FIELD OF THE INVENTION

The present invention relates to a method for identifying components of biological pathways in an individual or subject and determining if the individual or subject is a candidate for a clinical regimen or treatment. The invention also relates to using the methods to diagnose whether a subject is susceptible to cancer, autoimmune diseases, cell cycle disorders, or other disorders.

BACKGROUND

A central premise in modern cancer treatment is that patient diagnosis, prognosis, risk assessment, and treatment response prediction can be improved by stratification of cancers based on genomic, transcriptional and epigenomic characteristics of the tumor alongside relevant clinical information gathered at the time of diagnosis (for example, patient history, tumor histology and stage) as well as subsequent clinical follow-up data (for example, treatment regimens and disease recurrence events).

While several high-throughput technologies have been available for probing the molecular details of cancer, only a handful of successes have been achieved based on this paradigm. For example, 25% of breast cancer patients presenting with a particular amplification or overexpression of the ERBB2 growth factor receptor tyrosine kinase can now be treated with trastuzumab, a monoclonal antibody targeting the receptor (Vogel C, Cobleigh M A, Tripathy D, Gutheil J C, Harris L N, Fehrenbacher L, Slamon D J, Murphy M, Novotny W F, Burchmore M, Shak S, Stewart S J. First-line, single-agent Herceptin® (trastuzumab) in metastatic breast cancer. A preliminary report. Eur. J. Cancer 2001 January; 37 Suppl 1:2529).

However, even this success story is clouded by the fact that fewer than 50% of patients with ERBB2-positive breast cancers actually achieve any therapeutic benefit from trastuzumab, emphasizing our incomplete understanding of this well-studied oncogenic pathway and the many therapeutic-resistant mechanisms intrinsic to ERBB2-positive breast cancers (Park J W, Neve R M, Szollosi J, Benz C C. Unraveling the biologic and clinical complexities of HER2. Clin. Breast Cancer 2008 October; 8(5):392-401.)

This overall failure to translate modern advances in basic cancer biology is in part due to our inability to comprehensively organize and integrate all of the omic features now technically acquirable on virtually any type of cancer. Despite overwhelming evidence that histologically similar cancers are in reality a composite of many molecular subtypes, each with significantly different clinical behavior, this knowledge is rarely applied in practice due to the lack of robust signatures that correlate well with prognosis and treatment options.

Cancer is a disease of the genome that is associated with aberrant alterations that lead to disregulation of the cellular system. What is not clear is how genomic changes feed into genetic pathways that underlie cancer phenotypes. High-throughput functional genomics investigations have made tremendous progress in the past decade (Alizadeh A A, Eisen M B, Davis R E, Ma C, Lossos I S, Rosenwald A, Boldrick J C, Sabet H, Tran T, Yu X, eowell JI, Yang L, Marti G E, Moore T, Hudson J, Lu L, Lewis D B, Tibshirani R, SHERLOCK G, Chan W C, Greiner T C, Weisenburger D D, Armitage J O, Warnke R, Levy R, Wilson W, Greyer M R, Byrd J C, Botstein D, Brown P O, Staudt L M. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000 February; 403(6769):503-511.; Golub T R, Slonim D K, Tamayo P, Huard C, Gaasenbeek M, Mesirov J P, Coller H, Loh M L, Downing J R, Caligiuri M A, Bloomfield C D, Lander E S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999 October; 286(5439):531-537.; van de Vijver M J, He Y D, van t Veer L J, Dai H, Hart A A M, Voskuil D W, Schreiber G J, Peterse J L, Roberts C, Marton M J, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers E T, Friend S H, Bernards R. A Gene-Expression Signature as a Predictor of Survival in Breast Cancer. N Engl J Med 2002 December; 347(25):1999-2009.)

However, the challenges of integrating multiple data sources to identify reproducible and interpretable molecular signatures of tumorigenesis and progression remain elusive. Recent pilot studies by TCGA and others make it clear that a pathway-level understanding of genomic perturbations is needed to understand the changes observed in cancer cells. These findings demonstrate that even when patients harbor genomic alterations or aberrant expression in different genes, these genes often participate in a common pathway. In addition, and even more striking, is that the alterations observed (for example, deletions versus amplifications) often alter the pathway output in the same direction, either all increasing or all decreasing the pathway activation. (See Parsons D W, Jones S, Zhang X, Lin J C H, Leary R J, Angenendt P, Mankoo P, Carter H, Siu I, Gallia G L, Olivi A, McLendon R, Rasheed B A, Keir S, Nikolskaya T, Nikolsky Y, Busam D A, Tekleab H, Diaz L A, Hartigan J, Smith D R, Strausberg R L, Marie S K N, Shinjo S M O, Yan H, Riggins G J, Bigner D D, Karchin R, Papadopoulos N, Parmigiani G, Vogelstein B, Velculescu V E, Kinzler K W. An Integrated Genomic Analysis of Human Glioblastoma Multiforme. Science 2008 September; 321(5897):1807-1812.; Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008 October; 455(7216):1061-1068.)

Approaches for interpreting genome-wide cancer data have focused on identifying gene expression profiles that are highly correlated with a particular phenotype or disease state, and have led to promising results. Methods using analysis, of variance, false-discovery, and non-parametric methods have been proposed. (See Troyanskaya et al., 2002) have been proposed. Allison D B, Cui X, Page G P, Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nat. Rev. Genet. 2006 January; 7(1):55-65.; Dudoit S, Fridlyand J. A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol 2002 June; 3(7):RESEARCH0036-RESEARCH0036.21.; Tusher V G, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U.S.A. 2001 April; 98(9):5116-5121; Kerr M K, Martin M, Churchill G A. Analysis of variance for gene expression microarray data. J. Comput. Biol. 2000; 7(6):819-837; Storey J D, Tibshirani R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 2003 August; 100(16):9440-9445; and Troyanskaya O G, Garber M E, Brown P O, Botstein D, Altman R B. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 2002 November; 18(11):1454-1461.)

