Cancer Immunotherapy and Biomarkers

This study identifies three germline variants associated with immune-related adverse events in patients treated with immune checkpoint inhibitors.

This study identifies molecular and immune features associated with response to anti-PD-(L)1 therapy in NSCLC using a cohort of 393 patients.

CAMOIP provides a web tool for analyzing multi-omics data related to immunotherapy across cancers, enabling biomarker screening and mechanism analysis in over 4000 patients.

This study identifies HLA-DRB1 and HLA-DQA1 alleles associated with anti-drug antibodies and neutralizing antibodies in atezolizumab-treated cancer patients.

The paper develops DNA- and RNA-based models combining immune and tumor features to predict outcomes of checkpoint inhibition in melanoma.

This study predicts immune-related adverse events and objective response rates to anti-PD-1/PD-L1 therapy using enhancer RNA expression, identifying key regulatory genes involved in immune responses.

The paper identifies blood-based genomic and cellular biomarkers predictive of response to neoadjuvant PD-1 blockade in NSCLC patients using transcriptomic and mutation analysis.

The paper introduces easier, an R/Bioconductor package that predicts antitumor immune response from bulk RNA-seq data, outperforming existing methods in four cancers.

This study performs a meta-analysis of exhausted CD8+ T cells in humans and mice to identify robust therapeutic targets for immunotherapy.

This study identifies tumor antigens and immune subtypes in KIRC to guide mRNA vaccine development, finding LRP2 and DOCK8 as potential antigens and immune subtypes IS1-4 as suitable for vaccination.

IOBR is a computational tool that integrates multi-omics data to analyze tumor microenvironment and signatures, enabling batch analysis and comparison of immunotherapy-related features.

The paper analyzes AID-related mutations across cancers and finds they are associated with better outcomes in ICI-treated patients and contribute to immunogenic neoepitopes.

The paper uses single-cell RNA sequencing to study how PD-L1 and TGF-β blockade affect the tumor microenvironment, finding upregulated immune response genes and downregulated extracellular matrix genes.

The paper develops DASH, a machine learning algorithm to detect HLA LOH in cancer, showing 18% prevalence across 611 patients and correlations with PD-L1 and MSI.

The paper investigates how anti-cancer immunity evolves from preneoplasia to invasive lung adenocarcinomas, identifying changes in immune response and molecular features that may facilitate immune evasion.

The paper identifies immune cell-specific gene networks associated with response to nivolumab therapy in melanoma and highlights B cell genes as potential prognostic biomarkers.

The paper identifies proinflammatory monocytes/macrophages associated with resistance to PD-1/PD-L1 blockade in urothelial cancer using bulk and single-cell RNA sequencing.

The paper evaluates existing ICB biomarkers and identifies a novel signature to predict immunotherapy response in HNSCC using TCGA and clinical trial data.

The paper uses multi-modal single-cell screens to identify regulators of PD-L1 expression and develops a computational framework to improve analysis of perturbation data.

The paper uses Perturb-CITE-Seq to identify novel immune evasion mechanisms in cancer by analyzing multi-modal single-cell data from patient-derived models.

The paper develops a Bayesian network analysis pipeline to identify immune signaling changes in GEA patients before and after immunotherapy, detecting signals missed by conventional methods.

The paper characterizes the tumor microenvironment of early-stage lung adenocarcinoma, identifying tertiary lymphoid structures and CD8 T cell exclusion mediated by follicular regulatory T cells.

The paper examines PD-1/PD-L1/PD-L2 expression and methylation in low-grade glioma, finding associations with survival, immune infiltration, and immune-related biological processes.

This study ranks prostate cancer tumors along the IFN-γ/PD-L1 axis to prioritize patients for immune checkpoint therapy and identifies immune resistance mechanisms.

This paper challenges the predictive value of tumor mutational burden (TMB) for immunotherapy response, finding little evidence of its association and suggesting previous studies had methodological flaws.

The paper identifies RNA editing signatures that predict immunotherapy response in melanoma patients with high accuracy.

This paper identifies that immuno-positive neoantigens cluster in 3D genome regions and are enriched in active chromosomal compartments, suggesting 3D genome information can improve neoantigen prioritization.

The paper quantifies tumor immunogenicity using dN/dS ratios in the immunopeptidome and shows it predicts immunotherapy response by distinguishing adaptation from escape mechanisms.

The paper identifies cancer-specific driver gene mutations associated with immunotherapy outcomes in a multi-cancer cohort of 1,525 patients.

The paper shows that polygenic risk scores for skin autoimmunity affect the response to immune checkpoint blockade in bladder cancer, with higher vitiligo and psoriasis scores linked to better outcomes.