Functional precision cancer medicine—moving beyond pure genomics

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The essential job of precision medicine is to match the right drugs to the right patients. In cancer, precision medicine has been nearly synonymous with genomics. However, sobering recent studies have generally shown that most patients with cancer who receive genomic testing do not benefit from a genomic precision medicine strategy. Although some call the entire project of precision cancer medicine into question, I suggest instead that the tools employed must be broadened. Instead of relying exclusively on big data measurements of initial conditions, we should also acquire highly actionable functional information by perturbing—for example, with cancer therapies—viable primary tumor cells from patients with cancer.

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  1. 1.

    Precision oncology: a strategy we were not ready to deploy. Semin. Oncol. 43, 9–12 (2016).

  2. 2.

    , , & Precision medicine for cancer with next-generation functional diagnostics. Nat. Rev. Cancer 15, 747–756 (2015).

  3. 3.

    Perspective: the precision-oncology illusion. Nature 537, S63 (2016).

  4. 4.

    , & Precision oncology: origins, optimism, and potential. Lancet Oncol. 17, e81–e86 (2016).

  5. 5.

    & Limits to personalized cancer medicine. N. Engl. J. Med. 375, 1289–1294 (2016).

  6. 6.

    No solid evidence, only hollow argument for universal tumor sequencing: show me the data. JAMA Oncol. 2, 717–718 (2016).

  7. 7.

    et al. Consensus on precision medicine for metastatic cancers: a report from the MAP conference. Ann. Oncol. 27, 1443–1448 (2016).

  8. 8.

    et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344, 1031–1037 (2001).

  9. 9.

    et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 (2004).

  10. 10.

    et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004).

  11. 11.

    et al. Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma. Nature 467, 596–599 (2010).

  12. 12.

    et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N. Engl. J. Med. 363, 809–819 (2010).

  13. 13.

    et al. Programmed death-1 blockade with pembrolizumab in patients with classical Hodgkin lymphoma after brentuximab vedotin failure. J. Clin. Oncol. 34, JCO673467 (2016).

  14. 14.

    et al. Nivolumab for classical Hodgkin's lymphoma after failure of both autologous stem-cell transplantation and brentuximab vedotin: a multicentre, multicohort, single-arm phase 2 trial. Lancet Oncol. 17, 1283–1294 (2016).

  15. 15.

    et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 5, 43–51 (2015).

  16. 16.

    et al. Tumor genetic analyses of patients with metastatic renal cell carcinoma and extended benefit from mTOR inhibitor therapy. Clin. Cancer Res. 20, 1955–1964 (2014).

  17. 17.

    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).

  18. 18.

    et al. Genome sequencing identifies a basis for everolimus sensitivity. Science 338, 221 (2012).

  19. 19.

    & Translating cancer research into targeted therapeutics. Nature 467, 543–549 (2010).

  20. 20.

    et al. Routine molecular profiling of patients with advanced non-small-cell lung cancer: results of a 1-year nationwide programme of the French Cooperative Thoracic Intergroup (IFCT). Lancet 387, 1415–1426 (2016).

  21. 21.

    et al. A targeted next-generation sequencing assay detects a high frequency of therapeutically targetable alterations in primary and metastatic breast cancers: implications for clinical practice. Oncologist 19, 453–458 (2014).

  22. 22.

    et al. Institutional implementation of clinical tumor profiling on an unselected cancer population. JCI Insight 1, e87062 (2016).

  23. 23.

    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).

  24. 24.

    & Characteristics of exceptional or super responders to cancer drugs. Mayo Clin. Proc. 90, 1639–1649 (2015).

  25. 25.

    et al. Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs. J. Am. Med. Assoc. 311, 1998–2006 (2014).

  26. 26.

    et al. Personalized medicine for patients with advanced cancer in the phase I program at MD Anderson: validation and landmark analyses. Clin. Cancer Res. 20, 4827–4836 (2014).

  27. 27.

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

  28. 28.

    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).

  29. 29.

    & Precision medicine: lessons learned from the SHIVA trial. Lancet Oncol. 16, e579–e580 (2015).

  30. 30.

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

  31. 31.

    et al. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat. Med. 20, 682–688 (2014).

  32. 32.

    et al. Defining actionable mutations for oncology therapeutic development. Nat. Rev. Cancer 16, 319–329 (2016).

  33. 33.

    et al. ACMG recommendations for standards for interpretation and reporting of sequence variations: Revisions 2007. Genet. Med. 10, 294–300 (2008).

  34. 34.

    et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

  35. 35.

    et al. Prioritizing targets for precision cancer medicine. Ann. Oncol. 25, 2295–2303 (2014).

  36. 36.

    et al. A decision support framework for genomically informed investigational cancer therapy. J. Natl. Cancer Inst. 107, 107 (2015).

  37. 37.

    & Targeted therapies for CLL: practical issues with the changing treatment paradigm. Blood Rev. 30, 233–244 (2016).

  38. 38.

    et al. Defining a cancer dependency map. Cell 170, 564–576.e516 (2017).

  39. 39.

    & Highly specific prediction of antineoplastic drug resistance with an in vitro assay using suprapharmacologic drug exposures. J. Natl. Cancer Inst. 82, 582–588 (1990).

  40. 40.

