ArticleNature communications2024
Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances.Medical sciences (Basel, Switzerland) · 2025Pooled it
- Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.iScience · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Advanced immunotherapy across diseases and the role of artificial intelligence: A review.Biomolecules & biomedicine · 2026Review
- Racial differences in biomarkers, treatment, and outcomes in HR+/HER2- metastatic breast cancer in the United States.NPJ breast cancer · 2026Article
- Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation.Frontiers in immunology · 2026Review
- A Knowledge-Enhanced Multimodal Framework with Genomic Reconstruction for DLBCL Drug Response Prediction.Computational and structural biotechnology journal · 2026Article
- Development and validation of a circulating tumor DNA-based machine learning model for predicting immunotherapy response in non-small cell lung cancer.Translational cancer research · 2025Article
- Learning Patient Similarity from Genomics for Precision Oncology.medRxiv : the preprint server for health sciences · 2025Article
- Molecular analysis of lung adenocarcinomas from the SAFIR02-Lung cohort reveals new metastasis-associated copy-number alterations including frequent mutant-specific KRAS-allelic imbalance and identifies CDKN2A homozygous deletions as an independent biomarker of poor prognosis.British journal of cancer · 2025Article
- Case Report: Trametinib in the treatment of patients with metastatic lung adenocarcinoma harboringFrontiers in oncology · 2025Article
- Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers.Nature communications · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evaluating the effectiveness of cancer treatments in relation to specific tumor mutations is essential for improving patient outcomes and advancing the field of precision medicine. Here we represent a comprehensive analysis of 78,287 U.S. cancer patients with detailed somatic mutation profiling integrated with treatment and outcomes data extracted from electronic health records. We systematically identified 776 genomic alterations associated with survival outcomes across 20 distinct cancer types treated with specific immunotherapies, chemotherapies, or targeted therapies. Additionally, we demonstrate how mutations in particular pathways correlate with treatment response. Leveraging the large number of identified predictive mutations, we developed a machine learning model to generate a risk score for response to immunotherapy in patients with advanced non-small cell lung cancer (aNSCLC). Through rigorous computational analysis of large-scale clinico-genomic real-world data, this research provides insights and lays the groundwork for further advancements in precision oncology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.