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Background A randomized trial in glioblastoma patients with methylated-MGMT (m-MGMT) found an improvement in median survival of 16.7 months for combination therapy with temozolomide (TMZ) and lomustine, however the approach remains controversial and relatively under-utilized. Therefore, we sought to determine whether comprehensive genomic analysis can predict which patients would derive large, intermediate, or negligible benefits from the combination compared to single agent chemotherapy. Methods Comprehensive genomic information from 274 newly diagnosed patients with methylated-MGMT glioblastoma (GBM) was downloaded from TCGA. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotypes representing hallmark behavior of cancers. In silico responses to TMZ, lomustine, and combination treatment were biosimulated. Efficacy scores representing the effect of treatment for each treatment strategy were generated and compared to each other to ascertain the differential benefit in drug response. Results Differential benefits for each drug were identified, including strong, modest-intermediate, negligible, and deleterious (harmful) effects for subgroups of patients. Similarly, the benefits of combination therapy ranged from synergy, little or negligible benefit, and deleterious effects compared to single agent approaches. Conclusions The benefit of combination chemotherapy is predicted to vary widely in the population. Biosimulation appears to be a useful tool to address the disease heterogeneity, drug response, and the relevance of particular clinical trials observations to individual patients. Biosimulation has potential to spare some patients the experience of over-treatment while identifying patients uniquely situated to benefit from combination treatment. Validation of this new artificial intelligence tool is needed.

2017 Background: The Cellworks Singula Therapeutic Response Index (TRI) has been developed to assist clinicians and GBM patients in choosing between competing therapeutic options. In contrast to approaches that consider single aberrations, which often yield limited benefit, Cellworks utilizes an individual patient’s next generation sequencing results and a mechanistic multi-omics biology model, the Cellworks Omics Biology Model (CBM), to biosimulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. For any individual patient and alternative therapy, Cellworks integrates this biologically modeled multi-omics information into a continuous Singula TRI Score, scaled from 0 (low therapeutic benefit) to 100 (high therapeutic benefit). We demonstrate that Singula is strongly associated with OS and DFS beyond standard clinical factors, including patient age, patient gender, and physician prescribed treatments (PPT). Methods: In this study, Singula’s ability to predict response was evaluated in a retrospective cohort of 100 GBM patients with OS and DFS data from The Cancer Genome Atlas (TCGA) project, treated with PPT. As a primary analysis of the CBM and TRI Score, Cox Proportional Hazards (PH) regression and likelihood ratio (LR) tests were used to assess the hypothesis that Singula is predictive of OS and DFS above and beyond patient age, patient gender, and PPT. A p-value < 0.05 for the corresponding likelihood ratio statistic was required to be considered significant. Results: Multivariate analyses were performed to assess the performance of the Singula Therapy Response Index after adjusting for the contribution of standard clinical factors. The same Singula TRI algorithm and clinical cutoffs were used for all clinical outcome measures. These analyses, shown in the table, suggests that the proposed Singula TRI provides predictive value of OS and DFS above and beyond patient age, patient gender, and PPT. Conclusions: The Singula TRI Score provides a continuous measure scaled from 0 (low benefit) to 100 (high benefit) for alternative GBM therapeutic options. In this retrospective cohort, Singula was strongly predictive of OS and DFS and provided predictive value beyond PPT, patient age and gender. These results will be further validated in larger scale, prospectively designed clinical studies.[Table: see text]

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.

e14501 Background: UKT-03 evaluated TMZ plus Lomustine in a single arm phase II trial in newly diagnosed GBM patients. An overall survival of 23 months was a substantial improvement over historical experience. Patients with m-MGMT v. unmethylated tumors had a 2-yr survival of 75% and median survival not reached compared to 20% and 12.5 months, respectively. These data formed the basis for NOA-9, a randomized phase III trial in newly diagnosed, m-MGMT GBM which randomized 141 patients to standard therapy or experimental therapy with Lomustine and TMZ every 6 weeks. A superiority for the combination was observed: 48.1 v. 31.4 months for the standard arm in the ITT analysis. Nevertheless, many neurooncologists are reluctant to adopt this approach. The current standard of care uses single biomarker, m-MGMT, in contrast to comprehensive pathway analysis (CPA). We sought to determine if CPA could discriminate more effectively among each patient’s likelihood of benefiting from combination treatment. Methods: Cellworks Singula employs a novel Cellworks Omics Biology Model (CBM) to predict patient-specific biomarker and phenotype response of personalized GBM avatars to drug agents, radiation, and targeted therapies. The CBM was developed and validated using PubMed to generate protein network maps of patient-specific activated and inactivated disease pathways. CBM was used to simulate the TMZ and TMZ-Lomustine therapies for each patient in a TCGA cohort of 368 GBM patients. Omics data including methylation, whole exome sequencing, and copy number alterations were input into CBM. The Singula Composite Inhibition Score (CIS) was calculated based on the measured quantitative drug effects. Results: Though incremental gain from the combination was seen in all patients, CIS varied across the population with relative scores ranging from 32-82, with best responders have more than twice the benefit. Conclusions: CPA shows that m-MGMT is an excellent biomarker for determining the likelihood of benefit from TMZ and lomustine, with the caveat that CBM identifies 18% could be spared from TMZ exposure and would benefit from Lomustine alone. Otherwise, these data lend support for evolving the standard of care with combination therapy for patients with m-MGMT GBM and should help overcome a reluctance to employing combination therapy. Additionally, CBM has utility to individualize clinical decision making. [Table: see text]

