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The evidence framework for precision cancer medicine

Abstract

Precision cancer medicine (PCM) is a concept in which oncologists increasingly strive to tailor the use of targeted therapies in order to match the complexity of the cancer genome. This approach contradicts the historical framework used to support oncology practice that requires evidence from randomized controlled trials in order to change standards of care. This contrast demonstrates the irony of PCM: the therapies themselves are more precise than standard cytotoxic agents, although the clinical evidence supporting the benefits of these therapies is often considerably less precise. Nevertheless, the implementation of PCM should still be based on a framework of evidence-based development that supports clinical decision-making; this approach should not be simple off-label prescription of drugs following sequencing of a tumour biopsy sample. The clinical validation of increasingly complex diagnostic tests, the development of novel methods of evaluating efficacy, and the re-assessment of the standards of evidence sufficient to demonstrate the benefit of precision cancer therapies are all needed before PCM becomes the standard of care for patients with tumours harbouring genomic abnormalities of uncertain clinical significance.

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References

  1. Ottmann, O. G. et al. A phase 2 study of imatinib in patients with relapsed or refractory Philadelphia chromosome-positive acute lymphoid leukemias. Blood 100, 1965–1971 (2002).

    Article  CAS  Google Scholar 

  2. Sawyers, C. L. et al. Imatinib induces hematologic and cytogenetic responses in patients with chronic myelogenous leukemia in myeloid blast crisis: results of a phase II study. Blood 99, 3530–3539 (2002).

    Article  CAS  Google Scholar 

  3. O'Brien, S. G. et al. Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. N. Engl. J. Med. 348, 994–1004 (2003).

    Article  CAS  Google Scholar 

  4. Perez, E. A. et al. Four-year follow-up of trastuzumab plus adjuvant chemotherapy for operable human epidermal growth factor receptor 2-positive breast cancer: joint analysis of data from NCCTG N9831 and NSABP B-31. J. Clin. Oncol. 29, 3366–3373 (2011).

    Article  CAS  Google Scholar 

  5. Perez, E. A. et al. Trastuzumab plus adjuvant chemotherapy for human epidermal growth factor receptor 2-positive breast cancer: planned joint analysis of overall survival from NSABP B-31 and NCCTG N9831. J. Clin. Oncol. 32, 3744–3752 (2014).

    Article  CAS  Google Scholar 

  6. Rosell, R. et al. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomised phase 3 trial. Lancet Oncol. 13, 239–246 (2012).

    Article  CAS  Google Scholar 

  7. Fukuoka, M. et al. Biomarker analyses and final overall survival results from a phase III, randomized, open-label, first-line study of gefitinib versus carboplatin/paclitaxel in clinically selected patients with advanced non-small-cell lung cancer in Asia (IPASS). J. Clin. Oncol. 29, 2866–2874 (2011).

    Article  CAS  Google Scholar 

  8. Hauschild, A. et al. Dabrafenib in BRAF-mutated metastatic melanoma: a multicentre, open-label, phase 3 randomised controlled trial. Lancet 380, 358–365 (2012).

    Article  CAS  Google Scholar 

  9. Hyman, D. M. et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N. Engl. J. Med. 373, 726–736 (2015).

    Article  CAS  Google Scholar 

  10. Le Tourneau, C. et al. Treatment algorithms based on tumor molecular profiling: the essence of precision medicine trials. J. Natl Cancer Inst. 108, djv362 (2016).

    Article  Google Scholar 

  11. Meric-Bernstam, F. et al. A decision support framework for genomically informed investigational cancer therapy. J. Natl Cancer Inst. 107, djv098 (2015).

    Article  Google Scholar 

  12. Sukhai, M. A. et al. A classification system for clinical relevance of somatic variants identified in molecular profiling of cancer. Genet. Med. 18, 128–136 (2016).

    Article  CAS  Google Scholar 

  13. Papaemmanuil, E., Dohner, H. & Campbell, P. J. Genomic classification in acute myeloid leukemia. N. Engl. J. Med. 375, 900–901 (2016).

    Article  Google Scholar 

  14. Klco, J. M. et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. Cancer Cell 25, 379–392 (2014).

    Article  CAS  Google Scholar 

  15. Levis, M. Midostaurin approved for FLT3-mutated AML. Blood 129, 3403–3406 (2017).

    Article  CAS  Google Scholar 

  16. Kris, M. G. et al. Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs. JAMA 311, 1998–2006 (2014).

    Article  Google Scholar 

  17. Munoz, J., Schlette, E. & Kurzrock, R. Rapid response to vemurafenib in a heavily pretreated patient with hairy cell leukemia and a BRAF mutation. J. Clin. Oncol. 31, e351–352 (2013).

