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Background: DNA methyltransferase inhibition (DNMTi) with the hypomethylating agents (HMA) azacitidine (AZA) or decitabine, remains the mainstay of therapy for the majority of high-risk Myelodysplastic Syndromes (MDS) patients. Nevertheless, only 40-50% of MDS patients achieve clinical improvement with DNMTi. There is a need for a predictive clinical approach that can stratify MDS patients according to their chance of benefit from current therapies and that can identify and predict responses to new treatment options. Ideally, patients predicted to be non-responders (NR) could be offered alternative strategies while being spared protracted treatment with HMA alone that has a low likelihood of efficacy. Recently, an intriguing discovery of immune modulation by HMA has emerged. In addition to the benefits of unsilencing differentiation genes and tumor suppressor genes, HMA's reactivate human endogenous retroviral (HERV) genes leading to viral mimicry and upregulation of the immune response as a major mechanism of HMA efficacy. Although the PD-L1/PD1 blockade plus HMA has been recognized as a beneficial combination, there are no established markers to guide decision-making. We report here the utility of immunomic profiling of chromosome 9 copy number status as a significant mechanism of immune evasion and HMA resistance. Methods: 119 patients with known clinical responses to AZA were selected for this study. Publicly available data largely from TCGA and PubMed was utilized for this study. The aberration and copy number variations from individual cases served as input into the Cellworks Computation Omics Biology Model (CBM), a computational biology multi-omic software model, created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a disease model and then conduct biosimulations to measure the effect of AZA on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy. Results: Although AZA treatment increased tumor associated antigens and interferon signaling, it also increased PD-L1 expression to inactivate cytotoxic CD8(+) T cells. Copy number alterations of the chromosome 9p region were found to significantly drive PD-L1 expression with multiple genes such as CD274, IFNA1, IFNA2, JAK2, PDCD1LG and KDM4C playing a role in PD-L1 regulation further increasing immune suppression (Figure 1). Among 6 cases of chromosome 9p aberration in this dataset, 9p amp (n=2) were clinical non-responders (NR) while 9p del (n=4) were responders (R) to AZA. In principle, checkpoint immunotherapy could improve outcomes for patients with 9p abnormalities. Additionally, copy number variation loss of key genes located on chromosome 16 involved in antigen processing and presentation such as CIITA, CTCF, IRF8, PSMB10, NLRC5, and SOCS1 were found to negatively impact AZA sensitivity (NR=4; R=0); these patients would also be unlikely to respond to checkpoint immunotherapy. Also, aberrations in melanoma antigen gene (MAGE) family proteins (NR=2; R=O), and STT3A (NR=1; R=5) were found to impact AZA efficacy by decreasing antigen processing on tumor cells. Conclusion: Based on the results from the Cellworks Biosimulation Platform applied to the CBM, copy number variants of chromosome 9p and 16 can be converted into CBM-derived biomarkers for response to checkpoint immunotherapy in combination with HMA. Our results support a future prospective evaluation in larger cohorts of MDS patients. Figure 1 Figure 1. Disclosures Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees. Kumar: Cellworks Group Inc.: Current Employment. Castro: Bugworks: Consultancy; Exact sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Cellworks Group Inc.: Current Employment; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy. Grover: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Singh: Cellworks Group Inc.: Current Employment. Shyamasundar: Cellworks Group Inc.: Current Employment. Kulkarni: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Sahni: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Balakrishnan: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Palaniyeppa: Cellworks Group Inc.: Current Employment. Balla: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Abbvie: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings.

