Panomic genomic prevalence score

Abstract

Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. Here, we used molecular profiling data to identify biomarker signatures (biosignatures) that predict a tumor primary lineage, cancer category or type, organ group and / or histology. The signature may use genomic and transcriptome level information.

Description

CLAIM OF PRIORITY This application claims the benefit of U.S. Provisional Patent Application Serial Nos. 62 / 977,015, filed on February 14, 2020; 63 / 014,515, filed on April 23, 2020; 63 / 052,363, filed on July 15, 2020; and 63 / 145,305, filed on February 3, 2021; the entire contents of which applications are hereby incorporated by reference in their entirety. This application is related to International Patent Publication WO / 2020 / 146554, entitled Genomic Profiling Similarity and based on International Patent Application PCT / US2020 / 012815 filed on January 8, 2020, the entire contents of which application is hereby incorporated by reference in its entirety. TECHNICAL FIELD The present disclosure relates to the fields of data structures, data processing, and machine learning, and their use in precision medicine, e.g., tumor characterization including without limitation the use of molecular profiling to predict an attribute of a biological sample such as the primary origin, organ type, histology and / or cancer type. BACKGROUND Carcinoma of Unknown Primary (CUP) represents a clinically challenging heterogeneous group of metastatic malignancies in which a primary tumor remains elusive despite extensive clinical and pathologic evaluation. Approximately 2-4% of cancer diagnoses worldwide comprise CUP. See, e.g., Varadhachary. New Strategies for Carcinoma of Unknown Primary: the role of tissue of origin molecular profiling. Clin Cancer Res. 2013 Aug l;19(15):4027-33. In addition, some level of diagnostic uncertainty with respect to an exact tumor type classification is a frequent occurrence across oncologic subspecialties. Efforts to secure a definitive diagnosis can prolong the diagnostic process and delay treatment initiation. Furthermore, CUP is associated with poor outcome which might be explained by use of suboptimal therapeutic intervention. Immunohistochemical (IHC) testing is the gold standard method to diagnose the site of tumor origin, especially in cases of poorly differentiated or undifferentiated tumors. Assessing the accuracy in challenging cases and performing a meta-analysis of these studies reported that IHC analysis had an accuracy of 66% in the characterization of metastatic tumors. See, e.g., Brown RW, et al. Immunohistochemical identification of tumor markers in metastatic adenocarcinoma: a diagnostic adjunct in the determination of primary site. Am J Clin Pathol 1997, 107:12el9; Dennis JL, et al. Markers of adenocarcinoma characteristic of the site of origin: development of a diagnostic algorithm. Clin Cancer Res 2005, 11:3766e3772; Gamble AR, et al. Use of tumour marker immunoreactivity to identify primary site of metastatic cancer. BMJ 1993, 306:295e298; Park SY, et al. Panels of immimohistochemical markers help determine primary sites of metastatic adenocarcinoma. Arch Pathol Lab Med 2007, 131:1561el567; DeYoung BR, Wick MR. Immunohistologic evaluation of metastatic carcinomas of unknown origin: an algorithmic approach. Semin Diagn Pathol 2000, 17:184el93; Anderson GG, Weiss LM. Determining tissue of origin for metastatic cancers: meta-analysis and literature review of immunohistochemistry performance. Appl Immunohistochem Mol Morphol 2010, 18:3e8. Since therapeutic regimes can be dependent upon diagnosis, this represents an important unmet clinical need. To address these challenges, assays aiming at tissue-of-origin (TOO) identification based on assessment of differential gene expression have been developed and tested clinically. However, integration of such assays into clinical practice is hampered by relatively poor performance characteristics (from 83% to 89%) and limited sample availability. See, e.g., Pillai R, et al. Validation and reproducibility of a microarray-based gene expression test for tumor identification in formalin-fixed, paraffin-embedded specimens. J Mol Diagn 2011, 13:48e56; Rosenwald S, et al. Validation of a microRNA-based qRT-PCRtest for accurate identification of tumor tissue origin. Mod Pathol 2010, 23:814e823; Kerr SE, et al. Multisite validation study to determine performance characteristics of a 92-gene molecular cancer classifier. Clin Cancer Res 2012, 18:3952e3960; Kucab JE, et al. A Compendium of Mutational Signatures of Environmental Agents. Cell. 2019 May 2;177(4):821-836.el6. For example, a recent commercial RNA-based assay has a sensitivity of 83% in a test set of 187 tumors and confirmed results on only 78% of a separate 300 sample validation set. See Hainsworth JD, et al, Molecular gene expression profiling to predict the tissue of origin and direct site-specific therapy in patients with carcinoma of unknown primary site: a prospective trial of the Sarah Cannon research institute. J Clin Oncol. 2013 Jan 10;31(2):217-23. This may, at least in part, be a consequence of limitations of typical RNA-based assays in regards to normal cell contamination, RNA stability, and dynamics of RNA expression. Thus, there is a need for more robust approaches to TOO identification to aid cancer patients, particularly but not limited to CUP. Machine learning models can be configured to analyze labeled training data and then (haw inferences from the training data. Once the machine learning model has been trained, sets of data that are not labeled may be provided to the machine learning model as an input. The machine learning model may process the input data, e.g., molecular profiling data, and make predictions about the input based on inferences learned during training. The present disclosure further provides a voting methodology to combine multiple classifier models to achieve more accurate classification than that achieved by use a single model. Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. We have performed such profiling on well over 100,000 tumor patients from practically all cancer lineages. Patient and molecular data can be processed using machine learning algorithms to identify additional biomarker signatures that can be used to characterize various phenotypes of interest. Here, this “next generation profiling” (NGP) approach has been applied to build models to predict an attribute of a biological sample, mcludmg without limitation such as the primary origin, organ type, histology and / or cancer type. SUMMARY Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. Herein we provide systems and methods to predict attributes of a patient sample, including without limitation a tissue-of-origin (TOO). In an aspect, the disclosure provides a data processing apparatus for generating input data structure for use in training a machine learning model to predict at least one attribute of a biological sample, wherein the at least one attribute is selected from the group comprising a primary tumor origin, cancer / disease type, organ group, histology, and any combination thereof, the data processing apparatus including one or more processors and one or more storage devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising: obtaining, by the data processing apparatus one or more biomarker data structures and one or more sample data structures; extracting, by the data processing apparatus, first data representing one or more biomarkers associated with the sample from the one or more biomarker data structures, second data representing the sample data from the one or more sample data structures, and third data representing a predicted at least one attribute; generating, by the data processing apparatus, a data structure, for input to a machine learning model, based on the first data representing the one or more biomarkers and the second data representing the predicted at least one attribute and sample; providing, by the data processing apparatus, the generated data structure as an input to the machine learning model; obtaining, by the data processing apparatus, an output generated by the machine learning model based on the machine learning model’s processing of the generated data structure; determining, by the data processing apparatus, a difference between the third data representing a predicted predicted at least one attribute for the sample and the output generated by the machine learning model; and adjusting, by the data processing apparatus, one or more parameters of the machine learning model based on the difference between the third data representing a predicted predicted at least one attribute for the sample and the output generated by the machine learning model. In some embodiments, the set of one or more biomarkers include one or more biomarkers listed in any one of Tables 121-129, Tables 117-120, INSMI, any table selected from Tables 2-116, and any combination thereof, optionally wherein the set of one or more biomarkers comprises one or more biomarkers listed in any one of Table 117, Table 118, Table 119, Table 120, INSMI, or any combination thereof. In some embodiments, the set of one or more biomarkers include each of the biomarkers. In some embodiments, the set of one or more biomarkers includes at least one of these biomarkers, optionally wherein the set of one or more biomarkers comprises each of the biomarkers in Table 118, Table 119, Table 120, and INSMI, and wherein optionally the set of one or more biomarkers further comprises the markers in any table selected from Tables 2-116. In an aspect, the disclosure provides a data processing apparatus for generating input data structure for use in training a machine learning model to predict at least one attribute of a biological sample, wherein the at least one attribute is selected from the group comprising a primary tumor origin, cancer / disease type, organ group, histology, and any combination thereof, the data processing apparatus including one or more processors and one or more storage devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising: obtaining, by the data processing apparatus, a first data structure that structures data representing a set of one or more biomarkers associated with a biological sample from a first distributed data source, wherein the first data structure includes a key value that identifies the sample; storing, by the data processing apparatus, the first data structure in one or more memory devices; obtaining, by the data processing apparatus, a second data structure that structures data representing data for the at least one attribute for the sample having the one or more biomarkers from a second distributed data source, wherein the data for the at least one attribute includes data identifying a sample, at least one attribute, and an indication of the predicted at least one attribute, wherein second data structure also includes a key value that identifies the sample; storing, by the data processing apparatus, the second data structure in the one or more memory devices; generating, by the data processing apparatus and using the first data structure and the second data structure stored in the memory devices, a labeled training data structure that includes (i) data representing the set of one or more biomarkers and the sample, and (ii) a label that provides an indication of a predicted at least one attribute, wherein generating, by the data processing apparatus and using the first data structure and the second data structure includes correlating, by the data processing apparatus, the first data structure that structures the data representing the set of one or more biomarkers associated with the sample with the second data structure representing predicted at least one attribute data for the sample having the one or more biomarkers based on the key value that identifies the subject; and training, by the data processing apparatus, a machine learning model using the generated label training data structure, wherein training the machine learning model using the generated labeled training data structure includes providing, by the data processing apparatus and to the machine learning model, the generated label training data structure as an input to the machine learning model. In some embodiments, the operations further comprise: obtaining, by the data processing apparatus and from the machine learning model, an output generated by the machine learning model based on the machine learning model’s processing of the generated labeled training data structure; and determining, by the data processing apparatus, a difference between the output generated by the machine learning model and the label that provides an indication of the predicted at least one attribute. In some embodiments, the operations further comprise: adjusting, by the data processing apparatus, one or more parameters of the machine learning model based on the determined difference between the output generated by the machine learning model and the label that provides an indication of the predicted at least one attribute. In some embodiments, the set of one or more biomarkers include one or more biomarkers listed in any one of Tables 121-129, Tables 117-120, INSMI, any table selected from Tables 2-116, and any combination thereof, optionally wherein the set of one or more biomarkers comprises one or more biomarkers listed in any one of Table 117, Table 118, Table 119, Table 120, INSMI, or any combination thereof In some embodiments, the set of one or more biomarkers include each of the biomarkers. In some embodiments, the set of one or more biomarkers includes at least one of these biomarkers, optionally wherein the set of one or more biomarkers comprises each of the biomarkers in Table 118, Table 119, Table 120, and INSMI, and wherein optionally the set of one or more biomarkers further comprises the markers in any table selected from Tables 2-116. The disclosure also provides a method comprising steps that correspond to each of the operations described above. The disclosure also provides a system comprising one or more computers and one or more storage media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform each of the operations described above. The disclosure also provides a non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform the operations described above. In an aspect, the disclosure provides a method for determining at least one attribute of a biological sample, wherein the at least one attribute is selected from the group comprising a primary tumor origin, cancer / disease type, organ group, histology, and any combination thereof, the method comprising: for each particular machine learning model of a plurality of machine learning models that have each been trained to perform an prediction operation between received input data representing a sample and the at least one attribute: providing, to the particular machine learning model, input data representing a sample of a subject, wherein the sample was obtained from tissue or an organ of the subject; and obtaining output data, generated by the particular machine learning model based on the particular machine learning model’s processing the provided input data, that represents a probability or likelihood that the sample represented by the provided input data corresponds to the at least one attribute; providing, to a voting unit, the output data obtained for each of the plurality of machine learning models, wherein the provided output data includes data representing initial sample attributes determined by each of the plurality of machine learning models; and determining, by the voting unit and based on the provided output data, the predicted at least one attribute. In some embodiments, the predicted at least one attribute is determined by applying a majority rule to the provided output data, by using the provided output data as input into a dynamic voting model, or a combination thereof. In some embodiments, the determining, by the voting unit and based on the provided output data, the predicted at least one attribute comprises: determining, by the voting unit, a number of occurrences of each initial attribute class of the multiple candidate attribute classes; and selecting, by the voting unit, the initial attribute class of the multiple candidate attribute classes having the highest number of occurrences. In some embodiments, each machme learning model of the plurality of machme learning models comprises a random forest classification algorithm, boosted tree, support vector machine, logistic regression, k-nearest neighbor model, artificial neural network, naive Bayes model, quadratic discriminant analysis, Gaussian processes model, or any combination thereof In some embodiments, each machine learning model of the plurality of machine learning models comprises a random forest classification algorithm. In some embodiments, each machine learning model of the plurality of machine learning models comprises a boosted tree classification algorithm. In some embodiments, the plurality of machine learning models includes multiple representations of a same type of classification algorithm. In some embodiments, the input data represents a description of (i) sample attributes and (ii) origins. In some embodiments, the multiple candidate attribute classes include at least one class for prostate, bladder, endocervix, peritoneum, stomach, esophagus, ovary, parietal lobe, cervix, endometrium, liver, sigmoid colon, upper-outer quadrant of breast, uterus, pancreas, head of pancreas, rectum, colon, breast, intrahepatic bile duct, cecum, gastroesophageal junction, frontal lobe, kidney, tail of pancreas, ascending colon, descending colon, gallbladder, appendix, rectosigmoid colon, fallopian tube, brain, lung, temporal lobe, lower third of esophagus, upper-inner quadrant of breast, transverse colon, and skin. In some embodiments, the multiple candidate attribute classes include at least at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or all 21 of breast adenocarcinoma, central nervous system cancer, cervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, gastroesophageal adenocarcinoma, gastrointestinal stromal tumor (GIST), hepatocellular carcinoma, lung adenocarcinoma, melanoma, meningioma, ovarian granulosa cell tumor, ovarian & fallopian tube adenocarcinoma, pancreas adenocarcinoma, prostate adenocarcinoma, renal cell carcinoma, squamous cell carcinoma, thyroid cancer, urothelial carcinoma, uterine endometrial adenocarcinoma, and uterine sarcoma. In some embodiments, the sample attributes includes one or more biomarkers for the sample, wherein optionally the one or more biomarkers comprises one or more biomarkers listed in any one of Tables 121-129, Tables 117-120, INSMI, any table selected from Tables 2-116, and any combination thereof, optionally wherein the set of one or more biomarkers comprises one or more biomarkers listed in any one of Table 117, Table 118, Table 119, Table 120, INSMI, or any combination thereof. In some embodiments, the set of one or more biomarkers include each of the biomarkers. In some embodiments, the set of one or more biomarkers includes at least one of these biomarkers, optionally wherein the set of one or more biomarkers comprises each of the biomarkers in Table 118, Table 119, Table 120, and INSMI, and wherein optionally the set of one or more biomarkers further comprises the markers in any table selected from Tables 2-116. In some embodiments, the input data further includes data representing a description of the sample and / or subject. The disclosure also provides a system comprising one or more computers and one or more storage media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform each of the operations described above. The disclosure also provides a non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform the operations described above. 1. In an aspect, the disclosure provides a method for classifying a biological sample, the method comprising: obtaining, by one or more computers, first data representing one or more initial classifications for the biological sample that were previously determined based on RNA sequences of the biological sample; obtaining, by one or more computers, second data representing another initial classification for the biological sample that were previously determined based on DNA sequences of the biological sample; providing, by one or more computers, at least a portion of the first data and the second data as an input to a dynamic voting engine that has been trained to predict a target biological sample classification based on processing of multiple initial biological sample classifications; processing, by one or more computers, the provided input data through the dynamic voting engine; obtaining, by one or more computers, output data generated by the dynamic voting engine based on the dynamic voting engine’s processing of the provided input data; and determining, by one or more computers, a target biological sample classification for the biological sample based on the obtained output data. In some embodiments, the obtaining, by one or more computers, first data representing one or more initial classifications for the biological sample that were previously determined based on RNA sequences of the biological sample comprises: obtaining data representing a cancer type classification for the biological sample based the RNA sequences of the biological sample; obtaining data representing an organ from which the biological sample originated based on the RNA sequences of the biological sample; and obtaining data representing a histology for the biological sample based on the RNA sequences of the biological sample, and wherein providing at least a portion of the first data and the second data as an input to the dynamic voting engine comprises: providing the obtained data representing the cancer type classification, the obtained data representing the organ from which the biological sample originated, the obtained data representing the histology, and the second data as an input to the dynamic voting engine. In some embodiments, the dynamic voting engine comprises one or more machine learning model. In some embodiments, training the dynamic voting engine comprises: obtaining a labeled training data item that includes (I) one or more initial classifications that include data indicating a cancer classification type, data indicating an initial organ of origin, data indicating a histology, or data indicating output of a DNA analysis engine and (II) a target biological sample classification, generating training input data for input to the dynamic voting engine based on the obtained training data item, processing the generated training input data through the dynamic voting engine, obtaining output data generated by the dynamic voting engine based on the dynamic voting engine’s processing of the generated training input data, and adjusting one or more parameters of the dynamic voting engine based on the level of similarity between the output data and the label of the obtained training data item. In some embodiments, previously determining an initial classification for the biological sample based on DNA sequences of the biological sample comprises: receiving, by one or more computers, a biological signature representing the biological sample that was obtained from a cancerous neoplasm in a first portion of a body, wherein the model includes a cancerous biological signature for each of multiple different types of cancerous biological samples, wherein each of the cancerous biological signatures include at least a first cancerous biological signature representing a molecular profile of a cancerous biological sample from the first portion of one or more other bodies and a second cancerous biological signature representing a molecular profile of a cancerous biological sample from a second portion of one or more other bodies; performing, by one or more computers and using a pairwise-analysis model, pairwise analysis of the biological signature using the first cancerous biological signature and the second cancerous biological signature; generating, by one or more computers and based on the performed pairwise analysis, a likelihood that the cancerous neoplasm in the first portion of the body was caused by cancer in a second portion of the body; and storing, by one or more computers, the generated likelihood in a memory device. The disclosure also provides a system comprising one or more computers and one or more storage media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform each of the operations described above. The disclosure also provides a non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform the operations described above. In an aspect, the disclosure provides a method comprising: (a) obtaining a biological sample from a subject having a cancer; (b) performing at least one assay on the sample to assess one or more biomarkers, thereby obtaining a biosignature for the sample; (c) providing the biosignature into a model that has been trained to predict at least one attribute of the cancer, wherein the model comprises at least one pre-determined biosignature indicative of at least one attribute, and wherein the at least one attribute of the cancer is selected from the group comprising primary tumor origin, cancer / disease type, organ group, histology, and any combination thereof; (d) processing, by one or more computers, the provided biosignature through the model; and (e) outputting from the model a prediction of the at least one attribute of the cancer. In the methods provided herein, the biological sample may comprise formalin-fixed paraffin-embedded (FFPE) tissue, fixed tissue, a core needle biopsy, a fine needle aspirate, unstained slides, fresh frozen (FF) tissue, formalin samples, tissue comprised in a solution that preserves nucleic acid or protein molecules, a fresh sample, a malignant fluid, a bodily fluid, a tumor sample, a tissue sample, or any combination thereof. In some embodiments, the biological sample comprises cells from a solid tumor, a bodily fluid, or a combination thereof. In some embodiments, the bodily fluid comprises a malignant fluid, a pleural fluid, a peritoneal fluid, or any combination thereof. In some embodiments, the bodily fluid comprises peripheral blood, sera, plasma, ascites, wine, cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovial fluid, aqueous humor, amniotic fluid, cerumen, breast milk, broncheoalveolar lavage fluid, semen, prostatic fluid, Cowper’s fluid, pre-ejaculatory fluid, female ejaculate, sweat, fecal matter, tears, cyst fluid, pleural fluid, peritoneal fluid, pericardial fluid, lymph, chyme, chyle, bile, interstitial fluid, menses, pus, sebum, vomit, vaginal secretions, mucosal secretion, stool water, pancreatic juice, lavage fluids from sinus cavities, bronchopulmonary aspirates, blastocyst cavity fluid, or umbilical cord blood. In the methods provided herein, performing the at least one assay in step (b) may comprise determining a presence, level, or state of a protein or nucleic acid for each of the one or more biomarkers, wherein optionally the nucleic acid comprises deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or a combination thereof In some embodiments, the presence, level or state of at least one of the proteins is determined using a technique selected from immunohistochemistry (IHC), flow cytometry, an immunoassay, an antibody or functional fragment thereof, an aptamer, mass spectrometry, or any combination thereof, wherein optionally the presence, level or state of all of the proteins is determined using the technique; and / or the presence, level or state of at least one of the nucleic acids is determined using a technique selected from polymerase chain reaction (PCR), in situ hybridization, amplification, hybridization, microarray, nucleic acid sequencing, dye termination sequencing, pyrosequencing, next generation sequencing (NGS; high-throughput sequencing), whole exome sequencing, whole genome sequencing, whole transcriptome sequencing, or any combination thereof, wherein optionally the presence, level or state of all of the nucleic acids is determined using the technique. In some embodiments, the state of the nucleic acid comprises a sequence, mutation, polymorphism, deletion, insertion, substitution, translocation, fusion, break, duplication, amplification, repeat, copy number, copy number variation (CNV; copy number alteration; CNA), or any combination thereof. In some embodiments, the state of the nucleic acid consists of or comprises a copy number. In some embodiments, the at least one assay comprises next-generation sequencing, wherein optionally the next-generation sequencing is used to assess: i) at least one of the genes, genomic information / signatures, and fusion transcripts in any of Tables 121-130, or any combination thereof; ii) at least one of the genes and / or transcripts in any table selected from Tables 117-120, INSMI, and any combination thereof; iii) the whole exome or substantially the whole exome; iv) the whole transcriptome or substantially the whole transcriptome; v) at least one gene in any table selected from Tables 2-116, and any combination thereof; or vi) any combination thereof. In the methods provided herein, predicting the at least one attribute of the cancer may comprise determining a probability that the attribute is each member of a plurality of such attributes and selecting the attribute with the highest probability. In some embodiments of the methods provided herein, the primary tumor origin or plurality of primary tumor origins consists of, comprises, or comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, or all 38 of prostate, bladder, endocervix, peritoneum, stomach, esophagus, ovary, parietal lobe, cervix, endometrium, liver, sigmoid colon, upper-outer quadrant of breast, uterus, pancreas, head of pancreas, rectum, colon, breast, mtrahepatic bile duct, cecum, gastroesophageal junction, frontal lobe, kidney, tail of pancreas, ascending colon, descending colon, gallbladder, appendix, rectosigmoid colon, fallopian tube, brain, lung, temporal lobe, lower third of esophagus, upper-inner quadrant of breast, transverse colon, and skin. In some embodiments, the primary tumor origin or plurality of primary tumor origins consists of, comprises, or comprises at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or all 21 of breast adenocarcinoma, central nervous system cancer, cervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, gastroesophageal adenocarcinoma, gastrointestinal stromal tumor (GIST), hepatocellular carcinoma, lung adenocarcinoma, melanoma, meningioma, ovarian granulosa cell tumor, ovarian & fallopian tube adenocarcinoma, pancreas adenocarcinoma, prostate adenocarcinoma, renal cell carcinoma, squamous cell carcinoma, thyroid cancer, urothelial carcinoma, uterine endometrial adenocarcinoma, and uterine sarcoma. In some embodiments, the cancer / disease type consists of, comprises, or comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25,26, 27, or all 28 of adrenal cortical carcinoma; bile duct, cholangiocarcinoma; breast carcinoma; central nervous system (CNS); cervix carcinoma; colon carcinoma; endometrium carcinoma; gastrointestinal stromal tumor (GIST); gastroesophageal carcinoma; kidney renal cell carcinoma; liver hepatocellular carcinoma; lung carcinoma; melanoma; meningioma; Merkel; neuroendocrine; ovary granulosa cell tumor; ovary, fallopian, peritoneum; pancreas carcinoma; pleural mesothelioma; prostate adenocarcinoma; retroperitoneum; salivary and parotid; small intestine adenocarcinoma; squamous cell carcinoma; thyroid carcinoma; urothelial carcinoma; uterus. In some embodiments, the organ group consists of, comprises, or comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or all 17 of adrenal gland; bladder; brain; breast; colon; eye; female genital tract and peritoneum (FGTP); gastroesophageal; head, face or neck, NOS; kidney; liver, gallbladder, ducts; lung; pancreas; prostate; skin; small intestine; thyroid. In some embodiments, the histology consists of, comprises, or comprises at least 1,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, or all 29 of adenocarcinoma, adenoid cystic carcinoma, adenosquamous carcinoma, adrenal cortical carcinoma, astrocytoma, carcinoma, carcinosarcoma, cholangiocarcinoma, clear cell carcinoma, ductal carcinoma in situ (DCIS), glioblastoma (GBM), GIST, glioma, granulosa cell tumor, infiltrating lobular carcinoma, leiomyosarcoma, liposarcoma, melanoma, meningioma, Merkel cell carcinoma, mesothelioma, neuroendocrine, non-small cell carcinoma, oligodendroglioma, sarcoma, sarcomatoid carcinoma, serous, small cell carcinoma, squamous. In some embodiments of the methods provided herein, the at least one pre-determined biosignature indicative of the at least one attribute of the cancer, wherein optionally the at least one attribute is a cancer / disease type, comprises selections of biomarkers according to Table 118, wherein optionally: i. a pre-determined biosignature indicative of adrenal cortical carcinoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from INHA, MIB1, SYP, CDH1, NKX3-1, CALB2, KRT19, MUC1, S100A5, CD34, TMPRSS2, KRT8, NCAM2, ARG1, TG, NCAM1, SERPINA1, PSAP, TPM3, and ACVRL1; ii. a pre-determined biosignature indicative of bile duct, cholangiocarcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from HNF1B, VIL1, SERPINA1, ESRI, ANO1, SOX2, MUC4, S100A2, KRT5, KRT7, CNN1, AR, ENO2, S100A9, NKX2-2, SATB2, PSAP, S100A6, CALB2, and TMPRSS2; iii. a predetermined biosignature indicative of breast carcinoma consists of, comprises, or comprises at least, 1, 2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from GATA3, ANKRD30A, KRT15, KRT7, S100A2, PAX8, MUC4, KRT18, HNF1B, S100A1, PIP, SOX2, MDM2, MUC5AC, PMEL, TFF1, KRT16, KRT6B, S100A6, and SERPINB5; iv. a pre-determined biosignature indicative of central nervous system (CNS) consists of, comprises, or comprises at least, 1,2,3,4,5,6,7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SIOOB, KRT18, KRT8, SOX2, ANO1, NCAM1, PDPN, NKX2-2, KRT19, S100A14, S100A11, S100A1, MSH2, CEACAM1, GPC3, ERBB2, TG, KRT7, CGB3, and S100A2; v. a pre-determined biosignature indicative of cervix carcinoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from ESRI, CDKN2A, CCND1, LIN28A, PGR, SMARCB1, CEACAM4, SIOOB, FUT4, PSAP, MUC2, MDM2, NCAM1, SATB2, TNFRSF8, CD79A, S100A13, VHL, CD3G, and TPSAB1; vi. a pre-determined biosignature indicative of colon carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from CDX2, KRT7, MUC2, KRT20, MUC1, SATB2, VIL1, CEACAM5, CDH17, S100A6, CEACAM20, KRT6B, TFF3, FUT4, BCL2, KRT6A, KRT18, CEACAM18, TFF1, and MLH1; vii. a pre-determined biosignature indicative of endometrium carcinoma consists of, comprises, or comprises at least, 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from PAX8, PGR, ESRI, VHL, CALD1, LIN28B, NAPSA, KRT5, S100A6, DES, FLU, DSC3, SIOOP, CEACAM16, PDPN, ARG1, TLE1, WT1, BCL6, andMLHl; viii. a pre-determined biosignature indicative of gastrointestinal stromal tumor (GIST) consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from ANO1, SDC1, KRT19, MUC1, KRT8, ACVRL1, KIT, CDH1, S100A2, KRT7, ERBB2, S100A16, ENO2, S100A9, TPSAB1, KRT17, PAX8, PGR, ESRI, and VHL; ix. a pre-determined biosignature indicative of gastroesophageal carcinoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromFUT4, CDX2, SERPINB5, MUC5AC, AR, TFF1, NCAM2, TFF3, ISL1, ANO1, VIL1, PAX8, SOX2, CEACAM6, S100A13, ENO2, NAPSA, TPSAB1, SIOOB, and CD34; x. a pre-determined biosignature indicative of kidney renal cell carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromPAX8, CDH1, CDKN2A, SIOOP, S100A14, HAVCR1, HNF1B, KL, KRT7, MUC1, POU5F1, VHL, PAX2, AMACR, BCL6, S100A13, CA9, MDM2, SALL4, and SYP; xi. a pre-determined biosignature indicative of liver hepatocellular carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SERPINA1, CEACAM16, KRT19, AFP, MUC4, CEACAM5, MSH2, BCL6, DSC3, KRT15, S100A6, CEACAM20, GPC3, MUC1, CD34, VIL1, ERBB2, POU5F1, KRT18, andKRT16; xii. a predetermined biosignature indicative of lung carcinoma consists of, comprises, or comprises at least, 1, 2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromNAPSA, SOX2, CEACAM7, KRT7, S100A10, CEACAM6, S100A1, PAX8, AR, VHL, S100A13, CD99L2, KRT5, MUC1, CEACAM1, SFTPA1, TMPRSS2, TFF1, KRT15, andMUC4; xiii. a pre-determined biosignature indicative of melanoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SIOOB, KRT8, PMEL, KRT19, MUC1, MLANA, S100A14, S100A13, MIFF, S100A1, VIM, CDKN2A, ACVRL1, MS4A1, POU5F1, TPM1, UPK3A, SIOOP, GATA3, and CEACAM1; xiv. a pre-determined biosignature indicative of meningioma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SDC1, KRT8, ANO1, VIM, S100A14, S100A2, CEACAM1, MSH2, PGR, KRT10, TP63, CDS, INHA, CDH1, CCND1, MDM2, KRT16, SPN, SMARCB1, and S100A9; xv. a pre-determined biosignature indicative of Merkel cell carcinoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from ISL1, ERBB2, S100A12, S100A14, MYOG, SDC1, KRT7, S100PBP, MME, TMPRSS2, CEACAM5, CPS1, CR1, MUC4, CEACAM4, CA9, ENO2, FLU, LIN28B, and MLANA; xvi. a pre-determined biosignature indicative