Functional Precision Oncology Approach Using Nanoliter Droplet Array for Drug Sensitivity Testing in Lung Cancer.

Samples from recurrent, treatment-refractory cancers are rarely available, but would be valuable in understanding the molecular drivers of drug resistance. In leukemias, consecutive samples are readily available during treatment. Hence, we explored here the progression of adult acute myeloid leukemias (AML) by serial sampling and by integrating data from multiple platforms. Next-generation exome and RNA sequencing, and phosphoproteomic data were combined with comprehensive 240 cancer drug sensitivity and resistance testing (DSRT) of leukemic blasts ex-vivo before and after clinical relapse. The data were generated in an experimental diagnostic setting, with intent to improve and personalize treatment of patients with recurrent AML. A 54-year old AML-M5 patient with a FLT-3-ITD mutation and a normal karyotype was monitored by serial sampling. The patient was initially refractory to three consecutive high-dose induction treatments and had limited therapy options. AML blasts from the patient were screened with the DSRT platform. Results implied that the blast cells were 710-times more sensitive to temsirolimus and other rapamycin analogs as compared to normal BM cells, and showed a 1100-fold increased sensitivity to dasatinib. Proteomic analysis showed high phosphorylation of several signaling molecules, such as the insulin receptor and mTOR. Sequencing identified WT1 mutations and a NUP98-NSD1 fusion transcript, an infrequent event associated with poor prognosis in AML. Based on the DSRT results, the patient received compassionate off-label treatment with dasatinib, sunitinib and temsirolimus, resulting in a remarkable clinical remission, normalization of blast counts and a rapid recovery of neutrophil counts as a sign of selective elimination of the leukemic cells. The patient relapsed 4 weeks later, and at this point a new DSRT assay was performed, which showed the blast cells to be completely resistant to temsirolimus and less sensitive to dasatinib ex vivo. Consistent with this drug sensitivity profile was a genomic evolution of a distinct AML subclone with new changes, such as NF1 mutation and a microdeletion of the LEF1 gene, which were not observed in the pre-treatment sample. Taken together, we have demonstrated, how molecular profiling and functional ex vivo drug sensitivity and resistance data can be used to individually optimize patient treatment. Remission was achieved in a patient with advanced, treatment-refractory AML. Serial sampling from human AML patients coupled with molecular profiling and drug sensitivity testing may shed light on clonal progression of disease, and the molecular events underlying drug response. 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 4580. doi:1538-7445.AM2012-4580

There is an urgent need to develop better understanding of the drug responsivenes in ovarian cancer (OC), particularly in the clear cell and mucinous subtypes, which tend to be resistant to the commonly used platinum-based regimens. This would help to improve survival and facilitate precision medicine approaches to therapy. In order to discover previously unsuspected anti-cancer effects with approved and emerging drugs, we applied Drug Sensitivity and Resistance Testing (DSRT) technology (Pemovska et al., Cancer Discovery, 2013) to determine detailed dose-response effects of 300 drugs in 25 established OC cell lines (10 serous, 10 clear cell and 5 mucinous ovarian cancer cell lines). The panel covered 160 approved drugs as well as 162 emerging, investigational and pre-clinical oncology compounds such as kinase and non-kinase inhibitors. All drugs were tested in 5 different concentrations covering a 10,000-fold concentration range using Labcyte nano-dispensing technology, in order to generate detailed dose-response curves for each drug in each cell line. The area under the curve was estimated from the dose-response curve to obtain the drug sensitivity score. Bioinformatic processing of the drug response data from OC cell lines resulted in several key observations. First, the DSRT data made it possible to classify OC cell lines into four functional taxonomic subtypes based on comprehensive drug responses. Interestingly, these clusters did not depend on the histological origin of the OC cell lines. Second, the 322 drugs were clustered into functional subsets based on the response data across the 25 OC cell lines. Most of this clustering followed the expected chemical similarity and target space of the drugs, such as topoisomerase II inhibitors, MEK inhibitors, mTOR inhibitors and Taxanes each clustering in their own subgroups. Third, we found that many emerging, currently not-yet-approved drugs were active in subgroups of OC cell lines, including both kinase as well as non-kinase drugs, such as HDAC inhibitors Entinostat and Belinostat. DSRT technology provides a powerful strategy for assessing drug response in OC cell models, and it could also help to asses drug responses in patient-derived ex vivo model systems of OC. Analysis of correlations of DSRT data with genomic data of the cell lines is underway and will yield new translational and pharmacogenomic opportunities for OC. Citation Format: Akira Hirasawa, Astrid Murumägi, Mariliina Arjama, Bhagwan Yadav, John Patrick Mpindi, Krister Wennerberg, Tero Aittokallio, Daisuke Aoki, Olli Kallioniemi. Systematic high-throughput drug sensitivity and resistance testing (DSRT) of ovarian cancer cell lines indicates novel therapeutic possibilities with existing and emerging drugs. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5384. doi:10.1158/1538-7445.AM2014-5384