    , , & Chemotherapy sensitivity and resistance assays: a systematic review. J. Clin. Oncol. 22, 3618–3630 (2004).

  41. 41.

    et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov. 3, 1416–1429 (2013).

  42. 42.

    et al. Novel drug candidates for blast phase chronic myeloid leukemia from high-throughput drug sensitivity and resistance testing. Blood Cancer J. 5, e309 (2015).

  43. 43.

    et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci. Rep. 4, 5193 (2014).

  44. 44.

    et al. Ex vivo drug response profiling detects recurrent sensitivity patterns in drug-resistant acute lymphoblastic leukemia. Blood 129, e26–e37 (2017).

  45. 45.

    et al. Cancer therapy. Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility. Science 345, 216–220 (2014).

  46. 46.

    et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science 346, 1480–1486 (2014).

  47. 47.

    et al. Prospective cohort study using the breast cancer spheroid model as a predictor for response to neoadjuvant therapy—the SpheroNEO study. BMC Cancer 15, 519 (2015).

  48. 48.

    et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 4, 998–1013 (2014).

  49. 49.

    et al. The public repository of xenografts enables discovery and randomized phase II–like trials in ice. Cancer Cell 29, 574–586 (2016).

  50. 50.

    et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274.e22 (2016).

  51. 51.

    et al. Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy. Cell 160, 977–989 (2015).

  52. 52.

    & Mitochondria—judges and executioners of cell death sentences. Mol. Cell 61, 695–704 (2016).

  53. 53.

    et al. Mitochondria primed by death signals determine cellular addiction to antiapoptotic BCL-2 family members. Cancer Cell 9, 351–365 (2006).

  54. 54.

    et al. BH3 profiling identifies three distinct classes of apoptotic blocks to predict response to ABT-737 and conventional chemotherapeutic agents. Cancer Cell 12, 171–185 (2007).

  55. 55.

    et al. Pretreatment mitochondrial priming correlates with clinical response to cytotoxic chemotherapy. Science 334, 1129–1133 (2011).

  56. 56.

    , , & iBH3: simple, fixable BH3 profiling to determine apoptotic priming in primary tissue by flow cytometry. Biol. Chem. 397, 671–678 (2016).

  57. 57.

    et al. Relative mitochondrial priming of myeloblasts and normal HSCs determines chemotherapeutic success in AML. Cell 151, 344–355 (2012).

  58. 58.

    et al. Chronic lymphocytic leukemia requires BCL2 to sequester prodeath BIM, explaining sensitivity to BCL2 antagonist ABT-737. J. Clin. Invest. 117, 112–121 (2007).

  59. 59.

    et al. Selective BCL-2 inhibition by ABT-199 causes on-target cell death in acute myeloid leukemia. Cancer Discov. 4, 362–375 (2014).

  60. 60.

    et al. Activity of a selective inhibitor of nuclear export, selinexor (KPT-330), against AML-initiating cells engrafted into immunosuppressed NSG mice. Leukemia 30, 190–199 (2016).

  61. 61.

    et al. Activity of the type II JAK2 inhibitor CHZ868 in B cell acute lymphoblastic leukemia. Cancer Cell 28, 29–41 (2015).

  62. 62.

    et al. High-throughput measurement of single-cell growth rates using serial microfluidic mass sensor arrays. Nat. Biotechnol. 34, 1052–1059 (2016).

  63. 63.

    et al. Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate. Nat. Biotechnol. 34, 1161–1167 (2016).

  64. 64.

    et al. An implantable microdevice to perform high-throughput in vivo drug sensitivity testing in tumors. Sci. Transl. Med. 7, 284ra57 (2015).

  65. 65.

    et al. First in vivo testing of compounds targeting group 3 medulloblastomas using an implantable microdevice as a new paradigm for drug development. J. Biomed. Nanotechnol. 12, 1297–1302 (2016).

  66. 66.

    et al. Parallel in vivo assessment of drug phenotypes at various time points during systemic BRAF inhibition reveals tumor adaptation and altered treatment vulnerabilities. Clin. Cancer Res. 22, 6031–6038 (2016).

  67. 67.

    et al. A platform for rapid, quantitative assessment of multiple drug combinations simultaneously in solid tumors in vivo. PLoS One 11, e0158617 (2016).

  68. 68.

    et al. A technology platform to assess multiple cancer agents simultaneously within a patient's tumor. Sci. Transl. Med. 7, 284ra58 (2015).

  69. 69.

    et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).

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I would like to acknowledge P. Bhola for assistance with Figure 1 and the entire Letai laboratory for conversations over years that have stimulated ideas contained in this article. I also gratefully acknowledge funding from National Institutes of Health grant R01CA205967.

Author information


  1. Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.

    • Anthony Letai


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Competing interests

A.L. discloses consulting for AbbVie, Bayer, Astra Zeneca, XrX, Merrimack Pharmaceuticals, and Novartis; research sponsorship in his laboratory by AbbVie, AstraZeneca, XrX, and Novartis; inventorship on patents owned by Dana-Farber Cancer Institute regulating BH3 profiling and dynamic BH3 profiling; and being a cofounder and equity holder of Leap Oncology and Flash Therapeutics.

Corresponding author

Correspondence to Anthony Letai.