of neuroendocrine consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromNCAMl, ISL1, ENO2, POU5F1, TFF3, SYP, TPM4, S100A1, S100Z, MUC4, MPO, DSC3, CEACAM4, S100A7, ERBB2, CDX2, S100A11, KRT10, CEACAM5, and CEACAM3; xvii. a pre-determined biosignature indicative of ovary granulosa cell tumor consists of, comprises, or comprises at least, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromFOXL2, SDC1, MSH6, MUC1, KRT8, PGR, MME, SERPINA1, FLU, SIOOB, CEACAM21, AMACR, KRT1, SFTPA1, TPM1, CALCA, S100A11, NCAM1, ISL1, and ENO2; xviii. a pre-determined biosignature indicative of ovary, fallopian, peritoneum consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from WT1, PAX8, INHA, TFE3, S100A13, FOXL2, TLE1, MSLN, POU5F1, CEACAM3, ALPP, S100A10, FUT4, NKX3-1, CEACAM5, SOX2, ESRI, ENO2, ACVRL1, and SYP; xix. a pre-determined biosignature indicative of pancreas carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromPDXl, GATA3, ANO1, SERPINA1, ISL1, MUC5AC, FUT4, SMAD4, CDS, CALB2, S100A4, SMN1, ESRI, HNF1B, AMACR, MSH2, PDPN, MSLN, TFF1, and KRT6C; xx. a pre-determined biosignature indicative of pleural mesothelioma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from UPK3B, CALB2, WT1, SMARCB1, PDPN, INHA, CEACAM1, MSLN, KRT5, CA9, S100A13, SF1, CDH1, CDKN2A, FLU, SYP, CEACAM3, CPS1, SATB2, andBCL6; xxi. a pre-determmed biosignature indicative of prostate adenocarcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from KRT7, KLK3, NKX3-1, AMACR, S100A5, MUC1, MUC2, UPK3A, KL, CPS1, MSLN, PMEL, CNN1, SERPINA1, KRT2, CGB3, TMPRSS2, CEACAM6, SDC1, and AR; xxii. a pre-determined biosignature indicative of retroperitoneum consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromKRT19, KRT18, KRT8, TPM1, S100A14, CD34, TPM4, CDH1, CNN1, SDC1, AR, MDM2, KIT, TLE1, CPS1, CDK4, UPK3A, TMPRSS2, TPM3, and CEACAM1; xxiii. a pre-determined biosignature indicative of salivary and parotid consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromENO2, PIP, TPM1, KRT14, S100A1, ERBB2, TFF1, ALPP, DSC3, CTNNB1, CALB2, SALTA, ANO1, CEACAM16, HNF1B, KIT, ARG1, CEACAM18, TMPRSS2, and HAVCR1; xxiv. a pre-determined biosignature indicative of small intestine adenocarcinoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromPDXl, DES, MUC2, CDH17, CEACAM5, SERPINA1, KRT20, HNF1B, ESRI, ARG1, CD5, TLE1, PMEL, SOX2, SFTPA1, MME, CD99L2, MPO, SIOOP, and CA9; xxv. a pre-determined biosignature indicative of squamous cell carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from TP63, SOX2, KRT6A, KRT17, S100A1, CD3G, SFTPA1, AR, KRT5, SDC1, KRT20, DSC3, CNN1, MSH2, ESRI, S100A2, SERPLNB5, PDPN, S100A14, and TPM3; xxvi. a pre-determined biosignature indicative of thyroid carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from TG, PAX8, CPS1, S100A2, TPSAB1, CALB2, HNF1B, LNHA, ARG1, CNN1, CDK4, VIM, CEACAM5, TLE1, TFF3, KRT8, SIOOP, FOXL2, MUC1, and GATA3; xxvii. a pre-determined biosignature indicative of urothelial carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from GATA3, UPK2, KRT20, MUC1, S100A2, CPS1, TP63, CALB2, MITF, SIOOP, SERPINA1, DES, CTNNB1, MSLN, SALL4, VHL, KRT7, CD2, PAX8, and UPK3A; and / or xxviii. a pre-determined biosignature indicative of uterus consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromKRT19, KRT18, NCAM1, DES, FOXL2, CD79A, S100A14, ESRI, MSLN, MITF, UPK3B, TPM1, ENO2, SIOOP, MLH1, KRT8, CDH1, TPM4, SATB2, and MDM2. In some embodiments of the methods provided herein, the at least one pre-determined biosignature indicative of the at least one attribute of the cancer, wherein optionally the at least one attribute is an organ type, comprises selections of biomarkers according to Table 119; wherein optionally: i. a pre-determined biosignature indicative of adrenal gland consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from INHA, CDH1, SYP, MIB1, CALB2, KRT8, PSAP, KRT19, NCAM2, NKX3-1, ARG1, SERPINA1, CD34, TPM3, S100A7, ACVRL1, PMEL, CR1, ERG, and PECAM1; n. a predetermined biosignature indicative of bladder consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from GATA3, KRT20, UPK2, CPS1, SALL4, SERPINA1, DES, CALB2, MUC1, S100A2, MSLN, MITF, PAX8, S100A10, CNN1, UPK3A, CD3G, NAPSA, CD2, and MME; iii. a pre-determined biosignature indicative of brain consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from KRT8, ANO1, SIOOB, S100A14, SOX2, PDPN, CEACAM1, S100A2, NCAM1, MSH2, KRT18, NKX2-2, WT1, S100A1, GPC3, TLE1, CDS, S100Z, S100A16, and PGR; iv. a pre-determined biosignature indicative of breast consists of, comprises, or comprises at least, 1,2,3,4,5,6,7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from GATA3, ANKRD30A, KRT15, KRT7, S100A2, S100A1, MUC4, HNF1B, KRT18, SOX2, PIP, PAX8, MDM2, KRT16, MUC5AC, S100A6, TP63, TFF1, KRT5, and SERPINA1; v. a predetermined biosignature indicative of colon consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from CDX2, KRT7, MUC2, KRT20, MUC1, CEACAM5, CDH17, TFF3, KRT18, KRT6B, VIL1, SATB2, S100A6, SOX2, S100A14, HAVCR1, FUT4, ERG, HNF1B, and PTPRC; vi. a pre-determined biosignature indicative of eye consists of, comprises, or comprises at least, 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, or 20 features selected from PMEL, MLANA, MIFF, BCL2, S100A13, S100A2, S100A10, S100A1, MIB1, SOX2, ENO2, S100A16, VIM, VHL, PDPN, WT1, SIOOB, KRT7, KRT10, and PSAP; vii. a pre-determined biosignature indicative of female genital tract and peritoneum (FGTP) consists of, comprises, or comprises at least, 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from PAX8, ESRI, WT1, PGR, CDKN2A, FOXL2, KRT5, TPM4, SMARCB1, DES, TMPRSS2, CDK4, GATA3, AR, S100A13, MSH2, ANO1, CALB2, MS4A1, and CCND1; viii. a pre-determined biosignature indicative of gastroesophageal consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from CDX2, ANO1, FUT4, SERPINB5, SPN, NCAM2, VIL1, CD34, ENO2, TFF3, AR, S100A13, TPM1, CEACAM6, SOX2, PAX8, MUC5AC, CDH1, S100A11, and ISL1; ix. a pre-determined biosignature indicative of head, face or neck, NOS consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from KRT5, DSC3, TP63, HNF1B, MUC5AC, PAX5, KRT15, PGR, S100A6, TMPRSS2, MME, SIOOB, ENO2, CEACAM8, SALL4, ANO1, GATA3, LIN28B, CD99L2, and UPK3A; x. a pre-determined biosignature indicative of kidney consists of, comprises, or comprises at least, 1, 2, 3, 4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from PAX8, CDH1, HNF1B, S100A14, HAVCR1, CDKN2A, SIOOP, KL, KRT7, S100A13, VHL, PAX2, POU5F1, MUC1, AMACR, ENO2, MDM2, WT1, SYP, and AR; xi. a pre-determined biosignature indicative of liver, gallbladder, ducts consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SERPINA1, VIL1, HNF1B, ANO1, ESRI, SOX2, MUC4, S100A2, ENO2, CNN1, POU5F1, KRT5, S100A9, UPK3B, PSAP, KRT7, KL, TMPRSS2, SATB2, and S100A14; xii. a pre-determined biosignature indicative of lung consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromNAPSA, SOX2, SFTPA1, VHL, S100A1, S100A10, AR, TMPRSS2, CD99L2, CEACAM7, CEACAM6, KRT6A, KRT7, NCAM2, TP63, CEACAM1, MUC4, KRT20, CNN1, and ISL1; xiii. a pre-determined biosignature indicative of pancreas consists of, comprises, or comprises at least, 1,2,3,4,5,6,7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from PDX1, ANO1, SERPINA1, GATA3, ISL1, MUC5AC, SMAD4, FUT4, CDS, SMN1,NKX2-2, TFF1, AMACR, SOX2, HNF1B, S100Z, MSLN, DES, S100A4, and CALB2; xiv. a pre-determined biosignature indicative of prostate consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from KLK3, KRT7, NKX3-1, AMACR, CPS1, S100A5, UPK3A, KL, MUC1, CGB3, MUC2, TMPRSS2, MSLN, PMEL, S100A10, SERPINA1, KRT20, SFTPA1, BCL6, and TFF1; xv. a pre-determined biosignature indicative of skin consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SIOOB, KRT8, PMEL, KRT7, KRT19, GATA3, MDM2, AMACR, TPM1, TLE1, CEACAM19, CEACAM16, MLANA, TMPRSS2, AR, TFF3, BCL6, CR1, NCAM1, and MS4A1; xvi. a pre-determined biosignature indicative of small intestine consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromMUC2, CDH17, FLU, KRT20, CDX2, CDS, KRT7, MPO, CNN1, DSC3, DES, ANO1, S100A1, CALD1, TFF1, SPN, MITF, TMPRSS2, CALB2, and CEACAM16; and / or xvii. a predetermined biosignature indicative of thyroid consists of, comprises, or comprises at least, 1, 2, 3, 4, 5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from PAX8, TG,CPS1, SERPINB5, INHA, ARG1, CNN1, CEACAM5, TPSAB1, CALB2, HNF1B, VIM, CDK4, SIOOP, S100A2, LIN28B, TFF3, CGA, TLE1, and TPM3. In some embodiments of the methods provided herein, the at least one pre-determined biosignature indicative of the at least one attribute of the cancer, wherein optionally the at least one attribute is a histology, comprises selections of biomarkers according to Table 120; wherein optionally: i. a pre-determined biosignature indicative of adenocarcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from TMPRSS2, HNF1B, KRT5, MUC1, CEACAM5, MUC5AC, CDH17, TP63, ALPP, GATA3, CEACAM1, TFF3, S100A1, KRT8, PDX1, KRT17, CDH1, KLK3, CPS1, and S100A2; ii. a predetermined biosignature indicative of adenoid cystic carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from KRT14, KIT, TPM3, CGA, SMAD4, CTNNB1, DSC3, S100A6, TP63, TPM1, CALD1, MIB1, CD2, CDH1, ANO1, ENO2, CD3G, TPM2, CEACAM1, and BCL2; iii. a pre-determined biosignature indicative of adenosquamous carcinoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from TP63, SFTPA1, OSCAR, KRT19, KRT15, NAPSA, GPC3, MS4A1, S100A12, ERG, CEACAM6, VHL, SOX2, SERPINA1, KRT6A, CDKN2A, CD3G, PIP, NCAM2, and CEACAM7; iv. a pre-determined biosignature indicative of adrenal cortical carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from MIB1, INHA, CDH1, SYP, CALB2, NKX3-1, KRT19, ERBB2, MUC1, ARG1, VIM, CD34, CALD1, S100A9, MSLN, S100A10, CDS, PMEL, SDC1, and TP63; v. a pre-determined biosignature indicative of astrocytoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SIOOB, SOX2, NCAM1, MUC1, S100A4, KRT17, KRT8, S100A1, TPM4, CNN1, TPM2, OSCAR, AR, SDC1, SALL4, SMN1, SFTPA1, KIT, CA9, and S100A9; vi. a pre-determined biosignature indicative of carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from GATA3, MIFF, MUC5AC, PDPN, VIL1, CEACAM5, CDH1, CDH17, IL12B, SIOOP, KRT20, KRT7, SPN, TMPRSS2, ENO2, NKX2-2, PMEL, IMP3, BCL6, and S100A8; vii. a pre-determined biosignature indicative of carcinosarcoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromKRT6B, GPC3, MSLN, MUC1, S100A6, S100A2, MME, CDKN2A, CDH1, FOXL2, KRT7, CALB2, SFTPA1, ERG, PGR, KRT17, NAPSA, CALD1, LIN28B, and KIT; viii. a pre-determined biosignature indicative of cholangiocarcinoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SERPINA1, HNF1B, VIL1, TFF1, ENO2, NKX2-2, FUT4, MUC4, MLH1, TMPRSS2, WT1, KL, KRT7, ESRI, MDM2, SFTPA1, SMN1, KRT18, UPK3B, and COQ2; ix. a pre-determined biosignature indicative of clear cell carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from POU5F1, HAVCR1, CEACAM6, HNF1B, PAX8, NAPSA, CD34, MYOG, FOXL2, MITF, SIOOP, S100A9, S100A14, S100Z, WT1, CDH1, TTF1, SYP, MLH1, and KRT16; x. a pre-determined biosignature indicative of ductal carcinoma in situ (DOIS) consists of, comprises, or comprises at least, 1,2,3,4,5,6,7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from GATA3, HNF1B, DES, MME, ANKRD30A, SATB2, SOX2, NCAM2, PAX8, CEACAM4, PIP, MUC4, NKX3-1, SERPINA1, KRT20, KIT, NCAM1, KRT14, S100A2, and CDKN2A; xi. a pre-determined biosignature indicative of glioblastoma (GBM) consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SIOOB, KRT18, PDPN, NKX2-2, SOX2, NCAM1, KRT8, ERBB2, KRT15, KRT19, GATA3, CDKN2A, BCL6, S100A14, KRT10, UPK3A, SF1, CA9, CCND1, andKRTS; xii. a pre-determined biosignature indicative of GIST consists of, comprises, or comprises at least, 1, 2, 3, 4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from ANO 1, SDC1, MUC1, KRT19, KRT8, ACVRL1, KIT, ERBB2, CDH1, CEACAM19, FUT4, TFF3, S100A16, S100A13, ISL1, S100A9, TPSAB1, KRT18, IMP3, and KRT3; xiii. a pre-determined biosignature indicative of glioma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromKRT8, SIOOB, SYP, NCAM2, CD3G, SDC1, SOX2, CEACAM1, POU5F1, MIB1, SATB2, MDM2, NCAM1, KRT7, CGB3, CPS1, PDPN, CALCA, ERBB2, and TNFRSF8; xiv. a pre-determined biosignature indicative of granulosa cell tumor consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from FOXL2, SDC1, MSH6, KRT18, KRT8, MME, FLU, S100A9, CALCA, SIOOB, CCND1, CEACAM21, TLE1, SERPINA1, S100A11, SFTPA1, SYP, NCAM2, CD3G, and SOX2; xv. a pre-determined biosignature indicative of infiltrating lobular carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from CDH1, GATA3, S100A1, TFF3, CA9, MUC1,NKX3-1, ANKRD30A, SOX2, S100A5, MUC4, KRT7, OSCAR, MME, SERPINA1, CDK4, AR, CEACAM3, BCL6, and KRT5; xvi. a pre-determined biosignature indicative of leiomyosarcoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromKRT19, KRT8, KRT18, CNN1, TPM4, FOXL2, TPM2, TPM1, CD79A, CALB2, SATB2, S100A5, DES, S100A14, KRT2, ERBB2, PDPN, ENO2, CD2, and CALD1; xvii. a pre-determined biosignature indicative of liposarcoma consists of, comprises, or comprises at least, 1, 2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from KRT18, MDM2, CDK4, CDH1, KRT19, KRT7, PDPN, CD34, TPM4, CR1, ACVRL1, MME, KRT8, AMACR, CEACAM5, SIOOB, OSCAR, LIN28A, S100A12, and SDC1; xviii. a pre-determined biosignature indicative of melanoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SIOOB, PMEL, KRT19, KRT8, MUC1, S100A14, MLANA, S100A13, TPM1, MITF, VIM, CEACAM19, POU5F1, SATB2, CPS1, CDKN2A, KRT10, AR, ACVRL1, and LIN28A; xix. a pre-determined biosignature indicative of meningioma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from SDC1, KRT8, S100A14, ANO1, CEACAM1, VIM, KRT10, PGR, MSH2, CDS, S100A2, CDH1, TP63, SMARCB1, KRT16, S100A10, S100A4, DSC3, CCND1, and GATA3; xx. a pre-determined biosignature indicative of Merkel cell carcinoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from ISL1, ERBB2, MME, MYOG, CPS1, KRT7, SALL4, S100A12, S100A14, S100PBP, CR1, SMAD4, CEACAM5, MUC4, CA9, KRT10, SYP, CCND1, MSLN, and MLANA; xxi. a pre-determined biosignature indicative of mesothelioma consists of, comprises, or comprises at least, 1,2,3,4,5,6,7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from UPK3B, CALB2, PDPN, SMARCB1, MSLN, KRT5, CEACAM3, WT1, INHA, CEACAM1, CA9, TLE1, SATB2, CDH1, MUC2, CDKN2A, CEACAM18, MSH2, DSC3, andPTPRC; xxii. a predetermined biosignature indicative of neuroendocrine consists of, comprises, or comprises at least, 1, 2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from ISL1, NCAM1, S100A11, ENO2, S100A1, SYP, MUC1, TFF3, S100Z, PAX8, ERBB2, ESRI, S100A10, CEACAM5, SDC1, MUC4, MPO, S100A4, S100A7, and TP63; xxiii. a pre-determined biosignature indicative of non-small cell carcinoma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from ESRI, TMPRSS2, AR, S100A1, SFTPA1, MSLN, SOX2, ENO2, TP63, SMAD4, PTPRC, ISL1, CEACAM7, CEACAM20, S100Z, INHA, NCAM1, MUC2, TFF3, and PAX8; xxiv. a pre-determined biosignature indicative of oligodendroglioma consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromNCAMl, KRT18, CD2, S100A11, SYP, CDH1, S100A4, S100A14, CEACAM1, S100PBP, SDC1, SALL4, UPK2, COQ2, TPM2, CD99L2, TTF1, CD79A, INHA, and VIM; xxv. a pre-determined biosignature indicative of sarcoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected fromNCAMl, KRT19, S100A14, NKX2-2, KRT2, KRT7, SATB2, MYOG, CALD1, CEACAM19, CA9, KRT15, CDKN2A, SIOOP, WT1, TMPRSS2, S100A7, SERPINB5, DSC3, and ENO2; xxvi. a pre-determined biosignature indicative of sarcomatoid carcinoma consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from MME, VIM, S100A14, CD99L2, S100All,NKX3-l, SATB2, CPS1, MSLN, SFTPA1, POU5F1, CDH1, OSCAR, S100A5, IMP3, CEACAM1, PMS2, NCAM2, KRT15, and S100A12; xxvii. a pre-determined biosignature indicative of serous consists of, comprises, or comprises at least, 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from WT1, PAX8, KRT7, CDKN2A, MSLN, ACVRL1, SATB2, CDK4, DSC3, AR, S100A16, ANO1, S100A5, SDC1, IMP3, SERPINA1, KRT4, ESRI, FOXL2, andKRT15; xxviii. a predetermined biosignature indicative of small cell carcinoma consists of, comprises, or comprises at least, 1,2,3,4,5,6,7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from NCAM1, ISL1, PAX5, KIT, MUC4, S100A10, MUC1, CTNNB1, MITF, NKX2-2, S100A11, SMN1, MSLN, S100A6, BCL2, SYP, KL, CGB3, TPSAB1, TFF3; and / or xxix. a pre-determined biosignature indicative of squamous consists of, comprises, or comprises at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features selected from TP63, KRT5, KRT17, SOX2, AR, CD3G, KRT6A, S100A1, DSC3, SERPINB5, HNF1B, SDC1, S100A6, TPSAB1, KRT20, HAVCR1, TTF1, MSH2, PMS2, and CNNL The system and methods provided herein envision any combination of predetermined biosignatures above. See, e.g., FIGs. 4A-C and related text. If making selections of biomarkers from within the pre-determined biosignatures provided herein, one may choose biomarkers that provide the most informative predictions. For example, one may choose the top 1,2,3,4,5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 features, e.g., 3 or 5 or 10 or 20 features, or at least 3 or 5 or 10 or 20 features, with the highest Importance value for each pre-determined biosignature listed in Tables 118-120. In some embodiments of the methods provided herein, performing the at least one assay to assess the one or more biomarkers in step (b), including without limitation those described above with respect to Tables 118-120, comprises assessing the markers in the at least one pre-determined biosignature using DNA analysis and / or expression analysis, wherein: i. the DNA analysis consists of or comprises determining a sequence, mutation, polymorphism, deletion, insertion, substitution, translocation, fusion, break, duplication, amplification, repeat, copy number, copy number variation (CNV; copy number alteration; CNA), or any combination thereof; ii. the DNA analysis is performed using polymerase chain reaction (PCR), in situ hybridization, amplification, hybridization, microarray, nucleic acid sequencing, dye termination sequencing, pyrosequencing, next generation sequencing (NGS; high-throughput sequencing), whole exome sequencing, or any combination thereof; and / or iii. the expression analysis consists of or comprises analysis ofRNA, where optionally: i. the RNA analysis consists of or comprises determining a sequence, mutation, polymorphism, deletion, insertion, substitution, translocation, fusion, break, duplication, amplification, repeat, copy number, amount, level, expression level, presence, or any combination thereof; and / or ii. the RNA analysis is performed using polymerase chain reaction (PCR), in situ hybridization, amplification, hybridization, microarray, nucleic acid sequencing, dye termination sequencing, pyrosequencing, next generation sequencing (NGS; high-throughput sequencing), whole transcriptome sequencing, or any combination thereof; iv. the expression analysis consists of or comprises analysis of protein, where optionally: i. the protein analysis consists of or comprises determining a sequence, mutation, polymorphism, deletion, insertion, substitution, fusion, amplification, amount, level, expression level, presence, or any combination thereof; and / or ii. the protein analysis is performed using immunohistochemistry (IHC), flow cytometry, an immunoassay, an antibody or functional fragment thereof, an aptamer, mass spectrometry, or any combination thereof; and / or v. any combination thereof. In some embodiments, performing the assay to assess the one or more biomarkers in step (b) comprises assessing the markers in the at least one pre-determined biosignature using: a combination of the DNA analysis and the RNA analysis; a combination of the DNA analysis and the protein analysis; a combination of the RNA analysis and the protein analysis; or a combination of the DNA analysis, the RNA analysis, and the protein analysis. In some embodiments, performing the assay to assess the one or more biomarkers in step (b) comprises RNA analysis of messenger RNA transcripts. In some embodiments of the methods provided herein, the at least one pre-determined biosignature indicative of the at least one attribute of the cancer, optionally a cancer type or primary tumor origin, comprises selections of biomarkers according to at least one of FIGs. 6I-AC; wherein optionally: i. a pre-determined biosignature indicative of breast adenocarcinoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from GATA3, CDH1, PAX8, KRAS, ELK4, CCND1, MECOM, PBX1, CREBBP, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected fiom GATA3, NY-BR-1, KRT15, CK7, S100A2, RCCMa, MUC4, CK18, HNF1B and S100A1; ii. a pre-determined biosignature indicative of central nervous system cancer comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected fiom IDH1, SOX2, OLIG2, MYC, CREB3L2, SPECCI, EGFR, FGFR2, SETBP1, andZNF217, and / or expression analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected fiom SIOOB, CK18, CK8, SOX2, DOG1, CD56, PDPN, NKX2-2, CK19, and S100A14; m. a pre-determined biosignature indicative of cervical adenocarcinoma comprises DNA analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from TP53, MECOM, RPN1, U2AF1, GNAS, RAC1, KRAS, FL11, EXT1, and CDK6, and / or expression analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from ER, pl6, CYCLIND1, LIN28A, PR, SMARCB1, CEACAM4, SIOOB, CD15, and PSAP; iv. apre-determined biosignature indicative of cholangiocarcinoma comprises DNA analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from TP53, ARID1A, MAF, KRAS, CACNA1D, SPEN, SETBP1, CDK12, LHFPL6, and MDS2, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected fromHNFIB, VILLIN, ANTITRYPSIN, ER, DOG1, SOX2, MUC4, S100A2, KRT5, and CK7; v. a pre-determined biosignature indicative of colon adenocarcinoma comprises DNA analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from APC, CDX2, KRAS, SETBP1, FLT3, LHFPL6, CDKN2A, FLT1, ASXL1, and CDKN2B, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from CDX2, CK7, MUC2, CK20, MUC1, SATB2, VILLIN, CEACAM5, CDK17, and S100A6; vi. a pre-determined biosignature indicative of gastroesophageal adenocarcinoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from CDX2, ERG, TP53, KRAS, U2AF1, ZNF217, CREB3L2, IRF4, TCF7L2, and LHFPL6, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from CD15, CDX2, MASPIN, MUC5AC, AR, TFF1, NCAM2, TFF3, ISL1, and DOG1; vii. apre-determined biosignature indicative of gastrointestinal stromal tumor (GIST) comprises DNA analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from c-KIT (KIT), TP53, MAX, PDGFRA, TSHR, MSI2, SPEN, JAKI, SETBP1, and CDH11, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from DOG1, CD138, CK19, MUC1, CK8, ACVRL1, KIT, E-CADHERIN, S100A2, and CK7; viii. a pre-determined biosignature indicative of hepatocellular carcinoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from HLF, CACNA1D, HMGN2P46, KRAS, FANCF, PROC, ERG, FLT1, FGFR1, and ACSL6, and / or expression analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from ANTITRYPSIN, CEACAM16, CK19, AFP, MUC4, CEACAM5, MSH2, BCL6, DSC3, and KRT15; ix. a predetermined biosignature indicative of lung adenocarcinoma comprises DNA analysis of at least, 1,2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected fromNKX-2, KRAS, TP53, TPM4, CDX2, TERT, FOXA1, SETBP1, CDKN2A, andLHFPL6, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected fromNapsin A, SOX2, CEACAM7, CK7, S100A10, CEACAM6, S100A1, RCCMa, AR and VHL; x. a pre-determined biosignature indicative of melanoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from IRF4, SOX10, TP53, BRAF, FGFR2, TRIM27, EP300, CDKN2A, LRP1B, andNRAS, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from SIOOB, CK8, HMB-45, CD19, MUC1, MLANA, S100A14, S100A13, MITF, and S100A1; xi. a pre-determined biosignature indicative of meningioma comprises DNA analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from CHEK2, TP53, MYCL, THRAP3, MPL, EBF1, EWSR1, PMS2, FLU, andNTRK2, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from CD138, CK8, DOG1, VIM, S100A14, S100A2, CEACAM1, MSH2, PR, and KRT10; xii. a pre-determined biosignature indicative of ovarian granulosa cell tumor comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected fromFOXL2, TP53, EWSR1, CBFB, SPECCI, BCL3, MYH9, TSHR, GID4, and SOX2, and / or expression analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from FOXL2, CD138, MSH6, MUC1, CK8, PR, MME, ANTITRYPSIN, FLU, and SIOOB; xiii. a pre-determined biosignature indicative of ovarian & fallopian tube adenocarcinoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from TP53, MECOM, KRAS, TPM4, RAC1, ASXL1, EP300, CDX2, RPN1, and WT1, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from WT1, RCCMa, INHIBIN-alpha, TFE3, S100A13, FOLX2, TLE1, MSLN, POU5F1, and CEACAM3; xiv. a pre-determined biosignature indicative of pancreas adenocarcinoma comprises DNA analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from KRAS, CDKN2A, CDKN2B, FANCF, IRF4, TP53, ASXL1, SETBP 1, APC, and FOXO1, and / or expression analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected fromPDXl, GATA3, DOG1, ANTITRYPSIN, ISL1, MUC5AC, CD15, SMAD4, CD5, and CALB2; xv. a predetermined biosignature indicative of prostate adenocarcinoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from FOXA1, PTEN, KLK2, FOXO1, GATA2, FANCA, LHFPL6, KRAS, ETV6, and ERCC3, and / or expression analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from CK7, PSA, NKX3-1, AMACR, S100A5, MUC1, MUC2, UPK3A, KL and HEPPAR-1; xvi. a pre-determined biosignature indicative of renal cell carcinoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from VHL, TP53, EBF1, MAF, RAFI, CTNNA1, XPC, MUC1, KRAS, and BTG1, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from RCCMa, E-CADHERIN, pl6, SIOOP, S100A14, HAVCR1, HNF1B, KL, CK7, andMUCl; xvii. a pre-determined biosignature indicative ofsquamous cell carcinoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from TP53, SOX2, KLHL6, CDKN2A, LPP, CACNA1D, TFRC, KRAS, RPN1, and CDX2, and / or expression analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from P63, SOX2, CK6, KRT17, S100A1, CD3G, SFTPA1, AR, KRT5, and CD138; xviii. a pre-determined biosignature indicative of thyroid cancer comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from BRAF, NKX2-1, TP53, MYC, KDSR, TRRAP, CDX2, KRAS, FHIT, and SETBP1, and / or expression analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from THYROGLOBULIN, RCCMa, HEPPAR-1, S100A2, TPSAB1, CALB2, HNF1B, INHIBIN-alpha, ARG1, and CNN 1; xix. a pre-determined biosignature indicative of urothelial carcinoma comprises DNA analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from GATA3, ASXL1, CDKN2B, TP53, CTNNA1, CDKN2A, KRAS, IL7R, CREBBP, and VHL, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from GATA3, UPII, CK20, MUC1, S100A2, HEPPAR-1, P63, CALB2, MITF, and SIOOP; xx. a pre-determined biosignature indicative of uterine endometrial adenocarcinoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected fromPTEN, PAX8, PIK3CA, CCNE1, TP53, MECOM, ESRI, CDX2, CDKN2A, and KRAS, and / or expression analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from RCCMa, PR, ER, VHL, CALD1, LIN28B, Napsin A, KRT5, S100A6, and DES; and / or xxi. a pre-determined biosignature indicative of uterine sarcoma comprises DNA analysis of at least, 1, 2, 3,4, 5, 6, 7, 8, 9, or 10 features selected from RBI, SPECCI, FANCC, TP53, CACNA1D, JAKI, ETV1, PRRX1, PTCHI, and HOXD13, and / or expression analysis of at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 features selected from CK19, CK18, CD56, DES, FOXL2, CD79A, S100A14, ER, MSLN, and MITF. In some embodiments, the DNA analysis consists of or comprises determining a sequence, mutation, polymorphism, deletion, insertion, substitution, translocation, fusion, break, duplication, amplification, repeat, copy number, copy number variation (CNV; copy number alteration; CNA), or any combination thereof. In some embodiments, the DNA analysis is performed using polymerase chain reaction (PCR), in situ hybridization, amplification, hybridization, microarray, nucleic acid sequencing, dye termination sequencing, pyrosequencing, next generation sequencing (NGS; high-throughput sequencing), whole exome sequencing, or any combination thereof. In some embodiments, the expression analysis consists of or comprises analysis of RNA. In some embodiments, the RNA analysis consists of or comprises determining a sequence, mutation, polymorphism, deletion, insertion, substitution, translocation, fusion, break, duplication, amplification, repeat, copy number, amount, level, expression level, presence, or any combination thereof. In some embodiments, the RNA analysis is performed using polymerase chain reaction (PCR), in situ hybridization, amplification, hybridization, microarray, nucleic acid sequencing, dye termination sequencing, pyrosequencing, next generation sequencing (NGS; high-throughput sequencing), whole transcriptome sequencing, or any combination thereof. In some embodiments, the expression analysis consists of or comprises analysis of protein. In some embodiments, the protein analysis consists of or comprises determining a sequence, mutation, polymorphism, deletion, insertion, substitution, fusion, amplification, amount, level, expression level, presence, or any combination thereof. In some embodiments, the protein analysis is performed using immunohistochemistry (IHC), flow cytometry, an immunoassay, an antibody or functional fragment thereof, an aptamer, mass spectrometry, or any combination thereof. Any usefill combination of such analyses is contemplated by the invention. In the methods provided herein, the at least one pre-determined biosignature may comprise or may further comprise, as the case may be, selections of biomarkers according to any one of Tables 2116 assessed using DNA analysis. In some embodiments, the DNA analysis consists of or comprises determining a sequence, mutation, polymorphism, deletion, insertion, substitution, translocation, fusion, break, duplication, amplification, repeat, copy number, copy number variation (CNV; copy number alteration; CNA) or any combination thereof. In some embodiments, the DNA analysis is performed using polymerase chain reaction (PCR), in situ hybridization, amplification, hybridization, microarray, nucleic acid sequencmg, dye termmation sequencmg, pyrosequencmg, next generation sequencing (NGS; high-throughput sequencing), whole exome sequencing, or any combination thereof. In some embodiments, the at least one pre-determined biosignature comprising selections of biomarkers according to any one of Tables 2-116 comprises: i. a pre-determined biosignature indicative of adrenal cortical carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 2; ii. a pre-determined biosignature indicative of anus squamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25,26, 27,28,29,30,31,32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46, 47, 48,49, or at least 50 features selected from Table 3; iii. a pre-determined biosignature indicative of appendix adenocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45,46,47, 48, 49, or at least 50 features selected from Table 4; iv. a pre-determined biosignature indicative of appendix mucinous adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47,48,49, or at least 50 features selected from Table 5; v. a pre-determined biosignature indicative of bile duct NOS cholangiocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43, 44,45, 46,47,48,49, or at least 50 features selected from Table 6; vi. a pre-determined biosignature indicative of brain astrocytoma NOS origin consisting of, comprising, or comprising at least 1,2,3,4,5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25,26, 27,28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44, 45,46,47,48, 49, or at least 50 features selected from Table 7; vii. a pre-determined biosignature indicative of brain astrocytoma anaplastic origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43, 44,45, 46,47,48,49, or at least 50 features selected from Table 8; viii. a pre-determined biosignature indicative of breast adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1,2,3,4,5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25,26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43, 44,45,46,47, 48,49, or at least 50 features selected from Table 9; ix. a pre-determined biosignature indicative of breast carcinoma NOS consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 10; x. a pre-determined biosignature indicative of breast infiltrating duct adenocarcinoma origin consisting of, comprising, or comprising at least 1,2,3,4,5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25,26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43, 44, 45,46, 47,48,49, or at least 50 features selected from Table 11; xi. a pre-determined biosignature indicative of breast infiltrating lobular adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 12; xii. a pre-determined biosignature indicative of breast metaplastic carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 13; xiii. a pre-determined biosignature indicative of cervix adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 14; xiv. a pre-determined biosignature indicative of cervix carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 15; xv. a pre-determined biosignature indicative of cervix squamous carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42,43, 44,45, 46,47, 48, 49, or at least 50 features selected from Table 16; xvi. a pre-determined biosignature indicative of colon adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 17; xvii. a pre-determined biosignature indicative of colon carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 18; xviii. a pre-determined biosignature indicative of colon mucinous adenocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 19; xix. a pre-determined biosignature indicative of conjunctiva malignant melanoma NOS origin consisting of, comprising, or comprising at least 1,2,3, 4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25,26, 27,28, 29,30,31,32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46, 47, 48,49, or at least 50 features selected from Table 20; xx. a pre-determined biosignature indicative of duodenum and ampulla adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42, 43,44, 45,46,47,48,49, or at least 50 features selected from Table 21; xxi. a pre-determmed biosignature indicative of endometrial endometrioid adenocarcinoma origin consisting of, comprising, or comprising at least 1,2,3,4,5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 22; xxii. a pre-determined biosignature indicative of endometrial adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 23; xxiii. a pre-determined biosignature indicative of endometrial carcinosarcoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 24; xxiv. a pre-determined biosignature indicative of endometrial serous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 25; xxv. a pre-determined biosignature indicative of endometrium carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 26; xxvi. a pre-determined biosignature indicative of endometrium carcinoma undifferentiated origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47,48,49, or at least 50 features selected from Table 27; xxvii. a pre-determined biosignature indicative of endometrium clear cell carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43, 44,45, 46,47,48,49, or at least 50 features selected from Table 28; xxviii. a pre-determined biosignature indicative of esophagus adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 29; xxix. a pre-determined biosignature indicative of esophagus carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43,44,45,46, 47,48, 49, or at least 50 features selected from Table 30; xxx. a predetermined biosignature indicative of esophagus squamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 31; xxxi. a pre-determined biosignature indicative of extrahepatic cholangio common bile gallbladder adenocarcinoma NOS origm consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 32; xxxii. a pre-determined biosignature indicative of fallopian tube adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42,43, 44,45, 46,47, 48, 49, or at least 50 features selected from Table 33; xxxiii. a pre-determined biosignature indicative of fallopian tube carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45,46,47, 48, 49, or at least 50 features selected from Table 34; xxxiv. a pre-determined biosignature indicative of fallopian tube carcinosarcoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25,26, 27,28,29,30,31,32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46, 47, 48,49, or at least 50 features selected from Table 35; xxxv. a pre-determined biosignature indicative of fallopian tube serous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 36; xxxvi. a pre-determined biosignature indicative of gastric adenocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42,43, 44,45, 46,47, 48, 49, or at least 50 features selected from Table 37; xxxvii. a pre-determined biosignature