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Understanding inequities in precision oncology diagnostics

Nature Cancer volume 4, pages 787–794 (2023)Cite this article

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Abstract

Advances in molecular diagnostics have enabled the identification of targetable driver pathogenic variants, forming the basis of precision oncology care. However, the adoption of new technologies, such as next-generation sequencing (NGS) panels, can exacerbate healthcare disparities. Here, we summarize data on use patterns of advanced biomarker testing, highlight the disparities in both accessing NGS testing and using this data to match patients to appropriate personalized therapies and propose multidisciplinary strategies to address inequities looking forward.

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Fig. 1: Barriers to accessing and interpreting NGS technology for precision oncology.
Fig. 2: Overcoming major disparities in precision oncology diagnostics.

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References

  1. Park, J. J. H., Hsu, G., Siden, E. G., Thorlund, K. & Mills, E. J. An overview of precision oncology basket and umbrella trials for clinicians. CA Cancer J. Clin. 70, 125–137 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Schwartzberg, L., Kim, E. S., Liu, D. & Schrag, D. Precision oncology: who, how, what, when, and when not. Am. Soc. Clin. Oncol. Educ. Book 37, 160–169 (2017).

    Article  PubMed  Google Scholar 

  3. Kumar-Sinha, C. & Chinnaiyan, A. M. Precision oncology in the age of integrative genomics. Nat. Biotechnol. 36, 46–60 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jørgensen, J. T. The current landscape of the FDA approved companion diagnostics. Transl. Oncol. 14, 101063 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Malone, E. R., Oliva, M., Sabatini, P. J. B., Stockley, T. L. & Siu, L. L. Molecular profiling for precision cancer therapies. Genome Med. 12, 8 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 2017, PO.17.00011 (2017).

    PubMed  Google Scholar 

  7. Personalized Medicine Coalition. Personalized Medicine at the FDA: the Scope and Significance of Progress in 2021 https://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/Personalized_Medicine_at_FDA_The_Scope_Significance_of_Progress_in_2021.pdf (2021).

  8. Martin, N. A. et al. Adopting consensus terms for testing in precision medicine. JCO Precis. Oncol 5, 1563–1567 (2021).

    Article  Google Scholar 

  9. Howlader, N. et al. The effect of advances in lung-cancer treatment on population mortality. N. Engl. J. Med. 383, 640–649 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA Cancer J. Clin. 70, 7–30 (2020).

    Article  PubMed  Google Scholar 

  11. Qian, A. S. et al. Disparities in telemedicine during COVID-19. Cancer Med. 11, 1192–1201 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Leapman, M. S. et al. Mediators of racial disparity in the use of prostate magnetic resonance imaging among patients with prostate cancer. JAMA Oncol. 8, 687–696 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Brown, N. A. & Elenitoba-Johnson, K. S. J. Enabling precision oncology through precision diagnostics. Annu. Rev. Pathol. 15, 97–121 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Cheng, D. T. et al. Memorial Sloan Kettering—Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Landy, R. et al. Using prediction models to reduce persistent racial and ethnic disparities in the draft 2020 USPSTF lung cancer screening guidelines. J. Natl Cancer Inst. 113, 1590–1594 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rauscher, G. H., Allgood, K. L., Whitman, S. & Conant, E. Disparities in screening mammography services by race/ethnicity and health insurance. J. Womens Health 21, 154–160 (2012).

    Article  Google Scholar 

  17. Pinsky, P. F., Lau, Y. K. & Doubeni, C. A. Potential disparities by sex and race or ethnicity in lung cancer screening eligibility rates. Chest 160, 341–350 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wolfson, J. A., Sun, C. L., Wyatt, L. P., Hurria, A. & Bhatia, S. Impact of care at comprehensive cancer centers on outcome: results from a population-based study. Cancer 121, 3885–3893 (2015).

    Article  PubMed  Google Scholar 

  19. Sultan, D. H., Gishe, J., Hanciles, A., Comins, M. M. & Norris, C. M. Minority use of a National Cancer Institute-designated comprehensive cancer center and non-specialty hospitals in two Florida regions. J. Racial Ethn. Health Disparities 2, 373–384 (2015).

