Abstract 3188: Development of a cancer pharmacopeia-wide ex-vivo drug sensitivity and resistance testing (DSRT) platform for AML: Towards individually optimized therapy and improved understanding of drug resistance patterns
Authors: Malani Disha , Pemovska Tea , Kontro Mika , Murumägi Astrid , Kulesskiy Evgeny , Yadav Bhagwan , Bespalov Maxim , Eldfors Samuli , Elonen Erkki , Karjalainen Riikka , Knowles Jonathan KC , Majumder Muntasir Mamun , Mpindi John Patrick , Edgren Henrik , Szwajda Agnieszka , Venkata Naga Poojitha Kota , Turunen Laura , Mustjoki Satu , Wolf Maija , Aittokallio Tero , Heckman Caroline A. , Porkka Kimmo , Wennerberg Krister , Kallioniemi Olli 
Published: August 2012

Abstract
In order to discover unexpected anti-cancer efficacies of approved and emerging drugs, we established a diagnostic ex vivo drug sensitivity and resistance testing (DSRT) platform covering the entire cancer pharmacopeia as well as emerging anti-cancer compounds. Here, the platform was applied to analyze bone marrow (BM) mononuclear cells from 17 adult acute myeloid leukemia (AML) patients, 3 healthy donors as well as 7 AML cell lines. The DSRT panel covered FDA-approved small molecule oncology drugs (n=120), as well as emerging, investigational and pre-clinical oncology compounds (n=120), such as 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). To generate dose-response curves, each of the drugs was applied over a 10,000-fold concentration range. In addition, the samples underwent deep molecular profiling including exome- and transcriptome sequencing, as well as phosphoproteomic analysis. DSRT provided consistent and reliable data from ex vivo samples with a high correlation between data from individual healthy BM samples (r=0.93). Bioinformatic processing of the data from AML resulted in several key observations. First, overall drug response profiles of AML blast cells were distinctly different from healthy BM controls suggesting several potential leukemia-selective effects, such as multi-kinase (dasatinib), MEK, and mTOR inhibitors. Second, the overall drug responses from AML cell lines and the patient ex vivo samples showed differences, suggesting that ex vivo testing may reveal cancer-selective effects not previously seen in established cancer cell line panels. Third, the response data from patient samples clustered many drugs consistently into the expected functional classes (such as topoisomerase II inhibitors, MEK inhibitors and rapalogs), whereas other drug classes were more dispersed (such as FLT3 inhibitors with quizartinib clustering away from all other tyrosine kinase inhibitors), suggesting secondary targets playing a key role in drug efficacy. Fourth, analysis of serial samples from patients developing clinical resistance to targeted agents showed striking agreement between the ex-vivo DSRT profiles and clinical responses. In conclusion, comprehensive DSRT platform generated powerful novel insights on AML drug response and may enable individual optimization of therapies, particularly for recurrent leukemias. DSRT will also serve as a powerful hypothesis-generator for clinical trials, particularly for emerging drugs. The ability to correlate ex vivo response profiles for hundreds of drugs in clinical samples with deep molecular profiling data will yield exciting new translational and pharmacogenomic opportunities for cancer therapy.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3188. doi:1538-7445.AM2012-3188