SYSTEMS AND METHODS FOR THE GENERATION, VISUALIZATION AND CLASSIFICATION OF MOLECULAR FUNCTIONAL PROFILES

Description

FIELD

[0001] Aspects of the technology described herein relate to generating, visualizing and classifying molecular-functional (MF) profiles of cancer patients.

[0002] Some aspects of the technology described herein relate to generating a graphical user interface (GUI) for visualizing a molecular-functional profile of a cancer patient.

[0003] Some aspects of the technology described herein relate to identifying the type of MF profile of a patient, and predicting prognoses, identifying therapies, and / or otherwise aiding in the personalized care of the patient using the identified type.BACKGROUND

[0004] Correctly characterizing the type or types of cancer a patient or subject has and, potentially, selecting one or more effective therapies for the patient can be crucial for the survival and overall wellbeing of that patient. Advances in characterizing cancers, predicting prognoses, identifying effective therapies, and otherwise aiding in personalized care of patients with cancer are needed.

[0005] Beck et al. (PLOS Computational Biology, January 2013, Vol. 9, Issue 1, e1002875) describes a test to assess the prognostic significance of gene sets based on stratifying patients into prognostic subtypes with random gene sets, and the use of this test to identify prognostic signatures in breast and ovarian cancer.

[0006] US 2014 / 0342924 A describes the use of certain combinations of predictive markers whose expression correlates with responsiveness or non-responsiveness to a therapeutic regimen. These documents do not disclose determining a molecular-functional (MF) profile for a subject, by determining a gene group expression level for each of a set of gene groups comprising the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group and the fibroblast group as described herein, and identifying a MF profile cluster with which to associate the MF profile from amongst the MF profile clusters described herein.SUMMARY

[0007] Provided herein, inter alia, are systems and methods for generating a molecular-functional (MF) profile for a subject and identifying an existing MF profile cluster that is associated with the generated MF profile. Such information, in some embodiments, is output to a user in a graphical user interface (GUI).

[0008] Systems and methods for identifying a molecular-functional (MF) profile cluster with which to associate a MF profile for a subject comprises, in some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data for the subject; determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in a set of gene groups using the RNA expression data and / or WES data, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; and identifying a MF profile cluster with which to associate the MF profile for the subject from among multiple MF profile clusters that were generated by determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples for the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups, and clustering the plurality of MF profiles to obtain the MF profile clusters. Provided herein, inter alia, are systems and methods for generating MF profile clusters. Such information, in some embodiments, is stored in one or more databases.

[0009] Systems and methods for generating MF profile clusters comprises, in some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data for a plurality of subjects having a cancer of a particular type; determining a respective plurality of MF profiles for the plurality of subjects, in part, by determining, for each subject, a respective gene group expression level for each gene group in a set of gene groups using the RNA expression data and / or WES data, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising a first MF profile cluster, a second MF profile cluster, a third MF profile cluster, and a fourth MF profile cluster; and storing the plurality of MF profiles in association with information identifying the particular cancer type.

[0010] Provided herein, inter alia, are systems and methods for generating a molecular-functional (MF) profile for a subject using at least four (e.g., at least five) gene group expression levels and identifying an existing MF profile cluster that is associated with the generated MF profile. Such information, in some embodiments, is output to a user in a graphical user interface (GUI).

[0011] Systems and methods for identifying a molecular-functional (MF) profile cluster with which to associate a MF profile for a subject comprises, in some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data for the subject; determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in a set of gene groups using the RNA expression data and / or WES data, the set of gene groups comprising gene groups associated with cancer malignancy that consists of a tumor properties group and gene groups associated with cancer microenvironment that consists of a tumor-promoting immune microenvironment group, a an anti-tumor immune microenvironment group, an angiogenesis group, and a fibroblasts group; and identifying a MF profile cluster with which to associate the MF profile for the subject from among multiple MF profile clusters that were generated by determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples for the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups, and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0012] Provided herein, inter alia, are systems and methods for generating molecular-functional (MF) profile clusters, generating MF profiles for a subject, and associating the patient's MF profile with the MF profile cluster. Such information, in some embodiments, is output to a user in a graphical user interface (GUI).

[0013] Systems and methods for generating molecular-functional (MF) profile clusters, generating MF profiles for a subject, and associating the patient's MF profile with the MF profile cluster comprises, in some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data for a plurality of subjects; determining a respective plurality of MF profiles for the plurality of subjects, in part, by determining, for each subject, a respective gene group expression level for each gene group in a set of gene groups using the RNA expression data and / or WES data, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising a first MF profile cluster, a second MF profile cluster, a third MF profile cluster, and a fourth MF profile cluster; obtaining second RNA expression data from a subject, determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in the set of gene groups using the second RNA expression data; and identifying a MF profile cluster with which to associate the MF profile for the subject from among multiple MF profile clusters.

[0014] Provided herein, inter alia, are systems and methods for generating a MF profile and generating a MF portrait for visualizing the MF profile in a graphical user interface (GUI).

[0015] Systems and methods for generating a MF profile and generating a MF portrait for visualizing the MF profile in a graphical user interface (GUI) comprises, in some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data for a subject; determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in a set of gene groups using the RNA expression data and / or WES data, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; determining a first visual characteristic for a first GUI element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject; and presenting the generated personalized GUI to a user.

[0016] Provided herein, inter alia, are systems and methods for generating a MF profile by determining expression levels for e.g., four or five gene groups and generating a MF portrait for visualizing the MF profile in a graphical user interface (GUI).

[0017] Systems and methods for generating a MF profile by determining expression levels for e.g., four or five gene groups and generating a MF portrait for visualizing the MF profile in a graphical user interface (GUI) comprises, in some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data for a subject; determining a MF profile for the subject, in part, by determining a gene group expression level for each gene group in a set of gene groups using the RNA expression data and / or WES data, the set of gene groups comprising gene groups associated with cancer malignancy that consists of X and gene groups associated with cancer microenvironment that consist of a tumor-promoting immune microenvironment group, an anti-tumor immune microenvironment group, an angiogenesis group, and a fibroblasts group; determining a first visual characteristic for a first GUI element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject; and presenting the generated personalized GUI to a user.

[0018] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; and identifying, from among multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0019] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; and identifying, from among multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0020] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; and identifying, from among multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0021] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data from biological samples from a plurality of subjects, at least some of the subjects having a cancer of a particular type; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological sample; and storing the plurality of MF profiles in association with information identifying the particular cancer type.

[0022] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data from biological samples from a plurality of subjects, at least some of the subjects having a cancer of a particular type; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological sample; and storing the plurality of MF profiles in association with information identifying the particular cancer type.

[0023] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data from biological samples from a plurality of subjects, at least some of the subjects having a cancer of a particular type; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological sample; and storing the plurality of MF profiles in association with information identifying the particular cancer type.

[0024] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising a first gene group associated with cancer malignancy and a second gene group associated with cancer microenvironment, wherein the first and second gene groups are different, the determining comprising: determining a first gene group expression level for the first gene group, and determining a second gene group expression level for the second gene group; determining a first visual characteristic for a first graphical user interface (GUI) element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject, the GUI comprising: a first GUI portion associated with cancer malignancy and containing the first GUI element having the first visual characteristic, and a second GUI portion associated with cancer microenvironment and containing the second GUI element having the second visual characteristic; and presenting the generated personalized GUI to a user.

[0025] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising a first gene group associated with cancer malignancy and a second gene group associated with cancer microenvironment, wherein the first and second gene groups are different, the determining comprising: determining a first gene group expression level for the first gene group, and determining a second gene group expression level for the second gene group; determining a first visual characteristic for a first graphical user interface (GUI) element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject, the GUI comprising: a first GUI portion associated with cancer malignancy and containing the first GUI element having the first visual characteristic, and a second GUI portion associated with cancer microenvironment and containing the second GUI element having the second visual characteristic; and presenting the generated personalized GUI to a user.

[0026] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising a first gene group associated with cancer malignancy and a second gene group associated with cancer microenvironment, wherein the first and second gene groups are different, the determining comprising: determining a first gene group expression level for the first gene group, and determining a second gene group expression level for the second gene group; determining a first visual characteristic for a first graphical user interface (GUI) element using the first gene group expression level; determining a second visual characteristic for a second GUI element using the second gene group expression level; generating a personalized GUI personalized to the subject, the GUI comprising: a first GUI portion associated with cancer malignancy and containing the first GUI element having the first visual characteristic, and a second GUI portion associated with cancer microenvironment and containing the second GUI element having the second visual characteristic; and presenting the generated personalized GUI to a user.

[0027] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the tumor properties group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological sample, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0028] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the tumor properties group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological sample, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0029] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the tumor properties group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological sample, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0030] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0031] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0032] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0033] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, the cancer associated fibroblasts group, the angiogenesis group, and the complement inhibition group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0034] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, the cancer associated fibroblasts group, the angiogenesis group, and the complement inhibition group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0035] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject having a particular type of cancer; determining a molecular-functional (MF) profile for the subject at least in part by: determining, using the RNA expression data and reference RNA expression data, a gene group expression level for each gene group in a first set of gene groups associated with cancer malignancy and consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and determining, using the RNA expression data and the reference RNA expression data, a gene group expression level for each gene group in a second set of gene groups associated with cancer microenvironment and consisting of the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, the cancer associated fibroblasts group, the angiogenesis group, and the complement inhibition group; and accessing information specifying multiple MF profile clusters for the particular cancer type; identifying, from among the multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples, wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using the reference RNA expression data and RNA expression data from biological samples obtained from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

[0036] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining first RNA expression data and / or first whole exome sequencing (WES) data from biological samples from a plurality of subjects; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the first RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters including: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples; obtaining second RNA expression data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the second RNA expression data, a gene group expression level for each group in the set of gene groups; and identifying, from among the MF profile clusters, a particular MF profile cluster with which to associate the MF profile for the subject.

[0037] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining first RNA expression data and / or first whole exome sequencing (WES) data from biological samples from a plurality of subjects; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the first RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters including: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples; obtaining second RNA expression data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the second RNA expression data, a gene group expression level for each group in the set of gene groups; and identifying, from among the MF profile clusters, a particular MF profile cluster with which to associate the MF profile for the subject.

[0038] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining first RNA expression data and / or first whole exome sequencing (WES) data from biological samples from a plurality of subjects; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the first RNA expression data, a respective gene group expression level for each group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment; clustering the plurality of MF profiles to obtain MF profile clusters including: a first MF profile cluster associated with inflamed and vascularized biological samples and / or inflamed and fibroblast-enriched biological samples, a second MF profile cluster associated with inflamed and non-vascularized biological samples and / or inflamed and non-fibroblast-enriched biological samples, a third MF profile cluster associated with non-inflamed and vascularized biological samples and / or non-inflamed and fibroblast-enriched biological samples, and a fourth MF profile cluster associated with non-inflamed and non-vascularized biological samples and / or non-inflamed and non-fibroblast-enriched biological samples; obtaining second RNA expression data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the second RNA expression data, a gene group expression level for each group in the set of gene groups; and identifying, from among the MF profile clusters, a particular MF profile cluster with which to associate the MF profile for the subject.

[0039] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the tumor properties group; and second gene groups associated with cancer microenvironment consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group, determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

[0040] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the tumor properties group; and second gene groups associated with cancer microenvironment consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group, determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

[0041] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the tumor properties group; and second gene groups associated with cancer microenvironment consisting of the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group, determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

[0042] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, and the tumor-promotive immune group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

[0043] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, and the tumor-promotive immune group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

[0044] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, and the tumor-promotive immune group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

[0045] In one aspect, provided herein is a system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

[0046] In one aspect, provided herein is a method, comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.

[0047] In one aspect, provided herein is at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups comprising: first gene groups associated with cancer malignancy consisting of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and second gene groups associated with cancer microenvironment consisting of the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group; determining a first set of visual characteristics for a first plurality of graphical user interface (GUI) elements using the gene group expression levels determined for the first gene groups; determining a second set of visual characteristics for a second plurality of GUI elements using the gene group expression levels determined for the second gene groups; generating a personalized GUI personalized to the subject, the generating comprising: generating a first GUI portion associated with cancer malignancy and containing the first plurality of GUI elements having the determined first set of visual characteristics; and generating a second GUI portion associated with cancer microenvironment and containing the second plurality of GUI elements having the determined second set of visual characteristics; and presenting the generated personalized GUI to a user.Brief Description of the Drawings

[0048] Various aspects and embodiments will be described with reference to the following figures. The figures are not necessarily drawn to scale. FIG. 1A is a graphical representation of an exemplary bioinformatics pipeline for determining tumor functional properties in a molecular functional profile (MF profile), in accordance with some embodiments of the technology described herein. FIG. 1B is a graphical representation of tumor functional properties in a MF profile comprising 28 functional modules, in accordance with some embodiments of the technology described herein. The size of the modules correspond to their intensity rate. Colors reflect the module pro- or anti-cancer activity. Solid shades without cross-marking are assigned to the modules that promote tumor growth, while shades of with cross-marking are assigned to those having anticancer activity. The malignancy modules are collected in the Tumor Burden sector, which are located in the right top quarter of the graphical representation. FIG. 1C shows an exemplary MF profile, in accordance with some embodiments of the technology described herein. FIG. 2A is a block diagram of an illustrative environment 200 in which some embodiments of the technology described herein may be implemented. FIG. 2B is a block diagram of an illustrative graphical user interface 250 including patient data that may be presented to a user (e.g., a doctor), in accordance with some embodiments of the technology described herein. FIG. 2C is an illustrative example of the graphical user interface 250 of FIG. 2B, in accordance with some embodiments of the technology described herein. FIG. 3 is a graphic illustrating different types of screens that may be shown to a user of the software program. FIG. 4 is a screenshot of the user's account profile screen presented to the user in response to the user logging into the software program. FIG. 5 is a screenshot presenting the selected patient's information provided to the user in response to the user selecting the patient. FIG. 6 is a screenshot presenting that the patient's tumor biopsy sequencing data was downloaded (as shown in the lower right panel). FIG. 7 is a screenshot presenting the selected patient's report including information related to the patient's sequencing data, the patient, and the patient's cancer. FIG. 8 is a screenshot presenting information related to anti-PD1 immunotherapy provided in response to selecting anti-PD1 immunotherapy (as shown by highlighting) in the immunotherapy biomarkers portion of the screen (as shown in the left panel). FIG. 9 is a screenshot presenting selection of mutational burden biomarker by a user. FIG. 10 is a screenshot presenting information relating to the mutational burden biomarker (as shown in the middle panel) provided in response to the user selecting the mutational burden biomarker. FIG. 11 is a screenshot presenting that the mutational status gene group and neoantigens load gene group in the MF profile are highlighted in response to the user selecting the mutational burden biomarker (as shown in highlighting). FIG. 12 is a screenshot presenting that the T cells gene group in the MF profile is highlighted in response to the user selecting the CD8 T cell biomarker (as shown in highlighting). FIG. 13 is a screenshot presenting that the checkpoint inhibition gene group in the MF profile is highlighted in response to the user selecting the PDL1 expression biomarker. FIG. 14 is a screenshot presenting information related to sunitinib therapy provided in response to selecting sunitinib (as shown by highlighting) in the targeted therapy biomarkers portion of the screen (as shown in the left panel). FIG. 15 is a screenshot presenting clinical trial data relating to anti-PD1 therapy effectivity in patients having stage IV metastatic melanoma (as shown in the right panel) provided in response to the user selecting anti-PD1 immunotherapy (as shown in the left panel). FIG. 16 is a screenshot presenting clinical trial data relating to anti-CTLA4 therapy effectivity in patients having stage IV metastatic melanoma (as shown in the right panel) provided in response to the user selecting anti-CTLA4 immunotherapy (as shown in the left panel). FIG. 17 is a screenshot presenting clinical trial data relating to the NCT01295827 clinical trial of anti-PD1 treatment (as shown in the middle panel) provided in response to the user selecting the NCT01295827 clinical trial (as shown in the right panel). FIG. 18 is a screenshot presenting the treatment regimen of the selected clinical data provided in response to the user minimizing the therapy class description and drug description portions. The screen may also present information relating to ongoing clinical trials (marked by the letter A). FIG. 19 is a screenshot presenting a patient's MF profile (as shown in the middle panel). FIG. 20 is a screenshot presenting additional gene groups associated with the tumor properties gene group provided to the user in response to selecting the tumor properties gene group. FIG. 21 is a screenshot presenting information relating to the tumor proliferation rate (as shown in the right panel) provided in response to the user selecting the tumor proliferation rate gene group (as shown in highlighting) in the MF profile. FIG. 22 is a screenshot presenting information relating to the purity of the patient's tumor in the tumor purity portion (as shown in the lower right panel) and information relating to the clonal evolution of the patient's tumor in the tumor clones evolution portion (as shown in the lower right panel). FIG. 23 is a screenshot presenting information relating to the anti-tumor immune environment (as shown in the left panel) provided in response to the user selecting the anti-tumor immune environment gene group and information relating to the pro-tumor immune environment (as shown in the right panel) in response to the user selecting the pro-tumor immune environment gene group. FIG. 24 is a screenshot presenting information relating to expression of genes that determine T cell activity within the tumor in the anti-tumor microenvironment portion (as shown in the lower left panel) provided in response to the user selecting the T cell gene group in the MF profile (as shown by highlighting). FIG. 25 is a screenshot presenting information relating to expression of genes that determine cancer associated fibroblast activity within the tumor in the pro-tumor microenvironment portion (as shown in the lower right panel) provided in response to the user selecting the cancer associated fibroblast gene group in the MF profile (as shown by highlighting). FIG. 26 is a screenshot presenting information relating to the number of non-malignant cells in the patient's tumor (as shown in the lower left panel) provided in response to the user selecting tumor infiltrate in the anti-tumor immune environment portion (as shown in the upper left panel). FIG. 27 is a screenshot presenting information relating to the TCR repertoire in the patient's tumor (as shown in the lower right panel) provided in response to the user selecting tumor infiltrate in the pro-tumor immune environment portion (as shown in the upper right panel). FIG. 28 is a screenshot showing a MF profile presenting twenty-eight gene groups is shown in (as shown in the middle panel). FIG. 29 is a screenshot presenting the combo therapy portion (as shown in the right panel) provided to the user in response to selecting the combinational therapy portion (as shown in the middle panel). FIG. 30 is a screenshot presenting anti-PD1 therapy incorporated into the combo therapy portion (as shown in the upper right panel). FIG. 31 is a screenshot presenting information related to sunitinib treatment in the therapy biological influence portion (as shown in the lower middle panel) in response to the user selecting sunitinib in the targeted therapy biomarkers portion (as shown by highlighting). FIG. 32 is a screenshot presenting sunitinib incorporation in the combo therapy portion in response to the user selecting sunitinib. FIG. 33 is a screenshot presenting potential vaccine therapies such as a personalized neo-antigenic vaccine and an off the shelf vaccine provided to the user in response to selecting vaccine in the immunotherapy biomarkers portion (as shown in the left panel). FIG. 34 is a screenshot presenting information relating to treatment with a personalized neo-antigenic vaccine (as shown in the lower middle panel) provided to the user in response to selecting a personalized neo-antigenic vaccine (as shown by highlighting). FIG. 35 is a screenshot presenting incorporation of a personalized neo-antigenic vaccine in the combo therapy portion provided to the user in response to the user selecting the personalized neo-antigenic vaccine. FIG. 36 is a screenshot presenting the personalized neo-antigenic vaccine therapy, anti-PD1 therapy, and sunitinib therapy in the combo therapy portion provided to the user in response to the user incorporating each of these therapies into the combo therapy portion. FIG. 37 is a screenshot presenting an alert that substitution of sunitinib therapy with vemurafenib therapy is recognized by the software as an inappropriate combination for the patient. FIG. 38 is a block diagram of an illustrative computer system that may be used in implementing some embodiments of the technology described herein. FIG. 39A is a flowchart of an illustrative process 3900 for identifying an MF profile cluster with which to associate an MF profile for a subject, in accordance with some embodiments of the technology described herein. FIG. 39B is a flowchart of an illustrative process 3920 for generating MF profile clusters using RNA expression data obtained from subjects having a particular type of cancer, in accordance with some embodiments of the technology described herein. FIG. 39C is a flowchart of an illustrative process 3940 for identifying an MF profile cluster with which to associate an MF profile determined for a subject at least in part by determining the subject's expression levels for multiple gene groups, in accordance with some embodiments of the technology described herein. FIG. 39D is a flowchart of an illustrative process 3960 for generating MF profile clusters using RNA expression data obtained from subjects having a particular type of cancer, and associating a subject with one of the generated MF clusters based on the subject's MF profile, in accordance with some embodiments of the technology described herein. FIG. 40A is a flowchart of an illustrative process 4000 for generating an MF profile and generating an MF portrait for visualizing the MF profile in a graphical user interface (GUI), in accordance with some embodiments of the technology described herein. FIG. 40B is a flowchart of an illustrative process 4020 for generating an MF profile by determining expression levels for multiple gene groups and generating an MF portrait for visualizing the MF profile in a graphical user interface (GUI), in accordance with some embodiments of the technology described herein. FIG. 41A shows a series of MF profiles of melanoma patients (n=45) chosen randomly, in accordance with some embodiments of the technology described herein. FIG. 41B shows data from an unsupervised dense subgraph network cluster analysis of tumor functional processes calculated from RNA-Seq data of patient melanoma tumors (n=470 patients), in accordance with some embodiments of the technology described herein. The determined clusters were labeled Types A-D (1 st< - 4 th< MF profile clusters, respectively). FIG. 41C is a graphical representation of a correlation-based graph network of patients showing determined clusters, in accordance with some embodiments of the technology described herein. Each dot represents an individual melanoma patient, who is connected to other patients with a weight corresponding to its correlation value. The size of the dot corresponds to the vertex degree. FIG. 41D shows data from a k-means clustering analysis of tumor functional processes calculated from RNA-Seq data of melanoma tumors (n=470 patient tumors), in accordance with some embodiments of the technology described herein. The determined clusters were labeled Types A-D (1 st< - 4 th< MF profile clusters, respectively). FIG. 41E shows data from a cell composition analysis of melanoma tumors grouped into determined cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively)using MCP-counter and CIBERSORT, in accordance with some embodiments of the technology described herein. FIG. 41F shows data from a gene set enrichment analysis of melanoma tumors grouped into determined cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively), in accordance with some embodiments of the technology described herein. FIG. 41G is a graphical representation of functional process intensity associated with tumor growth (e.g., CAF, Angiogenesis, or Proliferation rate) or intratumoral immune infiltrate (e.g., effector cells or regulatory T cells (Tregs)) layered on cancers of determined cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively), in accordance with some embodiments of the technology described herein. FIG. 41H shows data from a log(p-value) t-test difference in process activity (enrichment score) between cancers of determined cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively), in accordance with some embodiments of the technology described herein. FIG. 41I shows Kaplan-Meier survival curves for melanoma patients split into cohorts according to the their MF profile determined cluster types (Types A-D; which are equivalent to the 1 st< - 4 th< types of portraits described herein, respectively) using unsupervised dense subgraph network clustering, in accordance with some embodiments of the technology described herein. FIG. 41J shows Kaplan-Meier survival curves for melanoma patients split into cohorts according to the their MF profile determined cluster types (Types A-D; which are equivalent to the 1 st< - 4 th< types of portraits described herein, respectively) using k-means clustering, in accordance with some embodiments of the technology described herein. FIG. 41K shows data from a purity, mutational load and mutational status analysis of melanoma tumors grouped according to their determined cluster Types A-D (which are equivalent to the 1 st< - 4 th< types of portraits described herein, respectively), in accordance with some embodiments of the technology described herein. FIG. 42A shows a MF profile type A (first type) as determined in accordance with some embodiments of the technology described herein. FIG. 42B shows a MF profile type B (second type), as determined in accordance with some embodiments of the technology described herein. FIG. 42C shows a MF profile type C (third type), as determined in accordance with some embodiments of the technology described herein. FIG. 42D shows a MF profile type D (fourth type), as determined in accordance with some embodiments of the technology described herein. FIG. 43A shows data from a tSNE analysis over non-normalized process enrichment scores, in accordance with some embodiments of the technology described herein. Each data point corresponds to an individual analyzed tumor sample. Different datasets (e.g., cancer types) are indicated by various grayscale intensities. FIG. 43B shows data from a tSNE analysis over process enrichment scores normalized within specific cancer types, in accordance with some embodiments of the technology described herein. Each data point corresponds to an individual tumor sample analyzed. Different datasets (e.g., cancer types) are indicated by various grayscale intensities. FIG. 43C shows data from an unsupervised dense subgraph network cluster analysis of tumor functional processes calculated from RNA-Seq data of different patient tumors. The following cancers were analyzed using TCGA data (listed n values indicate the numbers of individual patients): ACC - adrenocortical carcinoma (n=80), BLCA - bladder urothelial carcinoma (n=412), BRCA - breast invasive carcinoma (n=1100), CESC - cervical squamous cell carcinoma and endocervical adenocarcinoma (n=308), COAD - colon adenocarcinoma (n=461), ESCA - esophageal carcinoma (n=185), KIRC - kidney renal clear cell carcinoma (n=536), KIRP - kidney renal papillary cell carcinoma (n=291), LIHC - liver hepatocellular carcinoma (n=377), LUAD - lung adenocarcinoma (n=521), LUSC - lung squamous cell carcinoma (n=510), OV - ovarian serous cystadenocarcinoma (n=586), PAAD - pancreatic adenocarcinoma (n=185), PRAD - prostate adenocarcinoma (n=498), READ - rectal adenocarcinoma (n=172), SKCM - skin cutaneous melanoma (n=470), STAD - stomach adenocarcinoma (n=445), THCA - thyroid carcinoma (n=507), UCEC - uterine corpus endometrial carcinoma (n=548), CHOL - Cholangiocarcinoma - (n=36). FIG. 43D shows the frequency of determined cancer cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively) in patients having different malignant neoplasms, in accordance with some embodiments of the technology described herein. FIG. 43E shows data from an unsupervised dense subgraph network cluster analysis of tumor functional processes calculated from RNA-Seq data of patient having different malignant neoplasms, in accordance with some embodiments of the technology described herein. The determined clusters were labeled Types A-D (1 st< - 4 th< MF profile clusters, respectively). FIG. 43F shows Kaplan-Meier survival curves for patients having different malignant neoplasms split into cohorts according to the their determined cancer cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively), in accordance with some embodiments of the technology described herein. FIG. 44A shows data from a k-means clustering analysis of tumor functional processes calculated from RNA-Seq data for each cancer sample, in accordance with some embodiments of the technology described herein. The determined clusters were labeled Types A-D (1 st< - 4 th< MF profile clusters, respectively). FIG. 44B shows data from a k-means clustering analysis of tumor functional processes calculated from RNA-Seq data for merged pan-cancer tumors, in accordance with some embodiments of the technology described herein. The determined clusters were labeled Types A-D (1 st< - 4 th< MF profile clusters, respectively). FIG. 44C shows data from a log(p-value) t-test difference in process activity enrichment scores between determined cancer cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively) for merged pan-cancer tumors, in accordance with some embodiments of the technology described herein. FIG. 44D shows a heatmap of correlation between melanoma samples (n = 470) and the 10,000 most expressed genes, in accordance with some embodiments of the technology described herein. Pearson correlation matrices were clustered using Euclidean distance measured by the complete linkage method. Dense clusters are highlighted in column bar. FIG. 44E shows a heatmap of correlation between melanoma samples (n = 470) and to 298 genes constituting the functional processes, in accordance with some embodiments of the technology described herein. FIG. 44F shows a heatmap of correlation between melanoma samples (n = 470) and to 28 functional process scores, in accordance with some embodiments of the technology described herein. FIG. 44G shows a heatmap of correlation between 20 different carcinoma tumors, in accordance with some embodiments of the technology described herein. Panel (1) shows correlation with the 10,000 most expressed genes; panel (2) shows correlation with 298 genes constituting the functional processes; and panel (3) shows correlation with the 28 functional process scores. Pearson correlation matrices were clustered using Euclidean distance measured by the complete linkage method. Dense clusters are highlighted in column bar. FIG. 45A shows data from an unsupervised dense subgraph network cluster analysis of tumor functional processes calculated from RNA-Seq data of patient glioblastoma tumors (n=159) and glioma tumors (n=516), in accordance with some embodiments of the technology described herein. The determined clusters were labeled Types A-D (1 st< - 4 th< MF profile clusters, respectively). FIG. 45B shows data from a log(p-value) t-test difference in process activity enrichment scores between brain tumors determined to fall within cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively), in accordance with some embodiments of the technology described herein. FIG. 45C shows data from an unsupervised dense subgraph network cluster analysis of tumor functional processes calculated from RNA-Seq data of patient sarcoma tumors (n=261), in accordance with some embodiments of the technology described herein. The determined clusters were labeled Types A-D (1 st< - 4 th< MF profile clusters, respectively). FIG. 45D shows data from a log(p-value) t-test difference in process activity enrichment scores between sarcoma tumors determined to fall within cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively), in accordance with some embodiments of the technology described herein. FIG. 46A shows a heatmap showing processes of tumor MF profiles of melanoma patients treated with anti-CTLA4 therapy, in accordance with some embodiments of the technology described herein. Annotation of responders and non-responders, MF profile classification of determined cluster Types A-D (1 st< - 4 th< MF profile clusters, respectively), and total number of mutations is shown above the heatmap. The average MF profiles corresponding to patients from the heatmap and percent responders (R) and non-responders (N) for patients having the indicated tumor type are shown under the heatmap. FIG. 46B shows a heatmap showing processes of tumor MF profiles of melanoma patients treated with anti-PD1 therapy, in accordance with some embodiments of the technology described herein. FIG. 46C shows a heatmap showing processes of tumor MF profiles of melanoma patients treated with MAGE-A3 vaccine, in accordance with some embodiments of the technology described herein. FIG. 46D shows a heatmap showing processes of tumor MF profiles of mCRC patients from GSE5851 and HNSCC patients from GSE65021 treated with cetuximab, in accordance with some embodiments of the technology described herein. EGFR expression status is also indicated. FIG. 46E shows a heatmap showing processes of tumor MF profiles of ccRCC patients treated with sunitinib, in accordance with some embodiments of the technology described herein. FIG. 46F shows data of receiver operating characteristics for therapy response prediction based on MF profile type and AUC scores, in accordance with some embodiments of the technology described herein. FIG. 46G shows Kaplan-Meier survival curves for melanoma patients treated with anti-CTLA4 therapy split into cohorts according to the their determined MF profile type (Types A-D; 1 st< - 4 th< MF profile clusters, respectively), in accordance with some embodiments of the technology described herein. FIG. 46H shows Kaplan-Meier survival curves for melanoma patients treated with anti-PD1 therapy split into cohorts according to the their determined MF profile type (Types A-D; 1 st< - 4 th< MF profile clusters, respectively), in accordance with some embodiments of the technology described herein. FIG. 46I shows Kaplan-Meier survival curves for sunitinib treated patients having tumors with a high proliferation rate or a low proliferation rate, in accordance with some embodiments of the technology described herein. FIG. 47A shows a graphical representation of melanoma patients (dots) on two-dimensional coordinates of T cells and Cancer Associated Fibroblast process intensity from MF profile (z-scores), in accordance with some embodiments of the technology described herein. MF profile type (Types A-D; 1 st< - 4 th< MF profile clusters, respectively) is indicated for each patient. Dynamic changes in tumor MF profiles of five patients are shown by arrows. Larger dots indicate pre-treatment tumors. FIG. 47B shows a heatmap showing processes of determined MF profile type (Types A-D; 1 st< - 4 th< MF profile clusters, respectively) for melanoma patients before and after treatment with anti-PD1 therapy, in accordance with some embodiments of the technology described herein. Annotation of responders and non-responders, MF profile classification and total number of mutations is shown above the heatmap. Pre-treatment MF profiles for each patient are shown under the heatmap. FIG. 47C shows a graph of receiver operating characteristics for therapy response prediction based on tumor classification before treatment, in accordance with some embodiments of the technology described herein. FIG. 48A shows an exemplary MF profile useful for designing a combination therapy, in accordance with some embodiments of the technology described herein. FIG. 48B shows an exemplary MF profile type B useful for designing a combination therapy, in accordance with some embodiments of the technology described herein. FIG. 48C shows an exemplary MF profile type C useful for designing a combination therapy, in accordance with some embodiments of the technology described herein. FIG. 48D shows an exemplary MF profile type D useful for designing a combination therapy, in accordance with some embodiments of the technology described herein. FIG. 49A is a graphical representation of an exemplary MF profile having 28 functional processes, in accordance with some embodiments of the technology described herein. FIG. 49B is a graphical representation showing a visualization of a MF profile having 19 functional processes, in accordance with some embodiments of the technology described herein. FIG. 49C is a graphical representation showing a visualization of a MF profile having 5 functional processes, in accordance with some embodiments of the technology described herein. FIG. 50A shows data from a Pearson correlation analysis of functional modules which form the basis for the tumor MF profiles, in accordance with some embodiments of the technology described herein. FIG. 50B shows data from a Pearson correlation analysis of Effector cells functional activity module with NK cells, Checkpoint inhibition, MHC class II and Metastasis modules, in accordance with some embodiments of the technology described herein. FIG. 50C shows a graph showing SKCM and pan-cancer graph node connectivity percent (%) on different edge weight thresholds, in accordance with some embodiments of the technology described herein. The solid line shows 1% node connectivity. DETAILED DESCRIPTION