indicative of gastroesophageal junction adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42, 43,44, 45,46,47,48,49, or at least 50 features selected from Table 38; xxxviii. a pre-determined biosignature indicative of glioblastoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25,26, 27, 28,29,30,31,32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46, 47, 48,49, or at least 50 features selected from Table 39; xxxix. a pre-determined biosignature indicative of glioma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 40; xl. a pre-determined biosignature indicative of gliosarcoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42, 43,44,45,46,47, 48,49, or at least 50 features selected from Table 41; xli. a predetermined biosignature indicative of head, face or neck NOS squamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48, 49, or at least 50 features selected from Table 42; xlii. a pre-determined biosignature indicative of intrahepatic bile duct cholangiocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47,48,49, or at least 50 features selected from Table 43; xliii. a pre-determined biosignature indicative of kidney carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 44; xliv. a pre-determined biosignature indicative of kidney clear cell carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47,48,49, or at least 50 features selected from Table 45; xlv. a pre-determined biosignature indicative of kidney papillary renal cell carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43, 44,45, 46,47,48,49, or at least 50 features selected from Table 46; xlvi. a pre-determined biosignature indicative of kidney renal cell carcinoma NOS origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 47; xlvii. a pre-determined biosignature indicative of larynx NOS squamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 48; xlviii. a pre-determined biosignature indicative of left colon adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 49; xlix. a pre-determined biosignature indicative of left colon mucinous adenocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42,43, 44,45, 46,47, 48, 49, or at least 50 features selected from Table 50; 1. a pre-determined biosignature indicative of liver hepatocellular carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 51; li. a pre-determined biosignature indicative of lung adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47, 48,49, or at least 50 features selected from Table 52; hi. a pre-determmed biosignature indicative of lung adenosquamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 53; liii. a pre-determined biosignature indicative of lung carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42,43, 44,45, 46,47, 48, 49, or at least 50 features selected from Table 54; liv. a pre-determined biosignature indicative of lung mucinous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 55; Iv. a pre-determined biosignature indicative of lung neuroendocrine carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42,43, 44,45, 46,47, 48, 49, or at least 50 features selected from Table 56; Ivi. a pre-determined biosignature indicative of lung non-small cell carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 57; Ivii. a pre-determined biosignature indicative of lung sarcomatoid carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 58; Iviii. a pre-determined biosignature indicative of lung small cell carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42,43,44, 45,46,47,48, 49, or at least 50 features selected from Table 59; lix. a pre-determined biosignature indicative of lung squamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 60; lx. a pre-determined biosignature indicative of meninges meningioma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 61; Ixi. a pre-determined biosignature indicative of nasopharynx NOS squamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47,48,49, or at least 50 features selected from Table 62; Ixii. a pre-determined biosignature indicative of oligodendroglioma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 63; Ixiii. a pre-determined biosignature indicative of oligodendroglioma aplastic origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47,48,49, or at least 50 features selected from Table 64; Ixiv. a pre-determined biosignature indicative of ovary adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 65; Ixv. a pre-determined biosignature indicative of ovary carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42,43, 44,45, 46,47, 48, 49, or at least 50 features selected from Table 66; Ixvi. a pre-determined biosignature indicative of ovary carcinosarcoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 67; Ixvii. a pre-determined biosignature indicative of ovary clear cell carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 68; Ixviii. a pre-determined biosignature indicative of ovary endometrioid adenocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 69; Ixix. a pre-determined biosignature indicative of ovary granulosa cell tumor NOS origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25,26, 27,28, 29,30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45, 46,47,48,49, or at least 50 features selected from Table 70; Ixx. a pre-determined biosignature indicative of ovary high-grade serous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43, 44,45, 46,47,48,49, or at least 50 features selected from Table 71; Ixxi. a pre-determined biosignature indicative of ovary low-grade serous carcinoma origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43,44,45,46, 47,48, 49, or at least 50 features selected from Table 72; Ixxii. a pre-determined biosignature indicative of ovary mucinous adenocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43,44,45,46, 47,48, 49, or at least 50 features selected from Table 73; Ixxiii. a pre determined biosignature indicative of ovary serous carcinoma origin consistmg of, comprismg, or comprising at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43, 44,45,46,47,48, 49, or at least 50 features selected from Table 74; Ixxiv. a pre-determined biosignature indicative of pancreas adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 75; Ixxv. a pre-determined biosignature indicative of pancreas carcinoma NOS origin consisting of, comprising, or comprising at least 1,2,3,4,5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 76; Ixxvi. a pre-determined biosignature indicative of pancreas mucinous adenocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 77; Ixxvii. a pre-determined biosignature indicative of pancreas neuroendocrine carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 78; Ixxviii. a pre-determined biosignature indicative of parotid gland carcinoma NOS origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 79; Ixxix. a pre-determined biosignature indicative of peritoneum adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 80; Ixxx. a pre-determined biosignature indicative of peritoneum carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 81; Ixxxi. a pre-determined biosignature indicative of peritoneum serous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45,46,47, 48,49, or at least 50 features selected from Table 82; Ixxxii. a pre-determined biosignature indicative of pleural mesothelioma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 83; Ixxxiii. a pre-determined biosignature indicative of prostate adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 84; Ixxxiv. a pre-determined biosignature indicative of rectosigmoid adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 85; Ixxxv. a pre-determined biosignature indicative of rectum adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25,26, 27,28, 29,30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47,48,49, or at least 50 features selected from Table 86; Ixxxvi. a pre-determined biosignature indicative of rectum mucinous adenocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43, 44,45, 46,47,48,49, or at least 50 features selected from Table 87; Ixxxvii. a pre-determined biosignature indicative of retroperitoneum dedifferentiated liposarcoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 88; Ixxxviii. a pre-determined biosignature indicative of retroperitoneum leiomyosarcoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47,48,49, or at least 50 features selected from Table 89; Ixxxix. a pre-determined biosignature indicative of right colon adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43, 44,45, 46,47,48,49, or at least 50 features selected from Table 90; xc. a pre-determined biosignature indicative of right colon mucinous adenocarcinoma origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,43,44,45,46, 47,48, 49, or at least 50 features selected from Table 91; xci. a pre-determined biosignature indicative of salivary gland adenoidcystic carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 92; xcii. a pre-determined biosignature indicative of skin Merkel cell carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 93; xciii. a pre-determined biosignature indicative of skin nodular melanoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 94; xciv. a pre-determined biosignature indicative of skin squamous carcinoma origin consisting of, comprising, or comprising at least 1,2,3,4,5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43, 44, 45,46,47, 48,49, or at least 50 features selected from Table 95; xcv. a pre-determined biosignature indicative of skin melanoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42, 43,44,45,46,47, 48,49, or at least 50 features selected from Table 96; xcvi. a predetermined biosignature indicative of small intestine gastrointestinal stromal tumor (GIST) NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43, 44,45, 46,47,48,49, or at least 50 features selected from Table 97; xcvii. a pre-determined biosignature indicative of small intestine adenocarcinoma origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 98; xcviii. a pre-determined biosignature indicative of stomach gastrointestinal stromal tumor (GIST) NOS origin consisting of, comprising, or comprising at least 1, 2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25,26, 27,28, 29,30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45, 46, 47, 48,49, or at least 50 features selected from Table 99; xcix. a pre-determined biosignature indicative of stomach signet ring cell adenocarcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43,44,45,46, 47,48, 49, or at least 50 features selected from Table 100; c. a predetermined biosignature indicative of thyroid carcinoma NOS origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 101; ci. a pre-determined biosignature indicative of thyroid carcinoma anaplastic NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 102; cii. a pre-determined biosignature indicative of papillary carcinoma of thyroid origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 103; ciii. a pre-determined biosignature indicative of tonsil oropharynx tongue squamous carcinoma origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25,26, 27,28,29,30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45, 46,47,48,49, or at least 50 features selected from Table 104; civ. a pre-determined biosignature indicative of transverse colon adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42, 43,44, 45,46,47,48,49, or at least 50 features selected from Table 105; cv. a pre-determined biosignature indicative of urothelial bladder adenocarcinoma NOS origin consisting of, comprising, or comprising at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 106; cvi. a pre-determined biosignature indicative of urothelial bladder carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 107; cvii. a pre-determined biosignature indicative of urothelial bladder squamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41, 42,43, 44,45, 46,47, 48, 49, or at least 50 features selected from Table 108; cviii. a pre-determined biosignature indicative of urothelial carcinoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 109; cix. a pre-determined biosignature indicative of uterine endometrial stromal sarcoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 110; ex. a pre-determined biosignature indicative of uterus leiomyosarcoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41,42,43, 44,45, 46, 47, 48,49, or at least 50 features selected from Table 111; cxi. a pre-determined biosignature indicative of uterus sarcoma NOS origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42,43,44, 45, 46,47, 48,49, or at least 50 features selected from Table 112; cxii. a pre-determined biosignature indicative of uveal melanoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 113; cxiii. a pre-determined biosignature indicative of vaginal squamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40, 41,42, 43,44, 45,46,47,48, 49, or at least 50 features selected from Table 114; cxiv. a pre-determined biosignature indicative of vulvar squamous carcinoma origin consisting of, comprising, or comprising at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42,43, 44,45, 46,47, 48,49, or at least 50 features selected from Table 115; and / or cxv. a pre-determined biosignature indicative of skin trunk melanoma origin consisting of, comprising, or comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,40,41,42, 43,44, 45,46, 47,48, 49, or at least 50 features selected from Table 116. In some embodiments, the selections of biomarkers according to any one of Tables 2-116 comprises the top 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 100% of the feature biomarkers with the highest Importance value in the corresponding table / s. In some embodiments, the selections of biomarkers according to any one of Tables 2-116 comprises the top 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44, 45,46,47,48, 49 or 50 feature biomarkers with the highest Importance value in the corresponding table / s. In some embodiments, the selections of biomarkers according to any one of Tables 2-116 comprises at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 100% of the top 1,2,3,4,5,6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43,44,45,46, 47,48, 49, or 50 feature biomarkers with the highest Importance value in the corresponding table / s. In some embodiments, the selections of biomarkers according to any one of Tables 2-116 comprises at least 50%, 60%, 70%, 75%, 80%, 85%, 90%, 95%, or 100% of the top 5, 10, 15, 20, 25, 30, 35,40,45, 50, 60, 65, 70, 75, 80, 85, 90, 95, or 100 feature biomarkers with the highest Importance value in the corresponding table. If making selections of biomarkers from within the pre-determined biosignatures provided herein, one may choose biomarkers that provide the most informative predictions. For example, one may choose the top 1,2,3,4,5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,42,43,44, 45,46, 47,48, 49, or 50 features, e.g., 3 or 5 or 10 or 20 or 25 features, or at least 3 or 5 or 10 or 20 or 25 features, with the highest Importance value for each pre-determined biosignature listed in Tables 2-116. In some embodiments of the methods provided herein, step (b) comprises determining a gene copy number for at least one member of the biosignature, and step (d) comprises processing the gene copy number. In some embodiments, step (b) comprises determining a sequence for at least one member of the biosignature, and step (d) comprises processing the sequence. In some embodiments, step (b) comprises determining a sequence for a plurality of members of the biosignature, and step (d) comprises comparmg the sequence to a reference sequence (e.g., wild type) to identify microsatellite repeats, and identifying members of the biosignature that have microsatellite instability (MSI. In some embodiments, step (b) comprises determining a sequence for a plurality of members of the biosignature, and step (d) comprises comparing the sequence to a reference sequence (e.g., wild type) to identify a tumor mutational burden (TMB. In some embodiments, step (b) comprises determining an mRNA transcript level for at least 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42,43, 44,45,46,47, 48, 49, or at least 50 genes in any one of Tables 117-120, and / or INSMI, and step (d) comprises processing the transcript levels. In some embodiments, a gene copy number, CNV or CNA of a gene in the biosignature is determined by measuring the copy number of at least one proximate region to the gene, wherein optionally the proximate region comprises at least one location in the same subband, band, or arm of the chromosome wherein the gene is located. In some embodiments of the methods provided herein, the one or more biomarkers in the biosignature are assessed as described in their corresponding table, including without limitation Tables 2-116 or Tables 117-120. In some embodiments of the methods provided herein, the model comprises a plurality of intermediate models, wherein the plurality of intermediate models comprises at least one pairwise comparison module and / or at least one multi-class classification model. In some embodiments, the model calculates a statistical measure that the biosignature corresponds to at least one of the at least one pre-determined biosignatures. In some embodiments, the processing in step (d) comprises a pairwise comparison between candidate pre-determined biosignatures, and a probability is calculated that the biosignature corresponds to either one of the pairs of the at least one pre-determined biosignatures; and / or using at least one multi-class classification model to assess the biosignature. In some embodiments, the pairwise comparison between the two candidate primary tumor origins and / or the multi-class classification model is determined using a machine learning classification algorithm, wherein optionally the machine learning classification algorithm comprises a boosted tree. In some embodiments, the pairwise comparison between the two candidate primary tumor origins is applied to at least one pre-determined biosignature supplied herein, e.g., with respect to Tables 2-116; and / or the multi-class classification model is applied to at least one pre-determined biosignature supplied herein, e.g., with respect to Tables 118-120. In some embodiments, the methods supplied herein further comprise determining intermediate model predictions, wherein the intermediate model predictions comprise: a cancer type determined by the joint pairwise comparisons between at least one pair of pre-determined biosignatures supplied herein, e.g., with respect to Tables 2-116; a cancer / disease type determined by an intermediate multi-class model applied to at least one pre-determined biosignature supplied herein, e.g., with respect to Table 118, wherein optionally the intermediate multi-class model is applied to at least 1,2,3,4,5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22, 23,24, 25,26, 27, or 28 of the pre-determined biosignatures in Table 118; an organ group type determined by an intermediate multi-class model applied to at least one pre-determined biosignature supplied herein, e.g., with respect to Table 119, wherein optionally the intermediate multi-class model is applied to at least 1,2, 3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25, 26, or 27 of the predetermined biosignatures in Table 119; and / or a histology determined by an intermediate multi-class model applied to at least one pre-determined biosignature supplied herein, e.g., with respect to Table 120, wherein optionally the intermediate multi-class model is applied to at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21,22,23,24, 25,26, 27, 28, or 29 of the pre-determined biosignatures in Table 120. In some embodiments, the processing in step (d) comprises inputting the outputs of each of the utilized intermediate multi-class models into a final predictor model that provides the prediction in step (e), wherein optionally the final predictor model comprises a machine learning algorithm, wherein optionally the machine learning algorithm comprises a boosted tree. As described herein, the predicted at least one attribute of the cancer provided by the systems and methods herein can be provided at a desired level of granularity. In some embodiments, the predicted at least one attribute of the cancer comprises at least one of adrenal cortical carcinoma; anus squamous carcinoma; appendix adenocarcinoma, NOS; appendix mucinous adenocarcinoma; bile duct, NOS, cholangiocarcinoma; brain astrocytoma, anaplastic; brain astrocytoma, NOS; breast adenocarcinoma, NOS; breast carcinoma, NOS; breast infiltrating duct adenocarcinoma; breast infiltrating lobular carcinoma, NOS; breast metaplastic carcinoma, NOS; cervix adenocarcinoma, NOS; cervix carcinoma, NOS; cervix squamous carcinoma; colon adenocarcinoma, NOS; colon carcinoma, NOS; colon mucinous adenocarcinoma; conjunctiva malignant melanoma, NOS; duodenum and ampulla adenocarcinoma, NOS; endometrial adenocarcinoma, NOS; endometrial carcinosarcoma; endometrial endometrioid adenocarcinoma; endometrial serous carcinoma; endometrium carcinoma, NOS; endometrium carcinoma, undifferentiated; endometrium clear cell carcinoma; esophagus adenocarcinoma, NOS; esophagus carcinoma, NOS; esophagus squamous carcinoma; extrahepatic cholangio, common bile, gallbladder adenocarcinoma, NOS; fallopian tube adenocarcinoma, NOS; fallopian tube carcinoma, NOS; fallopian tube carcinosarcoma, NOS; fallopian tube serous carcinoma; gastric adenocarcinoma; gastroesophageal junction adenocarcinoma, NOS; glioblastoma; glioma, NOS; gliosarcoma; head, face or neck, NOS squamous carcinoma; intrahepatic bile duct cholangiocarcinoma; kidney carcinoma, NOS; kidney clear cell carcinoma; kidney papillary renal cell carcinoma; kidney renal cell carcinoma, NOS; larynx, NOS squamous carcinoma; left colon adenocarcinoma, NOS; left colon mucinous adenocarcinoma; liver hepatocellular carcinoma, NOS; lung adenocarcinoma, NOS; lung adenosquamous carcinoma; lung carcinoma, NOS; lung mucinous adenocarcinoma; lung neuroendocrine carcinoma, NOS; lung nonsmall cell carcinoma; lung sarcomatoid carcinoma; lung small cell carcinoma, NOS; lung squamous carcinoma; meninges meningioma, NOS; nasopharynx, NOS squamous carcinoma; oligodendroglioma, anaplastic; oligodendroglioma, NOS; ovary adenocarcinoma, NOS; ovary carcinoma, NOS; ovary carcinosarcoma; ovary clear cell carcinoma; ovary endometrioid adenocarcinoma; ovary granulosa cell tumor, NOS; ovary high-grade serous carcinoma; ovary low-grade serous carcinoma; ovary mucinous adenocarcinoma; ovary serous carcinoma; pancreas adenocarcinoma, NOS; pancreas carcinoma, NOS; pancreas mucinous adenocarcinoma; pancreas neuroendocrine carcinoma, NOS; parotid gland carcinoma, NOS; peritoneum adenocarcinoma, NOS; peritoneum carcinoma, NOS; peritoneum serous carcinoma; pleural mesothelioma, NOS; prostate adenocarcinoma, NOS; rectosigmoid adenocarcinoma, NOS; rectum adenocarcinoma, NOS; rectum mucinous adenocarcinoma; retroperitoneum dedifferentiated liposarcoma; retroperitoneum leiomyosarcoma, NOS; right colon adenocarcinoma, NOS; right colon mucinous adenocarcinoma; salivary gland adenoid cystic carcinoma; skin melanoma; skin melanoma; skin merkel cell carcinoma; skin nodular melanoma; skin squamous carcinoma; skin trunk melanoma; small intestine adenocarcinoma; small intestine gastrointestinal stromal tumor, NOS; stomach gastrointestinal stromal tumor, NOS; stomach signet ring cell adenocarcinoma; thyroid carcinoma, anaplastic, NOS; thyroid carcinoma, NOS; thyroid papillary carcinoma of thyroid; tonsil, oropharynx, tongue squamous carcinoma; transverse colon adenocarcinoma, NOS; urothelial bladder adenocarcinoma, NOS; urothelial bladder carcinoma, NOS; urothelial bladder squamous carcinoma; urothelial carcinoma, NOS; uterine endometrial stromal sarcoma, NOS; uterus leiomyosarcoma, NOS; uterus sarcoma, NOS; uveal melanoma; vaginal squamous carcinoma; vulvar squamous carcinoma; and any combination thereof. In some embodiments, the predicted at least one attribute of the cancer comprises at least one of breast adenocarcinoma, central nervous system cancer, cervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, gastroesophageal adenocarcinoma, gastrointestinal stromal tumor (GIST), hepatocellular carcinoma, lung adenocarcinoma, melanoma, meningioma, ovarian granulosa cell tumor, ovarian & fallopian tube adenocarcinoma, pancreas adenocarcinoma, prostate adenocarcinoma, renal cell carcinoma, squamous cell carcinoma, thyroid cancer, urothelial carcinoma, uterine endometrial adenocarcinoma, and uterine sarcoma. In some embodiments, the predicted at least one attribute of the cancer comprises at least one of bladder; skin; lung; head, face or neck (NOS); esophagus; female genital tract (FGT); brain; colon; prostate; liver, gall bladder, ducts; breast; eye; stomach; kidney; and pancreas. In some embodiments, the sample comprises a cancer of unknown primary (CUP). In an aspect, provided herein is a method of predicting at least one attribute of a cancer, the method comprising: (a) obtaining a biological sample from a subject having a cancer, wherein the biological sample can be a biological sample such as described above; (b) performing at least one assay to assess one or more biomarkers in the biological sample to obtain a biosignature for the sample, wherein the at least one assay can be as described above; (c) providing the biosignature into a model that has been trained to predict at least one attribute of the cancer, wherein the model comprises at least one intermediate model, wherein the at least one intermediate model comprises: (1) an first intermediate model trained to process DNA data using the predetermined biosignatures supplied herein with respect to Tables 2-116; (2) a second intermediate model trained to process RNA data using the predetermined biosignatures supplied herein with respect to Table 118; (3) a third intermediate model trained to process RNA data using the predetermined biosignatures supplied herein with respect to Table 119; and / or (4) a fourth intermediate model trained to process RNA data using the predetermined biosignatures supplied herein with respect to Table 120; (d) processing, by one or more computers, the provided biosignature through each of the plurality of intermediate models in part (c), providing the output of each of the plurality of intermediate models into a final predictor model, and processing by one or more computers, the output of each of the plurality of intermediate models through the final predictor model; and (e) outputting from the final predictor model a prediction of the at least one attribute of the cancer. In some embodiments, the predicted at least one attribute of the cancer is a tissue-of-origin selected from the group consisting of breast adenocarcinoma, central nervous system cancer, cervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, gastroesophageal adenocarcinoma, gastrointestinal stromal tumor (GIST), hepatocellular carcinoma, lung adenocarcinoma, melanoma, meningioma, ovarian granulosa cell tumor, ovarian & fallopian tube adenocarcinoma, pancreas adenocarcinoma, prostate adenocarcinoma, renal cell carcinoma, squamous cell carcinoma, thyroid cancer, urothelial carcinoma, uterine endometrial adenocarcinoma, uterine sarcoma, and any combination thereof. In some embodiments, step (b) comprises performing DNA analysis by sequencing genomic DNA from the biological sample, wherein the DNA analysis is performed for the genes in Tables 2-116. In some embodiments, step (b) comprises performing RNA analysis by sequencing messenger RNA transcripts from the biological sample, wherein the RNA analysis is performed for the genes in Table 117 or Tables 118120. In some embodiments, the at least one of the at least one intermediate model and final predictor model comprises a machine learning module, wherein optionally the machine learning module comprises one or more of a random forest, support vector machine, logistic regression, K-nearest neighbor, artificial neural network, naive Bayes, quadratic discriminant analysis, and Gaussian processes models, wherein optionally the machine learning module comprises an XGBoost decisiontree-based ensemble machine learning algorithm. The prediction of the at least one attribute of the cancer made using the systems and methods provided herein may be used in various settings. See, e.g., Example 3 herein. In some embodiments, the prediction is used to confirm a diagnosis. In some embodiments, the prediction is used to change a diagnosis. In some embodiments, the prediction is used to perform a quality check. In some embodiments, the prediction is used to indicate additional molecular testing to be performed. In some embodiments of the methods of the invention, the predicted at least one attribute comprises an ordered list, wherein optionally the list is ordered using a statistical measure. For example, the list may be ordered by confidence in the prediction. In some embodiments, the methods provided herein further comprise determining whether the prediction of the at least one attribute meets a threshold level, wherein optionally the threshold level is related to a probability of the prediction and / or a confidence in the prediction. In some embodiments, the methods provided herein further comprise generating a molecular profile that identifies the presence, level, or state of the biomarkers in the biosignature, e.g., whether each biomarker has a copy number alteration and / or mutation; and / or a TMB level, MSI, LOH, or MMR status; and / or expression level, wherein the expression level comprises that of at least one transcript and / or protein level. See, e.g., Example 1 for more details. In some embodiments, the methods provided herein further comprise selecting at least one treatment for the patient based at least in part upon the classified at least one attribute of the cancer, wherein optionally the treatment comprises administration of immunotherapy, chemotherapy, or a combination thereof. In an aspect, provided herein is a method comprising preparing a report, wherein the report comprises a summary or overview of the molecular profile generated herein, e.g., as described above, wherein the report identifies the classified at least one attribute of the cancer, wherein optionally the report further identifies the at least one treatment selected according to the methods provided herein, e.g., as described above. In some embodiments, the report is computer generated, is a printed report and / or a computer file, and / or is accessible via a web portal. Further provided herein is a system comprising one or more computers and one or more storage media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations described with reference to the methods described above. Relatedly, also provided herein is a non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations with reference to the methods described above. In an aspect, provided herein is a system for identifying a lineage for a cancer, the system comprising: (a) at least one host server; (b) at least one user interface for accessing the at least one host server to access and input data; (c) at least one processor for processing the inputted data; (d) at least one memory coupled to the processor for storing the processed data and instructions for carrying out operations with reference to the methods described above; and (e) at least one display for displaying the classified primary origin of the cancer. In some embodiments, the system further comprise at least one memory coupled to the processor for storing the processed data and instructions for selecting treatment and / or generating molecular profiling reports as described herein. In some embodiments, the at least one display comprises a report comprising the classified at least one attribute of the cancer. In an aspect, provided herein is a system for identifying at least one attribute of a sample obtained from a body, wherein the at least one attribute is selected from the group consisting of a primary tumor origin, cancer / disease type, organ group, histology, and any combination thereof, the system comprising: one or more processors and one or more memory units storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: obtaining, by the system, a sample biological signature representing the sample that was obtained from the body, wherein the sample comprises cancer cells; providing, by the system, the sample biological signature as an input to a model, wherein: the model is configured to perform analysis between the sample biological signature and each of multiple different biological signatures, wherein each of the multiple different biological signatures corresponds to a different attribute; and / or the model is a multi-class model wherein the classes comprise different attributes; and receiving, by the system, an output generated by the model that represents data indicating a likely attribute of the sample obtained from the body based on the pairwise analysis. In another aspect, provided herein is a system for identifying at least one attribute of a sample obtained from a body, wherein the at least one attribute is selected from the group consisting of a primary tumor origin, cancer / disease type, organ group, histology, and any combination thereof, the system comprising: one or more processors and one or more memory units storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: obtaining, by the system, a sample biological signature representing the sample that was obtained from the body; providing, by the system, the sample biological signature as an input to a model, wherein: the model is configured to perform analysis between the sample biological signature and each of multiple different biological signatures, wherein each of the multiple different biological signatures corresponds to a different attribute; and / or the model is a multi-class model wherein the classes comprise different attributes; and receiving, by the system, an output generated by the model that represents data indicating a probability that an attribute identified by the particular biological signature identifies a likely attribute of the sample. In still another aspect, provided herein is a system for identifying at least one attribute of a sample obtained from a body, wherein the at least one attribute is selected from the group consisting of a primary tumor origin, cancer / disease type, organ group, histology, and any combination thereof, the system comprising: one or more processors and one or more memory units storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: obtaining, by the system, a sample biological signature representing a biological sample that was obtained from the cancer sample in a first portion of the body, wherein the sample biological signature includes data describing a plurality of features of the biological sample, wherein the plurality of features include data describing the first portion of the body; providing, by the system, the sample biological signature as an input to a model, wherein: the model is configured to perform analysis between the sample biological signature and each of multiple different biological signatures, wherein each of the multiple different biological signatures corresponds to a different attribute; and / or the model is a multi-class model wherein the classes comprise different attributes; and receiving, by the system, an output generated by the model that represents data indicating a likely attribute of the sample obtained from the body. In some embodiments, the sample obtamed from the body is a biological sample as described above. In some embodiment, the at least one attribute is a primary tumor origin, cancer / disease type, organ group, and / or histology as described above. In some embodiments, the sample biological signature includes data representing features obtained based on performance of an assay to assess one or more biomarkers in the cancer sample, wherein optionally the assay is according to at least one assay described above. In some embodiments, the operations further comprise: determining, based on the output generated by the model, a proposed cancer treatment. In some embodiments, each of the multiple different biological signatures comprise pre-identified biosignatures as described above, e.g., with respect to Tables 2-116 or Tabled 118-120. In some embodiments, the operations further comprise: receiving, by the system, an output generated by the model that represents a likelihood that the sample obtained from the body in a first portion of the body originated from a cancer in a second portion of the body. In some embodiments, further comprising determining, by the system and based on the received output, whether the received output generated by the model satisfies one or more predetermined thresholds; and based on the determining, by the system, that the received output satisfies the one or more predetermined thresholds, determining, by the system, that the cancerous neoplasm in the first portion of the body originated from a cancer in a second portion of the body or that the cancerous neoplasm in the first portion of the body did not originate from a cancer in a second portion of the body. In some embodiments, the received output generated by the model includes a matrix data structure, wherein the matrix data structure includes a cell for each feature of the plurality of features evaluated by the pairwise model, wherein each of the cells includes data describing a probability that the corresponding feature indicates that the cancerous neoplasm in the first portion of the body was caused by cancer in the second portion of the first body. In an aspect, provided herein is a system for identifying at least one attribute of a cancer, wherein the at least one attribute is selected from the group consisting of a primary tumor origin, cancer / disease type, organ group, histology, and any combination thereof, the system comprising: one or more processors and one or more memory units storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: receiving, by the system storing a model that is configured to perform analysis of a biological signature, a sample biological signature representing a biological sample that was obtained from a cancerous neoplasm in a first portion of a body, wherein the model includes a cancerous biological signature for each of multiple different types of cancerous biological samples, wherein the cancerous biological signatures include at least a first cancerous biological signature representing a molecular profile of a cancerous biological sample from the first portion of one or more other bodies; performing, by the system and using the model, analysis of the sample biological signature using the cancerous biological signatures; generating, by the system and based on the performed analysis, a likelihood that the cancerous neoplasm in the first portion of the body was caused by cancer in a second portion of the body; providing, by the system, the generated likelihood to another device for display on the other device. In an aspect, provided herein is a system for training an analysis model for identifying at least one attribute of a cancer sample obtained from a body, wherein the at least one attribute is selected from the group consisting of a primary tumor origin, cancer / disease type, organ group, histology, and any combination thereof, the system comprising: one or more processors and one or more memory units storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: generating, by the system, an analysis model, wherein generating the analysis model includes generating a plurality of model signatures, wherein each model signature is configured to differentiate between at least one attribute within each of the at least one attribute; obtaining, by the system, a set of training data items, wherein each training data item represents DNA or RNA sequencing results and includes data indicating (i) whether or not a variant was detected in the sequencing results and (ii) a number of copies of a gene or transcript in the sequencing results; and training, by the system, an analysis model using the obtained set of training data items. In some embodiments, the plurality of model signatures are generated using random forest models, wherein optionally the random forest models comprise gradient boosted forests. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims. DESCRIPTION OF DRAWINGS FIG. 1A is a block diagram of an example of a prior art system for training a machine learning model. FIG. IB is a block diagram of a system that generates training data structures for training a machine learning model to predict a sample origin. FIG. IC is a block diagram of a system for using a trained machine learning model to predict a sample origin of sample data from a subject. FIG. ID is a flowchart of a process for generating training data structures for training a machine learning model to predict sample origin. FIG. IE is a flowchart of a process for using a trained machine learning model to predict sample origin of sample data from a subject. FIG. IF is an example of a system for performing pairwise to predict a sample origin. FIG. 1G is a block diagram of a system for predicting a sample origin using a voting unit to interpret output generated by multiple machine learning models that are each trained to perform pairwise analysis. FIG. 1H is a block diagram of system components that can be used to implement systems of FIGs. IB, IC, 1G, IF, and 1G. FIG. II illustrates a block diagram of an exemplary embodiment of a system for determining individualized medical intervention for cancer that utilizes molecular profiling of a patient’s biological specimen. FIGs. 2A-C are flowcharts of exemplary embodiments of (FIG. 2A) a method for determining individualized medical intervention for cancer that utilizes molecular profiling of a patient’s biological specimen, (FIG. 2B) a method for identifying signatures or molecular profiles that can be used to predict benefit from therapy, and (FIG. 2C) an alternate version of (FIG. 2B). FIGs. 3A-B use of biosignatures to predict a primary tumor lineage from a cancer sample. FIGs. 4A-B show schemes for classifying a tissue sample using RNA transcript analysis (FIG. 4A) or combined RNA and DNA analysis (FIG. 4B). FIG. 4C is flowchart of an example of a process 400C for training a dynamic voting engine. FIGs. 5A-E illustrate performance of the MDC / GPS to classify cancers using analysis of genomic DNA. FIGs. 6A-AL show further development of GPS using combined RNA and DNA analysis. FIGs. 7A-Q show an exemplary molecular profiling report that incorporates the Genomic Prevalence Score (GPS; also Genomic Profiling Similarity) information according to the systems and methods provided herein. FIGs. 8A-M show another exemplary molecular profiling report that incorporates the Genomic Prevalence Score information according to the systems and methods provided herein. DETAILED DESCRIPTION Described herein are methods and systems for characterizing various phenotypes of biological systems, organisms, cells, samples, or the like, by using molecular profiling, including systems, methods, apparatuses, and computer programs for training a machine learning model and then using the trained machine learning model to characterize such phenotypes. The term “phenotype” as used herein can mean any trait or characteristic that can be identified in part or in whole by using the systems and / or methods provided herein. In some implementations, the systems can include one or more computer programs on one or more computers m one or more locations, e.g., configured for use in a method described herein. Phenotypes to be characterized can be any phenotype of interest, including without limitation a tissue of origin, anatomical origin, histology, organ, medical condition, ailment, disease, disorder, or usefill combinations thereof A phenotype can be any observable characteristic or trait of, such as a disease or condition, a stage of a disease or condition, susceptibility to a disease or condition, prognosis of a disease stage or condition, a physiological state, or response / potential response (or lack thereof) to interventions such as therapeutics. A phenotype can result from a subject’s genetic makeup as well as the influence of environmental factors and the interactions between the two, as well as from epigenetic modifications to nucleic acid sequences. In various embodiments, a phenotype in a subject is characterized by obtaining a biological sample from a subject and analyzing the sample using the systems and / or methods provided herein. For example, characterizing a phenotype for a subject or individual can include detecting a disease or condition (including pre-symptomatic early stage detection), determining a prognosis, diagnosis, or theranosis of a disease or condition, or determining the stage or progression of a disease or condition. Characterizing a phenotype can include identifying appropriate treatments or treatment efficacy for specific diseases, conditions, disease stages and condition stages, predictions and likelihood analysis of disease progression, particularly disease recurrence, metastatic spread or disease relapse. A phenotype can also be a clinically distinct type or subtype of a condition or disease, such as a cancer or tumor. Phenotype determination can also be a determination of a physiological condition, or an assessment of organ distress or organ rejection, such as post-transplantation. The compositions and methods described herein allow assessment of a subject on an individual basis, which can provide benefits of more efficient and economical decisions in treatment. Theranostics includes diagnostic testing that provides the ability to affect therapy or treatment of a medical condition such as a disease or disease state. Theranostics testing provides a theranosis in a similar manner that diagnostics or prognostic testing provides a diagnosis or prognosis, respectively. As used herein, theranostics encompasses any desired form of therapy related testing, including predictive medicine, personalized medicine, precision medicine, integrated medicine, pharmacodiagnostics and Dx / Rx partnering. Therapy related tests can be used to predict and assess (hug response in individual subjects, thereby providing personalized medical recommendations. Predicting a likelihood of response can be determining whether a subject is a likely responder or a likely non-responder to a candidate therapeutic agent, e.g., before the subject has been exposed or otherwise treated with the treatment. Assessing a therapeutic response can be monitoring a response to a treatment, e.g., monitoring the subject’s improvement or lack thereof over a time course after initiating the treatment. Therapy related tests are usefill to select a subject for treatment who is particularly likely to benefit or lack benefit from the treatment or to provide an early and objective indication of treatment efficacy in an individual subject. Characterization using the systems and methods provided herem may indicate that treatment should be altered to select a more promising treatment, thereby avoiding the expense of delaying beneficial treatment and avoiding the financial and morbidity costs of less efficacious or ineffective treatment(s). In various embodiments, a theranosis comprises predicting a treatment efficacy or lack thereof, classifying a patient as a responder or non-responder to treatment. A predicted “responder” can refer to a patient likely to receive a benefit from a treatment whereas a predicted “non-responder” can be a patient unlikely to receive a benefit from the treatment. Unless specified otherwise, a benefit can be any clinical benefit of interest, including without limitation cure in whole or in part, remission, or any improvement, reduction or decline in progression of the condition or symptoms. The theranosis can be directed to any appropriate treatment, e.g., the treatment may comprise at least one of chemotherapy, immunotherapy, targeted cancer therapy, a monoclonal antibody, small molecule, or any usefill combinations thereof. The phenotype can comprise detecting the presence of or likelihood of developing a tumor, neoplasm, or cancer, or characterizing the tumor, neoplasm, or cancer (e.g., stage, grade, aggressiveness, likelihood of metastatis or recurrence, etc). In some embodiments, the cancer comprises an acute myeloid leukemia (AML), breast carcinoma, cholangiocarcinoma, colorectal adenocarcinoma, extrahepatic bile duct adenocarcinoma, female genital tract malignancy, gastric adenocarcinoma, gastroesophageal adenocarcinoma, gastrointestinal stromal tumors (GIST), glioblastoma, head and neck squamous carcinoma, leukemia, liver hepatocellular carcinoma, low grade glioma, lung bronchioloalveolar carcinoma (BAC), lung non-small cell lung cancer (NSCLC), lung small cell cancer (SCLC), lymphoma, male genital tract malignancy, malignant solitary fibrous tumor of the pleura (MSFT), melanoma, multiple myeloma, neuroendocrine tumor, nodal diffuse large B-cell lymphoma, non epithelial ovarian cancer (non-EOC), ovarian surface epithelial carcinoma, pancreatic adenocarcinoma, pituitary carcinomas, oligodendroglioma, prostatic adenocarcinoma, retroperitoneal or peritoneal carcinoma, retroperitoneal or peritoneal sarcoma, small intestinal malignancy, soft tissue tumor, thymic carcinoma, thyroid carcinoma, or uveal melanoma. The systems and methods herein can be used to characterize these and other cancers. Thus, characterizing a phenotype can be providing a diagnosis, prognosis or theranosis of one of the cancers disclosed herein. In various embodiments, the phenotype comprises a tissue or anatomical origin. For example, the tissue can be muscle, epithelial, connective tissue, nervous tissue, or any combination thereof. For example, the anatomical origin can be the stomach, liver, small intestine, large intestine, rectum, anus, lungs, nose, bronchi, kidneys, urinary bladder, urethra, pituitary gland, pineal gland, adrenal gland, thyroid, pancreas, parathyroid, prostate, heart, blood vessels, lymph node, bone marrow, thymus, spleen, skin, tongue, nose, eyes, ears, teeth, uterus, vagina, testis, penis, ovaries, breast, mammary glands, brain, spinal cord, nerve, bone, ligament, tendon, or any combination thereof. Additional non limiting examples of phenotypes of interest mclude clinical characteristics, such as a stage or grade of a tumor, or the tumor’s origin, e.g., the tissue origin. In various embodiments, phenotypes are determined by analyzing a biological sample obtained from a subject. A subject (individual, patient, or the like) can include, but is not limited to, mammals such as bovine, avian, canine, equine, feline, ovine, porcine, or primate animals (including humans and non-human primates). In preferred embodiments, the subject is a human subject. A subject can also include a mammal of importance due to being endangered, such as a Siberian tiger; or economic importance, such as an animal raised on a farm for consumption by humans, or an animal of social importance to humans, such as an animal kept as a pet or in a zoo. Examples of such animals include, but are not limited to, carnivores such as cats and dogs; swine including pigs, hogs and wild boars; rmninants or ungulates such as cattle, oxen, sheep, giraffes, deer, goats, bison, camels or horses. Also included are birds that are endangered or kept in zoos, as well as fowl and more particularly domesticated fowl, e.g., poultry, such as turkeys and chickens, ducks, geese, guinea fowl. Also included are domesticated swine and horses (including race horses). In addition, any animal species connected to commercial activities are also included such as those animals connected to agriculture and aquaculture and other activities in which disease monitoring, diagnosis, and therapy selection are routine practice in husbandry for economic productivity and / or safety of the food chain. The subject can have a pre-existing disease or condition, including without limitation cancer. Alternatively, the subject may not have any known pre-existing condition. The subject may also be non-responsive to an existing or past treatment, such as a treatment for cancer. Data Analysis and Machine Learning Aspects of the present disclosure are directed towards a system that generates a set of one or more training data structures that can be used to train a machine learning model to provide various classifications, such as characterizing a phenotype of a biological sample. As described above, characterizing a phenotype can include providing a diagnosis, prognosis, theranosis or other relevant classification. For example, the classification may include a disease state, a predicted efficacy of a treatment for a disease or disorder of a subject, or the anatomical origin of a sample having a particular set of biomarkers. Once trained, the trained machine learning model can then be used to process input data provided by the system and make predictions based on the processed input data. The input data may include a set of features related to a subject such as data representing one or more subject biomarkers and data representing a phenotype of interest, e.g., a disease and / or anatomical origin. In some embodiments, the input data may further include features representing an anatomical origin and the system may make a prediction describing whether the sample is from that anatomical origin. The prediction may include data that is output by the machine learning model based on the machine learning model’s processing of a specific set of features provided as an input to the machine learning model. The data may include without limitation data representing one or more subject biomarkers, data representmg a disease or anatomical origin, and data representmg a proposed treatment type as desired. As used herein, “biomarkers” or “sets of biomarkers” are used to train and test machine learning models and classify naive samples. Such references include particular biomarkers such as particular nucleic acids or proteins, and optionally also include a state of such nucleic acids or proteins. Examples of the state of a biomarker include various aspects that can be queried such as presence, level (quantity, concentration, etc), sequence, location, activity, structure, modifications, covalent or non-covalent binding partners, and the like. As a non-limiting examples, a set of biomarkers may include a gene or gene product (i.e., mRNA or protein) having a specified sequence (e.g., KRAS mutant), and / or a gene or gene product and a level thereof (e.g., amplified ERBB2 gene or overexpressed HER2 protein). Useful biomarkers and aspects thereof are further described below. Innovative aspects of the present disclosure include the extraction of specific data from incoming data streams for use in generating training data structures. An important aspect may be the selection of a specific set of one or more biomarkers for inclusion in the training data structure. This is because the presence, absence or other state of particular biomarkers may be indicative of the desired classification. For example, certain biomarkers may be selected to determine a desired phenotype, such as whether a treatment for a disease or disorder is of likely benefit, or a tumor origin. By way of example, in the present disclosure, the Applicant puts forth specific sets of biomarkers that, when used to train a machine learning model, result in a trained model that can more accurately predict a tumor origin than using a different set of biomarkers. See, e.g., Examples 1-3, Tables 121-130. The system is configured to obtain output data generated by the trained machine learning model based on the machine learning model’s processing of the input data. In various embodiments, the input data comprises biological data representing one or more biomarkers, data representing a disease or disorder, data representing a sample, data representing sample origins, or any combination thereof. The system may then predict an anatomical origin of a biological sample having a particular set of biomarkers. In some implementations, the disease or disorder may include a type of cancer and the anatomical origins can include various tissues and organs. In this setting, output of the trained machine learning model that is generated based on trained machine learning model processing of the input data that includes the set of biomarkers, the disease or disorder and various anatomical origins includes data representing the predicted anatomical origin of the biological sample. In some implementations, the output data generated by the trained machine learning model includes a probability of the desired classification. By way of illustration, such probability may be a probability that the biological sample is derived from tissue from a particular organ. In other implementations, the output data may include any output data generated by the trained machine learning model based on the trained machine learning model’s processing of the input data. In some embodiments, the input data comprises set of biomarkers, data representing the disease or disorder, data representing a sample, the data representing the sample origin, or any combination thereof. In some implementations, the training data structures generated by the present disclosure may include a plurality of training data structures that each include fields representing feature vector corresponding to a particular training sample. The feature vector includes a set of features derived from, and representative of, a training sample. The training sample may include, for example, one or more biomarkers of a biological sample, a disease or disorder associated with the biological sample, and an anatomical origin from the biological sample. The training data structures are flexible because each respective training data structure may be assigned a weight representing each respective feature of the feature vector. Thus, each training data structure of the plurality of training data structures can be particularly configured to cause certain inferences to be made by a machine learning model during training. Consider a non-limiting example wherein the model is trained to make a prediction of likely anatomical origin of a biological sample, e.g., a tumor sample. As a result, the novel training data structures that are generated in accordance with this specification are designed to improve the performance of a machine learning model because they can be used to train a machine learning model to predict an anatomical origin of a biological sample having a particular set of biomarkers. By way of example, a machine learning model that could not perform predictions regarding the anatomical origin of a biological sample having a particular set of biomarkers prior to being trained using the training data structures, system, and operations described by this disclosure can learn to make predictions regarding the anatomical origin of a biological sample having a particular set of biomarkers by being trained using the training data structures, systems and operations described by the present disclosure. Accordingly, this process takes an otherwise general purpose machine learning model and changes the general purpose machine leaning model into a specific computer for perform a specific task of performing predicting the anatomical origin of a biological sample having a particular set of biomarkers. FIG. 1A is a block diagram of an example of a prior art system 100 for training a machine learning model 110. In some implementations, the machine learning model may be, for example, a support vector machine. Alternatively, the machine learning model may include a neural network model, a linear regression model, a random forest model, a logistic regression model, a naive Bayes model, a quadratic discriminant analysis model, a K-nearest neighbor model, a support vector machine, or the like. The machine learning model training system 100 may be implemented as computer programs on one or more computers in one or more locations, in which the systems, components, and techniques described below can be implemented. The machine learning model training system 100 trains the machine learning model 110 using training data items from a database (or data set) 120 of training data items. The training data items may include a plurality of feature vectors. Each training vector may include a plurality of values that each correspond to a particular feature of a training sample that the training vector represents. The training features may be referred to as independent variables. In addition, the system 100 maintains a respective weight for each feature that is included in the feature vectors. The machine learning model 110 is configured to receive an input training data item 122 and to process the input training data item 122 to generate an output 118. The input training data item may include a plurality of features (or independent variables “X”) and a training label (or dependent variable “Y”). The machine learning model may be trained using the training items, and once trained, is capable of predicting X =XY). To enable machine learning model 110 to generate accurate outputs for received data items, the machine learning model training system 100 may train the machine learning model 110 to adjust the values of the parameters of the machine learning model 110, e.g., to determine trained values of the parameters from initial values. These parameters derived from the training steps may include weights that can be used during the prediction stage using the frilly trained machine learning model 110. In training, the machine learning model 110, the machine learning model training system 100 uses training data items stored in the database (data set) 120 of labeled training data items. The database 120 stores a set of multiple training data items, with each training data item in the set of multiple training items being associated with a respective label. Generally, the label for the training data item identifies a correct classification (or prediction) for the training data item, i.e., the classification that should be identified as the classification of the training data item by the output values generated by the machine learning model 110. With reference to FIG. 1 A, a training data item 122 may be associated with a training label 122a. The machine learning model training system 100 trains the machine learning model 110 to optimize an objective function. Optimizing an objective function may include, for example, minimizing a loss function 130. Generally, the loss function 130 is a function that depends on the (i) output 118 generated by the machine learning model 110 by processing a given training data item 122 and (ii) the label 122a for the training data item 122, i.e., the target output that the machine learning model 110 should have generated by processing the training data item 122. Conventional machine learning model training system 100 can train the machine learning model 110 to minimize the (cumulative) loss function 130 by performing multiple iterations of conventional machine learning model training techniques on training data items from the database 120, e.g., hinge loss, stochastic gradient methods, stochastic gradient descent with backpropagation, or the like, to iteratively adjust the values of the parameters of the machine learning model 110. A frilly trained machine learning model 110 may then be deployed as a predicting model that can be used to make predictions based on input data that is not labeled. FIG. IB is a block diagram of a system that generates training data structures for training a machine learning model to predict a sample origin. The system 200 mcludes two or more distributed computers 210, 310, a network 230, and an application server 240. The application server 240 includes an extraction unit 242, a memory unit 244, a vector generation unit 250, and a machine learning model 270. The machine learning model 270 may include one or more of a neural network model, a linear regression model, a random forest model, a logistic regression model, a naive Bayes model, a quadratic discriminant analysis, model, a K-nearest neighbor model, a support vector machine, or the like. Each distributed computer 210, 310 may include a smartphone, a tablet computer, laptop computer, or a desktop computer, or the like. Alternatively, the distributed computers 210, 310 may include server computers that receive data input by one or more terminals 205, 305, respectively. The terminal computers 205, 305 may include any user device including a smartphone, a tablet computer, a laptop computer, a desktop computer or the like. The network 230 may include one or more networks 230 such as a LAN, a WAN, a wired Ethernet network, a wireless network, a cellular network, the Internet, or any combination thereof. The application server 240 is configured to obtain, or otherwise receive, data records 220, 222, 224, 320 provided by one or more distributed computers such as the first distributed computer 210 and the second distributed computer 310 using the network 230. In some implementations, each respective distributed computer 210, 310 may provide different types of data records 220, 222, 224, 320. For example, the first distributed computer 210 may provide biomarker data records 220, 222, 224 representing biomarkers for a biological sample from a subject and the second distributed computer 310 may provide sample data 320 representing anatomical origin or other sample data for a subject obtained from the sample database 312. However, the present disclosure need not be limited to two computers 210, 310 providing data records 220, 222, 224, 230. Though such implementations can provide technical advantages such as load balancing, bandwidth optimization, or both, it is also contemplated that the data records 220, 222, 224, 230 can each be provided by the same computer. The biomarker data records 220, 222, 224 may include any type of biomarker data that describes biometric attributes of a biological sample. By way of example, the example of FIG. IB shows the biomarker data records as including data records representing DNA biomarkers 220, protein biomarkers 222, and RNA data biomarkers 224. These biomarker data records may each include data structures having fields that structure information 220a, 222a, 224a describing biomarkers of a subject such as a subject’s DNA biomarkers 220a, protein biomarkers 222a, or RNA biomarkers 224a. However, the present disclosure need not be so limited and any usefill biomarkers can be assessed. In some embodiments, the biomarker data records 220, 222, 224 include next generation sequencing data from DNA and / or RNA, including without limitation single variants, insertions and deletions, substitution, translocation, fusion, break, duplication, amplification, loss, copy number, repeat, total mutational burden, microsatellite instability, or the like. Alternatively, or in addition, the biomarker data records 220, 222, 224 may also include in situ hybridization data. Such in situ hybridization data may include DNA copy numbers, translocations, or the like. Alternatively, or in addition, the biomarker data records 220, 222, 224 may include RNA data such as gene expression or gene fusion, including without limitation data derived from whole transcriptome sequencmg. Alternatively, or in addition, the biomarker data records 220, 222, 224 may include protein expression data such as obtained using immunohistochemistry (IHC). Alternatively, or in addition, the biomarker data records 220, 222, 224 may include ADAPT data such as complexes. In some implementations, the biomarker data records 220, 222, 224 include one or more biomarkers and attributes listed in any one of Tables 2-116, Tables 117-120, ISNM1, Tables 121130. However, the present disclosure need not be so limited, and other types of biomarkers may be used as desired. For example, the biomarker data may be obtained by whole exome sequencing, whole transcriptome sequencing, whole genome sequencing, or a combination thereof. The sample data records 320 may describe various aspects of a biological sample, e.g., a tissue and / or organ from which the sample is derived. For example, the sample data records 320 obtained from the sample database 312 may include one or more data structures having fields that structure data attributes of a biological sample such as a disease or disorder 320a-l (“ailment”), a tissue or organ 320a-2 where the sample was obtained, a sample type 320a-3, a verified sample origin label 320a-4, or any combination thereof. The sample record 320 can include up to n data records describing a sample, where n is any positive integer greater than 0. For example, though the example of FIG. IB trains the machine learning model using patient sample data describing disease / disorder, tissue / organ where sample was obtained, and sample type, the present disclosure is not so limited. For example, in some implementations, the machine learning model 370 can be trained to predict the origin of sample using patient sample information that includes the tissue or organ 320a-2 where the sample was obtained and sample type 320a-3 without including the ailment or disorder 320a-l. Alternatively, or in addition, the sample data records 320 may also include fields that structure data attributes describing details of the biological sample, including attributes of a subject from which the sample is derived. An example of a disease or disorder may include, for example, a type of cancer. A tissue or organ may include, for example, a type of tissue (e.g., muscle tissue, epithelial tissue, connective tissue, nervous tissue, etc.) or organ (e.g., colon, lung, brain, etc.). A sample type may include data representing the type of sample, such as tumor sample, bodily fluid, fresh or frozen, biopsy, FFPE, or the like. In some implementations, attributes of a subject from which the sample is derived include clinical attributes such as pathology details of the sample, subject age and / or sex, prior subject treatments, or the like. If the sample is a metastatic sample of unknown primary origin (i.e., a cancer of unknown primary (CUPS)), the attributes may include the location from which the sample was taken. As a non-limiting example, a metastatic lesion of unknown primary origin may be found in the liver or brain. Accordingly, though the example of FIG. IB shows that sample data may include a disease or disorder, a tissue or organ, and a sample type, the sample data may include other types of information, as described herein. Moreover, there is no requirements that the sample data be limited to human “patients.” Instead, the sample data records 220, 222, 224 and biometric data records 320 may be associated with any desired subject including any non-human organism. In some implementations, each of the data records 220, 222, 224, 320 may mclude keyed data that enables the data records from each respective distributed computer to be correlated by application server 240. The keyed data may include, for example, data representing a subject identifier. The subject identifier may include any form of data that identifies a subject and that can associate biomarker for the subject with sample data for the subject. The first distributed computer 210 may provide 208 the biomarker data records 220, 222, 224 to the application server 240. The second distributed computer 310 may provide 210 the sample data records 320 to the application server 240. The application server 240 can provide the biomarker data records 220 and the sample data records 220, 222, 224 to the extraction unit 242. The extraction unit 242 can process the received biomarker data 220, 222, 224 and sample data records 320 in order to extract data 220a-l, 222a-l, 224a-l, 320a-l, 320a-2, 320a-3 that can be used to train the machine learning model. For example, the extraction unit 242 can obtain data structured by fields of the data structures of the biometric data records 220, 222, 224, obtain data structured by fields of the data structures of the outcome data records 320, or a combination thereof. The extraction unit 242 may perform one or more information extraction algorithms such as keyed data extraction, pattern matching, natural language processing, or the like to identify and obtain data 220a-1, 222a-1, 224a-1, 320a-l, 320a-2, 320a-3 from the biometric data records 220, 222, 224 and sample data records 320, respectively. The extraction unit 242 may provide the extracted data to the memory unit 244. The extracted data unit may be stored in the memory unit 244 such as flash memory (as opposed to a hard disk) to improve data access times and reduce latency in accessing the extracted data to improve system performance. In some implementations, the extracted data may be stored in the memory unit 244 as an in-memory data grid. In more detail, the extraction unit 242 may be configured to filter a portion of the biomarker data records 220, 222, 224 and the sample data records 320 such as 220a-1, 222a-1, 224a-l, 320a-l, 320a-2, 320a-3 that will be used to generate an input data structure 260 for processing by the machine learning model 270 from the portion of the sample data records 320a-4 that will be used as a label for the generated input data structure 260. Such filtering includes the extraction unit 242 separating the biomarker data and a first portion of the sample data that includes a disease or disorder 320a-1, tissue / organ 320a-l where sample was obtained (e.g., biopsied), sample type 320a-3 details, or any combination thereof, from the verified origin of the sample 320a-4. The verified sample origin of the sample may be a different tissue / organ or the same tissue / organ than the sample was obtained from. An example of who the tissue / organ that the sample was obtained from can be different than the verified origin can include instances where the disease or disorder has spread from a first tissue / organ to a second tissue / organ from which the sample was then obtained. The application server 240 can then use the biomarker data 220a-1, 222a-1, 224a-1, and the first portion of the sample data that includes the disease or disorder 320a-l, tissue or organ 320a-2, sample type details (not shown