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Background Following the positive outcome of the RATIFY phase 3 clinical trial, the multi-kinase inhibitor midostaurin was approved for the treatment of adult patients with newly diagnosed FLT3-mutated acute myeloid leukemia (AML). However, we and others have observed that single agent midostaurin yields responses also in a substantial portion of patients not carrying FLT3 mutations. The molecular basis and the kinase targets mediating these responses are poorly understood and no biomarkers predictive of response in FLT3 wildtype (wt) AML patients exist. To identify markers distinguishing the FLT3 wt responding subset of patients, we trained machine learning multi-marker models using AML patient baseline transcriptomic and mutational data to predict ex vivo responders vs. non-responders. Further, to better understand the molecular basis of midostaurin responses and to explore the unique signaling networks modulated by midostaurin, we profiled the sensitivities of AML patient samples to midostaurin in comparison to, and in combination with, several clinically relevant oncological targeted agents of diverse mechanistic classes. Results Midostaurin target space is unique and it retains anti-leukemic potency under cytoprotective conditions. We have previously established that single agent midostaurin is effective ex vivo in about 25% of FLT3 wt AML patient samples and retains potency in a cytoprotective medium that masks the effects of more selective FLT3 inhibitors such as quizartinib, crenolanib and sorafenib (Karjalainen et al, Blood 2017). To further investigate the unique pathways that midostaurin, but not other FLT3 inhibitors targets, we correlated the response patterns of 87 AML patient samples in cytoprotective medium to midostaurin and 261 other kinase inhibitors in our oncology compound collection. In unsupervised cluster analysis, midostaurin showed highly similar response patterns to AZD7762, OTS167, milciclib, pacritinib, ENMD-2076 and fostamatinib. Publicly available in vitro kinase profiling (Tang et al, Cell Chem. Biol. 2018) suggested that midostaurin does not inhibit most of the primary targets of these other inhibitors, with only aurora kinases, JAK kinases and SYK appearing to be shared potent targets. Midostaurin anti-leukemic potency is determined by the mutational background. Several multi-marker, supervised machine learning models were compared to extract biomarker signatures from either baseline transcriptomic or mutational data, in the task of predicting ex vivo midostaurin response in samples cultured in cytoprotective medium. In the full cohort (N=81), the presence of FLT3 mutations (both internal tandem repeat and tyrosine kinase domain mutations) was the strongest predictor of response. In the FLT3 wt cases (N=49), our results revealed that other select mutations correlated well with either response or non-response upon Bayesian Linear Regression analysis with cross-validation (Ammad-Ud-Din et al, Bioinformatics, 2017). Mutations in PTPN11, U2AF1, SRSF2, RUNX1, JAK2 and BCOR predicted midostaurin responders, while mutations in GATA2, WT1, NPM1 and IDH2 were enriched in non-responders (Figure 1). Baseline transcriptomic profiles, however, did not provide added value for the predictive power. Midostaurin efficacy can be enhanced by combination with other targeted agents. Combinatorial drug screening of midostaurin in cytoprotective medium revealed several synergizing drug classes, including BCL-2 and MDM2-p53 inhibitors. Further analysis of synergizing agents in broader AML patient sample cohorts is ongoing. Conclusions Our results show that midostaurin may reach its biological effects through inhibition of additional kinases than just FLT3. In both FLT3 mutant and wt cases, midostaurin responses are influenced by the overall mutational background. Furthermore, our data indicates that midostaurin efficacy can be enhanced through combination with other agents. Together, we have significantly expanded the understanding of molecular determinants of midostaurin response in primary AML cells, supporting predictive biomarker discovery efforts and development of synergistic drug combinations. The emerging hypotheses from this work will have to be tested in clinical studies. Disclosures Porkka: Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Marques Ramos:Novartis: Employment. Pallaud:Novartis: Employment. Aittokallio:Novartis: Research Funding. Wennerberg:Novartis: Research Funding.

