Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer
Year: 2024
Type: article
Abstract: Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multiplexed proteomic data set from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcomes. We designed a multiplex immunohistochemistry antibody panel to compare T-cell functionality and spatial localization in resected tumors from treatment-naïve patients with localized pancreatic ductal adenocarcinoma (PDAC) with resected tumors from a second cohort of patients treated with neoadjuvant agonistic CD40 (anti-CD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both cohorts were assayed, and over 1,000 tumor microenvironment (TME) features were quantified. We then trained ML models to accurately predict anti-CD40 treatment status and disease-free survival (DFS) following anti-CD40 therapy based on TME features. Through downstream interpretation of the ML models' predictions, we found anti-CD40 therapy reduced canonical aspects of T-cell exhaustion within the TME, as compared with treatment-naïve TMEs. Using automated clustering approaches, we found improved DFS following anti-CD40 therapy correlated with an increased presence of CD44+CD4+ Th1 cells located specifically within cellular neighborhoods characterized by increased T-cell proliferation, antigen experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of ML in molecular cancer immunology applications, highlight the impact of anti-CD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for anti-CD40-treated patients with PDAC.
Source: Cancer Immunology Research
Institutions: Oregon Health & Science University, OHSU Knight Cancer Institute, Akoya Biosciences (United States), Hospital of the University of Pennsylvania, Abramson Cancer Center, University of Pennsylvania, Harvard University, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Parker Institute for Cancer Immunotherapy, Moffitt Cancer Center
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: Susan G. Komen for the Cure, Eli Lilly and Company, AstraZeneca, Lustgarten Foundation, Celgene, Oregon Health and Science University, Dana-Farber Cancer Institute, Parker Institute for Cancer Immunotherapy, Prospect Creek Foundation, Revolution Medicines, Ipsen, National Institutes of Health, Knight Cancer Institute, Oregon Health and Science University, National Cancer Institute
Awards: Dana Farber/ Harvard Cancer Center SPORE in Gastrointestinal Cancer, G1797368095, G2242065166, G2789075706, G2883192542, G3045899018, G3101773556, G3754462075, G4402394640, G4717460674, G4860891374, G7144636301, Development and implementation of multiplex methods to understand the biology and heterogeneity of patient-derived cancer models, G9000040079, G9000050131, G9000099383, G9000136768, G9000160112, G9000176325, G9000207199, G9000272532, Leveraging Novel Randomized Clinical Trials of Vitamin D Supplementation in Patients with Colorectal Cancer: Impact on Survival and Anti-Tumor Immunity, Altered metabolism and machine learning for pancreatic cancer early detection
OpenAlex ID: W4391994490