[0049] Recent advances in personalized genomic sequencing and cancer genomic sequencing technologies have made it possible to obtain patient-specific information about cancer cells (e.g., tumor cells) and cancer microenvironments from one or more biological samples obtained from individual patients. This information can be used to characterize the type or types of cancer a patient or subject has and, potentially, select one or more effective therapies for the patient. This information may also be used to determine how a patient is responding over time to a treatment and, if necessary, to select a new therapy or therapies for the patient as necessary. This information may also be used to determine whether a patient should be included or excluded from participating in a clinical trial.

[0050] The inventors have recognized and appreciated that many different types of cancer including, but not limited to melanoma, sarcoma, and glioblastoma, may be characterized as or classified into one of four molecular function (MF) profiles, herein identified as first MF profile (1 st< MF profile), second MF profile (2 nd< MF profile), third MF profile (3 rd< MF profile), and fourth MF profile (4 th< MF profile).

[0051] First MF profile cancers may also be described as "inflamed / vascularized" and / or "inflamed / fibroblast-enriched"; Second MF profile cancers may also be described as "inflamed / non-vascularized" and / or "inflamed / non-fibroblast-enriched"; Third MF profile cancers may also be described as "non-inflamed / vascularized" and / or "non-inflamed / fibroblast-enriched"; and Fourth MF profile cancers may also be described as "non-inflamed / non- vascularized" and / or "non-inflamed / non-fibroblast-enriched" and / or "immune desert." Such characteristics of MF clusters may be calculated in a number of ways.

[0052] As used herein, "inflamed" refers to the level of compositions and processes related to inflammation in a cancer (e.g., a tumor). In some embodiments, inflamed cancers (e.g., tumors) are highly infiltrated by immune cells, and are highly active with regard to antigen presentation and T-cell activation. In some embodiments, inflamed cancers (e.g., tumors) may have an NK cell and / or a T cell z score of, for example, at least .60, at least 0.65, at least 0.70, at least 0.75, at least 0.80, at least 0.85, at least 0.90, at least 0.91, at least 0.92, at least 0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, at least 0.98, or at least 0.99. In some embodiments, inflamed cancers (e.g., tumors) may have an NK cell and / or a T cell z score of, for example, not less than .60, not less than 0.65, not less than 0.70, not less than 0.75, not less than 0.80, not less than 0.85, not less than 0.90, not less than 0.91, not less than 0.92, not less than 0.93, not less than 0.94, not less than 0.95, not less than 0.96, not less than 0.97, not less than 0.98, or not less than 0.99. In some embodiments, non-inflamed tumors are poorly infiltrated by immune cells, and have low activity with regard to antigen presentation and T-cell activation. In some embodiments, non-inflamed cancers (e.g., tumors) may have an NK cell and / or a T cell z score of, for example, less than -0.20, less than -0.25, less than -0.30, less than -0.35, less than -0.40, less than -0.45, less than -0.50, less than -0.55, less than -0.60, less than -0.65, less than -0.70, less than -0.75, less than -0.80, less than -0.85, less than -0.90, less than -0.91, less than -0.92, less than -0.93, less than -0.94, less than -0.95, less than -0.96, less than -0.97, less than -0.98, or less than -0.99. In some embodiments, non-inflamed cancers (e.g., tumors) may have an NK cell and / or a T cell z score of, for example, not more than -0.20, not more than -0.25, not more than -0.30, not more than -0.35, not more than -0.40, not more than -0.45, not more than -0.50, not more than -0.55, not more than -0.60, not more than -0.65, not more than -0.70, not more than -0.75, not more than -0.80, not more than -0.85, not more than -0.90, not more than -0.91, not more than -0.92, not more than -0.93, not more than -0.94, not more than -0.95, not more than -0.96, not more than -0.97, not more than -0.98, or not more than -0.99.

[0053] As used herein, "vascularized" refers to the formation of blood vessels in a cancer (e.g., a tumor). In some embodiments, vascularized cancers (e.g., tumors) comprise high levels of cellular compositions and process related to blood vessel formation. In some embodiments, vascularized cancers (e.g., tumors) may have an angiogenesis z score of, for example, at least .60, at least 0.65, at least 0.70, at least 0.75, at least 0.80, at least 0.85, at least 0.90, at least 0.91, at least 0.92, at least 0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, at least 0.98, or at least 0.99. In some embodiments, vascularized cancers (e.g., tumors) may have an NK cell and / or a T cell z score of, for example, not less than .60, not less than 0.65, not less than 0.70, not less than 0.75, not less than 0.80, not less than 0.85, not less than 0.90, not less than 0.91, not less than 0.92, not less than 0.93, not less than 0.94, not less than 0.95, not less than 0.96, not less than 0.97, not less than 0.98, or not less than 0.99. In some embodiments, non-vascularized cancers (e.g., tumors) comprise few or no compositions and processes related to blood vessel formation. In some embodiments, non-vascularized cancers (e.g., tumors) may have an angiogenesis z score of, for example, less than -0.20, less than -0.25, less than -0.30, less than -0.35, less than -0.40, less than -0.45, less than -0.50, less than -0.55, less than -0.60, less than -0.65, less than -0.70, less than -0.75, less than -0.80, less than -0.85, less than -0.90, less than -0.91, less than -0.92, less than -0.93, less than -0.94, less than -0.95, less than -0.96, less than -0.97, less than -0.98, or less than -0.99. In some embodiments, non-vascularized cancers (e.g., tumors) may have an angiogenesis z score of, for example, not more than -0.20, not more than -0.25, not more than -0.30, not more than -0.35, not more than -0.40, not more than -0.45, not more than -0.50, not more than -0.55, not more than -0.60, not more than -0.65, not more than -0.70, not more than -0.75, not more than -0.80, not more than -0.85, not more than -0.90, not more than -0.91, not more than -0.92, not more than -0.93, not more than -0.94, not more than -0.95, not more than -0.96, not more than -0.97, not more than -0.98, or not more than -0.99.

[0054] As used herein, "fibroblast enriched" refers to the level or amount of fibroblasts in a cancer (e.g., a tumor). In some embodiments, fibroblast enriched tumors comprise high levels of fibroblast cells. In some embodiments, fibroblast enriched cancers (e.g., tumors) may have a fibroblast (cancer associated fibroblast) z score of, for example, at least .60, at least 0.65, at least 0.70, at least 0.75, at least 0.80, at least 0.85, at least 0.90, at least 0.91, at least 0.92, at least 0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, at least 0.98, or at least 0.99. In some embodiments, fibroblast enriched cancers (e.g., tumors) may have an NK cell and / or a T cell z score of, for example, not less than .60, not less than 0.65, not less than 0.70, not less than 0.75, not less than 0.80, not less than 0.85, not less than 0.90, not less than 0.91, not less than 0.92, not less than 0.93, not less than 0.94, not less than 0.95, not less than 0.96, not less than 0.97, not less than 0.98, or not less than 0.99. In some embodiments, non-fibroblast-enriched cancers (e.g., tumors) comprise few or no fibroblast cells. In some embodiments, non-fibroblast-enriched cancers (e.g., tumors) may have a fibroblast (cancer associated fibroblast) z score of, for example, less than -0.20, less than -0.25, less than -0.30, less than -0.35, less than -0.40, less than -0.45, less than -0.50, less than -0.55, less than -0.60, less than -0.65, less than -0.70, less than -0.75, less than -0.80, less than -0.85, less than -0.90, less than -0.91, less than -0.92, less than -0.93, less than -0.94, less than -0.95, less than -0.96, less than -0.97, less than -0.98, or less than -0.99. In some embodiments, non-fibroblast-enriched cancers (e.g., tumors) may have a fibroblast (cancer associated fibroblast) z score of, for example, not more than -0.20, not more than -0.25, not more than -0.30, not more than - 0.35, not more than -0.40, not more than -0.45, not more than -0.50, not more than -0.55, not more than -0.60, not more than -0.65, not more than -0.70, not more than -0.75, not more than -0.80, not more than -0.85, not more than -0.90, not more than -0.91, not more than -0.92, not more than -0.93, not more than -0.94, not more than -0.95, not more than -0.96, not more than -0.97, not more than -0.98, or not more than -0.99.

[0055] Each subject biological sample may be assigned to one of four predefined MF profile clusters using a k-nearest neighbors classifier. The classifier may be trained on the data by which the MF profile clusters are defined and on their corresponding labels. Sample vectors for the k-nearest neighbors classifier may be found in Table 1, below. The classifier may then predict the type of MF profile (MF profile cluster) for the subject sample utilizing its relative processes intensity values. Relative processes intensity values may be calculated as Z-values (arguments of the standard normal distribution over training set of samples) of ssGSEA algorithm outputs inferred from the RNA sequence data from the subject sample as described herein. Table 1. Sample vectors for the k-nearest neighbors classifier (z -scores).MF profile typeFirstSecondThirdFourthAngiogenesis0.727815-0.59070.71314-0.42704Cancer Associated Fibroblasts0.596986-0.48710.82218-0.49264Receptor_tyrosine_kinases0.370197-0.43660.75614-0.33472NK_cells0.6246480.75725-0.3987-0.89695Checkpoint_inhibition0.6714910.74881-0.3928-0.92683Effector_cells0.6528370.77783-0.3953-0.93822T_cells0.7010670.74591-0.3827-0.9518Proliferation_rate-0.442440.10307-0.4570.509505

[0056] The identification and classification of 1 st< -4 th< MF profile cluster types as described herein were not known in the art, and such classifications provide more precise diagnoses that might not be seen by the use of any single marker or less complex combination of elements. The methods, systems, and graphical user interfaces (GUIs) based on such classifications described herein are newly available and no previously described techniques or methods existed to perform the elements of these techniques. Further, the four molecular function (MF) profiles were not known previously to exist and there could therefore be no motivation in the art to define these cancer types. Additionally, the types of analyses described herein would have been considered too involved, costly, and / or time consuming to perform without understanding the potential benefits that could be derived from such complex analyses based on the multiplicity and mutability of the involved factors.

[0057] The inventors have recognized and appreciated that several of the elements described herein add something more than what is well understood, routine, or conventional activity proposed by others in the field. These meaningful non-routine steps result in the improvements seen in the methods, systems, and GUIs described herein and include, but are not limited to: the analysis of gene expression levels and gene group expression levels for both cancer malignancy and cancer microenvironment; the combination(s) of specific genes used in the gene groups (or modules) provided herein; the recognition that many different cancers can be classified such that they are identifiable as one of 1 st< -4 th< MF profile cancer types; technical improvements in analyses that allow for more precise identification of cancers and resulting improvements in outcome for the patient; the creation of improved graphical user interfaces to aid in the analysis of an individual patient's cancer into cancer 1 st< -4 th< MF profile cancer types; the specification of treatments for individual patients based on the identified classification of one or more cancers in the patient (i.e., 1 st< -4 th< MF profile cancer types) and / or additional information about the patient or the patient's cancer.

[0058] Therefore, aspects of the present disclosure relate to methods and compositions for characterizing one or more cancers (e.g., tumors) of or in a patient. In some embodiments, characterizing a cancer (e.g., a tumor) comprises determining differentially expressed genes in a sample from a subject (e.g., a patient) having a cancer (e.g., a tumor). In some embodiments, characterizing a cancer (e.g., a tumor) comprises determining whether one or more genes are mutated in a sample from a subject having a cancer (e.g., a tumor). In certain embodiments, characterizing a cancer (e.g., a tumor) comprises identifying the cancer (e.g., a tumor) as a specific subtype of cancer selected from a 1 st< MF profile cancer type (inflamed / vascularized and / or inflamed / fibroblast enriched); a 2 nd< MF profile cancer type (inflamed / non-vascularized and / or inflamed / non-fibroblast enriched); a 3 rd< MF profile cancer type (non-inflamed / vascularized and / or non-inflamed / fibroblast enriched); and a 4 th< MF profile cancer type (non-inflamed / non-vascularized and / or non-inflamed / non-fibroblast enriched; also identified herein as "immune desert").

[0059] Such methods and compositions may be useful for clinical purposes including, for example, selecting a treatment, monitoring cancer progression, assessing the efficacy of a treatment against a cancer, evaluating suitability of a patient for participating in a clinical trial, or determining a course of treatment for a subject (e.g., a patient).

[0060] The methods and compositions described herein may also be useful for non-clinical applications including (as a non-limiting example) research purposes such as, e.g., studying the mechanism of cancer development and / or biological pathways and / or biological processes involved in cancer, and developing new therapies for cancer based on such studies.

[0061] Further, systems which present this information in a comprehensive and useable format will be needed to facilitate treatment of patients with such conditions. Therefore, provided herein are models and systems of cancer-immunity interrelationships for a particular patient that result in a profile designed to concisely and clearly describe important characteristics of cancerous cells (e.g., tumor cells) of the patient (referred to herein as, for example, "cancer malignancy"), as well as all the key processes in the cancer (e.g., tumor) microenvironment (discussed herein as, for example, "cancer microenvironment").

[0062] Such a model may take into consideration the full spectrum of non-malignant components in the cancer microenvironment, including fibroblasts and extracellular matrices, the network of blood and lymphatic vessels, tissue macrophages, dendritic and mast cells, different kinds of leukocytes / lymphocytes migrated to or proliferating within tumor, as well as intrinsic properties of malignant cells.

[0063] Certain aspects of the described model or system present the cellular composition of the cancerous cells (e.g., the tumor), while other aspects reflect the intensity of processes of the cancerous (e.g., the tumor) cells of the biological sample and / or patient. The presence and number of any cell type is an important but insufficient parameter because it is also necessary to understand how these cells function within the processes that make up the cancer (e.g., the tumor). The size of particular functional modules including, e.g., the intensity of processes ongoing in these modules, actually comprises both concentration and functional activity of the cell type. Therefore, a cancer (e.g., a tumor) "profile" that comprises a set of functional modules with an estimate of their intensity implicitly reflects the content of the different cell types within the cancer (e.g., the tumor).

[0064] Therefore, in some embodiments the model described herein enables the study of the structural-functional composition of a particular patient's tumor and / or cancerous cells and also allows the comparison of the same across different patients and groups of patients. As a non-limiting example, the described model has been used to compare human skin cutaneous melanoma (SKCM) tumors from 470 melanoma patients. Four general types of tumors were revealed (described here as tumor types 1 st< MF profile type, 2 nd< MF profile type, 3 rd< MF profile type, and 4 th< MF profile type) pertaining to 22%, 28%, 24%, and 24% of melanoma patients, respectively (representing 98% of total patients). Tumor types 1 st< MF profile type and 2 nd< MF profile type are characterized by excessive infiltration with cells of the immune system (so-called "inflamed" or "hot" tumors), and 3 rd< MF profile type and 4 th< MF profile type are considered poorly infiltrated (so-called "non-inflamed" or "cold" tumors), meaning they have no obvious signs of inflammation or recruitment of immune cells.

