Abstract: 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.

Abstract: 7027 Background: ATRA combined with arsenic trioxide revolutionized the treatment of APL. Based on promising in vitro data, several clinical trials evaluated ATRA combinations in non-APL AML, in which some patients seemed to benefit from the addition. Thus, predicting response a priori is imperative to determine the optimal treatment for each patient. The CBM was used to evaluate the impact of initial therapy with ATRA combined with cytarabine, etoposide, idarubicin (ATRA-CEI) to assess the biomarkers responsible for response in adults with AML. Methods: AML patients participating in clinical trial NCT00151242 had their leukemia sequenced as part of the trial, and genomic profiles were used for computational modeling by the CBM, which uses curated data about genomic aberrations from PubMed as input to generate disease-specific protein network maps and predict drug responses. Disease biomarkers unique to each patient were identified using biosimulation. Digital drug simulations were conducted by measuring the effect of ATRA-CEI on a composite cell growth score of cell proliferation, apoptosis and other hallmarks of cancer. ATRA-CEI was mapped to the patient genome along with a mechanism of action and validated based on the genomic profile and its biological consequences. Results: Of 171 patients treated with ATRA-CEI, 107 (63%) responded (R) and 64 did not (NR). A subset of 18 patients with favorable genomic features were found to be NR and their non-response was correctly predicted by CBM in all 18 cases. Mutations of DNMT3A, EZH2, ASXL, FLT-3, and GART amplification emerged as novel biomarkers of ATRA-CEI failure (only 37 of 107 responders (35%) with these findings, compared to 70 of 107 responders (65%) without these findings (p = 0.0027)). DNMT3A, EZH2, ASXL1 loss of function mutations activate FABP5, a key mechanism of ATRA resistance, and also activate ABCC1 (PgP), which reduces the efficacy of etoposide and idarubicin by upregulating MDR1. In general, monosomy 7 is expected to confer ATRA resistance due to the presence of EZH2 and KMT2E gene deletions. Indeed, 18 of 32 patients with monosomy 7 did not respond. However, the 14 who responded had co-occurrence of deletions involving IGFBP3, PMS2, HUS1, CDK5, XRCC2/4, AKR1B10, and others that overcame ATRA resistance associated with monosomy 7 and were identified by CBM. Use of CBM helps avoid unnecessary use of ATRA in patients unlikely to respond (19% of cases) thus reducing toxicity and cost without changing efficacy, and also identifies those likely to respond, even when they have monosomy 7, where non-response is the norm. Conclusions: ATRA benefits a subset of patients with non-APL AML. CBM predicted response using computational modeling of all genetic alternations, which explains its success versus traditional one-gene-one-drug approaches.

Abstract: 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.

Abstract: Background: The optimal treatment strategy for managing Acute Myeloid Leukemia (AML) and the use of reliable and predictive biomarkers to guide selection of cytotoxic chemotherapy regimens among patients with diverse genomic profiles remain unmet needs in the clinic. The combination of MEC [mitoxantrone (MIT), etoposide (VP16), and cytarabine (ARA-C)] is a commonly used regimen for relapsed or refractory AML patients. Unfortunately, many patients do not respond to MEC, and which of the three drug agents matters most for each individual patient is not known. Predictors of response are needed urgently. Methods: The Computational Omics Biology Model (CBM) is a computational multi-omic biology software model created using artificial intelligence heuristics and literature sourced from PubMed to generate a patient-specific protein network map. The aberration and copy number variations from individual cases served as input into the CBM. Disease-biomarkers unique to each patient were identified within patient-specific protein network maps. Biosimulations were conducted on the Cellworks Biosimulation Platform by measuring the effect of chemotherapy on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drugs was conducted by mapping the interaction of various drug combinations with the patient's genomic and pathway alterations based on signaling pathway mechanisms and their phenotypic consequences. The Cellworks Biosimulation Platform identified unique chromosomal signatures that permit a stratification of patients that are most likely to respond to MIT, VP16, or ARA-C as well as their combinations. 65 AML patients were selected for this study largely based on genomic data published in TCGA and PubMed: ARA-C [N=12, 7 responders (R) & 5 non-responders (NR)]ARA-C + MIT [N=30, 29 R & 1 NR]ARA-C + MIT + VP16 [N=23, 12 R & 11 NR] Results: Of the12 patients treated with ARA-C alone, 5 were predicted to be NR and 7 were predicted to be R. Of the 5 NR, 4 had 5q del which resulted in loss of APC, CSNK1A1 and SLC22A4 (nucleoside carrier) forming the non-response biomarkers for ARA-C. Notably, the biosimulation predicted lenalidomide to be beneficial for these patients. Out of 7 R, 4 patients also had 5q del, but were predicted to be R because of co-occurring aberrations involving CLSPN del, DHODH del, MSH2 del, EP300 del, CREBBP del, MSH6 Del, and RRM2 del. These genes were exclusively present in ARA-C responders. Of 53 patients who received ARA-C + VP16 + MIT or ARA-C + MIT, 41 patients were predicted to be R and 12 patients were predicted to be NR. The genomic aberrations predicted by biosimulation to be associated with response to this regimen include: NPM1-mut, TET2-mut, IDH1-mut, IDH2-mut, RAD17-del, NRAS-mut (Table 1). Notably, CBM predicted 19 of the 41 R had no genomic biomarkers of response to VP16 or MIT, suggesting these patients might have benefited equally from ARA-C alone with less toxicity and cost. Finally, 11/65 patients were predicted NR to MEC treatment. In the biosimulation, treatment failure was associated with high aberration frequencies of KMT2C-mut/del, FLT3 mut, TWIST1 del, LIMK1 del, SNAI2 amp, FNTA amp, and KAT6A amp. Of note, these genomic markers suggested a likelihood of benefit from other therapies, including vincristine, JQ1 and rigosertib. Conclusions: The Cellworks Biosimulation Platform identified novel polygenic biomarkers of response that can be employed to determine the optimal therapy for relapsed AML patients. Biosimulation permits avoidance of cytotoxic drugs with little chance of efficacy and reveals vulnerabilities in each patient's cancer that can be exploited to improve disease control. In AML, biosimulation promises to improve intensive therapy regimens by tailoring chemotherapy to optimize disease control and minimize toxicity. Figure 1 Figure 1. Disclosures Marcucci: Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings. Kumar: Cellworks Group Inc.: Current Employment. Castro: Cellworks Group Inc.: Current Employment; Omicure Inc: Consultancy; Caris Life Sciences Inc.: Consultancy; Exact sciences Inc.: Consultancy; Bugworks: Consultancy; Guardant Health Inc.: Speakers Bureau. Grover: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Alam: Cellworks Group Inc.: Current Employment. Azam: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Lunkad: Cellworks Group Inc.: Current Employment. Joseph: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Chauhan: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Behura: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Husain: Cellworks Group Inc.: Current Employment. Mandal: Cellworks Group Inc.: Current Employment. Raman: 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. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees.