High-Throughput Ex Vivo Drug Sensitivity and Resistance Testing (DSRT) Integrated with Deep Genomic and Molecular Profiling Reveal New Therapy Options with Targeted Drugs in Subgroups of Relapsed Chemorefractory AML

Ovarian cancer (OvCa) is the sixth most common cancer in women and a leading cause of death from gynecologic diseases. Poor prognosis in OvCa is due to late diagnosis and acquired resistance to conventional therapy. A significant setback for OvCa treatment is the lack of reliable biomarkers and the lack of effective targeted therapies. Our aim is to discover novel therapeutic opportunities with approved and emerging drugs for OvCa and then translate actionable drug efficacies and combinations as personalized therapy suggestions for the clinic. We have previously pioneered drug sensitivity and resistance testing (DSRT) of patient cells from leukemias for personalized medicine and for drug repositioning (Pemovska et al., Cancer Discovery, 2013; Pemovska et al. Nature, in press, 2014). Here, we tested primary cell cultures from the ascites fluid of relapsed chemorefractory OvCa patient samples using the DSRT platform for 305 drugs, consisting of both emerging and established cancer drugs. All drugs were tested in 5 concentrations to achieve a dose-response over a 10,000-fold concentration range. Both viability and cell toxicity assays were applied and results compared to a variety of normal cells and a database of >700 previously DSRT-tested cancer samples and cell lines of various types, including 30 OvCa cell lines. Genomic and transcriptomic profiling by next-gen sequencing was carried out in parallel. Most samples represent high-grade serous OvCa. Results from 5 primary ascites cultures tested so far showed a distinct drug sensitivity profile as compared to the 30 OvCa cell lines. The results on patient cells led to the discovery of previously unanticipated therapeutic possibilities. For example, in a 51-year old chemorefractory serous OvCa patient, genomic and transcriptomic analyses revealed a fusion gene of NRG-1, a target that was recently reported to involve the NRG1/ERBB3 activation loop in OvCa (Sheng et al. Cancer Cell, 2010). We found high expression of ERBB2 and ERBB3 by RNA seq and phospho-ERBB3, phospho-ERBB2 and phospho-EGFR by immunohistochemistry. In agreement with the molecular mechanism, DSRT analysis identified significant sensitivity of patient tumor cells to EGFR inhibitors, such as erlotinib and afatinib. Furthermore, drug combination testing identified highest combinatorial potential of dasatinib with erlotinib and afatinib in both viability and cell killing assays. In conclusion, DSRT testing together with genomic and transcriptomic profiling can identify and mechanistically validate tumor driver signals and pinpoint clinically actionable inhibitors and their combinations. Thus, this type of systems medicine profiling can significantly expand the power of current exclusively genomics-oriented personalized medicine approaches and help in drug repositioning to new indications. Citation Format: Astrid Murumägi, Akira Hirasawa, Mariliina Arjama, Katja Välimäki, Bhagwan Yadav, Jing Tang, Agnieszka Szwajda, Laura Turunen, John Patrick Mpindi, Teijo Pellinen, Krister Wennerberg, Ralf Bützow, Tero Aittokallio, Olli Kallioniemi. Novel therapeutic possibilities for chemorefractory ovarian cancer patients identified by functional ex vivo drug sensitivity testing of primary cells from ascites. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3746. doi:10.1158/1538-7445.AM2015-3746

