Opinion

Targeting minimal residual disease: a path to cure?

Published online:

Abstract

Therapeutics that block kinases, transcriptional modifiers, immune checkpoints and other biological vulnerabilities are transforming cancer treatment. As a result, many patients achieve dramatic responses, including complete radiographical or pathological remission, yet retain minimal residual disease (MRD), which results in relapse. New functional approaches can characterize clonal heterogeneity and predict therapeutic sensitivity of MRD at a single-cell level. Preliminary evidence suggests that iterative detection, profiling and targeting of MRD would meaningfully improve outcomes and may even lead to cure.

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References

  1. 1.

    et al. Acute Myeloid Leukemia (AML): different treatment strategies versus a common standard arm — combined prospective analysis by the German AML Intergroup. J. Clin. Oncol. 30, 3604–3610 (2012).

  2. 2.

    & Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J. 7, e577 (2017).

  3. 3.

    The aggressive peripheral T-cell lymphomas: 2017. Am. J. Hematol. 92, 706–715 (2017).

  4. 4.

    Chemotherapy combinations with monoclonal antibodies in non-Hodgkin's lymphoma. Semin. Hematol. 45, 90–94 (2008).

  5. 5.

    et al. Phase III study comparing cisplatin plus gemcitabine with cisplatin plus pemetrexed in chemotherapy-naive patients with advanced-stage non-small-cell lung cancer. J. Clin. Oncol. 26, 3543–3551 (2008).

  6. 6.

    et al. FOLFOXIRI (folinic acid, 5-fluorouracil, oxaliplatin and irinotecan) versus FOLFIRI (folinic acid, 5-fluorouracil and irinotecan) as first-line treatment in metastatic colorectal cancer (MCC): a multicentre randomised phase III trial from the Hellenic Oncology Research Group (HORG). Br. J. Cancer 94, 798–805 (2006).

  7. 7.

    et al. Phase II study of docetaxel, doxorubicin, and cyclophosphamide as first-line chemotherapy for metastatic breast cancer. J. Clin. Oncol. 19, 314–321 (2001).

  8. 8.

    et al. Phase III multicenter randomized trial of the Dartmouth regimen versus dacarbazine in patients with metastatic melanoma. J. Clin. Oncol. 17, 2745–2751 (1999).

  9. 9.

    Gastrointestinal stromal tumors. Curr. Treat. Opt. Oncol. 1, 267–273 (2000).

  10. 10.

    et al. Docetaxel plus prednisone or mitoxantrone plus prednisone for advanced prostate cancer. N. Engl. J. Med. 351, 1502–1512 (2004).

  11. 11.

    et al. Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR. N. Engl. J. Med. 362, 2380–2388 (2010).

  12. 12.

    et al. First-line ceritinib versus platinum-based chemotherapy in advanced ALK-rearranged non-small-cell lung cancer (ASCEND-4): a randomised, open-label, phase 3 study. Lancet 389, 917–929 (2017).

  13. 13.

    et al. Crizotinib therapy for advanced lung adenocarcinoma and a ROS1 rearrangement: results from the EUROS1 cohort. J. Clin. Oncol. 33, 992–999 (2015).

  14. 14.

    et al. Combined BRAF and MEK inhibition versus BRAF inhibition alone in melanoma. N. Engl. J. Med. 371, 1877–1888 (2014).

  15. 15.

    et al. Cobimetinib combined with vemurafenib in advanced BRAF(V600)-mutant melanoma (coBRIM): updated efficacy results from a randomised, double-blind, phase 3 trial. Lancet Oncol. 17, 1248–1260 (2016).

  16. 16.

    et al. Phase III randomized, intergroup trial assessing imatinib mesylate at two dose levels in patients with unresectable or metastatic gastrointestinal stromal tumors expressing the kit receptor tyrosine kinase: S0033. J. Clin. Oncol. 26, 626–632 (2008).

  17. 17.

    & Minimal residual disease in acute myeloid leukaemia. Nat. Rev. Clin. Oncol. 10, 460–471 (2013).

  18. 18.

    et al. Minimum lesion detectability as a measure of PET system performance. EJNMMI Phys. 4, 13 (2017).

  19. 19.

    & Preventing clonal evolutionary processes in cancer: Insights from mathematical models. Proc. Natl Acad. Sci. USA 112, 8843–8850 (2015).

  20. 20.

    & Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).

