Biology subjects

Wolpin, B. M.

Publications and source records attributed to Wolpin, B. M..

12 recordsLinked to original sources

bioRxiv2026

Characterization and therapeutic suppression of KEAP1-NRF2-driven resistance to KRAS inhibitors in pancreatic and lung cancer

The recent approval of KRAS inhibitors supports the therapeutic value of targeting mutant KRAS cancers. However, clinical efficacy is hindered by both primary and treatment-associated acquired resistance. We applied a CRISPR-Cas9 loss-of-function screen and identified loss of KEAP1 as a resistance mechanism to the KRASG12D-selective inhibitor MRTX1133 and the RAS(ON) multi-selective inhibitor RMC-7977 in pancreatic cancer models. RNA-sequencing analyses revealed a KEAP1KO transcriptome that is distinct from the ERK-, MYC-, and YAP/TAZ-TEAD-dependent transcriptional programs that drive KRAS inhibitor resistance, demonstrating a distinct mechanism of resistance. We then established a PDAC KEAP1-deficient (PKD) gene signature that was enriched in patients and preclinical models insensitive to KRAS inhibitor treatment. Finally, we observed that KEAP1-deficient cells exhibited elevated glutamine metabolism, and combination treatment with the glutamine antagonist DRP-104 (sirpiglenastat) enhanced KRAS inhibitor suppression of pancreatic and lung tumors. SIGNIFICANCEKEAP1 loss is associated with reduced response to KRAS inhibitor therapy. We demonstrate that KEAP1 loss-associated resistance can be overcome by pharmacologic inhibition of the KEAP1 loss-induced glutamine dependency, establishing a combination to enhance RAS inhibitor clinical efficacy.

cancer biology↗

bioRxiv2026

Pancreatic cancer-associated organ dysfunction promotes muscle autophagy and contributes to peripheral tissue wasting

Normal pancreas function supports both digestion and the hormonal regulation of whole-body metabolism. We find pancreatic ductal adenocarcinoma (PDAC) disrupts the normal function of the remaining pancreas, leading to altered systemic metabolism and peripheral tissue wasting that begins early in disease progression. Using mouse models of PDAC, we find small pancreas tumors lead to both endocrine and exocrine pancreatic dysfunction that results in systemic nutrient depletion and loss of both muscle and fat tissue. Providing free glucose in the diet that is absorbed despite pancreatic exocrine dysfunction causes hyperglycemia and blunts fat wasting without affecting muscle loss. Muscle mass can be restored by free dietary amino acids or pancreatic enzyme supplementation. Exocrine dysfunction causing reduced dietary protein digestion promotes muscle proteolysis and autophagy. Autophagy is a major driver of muscle wasting in PDAC, as muscle-specific deletion of the core autophagy gene Atg7 also reduces muscle wasting. Disrupting muscle autophagy without restoring systemic nutrition slows tumor growth and improves survival of mice with PDAC. Tracing the fate of amino acids released from muscle of mice with PDAC shows redistribution to both tumor and host tissues. Notably, improving nutrition in mice with disrupted muscle autophagy promotes tumor growth. Together, the data argue that early peripheral tissue wasting associated with early pancreatic cancer is driven by altered normal pancreatic organ function that leads to reduced nutrition and enhanced muscle autophagy, releasing nutrients to support both tumor and host metabolism.

cancer biology↗

bioRxiv2026

Integrated inference of cancer gene expression from cell-free plasma chromatin

Gene expression is a defining determinant of tumor identity, behavior, and therapeutic response, yet remains challenging to measure noninvasively. Here, we introduce APEX (Associating Plasma Epigenomic features with eXpression), a framework for inferring expression from circulating cell-free chromatin. Trained on [~]270,000 gene-sample pairs from matched tumor RNA-seq and plasma cfChIP-seq across multiple cancers and validated on >15 unseen cancer subtypes, APEX accurately infers cancer gene expression across a range of tumor fractions and outperforms existing plasma-based approaches by integrating positional histone mark and DNA fragmentation patterns across promoters and gene bodies. Using plasma alone, APEX enables classification of prognostically relevant basal and classical pancreatic cancer subtypes and identifies plasma-inferred NECTIN4 expression as a biomarker of response to enfortumab vedotin in metastatic bladder cancer. Together, these findings establish APEX as a biopsy-free approach for profiling tumor transcriptional states and extend liquid biopsy beyond genomic alterations to clinically relevant gene expression programs.