Introduction: Therapeutic options for patients with AML were recently expanded with FDA approval of four drugs in 2017. As their efficacy is limited in some patient subpopulations and relapse ultimately ensues, there remains an urgent need for additional treatment options tailored to well-defined patient subpopulations to achieve durable responses. Two comprehensive profiling efforts were launched to address this need-the multi-center Beat AML initiative, led by the Oregon Health & Science University (OHSU) and the AML Individualized Systems Medicine program at the Institute for Molecular Medicine Finland (FIMM). Methods: We performed a comparative analysis of the two large-scale data sets in which patient samples were subjected to whole-exome sequencing, RNA-seq, and ex vivo functional drug sensitivity screens: OHSU (121 patients and 160 drugs) and FIMM (39 patients and 480 drugs). We predicted ex vivo drug response [quantified as area under the dose-response curve (AUC)] using gene expression signatures selected with standard regression and a novel Bayesian model designed to analyze multiple data sets simultaneously. We restricted analysis to the 95 drugs in common between the two data sets. Results: The ex vivo responses (AUCs) of most drugs were positively correlated (OHSU: median Pearson correlation r across all pairwise drug comparisons=0.27; FIMM: median r=0.33). Consistently, a samples's ex vivo response to an individual drug was often correlated with the patient's Average ex vivo Drug Sensitivity (ADS), i.e., the average response across the 95 drugs (OHSU: median r across 95 drugs=0.41; FIMM: median r=0.58). Patients with a complete response to standard induction therapy had a higher ADS than those that were refractory (p=0.01). Further, patients whose ADS was in the top quartile had improved overall survival relative to those having an ADS in the bottom quartile (p The BCL-2 inhibitor venetoclax was the only drug whose response was negatively correlated with ADS in both data sets. We hypothesized that, whereas the predictive performance of many other drugs was likely dependent on ADS, the predictive performance of venetoclax (LASSO: r=0.53, p=0.01; Ridge: r=0.63, p=1.3x10-3) reflected specific gene expression biomarkers. To identify biomarkers associated with venetoclax sensitivity, we developed an integrative Bayesian machine learning method that jointly modeled both data sets, revealing several candidate biomarkers positively (BCL2 and FLT3) or negatively (CD14, MAFB, and LRP1) correlated with venetoclax response. We assessed these biomarkers in an independent data set that profiled ex vivo response to the BCL-2/BCL-XL inhibitor navitoclax in 29 AML patients (Lee et al.). All five biomarkers were validated in the Lee data set (Fig 1). Conclusions: The two independent ex vivo functional screens were highly concordant, demonstrating the reproducibility of the assays and the opportunity for their use in the clinic. Joint analysis of the two data sets robustly identified biomarkers of drug response for BCL-2 inhibitors. Two of these biomarkers, BCL2 and the previously-reported CD14, serve as positive controls credentialing our approach. CD14, MAFB, and LRP1 are involved in monocyte differentiation. The inverse correlation of their expression with venetoclax and navitoclax response is consistent with prior reports showing that monocytic cells are resistant to BCL-2 inhibition (Kuusanmäki et al.). These biomarker panels may enable better selection of patient populations likely to respond to BCL-2 inhibition than would any one biomarker in isolation. References: Kuusanmäki et al. (2017) Single-Cell Drug Profiling Reveals Maturation Stage-Dependent Drug Responses in AML, Blood 130:3821 Lee et al. (2018) A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia, Nat Commun 9:42 Disclosures Druker: Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Fred Hutchinson Cancer Research Center: Research Funding; Celgene: Consultancy; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; Aileron Therapeutics: Consultancy; Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; Patient True Talk: Consultancy; Millipore: Patents & Royalties; Monojul: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Beta Cat: Membership on an entity's Board of Directors or advisory committees; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Henry Stewart Talks: Patents & Royalties; Bristol-Meyers Squibb: Research Funding; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; McGraw Hill: Patents & Royalties; ARIAD: Research Funding; Novartis Pharmaceuticals: Research Funding. Heckman:Orion Pharma: Research Funding; Novartis: Research Funding; Celgene: Research Funding. Porkka:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Tyner:AstraZeneca: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Leap Oncology: Equity Ownership; Seattle Genetics: Research Funding; Syros: Research Funding; Takeda: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Aptose: Research Funding; Agios: Research Funding. Aittokallio:Novartis: Research Funding. Wennerberg:Novartis: Research Funding.