Target cell-related machine learning systems for identifying and / or detecting molecular signatures of disease and associated methods of use thereof

Abstract

Methods for identifying and selecting algorithms usable for detecting tumors and / or cancers in patient samples are provided. In some embodiments, the methods include determining gene expression levels of one or more of FGFR1OP2, CDH1, TP53, PIK3CA, S100A8, FAXDC2, CLEC12A, DMTN, MUC1, PSPH, MALAT1, and MKI67 present in a biological sample isolated from the subject that has one or more tumor educated cells that have been contacted directly or indirectly by the tumor and / or the cancer. Disclosed methods include, but are not limited to, generating gene expression profiles predictive of diseases and / or disorders, inhibiting gene expression changes resulting from initiation and / or progression of diseases and / or disorders, inhibiting proliferation and / or survival of target cells, inhibiting epithelial mesenchymal transition (EMT), inhibiting platelet aggregation, and inhibiting loss of immune cell targeting ability, among others. Also provided are systems for use in the presently disclosed methods.

Description

Attorney Docket No.3338 / 3 PCT DESCRIPTION TARGET CELL-RELATED MACHINE LEARNING SYSTEMS FOR IDENTIFYING AND / OR DETECTING MOLECULAR SIGNATURES OF DISEASE AND ASSOCIATED METHODS OF USE THEREOF CROSS REFERENCE TO RELATED APPLICATION

[0001] The presently disclosed subject matter claims the benefit of U.S. Provisional Patent Application Serial Nos.63 / 592,213, filed October 23, 2023; and 63 / 565,613, filed March 15, 2024. The disclosures of both of these applications are incorporated herein by reference in their entireties. TECHNICAL FIELD

[0002] The disclosed technology relates in some embodiments to machine learning systems and in some embodiments to methods for identifying and intervening in human disease based upon information obtained from target cells in the circulation. Target cells include cells that are normally found in the circulation but that have been affected by and / or modulated by a disease condition, for example by tumor education. Target cells can include white blood cells, platelets, and other immune cells as well as circulating tumor cells. Particularly, the presently disclosed subject matter relates in some embodiments to identifying and selecting algorithms usable for detecting target cells that contain a disease signature, optionally tumor educated cells, in clinical samples that can be used to determine risks of certain cancers, such as breast cancer. BACKGROUND

[0003] Early detection is of great importance for increasing survival rates for patients with cancer. Traditional detection methods, including tissue biopsies, are not only typically ordered late in the progression of cancer, but they are also limited in value due to their specific localization, their turnaround times for results, and their inability to provide assessment of minimal residual disease. As such, the results are not as timely as optimally needed, and disease progression can actually be advanced beyond what insight is provided by the procedures.

[0004] More recently, liquid biopsies have started to provide some relief to the aforementioned issues. They can be broader, more comprehensive, minimally invasive, and can also provide real time monitoring of tumor profiles and tumor evolution. Liquid biopsies to date generally focus on identifying characteristics of tumors, such as identifying circulating tumor DNA, tumor RNA, non-coding RNA, tumor cells, and the like. As would be expected, therefore, these signatures are robustly found only at higher burden of disease. As such, to date, the sensitivity of liquid biopsies for disease detection can be 30% or lower. Disease likely needs to be advanced for reliable detection to occur.

[0005] Described herein are a system and methods that utilize a different approach with novel types of biomarkers to identify disease in subjects. Further, since it is desired to have detection as early as possible in disease progression, approaches to identify biomarkers that may be physiologicallyAttorney Docket No.3338 / 3 PCT amplified at lower disease loads are particularly advantageous. Embodiments of the present disclosure are directed to these and other considerations. SUMMARY

[0006] This Summary lists several embodiments of the presently disclosed subject matter, and in many cases lists variations and permutations of these embodiments. This Summary is merely exemplary of the numerous and varied embodiments. Mention of one or more representative features of a given embodiment is likewise exemplary. Such an embodiment can typically exist with or without the feature(s) mentioned; likewise, those features can be applied to other embodiments of the presently disclosed subject matter, whether listed in this Summary or not. To avoid excessive repetition, this Summary does not list or suggest all possible combinations of such features.

[0007] Disclosed embodiments can include a method using machine learning algorithms to develop an algorithm or algorithms to identify biomarkers in target cells that can be associated with disease. Target cells may include white blood cells, disease modified cells, and / or tumor educated cells such as but not limited to leukocytes, platelets and other cells found in the circulation. Target cells may also include circulating tumor cells.

[0008] The method can include obtaining patient sample data comprising negative patient (e.g., a patient who does not have a disease, disorder, or condition of interest) samples and positive patient (e.g., a patient who does have a disease, disorder, or condition of interest) samples. The negative patient samples can be associated with individuals with no known disease diagnosis. The positive patient samples can be associated with individuals with positive diagnosis and can include biomarkers from target cells, such as white blood cells, disease modulated cells, and / or tumor educated cells such as but not limited to leukocytes, platelets and other cells found in the circulation. The method can in some embodiments include training a plurality of machine learning algorithms (MLAs) based on the negative patient samples, the positive patient samples, environmental factors, or any combination thereof. The environmental factors can in some embodiments include at least one of smoking history, current smoking status, menopausal status and / or hormone levels, body mass index (BMI), disease risk status, breast tissue density, and age of individuals within the patient sample data. The MLAs can be trained to identify the presence of disease using biomarkers from target cells, such as white blood cells, disease modulated cells, and / or tumor educated cells such as but not limited to leukocytes, platelets and other cells found in the circulation. The method can further include stratifying, via the first processor, the patient sample data by at least one pre-selected criterion. In some embodiments, the patient sample data is stratified by at least one of (i) a first group being under a predetermined age and a second group being over a predetermined age, or (ii) a third group being post-menopausal, a fourth group being pre- menopausal, and a fifth group being peri-menopausal. The method can include identifying a single model or a plurality of models, to deploy as a detecting model. A single model or a first model of a plurality of MLAs can be selected based on performance of the MLA for detecting the biomarkers fromAttorney Docket No.3338 / 3 PCT target cells such as white blood cells, disease modulated cells, and / or tumor educated cells such as but not limited to leukocytes, platelets and other cells found in a patient sample. In any embodiment described herein, the performance of a single algorithm or a plurality of MLAs for detecting the biomarkers from target cells such as white blood cells, disease modulated cells, and / or tumor educated cells such as but not limited to leukocytes, platelets and other cells found in clinical samples can be based on accuracy, sensitivity, or specificity of the MLAs.

[0009] In some embodiments, the presently disclosed subject matter also relates to methods for calculating and using sample biomarker transcript numbers from target cells as defined herein in sample data. The methods in some embodiments comprise accessing control data and sample data within a database. The control data can include both negative controls and positive controls, which in some embodiments can comprise target cells as defined herein including but not limited to tumor educated cells (e.g., leukocytes and / or platelets). The methods in some embodiments comprise analyzing the control data and sample data to identify standard deviations that are below predetermined deviation ranges. The methods can in some embodiments comprise analyzing the positive controls to identify whether one or more samples of the positive controls are above a predetermined amplification range. The methods can in some embodiments comprise calculating, for a portion of the control data that is below a respective predetermined deviation range and above a respective predetermined amplification range, control mean cycle threshold (CT) values. In some embodiments, the methods can comprise plotting a standard curve using the control mean CT values and known transcript numbers for biomarkers from target cells as defined herein in the control data. The method can include calculating gene expression levels of particular biomarkers in target cells as defined herein or tumor educated cells in the sample data using the standard curve.

[0010] In some embodiments, the presently disclosed subject matter also relates to methods for calculating and using sample biomarker transcript numbers from target cells as defined herein in sample data. The methods can in some embodiments comprise identifying a first model from a plurality of machine learning algorithms (MLAs) trained on negative patient samples, positive patient samples, and environmental factors including at least one of smoking history, menopausal status and / or hormone levels, disease risk status, breast tissue density, and age of individuals within patient sample data, the first model having an accuracy of over about 60% and a sensitivity of over about 50% for detecting cancer in a patient under the age of 50 by detecting biomarkers from target cells as defined herein in a patient sample. The methods can in some embodiments comprise receiving RNA quantification data for the patient sample. The methods can in some embodiments comprise receiving Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) analysis data, including quantitative and / or real- time RT-PCR data (qPCR), about a normalized patient sample. The method can include testing the RT- PCR / qPCR analysis data with the first model to identify a number of transcripts of the biomarkers in target cells as defined herein and to determine disease signal.Attorney Docket No.3338 / 3 PCT

[0011] The presently disclosed subject matter also relates in some embodiments to methods for calculating and using sample biomarker transcript numbers from target cells as defined herein in sample data. In some embodiments, the methods comprise identifying a first model from a plurality of machine learning algorithms (MLAs) trained on negative patient samples, positive patient samples, and environmental factors including at least one of smoking history, menopausal status, disease risk status, breast tissue density, and age of individuals within patient sample data, the first model having an accuracy of over about 60% and a sensitivity of over about 50% for detecting cancer in a patient over the age of 50 by detecting a presence of biomarkers from target cells as defined herein in a patient sample. The method can include receiving RNA quantification data for the patient sample. The method can include receiving Polymerase Chain Reaction (RT-PCR) analysis data, including quantitative and / or real-time RT-PCR data (qPCR), about a normalized patient sample. The method can include testing the RT-PCR / qPCR analysis data with the first model to identify a number of transcripts of the biomarkers in target cells as defined herein and to determine disease signal.

[0012] More particularly, in some embodiments the presently disclosed subject matter relates to a method comprising, consisting essentially of, or consisting of obtaining, via a first processor of a reverse transcriptase polymerase chain reaction (RT-PCR / qPCR) data analysis module, patient sample data comprising negative patient samples and positive patient samples, the negative patient samples being associated with individuals with no known cancer diagnosis, and the positive patient samples being associated with individuals with positive cancer diagnosis and comprising one or more biomarkers for target cells as defined herein, optionally tumor educated cells, for example leukocytes; training, via the first processor, a one or more machine learning algorithms (MLAs) based on the negative patient samples, the positive patient samples, and environmental factors including at least one of smoking history, current smoking status, menopausal status and / or hormone levels, BMI, disease risk status, breast tissue density, and age of individuals within the patient sample data in order to identify presence of the one or more biomarkers from target cells as defined herein, optionally tumor educated cells, for example leukocytes; stratifying, via the first processor, the patient sample data by at least one of a first group being under a predetermined age and a second group being over a predetermined age; or a third group being post-menopausal, a fourth group being pre-menopausal, and a fifth group being peri- menopausal; and identifying, via the first processor, a single or a first model from a plurality of MLAs to deploy as a detecting model, the first model being selected from the plurality of MLAs based on a performance of the plurality of MLAs for detecting the one or more biomarkers from target cells as defined herein, optionally leukocytes, in a patient sample.

[0013] In some embodiments, the performance of the one or more MLAs for detecting the one or more biomarkers from target cells as defined herein, optionally leukocytes, in the patient sample is based on accuracy, sensitivity, or specificity of a single or a plurality of MLAs for detecting the one or more biomarkers for target cells as defined herein, optionally tumor educated cells including but not limitedAttorney Docket No.3338 / 3 PCT to leukocytes. In some embodiments, the first model is selected in response to the first model having, for the second group accuracy of at least about 60% with a sensitivity of at least about 50% and / or a specificity of at least about 65%.

[0014] In some embodiments, the first model is selected in response to the first model havinga sensitivity of greater than about 87.0% and a specificity of greater than about 85.0%; or a sensitivity of greater than about 74.0% and a specificity of greater than about 85.0%.

[0015] In some embodiments, the presently disclosed methods comprise, consist essentially of, or consist of receiving, at the processor, ribonucleic acid (RNA) quantification data for the patient sample; calculating, via the processor, fluid volumes needed to normalize the patient sample; receiving, at the processor, RT-PCR / qPCR analysis data about a normalized patient sample; and testing, via the processor, the RT-PCR / qPCR analysis data with the first model to identify a number of copies of the target cells as defined herein, optionally leukocytes, and to increase the likelihood of signal detection with respect to a disease, disorder, or condition, optionally a tumor and / or a cancer.

[0016] In some embodiments, the presently disclosed methods further comprise receiving, at the first processor, control data comprising both negative controls and positive controls comprising target cells as defined herein, optionally leukocytes; analyzing, via the first processor, the control data and the patient sample to identify whether standard deviations are below a predetermined deviation range; analyzing the positive controls to identify whether one or more samples of the positive controls are above a predetermined amplification range; calculating, for a portion of the control data that is below a respective predetermined deviation range and above a respective predetermined amplification range, control mean CT values; plotting, via the processor, a standard curve using the control mean CT values and known transcript numbers of genes expressed by target cells as defined herein, optionally leukocytes, in the control data; and calculating, via the first processor, sample transcript numbers from target cells as defined herein, optionally leukocytes, in the patient sample data using the standard curve.

[0017] In some embodiments of the presently disclosed methods, the target cells as defined herein or tumor educated cells, optionally leukocytes, comprise one or more of eosinophils, monocytes, macrophages, NK cells, dendritic cells, platelets, or neutrophils that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or a component and / or secretion thereof, directly and / or indirectly. In some embodiments, a component of a tumor cell or previously tumor educated cell is any molecule of a plurality of molecules that are present on, in, and / or shed and / or otherwise released from a tumor cell or a previously tumor educated cell.

[0018] In some embodiments of the presently disclosed methods, the first model is effective to detect breast cancer in a patient.

[0019] In some embodiments of the presently disclosed methods, the one or more biomarkers in the patient sample are selected from the group consisting of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinaseAttorney Docket No.3338 / 3 PCT catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma Transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

[0020] In some embodiments of the presently disclosed methods, the one or more biomarkers in the patient sample are each of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

[0021] In some embodiments of the presently disclosed methods, the one or more upstream regulators in the patient samples (including but not limited to whole blood, white blood cells, and / or platelets isolated from a subject) include, but are not limited to gene products of the genes listed in Tables 25 and 27, including but not limited to STING1, TRAF2, ZNF281, BMP4, SMAD3, and ITGA11.

[0022] In some embodiments, the presently disclosed subject matter also relates to methods comprising, consisting essentially of, or consisting of accessing, via a processor, control data and sample data within a database, the control data comprising both negative controls and positive controls comprising target cells as defined herein, optionally leukocytes; analyzing, via the processor, the control data and sample data to identify standard deviations are below a predetermined deviation range; analyzing, via the processor, the positive controls to identify whether one or more samples of the positive controls are above a predetermined amplification range; calculating, via the processor and for a portion of the control data that is below a respective predetermined deviation range and above a respective predetermined amplification range, control mean CT values; plotting, via the processor, a standard curve using the control mean CT values and known transcript numbers of genes expressed by target cells as defined herein, optionally leukocytes, in the control data; and calculating, via the processor, sample transcript numbers from target cells as defined herein, optionally leukocytes, in the sample data using the standard curve. In some embodiments, the presently disclosed method further comprises normalizing raw expression levels of the one or more biomarkers with respect to the geometric mean of expression levels of one or more one or more controls, optionally wherein the one or more controls are selected from the group consisting of B2M, GAPDH, and EEF1G.

[0023] In some embodiments, the presently disclosed methods further comprise creating, via the processor, a first analysis model for the target cells as defined herein, optionally leukocytes, within samples by training a machine learning algorithm (MLA) based on normalized sample transcript number and environmental factors, the environmental factors including at least one of smoking history,Attorney Docket No.3338 / 3 PCT current smoking status, menopausal status and / or hormone levels, BMI, disease risk status, breast tissue density, and age of individuals within the sample data. In some embodiments, the MLA is selected from a plurality of machine learning algorithms characterized as one of regression-based algorithms, decision tree-based algorithms, deep learning algorithms, or neural networks.

[0024] In some embodiments, the presently disclosed methods further comprise retraining, via the processor and in response to a request for an upgrade to analysis software, the MLA using subsequent control data and subsequent sample data; and creating, via the processor, a second analysis model for the target cells as defined herein, optionally leukocytes, within samples based on the retraining.

[0025] In some embodiments, the first model is effective to detect breast cancer in a patient.

[0026] In some embodiments, the presently disclosed methods further comprise removing, via the processor, one or more outliers from the control data or the sample data responsive to determining that the one or more outliers are below the predetermined deviation range, optionally wherein the predetermined deviation range is selected from the group consisting of 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, and greater than 1.7 standard deviations.

[0027] In some embodiments, the presently disclosed methods further comprise removing, via the processor, one or more outliers from the control data and / or the sample data responsive to determining that the one or more outliers are above the predetermined amplification range.

[0028] In some embodiments of the presently disclosed methods, the target cells as defined herein, optionally leukocytes, comprise one or more of eosinophils, monocytes, macrophages, NK cells, dendritic cells, platelets, or neutrophils that have contacted a tumor cell and / or a component and / or secretion thereof, either directly or indirectly.

[0029] In some embodiments, the presently disclosed methods further comprise calculating sample transcript numbers from biomarkers expressed in the target cells as defined herein, optionally leukocytes, in the sample data identifying one or more biomarkers for the target cells as defined herein, optionally leukocytes, and further wherein the one or more biomarkers are selected from the group consisting of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67). In some embodiments, the one or more biomarkers are each of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucinAttorney Docket No.3338 / 3 PCT 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

[0030] In some embodiments, the presently disclosed subject matter relates to systems. In some embodiments, a system of the presently disclosed subject matter comprises one or more processors; memory in communication with the one or more processors and storing instructions that are configured to cause the system to identify a first model from one or more machine learning algorithms (MLAs) trained on one or more of negative patient samples, positive patient samples, and environmental factors including at least one of smoking history, current smoking status, menopausal status and / or hormone levels, disease risk status, breast tissue density, and age of individuals within patient sample data, the first model having a sensitivity of at least about 45% and / or a specificity of at least about 85% for detecting a disease, disorder, and / or condition, optionally a tumor and / or a cancer, in a patient under the age of 50 by detecting a presence of one or more biomarkers for a target cell, optionally a tumor educated cell, further optionally a leukocyte, in a patient sample; receive ribonucleic acid (RNA) quantification data for the patient sample; receive reverse transcriptase polymerase chain reaction (RT- PCR / qPCR) analysis data about a normalized patient sample; test the RT-PCR / qPCR analysis data with the first model to identify a gene expression profile from the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, with respect to one or more biomarkers, and increase the likelihood of signal detection with respect to a disease, disorder, or condition, optionally a tumor and / or a cancer based on the gene expression profile identified. In some embodiments, the first model is a decision tree-based algorithm. In some embodiments, the target cells as defined herein, optionally leukocytes, comprise one or more of eosinophils, monocytes, macrophages, NK cells, dendritic cells, platelets, or neutrophils that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or a component and / or secretion thereof, directly and / or indirectly. In some embodiments, the first model is effective to detect breast cancer in a patient.

[0031] In some embodiments of the systems of the presently disclosed subject matter, the one or more biomarkers are selected from the group consisting of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67). In some embodiments, the one or more biomarkers are each of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol- 4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associatedAttorney Docket No.3338 / 3 PCT (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

[0032] In some embodiments, the presently disclosed subject matter also relates to methods for detecting a tumor and / or a cancer in a subject. In some embodiments, the presently disclosed methods comprise, consist essentially of, or consist of determining gene expression levels of one or more biomarkers selected from the group consisting of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67) present in a biological sample isolated from the subject, the biological sample comprising one or more target cells as defined herein that have been contacted directly or indirectly by the tumor and / or the cancer and / or a previously tumor educated cell; and comparing the gene expression levels of the one or more biomarkers present in the biological sample with a control, wherein the control is optionally a biological sample isolated from a negative or positive control subject, wherein the comprising step provides an increased likelihood of signal detection with respect to a disease, disorder, or condition, optionally a tumor and / or a cancer, in the subject. In some embodiments, gene expression levels in the biological sample are determined for each of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67). In some embodiments, the determining comprises quantitatively analyzing transcription products of the one or more biomarkers in the biological sample. In some embodiments, the comparison is performed by a system of the presently disclosed subject matter. In some embodiments, the tumor and / or a cancer is a tumor and / or cancer of the breast. In some embodiments, the subject is a human. In some embodiments of the presently disclosed methods, the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, comprise one or more of eosinophils, monocytes, macrophages, NK cells, dendritic cells, platelets, or neutrophils that have contacted a tumor cell and / or a component thereof and / or a previously tumor educated cell and / or a component thereof, directly or indirectly.

[0033] In some embodiments, the presently disclosed methods further comprise administering to the subject at least one anti-tumor and / or anti-cancer therapy based on an outcome of the comparison step.

[0034] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting proliferation and / or survival (e.g., initiating apoptosis, necrosis, cell cycle arrest, etc.) of aAttorney Docket No.3338 / 3 PCT cell, optionally a tumor and / or cancer cell, and / or an immune cell that is induced to proliferate by direct or indirect contact with a tumor and / or cancer cell and / or a previously tumor educated cell, alone or in any combination. In some embodiments, the methods comprise, consist essentially of, or consist of contacting the tumor and / or cancer cell and / or the target cell (including but not limited to an immune cell) with an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety. In some embodiments, the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0035] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting gene expression changes associated with disease modulation, including tumor education, of immune cells. In some embodiments, the methods comprise, consist essentially of, or consist of contacting the immune cell with an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0036] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting downstream effects of tumor-education involving changes in the cytokine release (such as but not limited to IL6), processes and pathways involved in cell-cell communications, cellular immune response (to continue with the downstream pathways resulting from tumor education). In some embodiments, the presently disclosed subject matter also relates to the small molecule inhibitors such as crizotinib capable of inhibiting these downstream processes.

[0037] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting the growth of tumors and / or progression of tumor-associated diseases, disorders, and / or conditions in subjects. In some embodiments, the methods comprise, consist essentially of, or consist of administering to a subject in need thereof an effective amount of one or more small molecule drugs that comprise a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI- 7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0038] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting epithelial mesenchymal transition (EMT) associated with disease modulation, including tumor education, of immune cells in a subject. In some embodiments, the methods comprise, consist essentially of, or consist of administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drugAttorney Docket No.3338 / 3 PCT is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI- 7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0039] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting either cytokine signaling or any of the following: the S100A signaling pathway, a pathway related to cellular immune response and innate immunity, cell-to-cell signaling, cellular assembly and organization, pathogen-induced signaling, and / or inflammation related to associated tumor education of immune cells in a subject. In some embodiments, the methods comprise, consist essentially of, or consist of administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0040] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting upstream regulators of tumor education including but not limited to STING1, TRAF2, ZNF281, BMP4, SMAD2, SMAD3, ITGA11 and others associated with disease modulation, including tumor education, of target cells in a subject. In some embodiments, the methods comprise, consist essentially of, or consist of administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI- 7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0041] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting platelet aggregation associated with exposure to one or more disease or tumor-derived factors. In some embodiments, the methods comprise, consist essentially of, or consist of administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0042] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting loss of immune cell targeting ability associated with exposure to one or more disease or tumor-derived factors. In some embodiments, the methods comprise, consist essentially of, or consist of administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376Attorney Docket No.3338 / 3 PCT and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0043] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting loss of oncogenic signaling associated with exposure to one or more tumor-derived factors. In some embodiments, the presently disclosed methods comprise, consist essentially of, or consist of administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0044] In some embodiments, the presently disclosed subject matter also relates to methods for inhibiting an oncogenic pathway associated with education of an immune cell by a tumor and / or a cancer cell. In some embodiments, the methods comprise, consist essentially of, or consist of contacting the tumor and / or cancer cell and / or the immune cell with an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety. In some embodiments, the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof. In some embodiments, the immune cell is selected from the group consisting of eosinophils, monocytes, macrophages, natural killer (NK) cells, dendritic cells, neutrophils, T cells, B cells, mast cells, and other lymphocytes that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly. In some embodiments, the tumor and / or the cancer cell is a leukemia cell, a melanoma cell, an ovarian tumor and / or cancer cell, a breast tumor and / or cancer cell, a colorectal tumor and / or cancer cell, a lung squamous cancer cell, a sarcoma cell, a renal cell carcinoma cell, a pancreatic carcinoma cell, a squamous tumor of the head and neck cell, a brain cancer cell, a liver cancer cell, a prostate cancer cell, and a cervical cancer cell. In some embodiments, the oncogenic pathway is associated with a pathway listed in any one of Tables 4, 5, 14, 16, 21, 24, and / or 26.

[0045] In some embodiments, the presently disclosed subject matter also relates to methods for assessing the degree to which a tumor and / or cancer cell and / or a component and / or secretion thereof, induces alterations in biological activities of an immune cell. In some embodiments, the methods comprise, consist essentially of, or consist of contacting the immune cell with the tumor and / or cancer cell and / or a previously tumor educated cell and / or a component and / or secretion thereof, and identifying a change in one or more biological activities of the immune cell, wherein the change is relative to the immune cell under identical conditions but absent the contacting step. In some embodiments, the one or more biological activities are selected from but not limited to a change inAttorney Docket No.3338 / 3 PCT expression of at least one gene, a difference in proliferation, entry into EMT, and / or entry into an oncogenic pathway. In some embodiments, the oncogenic pathway is one of the pathways listed in any one of Tables 4, 5, 14, 16, 21, 24, and / or 26. In some embodiments, the immune cell is selected from but not limited to a white blood cell (WBC), a peripheral blood multinuclear cell (PBMC), or a platelet. In some embodiments, the tumor and / or cancer cell is a breast tumor and / or cancer cell, a lung tumor and / or cancer cell, or a pancreatic tumor and / or cancer cell, optionally wherein the tumor and / or cancer cell is from a cell line selected from but not limited to MDA-MB-231, MCF7, SKBR3, BT549, PAC1, and H1975.

