e21211 Background: Cancer management using cytotoxic drugs is hampered by limited efficacy. Hence, a personalized treatment approach matching chemotherapy with appropriate patients remains a persistent and unmet need in the clinic. Genomic heterogeneity among patients creates an opportunity to discern key genomic aberrations and pathways that confer resistance and response to standard treatment options. Pemetrexed, an antifolate, primarily inhibits thymidylate synthase (TYMS) while platinum compounds (carboplatin/cisplatin) trigger DNA damage similar to alkylating agents. When the DNA damage exceeds repair, apoptosis results. We conducted a pilot study using CBM to novel genomic biomarkers of response and resistance to pemetrexed–platinum treatment. Methods: 25 patients who received pemetrexed–platinum therapy were selected from TCGA data. Mutation and copy number aberrations from each case served as input into the CBM (generated from PubMed and other online resources) to create a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. Drug impact on the disease network was digitally simulated to determine treatment efficacy by measuring effect of chemotherapy on the cell growth score, i.e., a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. Effectively, the mechanism of action of each drug was mapped to each patient’s genome and biological consequences due to genomic abnormalities were correlated with response. Results: Among the 25 patients, 23 responders (R) and 2 non-responders (NR). The computer simulation correctly predicted response in 19/25 with 76% accuracy, 100% specificity and 73.91% sensitivity. CBM identified co-occurrence of deleted segments of chromosome 6q, 13q, 14q and 17q were responsible for pemetrexed-platinum response. The key genes governing response on these chromosomes included drug transporters and DNA repair pathways. ATP7B (13q) which exports platinum-based drugs was deleted in responders. The loss of REV3L (6q ), MBD1 (6q ), ERCC1 (6q), BRCA2 (13q), BRCA1 (17q), FANCM (14q), RAD51B (14q), XRCC3 (14q ) led to failure of DNA repair. RB1 (13q) del also led to failure of BRG1 mediated DNA repair . Combinations of these repair genes and transporters formed the major response criteria among the 23 responders while these characteristics were absent in non-responders. Conclusions: This pilot study highlights how CBM simulation platform can identify patients for therapy response prediction. Copy number changes impact responsiveness to chemotherapy and should be routinely assessed. We suggest that this approach should be validated prospectively in a larger patient cohort.

2519 Background: Despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of GBM patients remains relatively poor. Therapy selection is often based on information considering only a single aberration and ignoring other patient-specific omics data which could potentially enable more effective treatment selection. The Cellworks Singula™ report predicts response for physician prescribed therapies (PPT) using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. We test the hypothesis that Singula is a superior predictor of progression-free survival (PFS) and overall survival (OS) compared to PPT. Methods: Singula’s ability to predict response was evaluated in an independent, randomly selected, retrospective cohort of 109 GBM patients aged 17 to 83 years treated with PPT. Patient omics data was available from TCGA. Singula uses PubMed to generate protein interaction network activated and inactivated disease pathways. We simulated PPT for each patient and calculated the quantitative drug effect on a composite GBM disease inhibition score based on specific phenotypes while blinded to clinical response. Univariate and multivariate proportional hazards (PH) regression analyses were performed to determine if Singula provides predictive information for PFS and OS, respectively, above and beyond age and PPT. Results: In univariate analyses, Singula was a significant predictor of both PFS (HR = 4.130, p < 0.000) and OS (HR = 2.418, p < 0.0001). In multivariate PH regression analyses, Singula (HR = 4.033, p < 0.0001) remained an independent predictor of PFS after adjustment for PPT (p = 0.1453) and patient age (p = 0.4273). Singula (HR = 1.852, p = 0.0070) was also a significant independent predictor of OS after adjustment for PPT (p = 0.4127) and patient age (p = 0.0003). Results indicate that Singula is a superior predictor of both PFS and OS compared to PPT. Singula provided alternative therapy selections for 29 of 52 disease progressors detected by Cellworks. Conclusions: Singula is a superior predictor of PFS and OS in GBM patients compared to PPT. Singula can identify non-responders to PPT and provide alternative therapy selections.

