Swapnil Potdar

Differentiation status of primary chronic myeloid leukemia cells affects sensitivity to BCR-ABL1 inhibitors

Tyrosine kinase inhibitors (TKI) are the mainstay treatment of BCR-ABL1-positive leukemia and virtually all patients with chronic myeloid leukemia in chronic phase (CP CML) respond to TKI therapy. However, there is limited information on the cellular mechanisms of response and particularly on the effect of cell differentiation state to TKI sensitivity in vivo and ex vivo/in vitro. We used multiple, independent high-throughput drug sensitivity and resistance testing platforms that collectively evaluated 295 oncology compounds to characterize ex vivo drug response profiles of primary cells freshly collected from newly-diagnosed patients with BCR-ABL1-positive leukemia (n = 40) and healthy controls (n = 12). In contrast to the highly TKI-sensitive cells from blast phase CML and Philadelphia chromosome-positive acute lymphoblastic leukemia, primary CP CML cells were insensitive to TKI therapy ex vivo. Despite maintaining potent BCR-ABL1 inhibitory activity, ex vivo viability of cells was unaffected by TKIs. These findings were validated in two independent patient cohorts and analysis platforms. All CP CML patients under study responded to TKI therapy in vivo. When CP CML cells were sorted based on CD34 expression, the CD34-positive progenitor cells showed good sensitivity to TKIs, whereas the more mature CD34-negative cells were markedly less sensitive. Thus in CP CML, TKIs predominantly target the progenitor cell population while the differentiated leukemic cells (mostly cells from granulocytic series) are insensitive to BCR-ABL1 inhibition. These findings have implications for drug discovery in CP CML and indicate a fundamental biological difference between CP CML and advanced forms of BCR-ABL1-positive leukemia.

Abstract 3883: Gene expression predicts ex vivo drug sensitivity in acute myeloid leukemia

Introduction: Ex vivo drug sensitivity studies of samples derived from acute myeloid leukemia (AML) patients have been shown to be predictive of in vivo response. These findings are based on a limited number of well-characterized agents for which in vivo patient response data and ex vivo drug sensitivity data—on that same patient—are available. To show the feasibility of scaling such ex vivo studies to large drug screens, we characterized the reproducibility of expression-based models of drug response across two independent data sets—one generated at the Oregon Health and Science University (OHSU) and the second at the Institute for Molecular Medicine Finland (FIMM). Methods: We harmonized two large-scale AML ex vivo studies screened for drug response and profiled transcriptomically—OHSU (303 AML patient samples and 160 drugs) and FIMM (48 AML samples and 480 drugs). The two panels have 94 drugs in common. Log-logistic curves were fit to the dose-response data and area under the dose-response curves (AUCs) were calculated. Predictive modeling using Ridge regression or an integrative Bayesian approach was performed for each drug AUC independently using 202 highly-variable and/or cancer-associated genes as features. Results: For each of the 94 drugs in common between the two data sets, we trained a Ridge regression model on the OHSU data set, used the model to predict response in the FIMM data set, and calculated the Pearson correlation between the predicted and observed FIMM responses. 41 of the 94 drug models had a positive and statistically significant correlation [false discovery rate (FDR) -4 ) Conclusions: Our results using independent data sets and two statistical approaches suggest that certain drugs (including MEK and EGFR/VEGFR inhibitors) are amenable to expression-based predictive modeling in AML. Future work will focus on inferring individual biomarkers of response. Citation Format: Brian S. White, Suleiman A. Khan, Muhammad Ammad-ud-din, Swapnil Potdar, Mike J. Mason, Cristina E. Tognon, Brian J. Druker, Caroline A. Heckman, Olli P. Kallioniemi, Stephen E. Kurtz, Kimmo Porkka, Jeffrey W. Tyner, Tero Aittokallio, Krister Wennerberg, Justin Guinney. Gene expression predicts ex vivo drug sensitivity in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3883.