[0065] Generally, techniques described herein provide for improvements over conventional computer-implemented techniques for analysis of medical data such as evaluation of expression data (e.g., RNA expression data) and determining whether one or more therapies (e.g., targeted therapies and / or immunotherapies) will be effective in treating the subject. Additionally, some embodiments of the technology provided herein are directed to graphical user interfaces that present oncological data in a new way which is compact and highly informative. These graphical user interfaces not only reduce the cognitive load on users working with them, but may serve to reduce clinician errors and improve the functionality of a computer by providing all needed information in a single interactive interface. This eliminates the need for a clinician to consult different sources of information (e.g., view multiple different webpages, use multiple different application programs, etc.), which would otherwise place an additional burden on the processing, memory, and communications resources of the computer(s) used by the clinician.

[0066] As described herein, some embodiments relate to a software program for providing information related to a patient's cancer to a user (e.g., an oncologist or other doctor, a healthcare provider, a researcher, a patient, etc.). The software program may provide information about the patient, e.g., the patient's age, overall status, diagnosis, and treatment history.

[0067] In another aspect, the software program may provide information about the patient's cancer, e.g., tumor histology, tumor purity, tumor clone evolution, tumor cell composition, tumor cell infiltrate, gene expression levels, gene mutations, the results of medical examinations (e.g., MRI results) and sequencing data (e.g., RNA sequencing data and / or whole exome sequencing (WES) data).

[0068] In another aspect, the software program may provide information about potential treatments (e.g., immunotherapies, targeted therapies, etc.) and information related to potential treatments, e.g., prognostic factors, therapeutic efficacy, clinical trial efficacy, ongoing clinical trials, and relevant publications.

[0069] In another aspect, the software program may provide information about the patient's biomarkers (e.g., genetic biomarkers, cellular biomarkers, and expression biomarkers) and information related to the patient's biomarkers (e.g., a description of the biomarker, how the biomarker value was calculated, the patient's particular biomarker value compared to other patients, and related publications).

[0070] In yet another aspect, the software program may also allow the user to interactively design a panel of sequencing results (e.g., results related to the sequences or levels of specified biomarkers or genes) specific to the patient and / or a combination therapy for the patient.

[0071] As used herein, the term "patient" means any mammal, including mice, rabbits, and humans. In one embodiment, the patient is a human or non-human primate. The terms "individual" or "subject" may be used interchangeably with "patient."Obtaining Expression Data

[0072] Expression data (e.g., RNA expression data and / or whole exome sequencing (WES) data) as described herein may be obtained from a variety of sources. In some embodiments, expression data may be obtained by analyzing a biological sample from a patient. The biological sample may be analyzed prior to performance of the techniques described herein including the techniques for generating MF clusters, associating a patient's MF profile with one of the MF clusters, and generating an MF portrait from a patient's MF profile to provide a visualization for the MF profile. In some such embodiments, data obtained from the biological sample may be stored (e.g., in a database) and accessed during performance of the techniques described herein. In some embodiments, expression data is obtained from a database containing expression data for at least one patient.Biological Samples

[0073] Any of the methods, systems, assays, or other claimed elements may use or be used to analyze any biological sample from a subject (i.e., a patient or individual). In some embodiments, the biological sample may be any sample from a subject known or suspected of having cancerous cells or pre-cancerous cells.

[0074] The biological sample may be from any source in the subject's body including, but not limited to, any fluid [such as blood (e.g., whole blood, blood serum, or blood plasma), saliva, tears, synovial fluid, cerebrospinal fluid, pleural fluid, pericardial fluid, ascitic fluid, and / or urine], hair, skin (including portions of the epidermis, dermis, and / or hypodermis), oropharynx, laryngopharynx, esophagus, stomach, bronchus, salivary gland, tongue, oral cavity, nasal cavity, vaginal cavity, anal cavity, bone, bone marrow, brain, thymus, spleen, small intestine, appendix, colon, rectum, anus, liver, biliary tract, pancreas, kidney, ureter, bladder, urethra, uterus, vagina, vulva, ovary, cervix, scrotum, penis, prostate, testicle, seminal vesicles, and / or any type of tissue (e.g., muscle tissue, epithelial tissue, connective tissue, or nervous tissue).

[0075] The biological sample may be any type of sample including, for example, a sample of a bodily fluid, one or more cells, a piece of tissue, or some or all of an organ. In some embodiments, the sample may be from a cancerous tissue or organ or a tissue or organ suspected of having one or more cancerous cells. In some embodiments, the sample may be from a healthy (e.g., non-cancerous) tissue or organ. In some embodiments, a sample from a subject (e.g., a biopsy from a subject) may include both healthy and cancerous cells and / or tissue. In certain embodiments, one sample will be taken from a subject for analysis. In some embodiments, more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) samples may be taken from a subject for analysis. In some embodiments, one sample from a subject will be analyzed. In certain embodiments, more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more) samples may be analyzed. If more than one sample from a subject is analyzed, the samples may be procured at the same time (e.g., more than one sample may be taken in the same procedure), or the samples may be taken at different times (e.g., during a different procedure including a procedure 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 days; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 weeks; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 months, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years, or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 decades after a first procedure). A second or subsequent sample may be taken or obtained from the same region (e.g., from the same tumor or area of tissue) or a different region (including, e.g., a different tumor). A second or subsequent sample may be taken or obtained from the subject after one or more treatments, and may be taken from the same region or a different region. As a non-limiting example, the second or subsequent sample may be useful in determining whether the cancer in each sample has different characteristics (e.g., in the case of samples taken from two physically separate tumors in a patient) or whether the cancer has responded to one or more treatments (e.g., in the case of two or more samples from the same tumor prior to and subsequent to a treatment).

[0076] Any of the biological samples described herein may be obtained from the subject using any known technique. In some embodiments, the biological sample may be obtained from a surgical procedure (e.g., laparoscopic surgery, microscopically controlled surgery, or endoscopy), bone marrow biopsy, punch biopsy, endoscopic biopsy, or needle biopsy (e.g,, a fine-needle aspiration, core needle biopsy, vacuum-assisted biopsy, or image-guided biopsy). In some embodiments, each of the at least one biological samples is a bodily fluid sample, a cell sample, or a tissue biopsy.

[0077] In some embodiments, one or more than one cell (i.e., a cell sample) is obtained from a subject using a scrape or brush method. The cell sample may be obtained from any area in or from the body of a subject including, for example, from one or more of the following areas: the cervix, esophagus, stomach, bronchus, or oral cavity. In some embodiments, one or more than one piece of tissue (e.g., a tissue biopsy) from a subject may be used. In certain embodiments, the tissue biopsy may comprise one or more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10) samples from one or more tumors or tissues known or suspected of having cancerous cells.Sample Analysis

[0078] Methods and compositions described herein are based, at least in part, on the identification and characterization of certain biological processes and / or molecular and cellular compositions that are present within and / or surrounding the cancer (e.g., the tumor).

[0079] Biological processes within and / or surrounding cancer (e.g., a tumor) include, but are not limited to, angiogenesis, metastasis, proliferation, cell activation (e.g., T cell activation), tumor invasion, immune response, cell signaling (e.g., HER2 signaling), and apoptosis.

[0080] Molecular and cellular compositions within and / or surrounding cancer (e.g., a tumor) include, but are not limited to, nucleic acids (e.g., DNA and / or RNA), molecules (e.g., hormones), proteins (e.g., wild-type and / or mutant proteins), and cells (e.g., malignant and / or non-malignant cells).

[0081] The cancer microenvironment, as used herein, refers to the molecular and cellular environment in which the cancer (e.g., a tumor) exists including, but not limited to, blood vessels that surround and / or are internal to a tumor, immune cells, fibroblasts, bone marrow-derived inflammatory cells, lymphocytes, signaling molecules, and the extracellular matrix (ECM).

[0082] The molecular and cellular composition and biological processes present within and / or surrounding the tumor may be directed toward promoting cancer (e.g., tumor) growth and survival (e.g., pro-tumor) and / or inhibiting cancer (e.g., tumor) growth and survival (e.g., anti-tumor).

[0083] The cancer (e.g., tumor) microenvironment may comprise cellular compositions and biological processes directed toward promoting cancer (e.g., tumor) growth and survival (e.g., pro-tumor microenvironment) and / or inhibiting cancer (e.g., tumor) growth and survival (e.g., anti-tumor microenvironment). In some embodiments, the cancer (e.g., tumor) microenvironment comprises a pro-cancer (e.g., tumor) microenvironment. In some embodiments, the cancer (e.g., tumor) microenvironment comprises an anti-cancer (e.g., tumor) microenvironment. In some embodiments, the cancer (e.g., tumor) microenvironment comprises a pro-cancer (e.g., tumor) microenvironment and an anti-cancer (e.g., tumor) microenvironment. Any information relating to molecular and cellular compositions, and biological processes that are present within and / or surrounding cancer (e.g., a tumor) may be used in methods and compositions for characterization of cancers (e.g., tumors) as described herein. In some embodiments, cancer (e.g., a tumor) may be characterized based on gene group expression level (e.g., on gene group RNA expression level). In some embodiments, cancer (e.g., a tumor) is characterized based on protein expression. In some embodiments, cancer (e.g., a tumor) is characterized based on absence or presence of at least one mutation (e.g., mutational load). In some embodiments, the mutational load is estimated from whole exome sequencing data (WES). In some embodiments, cancer (e.g., a tumor) is characterized based on histology. In some embodiments, cancer (e.g., a tumor) is characterized based on tumor purity. Tumor purity may be determined using any means known in the art including, but not limited to, cell sorting-based technology (e.g., Fluorescent-Activated Cell Sorting (FACS)). In some embodiments, tumor purity is determined from whole exome sequencing (WES) data of paired tumor and non-cancerous (e.g., normal) tissue. In some embodiments, cancer (e.g., a tumor) is characterized based on the number of neoantigens. The number of neoantigens may be determined using any means known in the art including, but not limited to, the use of whole exome sequencing (WES) data of paired cancer (e.g., tumor) and non-cancerous tissues.

[0084] Methods and compositions for characterization of cancers as described herein may be applied to any cancer (e.g., any tumor). Exemplary cancers include, but are not limited to, adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma, endocervical adenocarcinoma, colon adenocarcinoma, esophageal carcinoma, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, prostate adenocarcinoma, rectal adenocarcinoma, skin cutaneous melanoma, stomach adenocarcinoma, thyroid carcinoma, uterine corpus endometrial carcinoma, and cholangiocarcinoma.

[0085] In one embodiment, cancers of any type (including all the types of cancer listed herein) may be classified as being 1 st< MF profile type (inflamed / vascularized and / or inflamed / fibroblast enriched), 2 nd< MF profile type (inflamed / non-vascularized and / or inflamed / non-fibroblast enriched), 3 rd< MF profile type (non-inflamed / vascularized and / or non-inflamed / fibroblast enriched), or 4 th< MF profile type (non-inflamed / non-vascularized and / or non-inflamed / non-fibroblast enriched) cancers (e.g., tumors).Expression Data

[0086] Expression data (e.g., indicating expression levels) for a plurality of genes may be used for any of the methods or compositions described herein. The number of genes which may be examined may be up to and inclusive of all the genes of the subject. In some embodiments, expression levels may be examined for all of the genes of a subject. As a non-limiting example, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 125 or more, 150 or more, 175 or more, 200 or more, 225 or more, 250 or more, 275 or more, or 300 or more genes may be used for any evaluation described herein. As another set of non-limiting examples, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 125, at least 150, at least 175, at least 200, at least 225, at least 250, at least 275, or at least 300 genes may be used for any evaluation described herein. In some embodiments, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 125, at least 150, at least 175, at least 200, at least 225, at least 250, at least 275, or at least 300 genes may be examined for each gene group or module evaluation described herein. In some embodiments, up to 50 modules (e.g., up to two, up to three, up to four, up to five, up to six, up to seven, up to eight, up to nine, up to ten, up to eleven, up to twelve, up to 13, up to 14, up to 15, up to 16, up to 17, up to 18, up to 19, up to 20, up to 21, up to 22, up to 23, up to 24, up to 25, up to 26, up to 27, up to 28, up to 29, up to 30, up to 31, up to 32, up to 33, up to 34, up to 35, up to 36, up to 37, up to 38, up to 39, up to 40, up to 41, up to 42, up to 43, up to 44, up to 45, up to 46, up to 47, up to 48, up to 49, or up to 50) modules or gene groups may be used for any evaluation described herein.

[0087] Any method may be used on a sample from a subject in order to acquire expression data (e.g., indicating expression levels) for the plurality of genes. As a set of non-limiting examples, the expression data may be RNA expression data, DNA expression data, or protein expression data.

[0088] DNA expression data, in some embodiments, refers to a level of DNA in a sample from a subject. The level of DNA in a sample from a subject having cancer may be elevated compared to the level of DNA in a sample from a subject not having cancer, e.g., a gene duplication in a cancer patient's sample. The level of DNA in a sample from a subject having cancer may be reduced compared to the level of DNA in a sample from a subject not having cancer, e.g., a gene deletion in a cancer patient's sample.

[0089] DNA expression data, in some embodiments, refers to data for DNA (or gene) expressed in a sample, for example, sequencing data for a gene that is expressed in a patient's sample. Such data may be useful, in some embodiments, to determine whether the patient has one or more mutations associated with a particular cancer.

[0090] RNA expression data may be acquired using any method known in the art including, but not limited to: whole transcriptome sequencing, total RNA sequencing, mRNA sequencing, targeted RNA sequencing, small RNA sequencing, ribosome profiling, RNA exome capture sequencing, and / or deep RNA sequencing. DNA expression data may be acquired using any method known in the art including any known method of DNA sequencing. For example, DNA sequencing may be used to identify one or more mutations in the DNA of a subject. Any technique used in the art to sequence DNA may be used with the methods and compositions described herein. As a set of non-limiting examples, the DNA may be sequenced through single-molecule real-time sequencing, ion torrent sequencing, pyrosequencing, sequencing by synthesis, sequencing by ligation (SOLiD sequencing), nanopore sequencing, or Sanger sequencing (chain termination sequencing). Protein expression data may be acquired using any method known in the art including, but not limited to: N-terminal amino acid analysis, C-terminal amino acid analysis, Edman degradation (including though use of a machine such as a protein sequenator), or mass spectrometry.

[0091] In some embodiments, the expression data comprises whole exome sequencing (WES) data. In some embodiments, the expression data comprises whole genome sequencing (WGS) data. In some embodiments, the expression data comprises next-generation sequencing (NGS) data. In some embodiments, the expression data comprises microarray data.

[0092] In some embodiments, expression data is used to determine gene group expression levels. In some embodiments, the gene group expression levels are calculated as a gene set enrichment analysis (GSEA) score for the gene group. In some embodiments, GSEA comprises calculating an enrichment score (ES), assessing ES significance, adjusting ES for multiple hypothesis testing, and weighting each gene. In some embodiments, each gene is weighted equally. In some embodiments, each gene is weighted according to their association with a phenotype.

[0093] In some embodiments, calculating an ES comprises ranking genes by their expression difference, calculating cumulative sum over ranked genes, and recording maximum deviation from zero as ES. In some embodiments, calculating cumulative sum over ranked genes comprises an increase in sum when a gene is present in a gene group and a decrease in sum when a gene is absent from gene group. In some embodiments, magnitude of increment depends on correlation of a gene and a phenotype.

[0094] In some embodiments, assessing ES significance comprises permutating phenotype labels. In some embodiments, assessing ES significance comprises calculating ES for permutated data. In some embodiments, assessing ES significance comprises comparing ES for non-permutated data to ES for permutated data.

[0095] In some embodiments, adjusting ES for multiple hypothesis testing comprises determining a normalized enrichment score (NES). In some embodiments, adjusting ES for multiple hypothesis testing determining a false discovery rate (FDR) for the NES. In some embodiments, determining FDR comprises comparing tail of the observed and null distributions for the NES. In some embodiments, GSEA score is calculated at least once. In some embodiments, GSEA score is calculated at least twice. In some embodiments, GSEA score is calculated once for positively scoring gene groups and once for negatively score gene groups.Datasets

[0096] Any dataset containing expression data may be used to generate MF profiles as described herein. In some embodiments, expression data may be obtained from one or more databases and / or any other suitable electronic repository of data. Examples of databases include, but are not limited to, CGP (Cancer Genome Project), CPTAC (Clinical Proteomic Tumor Analysis Consortium), ICGC (International Cancer Genome Consortium), and TCGA (The Cancer Genome Atlas). In some embodiments, expression data may be obtained from data associated with a clinical trial. In some embodiments, expression data may be predicted in association with a clinical trial based on one or more similar drugs (e.g., drugs of a similar class such as PD-1 inhibitors). In some embodiments, expression data may be obtained from a hospital database. In some embodiments, expression data may be obtained from a commercial sequencing supplier. In some embodiments, expression data may be obtained from a subject (e.g., a patient) and / or a subject's (e.g., a patient's) relative, guardian, or caretaker.Assays

[0097] Any of the biological samples described herein can be used for obtaining expression data using conventional assays or those described herein. Expression data, in some embodiments, includes gene expression levels. Gene expression levels may be detected by detecting a product of gene expression such as mRNA and / or protein.

[0098] In some embodiments, gene expression levels are determined by detecting a level of a protein in a sample and / or by detecting a level of activity of a protein in a sample. As used herein, the terms "determining" or "detecting" may include assessing the presence, absence, quantity and / or amount (which can be an effective amount) of a substance within a sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values and / or categorization of such substances in a sample from a subject.

[0099] The level of a protein may be measured using an immunoassay. Examples of immunoassays include any known assay (without limitation), and may include any of the following: immunoblotting assay (e.g., Western blot), immunohistochemical analysis, flow cytometry assay, immunofluorescence assay (IF), enzyme linked immunosorbent assays (ELISAs) (e.g., sandwich ELISAs), radioimmunoassays, electrochemiluminescence-based detection assays, magnetic immunoassays, lateral flow assays, and related techniques. Additional suitable immunoassays for detecting a level of a protein provided herein will be apparent to those of skill in the art.

[0100] Such immunoassays may involve the use of an agent (e.g., an antibody) specific to the target protein. An agent such as an antibody that "specifically binds" to a target protein is a term well understood in the art, and methods to determine such specific binding are also well known in the art. An antibody is said to exhibit "specific binding" if it reacts or associates more frequently, more rapidly, with greater duration and / or with greater affinity with a particular target protein than it does with alternative proteins. It is also understood by reading this definition that, for example, an antibody that specifically binds to a first target peptide may or may not specifically or preferentially bind to a second target peptide. As such, "specific binding" or "preferential binding" does not necessarily require (although it can include) exclusive binding. Generally, but not necessarily, reference to binding means preferential binding. In some examples, an antibody that "specifically binds" to a target peptide or an epitope thereof may not bind to other peptides or other epitopes in the same antigen. In some embodiments, a sample may be contacted, simultaneously or sequentially, with more than one binding agent that binds different proteins (e.g., multiplexed analysis).

[0101] As used herein, the term "antibody" refers to a protein that includes at least one immunoglobulin variable domain or immunoglobulin variable domain sequence. For example, an antibody can include a heavy (H) chain variable region (abbreviated herein as VH), and a light (L) chain variable region (abbreviated herein as VL). In another example, an antibody includes two heavy (H) chain variable regions and two light (L) chain variable regions. The term "antibody" encompasses antigen-binding fragments of antibodies (e.g., single chain antibodies, Fab and sFab fragments, F(ab')2, Fd fragments, Fv fragments, scFv, and domain antibodies (dAb) fragments (de Wildt et al., Eur J Immunol. 1996; 26(3):629-39.)) as well as complete antibodies. An antibody can have the structural features of IgA, IgG, IgE, IgD, IgM (as well as subtypes thereof). Antibodies may be from any source including, but not limited to, primate (human and non-human primate) and primatized (such as humanized) antibodies.

[0102] In some embodiments, the antibodies as described herein can be conjugated to a detectable label and the binding of the detection reagent to the peptide of interest can be determined based on the intensity of the signal released from the detectable label. Alternatively, a secondary antibody specific to the detection reagent can be used. One or more antibodies may be coupled to a detectable label. Any suitable label known in the art can be used in the assay methods described herein. In some embodiments, a detectable label comprises a fluorophore. As used herein, the term "fluorophore" (also referred to as "fluorescent label" or "fluorescent dye") refers to moieties that absorb light energy at a defined excitation wavelength and emit light energy at a different wavelength. In some embodiments, a detection moiety is or comprises an enzyme. In some embodiments, an enzyme is one (e.g., β-galactosidase) that produces a colored product from a colorless substrate.

[0103] It will be apparent to those of skill in the art that this disclosure is not limited to immunoassays. Detection assays that are not based on an antibody, such as mass spectrometry, are also useful for the detection and / or quantification of a protein and / or a level of protein as provided herein. Assays that rely on a chromogenic substrate can also be useful for the detection and / or quantification of a protein and / or a level of protein as provided herein.

[0104] Alternatively, the level of nucleic acids encoding a gene in a sample can be measured via a conventional method. In some embodiments, measuring the expression level of nucleic acid encoding the gene comprises measuring mRNA. In some embodiments, the expression level of mRNA encoding a gene can be measured using real-time reverse transcriptase (RT) Q-PCR or a nucleic acid microarray. Methods to detect nucleic acid sequences include, but are not limited to, polymerase chain reaction (PCR), reverse transcriptase-PCR (RT-PCR), in situ PCR, quantitative PCR (Q-PCR), real-time quantitative PCR (RT Q-PCR), in situ hybridization, Southern blot, Northern blot, sequence analysis, microarray analysis, detection of a reporter gene, or other DNA / RNA hybridization platforms.

[0105] In some embodiments, the level of nucleic acids encoding a gene in a sample can be measured via a hybridization assay. In some embodiments, the hybridization assay comprises at least one binding partner. In some embodiments, the hybridization assay comprises at least one oligonucleotide binding partner. In some embodiments, the hybridization assay comprises at least one labeled oligonucleotide binding partner. In some embodiments, the hybridization assay comprises at least one pair of oligonucleotide binding partners. In some embodiments, the hybridization assay comprises at least one pair of labeled oligonucleotide binding partners.

[0106] Any binding agent that specifically binds to a desired nucleic acid or protein may be used in the methods and kits described herein to measure an expression level in a sample. In some embodiments, the binding agent is an antibody or an aptamer that specifically binds to a desired protein. In other embodiments, the binding agent may be one or more oligonucleotides complementary to a nucleic acid or a portion thereof. In some embodiments, a sample may be contacted, simultaneously or sequentially, with more than one binding agent that binds different proteins or different nucleic acids (e.g., multiplexed analysis).

[0107] To measure an expression level of a protein or nucleic acid, a sample can be in contact with a binding agent under suitable conditions. In general, the term "contact" refers to an exposure of the binding agent with the sample or cells collected therefrom for suitable period sufficient for the formation of complexes between the binding agent and the target protein or target nucleic acid in the sample, if any. In some embodiments, the contacting is performed by capillary action in which a sample is moved across a surface of the support membrane.

[0108] In some embodiments, an assay may be performed in a low-throughput platform, including single assay format. In some embodiments, an assay may be performed in a high-throughput platform. Such high-throughput assays may comprise using a binding agent immobilized to a solid support (e.g., one or more chips). Methods for immobilizing a binding agent will depend on factors such as the nature of the binding agent and the material of the solid support and may require particular buffers. Such methods will be evident to one of ordinary skill in the art.Genes

[0109] The various genes recited herein are, in general, named using human gene naming conventions. The various genes, in some embodiments, are described in publically available resources such as published journal articles. The gene names may be correlated with additional information (including sequence information) through use of, for example, the NCBI GenBank ®< databases available at www <dot> ncbi <dot> nlm <dot> nih <dot> gov; the HUGO (Human Genome Organization) Gene Nomination Committee (HGNC) databases available at www <dot> genenames <dot> org; the DAVID Bioinformatics Resource available at www <dot> david <dot> ncifcrf <dot> gov. The gene names may also be correlated with additional information through printed publications from the foregoing organizations. It should be appreciated that a gene may encompass all variants of that gene. For organisms or subjects other than human subjects, corresponding specific-specific genes may be used. Synonyms, equivalents, and closely related genes (including genes from other organisms) may be identified using similar databases including the NCBI GenBank ®< databases described above.