genomics↗

bioRxiv2025

GLIS3 Marks a Neural-like Progenitor Cell State that Drives Metastasis in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a high rate of recurrence and metastasis despite intensive therapy. The neural-like progenitor (NRP) transcriptional program is enriched in residual disease after neoadjuvant chemotherapy and radiotherapy; however, the mechanisms for increased NRP expression in the post-treatment setting remain unclear. Here, we find that NRP signatures are strongly enriched in tissue injury and regeneration, and NRP cancer cells co-express transcription factors involved in pancreatic development. We identify both cell autonomous and ligand mediated mechanisms for inducing NRP expression in vitro. We establish and characterize isogenic mouse organoid overexpression models for transcription factors associated with the NRP, classical, and basal-like states. We discovered that Glis3 is a key NRP-associated transcription factor that drives malignant properties including greater clonogenicity, tumor growth, and metastasis compared to isogenic models for the classical and basal-like states. Our work highlights the emergence of clinically relevant developmental regeneration programs in the post-treatment context.

cancer biology↗

bioRxiv2025

Mechanisms of resistance to active state selective tri-complex RAS inhibitors

Tri-complex inhibitors (TCIs) act as molecular glues to recruit cyclophilin A (CYPA) to the active (GTP-bound or ON) conformation of RAS, which in turn prevents the activation of downstream effector proteins like RAF and PI3K. Emerging data demonstrate clinical activity, including tumor regressions, in patients with RAS driven cancers. Despite being promising therapeutic interventions, the mechanisms of resistance in patients treated with these inhibitors remain unknown. Here we studied matched baseline and post-progression specimens from patients treated with the RAS(ON) multi-selective inhibitor daraxonrasib (RMC-6236). Tissue or cell-free DNA specimens were collected from 40 patients with RAS-mutant non-small cell lung, colorectal, or other cancers. Eighteen patients (45%) were found to have acquired alterations in RAS signaling intermediates, including recurrent alterations in KRAS, BRAF, RAF1, MAP2K1/2 and PIK3CA. Preclinical resistance models mirrored the alterations observed in patients. We found that secondary KRAS Y64X mutations caused resistance by disrupting an important pi-pi interaction between KRAS and the indole ring of daraxonrasib, which lowers the affinity of the daraxonrasib:CYPA binary complex for active KRAS. We also identified kinase-dead and low-activity BRAF mutations in samples with acquired resistance. This is puzzling, because TCIs are expected to prevent the interaction between RAS and BRAF, which is needed for hypoactive mutants to dimerize and signal. We now show that RAF dimers are harder to displace from active RAS, as compared to their monomeric forms. Indeed, enhanced RAF dimerization attenuated the ability of TCIs to recruit CYPA to active RAS, resulting in diminished inhibition of oncogenic signaling and tumor growth. Thus, several clinical resistance alterations converge at attenuating the formation of the RAS:daraxonrasib:CYPA tri-complex, either by preventing daraxonrasib binding or by inducing RAF dimers.

cancer biology↗

bioRxiv2023

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (CD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict CD40 treatment status and disease-free survival (DFS) following CD40 therapy based upon TME features. Through downstream interpretation of the machine learning models predictions, we found CD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following CD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of CD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for CD40-treated patients with PDAC.

cancer biology↗

bioRxiv2022

Cancer tissue of origin constrains the growth and metabolism of metastases

Metastases arise from a subset of cancer cells that disseminate from the primary tumor; however, the factors that contribute to proliferation of cancer cells in a secondary site are incompletely understood. The ability of cancer cells to thrive in a new tissue site is influenced by genetic and epigenetic changes that are important for disease initiation and progression, but these factors alone do not predict if and where cancers metastasize. Specific cancer types metastasize to consistent subsets of tissues, suggesting that factors associated with the primary tumor influence the tissue environments where cancers can grow. Using pancreatic cancer as a model, we find that primary and metastatic tumors share many metabolic similarities and that the tumor initiating capacity and proliferation of both primary- and metastasis-derived cells is favored in the primary site relative to the metastatic site. Moreover, propagating lung or liver metastatic cells in vivo to enrich for tumor cells adapted to grow in the lung or the liver does not enhance their relative ability to form large tumors in those sites, change their preference to grow in the primary site, nor stably alter some aspects of their metabolism relative to primary tumors. We also analyzed primary liver and lung cancer cells and find that these cells also exhibit a preference to grow in their primary site relative to metastatic sites. Together, these data suggest that the cancer tissue-of-origin influences the metabolism of both primary and metastatic tumors and may impact whether cancer cells can thrive in a metastatic site.