in FIG. IB), or a combination thereof, to generate the input data structure 260. In addition, the application server 240 can use the second portion of the sample data describmg the verified origin of the sample 320a-4 as the label for the generated data structure. The application server 240 may process the extracted data stored in the memory unit 244 correlate the biomarker data 220a-l, 222a-l, 224a-l extracted from biomarker data records 220, 222, 224 with the first portion of the sample data 320a-l, 320a-2, 320a-3. The purpose of this correlation is to cluster biomarker data with sample data so that the sample data for the biological sample is clustered with the biomarker data for the same biological sample. In some implementations, the correlation of the biomarker data and the first portion of the sample data may be based on keyed data associated with each of the biomarker data records 220, 222, 224 and the sample data records 320. For example, the keyed data may include a sample identifier or a subject identifier, e.g., a subject from which the sample is derived. The application server 240 provides the extracted biomarker data 220a-l, 222a-l, 224a-l and the extracted first portion of the sample data 320a-1, 320a-2, 320a-3 as an input to a vector generation unit 250. The vector generation unit 250 is used to generate a data structure based on the extracted biomarker data 220a-1, 222a-1, 224a-l and the extracted first portion of the sample data 320a-1, 320a-2, 320a-3. The generated data structure is a feature vector 260 that includes a plurality of values that numerical represents the extracted biomarker data 220a-1, 222a-l, 224a-l and the extracted first portion of the sample data 320a-l, 320a-2, 320a-3. The feature vector 260 may include a field for each type of biomarker and each type of sample data. For example, the feature vector 260 may include one or more fields corresponding to (i) one or more types of next generation sequencing data such as single variants, insertions and deletions, substitution, translocation, fusion, break, duplication, amplification, loss, copy number, repeat, total mutational burden, microsatellite instability, (ii) one or more types of in situ hybridization data such as DNA copy number, gene copies, gene translocations, (iii) one or more types of RNA data such as gene expression or gene fusion, (iv) one or more types of protein data such as presence, level or cellular location obtained using immunohistochemistry, (v) one or more types of ADAPT data such as complexes, and (vi) one or more types of sample data such as disease or disorder, sample type, each sample details, or the like. The vector generation unit 250 is configured to assign a weight to each field of the feature vector 260 that indicates an extent to which the extracted biomarker data 220a-l, 222a-l, 224a-l and the extracted first portion of the sample data 320a-1, 320a-2, 320a-3 includes the data represented by each field. In one implementation, for example, the vector generation unit 250 may assign a ‘ 1’ to each field of the feature vector that corresponds to a feature found in the extracted biomarker data 220a-1, 222a-1, 224a-l and the extracted first portion of the sample data 320a-1, 320a-2, 320a-3. In such implementations, the vector generation unit 250 may, for example, also assign a ‘0’ to each field of the feature vector that corresponds to a feature not found in the extracted biomarker data 220a-l, 222a-1, 224a-l and the extracted first portion of the sample data 320a-1, 320a-2, 320a-3. The output of the vector generation unit 250 may include a data structures such as a feature vector 260 that can be used to train the machine learning model 270. The application server 240 can label the training feature vector 260. Specifically, the application server can use the extracted second portion of the sample data 320a-4 to label the generated feature vector 260 with a verified sample origin 320a-4. The label of the training feature vector 260 generated based on the verified sample origin 320a-4 can be used to predict the tissue or organ that was the origin for a biological sample represented by the sample record 320 and having disease or disorder 320a-l defined by the specific set of biomarkers 220a-1, 222a-l, 224a-l, each of which is described by described in the training data structure 260. The application server 240 can train the machine learning model 270 by providing the feature vector 260 as an input to the machine learning model 270. The machine learning model 270 may process the generated feature vector 260 and generate an output 272. The application server 240 can use a loss function 280 to determine the amount of error between the output 272 of the machine learning model 280 and the value specified by the training label, which is generated based on the second portion of the extracted sample data describing the verified sample origin 320a-4. The output 282 of the loss function 280 can be used to adjust the parameters of the machine learning model 282. In some implementations, adjusting the parameters of the machine learning model 270 may include manually timing of the machine learning model parameters model parameters. Alternatively, in some implementations, the parameters of the machine learning model 270 may be automatically tuned by one or more algorithms of executed by the application server 242. The application server 240 may perform multiple iterations of the process described above with reference to FIG. IB for each sample data record 320 stored in the sample database that correspond to a set of biomarker data for a biological sample. This may include hundreds of iterations, thousands of iterations, tens of thousands of iterations, hundreds of thousands of iterations, millions of iterations, or more, until each of the sample data records 320 stored in the sample database 312 and having a corresponding set of biomarker data for a biological sample are exhausted, until the machine learning model 270 is trained to within a particular margin of error, or a combination thereof. A machine learning model 270 is trained within a particular margin of error when, for example, the machine learning model 270 is able to predict, based upon a set of unlabeled biomarker data, disease or disorder data, and sample type data, an origin of an sample having the biomarker data. The origin may include, for example, a probability, a general indication of the confidence in the origin classification, or the like. FIG. IC is a block diagram of a system for using a trained machine learning model 370 to predict a sample origin of sample data from a subject. The machine learning model 370 includes a machine learning model that has been trained using the process described with reference to the system of FIG. IB above. For example, FIG. IB is an example of a machine learning model 370 that has been trained to predict sample origin using patient sample data that comprises data representmg a tissue / organ 422a where the sample was obtained and a sample type 420a. In the example of FIG. IB, a disease, disorder, or ailment was not used to train the model — though there may be implementations of the present disclosure where the machine learning model 370 can be trained using an ailment or disorder in addition to a tissue / organ 422a where the sample was obtained and a sample type 420a. The trained machine learning model 370 is capable of predicting, based on an input feature vector representative of a set of one or more biomarkers, a disease or disorder, and other relevant sample data such as sample type, a origin of a biological sample having the biomarkers. In some implementations, the “origin” may include an anatomical system, location, organ, tissue type, and the like. The application server 240 hosting the machine learning model 370 is configured to receive unlabeled biomarker data records 320, 322, 324. The biomarker data records 320, 322, 324 include one or more data structures that have fields structuring data that represents one or more particular biomarkers such as DNA biomarkers 320a, protein biomarkers 322a, RNA biomarkers 324a, or any combination thereof. As discussed above, the received biomarker data records may include various types of biomarkers not explicitly depicted by FIG. IC such as (i) next generation sequencing data from DNA and / or RNA, including without limitation single variants, insertions and deletions, substitution, translocation, fusion, break, duplication, amplification, loss, copy number, repeat, total mutational burden, microsatellite instability, or the like, (ii) one or more types of in situ hybridization data such as DNA copies, gene copies, gene translocations, (iii) one or more types of RNA data such as gene expression or gene fusion, (iv) one or more types of protein data such as presence, level or location obtained using immunohistochemistry, or (v) one or more types of ADAPT data such as complexes. In some implementations, the biomarker data records 320, 322, 324 include one or more biomarkers and attributes listed in any one of Tables 2-116, Tables 117-120, ISNM1, and / or Tables 121-130. However, the present disclosure need not be so limited, and other biomarkers may be used as desired. For example, the biomarker data may be obtained by whole exome sequencing, whole transcriptome sequencing, or a combination thereof. The application server 240 hosting the machine learning model 370 is also configured to receive sample data 420 representing a proposed origin data 422a for a biological sample described by the sample data 420a of the biological sample having biomarkers represented by the received biomarker data records 320, 322, 324. The proposed origin data 422a for the biological sample 420a are also unlabeled and merely a suggestion for the origin of a biological sample having biomarkers representing by biomarker data records 320, 322, 324. However, as discussed elsewhere herein, due to the potential for disease (e.g., cancer) to spread from, e.g., organ to organ, the tissue / organ 422a where a sample was obtained may not be the actual sample origin. In some implementations, the sample data 420 is received or provided 305 by a terminal 405 over the network 230 and the biomarker data is obtained from a second distributed computer 310. The biomarker data may be derived from laboratory machinery used to perform various assays. See, e.g., Example 1 herein. The sample data 420 can mclude data representmg a tissue / organ 422a where the sample was obtained and a sample type 420a. The tissue / organ 422a from where the sample was obtained may be referred to as the proposed origin of the sample. In other implementations, the sample data 420a, the proposed origin 422a, and the biomarker data 320, 322, 324 may each be received from the terminal 405. For example, the terminal 405 may be user device of a doctor, an employee or agent of the doctor working at the doctor’s office, or other human entity that inputs data representing a sample, data representing a proposed origin, and a data representing patient attributes for a the biological sample. In some implementations, the sample data 420 may include data structures structuring fields of data representing a proposed origin described by a tissue or organ name. In other implementations, the sample data 420 may include data structures structuring fields of data representing more complex sample data such as sample type, age and / or sex of the patient from which the sample is derived, or the like. The application server 240 receives the biomarker data records 320, 322, 324, the sample data 420, and the proposed origin data 422. The application server 240 provides the biomarker data records 320, 322, 324, the sample data 420, and the origin data 422 to an extraction unit 242 that is configured to extract (i) particular biomarker data such as DNA biomarker data 320a-l, protein expression data 322a-1, 324a-1, (ii) sample data 420a-l, and (iii) proposed origin data 422a-l from the fields of the biomarker data records 320, 322, 324 and the sample data records 420, 422. In some implementations, the extracted data is stored in the memory unit 244 as a buffer, cache or the like, and then provided as an input to the vector generation unit 250 when the vector generation unit 250 has bandwidth to receive an input for processing. In other implementations, the extracted data is provided directly to a vector generation unit 250 for processing. For example, in some implementations, multiple vector generation units 250 may be employed to enable parallel processing of inputs to reduce latency. The vector generation unit 250 can generate a data structure such as a feature vector 360 that includes a plurality of fields and includes one or more fields for each type of biomarker data and one or more fields for each type of origin data. For example, each field of the feature vector 360 may correspond to (i) each type of extracted biomarker data that can be extracted from the biomarker data records 320, 322, 324 such as each type of next generation sequencing data, each type of in situ hybridization data, each type of RNA or DNA data, each type of protein (e.g., immunohistochemistry) data, and each type of ADAPT data and (ii) each type of sample data that can be extracted from the sample data records 420,422 such as each type of disease or disorder, each type of sample, and each type of origin details. The vector generation unit 250 is configured to assign a weight to each field of the feature vector 360 that indicates an extent to which the extracted biomarker data 320a-l, 322a-l, 324a-l, the extracted sample 420a-1, and the extracted origin 422a-l includes the data represented by each field. In one implementation, for example, the vector generation unit 250 may assign a ‘ 1’ to each field of the feature vector 360 that corresponds to a feature found in the extracted biomarker data 320a-l, 322a-1, 324a-1, the extracted sample 420a-l, and the extracted ongm 422a-l. In such implementations, the vector generation unit 250 may, for example, also assign a ‘0’ to each field of the feature vector that corresponds to a feature not found in the extracted biomarker data 320a-l, 322a-1, 324a-1, the extracted sample 420a-l, and the extracted origin 422a-l. The output of the vector generation unit 250 may include a data structure such as a feature vector 360 that can be provided as an input to the trained machine learning model 370. The trained machine learning model 370 process the generated feature vector 360 based on the adjusted parameters that were determining during the training stage and described with reference to FIG. IB. The output 272 of the trained machine learning model provides an indication of the origin 422a-l of the sample 420a-l for the biological sample having biomarkers 320a-l, 322a-l, 324a-l. In some implementations, the output 272 may include a probability that is indicative of the origin 422a-1 of the sample 420a-l for the biological sample having biomarkers 320a-l, 322a-l, 324a-l. In such implementations, the output 272 may be provided 311 to the terminal 405 using the network 230. The terminal 405 may then generate output on a user interface 420 that indicates a predicted origin for the biological sample having the biomarkers represented by the feature vector 360. In other implementations, the output 272 may be provided to a prediction unit 380 that is configured to decipher the meaning of the output 272. For example, the prediction unit 380 can be configured to map the output 272 to one or more categories of effectiveness. Then, the output of the prediction unit 328 can be used as part of message 390 that is provided 311 to the terminal 305 using the network 230 for review by laboratory staff, a healthcare provider, a subject, a guardian of the subject, a nurse, a doctor, or the like. FIG. ID is a flowchart of a process 400 for generating training data structures for training a machine learning model to predict sample origin. In one aspect, the process 400 may include obtaining, from a first distributed data source, a first data structure that includes fields structuring data representing a set of one or more biomarkers associated with a biological sample (410), storing the first data structure in one or more memory devices (420), obtaining from a second distributed data source, a second data structure that includes fields structuring data representing the biological sample and origin data for the biological sample having the one or more biomarkers (430), storing the second data structure in the one or more memory devices (440), generating a labeled training data structure that structures data representing (i) the one or more biomarkers, (ii) a biological sample, (iii) an origin, and (iv) a predicted origin for the biological sample based on the first data structure and the second data structure (450), and training a machine learning model using the generated labeled training data (460). FIG. IE is a flowchart of a process 500 for using a trained machine learning model to predict sample origin of sample data from a subject. In one aspect, the process 500 may include obtaining a data structure representing a set of one or more biomarkers associated with a biological sample (510), obtaining data representing sample data for the biological sample (520), obtaining data representing a origin type for the biological sample (530), generating a data structure for mput to a machme learning model that structures data representing (i) the one or more biomarkers, (ii) the biological sample, and (iii) the origin type (540), providing the generated data structure as an input to the machine learning model that has been trained to predict sample origins using labeled training data structures structuring data representing one or more obtained biomarkers, one or more sample types, and one or more origins (550), and obtaining an output generated by the machine learning model based on the machine learning model processing of the provided data structure (560), and determining a predicted origin for the biological sample having the one or more biomarkers based on the obtained output generated by the machine learning model (570). Provided herein are methods of employing multiple machine learning models to improve classification performance. Conventionally, a single model is chosen to perform a desired prediction / classification. For example, one may compare different model parameters or types of models, e.g., random forests, support vector machines, logistic regression, k-nearest neighbors, artificial neural network, naive Bayes, quadratic discriminant analysis, or Gaussian processes models, during the training stage in order to identify the model having the optimal desired performance. Applicant realized that selection of a single model may not provide optimal performance in all settings. Instead, multiple models can be trained to perform the prediction / classification and the joint predictions can be used to make the classification. In this scenario, each model is allowed to “vote” and the classification receiving the majority of the votes is deemed the winner. This voting scheme disclosed herein can be applied to any machine learning classification, including both model building (e.g., using training data) and application to classify naive samples. Such settings include without limitation data in the fields of biology, finance, communications, media and entertainment. In some preferred embodiments, the data is highly dimensional “big data.” In some embodiments, the data comprises biological data, including without limitation biological data obtained via molecular profiling such as described herein. See, e.g., Example 1. The molecular profiling data can include without limitation highly dimensional next-generation sequencing data, e.g., for particular biomarker panels (see, e.g., Example 1) or whole exome and / or whole transcriptome data. The classification can be any usefill classification, e.g., to characterize a phenotype. For example, the classification may provide a diagnosis (e.g., disease or healthy), prognosis (e.g., predict a better or worse outcome), theranosis (e.g., predict or monitor therapeutic efficacy or lack thereof), or other phenotypic characterization (e.g., origin of a CUPs tumor sample). FIG. IF is an example of a system for performing pairwise analysis to predict a sample origin. A disease type can include, for example, an origin of a subject sample processed by the system. An origin of a subject sample can include, for example location of a subject’s body where a disease, such as cancer, originated. With reference to a practical example, a biopsy of a subject tumor may be obtained from a subject’s liver. Then, input data can be generated based on the biopsied tumor and provided as an input to the pairwise analysis model 340. The model can compare the generated input data to a corresponding biological signature of each known type of disease (e.g., different cancer types). Based on the output generated by the pairwise analysis model 340, the computer 310 can determine whether biopsied tumor represented by the input data originated in the liver or in some other portion of the subject’s body such as the pancreas. One or more treatments can then be determined based on the origin of the disease as opposed to the treatments being based on the biopsied tumor, alone. In more detail, the system 300 can include one or more processors and one or more memory writs 320 storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. In some implementations, the one or more processors and the one or memories 320 may be implemented in a computer such as a computer 310. The system 300 can obtain first biological signature data 322, 324 as an input. The first biological signature 322, 324 data can include one or more biomarkers 322, sample data 324, or both. Sample data 324 can include data representing the sample that was obtained from the body, e.g., a tissue sample, tumor sample, malignant fluid, or other sample such as described herein. In some implementations, the biological signature 322, 324 represents features of a disease, e.g., a cancer. In some implementations, the features may represent molecular data obtained using next generation sequencing (NGS). In some implementations, the features may be present in the DNA of a disease sample, including without limitation mutations, polymorphisms, deletions, insertions, substitutions, translocations, fusions, breaks, duplications, loss, amplification, repeats, or gene copy numbers. In some implementations, the features may be present in the RNA of a disease. The system can generate input data for input to a machine learning model 340 that has been trained to perform pairwise analysis. The machine learning model can include a neural network model, a linear regression model, a random forest model, a logistic regression model, a naive Bayes model, a quadratic discriminant analysis model, a K-nearest neighbor model, a support vector machine, or the like. The machine learning model 340 can be implemented as one or more computer programs on one or more computers in one or more locations. In some implementations, the generated input data may include data representing the biological signature 322, 324. In other implementations, the generated data that represents the biological signature can include a vector 332 generated using a vector generation unit 330. For example, the vector generation unit 330 can obtain biological signature data 322, 324 from the memory unit 320 and generate an input vector 333, based on the biological signature data 322, 324 that represents the biological signature data 322, 324 in a vector space. The generated vector 332 can be provided, as an input, to the pairwise analysis model 340. The pairwise analysis model 340 can be configured to perform pairwise analysis of the input vector 352 representing the biological signature 322, 324 with each biological signature 341-1, 341-2, 341-n, where n is any positive, non-zero integer. Each of the multiple different biological signatures correspond to a different type of disease, e.g., a different type of cancer. In some implementations, the model 340 can be a smgle model that is trained to determine a source of a sample based on m input sample by determining a level of similarity of features of an input sample to each of a plurality of biological signature classifications represented by biological signatures 341-1, 341-2, 341-n. In other implementations, the model 340 can include multiple different models that each perform a pairwise comparison between an input vector 332 and one biological signature such as 341-1. In such instances, output data generated by each of the models can be evaluated by a voting unit to determine a source of a sample represented by the processed input vector 332. The pairwise analysis model 340 can generate an output 342 that can be obtained by the system such as computer 310. The output 342 can indicate a likely disease type of the sample based on the pairwise analysis. In some implementations, the output 342 can include a matrix such as the matrix described in FIG. 5B. The system can determine, based on the generated matrix and using the prediction unit 350, data 360 indicating a likely disease type. Example 2 herein provides an implementation of such a system. In the Example, the models are trained to distinguish 115 disease types, where each disease type comprises a primary tumor origin and histology. In some embodiments, the data 360 provides a list of disease types ranked by probability. If desired, the data 360 can be presented as an aggregate of various disease types. In the Example, such aggregation of Organ Groups is presented, wherein each Organ Group comprises appropriate disease types. As an example, the Organ Group “colon” comprises the disease types “colon adenocarcinoma, NOS; colon carcinoma, NOS; colon mucinous adenocarcinoma” and the like. FIG. 1G is a block diagram of a system for predicting a sample origin using a voting unit to interpret output generated by multiple machine learning models that are each trained to perform pairwise analysis. The system 600 is similar to the system 300 of FIG. IF. However, instead of a single machine learning model 340 trained to perform pairwise analysis, the system 600 includes multiple machine learning models 340-0, 340-1 ... 340-x, where x is any non-zero integer greater than 1, that have been trained to perform pairwise analysis. The system 600 also include a voting unit 480. As a non-limiting example, system 600 can be used for predicting origin and related attributes of a biological sample having a particular set of biomarkers. See, e.g., Examples 2-3. Each machine learning model 370-0, 370-1, 370-x can include a machine learning model that has been trained to classify a particular type of input data 320-0, 320-1 ... 320-x, wherein x is any non-zero integer greater than 1 and equal to the number x of machine learning models. In some implementations, each machine learning models 340-0, 340-1, 340-x (labeled PW Compare Models in FIG. 1G) can be trained, or otherwise configured, to perform a particular pairwise comparison between (i) an input vector including data representing the sample data and (ii) another vector representing a particular biological signature including data representing a known disease type, portion of a subject body, or a both. Accordingly, in such implementations, the classification operation can include classifying (i) an input data vector including data representing sample data (e.g., sample origin, sample type, or the like) and (ii) one or more biomarkers associated with the sample as being sufficiently similar to a biological signature associated with the particular machme learning model or not sufficiently similar to the biological signature associated with the particular machine learning model. In some implementations, an input vector may be sufficiently similar to a biological signature if a similarity between the input vector and biological signature satisfies a predetermined threshold. In some implementations, each of the machine learning models 340-0, 340-1, 340-x can be of the same type. For example, each of the machine learning models 340-0, 340-1, 340-x can be a random forest classification algorithm, e.g., trained using differing parameters. In other implementations, the machine learning models 340-0, 340-1, 340-x can be of different types. For example, there can be one or more random forest classifiers, one or more neural networks, one or more K-nearest neighbor classifiers, other types of machine learning models, or any combination thereof. Input data such as 420 representing sample data and one or more biomarkers associated with the sample can be obtained by the application server 240. The sample data can include a sample type, sample origin, or the like, as described herein. In some implementations, the input data 420 is obtained across the network 230 from one or more distributed computers 310, 405. By way of example, one or more of the input data items 420 can be generated by correlating data from multiple different data sources 210,405. In such an implementation, (i) first data describing biomarkers for a biological sample can be obtained from the first distributed computer 310 and (ii) second data describing a biological sample and related data can be obtained from the second computer 405. The application server 240 can correlate the first data and the second data to generate an input data structure such as input data structure 420. This process is described in more detail in FIG. IC. The input data 420 can be provided to the vector generation unit 250. The vector generation unit 250 can generate input vectors 360-0, 360-1, 360-x that that each represent the input data 420. While some implementations may generate vectors 360-0, 360-1, 360-x serially, the present disclosure need not be so limited. In some implementations, each input data structure 320-0, 320-1, 320-x can include data representing biomarkers of a biological sample, data describing a biological sample and related data (e.g., a sample type, disease or disorder associated with the sample, and / or patient characteristics from which the sample is derived), or any combination thereof. The data representing the biomarkers of a biological sample can include data describing a specific subset or panel of genes or gene products. Alternatively, in some implementations, the data representing biomarkers of the biological sample can include data representing complete set of known genes or gene products, e.g., via whole exome sequencing and / or whole transcriptome sequencing. The complete set of known genes can include all of the genes of the subject from which the biological sample is derived. In some implementations, each of the machine learning models 340-0, 340-1, 340-x are the same type machine learning model such as a random forest model trained to classify the input data vectors as corresponding to a sample origin (e.g., tissue or organ) associated by the vector processed by the machine learning model. In such implementations, though each of the machine learning models 340-0, 340-1, 340-x is the same type of machine learning model, each of the machine learning models 340-0, 340-1, 340-x may be trained in different ways. The machine learning models 340-0, 340-1, 340-x can generate output data 372-0, 372-1, 372-x, respectively, representing whether a biological sample associated with input vectors 360-0, 360-1, 360-x is likely to be derived from an anatomical origin associated with the input vectors 360-0, 360-1, 360-x. In this example, the input data sets, and their corresponding input vectors, are the same — e.g., each set of input data has the same biomarkers, same sample type, same origin, or any combination thereof. Nonetheless, given the different training methods used to train each respective machine learning model 340-0, 340-1, 340-x may generate different outputs 372-0, 372-1, 372-x, respectively, based on each machine learning model 370-0, 370-1, 370-x processing the input vector 360-0, 361-1, 361-x, as shown in FIG. 1G. Alternatively, each of the machine learning models 340-0, 340-1, 340-x can be a different type of machine learning model that has been trained, or otherwise configured, to classify input data as most likely origin of a biological sample. For example, the first machine learning model 340-1 can include a neural network, the machine learning model 340-1 can include a random forest classification algorithm, and the machine learning model 340-x can include a K-nearest neighbor algorithm. In this example, each of these different types of machine learning models 340-0, 340-1, 340-x can be trained, or otherwise configured, to receive and process an input vector and determine whether the input vector is associated with to a sample origin also associated with the input vector. In this example, the input data sets, and their corresponding input vectors, can be the same — e.g., each set of input data has the same biomarkers, same sample type, same origin, or any combination thereof. Accordingly, the machine learning model 340-0 can be a neural network trained to process input vector 360-0 and generate output data 372-0 indicating whether the biological associated with the input vector 360-0 is likely to be from an origin also associated with input vector 360-0. In addition, the machine learning model 340-1 can be a random forest classification algorithm trained to process input vector 360-1, which for purposes of this example is the same as input vector 360-0, and generate output data 372-1 indicating whether the biological sample associated with the input vector 360-1 is likely to be from an origin also associated with the input vector 360-1. This method of input vector analysis can continue for each of the x inputs, x input vectors, and x machine learning models. Continuing with this example with reference to FIG. 1G the machine learning model 340-x can be a K-nearest neighbor algorithm trained to process input vector 360-x, which for purposes of this example is the same as input vector 360-0 and 360-1, and generate output data 372-x indicating whether the subject associated with the input vector 360-x is likely to be responsive or non-responsive to the treatment also associated with the input vector 360-x. Alternatively, each of the machine learning models 340-0, 340-1, 340-x can be the same type of machine learning models or different type of machine learning models that are each configured to receive different inputs. For example, the mput to the first machme learning model 340-0 can mclude a vector 360-0 that includes data representing a first subset or first panel of biomarkers from a biological sample and then predict, based on the machine learning models 340-0 processing of vector 360-0 whether the sample is more or less likely to be from a number of origins. In addition, in this example, an input to the second machine learning model 340-1 can include a vector 360-1 that includes data representing a second subset or second panel of biomarkers from the biological sample that is different than the first subset or first panel of biomarkers. Then, the second machine learning model can generate second output data 372-1 that is indicative of whether the sample associated with the input vector 360-1 is likely to be responsive or likely to be of an origin associated with the input vector 360-2. This method of input vector analysis can continue for each of the x inputs, x input vectors, and x machine learning models. The input to the xth machine learning model 340-x can include a vector 360-x that includes data representing an xth subset or xth panel of biomarkers of a subject that is different than (i) at least one, (i) two or more, or (iii) each of the other x-1 input data vectors 340-0 to 340-x-l. In some implementations, at least one of the x input data vectors can include data representing a complete set of biomarkers from the sample, e.g., next generation sequencing data. Then, the xth machine learning model 340-x can generate second output data 372-x, the second output data 372-x being indicative of whether the sample associated with the input vector 360-x is likely of an origin associated with the input vector 360-x. Multiple implementations of system 400 described above are not intended to be limiting, and instead, are merely examples of configurations of the multiple machine learning models 340-0, 340-1, 340-x, and their respective inputs, that can be employed using the present disclosure. With reference to these examples, the subject can be any human, non-human animal, plant, or other subject such as described herein. As described above, the input feature vectors can be generated, based on the input data, and represent the input data. Accordingly, each input vector can represent data that includes one or more biomarkers, a disease or disorder, a sample type, an origin, patient data, an origin of a sample having the biomarkers. In the implementation of FIG. 1G, the output data 372-0, 372-1, 372-x can be analyzed using a voting unit 480. For example, the output data 372-0, 372-1, 372-x can be input into the vote unit 480. In some implementations, the output data 372-0, 372-1, 372-x can be data indicating