[0110] In some embodiments, gene MK167 may be identified as GenBank ®< Accession number NM_002417.4 or NM_001145966.1; gene ESCO2 may be identified as GenBank ®< Accession number NM_001017420.2; gene CETN3 may be identified as GenBank ®< Accession number NM_001297765.1, NM_004365.3 or NM_001297768.1; gene CDK2 may be identified as GenBank ®< Accession number NM_001798.4, NM_052827.3 or NM_001290230.1; gene CCND1 may be identified as GenBank ®< Accession number NM_053056.2; gene CCNE1 may be identified as GenBank ®< Accession number NM_001238.3, NM_001322259.1, NM_001322261.1 or NM_001322262.1; gene AURKA may be identified as GenBank ®< Accession number NM_198433.2, NM_003600.3, NM_198434.2, NM_198435.2, NM_198436.2, NM_198437.2, NM_001323303.1, NM_001323304.1, or NM_001323305.1; gene AURKB may be identified as GenBank ®< Accession number NM_004217.3, NM_001256834.2, NM_001284526.1, NM_001313950.1, NM_001313951.1, NM_001313952.1, NM_001313954.1, NM_001313953.2 or NM_001313955.1; gene CDK4 may be identified as GenBank ®< Accession number NM_000075.3; gene CDK6 may be identified as GenBank ®< Accession number NM_001145306.1; gene PRC1 may be identified as GenBank ®< Accession number NM_199413.2 or NM_003981.3. ; gene E2F1 may be identified as GenBank ®< Accession number NM_005225.2; gene MYBL2 may be identified as GenBank ®< Accession number NM_002466.3 or NM_001278610.1; gene BUB1 may be identified as GenBank ®< Accession number NM_004336.4, NM_001278616.1, NM_001278617.1; gene PLK1 may be identified as GenBank ®< Accession number NM_005030.5; gene CCNB1 may be identified as GenBank ®< Accession number NM_031966.3, NM_001354845.1, NM_001354844.1; gene MCM2 may be identified as GenBank ®< Accession number NM_004526.3. ; gene MCM6 may be identified as GenBank ®< Accession number NM_005915.5; gene PIK3CA may be identified as GenBank ®< Accession number NM_006218.3; gene PIK3CB may be identified as GenBank ®< Accession number NM_006219.2 or NM_001256045.1; gene PIK3CG may be identified as GenBank ®< Accession number NM_002649.3, NM_001282427.1 or NM_001282426.1; gene PIK3CD may be identified as GenBank ®< Accession number NM_005026.4, NM_001350234.1, or NM_001350235.1; gene AKT1 may be identified as GenBank ®< Accession number NM_005163.2, NM_001014431.1, or NM_001014432.1; gene MTOR may be identified as GenBank ®< Accession number NM_004958.3 ; gene PTEN may be identified as GenBank ®< Accession number NM_001304717.2, NM_000314.6 or NM_001304718.1; gene PRKCA may be identified as GenBank ®< Accession number NM_002737.2; gene AKT2 may be identified as GenBank ®< Accession number NM_001330511.1, NM_001243027.2, NM_001243028.2, NM_001626.5; gene AKT3 may be identified as GenBank ®< Accession number NM_005465.4, NM_181690.2 or NM_001206729.1; gene BRAF may be identified as GenBank ®< Accession number NM_001354609.1 or NM_004333.5; gene FNTA may be identified as GenBank ®< Accession number NM_002027.2; gene FNTB may be identified as GenBank ®< Accession number NM_002028.3; gene MAP2K1 may be identified as GenBank ®< Accession number NM_002755.3; gene MKNK1 may be identified as GenBank ®< Accession number NM_003684.6, NM_198973.4 or NM_001135553.3; gene MKNK2 may be identified as GenBank ®< Accession number NM_017572.3 or NM_199054.2.MF Profiles

[0111] A "molecular functional tumor portrait (MF profile)," as described herein, refers to a graphical depiction of a tumor with regard to molecular and cellular composition, and biological processes that are present within and / or surrounding the tumor. Related compositions and processes present within and / or surrounding a tumor are presented in functional modules (also described herein as "gene groups") of a MF profile.

[0112] MF profiles may be constructed, in some embodiments, from gene expression data (for example sequencing data, e.g., whole exome sequencing data, RNA sequencing data, or other gene expression data) of normal tissue and / or tumor tissue. FIG. 1A shows an exemplary bioinformatics pipeline for constructing a tumor portrait from sequencing data. MF profiles produced in accordance with the bioinformatics pipeline in FIG. 1A may comprise functional modules depicted as circles and arrange in an circular pattern as shown in FIG. 1B. Each circle of the MF profile in FIG. 1B represents a functional module, which are labeled using lines. Related functional modules may be combined into a single functional module. For example, FIG. 1B shows that the anti-metastatic factors module, the metastatic factors module, and the tumor suppressors module may be combined into the malignant cell properties module.

[0113] FIG. 1C shows one embodiment of an MF profile as provided herein. As shown in FIG. 1C, the MF profile 100 comprises 28 functional modules, three of which are labeled as 110, 120 and 130. Module size indicates module intensity. For example, module 110 is larger than module 120 indicating that module 110 has increased module intensity as compared to module 120. The presence or absence of cross-hatching of the module indicates whether the module is a pro-tumor module or an anti-tumor module. Pro-tumor modules (e.g., module 120) are shown in solid shades without cross-marking thereof, and anti-tumor modules (e.g., module 130) are shown with cross-marking thereof. The depth of shading of the module indicates module intensity. Modules relating to tumor malignancy 140 are depicted in the top right quarter of the circle.MF Profile Modules

[0114] A "functional module" or "gene group," as described herein, refers to related compositions and processes present within and / or surrounding a tumor.

[0115] For example, an immune response / inflammation module provides information related to immune system composition and activity within a tumor. Examples of immune system composition and activity within a tumor presented in the immune response / inflammation module include, but are not limited to, the number of unique tumor antigens, MHC-restricted antigen presentation, expression of co-stimulatory compounds that are involved in T cell activation, intensities of activation and effector phases of adaptive and innate immune responses, proportions of different lymphoid and myeloid cell populations within a tumor, expression rates of cancer-promoting and anti-cancer cytokines, and intensities of immune response processes (e.g., activities of immunosuppressive cells and expression of immune checkpoint inhibitory molecules).

[0116] Exemplary modules in a MF profile may include, but are not limited to, Major histocompatibility complex I (MHCI) module, Major histocompatibility complex II (MHCII) module, Coactivation molecules module, Effector cells module, Effector T cell module; Natural killer cells (NK cells) module, T cell traffic module, T cells module, B cells module, B cell traffic module, Benign B cells module, Malignant B cell marker module, M1 signatures module, Th1 signature module, Antitumor cytokines module, Checkpoint inhibition (or checkpoint molecules) module, Follicular dendritic cells module, Follicular B helper T cells module, Protumor cytokines module, Regulatory T cells (Treg) module, Treg traffic module, Myeloid-derived suppressor cells (MDSCs) module, MDSC and TAM traffic module, Granulocytes module, Granulocytes traffic module, Eosinophil signature model, Neutrophil signature model, Mast cell signature module, M2 signature module, Th2 signature module, Th17 signature module, Protumor cytokines module, Complement inhibition module, Fibroblastic reticular cells module, Cancer associated fibroblasts (CAFs) module, Matrix formation (or Matrix) module, Angiogenesis module, Endothelium module, Hypoxia factors module, Coagulation module, Blood endothelium module, Lymphatic endothelium module, Proliferation rate (or Tumor proliferation rate) module, Oncogenes module, PI3K / AKT / mTOR signaling module, RAS / RAF / MEK signaling module, Receptor tyrosine kinases expression module, Growth Factors module, Tumor suppressors module, Metastasis signature module, Antimetastatic factors module, and Mutation status module. In certain embodiments, the modules may be described as "gene groups".

[0117] In some embodiments, the gene groups of the modules may comprise at least two genes (e.g., at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes as shown in the following lists; in some embodiments all of the listed genes are selected from each group; and in some embodiments the numbers of genes in each selected group are not the same.

[0118] In some embodiments, the modules in a MF profile may comprise or consist of: Major histocompatibility complex I (MHCI) module, Major histocompatibility complex II (MHCII) module, Coactivation molecules module, Effector cells (or Effector T cell) module, Natural killer cells (NK cells) module, T cells module, B cells module, M1 signatures module, Th1 signature module, Antitumor cytokines module, Checkpoint inhibition (or checkpoint molecules) module, Regulatory T cells (Treg) module, Myeloid-derived suppressor cells (MDSCs) module, Neutrophil signature model, M2 signature module, Th2 signature module, Protumor cytokines module, Complement inhibition module, Cancer associated fibroblasts (CAFs) module, Angiogenesis module, Endothelium module, Proliferation rate (or Tumor proliferation rate) module, PI3K / AKT / mTOR signaling module, RAS / RAF / MEK signaling module, Receptor tyrosine kinases expression module, Growth Factors module, Tumor suppressors module, Metastasis signature module, and Antimetastatic factors module. The MF profile may additionally include: T cell traffic module, Antitumor cytokines module, Treg traffic module, MDSC and TAM traffic module, Granulocytes or Granulocyte traffic module, Eosinophil signature model, Mast cell signature module, Th17 signature module, Matrix formation (or Matrix) module, and Hypoxia factors module. Such an MF profile could be useful for a subject with a solid cancer (e.g., a melanoma).

[0119] In some embodiments, the modules in a MF profile may comprise or consist of: Effector cells (or Effector T cell) module, Natural killer cells (NK cells) module, T cells module, Malignant B cell marker module, M1 signatures module, Th1 signature module, Checkpoint inhibition (or checkpoint molecules) module, Follicular dendritic cells module, Follicular B helper T cells module, Protumor cytokines module, Regulatory T cells (Treg) module, Neutrophil signature model, M2 signature module, Th2 signature module, Complement inhibition module, Fibroblastic reticular cells module, Angiogenesis module, Blood endothelium module, Proliferation rate (or Tumor proliferation rate) module, Oncogenes module, and Tumor suppressors module. The MF profile may additionally include: Major histocompatibility complex I (MHCI) module, Major histocompatibility complex II (MHCII) module, Coactivation molecules module, B cell traffic module, Benign B cells module, Antitumor cytokines module, Treg traffic module, Mast cell signature module, Th17 signature module, Matrix formation (or Matrix) module, Hypoxia factors module, Coagulation module, and Lymphatic endothelium module. Such an MF profile could be useful for a subject with follicular lymphoma.In some embodiments, the gene groups of the modules may comprise at least two genes (e.g., at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes as shown in the following lists; in some embodiments all of the listed genes are selected from each group; and in some embodiments the numbers of genes in each selected group are not the same): Major histocompatibility complex I (MHCI) module: HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2; Major histocompatibility complex II (MHCII) module: HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, and HLA-DRB6; Coactivation molecules module: CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28; Effector cells module: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; Effector T cell module: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; Natural killer cells (NK cells) module: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, EOMES, CLIC3, FGFBP2, KLRF1, and SH2D1B; T cell traffic module: CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, and CCL5; T cells module: EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD5, and CD28; B cells module: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, and STAP1; B cell traffic module: CXCL13 and CXCR5; Benign B cells module: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, and BLK; Malignant B cell marker module: MME, CD70, CD20, CD22, and PAX5; M1 signatures module: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3; Th1 signature module: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, and IL21; Antitumor cytokines module: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; Checkpoint inhibition (or checkpoint molecules) module: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR; Follicular dendritic cells module: CR1, FCGR2A, FCGR2B, FCGR2C, CR2, FCER2, CXCL13, MADCAM1, ICAM1, VCAM1, BST1, LTBR, and TNFRSF1A; Follicular B helper T cells module: CXCR5, B3GAT1, ICOS, CD40LG, CD84, IL21, BCL6, MAF, and SAP; Protumor cytokines module: IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, TNFSF13B, IL6, and IL7; Regulatory T cells (Treg) module: TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF18, TNFR2, and TNFRSF1B; Treg traffic module: CCL17, CXCL12, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; Myeloid-derived suppressor cells (MDSCs) module: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, and CD33; MDSC and TAM traffic module: CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; Granulocytes module: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, MPO, ELANE, PRTN3, CTSG, FCGR3B, CXCR1, CXCR2, CD177, PI3, FFAR2, PGLYRP1, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, and SIGLEC8; Granulocyte traffic module: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, and CCL26; Eosinophil signature model: PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, and PRG3; Neutrophil signature model: MPO, ELANE, PRTN3, CTSG, FCGR3B, CXCR1, CXCR2, CD177, PI3, FFAR2, and PGLYRP1; Mast cell signature module: CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, and SIGLEC8; M2 signature module: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; Th2 signature module: IL4, IL5, IL13, IL10, IL25, and GATA3; Th17 signature module: IL17A, IL22, IL26, IL17F, IL21, and RORC; Protumor cytokines module: IL10, TGFB1, TGFB2, TGFB3, IL22, and MIF; Complement inhibition module: CFD, CFI, CD55, CD46, CR1, and CD59; Fibroblastic reticular cells module: DES, VIM, PDGFRA, PDPN, NT5E, THY1, ENG, ACTA2, LTBR, TNFRSF1A, VCAM1, ICAM1, and BST1; Cancer associated fibroblasts (CAFs) module: COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, FBLN1, LUM, MFAP5, LGALS1, and PRELP; Matrix formation (or Matrix) module: MMP9, FN1, COL1A1, COL1A2, COL3A1, COL4A1, CA9, VTN, LGALS7, TIMP1, MMP2, MMP1, MMP3, MMP12, LGALS9, MMP7, and COL5A1; Angiogenesis module: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; Endothelium module: VEGFA, NOS3, KDR, FLT1, VCAM1, VWF, CDH5, MMRN1, CLEC14A, MMRN2, and ECSCR; Hypoxia factors module: LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; Coagulation module: HPSE, SERPINE1, SERPINB2, F3, and ANXA2; Blood endothelium module: VEGFA, NOS3, KDR, FLT1, VCAM1, VWF, CDH5, and MMRN1; Lymphatic endothelium module: CCL21 and CXCL12; Proliferation rate (or Tumor proliferation rate) module: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, PLK1, PRC1, CCNB1, MCM2, MCM6, CDK4, and CDK6; Oncogenes module: MDM2, MYC, AKT1, BCL2, MME, and SYK; PI3K / AKT / mTOR signaling module: PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; RAS / RAF / MEK signaling module: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; Receptor tyrosine kinases expression module: ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, and ABL1; Growth Factors module: NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, and FGF2; Tumor suppressors module: TP53, MLL2, CREBBP, EP300, ARID1A, HIST1H1, EBF1, IRF4, IKZF3, KLHL6, PRDM1, CDKN2A, RB1, EPHA7, TNFAIP3, TNFRSF14, FAS, SHP1, SOCS1, SIK1, PTEN, DCN, MTAP, AIM2, and MITF; Metastasis signature module: ESRP1, HOXA1, SMARCA4, TWIST1, NEDD9, PAPPA, CTSL, SNAI2, and HPSE; Antimetastatic factors module: NCAM1, CDH1, KISS1, BRMS1, ADGRG1, TCF21, PCDH10, and MITF; and Mutation status module: APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL. In certain embodiments, two or more genes from any combination of the listed modules may be included in an MF portrait.

[0120] In some embodiments, the gene groups of the modules may comprise at least two genes (e.g., at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes as shown in the following lists; in some embodiments all of the listed genes are selected from each group; and in some embodiments the numbers of genes in each selected group are not the same): Major histocompatibility complex I (MHCI) module: HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2; Major histocompatibility complex II (MHCII) module; HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, and HLA-DRB6; Coactivation molecules module: CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28; Effector cells (or Effector T cell) module: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; Natural killer cells (NK cells) module: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GNLY, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, EOMES, CLIC3, FGFBP2, KLRF1, and SH2D1B; T cells module: TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD5, and CD28; B cells module: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, FCRL5, PAX5, and STAP1; M1 signatures module: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3; Th1 signature module: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, and IL21; Checkpoint inhibition (or checkpoint molecules) module: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR; Regulatory T cells (Treg) module: TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, and TNFRSF1B; Myeloid-derived suppressor cells (MDSCs) module: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, and CD33; Neutrophil signature model: MPO, ELANE, PRTN3, CTSG, FCGR3B, CXCR1, CXCR2, CD177, PI3, FFAR2, and PGLYRP1; M2 signature module: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; Th2 signature module: IL4, IL5, IL13, IL10, IL25, and GATA3; Protumor cytokines module: IL10, TGFB1, TGFB2, TGFB3, IL22, and MIF; Complement inhibition module: CFD, CFI, CD55, CD46, and CR1; Cancer associated fibroblasts (CAFs) module: COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, COL6A3, FBLN1, LUM, MFAP5, and PRELP; Angiogenesis module: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, VCAM1, and MMRN1; Endothelium module: VEGFA, NOS3, KDR, FLT1, VCAM1, VWF, CDH5, MMRN1, CLEC14A, MMRN2, and ECSCR; Proliferation rate (or Tumor proliferation rate) module: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6, CDK4, and CDK6; PI3K / AKT / mTOR signaling module: PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; RAS / RAF / MEK signaling module: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; Receptor tyrosine kinases expression module: ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, and ABL1; Growth Factors module: NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, and FGF2; Tumor suppressors module: TP53, SIK1, PTEN, DCN, MTAP, AIM2, RB1, and MITF; Metastasis signature module: ESRP1, HOXA1, SMARCA4, TWIST1, NEDD9, PAPPA, and HPSE; and Antimetastatic factors module: NCAM1, CDH1, KISS1, and BRMS1. In some embodiments, the gene groups of the modules may further comprise at least two genes (e.g., at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes as shown in the following lists; in some embodiments all of the listed genes are selected from each group; and in some embodiments the numbers of genes in each selected group are not the same): T cell traffic module: CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, and CCL5; Antitumor cytokines module: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; Treg traffic module: CCL17, CXCL12, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; MDSC and TAM traffic module: CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; Granulocyte traffic module: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, and CCL26; Eosinophil signature model: PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, and PRG3; Mast cell signature module: CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, and SIGLEC8; Th17 signature module: IL17A, IL22, IL26, IL17F, IL21, and RORC; Matrix formation (or Matrix) module: FN1, CA9, MMP1, MMP3, MMP12, LGALS9, MMP7, MMP9, COL1A1, COL1A2, COL4A1, and COL5A1; and Hypoxia factors module: LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3. In certain embodiments, two or more genes from each of the listed modules are included. Any of the foregoing sets of modules may be used in a MF portrait for a subject with a solid cancer (e.g., melanoma).

[0121] In some embodiments, the gene groups of the modules may comprise at least two genes (e.g., at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes as shown in the following lists; in some embodiments all of the listed genes are selected from each group; and in some embodiments the numbers of genes in each selected group are not the same): Effector T cell module: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; Natural killer cells (NK cells) module: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, and KIR2DS5; T cells module: EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; Benign B cells module: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, and BLK; Malignant B cell marker module: MME, CD70, CD20, CD22, and PAX5; M1 signatures module: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3; Th1 signature module: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, and IL21; Checkpoint inhibition (or checkpoint molecules) module: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, and HAVCR2; Follicular dendritic cells module: CR1, FCGR2A, FCGR2B, FCGR2C, CR2, FCER2, CXCL13, MADCAM1, ICAM1, VCAM1, BST1, LTBR, and TNFRSF1A; Follicular B helper T cells module: CXCR5, B3GAT1, ICOS, CD40LG, CD84, IL21, BCL6, MAF, and SAP; Protumor cytokines module: IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, TNFSF13B, IL6, and IL7; Regulatory T cells (Treg) module: TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF18, and TNFR2; Neutrophil signature model: MPO, ELANE, PRTN3, and CTSG; M2 signature module: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; Th2 signature module: IL4, IL5, IL13, IL10, IL25, and GATA3; Complement inhibition module: CFD, CFI, CD55, CD46, CR1, and CD59; Fibroblastic reticular cells module: DES, VIM, PDGFRA, PDPN, NT5E, THY1, ENG, ACTA2, LTBR, TNFRSF1A, VCAM1, ICAM1, and BST1; Angiogenesis module: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, and CDH5; Blood endothelium module: VEGFA, NOS3, KDR, FLT1, VCAM1, VWF, CDH5, and MMRN1; Proliferation rate (or Tumor proliferation rate) module: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6; Oncogenes module: MDM2, MYC, AKT1, BCL2, MME, and SYK; and Tumor suppressors module: TP53, MLL2, CREBBP, EP300, ARID1A, HIST1H1, EBF1, IRF4, IKZF3, KLHL6, PRDM1, CDKN2A, RB1, EPHA7, TNFAIP3, TNFRSF14, FAS, SHP1, and SOCS1. In some embodiments, the gene groups of the modules may further comprise at least two genes (e.g., at least two genes, at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes as shown in the following lists; in some embodiments all of the listed genes are selected from each group; and in some embodiments the numbers of genes in each selected group are not the same): Coactivation molecules module: TNFRSF4 and CD28; B cell traffic module: CXCL13 and CXCR5; Antitumor cytokines module: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, FASLG; Treg traffic module: CCL17, CCR4, CCL22, and CXCL13; Eosinophil signature model: PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, and PRG3; Mast cell signature module: CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, and SIGLEC8; Th17 signature module: IL17A, IL22, IL26, IL17F, IL21, and RORC; Matrix formation (or Matrix) module: MMP9, FN1, COL1A1, COL1A2, COL3A1, COL4A1, CA9, VTN, LGALS7, TIMP1, and MMP2; Hypoxia factors module: LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; Coagulation module: HPSE, SERPINE1, SERPINB2, F3, and ANXA2; and Lymphatic endothelium module: CCL21 and CXCL12. In certain embodiments, two or more genes from each of the listed modules are included. Any of the foregoing sets of modules may be used in a MF portrait for a subject with a follicular lymphoma.

[0122] In some embodiments, the plurality of gene groups (or modules) associated with cancer malignancy is the tumor properties group. In some embodiments, the plurality of gene groups associated with cancer microenvironment are the tumor-promoting immune microenvironment group, the anti-tumor immune microenvironment group, the angiogenesis group, and the fibroblasts group.

[0123] In certain embodiments, the plurality of gene groups associated with cancer malignancy comprises at least three genes from the following group (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes are selected from each group; in some embodiments all of the listed genes are selected from each group): the tumor properties group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6, PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, AKT3, BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, MKNK2, ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, FGF2, TP53, SIK1, PTEN, DCN, MTAP, AIM2, RB1, ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, HPSE, KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, MITF, APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL. In certain embodiments, the plurality of gene groups associated with cancer microenvironment includes at least three genes from each of the following groups (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes are selected from each group; in some embodiments all of the listed genes are selected from each group): the anti-tumor immune microenvironment group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28, IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, NOS2, IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; the tumor-promoting immune microenvironment group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, VSIR, CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, CCL28, IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, CXCL8, CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, CTSG, IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1; the fibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; and the angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3. In some embodiments, an unequal number of genes may be selected from each of the listed groups for use. In specific embodiments, all or almost all of the listed genes are used.

[0124] In some embodiments, the plurality of gene groups associated with cancer malignancy are: the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group. In some embodiments, the plurality of gene groups associated with cancer microenvironment are: the cancer associated fibroblasts group, the angiogenesis group, the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, and the tumor-promotive immune group.

[0125] In some embodiments, the plurality of gene groups associated with cancer malignancy comprises at least three genes from each of the following groups (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes are selected from each group): the proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6; the PI3K / AKT / mTOR signaling group: PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; the RAS / RAF / MEK signaling group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; the receptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, and PDGFRB; the tumor suppressors group: TP53, SIK1, PTEN, DCN, MTAP, AIM2, and RB1; the metastasis signature group: ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, and HPSE; the anti-metastatic factors group: KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, and MITF; and the mutation status group: APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL.

