Repurposing FDA approved therapeutics

Abstract

An algorithm for identifying treatment targets for diseases and FDA-approved drugs that can be re-purposed for off-label use is described, as well as methods of using drugs identified by the algorithm to treat diseases including cancer. Also described are synergistic combinations of FDA-approved drugs for treating diffuse pleural mesothelioma.

Description

Attorney Docket No.:206339-0109-00WO REPURPOSING FDA APPROVED THERAPEUTICS CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 633,992, filed April 15, 2024, which is hereby incorporated by reference herein in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under W81XWH-21-1- 0868 awarded by the DoD / USAMRAA. The government has certain rights in the invention. BACKGROUND

[0003] Diffuse pleural mesothelioma (DPM) or malignant pleural mesothelioma (MPM) is a devastating lung cancer most commonly diagnosed at an advanced stage with poor prognosis for patients. The 5-year survival rate has remained stagnant at 5-10% due to the lack of effective therapies and the limited clinical benefit from resection (Janes SM, Alrifai D, Fennell DA, N Engl J Med., 2021;385(13):1207-1218). DPM tumors are described by three main histological subtypes: epithelioid (60-80% of tumors), sarcomatoid (<10% of tumors), and biphasic subtype (10-15% of tumors), which is a mixture of epithelioid and sarcomatoid tumor cells (Alì G, et al., J Thorac Dis.2018;10:S276-S284). While the diversity of DPM tumors result in a varied response to treatment (Ceresoli GL, et al., Lung Cancer. 2001;34(2):279-87), chemotherapy (cisplatin and the adjuvant pemetrexed) is still considered the first line therapy for unresectable DPM tumors (Popat S, et al., Ann Oncol. 2022;33(2):129-142). Unfortunately, chemotherapy prolongs life for an average of only a year after diagnosis (Borrelli EP, McGladrigan CG, Curr Treat Options Oncol., 2021;22(2):14). The most recent advancement in treating DPM, especially in cases of sarcomatoid histology, is the immunotherapy combination of nivolumab plus ipilimumab. This combination was approved in 2021, was repurposed from lung cancer, and resulted in an increase in survival of four months on average when compared to the first line chemotherapyAttorney Docket No.:206339-0109-00WO (Baas P, et al., Lancet.2021;397(10272):375-386). While these advancements in treatments are significant, there are still no therapies that lead to sustained tumor remission.

[0004] As such, there is a pressing need for compositions and methods that treat DPM. The present disclosure meets this long felt, but unmet, need. SUMMARY

[0005] In some embodiments, the disclosure relates to a composition for treating mesothelioma comprising at least one of dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, or tazemetostat. In some embodiments, the composition comprises at least one of gilteritinib, orlistat, osimertinib, sunitinib, sorafenib, or tazemetostat.

[0006] In some embodiments, the composition comprises two or more of dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, or tazemetostat. In some embodiments, the composition comprises two or more of gilteritinib, orlistat, osimertinib, sunitinib, sorafenib, or tazemetostat. In some embodiments, the composition comprises gilteritinib and regorafenib. In some embodiments, the composition comprises osimertinib and tazemetostat. In some embodiments, the composition comprises ribociclib and gilteritinib. In some embodiments, the composition comprises ribociclib and osimertinib. In some embodiments, the composition comprises ribociclib and regorafenib. In some embodiments, the composition comprises ribociclib and sorafenib. In some embodiments, the composition comprises sorafenib and gilteritinib. In some embodiments, the composition comprises sorafenib and tazemetostat. In some embodiments, the composition comprises sunitinib and gilteritinib. In some embodiments, the composition comprises sunitinib and osimertinib. In some embodiments, the composition comprises sunitinib and ribociclib. In some embodiments, the composition comprises sunitinib and tazemetostat. In some embodiments, the composition comprises tazemetostat and gilteritinib. In some embodiments, the composition comprises tazemetostat and orlistat. In some embodiments, the composition comprises dasatinib and osimertinib. In some embodiments, the composition comprises dasatinib and sunitinib. In some embodiments, the composition comprises orlistat and sunitinib.Attorney Docket No.:206339-0109-00WO

[0007] In some embodiments, the mesothelioma is diffuse pleural mesothelioma (DPM).

[0008] In some embodiments, the disclosure relates to a method for treating mesothelioma in a patient in need thereof, the method comprising administering at least one of dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, or tazemetostat to the subject. In some embodiments, the method comprises administering at least one of gilteritinib, orlistat, osimertinib, sunitinib, sorafenib, or tazemetostat to the subject.

[0009] In some embodiments, the method further comprises administering at least one additional agent to the subject. In some embodiments, the at least one additional agent comprises dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, or tazemetostat. In some embodiments, the method comprises administering a combination of gilteritinib and regorafenib. In some embodiments, the method comprises administering a combination of osimertinib and tazemetostat. In some embodiments, the method comprises administering a combination of ribociclib and gilteritinib. In some embodiments, the method comprises administering a combination of ribociclib and osimertinib. In some embodiments, the method comprises administering a combination of ribociclib and regorafenib. In some embodiments, the method comprises administering a combination of ribociclib and sorafenib. In some embodiments, the method comprises administering a combination of sorafenib and gilteritinib. In some embodiments, the method comprises administering a combination of sorafenib and tazemetostat. In some embodiments, the method comprises administering a combination of sunitinib and gilteritinib. In some embodiments, the method comprises administering a combination of sunitinib and osimertinib. In some embodiments, the method comprises administering a combination of sunitinib and ribociclib. In some embodiments, the method comprises administering a combination of sunitinib and tazemetostat. In some embodiments, the method comprises administering a combination of tazemetostat and gilteritinib. In some embodiments, the method comprises administering a combination of tazemetostat and orlistat. In some embodiments, the method comprises administering a combination of dasatinib and osimertinib. In some embodiments, the method comprises administering a combination ofAttorney Docket No.:206339-0109-00WO dasatinib and sunitinib. In some embodiments, the method comprises administering a combination of orlistat and sunitinib.

[0010] In some embodiments, the method comprises administering two or more inhibitors essentially simultaneously. In some embodiments, the method comprises administering two or more inhibitors sequentially.

[0011] In some embodiments, the mesothelioma is diffuse pleural mesothelioma (DPM).

[0012] In some embodiments, the disclosure relates to a method of repurposing FDA- approved therapeutics as therapeutics for additional diseases comprising the steps of: a) generating a first data set comprising a first set of genes, transcripts, markers or protein targets that are identified by a functional genomics screen as being essential for a disease or disease stage; b) generating a second data set comprising a second set of genes, transcripts, markers or protein targets associated with the disease or disease stage from multi-omics data comprising the steps of: i) obtaining multi-omic data related to the disease or disease stage; ii) running the multi-omic data through a biclustering algorithm; and iii) determining the second set of genes, transcripts, markers or protein targets associated with the disease or disease stage from the biclusters; c) generating a third data set comprising a set of genes, transcripts or protein targets for which there is a known FDA-approved therapeutic agent that targets the gene, transcript or protein targets; d) identifying a set of overlapping genes, transcripts or protein targets from the first, second and third data sets; and e) identifying an FDA-approved therapeutic agent for re-purposing when the target of the FDA-approved therapeutic agent is identified in the set of overlapping genes, transcripts or protein targets in step d.

[0013] In some embodiments, the functional genomics screen is a genomic screen, a proteomic screen, a metabolomic screen, a transcriptomic screen, or a drug screen. In some embodiments, the functional genomics screen is an siRNA screen, an shRNA screen, aAttorney Docket No.:206339-0109-00WO microRNA screen, a Zinc finger nuclease screen, a TALEN screen, a CRISPR-Cas9 screen, a CRIPSRi screen, a CRISPRa screen, a dual CRISPRi / a screen, or a CRISPRsc screen.

[0014] In some embodiments, the first data set is generated by comparing the results between more than one screen.

[0015] In some embodiments, genes, transcripts or proteins common to two or more screens are removed from the data set.

[0016] In some embodiments, the step of generating a second data set comprising a second set of genes, transcripts, markers or protein targets associated with the cancer from multi-omics data comprises the steps of: i) obtaining at least one multiomic data set for the disease or disease stage; ii) identifying a subset of genes, transcripts, markers or proteins from the at least one multiomic data set that are significantly variable within the at least one multiomic data set; iii) combining the subset of genes, transcripts, markers or proteins identified in step ii) with the first set of genes, transcripts, markers or protein targets that are identified by a functional genomics screen as being essential for the disease or disease stage; iv) constructing a set of biclusters from the combined set of genes, transcripts, markers or proteins assembled in step iii); v) identifying the subset of biclusters that are significantly co- expressed; vi) analyzing the bicluster set to identify the subset of biclusters that are significantly co-expressed and disease relevant; and vii) compiling the genes included in the subset of biclusters that are significantly co-expressed and disease relevant into the second dataset.

[0017] In some embodiments, the multiomics data comprises gene expression data, somatic mutation data, miRNA expression data, transcriptome expression data, chromatin accessibility data, protein expression data or phenotypic data or any combination thereof.Attorney Docket No.:206339-0109-00WO

[0018] In some embodiments, step ii) comprises generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set.

[0019] In some embodiments, the method of generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set comprises: a) systematically integrating mutations and defines a role for each gene wherein the role is protein affecting mutation (PAM), transcript fusions (Fusion), copy number alteration deletion (CNAdel), copy number alteration amplification (CNAamp), activating (Act), or loss of function (LoF); and b) assigning a binary value to each gene indicating the presence or absence of a mutation.

[0020] In some embodiments, the method further comprises the step of administering the identified FDA-approved therapeutic agent to a subject in need.

[0021] In some embodiments, the disease is cancer.

[0022] In some embodiments, the method comprises: a) generating a first data set comprising a first set of genes, transcripts, markers or protein targets that are identified by a functional genomics screen as being essential for tumor survival and growth of the cancer; b) generating a second data set comprising a second set of genes, transcripts, markers or protein targets associated with the cancer from multi-omics data comprising the steps of: i) obtaining multiomic data related to the cancer; ii) running the multiomic data through a biclustering algorithm; and iii) determining the second set of genes, transcripts, markers or protein targets associated with the cancer from the biclusters; c) generating a third data set comprising a set of genes, transcripts or protein targets for which there is a known FDA-approved therapeutic agent that targets the gene, transcript or protein targets; d) identifying a set of overlapping genes, transcripts or protein targets from the first, second and third data sets;Attorney Docket No.:206339-0109-00WO e) identifying an FDA-approved therapeutic agent for re-purposing when the target of the FDA-approved therapeutic agent is identified in the set of overlapping genes, transcripts or protein targets in step d); and f) administering the FDA-approved therapeutic agent identified in step e) to a subject diagnosed as having or being at increased risk of development or recurrence of the cancer.

[0023] In some embodiments, step ii) comprises generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set.

[0024] In some embodiments, the method of generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set comprises: a) systematically integrating mutations and defines a role for each gene wherein the role is PAM, fusion, CNAdel, CNAamp, Act, or LoF; and b) assigning a binary value to each gene indicating the presence or absence of a mutation.

[0025] In some embodiments, the cancer is mesothelioma, breast cancer, lung cancer, head and neck cancer, bladder cancer, stomach cancer, cancer of the nervous system, bone cancer, bone marrow cancer, brain cancer, colon cancer, esophageal cancer, endometrial cancer, gastrointestinal cancer, genital-urinary cancer, stomach cancer, lymphomas, melanoma, glioma, bladder cancer, pancreatic cancer, gum cancer, kidney cancer, retinal cancer, liver cancer, nasopharynx cancer, ovarian cancer, oral cancers, bladder cancer, hematological neoplasms, follicular lymphoma, cervical cancer, multiple myeloma, osteosarcomas, thyroid cancer, prostate cancer, colon cancer, prostate cancer, skin cancer, stomach cancer, testis cancer, tongue cancer, or uterine cancer.

[0026] In some embodiments, the method further comprises the step of administering the identified FDA-approved therapeutic agent in step e to a subject in need. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] For the purpose of illustrating the disclosure, there are depicted in the drawings certain embodiments of the present disclosure. Those of ordinary skill in the art will understand that the embodiments specifically described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of theAttorney Docket No.:206339-0109-00WO various embodiments of the present disclosure is defined solely by the claims. The features illustrated or described in connection with one exemplary embodiment may be combined with the features of other embodiments. Such modifications and variations are intended to be included within the scope of the present disclosure.

[0028] Figure 1, comprising Figure 1A and Figure 1B, depicts representative results of functional genomics screens in primary DPM cell lines from the epithelioid and sarcomatoid subtypes and their integration with a gene regulatory network and drug database. Figure 1A depicts a schematic of the functional genomic screen. Primary DPM cell lines M12T (epithelioid) and M24 (sarcomatoid) were transduced in biological triplicate with lentiviral particles containing sgRNAs from the Brunello library. Transduced cells were selected using puromycin and a statistically representative number of cells were carried for greater than 20 days. Genomic DNA was extracted, sgRNA sequences were amplified, sequenced, and analyzed for gene essentiality. Figure 1B depicts a representative plot of gene essentiality scores from M12T (x-axis) and M24 (y-axis) plotted for each of the ~19,000 genes analyzed. The red genes correspond to genes essential to the proliferation of M12T, blue to the proliferation of M24, and purple to the proliferation of both cell lines. Black highlighted genes are indicated as the 15 genes inhibited by FDA-approved drugs chosen for in vitro testing. Grey genes were either non-significant or commonly essential genes.

[0029] Figure 2, comprising Figure 2A through Figure 2D, depicts a flowchart describing the discovery of 1,921 DPM-specific essential genes. Figure 2A depicts a flowchart depicting that common essential genes were removed from genes essential to DPM proliferation for both M12T and M24. The union of these two lists identifies the DPM- specific essential genes. Figure 2B depicts a flowchart depicting the prioritization of 5,738 disease-relevant genes from the dpmSYGNAL network. Figure 2C depicts a flowchart depicting the prioritization of 368 FDA-approved inhibitors with known gene targets from DrugBank. Figure 2D depicts that the DPM-specific essential genes, disease-relevant genes, and FDA approved inhibitor target genes were intersected to repurpose FDA approved inhibitors for DPM.

[0030] Figure 3, comprising Figure 3A through Figure 3J, depicts representative results of screening for synergistic pairwise combinatorial drug interactions and describing the synergistic ranges and antitumor effects of tazemetostat with orlistat. Figure 3A depicts aAttorney Docket No.:206339-0109-00WO heat map illustrating the exemplary results of the screen for combinatorial drug interactions. The upper triangle contains observed log2(fold-change) for M24 and the lower triangle for M12T. Yellow dots indicate significant synergistic effects on proliferation and cell death (Benjamini-Hochberg corrected p-value < 0.05 and a change between the observed log2(fold change) and expected log2(fold change) > -0.2). Red outlines indicate significantly synergistic combinations in both cell lines. Figure 3B depicts a heat map illustrating representative results from replication of synergistic combinations in M3T, M14T, M50T, and M34. Dose response curves in the M12T (red background) and M24 (blue background) cell lines for orlistat (Figure 3C and Figure 3G) and tazemetostat (Figure 3D and Figure 3H), and concentrations chosen for the DRMs are indicated with dashed purple and red (IC50) lines. Inhibition percentage for DRM (Figure 3E and Figure 3I), ZIP synergy scores (Figure 3F and Figure 3J), and zones of significant synergy are indicated with a white box.

[0031] Figure 4, comprising Figure 4A through Figure 4N, depicts representative results from experiments determining the effect of tazemetostat and orlistat on the cell cycle and apoptosis. Figure 4A depicts a schematic of experiments where primary DPM cell lines M12T and M24 were plated into 6-well plates and treated with tazemetostat, orlistat, and the combination in triplicate. RNA was extracted 24 hours after drug treatment for the M12T cell line and 12 hours after treatment for the M24 cell line. Drug was applied to the cells prior to RNA extraction at the respective IC35s for the M12T (Figure 4B) and M24 (Figure 4C) cell lines. Semantic similarity between cell death processes and enriched biological processes from up-regulated genes from M12T (Figure 4D) and M24 (Figure 4F). Figure 4F depicts a heat map illustrating significant up-regulation of cell death processes associations (Jiang- Conrath Semantic Similarity Score > 0.8) for enriched biological processes in each cell line. For Figure 4E and Figure 4H, the red shading indicates significant in M12T, blue shading indicates significant in M24, and purple shading indicates significant in both cell lines. Semantic similarity between the cell cycle and enriched biological processes from down- regulated genes from M12T (Figure 4G) and M24 (Figure 4I). Figure 4H depicts significant downregulation of the cell cycle (Jiang-Conrath Semantic Similarity Score > 0.8) for enriched biological processes in each cell line. Figure 4J depicts validation of the apoptotic effects induced by tazemetostat and orlistat in the M12T and M24 cell lines. Significant differences from the vehicle control (p-value < 0.05) are indicated by star. Figure 4K depictsAttorney Docket No.:206339-0109-00WO validation of the anti-cell cycle effects induced by tazemetostat and orlistat in the M12T and M24 cell lines. Significant differences from the vehicle control (p-value < 0.05) are indicated by star. Differential gene expression of the ER stress response pathway in M12T (Figure 4L) and M24 (Figure 4M). Significantly differentially expressed genes are indicated by a yellow dot. Figure 4N depicts that genes of the ER stress response pathway were differentially expressed in M12T (red), M24 (blue), and both cell lines (purple).

[0032] Figure 5, comprising Figure 5A through Figure 5C, depicts representative experiments determining mechanistic observations of targeting genes involved in maintaining intracellular lipid concentrations of the M12T cell line. Figure 5A depicts log2(fold change) of target genes for orlistat (FASN and lipases) and tazemetostat (EZH1 and EZH2). Significantly differentially expressed genes are indicated by a yellow dot for each treatment. Figure 5B depicts metabolic pathway fluxes that were significantly affected (log2(foldchange) > 1.0, p-value < 0.1) by at least one treatment measured using METAFlux. Figure 5C depicts representative results from experiments where intracellular lipid concentrations were quantified by flow cytometry using Nile Red staining of live cells at 24 hours after treatment. Lipid levels significantly (p-value < 0.05) increased after treatment with tazemetostat and the combination and significantly (p-value < 0.05) decreased after treatment with orlistat.

[0033] Figure 6, comprising Figure 6A through Figure 6C, depicts representative in vivo results of treatments with orlistat, tazemetostat, orlistat with tazemetostat, and cisplatin. Figure 6A depicts representative images showing the tumor burden after murine intrapleural injection of luminescent H2373 cells when treated with vehicle, orlistat, tazemetostat, orlistat with tazemetostat, or cisplatin. Figure 6B depicts the average radiance for each mouse as measured on days 1, 9, 18, and 23 for each treatment group. Figure 6C depicts the weight distribution of each treatment group as measured on days 1, 9, 18, and 23 for each treatment group. NS = non-significant, * = p-value less than 0.05.

[0034] Figure 7, comprising Figure 7A through Figure 7AD, depicts representative dose response curves for all drugs tested in M12T (Figure 7A through Figure 7O) and M24 (Figure 7P through Figure 7AD) cell lines.

[0035] Figure 8, comprising Figure 8A through Figure 8L, depicts representative dose response matrices of osimertinib and sunitinib (Figure 8A through Figure 8D),Attorney Docket No.:206339-0109-00WO tazemetostat and sunitinib (Figure 8E through Figure 8H), and gilteritinib and sorafenib (Figure 8I through Figure 8L) in the M12T cell line.

