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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.

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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.

References

  1. Hodson, R. Precision medicine. Nature 537, S49 (2016).

    Article  CAS  PubMed  Google Scholar 

  2. Morash, M., Mitchell, H., Beltran, H., Elemento, O. & Pathak, J. The role of next-generation sequencing in precision medicine: a review of outcomes in oncology. J. Pers. Med. 8, E30 (2018).

    Article  PubMed  Google Scholar 

  3. Garraway, L. A., Verweij, J. & Ballman, K. V. Precision oncology: an overview. J. Clin. Oncol. 31, 1803–1805 (2013).

    Article  PubMed  Google Scholar 

  4. Marquart, J., Chen, E. Y. & Prasad, V. Estimation of the percentage of US patients with cancer who benefit from genome-driven oncology. JAMA Oncol. 4, 1093–1098 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Sun, L. et al. Association between KRAS variant status and outcomes with first-line immune checkpoint inhibitor-based therapy in patients with advanced non-small-cell lung cancer. JAMA Oncol. 7, 937–939 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Raponi, M., Winkler, H. & Dracopoli, N. C. KRAS mutations predict response to EGFR inhibitors. Curr. Opin. Pharmacol. 8, 413–418 (2008).

    Article  CAS  PubMed  Google Scholar 

  7. Hernán, M. A. & Robins, J. M. Using big data to emulate a target trial when a randomized trial is not available. Am. J. Epidemiol. 183, 758–764 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Danaei, G., García Rodríguez, L. A., Cantero, O. F., Logan, R. W. & Hernán, M. A. Electronic medical records can be used to emulate target trials of sustained treatment strategies. J. Clin. Epidemiol. 96, 12–22 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Liu, R. et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature 592, 629–633 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Singal, G. et al. Association of patient characteristics and tumor genomics with clinical outcomes among patients with non-small cell lung cancer using a clinicogenomic database. JAMA 321, 1391–1399 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zhang, Q., Gossai, A., Monroe, S., Nussbaum, N. C. & Parrinello, C. M. Validation analysis of a composite real-world mortality endpoint for patients with cancer in the United States. Health Serv. Res. 56, 1281–1287 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Curtis, M. D. et al. Development and validation of a high-quality composite real-world mortality endpoint. Health Serv. Res. 53, 4460–4476 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Carrigan, G. et al. Using electronic health records to derive control arms for early phase single-arm lung cancer trials: proof-of-concept in randomized controlled trials. Clin. Pharmacol. Ther. 107, 369–377 (2020).

    Article  PubMed  Google Scholar 

  14. Suissa, S. Immortal time bias in pharmaco-epidemiology. Am. J. Epidemiol. 167, 492–499 (2008).

    Article  PubMed  Google Scholar 

  15. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).

    Google Scholar 

  16. Petitjean, A., Achatz, M. I. W., Borresen-Dale, A. L., Hainaut, P. & Olivier, M. TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes. Oncogene 26, 2157–2165 (2007).

    Article  CAS  PubMed  Google Scholar 

  17. Wolfer, A. et al. MYC regulation of a ‘poor-prognosis’ metastatic cancer cell state. Proc. Natl Acad. Sci. U. S. A. 107, 3698–3703 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhao, R., Choi, B. Y., Lee, M.-H., Bode, A. M. & Dong, Z. Implications of genetic and epigenetic alterations of CDKN2A (p16(INK4a)) in Cancer. EBioMedicine 8, 30–39 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Cavanna, L., Citterio, C. & Orlandi, E. Immune checkpoint inhibitors in EGFR-mutation positive TKI-treated patients with advanced non-small-cell lung cancer network meta-analysis. Oncotarget 10, 209–215 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Mazieres, J. et al. Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the IMMUNOTARGET registry. Ann. Oncol. 0, 1321–1328 (2019).

    Article  CAS  Google Scholar 

  21. Rossi, G. et al. Precision medicine for NSCLC in the Era of immunotherapy: New biomarkers to select the most suitable treatment or the most suitable patient. Cancers 12, E1125 (2020).

    Article  PubMed  CAS  Google Scholar 

  22. Guibert, N. et al. Targeted sequencing of plasma cell-free DNA to predict response to PD1 inhibitors in advanced non-small cell lung cancer. Lung Cancer 137, 1–6 (2019).

    Article  PubMed  Google Scholar 

  23. Tsao, M.-S. et al. Prognostic and predictive importance of p53 and RAS for adjuvant chemotherapy in non-small-cell lung cancer. J. Clin. Oncol. 25, 5240–5247 (2007).

    Article  PubMed  Google Scholar 

  24. Wang, W.-X. et al. TP53 mutations predict for poor survival in ALK rearrangement lung adenocarcinoma patients treated with crizotinib. J. Thorac. Dis. 10, 2991–2998 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Witkiewicz, A. K. et al. RB-pathway disruption is associated with improved response to neoadjuvant chemotherapy in breast cancer. Clin. Cancer Res. 18, 5110–5122 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ertel, A. et al. RB-pathway disruption in breast cancer: differential association with disease subtypes, disease-specific prognosis and therapeutic response. Cell Cycle 9, 4153–4163 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tanioka, M. et al. Transcriptional CCND1 expression as a predictor of poor response to neoadjuvant chemotherapy with trastuzumab in HER2-positive/ER-positive breast cancer. Breast Cancer Res. Treat. 147, 513–525 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Chen, K., Quan, J., Yang, J. & Chen, Z. The potential markers of endocrine resistance among HR+/HER2+ breast cancer patients. Clin. Transl. Oncol. 22, 576–584 (2020).