Several pathway-level approaches use statistical tests based on overrepresentation of genesets to detect whether a pathway is perturbed in a disease condition. In these approaches, genes are ranked based on their degree of differential activity, for example as detected by either differential expression or copy number alteration. A probability score is then assigned reflecting the degree to which a pathway's genes rank near the extreme ends of the sorted list, such as is used in gene set enrichment analysis (GSEA) (Subramanian A, Tamayo P, Mootha V K, Mukherjee S, Ebert B L, Gillette M A, Paulovich A, Pomeroy S L, Golub T R, Lander E S, Mesirov J P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A. 2005 October; 102(43):15545-15550.). Other approaches include using a hypergeometric test-based method to identify Gene Ontology (Ashburner M, Ball C A, Blake J A, Botstein D, Butler H, Cherry J M, Davis A P, Dolinski K, Dwight S S, Eppig J T, Harris M A, Hill D P, Issel-Tarver L, Kasarskis A, Lewis S, Matese J C, Richardson J E, Ringwald M, Rubin G M, SHERLOCK G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000 May; 25(1):25-29.) or MIPS mammalian protein-protein interaction (Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, Montrone C, Mark P, Sttimpflen V, Mewes H, Ruepp A, Frishman D. The MIPS mammalian protein-protein interaction database. Bioinformatics 2005 March; 21(6):832-834.) categories enriched in differentially expressed genes (Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander E S, Golub T R. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. U.S.A. 1999 March; 96(6):2907-2912.).

Overrepresentation analyses are limited in their efficacy because they do not incorporate known interdependencies among genes in a pathway that can increase the detection signal for pathway relevance. In addition, they treat all gene alterations as equal, which is not expected to be valid for many biological systems.

Further complicating the issue is the fact that many genes (for example, microRNAs) are pleiotropic, acting in several pathways with different roles (Maddika S, Ande S R, Panigrahi S, Paranjothy T, Weglarczyk K, Zuse A, Eshraghi M, Manda K D, Wiechec E, Los M. Cell survival, cell death and cell cycle pathways are interconnected: implications for cancer therapy. Drug Resist. Updat. 2007 January; 10(1-2):13-29). Because of these factors, overrepresentation analyses often miss functionally-relevant pathways whose genes have borderline differential activity. They can also produce many false positives when only a single gene is highly altered in a small pathway. Our collective knowledge about the detailed interactions between genes and their phenotypic consequences is growing rapidly.

While the knowledge was traditionally scattered throughout the literature and hard to access systematically, new efforts are cataloging pathway knowledge into publicly available databases. Some of the databases that include pathway topology are Reactome (Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath G R, Wu G R, Matthews L, Lewis S, Bimey E, Stein L. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005 January; 33(Database issue):D428-32; Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999 January; 27(1):29-34.)) and the NCI Pathway Interaction Database. Updates to these databases are expected to improve our understanding of biological systems by explicitly encoding how genes regulate and communicate with one another. A key hypothesis is that the interaction topology of these pathways can be exploited for the purpose of interpreting high-throughput datasets.

Until recently, few computational approaches were available for incorporating pathway knowledge to interpret high-throughput datasets. However, several newer approaches have been proposed that incorporate pathway topology (Efroni S, Schaefer C F, Buetow K H. Identification of key processes underlying cancer phenotypes using biologic pathway analysis. PLoS ONE 2007; 2(5):e425.). One approach, called Signaling Pathway Impact Analysis (SPIA), uses a method analogous to Google's PageRank to determine the influence of a gene in a pathway (Tarca A L, Draghici S, Khatri P, Hassan S S, Mittal P, Kim J, Kim C J, Kusanovic J P, Romero R. A novel signaling pathway impact analysis. Bioinformatics 2009 January; 25(1):75-82.) In SPIA, more influence is placed on genes that link out to many other genes. SPIA was successfully applied to different cancer datasets (lung adenocarcinoma and breast cancer) and shown to outperform overrepresentation analysis and Gene Set Enrichment Analysis for identifying pathways known to be involved in these cancers. While SPIA represents a major step forward in interpreting cancer datasets using pathway topology, it is limited to using only a single type of genome-wide data.

New computational approaches are needed to connect multiple genomic alterations such as copy number, DNA methylation, somatic mutations, mRNA expression and microRNA expression. Integrated pathway analysis is expected to increase the precision and sensitivity of causal interpretations for large sets of observations since no single data source is likely to provide a complete picture on its own.

In the past several years, approaches in probabilistic graphical models (PGMs) have been developed for learning causal networks compatible with multiple levels of observations. Efficient algorithms are available to learn pathways automatically from data (Friedman N, Goldszmidt M. (1997) Sequential Update of Bayesian Network Structure. In: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI'97), Morgan Kaufmann Publishers, pp. 165-174; Murphy K, Weiss Y. Loopy belief propagation for approximate inference: An empirical study. In: Proceedings of Uncertainty in AI. 1999) and are well adapted to problems in genetic network inference (Friedman N. Inferring cellular networks using probabilistic graphical models. Science 2004 February; 303(5659):799-805.). As an example, graphical models have been used to identify sets of genes that form ‘modules’ in cancer biology (Segal E, Friedman N, Kaminski N, Regev A, Koller D. From signatures to models: understanding cancer using microarrays. Nat Genet 2005 June; 37 Suppl:S38-45.). They have also been applied to elucidate the relationship between tumor genotype and expression phenotypes (Lee S, Pe'er D, Dudley A M, Church GM, Koller D. Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. Proc. Natl. Acad. Sci. U.S.A. 2006 September; 103(38):14062-14067.), and infer protein signal networks (Sachs K, Perez 0, Pe'er D, Lauffenburger D A, Nolan G P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 2005 April; 308(5721):523-529.) and recombinatorial gene regulatory code (Beer M A, Tavazoie S. Predicting gene expression from sequence. Cell 2004 April; 117(2):185-198.). In particular, factor graphs have been used to model expression data (Gat-Viks I, Shamir R. Refinement and expansion of signaling pathways: the osmotic response network in yeast. Genome Research 2007 March; 17(3):358-367.; Gat-Viks I, Tanay A, Raijman D, Shamir R. The Factor Graph Network Model for Biological Systems. In: Hutchison D, Kanade T, Kittler J, Kleinberg J M, Mattem F, Mitchell J C, Naor M, Nierstrasz 0, Pandu Rangan C, Steffen B, Sudan M, Terzopoulos D, Tygar D, Vardi M Y, Weikum G, Miyano S, Mesirov J, Kasif S, Istrail S, Pevzner P A, Waterman M, editors. Berlin, Heidelberg: Springer Berlin Heidelberg; 2005 p. 31-47.; Gat-Viks I, Tanay A, Raijman D, Shamir R. A probabilistic methodology for integrating knowledge and experiments on biological networks. J. Comput. Biol. 2006 March; 13(2):165-181.).

