Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers

Sep 21, 2026

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Evaluating how specific tumor mutations influence cancer treatment effectiveness is essential for precision medicine. This study analyzes 78,287 U.S. cancer patients using somatic mutation profiling linked with treatment and outcome data from electronic health records. Across 20 cancer types, 776 genomic alterations associated with survival were identified for immunotherapies, chemotherapies, or targeted therapies. Mutations in particular pathways were shown to correlate with treatment response, and a machine-learning model produced an immunotherapy risk score for advanced non-small cell lung cancer.

LanguageEnglish

CategoryResearch & Report

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Pages10

Reading Time25 min

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Overview of real-world cancer data

Article [https://doi.org/10.1038/s41467-024-55251-5](https://doi.org/10.1038/s41467-024-55251-5) Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers | Received: 10 July 2024
Accepted: 3 December 2024
| Ruishan Liu 1,2, Shemra Rizzo3,6, Lisa Wang3,6, Nayan Chaudhary3,6,
Sophia Maund3,6, Marius Rene Garmhausen4,6, Sarah McGough3,
Ryan Copping 3,7 & James Zou 1,5,7 | | | --- | --- | --- | | | 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 clinicogenomic real-world data, this research provides insights and lays the groundwork for further advancements in precision oncology. | | | Precision medicine aims to characterize the unique responses of patients with speciﬁc genetic mutations to various treatments1. This understanding can enhance patient outcomes by tailoring treatment recommendations to individual tumor mutation proﬁles. The adoption of next-generation sequencing (NGS) has revolutionized genomics proﬁling, positioning it as an invaluable tool in cancer care2. Yet, despite the vast mutation data available, only a limited number of these mutations are associated with validated treatments3.
Recent studies have demonstrated that large-scale real-world clinico-genomics data can identify genomic biomarkers that predict patient response to cancer treatments. For example, using a cohort of 40,903 patients, researchers identiﬁed 458 statistically signiﬁcant gene-treatment interactions in eight common types of cancer4. | | In this study, we harness a substantially updated and broader dataset, with one and a half more years of data and patient follow-up (a Dec 31, 2022 data cut-off compared to a June 30, 2021 data cut-off previously), encompassing 12 additional cancer types (20 vs 8 previously), and more patients (78,287 vs 40,903 previously), to systematically identify mutations predictive of patient outcomes for speciﬁc treatments. Delving into the patients’ tumor mutation proﬁles, treatment histories, and survival outcomes, we characterize somatic genomic alterations that notably predict the patients’ survival on speciﬁc immunotherapies, chemotherapies, or targeted therapies.
Contemporary research accentuates the powerful capability of real-world data, especially those extracted from electronic health records (EHRs), in ascertaining treatment impacts, emulating control groups for singular clinical trials, and reﬁning eligibility benchmarks | 1Department of Electrical Engineering, Stanford University, Stanford, CA, USA. 2Department of Computer Science, University of Southern California, Los Angeles, CA, USA. 3Genentech, South San Francisco, CA, USA. 4F. Hoffmann-La Roche Ltd., Basel, Switzerland. 5Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. 6These authors contributed equally: Shemra Rizzo, Lisa Wang, Nayan Chaudhary, Sophia Maund, Marius Rene Garmhausen.7These authors jointly supervised this work: Ryan Copping, James Zou. e-mail: [copping.ryan@gene.com](copping.ryan@gene.com); [jamesz@stanford.edu](jamesz@stanford.edu) for

FAQs

What dataset and sources were used to study mutation-treatment effects?

The analysis used the Flatiron Health–Foundation Medicine U.S. de-identified clinico-genomic database (FH-FMI CGDB) with follow-up through December 31, 2022, sourced from ~280 U.S. cancer clinics.

How many cancer types and patients were included in the study?

The study covered 20 distinct cancer types and included 78,287 patients.

What did the researchers identify that relates mutations to treatment outcomes?

They systematically identified 776 genomic alterations associated with survival outcomes and showed how mutations in particular pathways correlate with treatment response.

Sep 21, 2026

Uploaded by Theodora