    Article  Google Scholar 

  18. Peters, S., Michielin, O. & Zimmermann, S. Dramatic response induced by vemurafenib in a BRAF V600E-mutated lung adenocarcinoma. J. Clin. Oncol. 31, e341–e344 (2013).

    Article  Google Scholar 

  19. Wagle, N. et al. Activating mTOR mutations in a patient with an extraordinary response on a phase I trial of everolimus and pazopanib. Cancer Discov. 4, 546–553 (2014).

    Article  Google Scholar 

  20. Prasad, V. Perspective: The precision-oncology illusion. Nature 537, S63 (2016).

    Article  CAS  Google Scholar 

  21. Tannock, I. F. & Hickman, J. A. Limits to personalized cancer medicine. N. Engl. J. Med. 375, 1289–1294 (2016).

    Article  Google Scholar 

  22. Tannock, I. F. & Hickman, J. A. Limits to precision cancer medicine. N. Engl. J. Med. 376, 96–97 (2017).

    PubMed  Google Scholar 

  23. Iyer, G. et al. Prevalence and co-occurrence of actionable genomic alterations in high-grade bladder cancer. J. Clin. Oncol. 31, 3133–3140 (2013).

    Article  CAS  Google Scholar 

  24. Radovich, M. et al. Clinical benefit of a precision medicine based approach for guiding treatment of refractory cancers. Oncotarget 7, 56491–56500 (2016).

    Article  Google Scholar 

  25. Tsimberidou, A. M. et al. Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative. Clin. Cancer Res. 18, 6373–6383 (2012).

    Article  CAS  Google Scholar 

  26. Von Hoff, D. D. et al. Pilot study using molecular profiling of patients' tumors to find potential targets and select treatments for their refractory cancers. J. Clin. Oncol. 28, 4877–4883 (2010).

    Article  CAS  Google Scholar 

  27. MacConaill, L. E. et al. Prospective enterprise-level molecular genotyping of a cohort of cancer patients. J. Mol. Diagn. 16, 660–672 (2014).

    Article  CAS  Google Scholar 

  28. Hakenberg, J. et al. Integrating 400 million variants from 80,000 human samples with extensive annotations: towards a knowledge base to analyze disease cohorts. BMC Bioinformatics 17, 24 (2016).

    Article  Google Scholar 

  29. Uzilov, A. V. et al. Development and clinical application of an integrative genomic approach to personalized cancer therapy. Genome Med. 8, 62 (2016).

    Article  Google Scholar 

  30. ECOG-ACRIN Cancer Research Group. Executive summary: interim analysis of the NCI-MATCH trial. ECOG-ACRIN Cancer Research Group http://ecog-acrin.org/nci-match-eay131/interim-analysis (2016).

  31. Marchione, M. Ultra-personal therapy: gene tumor boards guide cancer care. ABCNews http://abcnews.go.com/amp/Health/wireStory/ultra-personal-therapy-gene-tumor-boards-guide-cancer-50551995 (2017).

  32. Chakradhar, S. Group mentality: determining if targeted treatments really work for cancer. Nat. Med. 22, 222–224 (2016).

    Article  CAS  Google Scholar 

  33. Wagle, N. et al. Response and acquired resistance to everolimus in anaplastic thyroid cancer. N. Engl. J. Med. 371, 1426–1433 (2014).

    Article  Google Scholar 

  34. Li, M. M. et al. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J. Mol. Diagn. 19, 4–23 (2017).

    Article  CAS  Google Scholar 

  35. Hughes, K. S. et al. Identifying health information technology needs of oncologists to facilitate the adoption of genomic medicine: recommendations from the 2016 American Society of Clinical Oncology Omics and Precision Oncology Workshop. J. Clin. Oncol. 35, 3153–3159 (2017).

    Article  CAS  Google Scholar 

  36. Dienstmann, R., Rodon, J., Barretina, J. & Tabernero, J. Genomic medicine frontier in human solid tumors: prospects and challenges. J. Clin. Oncol. 31, 1874–1884 (2013).

    Article  Google Scholar 

  37. Garraway, L. A. Genomics-driven oncology: framework for an emerging paradigm. J. Clin. Oncol. 31, 1806–1814 (2013).

    Article  Google Scholar 

  38. Macconaill, L. E. & Garraway, L. A. Clinical implications of the cancer genome. J. Clin. Oncol. 28, 5219–5228 (2010).