Breast cancer is clinically and genomically heterogeneous and is composed of several pathologically and molecularly distinct subtypes. Patient responses to conventional and targeted therapeutics differ among subtypes motivating the development of marker guided therapeutic strategies. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways and drug response. In a test of 77 therapeutic compounds, nearly all drugs show differential responses across these cell lines and approximately half show subtype-, pathway and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance that may inform clinical drug deployment as well as efforts to combine drugs effectively.

The accumulation of high throughput molecular profiles of tumors at various levels has been a long and costly process worldwide. Combined analysis of gene regulation at various levels may point to specific biological functions and molecular pathways that are deregulated in multiple epithelial cancers and reveal novel subgroups of patients for tailored therapy and monitoring. We have collected high throughput data at several molecular levels derived from fresh frozen samples from primary tumors, matched blood, and with known micrometastases status, from approximately 110 breast cancer patients (further referred to as the MicMa dataset). These patients are part of a cohort of over 900 breast cancer cases with information about presence of disseminated tumor cells (DTC), long-term follow-up for recurrence and overall survival. The MicMa set has been used in parallel pilot studies of whole genome mRNA expression (1 Naume, B. et al., (2007), Presence of bone marrow micrometastasis is associated with different recurrence risk within molecular subtypes of breast cancer, 1: 160-171), arrayCGH (Russnes H G, Vollan H K M, Lingjaerde O C, Krasnitz A, Lundin P, Naume B, lie T, Borgen E, Rye I H, LangerOcl A, Chin S, Teschendorff A E, Stephens P J, MAner S, Schlichting E, Baumbusch L O, KAresen R, Stratton M P, Wigler M, Caldas C, Zetterberg A, Hicks J, BOrresen-Dale A. Genomic architecture characterizes tumor progression paths and fate in breast cancer patients. Sci Transl Med 2010 June; 2(38):38ra47), DNA methylation (Ronneberg J A, Fleischer T, Solvang H K, Nordgard S H, Edvardsen H, Potapenko I, Nebdal D, Daviaud C, Gut I, Bukholm I, Naume B, Borresen-Dale A, Tost J, Kristensen V. Methylation profiling with a panel of cancer related genes: association with estrogen receptor, TP53 mutation status and expression subtypes in sporadic breast cancer. Mol Oncol 2011 February; 5(1):61-76), whole genome SNP and SNP-CGH (Van, Loo P. et al., (2010), Allele-specific copy number analysis of tumors, 107: 16910-169154), whole genome miRNA expression analyses (5 Enerly, E. et al., (2011), miRNA-mRNA Integrated Analysis Reveals Roles for miRNAs in Primary Breast Tumors, 6: e16915-), TP53 mutation status dependent pathways and high throughput paired end sequencing (7 Stephens, P. J. et al., (2009), Complex landscapes of somatic rearrangement in human breast cancer genomes, 462: 1005-1010). This is a comprehensive collection of high throughput molecular data performed by a single lab on the same set of primary tumors of the breast.

A topic of great importance in cancer research is the identification of genomic aberrations that drive the development of cancer. Utilizing whole-genome copy number and expression profiles from the MicMa cohort, we defined several filtering steps, each designed to identify the most promising candidates among the genes selected in the previous step. The first two steps involve identification of commonly aberrant and in-cis correlated to expression genes, i.e. genes for which copy number changes have substantial effect on expression. Subsequently, the method considers in-trans effects of the selected genes to further narrow down the potential novel candidate driver genes (Miriam Ragle Aure, Israel Steinfeld Lars Oliver Baumbusch Knut Liestol Doron Lipson Bjorn Naume Vessela N. Kristensen Anne-Lise Borresen-Dale Ole-Christian Lingjwrde and Zohar Yakhini, (2011), A robust novel method for the integrated analysis of copy number and expression reveals new candidate driver genes in breast cancer). Recently we developed an allele-specific copy number analysis enabling us to accurately dissect the allele-specific copy number of solid tumors (ASCAT), and simultaneously estimating and adjusting for both tumor ploidy and nonaberrant cell admixture (Van, Loo P. et al., (2010), Allele-specific copy number analysis of tumors, 107: 16910-169154). This allows calculation of genome-wide allele-specific copy-number profiles from which gains, losses, copy number-neutral events, and loss of heterozygosity (LOH) can accurately be determined. Observing DNA aberrations in allele specific manner allowed us to construct a genome-wide map of allelic skewness in breast cancer, indicating loci where one allele is preferentially lost, whereas the other allele is preferentially gained. We hypothesize that these alternative alleles have a different influence on breast carcinoma development. We could also see that Basal-like breast carcinomas have a significantly higher frequency of LOH compared with other subtypes, and their ASCAT profiles show large-scale loss of genomic material during tumor development, followed by a whole-genome duplication, resulting in near-triploid genomes (Van et al. (2010)—supra). Distinct global DNA methylation profiles have been reported in normal breast epithelial cells as well as in breast tumors.

There is currently a need to provide methods that can be used in characterization, diagnosis, prevention, treatment, and determining outcome of diseases and disorders.