whether the biological sample associated with the input vector processed by the machine learning model is likely to be from a certain origin associated with the vector processed by the machine learning model. Data indicating whether the sample associated with the input vector, and generated by each machine learning model, can include a “0” or a “1.” A “0,” produced by a machine learning model 340-0 based on the machine learning model’s 340-0 processing of an input vector 360-0, can indicate that the sample associated with the input vector 360-0 is not likely to be from an origin associated with input vector 360-0. Similarity, as “1,” produced by a machine learning model 360-0 based on the machine learning model’s 370-0 processing of an input vector 360-0, can indicate that the sample associated with the input vector 360-0 is likely to be of an origin associated with the input vector 3600. Though the example uses “0” as not likely and “1” as likely, the present disclosure is not so limited. Instead, any value can be generated as output data to represent the output classes. For example, in some implementations “1” can be used to represent the “not likely” class and “0” to represent the “likely” class. In yet other implementations, the output data 372-0, 372-1, 372-x can include probabilities that indicate a likelihood that the sample associated with an input vector processed by a machine learning model is associated with a given origin (e.g., a given organ). In such implementations, for example, the generated probability can be applied to a threshold, and if the threshold is satisfied, then the subject associated with an input vector processed by the machine learning model can be determined to be likely to be of that origin. In some implementations, the machine learning models output an indication whether the sample is more likely to be from one origin versus another, instead of or in addition to indicating that the sample is more of less likely to be from a certain origin. For example, the machine learning model may indicate that the sample is more or less likely to be of prostatic origin (i.e., from the prostate), or the machine learning module may indicate whether the sample is most likely derived from the prostate or from the colon. Any such origins can be so compared. The voting unit 480 can evaluate the received output data 370-0, 372-1, 372-x and determine whether the sample associated with the processed input vectors 360-0, 360-1, 360-x is likely to be of an origin associated with the processed input vectors 360-0, 360-1, 360-x. The voting unit 480 can then determine, based on the set of received output data 370-0, 372-1, 372-x, whether the sample associated with input vectors 360-0, 360-1, 360-x is likely to be from an origin associated with the input vectors 360-0, 360-2, 360-x. In some implementations, the voting unit 480 can apply a “majority rule.” Applying a majority rule, the voting unit 480 can tally the outputs 372-0, 372-1, and 372-x indicating that the sample is from a given origin and outputs 372-0, 372-1, 372-x indicating that the sample is not from that origin (or is from a different origin as described above). Then, the class — e.g., from origin A or not from origin A, or from origin A and not from origin B, etc — having the majority predictions or votes is selected as the appropriate classification for the subject associated with the input vector 360-0, 360-1, 360-x. For example, the majority may determine that the sample is from origin A or is not from origin A, or alternately the majority may determine that the sample is from origin A or is from origin B. In some implementations, the voting unit 480 can complete a more nuanced analysis. For example, in some implementations, the voting unit 480 can store a confidence score for each machine learning model 340-0, 340-1, 340-x. This confidence score, for each machine learning model 340-0, 340-1, 340-x, can be initially set to a default value such as 0, 1, or the like. Then, with each round of processing of input vectors, the voting unit 480, or other module of the application server 240, can adjust the confidence score for the machine learning model 340-0, 340-1, 340-x based on whether the machine learning model accurately predicted the sample classification selected by the voting unit 480 during a previous iteration. Accordingly, the stored confidence score, for each machine learning model, can provide an indication of the historical accuracy for each machine learning model. In the more nuanced approached, the voting unit 480 can adjust output data 372-0, 372-0, 372-x produced by each machine learning model 340-0, 340-1, 340-x, respectively, based on the confidence score calculated for the machine learning model. Accordingly, a confidence score indicating that a machine learning mode is historically accurate can be used to boost a value of output data generated by the machine learning model. Similarly, a confidence score indicating that a machine learning model is historically inaccurate can be used to reduce a value of output data generated by the machine learning model. Such boosting or reducing of the value of output data generated by a machine learning model can be achieved, for example, by using the confidence score as a multiplier of less than one for reduction and more than 1 for boosting. Other operations can also be used to adjust the value of output data such as subtracting a confidence score from the value of the output data to reduce the value of the output data or adding the confidence score to the value of the output data to boost the value of the output data. Use of confidence scores to boost or reduce the value of output data generated by the machine learning models is particularly usefill when the machine learning models are configured to output probabilities that will be applied to one or more thresholds to determine whether a sample is or is not from an origin, or is from one of two possible origins. This is because using the confidence score to adjust the output of a machine learning model can be used to move a generated output value above or below a class threshold, thereby altering a prediction by a machine learning model based on its historical accuracy. Use of the voting unit 480 to evaluate outputs of multiple machine learning models can lead to greater accuracy in prediction of the origin of a sample for a particular set of subject biomarkers, as the consensus amongst multiple machine learning models can be evaluated instead of the output of only a single machine learning model. FIG. 1H is a block diagram of system components that can be used to implement systems of FIGs. IB, IC, 1G, IF, and 1G. Computing device 600 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Computing device 650 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, and other similar computing devices. Additionally, computing device 600 or 650 can include Universal Serial Bus (USB) flash (hives. The USB flash (hives can store operating systems and other applications. The USB flash (hives can include input / output components, such as a wireless transmitter or USB connector that can be inserted into a USB port of another computing device. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and / or claimed in this document. Computing device 600 mcludes a processor 602, memory 604, a storage device 608, a highspeed interface 608 connecting to memory 604 and high-speed expansion ports 610, and a low speed interface 612 connecting to low speed bus 614 and storage device 608. Each of the components 602, 604, 608, 608, 610, and 612, are interconnected using various busses, and can be mounted on a common motherboard or in other manners as appropriate. The processor 602 can process instructions for execution within the computing device 600, including instructions stored in the memory 604 or on the storage device 608 to display graphical information for a GUI on an external input / output device, such as display 616 coupled to high speed interface 608. In other implementations, multiple processors and / or multiple buses can be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 600 can be connected, with each device providing portions of the necessary operations, e.g., as a server bank, a group of blade servers, or a multiprocessor system. The memory 604 stores information within the computing device 600. In one implementation, the memory 604 is a volatile memory unit or units. In another implementation, the memory 604 is a non-volatile memory unit or units. The memory 604 can also be another form of computer-readable medium, such as a magnetic or optical disk. The storage device 608 is capable of providing mass storage for the computing device 600. In one implementation, the storage device 608 can be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product can be tangibly embodied in an information carrier. The computer program product can also contain instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer-or machine-readable medium, such as the memory 604, the storage device 608, or memory on processor 602. The high speed controller 608 manages bandwidth-intensive operations for the computing device 600, while the low speed controller 612 manages lower bandwidth intensive operations. Such allocation of functions is exemplary only. In one implementation, the high-speed controller 608 is coupled to memory 604, display 616, e.g., through a graphics processor or accelerator, and to highspeed expansion ports 610, which can accept various expansion cards (not shown). In the implementation, low-speed controller 612 is coupled to storage device 608 and low-speed expansion port 614. The low-speed expansion port, which can include various communication ports, e.g., USB, Bluetooth, Ethernet, wireless Ethernet can be coupled to one or more input / output devices, such as a keyboard, a pointing device, microphone / speaker pair, a scanner, or a networking device such as a switch or router, e.g., through a network adapter. The computing device 600 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a standard server 620, or multiple times in a group of such servers. It can also be implemented as part of a rack server system 624. In addition, it can be implemented m a personal computer such as a laptop computer 622. Alternatively, components from computing device 600 can be combined with other components in a mobile device (not shown), such as device 650. Each of such devices can contain one or more of computing device 600, 650, and an entire system can be made up of multiple computing devices 600, 650 communicating with each other. The computing device 600 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a standard server 620, or multiple times in a group of such servers. It can also be implemented as part of a rack server system 624. In addition, it can be implemented in a personal computer such as a laptop computer 622. Alternatively, components from computing device 600 can be combined with other components in a mobile device (not shown), such as device 650. Each of such devices can contain one or more of computing device 600, 650, and an entire system can be made up of multiple computing devices 600, 650 communicating with each other. Computing device 650 includes a processor 652, memory 664, and an input / output device such as a display 654, a communication interface 666, and a transceiver 668, among other components. The device 650 can also be provided with a storage device, such as a micro-drive or other device, to provide additional storage. Each of the components 650, 652, 664, 654, 666, and 668, are interconnected using various buses, and several of the components can be mounted on a common motherboard or in other manners as appropriate. The processor 652 can execute instructions within the computing device 650, including instructions stored in the memory 664. The processor can be implemented as a chipset of chips that include separate and multiple analog and digital processors. Additionally, the processor can be implemented using any of a number of architectures. For example, the processor 610 can be a CISC (Complex Instruction Set Computers) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor. The processor can provide, for example, for coordination of the other components of the device 650, such as control of user interfaces, applications run by device 650, and wireless communication by device 650. Processor 652 can communicate with a user through control interface 658 and display interface 656 coupled to a display 654. The display 654 can be, for example, a TFT (Thin-Fihn-Transistor Liquid Crystal Display) display or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 656 can comprise appropriate circuitry for (hiving the display 654 to present graphical and other information to a user. The control interface 658 can receive commands from a user and convert them for submission to the processor 652. In addition, an external interface 662 can be provide in communication with processor 652, so as to enable near area communication of device 650 with other devices. External interface 662 can provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces can also be used. The memory 664 stores information within the computing device 650. The memory 664 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. Expansion memory 674 can also be provided and connected to device 650 through expansion interface 672, which can include, for example, a SIMM (Single In Line Memory Module) card interface. Such expansion memory 674 can provide extra storage space for device 650, or can also store applications or other information for device 650. Specifically, expansion memory 674 can include instructions to carry out or supplement the processes described above, and can include secure information also. Thus, for example, expansion memory 674 can be provide as a security module for device 650, and can be programmed with instructions that permit secure use of device 650. In addition, secure applications can be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner. The memory can include, for example, flash memory and / or NVRAM memory, as discussed below. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 664, expansion memory 674, or memory on processor 652 that can be received, for example, over transceiver 668 or external interface 662. Device 650 can communicate wirelessly through communication interface 666, which can include digital signal processing circuitry where necessary. Communication interface 666 can provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication can occur, for example, through radio-frequency transceiver 668. In addition, shortrange communication can occur, such as using a Bluetooth, Wi-Fi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module 670 can provide additional navigation- and location-related wireless data to device 650, which can be used as appropriate by applications running on device 650. Device 650 can also communicate audibly using audio codec 660, which can receive spoken information from a user and convert it to usable digital information. Audio codec 660 can likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 650. Such sound can include sound from voice telephone calls, can include recorded sound, e.g., voice messages, music files, etc. and can also include sound generated by applications operating on device 650. The computing device 650 can be implemented in a number of different forms, as shown in the figure. For example, it can be implemented as a cellular telephone 680. It can also be implemented as part of a smartphone 682, personal digital assistant, or other similar mobile device. Various implementations of the systems and methods described here can be realized m digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations of such implementations. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the terms “machine-readable medium” or “computer-readable medium” refers to any computer program product, apparatus and / or device, e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs), used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor. To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. The systems and techniques described here can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here, or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), and the Internet. The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Computer Systems The practice of the present methods may also employ computer related software and systems. Computer software products as described herein typically include computer readable medium having computer-executable instructions for performing the logic steps of the method as described herein. Suitable computer readable medium include floppy disk, CD-ROM / DVD / DVD-ROM, hard-disk (hive, flash memory, ROM / RAM, magnetic tapes and etc. The computer executable instructions may be written in a suitable computer language or combination of several languages. Basic computational biology methods are described in, for example Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2.sup.nd ed., 2001). See U.S. Pat. No. 6,420,108. The present methods may also make use of various computer program products and software for a variety of purposes, such as probe design, management of data, analysis, and instrument operation. See, U.S. Pat. Nos. 5,593,839, 5,795,716, 5,733,729, 5,974,164, 6,066,454, 6,090,555, 6,185,561, 6,188,783, 6,223,127, 6,229,911 and 6,308,170. Additionally, the present methods relates to embodiments that include methods for providing genetic information over networks such as the Internet as shown in U.S. Ser. Nos. 10 / 197,621, 10 / 063,559 (U.S. Publication Number 20020183936), 10 / 065,856, 10 / 065,868, 10 / 328,818, 10 / 328,872, 10 / 423,403, and 60 / 482,389. For example, one or more molecular profiling techniques can be performed in one location, e.g., a city, state, country or continent, and the results can be transmitted to a different city, state, country or continent. Treatment selection can then be made in whole or in part in the second location. The methods as described herein comprise transmittal of information between different locations. Conventional data networking, application development and other functional aspects of the systems (and components of the individual operating components of the systems) may not be described in detail herein but are part as described herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent illustrative functional relationships and / or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in a practical system. The various system components discussed herein may include one or more of the following: a host server or other computing systems including a processor for processing digital data; a memory coupled to the processor for storing digital data; an input digitizer coupled to the processor for inputting digital data; an application program stored in the memory and accessible by the processor for directing processing of digital data by the processor; a display device coupled to the processor and memory for displaying information derived from digital data processed by the processor; and a plurality of databases. Various databases used herein may include: patient data such as family history, demography and environmental data, biological sample data, prior treatment and protocol data, patient clinical data, molecular profiling data of biological samples, data on therapeutic (hug agents and / or investigative (hugs, a gene library, a disease library, a drug library, patient tracking data, file management data, financial management data, billing data and / or like data usefill in the operation of the system. As those skilled in the art will appreciate, user computer may include an operating system (e.g., Windows NT, 95 / 98 / 2000, OS2, UNIX, Linux, Solaris, MacOS, etc.) as well as various conventional support software and drivers typically associated with computers. The computer may include any suitable personal computer, network computer, workstation, minicomputer, mainframe or the like. User computer can be in a home or medical / business environment with access to a network. In an illustrative embodiment, access is through a network or the Internet through a commercially-available web-browser software package. As used herein, the term “network” shall include any electronic communications means which incorporates both hardware and software components of such. Communication among the parties may be accomplished through any suitable communication channels, such as, for example, a telephone network, an extranet, an intranet, Internet, point of interaction device, personal digital assistant (e.g., Palm Pilot®, Blackberry®), cellular phone, kiosk, etc.), online communications, satellite communications, off-line communications, wireless communications, transponder communications, local area network (LAN), wide area network (WAN), networked or linked devices, keyboard, mouse and / or any suitable communication or data input modality. Moreover, although the system is frequently described herein as being implemented with TCP / IP communications protocols, the system may also be implemented using IPX, Appletalk, IP-6, NetBIOS, OSI or any number of existing or future protocols. If the network is in the nature of a public network, such as the Internet, it may be advantageous to presume the network to be insecure and open to eavesdroppers. Specific information related to the protocols, standards, and application software used in connection with the Internet is generally known to those skilled in the art and, as such, need not be detailed herein. See, for example, Dilip Naik, Internet Standards and Protocols (1998); Java 2 Complete, various authors, (Sybex 1999); Deborah Ray and Eric Ray, Mastering HTML 4.0 (1997); and Loshin, TCP / IP Clearly Explained (1997) and David Gourley and Brian Totty, HTTP, The Definitive Guide (2002), the contents of which are hereby incorporated by reference. The various system components may be independently, separately or collectively suitably coupled to the network via data links which includes, for example, a connection to an Internet Service Provider (ISP) over the local loop as is typically used in connection with standard modem communication, cable modem, Dish networks, ISDN, Digital Subscriber Line (DSL), or various wireless communication methods, see, e.g., Gilbert Held, Understanding Data Communications (1996), which is hereby incorporated by reference. It is noted that the network may be implemented as other types of networks, such as an interactive television (ITV) network. Moreover, the system contemplates the use, sale or distribution of any goods, services or information over any network having similar functionality described herein. As used herein, “transmit” may include sending electronic data from one system component to another over a network connection. Additionally, as used herein, “data” may include encompassing information such as commands, queries, files, data for storage, and the like in digital or any other form. The system contemplates uses in association with web services, utility computing, pervasive and individualized computing, security and identity solutions, autonomic computing, commodity computing, mobility and wireless solutions, open source, biometrics, grid computing and / or mesh computing. Any databases discussed herein may include relational, hierarchical, graphical, or object-oriented structure and / or any other database configurations. Common database products that may be used to implement the databases include DB2 by IBM (White Plains, NY), various database products available from Oracle Corporation (Redwood Shores, CA), Microsoft Access or Microsoft SQL Server by Microsoft Corporation (Redmond, Washington), or any other suitable database product. Moreover, the databases may be organized in any suitable manner, for example, as data tables or lookup tables. Each record may be a single file, a series of files, a linked series of data fields or any other data structure. Association of certain data may be accomplished through any desired data association technique such as those known or practiced in the art. For example, the association may be accomplished either manually or automatically. Automatic association techniques may include, for example, a database search, a database merge, GREP, AGREP, SQL, using a key field in the tables to speed searches, sequential searches through all the tables and files, sorting records in the file according to a known order to simplify lookup, and / or the like. The association step may be accomplished by a database merge function, for example, using a “key field” in pre-selected databases or data sectors. More particularly, a “key field” partitions the database according to the high-level class of objects defined by the key field. For example, certain types of data may be designated as a key field in a plurality of related data tables and the data tables may then be linked on the basis of the type of data in the key field. The data corresponding to the key field in each of the linked data tables is preferably the same or of the same type. However, data tables having similar, though not identical, data in the key fields may also be linked by using AGREP, for example. In accordance with one embodiment, any suitable data storage technique may be used to store data without a standard format. Data sets may be stored using any suitable technique, including, for example, storing individual files using an ISO / IEC 7816-4 file structure; implementing a domain whereby a dedicated file is selected that exposes one or more elementary files containing one or more data sets; using data sets stored in individual files using a hierarchical filing system; data sets stored as records in a single file (including compression, SQL accessible, hashed vione or more keys, numeric, alphabetical by first tuple, etc.); Binary Large Object (BLOB); stored as ungrouped data elements encoded using ISO / IEC 7816-6 data elements; stored as ungrouped data elements encoded using ISO / IEC Abstract Syntax Notation (ASN. 1) as in ISO / IEC 8824 and 8825; and / or other proprietary techniques that may include fractal compression methods, image compression methods, etc. In one illustrative embodiment, the ability to store a wide variety of information in different formats is facilitated by storing the information as a BLOB. Thus, any binary information can be stored in a storage space associated with a data set. The BLOB method may store data sets as ungrouped data elements formatted as a block of binary via a fixed memory offset using either fixed storage allocation, circular queue techniques, or best practices with respect to memory management (e.g., paged memory, least recently used, etc.). By using BLOB methods, the ability to store various data sets that have different formats facilitates the storage of data by multiple and unrelated owners of the data sets. For example, a first data set which may be stored may be provided by a first party, a second data set which may be stored may be provided by an unrelated second party, and yet a third data set which may be stored, may be provided by a third party unrelated to the first and second party. Each of these three illustrative data sets may contain different information that is stored using different data storage formats and / or techniques. Further, each data set may contain subsets of data that also may be distinct from other subsets. As stated above, in various embodiments, the data can be stored without regard to a common format. However, in one illustrative embodiment, the data set (e.g., BLOB) may be annotated in a standard manner when provided for manipulating the data. The annotation may comprise a short header, trailer, or other appropriate indicator related to each data set that is configured to convey information usefill in managing the various data sets. For example, the annotation may be called a “condition header”, “header”, “trailer”, or “status”, herein, and may comprise an indication of the status of the data set or may include an identifier correlated to a specific issuer or owner of the data. Subsequent bytes of data may be used to indicate for example, the identity of the issuer or owner of the data, user, transaction / membership account identifier or the like. Each of these condition annotations are further discussed herein. The data set annotation may also be used for other types of status information as well as various other purposes. For example, the data set annotation may include security information establishing access levels. The access levels may, for example, be configured to permit only certain individuals, levels of employees, companies, or other entities to access data sets, or to permit access to specific data sets based on the transaction, issuer or owner of data, user or the like. Furthermore, the security information may restrict / permit only certain actions such as accessing, modifying, and / or deleting data sets. In one example, the data set annotation indicates that only the data set owner or the user are permitted to delete a data set, various identified users may be permitted to access the data set for reading, and others are altogether excluded from accessing the data set. However, other access restriction parameters may also be used allowing various entities to access a data set with various permission levels as appropriate. The data, mcludmg the header or trailer may be received by a standalone interaction device configured to add, delete, modify, or augment the data in accordance with the header or trailer. One skilled in the art will also appreciate that, for security reasons, any databases, systems, devices, servers or other components of the system may consist of any combination thereof at a single location or at multiple locations, wherein each database or system includes any of various suitable security features, such as firewalls, access codes, encryption, decryption, compression, decompression, and / or the like. The computing unit of the web client may be further equipped with an Internet browser connected to the Internet or an intranet using standard dial-up, cable, DSL or any other Internet protocol known in the art. Transactions originating at a web client may pass through a firewall in order to prevent unauthorized access from users of other networks. Further, additional firewalls may be deployed between the varying components of CMS to further enhance security. Firewall may include any hardware and / or software suitably configured to protect CMS components and / or enterprise computing resources from users of other networks. Further, a firewall may be configured to limit or restrict access to various systems and components behind the firewall for web clients connecting through a web server. Firewall may reside in varying configurations including Statefill Inspection, Proxy based and Packet Filtering among others. Firewall may be integrated within an web server or any other CMS components or may further reside as a separate entity. The computers discussed herein may provide a suitable website or other Internet-based graphical user interface which is accessible by users. In one embodiment, the Microsoft Internet Information Server (IIS), Microsoft Transaction Server (MTS), and Microsoft SQL Server, are used in conjunction with the Microsoft operating system, Microsoft NT web server software, a Microsoft SQL Server database system, and a Microsoft Commerce Server. Additionally, components such as Access or Microsoft SQL Server, Oracle, Sybase, Informix MySQL, Interbase, etc., may be used to provide an Active Data Object (ADO) compliant database management system. Any of the communications, inputs, storage, databases or displays discussed herein may be facilitated through a website having web pages. The term “web page” as it is used herein is not meant to limit the type of documents and applications that might be used to interact with the user. For example, a typical website might include, in addition to standard HTML documents, various forms, Java applets, JavaScript, active server pages (ASP), common gateway interface scripts (CGI), extensible markup language (XML), dynamic HTML, cascading style sheets (CSS), helper applications, plug-ins, and the like. A server may include a web service that receives a request from a web server, the request including a URL (http: / / yahoo.com / stockquotes / ge) and an IP address (123.56.789.234). The web server retrieves the appropriate web pages and sends the data or applications for the web pages to the IP address. Web services are applications that are capable of interacting with other applications over a communications means, such as the internet. Web services are typically based on standards or protocols such as XML, XSLT, SOAP, WSDL and UDDL Web services methods are well known in the art, and are covered in many standard texts. See, e.g., Alex Nghiem, IT Web Services: A Roadmap for the Enterprise (2003), hereby incorporated by reference. The web-based clinical database for the system and method of the present methods preferably has the ability to upload and store clinical data files in native formats and is searchable on any clinical parameter. The database is also scalable and may use an EAV data model (metadata) to enter clinical annotations from any study for easy integration with other studies. In addition, the web-based clinical database is flexible and may be XML and XSLT enabled to be able to add user customized questions dynamically. Further, the database includes exportability to CDISC ODM. Practitioners will also appreciate that there are a number of methods for displaying data within a browser-based document. Data may be represented as standard text or within a fixed list, scrollable list, drop-down list, editable text field, fixed text field, pop-up window, and the like. Likewise, there are a number of methods available for modifying data in a web page such as, for example, free text entry using a keyboard, selection of menu items, check boxes, option boxes, and the like. The system and method may be described herein in terms of functional block components, screen shots, optional selections and various processing steps. It should be appreciated that such functional blocks may be realized by any number of hardware and / or software components configured to perform the specified functions. For example, the system may employ various integrated circuit components, e.g., memory elements, processing elements, logic elements, look-up tables, and the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. Similarly, the software elements of the system may be implemented with any programming or scripting language such as C, C++, Macromedia Cold Fusion, Microsoft Active Server Pages, Java, COBOL, assembler, PERL, Visual Basic, SQL Stored Procedures, extensible markup language (XML), with the various algorithms being implemented with any combination of data structures, objects, processes, routines or other programming elements. Further, it should be noted that the system may employ any number of conventional techniques for data transmission, signaling, data processing, network control, and the like. Still further, the system could be used to detect or prevent security issues with a client-side scripting language, such as JavaScript, VBScript or the like. For a basic introduction of cryptography and network security, see any of the following references: (1) “Applied Cryptography: Protocols, Algorithms, And Source Code In C,” by Bruce Schneier, published by John Wiley & Sons (second edition, 1995); (2) “Java Cryptography” by Jonathan Knudson, published by O’Reilly & Associates (1998); (3) “Cryptography & Network Security: Principles & Practice” by William Stallings, published by Prentice Hall; all of which are hereby incorporated by reference. As used herein, the term “end user”, “consumer”, “customer”, “client”, “treating physician”, “hospital”, or “business” may be used interchangeably with each other, and each shall mean any person, entity, machine, hardware, software or business. Each participant is equipped with a computing device in order to interact with the system and facilitate online data access and data input. The customer has a computing unit in the form of a personal computer, although other types of computing units may be used including laptops, notebooks, hand held computers, set-top boxes, cellular telephones, touch-tone telephones and the like. The owner / operator of the system and method of the present methods has a computing unit implemented in the form of a computer-server, although other implementations are contemplated by the system including a computing center shown as a main frame computer, a mini-computer, a PC server, a network of computers located in the same of different geographic locations, or the like. Moreover, the system contemplates the use, sale or distribution of any goods, services or information over any network having similar functionality described herein. In one illustrative embodiment, each client customer may be issued an “account” or “account number”. As used herein, the account