    Article  PubMed  Google Scholar 

  20. Palazzo, L. L., Sheehan, D. F., Tramontano, A. C. & Kong, C. Y. Disparities and trends in genetic testing and erlotinib treatment among metastatic non-small cell lung cancer patients. Cancer Epidemiol. Biomarkers Prev. 28, 926–934 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kirkwood, M. K. et al. The state of oncology practice in America, 2018: results of the ASCO Practice Census survey. J. Oncol. Pract. 14, e412–e420 (2018).

    Article  PubMed  Google Scholar 

  22. Burke, L. G. et al. Association of academic medical center presence with clinical outcomes at neighboring community hospitals among Medicare beneficiaries. JAMA Netw. Open 6, e2254559 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Mileham, K. F. et al. Defining comprehensive biomarker-related testing and treatment practices for advanced non-small-cell lung cancer: results of a survey of U.S. oncologists. Cancer Med. 11, 530–538 (2022).

    Article  PubMed  Google Scholar 

  24. Robert, N. J. et al. Biomarker tissue journey among patients (pts) with untreated metastatic non-small cell lung cancer (mNSCLC) in the U.S. Oncology Network community practices. J. Clin. Oncol. 39, 9004 (2021).

    Article  Google Scholar 

  25. Trosman, J. R. et al. Biomarker testing in non-small cell lung cancer (NSCLC): an assessment of current practices in precision oncology in the community setting—a trial of the ECOG-ACRIN Cancer Research Group (EAQ161CD). J. Clin. Oncol. 39, e18649 (2021).

    Article  Google Scholar 

  26. Boehmer, L. et al. Identifying barriers to equitable biomarker testing in underserved patients with NSCLC: a mixed-methods study to inform quality improvement opportunities. J. Clin. Oncol. 39, 123 (2021).

    Article  Google Scholar 

  27. Larson, K. L. et al. EGFR testing and erlotinib use in non-small cell lung cancer patients in Kentucky. PLoS ONE 15, e0237790 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lynch, J. A. et al. Underutilization and disparities in access to EGFR testing among Medicare patients with lung cancer from 2010–2013. BMC Cancer 18, 306 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Pezzi, T. A. et al. Association of Medicaid insurance with survival among patients with small cell lung cancer. JAMA Netw. Open 3, e203277 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Presley, C. J. et al. Disparities in next generation sequencing in a population-based community cohort of patients with advanced non-small cell lung cancer. J. Clin. Oncol. 35, 6563 (2017).

    Article  Google Scholar 

  31. Enewold, L. & Thomas, A. Real-world patterns of EGFR testing and treatment with erlotinib for non-small cell lung cancer in the United States. PLoS ONE 11, e0156728 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Williams, D. R. Race, socioeconomic status, and health. The added effects of racism and discrimination. Ann. NY Acad. Sci. 896, 173–188 (1999).

    Article  CAS  PubMed  Google Scholar 

  33. Churchwell, K. et al. Call to action: structural racism as a fundamental driver of health disparities: a presidential advisory from the American Heart Association. Circulation 142, e454–e468 (2020).

    Article  PubMed  Google Scholar 

  34. Levy, D. E. et al. Underutilization of BRCA1/2 testing to guide breast cancer treatment: Black and Hispanic women particularly at risk. Genet. Med. 13, 349–355 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Chapman-Davis, E. et al. Racial and ethnic disparities in genetic testing at a hereditary breast and ovarian cancer center. J. Gen. Intern. Med. 36, 35–42 (2021).

    Article  PubMed  Google Scholar 

  36. Armstrong, K., Micco, E., Carney, A., Stopfer, J. & Putt, M. Racial differences in the use of BRCA1/2 testing among women with a family history of breast or ovarian cancer. JAMA 293, 1729–1736 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Meyer, L. A. et al. Evaluating women with ovarian cancer for BRCA1 and BRCA2 mutations: missed opportunities. Obstet. Gynecol. 115, 945–952 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Shields, A. E., Burke, W. & Levy, D. E. Differential use of available genetic tests among primary care physicians in the United States: results of a national survey. Genet. Med. 10, 404–414 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Bruno, D. S., Hess, L. M., Li, X., Su, E. W. & Patel, M. Disparities in biomarker testing and clinical trial enrollment among patients with lung, breast, or colorectal cancers in the United States. JCO Precis. Oncol. 6, e2100427 (2022).