[0126] In some embodiments, the plurality of gene groups associated with cancer microenvironment comprises at least three genes from each of the following groups (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes are selected from each group): the cancer associated fibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; the angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; the antigen presentation group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, and CD28; the cytotoxic T and NK cells group: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; the B cells group: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, and BLK; the anti-tumor microenvironment group: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; the checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR; the Treg group: CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; the MDSC group: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; the granulocytes group: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, and CTSG; the tumor-promotive immune group: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1. In some embodiments, an unequal number of genes may be selected from each of the listed groups for use. In specific embodiments, all or almost all of the listed genes are used.

[0127] In some embodiments, the plurality of gene groups associated with cancer malignancy are: the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group. In some embodiments, the plurality of gene groups associated with cancer microenvironment are: the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group.

[0128] In some embodiments, the plurality of gene groups associated with cancer malignancy comprises at least three genes from each of the following groups (e.g., at least three genes, at least four genes, at least five genes, at least six genes, at least seven genes, at least eight genes, at least nine genes, at least ten genes, or more than ten genes are selected from each group): the proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6; the PI3K / AKT / mTOR signaling group: PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; the RAS / RAF / MEK signaling group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; the receptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, and PDGFRB; the growth factors group: NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, and FGF2; the tumor suppressors group: TP53, SIK1, PTEN, DCN, MTAP, AIM2, and RB1; the metastasis signature group: ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, and HPSE; the antimetastatic factors group: KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, and MITF; and the mutation status group: APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL. In some embodiments, the plurality of gene groups associated with cancer microenvironment comprises at least three genes from each of the following groups: the cancer associated fibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; the angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; the MHCI group: HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2; the MHCII group: HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, and HLA-DRB6; the coactivation molecules group: CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, and CD28; the effector cells group: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; the NK cells group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, and KIR2DS5; the T cell traffic group: CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, and CCL5; the T cells group: EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; the B cells group: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, and BLK; the M1 signatures group: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3; the Th1 signature group: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, and IL21; the antitumor cytokines group: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; the checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR; the Treg group: CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; the MDSC group: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; the granulocytes group: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, and CTSG; the M2 signature group: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; the Th2 signature group: IL4, IL5, IL13, IL10, IL25, and GATA3; the protumor cytokines group: IL10, TGFB1, TGFB2, TGFB3, IL22, and MIF; and the complement inhibition group: CFD, CFI, CD55, CD46, and CR1. In some embodiments, an unequal number of genes may be selected from each of the listed groups for use. In specific embodiments, all or almost all of the listed genes are used.

[0129] MF profiles may depict the intensity (e.g., amount) of a module or gene group using a distinguishing feature (e.g., color, shading or pattern, size, and / or shape). As used herein, "intensity" refers to an amount of a gene group expression level within a MF profile. For example, 2 nd< MF profile type cancers have an intense proliferation rate module indicative of a high proliferation rate of such cancers. Accordingly, in 2 nd< MF profile type cancers, the proliferation rate module is depicted in a larger size as an indication that this module is more abundant in the tumor than other modules. In some embodiments, the MF profile comprises modules of various sizes in which module size is indicative of module intensity. In some embodiments, the MF profile comprises modules of increasing sizes in which increasing module size is indicative of increasing module intensity.

[0130] MF profiles may depict a module as a pro-tumor module or anti-tumor module using a distinguishing feature (e.g., color, shading or pattern, size, and / or shape). In some embodiments, the MF profile comprises a pro-tumor module as one color or pattern and an anti-tumor module as another color or pattern. In some embodiments, the MF profile comprises a pro-tumor module as burgundy or a shade thereof and an anti-tumor module as blue or a shade thereof. In some embodiments, the MF profile comprises a pro-tumor module as solid shades without cross-marking and an anti-tumor module as shades with cross-marking.

[0131] MF profiles may comprise any number of functional modules. In some embodiments, the MF profile comprises at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, or at least 28 modules. In some embodiments, the MF profile comprises up to 2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, up to 10, up to 11, up to 12, up to 13, up to 14, up to 15, up to 16, up to 17, up to 18, up to 19, up to 20, up to 21, up to 22, up to 23, up to 24, up to 25, up to 26, up to 27, or up to 28 modules.MF Profile Types

[0132] The present disclosure is based, in part, on the finding that various cancers (e.g., tumors) can be categorized into four types (i.e., first MF profile type or "1 st< MF profile," second MF profile type or "2 nd< MF profile," third MF profile type or "3 rd< MF profile," and fourth MF profile type or "4 th< MF profile" cancers) based on certain properties of the cancer or tumor (e.g., expression data).

[0133] As used herein, the term "cancer type," "tumor type," or "MF profile type" refers to a cancer (e.g., a tumor) having certain features including certain molecular and cellular compositions, and biological processes.

[0134] MF profile type, in some embodiments, may provide information relating to a level of immune cells within and / or surrounding a tumor. For example, an "inflamed" or "hot" MF profile type includes a cancer (e.g., a tumor) that is highly infiltrated by immune cells, a "non-inflamed" or "cold" MF profile type describes a cancer (e.g., a tumor) that is poorly infiltrated by immune cells. In some embodiments, describing a cancer as a 1 st< MF profile type cancer indicates that the cancer (e.g., a tumor) is inflamed. In some embodiments, describing a cancer as a 2 nd< MF profile type cancer indicates that the cancer (e.g., a tumor) is inflamed. In some embodiments, describing a cancer as a 3 rd< MF profile type cancer indicates that the cancer (e.g., a tumor) is non-inflamed. In some embodiments, describing a cancer as a 4 th< MF profile type cancer indicates that the cancer (e.g., a tumor) is non-inflamed.

[0135] MF profile type, in some embodiments, provides information relating to an average ratio of malignant to nonmalignant cells of a tumor (e.g., tumor purity). In some embodiments, the average ratio of malignant to nonmalignant cells increases with MF profile type. For example, 4 th< MF profile > 3 rd< MF profile > 2 nd< MF profile > 1 st< MF profile with respect to an average ratio of malignant to nonmalignant cells.

[0136] In some embodiments, describing a cancer as a 1 st< MF profile type cancer indicates that the tumor has about 2 times (twice) as many nonmalignant cells as malignant cells. In some embodiments, describing a cancer as a 1 st< MF profile type cancer indicates that the tumor has an average ratio of malignant to nonmalignant cells of between 0.4 to 0.6. In some embodiments, describing a cancer as a 1 st< MF profile type cancer indicates that the cancer has an average ratio of malignant to nonmalignant cells of about 0.5.

[0137] In some embodiments, describing a cancer as a 2 nd< MF profile type cancer indicates that the cancer has about 1.5 times as many nonmalignant cells as malignant cells. In some embodiments, describing a cancer as a 2 nd< MF profile type cancer indicates that the cancer has an average ratio of malignant to nonmalignant cells between 0.6 to 0.7. In some embodiments, describing a cancer as a 2 nd< MF profile type cancer indicates that the cancer has an average ratio of malignant to nonmalignant cells of about 0.65.

[0138] In some embodiments, describing a cancer as a 3 rd< MF profile type cancer indicates that the cancer has about 1.3 times as many nonmalignant cells as malignant cells. In some embodiments, describing a cancer as a 3 rd< MF profile type cancer indicates that the cancer has an average ratio of malignant to nonmalignant cells between 0.7 to 0.8. In some embodiments, describing a cancer as a 3 rd< MF profile type cancer indicates that a tumor has an average ratio of malignant to nonmalignant cells of about 0.8.

[0139] In some embodiments, describing a cancer as a 4 th< MF profile type cancer indicates that the cancer has about 1.1 times as many nonmalignant cells s malignant cells. In some embodiments, describing a cancer as a 4 th< MF profile type cancer indicates that the cancer has an average ratio of malignant to nonmalignant cells between 0.8 to 0.9. In some embodiments, describing a cancer as a 4 th< MF profile type cancer indicates that the cancer has an average ratio of malignant to nonmalignant cells of about 0.85.

[0140] MF profile type, in some embodiments, provides information relating to tumor vascularization. In some embodiments, describing a cancer as a 1 st< MF profile type cancer indicates that the cancer (e.g., the tumor) is vascularized. In some embodiments, describing a cancer as a 2 nd< MF profile type cancer indicates that the cancer (e.g., the tumor) is nonvascularized. In some embodiments, describing a cancer as a 3 rd< MF profile type cancer indicates that the cancer (e.g., the tumor) is vascularized. In some embodiments, describing a cancer as a 4 th< MF profile type cancer indicates that the cancer (e.g., the tumor) is nonvascularized.

[0141] MF profile type, in some embodiments, provides information relating to levels of cancer associated fibroblasts (CAFs) within and / or surrounding a tumor. In some embodiments, describing a cancer as a 1 st< MF profile type cancer indicates that the cancer (e.g., the tumor) comprises CAFs. In some embodiments, describing a cancer as a 2 nd< MF profile type cancer indicates that the cancer (e.g., the tumor) is devoid of CAFs. In some embodiments, describing a cancer as a 3 rd< MF profile type cancer indicates that the cancer (e.g., the tumor) comprises CAFs. In some embodiments, describing a cancer as a 4 th< MF profile type cancer indicates that the cancer (e.g., the tumor) is devoid of CAFs.

[0142] MF profile type, in some embodiments, provides information relating to tumor proliferation rates. In some embodiments, describing a cancer as a 1 st< MF profile type cancer indicates that the cancer (e.g., the tumor) has an average proliferation rate. In some embodiments, describing a cancer as a 2 nd< MF profile type cancer indicates that the cancer (e.g., the tumor) has a high proliferation rate. In some embodiments, describing a cancer as a 3 rd< MF profile type cancer indicates that the cancer (e.g., the tumor) has an average proliferation rate. In some embodiments, describing a cancer as a 4 th< MF profile type cancer indicates that the cancer (e.g., the tumor) has a high proliferation rate.

[0143] MF profile type, in some embodiments, provides information relating to patient survival rate. In some embodiments, the patient survival rate increases with MF profile type. For example, 1 st< MF profile > 2 nd< MF profile > 3 rd< MF profile > 4 th< MF profile with respect to patient survival rate.

[0144] In some embodiments, describing a cancer as a 1 st< MF profile type cancer indicates a good patient survival rate. In some embodiments, describing a cancer as a 2 nd< MF profile type cancer indicates an optimal patient survival rate. In some embodiments, describing a cancer as a 3 rd< MF profile type cancer indicates that a poor patient survival rate. In some embodiments, describing a cancer as a 4 th< MF profile type cancer indicates that a poor patient survival rate.

[0145] MF profile type, in some embodiments, provides information relating to patient treatment. In some embodiments, the MF profile type provides information relating to an expected treatment outcome of a therapy. In some embodiments, the MF profile indicates that a specific treatment option is recommended. In some embodiments, the MF profile indicates that a specific treatment option is non-curative. In some embodiments, the MF profile indicates that a specific treatment option is dependent on a certain feature of a tumor, for example, mutational status of the tumor.

[0146] In some embodiments, identifying a cancer as a 1 st< MF profile type cancer indicates that a treatment selected from the group consisting of an angiogenesis inhibitor, a CAFs inhibitor, an immunosuppressive factor inhibitor, a MDSC inhibitor, a Treg inhibitor, a metastatic activity inhibitor, and an immunotherapy should be recommended or used. In some embodiments, identifying a cancer as a 1 st< MF profile type cancer indicates that treatment using a growth factor inhibitor dependent on a certain feature of a tumor (e.g., mutational status) should be recommended or used.

[0147] In some embodiments, identifying a cancer as a 2 nd< MF profile type cancer indicates that a treatment selected from the group consisting of an immunosuppressive factor inhibitor, a MDSC inhibitor, a Treg inhibitor, a metastatic activity inhibitor, a checkpoint inhibitor, and an immunotherapy should be recommended or used. In some embodiments, identifying a cancer as a 2 nd< MF profile type cancer indicates that treatment using a growth factor inhibitor dependent on a certain feature of a tumor (e.g., mutational status) should be recommended or used.

[0148] In some embodiments, identifying a cancer as a 3 rd< MF profile type indicates that a treatment selected from the group consisting of an angiogenesis inhibitor, a CAFs inhibitor, an immunosuppressive factor inhibitor, a M2 macrophage inhibitor, a MDSC inhibitor, and a Treg inhibitor should be recommended or used. In some embodiments, identifying a cancer as a 3 rd< MF profile type indicates that a checkpoint inhibitor should be recommended or used.

[0149] In some embodiments, identifying a cancer as a 4 th< MF profile type cancer indicates that a treatment such as an angiogenesis inhibitor and / or an immunotherapy should be recommended or used. In some embodiments, identifying a cancer as a 4 th< MF profile type indicates that a non-curative treatment option may be selected from the group consisting of a kinase inhibitor, a radiotherapy, and a chemotherapy.Visualization of MF Profiles

[0150] In some embodiments, a software program may provide a user with a visual representation of a patient's MF profile and / or other information related to a patient's cancer using an interactive graphical user interface (GUI).

[0151] In response to being launched, the interactive GUI may provide the user of the software program with initial information related to a patient's cancer. Subsequently, the user may interact with the GUI to obtain additional and / or alternative information related to a patient's cancer. FIGs. 3-37 show illustrative screenshots of the interactive graphical user interface and are described below.

[0152] FIG. 3 is a graphic illustrating different types of screens that may be shown to a user of the software program. Each of the different screens illustrated in FIG. 3 may be used to present different types of information to the user. A screenshot of a control screen of the software program is shown in the middle of FIG. 3. The control screen includes portions for presenting information relating to treatment selection, tumor properties, and clinical evidence of treatment efficacy and is described further with respect to FIGs. 7-37.

[0153] A user may interact with the control screen to obtain additional information about, for example, immunotherapy selection, targeted therapy selection, combination therapy design, tumor properties and tumor microenvironment, clinical evidence of targeted therapy efficacy, and clinical evidence of immunotherapy efficacy. The user may select a portion of the control screen (e.g., the immunotherapy portion) to view one or more additional screens presenting information relating to the selected portion. As shown in FIG. 3, arrows point from a portion of the control screen that may be selected toward the screens presenting additional information related to the selected portion.

[0154] For example, the user may select the immunotherapy selection portion of the control screen to view one or more screens presenting information relating to various immunotherapies, biomarkers associated with an immunotherapy (e.g., genetic biomarkers, cellular biomarkers, and expression biomarkers), immune cell properties of the patient's tumor, and clinical trials (e.g., information from and / or regarding published clinical trials and ongoing clinical trials).

[0155] In another example, the user may select the targeted therapy selection portion of the control screen to view one or more screens presenting information relating to various targeted therapies, biomarkers associated with targeted therapies (e.g., genetic biomarkers, cellular biomarkers, and / or expression biomarkers), properties of the patient's tumor associated with the targeted therapy, and clinical trials (e.g., published clinical trials and ongoing clinical trials).

[0156] In another example, the user may select the molecular-functional portrait (MF profile) portion of the control screen to view one or more screens presenting information relating to the patient's tumor microenvironment. Such information may include information about tumor properties (e.g., proliferation rate), angiogenesis, metastasis, cellular composition, cancer associated fibroblasts, pro-tumor immune environment, and anti-tumor immune environment.

[0157] In yet another example, the user may select the clinical evidence of treatment efficacy portion of the control screen to view one or more screens presenting information relating to a therapy (e.g., an immunotherapy or targeted therapy). Such information may include description of the therapy, therapy efficacy, potential adverse effects, related publications, treatment regimen, and patient survival data.

[0158] A user of the software program may interact with the GUI to log into the software program. FIG. 4 is a screenshot of the user's account profile screen presented to the user in response to the user logging into the software program. The user's account profile screen may provide information for one or more patients, such as patient identification and diagnosis (e.g., Hugo27, Melanoma, Stage: IV) in a patient selection portion (as shown in the upper left panel). The user's account profile screen may also provide reports generated from the patient's information by the software program in a report layout portion (as shown in the right panel). The report layout portion may provide the user with portions for viewing stored reports that were previously generated by the software program or for creating a new report.

[0159] In response to selection by a user, a selected portion of the GUI may be visually highlighted. As a set of non-limiting examples, a "visually highlighted" element may be highlighted through a difference in font (e.g., by italicizing, bolding, and / or underlining), by surrounding the section with a visual object (e.g., a box), by "popping" the element out (e.g., by increasing the zoom for that element), by changing the color of an element, by shading the element, by incorporation of movement into the element (e.g., by causing the element to move), any combination of the foregoing in a portion or the whole of the element, or in any other suitable way.

[0160] If a user's account profile screen provides information about one patient, the patient may be selected by the user to view a screen presenting the patient's information. If a user's account profile screen provides information about more than one patient, any one of the patients may be selected by the user to view a screen presenting the selected patient's information. The user may select a stored report to view a screen presenting information relating to the selected report. The user may select the create new report portion to view a screen for creating a new report. For example, the user may select the patient Hugo27, as shown in the upper left panel.

[0161] FIG. 5 is a screenshot presenting the selected patient's information provided to the user in response to the user selecting the patient. An overview of the patient's information is presented in the patient overview portion (as shown in the left panel) including clinical characteristics of the patient's disease (e.g., histology report). Additional information about the patient or the patient's cancer including overall status, disease characteristics and general recommendations (as shown in the upper middle panel) is provided. Information relating to the selected patient's sequencing data is presented in the Data Files portion (as shown in the right panel) including whole exome sequencing data (WES). The user may use the Upload Data File portion of the screen to upload the patient's tumor biopsy sequencing data.

[0162] FIG. 6 is a screenshot presenting that the patient's tumor biopsy sequencing data was downloaded (as shown in the lower right panel). The user may select start in the launch analysis portion of the screen (as shown in the lower middle panel) to view a report created from the patient's sequencing data and other information relating to the patient or the patient's cancer.

[0163] FIG. 7 is a screenshot presenting the selected patient's report including information related to the patient's sequencing data, the patient, and the patient's cancer. The therapy biomarkers portion (as shown in the left panel) presents information related to available therapies (e.g., immunotherapies and targeted therapies) and their predicted efficacy in the selected patient. Additional predictions of the efficacy of a therapy in the patient are provided in the machine predictor portion and additional portion (as shown in the left panel). The MF profile portion presents information relating to the molecular characteristics of a tumor including tumor genetics, pro-tumor microenvironment factors, and anti-tumor immune response factors (as shown in the middle panel). The clinical trials portion provides information relating to clinical trials (as shown in the right panel). The monotherapy or combinational therapy portion (as shown in the middle panel) may be selected by the user to interactively design a personalized treatment for a patient.

[0164] A user may select various portions of the screen to view additional information. For example, a user may select anti-PD1 in the immunotherapy biomarkers portion of the screen (as shown in the left panel) to view information relating to anti-PD1 treatment including biomarkers associated with anti-PD1 and tumor cell processes associated with anti-PD1 treatment.

[0165] FIG. 8 is a screenshot presenting information related to anti-PD1 immunotherapy provided in response to selecting anti-PD1 immunotherapy (as shown by highlighting) in the immunotherapy biomarkers portion of the screen (as shown in the left panel). Information relating to biomarkers associated with anti-PD1 immunotherapy is provided in the biomarkers portion (as shown in the right panel). The biomarkers portion presents genetic biomarkers, cellular biomarkers, and expression biomarkers, as well as patient specific information related to those biomarkers.

[0166] The user may select any one of the biomarkers presented in the biomarkers markers portion to view additional information relating to that biomarker including general information about the selected biomarker, patient specific information relating to the selected biomarker, information relating to tumor molecular processes associated with the selected biomarker, and treatment related information associated with the selected biomarker.

[0167] In response to selection by a user, the selected biomarker may be highlighted. FIG. 9 is a screenshot presenting the mutational burden biomarker (as shown by highlighting) was selected by the user. The user may select another portion of the mutational burden biomarker to view a screen presenting information relating to the mutational burden biomarker such as relevant publications.

[0168] FIG. 10 is a screenshot presenting information relating to the mutational burden biomarker (as shown in the middle panel) provided in response to the user selecting the mutational burden biomarker. The information may include a description of the biomarker, how the biomarker was calculated, the patient's particular biomarker value compared to other patients (as shown in a histogram), and information from publications relating to the selected biomarker.

[0169] Biomarkers are indicative of the molecular processes that take place in the tumor microenvironment. Accordingly, a patient's biomarkers provide information specific to the patient's tumor microenvironment. The system allows a user to interactively view biomarker information as it relates to a molecular process in the tumor. Gene groups relating to tumor molecular processes associated with a particular biomarker are highlighted in response to selecting that biomarker.

[0170] FIGs. 11-13 are screenshots demonstrating that tumor molecular processes gene groups presented in the MF profile that are associated with the selected biomarker are highlighted in response to the user selecting that biomarker.

[0171] For example, the user may select the mutational burden biomarker which is associated with the mutational status gene group and the neo-antigens load gene group in the tumor microenvironment. FIG. 11 is a screenshot presenting that the mutational status gene group and neo-antigens load gene group in the MF profile are highlighted in response to the user selecting the mutational burden biomarker (as shown in highlighting).

[0172] In another example, the user may select the CD8 T cells biomarker which is associated with the T cells gene group in the tumor microenvironment. FIG. 12 is a screenshot presenting that the T cells gene group in the MF profile is highlighted in response to the user selecting the CD8 T cell biomarker (as shown in highlighting).

[0173] In yet another example, the user may select the PDL1 expression biomarker which is associated with the checkpoint inhibition gene group in the tumor microenvironment. FIG. 13 is a screenshot presenting that the checkpoint inhibition gene group in the MF profile is highlighted in response to the user selecting the PDL1 expression biomarker.

[0174] The user may select a targeted therapy to view information relating to treatment with the selected targeted therapy including biomarkers associated with the selected therapy and tumor cell processes associated with the selected therapy. For example, the user may select the targeted therapy sunitinib.

[0175] FIG. 14 is a screenshot presenting information related to sunitinib therapy provided in response to selecting sunitinib (as shown by highlighting) in the targeted therapy biomarkers portion of the screen (as shown in the left panel). Information relating to biomarkers associated with sunitinib therapy is provided in the biomarkers portion (as shown in the right panel). The biomarkers portion presents genetic biomarkers, cellular biomarkers, and expression biomarkers, as well as patient specific information related to those biomarkers.

[0176] Biomarkers are predictive of the efficacy of a therapy. Accordingly, a patient's biomarkers are predictive of the patient's response to a therapy. The system allows a user to interactively view biomarker information as it relates to a predicted response to a therapy. Clinical evidence of treatment efficacy for a therapy (e.g., an immunotherapy or a targeted therapy) may be interactively viewed by the user. Figs. 15-18 are screenshots demonstrating that a user may select a therapy to view a screen presenting clinical trial data relating to the selected therapy.

[0177] For example, the user may select treatment with anti-PD1 immunotherapy. FIG. 15 is a screenshot presenting clinical trial data relating to anti-PD1 therapy effectivity in patients having stage IV metastatic melanoma (as shown in the right panel) provided in response to the user selecting anti-PD1 immunotherapy (as shown in the left panel).

[0178] In another example, the user may select treatment with anti-CTLA4 immunotherapy. FIG. 16 is a screenshot presenting clinical trial data relating to anti-CTLA4 therapy effectivity in patients having stage IV metastatic melanoma (as shown in the right panel) provided in response to the user selecting anti-CTLA4 immunotherapy (as shown in the left panel).

[0179] A particular clinical trial can be selected to view further information relating to the clinical trial such as therapy efficacy, adverse effects of the therapy, treatment regimen, and published results. FIG. 17 is a screenshot presenting clinical trial data relating to the NCT01295827 clinical trial of anti-PD1 treatment (as shown in the middle panel) provided in response to the user selecting the NCT01295827 clinical trial (as shown in the right panel).

[0180] A user can interactively view information relating to the clinical trial. For example, the user can minimize various portions of information to view information in other portions. FIG. 18 is a screenshot presenting the treatment regimen of the selected clinical data provided in response to the user minimizing the therapy class description and drug description portions. The screen may also present information relating to ongoing clinical trials (marked by the letter A).

[0181] Information relating to a patient's tumor microenvironment is based on expression of genes within the tumor microenvironment. The MF profile is a visual representation of gene groups within the tumor microenvironment that provide information about tumor properties, tumor processes (e.g., angiogenesis), tumor immune environment, and cellular composition (e.g., cancer associated fibroblasts). FIGs. 19-37 are screenshots demonstrating that a user may select portions of the MF profile to view screens presenting information related to the tumor microenvironment.