Introduction: Conventional cytotoxic chemotherapy regimens for adult acute myeloid leukemia (AML) are effective in curing less than 50% of the patients, and there is a major need for targeted drugs with better anti-cancer selectivity. Here, our aim was to i) identify potential clinically used or emerging cancer drugs by quantitative drug sensitivity and resistance testing (DSRT) of 16 AML cell lines ii) compare the cell line data with results obtained from tested 24 ex vivo AML patient specimens iii) identify genomic correlations potentially explaining drug responsiveness. Methods: The cancer pharmacopeia-wide drug collection is composed of 119 FDA approved and 90 investigational chemical compounds including cytotoxic agents and cell signaling molecule inhibitors. Each drug was tested over a 10,000-fold concentration range and that has generated quantitative five point dose-response curves. AML cells were plated in 384 well plates (where the drugs were pre-printed using an acoustic nano-dispensing technology, Labcyte®) and incubated in standard cell culture conditions. Cell viability was measured by Cell Titer Glow® luminescence assays. Analysis of dose response curves using Dotmatics® software resulted in IC50 values. Moreover, the genomic profiles of the AML cell lines were determined by microarrays and/or next-gen sequencing data for further integration with drug responses. Results: Comprehensive data analysis of 16 AML cell lines indicated that specific targeted drugs were selectively killing AML cells. The data analysis revealed relatively strong responses for MEK inhibitors in most AML cell lines (e.g. refametinib 87%, trametinib 82%, selumetinib 75%) while 21% of ex vivo AML patient samples were sensitive to these MEK inhibitors. In case of rapalog sensitivity, 80% of AML cell lines (e.g. temsirolimus 82%, everolimus 71%, sirolimus 81%) and 25% of ex vivo AML patient cases were responsive to the mTOR inhibitors. The AML cell lines carrying FLT3-ITD mutations were extremely sensitive to FLT3 inhibitors (e.g. quizartinib, lestaurtinib, tandutinib, and sorafenib) but very few responses to FLT3 inhibitors were observed in AML patients carrying an ITD mutation in the FLT3 kinase. Summary: Systematic DSRT profiling of AML cell lines illustrates drug sensitivity patterns to classify the cell lines as sensitive or resistant to specific classes of drugs. mTOR and MEK inhibitors were among the most effective inhibitors for most cell lines and also in some ex vivo patient cases suggesting that these drugs may have potential as therapeutic agents in AML. Also, bioinformatics predictions can be used to identify key synergistic combinations of tested drugs for effective AML therapy. Further integration of molecular profiles and functional responses of AML cell lines will help provide better understanding of drug efficacy based on known genetic background of the disease. Citation Format: Disha Malani, Astrid Murumägi, Tea Pemovska, Bhagwan Yadav, Evgeny Kulesskiy, Jing Tang, John Patrick Mpindi, Maija Wolf, Riikka Karjalainen, Tero Aittokallio, Caroline Heckman, Kimmo Porkka, Krister Wennerberg, Olli Kallioniemi. Identifying AML-specific key targeted drugs using high-throughput drug sensitivity and resistance testing profiles of AML cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5220. doi:10.1158/1538-7445.AM2013-5220

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

The heterogeneity of chemoresponses in high-grade serous ovarian cancer (HGS-OvCa) presents a major clinical challenge. The inherited or acquired nonresponsiveness to current therapies is likely to be one reason for relapse and treatment failure. Better models for predicting effective treatment options and drug combinations are needed. Although most tumors initially respond to standard platinum-taxane combination therapy, treatment resistance eventually evolves in 80-90% of the patients. Drug sensitivity and resistance testing (DSRT) was performed by using high-throughput screening (HTS) of 306 small-drug molecules on six primary cell lines from patients with disseminated HGS-OvCa. Commonly available ovarian cancer cell line OVCAR8 was used as a control. The drug effect was investigated with help of a quantitative scoring approach, drug sensitivity score (DSS). Sixteen drugs of clinical interest were investigated in more detail. Drug sensitivity varied greatly within primary cell lines. Out of 306 compounds, 29 were effective on the most resistant cell line, whereas 102 compounds showed effect on the most sensitive line. We found that 9 out of the 16 clinically important compounds gave no response on the tested cell lines. Seven compounds provided response on the screened cell lines: four HSP90 inhibitors, an HSP70 inhibitor, a Wee1 inhibitor, and a proteasome inhibitor. Of these seven compounds, the most effective drug compounds (the Wee1 inhibitor and two HSP90 inhibitors) were chosen for validation on primary HGS-OvCa cell lines to verify the HTS result. The effectiveness of currently used clinical treatment-line drugs, cisplatin and paclitaxel, was compared to the putative novel drug compounds on the primary cell lines and the control cell line. One primary cell line was completely resistant to the validated drug compounds as well as to cisplatin and paclitaxel combination treatment. Five cell lines were sensitive to cisplatin-paclitaxel treatment, but the sensitivity to the three most effective compounds varied greatly. The commonly available cell line OVCAR8 was the most sensitive of all tested cell lines. In conclusion, we found three potential drug compounds of clinical interest effective for disseminated HGS-OvCa. The analyses demonstrate that patient-derived primary HGS-OvCa cell lines have unique heterogeneous characteristics that are likely to have an effect on the choice of best drug candidates for each individual cell line, and furthermore for each patient. Consequently, more combinatorial drug treatment strategies should be validated to find new specific personalized drug treatment strategies. Citation Format: Pia Roering, Piia Mikkonen, Swapnil Potdar, Krister Wennerberg, Johanna Hynninen, Seija Grénman, Annika Auranen, Olli Carpén, Katja Kaipio. Drug sensitivity and resistance testing (DSRT) of clinically important compounds on primary ovarian cancer cell lines. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr A57.