e19502 Background: Despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of AML patients remains relatively poor. Therapy selection 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. 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 more accurate predictor of patient-specific therapy response than PPT. Methods: Singula’s ability to predict response was evaluated in an independent, randomly selected, retrospective cohort of 494 AML patients aged 2 to 85 years (median 54) treated with PPT. Patient omics data was available from PubMed. The accuracy of Singula was compared to that of PPT using McNemar’s test to account for the correlation between Singula and PPT. Multivariate logistic regression modeled complete response (CR) as a function of patient age, PPT, and Singula against any non-response (NR). Likelihood ratio tests were performed to further validate if Singula provides predictive information beyond PPT or patient age. Similar analyses were performed for overall survival (OS) using proportional hazards regression. Results: Singula was a better predictor for CR than PPT (McNemar’s χ2 = 72.0, p-value < 0.0001), with an overall accuracy of 88.5% (95% CI: 85.3%, 91.1%) compared to 70.2% (95% CI: 66.0%, 74.2%) for PPT. Singula exhibited a sensitivity and specificity of 97.1% and 68.0%, respectively. In multivariate regression analysis, Singula (p < 0.0001) remained an independent predictor for CR after adjusting for patient age (p = 0.0329) while PPT became not significant (p = 0.75). Singula was also an independent predictor for OS (p < 0.0001) after adjusting for patient age (p = 0.0018) and PPT (p = 0.0011). For all 100 true negatives, Singula generated alternative standard of care therapy selections with predicted clinical response. Conclusions: Singula is a superior independent predictor for CR and OS compared to PPT in AML patients. The Singula report can also validate therapy selection, correctly identify non-responders to PPT and further provide alternative therapy selections.

Background : Relapse is a major challenge in treating patients with MDS and AML. In this study we used a genomics-informed computational biology modeling (CBM) technique to understand the mechanisms of relapse after chemotherapy treatment and to postulate new re-induction treatment options. Methods : 120 patients with AML or MDS were recruited in the iCare1 prospective clinical study (NCT02435550), designed to assess predictive accuracy of CBM prediction by comparing computer predictions of treatment response to actual clinical outcomes. WES, CNV, and cytogenetics were obtained for 96 patients (Table). Genomic profiling was conducted by conventional cytogenetics, whole exome sequencing (SureSelectXT Clinical Research Exome, Agilent), and array CGH (Agilent). Somatic genomic mutations were inputted into CBM technology to create disease-specific protein network maps for each patient. A digital drug library of FDA-approved drugs was created for CBM by programming each agent's mechanism of action determined from published literature. Digital drug simulations of the patient's choice of therapy were tested at varying doses and predicted efficacy of the drugs were measured as a function of a disease inhibition score (DIS), defined as the degree to which disease pathways and phenotypes (cell proliferation and viability) were returned to a mutation-free state. Treating physicians were masked to the results of CBM predictions and the clinical outcomes were prospectively recorded. Clinical response for MDS patients was defined as CR+PR+HI (IWG 2006). Clinical response for AML patients was defined as CR+PR by IWG 2003 criteria. Results: 50 patients were eligible for evaluation based on length of follow-up. In 50 patients, 61 treatments were administered. CBM maps of relapsed samples from iCare 1 patients accurately matched the patient's nonresponse of treatment at relapse in 90% of patients and identified mechanisms for chemoresistance in these patients (Table 1). An example case-study is presented (CASE I: UFH-00012): AML patient, relapsed after 7+3 induction therapy. CBM predicted response to azacitidine (AZA) at first relapse and clinically was evaluated to be a responder with a blast reduction to 2%, normal CBC, and normal cytogenetics. After 9 months of AZA, the disease relapsed and was genetically profiled. CBM analysis and digital drug simulation of the relapsed disease genomics confirmed a non-response to AZA. A GOF mutation in EZH2 present in pre-AZA was a key driver of response while this was missing in the AZA-refractory sample. ASXL1 mutation was present in both samples and without the EZH2 mutation in after-AZA sample is posited as the mechanism for AZA resistance. Digital drug screening identified the combination of AZA with venetoclax as a possibly effective drug combination due to upregulation of BCL2 in this patient's disease network. Simplistic network schematics derived from the CBM analysis for pre and post AZA samples are illustrated in Figs 1 & Fig 2. Conclusions : Genomics-informed CBM accurately predicted response to standard of care in MDS and AML and identified mechanisms for induction failure. Loss of EZH2 mutation was identified as a possible mechanism for AZA-resistance in the face of ASXL1 mutation. CBM analysis of the refractory disease predicted a new therapy option for this patient based on the evolved disease characteristics. Disclosures Vali: Cell Works Group Inc.: Employment. Abbasi:Cell Works Group Inc.: Employment. Singh:Cellworks Research India Private Limited: Employment. Usmani:Cellworks Research India Private Limited: Employment. Lala:Cellworks Research India Private Limited: Employment. G:Cellworks Research India Private Limited: Employment. Radhakrishnan:Cellworks Research India Private Limited: Employment. Birajdar:Cellworks Research India Private Limited: Employment. Naga:Cellworks Research India Private Limited: Employment. Cogle:Celgene: Other: Steering Committee Member of Connect MDS/AML Registry.

Background The personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug. Methods We used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells. Results Here, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells. Conclusions These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches. Electronic supplementary material The online version of this article (doi:10.1186/s12967-015-0399-y) contains supplementary material, which is available to authorized users.

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.