[0182] FIG. 19 is a screenshot presenting a patient's MF profile (as shown in the middle panel). The MF profile may present any number of gene groups. As a non-limiting example, FIG. 19 presents five gene groups including the tumor properties gene group, angiogenesis gene group, cancer associated fibroblasts gene group (the fibroblasts group), pro-tumor immune environment gene group (tumor-promoting immune microenvironment group), and anti-tumor immune environment gene group (anti-tumor immune microenvironment group). Any one of these gene groups may be selected to view a screen presenting additional gene groups associated with the selected gene group and information relating to the selected gene group. For example, a user may select the tumor properties gene group of the MF profile to view additional gene groups associated with the tumor properties gene group and information related to particular tumor properties (e.g., tumor genetics and tumor cell properties).

[0183] FIG. 20 is a screenshot presenting additional gene groups associated with the tumor properties gene group provided to the user in response to selecting the tumor properties gene group. These gene groups include mutational status (mutation status) gene group, anti-metastatic (antimetastatic) factors gene group, metastatic factors (metastasis signature) gene group, tumor growth factors (growth factors) gene group, tumor suppressors gene group, oncogenes gene group (activated signaling pathways; including PI3K / AKT / mTOR signaling, RAS / RAF / MEK signaling, and Receptor tyrosine kinases expression), and proliferation rate gene group. Information relating to tumor genetics (as shown in the left panel) and tumor cell properties (as shown in the right panel) are provided in response to the user selecting the tumor properties gene group. Each of the additional gene groups may be selected to view information relating to the selected gene group. For example, a user may select the proliferation rate gene group in the MF profile.

[0184] FIG. 21 is a screenshot presenting information relating to the tumor proliferation rate (as shown in the right panel) provided in response to the user selecting the tumor proliferation rate gene group (as shown in highlighting) in the MF profile. The user may also view additional information relating to properties of the patient's tumor.

[0185] The user may view different screens presenting information relating to different tumor properties such as a screen presenting information related to tumor purity and tumor clone evolution. FIG. 22 is a screenshot presenting information relating to the purity of the patient's tumor in the tumor purity portion (as shown in the lower right panel) and information relating to the clonal evolution of the patient's tumor in the tumor clones evolution portion (as shown in the lower right panel).

[0186] The MF profile provides information relating to the pro-tumor immune environment (tumor-promoting immune microenvironment), and anti-tumor immune environment (anti-tumor immune microenvironment). For example, the user may select the anti-tumor immune environment (anti-tumor immune microenvironment) gene group in the MF profile to view information relating to the anti-tumor immune environment and the user may select the protumor immune environment (tumor-promoting immune microenvironment) gene group in the MF profile to view information relating to the pro-tumor immune environment (tumor-promoting immune microenvironment).

[0187] FIG. 23 is a screenshot presenting information relating to the anti-tumor immune environment (as shown in the left panel; anti-tumor immune microenvironment) provided in response to the user selecting the anti-tumor immune environment (anti-tumor immune microenvironment) gene group and information relating to the pro-tumor immune environment (as shown in the right panel; tumor-promoting immune microenvironment) in response to the user selecting the pro-tumor immune environment (tumor-promoting immune microenvironment) gene group. Additional gene groups relating to the tumor microenvironment are presented in the MF profile in response to selecting the anti-tumor and pro-tumor immune environment (anti-tumor immune microenvironment and tumor-promoting immune microenvironment) gene groups in the MF profile (as shown in the middle panel).

[0188] Any one of these additional gene groups in the MF profile may be selected to view information relating to that gene group. For example, the user may select the T cells gene group in the MF profile. FIG. 24 is a screenshot presenting information relating to expression of genes that determine T cell activity within the tumor in the anti-tumor microenvironment portion (as shown in the lower left panel) provided in response to the user selecting the T cell gene group in the MF profile (as shown by highlighting).

[0189] FIG. 25 is a screenshot presenting information relating to expression of genes that determine cancer associated fibroblast activity within the tumor in the pro-tumor microenvironment (anti-tumor immune microenvironment) portion (as shown in the lower right panel) provided in response to the user selecting the cancer associated fibroblast (fibroblasts) gene group in the MF profile (as shown by highlighting).

[0190] The user may select portions in the anti-tumor immune environment portion (as shown in the left panel; anti-tumor immune microenvironment) and the pro-tumor immune environment portion (as shown in the right panel; tumor-promoting immune microenvironment) to view additional information relating to anti-tumor cells and pro-tumor (or tumor promoting) cells within the tumor microenvironment (anti-tumor immune microenvironment and tumor-promoting immune microenvironment).

[0191] FIG. 26 is a screenshot presenting information relating to the number of nonmalignant cells in the patient's tumor (as shown in the lower left panel) provided in response to the user selecting tumor infiltrate in the anti-tumor immune environment portion (as shown in the upper left panel).

[0192] FIG. 27 is a screenshot presenting information relating to the TCR repertoire in the patient's tumor (as shown in the lower right panel) provided in response to the user selecting tumor infiltrate in the pro-tumor immune environment portion (as shown in the upper right panel; tumor-promotive immune infiltrate).

[0193] As disclosed herein, the MF profile may present five gene groups including the tumor properties gene group, angiogenesis gene group, cancer associated fibroblasts gene group (the fibroblasts group), pro-tumor immune environment gene group (tumor-promoting immune microenvironment group), and anti-tumor immune environment gene group (anti-tumor immune microenvironment group). Each of these gene groups may be selected by the user to view associated gene groups. When each of these gene groups is selected, the MF profile may present twenty-eight gene groups. A screen presenting a MF profile presenting twenty-eight gene groups (also described elsewhere herein) is shown in FIG. 28 (as shown in the middle panel).

[0194] The "combo therapy" (or "combination therapy") portion can be used to design a combination therapy based on one or more therapies. Combination therapies can be designed to target cancer (e.g., tumor) properties presented in the MF profile. For example, a combination therapy including a treatment to suppress pro-tumor process may be designed for a patient in which the MF profile showed active pro-tumor processes.

[0195] The combo therapy portion may present information to the user relating to the selected therapy including a description of the selected therapy, gene groups targeted by the selected therapy, clinical data related to the selected therapy, and predictions of the patient's response to the selected therapy based on information relating to the patient and the patient's cancer. Figs. 29-37 are screenshots demonstrating that a user may interactively design a combination therapy using the combo therapy portion.

[0196] FIG. 29 is a screenshot presenting the combo therapy portion (as shown in the right panel) provided to the user in response to selecting the combinational therapy portion (as shown in the middle panel).

[0197] FIG. 30 is a screenshot presenting anti-PD1 therapy incorporated into the combo therapy portion (as shown in the upper right panel). Gene groups targeted by anti-PD1 therapy in the MF profile are marked with arrows. Information relating to the biological influence of anti-PD1 therapy is presented in the therapy biological influence portion (as shown in the lower middle panel).

[0198] FIG. 31 is a screenshot presenting information related to sunitinib treatment in the therapy biological influence portion (as shown in the lower middle panel) in response to the user selecting sunitinib in the targeted therapy biomarkers portion (as shown by highlighting). The user may determine whether the selected treatment should be incorporated into the combination therapy based on this information.

[0199] FIG. 32 is a screenshot presenting sunitinib incorporation in the combo therapy portion in response to the user selecting sunitinib. Gene groups targeted by the anti-PD1 and sunitinib combination therapy are marked with arrows in the MF profile. Information relating to the combination of anti-PD1 and sunitinib therapy is presented in the proposed effectivity portion (as shown in the right panel) and in the potential adverse effects portion (as shown in the right panel). Information relating to published and ongoing clinical trials matching the selected combination therapy are presented in the ongoing and finished combo trials portion (as shown in the right panel).

[0200] The combination therapy may include more than two therapies. For example, a user may add a vaccine therapy to the anti-PD1 and sunitinib combination therapy designed by the user.

[0201] FIG. 33 is a screenshot presenting potential vaccine therapies such as a personalized neo-antigenic vaccine and an off the shelf vaccine provided to the user in response to selecting vaccine in the immunotherapy biomarkers portion (as shown in the left panel).

[0202] FIG. 34 is a screenshot presenting information relating to treatment with a personalized neo-antigenic vaccine (as shown in the lower middle panel) provided to the user in response to selecting a personalized neo-antigenic vaccine (as shown by highlighting).

[0203] FIG. 35 is a screenshot presenting incorporation of a personalized neo-antigenic vaccine in the combo therapy portion provided to the user in response to the user selecting the personalized neo-antigenic vaccine.

[0204] FIG. 36 is a screenshot presenting the personalized neo-antigenic vaccine therapy, anti-PD1 therapy, and sunitinib therapy in the combo therapy portion provided to the user in response to the user incorporating each of these therapies into the combo therapy portion.

[0205] Any one of the therapies in the combination therapy may be substituted for a different therapy. However, a particular combination therapy may be inappropriate for a patient. In response to the user's design of an inappropriate combination therapy, the software will provide an alert to the user indicating that the designed combo therapy is or may be inappropriate for the patient. The user may also receive an alert if the designed combination of therapies has a low effectivity score.

[0206] FIG. 37 is a screenshot presenting an alert that substitution of sunitinib therapy with vemurafenib therapy is recognized by the software as an inappropriate combination for the patient.Computer Implemented Methods For Generating, Visualizing and Classifying MF Profiles

[0207] Aspects of the technology described herein provide computer implemented methods for generating, visualizing and classifying molecular-functional (MF) profiles of cancer patients.

[0208] In some embodiments, a software program may provide a user with a visual representation of a patient's MF profile and / or other information related to a patient's cancer using an interactive graphical user interface (GUI). Such a software program may execute in any suitable computing environment including, but not limited to, a cloud-computing environment, a device co-located with a user (e.g., the user's laptop, desktop, smartphone, etc.), one or more devices remote from the user (e.g., one or more servers), etc.

[0209] For example, in some embodiments, the techniques described herein may be implemented in the illustrative environment 200 shown in FIG. 2A. As shown in FIG. 2A, within illustrative environment 200, one or more biological samples of a patient 202 may be provided to a laboratory 204. Laboratory 204 may process the biological sample(s) to obtain expression data (e.g., DNA, RNA, and / or protein expression data) and provide it, via network 208, to at least one database 206 that stores information about patient 202.

[0210] Network 208 may be a wide area network (e.g., the Internet), a local area network (e.g., a corporate Intranet), and / or any other suitable type of network. Any of the devices shown in FIG. 2A may connect to the network 208 using one or more wired links, one or more wireless links, and / or any suitable combination thereof.

[0211] In the illustrated embodiment of FIG. 2A, the at least one database 206 may store expression data for the patient, medical history data for the patient, test result data for the patient, and / or any other suitable information about the patient 202. Examples of stored test result data for the patient include biopsy test results, imaging test results (e.g., MRI results), and blood test results. The information stored in at least one database 206 may be stored in any suitable format and / or using any suitable data structure(s), as aspects of the technology described herein are not limited in this respect. The at least one database 206 may store data in any suitable way (e.g., one or more databases, one or more files). The at least one database 206 may be a single database or multiple databases.

[0212] As shown in FIG. 2A, illustrative environment 200 includes one or more external databases 216, which may store information for patients other than patient 202. For example, external databases 216 may store expression data (of any suitable type) for one or more patients, medical history data for one or more patients, test result data (e.g., imaging results, biopsy results, blood test results) for one or more patients, demographic and / or biographic information for one or more patients, and / or any other suitable type of information. In some embodiments, external database(s) 216 may store information available in one or more publically accessible databases such as TCGA (The Cancer Genome Atlas), one or more databases of clinical trial information, and / or one or more databases maintained by commercial sequencing suppliers. The external database(s) 216 may store such information in any suitable way using any suitable hardware, as aspects of the technology described herein are not limited in this respect.

[0213] In some embodiments, the at least one database 206 and the external database(s) 216 may be the same database, may be part of the same database system, or may be physically co-located, as aspects of the technology described herein are not limited in this respect.

[0214] In some embodiments, information stored in patient information database 206 and / or in external database(s) 216 may be used to perform any of the techniques described herein related to determining whether a subject is likely to respond positively or not likely to respond positively to an immune checkpoint blockade therapy. For example, the information stored in the database(s) 206 and / or 216 may be accessed, via network 208, by software executing on server(s) 210 to perform any one or more of the techniques described herein including with reference to FIGs. 39A, 39B, 39C, 39D, 40A and 40B.

[0215] For example, in some embodiments, server(s) 210 may access information stored in database(s) 206 and / or 216 and use this information to perform process 3900, described with reference to FIG. 39A, for identifying a MF profile cluster with which to associate an MF profile for a subject.

[0216] As another example, in some embodiments, server(s) 210 may access information stored in database(s) 206 and / or 216 and use this information to perform process 3920, described with reference to FIG. 39B, for generating MF profile clusters using RNA expression data obtained from subjects having a particular type of cancer.

[0217] As another example, in some embodiments, server(s) 210 may access information stored in database(s) 206 and / or 216 and use this information to perform process 3940, described with reference to FIG. 39C, for identifying an MF profile cluster with which to associate an MF profile determined for a subject at least in part by determining the subject's expression levels for multiple gene groups.

[0218] As another example, in some embodiments, server(s) 210 may access information stored in database(s) 206 and / or 216 and use this information to perform process 3960, described with reference to FIG. 39D, for generating MF profile clusters using RNA expression data obtained from subjects having a particular type of cancer, and associating a subject with one of the generated MF clusters based on the subject's MF profile.

[0219] As another example, in some embodiments, server(s) 210 may access information stored in database(s) 206 and / or 216 and use this information to perform process 4000, described with reference to FIG. 40A, for generating an MF profile for a subject and generating an MF portrait for visualizing the MF profile in a graphical user interface.

[0220] As another example, in some embodiments, server(s) 210 may access information stored in database(s) 206 and / or 216 and use this information to perform process 4020, described with reference to FIG. 40B, for presenting a generated personalized graphical user interface (GUI) to a user.

[0221] In some embodiments, server(s) 210 may include one or multiple computing devices. When server(s) 210 include multiple computing devices, the device(s) may be physically co-located (e.g., in a single room) or distributed across multi-physical locations. In some embodiments, server(s) 210 may be part of a cloud computing infrastructure. In some embodiments, one or more server(s) 210 may be co-located in a facility operated by an entity (e.g., a hospital, research institution) with which doctor 214 is affiliated. In such embodiments, it may be easier to allow server(s) 210 to access private medical data for the patient 202.

[0222] As shown in FIG. 2A, in some embodiments, the results of the analysis performed by server(s) 210 may be provided to doctor 214 through a computing device 214 (which may be a portable computing device, such as a laptop or smartphone, or a fixed computing device such as a desktop computer). The results may be provided in a written report, an e-mail, a graphical user interface, and / or any other suitable way. It should be appreciated that although in the embodiment of FIG. 2A, the results are provided to a doctor, in other embodiments, the results of the analysis may be provided to patient 202 or a caretaker of patient 202, a healthcare provider such as a nurse, or a person involved with a clinical trial.

[0223] In some embodiments, the results may be part of a graphical user interface (GUI) presented to the doctor 214 via the computing device 212. In some embodiments, the GUI may be presented to the user as part of a webpage displayed by a web browser executing on the computing device 212. In some embodiments, the GUI may be presented to the user using an application program (different from a web-browser) executing on the computing device 212. For example, in some embodiments, the computing device 212 may be a mobile device (e.g., a smartphone) and the GUI may be presented to the user via an application program (e.g., "an app") executing on the mobile device.

[0224] The GUI presented on computing device 212 provides a wide range of oncological data relating to both the patient and the patient's cancer in a new way that is compact and highly informative. Previously, oncological data was obtained from multiple sources of data and at multiple times making the process of obtaining such information costly from both a time and financial perspective. Using the techniques and graphical user interfaces illustrated herein, a user can access the same amount of information at once with less demand on the user and with less demand on the computing resources needed to provide such information. Low demand on the user serves to reduce clinician errors associated with searching various sources of information. Low demand on the computing resources serves to reduce processor power, network bandwidth, and memory needed to provide a wide range of oncological data, which is an improvement in computing technology.

[0225] FIG. 2B shows a block diagram of an illustrative GUI 250 containing information about patient 202. GUI 250 may include separate portions providing different types of information about patient 202. Illustrative GUI 150 includes the following portions: Patient Information Portion 252, Molecular-Functional (MF) Portrait Portion 260, Clinical Trial Information Portion 262, Immunotherapy Portion 254, Efficacy Predictor Portion 256, and Targeted Therapy Selection Portion 258.

[0226] Patient Information Portion 252 may provide general information about the patient and the patient's cancer. General information about the patient may include such information as the patient's name and date of birth, the patient's insurance provider, and contact information for the patient such as address and phone number. General information about the patient's cancer may include the patient's diagnosis, the patient's history of relapse and / or remission, and information relating to stage of the patient's cancer. Patient Information Portion 252 may also provide information relating to potential treatment options for the patient and / or previously administered treatments.

[0227] Molecular-Functional (MF) Portrait Portion 260 may include a molecular functional tumor portrait (MF profile) which refers to a graphical depiction of a tumor with regard to its molecular and cellular composition, and biological processes that are present within and / or surrounding the tumor. Further aspects relating to a patient's MF profile are provided herein.

[0228] Clinical Trial Information Portion 262 may include information relating to a clinical trial for a therapy that may be and / or will be administered to the patient. Clinical Trial Information Portion 262 may provide information about an ongoing clinical trial or a completed clinical trial. Information that may be provided in Clinical Trial Information Portion 262 may include information related to a therapy used in the clinical trial such as dosage and dosage regimen, number and diagnosis of patients participating in the clinical trial, and patient outcomes.

[0229] Immunotherapy Portion 254 may include patient specific information as it relates to an immunotherapy. Immunotherapy Portion 254 may provide such information for different immunotherapies, for example, immune checkpoint blockade therapies, anti-cancer vaccine therapies, and T cell therapies. Patient specific information relating to an immunotherapy may include information about the patient such as the patient's biomarkers associated with an immunotherapy and / or information about the patient's cancer such as composition of immune cells in the patient's tumor.

[0230] Efficacy Predictor Portion 256 may include information indicative of the patient's predicted response to an immunotherapy based on patient specific information presented in Immunotherapy Portion 254.

[0231] Targeted Therapy Selection Portion 258 may include patient specific information as it relates to a targeted therapy. Targeted Therapy Selection Portion 258 may provide such information for different targeted therapies, for example, a kinase inhibitor therapy, a chemotherapy, and anti-cancer antibody therapy. Patient specific information relating to an a targeted therapy may include information about the patient such as the patient's biomarkers associated with a targeted therapy and / or information about the patient's cancer such as whether a mutation is present in the patient's tumor.

[0232] An illustrative example of the graphical user interface 250 of FIG. 2B is shown as graphical user interface 270 of FIG. 2C. As shown in FIG. 2C, Patient Information Portion 272 may provide different information in different panels, for example, Overall Status panel, Disease Characteristics panel, and General Recommendations panel. Overall Status panel, in some embodiments, may provide general information about the patient such as patient name and patient age. Disease Characteristics panel, in some embodiments, may provide information about the patient's cancer such as type of cancer and stage of cancer. General Recommendations panel, in some embodiments, may provide previous treatments and possible treatment options for the patient.

[0233] Clinical Trial Information Portion 282a provides information relating to a clinical trial for anti-PD1 therapy. Clinical Trial Information Portion 282a (as shown in the upper portion) shows a graph providing patient overall response rate (ORR) for anti-PD1 therapy and other therapies such as vaccine or IFNα therapies. A user may select portions of the Clinical Trial Information Portion 282a to access information related to patient progression-free survival (PFS) and / or patient overall survival (OS). Clinical Trial Information Portion 282a (as shown in the lower portion) provides information relating to different clinical trials that may be presented to a user including a brief description of the clinical trial.

[0234] Clinical Trial Information Portion 282b provides information relating to a clinical trial for different targeted therapies. Clinical Trial Information Portion 282b (as shown in the upper portion) shows a graph providing patient overall response rate (ORR) for different targeted therapies including sunitinib (SU), imatinib (IM), vemurafenib (VER) and dabrafenib (DAB). A user may select portions of the Clinical Trial Information Portion 282b to access information related to patient progression-free survival (PFS) and / or patient overall survival (OS). Clinical Trial Information Portion 282b (as shown in the lower portion) provides information relating to different clinical trials that may be presented to a user including a brief description of the clinical trial.

[0235] Immunotherapy Portion 274 provides patient specific information associated with an immunotherapy and information indicative of the patient's predicted response to that immunotherapy. Immunotherapy Portion 274 provides such information for anti-PD1 therapy, a therapeutic cancer vaccine, IFNα therapy, IL2 therapy, anti-CTLA4 therapy, and anti-angiogenic therapy. Patient specific information shown in Immunotherapy Portion 274 includes the patient's biomarker information relating to various immunotherapies and the patient's therapy scores calculated from their biomarkers.

[0236] Efficacy Predictor Portion 276a provides information indicative of the patient's predicted response to anti-PD1 therapy based on patient specific information presented in Immunotherapy Portion 274. Efficacy Predictor Portion 276b provides information indicative of the patient's predicted response to anti-CTLA4 therapy based on patient specific information presented in Immunotherapy Portion 274.

[0237] Targeted Therapy Selection Portion 278 provides patient specific information associated with a targeted therapy and information indicative of the patient's predicted response to the targeted therapy. Targeted Therapy Selection Portion 278 provides such information for sunitinib (SU), imatinib (IM), vemurafenib (VER), dabrafenib (DAB), trametinib, and pazopanib. Patient specific information shown in Targeted Therapy Selection Portion 278 includes a patient's biomarker information relating to various targeted therapies and the patient's therapy scores calculated from their biomarkers.

[0238] An illustrative implementation of a computer system 3800 that may be used in connection with any of the embodiments of the technology described herein is shown in FIG. 38. The computer system 600 may include one or more computer hardware processors 3800 and one or more articles of manufacture that comprise non-transitory computer-readable storage media (e.g., memory 3820 and one or more non-volatile storage devices 3830). The processor(s) 3810 may control writing data to and reading data from the memory 3820 and the non-volatile storage device(s) 3830 in any suitable manner. To perform any of the functionality described herein, the processor(s) 3810 may execute one or more processor-executable instructions stored in one or more non-transitory computer-readable storage media (e.g., the memory 3820), which may serve as non-transitory computer-readable storage media storing processor-executable instructions for execution by the processor(s) 3810.

[0239] Systems and methods described herein provide for calculating an MF profile of a subject and associating the MF profile with an existing MF profile cluster. For example, computer-implemented processes for calculating a MF profile of a subject and associating the calculated MF profile with an existing MF profile cluster are described with reference to FIGs. 39A and 39C.

[0240] FIG. 39A is a flowchart of an illustrative computer-implemented process 3900 for identifying a MF profile cluster with which to associate an MF profile for a subject (e.g., a cancer patient), in accordance with some embodiments of the technology described herein. Process 3900 may be performed by any suitable computing device(s). For example, may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, or in any other suitable way.

[0241] Process 3900 begins at act 3902, where RNA expression data and / or whole exome sequencing (WES) data for a subject is obtained. RNA expression data may be acquired using any method known in the art, e.g., whole transcriptome sequencing, total RNA sequencing, and mRNA sequencing. In some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data comprises obtaining expression data from a biological sample from a patient and / or from a database storing such expression data. Further aspects relating to obtaining expression data are provided in section titled "Obtaining Expression Data".

[0242] Next, process 3900 proceeds to act 3904, where the MF profile for the subject is determined by determining a set of expression levels for a respective set of gene groups that includes gene groups associated with cancer malignancy and cancer microenvironment. The MF profile may be determined for a subject having any type of cancer, including any of the types described herein. The MF profile may be determined using any number of gene groups that relate to compositions and processes present within and / or surrounding the subject's tumor. Gene group expression levels, in some embodiments, may be calculated as a gene set enrichment (GSEA) score for the gene group. Further aspects relating to determining MF profiles are provided in section titled "MF Profiles".