[0046] In some embodiments, the presently disclosed subject matter also relates to methods for determining a degree to which a therapeutic agent can modulate an alteration in a biological activity of an immune cell induced by contact with a tumor and / or cancer cell and / or a previously tumor educated cell and / or a component and / or secretion thereof. In some embodiments, the methods comprise, consist essentially of, or consist of administering the therapeutic agent to an immune cell, the tumor and / or cancer cell and / or a previously tumor-educated cell, or any combination thereof; contacting the immune cell with the tumor and / or cancer cell and / or a previously tumor educated cell and / or a component and / or secretion thereof; and determining a change in one or more biological activities of the immune cell and / or a previously tumor educated cell, optionally wherein the one or more biological activities are selected from but not limited to a change in expression of at least one gene, a difference in proliferation, entry into EMT, entry into an oncogenic pathway, optionally one of the pathways, optionally one of the oncogenic pathways, listed in any of Tables 4, 5, 14, 16, 21, 24, and 26, wherein the change is relative to the immune cell and / or the tumor educated cell under identical conditions but absent the administering step.

[0047] In some embodiments, the tumor and / or cancer cell is a breast tumor and / or cancer cell, a lung tumor and / or cancer cell, or a pancreatic tumor and / or cancer cell. In some embodiments, the therapeutic agent is administered to the immune cell, the tumor and / or cancer cell, or both before the contacting step. In some embodiments, the therapeutic agent is administered to the immune cell, the tumor and / or cancer cell, or both after the contacting step. In some embodiments, the therapeutic agent is administered to the immune cell, the tumor and / or cancer cell, or both concurrently with the contacting step.

[0048] Further implementations, features, and aspects of the disclosed technology, and the advantages offered thereby, are described in greater detail hereinafter, and can be understood with reference to the following detailed description, accompanying drawings, and claims.

[0049] It is thus an object of the presently disclosed subject matter to provide methods for identifying and quantifying biomarkers from target cells as defined herein, optionally tumor educated cells, to identify a signal of disease, optionally cancer, optionally breast cancer, in a body.Attorney Docket No.3338 / 3 PCT

[0050] It is thus an object of the presently disclosed subject matter to provide machine learning systems and methods for employing biomarkers from target cells as defined herein, optionally tumor educated cells, for identifying the presence of disease in a body, optionally cancer, optionally breast cancer.

[0051] It is thus an object of the presently disclosed subject matter to provide methods for inhibiting disease modification of target cells as defined herein, optionally tumor educated cells, by administering to a subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0052] An object of the presently disclosed subject matter having been stated hereinabove, and which is achieved in whole or in part by the presently disclosed subject matter, other objects will become evident as the description proceeds when taken in connection with the accompanying Figures and non- limiting examples as best described herein below. BRIEF DESCRIPTION OF THE FIGURES

[0053] Figure 1 is a flow diagram illustrating an exemplary method for using machine learning algorithms to identify algorithms to detect biomarkers for target cells as defined herein, optionally tumor educated cells such as leukocytes or platelets, in accordance with certain embodiments of the presently disclosed subject matter.

[0054] Figure 2A is a flow diagram illustrating an exemplary method for calculating sample transcript numbers of genes expressed in target cells as defined herein, optionally tumor educated cells (e.g., leukocytes or platelets) in sample data, in accordance with certain embodiments of the presently disclosed subject matter. Figure 2B is a flow diagram illustrating an exemplary method for using machine learning algorithms to model and retrain a detecting algorithm to detect biomarkers for target cells as defined herein, in accordance with certain embodiments of the presently disclosed subject matter.

[0055] Figure 3A is a flow diagram illustrating an exemplary, non-limiting method for testing sample gene transcript numbers of target cells as defined herein, optionally tumor educated cells (e.g., leukocytes or platelets) in sample data, in accordance with certain embodiments of the disclosed technology. Figure 3B is a flow diagram illustrating an exemplary, non-limiting method for testing for sample transcript numbers of target cells as defined herein, optionally tumor educated cells (e.g., leukocytes or platelets) in sample data, in accordance with certain embodiments of the presently disclosed subject matter.

[0056] Figure 4 is a block diagram of an example RT-PCR / qPCR analysis module used to identify and quantify tumor-educated cells, according to an exemplary embodiment of the presently disclosed subject matter.Attorney Docket No.3338 / 3 PCT

[0057] Figure 5 is block diagram of an exemplary system that can be used to identify and quantify white blood cells, optionally tumor educated cells (e.g., leukocytes or platelets) according to an exemplary embodiment of the presently disclosed subject matter.

[0058] Figure 6 is an exemplary process flow for drawing samples and testing the samples for target cell gene expression for white blood cells, optionally tumor educated cells (e.g., leukocytes or platelets) according to an exemplary embodiment of the presently disclosed subject matter.

[0059] Figure 7A is an exemplary process map for an example RT-PCR / qPCR analysis module and Figure 7B is an exemplary process map for an example RNA quantification software, according to an exemplary embodiment of the presently disclosed subject matter.

[0060] Figure 8 shows a representative standard curve for calculating expression levels of gene biomarkers of interest and a Table with similar information for additional biomarker targets, including PCR amplification efficiency and a goodness of fit parameter for linear regression equations, according to an exemplary embodiment of the presently disclosed subject matter. With respect to the Table, PCR efficiency is defined as the percentage of target molecules that are copied per PCR cycle. An ideal efficiency is 100%, indicating amplification of all the target molecules with each successive cycle. However, a PCR efficiency of 100% is not always attainable and a range of 90% to 110% is considered acceptable. The efficiency of the qPCR reaction is influenced by many factors including target length, target sequence, primer sequence, buffer conditions, impurities present in the sample, cycling conditions, and the type of enzyme used. The precision of a particular PCR assay is dependent upon the efficiency of the reaction. To obtain the amplification efficiency of each target, a series of chemically synthesized controls with known copy numbers were quantified and used to generate a threshold cycle versus log copy number linear plot. All targets were run in triplicate wells for each diluted control. An 8-point tenfold dilution series spanning 7 orders of magnitude (2.4 x 108to 2.4 x 101copies per reaction) with equally spaced intermediate concentrations were created for each target. Linear regression analyses were used to plot a ‘best fit’ line through the points. Multiplexed primers and probes in the final plate design were used for these experiments. The amplification efficiency of each target was calculated using the following equation: As shown in the Table in the lower panel of Figure 8, the amplification efficiencies and linear regression coefficients of all targets fall within the prespecified ranges of 90 – 110% and greater than 0.9950, respectively.

[0061] Figure 9 is an exemplary flow diagram for training a machine learning algorithm and related models for identifying and quantifying target cell gene expression for white blood cells, optionally tumor educated cells (e.g., leukocytes or platelets) according to an exemplary embodiment of the presently disclosed subject matter.Attorney Docket No.3338 / 3 PCT

[0062] Figure 10A is a bar graph showing the effects of direct or indirect contact between cancer and immune cells from a phenotypic assay based on proliferation. Figure 10B is a bar graph showing the effects of small molecule inhibitors such as but not limited to crizotinib on proliferation resulting from indirect contact between breast cancer cancer cells and a human monocytic cell line.

[0063] Figures 11A and 11B are a Table and a bar graph showing the effect of crizotinib on biomarker gene expression changes normalized to THP1 alone for the noted genes upon contact with MDA-231 cells after 24 hours.

[0064] Figures 12A and 12B are a Table and a bar graph showing the effect of brigatinib on biomarker gene expression changes normalized to THP1 alone for the noted genes upon contact with MDA-231 cells after 24 hours.

[0065] Figure 13 shows the structures of exemplary, non-limiting small molecule inhibitors with a terminal monocyclic or polycyclic structure (boxes).

[0066] Figure 14 shows the structures of various exemplary, non-limiting small molecule scaffolds that can be employed in various compositions to inhibit the increase of proliferation generated upon contact immune and cancer cells.

[0067] Figures 15A and 15B are examples of structural scaffolds active in blocking signaling between white blood cells and cancer cells, optionally those participating in tumor education.

[0068] Figures 16A-16F: Verification of the qPCR data analysis software package. Positive control charts of three (3) targets for the V6 (4.0 × 10-7copies) control point obtained from manual calculations (Figure 16A, Figure 16C, and Figure 16E) and cropped LIS calculations (Figure 16B, Figure 16D, and Figure 16F). Data shows consistency in mean and standard deviations. Similar trends are obtained in positive corresponding positive control charts.

[0069] Figure 17 is a plate map to guide reaction setup for a test plate for a qPCR clinical assay.

[0070] Figure 18: Summary description of the IDBC prospective clinical study. Blood was collected from study participants in PAXgene RNA blood tubes and shipped to the Alberta Cancer Research Biobank (ACRB) for registration. ACRB then delivered blinded tubes to the present co-inventors for RNA extraction and normalization. Normalized RNA was returned to ACRB for aliquoting into randomly barcoded Matrix tubes and the deposit of individual Matrix tubes into the study biobank. De- identified and randomly barcoded aliquots were delivered to the present co-inventors for analysis.

[0071] Figures 19A and 19B: IDBC study performance of the presently disclosed Breast Cancer Test for the entire study cohort (see Figure 19A) and for participants under 50 and those aged 50 and over (see Figure 19B). Upper and lower whiskers of each histogram represent the upper and lower confidence intervals for each metric, respectively. Confidence intervals for accuracy, specificity and sensitivity of each dataset were computed through an exact binomial test (α = 0.005).

[0072] Figure 20 is a western blot showing the effects of crizotinib on EMT markers in in vitro models (e.g. THP1) contacted with breast cancer cells (e.g. MDA-MB-231).Attorney Docket No.3338 / 3 PCT

[0073] Figure 21 is a series of photomicrographs showing the effects of breast cancer cell conditioned media on human platelet aggregation.

[0074] Figures 22A and 22B are bar graphs showing increases in proliferation observed upon indirect (Figure 22A) and direct (Figure 22B) contact between cancer and immune cells (WBC).

[0075] Figure 23 is a series of bar graphs showing amplification of oncogenic proliferation signals with direct contact between human PBMCs and MDA-231.

[0076] Figure 24 is a series of bar graphs showing amplification of oncogenic proliferation signals with indirect contact between PBMCs and MDA-231 at 24 and 48 hours using breast cancer cell conditioned media.

[0077] Figures 25A and 25B are graphical summaries of the mechanism and pathways altered as a result of RNAseq analysis of tumor education and was generated using QIAGEN Inc.’s Ingenuity Pathway Analysis (IPA). See the discussion of Ingenuity Pathway Analysis available on Qiagen’s website.

[0078] Figures 26A and 26B are breast cancer test biomarker ROC curves for the noted genes.

[0079] Figures 27A-27C present differential gene expression in THP1 cells contacted with MDA-MB- 231 cells for 24 hours. (Figure 27A) a volcano plot showing the differentially expressed THP1 genes. (Figure 27B) The top 20 upregulated and / or downregulated THP1 genes. (Figure 27C) a Venn Diagram showing common genes affected by treatment vs. changes in genes specific to each cell type.

[0080] Figures 28A-C show Gene Ontology (GO) (Panel A), KEGG (Panel B), and Reactome Enrichment analyses (Panel C), of the differentially expressed genes in THP1 contacted with MDA- MB-231 cells. (Panel A) GO enrichment shows the most significant 30 terms. (Panel B) KEGG pathways are shown with padj < 0.05, demonstrating significant enrichment. (Panel C) Reactome enrichment shows the most significant 20 pathways.

[0081] Figure 29 is a bubble graph representation of the significant THP1 canonical pathways activated by contact with cancer cells. Activation Z score > 1.5.

[0082] Figure 30 presents the top cellular functions found to be modified by direct contact in IPA based on biofunction and disease.

[0083] Figure 31 lists the top upstream regulators activated in THP1 cells following direct 24 hr exposure to MDA-MB-231 cells.

[0084] Figures 32A and 32B present gene expression data for THP1 cells exposed to breast cancer cell conditioned media and secreted soluble factors after 24 hrs. Figure 32A shows that there were over 1,100 differentially expressed genes with fold change > 2 or < 2 with p < 0.05. Figure 32B indicates that there were 484 genes uniquely changed upon the exposure to the pre-conditioned media from MDA-231.

[0085] Figure 33 shows a representation of the affected canonical pathways from exposure to breast cancer cell conditioned media and secreted soluble factors after 24 hrs.Attorney Docket No.3338 / 3 PCT

[0086] Figure 34 compares upstream regulators between THP1 from direct (contact) and indirect (conditioned media / soluble factor) breast cancer cell exposure at 24 hrs.

[0087] Figure 35 is the KEGG enrichment analysis for THP1 indirectly exposed via breast cancer cell conditioned media and secreted soluble factors at 24 hrs.

[0088] Figure 36 is the GO enrichment ana analysis for THP1 indirectly exposed via breast cancer cell conditioned media and secreted soluble factors at 24 hrs.

[0089] Figure 37 represents the structures of various exemplary small molecule scaffolds that can be employed in various compounds to inhibit the increase of proliferation generated in a phenotypic assay upon contact immune and cancer cells, in particular MCF7 breast cancer cells.

[0090] Figure 38. Presents IDBC study participant disposition. The clinically eligible and evaluable dataset included participants meeting the study inclusion / exclusion criteria and evaluability criteria; of these, participants with evaluable assay results were included in the analyzable set. The independent test analysis set included participants who were analyzable and had a confirmed cancer diagnosis or absence of cancer diagnosis within the one-year post blood draw. All clinical validation data for the blood test were generated from the independent test set of participants.

[0091] Figures 39A-39F present overall clinical performance metrics of the disclosed blood test. Figure 39A. Blood test result tree. Sensitivity and specificity for (Figure 39B) women ages 24 to 81, (Figure 39C) participants under 50 and those aged 50 and over, (Figure 39D) pre-menopausal and post- menopausal participants, and (Figure 39E) as a function of tissue density. Figure 39F presents correlation of test performance compared to imaging across imaging BI-RADS categories. Upper and lower whiskers represent the upper and lower confidence intervals for each metric, respectively. DETAILED DESCRIPTION

[0092] The features and other aspects and principles of the disclosed computing embodiments can be implemented in various environments. Such environments and related applications can be specifically constructed for performing the various processes and operations of the disclosed embodiments or they can include a general-purpose computer or computing platform selectively activated or reconfigured by program code to provide the necessary functionality. Further, the processes disclosed herein can be implemented by a suitable combination of hardware, software, and / or firmware. For example, the disclosed embodiments can implement general purpose machines configured to execute software programs that perform processes consistent with the disclosed embodiments. Alternatively, the disclosed embodiments can implement a specialized apparatus or system configured to execute software programs that perform processes consistent with the disclosed embodiments. Furthermore, although some disclosed embodiments can be implemented by general purpose machines as computer processing instructions, all or a portion of the functionality of the disclosed embodiments can be implemented instead in dedicated electronics hardware.Attorney Docket No.3338 / 3 PCT

[0093] The disclosed embodiments also relate to tangible and non-transitory computer readable media that include program instructions or program code that, when executed by one or more processors, perform one or more computer-implemented operations. The program instructions or program code can include specially designed and constructed instructions or code, and / or instructions and code well- known and available to those having ordinary skill in the computer software arts. For example, the disclosed embodiments can execute high level and / or low-level software instructions, such as machine code (e.g., such as that produced by a compiler) and / or high-level code that can be executed by a processor using an interpreter.

[0094] The technology disclosed herein typically involves a high-level design effort to construct a computational system that can appropriately process unpredictable data. Mathematical algorithms can be used as building blocks for a framework, however certain implementations of the system can autonomously learn their own operation parameters, achieving better results, higher accuracy, fewer errors, fewer crashes, and greater speed.

[0095] Definitions

[0096] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the presently disclosed subject matter.

[0097] While the following terms are believed to be well understood by one of ordinary skill in the art, the following definitions are set forth to facilitate explanation of the presently disclosed subject matter.

[0098] All technical and scientific terms used herein, unless otherwise defined below, are intended to have the same meaning as commonly understood by one of ordinary skill in the art. References to techniques employed herein are intended to refer to the techniques as commonly understood in the art, including variations on those techniques or substitutions of equivalent techniques that would be apparent to one of skill in the art. While the following terms are believed to be well understood by one of ordinary skill in the art, the following definitions are set forth to facilitate explanation of the presently disclosed subject matter.

[0099] In describing the presently disclosed subject matter, it will be understood that a number of techniques and steps are disclosed. Each of these has individual benefit and each can also be used in conjunction with one or more, or in some cases all, of the other disclosed techniques.

[0100] Accordingly, for the sake of clarity, this description will refrain from repeating every possible combination of the individual steps in an unnecessary fashion. Nevertheless, the specification and claims should be read with the understanding that such combinations are entirely within the scope of the presently disclosed and claimed subject matter.

[0101] Following long-standing patent law convention, the terms “a”, “an”, and “the” refer to “one or more” when used in this application, including in the claims. For example, the phrase “an antibody” refers to one or more antibodies, including a plurality of the same antibody. Similarly, the phrase “atAttorney Docket No.3338 / 3 PCT least one”, when employed herein to refer to an entity, refers to, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100, or more of that entity, including but not limited to whole number values between 1 and 100 and greater than 100.

[0102] Unless otherwise indicated, all numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about”. The term “about”, as used herein when referring to a measurable value such as an amount of mass, weight, time, volume, concentration, or percentage, is meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1 % from the specified amount, as such variations are appropriate to perform the disclosed methods and / or employ the disclosed compositions. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by the presently disclosed subject matter.

[0103] As used herein, the phrase “component thereof” when employed in the context of a component of a cell, such as but not limited to a tumor cell, refers to any molecule that is present on (e.g., present on the surface including but not limited to attached to and / or traversing a cell membrane), in, and / or shed or released from the cell.

[0104] As used in this application, the terms “component,” “module,” “system,” “server,” “processor,” “memory,” and the like are intended to include one or more computer-related units, such as but not limited to hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and / or thread of execution and a component can be localized on one computer and / or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate by way of local and / or remote processes such as in accordance with a signal having one or more data packets, such as data from one component interacting with another component in a local system, distributed system, and / or across a network such as the Internet with other systems by way of the signal.

[0105] Certain embodiments and implementations of the disclosed technology are described above with reference to block and flow diagrams of systems and methods and / or computer program products according to example embodiments or implementations of the disclosed technology. It will be understood that one or more blocks of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, respectively, can be implemented by computer- executable program instructions. Likewise, some blocks of the block diagrams and flow diagrams mayAttorney Docket No.3338 / 3 PCT not necessarily need to be performed in the order presented, can be repeated, or may not necessarily need to be performed at all, according to some embodiments or implementations of the disclosed technology.

[0106] These computer-executable program instructions can be loaded onto a general-purpose computer, a special-purpose computer, a processor, or other programmable data processing apparatus to produce a particular machine, such that the instructions that execute on the computer, processor, or other programmable data processing apparatus create means for implementing one or more functions specified in the flow diagram block or blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement one or more functions specified in the flow diagram block or blocks.

[0107] As an example, embodiments or implementations of the disclosed technology can provide for a computer program product, including a computer-usable medium having a computer-readable program code or program instructions embodied therein, said computer-readable program code adapted to be executed to implement one or more functions specified in the flow diagram block or blocks. Likewise, the computer program instructions can be loaded onto a computer or other programmable data processing apparatus to cause a series of operational elements or steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions that execute on the computer or other programmable apparatus provide elements or steps for implementing the functions specified in the flow diagram block or blocks.

[0108] Accordingly, blocks of the block diagrams and flow diagrams support combinations of means for performing the specified functions, combinations of elements or steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each block of the block diagrams and flow diagrams, and combinations of blocks in the block diagrams and flow diagrams, can be implemented by special-purpose, hardware-based computer systems that perform the specified functions, elements or steps, or combinations of special-purpose hardware and computer instructions.

[0109] Certain implementations of the disclosed technology described above with reference to user devices can include mobile computing devices. Those skilled in the art recognize that there are several categories of mobile devices, generally known as portable computing devices that can run on batteries but are not usually classified as laptops. For example, mobile devices can include, but are not limited to portable computers, tablet PCs, internet tablets, PDAs, ultra-mobile PCs (UMPCs), wearable devices, and smart phones. Additionally, implementations of the disclosed technology can be utilized with internet of things (IoT) devices, smart televisions and media devices, appliances, automobiles, toys, and voice command devices, along with peripherals that interface with these devices.Attorney Docket No.3338 / 3 PCT

[0110] In this specification, numerous specific details have been set forth. It is to be understood, however, that implementations of the disclosed technology can be practiced without these specific details. In other instances, well-known methods, structures, and techniques have not been shown in detail in order not to obscure an understanding of this description. References to “one embodiment,” “an embodiment,” “some embodiments,” “example embodiment,” “various embodiments,” “one implementation,” “an implementation,” “example implementation,” “various implementations,” “some implementations,” etc., indicate that the implementation(s) of the disclosed technology so described can include a particular feature, structure, or characteristic, but not every implementation necessarily includes the particular feature, structure, or characteristic. Further, repeated use of the phrase “in one implementation” does not necessarily refer to the same implementation, although it may.

[0111] Throughout the specification and the claims, the following terms take at least the meanings explicitly associated herein, unless the context clearly dictates otherwise. The term “connected” means that one function, feature, structure, or characteristic is directly joined to or in communication with another function, feature, structure, or characteristic. The term “coupled” means that one function, feature, structure, or characteristic is directly or indirectly joined to or in communication with another function, feature, structure, or characteristic. The term “or” is intended to mean an inclusive “or.” Further, the terms “a,” “an,” and “the” are intended to mean one or more unless specified otherwise or clear from the context to be directed to a singular form. By “comprising” or “containing” or “including” is meant that at least the named element, or method step is present in article or method, but does not exclude the presence of other elements or method steps, even if the other such elements or method steps have the same function as what is named.

[0112] As discussed herein, a “patient” or “subject” or “body” can be a human or any animal. It should be appreciated that an animal can be a variety of any applicable type, including, but not limited to, mammal, veterinarian animal, livestock animal or pet-type animal, etc. As an example, the animal can be a laboratory animal specifically selected to have certain characteristics similar to a human (e.g., rat, dog, pig, monkey, or the like).

[0113] As used herein, the term “about,” when used in reference to a particular recited numerical value, means that the value may vary from the recited value by no more than in some embodiments, 10%, in some embodiments 15%, in some embodiments 20%, and in some embodiments 25% or more. By way of example and not limitation, the expression “about 100” can include in some embodiments 90 and 110 and all values in between (e.g., 91, 92, 93, 94, 95, 96, 97, 98, 99, etc.).

[0114] It is to be understood that the mention of one or more method steps does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Similarly, it is also to be understood that the mention of one or more components in a device or system does not preclude the presence of additional components or intervening components between those components expressly identified.Attorney Docket No.3338 / 3 PCT

[0115] Although embodiments are described herein with respect to systems or methods, it is contemplated that embodiments with identical or substantially similar features can alternatively be implemented as systems, methods and / or non-transitory computer-readable media.

[0116] As used herein, unless otherwise specified, the use of the ordinal adjectives “first,” “second,” “third,” etc., to describe a common object, merely indicates that different instances of like objects are being referred to, and is not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

[0117] As used herein, the phrase “transcript number”, and grammatical variants thereof, refers to an amount of a given gene product (e.g., an RNA, optionally an mRNA or other RNA generated by transcription in a cell) that is present in a cell, tissue, organ, or other biological sample that can be isolated from a subject. In some embodiments, the term “transcript number” is synonymous with the terms “copy number and “abundance” and refers to a quantitative and / or comparative value of the amount of the transcript in the biological sample. In some embodiments, transcript numbers are determined by standard molecular biological techniques including, but not limited to quantitative reverse transcription polymerase chain reaction (RT-PCR / qPCR) assays, digital PCR, and / or digital droplet PCR assays, Microarray Technology, RNA-seq (see e.g., Morin et al. (2008) Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. BioTechniques 45(1):81-94; Maher et al. (2009) Transcriptome sequencing to detect gene fusions in cancer. Nature 458(7234):97-101); Wang et al. (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10(1):57-63; Nariai et al. (2013) TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference. Bioinformatics.29:2292-2299.

[0118] As used herein, the phrase “target cell” refers to an epithelial cell, a mesenchymal cell, white blood cells, leukocytes, platelets, and other specific immune cells, including eosinophils, monocytes, macrophages, NK cells, dendritic cells, neutrophils, T cells, B cells, mast cells, plasma cells, and other lymphocytes that may or may not have been affected by the presence of a disease, disorder, or condition (including but not limited to a cell that has undergone at least one change in expression of at least one gene as a result of the presence of the disease), including but not limited to a cancer and / or a tumor. In some embodiments, a target cell has undergone a process of tumor education. Target cells may also include circulating tumor cells.