BRIEF DESCRIPTION OF THE INVENTION

In one embodiment, the invention provides a method of generating a dynamic pathway map (DPM), the method comprising: providing access to a pathway element database storing a plurality of pathway elements, each pathway element being characterized by its involvement in at least one pathway; providing access to a modification engine coupled to the pathway element database; using the modification engine to associate a first pathway element with at least one a priori known attribute; using the modification engine to associate a second pathway element with at least one assumed attribute; using the modification engine to cross-correlate and assign an influence level of the first and second pathway elements for at least one pathway using the known and assumed attributes, respectively, to form a probabilistic pathway model; and using the probabilistic pathway model, via an analysis engine, to derive from a plurality of measured attributes for a plurality of elements of a patient sample the DPM having reference pathway activity information for a particular pathway. In one preferred embodiment, the pathway element is a protein. In a more preferred embodiment, the protein is selected from the group consisting of a receptor, a hormone binding protein, a kinase, a transcription factor, a methylase, a histone acetylase, and a histone deacetylase. In an alternative preferred embodiment, the pathway element is a nucleic acid. In a more preferred embodiment, the nucleic acid is selected from the group consisting of a protein coding sequence, a genomic regulatory sequence, a regulatory RNA, and a trans-activating sequence. In another more preferred embodiment, the reference pathway activity information is specific with respect to a normal tissue, a diseased tissue, an ageing tissue, or a recovering tissue. In a preferred embodiment, the known attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity. In another preferred embodiment, the assumed attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity. In another alternative embodiment, the measured attributes are selected from the group consisting of a mutation, a differential genetic sequence object, a gene copy number, a transcription level, a translation level, a protein activity, and a protein interaction. In a preferred embodiment, the pathway is within a regulatory pathway network. In a more preferred embodiment, the regulatory pathway network is selected from the group consisting of an ageing pathway network, an apoptosis pathway network, a homeostasis pathway network, a metabolic pathway network, a replication pathway network, and an immune response pathway network. In a yet more preferred embodiment, the pathway is within a signaling pathway network. In an alternative yet more preferred embodiment, the pathway is within a network of distinct pathway networks. In a most preferred embodiment, the signaling pathway network is selected from the group consisting of a calcium/calmodulin dependent signaling pathway network, a cytokine mediated signaling pathway network, a chemokine mediated signaling pathway network, a growth factor signaling pathway network, a hormone signaling pathway network, a MAP kinase signaling pathway network, a phosphatase mediated signaling pathway network, a Ras superfamily mediated signaling pathway network, and a transcription factor mediated signaling pathway network.

The invention also provides a method of generating a dynamic pathway map (DPM), the method comprising: providing access to a model database that stores a probabilistic pathway model that comprises a plurality of pathway elements; wherein a first number of the plurality of pathway elements are cross-correlated and assigned an influence level for at least one pathway on the basis of known attributes; wherein a second number of the plurality of pathway elements are cross-correlated and assigned an influence level for at least one pathway on the basis of assumed attributes; and using a plurality of measured attributes for a plurality of elements of a patient sample, via an analysis engine, to modify the probabilistic pathway model to obtain the DPM, wherein the DPM has reference pathway activity information for a particular pathway.

In one preferred embodiment, the pathway is within a regulatory pathway network, a signaling pathway network, or a network of distinct pathway networks. In another preferred embodiment, the pathway element is a protein selected from the group consisting of a receptor, a hormone binding protein, a kinase, a transcription factor, a methylase, a histone acetylase, and a histone deacetylase or a nucleic acid is selected from the group consisting of a genomic regulatory sequence, a regulatory RNA, and a trans-activating sequence. In a still further preferred embodiment, the reference pathway activity information is specific with respect to a normal tissue, a diseased tissue, an ageing tissue, or a recovering tissue. In another preferred embodiment, the known attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity. In another preferred embodiment, the assumed attribute is selected from the group consisting of a compound attribute, a class attribute, a gene copy number, a transcription level, a translation level, and a protein activity. In a still further preferred embodiment, the measured attributes are selected from the group consisting of a mutation, a differential genetic sequence object, a gene copy number, a transcription level, a translation level, a protein activity, and a protein interaction.

The invention further provides a method of analyzing biologically relevant information, comprising: providing access to a model database that stores a dynamic pathway map (DPM), wherein the DPM is generated by modification of a probabilistic pathway model with a plurality of measured attributes for a plurality of elements of a first cell or patient sample; obtaining a plurality of measured attributes for a plurality of elements of a second cell or patient sample; and using the DPM and the plurality of measured attributes for the plurality of elements of the second cell or patient sample, via an analysis engine, to determine a predicted pathway activity information for the second cell or patient sample. In one preferred embodiment, the measured attributes for the plurality of elements of the first cell or patient sample are characteristic for a healthy cell or tissue, a specific age of a cell or tissue, a specific disease of a cell or tissue, a specific disease stage of a diseased cell or tissue, a specific gender, a specific ethnic group, a specific occupational group, and a specific species. In another preferred embodiment, the measured attributes for the plurality of elements of the second cell or patient sample are selected from the group consisting of a mutation, a differential genetic sequence object, a gene copy number, a transcription level, a translation level, a protein activity, and a protein interaction. In an alterative preferred embodiment, the first and second samples are obtained from the same cell or patient, and further comprising providing a treatment to the cell or patient before obtaining the plurality of measured attributes for the plurality of elements of the second cell or patient sample. In a more preferred embodiment, the treatment is selected from the group consisting of radiation, administration of a pharmaceutical to the patient, and administration of a candidate molecule to the cell. In another more preferred embodiment, the candidate molecule is a member of a library of candidate molecules. In another preferred embodiment, the predicted pathway activity information identifies an element as a hierarchical-dominant element in at least one pathway. In a more preferred embodiment, the predicted pathway activity information identifies an element as a disease-determinant element in at least one pathway with respect to a disease. In an alterative embodiment, the method further comprises a step of generating a graphical representation of predicted pathway activity information. In an alternative embodiment, the method further comprises a step of generating a treatment recommendation that is at least in part based on the predicted pathway activity information. In an alternative embodiment, the method further comprises a step of using the predicted pathway activity information to formulate a diagnosis, a prognosis for a disease, or a recommendation selected from the group consisting of a selection of a treatment option, and a dietary guidance. In an alternative embodiment, the method further comprises a step of using the predicted pathway activity information to identify an epigenetic factor, a stress adaptation, a state of an organism, and a state of repair or healing.

