Dan Theodorescu’s research while affiliated with Cedars-Sinai Medical Center and other places

Cisplatin-based chemotherapy is used across many common tumor types, but resistance reduces the likelihood of long-term survival. We previously found the puromycin-sensitive aminopeptidase, NPEPPS, as a druggable driver of cisplatin resistance in vitro and in vivo and in patient-derived organoids. Here, we present a general mechanism where NPEPPS interacts with the volume-regulated anion channels (VRACs) to control cisplatin import into cells and thus regulate cisplatin response across a range of cancer types. We also find the NPEPPS/VRAC gene expression ratio is a predictive measure of cisplatin response in multiple cancer cohorts, showing the broad applicability of this mechanism. Our work describes a specific mechanism of cisplatin resistance, which, given the characteristics of NPEPPS as a drug target, has the potential to improve cancer patient outcomes. In addition, we describe an intracellular mechanism regulating VRAC activity, which is critical for volume regulation in normal cells – a finding with functional implications beyond cancer.

Introduction: There have been many recent advancements in the treatment of bladder cancer including the approval of novel intravesical agents for non-muscle-invasive disease and systemic-targeted therapeutics for muscle-invasive and advanced disease. However, treatment strategies for bladder cancer are still largely based on clinicopathologic characteristics. Areas covered: Based on primary literature sourced from PubMed, Embase, and Cochrane Library, we review the current status of molecular markers and biomarker panels with respective to their value in predicting response to standard chemotherapeutics and novel agents in non-muscle-invasive, muscle-invasive, and advanced bladder cancer. Expert opinion: Several biomarkers based on molecular characterization of tumors and quantification of circulating tumor DNA have been associated with response or resistance to standard chemotherapeutics. More recent investigations have reported on predictive biomarkers for novel therapeutics in bladder cancer, although large-scale validation is still needed. Given the increasing therapeutic options for this disease, employment of such predictive biomarkers may help guide treatment selection and sequencing.

The standard of care for eligible patients with muscle-invasive bladder cancer (MIBC) is cisplatin-based neoadjuvant chemotherapy followed by radical cystectomy. However, platinum-based treatments leave up to 70% of MIBC patients with residual disease, and the 5-year survival rate for these individuals is under 30%. Understanding cisplatin resistance mechanisms will improve MIBC treatment by developing more effective therapeutics and precision medicine strategies. We previously identified NPEPPS, an M1 aminopeptidase, as a novel and potentially targetable mediator of platinum drug response. Recently, we have established that NPEPPS controls platinum drug import through its interactions with volume regulated anion channels (VRACs), which have been shown to import cisplatin to cells directly. We have shown that NPEPPS interacts with key VRAC subunit SWELL1 at the cell membrane and that intact SWELL1 is required for NPEPPS-mediated platinum resistance. We further discovered that NPEPPS cannot bind SWELL1 when its enzymatic aminopeptidase function is disrupted genetically or pharmacologically. In vivo studies showed that catalytic-dead NPEPPS-mutant MIBC xenografts were sensitive to cisplatin treatment compared to NPEPPS-wildtype xenografts. Patient cohort data analysis supported the model that the expression of NPEPPS and its relative expression with VRAC proteins are associated with overall survival in treatment-resistant MIBC patients. These findings shed light on how bladder cancer cells resist chemotherapy by disrupting the transport of drugs into cells and suggest new ways to target NPEPPS to enhance the effectiveness of platinum-based therapeutics in treating bladder cancer. Citation Format: Lily Elizabeth R. Feldman, Saswat Mohapatra, Robert Jones, Charlene Tilton, Tahlita Zuiverloon, James Costello, Dan Theodorescu. New mechanistic insights into cisplatin resistance in muscle-invasive bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB256.

There is an unmet need to improve the efficacy of platinum-based cancer chemotherapy, which is used in primary and metastatic settings in many cancer types. In bladder cancer, platinum-based chemotherapy leads to better outcomes in a subset of patients when used in the neoadjuvant setting or in combination with immunotherapy for advanced disease. Despite such promising results, extending the benefits of platinum drugs to a greater number of patients is highly desirable. Using the multiomic assessment of cisplatin-responsive and -resistant human bladder cancer cell lines and whole-genome CRISPR screens, we identified puromycin-sensitive aminopeptidase (NPEPPS) as a driver of cisplatin resistance. NPEPPS depletion sensitized resistant bladder cancer cells to cisplatin in vitro and in vivo. Conversely, overexpression of NPEPPS in sensitive cells increased cisplatin resistance. NPEPPS affected treatment response by regulating intracellular cisplatin concentrations. Patient-derived organoids (PDO) generated from bladder cancer samples before and after cisplatin-based treatment, and from patients who did not receive cisplatin, were evaluated for sensitivity to cisplatin, which was concordant with clinical response. In the PDOs, depletion or pharmacologic inhibition of NPEPPS increased cisplatin sensitivity, whereas NPEPPS overexpression conferred resistance. Our data present NPEPPS as a druggable driver of cisplatin resistance by regulating intracellular cisplatin concentrations. Significance Targeting NPEPPS, which induces cisplatin resistance by controlling intracellular drug concentrations, is a potential strategy to improve patient responses to platinum-based therapies and lower treatment-associated toxicities.