[0119] While certain embodiments of this disclosure have been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that this disclosure is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.Attorney Docket No.3338 / 3 PCT

[0120] This written description uses examples to disclose certain embodiments of the technology and also to enable any person skilled in the art to practice certain embodiments of this technology, including making and using any apparatuses or systems and performing any incorporated methods. The patentable scope of certain embodiments of the technology is defined in the claims, and can include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

[0121] Examples of the present disclosure related to systems and methods for identifying and selecting algorithms usable for detecting target cells as defined herein in patient samples that can be used to determine risks of certain cancers, such as breast cancer. More particularly, the disclosed technology relates to training models based on specific patient inputs, such as samples including biomarkers for target cells as defined herein, optionally tumor educated cells (e.g., leukocytes or platelets) and environmental factors such as smoking history, current smoking status, menopausal status and / or hormone levels, disease risk status, breast tissue density, and age of individuals within patient sample data. The systems and methods described herein utilize, in some instances, machine learning models, which are necessarily rooted in computers and technology. Machine learning models are a unique computer technology that involves training models to complete tasks and make decisions. The present disclosure details particular training data that can be tailored to best select models for detecting diseases, disorders, and conditions such as but not limited to cancer initiation and progression, including early-stage cancer progression, using specific biomarkers for genes for which gene expression changes are associated with the diseases, disorders, and conditions. In one exemplary embodiment, this can involve using target cells as defined herein, optionally tumor-educated immune cell biomarker information as inputs to classification algorithms, including but not limited to regression-based algorithms, decision tree-based algorithms, and / or neural network machine learning models, which can be applied to select, from the single or a plurality of machine learning algorithms (MLAs), a preferred algorithm or set of algorithms based on performance for detecting the biomarkers for tumor educated cells (e.g., leukocytes or platelets) in a patient sample. Using a machine learning model in this way can allow the system to target and select a particular model that can present the best sensitivity, specificity, or accuracy for a particular set of target cells as defined herein, optionally tumor educated cells (e.g., leukocytes or platelets), such as monocytes, macrophages, NK cells, dendritic cells, and / or neutrophils that have made contact with a tumor cell of the patient. This is a clear advantage and improvement over prior technologies that use off-the-shelf algorithms to merely take input data and determine if a patient has certain biomarkers, and which do not consider environmental factors that can affect the output.

[0122] The systems and methods described herein also improve, in some instances, the operation of computers and technology. One aspect of software-based solutions in the medical and disease analysisAttorney Docket No.3338 / 3 PCT space is to ensure that the so-called “medical device software” portion of the system is version controlled. This is pursuant to certain guidelines that require regulatory approval of medical device software. As such, the present disclosure provides separate modules that are controlled separately. A ribonucleic acid (RNA) analysis module 704 and a laboratory information system (LIS) module 702, which are not necessarily characterized as medical device software, can be separate from a reverse transcriptase polymerase chain reaction (RT-PCR / qPCR) analysis module 420, which in some embodiments can be characterized as medical device software. The RNA analysis module 704 can be called when samples are ready for RNA quantification. The outcome of this analysis is a record consisting of sample identity and quality checks, as well as concentrations of each RNA sample quantified and volumes of water that are required to be added to normalize the sample. The Laboratory Information System (LIS) module 705 can track samples during molecular processing, creating a record for traceability. The RT-PCR / qPCR analysis module 420, on the other hand, can be called when samples are ready for gene expression analysis. The RT-PCR / qPCR analysis module 420 can be maintained, operated, and controlled separately so as to maintain version control that can be employed by end users after regulatory accreditations. The RT-PCR / qPCR analysis module 420 can, of course, be retrained as needed and as is described herein.

[0123] Some implementations of the disclosed technology will be described more fully with reference to the accompanying Figures. This disclosed technology may, however, be embodied in many different forms and should not be construed as limited to the implementations set forth herein. The components described hereinafter as making up various elements of the disclosed technology are intended to be illustrative and not restrictive. Many suitable components that would perform the same or similar functions as components described herein are intended to be embraced within the scope of the disclosed electronic devices and methods.

[0124] Reference will now be made in detail to exemplary embodiments of the presently disclosed subject matter that are illustrated in the accompanying Figures and disclosed herein. Wherever convenient, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

[0125] Figure 1 is a flow diagram illustrating an exemplary method 100 for using machine learning algorithms to identify a detecting algorithm to detect biomarkers for target cells as defined herein, optionally tumor educated cells (e.g., leukocytes or platelets), in accordance with certain embodiments of the presently disclosed subject matter. The steps of method 100 can be performed by one or more components of system 500 (e.g., PCR analysis module 420, web server 510 of detection system 508, user device 502, etc.), as described in more detail with respect to Figures 4 and 5.

[0126] In block 102, a processor (e.g., processor 410) can obtain patient sample data comprising negative patient samples and / or positive patient samples. The negative patient samples can be associated with individuals with no known cancer diagnosis. The positive patient samples being associated withAttorney Docket No.3338 / 3 PCT individuals with positive cancer diagnosis, and the sample can include biomarkers for populations of target cells, optionally tumor educated cells (e.g., leukocytes or platelets). As discussed herein, prior liquid biopsy methods included identifying characteristics of tumors, such as circulating tumor DNA, tumor RNA, non-coding RNA, tumor cells, and the like. The instant method, however, incorporates a novel detecting mechanism that looks instead at the presence of target cells as defined herein in the circulation, optionally tumor educated cells (e.g., leukocytes or platelets). When target cells as defined herein contact and crosstalk with tumor cells and / or their components and / or their secretions and / or previously tumor educated cells and / or their components and / or their secretions, it can lead to modification of the characteristics of the target cells as defined herein through modification of gene expression and phenotype. This modification can occur through direct and / or indirect contact with the tumor cells and propagation of signals associated with such an interaction, and / or through interaction with components and / or secretions from the tumor cells and resulting signal propagation. The same process may occur in target cells through contact with previously tumor educated cells and / or their components and / or their secretions. As such, the present systems and methods utilize biomarkers from target cells that have contacted the tumor, cells associated with a tumor, and / or components and / or secretions from a tumor or related cells, including previously tumor educated cells and circulating tumor cells. Example tumor-educated cells usable with the present systems and methods include, but are not limited to, eosinophils, monocytes, macrophages, NK cells, dendritic cells, neutrophils, T cells, B cells Mast cells and other lymphocytes that may have been affected by the presence of disease, including a cancer tumor, and undergone a process of tumor education related to direct and / or indirect contact with tumor cells, and / or their related components and / or their related secretions.

[0127] The systems and methods described herein can be effective for detecting the presence of cancers. In some embodiments, the cancer can be selected from the group consisting of adrenal gland cancer, anal cancer, appendiceal cancer, ovarian cancer, uterine cancer, gastric cancer, breast cancer, lung cancer, bladder cancer, cervical cancer, stomach cancer, sarcoma cancer, liver cancer, esophageal cancer, laryngeal cancer, multiple myeloma, colorectal cancer, rectal cancer, skin cancer, pancreatic cancer, brain cancer, leukemia or lymphoma. The breast cancer can be estrogen receptor- positive / hormone (e.g., progesterone) receptor-positive / HER2-negative (ER+ / HR+ / HER2–) breast cancer; ER-negative / hormone receptor-positive / HER2-negative (ER- / HR+ / HER2–) breast cancer; ER- positive / HR-negative / HER2-negative (ER+ / HR- / HER2–) breast cancer; ER-positive / HR- positive / HER2-positive (ER+ / HR+ / HER2+) breast cancer; ER-negative / HR-negative / HER2-positive (ER- / HR- / HER2+) breast cancer; ER-negative / HR-negative / HER2-negative (triple negative breast cancer - TNBC), or inflammatory breast cancer.

[0128] Further, and as discussed above, target cells as defined herein, optionally tumor educated cells, can be a target for identifying the presence of cancers within a patient. Tumor-derived factors can educate target cells such as white blood cells or other cells and / or platelets within blood circulation,Attorney Docket No.3338 / 3 PCT through several modalities. Direct mechanisms include the transfer of RNA or protein cargo via exosomes, or receptor-mediated endocytosis of soluble factors within circulation. Contact driven signaling, either directly with tumors or tumor cells and / or components and / or secretions from tumors or tumor cells, as well as the same from previously tumor educated cells, can also drive education through changes in gene expression or changes in RNA processing, including alternative RNA splicing and other epigenetic processes. Tumor-derived factors can also alter the development and function of megakaryocytes (MKs) within the bone marrow, resulting in a distinct platelet population that enters circulation.

[0129] The mechanism for detecting said target cells as defined herein, optionally tumor educated cells, can in some embodiments include identifying, in patient samples, the biomarkers listed in Table 1, although other biomarkers individually or in combination can also be employed: Table 1 Exemplary Biomarkers GENE NAME, AND Chromosomal GENE Exemplary Exemplary SYMBOL GENBANK® SYMBOL Nucleic Acid Protein .1 .1 .1 .1 .1 .1 .1Attorney Docket No.3338 / 3 PCT S100 calcium binding NC_000001.11 S100A8 protein A8; also 153390032..153422583 Isoform a NM_001319196.1 NP_001306125.1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1Attorney Docket No.3338 / 3 PCT beta-2-microglobulin, NC_000015.10 B2M NM_004048.4 NP_004039.1 also referred to 44711517..44718145 .1 .1 sor,a p ura y o s ase on e nega ve pa en samp es, e pos ve pa en sampes, and environmental factors. The environmental factors can include, but are not limited to smoking history, current smoking status, menopausal status and / or hormone levels, BMI, and disease risk status, breast tissue density, and age of individuals within the patient sample data. Thus, in some embodiments the inputs into the algorithms can be based on the inputs listed in Table 2: Table 2 Exemplary Algorithm Inputs Inputs N li d i d t f h t t bi k l di g: 2, =

[0131] In block 106, method 100 can include stratifying, via the first processor, the patient sample data. Best performing algorithms can be selected from the plurality of MLAs based on performance. In some instances, algorithm performance can be correlated to age of patients. To compensate for these differences, participants can be stratified as pre-menopausal versus post-menopausal and peri- menopausal, and / or those patients younger or older than a particular age cutoff, for example patients under 50 versus over 50 years of age. As will be appreciated, the age-based stratification can use other age cutoffs, including by way of example only patients being over or under the age of 25, 30, 35, 40,Attorney Docket No.3338 / 3 PCT 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 (including all integers between 25 and 100). For each combination, a single algorithm or a best performing ensemble of algorithm can be identified using the model training approach described herein.

[0132] In block 108, method 100 can include identifying, via the first processor, a single algorithm or a first model of a plurality of MLAs to deploy as a detection model. The first model can be selected from the plurality of MLAs based on a performance of the plurality of MLAs for detecting the biomarkers for tumor educated cells (e.g., leukocytes or platelets) in a patient sample. Best performing algorithms can be defined as those demonstrating the highest capability of correctly identifying positive or negative samples in the hold out validation sets. The systems described herein, for example, were validated, and a total of 383 samples (132 positive and 251 negative cases) with known labels were used for model training and tuning. Training set performances of certain algorithms for each age group are presented In Table 3: Table 3 Exemplary Training Set Performances Age < 50 Age ≥ 50 Algorithm: Decision Tree PART

[0133] It is not measures of accuracy, sensitivity, and spe, o be employed for the presently disclosed subject matter. In this regard, in some embodiments the presently disclosed subject matter employs algorithms characterized by an accuracy of at least about 40% (e.g., 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, and in some embodiments 100%, and all numbers between 40% and 100% inclusive) and / or a sensitivity of at least about 50% (e.g., 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, and in some embodiments 100%, and all numbers between 50% and 100% inclusive) and / or a specificity of at least about 55% (e.g., 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, and in some embodiments 100%, and all numbers between 55% and 100% inclusive). It is noted that the presently disclosed subject matter remains useful when any of the above listed accuracy, sensitivity, and specificity values are combined with each other, although in some embodiments it can be beneficial to maximize each individually or all three together.

[0134] Method 100 can end after block 108. In other examples, additional steps can be performed. For example, the selected algorithm can be version-controlled and used to test for a number of transcripts in a patient sample. As such, method 100 can include receiving, at the processor, RNA quantification data for the patient sample. The method can include calculating, via the processor, fluidAttorney Docket No.3338 / 3 PCT volumes needed to normalize the patient sample. The method can include receiving, at the processor, qPCR analysis data about a normalized patient sample. To compensate for variations in RT-PCR enzyme (reverse transcriptase and Taq polymerase) activities, target transcript numbers can be normalized by dividing the target transcript number with the geometric mean of transcript numbers of the 3 reference targets (PC1, PC2 and PC3) on the assay according to Equation 1 below: Target^Transcript^Number^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ൌ ^Geometric^Mean^of^Transcript^Numbers^of^3^Reference^TargetsEquation 1

[0135] The method can further include testing, via the processor, the qPCR data with the first model (e.g., the version-controlled algorithm) to identify a number of transcripts of the target cells as defined herein, optionally tumor-educated cells and to determine cancer signal status.

[0136] The method can also be designed to automatically perform quality control checks and execute MLAs to make a prediction on whether the gene expression signature indicates the presence or absence of an active breast cancer signature. The PCR analysis module 420 can find the path where the code is saved, connect to the database (e.g., database 516), and read control and sample data from the database 516. This data can include sample IDs, target names, age, and transcript numbers. The next step can include checking for the No Template Control (NTC) values. Upon successful completion, the NTC rows can be removed, and the remainder of the data can be checked to ensure only numerical values are present.

[0137] Next, standard deviations (SD) of the sample positive controls can be checked to ensure they are less than a pre-determined threshold (e.g., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, or greater than 1.7 standard deviations). The positive control values are checked to confirm they amplified within a pre-defined range. If more than 1 positive control amplified out of range, that sample can be flagged and excluded from further analyses. The values of all remaining sample and control mean threshold cycles (CTs) are then checked for the SD criteria. For this, standard deviations for all triplicate values can be calculated and ensured they are equal to or below the minimum threshold of 0.5. If not, an outlier (minimum or maximum of the triplicate values) can removed to achieve a better SD. If the criterion is still not met, that sample and / or the control can be re-run or removed.

[0138] For all samples and controls that pass all quality control checks, the mean CT values can be calculated. The control mean CT values are used together with the known transcript numbers to plot a standard curve for each target. The R2goodness of fit parameter for each standard curve can be judged to be greater than or equal to a predetermined threshold (e.g., 0.95, 0.96, 0.97, 0.98, 0.99, 0.9925, 0.9950, or greater than 0.9950). If not, any single standard curve point that compromises the R2value can be removed. If more than one point needs to be removed for any target to achieve an acceptable curve, the entire plate can be rerun. With an acceptable standard curve, transcript numbers for the new samples can be calculated for each target by interpolation and used to make predictions.Attorney Docket No.3338 / 3 PCT

[0139] Figure 2A is a flow diagram illustrating an exemplary method 200 for calculating sample transcript numbers of biomarkers from target cells as defined herein optionally leukocytes or platelets in sample data, in accordance with certain embodiments of the disclosed technology. The steps of method 200 can be performed by one or more components of system 500 (e.g., PCR analysis module 420, web server 510 of detection system 508, user device 502, etc.), as described in more detail with respect to Figures 4 and 5. In block 202, method 200 can include accessing, via a processor (e.g., processor 410), control data and sample data within a database (e.g., database 516). The control data can include both negative controls and positive controls comprising target cells as defined herein.

[0140] In block 204, method 200 can include analyzing, via the processor, the control data and sample data to identify whether standard deviations are below a predetermined deviation range. For example, standard deviations (SD) of the sample positive controls can be checked to ensure they are less than a threshold of 0.5. In block 206, method 200 can include analyzing, via the processor, the positive controls to identify whether one or more samples of the positive controls are above a predetermined amplification range. For example, the positive control values are checked to confirm they amplified within a pre-defined range. If more than 1 positive control amplified out of range, that sample can be flagged and excluded from further analyses.

[0141] In block 208, method 200 can include calculating, via the processor and for a portion of the control data that is below a respective predetermined deviation range and above a respective predetermined amplification range, control mean CT values. In block 210, method 200 can include plotting, via the processor, a standard curve using the control mean CT values and known transcript numbers of target cells as defined herein target cells (as previously defined herein), optionally tumor- educated cells in the control data. In some embodiments, test plates can be pre-loaded with synthetic oligos of known transcript numbers that are amplified after every run and used to generate standard curves spanning at least 2, 3, 4, 5, 6, or greater than 6 orders of magnitude for each target. The standard curves can be used to calculate linear regression equations, as illustrated in Figure 8 for every plate, with y being the log transcript number and x the mean CT values after the RT-PCR run. The number of copies of each target in each sample are obtained from taking the anti-logarithmic value of y. In block 212, method 200 can include calculating, via the processor, sample transcript numbers of tumor- educated cells in the sample data using the standard curve.

[0142] Method 200 can end after block 212. In other examples, additional steps can be performed according to those steps described herein, including the steps shown in method 250 of Figure 2B. Figure 2B is a flow diagram illustrating an exemplary method 250 for using machine learning algorithms to identify a detection algorithm based upon biomarkers for target cells as defined herein, in accordance with certain embodiments of the disclosed technology. The steps of method 250 can be performed by one or more components of system 500 (e.g., PCR analysis module 420, web server 510 of detection system 508, user device 502, etc.), as described in more detail with respect to Figures 4 and 5. In blockAttorney Docket No.3338 / 3 PCT 252, method 250 can include creating, via the processor, a first analysis model for the target cells as defined herein, optionally tumor-educated cell, transcripts within samples by training an MLA based on normalized sample transcript number and environmental factors. The environmental factors including at least one of smoking history, current smoking status, menopausal status and / or hormone levels, disease risk status, breast tissue density, and age of individuals within the sample data, as described herein.

[0143] In block 254, method 250 can include retraining, via the processor and in response to a request for an upgrade to analysis software, the MLA(s) using subsequent control data and subsequent sample data. In block 256, method 250 can include creating, via the processor, a second analysis model for the target cell as defined herein, optionally tumor-educated cell, transcripts. Method 250 can end after block 256. In other examples, additional steps can be performed according to those steps described herein.

[0144] Figure 3A is a flow diagram illustrating an exemplary method 300 for testing for sample transcript numbers of target cells as defined herein, optionally tumor-educated cells in sample data, in accordance with certain embodiments of the disclosed technology. The steps of method 300 can be performed by one or more components of system 500 (e.g., PCR analysis module 420, web server 510 of detection system 508, user device 502, etc.), as described in more detail with respect to Figures 4 and 5. In block 302, method 300 can include identifying a first model from a plurality of MLAs trained on negative patient samples, positive patient samples, and environmental factors including at least one of smoking history, current smoking status, menopausal status or hormone levels, disease risk status, breast tissue density, and age of individuals within patient sample data. An exemplary algorithm of the presently disclosed subject matter had an accuracy of over 97% and a sensitivity of over 87% for detecting cancer in a patient under the age of 50 by detecting a presence of biomarkers for target cells as defined herein, optionally tumor-educated cells in a patient sample.

[0145] In block 304, method 300 can include receiving RNA quantification data for the patient sample. In block 306, method 300 can include receiving qPCR data about a normalized patient sample. In block 308, method 300 can include testing the qPCR data with the first model to identify a number of transcripts of the target cells as defined herein, and to determine cancer signal status. Method 300 can end after block 308. In other examples, additional steps can be performed according to those steps described herein.

[0146] Figure 3B is a flow diagram illustrating an exemplary method 350 for testing for sample transcript numbers of target cells as defined herein, optionally tumor-educated cells, in sample data, in accordance with certain embodiments of the disclosed technology. The steps of method 350 can be performed by one or more components of system 500 (e.g., PCR analysis module 420, web server 510 of detection system 508, user device 502, etc.), as described in more detail with respect to Figures 4 and 5. In block 352, method 350 can include identifying a single algorithm or a first model from a plurality of MLAs trained on negative patient samples, positive patient samples, and environmental factorsAttorney Docket No.3338 / 3 PCT including at least one of smoking history, current smoking status, menopausal status and / or hormone levels, BMI, disease risk status, breast tissue density, and age of individuals within patient sample data.

[0147] In block 354, method 350 can include receiving ribonucleic acid (RNA) quantification data for the patient sample. In block 356, method 350 can include receiving qPCR data about a normalized patient sample. In block 358, method 350 can include testing the qPCR analysis data with the first model to identify a number of transcripts from target cells as defined herein, optionally tumor-educated cells, and to determine cancer signal status. Method 350 can end after block 358. In other examples, additional steps can be performed according to those steps described herein.

[0148] Figure 4 is block diagram of an example PCR analysis module used to identify and, in some embodiments, quantify target cells as defined herein, optionally tumor-educated cells, according to an example implementation of the disclosed technology. According to some embodiments, the LIS module 702, RNA analysis module 704, user device 502, and web server 510, as depicted in Figure 5 and described below, can have a structure and components that are similar to those described with respect to PCR analysis module 420 shown in Figure 4. As shown, the PCR analysis module 420 can include a processor 410, an input / output (I / O) device 470, a memory 330 containing an operating system (OS) 440 and a program 450. In certain example implementations, the PCR analysis module 420 can be a single server or can be configured as a distributed computer system including multiple servers or computers that interoperate to perform one or more of the processes and functionalities associated with the disclosed embodiments. In some embodiments, PCR analysis module 420 can be one or more servers from a serverless or scaling server system. In some embodiments, the PCR analysis module 420 can further include a peripheral interface, a transceiver, a mobile network interface in communication with the processor 410, a bus configured to facilitate communication between the various components of the PCR analysis module 420, and a power source configured to power one or more components of the PCR analysis module 420.

[0149] A peripheral interface, for example, can include the hardware, firmware and / or software that enable(s) communication with various peripheral devices, such as media drives (e.g., magnetic disk, solid state, or optical disk drives), other processing devices, or any other input source used in connection with the disclosed technology. In some embodiments, a peripheral interface can include a serial port, a parallel port, a general-purpose input and output (GPIO) port, a game port, a universal serial bus (USB), a micro-USB port, a high-definition multimedia interface (HDMI) port, a video port, an audio port, a BLUETOOTH™ port, a near-field communication (NFC) port, another like communication interface, or any combination thereof.

[0150] In some embodiments, a transceiver can be configured to communicate with compatible devices and ID tags when they are within a predetermined range. A transceiver can be compatible with one or more of: radio-frequency identification (RFID), near-field communication (NFC),Attorney Docket No.3338 / 3 PCT BLUETOOTH™, low-energy BLUETOOTH™ (BLE), WIFI™, ZIGBEE™, ambient backscatter communications (ABC) protocols or similar technologies.

[0151] A mobile network interface can provide access to a cellular network, the Internet, or another wide-area or local area network. In some embodiments, a mobile network interface can include hardware, firmware, and / or software that allow(s) the processor(s) 410 to communicate with other devices via wired or wireless networks, whether local or wide area, private or public, as known in the art. A power source can be configured to provide an appropriate alternating current (AC) or direct current (DC) to power components.

[0152] The processor 410 can include one or more of a microprocessor, a microcontroller, a digital signal processor, a co-processor, or the like, or combinations thereof capable of executing stored instructions and operating upon stored data. The memory 330 can include, in some implementations, one or more suitable types of memory (e.g. such as volatile or non-volatile memory, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, flash memory, a redundant array of independent disks (RAID), and the like), for storing files including an operating system, application programs (including, for example, a web browser application, a widget or gadget engine, and or other applications, as necessary), executable instructions and data. In one embodiment, the processing techniques described herein can be implemented as a combination of executable instructions and data stored within the memory 330.

[0153] The processor 410 can be one or more known processing devices, such as, but not limited to, a microprocessor from the CORE™ family manufactured by INTEL™, the RYZEN™ family manufactured by AMD™, or a system-on-chip processor using an ARM™ or other similar architecture. The processor 410 can constitute a single core or multiple core processor that executes parallel processes simultaneously, a central processing unit (CPU), an accelerated processing unit (APU), a graphics processing unit (GPU), a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC) or another type of processing component. For example, the processor 410 can be a single core processor that is configured with virtual processing technologies. In certain embodiments, the processor 410 can use logical processors to simultaneously execute and control multiple processes. The processor 410 can implement virtual machine (VM) technologies, or other similar known technologies to provide the ability to execute, control, run, manipulate, store, etc. multiple software processes, applications, programs, etc. One of ordinary skill in the art would understand that other types of processor arrangements could be implemented that provide for the capabilities disclosed herein.

[0154] In accordance with certain example implementations of the disclosed technology, the PCR analysis module 420 can include one or more storage devices configured to store information used byAttorney Docket No.3338 / 3 PCT the processor 410 (or other components) to perform certain functions related to the disclosed embodiments. In one example, the PCR analysis module 420 can include the memory 330 that includes instructions to enable the processor 410 to execute one or more applications, such as server applications, network communication processes, and any other type of application or software known to be available on computer systems. Alternatively, the instructions, application programs, etc. can be stored in an external storage or available from a memory over a network. The one or more storage devices can be a volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, non-removable, or other type of storage device or tangible computer-readable medium.

[0155] The PCR analysis module 420 can include a memory 330 that includes instructions that, when executed by the processor 410, perform one or more processes consistent with the functionalities disclosed herein. Methods, systems, and articles of manufacture consistent with disclosed embodiments are not limited to separate programs or computers configured to perform dedicated tasks. For example, the PCR analysis module 420 can include the memory 330 that can include one or more programs 450 to perform one or more functions of the disclosed embodiments. For example, in some embodiments, the PCR analysis module 420 can additionally manage dialogue and / or other interactions with the customer via a program 450.

[0156] The processor 410 can execute one or more programs 450 located remotely from the PCR analysis module 420. For example, the PCR analysis module 420 can access one or more remote programs that, when executed, perform functions related to disclosed embodiments.