cancer biology↗

bioRxiv2021

Pancreatic cancer risk predicted from disease trajectories using deep learning

Pancreatic cancer is an aggressive disease that typically presents late with poor patient outcomes. There is a pronounced medical need for early detection of pancreatic cancer, which can be addressed by identifying high-risk populations. Here we apply artificial intelligence (AI) methods to a dataset of 6 million patient records with 24,000 pancreatic cancer cases in the Danish National Patient Registry (DNPR) and, for comparison, a dataset of three million records with 3,900 pancreatic cancer cases in the United States Department of Veterans Affairs (US-VA) healthcare system. In contrast to existing methods that do not use temporal information, we explicitly train machine learning models on the time sequence of diseases in patient clinical histories and test the ability to predict cancer occurrence in time intervals of 3 to 60 months after risk assessment. For cancer occurrence within 36 months, the performance of the best model (AUROC=0.88, DNPR), trained and tested on disease trajectories, exceeds that of a model without longitudinal information (AUROC=0.85, DNPR). Performance decreases when disease events within a 3 month window before cancer diagnosis are excluded from training (AUROC[3m]=0.83). Independent training and testing on the US-VA dataset reaches comparable performance (AUROC=0.78, AUROC[3m]=0.76). These results raise the state-of-the-art level of performance of cancer risk prediction on real-world data sets and provide support for the design of prediction-surveillance programs based on risk assessment in a large population followed by affordable surveillance of a relatively small number of patients at highest risk. Use of AI on real-world clinical records has the potential to shift focus from treatment of late-stage to early-stage cancer, benefiting patients by improving lifespan and quality of life.

bioinformatics↗

bioRxiv2020

Transcriptional subtype-specific microenvironmental crosstalk and tumor cell plasticity in metastatic pancreatic cancer

Bulk transcriptomic studies have defined classical and basal-like gene expression subtypes in pancreatic ductal adenocarcinoma (PDAC) that correlate with survival and response to chemotherapy; however, the underlying mechanisms that govern these subtypes and their heterogeneity remain elusive. Here, we performed single-cell RNA-sequencing of 23 metastatic PDAC needle biopsies and matched organoid models to understand how tumor cell-intrinsic features and extrinsic factors in the tumor microenvironment (TME) shape PDAC cancer cell phenotypes. We identify a novel cancer cell state that co-expresses basal-like and classical signatures, demonstrates upregulation of developmental and KRAS-driven gene expression programs, and represents a transitional intermediate between the basal-like and classical poles. Further, we observe structure to the metastatic TME supporting a model whereby reciprocal intercellular signaling shapes the local microenvironment and influences cancer cell transcriptional subtypes. In organoid culture, we find that transcriptional phenotypes are plastic and strongly skew toward the classical expression state, irrespective of genotype. Moreover, we show that patient-relevant transcriptional heterogeneity can be rescued by supplementing organoid media with factors found in the TME in a subtype-specific manner. Collectively, our study demonstrates that distinct microenvironmental signals are critical regulators of clinically relevant PDAC transcriptional states and their plasticity, identifies the necessity for considering the TME in cancer modeling efforts, and provides a generalizable approach for delineating the cell-intrinsic versus -extrinsic factors that govern tumor cell phenotypes.

cancer biology↗

bioRxiv2019

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate or the expression of a marker gene. Information-rich assays, such as gene-expression profiling, are generally not amenable to efficient profiling of a given perturbation across multiple cellular contexts. Here, we developed MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines, and combine it with Cell Hashing to further multiplex additional experimental conditions, such as multiple post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and can be used to predict long-term cell viability from short-term transcriptional responses to treatment.

cancer biology↗

bioRxiv2019

Microenvironmental adaptations drive obesity-associated pancreatic cancer

Obesity is a major modifiable risk factor for pancreatic ductal adenocarcinoma (PDAC), yet how and when obesity contributes to PDAC progression is not well understood. Leveraging an autochthonous mouse model, we demonstrate a causal and reversible role for obesity in early PDAC progression, showing that obesity markedly enhances tumorigenesis, while genetic or dietary induction of weight loss intercepts cancer development. Bulk and single cell molecular analyses of human and murine samples define microenvironmental consequences of obesity that promote tumor development rather than new driver gene mutations. We observe increased inflammation and fibrosis and also provide evidence for significant pancreatic islet cell adaptation in obesity-associated tumors. Specifically, we identify aberrant islet beta cell expression of the peptide hormone cholecystokinin (CCK) in tumors as an adaptive response to obesity. Furthermore, beta cell CCK expression promotes oncogenic Kras-driven pancreatic ductal tumorigenesis. Our studies argue that PDAC progression is driven by local obesity-associated changes in the tumor microenvironment - rather than systemic effects - and implicate endocrine-exocrine signaling beyond insulin in PDAC development. Furthermore, our demonstration that these obesity-associated adaptations are reversible supports the use of anti-obesity strategies to intercept PDAC early during progression.

cancer biology↗

bioRxiv2018

Bead Enhancement of EV Analysis

Extracellular vesicles (EVs) are recognized cancer biomarkers, however, clinical analysis has been difficult due to a lack of simple and sensitive assays. Here, we describe a bead-enhanced flow cytometry method, BEAD flow, using biotinylated EVs captured on streptavidin particles. With this method, we show analysis of patient-derived EVs using a panel of pancreatic cancer biomarkers. BEAD flow is easily translatable to any biomarker or cancer type and can be run with conventional flow cytometers, making it highly flexible and adaptable to diverse research and clinical needs.

cancer biology↗