Background: Therapy selection for MDS patients is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of MDS patients remains relatively poor. The Cellworks Singula™ report predicts response for physician prescribed treatments 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. Methods: The performance of Singula™ was evaluated in an independent, randomly selected, retrospective cohort of 144 MDS patients aged 28 to 89 years (median 69). The pre-defined Singula™ Classifier utilizes an individual's genomics profile to provide a dichotomous prediction of response or non-responses to a given physician prescribed treatment (PPT). Outcome data for these subjects, including measurement of complete response (CR), were obtained from 42 PubMed publications, each including patient genomics data of either karyotyping, targeted gene panels, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized these data to generate a Singula™ classifier of responder vs non-responder in this MDS cohort. Statistical analyses, including assessments of accuracy, sensitivity, specificity, negative (NPV) and positive predictive (PPV) values were performed on the merged data to compare the Singula™ predicted response with the actual observed CR. Multivariate logistic regression models of complete response were performed incorporating covariates for patient age, PPT, and the Singula™ Classifier. Results: Table 1 reveals that the pre-defined Singula™ classifier had 90.3% (Exact 95% CI: 84.2%, 94.6%) accuracy in predicting observed patient response from the physician prescribed treatment. In this study, Singula™ was able to accurately identify responders with 90.0% (81.2%, 95.6%) sensitivity. Importantly, Singula™ had 90.6% (80.7%, 96.5%) specificity for the subset of 64 patients (44.4%) that had a non-response. For 32% (17/54) of the non-responders patients, Singula™ provided an alternative Standard of Care treatment therapy, as shown in Table 2. The remaining 37 patients were predicted to be non-responders to all remaining Standard of Care options, so did not have alternate treatment predictions. Assuming at least 4% of these non-responding patients would have responded to the alternative Singula™ prescribed therapy, then these data support that Singula™ improves prediction of CR compared to the original PPT (McNemar's p-value < 0.05). In multivariate logistic regression models of CR that included patient age and prescribed drug therapy, the Singula™ Classifier remained an independent, significant predictor of CR (OR > 100, p-value < 0.0001), while both patient age (p = 0.372) and drug therapy (p = 0.720) fell off the model. Conclusions: Cellworks Singula™ has high accuracy and sensitivity in predicting CR for MDS patient response to physician prescribed therapies. Singula™ also has high specificity in identifying patients who are unlikely to respond to physician prescribed therapies and provides alternative treatment recommendations for these patients. The Singula™ Classifier is an independent and superior predictor of CR compared with other clinical (age) or therapeutic (PPT) factors. Figure Disclosures Stein: Amgen: Consultancy, Speakers Bureau; Stemline: Consultancy, Speakers Bureau. Watson:BioAI Health 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; 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. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Prakash:Cellworks Research India Private Limited: Current Employment. Behura:Cellworks Research India Private Limited: Current Employment. Balakrishnan:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Marcucci:Abbvie: Speakers Bureau; Novartis: Speakers Bureau; Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial); Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Iaso Bio: Membership on an entity's Board of Directors or advisory committees.

Background:Early T-cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL), an orphan disease, is a sub-type of T-Cell Acute Lymphoblastic Leukemia (T-ALL) with very poor prognosis and limited therapy options. ETP-ALL is a heterogeneous disease with many distinct genomic profiles, often with more myeloid than lymphoid characteristics. However, standard of care (SOC) drugs for acute myeloid leukemia (AML) have shown limited efficacy for ETP-ALL (PMID: 32733662, 25435716). The genomic profiles of ETP-ALL patients have more complex cytogenetics and larger numbers of genomic aberrations when compared to non-ETP-ALL (T-ALL) profiles (PMID: 22237106, 30641417). We present an alternative multi-gene analysis approach using the Cellworks Omics Biology Model (CBM) workflow to identify unique, intersecting protein pathways in patient-specific disease profiles. The CBM predictive workflow was used to design novel personalized therapy options for an ETP-ALL representative PEER human lymphoid cell line in comparison to a T-ALL JURKAT cell line. The predicted combination therapies were then validated in a lab model. Methods:A PEER cell line was selected to represent ETP-ALL and a JURKAT cell line was selected as a representative for non-ETP T-ALL. Next Generation Sequencing (NGS) was performed for the PEER cell line. For the JURKAT cell line, publicly available NGS whole exome sequencing from cBioPortal and Sanger, along with array CGH from Agilent, were used. The genomic data for the PEER and JURKAT cell lines were used as inputs to the CBM to generate dynamic patient-specific disease protein network maps. Biomarkers and pathway characteristics unique to the PEER and JURKAT cell lines were identified. A digital drug library of targeted FDA-approved agents was simulated on the disease models using both single drug agents and drug combinations at varying