[0036] Figure 9, comprising Figure 9A through Figure 9L, depicts representative dose response matrices of osimertinib and sunitinib (Figure 9A through Figure 9D), tazemetostat and sunitinib (Figure 9E through Figure 9H), and gilteritinib and sorafenib (Figure 9I through Figure 9L) in the M24 cell line.

[0037] Figure 10, comprising Figure 10A through Figure 10F, depicts semantic similarity between cell death processes and enriched biological processes from up-regulated genes from M12T (Figure 10A) and M24 (Figure 10C). Figure 10B depicts significant up- regulation of cell death processes associations (Jiang-Conrath Semantic Similarity Score > 0.8) for enriched biological processes in each cell line. For Figure 10B and Figure 10E, the red shading indicates significant in M12T, blue shading indicates significant in M24, and purple shading indicates significant in both cell lines. Semantic similarity between the cell cycle and enriched biological processes from down-regulated genes from M12T (Figure 10D) and M24 (Figure 10F).

[0038] Figure 11 depicts an exemplary computing device.

[0039] Figure 12 depicts a schematic of the system and method to repurpose FDA- approved gene inhibitors.

[0040] Figure 13 depicts a diagram demonstrating that OncoMerge integrates PAMs, fusions, and CNAs into an integrated mutation matrix with the most suitable mutation type for each gene. The input data for OncoMerge includes the PAM, transcript fusion, and CNA matrices. OncoMerge then generates six matrices (PAM, Fusion, CNAamp, CNAdel, Act, and LoF) and uses mutational frequency and statistical filters to determine each gene’s most suitable somatic mutation role.

[0041] Figure 14 depicts an OncoMerge flow-chart that describes how the putative protein affecting mutation (PAM), transcript fusions (Fusion), and putative copy number alteration (CNA) data are integrated and filtered to generate an integrated mutation matrix.

[0042] Figure 15, comprising Figure 15A through Figure 15G, depicts exemplary data demonstrating the performance of OncoMerge filters and sensitivity analyses. Figure 15A depicts data demonstrating the impact of filter sets on the number of somatically mutated genes inferred by OncoMerge in at least one cancer. Figure 15B depicts dataAttorney Docket No.:206339-0109-00WO demonstrating the impact of filter sets on the distribution of genes per CNA locus using the same set of filtering conditions (y axis is distributed on a log scale). The dashed line indicates the 10 genes per loci cutoff that invoke the MFF filter. Figure 15C depicts data demonstrating the enrichment of the gold standard (GS) activating (Act) or loss of function (LoF) somatic mutations with OncoMerge (OM) Act or LoF somatic mutations for each filtering condition: no filters (None); PQ filter; MFF; combined PQ and MFF; and combined PQ, MFF, and MHC. Significant enrichments from Figure 15C are highlighted in red and had p value less than or equal to the Bonferroni corrected a level of 4.8×10-4(α = 0.05, number of tests = 105, Bonferroni corrected α = α / number of tests = 0.05 / 105 = 4.8×10-4). The orange arrowheads indicate OM Act vs. GS Act, and the green arrowheads indicate OM LoF vs. GS LoF. Figure 15D depicts data comparing GISTIC thresholds: cutoff of one equates to shallow amplification and deletions, and cutoff of two equates to deep amplifications and deletions. Figure 15E depicts data comparing the possible values for the MFF filter parameter maximum number of genes in the loci (Max. loci genes) with 1, 5, 10, 15, and 20 genes. Figure 15F depicts data comparing the possible cutoff values of the minimum mutation frequency (Min. mut. freq.) with 1%, 5%, 10%, and 20%. Figure 15G depicts data comparing the possible cutoff values of the PQ filter permuted q value (Perm. q value) with 0.01, 0.05, 0.1, and 0.2. Significant enrichments from (Figure 15D) – (Figure 15G) are highlighted in red and had p value less than or equal to the Bonferroni corrected a level of 2.4×10-3(α = 0.05, number of tests per parameters = 21, Bonferroni corrected α = α / number of tests per parameter = 0.05 / 21 = 2.4×10-3). The purple arrowheads indicate the final parameterization chosen for OncoMerge.

[0043] Figure 16 depicts data demonstrating the average contribution of somatic mutation type to the final mutation frequency.

[0044] Figure 17, comprising Figure 17A through Figure 17E, depicts data demonstrating a summary of the effect on number and frequency of somatic mutations after integrating mutation types. Figure 17A depicts data demonstrating the frequency of hypermutation and MSI across cancers. Figure 17B depicts data demonstrating the number and distribution of mutation types. Figure 17C depicts data demonstrating the number of somatically mutated genes added because of integration. Figure 17D depicts data demonstrating the integrated somatic mutation frequencies. Figure 17E depicts dataAttorney Docket No.:206339-0109-00WO demonstrating the increases in somatic mutation frequency relative to PAM frequency after integration.

[0045] Figure 18, comprising Figure 18A through Figure 18C, depicts data demonstrating pan-cancer somatic mutations with a consistent functional impact across at least five cancers. Figure 18A depicts pan-cancer somatic mutations from the loss of functions group. Figure 18B depicts pan-cancer somatic mutations from the activating group. Figure 18C depicts prior knowledge of tumor suppressor or oncogene status for each somatically mutated gene (black square indicates known tumor suppressor or oncogene activity).

[0046] Figure 19, comprising Figure 19A through Figure 19E, depicts data demonstrating improvements in downstream SYGNAL analysis by comparing gene regulatory networks (GRNs) constructed with an OncoMerge integrated somatic mutation matrix vs. a legacy network using only PAMs. Figure 19A depicts data demonstrating the average degree of nodes in the PanCaner SYGNAL networks. Figure 19B depicts data demonstrating mutations per cancer network. Figure 19C depicts data demonstrating mutations that overlap with genes previously associated with a specific cancer in DisGeNET. Figure 19D depicts data demonstrating TFs per cancer network. Figure 19E depicts data demonstrating TFs that overlap with genes previously associated with a specific cancer in DisGeNET.

[0047] Figure 20, comprising Figure 20A through Figure 20C, depicts the architecture of functional disease-specific TF regulatory networks from human tumors. Figure 20A depicts a schematic of an active TF regulatory network construction pipeline: (1) TFs from all cancer regulatory networks were identified, (2) a putative map of TF regulatory network interactions was constructed, (3) TF → TF relationships were filtered using Pearson’s correlations computed from patient tumor data, and (4) compute the triad significance profiles using mfinder. Figure 20B depicts a comparison of active TF regulatory network based on SYGNAL GRNs (red) to the static TF regulatory network based on ENCODE DNA binding and accessibility (blue, Neph et al.32). Figure 20C depicts FANMOD enrichment normalized Z scores for the three most enriched motifs from the active TF regulatory network after incorporating TF regulatory interaction roles (activation or repression). The first row, titled Coherent motifs, is shaded when the motif configuration isAttorney Docket No.:206339-0109-00WO coherent and white when it is incoherent. Normalized Z scores are reported for each cancer, and diagonal dashed lines are inserted when no Z score was returned. The network motif can be found at the bottom of each column, colored with regulatory roles. C1, C2, C3, C4 = coherent FFLs. I1, I2, I3, I4 = incoherent FFLs. DETAILED DESCRIPTION

[0048] The present disclosure relates, in part, to compositions and methods to treat diffuse pleural mesothelioma. The present disclosure is based, in part, on a method to identify FDA-approved therapeutics for the treatment of additional diseases and disorders. Before any embodiments of the disclosure are explained in detail, it is to be understood that the disclosure is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the following drawings. The disclosure is capable of other embodiments and of being practiced or of being carried out in various ways

[0049] In some embodiments, the disclosure provides an analysis pipeline to identify FDA-approved therapeutic agents, or combinations thereof, that have potential for treating one or more additional diseases or disorders.

[0050] In some embodiments, the disclosure is based, in part, on the discovery that FDA-approved therapeutic agents used in the treatment of other diseases and disorders can be repurposed for the treatment of mesothelioma. In some embodiments, the mesothelioma is diffuse pleural mesothelioma. Therefore, in some embodiments, the disclosure relates to the use of one or more of dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat for the treatment of mesothelioma. In some embodiments, the disclosure relates to the use of one or more of dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat for the treatment of diffuse pleural mesothelioma.

[0051] The present disclosure is also based, in part, on the discovery that some combinations of FDA-approved therapeutic agents have synergistic activity when administered in combination. Accordingly, in some embodiments, the disclosure provides combinations of two or more FDA-approved therapeutics and methods of use thereof for treating diseases and disorders. In some embodiments, the combination comprises two orAttorney Docket No.:206339-0109-00WO more of dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat. Definitions

[0052] Unless defined otherwise, 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 disclosure belongs.

[0053] As used herein, each of the following terms has the meaning associated with it in this section.

[0054] The articles “a” and “an” are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.

[0055] “About” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, or ±0.1% from the specified value, as such variations are appropriate to perform the disclosed methods.

[0056] The term “abnormal” when used in the context of organisms, tissues, cells or components thereof, refers to those organisms, tissues, cells or components thereof that differ in at least one observable or detectable characteristic (e.g., age, treatment, time of day, etc.) from those organisms, tissues, cells or components thereof that display the “normal” (expected) respective characteristic. Characteristics which are normal or expected for one cell or tissue type, might be abnormal for a different cell or tissue type.

[0057] The term “anti-tumor effect” as used herein, refers to a biological effect which can be manifested by a decrease in tumor volume, a decrease in the number of tumor cells, a decrease in the number of metastases, an increase in life expectancy, or amelioration of various physiological symptoms associated with the cancerous condition. An “anti-tumor effect” can also be manifested by the ability of the peptides, polynucleotides, cells and antibodies of the disclosure in prevention of the occurrence of tumor in the first place.

[0058] The term “analog” as used herein generally refers to compounds that are generally structurally similar to the compound of which they are an analog, or “parent” compound. Generally, analogs will retain some characteristics of the parent compound, e.g., aAttorney Docket No.:206339-0109-00WO biological or pharmacological activity. An analog may lack other, less desirable characteristics, e.g., antigenicity, proteolytic instability, toxicity, and the like. An analog includes compounds in which a particular biological activity of the parent is reduced, while at least one distinct biological activities of the parent are unaffected in the “analog.”

[0059] “Cancer,” as used herein, refers to the abnormal growth or division of cells. Generally, the growth and / or life span of a cancer cell exceeds, and is not coordinated with, that of the normal cells and tissues around it. Cancers may be benign, pre-malignant or malignant. Cancer occurs in a variety of cells and tissues, including the oral cavity (e.g., mouth, tongue, pharynx, etc.), digestive system (e.g., esophagus, stomach, small intestine, colon, rectum, liver, bile duct, gall bladder, pancreas, etc.), respiratory system (e.g., larynx, lung, bronchus, etc.), bones, joints, skin (e.g., basal cell, squamous cell, meningioma, etc.), breast, genital system, (e.g., uterus, ovary, prostate, testis, etc.), urinary system (e.g., bladder, kidney, ureter, etc.), eye, nervous system (e.g., brain, etc.), endocrine system (e.g., thyroid, etc.), and hematopoietic system (e.g., lymphoma, myeloma, leukemia, acute lymphocytic leukemia, chronic lymphocytic leukemia, acute myeloid leukemia, chronic myeloid leukemia, etc.). Cancer cells can spread locally or through the bloodstream and lymphatic system to other parts of the body. Examples of various cancers include but are not limited to: diffuse pleural mesothelioma, breast cancer, prostate cancer, ovarian cancer, cervical cancer, skin cancer, pancreatic cancer, colorectal cancer, renal cancer, liver cancer, brain cancer, lymphoma, leukemia, lung cancer, sarcoma and the like. Diffuse pleural mesothelioma (DPM) and malignant pleural mesothelioma (MPM) may be used interchangeably.

[0060] The term “clinical factors” as used herein, refers to any data that a medical practitioner may consider in determining a diagnosis or prognosis of disease. Such factors include, but are not limited to, the patient's medical history, a physical examination of the patient, complete blood count, analysis of the activity of enzymes, examination of cells, cytogenetics, and immunophenotyping of blood cells. As used herein, an “immunoassay” refers to any binding assay that uses an antibody capable of binding specifically to a target molecule to detect and quantify the target molecule.

[0061] The term “compound,” as used herein, unless otherwise indicated, refers to any specific chemical compound disclosed herein. In one embodiment, the term also refers toAttorney Docket No.:206339-0109-00WO stereoisomers and / or optical isomers (including racemic mixtures) or enantiomerically enriched mixtures of disclosed compounds.

[0062] A “disease” is a state of health of an animal wherein the animal cannot maintain homeostasis, and wherein if the disease is not ameliorated then the animal’s health continues to deteriorate.

[0063] In contrast, a “disorder” in an animal is a state of health in which the animal is able to maintain homeostasis, but in which the animal’s state of health is less favorable than it would be in the absence of the disorder. Left untreated, a disorder does not necessarily cause a further decrease in the animal’s state of health.

[0064] A disease or disorder is “alleviated” if the severity of a sign or symptom of the disease or disorder, the frequency with which such a sign or symptom is experienced by a patient, or both, is reduced.

[0065] As used herein, the terms “therapy” or “therapeutic regimen” refer to those activities taken to prevent, treat or alter a disease or disorder, e.g., a course of treatment intended to reduce or eliminate at least one sign or symptom of a disease or disorder using pharmacological, surgical, dietary and / or other techniques. A therapeutic regimen may include a prescribed dosage of one or more compounds or surgery. Therapies will most often be beneficial and reduce or eliminate at least one sign or symptom of the disorder or disease state, but in some instances the effect of a therapy will have non-desirable or side-effects. The effect of therapy will also be impacted by the physiological state of the subject, e.g., age, gender, genetics, weight, other disease conditions, etc.

[0066] An “effective amount” as used herein, means an amount which provides a therapeutic, prophylactic, or other desired benefit.

[0067] The term “inhibit,” as used herein, means to suppress or block an activity or function by at least about ten percent relative to a control value. In one embodiment, the activity is suppressed or blocked by 50% compared to a control value. In one embodiment, the activity is suppressed or blocked by 75%. In one embodiment, the activity is suppressed or blocked by 95%.

[0068] As used herein, the term “pharmaceutically acceptable” refers to a material, such as a carrier or diluent, which does not abrogate the biological activity or properties of the compound, and is relatively non-toxic, i.e., the material may be administered to anAttorney Docket No.:206339-0109-00WO individual without causing an undesirable biological effect or interacting in a deleterious manner with any of the components of the composition in which it is contained.

[0069] As used herein, the term “pharmaceutical composition” refers to a mixture of at least one compound useful within the disclosure with a pharmaceutically acceptable carrier. The pharmaceutical composition facilitates administration of the compound to a patient or subject. Multiple techniques of administering a compound exist in the art including, but not limited to, intravenous, oral, aerosol, parenteral, ophthalmic, pulmonary, and topical administration.

[0070] As used herein, the term “pharmaceutically acceptable carrier” means a pharmaceutically acceptable material, composition, or carrier, such as a liquid or solid filler, stabilizer, dispersing agent, suspending agent, diluent, excipient, thickening agent, solvent or encapsulating material, involved in carrying or transporting a compound useful within the disclosure within or to the patient such that it may perform its intended function. Typically, such constructs are carried or transported from one organ, or portion of the body, to another organ, or portion of the body. Each carrier must be “acceptable” in the sense of being compatible with the other ingredients of the formulation, including the compound useful within the disclosure, and not injurious to the patient. Some examples of materials that may serve as pharmaceutically acceptable carriers include: sugars, such as lactose, glucose and sucrose; starches, such as corn starch and potato starch; cellulose, and its derivatives, such as sodium carboxymethyl cellulose, ethyl cellulose and cellulose acetate; powdered tragacanth; malt; gelatin; talc; excipients, such as cocoa butter and suppository waxes; oils, such as peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, corn oil and soybean oil; glycols, such as propylene glycol; polyols, such as glycerin, sorbitol, mannitol and polyethylene glycol; esters, such as ethyl oleate and ethyl laurate; agar; buffering agents, such as magnesium hydroxide and aluminum hydroxide; surface active agents; alginic acid; pyrogen-free water; isotonic saline; Ringer’s solution; ethyl alcohol; phosphate buffer solutions; and other non-toxic compatible substances employed in pharmaceutical formulations. As used herein, “pharmaceutically acceptable carrier” also includes any and all coatings, antibacterial and antifungal agents, and absorption delaying agents, and the like that are compatible with the activity of the compound useful within the disclosure and are physiologically acceptable to the patient. Supplementary active compounds may also beAttorney Docket No.:206339-0109-00WO incorporated into the compositions. The “pharmaceutically acceptable carrier” may further include a pharmaceutically acceptable salt of the compound useful within the disclosure. Other additional ingredients that may be included in the pharmaceutical compositions used in the practice of the disclosure are known in the art and described, for example in Remington's Pharmaceutical Sciences (Genaro, Ed., Mack Publishing Co., 1985, Easton, PA), which is incorporated herein by reference.

[0071] As used herein, the term “potency” refers to the dose needed to produce half the maximal response (ED50).

[0072] As used herein, the term “efficacy” refers to the maximal effect (Emax) achieved within an assay.

[0073] “Sample” or “biological sample” as used herein means a biological material from a subject, including but is not limited to organ, tissue, exosome, blood, plasma, saliva, urine and other body fluid. A sample can be any source of material obtained from a subject.

[0074] The terms “subject,” “patient,” “individual,” and the like are used interchangeably herein, and refer to any animal, or cells thereof whether in vitro or in situ, amenable to the methods described herein. In certain non-limiting embodiments, the patient, subject or individual is a human.

[0075] The term “therapeutically effective amount” refers to the amount of the subject compound or composition that will elicit the biological, physiologic, clinical or medical response of a cell, tissue, organ, system, or subject that is being sought by the researcher, veterinarian, medical doctor or other clinician. The term “therapeutically effective amount” includes that amount of a compound or composition that, when administered, is sufficient to prevent development of, or treat to some extent, one or more of the signs or symptoms of the disorder or disease being treated. The therapeutically effective amount will vary depending on the compound or composition, the disease and its severity and the age, weight, etc., of the subject to be treated.

[0076] In the context of administration of two or more agents, “synergistically effective amounts” are amounts of the agents that either (i) produce greater than additive therapeutic effects, compared to monotherapy with the agents; or (ii) produce at least comparable therapeutic effects and reduce toxic side effects, due to lower effective dosing or less frequent dosing, compared to monotherapy with the agents. An indication of suchAttorney Docket No.:206339-0109-00WO synergy can be provided in in vitro studies, e.g., with GBM cell lines, in studies to evaluate the killing of tumor cell lines in vitro or inhibition of cell growth. Synergy can be demonstrated in clinical trials in which the effects of monotherapy and combination therapy are compared and statistically analyzed.