[0157] The memory 330 can include one or more memory devices that store data and instructions used to perform one or more features of the disclosed embodiments. The memory 330 can also include any combination of one or more databases controlled by memory controller devices (e.g., server(s), etc.) or software, such as document management systems, MICROSOFT™ SQL databases, SHAREPOINT™ databases, ORACLE™ databases, SYBASE™ databases, or other relational or non- relational databases. The memory 330 can include software components that, when executed by the processor 410, perform one or more processes consistent with the disclosed embodiments. In some embodiments, the memory 330 can include a system database 460 for storing related data to enable the PCR analysis module 420 to perform one or more of the processes and functionalities associated with the disclosed embodiments.

[0158] The system database 460 can include stored data relating to status data (e.g., average session duration data, location data, idle time between sessions, and / or average idle time between sessions) and historical status data. According to some embodiments, the functions provided by the system database 460 can also be provided by a database that is external to the PCR analysis module 420, such as the database 516 as shown in Figure 5.

[0159] The PCR analysis module 420 can also be communicatively connected to one or more memory devices (e.g., databases) locally or through a network. The remote memory devices can beAttorney Docket No.3338 / 3 PCT configured to store information and can be accessed and / or managed by the PCR analysis module 420. By way of example, the remote memory devices can be document management systems, MICROSOFT™ SQL database, SHAREPOINT™ databases, ORACLE™ databases, SYBASE™ databases, or other relational or non-relational databases. Systems and methods consistent with disclosed embodiments, however, are not limited to separate databases or even to the use of a database.

[0160] The PCR analysis module 420 can also include one or more I / O devices 470 that can comprise one or more interfaces for receiving signals or input from devices and providing signals or output to one or more devices that allow data to be received and / or transmitted by the PCR analysis module 420. For example, the PCR analysis module 420 can include interface components, which can provide interfaces to one or more input devices, such as one or more keyboards, mouse devices, touch screens, track pads, trackballs, scroll wheels, digital cameras, microphones, sensors, and the like, that enable the PCR analysis module 420 to receive data from a user (such as, for example, via the user device 502).

[0161] In examples of the disclosed technology, the PCR analysis module 420 can include any number of hardware and / or software applications that are executed to facilitate any of the operations. The one or more I / O interfaces can be utilized to receive or collect data and / or user instructions from a wide variety of input devices. Received data can be processed by one or more computer processors as desired in various implementations of the disclosed technology and / or stored in one or more memory devices.

[0162] The PCR analysis module 420 can contain programs that train, implement, store, receive, retrieve, and / or transmit one or more machine learning models. Machine learning models can include a neural network model, a generative adversarial model (GAN), a recurrent neural network (RNN) model, a deep learning model (e.g., a long short-term memory (LSTM) model), a random forest model, a convolutional neural network (CNN) model, a support vector machine (SVM) model, logistic regression, XGBoost, and / or another machine learning model. Models can include an ensemble model (e.g., a model comprised of a plurality of models). In some embodiments, training of a model can terminate when a training criterion is satisfied. Training criterion can include a number of epochs, a training time, a performance metric (e.g., an estimate of accuracy in reproducing test data), or the like. The PCR analysis module 420 can be configured to adjust model parameters during training. Model parameters can include weights, coefficients, offsets, or the like. Training can be supervised or unsupervised.

[0163] The PCR analysis module 420 can be configured to train machine learning models by optimizing model parameters and / or hyperparameters (hyperparameter tuning) using an optimization technique, consistent with disclosed embodiments. Hyperparameters can include training hyperparameters, which can affect how training of the model occurs, or architectural hyperparameters, which can affect the structure of the model. An optimization technique can include a grid search, a random search, a gaussian process, a Bayesian process, a Covariance Matrix Adaptation EvolutionAttorney Docket No.3338 / 3 PCT Strategy (CMA-ES), a derivative-based search, a stochastic hill-climb, a neighborhood search, an adaptive random search, or the like. The PCR analysis module 420 can be configured to optimize statistical models using known optimization techniques.

[0164] The PCR analysis module 420 can also contain one or more prediction models. Prediction models can include statistical algorithms that are used to determine the probability of an outcome, given a set amount of input data. For example, prediction models can include regression models that estimate the relationships among input and output variables. Prediction models can also sort elements of a dataset using one or more classifiers to determine the probability of a specific outcome. Prediction models can be parametric, non-parametric, and / or semi-parametric models.

[0165] In some examples, prediction models can cluster points of data in functional groups such as “random forests.” Random Forests can comprise combinations of decision tree predictors. In some embodiments, decision trees can comprise a data structure that maps observations about something, in the “branch” of the tree, to conclusions about that thing’s target value, in the “leaves” of the tree. Each tree can depend on the values of a random vector sampled independently and with the same distribution for all trees in the forest. Prediction models can also include artificial neural networks. Artificial neural networks can model input / output relationships of variables and parameters by generating a number of interconnected nodes which contain an activation function. The activation function of a node can define a resulting output of that node given an argument or a set of arguments. Artificial neural networks can generate patterns to the network via an ‘input layer’, which communicates to one or more “hidden layers” where the system determines regressions via weighted connections. Prediction models can additionally or alternatively include classification and regression trees, or other types of models known to those skilled in the art. To generate prediction models, the notification system can analyze information applying machine-learning methods.

[0166] While the PCR analysis module 420 has been described as one form for implementing the techniques described herein, other, functionally equivalent, techniques can be employed. For example, some or all of the functionality implemented via executable instructions can also be implemented using firmware and / or hardware devices such as application specific integrated circuits (ASICs), programmable logic arrays, state machines, etc. Furthermore, other implementations of the PCR analysis module 420 can include a greater or lesser number of components than those illustrated.

[0167] Figure 5 is a block diagram of an example system 500 that can be used to view and interact with detection system 508, according to an example implementation of the disclosed technology. The components and arrangements shown in Figure 5 are not intended to limit the disclosed embodiments as the components used to implement the disclosed processes and features can vary. As shown, detection system 508 can interact with a user device 502 via a network 506. In certain example implementations, the detection system 508 can include the LIS module 702, the RNA module, a PCR analysis module 420, a local network 512, a web server 510, and a database 516.Attorney Docket No.3338 / 3 PCT

[0168] In some embodiments, a user can operate the user device 502. The user device 502 can include one or more of a mobile device, smart phone, general purpose computer, tablet computer, laptop computer, telephone, public switched telephone network (PSTN) landline, smart wearable device, voice command device, other mobile computing device, or any other device capable of communicating with the network 506 and ultimately communicating with one or more components of the detection system 508. In some embodiments, the user device 502 can include or incorporate electronic communication devices for hearing or vision impaired users.

[0169] The network 506 can be of any suitable type, including individual connections via the internet such as cellular or WIFI™ networks. In some embodiments, the network 506 can connect terminals, services, and mobile devices using direct connections such as radio-frequency identification (RFID), near-field communication (NFC), BLUETOOTH™, low-energy BLUETOOTH™ (BLE), WIFI™, ZIGBEE™, ambient backscatter communications (ABC) protocols, USB, WAN, or LAN. Because the information transmitted can be personal or confidential, security concerns can dictate one or more of these types of connections be encrypted or otherwise secured. In some embodiments, however, the information being transmitted can be less personal, and therefore the network connections can be selected for convenience over security.

[0170] The network 506 can include any type of computer networking arrangement used to exchange data. For example, the network 506 can be the Internet, a private data network, virtual private network (VPN) using a public network, and / or other suitable connection(s) that enable(s) components in the system 500 environment to send and receive information between the components of the system 500. The network 506 can also include a PSTN and / or a wireless network.

[0171] Screening system 508 can be associated with and optionally controlled by one or more entities such as a business, corporation, individual, partnership, or any other entity that provides one or more of goods, services, and consultations to individuals such as customers. In some embodiments, the screening system 508 can be controlled by a third party on behalf of another business, corporation, individual, partnership. The screening system 508 can include one or more servers and computer systems for performing one or more functions associated with products and / or services that the organization provides.

[0172] Web server 510 can include a computer system configured to generate and provide one or more websites accessible to a user (in some embodiments, for access to remote software) as well as any other individuals involved in access system 508’s normal operations. Web server 510 can include a computer system configured to receive communications from user device 502 via for example, a mobile application, a chat program, an instant messaging program, a voice-to-text program, an SMS message, email, or any other type or format of written or electronic communication. Web server 510 can have one or more processors 522 and one or more web server databases 524, which can be any suitable repository of website data. Information stored in web server 510 can be accessed (e.g., retrieved,Attorney Docket No.3338 / 3 PCT updated, and added to) via local network 512 and / or network 506 by one or more devices or systems of system 500. In some embodiments, web server 510 can host websites or applications that can be accessed by the user device 502. For example, web server 510 can host a healthcare service provider website that a user device can access by providing an attempted login that are authenticated by the PCR analysis module 420. According to some embodiments, web server 510 can include software tools, similar to those described with respect to user device 502 above, that can allow web server 510 to obtain network identification data from user device 502. The web server can also be hosted by an online provider of website hosting, networking, cloud, or backup services, such as MICROSOFT AZURE™ or AMAZON WEB SERVICES™.

[0173] The local network 512 can include any type of computer networking arrangement used to exchange data in a localized area, such as WIFI™, BLUETOOTH™, Ethernet, and other suitable network connections that enable components of the detection system 508 to interact with one another and to connect to the network 506 for interacting with components in the system 500 environment. In some embodiments, the local network 512 can include an interface for communicating with or linking to the network 506. In other embodiments, certain components of the detection system 508 can communicate via the network 506, without a separate local network 506.

[0174] The detection system 508 can be hosted in a cloud computing environment (not shown). The cloud computing environment can provide software, data access, data storage, and computation. Furthermore, the cloud computing environment can include resources such as applications (apps), virtual machines (VMs), virtualized storage (VS), or hypervisors (HYP). User device 502 can be able to access detection system 508 using the cloud computing environment. User device 502 can be able to access detection system 508 using specialized software. The cloud computing environment can eliminate the need to install specialized software on user device 502.

[0175] In accordance with certain example implementations of the disclosed technology, the detection system 508 can include one or more computer systems configured to compile data from a plurality of sources including in some embodiments the PCR analysis module 420, web server 510, and / or the database 516. The PCR analysis module 420 can correlate compiled data, analyze the compiled data, arrange the compiled data, generate derived data based on the compiled data, and store the compiled and derived data in a database such as the database 516. According to some embodiments, the database 516 can be a database associated with an organization and / or a related entity that stores a variety of information relating to customers, transactions, ATM, and business operations. The database 516 can also serve as a back-up storage device and can contain data and information that is also stored on, for example, database 460, as discussed with reference to Figure 4.

[0176] Figure 6 is an example process flow for accepting samples based on predefined quality metrics, processing the sample and testing for target cell gene expression as defined herein, according to an example implementation of the disclosed technology. Figure 7A is a process map for an exampleAttorney Docket No.3338 / 3 PCT PCR analysis module, according to an example implementation of the disclosed technology. Figure 7B is a process map for an example RNA analysis module, according to an example implementation of the disclosed technology. Figures 8show representative standard curves for calculating target transcript numbers, according to an example implementation of the disclosed technology.

[0177] Figure 9 is an example flow diagram for training a machine learning based model for identifying and quantifying target cells as defined herein, according to an example implementation of the disclosed technology. The flow diagram also shows a method of deploying a model (e.g., a version- controlled algorithm) and testing the deployed model. In this example, testing of the disclosed systems and methods was performed to evaluate systems using age-grouping as an input for MLA training. To evaluate model performance, a blind test set of 695 samples, consisting of 96 positive and 599 negative samples, were used for independent testing on the best performing algorithms for each age group. Clinical performance metrics are reported for the blind test set with 99.5% confidence intervals (CI) computed through an exact binomial test. The model demonstrated an overall testing accuracy of 92.2% (CI: 88.9% – 94.6%) with a specificity of 94.3% (CI: 91.0% – 96.4%) and sensitivity of 79.2% (CI: 65.5% – 88.4%) for cancer detection. Higher performance was observed in the group of women under 50 with a specificity of 99.0% (CI: 94.2% – 99.8%) and a sensitivity of 91.7% (CI: 51.1% – 99.1%). During software system upgrades, models can be validated by using the same data on which the algorithms were trained, tuned and cross-validated.

[0178] In some embodiments, rather than determining gene expression levels via transcript analysis, gene expression levels are determined at the protein level. In such embodiments, mass spectroscopy, ELISA, and other antibody-based protein detection techniques, including flow cytometry of blood cells, can be employed.

[0179] Crosstalk Between Immune and Breast Cancer (e.g., TNBC) Cells Promotes Transcriptional Reprogramming into a Pro-Tumorigenic Phenotype. Early detection and diagnosis of cancer can have a significant impact on improving mortality and better treatment outcomes. Liquid biopsy is an actively emerging tool offering a convenient non-invasive way of detecting and identifying a cancer signal from a blood sample. Recently several liquid biopsy tests have been described and implemented in clinical practice. However, the majority of these tests are based on circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating free DNA (cfDNA), or exosomes which possess a number of limitations associated with the technology. Alternative approaches have evolved including RNA signatures from tumor-educated platelets, leukobiopsy, micro and oncRNAs. Disclosed herein is a validated whole blood assay to identify breast cancer, based on a gene expression panel of 12 RNA biomarkers. As the oncogenic signal detected by liquid biopsies can also be translated to impact the immune response to cancer, an aim was to gain insight into the interactions between white blood cells and other cells in the circulation and tumor cells. To elucidate the potential mechanism of the immune- cancer crosstalk, we devised co-cultivation studies by exposing human monocytic cell line model,Attorney Docket No.3338 / 3 PCT THP1, to breast cancer cells. RNAseq and pathway analysis of THP1 cells alone were performed, and in comparison, to post 24 hours contact with breast cancer cells and identified multiple oncogenic pathways upregulated in THP1 cells as a result of this exposure. KEGG and reactome enrichment analysis revealed that several key EMT, inflammation and signaling pathways were activated in THP1 cells leading to their transcriptional reprogramming into a pro-oncogenic phenotype. As the tumor education of immune cells plays an important role in promoting cancer and metastasis, the findings reported above provide an important insight into the mechanism of this transformation and its diagnostic use as well as the potential targeted therapeutic strategies to interfere with or reverse cell-cell communication leading to a modified or tumorigenic phenotype.

[0180] Diagnostic and Treatment Methods and Compositions for Use Thereof

[0181] The presently disclosed subject matter relates in some embodiments to methods for detection and treatment of diseases, disorders, and conditions, including but not limited to cancer, using a unique biomarker identification process coupled with a unique software approach. This involves the identification, validation, and correlation of unique biomarkers from whole blood and / or components and / or fractions thereof that allow detection of diseases, disorders, and conditions and promotes establishing effective treatments thereof. The biomarkers arise from a recently discovered interaction of disease cells with other cells in the body, and particularly the circulatory system, and represent a physiological response to the presence of disease. This response is multifactorial, and benefits from the use of machine learning and, in some embodiments artificial intelligence, in biomarker selection and correlation. Software algorithms trained with samples and / or data from the disease condition enable and enhance the ability to detect and classify disease across a range of individuals, including those with diverse ethnicity, across a range of age, and with different molecular (or other) manifestations of the same disease.

[0182] By way of example and not limitation, the steps involved in the methods encompass a range of investigations and typically includes the use of in vitro models, along with relevant clinical samples and data analysis. By combining in vitro discovery with clinical investigation, information is developed that increases the relevance of early findings for establishing an effective biomarker and software approach. Importantly, in vitro models should support pertinent dynamic conditions that exist in the body as part of human disease and thereby create a relevant environment for investigation. Examples of such conditions are those that can participate in the induction of phenotypic changes in appropriate cell lines, such as epithelial to mesenchymal transition (EMT), or mesenchymal to epithelial transition (MET) or other functional alterations, such as increase of proliferation or any other amplification of the oncogenic signal.

[0183] Using breast cancer as a reference case, in vitro models were used at several points in method development to investigate solid tumor cell lines derived from different molecular subtypes (hormone responsive, Her2 positive, triple negative). Data was compiled to arrive at conclusions for the generalAttorney Docket No.3338 / 3 PCT condition of breast cancer, resulting in a pre-validated panel of tens of potential biomarkers. These pre- selected biomarkers were then examined in clinical samples obtained from female subjects who volunteered for study and who represented populations with and without breast cancer. Specific clinical information accompanying samples provided by these subjects allowed classification based on a group of factors and eventual correlation of biomarker expression to disease status. This included the ability to identify no disease, invasive disease and abnormal but non-cancer conditions - for example, DCIS, LCIS, and breast tissue atypia.

[0184] Two important and associated activities that were employed along with generating in vitro and clinical sample information and biomarkers were: (1) the development of algorithms that can be incorporated into custom software and that are based on principles of machine learning and in some cases artificial intelligence; and (2) molecular mechanisms associated with development of target biomarker expression.

[0185] Clinical sample information resulting from detailed standard of care analyses (such as imaging / scanning, biopsy pathology and surgical pathology) establishes sample classification and provides confirmed positives and negatives, along with other categorization, such as disease stage, molecular subtype and grade. For samples that are not either normal or disease, classifications such as atypia or benign are also available. These categorized samples then provide the training set to develop algorithms that are capable of coordinating and matching biomarkers to the different classifications in a predictive manner. While biomarker selection and algorithm development may be examined independently, they are often used together to develop a functional relationship between samples, biomarkers and predictive capability. As for molecular mechanisms, the subject method takes advantage a previously undescribed relationship between disease cells and other cells in the body, which can be partially defined by an unexpected growth rate dependence. This dependence can arise either as a consequence of direct contact between cell types or through exposure to soluble factors secreted by the disease cells and to which other cells are exposed. This dependence can be further characterized by gene expression changes in one or both cell populations that may be indicative of phenotypic modifications that are relevant to the ability of disease to become established in the body and subsequently progress.

[0186] The molecular mechanism that is involved in the current method is at least related to what has previously been described as tumor education. This effect has been seen in cancer cells exposed to other cell types in vivo and / or in vitro; however limited mechanistic information has been described. In the current method, a variety of important cell signaling, epigenetic and gene expression changes are presented. These changes are directly related to the expression of RNAs and proteins that have been used in the method as biomarkers of disease (for example, breast cancer) through tumor education or other similar processes. Importantly, the method describes a phenotypic assay approach for use in measuring the effects of tumor education, and for identifying small molecule drugs or other compoundsAttorney Docket No.3338 / 3 PCT that have the ability to interrupt, block or otherwise abrogate aspects of the tumor education process. This assay approach is particularly useful for 2 exemplary uses: (1) elucidation of the biomolecular pathways and factors leading to phenotypic changes related to target cell as defined herein properties, optionally those derived through tumor education that can cause disease to become established and progress; and (2) identification of small molecules, biologic molecules or other moieties that can block the phenotypic changes and / or offer therapeutic potential to restore a disease limited or disease free physiological condition in an experimental system or a living body.

[0187] Exemplary molecular scaffolds have been identified through work with the described phenotypic assay. These scaffolds have shown the ability to interrupt key steps in the tumor education process and have also been shown to be effective in vivo as therapeutic compounds previously identified to have anti-disease properties, including in cancer and breast cancer. An important aspect of the scaffolds, as presented below, is a terminal monocyclic or polycyclic structure.

[0188] This method of generation of a biomarker signature that detects disease has been used for breast cancer. Additional uses in breast cancer can include the prediction of best treatments based on the molecular signatures.

[0189] The presently disclosed process of detecting a cancer signature can in some embodiments involve the physiological amplification of RNA biomarkers associated with disease cell interaction with other cell types, including immune cells and platelets. This process is at least related to tumor education of these cells.

[0190] To investigate the mechanism of tumor education of cells involved in generation of the current biomarker signature, we developed and employed a phenotypic assay involving in vitro contact studies with immune and breast cancer cell lines. This work was expanded to include in vitro studies of breast cancer cell lines and freshly isolated human white blood cells, peripheral mononuclear blood cells (PMBCs), platelets of any other isolated immune cells. Findings correlated with the clinical data obtained from patient samples.

[0191] We identified that the direct contact of tumor and immune cells as well as soluble factors secreted by tumor cells in solution lead to amplified cancer signal and immune cell alteration, including but not limited to an increased proliferation of both the immune and cancer cell types and a change in gene expression and signal pathway activation. Importantly, these effects were counteracted by application of several small molecule inhibitors, but unaffected by others. Therefore, the present method provides a novel mechanism of measuring the ability of cancer cells to influence immune cells through a process similar to tumor education and that can be used to identify and investigate the ability of targeted treatments to interrupt or reverse the process. Importantly, this model supports the identification and validation of biomarkers that have been used to identify the presence of disease in vivo by a method involving a series of RNA biomarkers and software / algorithms.Attorney Docket No.3338 / 3 PCT

[0192] We identified multiple classes of compounds capable of inhibiting a signal that can be amplified by exposure to tumor cells, examples of which include but not limited to the compounds depicted in Figures 13-15.

[0193] Furthermore, we have demonstrated that the small molecule inhibitors can affect gene expression changes of the biomarkers selected for disease identification and can lead to modulation of physiological amplification of the oncogenic signatures and pathways leading to cancer progression (this has not been described before and is an unexpected finding). Therefore, the current invention constitutes a method of detecting the presence of active disease, including disease that is treatable with targeted small molecule inhibitors, and offers a way to extinguish disease promoting signaling that is associated with tumor cells and their components or secretions.

[0194] We also identified that the contact between the immune and cancer cells leads to the increase of the epithelial mesenchymal transition (EMT) (such as but not limited to vimentin or beta-catenin expression; see Figure 20). Importantly, this upregulation of EMT resulting from the apparent tumor- education of the immune cells, in particular but not limited to monocytes, can be reversed by small molecule inhibitors, implying that the effects of tumor exposure are not permanent.

[0195] Therefore, in some embodiments, the presently disclosed subject matter relates to methods for inhibiting tumor growth and / or blocking the effects of tumor cell exposure on other cell types, leading to a reduction or elimination of tumor education and its impacts on target cells. This may include non-modification of immune responses and / or abrogation / elimination of disease progression. With appropriately informed selection and / or development of appropriate inhibitors, including small molecule drugs, it is possible that existing disease could be reversed.

[0196] Through additional work, we have investigated the effect of the indirect contact of cancer cells with human primary freshly isolated platelets. Solid tumor pre-conditioned media served as a surrogate for indirect communication between cancer cells and white blood cells or other cells in the circulation by way of secreted molecule exposure.

[0197] Unexpectedly, it has been demonstrated that the contact of cultured cancer cells with isolated human platelets caused their aggregation (see Figure 21).

[0198] To translate the proposed mechanism from cell models to human immune cells we have performed the direct and indirect contact studies with freshly isolated human white blood cells and solid tumor cancer cells which also unexpectedly leads to the increase of the oncogenic signal such as but not limited to proliferation (see Figure 22). Similar results were observed upon incubation of the freshly isolated human PBMCs with direct and indirect contact with solid cancer cell lines (see Figures 23 and 24).

[0199] To define the mechanism of target cell (as previously defined herein) changes, optionally through tumor-education of immune cells and / or platelets, we have performed RNAseq characterization isolated immune and cancer cells prior to and post direct or indirect contact. Through transcriptomicsAttorney Docket No.3338 / 3 PCT and pathway analysis we identified the most significantly affected molecular pathways upregulated or downregulated in human immune cells such as but not limited to monocytes (Table 4). Therefore, in some embodiments the presently disclosed subject matter relates to immune cell transformation occurring upon direct or indirect interaction with cancer cells. Table 4 Comparison Analysis of the Canonical Pathways Significantly Changed in Human Monocytic Cell Line Upon Direct or Indirect Contact with Human Cancer Cells*Canonical Pathways THP1+ THP1+ Pre_cond Pre-cond MDA- MDA-231 THP1 Z- THP1 p-Attorney Docket No.3338 / 3 PCT Coronavirus Pathogenesis Pathway 1.63 0.13 2.89 0.04 Pulmonary Healing Signaling Pathway 5.25 0.00 2.83 0.00Attorney Docket No.3338 / 3 PCT Pyroptosis Signaling Pathway 3.30 0.01 2.32 0.00 Pancreatic Adenocarcinoma Signaling 3.15 0.01 2.31 0.00Attorney Docket No.3338 / 3 PCT Inflammasome pathway 2.45 0.02 1.63 0.00 Inhibition of Angiogenesis by TSP1 2.12 0.01 1.63 0.00Attorney Docket No.3338 / 3 PCT Dilated Cardiomyopathy Signaling Pathway 3.05 0.25 0.00 0.01 White Adipose Tissue Browning Pathway 2.84 0.02 0.00 0.01. . . pathway-analysis.

[0200] A graphical summary of these pathways is presented in Figure 25.