doses. The treatment impact was assessed by quantitatively measuring drug effect on a cell growth score, which is a composite of the quantified values of cell proliferation, survival and apoptosis along with impact on the patient-specific disease biomarker score. Comparative dose response studies were run to assess IC50 differences for both cell lines. Cellworks VenturaTM predicted novel therapy combinations for the ETP-ALL representative PEER cell line, which were then prospectively validated by in vitro experiments. The same therapy options were predicted to be less effective in the T-ALL representative JURKAT cell line, which was also confirmed by in vitro studies. Results:The CBM predicted three novel combination therapies for the ETP-ALL representative PEER cell line: nilotinib + cytarabine, bortezomib + cytarabine and bortezomib + idarubicin. All three therapies were predicted to be less effective in JURKAT cells. In vitro, PEER cells were sensitive to all 3 combinations, as predicted by the CBM; whereas, JURKAT cell lines were not sensitive to the first 2 combinations (as predicted), but were sensitive to bortezomib + idarubicin. The CBM analysis is supported by scientific rationales for these combinations based on the genomics-driven disease characteristics of the cell-line. The reasons for drug sensitivity and resistance were determined. These combinations were then prospectively validated in vitro on both cell lines and the experimental responses matched the predicted outcomes. Conclusion:The Cellworks Omics Biology Model integrates the multiple genomic abnormalities in a patient to identify disease network characteristics unlike other NGS analytic tools that attempt to interpret the impact of each genomic alteration in isolation. CBM identified 3 novel therapy options for ETP-ALL that were validated in vitro, similar to anecdotal experience in vivo. This predictive technology can improve clinical decision-making and identify novel treatment options. Disclosures Howard: Cellworks:Consultancy;Servier:Consultancy, Other: Speaker;EUSA Pharma:Consultancy;Sanofi:Consultancy, Other: Speaker;Boston Scientific:Consultancy.Kumar:Cellworks Research India Private Limited:Current Employment.Pampana:Cellworks Research India Private Limited:Current Employment.Ullal:Cellworks Research India Private Limited:Current Employment.Tyagi:Cellworks Research India Private Limited:Current Employment.Lala:Cellworks Research India Private Limited:Current Employment.Kumari:Cellworks Research India Private Limited:Current Employment.Joseph:Cellworks Research India Private Limited:Current Employment.Raju:Cellworks Research India Private Limited:Current Employment.Balakrishnan:Cellworks Research India Private Limited:Current Employment.Mundkur:Cellworks Group Inc.:Current Employment.Macpherson:Cellworks Group Inc.:Current Employment.Nair:Cellworks Research India Private Limited:Current Employment.Kapoor:Cellworks Research India Private Limited:Current Employment.

e21208 Background: Paclitaxel and carboplatin (PC) is used to treat a wide variety of malignancies including gynecologic, breast, lung, and occult primary cancers. In NSCLC, PC led to a substantial improvement in 1-yr survival from 10% (P alone) to approximately 50% seen with the combination. Nevertheless, a large proportion of patients do not respond. An optimal cytotoxic strategy for managing NSCLC and the discovery of chemotherapy biomarkers to guide treatment selection remain unmet needs in the clinic. Cellworks CBM platform identified a unique chromosomal signature which permits a stratification of which patients are most likely to respond to PC treatment. Methods: 22 patients treated with PC were published in TCGA dataset and selected for analysis. The mutation and copy number aberrations from individual cases 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. Digital drug simulations were conducted by measuring effect of PC on a cell growth score comprised of a composite of cell proliferation, apoptosis, and other cancer hallmarks. Drug simulations were systematically conducted to identify and evaluate therapeutic efficacy. The drug combination was mapped to the patient genome along with a rational mechanism of action and validated based on the genomic profile and its biological consequences. Results: Of the 22 patients treated with PC, 13 had clinical responses and 9 were non-responders. The computer simulation correctly predicted response in 16/22 with 72.73% accuracy, 55.56% specificity and 84.62% sensitivity. CBM identified novel amplified segments of Chromosome 11p and 1p were responsible for non-responsiveness to PC. Key genes on these chromosomes were identified belonging to the autophagy, reactive oxygen species (ROS) scavenging, DNA repair, and microtubule polymerization pathways. Amplification of AMBRA1, ATG13 and TRAF6 (11p) led to autophagy upregulation resulting in low ROS level, a well-documented resistance loop for chemotherapy. SIRT3 and CAT (11p), ROS scavenging genes, were also upregulated due to increase in copy number. CTH (1p) is another key enzyme involved in GSH-mediated ROS scavenging and was also upregulated. Biosimulation indicated a low ROS level was the key reason of resistance to PC. Heightened DNA damage repair due FANCF and ZNF143 (11p amp) and USP1 (1p amp), was another cause of PC resistance. These discoveries suggest that a combination of an autophagy inhibitor / BCL2 mimetic might prove useful to reverse PC resistance associated with 11p and 1p amp. Conclusions: This study highlights how CBM simulation platform can help to identify novel patient segments for therapy response prediction and use drug re-purposing to overcome chemotherapy resistance.