[0077] To “treat” a disease or disorder as the term is used herein, means to reduce the frequency or severity of at least one sign or symptom of a disease or disorder experienced by a subject. The terms "treatment", "treating", "treat" and the like are used herein to generally refer to obtaining a desired pharmacologic and / or physiologic effect. The effect can be prophylactic in terms of completely or partially preventing a disease or symptom(s) thereof and / or may be therapeutic in terms of a partial or complete stabilization or cure for a disease and / or adverse effect attributable to the disease. The term “treatment" encompasses any treatment of a disease in a mammal, particularly a human, and includes: (a) preventing the disease and / or symptom(s) from occurring in a subject who may be predisposed to the disease or symptom but has not yet been diagnosed as having it; (b) inhibiting the disease and / or symptom(s), e.g., slowing or arresting their development (e.g., halting the growth of tumors, slowing the rate of tumor growth, halting the rate of cancer cell proliferation, and the like); or (c) relieving the disease symptom(s), i.e., causing regression of the disease and / or symptom(s) (e.g., causing decrease in tumor size, reducing the number of cancer cells present, and the like). Those in need of treatment include those already inflicted (e.g., those with cancer, those with an infection, those with a metabolic disorder, those with macular degeneration, etc.) as well as those in which prevention is desired (e.g., those with increased susceptibility to cancer, those with an increased likelihood of infection, those suspected of having cancer, those suspected of harboring an infection, those with increased susceptibility for metabolic disease, those with increased susceptibility for macular degeneration, etc.).

[0078] “Sample” or “biological sample” as used herein means a biological material isolated from a subject. The biological sample may contain any biological material suitable for detecting a mRNA, polypeptide or other marker of a physiologic or pathologic process in a subject, and may comprise fluidRanges: throughout this disclosure, various embodiments of the disclosure can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, theAttorney Docket No.:206339-0109-00WO description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.

[0079] “Omics” refers to fields of study or collections of data that characterize multiple biological molecules, sometimes relative to each other, in the context of cells or organisms. Examples include genomics (evaluation / data pertaining to the genome of a cell or organism); proteomics (proteins); metabolomics (metabolites); transcriptomics (RNA molecules); and other specialized classes of information, such as information about subsets or subtypes of any of the foregoing). Omics data may contain information about hundreds or thousands of species within a class of molecule. “Multi-omics” data refers to more than one type of omics data. Description

[0080] The present disclosure relates to systems and methods to identify and repurpose FDA-approved drugs to treat diseases and disorders, such as cancer. The present disclosure is based, in part, on the unexpected discovery that FDA-approved therapeutic agents used in the treatment of specific diseases and disorders can be repurposed to treat additional diseases and disorder. For example, the data provide herein demonstrate re- purposing of FDA approved therapeutics for the treatment of diffuse pleural mesothelioma. Therefore, in some embodiments, the present disclosure relates to methods of use of an FDA- approved drug for an unapproved use (sometimes called an “off-label” use) to treat a disease or medical condition. In some embodiments, the present disclosure relates to compositions and methods to treat cancer. In some embodiments, the present disclosure relates to compositions and methods to treat diffuse pleural mesothelioma.

[0081] The present disclosure is also based, in part, on the discovery that FDA- approved therapeutic agents can be used in combination, and doing so have an additive or synergistic effect in the treatment of a disease or disorder. Accordingly, in someAttorney Docket No.:206339-0109-00WO embodiments, the disclosure provides synergistic combinations of FDA-approved therapeutics for FDA-approved use or for off-label use. Methods for Identifying Drug Candidates for Repurposing

[0082] Exemplary systems and method for identifying and repurposing FDA- approved drugs for the treatment of cancer is described in Figure 12. It should be understood that while the methods depicted in those figures will be described with reference to application of the method to a specific set of data (e.g., data related to diffuse pleural mesothelioma), to identifying and repurposing FDA-approved drugs for diffuse pleural mesothelioma, the method is applicable in other contexts and may be applied to mapping disease-relevant gene regulatory interactions for other diseases and disorders, including other cancers. It should also be understood that it is not, strictly speaking, necessary to perform each step described and / or depicted in the methods below as, in some circumstances, execution of a portion of the methods described herein may yield data that are useful in exploring potential treatments for the condition(s) in question.

[0083] Figure 12 depicts an exemplary flow chart showing an exemplary method 200 of identifying candidate drugs 300 that are candidates for off-label use or repurposing. Generally speaking, the candidates identified from a screen of candidate genes, transcripts, epigenetic markers or proteins associated with a specific disease or disorder, dataset A 205, and the candidates identified from a multiomic biclustering analysis of candidate genes, transcripts, epigenetic markers or proteins associated with a specific disease or disorder, dataset B 239, are compared to identify a dataset AB 261 comprising the subset of genes and / or proteins that overlap in dataset A 205 and dataset B 239. FDA-approved, gene- targeting drugs from dataset C 245 are then assessed to determine if one or more FDA- approved therapeutic has been identified as a modulator of one or more gene and / or protein from dataset AB. The listing of candidate drugs 300 reflects the output of a subset of FDA- approved therapeutics that are candidates for repurposing for the treatment of the disease or disorder that was associated with the data analyzed to generate dataset AB 261.

[0084] In some embodiments, the candidate genes, transcripts, epigenetic markers or proteins from dataset A 205 are identified by a functional genomics screen 201 as being essential genes for a specific disease or disease state. Methods to analyze screens to identifyAttorney Docket No.:206339-0109-00WO candidate genes, transcripts, epigenetic markers or proteins of interest are well known in the art, including but not limited to a genomic screen, a CRISPR screen, a proteomic screen, a metabolomic screen, a transcriptomic screen, or a drug screen. In some embodiments, the screen is a functional genomic screen. In some embodiments, the functional genomic screen is one or more selected from the group consisting of an siRNA screen, an shRNA screen, a microRNA screen, a Zinc finger nuclease screen, a TALEN screen, a CRISPR-Cas9 screen, a CRISPRi screen, a CRISPRa screen, a dual CRISPRi / a screen, and a CRISPRsc screen.

[0085] In some embodiments, the screen comprises contacting a cell line with a library of molecules (e.g., a CRISPR-Cas9 and a library of sgRNA) to generating knockout cells for each gene, RNA or protein and evaluating viability of the knockout cells to identify target genes, RNA or protein that are essential for viability of the cell line. In some embodiments, the cell line is an established disease model cell line. In some embodiments, the cell line comprises at least one mutation known to be a driver of a specific disease or disorder. In some embodiments, the cell line is a patient-derived disease cell line.

[0086] In some embodiments, the screen further comprises contacting a second cell line with a CRISPR-Cas9 protein and a library of sgRNA to generating knockout cells for each known protein and evaluating viability of the knockout cells to identify target genes that are essential for viability of the second cell line. In some embodiments, the genes that result in loss of viability to both the first (disease-specific) cell line and the second cell line cell line are likely common essential genes and are removed as disease-associated gene candidates. In some embodiments, the second cell line is a second established disease model cell line. In some embodiments, the second cell line comprises at least one mutation known to be a driver of a second specific disease or disorder. In some embodiments, the cell line is a patient derived disease cell line. In some embodiments, the second cell line is a normal (non-disease associated) cell line.

[0087] In some embodiments, the screen 201 generates one or more dataset (dataset A1, dataset A2, … dataset An in Figure 12) comprising candidate genes, transcripts, epigenetic markers or proteins. The screen may be performed on multiple cell lines or multiple screens may be performed on one cell line or a combination thereof. One or more of dataset A1, dataset A2, … dataset Anmay be compared to generate dataset A 205 comprising a subset ofAttorney Docket No.:206339-0109-00WO candidate genes, transcripts, epigenetic markers or proteins identified in one or more dataset from the screen.

[0088] In some embodiments, the set of candidate genes, transcripts, epigenetic markers or proteins which are identified as dataset B 239 are derived from multi-omic data. In some embodiments, the multi-omics data 221 are obtained from a database comprising data compiled from analyses of multiple disease samples. For example, in some embodiments, a set of candidate genes, transcripts, epigenetic markers or proteins from dataset B 239 are derived from a multi-omics biclustering analysis performed on a disease- specific subset of data. Any available database comprising appropriate disease data, may be used for the multi-omics biclustering analysis to generate dataset B 239. For example, in some embodiments the multi-omics data set comprises, but is not limited to, gene expression data, somatic mutation data, miRNA expression data, transcriptomic data, chromatin accessibility data, protein expression, metabolomic data, clinical survival data or phenotypic data or any combination thereof. Additionally, any given set of multi-omics data may be updated periodically to include a different (usually larger) set of patients and / or controls.

[0089] In some embodiments, two or more multi-omics data sets may be combined into a single matrix prior to biclustering analysis. For example, in some embodiments, somatic mutation data and copy number variation data are combined prior to bicluster analysis. In some embodiments, the multi-omics data may comprise indicators of somatic mutations causing or associated with a specific disease or disorder. Somatic mutations include, but are not limited to, a protein-affecting mutation, a gene fusion, a copy number alteration, a copy number alteration amplification, a copy number alteration deletion, a gain of function mutation, or a loss of function mutation.

[0090] Methods for merging multi-omics data into a single matrix include, but are not limited to, the use of the OncoMerge platform as described in Example 2 and outlined in Figure 13. In some embodiments, the method integrates two or more mutation matrices comprising data on two or more different mutation types into a single integrated mutation matrix with the most suitable mutation type for each gene. For example, in some embodiments, mutation matrices for two or more of protein affecting mutations (PAM), transcript fusions, and copy number alteration (CNA) are combined into an integrated mutation matrix.Attorney Docket No.:206339-0109-00WO

[0091] In some embodiments, mutational frequency and statistical filters are used to generate multiple matrices from an integrated mutation matrix. In some embodiments, mutational frequency and statistical filters are used to generate matrices for each of: PAM, Transcript Fusion, CNAamp, CNAdel, Act, and LoF from the integrated mutation matrix.

[0092] In some embodiments, the two or more mutation matrices are merged into a single comprehensive mutation matrix, prior to biclustering analysis. For example, in some embodiments matrices for two or more of: PAM, Transcript Fusion, CNAamp, CNAdel, Act, and LoF are combined into a single comprehensive mutation matrix. In some embodiments, the comprehensive mutation matrix is binarized for the presence or absence of any mutation type prior to biclustering analysis.

[0093] In some embodiments, the multi-omics data 221 are standardized, normalized, batch-corrected, platform-corrected, or any combination thereof. In some embodiments the normalized data is used to generate a corrected matrix 223. Systems and methods to standardize, normalize, and batch correct multi-omics data are well known in the art. In some embodiments, the multi-omics data may be normalized by variance stabilized transformation in DESeq2.

[0094] In some embodiments, the multi-omics data 221 or the corrected matrix 223 is then binarized to generate one or more binary mutation matrices 225 such as binary mutation matrix Z1, binary mutation matrix Z2, … binary mutation matrix Zn. In some embodiments, each binary mutation matrix indicates the presence or absence of a mutation. The mutation may be a somatic mutation. The somatic mutations may be any one or more of but not limited to a protein-affecting mutation, a gene fusion, a copy number alteration, a copy number alteration amplification, a copy number alteration deletion, a gain of function mutation, or a loss of function mutation. The step of binarizing the multi-omics data 221 or the corrected matrix 223 can be performed by any one or more technique known in the art with or without slight modifications. By way of example, the frequency, statistical significance, and mutational frequency of somatic mutations may be calculated for the multi- omics data 221 or the corrected matrix 223. Exemplary algorithms that may be applied to the multi-omics data 221 or the corrected matrix 223 include, but are not limited to, one or more of MutSig2CV, PRADA, or GISTIC2.0. By way of example, systems and methods may be applied to the binary mutation matrices 225 wherein the binary mutation matrix Z1, binaryAttorney Docket No.:206339-0109-00WO mutation matrix Z2, … binary mutation matrix Zn are run through a decision tree algorithm to classify each mutation. Exemplary classifications include, but are not limited to, a protein- affecting mutation, a gene fusion, a copy number alteration, a copy number alteration amplification, a copy number alteration deletion, a gain of function mutation, or a loss of function mutation.

[0095] In some embodiments, the binary mutation matrices 225 are combined to generate a comprehensive mutation matrix 227. The comprehensive mutation matrix 227 may indicate the presence or absence of a mutation. The step of generating a comprehensive mutation matrix 227 can be performed by a technique known in the art with or without slight modifications.

[0096] In some embodiments, the multi-omics data, or a mutation matrix (e.g., a comprehensive mutation matrix 227) is run through a biclustering algorithm 230 to generate a set of biclusters representing the subset of genes that are significantly co-expressed across the multi-omics data set. In some embodiments, the genes from a functional genomics screen are included in the data set or mutation matrix before biclustering analysis is performed. In some embodiments, the SYstems Genetics Network AnaLysis (SYGNAL) pipeline is used to generate biclusters from the multi-omics data as described in U.S. Patent No. US11657895B2, which is incorporated by reference herein in its entirety.

[0097] In some embodiments, the biclusters are post-processed to determine a set of candidate genes, transcripts, epigenetic markers or proteins in dataset B 239. Techniques to post-process biclusters are well known in the art. Exemplary post-processing techniques that can be applied include, but are not limited to: determining co-expression quality via variance explained by first principal component, functional enrichment with gene ontology (GO) biological process terms, association with the hallmarks of cancer, and association of bicluster eigengene with overall patient survival. In some embodiments, the subset of candidate genes, transcripts, epigenetic markers or proteins that are significantly co- expressed and disease relevant (i.e., associated with a hallmark of cancer or patient survival) are then compiled as dataset B 239.

[0098] In some embodiments, the candidate genes, transcripts, epigenetic markers or proteins from dataset A 205 and dataset B 239 are then compared to generate a dataset ABAttorney Docket No.:206339-0109-00WO 261 comprising the subset of the candidate genes, transcripts, epigenetic markers or proteins that overlap in dataset A 205 and dataset B 239.

[0099] In some embodiments, the FDA-approved, gene-targeting drugs from Dataset C 245 are derived from a drug repository DrugBank 241. In some embodiments, the drugs from the drug repository DrugBank 241 are run through one or more drug filtering and / or processing algorithms. Drug filtering and / or processing algorithms are well known in the art, and include, but are not limited to, a drug repurposing algorithm 243. Drugs from the drug repository DrugBank 241 having a known target may be compiled as Dataset C 245. In some embodiments, a drug is included in Dataset C only if it is an inhibitory therapeutic. In some embodiments, a drug is included in Dataset C only if it is FDA-approved.

[0100] In some embodiments, list of gene or protein targets of the FDA-approved drugs from Dataset C 245 are then comparted to the disease-associated candidate genes, transcripts, epigenetic markers or proteins from Dataset AB to generate a list of candidate drugs 300 or therapeutics for repurposing for off-label use for treatment of a disease or disorder. Computer Systems and Methods

[0101] In some embodiments of the present disclosure, software or code for executing any number of the bioinformatic analysis required for execution of the methods of the disclosure may be stored on a non-transitory computer-readable medium, wherein the software performs some or all of the steps of the present disclosure when executed on a processor.

[0102] Embodiments of the disclosure relate to algorithms executed in computer software. Though certain embodiments may be described as written in particular programming languages, or executed on particular operating systems or computing platforms, it is understood that the system and method of the present disclosure is not limited to any particular computing language, platform, or combination thereof. Software executing the algorithms described herein may be written in any programming language known in the art, compiled or interpreted, including but not limited to R, C, C++, C#, Objective-C, Java, JavaScript, MATLAB, Python, PHP, Perl, Ruby, or Visual Basic. It is further understood that elements of the present disclosure may be executed on any acceptable computing platform,Attorney Docket No.:206339-0109-00WO including but not limited to a server, a cloud instance, a workstation, a thin client, a mobile device, an embedded microcontroller, a television, or any other suitable computing device known in the art.

[0103] Parts of this disclosure are described as software running on a computing device. Though software described herein may be disclosed as operating on one particular computing device (e.g. a dedicated server or a workstation), it is understood in the art that software is intrinsically portable and that most software running on a dedicated server may also be run, for the purposes of the present disclosure, on any of a wide range of devices including desktop or mobile devices, laptops, tablets, smartphones, watches, wearable electronics or other wireless digital / cellular phones, televisions, cloud instances, embedded microcontrollers, thin client devices, or any other suitable computing device known in the art.

[0104] Similarly, parts of this disclosure are described as communicating over a variety of wireless or wired computer networks. For the purposes of this disclosure, the words “network”, “networked”, and “networking” are understood to encompass wired Ethernet, fiber optic connections, wireless connections including any of the various 802.11 standards, cellular WAN infrastructures such as 3G, 4G / LTE, or 5G networks, Bluetooth®, Bluetooth® Low Energy (BLE) or Zigbee® communication links, or any other method by which one electronic device is capable of communicating with another. In some embodiments, elements of the networked portion of the disclosure may be implemented over a Virtual Private Network (VPN).

[0105] Figure 11 and the following discussion are intended to provide a brief, general description of a suitable computing environment in which the disclosure may be implemented. While the disclosure is described above in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a computer, those skilled in the art will recognize that the disclosure may also be implemented in combination with other program modules.

[0106] Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the disclosure may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics,Attorney Docket No.:206339-0109-00WO minicomputers, mainframe computers, and the like. The disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0107] Figure 11 depicts an illustrative computer architecture for a computer 100 for practicing the various embodiments of the disclosure. The computer architecture shown in Figure 11 illustrates a conventional personal computer, including a central processing unit 150 (“CPU”), a system memory 105, including a random access memory 110 (“RAM”) and a read-only memory (“ROM”) 115, and a system bus 135 that couples the system memory 105 to the CPU 150. A basic input / output system containing the basic routines that help to transfer information between elements within the computer, such as during startup, is stored in the ROM 115. The computer 100 further includes a storage device 120 for storing an operating system 125, application / program 130, and data.

[0108] The storage device 120 is connected to the CPU 150 through a storage controller (not shown) connected to the bus 135. The storage device 120 and its associated computer-readable media provide non-volatile storage for the computer 100. Although the description of computer-readable media contained herein refers to a storage device, such as a hard disk or CD-ROM drive, it should be appreciated by those skilled in the art that computer-readable media can be any available media that can be accessed by the computer 100.

[0109] By way of example, and not to be limiting, computer-readable media may comprise computer storage media. Computer storage media includes volatile and non- volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.

[0110] According to various embodiments of the disclosure, the computer 100 may operate in a networked environment using logical connections to remote computers through aAttorney Docket No.:206339-0109-00WO network 140, such as TCP / IP network such as the Internet or an intranet. The computer 100 may connect to the network 140 through a network interface unit 145 connected to the bus 135. It should be appreciated that the network interface unit 145 may also be utilized to connect to other types of networks and remote computer systems.

[0111] The computer 100 may also include an input / output controller 155 for receiving and processing input from a number of input / output devices 160, including a keyboard, a mouse, a touchscreen, a camera, a microphone, a controller, a joystick, or other type of input device. Similarly, the input / output controller 155 may provide output to a display screen, a printer, a speaker, or other type of output device. The computer 100 can connect to the input / output device 160 via a wired connection including, but not limited to, fiber optic, Ethernet, or copper wire or wireless means including, but not limited to, Wi-Fi, Bluetooth, Near-Field Communication (NFC), infrared, or other suitable wired or wireless connections.

[0112] As mentioned briefly above, a number of program modules and data files may be stored in the storage device 120 and / or RAM 110 of the computer 100, including an operating system 125 suitable for controlling the operation of a networked computer. The storage device 120 and RAM 110 may also store one or more applications / programs 130. In particular, the storage device 120 and RAM 110 may store an application / program 130 for providing a variety of functionalities to a user. For instance, the application / program 130 may comprise many types of programs such as a word processing application, a spreadsheet application, a desktop publishing application, a database application, a gaming application, internet browsing application, electronic mail application, messaging application, and the like. According to an embodiment of the present disclosure, the application / program 130 comprises a multiple functionality software application for providing word processing functionality, slide presentation functionality, spreadsheet functionality, database functionality and the like.