    Article  PubMed  Google Scholar 

  40. Kehl, K. L., Lathan, C. S., Johnson, B. E. & Schrag, D. Race, poverty, and initial implementation of precision medicine for lung cancer. J. Natl Cancer Inst. 111, 431–434 (2019).

    Article  PubMed  Google Scholar 

  41. Sheinson, D. M. et al. Trends in use of next-generation sequencing in patients with solid tumors by race and ethnicity after implementation of the Medicare national coverage determination. JAMA Netw. Open 4, e2138219 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Bruno, D. S. et al. Racial disparities in biomarker testing and clinical trial enrollment in non-small cell lung cancer (NSCLC). J. Clin. Oncol. 39, 9005 (2021).

    Article  Google Scholar 

  43. Aldrighetti, C. M., Niemierko, A., Van Allen, E., Willers, H. & Kamran, S. C. Racial and ethnic disparities among participants in precision oncology clinical studies. JAMA Netw. Open 4, e2133205 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Gierman, H. J. et al. Genomic testing and treatment landscape in patients with advanced non-small cell lung cancer (aNSCLC) using real-world data from community oncology practices. J. Clin. Oncol. 37, 1585 (2019).

    Article  Google Scholar 

  45. Quinn, R., Patel, R., Sison, C., Singh, A. & Zhu, X. H. Impact of precision medicine on clinical outcomes: a single-institution retrospective study. Front. Oncol. 11, 659113 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Nadler, E. et al. Treatment patterns and clinical outcomes in patients with advanced non-small cell lung cancer initiating first-line treatment in the US community oncology setting: a real-world retrospective observational study. J. Cancer Res. Clin. Oncol. 147, 671–690 (2021).

    Article  PubMed  Google Scholar 

  47. Sturgill, E. G. et al. Next-generation sequencing of patients with breast cancer in community oncology clinics. JCO Precis. Oncol. 5, 1297–1311 (2021).

    Article  PubMed  Google Scholar 

  48. Doshi, J. A., Li, P., Huo, H., Pettit, A. R. & Armstrong, K. A. Association of patient out-of-pocket costs with prescription abandonment and delay in fills of novel oral anticancer agents. J. Clin. Oncol. 36, 476–482 (2018).

    Article  CAS  PubMed  Google Scholar 

  49. Kato, S. et al. Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-One strategy. Nat. Commun. 11, 4965 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Freedman, A., Klabunde, C. & Wiant, K. Use of next-generation sequencing tests to guide cancer treatment: results from a nationally representative survey of oncologists in the United States. JCO Precis. Oncol. 2, PO.18.00169 (2018).

    PubMed  PubMed Central  Google Scholar 

  51. Gray, S. W., Hicks-Courant, K., Cronin, A., Rollins, B. J. & Weeks, J. C. Physicians’ attitudes about multiplex tumor genomic testing. J. Clin. Oncol. 32, 1317–1323 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Kuderer, N. M. et al. Comparison of 2 commercially available next-generation sequencing platforms in oncology. JAMA Oncol. 3, 996–998 (2017).

    Article  PubMed  Google Scholar 

  53. Levit, L. A. et al. Implementing precision medicine in community-based oncology programs: three models. J. Oncol. Pract. 15, 325–329 (2019).

    Article  PubMed  Google Scholar 

  54. Zeng, J. et al. Operationalization of next-generation sequencing and decision support for precision oncology. JCO Clin. Cancer Inform. 3, 1–12 (2019).

    PubMed  Google Scholar 

  55. De Paoli-Iseppi, R. et al. Comparison of whole-exome sequencing of matched fresh and formalin fixed paraffin embedded melanoma tumours: implications for clinical decision making. Pathology 48, 261–266 (2016).