    Article  CAS  PubMed  Google Scholar 

  29. Rastelli, F. & Crispino, S. Factors predictive of response to hormone therapy in breast cancer. Tumori 94, 370–383 (2008).

    Article  PubMed  Google Scholar 

  30. de Bruin, E. C. et al. Reduced NF1 expression confers resistance to EGFR inhibition in lung cancer. Cancer Discov. 4, 606–619 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Hsu, C. P. et al. Clinical significance of tumor suppressor PTEN in colorectal carcinoma. Eur. J. Surg. Oncol. 37, 140–147 (2011).

    Article  CAS  PubMed  Google Scholar 

  32. Carbuccia, N. et al. Mutations of ASXL1 gene in myeloproliferative neoplasms. Leukemia 23, 2183–2186 (2009).

    Article  CAS  PubMed  Google Scholar 

  33. Endele, S. et al. Mutations in GRIN2A and GRIN2B encoding regulatory subunits of NMDA receptors cause variable neurodevelopmental phenotypes. Nat. Genet. 42, 1021–1026 (2010).

    Article  CAS  PubMed  Google Scholar 

  34. Carvill, G. L. et al. GRIN2A mutations cause epilepsy-aphasia spectrum disorders. Nat. Genet. 45, 1073–1076 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Odogwu, L. et al. FDA approval summary: dabrafenib and trametinib for the treatment of metastatic non-small cell lung cancers harboring BRAF V600E mutations. Oncologist 23, 740–745 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Eng, C. BRAF mutation in colorectal cancer: an enigmatic target. J. Clin. Oncol. 39, 259–261 (2021).

    Article  PubMed  Google Scholar 

  37. Powles, T. et al. Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet 391, 748–757 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Balar, A. V. et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet Lond. Engl. 389, 67–76 (2017).

    Article  CAS  Google Scholar 

  39. West, H. et al. Atezolizumab in combination with carboplatin plus nab-paclitaxel chemotherapy compared with chemotherapy alone as first-line treatment for metastatic non-squamous non-small-cell lung cancer (IMpower130): a multicentre, randomised, open-label, phase 3 trial. Lancet Oncol. 20, 924–937 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Peters, S. et al. Phase II Trial of Atezolizumab as first-line or subsequent therapy for patients with programmed death-ligand 1-selected advanced non-small-cell lung cancer (BIRCH). J. Clin. Oncol. 35, 2781–2789 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Rittmeyer, A. et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet 389, 255–265 (2017).

    Article  PubMed  Google Scholar 

  42. Chakravarty, D. et al. OncoKB: A precision oncology knowledge base. JCO Precis. Oncol. 2017, 1–16 (2017).

    Google Scholar 

  43. Kron, A. et al. Impact of TP53 mutation status on systemic treatment outcome in ALK-rearranged non-small-cell lung cancer. Ann. Oncol. 29, 2068–2075 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Liu, C. et al. The superior efficacy of anti-PD-1/PD-L1 immunotherapy in KRAS-mutant non-small cell lung cancer that correlates with an inflammatory phenotype and increased immunogenicity. Cancer Lett. 470, 95–105 (2020).

    Article  CAS  PubMed  Google Scholar 

  45. McGough, S. F. et al. Penalized regression for left-truncated and right-censored survival data. Stat. Med. 40, 5487–5500 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Dong, Z.-Y. et al. Potential predictive value of TP53 and KRAS mutation status for response to PD-1 blockade immunotherapy in lung adenocarcinoma. Clin. Cancer Res. 23, 3012–3024 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Okamoto, I. et al. Real world treatment and outcomes in EGFR mutation-positive non-small cell lung cancer: Long-term follow-up of a large patient cohort. Lung Cancer 117, 14–19 (2018).

    Article  PubMed  Google Scholar 

  48. Papillon-Cavanagh, S., Doshi, P., Dobrin, R., Szustakowski, J. & Walsh, A. M. STK11 and KEAP1 mutations as prognostic biomarkers in an observational real-world lung adenocarcinoma cohort. ESMO Open 5, e000706 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Frampton, G. M. et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat. Biotechnol. 31, 1023–1031 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. He, J. et al. Integrated genomic DNA/RNA profiling of hematologic malignancies in the clinical setting. Blood 127, 3004–3014 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Woodhouse, R. et al. Clinical and analytical validation of FoundationOne Liquid CDx, a novel 324-Gene cfDNA-based comprehensive genomic profiling assay for cancers of solid tumor origin. PLoS ONE 15, e0237802 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Carrigan, G. et al. An evaluation of the impact of missing deaths on overall survival analyses of advanced non-small cell lung cancer patients conducted in an electronic health records database. Pharmacoepidemiol. Drug Saf. 28, 572–581 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. André, F. et al. AACR project GENIE: Powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).

    Article  Google Scholar 

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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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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

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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 28, 1656–1661 (2022). https://doi.org/10.1038/s41591-022-01873-5

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