ALBERT

Description: 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.

Description: Introduction Adult acute myeloid leukemia (AML) exemplifies the challenges of modern cancer drug discovery and development in that molecularly targeted therapies are yet to be translated into clinical use. No effective second-line therapy exists once standard chemotherapy fails. While many genetic events have been linked with the onset and progression of AML, the fundamental disease mechanisms remain poorly understood. There is significant genomic and molecular heterogeneity among patients. Several targeted therapies have been investigated for improved second-line AML therapy but none has been approved for clinical use to date. It would be critically important to identify patient subgroups that would benefit from such therapies and to identify combinations of drugs that are likely to be effective. Methods To identify and optimize novel therapies for AML, we studied 28 samples from 18 AML patients with an individualized systems medicine (ISM) approach. The ISM platform includes functional profiling of AML patient cells ex vivo with drug sensitivity and resistance testing (DSRT), comprehensive molecular profiling as well as clinical background information. Data integration was done to identify disease- and patient-specific molecular vulnerabilities for translation in the clinic. The DSRT platform comprises 306 anti-cancer agents, each tested in a dose response series. We calculated differential drug sensitivity scores by comparing AML responses to those of control cells in order to distinguish cancer-specific drug effects. Next generation RNA- and exome-sequencing was used to identify fusion transcripts and mutations that link to drug sensitivities. Results Individual AML patient samples had a distinct drug sensitivity pattern, but unsupervised hierarchical clustering of the drug sensitivity profiles of the 28 AML patient samples identified 5 functional AML drug response subtypes. Each subtype was characterized by distinct combinations of sensitivities: Bcl-2 inhibitors (e.g. navitoclax; Group 1), JAK inhibitors (e.g. ruxolitinib) (Group 2) and MEK inhibitors (e.g. trametinib) (Groups 2 and 4), PI3K/mTOR inhibitors (e.g. temsirolimus; Groups 4 and 5), broad spectrum receptor tyrosine kinase inhibitors (e.g. dasatinib) (Groups 3, 4 and 5) and FLT3 inhibitors (e.g. quizartinib, sunitinib) (Group 5). Correlation of overall drug responses with genomic profiles revealed that RAS and FLT3 mutations were significantly linked with the drug response subgroups 4 and 5, respectively. Activating FLT3 mutations contributed to sensitivity to FLT3 inhibitors, as expected, but also to tyrosine kinase inhibitors not targeting FLT3, such as dasatinib. Hence, these data point to the potential synergistic combinatorial effects of FLT3 inhibitors with dasatinib for improved therapy outcome (Figure). Early clinical translational results based on compassionate use support this hypothesis. Therefore, by combinations of drugs we expect to see synergistic drug responses that can be translated into efficacious and safe therapies for relapsed AML cases in the clinic. Clinical application of DSRT results in the treatment of eight recurrent chemorefractory patients led to objective responses in three cases according to ELN criteria, whereas four of the remaining five patients had meaningful responses not meeting ELN criteria. After disease progression, AML patient cells showed ex vivo resistance to the drugs administered to the patients, as well as significant changes in clonal architecture during treatment response. Furthermore, we saw genomic alterations potentially explaining drug resistance, such as appearance of novel fusion genes. Summary The ISM approach represents an opportunity for improving therapies for cancer patients, one patient at the time. We show that the platform can be used to identify functional groups of AML linking to vulnerabilities to single targeted drugs and, importantly, unexpected drug combinations. This information can in turn be used for personalized medicine strategies and for creating hypotheses to be explored in systematic clinical trials, both for approved and investigational drugs. Disclosures: Off Label Use: Many of the compounds included in our DSRT platform are not indicated for AML therapy. Mustjoki:BMS: Honoraria, Research Funding; Novartis: Honoraria. Porkka:Novartis: Honoraria, Research Funding; BMS: Honoraria, Research Funding. Kallioniemi:Medisapiens: Membership on an entity’s Board of Directors or advisory committees; Roche: Research Funding.

Description: Abstract 2487 Introduction: The molecular drivers of adult AML as well as the determinants of drug response are poorly understood. While AML genomes have recently been sequenced, many cases do not harbor druggable mutations. Treatment options are particularly limited for relapsed and refractory AML. Due to the molecular heterogeneity of the disease, optimal therapy would likely consist of individualized combinations of targeted and non-targeted drugs, which poses significant challenges for the conventional paradigm of clinical drug testing. In order to better understand the molecular driver signals, identify individual variability of drug response, and to discover clinically actionable therapeutic combinations and future opportunities with emerging drugs, we established a diagnostic ex-vivo drug sensitivity and resistance testing (DSRT) platform for adult AML covering the entire cancer pharmacopeia as well as many emerging anti-cancer compounds. Methods: DSRT was implemented for primary cells from adult AML patients, focusing on relapsed and refractory cases. Fresh mononuclear cells from bone marrow aspirates (〉50% blast count) were screened in a robotic high-throughput screening system using 384-well plates. The primary screening panel consisted of a comprehensive collection of FDA/EMA-approved small molecule and conventional cytotoxic drugs (n=120), as well as emerging, investigational and pre-clinical oncology compounds (currently n=90), such as major kinase (e.g. RTKs, checkpoint and mitotic kinases, Raf, MEK, JAKs, mTOR, PI3K), and non-kinase inhibitors (e.g. HSP, Bcl, activin, HDAC, PARP, Hh). The drugs are tested over a 10,000-fold concentration range resulting in a dose-response curve for each compound and with combinations of effective drugs explored in follow-up screens. The same samples also undergo deep molecular profiling including exome- and transcriptome sequencing, as well as phosphoproteomic analysis. Results: DSRT data from 11 clinical AML samples and 2 normal bone marrow controls were bioinformatically processed and resulted in several exciting observations. First, overall drug response profiles of the AML samples and the controls were distinctly different suggesting multiple leukemia-selective inhibitory effects. Second, the MEK and mTOR signaling pathways emerged as potential key molecular drivers of AML cells when analyzing targets of leukemia-specific active drugs. Third, potent new ex-vivo combinations of approved targeted drugs were uncovered, such as mTOR pathway inhibitors with dasatinib. Fourth, data from ex-vivo DSRT profiles showed excellent agreement with clinical response when serial samples were analyzed from leukemia patients developing clinical resistance to targeted agents. Summary: The rapid and comprehensive DSRT platform covering the entire cancer pharmacopeia and many emerging agents has already generated powerful insights into the molecular events underlying adult AML, with significant potential to facilitate individually optimized combinatorial therapies, particularly for recurrent leukemias. DSRT will also serve as a powerful hypothesis-generator for clinical trials, particularly for emerging drugs and drug combinations. The ability to correlate response profiles of hundreds of drugs in clinical ex vivo samples with deep molecular profiling data will yield exciting new translational and pharmacogenomic opportunities for clinical hematology. Disclosures: Mustjoki: Novartis: Honoraria; Bristol-Myers Squibb: Honoraria. Porkka:Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding. Kallioniemi:Abbot/Vysis: Patents & Royalties; Medisapiens: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Bayer Schering Pharma: Research Funding; Roche: Research Funding.