or account number may include any device, code, number, letter, symbol, digital certificate, smart chip, digital signal, analog signal, biometric or other identifier / indicia suitably configured to allow the consumer to access, interact with or communicate with the system (e.g., one or more of an authorization / access code, personal identification number (PEN), Internet code, other identification code, and / or the like). The account number may optionally be located on or associated with a charge card, credit card, debit card, prepaid card, embossed card, smart card, magnetic stripe card, bar code card, transponder, radio frequency card or an associated account. The system may include or interface with any of the foregoing cards or devices, or a fob having a transponder and RFID reader in RF communication with the fob. Although the system may include a fob embodiment, the methods is not to be so limited. Indeed, system may include any device having a transponder which is configured to communicate with RFID reader via RF communication. Typical devices may include, for example, a key ring, tag, card, cell phone, wristwatch or any such form capable of being presented for interrogation. Moreover, the system, computing unit or device discussed herein may include a “pervasive computing device,” which may include a traditionally noncomputerized device that is embedded with a computing unit. The account number may be distributed and stored in any form of plastic, electronic, magnetic, radio frequency, wireless, audio and / or optical device capable of transmitting or downloading data from itself to a second device. As will be appreciated by one of ordinary skill in the art, the system may be embodied as a customization of an existing system, an add-on product, upgraded software, a standalone system, a distributed system, a method, a data processing system, a device for data processing, and / or a computer program product. Accordingly, the system may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining aspects of both software and hardware. Furthermore, the system may take the form of a computer program product on a computer-readable storage medium having computer-readable program code means embodied m the storage medium. Any suitable computer-readable storage medium may be used, including hard disks, CD-ROM, optical storage devices, magnetic storage devices, and / or the like. The system and method is described herein with reference to screen shots, block diagrams and flowchart illustrations of methods, apparatus (eg., systems), and computer program products according to various embodiments. It will be understood that each functional block of the block diagrams and the flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions that execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks. These computer program instructions may 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 which implement the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Accordingly, functional blocks of the block diagrams and flowchart illustrations support combinations of means for performing the specified functions, combinations of steps for performing the specified functions, and program instruction means for performing the specified functions. It will also be understood that each functional block of the block diagrams and flowchart illustrations, and combinations of functional blocks in the block diagrams and flowchart illustrations, can be implemented by either special purpose hardware-based computer systems which perform the specified functions or steps, or suitable combinations of special purpose hardware and computer instructions. Further, illustrations of the process flows and the descriptions thereof may make reference to user windows, web pages, websites, web forms, prompts, etc. Practitioners will appreciate that the illustrated steps described herein may comprise in any number of configurations including the use of windows, web pages, web forms, popup windows, prompts and the like. It should be further appreciated that the multiple steps as illustrated and described may be combined into single web pages and / or windows but have been expanded for the sake of simplicity. In other cases, steps illustrated and described as single process steps may be separated into multiple web pages and / or windows but have been combined for simplicity. Molecular Profiling The molecular profiling approach provides a method for selecting a candidate treatment for an individual that could favorably change the clinical course for the individual with a condition or disease, such as cancer. The molecular profiling approach provides clinical benefit for individuals, such as identifying therapeutic regimens that provide a longer progression free survival (PFS), longer disease free survival (DFS), longer overall survival (OS) or extended lifespan. Methods and systems as described herein are directed to molecular profiling of cancer on an individual basis that can identify optimal therapeutic regimens. Molecular profiling provides a personalized approach to selecting candidate treatments that are likely to benefit a cancer. The molecular profiling methods described herein can be used to guide treatment in any desired setting, including without limitation the fiont-line / standard of care setting, or for patients with poor prognosis, such as those with metastatic disease or those whose cancer has progressed on standard front line therapies, or whose cancer has progressed on previous chemotherapeutic or hormonal regimens. The systems and methods of the invention may be used to classify patients as more or less likely to benefit or respond to various treatments. Unless otherwise noted, the terms “response” or “non-response,” as used herein, refer to any appropriate indication that a treatment provides a benefit to a patient (a “responder” or “benefiter”) or has a lack of benefit to the patient (a “non-responder” or “non-benefiter”). Such an indication may be determined using accepted clinical response criteria such as the standard Response Evaluation Criteria in Solid Tumors (RECIST) criteria, or any other usefill patient response criteria such as progression free survival (PFS), time to progression (TTP), disease free survival (DFS), time-to-next treatment (TNT, TTNT), time-to-treatment failure (TTF, TTTF), tumor shrinkage or disappearance, or the like. RECIST is a set of rules published by an international consortium that define when tumors improve (“respond”), stay the same (“stabilize”), or worsen (“progress”) during treatment of a cancer patient. As used herein and unless otherwise noted, a patient “benefit” from a treatment may refer to any appropriate measure of improvement, including without limitation a RECIST response or longer PFS / TTP / DFS / TNT / TTNT, whereas “lack of benefit” from a treatment may refer to any appropriate measure of worsening disease during treatment. Generally disease stabilization is considered a benefit, although in certain circumstances, if so noted herein, stabilization may be considered a lack of benefit. A predicted or indicated benefit may be described as “indeterminate” if there is not an acceptable level of prediction of benefit or lack of benefit. In some cases, benefit is considered indeterminate if it cannot be calculated, e.g., due to lack of necessary data. Personalized medicine based on pharmacogenetic insights, such as those provided by molecular profiling as described herein, is increasingly taken for granted by some practitioners and the lay press, but forms the basis of hope for improved cancer therapy. However, molecular profiling as taught herein represents a fundamental departure from the traditional approach to oncologic therapy where for the most part, patients are grouped together and treated with approaches that are based on findings from light microscopy and disease stage. Traditionally, differential response to a particular therapeutic strategy has only been determined after the treatment was given, i.e., a posteriori. The “standard” approach to disease treatment relies on what is generally true about a given cancer diagnosis and treatment response has been vetted by randomized phase III clinical trials and forms the “standard of care” in medical practice. The results of these trials have been codified in consensus statements by guidelines organizations such as the National Comprehensive Cancer Network and The American Society of Clinical Oncology. The NCCN Compendium™ contains authoritative, scientifically derived information designed to support decision-making about the appropriate use of (hugs and biologies in patients with cancer. The NCCN Compendium™ is recognized by the Centers for Medicare and Medicaid Services (CMS) and United Healthcare as an authoritative reference for oncology coverage policy. On-compendium treatments are those recommended by such guides. The biostatistical methods used to validate the results of clinical trials rely on minimizing differences between patients, and are based on declaring the likelihood of error that one approach is better than another for a patient group defined only by light microscopy and stage, not by individual differences in tumors. The molecular profiling methods described herein exploit such individual differences. The methods can provide candidate treatments that can be then selected by a physician for treating a patient. Molecular profiling can be used to provide a comprehensive view of the biological state of a sample. In an embodiment, molecular profiling is used for whole tumor profiling. Accordingly, a number of molecular approaches are used to assess the state of a tumor. The whole tumor profiling can be used for selecting a candidate treatment for a tumor. Molecular profiling can be used to select candidate therapeutics on any sample for any stage of a disease. In embodiment, the methods as described herein are used to profile a newly diagnosed cancer. The candidate treatments indicated by the molecular profiling can be used to select a therapy for treating the newly diagnosed cancer. In other embodiments, the methods as described herein are used to profile a cancer that has already been treated, e.g., with one or more standard-of-care therapy. In embodiments, the cancer is refractory to the prior treatment / s. For example, the cancer may be refractory to the standard of care treatments for the cancer. The cancer can be a metastatic cancer or other recurrent cancer. The treatments can be on-compendium or off-compendium treatments. Molecular profiling can be performed by any known means for detecting a molecule in a biological sample. Molecular profiling comprises methods that include but are not limited to, nucleic acid sequencing, such as a DNA sequencing or RNA sequencing; immunohistochemistry (IHC); in situ hybridization (ISH); fluorescent in situ hybridization (FISH); chromogenic in situ hybridization (CISH); PCR amplification (e.g., qPCR or RT-PCR); various types of microarray (mRNA expression arrays, low density arrays, protein arrays, etc); various types of sequencing (Sanger, pyrosequencing, etc); comparative genomic hybridization (CGH); high throughput or next generation sequencing (NGS); Northern blot; Southern blot; immunoassay; and any other appropriate technique to assay the presence or quantity of a biological molecule of interest. In various embodiments, any one or more of these methods can be used concurrently or subsequent to each other for assessmg target genes disclosed herein. Molecular profiling of individual samples is used to select one or more candidate treatments for a disorder in a subject, e.g., by identifying targets for (hugs that may be effective for a given cancer. For example, the candidate treatment can be a treatment known to have an effect on cells that differentially express genes as identified by molecular profiling techniques, an experimental (hug, a government or regulatory approved drug or any combination of such (hugs, which may have been studied and approved for a particular indication that is the same as or different from the indication of the subject from whom a biological sample is obtain and molecularly profiled. When multiple biomarker targets are revealed by assessing target genes by molecular profiling, one or more decision rules can be put in place to prioritize the selection of certain therapeutic agent for treatment of an individual on a personalized basis. Rules as described herein aide prioritizing treatment, e.g., direct results of molecular profiling, anticipated efficacy of therapeutic agent, prior history with the same or other treatments, expected side effects, availability of therapeutic agent, cost of therapeutic agent, drug-drug interactions, and other factors considered by a treating physician. Based on the recommended and prioritized therapeutic agent targets, a physician can decide on the course of treatment for a particular individual. Accordingly, molecular profiling methods and systems as described herein can select candidate treatments based on individual characteristics of diseased cells, e.g., tumor cells, and other personalized factors in a subject in need of treatment, as opposed to relying on a traditional one-size fits all approach that is conventionally used to treat individuals suffering from a disease, especially cancer. In some cases, the recommended treatments are those not typically used to treat the disease or disorder inflicting the subject. In some cases, the recommended treatments are used after standard-of-care therapies are no longer providing adequate efficacy. The treating physician can use the results of the molecular profiling methods to optimize a treatment regimen for a patient. The candidate treatment identified by the methods as described herein can be used to treat a patient; however, such treatment is not required of the methods. Indeed, the analysis of molecular profiling results and identification of candidate treatments based on those results can be automated and does not require physician involvement. Biological Entities Nucleic acids include deoxyribonucleotides or ribonucleotides and polymers thereof in either single- or double-stranded form, or complements thereof. Nucleic acids can contain known nucleotide analogs or modified backbone residues or linkages, which are synthetic, naturally occurring, and non-naturally occurring, which have similar binding properties as the reference nucleic acid, and which are metabolized in a manner similar to the reference nucleotides. Examples of such analogs include, without limitation, phosphorothioates, phosphoramidates, methyl phosphonates, chiral-methyl phosphonates, 2-O-methyl ribonucleotides, peptide-nucleic acids (PNAs). Nucleic acid sequence can encompass conservatively modified variants thereof (e.g., degenerate codon substitutions) and complementary sequences, as well as the sequence explicitly indicated. Specifically, degenerate codon substitutions may be achieved by generating sequences in which the third position of one or more selected (or all) codons is substituted with mixed-base and / or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chern. 260:2605-2608 (1985); Rossolini et al., Mol. Cell Probes 8:91-98 (1994)). The term nucleic acid can be used interchangeably with gene, cDNA, mRNA, oligonucleotide, and polynucleotide. A particular nucleic acid sequence may implicitly encompass the particular sequence and “splice variants” and nucleic acid sequences encoding truncated forms. Similarly, a particular protein encoded by a nucleic acid can encompass any protein encoded by a splice variant or truncated form of that nucleic acid. “Splice variants,” as the name suggests, are products of alternative splicing of a gene. After transcription, an initial nucleic acid transcript may be spliced such that different (alternate) nucleic acid splice products encode different polypeptides. Mechanisms for the production of splice variants vary, but include alternate splicing of exons. Alternate polypeptides derived from the same nucleic acid by read-through transcription are also encompassed by this definition. Any products of a splicing reaction, including recombinant forms of the splice products, are included in this definition. Nucleic acids can be truncated at the 5’ end or at the 3’ end. Polypeptides can be truncated at the N-terminal end or the C-terminal end. Truncated versions of nucleic acid or polypeptide sequences can be naturally occurring or created using recombinant techniques. The terms “genetic variant” and “nucleotide variant” are used herein interchangeably to refer to changes or alterations to the reference human gene or cDNA sequence at a particular locus, including, but not limited to, nucleotide base deletions, insertions, inversions, and substitutions in the coding and non-coding regions. Deletions may be of a single nucleotide base, a portion or a region of the nucleotide sequence of the gene, or of the entire gene sequence. Insertions may be of one or more nucleotide bases. The genetic variant or nucleotide variant may occur in transcriptional regulatory regions, untranslated regions of mRNA, exons, introns, exon / intron junctions, etc. The genetic variant or nucleotide variant can potentially result in stop codons, frame shifts, deletions of amino acids, altered gene transcript splice forms or altered amino acid sequence. An allele or gene allele comprises generally a naturally occurring gene having a reference sequence or a gene containing a specific nucleotide variant. A haplotype refers to a combination of genetic (nucleotide) variants in a region of an mRNA or a genomic DNA on a chromosome found in an individual. Thus, a haplotype includes a number of genetically linked polymorphic variants which are typically inherited together as a unit. As used herein, the term “amino acid variant” is used to refer to an amino acid change to a reference human protein sequence resulting from genetic variants or nucleotide variants to the reference human gene encoding the reference protein. The term “amino acid variant” is intended to encompass not only smgle ammo acid substitutions, but also ammo acid deletions, insertions, and other significant changes of amino acid sequence in the reference protein. The term “genotype” as used herein means the nucleotide characters at a particular nucleotide variant marker (or locus) in either one allele or both alleles of a gene (or a particular chromosome region). With respect to a particular nucleotide position of a gene of interest, the nucleotide(s) at that locus or equivalent thereof in one or both alleles form the genotype of the gene at that locus. A genotype can be homozygous or heterozygous. Accordingly, “genotyping” means determining the genotype, that is, the nucleotide(s) at a particular gene locus. Genotyping can also be done by determining the amino acid variant at a particular position of a protein which can be used to deduce the corresponding nucleotide variant(s). The term “locus” refers to a specific position or site in a gene sequence or protein. Thus, there may be one or more contiguous nucleotides in a particular gene locus, or one or more amino acids at a particular locus in a polypeptide. Moreover, a locus may refer to a particular position in a gene where one or more nucleotides have been deleted, inserted, or inverted. Unless specified otherwise or understood by one of skill in art, the terms “polypeptide,” “protein,” and “peptide” are used interchangeably herein to refer to an amino acid chain in which the amino acid residues are linked by covalent peptide bonds. The amino acid chain can be of any length of at least two amino acids, including full-length proteins. Unless otherwise specified, polypeptide, protein, and peptide also encompass various modified forms thereof, including but not limited to glycosylated forms, phosphorylated forms, etc. A polypeptide, protein or peptide can also be referred to as a gene product. Lists of gene and gene products that can be assayed by molecular profiling techniques are presented herein. Lists of genes may be presented in the context of molecular profiling techniques that detect a gene product (e.g., an mRNA or protein). One of skill will understand that this implies detection of the gene product of the listed genes. Similarly, lists of gene products may be presented in the context of molecular profiling techniques that detect a gene sequence or copy number. One of skill will understand that this implies detection of the gene corresponding to the gene products, including as an example DNA encoding the gene products. As will be appreciated by those skilled in the art, a “biomarker” or “marker” comprises a gene and / or gene product depending on the context. The terms “label” and “detectable label” can refer to any composition detectable by spectroscopic, photochemical, biochemical, immunochemical, electrical, optical, chemical or similar methods. Such labels include biotin for staining with labeled streptavidin conjugate, magnetic beads (e.g., DYNABEADS™), fluorescent dyes (e.g., fluorescein, Texas red, rhodamine, green fluorescent protein, and the like), radiolabels (e.g., 3H, 125I, 35S, 14C, or 32P), enzymes (e.g., horse radish peroxidase, alkaline phosphatase and others commonly used in an ELISA), and calorimetric labels such as colloidal gold or colored glass or plastic (e.g., polystyrene, polypropylene, latex, etc) beads. Patents teaching the use of such labels include U.S. Pat. Nos. 3,817,837; 3,850,752; 3,939,350; 3,996,345; 4,277,437; 4,275,149; and 4,366,241. Means of detecting such labels are well known to those of skill in the art. Thus, for example, radiolabels may be detected using photographic film or scintillation counters, fluorescent markers may be detected using a photodetector to detect emitted light. Enzymatic labels are typically detected by providing the enzyme with a substrate and detecting the reaction product produced by the action of the enzyme on the substrate, and calorimetric labels are detected by simply visualizing the colored label. Labels can include, e.g., ligands that bind to labeled antibodies, fluorophores, chemiluminescent agents, enzymes, and antibodies which can serve as specific binding pair members for a labeled ligand. An introduction to labels, labeling procedures and detection of labels is found in Polak and Van Noorden Introduction to Immunocytochemistry, 2nd ed., Springer Verlag, NY (1997); and in Haugland Handbook of Fluorescent Probes and Research Chemicals, a combined handbook and catalogue Published by Molecular Probes, Inc. (1996). Detectable labels include, but are not limited to, nucleotides (labeled or unlabelled), compomers, sugars, peptides, proteins, antibodies, chemical compounds, conducting polymers, binding moieties such as biotin, mass tags, calorimetric agents, light emitting agents, chemiluminescent agents, light scattering agents, fluorescent tags, radioactive tags, charge tags (electrical or magnetic charge), volatile tags and hydrophobic tags, biomolecules (e.g., members of a binding pair antibody / antigen, antibody / antibody, antibody / antibody fragment, antibody / antibody receptor, antibody / protein A or protein G, hapten / anti-hapten, biotin / avidin, biotin / streptavidin, folic acid / folate binding protein, vitamin B12 / intrinsic factor, chemical reactive group / complementary chemical reactive group (e.g., sulfhydryl / maleimide, sulfhydryl / haloacetyl derivative, amine / isotriocyanate, amine / succinimidyl ester, and amine / sulfonyl halides) and the like. The terms “primer”, “probe,” and “oligonucleotide” are used herein interchangeably to refer to a relatively short nucleic acid fragment or sequence. They can comprise DNA, RNA, or a hybrid thereof, or chemically modified analog or derivatives thereof. Typically, they are single-stranded. However, they can also be double-stranded having two complementing strands which can be separated by denaturation. Normally, primers, probes and oligonucleotides have a length of from about 8 nucleotides to about 200 nucleotides, preferably from about 12 nucleotides to about 100 nucleotides, and more preferably about 18 to about 50 nucleotides. They can be labeled with detectable markers or modified using conventional manners for various molecular biological applications. The term “isolated” when used in reference to nucleic acids (e.g., genomic DNAs, cDNAs, mRNAs, or fragments thereof) is intended to mean that a nucleic acid molecule is present in a form that is substantially separated from other naturally occurring nucleic acids that are normally associated with the molecule. Because a naturally existing chromosome (or a viral equivalent thereof) includes a long nucleic acid sequence, an isolated nucleic acid can be a nucleic acid molecule having only a portion of the nucleic acid sequence in the chromosome but not one or more other portions present on the same chromosome. More specifically, an isolated nucleic acid can include naturally occurring nucleic acid sequences that flank the nucleic acid in the naturally existing chromosome (or a viral equivalent thereof). An isolated nucleic acid can be substantially separated from other naturally occurring nucleic acids that are on a different chromosome of the same organism. An isolated nucleic acid can also be a composition in which the specified nucleic acid molecule is significantly enriched so as to constitute at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, or at least 99% of the total nucleic acids in the composition. An isolated nucleic acid can be a hybrid nucleic acid having the specified nucleic acid molecule covalently linked to one or more nucleic acid molecules that are not the nucleic acids naturally flanking the specified nucleic acid. For example, an isolated nucleic acid can be in a vector. In addition, the specified nucleic acid may have a nucleotide sequence that is identical to a naturally occurring nucleic acid or a modified form or mutein thereof having one or more mutations such as nucleotide substitution, deletion / insertion, inversion, and the like. An isolated nucleic acid can be prepared from a recombinant host cell (in which the nucleic acids have been recombinantly amplified and / or expressed), or can be a chemically synthesized nucleic acid having a naturally occurring nucleotide sequence or an artificially modified form thereof. The term “high stringency hybridization conditions,” when used in connection with nucleic acid hybridization, includes hybridization conducted overnight at 42 °C in a solution containing 50% formamide, 5xSSC (750 mM NaCl, 75 mM sodium citrate), 50 mM sodium phosphate, pH 7.6, 5xDenhardt’s solution, 10% dextran sulfate, and 20 microgram / ml denatured and sheared salmon sperm DNA, with hybridization filters washed in 0.1 x SSC at about 65 °C. The term “moderate stringent hybridization conditions,” when used in connection with nucleic acid hybridization, includes hybridization conducted overnight at 37 °C in a solution containing 50% formamide, 5xSSC (750 mM NaCl, 75 mM sodium citrate), 50 mM sodium phosphate, pH 7.6, 5 xDenhardt’s solution, 10% dextran sulfate, and 20 microgram / ml denatured and sheared salmon sperm DNA, with hybridization filters washed in 1 xSSC at about 50 °C. It is noted that many other hybridization methods, solutions and temperatures can be used to achieve comparable stringent hybridization conditions as will be apparent to skilled artisans. For the purpose of comparing two different nucleic acid or polypeptide sequences, one sequence (test sequence) may be described to be a specific percentage identical to another sequence (comparison sequence). The percentage identity can be determined by the algorithm of Karlin and Altschul, Proc. Natl. Acad. Sci. USA, 90:5873-5877 (1993), which is incorporated into various BLAST programs. The percentage identity can be determined by the “BLAST 2 Sequences” tool, which is available at the National Center for Biotechnology Information (NCBI) website. See Tatusova and Madden, FEMS Microbiol. Lett., 174(2):247-250 (1999). For pairwise DNA-DNA comparison, the BLASTN program is used with default parameters (e.g., Match: 1; Mismatch: -2; Open gap: 5 penalties; extension gap: 2 penalties; gap x dropoff: 50; expect: 10; and word size: 11, with filter). For pairwise protein-protein sequence comparison, the BLASTP program can be employed usmg default parameters (e.g., Matrix: BLOSUM62; gap open: 11; gap extension: 1; xdropoff: 15; expect: 10.0; and wordsize: 3, with filter). Percent identity of two sequences is calculated by aligning a test sequence with a comparison sequence using BLAST, determining the number of amino acids or nucleotides in the aligned test sequence that are identical to amino acids or nucleotides in the same position of the comparison sequence, and dividing the number of identical amino acids or nucleotides by the number of amino acids or nucleotides in the comparison sequence. When BLAST is used to compare two sequences, it aligns the sequences and yields the percent identity over defined, aligned regions. If the two sequences are aligned across their entire length, the percent identity yielded by the BLAST is the percent identity of the two sequences. If BLAST does not align the two sequences over their entire length, then the number of identical amino acids or nucleotides in the unaligned regions of the test sequence and comparison sequence is considered to be zero and the percent identity is calculated by adding the number of identical amino acids or nucleotides in the aligned regions and dividing that number by the length of the comparison sequence. Various versions of the BLAST programs can be used to compare sequences, e.g., BLAST 2.1.2 or BLAST+ 2.2.22. A subject or individual can be any animal which may benefit from the methods described herein, including, e.g., humans and non-human mammals, such as primates, rodents, horses, dogs and cats. Subjects include without limitation a eukaryotic organisms, most preferably a mammal such as a primate, e.g., chimpanzee or human, cow; dog; cat; a rodent, e.g., guinea pig, rat, mouse; rabbit; or a bird; reptile; or fish. Subjects specifically intended for treatment using the methods described herein include humans. A subject may also be referred to herein as an individual or a patient. In the present methods the subject has colorectal cancer, e.g., has been diagnosed with colorectal cancer. Methods for identifying subjects with colorectal cancer are known in the art, e.g., using a biopsy. See, e.g., Fleming et al., J Gastrointest Oncol. 2012 Sep; 3(3): 153—173; Chang et al., Dis Colon Rectum. 2012; 55(8):831-43. Treatment of a disease or individual according to the methods described herein is an approach for obtaining beneficial or desired medical results, including clinical results, but not necessarily a ewe. For purposes of the methods described herein, beneficial or desired clinical results include, but are not limited to, alleviation or amelioration of one or more symptoms, diminishment of extent of disease, stabilized (i.e., not worsening) state of disease, preventing spread of disease, delay or slowing of disease progression, amelioration or palliation of the disease state, and remission (whether partial or total), whether detectable or undetectable. Treatment also includes prolonging survival as compared to expected survival if not receiving treatment or if receiving a different treatment. A treatment can include administration of various small molecule (hugs or biologies such as immunotherapies, e.g., checkpoint inhibitor therapies. A biomarker refers generally to a molecule, including without limitation a gene or product thereof, nucleic acids (e.g., DNA, RNA), protein / peptide / polypeptide, carbohydrate structure, lipid, glycolipid, characteristics of which can be detected in a tissue or cell to provide information that is predictive, diagnostic, prognostic and / or theranostic for sensitivity or resistance to candidate treatment. Biological Samples A sample as used herein includes any relevant biological sample that can be used for molecular profiling, e.g., sections of tissues such as biopsy or tissue removed during surgical or other procedures, bodily fluids, autopsy samples, and frozen sections taken for histological purposes. Such samples include blood and blood fractions or products (e.g., serum, buffy coat, plasma, platelets, red blood cells, and the like), sputum, malignant effusion, cheek cells tissue, cultured cells (e.g., primary cultures, explants, and transformed cells), stool, wine, other biological or bodily fluids (e.g., prostatic fluid, gastric fluid, intestinal fluid, renal fluid, lung fluid, cerebrospinal fluid, and the like), etc. The sample can comprise biological material that is a fresh frozen & formalin fixed paraffin embedded (FFPE) block, formalin-fixed paraffin embedded, or is within an RNA preservative + formalin fixative. More than one sample of more than one type can be used for each patient. In a preferred embodiment, the sample comprises a fixed tumor sample. The sample used in the systems and methods of the invention can be a formalin fixed paraffin embedded (FFPE) sample. The FFPE sample can be one or more of fixed tissue, unstained slides, bone marrow core or clot, core needle biopsy, malignant fluids and fine needle aspirate (FNA). In an embodiment, the fixed tissue comprises a tumor containing formalin fixed paraffin embedded (FFPE) block from a surgery or biopsy. In another embodiment, the unstained slides comprise unstained, charged, unbaked slides from a paraffin block. In another embodiment, bone marrow core or clot comprises a decalcified core. A formalin fixed core and / or clot can be paraffin-embedded. In still another embodiment, the core needle biopsy comprises 1, 2, 3,4, 5, 6, 7, 8, 9, 10 or more, e.g., 3-4, paraffin embedded biopsy samples. An 18 gauge needle biopsy can be used. The malignant fluid can comprise a sufficient volume of fresh pleural / ascitic fluid to produce a 5x5x2mm cell pellet. The fluid can be formalin fixed in a paraffin block. In an embodiment, the core needle biopsy comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more, e.g., 4-6, paraffin embedded aspirates. A sample may be processed according to techniques understood by those in the art. A sample can be without limitation fresh, frozen or fixed cells or tissue. In some embodiments, a sample comprises formalin-fixed paraffin-embedded (FFPE) tissue, fresh tissue or fresh frozen (FF) tissue. A sample can comprise cultured cells, including primary or immortalized cell lines derived from a subject sample. A sample can also refer to an extract from a sample from a subject. For example, a sample can comprise DNA, RNA or protein extracted from a tissue or a bodily fluid. Many techniques and commercial kits are available for such purposes. The fresh sample from the individual can be treated with an agent to preserve RNA prior to further processing, e.g., cell lysis and extraction. Samples can include frozen samples collected for other pwposes. Samples can be associated with relevant information such as age, gender, and clinical symptoms present in the subject; source of the sample; and methods of collection and storage of the sample. A sample is typically obtained from a subject. A biopsy comprises the process of removing a tissue sample for diagnostic or prognostic evaluation, and to the tissue specimen itself. Any biopsy technique known in the art can be applied to the molecular profiling methods of the present disclosure. The biopsy technique applied can depend on the tissue type to be evaluated (e.g., colon, prostate, kidney, bladder, lymph node, liver, bone marrow, blood cell, lung, breast, etc.), the size and type of the tumor (e.g., solid or suspended, blood or ascites), among other factors. Representative biopsy techniques include, but are not limited to, excisional biopsy, incisional biopsy, needle biopsy, surgical biopsy, and bone marrow biopsy. An “excisional biopsy” refers to the removal of an entire tumor mass with a small margin of normal tissue surrounding it. An “incisional biopsy” refers to the removal of a wedge of tissue that includes a crosssectional diameter of the tumor. Molecular profiling can use a “core-needle biopsy” of the tumor mass, or a “fine-needle aspiration biopsy” which generally obtains a suspension of cells from within the tumor mass. Biopsy techniques are discussed, for example, in Harrison’s Principles of Internal Medicine, Kasper, et al., eds., 16th ed., 2005, Chapter 70, and throughout Part V. Unless otherwise noted, a “sample” as referred to herein for molecular profiling of a patient may comprise more than one physical specimen. As one non-limiting example, a “sample” may comprise multiple sections from a tumor, e.g., multiple sections of an FFPE block or multiple coreneedle biopsy sections. As another non-limiting example, a “sample” may comprise multiple biopsy specimens, e.g., one or more surgical biopsy specimen, one or more core-needle biopsy specimen, one or more fine-needle aspiration biopsy specimen, or any usefill combination thereof. As still another non-limiting example, a molecular profile may be generated for a subject using a “sample” comprising a solid tumor specimen ...

