Biology subjects

Betre, K.

Publications and source records attributed to Betre, K..

3 recordsLinked to original sources

bioRxiv2025

A window trial in metastatic pancreatic ductal adenocarcinoma reveals resistance mechanisms to targeting the KRAS-MEK pathway

Copy number alterations of KRAS, mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), and MYC occur in 30-40% of PDAC. Here we demonstrate that KRAS and MYC are frequently co-gained and accompanied with worse prognosis in PDAC. In a Window-of-Opportunity clinical trial for metastatic PDAC, serial biopsies and deep multi-omics analyses were utilized to explore resistance mechanisms to MEK inhibition, as a surrogate for KRAS inhibition. Tumors from four of 14 patients showed Ki-67/CA19-9-based biomarker response (BR). Non-BR tumors were enriched for KRAS/MYC co-gain and KRASG12D variant. A transcriptomic signature of BR tumors was inversely correlated with KRASG12D/MYC co-gain in a large PDAC dataset and predictive for KRAS inhibitor response in multiple models. Finally, co-targeting KRAS and MYC was synergistic in KRASG12D/MYC co-gain PDAC. Together, this study provides insight into KRAS inhibitor resistance and supports MYC as an important target to improve patient outcomes in this deadly disease.

cancer biology↗

bioRxiv2025

mRNA stability factor HuR promotes immune evasion in pancreatic ductal adenocarcinoma

The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by a limited infiltration of tumor-specific T cells and anti-tumor T cell activity. Extracellular factors in the PDAC TME have been widely reported to mediate immune suppression, but the contribution from tumor-intrinsic factors is not well understood. The RNA-binding protein, HuR (ELAVL1), is enriched in PDAC and negatively correlates with T cell infiltration. In an immunocompetent Kras-p53-Cre (KPC) orthotopic model of PDAC, we found that genetic disruption of HuR impaired tumor growth due to a novel role of HuR inducing T-cell suppression. Importantly, we found that HuR depletion in tumors enhanced both T cell number and activation states and diminished myeloid phenotypes by comprehensive spatial profiling of the PDAC TME. Mechanistically, HuR mediated the stabilization of mTOR pathway transcripts, and inhibition of mTOR activity rescued the impaired function of local T cells. Translating these findings, we demonstrated that HuR depletion sensitized PDAC tumors to immune checkpoint blockade, while isogenic, wildtype tumors are resistant. For the first time, we show that HuR facilitates tumor immune suppression in PDAC by inhibiting T cell infiltration and function and implicate targeting HuR as a potential therapeutic strategy in combination with immunotherapy. SIGNIFICANCEThis study identified a novel mechanism that HuR supports pancreatic tumor growth by restricting T cell infiltration, promoting immune evasion. Our work supports targeting strategies against HuR in PDAC with the goal of enhancing PDAC sensitivity to immune-based cancer therapies, such as checkpoint blockade and T cell transfer.

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↗