[0113] The computer 100 in some embodiments can include a variety of sensors 165 for monitoring the environment surrounding and the environment internal to the computer 100. These sensors 165 can include a Global Positioning System (GPS) sensor, a photosensitive sensor, a gyroscope, a magnetometer, thermometer, a proximity sensor, anAttorney Docket No.:206339-0109-00WO accelerometer, a microphone, biometric sensor, barometer, humidity sensor, radiation sensor, or any other suitable sensor. Off-Label Use of FDA Therapeutics

[0114] In some embodiments, the present disclosure provides pharmaceutical compositions comprising at least one FDA-approved therapeutic agent as identified in a drug repurposing analysis as described above, for off-label use for the treatment of a disease or disorder. In some embodiments, the disease or disorder is a cancer.

[0115] Although certain examples and embodiments disclosed herein may be related to one or more different types of cancer, for example diffuse pleural mesothelioma, it is understood that the methods and systems disclosed herein may be applied to any cancer, including but not limited to breast cancer, lung cancer, head and neck cancer, bladder cancer, stomach cancer, cancer of the nervous system, bone cancer, bone marrow cancer, brain cancer, colon cancer, esophageal cancer, endometrial cancer, gastrointestinal cancer, genital- urinary cancer, stomach cancer, lymphomas, melanoma, glioma, bladder cancer, pancreatic cancer, gum cancer, kidney cancer, retinal cancer, liver cancer, nasopharynx cancer, ovarian cancer, oral cancers, bladder cancer, hematological neoplasms, follicular lymphoma, cervical cancer, multiple myeloma, osteosarcomas, thyroid cancer, prostate cancer, colon cancer, prostate cancer, skin cancer, stomach cancer, testis cancer, tongue cancer, or uterine cancer. In some embodiments, the cancer is a pre-cancer.

[0116] In one embodiment, the disclosure provides for use of at least one FDA- approved therapeutic agent identified in a drug repurposing analysis as described above, for use for the treatment of diffuse pleural mesothelioma. In some embodiments, the FDA- approved therapeutic agent that is re-purposed for the treatment of diffuse pleural mesothelioma is dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, or tazemetostat, or a combination thereof.

[0117] In one embodiment, the disclosure provides for use of a combination of FDA- approved therapeutic agents for use for the treatment of diffuse pleural mesothelioma. In some embodiments, at least one of the FDA-approved therapeutic agents was identified in a drug re-purposing assay as described above as being a candidate for treatment of diffuse pleural mesothelioma. In some embodiments, the combination of therapeutic agents has aAttorney Docket No.:206339-0109-00WO synergistic effect for the treatment of diffuse pleural mesothelioma comprises one or more of dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, or tazemetostat.

[0118] In some embodiments, the synergistic combination of FDA-approved therapeutics for use for the treatment of diffuse pleural mesothelioma comprises the combination of gilteritinib and regorafenib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of osimertinib and tazemetostat. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of ribociclib and gilteritinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of ribociclib and osimertinib. In some embodiments, the synergistic combination of FDA- approved therapeutics comprises the combination of ribociclib and regorafenib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of ribociclib and sorafenib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sorafenib and gilteritinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sorafenib and tazemetostat. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sunitinib and gilteritinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sunitinib and osimertinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sunitinib and ribociclib. In some embodiments, the synergistic combination of FDA- approved therapeutics comprises the combination of sunitinib and tazemetostat. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of tazemetostat and gilteritinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of tazemetostat and orlistat. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of dasatinib and osimertinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of dasatinib and sunitinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of orlistat and sunitinib.Attorney Docket No.:206339-0109-00WO Pharmaceutical Compositions

[0119] In some embodiments, at least one FDA-approved therapeutic agent identified according to a drug repurposing screen as disclosed herein is administered to a subject having a disease or disorder for which the FDA-approved therapeutic agent was identified by the drug repurposing screen as being a candidate.

[0120] In some embodiments, at least one of dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat, or any combination thereof, is administered to a subject having or at risk of diffuse pleural mesothelioma to treat, prevent progression of or reduce the risk of recurrent of diffuse pleural mesothelioma. In some embodiments, at least one of dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat, or any combination thereof is administered alone, or in combination with other drugs and / or agents as pharmaceutical compositions.

[0121] In some embodiments, the present disclosure provides pharmaceutical compositions comprising at least one agent for use in the methods of the disclosure. The relative amounts of the agent(s), any pharmaceutically acceptable carrier, and any additional ingredients in a pharmaceutical composition of the disclosure will vary, depending upon the identity, size, and condition of the subject treated and further depending upon the route by which the composition is to be administered.

[0122] The formulations of the pharmaceutical compositions described herein may be prepared by any method known or hereafter developed in the art of pharmacology. In general, such preparatory methods include the step of bringing the active ingredient into association with a carrier or at least one other accessory ingredient. Said compositions may comprise additional medicinal agents, pharmaceutical agents, carriers, buffers, adjuvants, dispersing agents, diluents, and the like depending on the intended use and application.

[0123] Examples of suitable pharmaceutical carriers, excipients and / or diluents are well known in the art and include, but are not limited to, a gum, a starch (e.g. corn starch, pregelatinized starch), a sugar (e.g., lactose, mannitol, sucrose, dextrose), a cellulosic material (e.g. microcrystalline cellulose), an acrylate (e.g. polymethylacrylate), calcium carbonate, magnesium oxide, talc, or mixtures thereof.Attorney Docket No.:206339-0109-00WO

[0124] Pharmaceutically acceptable carriers for liquid formulations are aqueous or non-aqueous solutions, suspensions, emulsions or oils, Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, and injectable organic esters such as ethyl oleate. Examples of oils are those of animal, vegetable, or synthetic origin, for example, peanut oil, soybean oil, olive oil, sunflower oil, turmeric oil, fish-liver oil, another marine oil, or a lipid from milk or eggs.

[0125] Aqueous carriers include water, alcoholic / aqueous solutions, emulsions or suspensions, including saline and buffered media such as phosphate buffered saline solutions, water, emulsions, such as oil / water emulsions, various types of wetting agents, sterile solutions etc. Compositions comprising such carriers can be formulated by well-known conventional methods. Suitable carriers may comprise any material which, when combined with the biologically active compound of the disclosure, retains the biological activity. Preparations for parenteral administration may include sterile aqueous or non-aqueous solutions, suspensions, and emulsions. Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, vegetable oils such as olive oil, and injectable organic esters such as ethyl oleate. Aqueous carriers include water, alcoholic / aqueous solutions, emulsions or suspensions, including saline and buffered media. Parenteral vehicles may include sodium chloride solution, Ringer's dextrose, dextrose and sodium chloride, lactated Ringer's, or fixed oils. Intravenous vehicles may include fluid and nutrient replenishers, electrolyte replenishers (such as those based on Ringer's dextrose), and the like. Preservatives and other additives may also be present including, for example, antimicrobials, antioxidants, chelating agents, and inert gases and the like, in addition, the pharmaceutical composition of the present disclosure might comprise proteinaceous carriers, e.g., serum albumin or immunoglobulin, in some embodiments of human origin.

[0126] The composition may contain at least one added materials such as carriers and / or excipients. As used herein, “carriers” and “excipients” generally refer to substantially inert, non-toxic materials that do not deleteriously interact with other components of the composition. These materials may be used to increase the amount of solids in particulate pharmaceutical compositions, such as to form a powder of drug particles. Examples of suitable carriers include water, silicone, gelatin, waxes, and the like.Attorney Docket No.:206339-0109-00WO

[0127] Examples of normally employed “excipients,” include pharmaceutical grades of mannitol, sorbitol, inositol, dextrose, sucrose, lactose, trehalose, dextran, starch, cellulose, sodium or calcium phosphates, calcium sulfate, citric acid, tartaric acid, glycine, high molecular weight polyethylene glycols (PEG), and the like and combinations thereof. In one embodiment, the excipient may also include a charged lipid and / or detergent in the pharmaceutical compositions. Suitable charged lipids include, without limitation, phosphatidylcholines (lecithin), and the like. Detergents will typically be a nonionic, anionic, cationic or amphoteric surfactant. Examples of suitable surfactants include, for example, Tergitol® and Triton® surfactants (Union Carbide Chemicals and Plastics, Danbury, Conn.), polyoxyethylenesorbitans, for example, TWEEN surfactants (Atlas Chemical Industries, Wilmington, Del.), polyoxyethylene ethers, for example, Brij®, pharmaceutically acceptable fatty acid esters, for example, lauryl sulfate and salts thereof (SDS), and the like. Such materials may be used as stabilizers and / or anti-oxidants. Additionally, they may be used to reduce local irritation at the site of administration.

[0128] In some embodiment, the composition is formulated in a lyophilized form. In certain embodiments, the lyophilized formulation of the composition allows for maintaining structure and achieving remarkably superior long-term stability conditions which might occur during storage or transportation of the composition. Formulations

[0129] The therapeutic agent or combination thereof described herein can be administered at therapeutically effective doses. Therapeutically effective doses can be determined by one of ordinary skill in the art based on the type of therapeutic agent or combination thereof administered. Dosage, routes of administration, and administration schedules described in the art can be used. Representative doses are available in the Merck Manual Professional Edition (see the internet at merckmanuals(dot)com / professional).

[0130] Further, doses of a therapeutic agent, or combination thereof, administered to animals can be converted to equivalent doses for humans based on the body surface area (BSA) (represented in mg / m2) normalization method (see, e.g., Reagan-Shaw, S. et al., “Dose translation from animal to human studies revisited,” FASEB J.22, 659-661 (2007); and “Guidance for Industry—Estimating the Maximum Safe Starting Dose in Initial ClinicalAttorney Docket No.:206339-0109-00WO Trials for Therapeutics in Adult Healthy Volunteers,” U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), July 2005, Pharmacology and Toxicology; which are incorporated by reference herein).

[0131] In some embodiments, the therapeutic agent or combination thereof described herein are administered in therapeutically effective amounts for periods of time effective to treat a cancer or tumor. The effective amount of the therapeutic agent or combination thereof described herein can be determined by one of ordinary skill in the art and includes dosage amounts for a mammal of from about 0.5 to about 200 mg / kg, about 0.5 to about 150 mg / kg, about 0.5 to 100 mg / kg, about 0.5 to about 75 mg / kg, about 0.5 to about 50 mg / kg, about 0.01 to about 50 mg / kg, about 0.05 to about 25 mg / kg, about 0.1 to about 25 mg / kg, about 0.5 to about 25 mg / kg, about 1 to about 20 mg / kg, about 1 to about 10 mg / kg, about 20 mg / kg of body weight, about 10 mg / kg, about 5 mg / kg, about 2.5 mg / kg, about 1.0 mg / kg, or about 0.5 mg / kg of body weight of the therapeutic agent or combination thereof described herein, or any range derivable therein. In some embodiments, the dosage amounts of the therapeutic agent or combination thereof described herein are from about 0.01 mg / kg to about 10 mg / kg of body weight. In some embodiments, the dosage amount of the inhibitors is from about 0.01 mg / kg to about 5 mg / kg, or from about 0.01 mg / kg to about 2.5 mg / kg of body weight. The compositions described herein can be administered in a single dose or in the form of individual divided doses, such as from 1 to 4 times per day, or once every 2 days, 3 days, 4 days, 5 days, 6 days, weekly, or monthly. The compositions described herein can also be administered for various treatment cycles, such as 2, 3, 4, 5, 6, 7, 8, 9, 10 treatment cycles. The treatment cycles can be different lengths of time depending on the cancer to be treated, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 week treatment cycles. In addition, the effective amount of at least one therapeutic agent can be determined during pre-clinical trials and clinical trials by methods known to physicians and clinicians.

[0132] Alternatively, administration of a specific amount of may be given which is not based upon the weight of the patient such as an amount in the range of 1 µg-100 µg, 1 mg-100 mg, or 1 gm-100 gm. For example, site specific administration may be to body compartment or cavity such as CT-guided percutaneous intratumoral injection of an effective amount of at least one therapeutic agent. In one embodiment, site specific administration of an effective amount of at least one therapeutic agent is performed through endoscopicAttorney Docket No.:206339-0109-00WO ultrasound (EUS) guided delivery. However, the method of delivery is not limited to the above methods, and includes the delivery of at least one or more treatment element to a target site with the aid of a device for example including but not limited to guiding catheter, catheter, endoscope, trocar, introducer, endoscope working channel, endoscope with ultrasound probe, sheath introducer, sleeve, stepper, port, or the like as is known in the art.

[0133] In some embodiments, at least one therapeutic agent of the present disclosure is formulated according to known methods to prepare pharmaceutically useful compositions, whereby these materials, or their functional derivatives, are combined in admixture with a pharmaceutically acceptable carrier vehicle. Suitable vehicles and their formulation, inclusive of other human proteins, e.g., human serum albumin, are described, for example, in Remington's Pharmaceutical Sciences (16th ed., Osol, A. ed., Mack Easton Pa. (1980)). In order to form a pharmaceutically acceptable composition suitable for effective administration, such compositions will contain an effective amount of the above-described compounds together with a suitable amount of carrier vehicle. Additional pharmaceutical methods may be employed to control the duration of action. Controlled release preparations may be achieved through the use of polymers to complex or absorb the compounds. Another possible method to control the duration of action by controlled release preparations is to incorporate the compounds of the present disclosure into particles of a polymeric material such as polyesters, polyamino acids, hydrogels, poly(lacticacid) or ethylene vinylacetate copolymers. Alternatively, instead of incorporating these agents into polymeric particles, it is possible to entrap these materials in microcapsules prepared, for example, by interfacial polymerization, for example, hydroxymethylcellulose or gelatin-microcapsules and poly(methylmethacylate)-microcapsules, respectively, or in colloidal drug delivery systems, for example, liposomes, albumin microspheres, microemulsions, nanoparticles, and nanocapsules or in macroemulsions. Exemplary microparticles and nanoparticles that can be used to encapsulate a therapeutic agent of the disclosure include, but are not limited to, microparticles and nanoparticles as described in US Patent Publication No.: US 2017 / 0273909A1, the contents of which are incorporated herein in their entirety. Therefore, in one embodiment, the therapeutic agent of the disclosure is encapsulated in a crystalline or semi-crystalline matrix which confers enhanced stability to the agent.Attorney Docket No.:206339-0109-00WO

[0134] The microparticles of this disclosure may have a size range from about 1 to 250 microns diameter, 10 to 200 microns diameter, 10 to 130 microns diameter, or about 10 to 90 microns diameter. The amount of therapeutic agent present in the formulation depends on the desired daily release dosage and thus on the biodegradation rate of the encapsulating matrix. The exact amount of therapeutic agent may be ascertained by bioavailability trials.

[0135] The treatment may be given in a single dose schedule, or as a multiple dose schedule in which a primary course of treatment may be with 1-100 separate doses, followed by other doses given at subsequent time intervals required to maintain and or reinforce the response, for example, at 1-4 months for a second dose, and if needed, a subsequent dose(s) after several months. Examples of suitable treatment schedules include: (i) 0, 1 month and 6 months, (ii) 0, 7 days and 1 month, (iii) 0 and 1 month, (iv) 0 and 6 months, or other schedules sufficient to elicit the desired responses expected to reduce disease symptoms, or reduce severity of disease. Methods of Treatment

[0136] The present disclosure also provides a method of treating or preventing a disease or disorder in a subject.

[0137] In one embodiment, the disease or disorder is cancer or a cancer-associated disease or disorder. The following are non-limiting examples of cancers that can be treated by the disclosed methods and compositions: acute lymphoblastic leukemia, acute myeloid leukemia, adrenocortical carcinoma, appendix cancer, basal cell carcinoma, bile duct cancer, bladder cancer, bone cancer, brain and spinal cord tumors, brain stem glioma, brain tumor, breast cancer, bronchial tumors, burkitt lymphoma, carcinoid tumor, central nervous system atypical teratoid / rhabdoid tumor, central nervous system embryonal tumors, central nervous system lymphoma, cerebellar astrocytoma, cerebral astrocytoma / malignant glioma, cerebral astrocytotna / malignant glioma, cervical cancer, childhood visual pathway tumor, chordoma, chronic lymphocytic leukemia, chronic myelogenous leukemia, chronic myeloproliferative disorders, colon cancer, colorectal cancer, craniopharyngioma, cutaneous cancer, cutaneous t- cell lymphoma, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer, ewing family of tumors, extracranial cancer, extragonadal germ cell tumor, extrahepatic bile duct cancer, extrahepatic cancer, eye cancer, fungoides, gallbladder cancer, gastric (stomach)Attorney Docket No.:206339-0109-00WO cancer, gastrointestinal cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (gist), germ cell tumor, gestational cancer, gestational trophoblastic tumor, glioblastoma, glioma, hairy cell leukemia, head and neck cancer, hepatocellular (liver) cancer, histiocytosis, hodgkin lymphoma, hypopharyngeal cancer, hypothalamic and visual pathway glioma, hypothalamic tumor, intraocular (eye) cancer, intraocular melanoma, islet cell tumors, kaposi sarcoma, kidney (renal cell) cancer, langerhans cell cancer, langerhans cell histiocytosis, laryngeal cancer, leukemia, lip and oral cavity cancer, liver cancer, lung cancer, lymphoma, macroglobulinemia, malignant fibrous histiocvtoma of bone and osteosarcoma, medulloblastoma, medulloepithelioma, melanoma, merkel cell carcinoma, mesothelioma, metastatic squamous neck cancer with occult primary, mouth cancer, multiple endocrine neoplasia syndrome, multiple myeloma, mycosis, myelodysplastic syndromes, myelodysplastic / myeloproliferative diseases, myelogenous leukemia, myeloid leukemia, myeloma, myeloproliferative disorders, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroblastoma, non-hodgkin lymphoma, non-small cell lung cancer, oral cancer, oral cavity cancer, oropharyngeal cancer, osteosarcoma and malignant fibrous histiocytoma, osteosarcoma and malignant fibrous histiocytoma of bone, ovarian, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, ovarian low malignant potential tumor, pancreatic cancer, papillomatosis, paraganglioma, parathyroid cancer, penile cancer, pharyngeal cancer, pheochromocytoma, pineal parenchymal tumors of intermediate differentiation, pineoblastoma and supratentorial primitive neuroectodermal tumors, pituitary tumor, plasma cell neoplasm, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system cancer, primary central nervous system lymphoma, prostate cancer, rectal cancer, renal cell (kidney) cancer, renal pelvis and ureter cancer, respiratory tract carcinoma involving the nut gene on chromosome 15, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, sezary syndrome, skin cancer (melanoma), skin cancer (nonmelanoma), skin carcinoma, small cell lung cancer, small intestine cancer, soft tissue cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer , stomach (gastric) cancer, supratentorial primitive neuroectodermal tumors, supratentorial primitive neuroectodermal tumors and pineoblastoma, T-cell lymphoma, testicular cancer, throat cancer, thymoma and thymic carcinoma, thyroid cancer, transitional cell cancer, transitional cell cancer of the renal pelvis and ureter, trophoblastic tumor, urethralAttorney Docket No.:206339-0109-00WO cancer, uterine cancer, uterine sarcoma, vaginal cancer, visual pathway and hypothalamic glioma, vulvar cancer, waldenstrom macroglobulinemia, and wilms tumor.