[0201] To define the mechanism of target cell (as previously defined herein) changes, optionally through tumor-education of immune cells and / or platelets, RNAseq characterization of isolated immune cell from cancer patients including white blood cells or platelets was performed and compared to the immune cells of the non-cancer samples. Through transcriptomics and pathway analysis we identified the most significantly affected molecular pathways upregulated or downregulated in human immune cells (white blood cells (WBC)) (Table 5). Therefore, in some embodiments the presently disclosed subject matter relates to immune cell transformation occurring in cancer patients as a result of oncogenic transformation of the immune cells driven by the direct or indirect contact.Attorney Docket No.3338 / 3 PCT Table 5 Ingenuity Pathway Analysis (IPA) of the Significant Pathways Affected in Isolated White Blood Cells vs. Non-cancer White Blood Cells* Ingenuity Canonical PathwaysSignaling Pathway Category z-scorep-valueAttorney Docket No.3338 / 3 PCT Signaling by NOTCH1 -1.53 0.04 Signaling by NOTCH Phagosome Formation Cellular Immune eeAttorney Docket No.3338 / 3 PCT Neutrophil degranulation -5.62 0.00 Innate Immune System *(The functional and comparison analyses were generated through the use of IPA (see the website of

[0202] To define the mechanism of target cell (as previously defined herein) changes, optionally through tumor-education of immune cells and / or platelets, the upstream regulators or effectors were identified through transcriptomics and pathway analysis by using RNAseq characterization of isolated immune cell from cancer patients including white blood cells or platelets and compared to the immune cells of the non-cancer samples. We identified the most significantly affected upstream regulators upregulated or downregulated in human immune cells, white blood cells (see Table 6). Table 6 Upstream Regulators Identified in Cancer WBCs Significantly Affected vs. Non-cancer WBCs Upstream Regulator Predicted Activation State Activation z-score p-value of overlapAttorney Docket No.3338 / 3 PCT YBX1 Inhibited -1.61 0.02 FGF1 Inhibited -1.63 0.01 [002 methods for in b t ng pro erat on o tumor and / or cancer ce s, mmune ce s t at are nduced to pro erate by direct or indirect contact with a tumor and / or cancer cell and / or their components and / or their secretions and / or the same with or from previously tumor educated cells, optionally wherein the tumor and / or the cancer is in a group contemplated by the present subject matter and interacting with the target cells identified as having or being at risk for developing characteristics associated with tumor education. In some embodiments, the methods comprise, consist essentially of, or consist of contacting the tumor and / or cancer cell and / or the target cells as defined herein, with an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, for purposes of affecting cell-cell interaction and / or direct or indirect signaling, including any arising from previously tumor educated cells, to abrogate potential transformation of the target cells. Exemplary small molecule drugs include gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof. These exemplary small molecules and their targets are depicted in Figures 13-15. In Figure 13, the terminal monocyclic or polycyclic moiety is indicated with a box.

[0204] In some embodiments, the derivatives of the small molecule drug have the following generic formula:Attorney Docket No.3338 / 3 PCT wherein R1 can be H, CH3, CH2-CH3, CH2-OH, CH2-NH2, or O-(CH2)n–CH3, where n≥1; or can be a carbocycle (C5-C10) or heterocycle (C2-C10) and R2 can be H, CH3, CH2-CH3, CH2-OH, or CH2-NH2. Other possible substituents for R2 include but are not limited to those depicted in Figure 14 Examples of small molecule inhibitors are also depicted in Figures 15A and 15 B

[0205] The presently disclosed subject matter also relates in some embodiments to methods for inhibiting gene expression changes associated with directly and / or indirectly derived tumor education of target cells as defined herein, optionally immune cells or platelets. In some embodiments, the methods comprise, consist essentially of, or consist of contacting an immune cell with an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0206] The presently disclosed subject matter also relates in some embodiments to methods for inhibiting the growth of a tumor and / or progression of a tumor-associated disease in a subject. In some embodiments, the methods comprise, consist essentially of, or consist of administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0207] The presently disclosed subject matter also relates in some embodiments to methods for inhibiting epithelial mesenchymal transition (EMT) associated with directly and / or indirectly derived tumor education of target cells as defined herein, optionally immune cells or platelets, in a subject. In some embodiments, the methods comprise, consist essentially of, or consist of administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

[0208] The presently disclosed subject matter also relates in some embodiments to methods for inhibiting platelet aggregation, inhibiting loss of immune cell targeting ability, inhibiting oncogenic signaling, or any combination thereof associated with exposure to one or more tumor-derived factors such as cells and / or their contents and / or their secretions and / or the same from previously tumor educated cells. In some embodiments, the methods comprise, consist essentially of, or consist of administering to the subject an effective amount of a small molecule drug that comprises a terminalAttorney Docket No.3338 / 3 PCT monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, SGI-7979, LDN19389, X-376 and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof. EXAMPLES

[0209] The following EXAMPLES provide illustrative embodiments. In light of the present disclosure and the general level of skill in the art, those of skill will appreciate that the following EXAMPLES are intended to be exemplary only and that numerous changes, modifications, and alterations can be employed without departing from the scope of the presently disclosed subject matter. Introduction to the EXAMPLES

[0210] Disclosed herein are exemplary embodiments of the presently disclosed subject matter; methods for identifying and intervening in disease based upon information contained in target cells (as previously described herein) as well as use of certain biomarkers in an in vitro diagnostic test for the detection of a breast cancer signature that provides information to clinicians that might be used in directing further detection tests required to make a diagnosis. The development and functional capacity of the test was derived from molecular processes related to target cell (as previously defined herein) characteristics, including those associated with cell modification, including tumor education of cells (e.g., leukocytes or platelets or others). The methods by which the use of these characteristics arise includes the ability to modulate their occurrence and the ability to employ the characteristics in diagnostic procedures. The exemplary test process generally involves collection of a blood sample into a vacuum collection tube, molecular processing of the blood sample, and result generation through software analyses. The software package is based on a single or group of ML algorithms. A custom laboratory information system (LIS) has been developed to assist clinical laboratory staff in managing all processes involved in generating test results from a blood sample. It also allows for the monitoring and review of internal quality control criteria at two critical steps: RNA quantification and RT- PCR / qPCR processing. The software system for the test in some embodiments includes the LIS and data analysis software modules. Quality control checks include, but are not limited to, evaluation of variation in data points from previous runs (in some embodiments, the last 20 runs) to identify potential trends or shifts, positive and negative controls.

[0211] Cell-cell communication within the tissue microenvironment (TME) and the broader in vivo physiology is an important part of the mechanism of cancer development and progression. The interaction between tumor and non-tumor cells may result in activation of multiple signaling and gene expression pathways leading disease progression / spread. Cancer cells can also reprogram normal cells, including immune cells, through direct contact or via secreted molecules causing the normal / immune cells to undergo changes. These changes in signaling, gene expression or function / phenotype have the potential to be used for the creation and establishment of cancer detection and treatment approaches,Attorney Docket No.3338 / 3 PCT technology or products. We have developed and implemented a novel whole blood RNA gene expression biomarker panel capable of identifying the presence of a breast cancer signature that functions even at early stages of disease. We have demonstrated that the expression profiles for genes in this panel are different in cells from individuals with and without breast cancer.

[0212] Upon receipt of a new blood sample for processing, molecular processing involves nucleic acid extraction, quantification, normalization, and qPCR. A new and unique accession can be created for each sample after the external reference has been entered, blood tube barcode has been scanned and related fields populated, the patient, provider, facility and collector. Progression of a sample through each of these processes involves change of status within a LIS. Such a LIS is equipped with the ability to automatically compute reagent calculations needed for each step, dependent on number of samples being analyzed. The process flow chart of the exemplary breast cancer test is presented in Figure 6.

[0213] Two independent software packages are automatically called by the LIS to assist with data analysis and monitoring of internal quality control criteria. RNA quantification software (in some embodiments, RNA Module 704; see Figure 5) can be called when enough samples are ready for RNA quantification, a plate has been designed, locked and RNA quantified by laboratory staff. The outcome of this analysis is a report consisting of identity and quality checks, as well as concentrations of each RNA sample quantified and volumes of water that are required to be added to normalize the sample. Similarly, qPCR analysis software (in some embodiments, PCT Module 420; see Figure 5) can be called when samples have been added to the PCR plate, test plate and reagent calculations have been printed, locked, and laboratory staff have successfully generated qPCR data. This package is responsible for providing the test result upon analysis of gene expression data. Both packages automatically progress successfully quantified samples through the next status within the LIS.

[0214] These software packages are also responsible for monitoring and review of quality control criteria and therefore act as key decision points for sample rejection based on quality or quantity. In some embodiments, internal quality control checks can include: 1. Positive controls for plates to ensure reproducible results are generated from plate to plate; 2. Negative controls to check for reagent or sample contamination; 3. Reproducibility of replicates to compensate for technical noise and increase precision in results; 4. Regression of standard curves to verify linearity of standards and ensure accurate calculations of unknown variables; and 5. Positive controls for samples to evaluate quality differences that might exist among samples. This criterion is in some embodiments only checked during qPCR.

[0215] Failure of each of these criteria may be associated to the occurrence of either random or systematic errors in the laboratory. To support documentation of data analyses and evaluation of qualityAttorney Docket No.3338 / 3 PCT control criteria, all error messages and / or warnings can be saved in a unique log file for each plate and for each sample within the LIS.

[0216] Design and development of three software packages that make up the exemplary software system for the test process is presented herein. For each software package, user needs are defined and input / output specifications are presented. For data analysis software packages, process flowcharts are presented together with an architecture and execution sequence. Finally, a detailed description is provided on how the functional and performance requirements of each software were verified and validated. Guidance standards used for design and development of the software system were from the Clinical Laboratory Improvement Amendments (CLIA), Clinical and Laboratory Standards Institute (CLSI), the International Organization for Standardization (ISO), and the United States Food and Drug Administration (US FDA). EXAMPLE 1 Tumor-Educated Monocytes Show Breast Cancer-Subtype Specific Reprograming

[0217] To gain insight into gene expression changes associated with the disease state vs. normal physiology and to elucidate mechanisms involved in the induction and propagation of changes associated with the presence of breast cancer, including the reprogramming (or education of) cells, we first performed co-culture studies in vitro, exposing a human monocytic cell line to cultured breast cancer cells. Here, we describe effects of co-culture exposure on the monocytic cells, including significant changes in the monocyte transcriptome (Table 4 and Figure 27). This is further exemplified by Gene Ontology, KEGG, and Reactome enrichment analyses presented in Figure 28. Importantly, the changes induced in the in vitro model are consistent with those observed in clinical samples. This indicates that the in vitro system is representative of processes occurring in vivo, and provides a relevant model to identify important molecular steps participating in, and evaluate potential therapeutic compounds for activity against, cancer cell (tumor) driven reprogramming, education or other means of cancer cells modifying normal cells, including immune cells, in vivo. Additional data based upon cellular contact is presented in Figures 29-31, with some important categories (Figures 29 and 30) and genes (Figure 31) identified. Data confirming pathway analysis predictions, showing that EMT protein biomarkers are up regulated upon direct and indirect contact, were also collected for beta-catenin, vimentin, and IL-6.

[0218] Many of the effects seen upon cell co-culture were also found in work with monocytes exposed to conditioned media and soluble factors from cancer cell line cultures with no cell-cell contact. A volcano plot and Venn Diagram showing numbers of regulated genes is presented in Figure 32. A selection of important canonical pathways from IPA are presented in Figure 33. Figure 34 shows how key regulators aligned in both tested environments. KEGG and Gene Ontology enrichment for the indirect case are presented in Figures 35 and 36, respectively, and show similarity to the contact dependent data. A graphical depiction of the non-contract conditioned media network from IPA isAttorney Docket No.3338 / 3 PCT provided in Figure 25A. Taken together, this data shows that indirect communication between breast cancer and human monocytes can lead to induced changes in the monocytes that represent significant changes in gene expression.

[0219] Changes in biomarker expression were also described and correlated to different breast cancer profiles in clinical samples. Similarly, using RNAseq and pathway analysis tools, we demonstrated that changes induced by contact with more aggressive triple negative (TNBC) cells or their secretions are distinct from the alterations triggered upon contact with ER+ breast cancer cells / secretions. We also showed that cancer-immune crosstalk triggers epithelial to mesenchymal transition (EMT) in the monocytic cell line (Figure 20), as well as activated multiple pathways associated with cell migration and invasion, cell chemotaxis and cell-to-cell interactions (Table 4). As the interplay between cancer and immune cells constitutes an important role in cancer progression, the findings reported here provide important insight into the mechanisms of disease-dependent reprogramming of normal cells, particularly immune cells, contributing to disease establishment and progression. Genes that are significantly modified as part of this process function as biomarkers in the currently disclosed subject matter and can be used to detect the presence of active disease processes. EXAMPLE 2 Laboratory Information System (LIS)

[0220] User Needs

[0221] A custom LIS is able to assist clinical laboratory staff (users) in managing all activities involved in the test process and represents a key component of the overall testing system. This includes generating test results from a blood sample and integration with data analysis packages that are capable of monitoring and reviewing predetermined internal quality control parameters. To develop and implement such a system, it should be able to provide oversight on the performance of any laboratory executing the test. The following information supports defining and establishing a LIS suitable for molecular blood testing in support of the described subject matter.

[0222] Input and Output Specifications

[0223] Tables 7 and 8 describe the input and output specifications required for the LIS software. Specifications are categorized into customer, regulatory, safety, and product requirements. All are addressed as part of LIS versioning and release processes. Adequately specifying input and output parameters is an important step in establishing a functional software system and test. Table 7 Specifications of Exemplary LIS Software FUNCTIONAL REQUIREMENTS edAttorney Docket No.3338 / 3 PCT The LIS can execute the two independent data analysis software packages when data has been successfully imported at the RNA quantification and qPCR steps. ssAccession Status Meaning P di N P di A i r is toAttorney Docket No.3338 / 3 PCT Untested Plate has been created and available for accessions to be loaded Testing Plate being tested either by the RNA or PCR data analysis software package

[0224] Objective: To verify input and output specifications for the LIS and its ability to coordinate testing, new accessions were created and tracked through the entire breast cancer assay process.

[0225] Acceptance Criteria: All 14 accessions were successfully created in a database with a pending status and successfully progressed through the process until test reports were generated.

[0226] Study Design: 14 new sample accessions were created as part of the pre-analytical testing process. The statuses of accessions were updated when blood samples were shipped and when they had been received. Sample biometrics and collection times were updated using information from the forms and accessions were staged for molecular processing. First, RNA was extracted, quantified and normalized, and the qPCR reaction was setup and run. Raw qPCR data were imported into the LIS and the PCR data analysis software was executed. Data was reviewed and a results report generated for each accession.

[0227] Results. In the analytical testing phase, RNA is extracted, and status is changed to ‘Extracted’. First, samples to be extracted are selected and their status changed to ‘Extracted’ by clicking on the ‘Move to Extracted’ icon in the Workflow section. Depending on the number of samples chosen, the LIS automatically creates an RNA Extraction Plate Map and reagent preparation tables for resuspension and DNase reagent mixtures for use in the extraction process.

[0228] Extracted samples are ready for RNA quantification. In the Lab Processing section, a new RNA quantification plate must be created before accessions to be extracted are displayed in the sample panel. The status of accessions changes from ‘Extracted’ to ‘Loaded RNA Plate’ after they are loaded onto the RNA plate and to ‘Ready for Quantification’ when the plate has been locked. Once output data from the microplate reader is imported into the LIS and the RNA quantification software package is successfully executed, the status will change to ‘RNA Quantified’ and the operator will be able to print the RNA Quantification and Normalization Report by clicking on the Print Calculations tab. This report provides details on whether all plate quality control checks were passed, the accession numbers, RNA stock concentration, volumes of RNA and water required for normalization and accession-specific comments on sample quality control checks. During RNA quantification, the status of the plate changes from ‘Untested’ when it was created to ‘Testing’ when the data analysis package has been executed, toAttorney Docket No.3338 / 3 PCT ‘Processed’ when the software package has been successfully executed. Failure of any of the plate quality control checks will result in the status being changed to ‘Rejected’.

[0229] The status of quantified samples that are ready for PCR are changed to ‘Ready for PCR’. cDNA is synthesized and the qPCR reaction is setup. Like RNA quantification, accessions ready for PCR can only be viewed in the sample panel after a new PCR plate has been created and the status of the plates will change similarly. Execution of the RNA and PCR data analysis software results in deposition of plate data in the ‘Plate Results’ tab of the Customer Service sectionOnce a results report has been generated and sent to the ordering physician, the status of the accession changes to ‘Closed / Completed’

[0230] Conclusion: Taken together, these results verify the input and output specifications of the exemplary LIS and its ability to coordinate testing through interaction with the other components shown in Figure 5. New and unique accessions were created and progressed through the pre-analytical, analytical and post-analytical testing phases. The two independent software packages were executed by the LIS at the RNA quantification and qPCR steps, respectively. Data from the software packages was written back into the LIS and available to be reviewed and used for troubleshooting as required. A results report was generated for each accession using prediction data from the PCR data analysis package and the accession status changed to ‘Closed / Completed’. EXAMPLE 4 Design Validation

[0231] Objective: To validate user needs of a custom LIS and create a procedure for ongoing verification and validation of the software system as part of control of design and development changes.

[0232] Acceptance Criteria: The LIS successfully executed the data analysis packages during RNA quantification and qPCR, and data from these packages were written back to the LIS for review. Predetermined internal quality control checks were performed by the LIS and available for review by laboratory operators.

[0233] Study Design: The ability of the LIS to automatically execute the RNA quantification and PCR software modules was validated by importing data for each process and monitoring the changes in the accession and sample status. Plate data from the software packages was also validated to have been deposited at the appropriate sections in the LIS.

[0234] Results / Conclusion: Upon successful importation of output data from the RNA quantification and qPCR instruments, the LIS automatically executed the analysis packages and plate status changed from ‘Untested’ to ‘Testing’ to ‘Processed’ when results were written in the ‘Plate Results’ tab of the accession detail section of the LIS. The accession status also changed as indicated in Table 8. Successful execution of the data analysis packages validated the LIS as tracking and monitoring component of an exemplary software system of the presently disclosed subject matter that can provide oversight on the performance of partner laboratories who execute the test.Attorney Docket No.3338 / 3 PCT

[0235] A software validation LIS procedure has been developed and approved for use in verifying and validating the system during design and development changes of any of the software components. This procedure is used to verify calculations and execution of software components in the event that no changes have been performed over a 1-year period. EXAMPLE 5 RNA Quantification Data Analysis Software Module

[0236] All extracted RNA samples in the exemplary breast cancer test process proceed to RNA quantification and normalization. It is important to ensure that all reagent calculations are automatically generated depending on the number of samples chosen for quantification and the entire system can be automated to increase processing throughput. Exemplary, non-limiting input and output specifications of exemplary RNA quantification and normalization procedures are presented in Table 9. Table 9 Input and Output Specifications of Exemplary RNA Quantification and Normalization Procedures PERFORMANCE REQUIREMENTS is S. tic

[0237] Tab nstitute the RNA quantificationso ware / mo u e. ere app ca e, e npu s, purpose an ou pu s of each module are provided. Table 10 Architecture of an Exemplary RNA Quantification and Normalization Software Package # Module Name Description he ns ntoAttorney Docket No.3338 / 3 PCT Output(s): All the results are written back into the database, including updates for rejecting / accepting samples. nd ple um be rol get wn es nd his rd ent or be ity his he ber ble R2he nd ity isAttorney Docket No.3338 / 3 PCT 6 UpdateControlValues This function is used for communication between the software and the database to update the control mean RFU values after quality is he nd in nd ls) nd ed ally q p . run was “ReadWriteDB.R”. It connected to the database and started reading the data by finding all the plates that had just been run and searching for the plate data. This included the Control and sample data, including IDs and RFUs. The next step was to pass it to the RNADataAnalysis function to check the quality of the data and calculate the concentrations for the new samples. The concentrations were read from the database as well. The first quality control check was to ensure the RNA quantification reagents and plate was not contaminated with interfering nucleic acids. For this, the mean relative fluorescence units (RFU) of the negative control wells were checked to ensure that they were not greater than a specified threshold (e.g., 0.20). Otherwise, the entire plate was re-run and a log provided of the possibility of contamination. Next, the standard deviation (SD) of the triplicate RFU values was checked for each control / new sample to be less than or equal to a specified threshold (e.g., 0.5). If not, an outlier (minimum or maximum of the triplicate values) was removed to get better SD. If the SD after removing the outlier was not less than the threshold, the sample was re-run.

[0239] For samples and controls that passed all the quality control checks, mean RFU values from the triplicate values were calculated. The blank mean RFU was then removed from all sample mean and control means. A check was also undertaken to confirm that all the sample’s mean RFUs wereAttorney Docket No.3338 / 3 PCT smaller than the largest control mean RFU. If not, that sample was excluded from analysis, and a request made for it to be diluted and re-run. Further, the control mean RFU values and their known standard concentrations were used to plot a standard curve. The linear regression coefficient (R2) of the standard curve must be greater than or equal to a specified threshold (e.g., 0.9950) for the plate to be acceptable. If not, any single standard curve point that compromised the R2value was removed. If more than two points needed to be removed to achieve an acceptable curve, the entire plate was rerun. This part was done in findStandardCurve function. The acceptable standard curve was then used to calculate concentrations for the new samples through interpolation. RNA concentrations were used as inputs by the LIS in calculating normalization volumes that can be documented in an RNA Normalization Report. This Report can also include a summary of the reagents and equipment used in the procedure, identity of the operator, and a summary of all performed quality control checks.

[0240] A process map of the RNA quantification software is presented in Figure 7B. Briefly, raw RNA output data from the Fluoroskan microplate spectrophotometer was exported and used as an input for data analysis. The software managed and collated the raw data by identifying reagent, plate, and operator details as well as creating a data matrix of RFUs for all wells. Next, the standard deviation (both controls and samples) and negative and positive quality control checks were performed and RFUs were normalized by subtracting the mean RFU of the blank wells from those of all controls and unknowns. A standard curve was then plotted, and the linear regression coefficient evaluated. For all plates that passed these criteria, the RNA concentrations and normalization volumes of samples were calculated, and a report generated Detailed descriptions of all quality control checks and acceptance and rejection. criteria were provided and evaluated as part of internal quality control processes.

[0241] Design Verification. To verify input and output specifications, reports from quantification of 12 RNA samples were compared when raw data was analyzed manually in Microsoft Excel and by the RNA quantification software package. For both analyses, total RNA was extracted from whole blood and quantified using test instructions. Calculations for volumes of reagents required to prepare TE buffer and ribogreen working solutions were performed in Microsoft Excel and compared to calculations from the software .Analyses of raw data was performed manually by implementing software inputs (Figure 7B and) within Microsoft Excel and a report was generated showing the standard curve, normalization volumes and identity of any samples that had QA / QC failures. All experiments were set up in triplicate and statistical analyses were performed as recommended in CLSI EP6-A3 and in FDA guidance for evaluating diagnostic tests (United States Food and Drug Administration. Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests. Guidance for Industry and FDA Staff (2007)).

[0242] Design Validation. To validate user needs for RNA quantification and normalization, the software package was used to analyze raw data from an RNA output file consisting of 24 RNA samples. Total RNA was extracted from whole blood samples and quantified. Validation was completed byAttorney Docket No.3338 / 3 PCT tracking samples through the workflow, generating a plate map, reviewing reagent calculations, quality control checks, RNA concentrations and normalization volumes of all samples. Work described in this EXAMPLE can be necessary for managing input of RNA data, which is a key parameter for setting up qPCR analyses of target biomarker expression and classification of samples as related to a breast cancer signature. EXAMPLE 6 Exemplary PCR Data Analysis Software Package

[0243] All samples that have undergone qPCR in the breast cancer test process may proceed to data analysis. An efficient software package that reliably evaluates predetermined quality control checks and executes predictive algorithms is required for this purpose. Further, it may be important for this package to be incorporated within a LIS to simplify sample tracking and report generation.

[0244] Table 11 describes the input and output specifications required for PCR data analysis using a software package. Table 11 Input and Output Specifications of an Exemplary PCR Data Analysis Software Package INPUTS OUTPUTS ta. he on es for ple ort tus om he ed nto ataAttorney Docket No.3338 / 3 PCT INPUTS OUTPUTS sam les after a PCR run to ensure cer CR ate he IS ese ks ch he are n a ce ed e’. are laryqPCR data analysis software package. Where applicable, inputs, purpose and outputs of each module are provided. Table 12 Architecture of an Exemplary PCR Data Analysis Software Package # Module Name Description all ts teAttorney Docket No.3338 / 3 PCT Purpose: NTC reactions check for reagent or sample contamination by ensuring no nucleic acids are loaded unto ed on un no es e- st. d. ls ck ss a on ls es C to if he he to t. he is isAttorney Docket No.3338 / 3 PCT 6 DBCheckSD Input(s): Triplicate CT value of a sample or control. Purpose: Triplicate CT values (groups of 3) are used to or on al on 5. he til on nt te er or ed to es ch of in he all e- be of If rd If an a e-Attorney Docket No.3338 / 3 PCT 8 CombineResultsFinalKit2 Input(s): Copy number data for new samples a function name to write the results / predictions for the new samples. en es he ed nt er st n n ch he ng he ng he es he T he he TAttorney Docket No.3338 / 3 PCT values after quality control checks and if any outlier from the triplicate value is removed. edus of their breast cancer signal in order for the test to function as intended. EXAMPLE 7 Exemplary qPCR Data Analysis Software Execution Sequence

[0246] The exemplary software packages consisted of 16 modules and was designed to automatically evaluate quality control checks and execute ML-based algorithms to make a prediction on whether a given gene expression signature indicated the presence or absence of a breast cancer signature. The main code to be run was “ReadDB.R”. This module found the path where the code was saved in, connected to the database, and read control and sample data from the database. This included sample IDs, target names, age, and transcript numbers. The next step was to check for the NTC values (DBCheckNTC). Upon successful completion, the rest of the data were checked to ensure only numerical values were present.

[0247] Next, standard deviations (SD) of the sample positive controls were checked to ensure they were less than the threshold of, for example, 0.5. Positive control values were checked to confirm they amplified within a pre-defined range. If more than one positive control amplified out of range, that sample was flagged and excluded from further analyses. The values of all remaining sample and control CTs were then checked for the SD criterion.