Background: Monosomy 7/Del 7 (-7) or its long arm (del(7q)) is one of the most common cytogenetic abnormalities in pediatric and adult myeloid malignancies, particularly in adverse-risk acute myeloid leukemias (AMLs). In general, (-7) is associated with poor response to induction chemotherapy (PMID 12393746). At the same time, not all patients fare poorly so the ability to identify responders and non-responders remains a high priority. Aim: To predict the response for induction chemotherapy in AML patients with (-7) and identify novel genomic signatures of response and resistance. Methods: Genomic data from 13 consecutive patients with (-7) were analyzed using the Cellworks Omics Biology Model (CBM) to generate patient-specific protein network models. All data was anonymized, de-identified and exempt from IRB review. For each model, disease simulations were performed and patients were segregated into HOXA-upregulated and HOXA-downregulated cohorts based on the simulation levels of HOXA5 and HOXA9. Digital drug simulations for induction chemotherapy were accomplished by measuring the impact of drug effect on a cell growth score, a composite of cell proliferation, viability and apoptosis indices. Each patient-specific model was analyzed to identify mechanisms underlying treatment outcomes. Results: 7/13 (54%) of (-7) patients failed to achieve remission after induction chemotherapy (Table 1) which highlighted that (-7) alone does not confer resistance to chemotherapy. CBM identified other genomic alterations that determine chemotherapy response, including DNA repair deficiency genes, mismatch repair (MMR), and homologous recombination repair (HRR) genes. DNA methylation and histone methylation (H3K27me) impacting HOXA gene expression, mainly HOXA5 and HOXA9, were identified as upstream regulators of DNA repair genes. Loss or reduced levels of EZH2 is associated with lower H3K27 methylation and thereby higher expression of HOXA5 and HOXA9 gene targets. High active levels of HOXA correlated with low rates of successful remission induction (22%, n=7) and lower activity levels of HOXA correlated with successful induction chemotherapy (100%, n=4) (Table 1). CBM identified that (-7) results in a decreased expression of EZH2, CARD11, EIF3, PMS2, HUS1, KMT2C (MLL3), CDK5 and IKZF1 genes. Since EZH2 abnormalities alone are not implicated in poor prognosis for AML patients, we sought other aberrations that prevent HOXA upregulation. Using CBM, we identified multiple accompanying aberrations which regulate HOXA genes. Deletions of KAT6A, ASXL1, DNMT3B, DNMT3L genes and high KMT2A-partial tandem duplication correspond to HOXA-upregulation whereas EED amplification, gain of function mutations in DNMT3A, mutations in IDH1/2, MYC and KAT6A amplification and KDM4A deletion result in HOXA-downregulation. Conclusion: Alterations of chromatin regulation have consequences for transcription factors that regulate expression of DNA repair genes. Under conditions where DNA repair is enhanced, induction chemotherapy was 78% less likely to effect remission in (-7) AML patients undergoing induction chemotherapy. Loss of H3K27 methylation associated with loss of PRC2 function by any means resulted in HOXA-upregulation and upregulation of DNA repair genes induced resistance to induction therapy. On the other hand, CBM analysis identified genetic signatures associated with a 100% remission rate from AML induction therapy despite the presence of (-7). Generation of H3K27me caused by PRC2 activation resulting from numerous mechanisms led to HOXA-downregulation and 100% response to induction therapy. Stratification of patients harboring (-7) by HOXA biomarker analysis could inform treatment planning, avoid drug-related adverse events and reduce treatment costs after validation with a larger prospective dataset. Disclosures Castro: Cellworks Group Inc: Consultancy. Watson:Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kumar:Cellworks Research India Private Limited: Current Employment. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Mohapatra:Cellworks Research India Private Limited: Current Employment. G:Cellworks Research India Private Limited: Current Employment. Agarwal:Cellworks Research India Private Limited: Current Employment. Suseela:Cellworks Research India Private Limited: Current Employment. Ganesh:Cellworks Research India Private Limited: Current Employment. Sauban:Cellworks Research India Private Limited: Current Employment. Kumar:Cellworks Research India Private Limited: Current Employment. Raman:Cellworks Research India Private Limited: Current Employment. Singh:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Lunkad:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Howard:Servier: Consultancy, Other: Speaker; Boston Scientific: Consultancy; Sanofi: Consultancy, Other: Speaker; EUSA Pharma: Consultancy; Cellworks: Consultancy.

Background: HMAs (e.g., azacitidine (AZA), decitabine (DAC)) are approved agents for the treatment of patients with MDS. Despite their widespread use, only 50% of patients respond to HMA without reliable biomarkers of response despite knowing the patient's genomic and clinical (non-genomic) characteristics prior to treatment. Unfortunately, no comprehensive method exists to predict HMA response in a patient using both genomic and non-genomic parameters, though we recently developed a genomics-informed computational biology method (CBM) for MDS (PMID 27855285). Predicting treatment response would improve management of MDS patients by empowering the clinician to restrict treatment-related adverse events to those who would benefit and also reduce health care costs.