  21. 21.

    et al. Leukemic stem cell frequency: a strong biomarker for clinical outcome in acute myeloid leukemia. PLoS ONE 9, e107587 (2014).

  22. 22.

    et al. Postoperative adjuvant chemotherapy or BCG for colon cancer: results from NSABP protocol C-01. J. Natl Cancer Inst. 80, 30–36 (1988).

  23. 23.

    et al. The benefit of leucovorin-modulated fluorouracil as postoperative adjuvant therapy for primary colon cancer: results from National Surgical Adjuvant Breast and Bowel Project protocol C-03. J. Clin. Oncol. 11, 1879–1887 (1993).

  24. 24.

    et al. Improved overall survival with oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment in stage II or III colon cancer in the MOSAIC trial. J. Clin. Oncol. 27, 3109–3116 (2009).

  25. 25.

    Early Breast Cancer Trialists' Collaborative Group. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet 379, 432–444 (2012).

  26. 26.

    et al. A systematic meta-analysis of randomized controlled trials of adjuvant chemotherapy for localized resectable soft-tissue sarcoma. Cancer 113, 573–581 (2008).

  27. 27.

    et al. Adjuvant endocrine monotherapy for postmenopausal early breast cancer patients with hormone-receptor positive: a systemic review and network meta-analysis. Breast Cancer (2017).

  28. 28.

    et al. Vinorelbine plus cisplatin versus observation in resected non-small-cell lung cancer. N. Engl. J. Med. 352, 2589–2597 (2005).

  29. 29.

    et al. The effectiveness of combinations of antileukemic agents in inducing and maintaining remission in children with acute leukemia. Blood 26, 642–656 (1965).

  30. 30.

    , , , & Minimal residual disease-directed therapy in acute myeloid leukemia. Blood 125, 2331–2335 (2015).

  31. 31.

    Flow cytometric monitoring of residual disease in acute leukemia. Methods Mol. Biol. 999, 123–136 (2013).

  32. 32.

    et al. Standardized flow cytometry for highly sensitive MRD measurements in B-cell acute lymphoblastic leukemia. Blood 129, 347–357 (2017).

  33. 33.

    & MRD in AML: does it already guide therapy decision-making? Hematol. Am. Soc. Hematol. Educ. Program 2016, 356–365 (2016).

  34. 34.

    Minimal residual disease in acute lymphoblastic leukemia. Hematol. Am. Soc. Hematol. Educ. Program 2010, 7–12 (2010).

  35. 35.

    et al. Clinical significance of minimal residual disease quantification in adult patients with standard-risk acute lymphoblastic leukemia. Blood 107, 1116–1123 (2006).

  36. 36.

    , & Has MRD monitoring superseded other prognostic factors in adult ALL? Blood 120, 4470–4481 (2012).

  37. 37.

    et al. Prospective minimal residual disease monitoring to predict relapse of acute promyelocytic leukemia and to direct pre-emptive arsenic trioxide therapy. J. Clin. Oncol. 27, 3650–3658 (2009).

  38. 38.

    et al. Minimal residual disease monitoring by quantitative RT-PCR in core binding factor AML allows risk stratification and predicts relapse: results of the United Kingdom MRC AML-15 trial. Blood 120, 2826–2835 (2012).

  39. 39.

    et al. Oncogenetics and minimal residual disease are independent outcome predictors in adult patients with acute lymphoblastic leukemia. Blood 123, 3739–3749 (2014).

  40. 40.

    et al. Clinical significance of minimal residual disease in childhood acute lymphoblastic leukemia and its relationship to other prognostic factors: a Children's Oncology Group study. Blood 111, 5477–5485 (2008).

  41. 41.

    et al. Relation of clinical response and minimal residual disease and their prognostic impact on outcome in acute myeloid leukemia. J. Clin. Oncol. 33, 1258–1264 (2015).

  42. 42.

    et al. Prognostic relevance of treatment response measured by flow cytometric residual disease detection in older patients with acute myeloid leukemia. J. Clin. Oncol. 31, 4123–4131 (2013).

  43. 43.

    et al. Adult patients with acute lymphoblastic leukemia and molecular failure display a poor prognosis and are candidates for stem cell transplantation and targeted therapies. Blood 120, 1868–1876 (2012).

  44. 44.

    et al. High prognostic impact of flow cytometric minimal residual disease detection in acute myeloid leukemia: data from the HOVON/SAKK AML 42A study. J. Clin. Oncol. 31, 3889–3897 (2013).