[0248] For all samples and controls that pass all quality control checks, mean CT values were calculated. The Control mean CT values were used together with the known copy numbers to plot a standard curve for each target. The R2for each standard curve should be greater than or equal to a threshold (e.g., 0.9950). If not, any single standard curve point that compromised the R2value was removed. If more than one point needed to be removed for any target to achieve an acceptable curve, the entire plate was rerun. These calculations were performed by the StandardCurve module. With an acceptable standard curve, copy numbers for the new samples were calculated for each target by interpolation and used to make predictions using the CombineResultsFinalKit2 model.

[0249] A process map of the PCR data analysis software package is presented in Figure 7A. Briefly, raw qPCR output data from the Quant Studio 7 qPCR system was exported and used as an input for data analysis. Data management and collation involved the following tasks: A. Finding the date, user, and instrument IDs from the input file using locations specified in the code. B. Finding the required data that is to be used to define the data matrix for calculating patient sample parameters. C. Identifying well position, sample name, target name, and CT columns in the data matrix.Attorney Docket No.3338 / 3 PCT D. Reading the control data from the ‘CopyNumbers.csv’ file. This information is used to calculate the unknown copy number of each target. E. Reading the control IDs from the ‘ControlIDs.csv’ file. This information is used to extract new unknown sample IDs and to ensure amplification data is available for all controls on the plate.

[0250] Next, the plate negative control check was performed to ensure there was no sample contamination during reaction setup. Positive control checks were then carried out for the entire plate and the samples to ensure the protocol ran as expected and samples were of sufficient quality. The standard deviation (both controls and samples) check was then performed, and outliers removed, where applicable. A standard curve was then plotted for each target and the linear regression coefficient evaluated. Sample copy numbers were calculated using the standard curve equation of each target and put in the data matrix of that sample. Detailed descriptions of all quality control checks acceptance and rejection criteria were provided. These activities ensured that information provided by the qPCR analysis software was accurate and reliable. EXAMPLE 8 Development of Machine Learning Algorithms (MLAs) for Making Test Predictions

[0251] MLAs were used to develop the analysis software model for the exemplary breast cancer test. The inputs for these algorithms (for each sample analyzed) were: 1. Normalized gene expression data for each of the 12 biomarkers (qPCR transcript numbers); 2. Smoking history and current status (current smoker = 1, former smoker = 2, never smoker = 3); 3. Menopausal status (unknown menopausal status = 0, pre-menopausal = 1, post- menopausal = 2); and 4. Age (expressed in number of years).

[0252] Figure 9 provides an overall description of how the machine learning process was employed. Biomarker and clinical data from each clinical study sample was generated and supplied to the algorithms, and the training and tuning process was conducted in an iterative fashion (model development). Known sample status based on radiology and biopsy outcomes was used as the measure of ‘correct’ or ‘incorrect’ predictions and used to optimize the models making the predictions. In summary, training data was pre-processed, parsed, run on different MLAs to identify key features and create models. Model parameters were then finetuned through validation to identify key drivers of model performance and to optimize the overall predictive ability. Once development (training and validation) was complete for the two models used for participant groups, the models were locked and deployed in the production environment (version 1.0) for use in establishing analytical and clinical performance metrics for the assay using independent (new) sample data as the test set. All performanceAttorney Docket No.3338 / 3 PCT results for clinical accuracy, sensitivity, and specificity were generated from these independent (new) samples. For guidance during the model development and independent evaluation process, the work used the U.S. FDA’s Document on the Proposed Regulatory Framework for Modifications to Medical Device Software and IEC 62304:2006+AMD1:2015 to meet state-of-the-art requirements for software lifecycle processes.

[0253] A total of 383 samples (132 positive and 251 negative cases) with known labels were used for model training and tuning. The best set of performing algorithms were identified from a group of 17 MLAs categorized as either regression-based algorithms, decision tree-based algorithms or neural networks. It was observed from the results that algorithm performance was directly correlated to age of patients. To compensate for these differences, participants were stratified as either pre-menopausal versus post-menopausal and those younger than 50 versus aged 50 and over. For each combination, best performing algorithms were identified using the same model training and tuning approach described above. It was identified that better performance was obtained for the age categorization. Best performing algorithms are defined as those that demonstrated the highest capability of correctly identifying positive or negative samples in the validation sets. Training set performances of the best performing algorithms for each age group are presented below and are used to encompass models for identifying a breast cancer signal or lack thereof: 1. Age < 50 ^ Model 1 ^ Accuracy: 97.7% (CI: 91.8% – 99.4%) ^ Specificity: 100% (CI: 89.5% – 100%) ^ Sensitivity: 88.0% (CI: 74.9% – 93.7%) 2. Age ≥ 50 ^ Model 2 ^ Accuracy: 87.8% (CI: 82.8% – 93.1%) ^ Specificity: 100% (CI: 91.5% – 100%) ^ Sensitivity: 75.0% (CI: 62.8% – 83.7%) EXAMPLE 9 Design Verification

[0254] For verifying performance, an important consideration was evaluation of internal quality control checks on each plate that is run and seamless integration of the PCR software package into the LIS. Internal quality checks included positive and negative controls for plates, reproducibility of technical replicates, regression of standard curves, and positive control checks using Westgard rules. All quality control criteria are required to be met to ensure validity of test results. Consequently, each run should pass these criteria before data can be used in subsequent steps.Attorney Docket No.3338 / 3 PCT

[0255] To evaluate each criterion, data from the qPCR system was exported and established as a readable file. This data file was the source for all subsequent sample analyses. To support documentation of analysis progress and quality control criteria, the following additional files were created and archived:

[0256] 1) Any errors / warnings were saved into a unique log file for each plate and for each sample. This log file provided information at the level of individual wells on the plate.

[0257] 2) A final report was generated to present analysis results. This file was intrinsically linked to all quality conditions evaluated.

[0258] Objective: To verify the input and output specifications of the PCR data analysis software package.

[0259] Acceptance criteria: The same mean and standard deviations should be obtained in manual and LIS calculations. Positive control charts should show the same trends.

[0260] Study Design: Total RNA was extracted from whole blood samples and quantified using the RNA quantification and normalization protocol. cDNA was synthesized and the qPCR reaction was setup to examine expression of the 12 biomarkers along with positive controls, negative controls (no template controls) and synthetic oligonucleotide standards.20 PCR plates were run, and positive control data was used to create positive control charts. For each plate, 48 positive control charts from 12 targets (4 charts per target) were generated and used to monitor consistency in runs and to guide in troubleshooting occurrence of random and systematic errors. To verify the PCR data analysis software package, control charts from 3 control points were plotted manually and compared to charts generated by the LIS. Westgard rules used for ongoing monitoring of process performance were employed. All experiments were set up in triplicates. Statistical analyses were performed as recommended in CLSI EP6-A (CLSI / NCCLS. 2003. Evaluation of the linearity of quantitative measurement procedures: a statistical approach. Approved guideline. CLSI document EP6-A. Clinical and Laboratory Standards Institute, Wayne, Pennsylvania), MIQE Guidelines (Bustin et al. (2009) The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clinical Chemistry 55(4):611-622), and the FDA guidance for evaluating diagnostic tests (United States Food and Drug Administration. Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests. Guidance for Industry and FDA Staff (2007)).

[0261] Results / Conclusion: As shown in Figures 16A-16F, the same mean and standard deviations were obtained when amplification data of SY241, SY245, and SY264 were analyzed manually (Figures 16A, 16C, and 16E) compared to when analyzed by the software package in the LIS (Figures 16B, 16D, and 16F). Positive control charts showed the same trends between both data analysis approaches. These results verified the PCR data analysis software for use in the exemplary breast cancer testing process.Attorney Docket No.3338 / 3 PCT EXAMPLE 10 Design Validation

[0262] Upon completion of design verification, it was important to validate user needs for the PCR data analysis software package after it was incorporated within the exemplary custom LIS. For this, the ability of the software to evaluate predetermined quality control checks and execute the locked predictive model was validated.

[0263] Objective: To validate user needs for the PCR data analysis software package.

[0264] Acceptance criteria: Samples should be successfully progressed through the workflow using the LIS and the PCR data analysis software called by the LIS when raw qPCR data has been imported. Target amplification data and sample outcomes from locked and version-controlled predictive models should be successfully written back to the LIS for report generation.

[0265] Study Design: Total RNA from 3 samples was extracted and quantified from whole blood samples according to test instruct. cDNA was synthesized and the qPCR reaction was setup. The qPCR plate map was printed and used as a guide for sample loading during reaction setup. The ability of the LIS to execute the data analysis package was validated when raw qPCR data was imported by checking the status of the plate. Once the plate had been processed, LIS-PCR software connectivity was further evaluated by checking for amplification and sample prediction data that was written back in the LIS. All experiments were set up in triplicates as recommended by the MIQE Guidelines (Bustin et al., 2009). Statistical analyses were performed as recommended in CLSI EP6-A (CLSI / NCCLS. 2003. Evaluation of the linearity of quantitative measurement procedures: a statistical approach. Approved guideline. CLSI document EP6-A. Clinical and Laboratory Standards Institute, Wayne, Pennsylvania), MIQE Guidelines ((Bustin et al., 2009), and the FDA guidance for evaluating diagnostic tests (United States Food and Drug Administration. Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests. Guidance for Industry and FDA Staff (2007)).

[0266] Results / Conclusion: Once the PCR plate was setup and locked in the LIS, a plate map (Figure 17) was available to be printed and used as a guide for reaction setup. qPCR was run and raw data imported. Plate status changed from ‘Untested’ to ‘Testing’ and then to ‘Closed / Completed’ when the data analysis software was automatically executed by the LIS

[0267] The status of individual samples had changed from 'Ready for PCR' to 'Loaded PCR plate' when the plate was designed and locked. Upon execution of the software, sample status changed from 'Loaded PCR plate' to 'To Review' and plate results were available for review on the Results and Plate Results tabs. Amplification data of all 12 targets for the 3 samples on the plate was written back to the LIS where it is stored for use in future analysis and for troubleshooting failed quality control checks. A screenshot of amplification data in the LIS is shown in Figure 18. Finally, a results report was generated for each sample using sample predictions from the PCR software. This report provided details on sample collection, receipt and report, order providers, patient information, and outcome of the exemplary breastAttorney Docket No.3338 / 3 PCT cancer test. Successful sample and plate tracking, execution of the PCR data analysis software, transfer of gene expression, and sample prediction data to the LIS for report generation validated the software package for use with the exemplary breast cancer test. EXAMPLE 11 Validation of Predictive Models

[0268] Objective: To validate training set performance of locked models (version 1.0) used to make predictions from gene expression data obtained using the exemplary breast cancer testing process.

[0269] Acceptance criteria: Clinical accuracy, sensitivity, and specificity in the independent test set being within the confidence interval limits of the training set performance (see above).

[0270] Study Design: To validate performance of the locked predictive models, a blind test set of 695 samples obtained from the IDBC clinical study consisting of 96 positive and 599 negative samples, was used for independent model testing in each age group. Molecular processing was undertaken for all study samples, culminating in generation of gene expression data on a qPCR platform according to the presently disclosed sample testing process (see Figure 18). Raw qPCR output data was fed into the locked models to determine the presence or absence of a breast cancer signal. Internal quality control checks were performed with custom data analyses packages to assess sample quantity and quality at the RNA quantification and qPCR steps. In addition to these controls, practices and procedures were put in place to reduce the chance of possible failures and mitigate errors in the testing process. Clinical performance metrics were reported for the blind test set with 99.5% confidence intervals (CI) computed through an exact binomial test.

[0271] Results / Conclusion: For the entire independent test sample set, the exemplary breast cancer test of the presently disclosed subject matter demonstrated an overall accuracy of 92.2% (CI: 88.9% – 94.6%) with a specificity of 94.3% (CI: 91.0% – 96.4%) and sensitivity of 79.2% (CI: 65.5% – 88.4%) for cancer detection. Model performance for the entire cohort, participants younger than 50 and those aged 50 and above are presented in Figures 19A and 19BF. Table 13 shows evidence of the validation of the locked models demonstrated by the clinical accuracy, specificity, and sensitivity obtained in the independent test set being within the lower and upper confidence limits of the performance obtained in the training set. Table 13 Validation of Locked Models Used to Make Predictions for the Exemplary Breast Cancer Test Predictive Model ID 1 (Age < 50)Attorney Docket No.3338 / 3 PCT Sensitivity 74.9%-93.7% 91.7% PASS Predictive Model ID 2 (Age ≥ 50)p y p y g p g s

[0272] An exemplary workflow for a test ordered by a medical professional is as follows. The medical professional requisitions or prescribes the test, and the subject attends a phlebotomist appointment to have his or her blood drawn according to test instruction. The blood sample is sent to an approved clinical laboratory equipped to perform the test, including having the appropriate LIS, and the sample is accessioned into the LIS. The sample is then processed according to the test steps programmed into the LIS, and the LIS manages all of necessary QA / QC steps associated with RNA extraction, RNA quantification, and RNA normalization.

[0273] Once converted to cDNA, the sample is loaded onto a PCR plate, prepared for qPCR, and run. Results are determined by LIS / qPCR software processing steps and a report is generated. The report, indicating a positive or negative result for the presence of a breast cancer signal, is communicated to the requisitioning / prescribing medical professional. The physician considers the results according to the subject's history and clinical characteristics. If the report indicates a positive result, the physician can order follow-up imaging (for example, a mammogram and ultrasound and / or MRI). If the imaging confirmed a concern, the subject can be recommended for biopsy. If a biopsy diagnosis is cancer, the subject can continue to treatment. If follow-up imaging or biopsy is negative, the subject is likely recommended for shorter interval screening (for example, repeat blood testing with imaging at 6 months). For a negative result, the medical professional has confidence in recommending maintaining standard of care screening but with an option to increase testing frequency based on factors such as the subjects age, family history, and clinical characteristics. The performance characteristics of the test can affect medical professional decisions in the following ways and can offer advantages for promoting early-stage detection: ^ Test performance has been shown to be independent of breast tissue density ^ The test has been shown to have good detection of Stage 1 and 2 breast cancer ^ The test has been shown to have good detection of breast tumors less than 1cm in diameter ^ The test has been shown to very accurately identify subjects with no breast cancerAttorney Docket No.3338 / 3 PCT ^ The test has been shown to have very good performance in younger women ^ The test has no exposure to radiation ^ Samples for the test can be collected in community locations

[0274] Each of these characteristics provides medical professionals with ways to perform a new kind of testing in groups not well served by the existing standard of care (including those who do not participate in mammography screening when eligible) and to receive a different type of information (molecular results) than obtained through imaging (such as mammography). This can promote more testing and more efficiency in the overall diagnostic pathway, for example, by testing more women and shortening the amount of time to determine if a biopsy is needed. The ability to triage subjects who show up with factors such as breast pain or other non-specific concerns is also an important use of the test that promotes efficiency in patient management. Introduction to EXAMPLE 13

[0275] Early detection and diagnosis of cancer can have a significant impact on improving mortality and better treatment outcomes. Liquid biopsy is an actively emerging tool offering a convenient non- invasive way of detecting and identifying a cancer signal form a blood sample. Recently several liquid biopsy tests have been described and implemented in clinical practice. However, the majority of these tests are based on circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating free DNA (cfDNA), or exosomes which possess a number of limitations associated with the technology. Alternative approaches have evolved including RNA signatures from tumor-educated platelets, leukobiopsy, micro and oncRNAs. As the oncogenic signal detected by liquid biopsies can also be translated to impact the immune response to cancer, an aim was to gain insight into the interactions between white blood cells and other cells in the circulation and tumor cells.

[0276] Cell-cell communication within the tissue microenvironment (TME) and the broader in vivo physiology is an important part of the mechanism of cancer development and progression. The interaction between tumor and non-tumor cells may result in activation of multiple signaling and gene expression pathways leading to disease progression / spread. Cancer cells can also reprogram normal cells, including immune cells, through direct contact or via secreted molecules causing the normal / immune cells to undergo changes. These changes in signaling, gene expression, or function / phenotype have the potential to be used for the creation and establishment of cancer detection and treatment approaches, technology or products. Disclosed herein is a whole blood RNA gene expression biomarker panel capable of identifying the presence of a breast cancer signature that functions even at early stages of disease. The expression profiles for genes in this panel are shown to be different in cells from individuals with and without breast cancer.

[0277] Tumor-derived factors can educate white blood cells or other immune cells and / or platelets within blood circulation through several modalities. Direct mechanisms include the transfer of RNA or protein cargo via exosomes, or receptor-mediated endocytosis of soluble factors within circulation.Attorney Docket No.3338 / 3 PCT Contact driven signaling, either directly with tumors or tumor cells and / or components and / or secretions from tumors or tumor cells, as well as the same from previously tumor educated cells, can also drive education through changes in gene expression or changes in RNA processing, including alternative RNA splicing. Tumor-derived factors can also alter the development and function of megakaryocytes (MKs) within the bone marrow, resulting in a distinct platelet population that enters circulation. Tumor- educated immune cells can be modulated and transformed or affected in the presence of disease, including a cancer tumor, and undergone a process of tumor education related to direct and / or indirect contact with tumor cells, and / or their related components and / or their related secretions. The direct or indirect communication between the tumor and immune cells leading to the propagation of the cancer signal can be measured and utilized in the diagnostics or to develop new targeted cancer therapies.

[0278] To dissect the mechanism in more detail, several strategies were utilized to imitate the cross- cell communication which included different cell models, freshly isolated blood components, and freshly isolated fractionated blood components from cancer patients vs. normal blood. EXAMPLE 13 Initial RNAseq Analyses

[0279] RNAseq with different subtypes, such as, comparison analysis of THP1 contacted with MDA-MB-231 (TNBC subtype) vs. THP1 contacted with MCF7 (ER+ subtype) and SK-BR-3 (HER2+) consist of the data with the cell-based models: The first method included the contact studies with the breast cancer cell models and immune cell models. The breast cancer cell models representative of all subtypes (ER+; HER2+, and triple negative (TNBC) were directly and indirectly contacted with the immune cell models (THP1-human monocytic cell model or freshly isolated white blood cells, platelets or PBMCs). First, we devised co-cultivation studies by exposing human monocytic cell line model, THP1, to breast cancer cells. RNAseq and pathway analysis of THP1 cells alone were performed, and in comparison, to post 24 hours contact with breast cancer cells and identified multiple oncogenic pathways significantly upregulated or downregulated in THP1 cells as a result of this exposure.

[0280] To define the mechanism of target cell changes, optionally through tumor-education of immune cells and / or platelets, RNAseq characterizations of isolated immune and cancer cell models prior to and post direct contact with different breast cancer cell lines were performed. Through transcriptomics and pathway analysis we identified the most significantly affected molecular pathways upregulated or downregulated in models of human immune cells such as but not limited to monocytes (THP1; see Table 14). Therefore, in some embodiments the presently disclosed subject matter relates to immune cell transformation occurring upon direct or indirect interaction with cancer cells.

[0281] These findings provide an important insight into the mechanism of this transformation and its potential use in diagnostic as well as potential targeted therapeutic strategies to interfere with or reverse cell-cell communication leading to a modified or tumorigenic phenotype. Collectively, thisAttorney Docket No.3338 / 3 PCT information supported the concept of using target cells as a source of information of the currently disclosed subject matter.

[0282] To define the mechanism of changed in target cells through tumor-education of immune cells, RNAseq characterization isolated immune and cancer cell models prior to and post direct contact with different the breast cancer cell lines. Through transcriptomics and pathway analysis we identified the most significantly affected upstream regulators driving this process of tumor education in models of human immune cells such as but not limited to monocytes (Table 15). The upstream regulators presented in Table YYY represent the significant and shared between three datasets.

[0283] Several small molecule hits inhibiting the increase of the proliferation of THP1 directly contacted with the MCF7 were identified based on a phenotypic assay evaluating cell proliferation indicating the process of tumor education of target cells can be effectively interrupted. Exemplary such small molecules are presented in Figure 37. Table 14 Canonical Pathways Significantly Affected Upon THP1 Contact with Breast Cancer Cell Lines Canonical Pathways THP1+ THP1+ THP1+ THP1+ THP1+ THP1+ SKBR3 SKBR3 MCF7 MCF7 MDA- MDA- ueAttorney Docket No.3338 / 3 PCT Integrin Signaling 4.32 0.03 4.24 0.00 5.15 0.00 Wound Healing Signaling Pathway 4.99 0.00 3.00 0.00 5.09 0.00Attorney Docket No.3338 / 3 PCT Regulation of Actin-based Motility by Rho 1.90 0.05 2.65 0.04 2.31 0.01Shared Upstream Regulators from Comparison Analysis of the Upstream Regulators in THP1 Contacted with MCF7 vs. THP1 contacted with MDA-231, vs. THP1 contacted with SKBR3 Upstream Regulators THP1+ SKBR3 THP1+ MCF7 THP1+ MDA-231 Z score p value Z score p value Z score p valueAttorney Docket No.3338 / 3 PCT IL1B 5.75 0.00 2.23 0.00 5.30 0.00 RELA 4.17 0.00 3.38 0.00 5.27 0.00Attorney Docket No.3338 / 3 PCT PPRC1 4.62 0.00 2.14 0.00 3.88 0.00 CHD1 3.87 0.00 2.00 0.01 3.87 0.00Attorney Docket No.3338 / 3 PCT TNFSF14 2.99 0.00 1.36 0.00 3.03 0.00 IL4 2.43 0.00 1.67 0.00 3.02 0.00Attorney Docket No.3338 / 3 PCT SMARCA2 1.91 0.01 1.30 0.01 2.18 0.01 Growth hormone 1.80 0.00 1.61 0.00 2.11 0.00Attorney Docket No.3338 / 3 PCT SALL4 -2.36 0.00 -1.50 0.00 -2.11 0.00 NUMB -1.98 0.00 -2.20 0.00 -2.21 0.00Attorney Docket No.3338 / 3 PCT CBX5 -4.70 0.00 -4.85 0.00 -5.28 0.00 GLI1 -4.83 0.00 -2.80 0.00 -5.66 0.00Ingenuity Pathway Analysis Comparing Differential Gene Expression in Cancer Platelets vs. Normal Platelets

[0284] The second method included the analysis of the gene expression changes in isolated white blood cells from cancer patients compared to normal isolated WBC or in isolated cancer platelets compared to normal platelets using RNAseq analysis.

[0285] The overall methodology for the clinical research study is outlined below:

[0286] To define the mechanism of target cell changes, through tumor-education of immune cells and / or platelets, RNAseq characterization of isolated immune cells from cancer patients including white blood cells or platelets and compared to the immune cells of the non-cancer samples was performed. Through transcriptomics and pathway analysis we identified the most significantly affected molecular pathways upregulated or downregulated in human immune cells (white blood cells or platelets). Therefore, in some embodiments the presently disclosed subject matter relates to immune cell transformation occurring in cancer patients as a result of oncogenic transformation of the immune cells driven by the direct or indirect contact with oncogenic cells or factors.

[0287] To define the mechanism tumor-education of platelets, RNAseq characterization of isolated platelets from cancer patients and compared to the profiles of isolated non-cancer platelets were performed. Through transcriptomics and pathway analysis we identified the most significantly affected molecular pathways upregulated or downregulated in human platelets (Table 16). The significant upstream regulators driving this transformation in this dataset are shown in Tables 17 and 18. Table 16 Canonical Pathways with Categories Significantly Changed in Cancer Platelets vs. Normal Ingenuity Canonical Pathways z-score p valueAttorney Docket No.3338 / 3 PCT Role of Hypercytokinemia / hyperchemokinemia in the Pathogenesis of Influenza 1.63 0.00Significant Regulators Changed in the Cancer Platelets vs. Normal Platelets Symbol Expr Fold Expr p- Drugs affecting the regulators Change valueSignificant Upstream Regulators in the Dataset Potentially Driving Tumor-education of Platelets in Cancer Patients p-value Upstream Activation ,Attorney Docket No.3338 / 3 PCT IRF4 -1.52 0.01 CXCL10,GBP1,ICA1,IFNG,STAT2 ELF3-AS1 0.00 0.01 H2AC25,H2BC17,H4C14,H4C3 L , 9, E 1, s canbe targeted with the small molecules inhibitors which will lead to the reversal of this process.