In another embodiment, The invention provides a transformation method for creating a matrix of integrated pathway activities (IPAs) for predicting a clinical outcome for an individual in need, the method comprising the steps of (i) providing a set of curated pathways, wherein the pathways comprise a plurality of entities; (ii) converting each curated pathway into a distinct probabilistic graphical model (PGM), wherein the PGM is derived from factor graphs of each curated pathway, (iii) providing a biological sample from the individual wherein the biological sample comprises at least one endogenous entity comprised in one of the curated pathways; (iv) determining the levels of endogenous entity in the biological sample; (v) comparing the levels of the endogenous entity with those levels of the entity in a previously determined control sample from another individual; (vi) determining whether the levels of the endogenous entity relative to the control entity levels are activated, nominal, or inactivated; (vii) assigning the endogenous entity a numeric state, wherein the state representing activated is +1, the state representing nominal activity is 0, and wherein the state representing inactivated is −1; (viii) repeating steps ii through (vi) for another endogenous entity; (x) compiling the numeric states of each endogenous entity into a matrix of integrated pathway activities (IPAs), (x) wherein the matrix of integrated pathway activities is A wherein A, represents the inferred activity of entity i in biological sample j; the method resulting in a matrix of integrated pathway activities for predicting a clinical outcome for the individual.

In one embodiment the method for creating a matrix of IPAs comprises predicting a clinical outcome, providing a diagnosis, providing a treatment, delivering a treatment, administering a treatment, conducting a treatment, managing a treatment, or dispensing a treatment to an individual in need. In another embodiment, the set of curated pathways is from an analysis of human biology. In yet another alternative embodiment, the set of curated pathways is from an analysis of non-human biology. In another embodiment, the determining of the levels of the endogenous entity relative to the control entity levels is performed using Student's t-test. In an alternative embodiment, the determining of the levels of the endogenous entity relative to the control entity levels is performed using ANOVA. In another embodiment, the transforming method comprise the steps of wherein a plurality of matrices of integrated pathway activities from more than one individual are combined, the combined plurality of matrices resulting in a cluster, and where the distances between the individuals' matrices of the resulting cluster are determined. In one embodiment, the determined distances are analysed using K-means cluster analysis. In another alternative embodiment, the determined distances are analysed using K2-means cluster analysis. In a yet other embodiment, the transforming method comprises the step of determining the levels of endogenous entity in the biological sample comprises detecting the endogenous entity with an antibody and thereby determining the levels of endogenous entity. In an alternative embodiment the step of determining the levels of endogenous entity in the biological sample comprises detecting the endogenous entity with a nucleic acid probe and thereby determining the levels of endogenous entity. In another alternative embodiment, the step of determining the levels of endogenous entity in the biological sample comprises detecting the endogenous entity with an organic reagent, wherein the organic reagent binds to the endogenous entity thereby resulting in a detectable signal and thereby determining the levels of endogenous entity.

In a still further alternative embodiment, the step of determining the levels of endogenous entity in the biological sample comprises detecting the endogenous entity with an inorganic reagent, wherein the inorganic reagent binds to the endogenous entity thereby resulting in a detectable signal and thereby determining the levels of endogenous entity. In another alternative embodiment, the step of determining the levels of endogenous entity in the biological sample comprises detecting the endogenous entity with an organic reagent, wherein the organic reagent reacts with the endogenous entity thereby resulting in a detectable signal and thereby determining the levels of endogenous entity. In another alternative embodiment, the step of determining the levels of endogenous entity in the biological sample comprises detecting the endogenous entity with an inorganic reagent, wherein the inorganic reagent reacts with the endogenous entity thereby resulting in a detectable signal and thereby determining the levels of endogenous entity. In a preferred embodiment, the step of determining the levels of endogenous entity in the biological sample comprises measuring the absorbance of the endogenous entity at the optimal wavelength for the endogenous entity and thereby determining the levels of endogenous entity. In an alternative preferred embodiment, the step of determining the levels of endogenous entity in the biological sample comprises measuring the fluorescence of the endogenous entity at the optimal wavelength for the endogenous entity and thereby determining the levels of endogenous entity. In a still further alternative preferred embodiment, the step of determining the levels of endogenous entity in the biological sample comprises reacting the endogenous entity with an enzyme, wherein the enzyme selectively digests the endogenous entity to create at least one product, detecting the at least one product, and thereby determining the levels of endogenous entity. In a more preferred embodiment, the step of reacting the endogenous entity with an enzyme results in creating at least two products. In a yet more preferred embodiment, the step of reacting the endogenous entity with an enzyme resulting at least two products is followed by a step of treating the products with another enzyme, wherein the enzyme selectively digests at least one of the products to create at least a third product, detecting the at least a third product, and thereby determining the levels of endogenous entity.