[0243] Next, process 3900 proceeds to act 3906, where a MF profile cluster with which to associate the MF profile of the subject is identified. The MF profile of the subject may be associated with any of the types of MF profile clusters types described herein. A subject's MF profile may be associated with one or multiple of the MF profile clusters in any suitable way. For example, an MF profile may be associated with one of the MF profile clusters using a similarity metric (e.g., by associating the MF profile with the MF profile cluster whose centroid is closest to the MF profile according to the similarity metric). As another example, a statistical classifier (e.g., k-means classifier or any other suitable type of statistical classifier) may be trained to classify the MF profile as belonging to one or multiple of the MF clusters. Further aspects relating to determining MF profiles are provided in section "MF Profiles".

[0244] Optionally, process 3900 proceeds to act 3908, where a therapy for the subject is identified based on the identified MF profile cluster. The identified therapy may be any type of anti-cancer therapy depending on the patient's cancer and their identified MF profile cluster. A single anti-cancer therapy or a combination of anti-cancer therapies may be identified in act 3908. Identifying a therapy based on the MF profile cluster includes excluding those therapies that may be ineffective or harmful to the subject in order to identify a suitable therapy for the subject. Further aspects related to using a patient's identified MF profile cluster for clinical purposes are provided in section "Applications".

[0245] The MF profile of the subject may be output to a user, in some embodiments, by displaying the MF profile to the user in a graphical user interface (GUI), including the information about the MF profile in a report, sending an email to the user, and / or in any other suitable way. For example, the MF profile of the subject and other patient related information may be provided to a user in a GUI as shown in FIGs. 3-37.

[0246] In this way, a patient's MF profile can be identified and used for various clinical purposes including assessing the efficacy of a treatment for cancer and / or evaluating suitability of a patient for participating in a clinical trial.

[0247] FIG. 39C is a flowchart of an illustrative computer-implemented process 3940 for identifying an existing MF profile cluster with which to associate a MF profile for a subject (e.g., a cancer patient), in accordance with some embodiments of the technology described herein. Process 3940 may be performed by any suitable computing device(s). For example, may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, or in any other suitable way.

[0248] Process 3940 begins at act 3942, where RNA expression data and / or whole exome sequencing (WES) data for a subject having a particular type of cancer is obtained. RNA expression data may be acquired using any method known in the art, e.g., whole transcriptome sequencing, total RNA sequencing, and mRNA sequencing. In some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data comprises obtaining expression data from a biological sample from a patient and / or from a database storing such expression data. Further aspects relating to obtaining expression data are provided in section "Obtaining Expression Data".

[0249] Next, process 3940 proceeds to act 3944, where the MF profile for the subject is determined by determining a set of expression levels for a respective set of gene groups that includes at least one gene group associated with cancer malignancy and at least four gene groups associated with cancer microenvironment. The at least one gene group associated with cancer malignancy, in some embodiments, consists of a tumor properties gene group. The at least four gene groups associated with cancer microenvironment, in some embodiments, consists of tumor-promoting immune microenvironment group, anti-tumor immune microenvironment group, angiogenesis group, and fibroblasts group.

[0250] It should be appreciated that act 3944 may be performed using any number of gene groups associated with cancer malignancy and cancer microenvironment. For example, MF profiles may be determined using set of gene groups that includes 19 gene groups where the gene groups associated with cancer malignancy consists of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group, and the gene groups associated with cancer microenvironment consists of the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group.

[0251] In another example, MF profiles may be determined using set of gene groups that includes 30 gene groups where the gene groups associated with cancer malignancy consists of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group, and the gene groups associated with cancer microenvironment consists of the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, the cancer associated fibroblasts group, the angiogenesis group, and the complement inhibition group.

[0252] The MF profile may be determined using any number of gene groups (or functional modules) that relate to compositions and processes present within and / or surrounding the subject's tumor. Gene groups may comprise any number of genes and may be related to any composition and process. Further aspects relating to the gene groups are provided in section "MF Profile Modules". Gene group expression levels, in some embodiments, may be calculated as a gene set enrichment (GSEA) score for the gene group. Further aspects relating to determining MF profiles are provided in section "MF Profiles".

[0253] Next, process 3940 proceeds to act 3946, where information specifying MF profile clusters for the particular cancer type are accessed. Different MF profile clusters are accessed for different cancers. For example, MF profile clusters associated with lung cancer are accessed when process 3940 is performed for a patient having lung cancer and MF profile clusters associated with melanoma are accessed when process 3940 is performed for a patient having melanoma. Any number of MF profile clusters for the particular cancer may be accessed including at least two, at least 5, at least 10 or at least 20. The number of accessed MF profiles, in some embodiments, may be between 2-20, between 2-10, or between 15-20. The number of accessed MF profile clusters may vary depending on the particular cancer with which the MF profile clusters are associated. For example, 5 MF profile clusters may be accessed when the particular cancer type is lung cancer and 12 MF profile clusters may be accessed when the particular cancer is melanoma. Accessing information specifying MF profile clusters for the particular cancer may include accessing information from a variety of sources and / or a variety of databases.

[0254] Next, process 3940 proceeds to act 3948, where a MF profile cluster with which to associate the MF profile of the subject is identified. The MF profile of the subject may be associated with any of the types of MF profile clusters types described herein. A subject's MF profile may be associated with one or multiple of the MF profile clusters in any suitable way. For example, an MF profile may be associated with one of the MF profile clusters using a similarity metric (e.g., by associating the MF profile with the MF profile cluster whose centroid is closest to the MF profile according to the similarity metric). As another example, a statistical classifier (e.g., k-means classifier or any other suitable type of statistical classifier) may be trained to classify the MF profile as belonging to one or multiple of the MF clusters. Further aspects relating to determining MF profiles are provided in section "MF Profiles".

[0255] The MF profile of the subject may be output to a user, in some embodiments, by displaying the MF profile to the user in a graphical user interface (GUI), including the information about the MF profile in a report, sending an email to the user, and / or in any other suitable way. For example, the MF profile of the subject and other patient related information may be provided to a user in a GUI as shown in FIGs. 3-37.

[0256] In this way, a patient's MF profile can be identified and used for various clinical purposes including assessing the efficacy of a treatment for cancer and / or evaluating suitability of a patient for participating in a clinical trial.

[0257] Systems and methods described herein provide for generating MF profile clusters and for generating a MF profile for a patient and associating that MF profile to a generated MF cluster. For example, a computer-implemented process 3920 for generating MF profile clusters using RNA expression data obtained from subjects having a particular type of cancer is described with reference to FIG. 39B. As another example, a computer-implemented process 3960 for generating MF profile clusters using RNA expression data obtained from subjects having a particular type of cancer, and associating a subject with one of the generated MF clusters based on the subject's MF profile is described with reference to FIG. 39D.

[0258] FIG. 39B is a flowchart of an illustrative computer-implemented process 3920 for generating MF profile clusters using expression data obtained from subjects having a particular type of cancer, in accordance with some embodiments of the technology described herein. MF profile clusters may be generated for any cancer using expression data obtained from patients having that type of cancer. For example MF profile clusters associated with melanoma may be generated using expression data from melanoma patients. In another example MF profile clusters associated with lung cancer may be generated using expression data from lung cancer patients.

[0259] Process 3920 begins at act 3922, where RNA expression data and / or whole exome sequencing (WES) data for a plurality of subjects having a particular cancer are obtained. The plurality of subjects for which expression data is obtained may comprise any number of patients having a particular cancer. For example, expression data may be obtained for a plurality of melanoma patients, for example, 100 melanoma patients, 1000 melanoma patients, or any number of melanoma patients as the technology is not so limited. RNA expression data may be acquired using any method known in the art, e.g., whole transcriptome sequencing, total RNA sequencing, and mRNA sequencing. Further aspects relating to obtaining expression data are provided in section "Obtaining Expression Data".

[0260] Next, process 3920 proceeds to act 3924, where the MF profile for each subject in the plurality of subject is determined by determining a set of expression levels for a respective set of gene groups that includes gene groups associated with cancer malignancy and cancer microenvironment. MF profiles may be determined using any number of gene groups that relate to compositions and processes present within and / or surrounding the subject's tumor. Gene group expression levels, in some embodiments, may be calculated as a gene set enrichment (GSEA) score for the gene group. Further aspects relating to determining MF profiles are provided in section titled "MF Profiles".

[0261] Next, process 3920 proceeds to act 3926, where the plurality of MF profiles are clustered to obtain MF profile clusters. MF profiles may be clustered using any of the techniques described herein including, for example, community detection clustering, dense clustering, k-means clustering, or hierarchical clustering. MF profiles may be clustered for any type of cancer using MF profiles generated for patients having that type of cancer. MF profile clusters, in some embodiments, comprises a 1 st< MF profile cluster, a 2 nd< MF profile cluster, a 3 rd< MF profile, and a 4 th< MF profile. The relative sizes of 1 st< - 4 th< MF clusters may vary among cancer types. For example, the size of the 3 rd< MF profile cluster (shown as C) was larger for ACC (adrenocortical carcinoma) than that of BLCA (bladder urothelial carcinoma. MF profiles were clustered for different cancers as shown in Example 4. Further aspects relating to MF profile clusters are provided in section titled "MF profiles".

[0262] Next, process 3920 proceeds to act 3928, where the plurality of MF profiles in association with information identifying the particular cancer type are stored. MF profiles may be stored in a database in any suitable format and / or using any suitable data structure(s), as aspects of the technology described herein are not limited in this respect. The database may store data in any suitable way, for example, one or more databases and / or one or more files. The database may be a single database or multiple databases.

[0263] In this way, MF profile clusters can be stored and used as existing MF profile clusters with which a patient's MF profile can be associated. Existing MF profiles clusters, in some embodiments, may be associated with a patient's MF profile generated using five gene groups, 19 gene groups, or 30 gene groups as described with respect to FIG. 39C.

[0264] FIG. 39D is a flowchart of an illustrative computer-implemented process 3960 for generating MF profile clusters using expression data obtained from subjects having a particular type of cancer, and associating a subject with one of the generated MF clusters based on the subject's MF profile, in accordance with some embodiments of the technology described herein. Process 3960 may be performed by any suitable computing device(s). For example, may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, or in any other suitable way.

[0265] Process 3960 begins at act 3962, where RNA expression data and / or whole exome sequencing (WES) data for each subject in a plurality of subjects having a particular type of cancer is obtained. RNA expression data may be acquired using any method known in the art, e.g., whole transcriptome sequencing, total RNA sequencing, and mRNA sequencing. In some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data comprises obtaining expression data from a biological sample from a patient and / or from a database storing such expression data. Further aspects relating to obtaining expression data are provided in section "Obtaining Expression Data".

[0266] Next, process 3960 proceeds to act 3964, where the MF profile for each subject in the plurality of subjects is determined by determining a set of expression levels for a respective set of gene groups that includes at least one gene group associated with cancer malignancy and at least four gene groups associated with cancer microenvironment. The MF profile may be determined using any number of gene groups (or functional modules) that relate to compositions and processes present within and / or surrounding the subject's tumor. Gene groups may comprise any number of genes and may be related to any composition and process. Further aspects relating to the gene groups are provided in section "MF Profile Modules". Gene group expression levels, in some embodiments, may be calculated as a gene set enrichment (GSEA) score for the gene group. Further aspects relating to determining MF profiles are provided in section "MF Profiles".

[0267] Next, process 3960 proceeds to act 3966, where the plurality of MF profiles are clustered to obtain MF profile clusters. MF profiles may be clustered using any of the techniques described herein including, for example, community detection clustering, dense clustering, k-means clustering, or hierarchical clustering. MF profiles may be clustered for any type of cancer using MF profiles generated for patients having that type of cancer. MF profile clusters, in some embodiments, comprises a 1 st< MF profile cluster, a 2 nd< MF profile cluster, a 3 rd< MF profile, and a 4 th< MF profile. The relative sizes of 1 st< - 4 th< MF clusters may vary among cancer types. For example, the size of the 3 rd< MF profile cluster (shown as C) was larger for ACC (adrenocortical carcinoma) than that of BLCA (bladder urothelial carcinoma. MF profiles were clustered for different cancers as shown in Example 4. Further aspects relating to MF profile clusters are provided in section titled "MF profiles".

[0268] Next, process 3960 proceeds to act 3968, where RNA expression data and / or whole exome sequencing (WES) data for an additional subject is obtained. Expression data for an additional subject may be obtained by any suitable means as described in further detail in section "Obtaining Expression Data". Expression data for the additional subject may be obtained in the same manner used for obtaining expression data of the plurality of subjects. Alternatively or in addition to, expression data for the additional subject may be obtained in a manner different from that used to obtain expression data of the plurality of subjects. Further aspects relating to obtaining expression data are provided in section "Obtaining Expression Data".

[0269] Next, process 3960 proceeds to act 3970, where MF profiles for the additional subject are determined using the additional subject's expression data. The MF profile for the additional subject is determined by determining a set of expression levels for a respective set of gene groups that includes at least one gene group associated with cancer malignancy and at least four gene groups associated with cancer microenvironment. The MF profile may be determined using any number of gene groups (or functional modules) that relate to compositions and processes present within and / or surrounding the subject's tumor. Gene groups may comprise any number of genes and may be related to any composition and process. Further aspects relating to the gene groups are provided in section "MF Profile Modules". Gene group expression levels, in some embodiments, may be calculated as a gene set enrichment (GSEA) score for the gene group. Further aspects relating to determining MF profiles are provided in section "MF Profiles".

[0270] Next, process 3960 proceeds to act 3972, where a MF profile cluster with which to associate the MF profile of the subject is identified. The MF profile of the subject may be associated with any of the types of MF profile clusters determined in act 3966. A subject's MF profile may be associated with one or multiple of the MF profile clusters in any suitable way. For example, an MF profile may be associated with one of the MF profile clusters using a similarity metric (e.g., by associating the MF profile with the MF profile cluster whose centroid is closest to the MF profile according to the similarity metric). As another example, a statistical classifier (e.g., k-means classifier or any other suitable type of statistical classifier) may be trained to classify the MF profile as belonging to one or multiple of the MF clusters. Further aspects relating to determining MF profiles are provided in section "MF Profiles".

[0271] Optionally, process 3960 proceeds to act 3974, where a therapy for the subject is identified based on the identified MF profile cluster. The identified therapy may be any type of anti-cancer therapy depending on the patient's cancer and their identified MF profile cluster. A single anti-cancer therapy or a combination of anti-cancer therapies may be identified in act 3974. Identifying a therapy based on the MF profile cluster includes excluding those therapies that may be ineffective or harmful to the subject in order to identify a suitable therapy for the subject. Further aspects related to using a patient's identified MF profile cluster for clinical purposes are provided in section "Applications".

[0272] The MF profile of the subject may be output to a user, in some embodiments, by displaying the MF profile to the user in a graphical user interface (GUI), including the information about the MF profile in a report, sending an email to the user, and / or in any other suitable way. For example, the MF profile of the subject and other patient related information may be provided to a user in a GUI as shown in FIGs. 3-37.

[0273] In this way, a patient's MF profile can be identified and used for various clinical purposes including assessing the efficacy of a treatment for cancer and / or evaluating suitability of a patient for participating in a clinical trial.

[0274] Systems and methods described herein provide for generating a MF profile for a patient and generating a visualization of the generated MF profile as a MF portrait. For example, a computer-implemented process for generating a MF profile and an associated MF portrait is shown in FIG. 40A, and a computer-implemented process for generating a MF profile using five gene groups and an associated MF portrait is shown in FIG. 40B.

[0275] FIG. 40A is a flowchart of an illustrative computer-implemented process 4000 for generating a MF profile and an associated MF portrait, in accordance with some embodiments of the technology described herein. Process 4000 may be performed by any suitable computing device(s). For example, may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, or in any other suitable way.

[0276] Process 4000 begins at act 4002, where RNA expression data and / or whole exome sequencing (WES) data for a subject having a particular type of cancer is obtained. RNA expression data may be acquired using any method known in the art, e.g., whole transcriptome sequencing, total RNA sequencing, and mRNA sequencing. In some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data comprises obtaining expression data from a biological sample from a patient and / or from a database storing such expression data. Further aspects relating to obtaining expression data are provided in section "Obtaining Expression Data".

[0277] Next, process 4000 proceeds to act 4004, where the MF profile for the subject is determined by determining a set of expression levels for a respective set of gene groups that includes gene groups associated with cancer malignancy and gene groups associated with cancer microenvironment. The MF profile may be determined for a subject having any type of cancer, including any of the types described herein. The MF profile may be determined using any number of gene groups (or functional modules) that relate to compositions and processes present within and / or surrounding the subject's tumor. Gene group expression levels, in some embodiments, are calculated as a gene set enrichment (GSEA) score for the gene group. Further aspects relating to determining MF profiles are provided in section "MF Profiles".

[0278] Next, process 4000 proceeds to act 4006, where a first set of visual characteristics for a first plurality of GUI elements using the first gene group expression levels are determined. Examples of visual characteristics for a GUI element include color, shading or pattern, size, and / or shape. A set of visual characteristics may contain any number of visual characteristics. GUI elements, for example, include genes, gene groups, biomarkers, and biomarker information. A plurality of GUI elements may contain any number of GUI elements. Further aspects of visual characteristics and GUI elements are shown in and / or described with reference to FIGs. 3-37.

[0279] Next, process 4000 proceeds to act 4008, where a second set of visual characteristics for a second plurality of GUI elements using the second gene group expression levels are determined. Examples of visual characteristics for a GUI element include color, shading or pattern, size, and / or shape. A set of visual characteristics may contain any number of visual characteristics. GUI elements, for example, include genes, gene groups, biomarkers, and biomarker information. A plurality of GUI elements may contain any number of GUI elements. Further aspects of visual characteristics and GUI elements are shown in and / or described with reference to FIGs. 3-37.

[0280] Next, process 4000 proceeds to act 4010, where a GUI containing a first portion including the first GUI element and a second portion including the second GUI element is generated. For example, the MF profile of the subject and other patient related information may be provided to a user in a GUI as shown in FIGs. 3-37. Further aspects relating to the GUI as shown in FIGs. 3-37 are provided in section "Visualization of MF Profiles".

[0281] Next, process 4000 proceeds to act 4012, where the generated personalized GUI is presented to a user. In some embodiments, the GUI may be presented to the user as part of a webpage displayed by a web browser. In some embodiments, the GUI may be presented to the user using an application program (different from a web-browser). For example, in some embodiments, the GUI may be presented to the user via an application program (e.g., "an app") executing on a mobile device.

[0282] FIG. 40B is a flowchart of an illustrative computer-implemented process 4020 for generating a MF profile using at least one gene group associated with cancer malignancy and at least four gene groups associated with cancer microenvironment, and an associated MF portrait, in accordance with some embodiments of the technology described herein. Process 4020 may be performed by any suitable computing device(s). For example, may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, or in any other suitable way.

[0283] Process 4020 begins at act 4022, where RNA expression data and / or whole exome sequencing (WES) data for a subject having a particular type of cancer is obtained. RNA expression data may be acquired using any method known in the art, e.g., whole transcriptome sequencing, total RNA sequencing, and mRNA sequencing. In some embodiments, obtaining RNA expression data and / or whole exome sequencing (WES) data comprises obtaining expression data from a biological sample from a patient and / or from a database storing such expression data. Further aspects relating to obtaining expression data are provided in section "Obtaining Expression Data".

[0284] Next, process 4020 proceeds to act 4024, where the MF profile for the subject is determined by determining a set of expression levels for a respective set of gene groups that includes at least one gene group associated with cancer malignancy and at least four gene groups associated with cancer microenvironment. The at least one gene group associated with cancer malignancy, in some embodiments, consists of a tumor properties gene group. The at least four gene groups associated with cancer microenvironment, in some embodiments, consists of tumor-promoting immune microenvironment group, anti-tumor immune microenvironment group, angiogenesis group, and fibroblasts group.

[0285] It should be appreciated that act 4024 may be performed using any number of gene groups associated with cancer malignancy and cancer microenvironment. For example, MF profiles may be determined using set of gene groups that includes 19 gene groups where the gene groups associated with cancer malignancy consists of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group, and the gene groups associated with cancer microenvironment consists of the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group.

[0286] In another example, MF profiles may be determined using set of gene groups that includes 30 gene groups where the gene groups associated with cancer malignancy consists of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group, and the gene groups associated with cancer microenvironment consists of the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, the cancer associated fibroblasts group, the angiogenesis group, and the complement inhibition group.

[0287] The MF profile may be determined using any number of gene groups (or functional modules) that relate to compositions and processes present within and / or surrounding the subject's tumor. Gene groups may comprise any number of genes and may be related to any composition and process. Further aspects relating to the gene groups are provided in section "MF Profile Modules". Gene group expression levels, in some embodiments, may be calculated as a gene set enrichment (GSEA) score for the gene group. Further aspects relating to determining MF profiles are provided in section "MF Profiles".

[0288] Next, process 4020 proceeds to act 4026, where a first set of visual characteristics for a first plurality of GUI elements using the first gene group expression levels are determined. Examples of visual characteristics for a GUI element include color, shading or pattern, size, and / or shape. A set of visual characteristics may contain any number of visual characteristics. GUI elements, for example, include genes, gene groups, biomarkers, and biomarker information. A plurality of GUI elements may contain any number of GUI elements. Further aspects of visual characteristics and GUI elements are shown in and / or described with reference to FIGs. 3-37.

[0289] Next, process 4020 proceeds to act 4028, where a second set of visual characteristics for a second plurality of GUI elements using the second gene group expression levels are determined. Examples of visual characteristics for a GUI element include color, shading or pattern, size, and / or shape. A set of visual characteristics may contain any number of visual characteristics. GUI elements, for example, include genes, gene groups, biomarkers, and biomarker information. A plurality of GUI elements may contain any number of GUI elements. Further aspects of visual characteristics and GUI elements are shown in and / or described with reference to FIGs. 3-37.

[0290] Next, process 4020 proceeds to act 4030, where a GUI containing a first portion including the first GUI element and a second portion including the second GUI element is generated. For example, the MF profile of the subject and other patient related information may be provided to a user in a GUI as shown in FIGs. 3-37. Further aspects relating to the GUI as shown in FIGs. 3-37 are provided in section "Visualization of MF Profiles".

[0291] Next, process 4020 proceeds to act 4032, where the generated personalized GUI is presented to a user. In some embodiments, the GUI may be presented to the user as part of a webpage displayed by a web browser. In some embodiments, the GUI may be presented to the user using an application program (different from a web-browser). For example, in some embodiments, the GUI may be presented to the user via an application program (e.g., "an app") executing on a mobile device.

[0292] Such MF portraits provided in the GUI can used for various clinical purposes described herein including assessing the efficacy of a treatment for cancer and / or evaluating suitability of a patient for participating in a clinical trial.Applications

[0293] Methods and compositions for tumor type characterization as described herein may be used for various clinical purposes including, but not limited to, monitoring the progress of cancer in a subject, assessing the efficacy of a treatment for cancer, identifying patients suitable for a particular treatment, evaluating suitability of a patient for participating in a clinical trial and / or predicting relapse in a subject. Accordingly, described herein are diagnostic and prognostic methods for cancer treatment based on tumor type described herein.

[0294] Methods and compositions described herein can be used to evaluate the efficacy of a cancer treatment, such as those described herein, given the correlation between cancer type (e.g., tumor types) and cancer prognosis. For example, multiple biological samples, such as those described herein, can be collected from a subject to whom a treatment is performed either before and after the treatment or during the course of the treatment. The cancer type (e.g., the tumor type) in the biological sample from the subject can be determined using any of the methods described herein. For example, if the cancer type indicates that the subject has a poor prognosis and the cancer type changes to a cancer type indicative of a favorable prognosis after the treatment or over the course of treatment (e.g., 1 st< MF profile cancer type in a later collected sample when compared to 4 th< MF profile cancer type in an earlier collected sample), it indicates that the treatment is effective.

[0295] If the subject is identified as not responsive to the treatment based on cancer type (e.g., no change in cancer type is identified in response to treatment), a higher dose and / or greater frequency of dosage of the anti-cancer therapeutic agent may be administered to the identified subject. Alternatively, an alternative treatment can be administered to a subject who is found to not be responsive to a first or subsequent treatment. In some embodiments, the dosage or frequency of dosage of the therapeutic agent is maintained, lowered, or ceased in a subject identified as responsive to the treatment or not in need of further treatment. In certain embodiments, the dosage or frequency of dosage of the therapeutic agent is increased in a subject identified as non-responsive to the treatment. In some embodiments, a first therapeutic agent is halted and a new (second) therapeutic is used to treat the subject; or (alternatively) an additional (second) therapeutic is added in a subject identified as non-responsive to the first therapeutic agent.