[0289] Furthermore, master regulators and causal networks involved in the process of tumor- education of platelets were identified (see Table 19). Table 19 Casual Networks Affected upon Tumor Education in Cancer Platelets vs. Normal Platelets Master Activation p-value Participating regulatorsAttorney Docket No.3338 / 3 PCT RIGI 2.84 0.00 AKT (family), NFKB (complex), RELA, RIGI, STAT1 BCL10 2.53 0.00 AKT (family), BCL10, CASP8, MAP3K14, NFKB (complex) , , , ,Attorney Docket No.3338 / 3 PCT TASL 1.89 0.00 CASP8, IRF5, STAT1, TASL CGAS 1.89 0.01 CGAS, IFN TYPE 1 (family), IRF3, STAT1 ,Attorney Docket No.3338 / 3 PCT IRF9 1.41 0.01 IRF9 TLR6 1.41 0.02 TLR6 4 , , ,Attorney Docket No.3338 / 3 PCT ADAR -1.81 0.01 ADAR, CASP8, EIF2AK2, FOS, IRF3, JUN, NFKB (complex), STAT1, STAT3 Kn but not limited to the ones shown in Table 20. Table 20 Diseases or Functions Identified in the Cancer Platelets vs. Normal Dataset Categories Diseases or Functions Activation z- p-value Annotation scoreAttorney Docket No.3338 / 3 PCT Free Radical Scavenging Synthesis of reactive oxygen species2.17 0.01Ingenuity Pathway Analysis (IPA) Comparing Cancer White Blood Cell (WBC) Samples vs. Normal WBC Samples

[0291] To define the mechanism tumor-education of white blood cells, RNAseq characterization of isolated white blood cells from cancer patients and compared to the profiles of isolated non-cancer WBC was performed. Through transcriptomics and pathway analysis, the most significantly affected molecular pathways upregulated or downregulated in human WBC were identified (see Table 21).Attorney Docket No.3338 / 3 PCT Table 21 Canonical Pathways Significantly Changed in Cancer WBC vs. Normal WBC Ingenuity Canonical Pathways z-score p-value Signaling Pathway Category Expression and translocation of 12.69 0.00 Olfactory Signaling Pathway ar ar ns s ;Attorney Docket No.3338 / 3 PCT IL-6 Signaling -1.73 0.05 Cellular Immune Response; Cytokine Signaling s ar d sse upsream reguaors rvng e mecansm o umor e uca on o e n cancer are listed in Table 22. The key regulators with the most significance are RNA polymerase II, TRAF3IP2, PTPRR, MIAT, NR3C1. Table 22 Upstream Regulators Affected in Cancer WBC vs. Normal WBC Upstream Regulator Activation z- p-value of overlapAttorney Docket No.3338 / 3 PCT CD3E 2.67 0.01 PPP2R2A 2.65 0.01Attorney Docket No.3338 / 3 PCT FOXO4 -2.61 0.01 GROWTH HORMONE (family) -2.61 1.00

[0293] Thite blood cells in cancer patients are presented in Table 23. Table 23 Master Regulators and Casual Networks Significantly Changed in Cancer WBC vs. Normal WBC Master Regulator Activation z-score p-value of overlap PTPRR 5.48 0.00Attorney Docket No.3338 / 3 PCT CD151 1.82 0.02 ETV3 1.81 0.03Attorney Docket No.3338 / 3 PCT ITGA11 1.41 0.01 S100A11 1.41 0.02Attorney Docket No.3338 / 3 PCT HSPH1 -1.41 0.03 CYTOKINE (family) -1.41 0.03Attorney Docket No.3338 / 3 PCT GLI1 -2.22 0.00 ITGAV -2.24 0.01Comparison of Cell-based Models and Clinical Datasets

[0294] The comparison analyses between the affected pathways in clinical samples vs. cell models described above (e.g., gene expression, pathway analysis and upstream regulators) was performed to link the mechanisms between the cell-based models and the clinical datasets.

[0295] Furthermore, to link the transcriptomics, gene expression and Ingenuity pathway analysis between the data in cell models to the clinical data, we have performed comparison analysis between the THP1 exposed to MDA-2341 and cancer WBC vs normal WBC.

[0296] The analysis of the canonical pathways in THP1 contacted with MDA-231 compared to Cancer WBC vs. normal is presented in Table 24. A comparison analysis of the upstream regulators between cancer WBC vs. normal WBC vs. THP1 contacted with MDA-231 vs. THP1 alone is presented in Table 25. Table 24 Canonical Pathway Analysis Comparing Cancer WBC vs. Normal and THP1 Contacted with MDA-231 vs. THP1 Alone Canonical Pathways Cancer WBC THP1+MDA- Signaling Pathway CategoriesAttorney Docket No.3338 / 3 PCT G-Protein Coupled Receptor -1.42 0.06 5.08 0.00 Intracellular and Second Messenger Signaling Signaling g t; dComparison Analysis of the Upstream Regulators Between Cancer WBC vs. Normal WBC vs. THP1 Contacted with MDA-231 vs. THP1 Alone Upstream Regulators CW vs. NW THP1+MDA-231 eAttorney Docket No.3338 / 3 PCT ZNF281 1.41 0.02 2.65 0.00 MACROH2A1 1.40 0.05 -1.34 0.00

[0297] Next,d cancer platelets vs. normal platelets was performed. The results are summarized in Table 26. Table 26 Comparison Analysis of the Canonical Pathways Between Cancer Platelets / Normal Platelets vs. THP1 contacted with MDA-231 vs THP1 Alone Canonical Pathways Cancer Platelets THP1+MDA 231 Signaling Pathway Categories: v n rm l sAttorney Docket No.3338 / 3 PCT Pyroptosis Signaling 1.63 0.00 3.30 0.01 Apoptosis; Cellular Stress and Pathway Injury; Pathogen-Influenced Signaling - ty eplatelets / normal platelets vs. THP1 contacted with MDA-231 vs. THP1 alone was also was performed. The results are summarized in Table 27. Table 27 Comparison Analysis of the Upstream Regulators Between Cancer Platelets / Normal Platelets vs. THP1 Contacted with MDA-231 vs. THP1 Alone Upstream Cancer Platelets vs THP1+MDA 231 R l t l l t l tAttorney Docket No.3338 / 3 PCT

[0299] Comparison analysis of the casual networks between cancer platelets / normal platelets vs. thp1 contacted with mda-231 vs. thp1 alone was also was performed. The results are summarized in Table 28. Table 28 Comparison Analysis of the Casual Networks Between Cancer Platelets / Normal Platelets vs. THP1 Contacted with MDA-231 vs. THP1 Alone Cancer Platelets vs Causal THP1+MDA 231 normal platelets eMater a s and Met ods or EXAMPLES 17-19

[0300] Study design. Identify Breast Cancer (IDBC, United States Clinical Trials Study ID: NCT04495244) is a multi-site, observational clinical study in women undergoing screening. For this analysis, 1396 participants from sites in Calgary (University of Calgary, Foothills Hospital Women’s Health Centre, Calgary, Alberta, Canada), Manchester (Manchester University NHS Foundation Trust, United Kingdom) and Oklahoma City (Mercy Health, Oklahoma, United States of America were recruited between May 2018 and October 2021. Exclusion criteria included breast surgery within the past year or a current or previous cancer diagnosis except non-melanoma skin cancer. The study was approved by the Institutional Review Board or an independent ethics committee at each participating site and informed consent was obtained for all clinical study participants. The study was conducted in accordance with the International Conference on Harmonization for Good Clinical Practice guidelines.Attorney Docket No.3338 / 3 PCT IDBC clinical study samples were divided into three pre-specified groups: (a) discovery, (b) training and validation with selected and optimized assay and classifiers, and (c) independent dataset for establishing clinical performance metrics (Figure 38).

[0301] Participants. Women aged 25 to 80 years with a normal screening mammogram or physical exam, or a Breast Imaging Reporting & Data System (BI-RADS; American College of Radiology) assessment category of 3-5 from breast imaging, were recruited. After consent, participants completed an intake questionnaire (demographics, medical history) and provided a 2.5 mL blood sample collected in a PAXgene Blood RNA Tube (BD). Participants continued with standard of care, which included imaging (mammogram, ultrasound and / or MRI) and other diagnostic procedures (biopsy and pathology) when indicated. PPV for imaging-based detection of breast cancer was calculated for this cohort (American College of Radiology). True Positives (TP) were defined as breast cancers confirmed by pathology (from biopsy or surgery) within 365 days of the blood draw. Participants with a benign (negative) biopsy or absence of a breast cancer diagnosis for up to 365 days of the blood draw were classified as not having breast cancer (True Negatives-TN). False Positives (FP) are those with a negative biopsy result. PPV (positive biopsy rate, PBR) was defined as TP / (TP+FP for those who received a biopsy).

[0302] Study objectives and measurement of test performance. The primary objective of the study was to evaluate clinical performance of the Syantra DXTMBreast Cancer test (hereinafter “the blood test”) in an independent and blinded sample cohort. For this objective, clinical samples were evaluated using the blood test and a result was generated (signal positive or signal negative). False Negatives (FN) are those with a negative result from the blood test, but with a biopsy-confirmed breast cancer. Clinical sensitivity of the test was defined as TP / (TP+FN) and specificity as TN / (TN+FP). Negative Predictive Value was defined as TN / (TN+FN).

[0303] Clinical Data Collection. Recruitment sites collected information on participant medical history and screening or diagnostic procedures. For participants with a breast cancer diagnosis, surgical and pathological procedures as well as treatments were recorded.

[0304] Sample collection, processing, and analysis. Samples and intake questionnaires from clinical study sites were transferred to the Alberta Cancer Research Biobank (ACRB; Alberta Health Services, Calgary, Alberta, Canada) for blinding of diagnosis, relabeling, databasing and archiving in preparation 168 for molecular processing. The clinical study database was maintained at Cytel Inc.

[0305] Blinded samples were then sent to Syantra Inc. (Calgary, AB, Canada) for extraction, quantification, and normalization of nucleic acids. Upon completion, normalized samples were sent back to the ACRB for aliquoting, randomization, blinding and storage. For evaluation of test performance in an independent cohort, ACRB provided blinded, randomized samples for the Syantra DXTMBreast Cancer test to be run. Upon completion, outcomes were reported to the ACRB for correlation to participant status.Attorney Docket No.3338 / 3 PCT

[0306] Breast cancer detection blood test. Whole blood samples were processed by trained medical laboratory personnel under a certified quality management system (ISO 13485:2016). Processing included extraction of nucleic acids with the MAGMAXTMfor Stabilized Blood Tubes RNA Isolation Kit, compatible with PAXGENETMBlood RNA Tubes (ThermoFisher Scientific), quantification using the QUANT-ITTMRIBOGREEN® Assay Kit (ThermoFisher Scientific), normalization, and a customized two-step qPCR process. This process includes the use of custom PCR plates to determine transcript-level gene expression profiles of 12 biomarkers based on amplification profiles of validated nucleic acid controls. Quality controls were evaluated during analytical steps of the process as documented in the assay’s Individualized Quality Control Plan.

[0307] Classification of breast cancer signal. Custom analysis software was designed and developed to classify samples using source training models that recognized patterns of gene expression indicative of the presence or absence of the breast cancer signal. Raw gene expression data from the PCR process, age, menopausal and smoking status of the patient were used as inputs into data analysis packages. The classifiers were trained on 349 samples (119 signal detected and 230 signal not detected) using a combination of machine learning-based algorithms. The final classifier model was trained to target at least 85% specificity for acceptance. The locked and version-controlled software was used for blinded analyses to establish clinical performance metrics of the test.

[0308] Statistical analysis. Descriptive statistics were used to report demographics and baseline characteristics, with the number and percentage of participants in each category calculated and reported for categorical variables. For continuous variables, the total number of participants (n), the mean, lower and upper confidence intervals were calculated. The 95% confidence intervals (CI) for clinical test performance metrics (sensitivity, specificity, and accuracy) were computed using an exact binomial test (α = 0.05) unless otherwise stated. EXAMPLE 17 Participant Disposition

[0309] A total of 1396 participants were recruited resulting in 275 participants being diagnosed with breast cancer and 1121 participants with no breast cancer diagnosis. Of these, 1325 were evaluable with 71 failing quality control and excluded from the study (Figure 38). The most common reasons for exclusion were insufficient RNA amounts (n = 47), low quality of RNA (n = 16) and study inclusion criteria failures (n = 5). No serious adverse events related to sample collection were reported. Participant recruitment according to clinical study sites is presented in Table 29.Attorney Docket No.3338 / 3 PCT Table 29 Clinical Demographics Cancer AllCharacteristicNo Cancer(n=590)(n=97) (n=687) Age (y) Age range (mean) 24-81 (54) 36-75 (60) 24-81 (55) <50 n (%) 190 (32.3) 13 (13.4) 203 (29.6) ≥50 n (%) 399 (67.7) 84 (86.6) 483 (70.4) Missing n (%) 1 0 1 Hormone Replacement Therapy (HRT) Current, n (%) 84 (14.5) 7 (7.4) 91 (13.5) In Past, n (%) 83 (14.3) 22 (23.2) 105 (15.6) Never, n (%) 413 (71.2) 66 (69.5) 479 (71.0) Missing, n (%) 10 (1.7) 2 (2.1) 12 (1.7) Hormone Birth Control (HBC) Current, n (%) 33 (6.1) 3 (3.4) 36 (5.7) In Past, n (%) 408 (75.4) 63 (72.4) 471 (75) Never, n (%) 100 (18.5) 21 (24.1) 121 (19.3) Missing, n (%) 541 (49) 87 (10) 628 (60) Smoking Status Current, n (%) 42 (7.2) 20 (20.6) 62 (9.1) In Past, n (%) 170 (29) 36 (37.1) 206 (30.1) Never, n (%) 375 (63.9) 41 (42.3) 416 (60.8) Missing, n (%) 587 (3) 97 (0) 684 (3) Ethnicity Caucasian, n (%) 529 (90.0) 88 (90.7) 617 (89.8) Black, n (%) 5 (0.8) 2 (2.1) 7 (1) Indigenous / Native American, n (%) 5 (0.8) 1 (1) 6 (0.9) Asian, n (%) 36 (6.1) 5 (5.2) 41 (6.0) Other, n (%) 13 (2.2) 0 (0) 13 (1.9) Missing, n (%) 2 (0.3) 1 (1.0) 3 (0.4) Breast density Almost entirely fat (A), n (%) 56 (9.5) 25 (25.8) 81 (11.8) Scattered fibroglandular densities (B), n (%) 188 (31.9) 46 (47.4) 234 (34.0) Heterogeneously dense (C), n (%) 198 (33.6) 15 (15.5) 213 (31) Extremely dense (D), n (%) 109 (18.5) 10 (10.3) 119 (17.3)Attorney Docket No.3338 / 3 PCT Missing, n (%) 51 (8.6) 1 (1.0) 40 (5.8) BI-RADS Scoring BI-RADS 1, n (%) 120 (20.3) 0 120 (17.5) BI-RADS 2, n (%) 175 (29.7) 0 175 (25.5) BI-RADS 3, n (%) 41 (6.9) 4 (4.1) 45 (6.6) BI-RADS 4, n (%) 147 (24.9) 31 (32) 178 (25.9) BI-RADS 5, n (%) 8 (1.4) 56 (57.7) 64 (9.3) Missing, n (%) 99 (16.8) 6 (6.2) 105 (15.3) Biopsies 199 (33.7) 97 (100)1296 (43.0) Cancer Subtype IDC, n (%) 77 (79.4) ILC, n (%) 8 (8.2) Other, n (%) 6 (6.2) Luminal A, n (%) 75 (77.3) Luminal B, n (%) 10 (10.3) TNBC, n (%) 6 (6.2) HER2, n (%) 2 (2.1) Missing, n (%) 4 (4.1)1Four cancers were detected at surgery and not at biopsy.

[0310] Of the 1325 evaluable samples, 289 were used for target biomarker optimization that involved transcriptomic analyses and correlation of the biomarker signature to breast cancer risk factors. A set of 349 samples, consisting of 119 breast cancer and 230 non breast cancer samples, were used for training and tuning of classification algorithms for identifying the presence and absence of the active signal. A locked version of the test incorporating models for classification of the breast cancer signal status for tested samples was established upon successful completion of design and development activities as described in the certified quality management system. Next, applicable analytical performance characteristics and quality control metrics for the test were established using guidance from the Clinical Laboratory Improvement Amendments (CLIA), Clinical and Laboratory Standards Institute (CLSI), the International Organization for Standardization (ISO), and the United States Food and Drug Administration (US FDA), prior to evaluating clinical performance. EXAMPLE 18 Blinded Analysis Results

[0311] The blood test was evaluated in the independent blinded test set of blood samples from 687 women ages 24 to 81 (Figure 39A). Table 30 provides a summary of participant demographics. The population was predominantly Caucasian (90.2%). There were 296 participants with a biopsy (43%)Attorney Docket No.3338 / 3 PCT and the invasive breast cancer rate was 14.2% (142 breast cancers per 1000). There were 97 breast cancers in the 296 participants receiving a biopsy, resulting in a PPV of 33% for breast imaging. Breast cancers were predominantly invasive ductal carcinomas (IDC; 86%), with 5.3% invasive lobular carcinomas (ILC) and remainder mixed; 80.6% of the breast cancers were Luminal A phenotype (ER+ / PR+ / HER2-), 10.8% Luminal B (ER+ / PR+ / HER2+), 6.5% TNBC (ER- / PR- / HER2-) and 2.1% HER2+ / ER- / PR-. For subjects with invasive breast cancer, the median tumor size was 14 mm. Table 30 Clinical Study Site Participants Site No Biopsy Benign No Cancer Cancer All Calgary (Canada) 223 (56.9) 172 (86.9) 395 (66.9) 43 (44.3) 439 (63.8) Manchester (UK) 36 (9.2) 17 (8.6) 53 (9) 52 (53.6) 105 (15.3) Oklahoma City (USA) 133 (33.9) 9 (4.5) 142 (24.1) 2 (2.1) 144 (20.9)

[0312] The blood test was able to identify women with no breast cancer diagnosis with a specificity of 94.2% (92.0 – 96.0%, 95% CI) and those with breast cancer with a sensitivity of 78.4% (68.8% – 86.1%, 95% CI) (Figure 39B). Of the 110 women receiving a positive blood test result, 76 women received a pathology result positive for breast cancer and 34 participants were classified as false positive (69.1% PPV). The 34 false positive participants included 23 participants who were positive by imaging and had a negative biopsy result and 11 participants with negative imaging and no biopsy. The blood test returned a negative result for 577 participants. This included 376 participants who did not have positive imaging and did not have a biopsy (assumed no breast cancer) and 180 participants who received a negative biopsy. There were 21 classified as false negative including 2 participants with 2mm tumors originally classified as DCIS at biopsy. The overall NPV was equal to 96.4%. Taken together, these results established performance characteristics for the blood test that were similar or superior to current standard of care for breast cancer detection. EXAMPLE 19 Sub-group Analysis of Blood Test Results

[0313] Study participants under age 50 (n = 203; Figure 39C) showed a specificity of 98.9 (CI: 96.2% – 99.9%) and a sensitivity of 84.6% (CI: 54.6% – 98.1%). For participants aged 50 and over (n = 483), the specificity was 92.0 (CI: 88.9% – 94.4%) and a sensitivity of 77.4% 62 (CI: 67.0% – 85.8%). Similar results were found based on menopausal status (Figure 39D), with specificity for pre-menopausal participants being 96.3% (N = 217, CI: 92.9% – 98.4%) and sensitivity of 87.5% (N = 24, CI: 67.6 - 97.3%) compared to post-menopausal women (n = 437) with a specificity of 92.9% (CI: 89.7% – 95.3%) and a sensitivity of 75.0% (CI: 63.4% – 84.5%).Attorney Docket No.3338 / 3 PCT

[0314] In terms of tumor size, small tumors under 10 mm were detected with a sensitivity of 71.4% (see Table 31). Sensitivity for tumors between 10 mm and 20 mm was 76.3%, between 20 mm and 50 mm was 85.7%, and for tumors greater than 50 mm, the sensitivity was 100%. Table 31 Sensitivities Based on Tumor Size Tumor Size n Sensitivity 95% CI Lower Limit 95% CI Upper Limit ≤10 mm 28 71.4% 51.3% 86.8% >10 mm and ≤20 mm 38 76.3% 59.8% 88.6% >20 mm and ≤50 mm 21 85.7% 63.7% 97.0% >50 mm 2 100.00% 15.8% 100.0%

[0315] As shown in Figure 39E, the blood test had a specificity of 93.9% (CI: 88.0% – 96.9%) for women with less-dense tissue (densities A and B; n = 244) and 94.1% (CI: 89.1% – 96.9%) for those presenting with dense or extremely dense tissue (densities C and D; n = 307). Sensitivity for women with densities A and B (n = 71) was 77.5% (CI: 61.2% – 88.2%) and 80.0% (CI: 51.9% – 93.7%) for the density C and D participants (n = 25). For women with extremely dense breast tissue (D density), a sensitivity of 90.0% and specificity of 95.4% were obtained. Further, test performance correlated well with imaging-based BIRADS categories (1-5; Figure 39F), with no statistical difference between categories.

[0316] Smoking status of participants was found to potentially influence test performance. Specificities of 96.3%, 94.2% and 76.7% were obtained for non- (n = 377), former (n = 171), and current (n = 43) smokers, respectively. Conversely, more current smokers (n = 20) who had breast cancer were detected by the test (sensitivity, 90%) compared to non- (n = 42) or former (n = 36) smokers who had sensitivities of 76.2% and 72.2%, respectively. Discussion of the EXAMPLES

[0317] Provided herein is information related to the effects of cancer cells on leukocytes to exemplify the types of alterations that can be induced by tumor education when different cells types, including a model leukocyte are in direct and indirect contact. Importantly, these alterations are consistent with those observed in clinical samples. This indicates that the in vitro system is representative of processes occurring in vivo and provides a relevant methodology to focus on important molecular steps associated with tumor education and other co-culture effects. The in vitro model also provides an environment to manipulate pathways with inhibitors and identify / evaluate key mediators and regulatory steps that may participate in tumor education processes.

[0318] Descriptions of user needs, input and output specifications, verification, and validations of the three software components that constitute an exemplary software system of the presently disclosedAttorney Docket No.3338 / 3 PCT subject matter are presented herein. A process flowchart of the exemplary breast cancer test is also presented highlighting key decision points in the analytical testing phase. A process map of each data analysis software package is presented highlighting key steps that were considered for design and development. The software architecture and sequence of each data analysis software is also provided.

[0319] A procedure describing how a physician would use the test in subjects being screened or investigated for the presence of breast cancer is provided. The process can add efficiency to the existing imaging-based methods of patient management and can encourage additional participation in testing from groups that are not well served by the existing standard of care, including younger women and women with dense breast tissue.

[0320] Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the preceding illustrative EXAMPLES, make and utilize the compounds of the presently disclosed subject matter and practice the methods of the presently disclosed subject matter. The preceding EXAMPLES therefore particularly point out embodiments of the presently disclosed subject matter and are not to be construed as limiting in any way the remainder of the disclosure.

[0321] Early and effective detection of breast cancer has been demonstrated to positively impact patient health outcomes (see e.g., Nazari & Mukherjee (2018) An overview of mammographic density and its association with breast cancer. Breast Cancer 25:259-267; Ginsburg et al. (2020) Breast Cancer Early Detection: A Phased Approach to Implementation. Cancer 126:2379-2393; Wei et al. (2021) Early Breast Cancer Detection Using Untargeted and Targeted 391 Metabolomics. J Proteome Res 20:3133). The last few decades have seen significant advancements in breast cancer detection such as innovations in breast imaging and localization approaches; implementation of genetic, genomic, and proteomic profiles related to disease development and progression; and integration of machine learning, artificial intelligence and deep learning models for image analysis and interpretation of genetic information (Nazari & Mukherjee (2018) An overview of mammographic density and its association with breast cancer. Breast Cancer 25:259-267; Chen & Zhao (2019) Next-generation sequencing in liquid biopsy: cancer screening and early detection. Hum Genomics 13(1): 34; Lotter et al. (2021) Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation- efficient deep learning approach. Nat Med 27:244-249; Barrios (2022) Global challenges in breast cancer detection and treatment. Breast 62:S3-S6). However, there are still several unmet needs posed by the current standards of care for breast screening such as decreased sensitivity in detecting early invasive breast cancer in younger women and in women with dense breast tissue and limitations in screening program access and participation (Smith (2003) An Overview of Mammography: Benefits and Limitations. Journal of the National Comprehensive Cancer Network. J Natl Compr Canc Netw 1(2):264-271; Løberg et al. (2015) Benefits and harms of mammography screening. Breast Cancer Res 17(1):63; Nazari & Mukherjee (2018) An overview of mammographic density and its association with breast cancer. Breast Cancer 25:259-267).Attorney Docket No.3338 / 3 PCT

[0322] A blood test has the potential to address gaps to increase early detection. Establishment of clinical assay performance characteristics is a key determinant to how best the test would fit into the patient clinical care pathway. For a screening test, it is desired to have a high specificity, reducing the number of follow-up procedures for those negative for cancer. However, it is important to also have high sensitivity to decrease the rate of missed cancers. For a test used as part of breast screening, the ability to detect tumors when they are small (<20mm) is critical as this early stage is associated with 99% five-year survival rates.

[0323] Recent advances in molecular techniques and computational analyses have facilitated the development and demonstration of liquid biopsies for breast cancer detection including Galleri (Grail) and Shield (Freenome). Though promising with high specificity levels, these tests have not demonstrated detection of breast cancer at small tumor sizes and / or early stages needed for screening applications. This is potentially due to the dependence on circulating tumor DNA (ctDNA) in plasma, which is very low in breast cancer at early stages (Venetis et al. (2023) Liquid biopsy: Cell-free DNA based analysis in breast cancer. The Journal of Liquid Biopsy 1:100002; Arko-Boham et al. (2019) Circulating cell-free DNA integrity as a diagnostic and prognostic biomarker for breast and prostate cancers. Cancer Genet 235-236:65-71; Vlataki et al. Circulating Tumor DNA in the Management of Early-Stage Breast Cancer. Cells 12(12):1573. While an overall clinical sensitivity of 30.5% (26.7- 34.6%) was obtained for Grail's targeted methylation-based, ctDNA multi-cancer early breast cancer detection test, stage I sensitivity was 2.6%28. A different approach is based on circulating tumor cells (CTC) as in the TruCheck test (Datar Genetics). Early results were reported on 141 participants showing detection across stages with a specificity of 93.1% and sensitivity of 94.64% (Crook et al. (2022) Accurate Screening for Early-Stage Breast Cancer by Detection and Profiling of Circulating Tumor Cells. Cancers (Basel) 14(14):3341. However, further details on the cohort and study design are needed to provide context for detailed interpretation of these results.

