Identification Of AML Subtype-Selective Drugs By Functional Ex Vivo Drug Sensitivity and Resistance Testing and Genomic Profiling

Abstract

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.