[0296] In some embodiments, cancer types can also be used to identify a cancer that may be treatable using a specific anti-cancer therapeutic agent (e.g., a chemotherapy). To practice this method, the cancer type in a sample (e.g., a tumor biopsy) collected from a subject having cancer can be determined using methods described herein. If the cancer type is identified as being susceptible to treatment with an anti-cancer therapeutic agent, the method may further comprise administering to the subject having the cancer an effective amount of the anti-cancer therapeutic agent.

[0297] In some embodiments, the methods and compositions for cancer type characterization as described herein may be relied on in the development of new therapeutics for cancer. In some embodiments, the cancer type may indicate or predict the efficacy of a new therapeutic or the progression of cancer in a subject prior to, during, or after the administration of the new therapy.

[0298] In some embodiments, methods and compositions for cancer type characterization as described herein may be used to evaluate suitability of a patient for participating in a clinicial trial. In some embodiments, the cancer type may be used to include patients in a clinical trial. In some embodiments, patients having a specified cancer type (e.g., type A, or 1 st< MF profile) are included in a clinical trial. Herein, cancer types A-D correspond to the 1 st< - 4 th< MF profile types, respectively. In some embodiments, patients having any one of two specified cancer types (e.g., 1 st< MF profile or 4 th< MF profile) are included in a clinical trial. In some embodiments, patients having any one of three specified cancer types (e.g., patients having a 1 st< MF profile, a 2 nd< MF profile, or a 3 rd< MF profile) are included in a clinical trial. In some embodiments, patients having any one of four specified cancer types (e.g., patients having a 1 st< MF profile, a 2 nd< MF profile, a 3 rd< MF profile, or a 4 th< MF profile) are included in a clinical trial.

[0299] In some embodiments, the cancer type may be used to exclude patients in a clinical trial. In some embodiments, patients having a specified cancer type (e.g., 1 st< MF profile) are excluded from a clinical trial. In some embodiments, patients having any one of two specified cancer types (e.g., 1 st< MF profile or 4 th< MF profile) are excluded from a clinical trial. In some embodiments, patients having any one of three specified cancer types (e.g., patients having a 1 st< MF profile, a 2 nd< MF profile, or a 3 rd< MF profile) are excluded from a clinical trial. In some embodiments, patients having any one of four specified cancer types (e.g., patients having a 1 st< MF profile, a 2 nd< MF profile, a 3 rd< MF profile, or a 4 th< MF profile) are excluded from a clinical trial.

[0300] Further, methods and compositions for tumor type characterization as described herein may be applied for non-clinical uses including, for example, for research purposes. In some embodiments, the methods described herein may be used to study cancer cell function. For example, the methods described herein may be used to evaluate a tumor process (e.g., tumor metastasis), which can be used for various purposes including identifying targets that specifically effect the tumor process being evaluated.Methods of Treatment

[0301] In certain methods described herein, an effective amount of anti-cancer therapy described herein may be administered or recommended for administration to a subject (e.g., a human) in need of the treatment via a suitable route (e.g., intravenous administration).

[0302] The subject to be treated by the methods described herein may be a human patient having, suspected of having, or at risk for a cancer. Examples of a cancer include, but are not limited to, melanoma, lung cancer, brain cancer, breast cancer, colorectal cancer, pancreatic cancer, liver cancer, prostate cancer, skin cancer, kidney cancer, bladder cancer, or prostate cancer. The subject to be treated by the methods described herein may be a mammal (e.g., may be a human). Mammals include, but are not limited to: farm animals (e.g., livestock), sport animals, laboratory animals, pets, primates, horses, dogs, cats, mice, and rats.

[0303] A subject having a cancer may be identified by routine medical examination, e.g., laboratory tests, biopsy, PET scans, CT scans, or ultrasounds. A subject suspected of having a cancer might show one or more symptoms of the disorder, e.g., unexplained weight loss, fever, fatigue, cough, pain, skin changes, unusual bleeding or discharge, and / or thickening or lumps in parts of the body. A subject at risk for a cancer may be a subject having one or more of the risk factors for that disorder. For example, risk factors associated with cancer include, but are not limited to, (a) viral infection (e.g., herpes virus infection), (b) age, (c) family history, (d) heavy alcohol consumption, (e) obesity, and (f) tobacco use.

[0304] "An effective amount" as used herein refers to the amount of each active agent required to confer therapeutic effect on the subject, either alone or in combination with one or more other active agents. Effective amounts vary, as recognized by those skilled in the art, depending on the particular condition being treated, the severity of the condition, the individual patient parameters including age, physical condition, size, gender and weight, the duration of the treatment, the nature of concurrent therapy (if any), the specific route of administration and like factors within the knowledge and expertise of the health practitioner. These factors are well known to those of ordinary skill in the art and can be addressed with no more than routine experimentation. It is generally preferred that a maximum dose of the individual components or combinations thereof be used, that is, the highest safe dose according to sound medical judgment. It will be understood by those of ordinary skill in the art, however, that a patient may insist upon a lower dose or tolerable dose for medical reasons, psychological reasons, or for virtually any other reasons.

[0305] Empirical considerations, such as the half-life of a therapeutic compound, generally contribute to the determination of the dosage. For example, antibodies that are compatible with the human immune system, such as humanized antibodies or fully human antibodies, may be used to prolong half-life of the antibody and to prevent the antibody being attacked by the host's immune system. Frequency of administration may be determined and adjusted over the course of therapy, and is generally (but not necessarily) based on treatment, and / or suppression, and / or amelioration, and / or delay of a cancer. Alternatively, sustained continuous release formulations of an anti-cancer therapeutic agent may be appropriate. Various formulations and devices for achieving sustained release are known in the art.

[0306] In some embodiments, dosages for an anti-cancer therapeutic agent as described herein may be determined empirically in individuals who have been administered one or more doses of the anti-cancer therapeutic agent. Individuals may be administered incremental dosages of the anti-cancer therapeutic agent. To assess efficacy of an administered anti-cancer therapeutic agent, one or more aspects of a cancer (e.g., tumor formation, tumor growth, or cancer or tumor Type A-D) may be analyzed.

[0307] Generally, for administration of any of the anti-cancer antibodies described herein, an initial candidate dosage may be about 2 mg / kg. For the purpose of the present disclosure, a typical daily dosage might range from about any of 0.1 µg / kg to 3 µg / kg to 30 µg / kg to 300 µg / kg to 3 mg / kg, to 30 mg / kg to 100 mg / kg or more, depending on the factors mentioned above. For repeated administrations over several days or longer, depending on the condition, the treatment is sustained until a desired suppression or amelioration of symptoms occurs or until sufficient therapeutic levels are achieved to alleviate a cancer, or one or more symptoms thereof. An exemplary dosing regimen comprises administering an initial dose of about 2 mg / kg, followed by a weekly maintenance dose of about 1 mg / kg of the antibody, or followed by a maintenance dose of about 1 mg / kg every other week. However, other dosage regimens may be useful, depending on the pattern of pharmacokinetic decay that the practitioner (e.g., a medical doctor) wishes to achieve. For example, dosing from one-four times a week is contemplated. In some embodiments, dosing ranging from about 3 µg / mg to about 2 mg / kg (such as about 3 µg / mg, about 10 µg / mg, about 30 µg / mg, about 100 µg / mg, about 300 µg / mg, about 1 mg / kg, and about 2 mg / kg) may be used. In some embodiments, dosing frequency is once every week, every 2 weeks, every 4 weeks, every 5 weeks, every 6 weeks, every 7 weeks, every 8 weeks, every 9 weeks, or every 10 weeks; or once every month, every 2 months, or every 3 months, or longer. The progress of this therapy may be monitored by conventional techniques and assays and / or by monitoring cancer Types A-D (; 1 st< - 4 th< MF profile clusters, respectively) as described herein. The dosing regimen (including the therapeutic used) may vary over time.

[0308] When the anti-cancer therapeutic agent is not an antibody, it may be administered at the rate of about 0.1 to 300 mg / kg of the weight of the patient divided into one to three doses, or as disclosed herein. In some embodiments, for an adult patient of normal weight, doses ranging from about 0.3 to 5.00 mg / kg may be administered. The particular dosage regimen, e.g.., dose, timing, and / or repetition, will depend on the particular subject and that individual's medical history, as well as the properties of the individual agents (such as the half-life of the agent, and other considerations well known in the art).

[0309] For the purpose of the present disclosure, the appropriate dosage of an anti-cancer therapeutic agent will depend on the specific anti-cancer therapeutic agent(s) (or compositions thereof) employed, the type and severity of cancer, whether the anti-cancer therapeutic agent is administered for preventive or therapeutic purposes, previous therapy, the patient's clinical history and response to the anti-cancer therapeutic agent, and the discretion of the attending physician. Typically the clinician will administer an anti-cancer therapeutic agent, such...

Claims

1. A method comprising: using at least one computer hardware processor to perform: obtaining RNA expression data and / or whole exome sequencing (WES) data for a biological sample from a subject; determining a molecular-functional (MF) profile for the subject at least in part by determining, using the RNA expression data, a gene group expression level for each gene group in a set of gene groups, the set of gene groups comprising gene groups associated with cancer malignancy and different gene groups associated with cancer microenvironment, wherein the gene groups associated with cancer malignancy comprise at least one gene group comprising at least three genes selected from the tumor properties group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6, PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, AKT3, BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, MKNK2, ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB, NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, FGF2, TP53, SIK1, PTEN, DCN, MTAP, AIM2, RB1, ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, HPSE, KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, MITF, APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL; and wherein the gene groups associated with cancer microenvironment comprise: at least one gene group comprising at least three genes selected from the anti-tumor immune microenvironment group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28, IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, NOS2, IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; at least one gene group comprising at least three genes selected from the tumor-promoting immune microenvironment group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, VSIR, CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, CCL28, IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, CXCL8, CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, CTSG, IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1; at least one gene group comprising at least three genes selected from the fibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; and at least one gene group comprising at least three genes selected from the angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; identifying, from among multiple MF profile clusters, an MF profile cluster with which to associate the MF profile for the subject, the MF profile clusters comprising a first MF profile cluster, a second MF profile cluster, a third MF profile cluster, and a fourth MF profile cluster, and identifying at least one first therapy for the subject based on the identified MF profile cluster; wherein the MF profile clusters were generated by: determining a plurality of MF profiles for a respective plurality of subjects using RNA expression data obtained from biological samples from the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters.

2. The method of claim 1, further comprising providing a user with a visual representation of a patient's MF profile and / or other information related to a patient's cancer using an interactive graphical user interface (GUI).

3. The method of claim 1 or claim 2, wherein the subject has a particular cancer type and the method comprises accessing information specifying multiple MF profile clusters for the particular cancer type.

4. The method of claim 1 or any other preceding claim, wherein the gene groups associated with cancer malignancy are: the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and wherein determining the MF profile for the subject comprises determining a gene group expression level for each of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group.

5. The method of claim 4, wherein: (a) the gene groups associated with cancer malignancy comprise: a gene group comprising at least three genes selected from the proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6; a gene group comprising at least three genes selected from the PI3K / AKT / mTOR signaling group: PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; a gene group comprising at least three genes selected from the RAS / RAF / MEK signaling group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; a gene group comprising at least three genes selected from the receptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, and PDGFRB; a gene group comprising at least three genes selected from the tumor suppressors group: TP53, SIK1, PTEN, DCN, MTAP, AIM2, and RB1; a gene group comprising at least three genes selected from the metastasis signature group: ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, and HPSE; a gene group comprising at least three genes selected from the anti-metastatic factors group: KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, and MITF; and a gene group comprising at least three genes selected from the mutation status group: APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL; and / or (b) determining the MF profile comprises determining the gene group expression level for the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group using the gene expression level obtained from the RNA sequence data for at least three genes in teach of the respective groups.

6. The method of claim 1 or any other preceding claim, wherein the gene groups associated with cancer microenvironment are: the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group; and wherein determining the MF profile for the subject comprises determining a gene group expression level for each of the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the cancer associated fibroblasts group, the angiogenesis group, and the tumor-promotive immune group.

7. The method of claim 6, wherein: (a) the gene groups associated with cancer microenvironment comprise: a gene group comprising at least three genes selected from the cancer associated fibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; a gene group comprising at least three genes selected from the angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; a gene group comprising at least three genes selected from the antigen presentation group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, and CD28; a gene group comprising at least three genes selected from the cytotoxic T and NK cells group: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; a gene group comprising at least three genes selected from the B cells group: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, and BLK; a gene group comprising at least three genes selected from the anti-tumor microenvironment group: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; a gene group comprising at least three genes selected from the checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR; a gene group comprising at least three genes selected from the Treg group: CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; a gene group comprising at least three genes selected from the MDSC group: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; a gene group comprising at least three genes selected from the granulocytes group: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, and CTSG; a gene group comprising at least three genes selected from the tumor-promotive immune group: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1; and / or (b) determining the MF profile comprises determining the gene group expression level for the cancer associated fibroblasts group, the angiogenesis group, the antigen presentation group, the cytotoxic T and NK cells group, the B cells group, the anti-tumor microenvironment group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, and the tumor-promotive immune group using the gene expression level obtained from the RNA sequence data for at least three genes in each of the respective groups.

8. The method of claim 1 or any other preceding claim, (a) wherein the gene groups associated with cancer malignancy are: the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and wherein determining the MF profile for the subject comprises determining a gene group expression level for each of the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK signaling group, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group; and / or (b)wherein the gene groups associated with cancer microenvironment are: the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group; and wherein determining the MF profile for the subject comprises determining a gene group expression level for each of the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group.

9. The method of claim 8 or any other preceding claim, wherein: (a) the gene groups associated with cancer malignancy comprise: a gene group comprising at least three genes selected from the proliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, and MCM6; a gene group comprising at least three genes selected from the PI3K / AKT / mTOR signaling group: PIK3CA, PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; a gene group comprising at least three genes selected from the RAS / RAF / MEK signaling group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; a gene group comprising at least three genes selected from the receptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR, ERBB2, FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, and PDGFRB; a gene group comprising at least three genes selected from the growth factors group: NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, and FGF2; a gene group comprising at least three genes selected from the tumor suppressors group: TP53, SIK1, PTEN, DCN, MTAP, AIM2, and RB1; a gene group comprising at least three genes selected from the metastasis signature group: ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, and HPSE; a gene group comprising at least three genes selected from the anti-metastatic factors group: KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, and MITF; and a gene group comprising at least three genes selected from the mutation status group: APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, and VHL; and / or (b) the gene groups associated with cancer microenvironment comprise: a gene group comprising at least three genes selected from the cancer associated fibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; a gene group comprising at least three genes selected from the angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1, PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1, MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; a gene group comprising at least three genes selected from the MHCI group: HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2; a gene group comprising at least three genes selected from the MHCII group: HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, and HLA-DRB6; a gene group comprising at least three genes selected from the coactivation molecules group: CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, and CD28; a gene group comprising at least three genes selected from the effector cells group: IFNG, GZMA, GZMB, PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; a gene group comprising at least three genes selected from the NK cells group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, and KIR2DS5; a gene group comprising at least three genes selected from the T cell traffic group: CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11, CCL21, CCL2, CCL3, CCL4, and CCL5; a gene group comprising at least three genes selected from the T cells group: EOMES, TBX21, ITK, CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; a gene group comprising at least three genes selected from the B cells group: CD19, MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, and BLK; a gene group comprising at least three genes selected from the M1 signatures group: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3; a gene group comprising at least three genes selected from the Th1 signature group: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, and IL21; a gene group comprising at least three genes selected from the antitumor cytokines group: HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; a gene group comprising at least three genes selected from the checkpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA, HAVCR2, and VSIR; a gene group comprising at least three genes selected from the Treg group: CXCL12, TGFB1, TGFB2, TGFB3, FOXP3, CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5, CXCL13, and CCL28; a gene group comprising at least three genes selected from the MDSC group: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1, and CXCL8; a gene group comprising at least three genes selected from the granulocytes group: CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, and CTSG; a gene group comprising at least three genes selected from the M2 signature group: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; a gene group comprising at least three genes selected from the Th2 signature group: IL4, IL5, IL13, IL10, IL25, and GATA3; a gene group comprising at least three genes selected from the protumor cytokines group: IL10, TGFB1, TGFB2, TGFB3, IL22, and MIF; and a gene group comprising at least three genes selected from the complement inhibition group: CFD, CFI, CD55, CD46, and CR1.

10. The method of claim 9 or any other preceding claim, wherein determining the MF profile comprises: (a) determining the gene group expression level for the proliferation rate group, the PI3K / AKT / mTOR signaling group, the RAS / RAF / MEK, the receptor tyrosine kinases expression group, the growth factors group, the tumor suppressors group, the metastasis signature group, the anti-metastatic factors group, and the mutation status group using the gene expression level obtained from the RNA sequence data for at least three genes in each of the respective groups; and / or (b) determining the gene group expression level for the cancer associated fibroblasts group, the angiogenesis group, the MHCI group, the MHCII group, the coactivation molecules group, the effector cells group, the NK cells group, the T cell traffic group, the T cells group, the B cells group, the M1 signatures group, the Th1 signature group, the antitumor cytokines group, the checkpoint inhibition group, the Treg group, the MDSC group, the granulocytes group, the M2 signature group, the Th2 signature group, the protumor cytokines group, and the complement inhibition group, using the gene expression level obtained from the RNA sequence data for at least three genes in each of the respective groups.

11. The method of claim 1 or any other preceding claim, wherein identifying the at least one therapy comprises identifying at least one therapy selected: (a) from the group consisting of: chemotherapy, antibody drug conjugates, hormonal therapy, viral therapy, genetic therapy, non-immune protein therapy, antiangiogenic agents, anti-cancer vaccines, radiotherapy, soluble receptor therapy, cell based therapies, immunotherapy, and targeted therapy; and / or (b) from the group consisting of: HGFR inhibitors, EGFR inhibitors, VEGF inhibitors, PDGF inhibitors, CXR2 inhibitors, CXCR4 inhibitors, DPP-4 inhibitors, galectin inhibitors, antifibrotic agents, LPR1 inhibitors, TGF-beta inhibitors, IL5 inhibitors, IL4 inhibitors, IL13 inhibitors, IL22 inhibitors, CSF1R inhibitors, IDO inhibitors, LPR1 inhibitors, CD25 inhibitors, GITR inhibitors, PD1 inhibitors, CTLA1 inhibitors, PDL1 inhibitors, LAG3 inhibitors, TIM3 inhibitors, vaccines, PRIMA-1 analogues, CD40 agonists, ICOS agonists, OX40 agonists, Bcl-2 inhibitors, AKT inhibitors, MYC-targeting siRNA, pan-tyrosine kinase inhibitors, CDK4 / 6 inhibitors, Aurora A inhibitors, vaccines, LAG3 inhibitors, and any antibody-drug conjugate; and / or (c) from the group consisting of: HGFR inhibitors, EGFR inhibitors, VEGF inhibitors, PDGF inhibitors, CXR2 inhibitors, galectin inhibitors, antifibrotic agents, LPR1 inhibitors, TGF-beta inhibitors, IL5 inhibitors, IL4 inhibitors, IL13 inhibitors, IL22 inhibitors, CSF1R inhibitors, IDO inhibitors, CXCR4 inhibitors, CD25 inhibitors, GITR inhibitors, PD1 inhibitors, CTLA1 inhibitors, PDL1 inhibitors, LAG3 inhibitors, TIM3 inhibitors, and vaccines; and / or (d) from the group consisting of: HGFR inhibitors, EGFR inhibitors, PRIMA-1 analogues, TGF-beta inhibitors, IL22 inhibitors, CSF1R inhibitors, IDO inhibitors, LPR1 inhibitors, CXCR4 inhibitors, CD25 inhibitors, GITR inhibitors, CD40 agonists, ICOS agonists, OX40 agonists, and vaccines; and / or (e) from the group consisting of: Bcl-2 inhibitors, AKT inhibitors, MYC-targeting siRNA, PRIMA-1 analogues, VEGF inhibitors, PDGF inhibitors, CXR2 inhibitors, galectin inhibitors, antifibrotic agents, LPR1 inhibitors, TGF-beta inhibitors, IL5 inhibitors, IL4 inhibitors, IL13 inhibitors, CSF1R inhibitors, IDO inhibitors, CXCR4 inhibitors, and vaccines; and / or (f) from the group consisting of: antibody-drug conjugates, HGFR inhibitors, EGFR inhibitors, VEGF inhibitors, PDGF inhibitors, CXCR2 inhibitors, galectin inhibitors, antifibrotic agents, LPR1 inhibitors, TGF-beta inhibitors, IL22 inhibitors, and CXCL10 disrupting inhibitors; and / or (g) from the group consisting of: Bcl-2 inhibitors, AKT inhibitors, MYC-targeting siRNA, chemotherapy, pan-tyrosine kinase inhibitors, CDK4 / 6 inhibitors, Aurora A inhibitors, and DPP-4 inhibitors.

12. The method of claim 1 or any other preceding claim, wherein: (i) obtaining the RNA expression data is performed using whole transcriptome sequencing or mRNA sequencing; and / or (ii) each of the biological samples is from a tumor or tissue known or suspected of having cancerous cells; and / or (iii) the WES data is used to quantify tumor burden (purity), identify specific mutations, and / or to calculate the number of neoantigens.

13. The method of claim 1 or any other preceding claim, further comprising using the at least one computer hardware processor to perform: accessing information stored in one or more databases, the information comprising the MF profile clusters; or generating the MF profile clusters, the generating comprising: (a) obtaining RNA expression data from biological samples obtained from a plurality of subjects; determining a respective plurality of MF profiles for the plurality of subjects, each of the plurality of MF profiles containing a gene group expression level for each gene group in the set of gene groups; and clustering the plurality of MF profiles to obtain the MF profile clusters, optionally wherein clustering the plurality of MF profiles is performed by using a k-means clustering technique; or (b) obtaining RNA expression data and / or whole exome sequencing (WES) data from biological samples from a plurality of subjects, at least some of the subjects having a cancer of a particular type; determining a respective plurality of molecular-functional (MF) profiles for the plurality of subjects at least in part by, for each of the plurality of subjects, determining, using the RNA expression data, a respective gene group expression level for each group in the set of gene groups clustering the plurality of MF profiles to obtain the MF profile clusters, optionally wherein clustering the plurality of MF profiles is performed by using a k-means clustering technique; and storing the plurality of MF profiles in association with information identifying the particular cancer type.

14. The method of claim 1 or any other preceding claim, wherein the method further comprises: determining at least one visual characteristic of a first graphical user interface (GUI) element using a first gene group expression level for at least one gene group associated with cancer malignancy and at least one visual characteristic of a second GUI element using a second gene group expression level for at least one gene group associated with cancer microenvironment; generating a personalized GUI personalized to the subject, the GUI comprising: a first portion associated with cancer malignancy and containing the first GUI element; and a second portion associated with cancer microenvironment and containing the second GUI element, wherein the second portion is different from the first portion; and presenting the generated personalized GUI to a user, optionally wherein determining the at least one visual characteristic of the first GUI element comprises determining size and / or colour of the first GUI element using the first gene group expression level, and / or wherein the first portion comprises a first plurality of GUI elements representing a respective plurality of gene groups associated with cancer malignancy, and / or wherein the second portion comprises a second plurality of GUI elements representing a respective plurality of gene groups associated with cancer microenvironment.

15. The method of claim 1 or any other preceding claim, wherein determining the MF profile for the subject comprises: determining a first gene group expression level for a first gene group of the gene groups associated with cancer malignancy and for a second gene group of the gene groups associated with cancer microenvironment using a gene set enrichment analysis (GSEA) technique; and / or determining a first gene group expression level for a first gene group of the gene groups associated with cancer malignancy and for a second gene group of the gene groups associated with cancer microenvironment using a mutation count technique.

16. A system, comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform the method of any of claims 1 to 15.

17. At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform the method of any of claims 1 to 15.