Claims

1. A method for classifying a biological sample, the method comprising:obtaining, by one or more computers, first data representing one or more initial classifications for the biological sample that were previously determined based on RNA sequences of the biological sample, wherein the biological sample includes a cancer sample from a subject that has a cancer;obtaining, by one or more computers, second data representing another initial classification for the biological sample that were previously determined based on DNA sequences of the biological sample;providing, by the one or more computers, at least a portion of the first data and the second data as input data to a dynamic voting engine that has been trained to predict a target biological sample classification based on processing of multiple initial biological sample classifications, wherein training the dynamic voting engine comprises:obtaining a labeled training data item that includes:(I) one or more initial training classifications that include data indicating an initial organ of origin of the cancer; and(II) a label of a biological sample classification;generating training input data for input to the dynamic voting engine based on the obtained training data item;processing the generated training input data through the dynamic voting engine;obtaining output data generated by the dynamic voting engine based on the dynamic voting engine’s processing of the generated training input data; andadjusting one or more parameters of the dynamic voting engine based on a level of similarity between the output data and the label of the labeled training data item;processing, by the one or more computers, the provided input data through the dynamic voting engine;obtaining, by the one or more computers, output data generated by the dynamic voting engine based on the dynamic voting engine’s processing of the provided input data; anddetermining, by the one or more computers, the target biological sample classification for the biological sample based on the obtained output data, wherein the target2021221048   29 May 2026biological sample classification includes an attribute for the cancer, and wherein the attribute for the cancer is a tissue-of origin (TOO).

2. The method of claim 1, wherein obtaining, by the one or more computers, the first data representing the one or more initial classifications for the biological sample that were previously determined based on RNA sequences of the biological sample comprises:obtaining data representing a cancer type classification for the biological sample based the RNA sequences of the biological sample;obtaining data representing an organ from which the biological sample originated based on the RNA sequences of the biological sample; andobtaining data representing a histology for the biological sample based on the RNA sequences of the biological sample,wherein providing at least a portion of the first data and the second data as an input to the dynamic voting engine comprises:providing the obtained data representing the cancer type classification, the obtained data representing the organ from which the biological sample originated, the obtained data representing the histology, and the second data as an input to the dynamic voting engine.

3. The method of claim 1 or claim 2, wherein the dynamic voting engine comprises one or more machine learning models.

4. The method of claim 3, wherein the one or more machine learning models comprise one or more of a random forests, support vector machines, logistic regressions, K-nearest neighbors, artificial neural networks, naive Bayes, quadratic discriminant analysis, Gaussian processes models, decision trees, or any combination thereof.

5. The method of any one of the preceding claims, wherein the one or more initial training classifications further include data indicating a cancer classification type, data indicating a histology, or data indicating output of a DNA analysis engine.

6. The method of claim 5, wherein the one or more initial training classifications are determined based on or derived from simulated data that is generated to represent initial2021221048   29 May 2026classifications that ought to be generated by the one or more initial classification engines when the one or more initial classification engines process RNA sequences derived from the biological sample.

7. The method of any one of the preceding claims, wherein previously determining the other initial classification for the biological sample based on DNA sequences of the biological sample is performed by a pairwise-analysis model and comprises:receiving, by one or more computers, a biological signature representing the biological sample that was obtained from a cancerous neoplasm in a first portion of a body, wherein the pairwise-analysis model includes a cancerous biological signature for each of multiple different types of cancerous biological samples, wherein each of the cancerous biological signatures includes at least a first cancerous biological signature representing a molecular profile of a cancerous biological sample from the first portion of one or more other bodies and a second cancerous biological signature representing a molecular profile of a cancerous biological sample from a second portion of one or more other bodies;performing, by one or more computers and using the pairwise-analysis model, pairwise analysis of the biological signature using the first cancerous biological signature and the second cancerous biological signature;generating, by one or more computers and based on the performed pairwise analysis, a likelihood that the cancerous neoplasm in the first portion of the body was caused by cancer in a second portion of the body; andstoring, by one or more computers, the generated likelihood in a memory device.

8. The method of any one of the preceding claims, wherein the one or more initial classifications and the other initial classification are determined by respective models, and wherein the dynamic voting engine uses more weight for a model that is more accurate than another model.

9. The method of any preceding claim, wherein the other initial classification for the biological sample is determined based on copy number alterations of the DNA sequences of the biological sample.2021221048   29 May 202610. The method of any one of claims 1-9, wherein the one or more initial classifications are determined based on or derived from actual data generated by one or more initial classification engines selected from a group comprising:(a) a cancer type classification engine;(b) an initial organ of origin engine; and(c) a histology engine, wherein the one or more initial classification engines include initial organ of origin engine, and wherein the one or more initial classification engines process data derived from the biological sample.

11. The method of claim 10, wherein data for transcript expression levels is used to train the one or more initial classification engines for the attribute for the cancer.

12. The method of claim 11, wherein the transcript expression levels are selected from transcript features selected from the group consisting of ACVRL1, AFP, ALPP, AMACR, ANKRD30A, ANO1, AR, ARG1, BCL2, BCL6, CA9, CALB2, CALCA, CALD1, CCND1, CD1A, CD2, CD34, CD3G, CD5, CD79A, CD99L2, CDH1, CDH17, CDK4, CDKN2A, CDX2, CEACAM1, CEACAM16, CEACAM18, CEACAM19, CEACAM20, CEACAM21, CEACAM3, CEACAM4, CEACAM5, CEACAM6, CEACAM7, CEACAM8, CGA, CGB3, CNN1, COQ2, CPS1, CR1, CR2, CTNNB1, DES, DSC3, ENO2, ERBB2, ERG, ESR1, FLI1, FOXL2, FUT4, GATA3, GPC3, HAVCR1, HNF1B, IL12B, IMP3, INHA, ISL1, KIT, KL, KLK3, KRT1, KRT10, KRT14, KRT15, KRT16, KRT17, KRT18, KRT19, KRT2, KRT20, KRT3, KRT4, KRT5, KRT6A, KRT6B, KRT6C, KRT7, KRT8, LIN28A, LIN28B, MAGEA2, MDM2, MIB1, MITF, MLANA, MLH1, MME, MPO, MS4A1, MSH2, MSH6, MSLN, MTHFR, MUC1, MUC2, MUC4, MUC5AC, MYOD1, MYOG, NANOG, NAPSA, NCAM1, NCAM2, NKX2-2, NKX3-1, OSCAR, PAX2, PAX5, PAX8, PDPN, PDX1, PECAM1, PGR, PIP, PMEL, PMS2, POU5F1, PSAP, PTPRC, S100A1, S100A10, S100A11, S100A12, S100A13, S100A14, S100A16, S100A2, S100A4, S100A5, S100A6, S100A7, S100A7A, S100A7L2, S100A8, S100A9, S100B, S100P, S100PBP, S100Z, SALL4, SATB2, SDC1, SERPINA1, SERPINB5, SF1, SFTPA1, SMAD4, SMARCB1, SMN1, SOX2, SPN, SYP, TFE3, TFF1, TFF3, TG, TLE1, TMPRSS2, TNFRSF8, TP63, TPM1, TPM2, TPM3, TPM4, TPSAB1, TTF1, UPK2, UPK3A, UPK3B, VHL, VIL1, VIM, and WT1.2021221048   29 May 202613. The method of any one of the preceding claims, wherein the target biological sample classification further includes a cancer type or a histology of a cancer.

14. The method of any preceding claim, wherein the biological sample comprises formalin-fixed paraffin-embedded (FFPE) tissue, fixed tissue, a core needle biopsy, a fine needle aspirate, unstained slides, fresh frozen (FF) tissue, formalin samples, tissue comprised in a solution that preserves nucleic acid or protein molecules, a fresh sample, a malignant fluid, a bodily fluid, a tumor sample, a tissue sample, or any combination thereof.

15. The method of any preceding claim, wherein the first data and / or the second data were determined based on a mutation, polymorphism, deletion, insertion, substitution, translocation, fusion, break, duplication, amplification, repeat, copy number, copy number variation, or any combination thereof.

16. The method of any preceding claim, further comprising:obtaining the biological sample; andperforming at least one assay on the biological sample to obtain the RNA sequences and the DNA sequences.

17. A system comprising one or more computers and one or more storage media storing instructions that, when executed by the one or more computers, cause the one or more computers to perform the method of any one of claims 1-16.

18. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform the method of any one of claims 1-16.

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