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Multimodal integration of blood RNA and ctDNA reflects response to immunotherapy in metastatic urothelial cancer.
JCI Insight
January 2025
Medical Oncology Department, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, Netherlands.
Background: Previously, we demonstrated that changes in circulating tumor DNA (ctDNA) are promising biomarkers for early response prediction (ERP) to immune checkpoint inhibitors (ICI) in metastatic urothelial cancer (mUC). In this study, we investigated the value of whole blood immunotranscriptomics for ERP-ICI and integrated both biomarkers into a multimodal model to boost accuracy.
Methods: Blood samples of 93 patients were collected at baseline and after 2-6 weeks of ICI for ctDNA (N=88) and immunotranscriptome (N=79) analyses.
Systematic bias in malaria parasite relatedness estimation.
G3 (Bethesda)
January 2025
Infectious Disease Epidemiology and Analytics G5 Unit, Institut Pasteur, Université Paris Cité, Paris 75015, France.
Genetic studies of Plasmodium parasites increasingly feature relatedness estimates. However, various aspects of malaria parasite relatedness estimation are not fully understood. For example, relatedness estimates based on whole-genome-sequence (WGS) data often exceed those based on sparser data types.
View Article and Find Full Text PDFElevated protein lactylation promotes immunosuppressive microenvironment and therapeutic resistance in pancreatic ductal adenocarcinoma.
J Clin Invest
January 2025
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Metabolic reprogramming shapes tumor microenvironment (TME) and may lead to immunotherapy resistance in pancreatic ductal adenocarcinoma (PDAC). Elucidating the impact of pancreatic cancer cell metabolism in the TME is essential to therapeutic interventions. "Immune cold" PDAC is characterized by elevated lactate levels resulting from tumor cell metabolism, abundance of pro-tumor macrophages, and reduced cytotoxic T cell in the TME.
View Article and Find Full Text PDFAttention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.
Brief Bioinform
November 2024
School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129 Shaanxi, China.
The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question remains: how to model the presentation of neoantigens by major histocompatibility complex class I molecules and the recognition of the peptide-MHC-I (pMHC-I) complex by T cell receptors (TCRs). Accurate prediction of pMHC-I binding and TCR recognition remains a significant computational challenge in immunology due to intricate binding motifs and the long-tail distribution of known binding pairs in public databases.
View Article and Find Full Text PDFA comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions.
Brief Bioinform
November 2024
Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, 999077, China.
The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis.
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