In another preferred embodiment the individual is selected from the group of a healthy individual, an asymptomatic individual, and a symptomatic individual. In a more preferred embodiment, the individual is selected from the group consisting of an individual diagnosed with a condition, the condition selected from the group consisting of a disease and a disorder. In a preferred embodiment, the condition is selected from the group consisting of acquired immunodeficiency syndrome (AIDS), Addison's disease, adult respiratory distress syndrome, allergies, ankylosing spondylitis, amyloidosis, anemia, asthma, atherosclerosis, autoimmune hemolytic anemia, autoimmune thyroiditis, benign prostatic hyperplasia, bronchitis, Chediak-Higashi syndrome, cholecystitis, Crohn's disease, atopic dermatitis, dermnatomyositis, diabetes mellitus, emphysema, erythroblastosis fetalis, erythema nodosum, atrophic gastritis, glomerulonephritis, Goodpasture's syndrome, gout, chronic granulomatous diseases, Graves' disease, Hashimoto's thyroiditis, hypereosinophilia, irritable bowel syndrome, multiple sclerosis, myasthenia gravis, myocardial or pericardial inflammation, osteoarthritis, osteoporosis, pancreatitis, polycystic ovary syndrome, polymyositis, psoriasis, Reiter's syndrome, rheumatoid arthritis, scleroderma, severe combined immunodeficiency disease (SCID), Sjogren's syndrome, systemic anaphylaxis, systemic lupus erythematosus, systemic sclerosis, thrombocytopenic purpura, ulcerative colitis, uveitis, Werner syndrome, complications of cancer, hemodialysis, and extracorporeal circulation, viral, bacterial, fangal, parasitic, protozoal, and helminthic infection; and adenocarcinoma, leukemia, lymphoma, melanoma, myeloma, sarcoma, teratocarcinoma, and, in particular, cancers of the adrenal gland, bladder, bone, bone marrow, brain, breast, cervix, gall bladder, ganglia, gastrointestinal tract, heart, kidney, liver, lung, muscle, ovary, pancreas, parathyroid, penis, prostate, salivary glands, skin, spleen, testis, thymus, thyroid, and uterus, akathesia, Alzheimer's disease, amnesia, amyotrophic lateral sclerosis (ALS), ataxias, bipolar disorder, catatonia, cerebral palsy, cerebrovascular disease Creutzfeldt-Jakob disease, dementia, depression, Down's syndrome, tardive dyskinesia, dystonias, epilepsy, Huntington's disease, multiple sclerosis, muscular dystrophy, neuralgias, neurofibromatosis, neuropathies, Parkinson's disease, Pick's disease, retinitis pigmentosa, schizophrenia, seasonal affective disorder, senile dementia, stroke, Tourette's syndrome and cancers including adenocarcinomas, melanomas, and teratocarcinomas, particularly of the brain. In an alternative preferred embodiment, the condition is selected from the group consisting of cancers such as adenocarcinoma, leukemia, lymphoma, melanoma, myeloma, sarcoma, teratocarcinoma, and, in particular, cancers of the adrenal gland, bladder, bone, bone marrow, brain, breast, cervix, gall bladder, ganglia, gastrointestinal tract, heart, kidney, liver, lung, muscle, ovary, pancreas, parathyroid, penis, prostate, salivary glands, skin, spleen, testis, thymus, thyroid, and uterus; immune disorders such as acquired immunodeficiency syndrome (AIDS), Addison's disease, adult respiratory distress syndrome, allergies, ankylosing spondylitis, amyloidosis, anemia, asthma, atherosclerosis, autoimmune hemolytic anemia, autoimmune thyroiditis, bronchitis, cholecystitis, contact dermatitis, Crohn's disease, atopic dermatitis, dermatomyositis, diabetes mellitus, emphysema, episodic lymphopenia with lymphocytotoxins, erythroblastosis fetalis, erythema nodosum, atrophic gastritis, glomerulonephritis, Goodpasture's syndrome, gout, Graves' disease, Hashimoto's thyroiditis, hypereosinophilia, irritable bowel syndrome, multiple sclerosis, myasthenia gravis, myocardial or pericardial inflammation, osteoarthritis, osteoporosis, pancreatitis, polymyositis, psoriasis, Reiter's syndrome, rheumatoid arthritis, scleroderma, Sjogren's syndrome, systemic anaphylaxis, systemic lupus erythematosus, systemic sclerosis, thrombocytopenic purpura, ulcerative colitis, uveitis, Werner syndrome, complications of cancer, hemodialysis, and extracorporeal circulation, viral, bacterial, fungal, parasitic, protozoal, and helminthic infections, trauma, X-linked agammaglobinemia of Bruton, common variable immunodeficiency (CVI), DiGeorge's syndrome (thymic hypoplasia), thymic dysplasia, isolated IgA deficiency, severe combined immunodeficiency disease (SCID), immunodeficiency with thrombocytopenia and eczema (Wiskott-Aldrich syndrome), Chediak-Higashi syndrome, chronic granulomatous diseases, hereditary angioneurotic edema, and immunodeficiency associated with Cushing's disease; and developmental disorders such as renal tubular acidosis, anemia, Cushing's syndrome, achondroplastic dwarfism, Duchenne and Becker muscular dystrophy, epilepsy, gonadal dysgenesis, WAGR syndrome (Wilms' tumor, aniridia, genitourinary abnormalities, and mental retardation), Smith-Magenis syndrome, myelodysplastic syndrome, hereditary mucoepithelial dysplasia, hereditary keratodermas, hereditary neuropathies such as Charcot-Marie-Tooth disease and neurofibromatosis, hypothyroidism, hydrocephalus, seizure disorders such as Syndenham's chorea and cerebral palsy, spina bifida, anencephaly, craniorachischisis, congenital glaucoma, cataract, sensorineural hearing loss, and any disorder associated with cell growth and differentiation, embryogenesis, and morphogenesis involving any tissue, organ, or system of a subject, for example, the brain, adrenal gland, kidney, skeletal or reproductive system. In another preferred embodiment, the condition is selected from the group consisting of endocrinological disorders such as disorders associated with hypopituitarism including hypogonadism, Sheehan syndrome, diabetes insipidus, Kaftan's disease, Hand-Schuller-Christian disease, Letterer-Siwe disease, sarcoidosis, empty sella syndrome, and dwarfism; hyperpituitarism including acromegaly, giantism, and syndrome of inappropriate antidiuretic hormone (ADH) secretion (SIADH); and disorders associated with hypothyroidism including goiter, myxedema, acute thyroiditis associated with bacterial infection, subacute thyroiditis associated with viral infection, autoimmune thyroiditis (Hashimoto's disease), and cretinism; disorders associated with hyperthyroidism including thyrotoxicosis and its various forms, Grave's disease, pretibial myxedema, toxic multinodular goiter, thyroid carcinoma, and Plummer's disease; and disorders associated with hyperparathyroidism including Conn disease (chronic hypercalemia); respiratory disorders such as allergy, asthma, acute and chronic inflammatory lung diseases, ARDS, emphysema, pulmonary congestion and edema, COPD, interstitial lung diseases, and lung cancers; cancer such as adenocarcinoma, leukemia, lymphoma, melanoma, myeloma, sarcoma, teratocarcinoma, and, in particular, cancers of the adrenal gland, bladder, bone, bone marrow, brain, breast, cervix, gall bladder, ganglia, gastrointestinal tract, heart, kidney, liver, lung, muscle, ovary, pancreas, parathyroid, penis, prostate, salivary glands, skin, spleen, testis, thymus, thyroid, and uterus; and immunological disorders such as acquired immunodeficiency syndrome (AIDS), Addison's disease, adult respiratory distress syndrome, allergies, ankylosing spondylitis, amyloidosis, anemia, asthma, atherosclerosis, autoimmune hemolytic anemia, autoimmune thyroiditis, bronchitis, cholecystitis, contact dermatitis, Crohn's disease, atopic dermatitis, dermatomyositis, diabetes mellitus, emphysema, episodic lymphopenia with lymphocytotoxins, erythroblastosis fetalis, erythema nodosum, atrophic gastritis, glomerulonephritis, Goodpasture's syndrome, gout, Graves' disease, Hashimoto's thyroiditis, hypereosinophilia, irritable bowel syndrome, multiple sclerosis, myasthenia gravis, myocardial or pericardial inflammation, osteoarthritis, osteoporosis, pancreatitis, polymyositis, psoriasis, Reiter's syndrome, rheumatoid arthritis, scleroderrna, Sjogren's syndrome, systemic anaphylaxis, systemic lupus erythematosus, systemic sclerosis, thrombocytopenic purpura, ulcerative colitis, uveitis, Werner syndrome, complications of cancer, hemodialysis, and extracorporeal circulation, viral, bacterial, fungal, parasitic, protozoal, and helminthic infections, and irauma.