    Article  PubMed  Google Scholar 

  56. Shi, Y. et al. A prospective, molecular epidemiology study of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology (PIONEER). J. Thorac. Oncol. 9, 154–162 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Spratt, D. E. et al. Racial/ethnic disparities in genomic sequencing. JAMA Oncol. 2, 1070–1074 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Nassar, A. H. et al. Ancestry-driven recalibration of tumor mutational burden and disparate clinical outcomes in response to immune checkpoint inhibitors. Cancer Cell 40, 1161–1172 (2022).

    Article  CAS  PubMed  Google Scholar 

  59. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kim, M. S. et al. Testing the generalizability of ancestry-specific polygenic risk scores to predict prostate cancer in sub-Saharan Africa. Genome Biol. 23, 194 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Rebbeck, T. R. et al. Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations. Hum. Mutat. 39, 593–620 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Manrai, A. K. et al. Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375, 655–665 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Lau-Min, K. S., Guerra, C. E., Nathanson, K. L. & Bekelman, J. E. From race-based to precision oncology: leveraging behavioral economics and the electronic health record to advance health equity in cancer care. JCO Precis. Oncol. 5, 403–407 (2021).

    Article  Google Scholar 

  64. Tate, J. G. et al. COSMIC: the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 47, D941–D947 (2019).

    Article  CAS  PubMed  Google Scholar 

  65. Sheinson, D. M., Wong, W. B., Flores, C., Ogale, S. & Gross, C. P. Association between Medicare’s national coverage determination and utilization of next-generation sequencing. JCO Oncol. Pract. 17, e1774–e1784 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  66. LUNGevity Foundation. LUNGevity State Scorecard: State Medicaid Coverage Policy and Impact on Lung Cancer Outcomes; https://www.lungevity.org/public-policy/lung-cancer-scorecard (2020).

  67. Flowers, M. E. et al. Public Act 102-0203; https://www.senatorholmes.com/legislation/my-legislation (2021).

  68. Kesselheim, A. S., Avorn, J. & Sarpatwari, A. The high cost of prescription drugs in the United States: origins and prospects for reform. JAMA 316, 858–871 (2016).

    Article  PubMed  Google Scholar 

  69. Essien, U. R., Dusetzina, S. B. & Gellad, W. F. A policy prescription for reducing health disparities—achieving pharmacoequity. JAMA 326, 1793–1794 (2021).

    Article  PubMed  Google Scholar 

  70. Hunter, A., Wright, P., Cappelli, M., Kasaboski, A. & Surh, L. Physician knowledge and attitudes towards molecular genetic (DNA) testing of their patients. Clin. Genet. 53, 447–455 (1998).

    Article  CAS  PubMed  Google Scholar 

  71. Myers, R. E. et al. A survey of physician receptivity to molecular diagnostic testing and readiness to act on results for early-stage colon cancer patients. BMC Cancer 16, 766 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Association of Community Cancer Centers. Precision Medicine: Transforming Complex to Clear https://www.accc-cancer.org/home/learn/precision-medicine (2022).

  73. Bosompra, K. et al. Likelihood of undergoing genetic testing for cancer risk: a population-based study. Prev. Med. 30, 155–166 (2000).

    Article  CAS  PubMed  Google Scholar 

  74. Armstrong, K. et al. Factors associated with decisions about clinical BRCA1/2 testing. Cancer Epidemiol. Biomarkers Prev. 9, 1251–1254 (2000).

    CAS  PubMed  Google Scholar 

  75. Gray, S. W. et al. Attitudes of patients with cancer about personalized medicine and somatic genetic testing. J. Oncol. Pract. 8, 329–335 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Bussey-Jones, J. et al. Asking the right questions: views on genetic variation research among Black and white research participants. J. Gen. Intern. Med. 24, 299–304 (2009).

