Published OnlineFirst September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350 RESEARCH ARTICLE Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia Tea Pemovska 1 , Mika Kontro 2 , Bhagwan Yadav 1 , Henrik Edgren 1 , Samuli Eldfors1 , Agnieszka Szwajda 1 , Henrikki Almusa 1 , Maxim M. Bespalov 1 , Pekka Ellonen 1 , Erkki Elonen 2 , Bjørn T. Gjertsen5 , 6 , Riikka Karjalainen 1 , Evgeny Kulesskiy 1 , Sonja Lagström 1 , Anna Lehto 1 , Maija Lepistö1 , Tuija Lundán 3 , Muntasir Mamun Majumder 1 , Jesus M. Lopez Marti 1 , Pirkko Mattila 1 , Astrid Murumägi 1 , Satu Mustjoki 2 , Aino Palva 1 , Alun Parsons 1 , Tero Pirttinen 4 , Maria E. Rämet 4 , Minna Suvela 1 , Laura Turunen 1 , Imre Västrik 1 , Maija Wolf 1 , Jonathan Knowles 1 , Tero Aittokallio 1 , Caroline A. Heckman 1 , Kimmo Porkka 2 , Olli Kallioniemi 1 , and Krister Wennerberg 1 ABSTRACT We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profi ling and ex vivo drug sensitivity and resistance testing (DSRT) of patients’ cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered fi ve major taxonomic drug-response sub- types based on DSRT profi les, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3 -ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed signifi cant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs. SIGNIFICANCE: Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing. Cancer Discov; 3(12); 1416–29. ©2013 AACR. See related commentary by Hourigan and Karp, p. 1336. Authors’ Affi liations: 1 Institute for Molecular Medicine Finland, FIMM; T. Aittokallio, C.A. Heckman, K. Porkka, O. Kallioniemi, and K. Wennerberg 2 Hematology Research Unit Helsinki, Helsinki University Central Hospital, shared senior authorship of this article. University of Helsinki, Helsinki; 3 Department of Clinical Chemistry and Current address for M.M. Bespalov: Stem cells and Neurogenesis Unit, TYKSLAB, Turku University Central Hospital, University of Turku, Turku; Division of Neuroscience, San Raffaele Scientifi c Institute, Milan, Italy. 4 Department of Internal Medicine, Tampere University Hospital, Tampere, Finland; 5 Department of Clinical Science, Hematology Section, University Corresponding Author: Krister Wennerberg, Institute for Molecular Medi- of Bergen; and 6 Department of Internal Medicine, Hematology Section, cine Finland, University of Helsinki, Tukholmankatu 8, 00290 Helsinki, Haukeland University Hospital, Bergen, Norway Finland. Phone: 358-9-191-25764; Fax: 358-9-191-25737; E-mail: krister .wennerberg@fi mm.fi Note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/). doi: 10.1158/2159-8290.CD-13-0350 T. Pemovska and M. Kontro shared fi rst authorship of this article. © 2013 American Association for Cancer Research. 1416 | CANCER DISCOVERYDECEMBER 2013 www.aacrjournals.org Downloaded from cancerdiscovery.aacrjournals.org on September 24, 2021. © 2013 American Association for Cancer Research. Published OnlineFirst September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350 INTRODUCTION therapy ( 10 ). In light of the genomic and molecular diversity of AML, and its continuous evolution in response to chemother- Adult acute myeloid leukemia (AML) is a prototype exam- apy, it is important to better understand the potential utility of ple of the challenges of modern cancer drug discovery, devel- all targeted cancer drugs that are already available in the clinic. opment, and patient therapy. With the exception of the These drugs could be systematically repurposed as off-label retinoic acid–sensitive acute promyelocytic leukemia subtype, indications to responding subgroups of AML. Furthermore, molecularly targeted therapeutic approaches for AML are yet comparative information on the effi cacy of the hundreds of to be translated into clinical advances. The disease has tra- emerging targeted anticancer agents, as well as their potential ditionally been subdivided into different subtypes (M0–M7) combinations, in patient-derived ex vivo samples could dramati- based on cellular lineage and biomarkers ( 1 ). Current World cally help prioritize clinical development of such agents. Health Organization classifi cation refl ects the fact that a grow- To facilitate testing of already clinically available drugs as ing number of AML