The Invention also provides the transforming method as disclosed herein wherein matrix A can then be used in place of the original constituent datasets to identify associations with clinical outcomes. In a more preferred embodiment the curated pathways are selected from the group consisting of biochemical pathways, genetic pathways, metabolic pathways, gene regulatory pathways, gene transcription pathways, gene translation pathways. In another more preferred embodiment, the entities are selected from the group consisting of nucleic acids, peptides, proteins, peptide nucleic acids, carbohydrates, lipids, proteoglycans, factors, co-factors, biochemical metabolites, organic compositions, inorganic compositions, and salts. In a yet other preferred embodiment, the biological sample is selected from the group consisting of patient samples, control samples, experimentally-treated animal samples, experimentally-treated tissue culture samples, experimentally-treated cell culture samples, and experimentally-treated in vitro biochemical composition samples. In a more preferred embodiment, the biological sample is a patient sample.

The invention also provides a probabilistic graphical model (PGM) framework having an output that infers the molecular pathways altered in a patient sample, the PGM comprising a plurality of factor graphs, wherein the factor graphs represent integrated biological datasets, and wherein the inferred molecular pathways that are altered in a patient sample comprise molecular pathways known from data and wherein said molecular pathways effect a clinical or non-clinical condition, wherein the inferred molecular pathways are known to be modulated by a clinical regimen or treatment, and wherein the output indicates a clinical regimen. In a preferred embodiment, the data is selected from experimental data, clinical data, epidemiological data, and phenomenological data. In another preferred embodiment, the condition is selected from the group consisting of a disease and a disorder. In a more preferred embodiment, the condition is selected from the group consisting of acquired immunodeficiency syndrome (AIDS), Addison's disease, adult respiratory distress syndrome, allergies, ankylosing spondylitis, amyloidosis, anemia, asthma, atherosclerosis, autoimmune hemolytic anemia, autoimmune thyroiditis, benign prostatic hyperplasia, bronchitis, Chediak-Higashi syndrome, cholecystitis, Crohn's disease, atopic dermatitis, dermnatomyositis, diabetes mellitus, emphysema, erythroblastosis fetalis, erythema nodosum, atrophic gastritis, glomerulonephritis, Goodpasture's syndrome, gout, chronic granulomatous diseases, Graves' disease, Hashimoto's thyroiditis, hypereosinophilia, irritable bowel syndrome, multiple sclerosis, myasthenia gravis, myocardial or pericardial inflammation, osteoarthritis, osteoporosis, pancreatitis, polycystic ovary syndrome, polymyositis, psoriasis, Reiter's syndrome, rheumatoid arthritis, scleroderma, severe combined immunodeficiency disease (SCID), Sjogren's syndrome, systemic anaphylaxis, systemic lupus erythematosus, systemic sclerosis, thrombocytopenic purpura, ulcerative colitis, uveitis, Werner syndrome, complications of cancer, hemodialysis, and extracorporeal circulation, viral, bacterial, fungal, parasitic, protozoal, and helminthic infection; and adenocarcinoma, leukemia, lymphoma, melanoma, myeloma, sarcoma, teratocarcinoma, and, in particular, cancers of the adrenal gland, bladder, bone, bone marrow, brain, breast, cervix, gall bladder, ganglia, gastrointestinal tract, heart, kidney, liver, lung, muscle, ovary, pancreas, parathyroid, penis, prostate, salivary glands, skin, spleen, testis, thymus, thyroid, and uterus, akathesia, Alzheimer's disease, amnesia, amyotrophic lateral sclerosis (ALS), ataxias, bipolar disorder, catatonia, cerebral palsy, cerebrovascular disease Creutzfeldt-Jakob disease, dementia, depression, Down's syndrome, tardive dyskinesia, dystonias, epilepsy, Huntington's disease, multiple sclerosis, muscular dystrophy, neuralgias, neurofibromatosis, neuropathies, Parkinson's disease, Pick's disease, retinitis pigmentosa, schizophrenia, seasonal affective disorder, senile dementia, stroke, Tourette's syndrome and cancers including adenocarcinomas, melanomas, and teratocarcinomas, particularly of the brain. In an alternative more preferred embodiment, the condition is selected from the group consisting of cancers such as adenocarcinoma, leukemia, lymphoma, melanoma, myeloma, sarcoma, teratocarcinoma, and, in particular, cancers of the adrenal gland, bladder, bone, bone marrow, brain, breast, cervix, gall bladder, ganglia, gastrointestinal tract, heart, kidney, liver, lung, muscle, ovary, pancreas, parathyroid, penis, prostate, salivary glands, skin, spleen, testis, thymus, thyroid, and uterus; immune disorders such as acquired immunodeficiency syndrome (AIDS), Addison's disease, adult respiratory distress syndrome, allergies, ankylosing spondylitis, amyloidosis, anemia, asthma, atherosclerosis, autoimmune hemolytic