  45. 45.

    et al. Clinical significance of minimal residual disease in childhood acute lymphoblastic leukemia. N. Engl. J. Med. 339, 591–598 (1998).

  46. 46.

    et al. Minimal residual disease in adolescent (older than 14 years) and adult acute lymphoblastic leukemias: early immunophenotypic evaluation has high clinical value. Blood 101, 4695–4700 (2003).

  47. 47.

    et al. Successful therapy reduction and intensification for childhood acute lymphoblastic leukemia based on minimal residual disease monitoring: study ALL10 From the Dutch Childhood Oncology Group. J. Clin. Oncol. 34, 2591–2601 (2016).

  48. 48.

    et al. Detection of MRD may predict the outcome of patients with Philadelphia chromosome-positive ALL treated with tyrosine kinase inhibitors plus chemotherapy. Blood 122, 1214–1221 (2013).

  49. 49.

    & How I treat newly diagnosed chronic phase CML. Blood 120, 1390–1397 (2012).

  50. 50.

    & Moving treatment-free remission into mainstream clinical practice in CML. Blood 128, 17–23 (2016).

  51. 51.

    et al. Safety and efficacy of imatinib cessation for CML patients with stable undetectable minimal residual disease: results from the TWISTER study. Blood 122, 515–522 (2013).

  52. 52.

    et al. Loss of major molecular response as a trigger for restarting tyrosine kinase inhibitor therapy in patients with chronic-phase chronic myelogenous leukemia who have stopped imatinib after durable undetectable disease. J. Clin. Oncol. 32, 424–430 (2014).

  53. 53.

    et al. Discontinuation of dasatinib in patients with chronic myeloid leukaemia who have maintained deep molecular response for longer than 1 year (DADI trial): a multicentre phase 2 trial. Lancet Haematol. 2, e528–535 (2015).

  54. 54.

    et al. Discontinuation of dasatinib or nilotinib in chronic myeloid leukemia: interim analysis of the STOP 2G-TKI study. Blood 129, 846–854 (2017).

  55. 55.

    et al. Long-term follow-up of the French stop imatinib (STIM1) Study in patients with chronic myeloid leukemia. J. Clin. Oncol. 35, 298–305 (2017).

  56. 56.

    Quantitative approaches to analyzing imatinib-treated chronic myeloid leukemia. Trends Pharmacol. Sci. 28, 197–199 (2007).

  57. 57.

    et al. Dynamics of chronic myeloid leukaemia. Nature 435, 1267–1270 (2005).

  58. 58.

    , , , & Nilotinib exerts equipotent antiproliferative effects to imatinib and does not induce apoptosis in CD34+ CML cells. Blood 109, 4016–4019 (2007).

  59. 59.

    et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).

  60. 60.

    et al. Cancer stem cell definitions and terminology: the devil is in the details. Nat. Rev. Cancer 12, 767–775 (2012).

  61. 61.

    et al. Interrupted versus continued maintenance therapy in childhood acute leukemia. Cancer 36, 341–352 (1975).

  62. 62.

    , , , & Treatment of acute lymphoblastic leukemia. 30 years' experience at St. Jude Children's Research Hospital. N. Engl. J. Med. 329, 1289–1295 (1993).

  63. 63.

    et al. Activating mutations in the NT5C2 nucleotidase gene drive chemotherapy resistance in relapsed ALL. Nat. Med. 19, 368–371 (2013).

  64. 64.

    et al. Relapse-specific mutations in NT5C2 in childhood acute lymphoblastic leukemia. Nat. Genet. 45, 290–294 (2013).

  65. 65.

    et al. Hematopoietic malignancies demonstrate loss-of-function mutations of BAX. Blood 91, 2991–2997 (1998).

  66. 66.

    et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kalpha inhibitor. Nature 518, 240–244 (2015).

  67. 67.

    et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

  68. 68.

    et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

  69. 69.

    , & A conduit to metastasis: circulating tumor cell biology. Genes Dev. 31, 1827–1840 (2017).

  70. 70.

    et al. Microfluidic, marker-free isolation of circulating tumor cells from blood samples. Nat. Protoc. 9, 694–710 (2014).

  71. 71.

    et al. Circulating tumor cells predict survival benefit from treatment in metastatic castration-resistant prostate cancer. Clin. Cancer Res. 14, 6302–6309 (2008).