Hypomethylating agents (HMAs) (azacitidine (aza), decitabine (dec)) and lenalidomide (len) are approved agents and used to treat patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML). Despite their widespread use, HMAs fail in the majority of these patients, and len fails in 75% of non-del(5q) MDS. Currently, no method exists to predict disease response, thus the management of MDS and AML patients is challenging. Methods: Patients with AML or MDS were recruited to a clinical trial (NCT02435550) designed to assess predictive values by comparing computer predictions of drug response to actual clinical response. Genomic profiling was conducted by cytogenetics, whole exome sequencing, and array CGH. Genomic results were inputted into a computational software (Cellworks), which generates disease-specific protein network maps using PubMed and other resources. Digital drug simulations were conducted by quantitatively measuring drug effect on a cell growth score (proliferation + viability + apoptosis). Each patient-specific protein network was screened for the extent by which aza, dec or len reduced disease growth in a dose-respondent manner. Treatment was physician’s choice of SOC. Clinical outcomes were prospectively recorded. IWG criteria were used to define response. Western blot assays were performed to validate the predicted protein network perturbations. Fisher’s exact test was used to compare prediction values of the genomics-informed computer method versus empiric drug administration. Results: 88 patients have had all molecular tests and computational modeling performed. Lab validation of computer-predicted, activated protein networks in 19 samples from 13 different patients showed correct prediction of 5 activated networks (Akt2, Akt3, PIK3CA, p38, Erk1/2) in 17 samples, with 89% accuracy. At the time of this report, 26/88 patients were eligible for efficacy evaluation. 8/26 patients showed clinical response to SOC therapy, 18/26 did not. 24/26 outcome predictions were correctly matched to their clinical outcomes, and 2/20 were incorrectly matched, resulting in 92% prediction accuracy, 80% PPV, 100% NPV, 100% sensitivity, and 89% specificity. The accuracy of the genomics-informed computer method was significantly greater than empiric drug administration (p=1.664e-05). New genomic signature rules were discovered to correlate with clinical response after aza, dec or len. Summary: A computational method that models multiple genomic abnormalities simultaneously showed high predictive value of protein network aberrations and clinical outcomes after SOC treatments. The network method uncovered molecular reasons for drug failure and highlighted resistance pathways that could be targeted to recover chemosensitivity. This technology could also be used to establish eligibility criteria for precision enrollment in drug development trials Citation Format: Leylah Drusbosky, Kimberly E. Hawkins, Shireen Vali, Taher Abbasi, Ansu Kumar, Neeraj Kumar Singh, Kabya Basu, Chandan Kumar, Amjad Husain, Caitlin Tucker, Randy A. Brown, Maxim Norkin, John Hiemenz, Jack Hsu, John Wingard, Christopher R. Cogle. iCare 1: A prospective clinical trial to predict treatment response based on mutanome-informed computational biology in patients with AML and MDS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr CT085. doi:10.1158/1538-7445.AM2017-CT085

Introduction Ursolic acid (UA) is a pentacyclic triterpene acid present in many plants, including apples, basil, cranberries, and rosemary. UA suppresses proliferation and induces apoptosis in a variety of tumor cells via inhibition of nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB). Given that single agent therapy is a major clinical obstacle to overcome in the treatment of cancer, we sought to enhance the anti-cancer efficacy of UA through rational design of combinatorial therapeutic regimens that target multiple signaling pathways critical to carcinogenesis. Methodology Using a predictive simulation-based approach that models cancer disease physiology by integrating signaling and metabolic networks, we tested the effect of UA alone and in combination with 100 other agents across cell lines from colorectal cancer, non-small cell lung cancer and multiple myeloma. Our predictive results were validated in vitro using standard molecular assays. The MTT assay and flow cytometry were used to assess cellular proliferation. Western blotting was used to monitor the combinatorial effects on apoptotic and cellular signaling pathways. Synergy was analyzed using isobologram plots. Results We predictively identified c-Jun N-terminal kinase (JNK) as a pathway that may synergistically inhibit cancer growth when targeted in combination with NFκB. UA in combination with the pan-JNK inhibitor SP600125 showed maximal reduction in viability across a panel of cancer cell lines, thereby corroborating our predictive simulation assays. In HCT116 colon carcinoma cells, the combination caused a 52% reduction in viability compared with 18% and 27% for UA and SP600125 alone, respectively. In addition, isobologram plot analysis reveals synergy with lowered doses of the drugs in combination. The combination synergistically inhibited proliferation and induced apoptosis as evidenced by an increase in the percentage sub-G1 phase cells and cleavage of caspase 3 and poly ADP ribose polymerase (PARP). Combination treatment resulted in a significant reduction in the expression of cyclin D1 and c-Myc as compared with single agent treatment. Conclusions Our findings underscore the importance of targeting NFκB and JNK signaling in combination in cancer cells. These results also highlight and validate the use of predictive simulation technology to design therapeutics for targeting novel biological mechanisms using existing or novel chemistry.