Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Autor:	Blise KE; Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon.; The Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon., Sivagnanam S; The Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.; Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, Oregon., Betts CB; The Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.; Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, Oregon.; Current affiliation: Akoya Biosciences, Marlborough, Massachusetts., Betre K; Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, Oregon., Kirchberger N; Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, Oregon., Tate BJ; The Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.; Immune Monitoring and Cancer Omics Services, Oregon Health and Science University, Portland, Oregon., Furth EE; Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.; Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania., Dias Costa A; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts., Nowak JA; Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts., Wolpin BM; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts., Vonderheide RH; Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.; Parker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania., Goecks J; Department of Biomedical Engineering, Oregon Health and Science University, Portland, Oregon.; The Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.; Current affiliation: Department of Machine Learning, H. Lee Moffitt Cancer Center, Tampa, Florida.; Current affiliation: Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center, Tampa, Florida., Coussens LM; The Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.; Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, Oregon., Byrne KT; The Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.; Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, Oregon.
Jazyk:	angličtina
Zdroj:	Cancer immunology research [Cancer Immunol Res] 2024 May 02; Vol. 12 (5), pp. 544-558.
DOI:	10.1158/2326-6066.CIR-23-0873
Abstrakt:	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. (©2024 American Association for Cancer Research.)
Databáze:	MEDLINE
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