Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to Immunotherapy

7 Jan 2020

Abstract excerpt

PURPOSE: Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response to immunotherapy. Due to the substantial heterogeneity, an integrative approach incorporating diverse molecular features is needed to characterize immunologic features underlying primary resistance to immunotherapy and for the establishment of novel predictive...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer Immunotherapy and Biomarkers
Share to Ferroptosis and cancer prognosis
Share to vaccines and immunoinformatics approaches

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 31911545
DOI: 10.1158/1078-0432.ccr-19-1744

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to ImmunotherapyPMID 31911545Read ↗

Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanomaSemantic proximity Read ↗

A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape.Semantic proximity Read ↗

A Pan-Cancer Immunogenomic Atlas for Immune Checkpoint Blockade ImmunotherapySemantic proximity Read ↗

A mutational signature and significantly mutated driver genes associated with immune checkpoint inhibitor response across multiple cancers.Semantic proximity Read ↗

Single-cell spatial multiomics reveals tumor microenvironment vulnerabilities in cancer resistance to immunotherapySemantic proximity Read ↗

A Large-Scale Meta-Analysis Reveals Positive Feedback between Macrophages and T Cells That Sensitizes Tumors to Immunotherapy.Semantic proximity Read ↗

Integrated genomic analysis identifies a genetic mutation model predicting response to immune checkpoint inhibitors in melanoma.Semantic proximity Read ↗

Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition.Semantic proximity Read ↗

Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.Semantic proximity Read ↗

Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma.Semantic proximity Read ↗

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.Semantic proximity Read ↗

Insights and Strategies of Melanoma Immunotherapy: Predictive Biomarkers of Response and Resistance and Strategies to Improve Response RatesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.