[0138] In various embodiments, the one or more therapeutic agents, or any combination thereof, is administered orally, intraoperatively, intratumorally, intravenously, intravascularly, intramuscularly, subcutaneously, intracerebrally, intraperitoneally, by soft tissue injection, by surgical placement, by arthroscopic placement, and by percutaneous insertion, e.g., direct injection, cannulation or catheterization. Any administration may be a single administration of a composition of disclosure or multiple administrations. Administrations may be to single site or to more than one site in the subject being treated. Multiple administrations may occur essentially at the same time or separated in time.

[0139] In certain embodiments, the composition of the disclosure is administered during surgical resection or debulking of a tumor or diseased tissue. For example, in subjects undergoing surgical treatment of diseased tissue or tumor, the composition may be administered to the site in order to further treat the tumor.

[0140] Subjects to which administration of the pharmaceutical compositions of the disclosure is contemplated include, but are not limited to, humans and other primates, mammals including commercially relevant mammals such as non-human primates, cattle, pigs, horses, sheep, cats, and dogs.

[0141] Pharmaceutical compositions of the present disclosure may be administered in a manner appropriate to the disease to be treated (or prevented). The quantity and frequency of administration will be determined by such factors as the condition of the subject, and the type and severity of the subject’s disease, although appropriate dosages may be determined by clinical trials.

[0142] When “therapeutic amount” is indicated, the precise amount of the compositions of the present disclosure to be administered can be determined by a physician with consideration of individual differences in age, weight, disease type, extent of disease, and condition of the patient (subject).

[0143] The administration of the subject compositions may be carried out in any convenient manner, including by aerosol inhalation, injection, ingestion, transfusion, implantation or transplantation. The compositions described herein may be administered to aAttorney Docket No.:206339-0109-00WO subject subcutaneously, intradermally, intratumorally, intranodally, intramedullary, intramuscularly, by intravenous (i.v.) injection, or intraperitoneally.

[0144] The composition comprising at least one therapeutic agent as described herein can be incorporated into any formulation known in the art. For example, the therapeutic agent may be incorporated into formulations suitable for oral, parenteral, intravenous, subcutaneous, percutaneous, topical, buccal, or another route of administration. Suitable compositions include, but are not limited to, tablets, capsules, caplets, pills, gel caps, troches, dispersions, suspensions, solutions, syrups, granules, beads, transdermal patches, gels, powders, pellets, magmas, lozenges, creams, pastes, plasters, lotions, discs, suppositories, liquid sprays for nasal or oral administration, dry powder or aerosolized formulations for inhalation, compositions and formulations for intravesical administration and the like. It should be understood that the formulations and compositions that would be useful in the present disclosure are not limited to the particular formulations and compositions that are described herein.

[0145] Although the description of pharmaceutical compositions provided herein are principally directed to pharmaceutical compositions which are suitable for ethical administration to humans, it will be understood by the skilled artisan that such compositions are generally suitable for administration to animals of all sorts. Modification of pharmaceutical compositions suitable for administration to humans in order to render the compositions suitable for administration to various animals is well understood, and the ordinarily skilled veterinary pharmacologist can design and perform such modification with merely ordinary, if any, experimentation. Subjects to which administration of the pharmaceutical compositions of the disclosure is contemplated include, but are not limited to, humans and other primates, mammals including commercially relevant mammals such as non-human primates, cattle, pigs, horses, sheep, cats, and dogs.

[0146] In the method of treatment, the administration of the composition of the disclosure may be for either “prophylactic” or “therapeutic” purpose. When provided prophylactically, the composition of the present disclosure is provided in advance of any sign or symptom, although in particular embodiments the disclosure is provided following the onset of at least one sign or symptom to prevent further signs or symptoms from developing or to prevent present signs or symptoms from becoming more severe. The prophylacticAttorney Docket No.:206339-0109-00WO administration of the composition serves to prevent or ameliorate subsequent signs or symptoms. When provided therapeutically, the pharmaceutical composition is provided at or after the onset of at least one sign or symptom. Thus, the present disclosure may be provided either prior to the anticipated exposure to a disease-causing agent or disease state or after the initiation of the disease or disorder.

[0147] In the context of methods of treatment, ordinals describing an agent (e.g., “first agent,” “second agent,” “third agent”) are used to uniquely identify and distinguish one agent from another and to indicate that the agents are different from each other, but are not intended to imply an order or a priority for the agents or steps of a method that employ them. Because in this circumstance the ordinals do not imply an order, it is possible to characterize embodiments of the disclosure with discontinuous ordinals (e.g., an embodiment with defined first, second, and fourth agents is simply an embodiment with three uniquely defined agents (with no implication that a “third” uniquely defined agent must be present).

[0148] Ordinals used to describe steps of a method (e.g., “first administering,” “second administering”) are indicative of an order of steps.

[0149] In the context of administration of two or more agents, “synergistically effective amounts” are amounts of the agents that either (i) produce greater than additive therapeutic effects, compared to monotherapy with the agents; or (ii) produce at least comparable therapeutic effects and reduce toxic side effects, due to lower effective dosing or less frequent dosing, compared to monotherapy with the agents. An indication of such synergy can be provided in in vitro studies, e.g., with mesothelioma cell lines, in studies to evaluate the killing of tumor cell lines in vitro or inhibition of cell growth. Synergy can be demonstrated in clinical trials in which the effects of monotherapy and combination therapy are compared and statistically analyzed.

[0150] In various embodiments, the disclosure provides methods of killing cancer cells. In some embodiments, the cancer cells are mesothelioma cells. In some embodiments, the method comprises contacting the cells with a composition comprising at least one FDA- approved therapeutic agent, wherein the FDA-approved therapeutic agent has been approved for a use that is not treatment of mesothelioma. FDA-approved therapeutic agents that are approved for a use that is not treatment of mesothelioma, but that have been identified by a drug repurposing screen, as disclosed herein, for the treatment of mesothelioma, include, butAttorney Docket No.:206339-0109-00WO are not limited to, dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat. In some embodiments, the method further comprises contacting the cells, separately, simultaneously, or sequentially, with a second FDA-approved therapeutic agent. In some embodiments, the combination of a first and second FDA- approved therapeutic agent has a synergistic effect for the treatment of a disease or disorder. In some embodiments, the disease or disorder is mesothelioma. In some embodiments, the synergistic combination of FDA-approved therapeutics for use for the treatment of diffuse pleural mesothelioma comprises the combination of gilteritinib and regorafenib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of osimertinib and tazemetostat. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of ribociclib and gilteritinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of ribociclib and osimertinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of ribociclib and regorafenib. In some embodiments, the synergistic combination of FDA- approved therapeutics comprises the combination of ribociclib and sorafenib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sorafenib and gilteritinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sorafenib and tazemetostat. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sunitinib and gilteritinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sunitinib and osimertinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sunitinib and ribociclib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of sunitinib and tazemetostat. In some embodiments, the synergistic combination of FDA- approved therapeutics comprises the combination of tazemetostat and gilteritinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of tazemetostat and orlistat. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of dasatinib and osimertinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprisesAttorney Docket No.:206339-0109-00WO the combination of dasatinib and sunitinib. In some embodiments, the synergistic combination of FDA-approved therapeutics comprises the combination of orlistat and sunitinib.

[0151] In some embodiments, the method comprises contacting the cells with a composition comprising a first therapeutic agent as described herein before contacting the cells with a second therapeutic agent. Examples of contacting the cells with a first therapeutic agent as described herein before contacting the cells with a second therapeutic agent include, but are not limited to, contacting the cells with a composition comprising a first therapeutic agent 4 weeks, 3 weeks, 2 weeks, 7 days, 6 days, 5 days, 4 days, 3 days, 2 days, 36 hours, 24 hours, 22 hours, 20 hours, 18 hours, 16 hours, 14 hours, 12 hours, 10 hours, 9 hours, 8 hours, 7 hours, 6 hours, 5 hours, 4 hours, 3 hours, 2.5 hours, 2 hours, 1.5 hours, 1 hour, 50 minutes, 45 minutes, 40 minutes, 35 minutes, 30 minutes, 25 minutes, 20 minutes, 15 minutes, 10 minutes, 5 minutes, 4 minutes, 3 minutes, 2 minutes, 1 minute, and 30 seconds before contacting the cells with a second therapeutic agent. In some embodiments, the cells are contacted with a composition comprising a first therapeutic agent multiple times before the cells are contacted with a second therapeutic agent.

[0152] In some embodiments, the method comprises administering a first composition comprising a first therapeutic agent and a second composition comprising a second therapeutic agent to a subject simultaneously or essentially simultaneously.

[0153] In some embodiments, the method comprises administering single composition comprising a first therapeutic agent and a second therapeutic agent to a subject.

[0154] In some embodiments, the method comprises administering to a subject a first composition comprising a first therapeutic agent as described herein before administering to the same subject a second composition comprising a second therapeutic agent. In some embodiments, a first composition comprising a first therapeutic agent is administered to the subject more than 4 weeks, 3 weeks, 2 weeks, 7 days, 6 days, 5 days, 4 days, 3 days, 2 days, 36 hours, 24 hours, 22 hours, 20 hours, 18 hours, 16 hours, 14 hours, 12 hours, 10 hours, 9 hours, 8 hours, 7 hours, 6 hours, 5 hours, 4 hours, 3 hours, 2.5 hours, 2 hours, 1.5 hours, 1 hour, 50 minutes, 45 minutes, 40 minutes, 35 minutes, 30 minutes, 25 minutes, 20 minutes, 15 minutes, 10 minutes, 5 minutes, 4 minutes, 3 minutes, 2 minutes, 1 minute, and 30 seconds before a second composition comprising a second therapeutic agent is administeredAttorney Docket No.:206339-0109-00WO to the subject. In some embodiments, the composition comprising the first therapeutic agent is administered multiple times before the second composition comprising the second therapeutic agent. In some embodiments, the composition comprising the second therapeutic agent is administered multiple times following administration of the first composition comprising the first therapeutic agent. In some embodiments, each of the first composition comprising the first therapeutic agent and the second composition comprising the second therapeutic agent are administered multiple times over the course of treatment. Embodiments

[0155] The disclosure includes at least the following numbered embodiments: 1. A composition for treating mesothelioma, wherein the composition comprises one or more inhibitors selected from the group consisting of: dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat. 2. The composition of embodiment 1, wherein the inhibitor is selected from the group consisting of: gilteritinib, orlistat, osimertinib, sunitinib, sorafenib, and tazemetostat. 3. The composition of embodiments 1 or 2, wherein the composition comprises two or more inhibitors. 4. The composition of embodiment 3, wherein the two or more inhibitors are selected from the group consisting of: a) gilteritinib and regorafenib; b) osimertinib and tazemetostat; c) ribociclib and gilteritinib; d) ribociclib and osimertinib; e) ribociclib and regorafenib; f) ribociclib and sorafenib; g) sorafenib and gilteritinib; h) sorafenib and tazemetostat; i) sunitinib and gilteritinib; j) sunitinib and osimertinib; k) sunitinib and ribociclib;Attorney Docket No.:206339-0109-00WO l) sunitinib and tazemetostat; m) tazemetostat and gilteritinib; n) tazemetostat and orlistat; o) dasatinib and osimertinib; p) dasatinib and sunitinib; and q) orlistat and sunitinib. 5. The composition of embodiments 3 or 4, wherein the two inhibitors or more are selected from the group consisting of: a) tazemetostat and orlistat, b) sunitinib and tazemetostat, c) sunitinib and osimertinib, and d) sorafenib and gilteritinib. 6. The composition of any one of embodiments 1-5, wherein the mesothelioma is diffuse pleural mesothelioma (DPM). 7. A method for treating mesothelioma in a patient in need thereof, the method comprising administering one or more inhibitors selected from the group consisting of: dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat to the subject. 8. The method of embodiment 7, wherein the inhibitor is selected from the group consisting of: gilteritinib, orlistat, osimertinib, sunitinib, sorafenib, and tazemetostat. 9. The method of embodiments 7 or 8, wherein the method further comprises administering at least one additional agent to the subject. 10. The method of embodiment 9, wherein the at least one additional agent comprises an inhibitor selected from the group consisting of: dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat. 11. The method of any one of embodiments 7-10, wherein the method comprises administering two or more inhibitors selected from the group consisting of: a) gilteritinib and regorafenib; b) osimertinib and tazemetostat; c) ribociclib and gilteritinib; d) ribociclib and osimertinib;Attorney Docket No.:206339-0109-00WO e) ribociclib and regorafenib; f) ribociclib and sorafenib; g) sorafenib and gilteritinib; h) sorafenib and tazemetostat; i) sunitinib and gilteritinib; j) sunitinib and osimertinib; k) sunitinib and ribociclib; l) sunitinib and tazemetostat; m) tazemetostat and gilteritinib; n) tazemetostat and orlistat; o) dasatinib and osimertinib; p) dasatinib and sunitinib; and q) orlistat and sunitinib. 12. The method of any one of embodiments 7-11, wherein the method comprises administering two or more inhibitors selected from the group consisting of: a) tazemetostat and orlistat, b) sunitinib and tazemetostat, c) sunitinib and osimertinib, and d) sorafenib and gilteritinib. 13. The method of any one of embodiments 7-12, wherein the method comprises administering two or more inhibitors essentially simultaneously. 14. The method of any one of embodiments 7-12, wherein the method comprises administering two or more inhibitors sequentially. 15. The method of any one of embodiments 7-14, wherein the mesothelioma is diffuse pleural mesothelioma (DPM). 16. A method of repurposing FDA-approved therapeutics as therapeutics for additional diseases comprising the steps of: a) generating a first data set comprising a first set of genes, transcripts, markers or protein targets that are identified by a functional genomics screen as being essential for a disease or disease stage;Attorney Docket No.:206339-0109-00WO b) generating a second data set comprising a second set of genes, transcripts, markers or protein targets associated with the disease or disease stage from multi-omics data comprising the steps of: i) obtaining multi-omic data related to the disease or disease stage; ii) running the multi-omic data through a biclustering algorithm; and iii) determining the second set of genes, transcripts, markers or protein targets associated with the disease or disease stage from the biclusters; c) generating a third data set comprising a set of genes, transcripts or protein targets for which there is a known FDA-approved therapeutic agent that targets the gene, transcript or protein targets; d) identifying a set of overlapping genes, transcripts or protein targets from the first, second and third data sets; and e) identifying an FDA-approved therapeutic agent for re-purposing when the target of the FDA-approved therapeutic agent is identified in the set of overlapping genes, transcripts or protein targets in step d. 17. The method of embodiment 16, wherein the functional genomics screen is selected from the group consisting of: a genomic screen, a proteomic screen, a metabolomic screen, a transcriptomic screen, and a drug screen. 18. The method of embodiment 16, wherein the functional genomics screen is selected from the group consisting of: an siRNA screen, an shRNA screen, a microRNA screen, a Zinc finger nuclease screen, a TALEN screen, a CRISPR-Cas9 screen, a CRIPSRi screen, a CRISPRa screen, a dual CRISPRi / a screen, and a CRISPRsc screen. 19. The method of any one of embodiments 16-18, wherein the first data set is generated by comparing the results between more than one screen. 20. The method of any one of embodiments 16-19, wherein genes, transcripts or proteins common to two or more screens are removed from the data set. 21. The method of any one of embodiments 16-20, wherein the step of generating a second data set comprising a second set of genes, transcripts, markers or protein targets associated with the cancer from multi-omics data comprises the steps of: i) obtaining at least one multiomic data set for the disease or disease stage;Attorney Docket No.:206339-0109-00WO ii) identifying a subset of genes, transcripts, markers or proteins from the at least one multiomic data set that are significantly variable within the at least one multiomic data set; iii) combining the subset of genes, transcripts, markers or proteins identified in step ii) with the first set of genes, transcripts, markers or protein targets that are identified by a functional genomics screen as being essential for the disease or disease stage; iv) constructing a set of biclusters from the combined set of genes, transcripts, markers or proteins assembled in step iii); v) identifying the subset of biclusters that are significantly co-expressed; vi) analyzing the bicluster set to identify the subset of biclusters that are significantly co-expressed and disease relevant; and vii) compiling the genes included in the subset of biclusters that are significantly co-expressed and disease relevant into the second dataset. 22. The method of any one of embodiments 16-21, wherein the multiomics data comprises gene expression data, somatic mutation data, miRNA expression data, transcriptome expression data, chromatin accessibility data, protein expression data or phenotypic data or any combination thereof. 23. The method of any one of embodiments 16-22, wherein step ii) comprises generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set. 24. The method of any one of embodiments 16-23, wherein the method of generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set comprises: a) systematically integrating mutations and defines a role for each gene, wherein the role is protein affecting mutation (PAM), transcript fusions (Fusion), copy number alteration deletion (CNAdel), copy number alteration amplification (CNAamp), activating (Act), or loss of function (LoF); and b) assigning a binary value to each gene indicating the presence or absence of a mutation.Attorney Docket No.:206339-0109-00WO 25. The method of any one of embodiments 16-24, wherein the disease is cancer. 26. The method of any one of embodiments 16-25, wherein the method comprises: a) generating a first data set comprising a first set of genes, transcripts, markers or protein targets that are identified by a functional genomics screen as being essential for tumor survival and growth of the cancer; b) generating a second data set comprising a second set of genes, transcripts, markers or protein targets associated with the cancer from multi-omics data comprising the steps of: i) obtaining multiomic data related to the cancer; ii) running the multiomic data through a biclustering algorithm; and iii) determining the second set of genes, transcripts, markers or protein targets associated with the cancer from the biclusters; c) generating a third data set comprising a set of genes, transcripts or protein targets for which there is a known FDA-approved therapeutic agent that targets the gene, transcript or protein targets; d) identifying a set of overlapping genes, transcripts or protein targets from the first, second and third data sets; e) identifying an FDA-approved therapeutic agent for re-purposing when the target of the FDA-approved therapeutic agent is identified in the set of overlapping genes, transcripts or protein targets in step d); and f) administering the FDA-approved therapeutic agent identified in step e) to a subject diagnosed as having or being at increased risk of development or recurrence of the cancer. 27. The method of any one of embodiments 16-26, wherein step ii) comprises generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set. 28. The method of any one of embodiments 16-27, wherein the method of generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set comprises:Attorney Docket No.:206339-0109-00WO a) systematically integrating mutations and defines a role for each gene, wherein the role is PAM, fusion, CNAdel, CNAamp, Act, or LoF; and b) assigning a binary value to each gene indicating the presence or absence of a mutation. 29. The method of any one of embodiments 16-28, wherein the cancer is mesothelioma, breast cancer, lung cancer, head and neck cancer, bladder cancer, stomach cancer, cancer of the nervous system, bone cancer, bone marrow cancer, brain cancer, colon cancer, esophageal cancer, endometrial cancer, gastrointestinal cancer, genital-urinary cancer, stomach cancer, lymphomas, melanoma, glioma, bladder cancer, pancreatic cancer, gum cancer, kidney cancer, retinal cancer, liver cancer, nasopharynx cancer, ovarian cancer, oral cancers, bladder cancer, hematological neoplasms, follicular lymphoma, cervical cancer, multiple myeloma, osteosarcomas, thyroid cancer, prostate cancer, colon cancer, prostate cancer, skin cancer, stomach cancer, testis cancer, tongue cancer, or uterine cancer. 30. The method of any one of embodiments 16-29, wherein the method further comprises the step of administering the identified FDA-approved therapeutic agent in step e to a subject in need. EXPERIMENTAL EXAMPLES

[0156] The disclosure is further described in detail by reference to the following experimental examples. These examples are provided for purposes of illustration only and are not intended to be limiting unless otherwise specified. Thus, the disclosure should in no way be construed as being limited to the following examples, but rather, should be construed to encompass any and all variations which become evident as a result of the teaching provided herein.

