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While many resources exist for the drug screening of bladder cancer cell lines in 2D culture, it is widely recognized that screening in 3D culture is more representative of in vivo response. Importantly, signaling changes between 2D and 3D culture can result in changes to drug response. To address the need for 3D drug screening of bladder cancer cell lines, we screened 17 bladder cancer cell lines using a library of 652 investigational small-molecules and 3 clinically relevant drug combinations in 3D cell culture. Our goal was to identify compounds and classes of compounds with efficacy in bladder cancer. Utilizing established genomic and transcriptomic data for these bladder cancer cell lines, we correlated the genomic molecular parameters with drug response, to identify potentially novel groups of tumors that are vulnerable to specific drugs or classes of drugs. Importantly, we demonstrate that MEK inhibitors are a promising targeted therapy for the basal subtype of bladder cancer, and our data indicate that drug screening of 3D cultures provides an important resource for hypothesis generation.

PURPOSE: There is a need for biomarkers of drug efficacy for targeted therapies in triple-negative breast cancer (TNBC). As a step toward this, we identify multi-omic molecular determinants of anti-TNBC efficacy in cell lines for a panel of oncology drugs. METHODS: Using 23 TNBC cell lines, drug sensitivity scores (DSS(3)) were determined using a panel of investigational drugs and drugs approved for other indications. Molecular readouts were generated for each cell line using RNA sequencing, RNA targeted panels, DNA sequencing, and functional proteomics. DSS(3) values were correlated with molecular readouts using an FDR-corrected significance cutoff of p* < 0.05 and yielded molecular determinant panels that predict anti-TNBC efficacy. RESULTS: Six molecular determinant panels were obtained from 12 drugs we prioritized based on their efficacy. Determinant panels were largely devoid of DNA mutations of the targeted pathway. Molecular determinants were obtained by correlating DSS(3) with molecular readouts. We found that co-inhibiting molecular correlate pathways leads to robust synergy across many cell lines. CONCLUSIONS: These findings demonstrate an integrated method to identify biomarkers of drug efficacy in TNBC, where DNA predictions correlate poorly with drug response. Our work outlines a framework for the identification of novel molecular determinants and optimal companion drugs for combination therapy based on these correlates.

Metastatic triple-negative breast cancer (TNBC) has a median survival of 13 months, and only 5 months if brain metastases are present. Advances in immunotherapy are promising, but only add weeks to a few months of progression-free survival to this guarded prognosis. Thus, there is a large unmet need for innovations that improve patient outcomes in the metastatic setting when patients are refractory to first-line treatment. Recently, we published an innovative strategy to identify multi-omic molecular correlates of drug response in TNBC. From this work, we now apply the strategy to 3D cell culture models and patient derived xenografts (PDXs) that are more representative of in vivo drug response in real patients. In brief, we have tested the dose response for a panel of 387 investigational drugs and plotted this readout against molecular characterization in 35 TNBC cell lines and PDXs. Using linear regression, we have identified statistically significant positively and negatively correlating DNA, RNA, and protein predictors of drug efficacy. From this expanded multi-omic set, we have identified a multi-omic signature for sensitivity to gemcitabine, a drug widely used in TNBC, but at present, without any a priori knowledge of who is likely to benefit. Moreover, we noted a significant correlation of response with MERIT40, an AKT substrate. MERIT40 is critical for localization of BRCA1 to facilitate DNA damage repair. MERIT40 promotes resolution of DNA damage caused by chemotherapy, and thus, based on our published work and supporting literature, PI3K/AKT inhibition is a rational synergistic partner with gemcitabine. Moreover, further supporting our hypothesis, preliminary results indicate Chou-Talalay synergy at therapeutically relevant doses when gemcitabine is combined with PI3K/AKT inhibition. In future studies, we will evaluate the ability of the gemcitabine biomarker to predict response, and the efficacy of the prioritized combinations using ex vivo patient models of metastatic breast cancer established in our laboratory. Citation Format: Nathan M. Merrill, Nathalie M. Vandecan, Athena M. Apfel, Peter J. Ulintz, Liwei Bao, Xu Cheng, Aki Morikawa, Sofia D. Merajver, Matthew B. Soellner. Optimizing gemcitabine in metastatic TNBC by rational PI3K/AKT combination [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1187.

