Cancer Immunotherapy and Biomarkers

The paper identifies metabolic gene signatures associated with response to immune checkpoint blockade therapy using single-cell RNA-seq data.

INAEME improves neoantigen prediction by considering all mutational events, increasing neoantigen detection from 8.45% to 23.65% in TCGA samples.

This study uses multiple instance learning to predict immune checkpoint blockade efficacy based on qualitative neoantigen profiles, showing better performance than quantitative approaches.

IMPACT is a web server that explores immunotherapeutic predictive and cancer prognostic biomarkers using a large dataset of 6,276 patients treated with immune checkpoint inhibitors.

The paper studies the spatial spread of IFN-gamma in melanomas and finds it is confined, leading to non-genetic cellular variability and altered T cell susceptibility.

The paper uses topic modeling to analyze T cell dynamics in cancer patients, identifying three latent topics associated with clinical outcomes and immune responses.

The paper identifies key predictors of PD-1 blockade response in renal cell carcinoma by analyzing the tumor microenvironment of metastatic sites and developing a predictive TID score.

The paper investigates PD1 activation mechanism using molecular docking and simulations, finding a high molecular weight complex that explains PD1-PDL1 interaction and activation.

The paper uses a QSP model to generate virtual patients for NSCLC and predicts response to PD-L1 inhibition using immunogenomic data.

This paper proposes a drug combination for Melanoma treatment using machine learning on genes and proteins, showing a 74-fold increase in effectiveness compared to single drugs.

The paper characterizes genetic immune escape (GIE) across primary and metastatic tumors, finding high prevalence and type-specific differences in immune evasion mechanisms.

The paper introduces ImogiMap, a statistical framework to identify tumor-immune interactions by analyzing their co-association with immune phenotypes, and identifies the CD86/CD70 axis as a potential immunotherapy target in endometrial carcinoma.

The paper examines the prognostic role of immunoproteasome expression in solid tumours, finding it correlates with immune infiltration and survival outcomes, but varies by tumour type and immune context.

This paper introduces DeepGeni, an interpretable autoencoder for microbiome analysis, showing improved prediction of melanoma immunotherapy response and identifying key microbial taxa.

This paper introduces probabilistic mixture models to calibrate panel-derived tumor mutational burden (TMB) to exomic TMB, showing improved accuracy over linear regression.

The paper defines an adaptive-to-innate immune ratio from bulk RNAseq data and shows it correlates with improved prognosis and immunotherapy response, with gender-specific differences.

This study identifies a decoupling signature that reduces the correlation between cytotoxic and exhausted T cell activities, improving the prediction of immunotherapy response in melanoma.

This study identifies cAMP as an endogenous inhibitor of PD-1/PD-L1 binding through a combination of experimental and computational methods.

The paper identifies genetic interactions between cancer mutations and immune resistance genes using a CRISPR double knockout screen, revealing potential targets for existing drugs.

This study identifies immunomodulatory peptides deregulated in breast cancer using single-cell and spatial transcriptomics, and proposes drugs to modulate their expression.

The paper identifies T cell receptor convergence as a marker of antigen-specific T cell responses in cancer immunotherapies, showing better predictive accuracy than existing TCR repertoire indexes.

TSNAdb v2.0 updates the tumor-specific neoantigen database with stricter identification criteria, predicted neoantigens from three mutation types, and experimentally validated neoantigens classified by evidence.

Seq2Neo predicts neoantigen immunogenicity using a CNN model trained on genomic alterations, outperforming existing tools in independent datasets.

The paper identifies four immune clusters in pediatric nervous system tumors using RNA-seq data and validates them with multiple methods, highlighting the need for personalized immunogenomic profiling.

TIGER is a gene expression resource for tumor immunotherapy that includes bulk and single-cell data, enabling analysis of immune cell subsets and their association with treatment response.

This study maps the single-cell landscape of acral melanoma and analyzes the molecular regulatory network of its tumor microenvironment, identifying nine cell types and key interactions.

This study identifies CD58 as a prognostic marker in colorectal cancer using multi-omics analysis, showing its role in immunotherapy response based on microsatellite instability status.

This study shows HSF1 suppresses CD8+ T cell infiltration in breast tumors by reducing CCL5 expression, thereby preventing immune-mediated tumor destruction.

The paper analyzes HLA expression across 33 cancer types to show its role in predicting patient survival and immune evasion mechanisms.

The paper shows that stronger systemic and liver immune responses improve liver cancer outcomes by reducing tumor burden and correlating with better survival.