Abstract 4207: Selective drug sensitivity score (DSS) for indolent and aggressive prostate cancer cell lines

Prostate cancer (PC) is the most common malignancy in men and the second leading cause of cancer-related deaths. The majority of the PCs are classified as adenocarcinomas characterized by the expression of androgen receptor (AR) and prostate-specific antigen (PSA). Two of the most commonly used cell lines are LNCaP and PC-3 cells, derived from lymph node and bone metastases, respectively. Also VCaP cells, derived from vertebral metastases, are widely used in prostate cancer research. It has been well established that LNCaP and VCaP cells represent the conventional indolent form of PC expressing AR and PSA and are androgen-dependent. PC-3 cells, on the other hand, do not express AR and PSA, are androgen-independent, and represent the highly aggressive form. The drug sensitivity of the cell lines was assessed by applying a large panel of drugs covering cancer chemotherapeutics and clinically available and emerging drugs including conventional chemotherapy, kinase inhibitors, metabolic modifiers, rapalogs, differentiating/epigenetic modifiers, kinesin inhibitors, apoptotic modulators, NSAIDs, hormone therapy, immunomodulators and HSP inhibitors. A panel of 460 compounds was tested in five concentrations covering a 10.000-fold drug-relevant concentration range in 384-well format. Cells were seeded to pre-drugged plates, followed by cell viability measurements (CellTiter-Glo) after 72 hours. Maximal and minimal responses to drugs were analyzed, the EC50 values were calculated and Drug Sensitivity Score (DSS) was calculated for each drug as a measure of reduced viability. A selective Drug Sensitivity Score (sDSS) was calculated to identify the selective drug response pattern of each three cancer cell lines. As expected, the results indicate that LNCaP and VCaP cells in general were more sensitive to drugs of different categories than PC-3 cells. According to DSS analysis, all three cell lines showed sensitivity to conventional chemotherapy and kinase inhibitors. However, PC-3 cells were more sensitive to kinase inhibitors than conventional chemotherapy. Determining sDSS revealed specific sensitivities of each cell line. LNCaP cells were sensitive to kinase inhibitors, such as mTOR and AKT inhibitors. Also VCaP cells showed selective sensitivity to kinase inhibitors, especially Aurora kinase and IGF1R inhibitors. In addition to kinase inhibitors, VCaP cells were selectively sensitive to HDAC inhibitors. Furthermore, PC-3 cells were sensitive to e.g. CDK inhibitors. We conclude that the cell-based compound screening combined with DSS and sDSS analysis provides a possibility to profile cellular responses to an extensive collection of anti-cancer compounds enabling repurposing of existing drugs to new indications, identification of vulnerabilities in different types of cancer cells and functional investigation of cellular pathways behind drug sensitivity or resistance. Citation Format: Jenni Maki-Jouppila, Jenni Bernoulli, Johanna Tuomela, Mari I. Suominen, Jussi M. Halleen, Sanna Timonen, Elina Huovari, Katja Suomi, Swapnil Potdar, Paivi Ostling, Jani Saarela, Katja M. Fagerlund. Selective drug sensitivity score (DSS) for indolent and aggressive prostate cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4207. doi:10.1158/1538-7445.AM2017-4207

Abstract 3838: Drug sensitivity profile of 5TGM1 murine multiple myeloma cell line emphasizes the translational potential of the syngeneicin vivomodel