  72. 72.

    et al. Diagnostic leukapheresis enables reliable detection of circulating tumor cells of nonmetastatic cancer patients. Proc. Natl Acad. Sci. USA 110, 16580–16585 (2013).

  73. 73.

    US National Library of Medicine. ClinicalTrials.gov (2017)

  74. 74.

    , , & Does the mobilization of circulating tumour cells during cancer therapy cause metastasis? Nat. Rev. Clin. Oncol. 14, 32–44 (2017).

  75. 75.

    et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17, 223–238 (2017).

  76. 76.

    et al. Gefitinib treatment in EGFR mutated caucasian NSCLC: circulating-free tumor DNA as a surrogate for determination of EGFR status. J. Thorac. Oncol. 9, 1345–1353 (2014).

  77. 77.

    et al. ctDNA determination of EGFR mutation status in European and Japanese patients with advanced NSCLC: the ASSESS study. J. Thorac. Oncol. 11, 1682–1689 (2016).

  78. 78.

    et al. Prospective validation of rapid plasma genotyping for the detection of EGFR and KRAS mutations in advanced lung cancer. JAMA Oncol. 2, 1014–1022 (2016).

  79. 79.

    et al. Osimertinib benefit in EGFR-mutant NSCLC patients with T790M-mutation detected by circulating tumour DNA. Ann. Oncol. 28, 784–790 (2017).

  80. 80.

    Functional precision cancer medicine-moving beyond pure genomics. Nat. Med. 23, 1028–1035 (2017).

  81. 81.

    & Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).

  82. 82.

    & Single-cell analyses to tailor treatments. Sci. Transl Med. 9, eaan4730 (2017).

  83. 83.

    et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

  84. 84.

    et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

  85. 85.

    et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  86. 86.

    , & Scaling by shrinking: empowering single-cell 'omics' with microfluidic devices. Nat. Rev. Genet. 18, 345–361 (2017).

  87. 87.

    et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

  88. 88.

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

  89. 89.

    et al. Overview of a chemoresponse assay in ovarian cancer. Clin. Transl Oncol. 16, 761–769 (2014).

  90. 90.

    et al. American Society of Clinical Oncology Technology Assessment: chemotherapy sensitivity and resistance assays. J. Clin. Oncol. 22, 3631–3638 (2004).

  91. 91.

    , & Examining the utility of patient-derived xenograft mouse models. Nat. Rev. Cancer 15, 311–316 (2015).

  92. 92.

    et al. Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat. Rev. Cancer 17, 254–268 (2017).

  93. 93.

    et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).

  94. 94.

    et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).

  95. 95.

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

  96. 96.

    & Dynamic BH3 profiling-poking cancer cells with a stick. Mol. Cell Oncol. 3, e1040144 (2016).

  97. 97.

    et al. Blastic plasmacytoid dendritic cell neoplasm is dependent on BCL2 and sensitive to venetoclax. Cancer Discov. 7, 156–164 (2017).

  98. 98.

    et al. The public repository of xenografts enables discovery and randomized phase II-like trials in mice. Cancer Cell 30, 183 (2016).

  99. 99.

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

  100. 100.

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

  101. 101.

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

  102. 102.

    et al. Using buoyant mass to measure the growth of single cells. Nat. Methods 7, 387–390 (2010).

  103. 103.

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

  104. 104.

    et al. Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation. Nat. Commun. 8, 1613 (2017).

  105. 105.

    & Live-cell mass profiling: an emerging approach in quantitative biophysics. Nat. Methods 11, 1221–1228 (2014).

  106. 106.

    et al. Characterizing deformability and surface friction of cancer cells. Proc. Natl Acad. Sci. USA 110, 7580–7585 (2013).

  107. 107.

    et al. Hydrodynamic stretching of single cells for large population mechanical phenotyping. Proc. Natl Acad. Sci. USA 109, 7630–7635 (2012).

  108. 108.

    et al. Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat. Methods 12, 199–202 (2015).

  109. 109.

    , & Analyzing cell mechanics in hematologic diseases with microfluidic biophysical flow cytometry. Lab Chip 8, 1062–1070 (2008).

  110. 110.

    et al. Single-cell phosphoproteomics resolves adaptive signaling dynamics and informs targeted combination therapy in glioblastoma. Cancer Cell 29, 563–573 (2016).