    Article  Google Scholar 

  39. Meador, C. B. et al. Beyond histology: translating tumor genotypes into clinically effective targeted therapies. Clin. Cancer Res. 20, 2264–2275 (2014).

    Article  CAS  Google Scholar 

  40. Nikanjam, M., Liu, S., Yang, J. & Kurzrock, R. Dosing three-drug combinations that include targeted anti-cancer agents: analysis of 37,763 patients. Oncologist 22, 576–584 (2017).

    Article  CAS  Google Scholar 

  41. Liu, S., Nikanjam, M. & Kurzrock, R. Dosing de novo combinations of two targeted drugs: towards a customized precision medicine approach to advanced cancers. Oncotarget 7, 11310–11320 (2016).

    PubMed  PubMed Central  Google Scholar 

  42. Schwaederle, M. et al. Impact of precision medicine in diverse cancers: a meta-analysis of phase II clinical trials. J. Clin. Oncol. 33, 3817–3825 (2015).

    Article  CAS  Google Scholar 

  43. Jardim, D. L. et al. Impact of a biomarker-based strategy on oncology drug development: a meta-analysis of clinical trials leading to FDA approval. J. Natl Cancer Inst. 107, djv253 (2015).

    Article  Google Scholar 

  44. Herbst, R. S. et al. Lung Master Protocol (Lung-MAP) — a biomarker-driven protocol for accelerating development of therapies for squamous cell lung cancer: SWOG S1400. Clin. Cancer Res. 21, 1514–1524 (2015).

    Article  CAS  Google Scholar 

  45. LoRusso, P. M. et al. Pilot trial of selecting molecularly guided therapy for patients with non-V600 BRAF-mutant metastatic melanoma: experience of the SU2C/MRA melanoma dream team. Mol. Cancer Ther. 14, 1962–1971 (2015).

    Article  CAS  Google Scholar 

  46. Bashaw, E. D. & Fang, L. Clinical pharmacology and orphan drugs: an informational inventory 2006–2010. Clin. Pharmacol. Ther. 91, 932–936 (2012).

    Article  CAS  Google Scholar 

  47. U.S. Food and Drug Administration. FDA grants accelerated approval to pembrolizumab for first tissue/site agnostic indication. U.S. Food and Drug Administration https://www.fda.gov/Drugs/InformationOnDrugs/ApprovedDrugs/ucm560040.htm (2017).

  48. Ritter, D. I. et al. Somatic cancer variant curation and harmonization through consensus minimum variant level data. Genome Med. 8, 117 (2016).

    Article  Google Scholar 

  49. Gee, A. W., Balogh, E., Patlak, M. & Nass, S. J. The drug development daradigm in oncology. Proceedings of a workshop. (The National Academies Press, 2017).

    Google Scholar 

  50. Lopez-Chavez, A. et al. Molecular profiling and targeted therapy for advanced thoracic malignancies: a biomarker-derived, multiarm, multihistology phase II basket trial. J. Clin. Oncol. 33, 1000–1007 (2015).

    Article  CAS  Google Scholar 

  51. Meric-Bernstam, F. et al. Feasibility of large-scale genomic testing to facilitate enrollment onto genomically matched clinical trials. J. Clin. Oncol. 33, 2753–2762 (2015).

    Article  Google Scholar 

  52. Le Tourneau, C. et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol. 16, 1324–1334 (2015).

    Article  CAS  Google Scholar 

  53. Haslem, D. S. et al. A retrospective analysis of precision medicine outcomes in patients with advanced cancer reveals improved progression-free survival without increased health care costs. J. Oncol. Pract. 13, e108–e119 (2017).

    Article  Google Scholar 

  54. Wheler, J. J. et al. Cancer therapy directed by comprehensive genomic profiling: a single center study. Cancer Res. 76, 3690–3701 (2016).

    Article  CAS  Google Scholar 

  55. Massard, C. et al. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: results of the MOSCATO 01 trial. Cancer Discov. 7, 586–595 (2017).

    Article  CAS  Google Scholar 

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All authors made a substantial contribution to all aspects of the preparation of this manuscript.

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Correspondence to Jeffrey A. Moscow.

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Moscow, J., Fojo, T. & Schilsky, R. The evidence framework for precision cancer medicine. Nat Rev Clin Oncol 15, 183–192 (2018). https://doi.org/10.1038/nrclinonc.2017.186

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