[0324] The clinical performance of a new blood test platform for early cancer detection based on a panel of RNA transcripts, custom qPCR assays, and specially developed software informed by machine learning is disclosed herein. This test uses whole blood instead of blood fractions and uses a proprietary panel of RNA transcripts selected to enable detection of early-stage breast cancer. This non- randomized, blinded, non-interventional, longitudinal, and multi-center clinical study (NCT04495244) was aimed at evaluating clinical performance metrics of the test in a population of women aged 25 - 80 years as part of regular breast screening. Results revealed an overall clinical accuracy of 92.2% in the independent test set that consisted of 68% of cancers 20mm or below in size (T1 stage). The test had an overall specificity of 94.3%, a sensitivity of 79.2%, and was importantly able to detect small tumors (≤10mm) with a sensitivity of 71.4%. Test sensitivity and specificity was highest for a sub-population of younger 333 women (under 50 and / or premenopausal) and women with extremely dense tissue (D density), two groups currently underserved by mammography-based standard of care. Limitations ofAttorney Docket No.3338 / 3 PCT the study included the need for higher participant numbers to perform more representative clinical evaluations in the different sub-populations and low ethnic diversity. Recognizing these limitations, the test performance within the 95% confidence intervals demonstrates the test performed well at both identifying those negative for breast cancer as well as those diagnosed with the disease for this cohort. Important context for the performance results of the test are the characteristics of the cohort. This cohort of women provided a blood sample during their screening and diagnostic journeys. For the women who had a positive image (defined as BIRADS category 3-5) and subsequent biopsy, 67% did not have breast cancer (PPV 33%). This agrees with previously reported results indicating 70-80% of biopsies are negative for breast cancer Ghosh et al. (2005) Breast biopsy utilization: a population-based study. Arch Intern Med 165(14):1593-1598. However, for the same group of women, almost 70% of the positive results from the blood test were confirmed breast cancer (PPV 69%). This indicates potential for the blood test to improve the breast screening and diagnosis pathway, possibly leading to fewer negative biopsies. Further, 96.2% of negative test results obtained from use of our blood test were confirmed as negative indicating the blood test performed well at detecting participants negative for the presence of breast cancer. These results indicate an ability to detect small tumors and early-stage cancers with high performance, while also identifying those negative for breast cancer, supporting a role in the breast screening care pathway. This is particularly true for younger women and women with dense tissue who can be challenging to detect early. Young women are an important underserved population due to the increased prevalence of dense tissue, decreased performance of mammography, decreased coverage of screening, and increased percentage of aggressive cancers (for review: Stibbards-Lyle (2024) Status of breast cancer detection in young women and potential of liquid biopsy. Front Oncol 14:1398196). Women under age 50 represent almost 20% of breast cancer diagnoses (Xu et al. (2024) Breast Cancer Incidence Among US Women Aged 20 to 49 Years by Race, Stage, and Hormone Receptor Status. in JAMA Network Open, Vol.7: E2353331; Ademuyiwa et al. (2015) Managing breast cancer in younger women: Challenges and solutions. Breast Cancer: Targets and Therapy 8:1-12; Heer et al. (2020) Global burden and trends in premenopausal and postmenopausal breast cancer: a population-based study. Lancet Glob Health 8:e1027-e1037; Xu et al. (2024) Breast Cancer Incidence Among US Women Aged 20 to 49 Years by Race, Stage, and Hormone Receptor Status. JAMA Network Open, Vol.7:E2353331.

[0325] A blood test for breast cancer detection would be valuable for women who cannot undergo diagnostic mammography and where mammography is not widely available. In countries where there is limited access to mammography, a blood test could identify women to prioritize for triple assessment (that is, history / examination, imaging and histology) as part of the breast cancer screening and diagnosis workflow. With highest performance in women under 50 and women with extremely dense breast tissue, this blood test may provide a supplemental technology to enable significant advances in earlier detection in these women.Attorney Docket No.3338 / 3 PCT

[0326] Specificity and sensitivity tend to have an inverse correlation where maximizing one leads to a decrease in the other. The balance is to have a screening test that is efficient at identifying those likely not to have cancer, while still catching the low number of individuals positive for cancer at an early stage. Future studies are needed to further evaluate the effect of this blood test on patient outcomes and to investigate the most efficient and effective clinical implementation pathways for the test.

[0327] The RNA-based whole blood test evaluated in this study showed a sensitivity for tumors 20mm and below (T1 stage) of 76.3%, which is much higher than other liquid biopsies or blood tests and demonstrates potential for the test to detect breast cancer at a stage where is it most treatable with 99% 5-year survival rates. Further, sensitivity in tumors 10mm or less was 71%. No other blood tests for breast cancer early detection have reported detection of tumors 10mm or smaller. These results demonstrate that our novel blood test platform detects early breast cancer with high sensitivity and specificity rates, including in populations underserved by current screening programs. The blood test has great potential for enabling more women to receive early-stage diagnosis leading to improved outcomes.

[0328] Summarily, the presently disclosed subject matter relates in some embodiments to the identification and use of target cells in the blood circulation that carry gene expression profiles that are associated with diseases, disorder, and / or conditions. The presently disclosed subject matter covers in some embodiments cells that express signatures associated with diseases, disorders, and / or conditions, as well as those that have acquired the signatures by way of active processes, such as tumor education. The presently disclosed subject matter describes in some embodiments how to use target cell disease signatures to create testing systems for detecting the presence of numerous cancers, especially at early stage, with breast cancer being a detailed example. Testing systems include assay designs, biomarkers, software, machine learning algorithms and controls necessary to identify the presence or absence of signals associated with diseases, disorders, and / or conditions. Analytical requirements for validating test results are also included.

[0329] The presently disclosed subject matter further relates in some embodiments to the molecular pathways by which disease-related gene expression profiles (e.g., biomarkers) arise in target cells, and a class of small molecule drugs that modulate those changes in gene expression. In this way, both a new therapeutic route and the chemical scaffold(s) for achieving new drugs are provided for intervening in diseases such as cancer. The presently disclosed subject matter also describes in some embodiments ways to determine whether the new therapeutics are working, via the testing systems.

[0330] Taken together, the presently disclosed subject matter captures a novel, complete cycle for identifying diseases, disorders, and / or conditions such as but not limited to cancer early, facilitating intervention with new drugs, and assuring effective treatment. This fills several important gaps in current diagnostic and therapeutic practice and provides a robust technology platform for further innovation.Attorney Docket No.3338 / 3 PCT REFERENCES

[0331] All references listed in the instant disclosure, including but not limited to all patents, patent applications and publications thereof, scientific journal articles, and database entries (including but not limited to UniProt, EMBL, and GENBANK® biosequence database entries and including all annotations available therein) are incorporated herein by reference in their entireties to the extent that they supplement, explain, provide a background for, and / or teach methodology, techniques, and / or compositions employed herein. The discussion of the references is intended merely to summarize the assertions made by their authors. No admission is made that any reference (or a portion of any reference) is relevant prior art. Applicants reserve the right to challenge the accuracy and pertinence of any cited reference.

[0332] Bustin et al. (2009) The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clinical Chemistry 55(4):611-622.

[0333] CLSI / NCCLS (2003) Evaluation of the linearity of quantitative measurement procedures: a statistical approach. Approved guideline. CLSI document EP6-A. Clinical and Laboratory Standards Institute, Wayne, Pennsylvania, United States of America.

[0334] United States Food and Drug Administration. (2007) Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests. Guidance for Industry and FDA Staff.

[0335] It will be understood that various details of the presently disclosed subject matter may be changed without departing from the scope of the presently disclosed subject matter. Furthermore, the foregoing description is for the purpose of illustration only, and not for the purpose of limitation.

Claims

Attorney Docket No.3338 / 3 PCT CLAIMS What is claimed is:

1. A method comprising: obtaining, via a first processor of a reverse transcriptase polymerase chain reaction / quantitative polymerase chain reaction (RT-PCR / qPCR) data analysis module, patient sample data comprising negative patient samples and positive patient samples, the negative patient samples being associated with individuals with no known diagnosis with respect to a disease, disorder, or condition of interest, optionally a tumor or a cancer, and the positive patient samples being associated with individuals with positive diagnosis with respect to the disease, disorder, or condition of interest, optionally the tumor or the cancer and comprising one or more biomarkers from target cells as defined herein, optionally tumor educated cells, further optionally leukocytes; training, via the first processor, a one or more machine learning algorithms (MLAs) based on the negative patient samples, the positive patient samples, and environmental factors including at least one of smoking history, current smoking status, menopausal status and / or hormone levels, BMI, disease risk status, breast tissue density, and age of individuals within the patient sample data in order to identify presence of the one or more biomarkers from target cells as defined herein, optionally tumor educated cells, further optionally leukocytes; and identifying, via the first processor, a single or a first model from a plurality of MLAs to deploy as a detecting model, the first model being selected from the plurality of MLAs based on a performance of the plurality of MLAs for detecting the one or more biomarkers from target cells as defined herein, optionally tumor educated cells, further optionally leukocytes, in a patient sample.

2. The method of Claim 1, wherein the performance of the one or more MLAs for detecting the one or more biomarkers from target cells as defined herein, optionally tumor educated cells, optionally leukocytes, in the patient sample is based on accuracy, sensitivity, or specificity of a single or a plurality of MLAs for detecting the one or more biomarkers from target cells as defined herein, optionally tumor educated cells including but not limited to leukocytes.

3. The method of Claim 2, wherein the first model is selected in response to the first model having a sensitivity of at least 45% and / or a specificity of at least 85%.

4. The method of any one of Claims 1-3, further comprising stratifying, via the first processor, the patient sample data by at least one pre-selected criterion, optionally wherein stratifying comprises consideration of at least one of: a first group being under a predetermined age and a second group being over a predetermined age; orAttorney Docket No.3338 / 3 PCT a third group being post-menopausal, a fourth group being pre-menopausal, and a fifth group being peri-menopausal; and 5. The method of any one of Claims 1 to 4, wherein the first model is selected in response to the first model having: (i) a sensitivity of greater than 87.0% and a specificity of greater than 85.0%; or (ii) a sensitivity of greater than 74.0%, and a specificity of greater than 85.0%.

6. The method of any one of Claims 1 to 5, further comprising: receiving, at the processor, ribonucleic acid (RNA) quantification data for the patient sample; calculating, via the processor, fluid volumes needed to normalize the patient sample; receiving, at the processor, RT-PCR / qPCR analysis data about a normalized patient sample; and testing, via the processor, the RT-PCR / qPCR analysis data with the first model to identify a number of copies of the biomarkers from target cells as defined herein, optionally tumor educated cells, optionally leukocytes, and to increase the likelihood of signal detection with respect to a disease, disorder, or condition, optionally a tumor and / or a cancer.

7. The method of any one of Claims 1 to 6, further comprising: receiving, at the first processor, control data comprising both negative controls and positive controls comprising target cells as defined herein, optionally tumor educated cells, optionally leukocytes; analyzing, via the first processor, the control data and the patient sample to identify whether standard deviations are below a predetermined deviation range; analyzing the positive controls to identify whether one or more samples of the positive controls are above a predetermined amplification range; calculating, for a portion of the control data that is below a respective predetermined deviation range and above a respective predetermined amplification range, control mean CT values; plotting, via the processor, a standard curve using the control mean CT values and known gene transcript numbers expressed by target cells as defined herein, optionally tumor educated cells, optionally leukocytes, in the control data; and calculating, via the first processor, sample transcript numbers of genes in target cells as defined herein, optionally tumor educated cells, optionally leukocytes, in the patient sample data using the standard curve.

8. The method of any one of Claims 1 to 7, wherein the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, comprise one or more of eosinophils, monocytes, macrophages, NK cells, dendritic cells, neutrophils, T cells, B cells Mast cells and otherAttorney Docket No.3338 / 3 PCT lymphocytes that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly.

9. The method of any one of Claims 1 to 8, wherein the first model is effective to detect breast cancer in a patient.

10. The method of any one of Claims 1 to 9, wherein the one or more biomarkers in the patient sample are selected from the group consisting of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma Transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

11. The method of any one of Claims 1 to 10, wherein the one or more biomarkers in the patient sample are each of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

12. A method comprising: accessing, via a processor, control data and sample data within a database, the control data comprising both negative controls and positive controls comprising target cells as defined herein, optionally tumor educated cells, optionally leukocytes; analyzing, via the processor, the control data and sample data to identify standard deviations are below a predetermined deviation range; analyzing, via the processor, the positive controls to identify whether one or more samples of the positive controls are above a predetermined amplification range; calculating, via the processor and for a portion of the control data that is below a respective predetermined deviation range and above a respective predetermined amplification range, control mean CT values; plotting, via the processor, a standard curve using the control mean CT values and known transcript numbers of genes expressed by target cells as defined herein, optionally tumor educated cells, optionally leukocytes, in the control data; andAttorney Docket No.3338 / 3 PCT calculating, via the processor, sample transcript numbers of target cells as defined herein, optionally tumor educated cells, optionally leukocytes, in the sample data using the standard curve.

13. The method of Claim 12, further comprising normalizing raw expression levels of the one or more biomarkers with respect to the geometric mean of expression levels of one or more positive controls, optionally wherein the one or more positive controls are selected from the group consisting of B2M, GAPDH, and EEF1G.

14. The method of Claim 13, further comprising creating, via the processor, a first analysis model for the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, within samples by training a machine learning algorithm (MLA) based on normalized sample transcript number and environmental factors, the environmental factors including at least one of smoking history, current smoking status, menopausal status and / or hormone levels, body mass index (BMI), disease risk status, breast tissue density, and age of individuals within the sample data.

15. The method of Claim 14, wherein the MLA is selected from a plurality of machine learning algorithms characterized as one of regression-based algorithms, decision tree-based algorithms, deep learning algorithms, or neural networks.

16. The method of Claim 14 or Claim 15, further comprising: retraining, via the processor and in response to a request for an upgrade to analysis software, the MLA using subsequent control data and subsequent sample data; and creating, via the processor, a second analysis model for the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, within samples based on the retraining.

17. The method of any one of Claims 14 to 16, wherein the method is effective to detect breast cancer in a patient.

18. The method of any one of Claims 12 to 17, further comprising removing, via the processor, one or more outliers from the control data or the sample data responsive to determining that the one or more outliers are below the predetermined deviation range, optionally wherein the predetermined deviation range is selected from the group consisting of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, and greater than 1.7 standard deviations.

19. The method of any one of Claims 12 to 18, further comprising removing, via the processor, one or more outliers from the positive controls responsive to determining that the one or more outliers are above the predetermined amplification range.

20. The method of any one of Claims 12 to 19, wherein the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, comprise one or more of eosinophils, monocytes, macrophages, NK cells, dendritic cells, neutrophils, T cells, B cells Mast cells and otherAttorney Docket No.3338 / 3 PCT lymphocytes that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly.

21. The method of any of Claims 12 to 20, wherein calculating sample transcript numbers of biomarkers expressed in the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, in the sample data comprises identifying one or more biomarkers for the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, and further wherein the one or more biomarkers are selected from the group consisting of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

22. The method of Claim 21, wherein the one or more biomarkers are each of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol- 4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

23. A system comprising: one or more processors; memory in communication with the one or more processors and storing instructions that are configured to cause the system to: identify a first model from one or more machine learning algorithms (MLAs) trained on one or more of negative patient samples, positive patient samples, and environmental factors including at least one of smoking history, current smoking status, menopausal status and / or hormone levels, BMI, disease risk status, breast tissue density, and age of individuals within patient sample data, the first model having a sensitivity of at least 45% and / or a specificity of at least 85% for detecting a disease, disorder, and / or condition, optionally a tumor and / or a cancer, in a patient under the age of 50 by detecting a presence of one or more biomarkers for a target cell, optionally a tumor educated cell, further optionally a leukocyte, in a patient sample;Attorney Docket No.3338 / 3 PCT receive ribonucleic acid (RNA) quantification data for the patient sample; receive polymerase chain reaction / quantitative polymerase chain reaction (RT- PCR / qPCR) analysis data about a normalized patient sample; test the RT-PCR / qPCR analysis data with the first model to identify a gene expression profile of the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, with respect to one or more biomarkers; and increase the likelihood of signal detection with respect to a disease, disorder, or condition, optionally a tumor and / or a cancer, based on the gene expression profile identified.

24. The system of Claim 23, wherein the first model comprises a decision tree-based algorithm.

25. The system of Claim 23 or Claim 24, wherein the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, comprise one or more of eosinophils, monocytes, macrophages, NK cells, dendritic cells, neutrophils, T cells, B cells, mast cells, and / or other lymphocytes that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly.

26. The system of any one of Claims 23 to 25, wherein the first model is effective to detect breast cancer in a patient.

27. The system of any one of Claims 23 to 26, wherein the one or more biomarkers are selected from the group consisting of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

28. The system of any of Claims 23 to 27, wherein the one or more biomarkers are each of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

29. A method for detecting a tumor and / or a cancer in a subject, the method comprising:Attorney Docket No.3338 / 3 PCT (a) determining gene expression levels of one or more biomarkers selected from the group consisting of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67) present in a biological sample isolated from the subject, the biological sample comprising one or more target cells as defined herein, optionally tumor educated cells, optionally leukocytes and / or platelets, that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly; and (b) comparing the gene expression levels of the one or more biomarkers present in the biological sample with a control, wherein the control is optionally a biological sample isolated from a negative control subject, wherein the comparing step provides an increased likelihood of signal detection with respect to a disease, disorder, and / or condition, optionally a tumor and / or a cancer, in the subject.

30. The method of Claim 29, wherein gene expression levels in the biological sample are determined for each of FGFR1 oncogene partner 2 (FGFR1OP2), cadherin 1 (CDH1), tumor protein p53 (TP53), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), S100 calcium binding protein A8 (S100A8), fatty acid hydroxylase domain containing 2 (FAXDC2), C-type lectin domain family 12 member A (CLEC12A), dematin actin binding protein (DMTN), mucin 1, cell surface associated (MUC1), phosphoserine phosphatase (PSPH)), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), and marker of proliferation Ki-67 (MKI67).

31. The method of Claim 29 or Claim 30, wherein the determining comprises quantitatively analyzing transcription products of the one or more biomarkers in the biological sample.

32. The method of any one of claims 29 to 31 wherein the comparing is performed by the system of Claim 23.

33. The method of any one of Claims 29 to 32, wherein the tumor and / or a cancer is a tumor and / or cancer of the breast.

34. The method of any one of Claims 29 to 33, wherein the subject is a human.

35. The method of any one of Claims 29 to 34, wherein the target cells as defined herein, optionally tumor educated cells, optionally leukocytes, comprise one or more of eosinophils, monocytes, macrophages, NK cells, dendritic cells, neutrophils, T cells, B cells, mast cells and otherAttorney Docket No.3338 / 3 PCT lymphocytes that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly.

36. The method of any one of Claims 29 to 35, further comprising administering to the subject at least one anti-tumor and / or anti-cancer therapy based on an outcome of the comparison step.

37. A method for inhibiting proliferation and / or survival of a cell, including but not limited to apoptosis, necrosis, cell cycle arrest, and senescence of a target cell as defined herein, optionally tumor educated cells, optionally leukocytes and / or platelets that that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly, and are induced to proliferate, the method comprising contacting the tumor and / or cancer cells and / or the target cells with an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety.

38. The method of Claim 37, wherein the small molecule drug is selected from the group consisting of but not limited to gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

39. A method for inhibiting a gene expression changes associated with target cells as defined herein, optionally tumor educated cells, optionally leukocytes and / or platelets, that that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly, and as a result are induced to modify gene expression, the method comprising contacting the tumor and / or cancer cells and / or the target cells with an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

40. A method for inhibiting the growth of a tumor and / or progression of a tumor-associated disease in a subject, the method comprising administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.Attorney Docket No.3338 / 3 PCT 41. A method for inhibiting epithelial mesenchymal transition (EMT) associated with target cells as defined herein, optionally tumor educated cells, optionally leukocytes and / or platelets that that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly, and are induced to undergo EMT, the method comprising administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

42. A method for inhibiting platelet aggregation associated with exposure to one or more tumor- derived factors, the method comprising administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

43. A method for inhibiting loss of immune cell targeting ability associated with exposure to one or more tumor cells and / or tumor-derived factors and / or previously tumor educated cells and their factors, the method comprising administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

44. A method for inhibiting acquisition of oncogenic signaling associated with exposure to one or more tumor cells and / or tumor-derived factors and / or previously tumor educated cells and their factors, the method comprising administering to the subject an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety, optionally wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

45. A method for inhibiting an oncogenic pathway associated with education of an immune cell by a tumor and / or a cancer cell, the method comprising contacting the tumor and / or cancer cellAttorney Docket No.3338 / 3 PCT and / or the immune cell with an effective amount of a small molecule drug that comprises a terminal monocyclic or polycyclic moiety.

46. The method of Claim 45, wherein the small molecule drug is selected from the group consisting of gilteritinib, abemaciclib, crizotinib, brigatinib, dasatinib, and derivatives thereof, wherein the derivatives thereof comprise the terminal monocyclic or polycyclic moiety of the parent molecule or a substituted derivative thereof.

47. The method of Claim 45 or Claim 46, wherein the immune cell is selected from the group consisting of eosinophils, monocytes, macrophages, natural killer (NK) cells, dendritic cells, neutrophils, T cells, B cells, mast cells, and other lymphocytes that have contacted a tumor cell and / or a component and / or secretion thereof, and / or a previously tumor educated cell and / or component and / or secretion thereof, directly and / or indirectly.

48. The method of any one of Claims 45-47, wherein the tumor and / or the cancer cell is a leukemia cell, a melanoma cell, an ovarian tumor and / or cancer cell, a breast tumor and / or cancer cell, a colorectal tumor and / or cancer cell, a lung squamous cancer cell, a sarcoma cell, a renal cell carcinoma cell, a pancreatic carcinoma cell, a squamous tumor of the head and neck cell, a brain cancer cell, a liver cancer cell, a prostate cancer cell, and a cervical cancer cell.

49. The method of any one of Claims 45 to 48, wherein the oncogenic pathway is associated with a pathway optionally an oncogenic pathway listed in any of Tables 4, 5, 14, 16, 21, 24, and 26.

50. A method for assessing a degree to which a tumor and / or cancer cell and / or a component and / or secretion thereof induces alterations in biological activities of an immune cell, the method comprising contacting the immune cell with the tumor and / or cancer cell and / or a previously tumor educated cell and / or a component and / or secretion thereof, and identifying a change in one or more biological activities of the immune cell, optionally wherein the one or more biological activities are selected from but not limited to a change in expression of at least one gene, a difference in proliferation, entry into EMT, entry into an oncogenic pathway, optionally one of the pathways, optionally one of the oncogenic pathways, listed in any of Tables 4, 5, 14, 16, 21, 24, and 26, wherein the change is relative to the immune cell under identical conditions but absent the contacting step.

51. The method of Claim 50, wherein the immune cell is selected from but not limited to a white blood cell (WBC), a peripheral blood multinuclear cell (PBMC), and a platelet.

52. The method of Claim 50, wherein the tumor and / or cancer cell is a breast tumor and / or cancer cell, a lung tumor and / or cancer cell, or a pancreatic tumor and / or cancer cell, optionally wherein the tumor and / or cancer cell is from a cell line selected from but not limited to MDA- MB-231, MCF7, SKBR3, PAC1, H1975, and BT-549.

53. A method for determining a degree to which a therapeutic agent can modulate an alteration in a biological activity of an immune cell induced by contact with a tumor and / or cancer cellAttorney Docket No.3338 / 3 PCT and / or a previously tumor educated cell and / or a component and / or secretion thereof, the method comprising: (a) administering the therapeutic agent to an immune cell, the tumor and / or cancer cell and / or a previously tumor-educated cell, or any combination thereof; (b) contacting the immune cell with the tumor and / or cancer cell and / or a previously tumor educated cell and / or a component and / or secretion thereof; and (c) determining a change in one or more biological activities of the immune cell and / or a previously tumor educated cell, optionally wherein the one or more biological activities are selected from but not limited to a change in expression of at least one gene, a difference in proliferation, entry into EMT, entry into an oncogenic pathway, optionally one of the pathways, optionally one of the oncogenic pathways, listed in any one of Tables 4, 5, 14, 16, 21, 24, and 26, wherein the change is relative to the immune cell and / or the tumor educated cell under identical conditions but absent the administering step.

54. The method of Claim 53, wherein the tumor and / or cancer cell is a breast tumor and / or cancer cell, a lung tumor and / or cancer cell, or a pancreatic tumor and / or cancer cell.

55. The method of Claim 53 or Claim 54, wherein the therapeutic agent is administered to the immune cell, the tumor and / or cancer cell, the tumor educated cell, or any combination thereof before the contacting step.

56. The method of any one of Claims 53 to 55, wherein the therapeutic agent is administered to the immune cell, the tumor and / or cancer cell, the tumor educated cell, or any combination thereof after the contacting step.

57. The method of any one of Claims 53 to 56, wherein the therapeutic agent is administered to the immune cell, the tumor and / or cancer cell, the tumor educated cell, or any combination thereof concurrently with the contacting step.

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