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

Acute myeloid leukemia (AML) is an aggressive, heterogeneous disease with few options for targeted therapy. Here, we describe a novel translational strategy termed Individualized Systems Medicine (ISM), in which we profile primary AML patient cells functionally, molecularly and clinically to identify novel treatment strategies for patients, monitor and predict disease progression and follow-up therapies, and elucidate drug response and resistance mechanisms. We developed a comprehensive ex vivo drug sensitivity and resistance testing (DSRT) strategy to screen AML patient blast cells ex vivo against a set of 202 conventional chemotherapeutic and targeted approved (n=119) and investigational (n=83) drugs. Quantitative leukemia-selective drug sensitivity scores for each drug were determined by comparing the area under the dose response curve from the patient cells to that of healthy control mononuclear cells. Analysis of consecutive samples from the same patients with DSRT and next-generation sequencing was applied to infer clonal evolution and potential mechanisms of drug response and resistance. Twenty-four samples from 16 recurrent and refractory AML patients were profiled by DSRT, sequencing and proteomic approaches. Several approved and late stage clinical investigated targeted drugs including multi-kinase inhibitors (e.g. dasatinib, sunitinib), TORC1 inhibitors (e.g. temsirolimus), JAK inhibitors (e.g. ruxolitinib) and MEK inhibitors (e.g. trametinib, selumetinib) showed selective leukemic-specific responses in 10-30% of AML samples from patients with recurrent disease. In two refractory AML cases where dasatinib, sunitinib and temsirolimus showed selective responses, the clinical administration of these compounds resulted in complete and partial remission, but was followed by resistance to the applied drugs. Re-sampling and DSRT retesting of cells confirmed diminished sensitivities to the administered drugs, but also indicated new acquired drug sensitivities. Exome and RNA sequencing of the serial samples from both patients revealed diverse subclonal populations characterized by multiple somatic mutations, which were either lost or gained during disease progression and represented drug sensitive or resistant subclones. In conclusion, our results suggest that an ISM strategy based on consecutive cancer sampling, ex vivo DSRT and analysis of clonal evolution could facilitate the rapid design of improved combinatorial therapies for AML. This strategy can also help tailor optimized therapies for patients, and prioritize introduction of new drugs for clinical testing. Citation Format: Krister Wennerberg, Tea Pemovska, Mika Kontro, Bhagwan Yadav, Evgeny Kulesskiy, Henrik Edgren, Samuli Eldfors, Riikka Karjalainen, Naga Poojitha Kota Venkata, Anna Lehto, Muntasir Mamun Majumder, Disha Malani, Astrid Murumägi, Laura Turunen, Jonathan Knowles, Tero Aittokallio, Caroline Heckman, Kimmo Porkka, Olli Kallioniemi. Comprehensive ex vivo drug sensitivity testing combined with in depth molecular profiling of AML patients cells provides individualized treatment strategies and reveals mechanisms of drug resistance. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 65. doi:10.1158/1538-7445.AM2013-65

Development of a Cancer Pharmacopeia-Wide Ex-Vivo Drug Sensitivity and Resistance Testing (DSRT) Platform: Identification of MEK and mTOR As Patient-Specific Molecular Drivers of Adult AML and Potent Therapeutic Combinations with Dasatinib

A personalised medicine drug sensitivity and resistance testing platform and utilisation of acoustic droplet ejection at the Institute for Molecular Medicine Finland