Triple-negative breast cancer (TNBC) is one of the deadliest forms of breast cancer due to limited treatment options beyond conventional chemotherapy. TNBC constitutes approximately 10-20% of total breast cancer cases in the US; however, TNBC tends to be more aggressive, higher grade, and have a poorer prognosis than other forms of breast cancer. While oncogenic aberrations have been identified in TNBC, such as the PI3K/AKT/mTOR pathway that is misregulated in ~25% of tumors, there is a general lack of predictive markers of therapeutic efficacy to identify subsets of patients most likely to positively respond to a given targeted or conventional therapy. Simply relying on mutation status is undependable, as some patients without PI3K mutations respond to PI3K inhibitors and some patients with PI3K mutations do not respond. Thus, there is a critical need for biomarkers of drug efficacy in TNBC to help design precision medicine clinical trials based on systematically derived predictive biomarkers. To fill this gap, we devised a strategy to identify biomarkers of drug efficacy in TNBC. Using 23 TNBC cell lines, drug sensitivity scores (DSS3) were determined across a panel of investigational drugs and drugs approved for other indications. Molecular readouts were generated for each cell line using RNA sequencing, RNA targeted panels, DNA sequencing, and functional proteomics. DSS3 values were correlated with molecular readouts using an FDR-corrected significance cutoff of p* < 0.05 and yielded molecular determinant panels that predict anti-TNBC efficacy. Molecular determinant panels were obtained from 12 drugs that were prioritized on the basis of their efficacy. Six molecular determinant panels were obtained by correlating DSS3 with molecular readouts. We found that coinhibiting correlated pathways led to robust synergy across many cell lines. Together, our work presents an integrated method to identify biomarkers of drug efficacy in TNBC, where DNA predictions correlate poorly with drug response. Furthermore, we outline a framework to identify optimal companion drugs for combination therapy. Citation Format: Nathan M. Merrill, Eric J. Lachacz, Nathalie M. Vandecan, Peter J. Ulintz, Li Wei Bao, John P. Lloyd, Aki Morikawa, Sofia D. Merajver, Matthew B. Soellner. Molecular determinants of drug response in TNBC cell lines [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 38.

543 Background: Neoadjuvant chemotherapy prior to radical cystectomy (RC) for muscle-invasive urothelial carcinoma (UCCx) is the standard of care though the absolute survival benefit is small, and some patients progress during chemotherapy. While progress has been made in the prediction of sensitivity to platinum-based chemotherapies, providing more accurate, personalized, and clinically-relevant chemotherapy response prediction is an unmet need. We present our early and ongoing experience with rapid, organoid-based drug-screening. Methods: one gram of tumor was procured from patients undergoing TURBT or RC and divided between DNA/RNA sequencing, organoid drug-screening, and patient-derived xenografts. Tissue was dissociated, filtered, and resuspended in organoid media for serial passage and drug screening. Drugs were tested at “Cmax” concentration, which is the maximum plasma concentration in human trials so as to provide physiologic relevance. Results were normalized to control such that a value of 100 indicated no drug effect compared to control, and a value of 0 indicated complete response. Number of drugs screened was dependent upon tissue available, but was often 20-30. Results: 25 patients have undergone rapid organoid development and drug testing to date. Clinical correlations with chemotherapy response are ongoing. Drug response analyses were available 5-10 days following procedure. Select drug response data from the 9 most recent patient organoid samples are presented in the Table. DNA and RNA sequencing and PDX models are in progress. Conclusions: This platform allows for the rapid determination of neoadjuvant chemotherapy response and may further guide selection of therapeutic agents in patients with locally advanced bladder cancer.[Table: see text]

Triple negative breast cancer (TNBC) is one of the deadliest forms of breast cancer due to limited treatment options beyond conventional chemotherapy. While making up approximately 10-20% of total breast cancer cases in the US, TNBC carries a serious burden of disease as it tends to be more aggressive, higher grade, and have a poorer prognosis than other forms of breast cancer. Though new therapies, such as PARP inhibitors, have shown promise in clinical trials in very select patients (e.g. BRCA1 mutation carriers), there is a general lack of predictive markers of therapeutic efficacy to identify subsets of patients most likely to positively respond to a given targeted or conventional therapy. Thus, there is a critical need for biomarkers of drug efficacy in TNBC to help design precision medicine clinical trials based on systematically derived predictive biomarkers. To fill this gap, we devised a strategy to identify biomarkers of drug efficacy in TNBC. As a paradigm of our approach, we treated a panel of 23 TNBC cell lines with our potent c-Src/p38 inhibitor, UM-164, and determined a drug sensitivity score (DSS) in each line. We then calculated correlations of DSS with a variety of molecular readouts, including RNA sequencing, reverse-phase protein array, DNA sequencing array, and Nanostring RNA and miRNA arrays. From these correlations, we have identified expression DJ1, MAPK11, and PCM1 as strong predictors of UM-164 efficacy in TNBC. Building on this, we used our results to identify companion drugs that lead to synergy or test others that could instead result in antagonism when used in combination. To verify our results, independently targeting the DJ1 pathway, we show that AKT inhibition results in synergy when combined with UM-164. The outcome of this research yields a robust and generalizable methodology for the identification of biomarkers in cancer to design Phase I-III trials and for generating the mechanistic evidence of effective rational combinations. Citation Format: Nathan M. Merrill, Nathalie M. Vandecan, John P. Lloyd, Eric J. Lachacz, Peter J. Ulintz, Sofia D. Merajver, Matthew B. Soellner. Identification of biomarkers for UM-164 in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3989.