Cancer Immunotherapy and Biomarkers

This study identifies DUX4 as a common driver of immune evasion and immunotherapy failure in metastatic cancers, associated with poor prognosis and resistance to PD-L1 blockade.

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.

This study shows that overexpressing an allogeneic MHC-I molecule on tumor cells can induce anti-tumor immunity in PD-1 knockout mice, even without PD-1 signaling.

This study develops nanoPDLIM2, a PDLIM2-expressing nanoparticle, which enhances PD-1 blockade and chemotherapy efficacy in lung cancer by reactivating tumor immunity and reducing drug resistance.

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.

This paper shows that NR0B2 in myeloid cells reduces Treg expansion and tumor growth by modulating the NLRP3 inflammasome, with small molecule agonists improving ICB efficacy in breast cancer.

This study shows that combining ketogenic diet and HDAC inhibitors with ICB therapy enhances anti-tumor effects in prostate cancer by modulating epigenetic and immune mechanisms.

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 investigates how ERBB signaling contributes to immune evasion in KRAS-driven lung adenocarcinoma using a novel mouse model and finds that transient ERBB blockade can restore immune surveillance when combined with PD1 blockade.

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 develops co-culture strategies of tumor organoids with autologous T-cells to screen immune-checkpoint and targeted therapy combinations.

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.