As almost all therapeutic approaches for ovarian cancer (OC) are organ-based therapies that have been tested in large-scale clinical studies, no precision medicine approach to OC currently exists. There is an urgent need to develop a better understanding of the drug responsiveness in OC, particularly in the clear cell and mucinous subtypes, which tend to be resistant to the commonly used platinum-based regimens. This would help to improve survival and facilitate precision medicine therapy approaches. In order to discover previously unsuspected anti-cancer effects of approved and emerging drugs, we applied Drug Sensitivity and Resistance Testing (DSRT) technology (Pemovska et al., Cancer Discovery, 2013) to determine detailed dose-response effects of 306 drugs in 29 established OC cell lines (12 clear cell, 6 mucinous, 1 endometrioid, and 10 serous and other OC cell lines). The panel covered 137 approved drugs as well as emerging, investigational and pre-clinical oncology compounds such as kinase and non-kinase inhibitors. All drugs were tested at 5 different concentrations, covering a 10,000-fold concentration range by using Labcyte nano-dispensing technology, in order to generate detailed dose-response curves for each drug in each cell line. The area under the curve was estimated from the dose-response curve to obtain the drug sensitivity score. Bioinformatic processing of the drug response data from OC cell lines resulted in several key observations. First, the clusters did not depend on the histological origin of the OC cell lines but were clustered according to drug categories. Second, our result showed that many emerging or non-approved drugs were sensitive in subgroups of OC cell lines. For example, MEK inhibitors and mTOR inhibitors show high sensitivity in many OC cell lines. They can be candidates for drug repositioning. Third, we confirmed reproducibility of our findings. In summary, as per the data from direct testing of drug efficacy, several emerging anti-cancer drugs show potential responses in subsets of OC cell lines. DSRT technology provides a powerful strategy for assessing drug response in OC cell models, and it could also help asses drug responses in patient-derived ex vivo model systems of OC. Analysis of correlations of DSRT data with genomic data of the cell lines is underway and will yield new translational and pharmacogenomic opportunities for treating OC. Citation Format: Akira Hirasawa, Astrid Murumägi, Mariliina Arjama, Bhagwan Yadav, John Patrick Mpindi, Krister Wennerberg, Tero Aittokallio, Daisuke Aoki, Olli Kallioniemi. High-throughput drug sensitivity and resistance testing of ovarian cancer cell lines provides useful strategy for assessing drug repositioning and therapeutic possibilities of emerging drugs [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr POSTER-TECH-1111.

Comparison of two supporting matrices for patient-derived cancer cells in 3D drug sensitivity and resistance testing assay (3D-DSRT).

Identification of signaling pathways that are required for the growth and differentiation block of cells from adult acute myeloid leukemia (AML) is urgently required to facilitate development of novel therapies. Here, we describe an approach to functionally determine molecular drivers of AML by quantitative drug sensitivity and resistance testing (DSRT) of AML blast cells in primary culture ex vivo. The selection of drugs covered the entire cancer pharmacopeia and much of the pipeline of drugs under development in the industry: 120 FDA approved small molecular cancer drugs and 120 emerging drugs, investigational compounds and signal transduction inhibitors. All compounds were tested over a 10,000-fold concentration range to generate quantitative and reliable dose-response data. In addition, whole exome and transcriptome sequencing and phophoproteomic profiling were also performed to derive a comprehensive understanding of the molecular AML-related aberrations on an individual basis. Comparison of 17 AML patient samples and 3 healthy bone marrow control samples based on ex vivo drug responses identified several classes of approved and investigational drugs that showed selective anti-AML activities: mTOR inhibitors (e.g. temsirolomus, everolimus, sirolimus), MEK inhibitors (e.g. AS703026, GSK1120212, RDEA119, selumetinib), tyrosine kinase inhibitors (e.g. dasatinib, ponatinib, sunitinib), Bcl-2 inhibitors (navitoclax) and HSP90 inhibitors (e.g. BIIB021, NVP-AUY922, tanespimycin). In particular, the rapamycin class of mTOR inhibitors and allosteric MEK inhibitors stood out as effective and selective inhibitors in 8/17 (47%) and 9/17 (52%) of the patients, respectively. Simultaneous data from other targeted inhibitors made it possible to dissect the critical steps in signaling and therapeutic efficacy. For example, PI3K and Akt inhibitors were not effective in these patients, suggesting that the mTOR dependency is mediated through a PI3K-Akt-independent pathway. Similarly, the dependency of MEK signaling appears to be through a Ras-Raf-independent pathway since Raf inhibitors were not effective. In conclusion, the DSRT platform allows us to derive quantitative data on the ex vivo drug response profiles of AML cells from individual patients. This information could be used as a diagnostic tool to optimize personalized therapies in the future. Our data demonstrate that mTOR and MEK signaling and the associated inhibitors are the most promising leads for improved AML therapeutics. This analysis also demonstrates gaps in our current understanding of the redundancy of key cancer cell signaling pathways and proves the significant value of data from experimental drug response testing ex vivo. 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 895. doi:1538-7445.AM2012-895

<div>Abstract<p>We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and <i>ex vivo</i> 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 five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., <i>MLL</i> gene fusions in subgroup IV and <i>FLT3</i>-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas <i>ex vivo</i> DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs.</p><p><b>Significance:</b> 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. <i>Cancer Discov; 3(12); 1416–29. ©2013 AACR.</i></p><p>See related commentary by Hourigan and Karp, p. 1336</p><p>This article is highlighted in the In This Issue feature, p. 1317</p></div>