[0157] Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, make and utilize the present disclosure and practice the claimed methods. The following working examples therefore, specifically point out certain embodiments of the present disclosure, and are not to be construed as limiting in any way the remainder of the disclosure.Attorney Docket No.:206339-0109-00WO Example 1: Repurposing FDA-approved inhibitors for pleural mesothelioma

[0158] Drug repurposing is the use of a drug outside of its original indication (Jourdan JP, et al., J Pharm Pharmacol.2020;72(9):1145-1151). It has been an important solution to treating a variety of diseases (Pushpakom S, et al., Nat Rev Drug Discov. 2019;18(1):41-58) and can be especially useful in developing treatments for rare, orphaned cancers like diffuse pleural mesothelioma (DPM) (Kulkarni NS, et al., Life Sci. 2022;304:120716; Gray SG, et al., Transl Lung Cancer Res.2020;9:S100-S119). Current methods used to repurpose drugs include serendipity (Ban TA, Dialogues Clin Neurosci. 2006;8(3):335-44), in silico predictive modeling (Sohraby F, Bagheri M, et al., Methods Mol Biol.2019;1903:23-43; He B, et al., Front Oncol.2021;11:711225), and screening drugs one- at-a-time against immortalized cell lines (Huang K, et al., Nat Med.2024;30(12):3601-3613; Yang W, et al., Nucleic Acids Res.2013;41:D955-61). However, these methods are fiscally and temporally restrictive, and have limitations caused by the large amounts of quality data they require (Chang Y, et al., Pharmaceutics.2022;15(1):49) and the lack of clinical translatability after immortalized cell line testing (Dell'Anno I, et al., Cancers (Basel). 2022;14(10):2527). While immortalized cell lines have long been the standard model system used for drug discovery (Richter M, et al., Front Cell Dev Biol.2021;9:711381), they lack tumor heterogeneity and do not capture critical aspects of tumor biology that are recapitulated in patient-derived primary cell lines (Richter M, et al., Front Cell Dev Biol. 2021;9:711381). Patient-centric approaches to counteract these constraints are becoming crucial to therapeutic design and development (Crawford LS, et al., Learn Health Syst. 2017;1(3):e10027).

[0159] The current drug repurposing limitations are addressed herein with a disease- centric drug repurposing strategy. DPM-specific gene vulnerabilities were identified by applying functional genomics CRISPR-Cas9 pooled library screens to patient-derived primary cell lines that recapitulate DPM disease biology (Doench JG, et al., Nat Biotechnol. 2016;34(2):184-191; Crawford LS, et al., Learn Health Syst.2017;1(3):e10027; Xu D, et al., Mol Cancer Ther.2020;19(2):661-672. Next, the gene vulnerabilities are integrated with gene regulatory networks (GRNs) underlying DPM tumor biology constructed from patient multi-omic profiles (Plaisier CL, et al., Cell Syst.2016;3(2):172-186). GRNs have been used to elucidate the underlying biology of cancer from large amount of complicated dataAttorney Docket No.:206339-0109-00WO (Seçilmiş D, et al., NPJ Syst Biol Appl.2020;6(1):37) to identify disease-relevant interactions that can be exploited for treatment (Plaisier CL, et al., Cell Syst.2016;3(2):172- 186). The disease-specific gene vulnerabilities are then paired with inhibitors, which mimic the gene knockout mechanism of action from the functional genomics screens, and filtered to select for those currently approved by the FDA to expedite the translational process to bedside applications. This process subsequently prioritizes drugs that are most likely to have antitumor effects, reducing the amount of unsuccessful testing. Additionally, effective drugs can be paired together to identify synergistic combinations targeting multiple biological vulnerabilities (Borea F, et al., Int J Mol Sci.2023;24(11):9165; Jin H, et al., Nat Rev Drug Discov.2023;22(3):213-234) to provide potent tumor reduction while reducing toxic side- effects in patients (Lehár J, et al., Nat Biotechnol.2009;27(7):659-66.; Chen D, et al., BMC Syst Biol.2015;9:56. This drug repurposing strategy was successfully implemented to identify nine FDA-approved inhibitors and 17 synergistic combinations effective against the epithelioid and sarcomatoid subtypes. Furthermore, antitumor effects of the most synergistic combination were demonstrated in vitro and in vivo, providing evidence for the clinical relevancy of the repurposing pipeline. The methods are now described. Cell Lines

[0160] Six primary human mesothelioma cell lines, consisting of four epithelioid subtype cell lines (M3T, M12T, M14T, M50T) (Chernova T, Sun XM, Powley IR, et al., Cell Death Differ., 2016;23(7):1152-1164) and two sarcomatoid subtype cell lines (M24, M34) (Al-Taei S, Salimu J, Lester JF, et al., Lung Cancer, 2012;77(2):312-318) were obtained. Cells were grown in gelatin-coated cell culture-treated flasks under the same conditions in which they were derived. Epithelioid cell lines were cultured with RPMI-1640 growth media supplemented with 2 mM L-glutamine, 100 U / mL penicillin, 100 μg / mL streptomycin, 20 ng / mL hEGF, 1 μg / mL hydrocortisone, 2 μg / mL heparin, and 10% FBS at 37 °C and 5% CO2(Chernova T, Sun XM, Powley IR, et al., Cell Death Differ., 2016;23(7):1152-1164). Sarcomatoid cell lines were cultured with RPMI-1640 growth media supplemented with 2 mM L-glutamine, 100 U / mL penicillin, 100 μg / mL streptomycin, 25mM HEPES buffer,Attorney Docket No.:206339-0109-00WO 100 mM sodium pyruvate, and 5% FBS at 37 °C and 5% CO2 (Al-Taei S, Salimu J, Lester JF, et al., Lung Cancer, 2012;77(2):312-318). CRISPR-Cas9 functional genomics screens

[0161] The Brunello single vector human CRISPR-Cas9 knockout pooled library containing an average of four sgRNAs per gene was used to induce knockouts in 19,114 genes (Doench JG, Fusi N, Sullender M, et al., Nat Biotechnol., 2016;34(2):184-191) in the M12T and M24 cell lines. Lentivirus containing the library was purchased from Addgene (Addgene #73179-LV). Cells were transduced in biological triplicate in suspension with 8 μg / mL polybrene (Fisher Scientific #TR1003G) at a transduction efficiency between 0.2-0.4 and incubated overnight. Puromycin (Fisher Scientific, BP2956100) concentration was determined to be 1.25 μg / mL using kill curves for both cell lines. Puromycin selection was applied for 72 hours following transduction. Transduced cells were then seeded into three five-layer-T175 flasks (FisherSci #353144) at a concentration of 15 million cells per flask. The replicates were periodically passaged for at least 20 days, representing 8-9 doubling times (Toledo CM, et al., Cell Rep, 2015;13(11):2425-2439), with 45 million cells were carried over at each passage. DNA was extracted using the cell culture DNA midi kit (Qiagen #13343), and PCR amplified (Doench JG, Fusi N, Sullender M, et al., Nat Biotechnol., 2016;34(2):184-191). The transduction efficiency of the M12T cell line was ~37.5% with a dynamic range of ~500 cells per sgRNA, and the transduction efficiency for the M24 cell line was ~20% with a dynamic range of ~267 cells per sgRNA. Purified PCR products were sequenced to generate 39 million to 83 million reads per sample (Illumina). Reads were mapped to sgRNA barcodes and counts for each sgRNA barcode were tabulated using the PoolQ 3.0 algorithm. All samples had at least 29 million reads that mapped to sgRNA barcodes (Table 1). The Joint Analysis of CRISPR / Cas9 Knockout Screens (JACKS) was applied to provide gene level essentiality, median log2(fold-change), and p-values (Allen F, Behan F, Khodak A, et al., Genome Res., 2019;29(3):464-471). Genes were considered significant if they had an essentiality score < 0 and a Benjamini-Hochberg FDR corrected p- value < 0.001. Commonly essential genes across multiple cell types were obtained from the Dependency Map’s (DepMap) Project Achilles and Cancer Cell Line Encyclopedia (CCLE) projects and were excluded to identify mesothelioma-specific essential genes (Meyers RM, etAttorney Docket No.:206339-0109-00WO al., Nat Genet., 2017;49(12):1779-1784; Tsherniak A, et al., Cell, 2017;170(3):564-576.e16). Genes demonstrated to be associated with DPM were identified from DisGeNET (Piñero J, et al., Nucleic Acids Res., 2020;48(D1):D845-D855) and compared to essential genes using hypergeometric enrichment analysis. Table 1. Functional genomics screen barcodes. Barcode Condition Reads matching Reads matching % Normalize construct+sample sample barcode Match match barcode CGGTTCAA M24_1 33,506,429 39,014,064 85.88 16.08 GCTGGATT M24_2 45,398,852 56,051,672 80.99 16.52 TAACTCGG M24_3 65,930,451 76,197,728 86.53 17.06 TAACAGTT pDNA 96,746,226 102,835,801 94.08 17.61 ATACTCAA M12T_1 53,944,829 78,425,876 68.78 16.77 GCTGAGAA M12T_2 29,461,222 43,186,436 68.22 15.90 ATTGGAGG M12T_3 33,176,029 82,566,997 40.18 16.07 Read counts for sample barcodes with associated conditions Patient Data Acquisition

[0162] Multi-omic profiles containing gene expression, somatic mutation, and phenotypic data (overall survival, age, and sex) were obtained for 211 patient tumors from Bueno et al. (Bueno R, Stawiski EW, Goldstein LD, et al., Nat Genet., 2016;48(4):407-416) for use in this study. RNA-seq counts were normalized using variance stabilized transformation (vst) in DESeq2 (Love MI, Huber W, Anders S., Genome Biol., 2014;15(12):550). Somatic protein affecting and copy number mutations were integrated into a comprehensive mutation matrix using OncoMerge (Striker SS, Wilferd SF, Lewis EM, O’Connor SA, Plaisier CL, Cell Rep Methods, 2023;3(4):100442). Clinical phenotype information was obtained for overall survival, age, and sex.

[0163] The TCGA mesothelioma multi-omic profiles containing gene expression, miRNA expression, somatic mutation, and phenotypic data (overall survival, age, and gender) were obtained for 87 patients (Thorsson V, Gibbs DL, Brown SD, et al., Immunity, 2018;48(4):812-830.e14). The standardized, normalized, batch-corrected and platform- corrected data matrices and mutation data were generated by the PanCancer Atlas consortium, available at the publication page (gdc.cancer(dot)gov / about-Attorney Docket No.:206339-0109-00WO data / publications / pancanatlas). Somatic protein affecting, gene fusion, and copy number mutations were integrated into a comprehensive mutation matrix using OncoMerge (Striker SS, Wilferd SF, Lewis EM, O’Connor SA, Plaisier CL, Cell Rep Methods, 2023;3(4):100442). dpmSYGNAL gene regulatory network construction

[0164] The SYstems Genetics Network AnaLysis (SYGNAL) pipeline was applied to the Bueno cohort (Bueno R, Stawiski EW, Goldstein LD, et al., Nat Genet., 2016;48(4):407- 416) to construct the dpmSYGNAL network using the methods and command line parameters described in Plaisier et al. (Plaisier CL, O’Brien S, Bernard B, et al., Cell Syst., 2016;3(2):172-186). The 10,951 genes in the expression matrix were defined as the top 8,000 most variant genes within the patient cohort and supplemented with 2,951 genes from the union of the M12T and M24 essential gene lists. Three SYGNAL networks were constructed: two trained on miRNA target gene relationships utilizing PITA (Kertesz M, et al., Nat Genet. 2007;39(10):1278-84) and TargetScan (McGeary SE, et al., Science. 2019;366(6472):eaav1741), and one trained on TF target gene relationships (TFBS_DB) (Plaisier CL, O’Brien S, Bernard B, et al., Cell Syst., 2016;3(2):172-186). A total of 2,190 biclusters were constructed using SYGNAL, with 730 biclusters per trained SYGNAL network. The 2,190 biclusters were post-processed to determine: (i) co-expression quality via variance explained by first principal component (empirical p-value < 0.05 and variance explained ≥ 0.3); (ii) functional enrichment with GO biological process terms (BH-corrected p-value ≤ 0.05) (Plaisier CL, et al., Genome Res.2012;22(11):2302-14); (iii) association with the hallmarks of cancer (Jiang-Conrath Semantic Similarity Score ≥ 0.8, permuted p-value ≤ 5.1 × 10−4) (Plaisier CL, et al., Genome Res.2012;22(11):2302-14; Hanahan D, et al., Cell. 2011;144(5):646-74); and (iv) association of the bicluster eigengene with overall patient survival (Cox proportional hazards regression p-value ≤ 0.05). Disease relevant genes used to identify FDA-approved inhibitors were defined as genes from biclusters that were significantly co-expressed and disease relevant, i.e., associated with a hallmark of cancer or overall patient survival. Identifying FDA approved inhibitors from DrugBankAttorney Docket No.:206339-0109-00WO

[0165] The DrugBank database version 5.1.7 (Wishart DS, Feunang YD, Guo AC, et al., Nucleic Acids Res., 2018;46(D1):D1074-D1082) was downloaded as an xml file and queried using the R package dbparser. The drugs, targets, carriers / enzymes / targets / transporters, and drug interactions tables were exported and joined using the pandas Python package. The merged DrugBank matrix included the drug name, target gene name, activity, and FDA approval status. Candidate drugs were identified as FDA-approved inhibitors of a specific gene target. FDA-approved inhibitor repurposing for DPM

[0166] The essential genes from the union of the M12T and M24 functional genomics screens (JACKS average log2(fold-change) < -0.7 and Benjamini-Hochberg FDR corrected p-value < 0.001) were intersected with disease relevant genes from dpmSYGNAL (associated with a hallmark of cancer or overall patient survival) and intersected again with the genes targeted by FDA-approved inhibitors from DrugBank. To minimize potential toxic interactions, contraindications to cisplatin and pemetrexed were used to deprioritize inhibitors for genes with more than one candidate inhibitor (Harden SV, Darlison L, Beckett P, Bibby AC, Br J Cancer, 2020;123(11):1588-1589). Drug reconstitution

[0167] Each drug was reconstituted to a concentration of 20 mM in solvent and sonicated according to manufacturer’s instructions (Table 2). All drugs were filter sterilized using 13 mm 0.22 µm PTFE LeurLock syringe filters (VWR, 10059-502) and aliquoted into stocks before storage. Drugs were thawed at room temperature and sonicated if indicated before use. Drugs were used within two weeks of reconstitution. Table 2. Reconstitution of each drug to a concentration of 20mM SolventStorage MW Amt. Vol. Conc. Sonic. (°C, Inhibitor Source Part # (g / mol)(mg) Name(mL) (mM) (mins) † days) Bumetanide MCE HY-17468 364.42 10 DMSO 1.372 20 15 -80, 14 Caffeine APExBIO N2379 194.19 10 DMSO 2.576 20 10 -80,14Attorney Docket No.:206339-0109-00WO Cannot Cisplatin SA P4394 300.05 10 NaCl 1.666 20 15, 30°C be stored Dasatinib APExBIO A3017 488.01 10 DMSO 1.025 20 10 -80, 14 Flecainide AcetateMCE HY-W010950 474.39 10 DMSO 1.054 20 10 -80, 14Gilteritinib MCE HY-12432 552.71 10 DMSO 0.905 20 15 -80, 14 Orlistat FS AAJ62999MF 495.73 10 EtOH 1.009 20 None -20, 14 Osimertinib MCE HY-15772 499.61 10 DMSO 2.000 20 10 -80, 14 Pemetrexed MCE HY-10820 427.41 10 DMSO 1.170 20 10 -80, 14 Regorafenib MCE HY-10331 482.82 10 DMSO 1.036 20 15 -80, 14 Ribociclib MCE HY-15777 434.54 10 DMSO 1.151 20 15 -80, 14 Sorafenib MCE HY-10201 464.83 10 DMSO 1.076 20 None -80, 14 Sunitinib MCE HY-10255A 398.47 10 DMSO 1.255 20 15 -80, 14 Talazoparib MCE HY-16106 380.35 10 DMSO 1.315 20 10 -80, 14 Tazemetostat MCE HY-13803 572.74 10 DMSO 0.873 20 10 -80, 14 FS = Fisher Scientific, MCE = MedChemExpress, SA = Sigma Aldrich, MW = molecular weight, Amt. = amount, Vol. = volume, Conc. = concentration, Sonic. = sonication. † Drugs were sonicated in S Heated Ultrasonic Cleaner with 3 Modes and High Power, Elma (VWR, 76326-680) Hall MD, Telma KA, Chang KE, et al., Cancer Res.2014;74(14):3913-3922 Quantifying relative cell viability

[0168] Cells were plated at 15,000 cells per well onto a tissue culture-treated white 96-well plate and allowed to adhere overnight (24 hours). Drugs, drug combinations, negative controls, and vehicle controls were added and incubated for 48 hours. Each perturbation was assayed in biological triplicate, except for dose response matrices which used biological duplicates. Luminescence was converted into cell counts by comparison to an eight-point serial dilution standard curve. Relative cell viability was quantified using the CellTiter-Glo Luminescent Cell Viability Assay (Promega, G7570). Luminescence was measured using a BioTek Synergy H1 plate reader. Comparisons for each perturbation were computed against the appropriate negative vehicle control. Dose response curves (DRCs) to determine IC50, IC35and AUC for drugs

[0169] Drug efficacy was evaluated in the M12T and M24 cell lines. Single agent drugs were applied to the cells at 23 different drug concentrations over a log10-scaled range from 0.001 µM to 200 µM with vehicle and negative controls. Cell viability was quantified as previously described. The IC50 and IC35 concentrations were calculated from the DRCsAttorney Docket No.:206339-0109-00WO generated using the R package drc v3.0-1 (Ritz C, Baty F, Streibig JC, Gerhard D, PLoS One, 2015;10(12):e0146021) for each drug in both the M12T and M24 cell lines (Figure 7). The only exception was for dasatinib. Dasatinib exhibited a double sigmoid curve (Figure 7D & Figure 7S) which could not be modeled accurately using drc package. The IC35 and IC50 for dasatinib were manually calculated using the concentration of drug resulting in a decrease in the log2(fold-change) by 35% and 50%, respectively. The area under the curve (AUC) was calculated using the python SciPy package integrate.simpson v1.9.3 (Virtanen P, Gommers R, Oliphant TE, et al., Nat Methods, 2020;17(3):261-272). Comparisons for each perturbation were plotted using the log2(fold-change) derived from the vehicle control. Drugs at least as antiproliferative as cisplatin (AUC > 0.6) were considered significantly antiproliferative. Combinatorial response screens (CRSs) identifying synergy between drugs for the M12T and M24 cell lines

[0170] Drugs at least as antiproliferative as the cisplatin (AUC > 0.6) were tested for synergy pairwise in the M12T and M24 cell lines. Pairwise combinations were applied to the cells at their IC35 concentration in triplicate. Cell viability was quantified as described previously. Synergy was calculated for each pair by comparing combinations to the single agent treatments alone using the Bliss independence model described in Plaisier et al., 2016 (Plaisier CL, O’Brien S, Bernard B, et al., Cell Syst., 2016;3(2):172-186). Briefly, fold changes for single drugs (FCD1and FCD2) were used to calculate an estimated Blissindependence combined fold-change (FCD1+D2,Estimated) using the formula:^^^^ = (^^^ଶ(ி^ವభ)ା^^^ଶ(ி^ವమ)ି^^^ଶ(ி^ವభ)∗^^^ଶ(ி^ವమ))^^ା^ଶ,ா^௧^^^௧^ௗ 2

[0171] and standard error of the observed fold-change were used to calculate a two-tailed t-test p-value to determine if the FCD1+D2,Estimatecould have been drawn from the same distribution as FCD1+D2,Observed. If FCD1+D2,Observed was significantly greater than FCD1+D2,Estimate then synergy was observed (Benjamini-Hochberg corrected p-value < 0.05, FCD1+D2,Observed - FCD1+D2,Estimate < -0.2). If FCD1+D2,Observedwas significantly smaller than FCD1+D2,Estimate, then antagonism was observed. Finally, if FCD1+D2,Observedand FCD1+D2,Estimatewere indistinguishable, then independence or additivity was observed.Attorney Docket No.:206339-0109-00WO Replicating drug efficacy and synergistic response in independent patient cell lines

[0172] The top four synergistic drug pairs identified in the combinatorial response screens were applied to the M3T, M14T, M50T, and M34 patient-derived cell lines to test for replication of single drug efficacy and synergistic response. Dose response curves were performed as previously described to identify the IC35 for the six individual drugs comprising these combinations: gilteritinib, sorafenib, osimertinib, sunitinib, tazemetostat, and orlistat. These IC35concentrations were used in a combinatorial response screen to determine synergistic responses in the gilteritinib with sorafenib, orlistat with tazemetostat, osimertinib with sunitinib, and sunitinib with tazemetostat combinations against the four additional cell lines. Pairwise dose response matrices (DRMs) to test synergy across concentrations

[0173] Dose response matrices (DRMs) were performed to capture the effects of the drug at concentrations characterizing the inflection points and the IC50 of the dose response curve. Subtle variations in drug concentrations from batch to batch and the decline in efficacy following reconstitution cause these points to shift to higher concentrations. Therefore, a dose response curve was performed for each drug immediately prior to a pairwise dose response matrix to avoid these issues. Each single agent drug was tested against both the M12T and M24 cell lines using the same 23 concentrations as the original dose response curve and cell viability was quantified using luminescence as described previously. The same drug stock was then used within five days for the pairwise dose response matrix.