Abstract: Background: Pediatric AML (pAML) treatment outcomes can vary due to genomic heterogeneity. Thus, selecting the right drugs for a given patient is challenging. There is a need for a priori means of predicting treatment responses based on tumor "omics". Computational biology modeling (CBM) is a precision medicine approach by which biological pathways of tumorigenesis are mapped using mathematical principles to yield a virtual, interactive tumor model. This model can be customized based on a patient's omics and analyzed virtually for response to therapies. Aim: To define prediction values of a CBM precision medicine approach in matching clinical response to ADE therapy in a cohort of pAML patients. Methods: Thirty pAML patients that were treated ADE chemotherapy were utilized with information on the clinical, genomic (cytogenetics, mutations) and protein expression data from this cohort of pAML patients used for the CBM. From cytogenetics results, gene copy number variations were coded as either knocked-down (KD) or over-expressed (OE). From NGS results (2 gene panel - CEBPA, NPM1), gene mutations were coded as either loss or gain of function (LOF or GOF). For protein expression data, proteins that were 〉 2sigma from the mean were coded as KD if their value was 〈 0 or OE if their value was 〉 0. Proteins with values 〈 2sigma from the mean were not included in the CBM as perturbed. The LOF, GOF, KD, OE data was input in the CBM software system (Cellworks Group) to generate patient-specific maps of AML. Each map showed unique interplay of dysregulated networks for the patient's AML. Digital drug simulations were then conducted in each map to measure the impact of cytarabine, daunorubicin and etoposide alone and in combination to predict AML disease inhibition score (DIS) (composite of cell proliferation, viability, apoptosis and impact on patient-specific biomarkers). Response to treatment is determined based on a threshold DIS range derived through AML training datasets comprising omics and clinical outcome. Clinical outcome data for these pAML patients treated with ADE was compared with CBM predictions. Clinical response was defined as complete response at the end of consolidation therapy as per International Working Group 2006 criteria. Results: Assessment was made for 30 patients, 14 female, median age 14 years, all of which achieved CR, with predictions made for all but one which lacked sufficient genomic inputs. CBM accurately predicted the clinical outcomes of 28 of 29 responders, with an accuracy and positive predictive value of 96 %. Multivariate analysis of predictive score with age at diagnosis, DFS and OS is positively correlated with Pearson coefficient of 0.22, 0.54 and 0.49. Analysis of the individual drug responses of each patient indicated that some of the drugs were predicted to be non-responsive based on the patient-disease pathway characteristics, and could have been eliminated from the treatment, thus reducing the overall adverse impact of the very intensive therapy regimen. There were profiles in which AraC was a responder due to decreased mismatch repair pathway in the disease network resulting from presence of aberrations such as KMT2A-AFDN, RUNX1-RUNX1T1, CEBPA LOF, KDM1A OE, MSH2 KD etc., while Daunorubicin and Etoposide were predicted as non-responders due to presence of an intact homologous DNA repair pathway as a result of absence of aberrations in HR pathway genes. CBM analysis of patient "omics" driven disease characteristics could have eliminated additional drugs for such patients. Conclusion: The CBM prediction of ADE in pAML patients based on genomic, proteomic and clinical data showed high predictive accuracy of 96.55%. CBM analysis of patients' genomics and proteomics driven disease characteristics and individual drug response prediction indicated that the intensive therapy regimen can be tailored for each patient to minimize toxicity by removing non-responsive drugs. The study validates the approach to a priori predict response and identify optimal therapy option for the patient. Disclosures Cogle: Celgene: Other: Steering Committee Member of Connect MDS/AML Registry. Abbasi:Cell Works Group Inc.: Employment. Singh:Cellworks Research India Private Limited: Employment. Sauban:Cellworks Research India Private Limited: Employment. Raman:Cellworks Research India Private Limited: Employment. Vidva:Cellworks Research India Private Limited: Employment. Tyagi:Cellworks Research India Private Limited: Employment. Talawdekar:Cellworks Research India Private Limited: Employment. Das:Cellworks Research India Private Limited: Employment. Vali:Cell Works Group Inc.: Employment.

Abstract: 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.