    Article  PubMed  Google Scholar 

  77. Shirdarreh, M., Aziza, O., Pezo, R. C., Jerzak, K. J. & Warner, E. Patients’ and oncologists’ knowledge and expectations regarding tumor multigene next-generation sequencing: a narrative review. Oncologist 26, e1359–e1371 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Tzeng, J. P. et al. Women’s experiences with genomic testing for breast cancer recurrence risk. Cancer 116, 1992–2000 (2010).

    Article  PubMed  Google Scholar 

  79. Bradbury, A. R. Implementation of precision cancer medicine: progress and the path to realizing the promise of tumor sequencing. J. Oncol. Pract. 15, 297–299 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Bradbury, A. R. et al. A randomized study of genetic education versus usual care in tumor profiling for advanced cancer in the ECOG-ACRIN Cancer Research Group (EAQ152). Cancer 128, 1381–1391 (2022).

    Article  CAS  PubMed  Google Scholar 

  81. Pishvaian, M. J. et al. Overall survival in patients with pancreatic cancer receiving matched therapies following molecular profiling: a retrospective analysis of the Know Your Tumor registry trial. Lancet Oncol. 21, 508–518 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Johnson, A. et al. The right drugs at the right time for the right patient: the MD Anderson precision oncology decision support platform. Drug Discov. Today 20, 1433–1438 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Akkari, Y., Smith, T., Westfall, J. & Lupo, S. Implementation of cancer next-generation sequencing testing in a community hospital. Cold Spring Harb. Mol. Case Stud. 5, a003707 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Rebbeck, T. R. et al. A framework for promoting diversity, equity, and inclusion in genetics and genomics research. JAMA Health Forum 3, e220603 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Arora, K. et al. Genetic ancestry correlates with somatic differences in a real-world clinical cancer sequencing cohort. Cancer Discov. 12, 2552–2565 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Haddad, T. et al. Impact of a cognitive computing clinical trial matching system in an ambulatory oncology practice. J. Clin. Oncol. 36, 6550 (2018).

    Article  Google Scholar 

  87. Mazor, T. et al. MatchMiner: computational matching of cancer patients to precision medicine clinical trials. Eur. J. Cancer 138, S18 (2020).

    Article  Google Scholar 

  88. Mazor, T. et al. Abstract A024: MatchMiner: an open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials. Mol. Cancer Ther. 18, A024 (2019).

    Article  Google Scholar 

  89. How Advances in Precision Oncology Are Shaping a New Diagnostic and Treatment Frontier https://www.ajmc.com/view/how-advances-in-precision-oncology-new-diagnostic-and-treatment-frontier (2020).

  90. Tanaka, I., Furukawa, T. & Morise, M. The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence. Cancer Cell Int. 21, 454 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

T.R.R. received support for this work from the Dana-Farber–Harvard Cancer Center Cancer Center Support Grant (P30-CA006516) and the Zhu Family Center for Global Cancer Prevention.

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  1. These authors contributed equally: Ritika Dutta, Mounica Vallurupalli.

Authors and Affiliations

  1. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Ritika Dutta

  2. Harvard Medical School, Boston, MA, USA

    Ritika Dutta

  3. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Mounica Vallurupalli, Quinn McVeigh & Timothy R. Rebbeck

  4. Cancer Program, Broad Institute, Cambridge, MA, USA

    Mounica Vallurupalli, Quinn McVeigh & Franklin W. Huang

  5. Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Franklin W. Huang

  6. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Franklin W. Huang

  7. San Francisco Veterans Health Care System, San Francisco, CA, USA

    Franklin W. Huang

  8. Harvard TH Chan School of Public Health, Boston, MA, USA

    Timothy R. Rebbeck

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Correspondence to Franklin W. Huang or Timothy R. Rebbeck.

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Competing interests

R.D. has received consulting fees from Acuta Capital Partners. Q.M. has received research funding from Bayer. A family member of T.R.R. is a consultant to AstraZeneca. All other authors declare no competing interests.

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Dutta, R., Vallurupalli, M., McVeigh, Q. et al. Understanding inequities in precision oncology diagnostics. Nat Cancer 4, 787–794 (2023). https://doi.org/10.1038/s43018-023-00568-1

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