cases can be categorized on the basis of well as emerging targeted inhibitors for patients with AML, their underlying genetic abnormalities that defi ne distinct clin- we undertook a comprehensive functional strategy to directly icopathologic entities ( 2 ). Genomic changes in AML are now determine the drug dependency of cancer cells based on ex vivo relatively well understood, with each AML sample containing drug sensitivity and resistance testing (DSRT). First, we applied roughly 400 genomic variants, of which an average of 13 reside a systematic large panel of drugs covering both cancer chemo- in coding regions ( 3, 4 ). Recurrent changes have highlighted therapeutics and many clinically available and emerging molec- potential driver genes, including NPM1 , CEBPA , DNMT3A , ularly targeted drugs. Second, we developed a new way to score TET2 , RUNX1 , ASXL1 , IDH2 , and MLL , with mutations in FLT3 , for differential drug response in AML cells as compared with IDH1 , KIT , and RAS genes modifying the disease phenotype ( 5 ). the effi cacy of these drugs in normal bone marrow cells. Third, Although several of the recurrent genetic alterations link to trac- we verifi ed DSRT predictions in vivo by treating patients with table drug targets, genetic testing of patients with AML has yet AML with off-label drugs. Fourth, we assessed the molecular to result in effective personalized or stratifi ed therapies. mechanisms underlying development of cancer progression Patients with AML have a poor outcome, with a 5-year and drug resistance by repeat sampling from relapsed patients, survival of 30% to 40% ( 6, 7 ). The standard therapy for most followed by genomic and transcriptomic profi ling as well as adult patients with AML is conventional chemotherapy con- a new DSRT analysis to understand both coresistance as well sisting of the nucleoside analog cytarabine combined with as new vulnerabilities. Taken together, we term this approach a topoisomerase II inhibitor ( 8, 9 ). A number of second-line individualized systems medicine (ISM; Fig. 1 ). treatment options have been applied in patients with AML after Here, we demonstrate that the ISM strategy made it possible to relapse, but the response rates have remained low. Furthermore, (i) create a taxonomy of comprehensive drug responses in AML, patients with AML at relapse exhibit an increased number of (ii) identify clinically actionable AML-selective targeted drugs, genetic alterations, which can be attributed to disease progres- (iii) clinically apply such therapies for individual chemorefrac- sion and/or DNA-damaging agents used for routine chemo- tory AML patients predicted to be sensitive to targeted drugs, DECEMBER 2013CANCER DISCOVERY | 1417 Downloaded from cancerdiscovery.aacrjournals.org on September 24, 2021. © 2013 American Association for Cancer Research. Published OnlineFirst September 20, 2013; DOI: 10.1158/2159-8290.CD-13-0350 RESEARCH ARTICLE Pemovska et al. Individualized drug treatment selection DSRT 100 Effective drugs 80 60 40 % Survival 20 Selective DSS Selective Resistant drugs 0 –9 –8 –7 –6 –5 Log conc (mol/L) Understanding biology of disease Diagnosis Understanding Relapse 1 Molecular profiling Result drug sensitivity and resistance Relapse 2 Genome Transcriptome Signalome Rapid introduction of therapies Figure 1. Functional ISM platform for improved AML therapy. The platform involves (i) comprehensive direct DSRT of 187 approved and investigational oncology compounds in ex vivo primary cells from serial AML samples; (ii) clinical implementation of testing results in individual patients with relapsed and refractory disease; (iii) deep molecular and genomic profi ling of the patients with AML from consecutive samples before and after relapse and drug resistance for monitoring disease progression and clonal evolution; and (iv) integrating drug sensitivity, next-generation sequencing, and clinical follow-up data to understand the biology of disease, drug sensitivity, and resistance that can lead to rapid introduction of novel therapies to the clinic. DSS, Drug Sensitivity Score. and (iv) follow individually optimized therapies in patients by out a pilot test for the implementation of optimized therapies analysis of the clonal evolution of leukemic