[0174] Gilteritinib with sorafenib, orlistat with tazemetostat, osimertinib with sunitinib, and sunitinib with tazemetostat were tested for areas of synergy across multiple concentrations. Six concentrations were chosen from the dose response curve for each drug centered on the IC50 and capturing the inflection points of the sigmoidal curve. For each pairwise combination the six concentrations for the single drugs were plated in a six-by-six matrix and included a vehicle control. Every combination was plated in biological duplicate. Cell viability was quantified as described previously. SynergyFinder 2.0 was used to calculate synergy scores for each concentration pair using the zero-interaction potency (ZIP) scoring function (Ianevski A, Giri AK, Aittokallio T, Nucleic Acids Res., 2020;48(W1):W488-W493).Attorney Docket No.:206339-0109-00WO RNA-seq of synergistic antiproliferative drug pairs

[0175] The transcriptomic effects of the gilteritinib with sorafenib, orlistat with tazemetostat, osimertinib with sunitinib, and sunitinib with tazemetostat and their respective single agents were characterized via RNA-seq. Cells were incubated overnight on tissue culture-treated 6-well plates at 450,000 cells / well for both the M12T and M24 cell lines. Single-agent drugs and drug combinations were applied at their respective IC35concentrations (Table 3) with a negative vehicle control in triplicate. Cells were collected 24 hours after drug application for all M12T cell line treatments. The osimertinib with sunitinib combination, sunitinib with tazemetostat combination, and their respective single agents for the M24 cell line were also collected at twenty-four hours after drug application. Due to significantly decreased cell viability and RNA quality at 24 hours, cells were collected from the gilteritinib with sorafenib and orlistat with tazemetostat combinations with their respective single agents for M24 cells at eight hours after drug application. Total RNA was extracted using the Qiagen RNeasy kit according to the manufacturer's instructions (Qiagen #74104). Each sample was assessed for quality using Agilent 4200 TapeStation, and all samples had an RNA integrity number (RINe) > 9.0.

[0176] Messenger RNA was purified from total RNA using poly-T oligo-attached magnetic beads. After fragmentation, the first strand cDNA was synthesized using random hexamer primers followed by second strand cDNA synthesis. The library was ready after end repair, A-tailing, adapter ligation, size selection, amplification, and purification. The library was checked with Qubit and real-time PCR for quantification and bioanalyzer for size distribution detection. Quantified libraries were pooled and sequenced using Illumina High Throughput Sequencing (Novogene Bioinformatics Technology Co., Ltd.) at a read depth of at least 20 million paired reads per sample. Adapter sequences were removed from sequencing reads using Cutadapt v4.1. Trimmed sequences were aligned to genome build GRCh38.p12 using STAR 2.7.1. Counts of how many read map to each gene were computed using htseq-count v0.11.1. Differential expression analysis was performed using DESeq2 v1.36.0. Genes were considered differentially expressed if they had a p-value < 0.05; up- regulated genes had a cutoff of log2(fold change) > 1.0, while down-regulated genes had a cutoff of log2(fold change) < -1.0. Functional enrichment analysis was performed on theAttorney Docket No.:206339-0109-00WO differential expression results using enrichr v0.14.0 (GSEApy) to identify functionally enriched Gene Ontology : Biological Process (GO:BP) terms (GO_Biological_Process_2021) from the differential expression data.

[0177] A combinatorial response screen was performed in parallel to the RNA extractions. Cells were treated with gilteritinib with sorafenib, tazemetostat with orlistat, osimertinib with sunitinib, and sunitinib with tazemetostat, as well as with each single agent. Cell viability was quantified using luminescence as described previously at 8-, 12-, and 24- hour time intervals to identify when synergy occurred, which determined when RNA should be extracted. Results confirmed synergy was occurring for the same drug aliquot, drug concentration, and cell passage used for RNA-seq studies (Table 4). Table 3. FDA approved inhibitor dose response curve statistics from application to primary DPM cell lines, lines M12T and M24 M12T (Epithelioid) M24 (Sarcomatoid) Clinical trial pha Avg. FDA approvedGene se AUC IC IAUCtarget50C35AUC IC IC inhibitor50 35(NCT number ) Bumetanide SLC12A2 NA 0.24 NA NA -0.04 NA NA 0.10 Caffeine PDE1B NA 0.26 NA NA 0.13 NA NA 0.19 CisplaƟn† Standardof care NA 0.54 25.9 17.6 0.63 32.6 20.7 0.59DasaƟnib YES1 Phase II(NCT00509041 #)0.85 35 0.06 0.95 20.0 6.1 0.90Flecainide acetateSCN4A NA 0.51 NA NA 0.19 NA NA 0.35GilteriƟnib AXL NA 0.93 24.5 22.5 0.99 8.6 3.9 0.96 Orlistat FASN NA 0.57 38.9 26.9 0.83 12.4 9.8 0.70 OsimerƟnib EGFRPhase III, NSCLC(NCT03521154) 0.95 15.1 12.8 0.96 14.1 12.4 0.96PemetrexedATIC,GART Standard of care 0.3 NA NA 0.29 NA NA 0.29Regorafenib FRK,NTRK1NA 0.80 11.9 9.6 0.93 3.8 2.5 0.87RibociclibCDK4,Phase II CDK6 (NCT02187783 #)0.77 52.1 38.9 0.71 84.5 59.8 0.74Sorafenib RAF1 Phase II(NCT00107432 #)0.93 13.6 11.2 0.97 9.7 7.5 0.95SuniƟnibPDGFRA,Phase II PDGFRB (NCT00392444 #)0.96 13.2 12.1 0.96 13.3 11.8 0.96Attorney Docket No.:206339-0109-00WO Talazoparib PARP1 Phase II(NCT04462809)0.52 NA NA 0.34 NA NA 0.43Tazemetostat EZH2 Phase II(NCT02860286 #)0.72 76.2 73.0 0.73 74.9 71.3 0.73† Cisplatin is a chemotherapy included as the first line therapy. National Clinical Trial (NCT) number from ClinicalTrials(dot)gov. # Trial is completed. Table 4: Combinatorial response screen results for M12T and M24 RNAseq validation of synergy at timepoint total RNA is extracted Log2 Log2 Log2 Treatment Drug 1 Drug 2 both Log2 expected Benjamini- Drug 1 Drug 2Celltime observed observed observed expected fold t-sta Hochberg line(hours) fold fold fold fold change tistic p-value corrected change change change change p-value p-value Gilteritinib Sorafenib-0.96 -1.01 -8.33 -2.95 4.34E-06 -66.00 3.16E-07 4.34E-06Sunitinib OsimertinibM12T 24-0.92 -1.03 -5.17 -2.92 5.10E-05 -64.31 3.50E-07 5.10E-05Sunitinib Tazemetostat -0.92 -1.12 -4.53 -3.08 2.80E-02 -40.38 2.25E-06 2.80E-02Tazemetostat Orlistat -1.12 -0.16 -9.32 -1.47 1.65E-07 -66.12 3.13E-07 1.65E-07Sunitinib Osimertinib 8-0.15 -0.21 -0.49 -0.39 2.00E-02 -17.78 5.88E-05 1.77E-02Sunitinib TazemetostatM24-0.15 -0.13 -0.36 -0.3 5.00E-02 -13.57 1.71E-04 5.32E-02Gilteritinib Sorafenib 24-0.96 -1.01 -8.33 -2.95 4.34E-06 -66.00 3.16E-07 4.34E-06Tazemetostat Orlistat -0.92 -1.03 -9.76 -2.92 1.12E-06 -66.14 3.13E-07 1.12E-06Quantifying relative apoptosis using caspase 3 / 7 luminescence

[0178] M12T and M24 cells were plated at 15,000 cells per well in a tissue culture- treated white 96-well plate (Thermo Scientific) and allowed to adhere overnight. Tazemetostat and orlistat as single agents and in combination were applied to the cells at each drug’s respective IC35in triplicate and incubated for 24 hours with negative and vehicle controls. Apoptotic effects were quantified by measuring caspase 3 / 7 activity using the Caspase-Glo 3 / 7 assay (Promega, G8093) according to the manufacturer’s instructions. Fold- changes against the vehicle controls were quantified for each drug application and significance was determined using a Student’s T-test. Quantifying the cell cycle using flow cytometryAttorney Docket No.:206339-0109-00WO

[0179] The effect of tazemetostat and orlistat individually and in combination on the cell cycle was quantified using flow cytometry in the M12T and M24 cell lines. Cell culture treated T175 flasks were seeded at a concentration of 10 x 106cells in triplicate for a vehicle control, tazemetostat and orlistat as single agents, and in combination at their IC35s (Table 3) for 24 hours. Cells were removed from the flasks, fixed with 80% methanol for 30 minutes at -20°C, and stained with FxCycle™ PI / RNase Staining Solution (Thermo Fisher, F10797). Flow cytometry was performed using a Thermo Fisher Invitrogen Attune NXT Flow Cytometer. Data analysis was performed using using the FlowJo™ v10.8 Software (BD Life Sciences), and the cell cycle was quantified using the Dean-Jett-Fox model to identify the percent of cells in G0 / G1, S, and G2 / M. Defining sets of GO:BP terms that best capture cell proliferation and cell death via semantic similarity

[0180] Search terms ‘cell cycle’ for proliferation and ‘cell death’ were used to identify the Gene Ontology Biological Process (GO:BP) terms that functionally described the cell cycle and seven types of cell death (cell death, apoptosis, autophagy, mitoptosis, necrosis, cell death in response to oxidative stress, and pyroptosis). All terms closely related to the cell cycle or type of cell death were manually curated into eight different sets of GO:BP terms. Jiang-Conrath semantic similarity scores were calculated between all of the GO:BP terms within a set using GOSemSim v3.16 (Yu G, Methods Mol Biol., 2020;2117:207-215). KMeans clustering through sklearn v1.1.3 (jmlr.csail.mit(dot)edu / papers / v12 / pedregosa11a(dot)html) was applied for each set of GO:BP terms with the number of clusters (k) chosen as the smallest number of clusters that had a minimum within cluster semantic similarity score ≥ 0.8. All GO:BP terms with the highest average within cluster semantic similarity score were chosen to represent the process for future semantic similarity analyses (Table 5). Table 5. Final list of GO:BP terms representing the cell cycle and processes of cell death. Process Gene Ontology: Biological Process term(s)Attorney Docket No.:206339-0109-00WO Cell cycleGO:0045930, GO:0045787, GO:0033262, GO:1902749,GO:1902806, GO:0090266, GO:1901990, GO:1901992, GO:1902751, GO:0051726, GO:0000083 Cell deathGO:0010941GO:0008637, GO:1902165, GO:0043280, GO:1904035, GO:2001236, GO:1900119, GO:2001268, GO:0001844, GO:1902230, GO:2001239, GO:0043066, GO:1902175, GO:0006309, GO:1902172, GO:2001243, GO:1902235, Apoptosis GO:1902237, GO:0097194, GO:0043653, GO:1902043, GO:1901029, GO:0036462, GO:1902041, GO:0008635, GO:1902110, GO:2001235, GO:0043154, GO:2000353, GO:0043065, GO:1902255, GO:2001238, GO:0030263, GO:0006915, GO:1900118, GO:2001269, GO:1901028 AutophagyGO:1903146Mitoptosis GO:0000423 NecroptosisGO:0060544Oxidative Stress GO:1903206, GO:1902175 PyroptosisGO:0070269Enrichment of the proliferation and cell death

[0181] Enriched GO:BP terms were identified for gene sets using GSEApy prerank v0.14.0 (Fang Z, Liu X, Peltz G, Bioinformatics, 2023;39(1):btac757). The Benjamini- Hochberg multiple hypothesis correction was applied to the GSEA p-values. The Jiang- Conrath semantic similarity score was used to link enriched GO:BP terms with proliferation or cell death using the curated sets of GO:BP terms defined. Down-regulation of proliferation was defined as significant enrichment of a GO:BP term in the downregulated genes (GSEA NES < 0 and FDR p-value ≤ 0.05) that had significant semantic similarity with the cell cycle GO:BP terms (Jiang-Conrath semantic similarity score ≥ 0.8). Up-regulation of cell death pathways was defined as a significant enrichment of a GO:BP term in the up-regulated genes (GSEA NES > 0 and FDR p-value < 0.05) that had significant semantic similarity to the cell death GO:BP terms (Jiang-Conrath semantic similarity score ≥ 0.8). Determining flux of metabolic pathways using METAFlux

[0182] METAFlux is a computational pipeline that provides flux balance analysis of bulk RNA-seq data (Huang Y, et al., Nat Commun.2023;14(1):4883). The bulk RNA-seq data for the M12T cell line vehicle, orlistat, tazemetostat, and orlistat with tazemetostatAttorney Docket No.:206339-0109-00WO treatments was analyzed using METAFlux to determine the directional flow of metabolites through lipid-related pathways. Flux comparisons between the vehicle control and three drug treatments were made using Student’s t-test and represented using log2(fold change) of the mean flux between triplicates. Metabolic pathways were considered significant if the |log2(FC)| > 1.0 with a p-value < 0.1 in at least one condition. Quantifying changes in cellular lipid concentrations

[0183] The effect of tazemetostat and orlistat on intracellular lipid concentrations was quantified using flow cytometry with the M12T cell line. Cells were plated in T175 flasks at a concentration of 10 x 106cells in triplicate for each sample and incubated overnight. Samples included a vehicle control, tazemetostat, orlistat, or a combination of tazemetostat with orlistat, which were applied to the cells for 24 and 48 hours. The IC50 concentrations were used for the single agents of tazemetostat and orlistat. The combination of tazemetostat with orlistat was applied at the lowest observed synergistic concentrations from the dose response matrix, which was 40 µM and 9 µM, respectively. Cells were removed from the flasks and stained with a LIVE / DEAD™ Fixable Violet Dead Cell Stain Kit (ThermoFisher, L34975) according to the manufacturer’s directions prior to fixation with 4% PFA (FisherSci, AAJ19943K2). Intracellular lipids were then stained with 300 nmol / L Nile Red (Sigma Aldrich, N3013) (Boumelhem BB, et al., J Cell Sci.2022;135(5):jcs258322). Flow cytometry was performed using a Thermo Fisher Invitrogen Attune NXT Flow Cytometer. The flow cytometry data were analyzed using FlowJo™ v10.8 Software (BD Life Sciences). In vivo analysis of tazemetostat with orlistat using subcutaneous murine models

[0184] The National Cancer Institute (NCI)-derived DPM cell line H2373 was obtained from American Type Culture Collection (CRL-5943) and was used to generate pleural orthotopic tumors in murine models. Prior to establishing the tumors, synergy between the tazemetostat and orlistat drug combination was measured against the H2373 in vitro. Dose response curves were performed using ten concentrations for both tazemetostat [0 µM, 0.781 µM, 1.56 µM, 3.12 µM, 6.26 µM, 12.5 µM, 25 µM, 50 µM, 100 µM, and 200 µM] and orlistat [0 µM, 1.953 µM, 3.906 µM, 7.8125 µM, 15.625 µM, 31.25 µM, 62.50 µM, 125 µM, 250 µM, and 500 µM] prior to a combinatorial screen to determine the IC35 at 72Attorney Docket No.:206339-0109-00WO hours after drug application. A combinatorial response screen was performed to confirm a synergistic response against the H2373 cell line by applying tazemetostat and orlistat at their IC35concentrations and comparing the observed effect of the combination against the expected additive effect calculated by the Bliss Independence Model from the single agents. Cell viability for both assays was measured using the CellTiter 96® AQueous assay (Promega, G3582).