Multiple myeloma (MM) is the second most common hematologic malignancy that originates from B-cells (plasma cells) and causes 2% of cancer-related deaths. Symptoms of MM include bone pain caused by multiple osteolytic lesions, pathologic fractures, and hypercalcemia. Typically, MM has a low growth fraction and it is highly dependent on the microenvironment. These properties have made it hard to target by conventional chemotherapy, but could now be exploited by novel stroma-targeting drugs and immunotherapy. These new approaches underline the need for well characterized models with functional immune system and appropriate tumor microenvironment. To gain additional information supporting the use of the syngeneic 5TGM1 murine multiple myeloma model in drug development, we tested drug sensitivity of 5TGM1 cells by screening an extensive panel of drugs. The compound library consisting of 460 compounds included conventional chemotherapy, kinase inhibitors, metabolic modifiers, rapalogs, differentiating/epigenetic modifiers, kinesin inhibitors, apoptotic modulators, NSAIDs, hormone therapy, immunomodulators and HSP inhibitors. The compounds were tested in five concentrations covering a 10.000-fold drug-relevant concentration range in 384-well format. Cells were seeded to plates with a compound library, followed by cell viability measurements (CellTiter-Glo) after 72 hours. Maximal and minimal responses to drugs were analyzed, and the EC50 values were calculated. Drug Sensitivity Score (DSS) was calculated for each drug as a measure of reduced viability. According to DSS analysis, 5TGM1 cells showed sensitivity to conventional chemotherapy, such as antimitotic drugs, and kinase inhibitors, such as MEK1/2 inhibitors. In addition, the cells showed particular sensitivity to several HSP90 inhibitors currently in phase I/II clinical development for MM. Lenalidomide and pomalidomide, efficient in treating multiple myeloma in humans, both gave low DSS value indicating that 5TGM1 cells are not sensitive to these drugs, which is expected because they do not bind to murine form of the target cereblon. In contrast, 5TGM1 cells were highly sensitive to the proteasome inhibitor bortezomib (DSS 32.2), which is currently in clinical use. In conclusion, the murine 5TGM1 cells show sensitivity to various MM drugs used in the clinic and under development. Evaluating the effects of the microenvironment on the growth and drug sensitivity of 5TGM1 cells in vitro and in vivo will be essential. Furthermore, the cell-based compound screening combined with DSS analysis provides a possibility to profile cellular responses to an extensive collection of anti-cancer compounds enabling identification of vulnerabilities in cancer cells and functional investigation of cellular pathways behind drug sensitivity or resistance. Citation Format: Jenni Maki-Jouppila, Jenni Bernoulli, Mari I. Suominen, Tiina Kahkonen, Jussi M. Halleen, Sanna Timonen, Elina Huovari, Katja Suomi, Swapnil Potdar, Maria Nurmi, Paivi Ostling, Jani Saarela, Katja M. Fagerlund. Drug sensitivity profile of 5TGM1 murine multiple myeloma cell line emphasizes the translational potential of the syngeneic in vivo model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3838. doi:10.1158/1538-7445.AM2017-3838

Abstract 5732: PI3K/Akt activity regulates androgen receptor expression and predicts poor clinical outcome in non-metastatic hormone-naïve prostate cancer

Activation of PI3K/Akt pathway is associated with adverse outcome and aggressive disease in many cancers. In prostate cancer (PCa), the activity of this pathway has been shown to promote disease progression and metastasis. However, it is still controversial how PI3K/Akt regulates androgen receptor (AR), a central signaling molecule in prostate pathophysiology, and whether it has an active role in hormone naive non-metastatic PCa. Here, we show using immunohistochemistry (IHC) and advanced quantitative multiplexed IHC that the expression of phosphorylated-Akt(S473) and AR are highly correlated in clinical PCa, even at the cellular level. Furthermore, we found that high expression of p-Akt(S473) predicts poor clinical outcome in two independent hormone-naive non-metastatic PCa cohorts. To study whether PI3K/Akt regulates AR expression, we performed an in vitro drug screen with 32 PI3K/Akt/mTOR inhibitors in PC346C, an AR expressing cell line derived from a hormone-naive primary tumor of prostate. We observed a strong correlation between p-Akt(S473) and AR also in vitro in individual cells independent of the inhibitor used. Although both PI3K and Akt specific inhibition reduced cell viability, the response in nuclear expression of AR was highly dependent on the target of inhibition: Akt specific inhibition reduced AR nuclear expression and resulted in large, spindle-shaped cells, whereas PI3K specific inhibition increased AR nuclear expression and resulted in smaller, round-shaped cells. These data suggest that PI3K and Akt have different roles in sustaining AR activity in PCa as perturbations of the two components leads to differential responses in terms of AR nuclear expression and cell morphology. In conclusion, activated Akt associates with AR expression and predicts poor clinical outcome in hormone-naive non-metastatic PCa. Furthermore, the differing roles of PI3K and Akt in AR regulation warrants for further studies as it may have implications in the design of PCa therapy targeting PI3K/Akt, especially when the inhibitors are administered in combination with anti-androgens. Citation Format: Sami Blom, Petra Maki-Teeri, Andrew Erickson, Lassi Paavolainen, Tuomas Mirtti, Antti Rannikko, Swapnil Potdar, Paivi Ostling, Wytske van Weerden, Olli Kallioniemi, Teijo Pellinen. PI3K/Akt activity regulates androgen receptor expression and predicts poor clinical outcome in non-metastatic hormone-naive prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5732. doi:10.1158/1538-7445.AM2017-5732