Simple Summary The search to identify and understand proteins which are strongly associated with cancer is critical to our abilities to treat the disease. CD44 is a protein which is known to be strongly associated with cancer progression. While the precise role that CD44 plays in cancer development and resistance to chemotherapy agents remains to be fully determined, the protein provides three avenues for possible novel therapeutic approaches. First, since CD44 is found on the surface of cancer cells, it is more accessible to the therapeutic agents which block its ability to carry out its negative functions. Second, since CD44 binds a sugar moiety called hyaluronic acid, chemotherapy agents can be packaged with hyaluronic acid so that they preferentially bind cancer cells with high levels of CD44 on their surface, resulting in a greater efficacy in treatment. Third, the levels of CD44 can be assessed in urine voided from bladder cancer patients, possibly informing on the aggressiveness or stage of the cancer without the need for invasive testing. All told, CD44 protein provides a number of opportunities to better monitor and therapeutically target cancer and deserves increased resources to further clarify this potential. Abstract The glycoprotein CD44, with its many isoforms and variations in carbohydrate patterning, participates in a diverse set of cellular functions. This fact leads to the protein playing a role in many normal and pathologic cellular processes including a role in cancer progression and metastasis. These same facts make CD44 a strong therapeutic target in many cancer types, including bladder cancer.

533 Background: Accurate prediction of response to neoadjuvant chemotherapy (NAC) is essential for optimizing treatment outcomes in muscle-invasive bladder cancer (MIBC). We present a cutting-edge multimodal deep learning model aimed at predicting outcomes of NAC from accessible H & E images and molecular data. Methods: We designed a Graph-based Multi-modal Late Fusion (GMLF) deep learning model that integrates three types of data from the S1314-COXEN clinical trial: 1) Neural embeddings from 182 whole slide images (WSIs), generated via the ResNet50 architecture. 2) Cell types based on morphology, including cancer, necrotic, immune, and stromal cells, and their spatial positions within the tumor microenvironment extracted from WSIs using the HoVer-Net framework, a multiple-branch convolutional neural network for predicting cell types. 3) Patient-level RNA expression data derived from 1,071-dimensional gene expression vectors. The dataset was randomly divided into 80% for model training, using 5-fold cross-validation (5-fold CV), and 20% for internal validation. Results: Our dataset included 182 WSIs of 180 patients who were randomized to receive either gemcitabine-cisplatin or dose-dense methotrexate-vinblastine-adriamycin-doxorubicin-cisplatin. Of all patients, 30.8% demonstrated a complete pathological response (pT0 after radical cystectomy). Our GMLF model achieved an AUROC of 0.7417 ± 0.1021 for predicting whether a patient showed a complete pathologic response to NAC in a 5-fold CV. Using an 80/20 training and validation split, the model yielded an AUROC of 0.7236. Interestingly, our model emphasized the contribution of spatial information collected from WSIs. For selecting the best architecture for the histology branch, the Slidegraph+ framework, a graph neural network-based model, achieved an AUROC of 0.6938 ± 0.0565, significantly outperforming patch-level models as the second-best, which achieved AUROC of 0.5572 ± 0.1793. Shapley Additive Explanation (SHAP) analysis revealed RNA expression branch as the most influential, with a mean SHAP magnitude of 0.13, followed by the neural embeddings at 0.12. SHAP-based interpretation identified the most important expressed genes impacting model predictability, including TP63. Our gene set analysis identified the most influential pathways for predicting response, which included basal differentiation and myo-fibroblast enrichment (p-value < 0.05). Conclusions: Our interpretable GMLF model accurately predicts NAC response in patients with MIBC by integrating standard H&E images and RNA expression data. Our model interpretations not only revealed the importance of each modality but also uncovered histopathological, cellular, and molecular underpinnings of response to NAC in MIBC. These findings open the door to AI-guided development of precision therapies for MIBC. Clinical trial information: NCT02177695 .

Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.