[0185] H2373 cells stably expressing the luciferase reporter vector pGL4.51[luc2 / CMV / Neo] (Promega) were injected into the thoracic cavity of 6-8-week-old NOD.Cg-PrkdcscidIl2rgtm1Wjl / SzJ (NSG) mice at a concentration of 1 x 106cells / mice. Treatments were applied after establishing the xenografts for two weeks. A total of 40 xenografts were randomized into five groups: vehicle, orlistat, tazemetostat, combination of orlistat and tazemetostat, and cisplatin. Tazemetostat was delivered orally once a week at a concentration of 75 mg / kg. Orlistat was delivered via intrapleural injection at a concentration of 180 mg / kg. These concentrations and delivery methods were also used for the combination therapy. Cisplatin was delivered via intraperitoneal injection once a week at a concentration of 3 mg / kg. Treatments were delivered for three weeks, and tumor volume and mouse body weight data were collected on days 1, 9, 18, and 23. An unpaired Student’s t-test was used to calculate significance (p-value < 0.05) between means of treatments. Synergy was calculated using the Bliss Independence Model applied to the median average radiance of mice within a treatment cohort. The results are now described. Discovering gene knockouts that affect cellular proliferation in DPM tumor cells

[0186] Genes with therapeutic potential were systematically and unbiasedly identified by conducting a functional genomics screen in two primary low-passage number DPM cell lines representing the epithelioid (M12T) and sarcomatoid (M24) subtypes (Figure 1A). The genes are potentially therapeutic because knockout of the gene significantly reduces cell viability of proliferation. The screens were strategically conducted in cell lines representing subtypes that account for most of the histologic spectrum of DPM tumors (Alì G, Bruno R, Fontanini G, J Thorac Dis., 2018;10(Suppl 2):S276-S284). sgRNAs targeting each gene wereAttorney Docket No.:206339-0109-00WO integrated into a gene level essentiality score that corrects for noise and sgRNA efficiency and quantifies the effect each gene knockout has on proliferation (Allen F, Behan F, Khodak A, et al., Genome Res., 2019;29(3):464-471) (Figure 1B).2,727 essential genes for M12T and 2,964 for M24 were discovered (gene essentiality score < 0 and BH corrected value < 0.001; Table 6). Next, 1,034 DPM-specific essential genes for M12T and 1,517 for M24 were identified by excluding commonly essential genes (Meyers RM, Bryan JG, McFarland JM, et al., Nat Genet., 2017;49(12):1779-1784; Tsherniak A, Vazquez F, Montgomery PG, et al., Cell, 2017;170(3):564-576.e16) (Figure 2A).1,921 DPM-specific essential genes were identified by taking the union of M12T and M24 which maximizes the therapeutic potential for both subtypes (Figure 2A). The DPM-specific essential genes were significantly enriched with 85 genes previously associated with DPM (Piñero J, Ramírez-Anguita JM, Saüch- Pitarch J, et al., Nucleic Acids Res., 2020;48(D1):D845-D855) (p-value = 1.8 x 10-2). The 1,921 DPM-specific essential genes that significantly decrease cell viability or proliferation are excellent targets for repurposing FDA-approved inhibitors.O 0W067 _50 b_0513 27920527- db 5_ _b 5_7_5_ _b 66_d9s_ b0bfsbd_ds2 _sbbdb_s db_s d_ds_s_bbd_1t,f0 6 t,1bft,13f _t,6bft, bft,3bftft, sbf- 2289_1 4b 4_ 24 d_2 0 28 ,4 01t,_0275 5_ 24 6_ 943s3rbdb6e0t_d__b sdbfb_td_ b_db b_d d_ b b_d d_ bdssbf sbf_s ,2b 2s8bf_s_bsbsbf1 _sbsbf_s8:ult.c ,oi4b4 6t,2 ft, 2_t,ft,5 ft,t,81ft,t, bft,042 5575 25 b5d53 14 32_55694 _b25374 48_bNd_teet b _ad n_i _ an 6s c_ _s_bn 4_bd3_ _n d_6bs atcesntas_ _n 3_d_secsftt, ac5snta_sna acecst bfs btcsf na act tet,cstsnta becsftt,kccoft,63oss4 5 gr 0gr eg53_gr eg ,8 eg gr27 eg gr eg 63D a1L__at,23at, ra n at, ra 21 ra at5_ ra atra 5_bna65_b14t,7ac 36t,_bt, ,50nat, ,83t,nayAdeN_cssnbte3r Gg_dn_4_ 3 ste1_36d_363_cs7asbna5_t34 83cstn grna1_sb1_na e5g_ _na2_ ego Y ftS t, rt4 atc,sfttm0 e ,c8staatecst,c6s9te 4n_acsftnacra nac na rat,5 cs ste t, cs stecs t,Ap 27d _5 gn_anr 0act1_gnraetg3r2 ga_nr batd_ e 13 tegs gra_n gr r 7at5 t_egn gr rtae 7tgr5_nsatc,e s5at07 c,s0t t1 , a66g7c,s0bft t1 , aat, ,3aat, ,2 at, a6t,09cst 06 72 cst 67 54 25cstreagtr _,a6tate, i 5gr _aati4_ eat74gr _6atat3i6 4__ eatgr2a_a_tateigr4a_ _tatati4_ eatgra00 p,39658_ t_4_1_b,d6 p,i5010 p,43__5_5_b,d0 p,n6029 aicp t,ipp, ,ipp,ip t,s, 3 , 6 0 , 8 , 0_4_t8 8 4 7 9 4 9 2 0 9e5_1_5_1_4_5_1_1_4 4 4ati apti apt_isap8bft ti apti ap2 t_isapbft ti apti gp2 ra atti apti apti a a a a a_a_a_aptip tip tip tip tip 0 tip tip tipN N N N N N N N N N N N N Y N N N N N N N N N N N NYY Y Y Y Y Y Y YY Y Y Y Y Y Y N Y Y Y Y Y Y Y Y Y0007 7 1 7 7 8 7 6 3 0 0 8+00+00+00+00+00+00+00 0+0+0+ 030 0 9 9 8 8 7 7 7 7 676462616-3-3-2-2-2-2-2-2 2 2 2 2 2 2 2sEtl0E E E E E E E E E E E E E E E E E-E-E-E-E-E-E-E-E0000000000000000 0 7 8 4 0 1 6 5 6 2 2 5 2 9 8 3 4 9ud. . . . . . . .0.0.si0 0 0 0 0 0 0 0 0 0 6.3.1.7.9.6.3.9.9.6.4.1.8.7.0.2.6eo 4 6 1 1 3 8 6 7 4 2 5 7 5 3 4 1t ram noecerraS38. 78. 51. 65. 16. 06. 45. 35. 46. 4525250594945454140493838373635353c42. .s2 -2-3-2-2-2-2-2-2-2-2.-2. .-2-2.-2.-2. . . .-2-2-2-2.-2.-2.-2.-2.-2.-2-sM cimo99. 8273.34605. 600608881738. 17. 18. 75. 76. 0726962636575492443564-2.-3.-2-2.-2.-2.-2.-2.-2-2-2-2-2-2.-2.-2. . . . . . . . .-2-2-2-2-2-2-2-2-2-neglano 0i0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 001 0 018 0 0t +0+0 0+0 0 0 0+0 0+0+0+0+0+0+0+0+0 0+1 0+0+2 0 0cE+0En0u. 0E+0. 0E0. 0E+0. 0E+0. 0E+0. 0E0. 0E+0. 0E0. 0E0. 0E0. 0E00E00E00E00E00E+00E00E- 00E04E90E- +00E07E+50E000fd0 0 0 0 0 0 0 0 0 0 0.0.0.0.0.0.0.0.0.0. .0.0. .0.0i 1 1touileohkticpE 17. 62. 395958577776828398293528971495186307246597806548oTn213-3.-2.-2. . . . . . . . . . . . . . . . . .-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-2-1-2.-2.-2.-2.-2- k M 9sac-98. 9R3 5. 031. 730. 830. 438. 329. 929. 125. 125. 31. 31. 76. 39. 00. 16. 97. 90. 65. 50. 46. 86. 09. 81. 56. 52.P- - - - - - - - -2-3-3-2-2-3-2-2-3-2-3-1-2-2-2-2-3-SIR CD:IlL6o2RA31CBP1S PNX413 65 0 5 2 P 1A1 K 53LA3 8L3BP 2K8SB B A 172MP L 7C S2 6Le b L SPNl mORP CKVFN R SNPRPRMSLPPRPRULBPRDCPR 3FESCR LTRTPR SP ONNEECPYDC FIEPRbyP H RaSTO 8W06 0 994 205 _0 b5 2852_751- d5_7_ b_ _5b _bbdbd _ _b9s0bdf1t_,s d_ dbsb__ss_sbdbbftbft_ds_sbbft0 6f- 2t2, f4t, ft9_ 205,,70,4 0ft,5022423,4 8113b 43d6__5_ _b_b_b_b24_ _bsbdbd d_d d ds_ _ _ bd d_0 72:1b.3f_to_,s_bf sbfbf stbsbsb _,ft,ftftsbsfbft b41t,t,d_6_7325 80 402,2,2t, ,85565624 73 45n 5_6_ 6__n_ _ _5_ _Ntsb ac n nbdancancanan nae ft,k6staecs atcst_sb ste st cest cestaecsctstec1o7 g_rae eftD ntgrgr ,5 gra gra gra graegr 9779 1 graa,c8 a3 t,at, 1_t,t,t,t,a2 3 5t,26314 b375 6 8t,_ _ _ 34533201 63 b b b 7ysetnerg_2 4 d_ _ _ _ _2 d_d_d_4_rnaac46_n_n sbf nanca 02 nana _n sbf sbf sbf45 naot t,t 78st2 acs acs t, st cst3cst cstacs t, t, t, 3cst6eg _bd tete832egeg _bdegeg te227469 _321b egA936__ rabnt, _5s gbrgr _fat,at,nraadact r,a2t, _0srbaft r,a6t,gr _ _ _d_0at,nan n sracacacbft,4_cssbt0fe7_4t,8 7a7 006 st 11562t,47486 67 stststt, 56t126_2_ eg5 _a_a1 _a_a 4_ eg 0876 egeg 10 _a,grt 0890ai4_5_a art, p, 5384513_nanticacp,ti4pa,t5,_t nipt ,i6p, _t nipt ,ip,atir467 ac 512ac 2 6pa,t2,_30 n_anracact r,a0t5,_t 8niapc,25_7 3 5 3 bad stst 5 5 1st 422st22514586stst8506st15t_iapt _iapt _iapt _iap3 t_ispbefgtr eagtr _aatt _iapt _iapt eigpr _aatt _iapt eigp6 r _aatt _iap9 t _iapt _iapt eigpr eagtr _aatt _iapt eigpr _aattip 5N N N N N N N N N N N N N N N N Y N N N N N N N N N N N N N N N N N N NY Y N Y Y Y N Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y85857545259444343434047 6 6 1 0 8 8 4 4 9 9 9 9 8 8 7 6 3 2 2 2 2 1 0 82 2 2 2 2 2 2 2 2 2 232323232322 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0- - 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Claims

1. Attorney Docket No.:206339-0109-00WO CLAIMS What is claimed is:

1. A composition for treating mesothelioma, wherein the composition comprises one or more inhibitors selected from the group consisting of: dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat.

2. The composition of claim 1, wherein the inhibitor is selected from the group consisting of: gilteritinib, orlistat, osimertinib, sunitinib, sorafenib, and tazemetostat.

3. The composition of claim 1, wherein the composition comprises two or more inhibitors.

4. The composition of claim 3, wherein the two or more inhibitors are selected from the group consisting of: a) gilteritinib and regorafenib; b) osimertinib and tazemetostat; c) ribociclib and gilteritinib; d) ribociclib and osimertinib; e) ribociclib and regorafenib; f) ribociclib and sorafenib; g) sorafenib and gilteritinib; h) sorafenib and tazemetostat; i) sunitinib and gilteritinib; j) sunitinib and osimertinib; k) sunitinib and ribociclib; l) sunitinib and tazemetostat; m) tazemetostat and gilteritinib; n) tazemetostat and orlistat; o) dasatinib and osimertinib; p) dasatinib and sunitinib; and q) orlistat and sunitinib.Attorney Docket No.:206339-0109-00WO 5. The composition of claim 4, wherein the two or more inhibitors are selected from the group consisting of: a) tazemetostat and orlistat, b) sunitinib and tazemetostat, c) sunitinib and osimertinib, and d) sorafenib and gilteritinib.

6. The composition of claim 1, wherein the mesothelioma is diffuse pleural mesothelioma (DPM).

7. A method for treating mesothelioma in a patient in need thereof, the method comprising administering one or more inhibitors selected from the group consisting of: dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat to the subject.

8. The method of claim 7, wherein the inhibitor is selected from the group consisting of: gilteritinib, orlistat, osimertinib, sunitinib, sorafenib, and tazemetostat.

9. The method of claim 7, wherein the method further comprises administering at least one additional agent to the subject.

10. The method of claim 9, wherein the at least one additional agent comprises an inhibitor selected from the group consisting of: dasatinib, gilteritinib, orlistat, osimertinib, regorafenib, ribociclib, sunitinib, sorafenib, and tazemetostat.

11. The method of claim 9, wherein the method comprises administering two or more inhibitors selected from the group consisting of: a) gilteritinib and regorafenib; b) osimertinib and tazemetostat; c) ribociclib and gilteritinib; d) ribociclib and osimertinib;Attorney Docket No.:206339-0109-00WO e) ribociclib and regorafenib; f) ribociclib and sorafenib; g) sorafenib and gilteritinib; h) sorafenib and tazemetostat; i) sunitinib and gilteritinib; j) sunitinib and osimertinib; k) sunitinib and ribociclib; l) sunitinib and tazemetostat; m) tazemetostat and gilteritinib; n) tazemetostat and orlistat; o) dasatinib and osimertinib; p) dasatinib and sunitinib; and q) orlistat and sunitinib.

12. The method of claim 11, wherein the method comprises administering two or more inhibitors selected from the group consisting of: a) tazemetostat and orlistat, b) sunitinib and tazemetostat, c) sunitinib and osimertinib, and d) sorafenib and gilteritinib.

13. The method of claim 9, wherein the method comprises administering two or more inhibitors essentially simultaneously.

14. The method of claim 9, wherein the method comprises administering two or more inhibitors sequentially.

15. The method of claim 9, wherein the mesothelioma is diffuse pleural mesothelioma (DPM).Attorney Docket No.:206339-0109-00WO 16. A method of repurposing FDA-approved therapeutics as therapeutics for additional diseases comprising the steps of: a) generating a first data set comprising a first set of genes, transcripts, markers or protein targets that are identified by a functional genomics screen as being essential for a disease or disease stage; b) generating a second data set comprising a second set of genes, transcripts, markers or protein targets associated with the disease or disease stage from multi-omics data comprising the steps of: i) obtaining multi-omic data related to the disease or disease stage; ii) running the multi-omic data through a biclustering algorithm; and iii) determining the second set of genes, transcripts, markers or protein targets associated with the disease or disease stage from the biclusters; c) generating a third data set comprising a set of genes, transcripts or protein targets for which there is a known FDA-approved therapeutic agent that targets the gene, transcript or protein targets; d) identifying a set of overlapping genes, transcripts or protein targets from the first, second and third data sets; and e) identifying an FDA-approved therapeutic agent for re-purposing when the target of the FDA-approved therapeutic agent is identified in the set of overlapping genes, transcripts or protein targets in step d.

17. The method of claim 16, wherein the functional genomics screen is selected from the group consisting of: a genomic screen, a proteomic screen, a metabolomic screen, a transcriptomic screen, and a drug screen.

18. The method of claim 17, wherein the functional genomics screen is selected from the group consisting of: an siRNA screen, an shRNA screen, a microRNA screen, a Zinc finger nuclease screen, a TALEN screen, a CRISPR-Cas9 screen, a CRIPSRi screen, a CRISPRa screen, a dual CRISPRi / a screen, and a CRISPRsc screen.Attorney Docket No.:206339-0109-00WO 19. The method of claim 16, wherein the first data set is generated by comparing the results between more than one screen.

20. The method of claim 19, wherein genes, transcripts or proteins common to two or more screens are removed from the data set.

21. The method of claim 16, wherein the step of generating a second data set comprising a second set of genes, transcripts, markers or protein targets associated with the cancer from multi-omics data comprises the steps of: i) obtaining at least one multiomic data set for the disease or disease stage; ii) identifying a subset of genes, transcripts, markers or proteins from the at least one multiomic data set that are significantly variable within the at least one multiomic data set; iii) combining the subset of genes, transcripts, markers or proteins identified in step ii) with the first set of genes, transcripts, markers or protein targets that are identified by a functional genomics screen as being essential for the disease or disease stage; iv) constructing a set of biclusters from the combined set of genes, transcripts, markers or proteins assembled in step iii); v) identifying the subset of biclusters that are significantly co-expressed; vi) analyzing the bicluster set to identify the subset of biclusters that are significantly co-expressed and disease relevant; and vii) compiling the genes included in the subset of biclusters that are significantly co-expressed and disease relevant into the second dataset.

22. The method of claim 21, wherein the multiomics data comprises gene expression data, somatic mutation data, miRNA expression data, transcriptome expression data, chromatin accessibility data, protein expression data or phenotypic data or any combination thereof.Attorney Docket No.:206339-0109-00WO 23. The method of claim 21, wherein step ii) comprises generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set.

24. The method of claim 23, wherein the method of generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set comprises: a) systematically integrating mutations and defines a role for each gene wherein the role is protein affecting mutation (PAM), transcript fusions (Fusion), copy number alteration deletion (CNAdel), copy number alteration amplification (CNAamp), activating (Act), or loss of function (LoF); and b) assigning a binary value to each gene indicating the presence or absence of a mutation.

25. The method of claim 16, wherein the disease is cancer.

26. The method of claim 25 wherein the method comprises: a) generating a first data set comprising a first set of genes, transcripts, markers or protein targets that are identified by a functional genomics screen as being essential for tumor survival and growth of the cancer; b) generating a second data set comprising a second set of genes, transcripts, markers or protein targets associated with the cancer from multi-omics data comprising the steps of: i) obtaining multiomic data related to the cancer; ii) running the multiomic data through a biclustering algorithm; and iii) determining the second set of genes, transcripts, markers or protein targets associated with the cancer from the biclusters; c) generating a third data set comprising a set of genes, transcripts or protein targets for which there is a known FDA-approved therapeutic agent that targets the gene, transcript or protein targets; d) identifying a set of overlapping genes, transcripts or protein targets from the first, second and third data sets;Attorney Docket No.:206339-0109-00WO e) identifying an FDA-approved therapeutic agent for re-purposing when the target of the FDA-approved therapeutic agent is identified in the set of overlapping genes, transcripts or protein targets in step d); and f) administering the FDA-approved therapeutic agent identified in step e) to a subject diagnosed as having or being at increased risk of development or recurrence of the cancer.

27. The method of claim 26, wherein step ii) comprises generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set.

28. The method of claim 27, wherein the method of generating a binary mutation matrix for the presence or absence of mutations within the multiomics data set comprises: a) systematically integrating mutations and defines a role for each gene wherein the role is PAM, fusion, CNAdel, CNAamp, Act, or LoF; and b) assigning a binary value to each gene indicating the presence or absence of a mutation.

29. The method of claim 26, wherein the cancer is mesothelioma, breast cancer, lung cancer, head and neck cancer, bladder cancer, stomach cancer, cancer of the nervous system, bone cancer, bone marrow cancer, brain cancer, colon cancer, esophageal cancer, endometrial cancer, gastrointestinal cancer, genital-urinary cancer, stomach cancer, lymphomas, melanoma, glioma, bladder cancer, pancreatic cancer, gum cancer, kidney cancer, retinal cancer, liver cancer, nasopharynx cancer, ovarian cancer, oral cancers, bladder cancer, hematological neoplasms, follicular lymphoma, cervical cancer, multiple myeloma, osteosarcomas, thyroid cancer, prostate cancer, colon cancer, prostate cancer, skin cancer, stomach cancer, testis cancer, tongue cancer, or uterine cancer.

30. The method of claim 16, wherein the method further comprises the step of administering the identified FDA-approved therapeutic agent in step e to a subject in need.

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