anemia, autoimmune thyroiditis, bronchitis, cholecystitis, contact dermatitis, Crohn's disease, atopic dermatitis, dermatomyositis, diabetes mellitus, emphysema, episodic lymphopenia with lymphocytotoxins, erythroblastosis fetalis, erythema nodosum, atrophic gastritis, glomerulonephritis, Goodpasture's syndrome, gout, Graves' disease, Hashimoto's thyroiditis, hypereosinophilia, irritable bowel syndrome, multiple sclerosis, myasthenia gravis, myocardial or pericardial inflammation, osteoarthritis, osteoporosis, pancreatitis, polymyositis, psoriasis, Reiter's syndrome, rheumatoid arthritis, scleroderma, Sjogren's syndrome, systemic anaphylaxis, systemic lupus erythematosus, systemic sclerosis, thrombocytopenic purpura, ulcerative colitis, uveitis, Werner syndrome, complications of cancer, hemodialysis, and extracorporeal circulation, viral, bacterial, fungal, parasitic, protozoal, and helminthic infections, trauma, X-linked agammaglobinemia of Bruton, common variable immunodeficiency (CVI), DiGeorge's syndrome (thymic hypoplasia), thymic dysplasia, isolated IgA deficiency, severe combined immunodeficiency disease (SCID), immunodeficiency with thrombocytopenia and eczema (Wiskott-Aldrich syndrome), Chediak-Higashi syndrome, chronic granulomatous diseases, hereditary angioneurotic edema, and immunodeficiency associated with Cushing's disease; and developmental disorders such as renal tubular acidosis, anemia, Cushing's syndrome, achondroplastic dwarfism, Duchenne and Becker muscular dystrophy, epilepsy, gonadal dysgenesis, WAGR syndrome (Wilms' tumor, aniridia, genitourinary abnormalities, and mental retardation), Smith-Magenis syndrome, myelodysplastic syndrome, hereditary mucoepithelial dysplasia, hereditary keratodermas, hereditary neuropathies such as Charcot-Marie-Tooth disease and neurofibromatosis, hypothyroidism, hydrocephalus, seizure disorders such as Syndenham's chorea and cerebral palsy, spina bifida, anencephaly, craniorachischisis, congenital glaucoma, cataract, sensorineural hearing loss, and any disorder associated with cell growth and differentiation, embryogenesis, and morphogenesis involving any tissue, organ, or system of a subject, for example, the brain, adrenal gland, kidney, skeletal or reproductive system. In a yet other more preferred embodiment, the condition is selected from the group consisting of endocrinological disorders such as disorders associated with hypopituitarism including hypogonadism, Sheehan syndrome, diabetes insipidus, Kallman's disease, Hand-Schuller-Christian disease, Letterer-Siwe disease, sarcoidosis, empty sella syndrome, and dwarfism; hyperpituitarism including acromegaly, giantism, and syndrome of inappropriate antidiuretic hormone (ADH) secretion (SIADH); and disorders associated with hypothyroidism including goiter, myxedema, acute thyroiditis associated with bacterial infection, subacute thyroiditis associated with viral infection, autoimmune thyroiditis (Hashimoto's disease), and cretinism; disorders associated with hyperthyroidism including thyrotoxicosis and its various forms, Grave's disease, pretibial myxedema, toxic multinodular goiter, thyroid carcinoma, and Plummer's disease; and disorders associated with hyperparathyroidism including Conn disease (chronic hypercalemia); respiratory disorders such as allergy, asthma, acute and chronic inflammatory lung diseases, ARDS, emphysema, pulmonary congestion and edema, COPD, interstitial lung diseases, and lung cancers; cancer such as adenocarcinoma, leukemia, lymphoma, melanoma, myeloma, sarcoma, teratocarcinoma, and, in particular, cancers of the adrenal gland, bladder, bone, bone marrow, brain, breast, cervix, gall bladder, ganglia, gastrointestinal tract, heart, kidney, liver, lung, muscle, ovary, pancreas, parathyroid, penis, prostate, salivary glands, skin, spleen, testis, thymus, thyroid, and uterus; and immunological disorders such as acquired immunodeficiency syndrome (AIDS), Addison's disease, adult respiratory distress syndrome, allergies, ankylosing spondylitis, amyloidosis, anemia, asthma, atherosclerosis, autoimmune hemolytic anemia, autoimmune thyroiditis, bronchitis, cholecystitis, contact dermatitis, Crohn's disease, atopic dermatitis, dermatomyositis, diabetes mellitus, emphysema, episodic lymphopenia with lymphocytotoxins, erythroblastosis fetalis, erythema nodosum, atrophic gastritis, glomerulonephritis, Goodpasture's syndrome, gout, Graves' disease, Hashimoto's thyroiditis, hypereosinophilia, irritable bowel syndrome, multiple sclerosis, myasthenia gravis, myocardial or pericardial inflammation, osteoarthritis, osteoporosis, pancreatitis, polymyositis, psoriasis, Reiter's syndrome, rheumatoid arthritis, scleroderma, Sjogren's syndrome, systemic anaphylaxis, systemic lupus erythematosus, systemic sclerosis, thrombocytopenic purpura, ulcerative colitis, uveitis, Werner syndrome, complications of cancer, hemodialysis, and extracorporeal circulation, viral, bacterial, fungal, parasitic, protozoal, and helminthic infections, and trauma.

