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Systematic pan-cancer analysis of mutation–treatment interactions using large real-world clinicogenomics data

Abstract

Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation–mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology.

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Fig. 1: Overview of mutation-survival associations.
Fig. 2: Gene–treatment interactions for the selected 42 prognostic genes; results for all 499 genes are shown in Supplementary Data.
Fig. 3: Mutation–mutation interactions for genes that modify the effect of anchor target genes.

Data availability

The FH-FMI CGDB data used in this study were licensed from Flatiron Health (https://flatiron.com/real-world-evidence/) and Foundation Medicine. These de-identified data may be made available upon request; interested researchers can contact DataAccess@flatiron.com and cgdb-fmi@flatiron.com. The GENIE BPC data access information is available at https://www.aacr.org/professionals/research/aacr-project-genie/. OncoKB is available at https://www.oncokb.org/. The findings from our study are visualized in www.precision-cancer.org.

Code availability

The open-source Python code for the computational analysis in this paper is available at https://github.com/RuishanLiu/precision-cancer.

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Acknowledgements

We thank A. Gentles, L. Zou, D. Hibar, M. Taylor, Y. Lu and J. Caswell-Jin for comments and discussions. S.R., M.R.G., N.P., C.H, L.W., C.H. and R.C. are supported by funding from Roche. J.Z. is supported by National Science Foundation CAREER, a Sloan Fellowship, a grant from the Emerson Collective and the Chan-Zuckerberg Investigator award.

Author information

Authors and Affiliations

Authors

Contributions

R.L., S.R., R.C., J.N. and J.Z. designed the study. R.L. carried out the data analysis. S.R., S.W., M.R.G., N.P., Z.H., L.W., C.H., N.C. and J.N. verified the analysis and provided technical support and interpretations of the findings. Z.H. created data visualizations. J.N., R.C. and J.Z. supervised the study. All the authors contributed to writing the manuscript and reviewed and approved the final manuscript.

Corresponding author

Correspondence to James Zou.

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Competing interests

S.R., M.R.G., N.P., N.C., L.W., C.H. and R.C. are employees of Roche-Genentech.

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Peer review information

Nature Medicine thanks R. Stephanie Huang, Joe Zhang, Geoff Hall and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling editors: Javier Carmona and Joao Monteiro in collaboration with the Nature Medicine team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Histogram of the diagnosis dates stratified by cancer types.

The time interval is one month.

Extended Data Fig. 2 Histogram of the sample collection dates for the earliest FMI test.

The patients are binned relative to lines of treatment.

Extended Data Fig. 3 OS HR for the subset of mutations in each gene that are assessed by FMI to be likely pathogenic.

Entries with OS HR with two-sided Wald test P value <0.05 and shown in Fig. 1c are colored.

Extended Data Fig. 4 OS HR for individual genes after removing FMI baitsets used for liquid biopsy.

Entries with OS HR with two-sided Wald test P value <0.05 and shown in Fig. 1c are colored.

Extended Data Fig. 5 Refined mutation–mutation interactions analysis for specific mutations targeted by therapies.

Anchor genes and modifier genes are selected in the same way as in Fig. 3. For each anchor gene, we only consider mutations subtypes that are specifically focused on by FDA-approved targeted therapies (written below the gene name). The size of a modifier gene’s circle indicates what fraction of patients with the anchor gene mutated in the pathogenic subtypes also have mutation in the modifier gene. The modifier’s color indicates its positive (blue for HR <1) or negative (red for HR >1) impact on the survival of patients who have the anchor gene pathogenic mutation. Modifiers are genes with significant anchor-modifier interactions (two-sided Wald test P value <0.05).

Extended Data Fig. 6 Balance assessment between experiment and control cohorts for baseline covariates.

Here we use the analysis of the prognostic effect of TP53 mutation on overall survival as an example; similar results are seen for other genes. For each cancer, we plot the standardized mean difference (SMD) for every baseline covariate between the experiment cohort (patients with TP53 mutation) and control cohort (patients without TP53 mutation). SMD close to 0 represents that the two cohorts are balanced. The inverse propensity weighting used in our analysis (IPTW) effectively balances the two cohorts. Raw corresponds to the unadjusted cohorts.

Extended Data Fig. 7 Balance assessment between experiment and control cohorts for covariate missingness.

Here we report the standardized mean difference (SMD) between the experiment and control cohorts for the missingness of ECOG value as an example; similar results are seen for other covariates. For each cancer, we plot SMD evaluated for the prognostic effect of the top ten most frequent mutations on overall survival. SMD close to 0 represents that the two cohorts are balanced. The inverse propensity weighting used in our analysis (IPTW) effectively balances the two cohorts in regard to covariate missingness. Raw corresponds to the unadjusted cohorts.

Supplementary information

Supplementary Information

Supplementary Tables 1–16.

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Supplementary Data

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Liu, R., Rizzo, S., Waliany, S. et al. Systematic pan-cancer analysis of mutation–treatment interactions using large real-world clinicogenomics data. Nat Med (2022). https://doi.org/10.1038/s41591-022-01873-5

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