  111. 111.

    et al. Bioluminescence microscopy as a method to measure single cell androgen receptor activity heterogeneous responses to antiandrogens. Sci. Rep. 6, 33968 (2016).

  112. 112.

    et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

  113. 113.

    et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).

  114. 114.

    et al. Next Generation Flow for highly sensitive and standardized detection of minimal residual disease in multiple myeloma. Leukemia 31, 2094–2103 (2017).

  115. 115.

    et al. Genetic interrogation of circulating multiple myeloma cells at single-cell resolution. Sci. Transl Med. 8, 363ra147 (2016).

  116. 116.

    et al. Location of residual viable tumor cells after neoadjuvant chemotherapy: a new concept with high prognostic performance in osteosarcoma. J. Surg. Oncol. 115, 752–759 (2017).

  117. 117.

    et al. Characterization of residual tumours at the primary site in patients with a near pathological complete response after neoadjuvant chemoradiotherapy for oesophageal cancer. Br. J. Surg. 103, 1874–1879 (2016).

  118. 118.

    et al. Using the neoadjuvant chemotherapy paradigm to develop precision therapy for muscle-invasive bladder cancer. Urol. Oncol. 34, 469–476 (2016).

  119. 119.

    et al. Guidelines for preventing infectious complications among hematopoietic cell transplantation recipients: a global perspective. Biol. Blood Marrow Transplant 15, 1143–1238 (2009).

  120. 120.

    Address to the New York State Agricultural Association (Syracuse, 1903).

  121. 121.

    & Preleukemia: one name, many meanings. Leukemia 31, 534–542 (2017).

  122. 122.

    et al. Adverse genomic alterations and stemness features are induced by field cancerization in the microenvironment of hepatocellular carcinomas. Oncotarget 8, 48688–48700 (2017).

  123. 123.

    , & Evidence for field effect cancerization in colorectal cancer. Genomics 103, 211–221 (2014).

  124. 124.

    et al. Molecular etiology of second primary tumors in contralateral tonsils of human papillomavirus-associated index tonsillar carcinomas. Oral Oncol. 49, 244–248 (2013).

  125. 125.

    , & Evidence for field cancerization of the prostate. Prostate 69, 1470–1479 (2009).

  126. 126.

    et al. Clonality and field cancerization in intraductal papillary-mucinous tumors of the pancreas. Cancer 92, 1807–1817 (2001).

  127. 127.

    et al. Determining the origin of synchronous multifocal bladder cancer by exome sequencing. BMC Cancer 15, 871 (2015).

  128. 128.

    et al. Molecular determinants of tumor recurrence in the urinary bladder. Future Oncol. 5, 843–857 (2009).

  129. 129.

    et al. Molecular evidence supporting field effect in urothelial carcinogenesis. Clin. Cancer Res. 11, 6512–6519 (2005).

  130. 130.

    et al. 2-Hydroxyglutarate in IDH mutant acute myeloid leukemia: predicting patient responses, minimal residual disease and correlations with methylcytosine and hydroxymethylcytosine levels. Leuk. Lymphoma 54, 408–410 (2013).

  131. 131.

    et al. Single cells from human primary colorectal tumors exhibit polyfunctional heterogeneity in secretions of ELR+ CXC chemokines. Integr. Biol. 5, 1272–1281 (2013).

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Acknowledgements

The authors thank C. Love and M. Stevens for thoughtful review and comments. D.M.W. and S.R.M. are supported by the Koch Institute–Dana-Farber/Harvard Cancer Center Bridge Project, the National Cancer Institute (NCI) R33 CA191143 and the NCI Cancer Systems Biology Consortium U54 CA217377. D.M.W. is a Leukaemia and Lymphoma Society Scholar. M.A.M. is supported by NCI K08 CA212252.

Author information

Affiliations

  1. Marlise R. Luskin and Mark A. Murakami are at the Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.

    • Marlise R. Luskin
    •  & Mark A. Murakami
  2. Scott R. Manalis is at the Koch Institute for Integrative Cancer Research and the Departments of Biological Engineering and Mechanical Engineering at the Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

    • Scott R. Manalis
  3. David M. Weinstock is at the Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA, and at the Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA.

    • David M. Weinstock

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Contributions

M.R.L, M.A.M., S.R.M. and D.M.W. researched data for the article, substantially contributed to discussion of content, wrote the article and reviewed and edited the manuscript before submission.

Competing interests

D.M.W. declares that he is a consultant for and receives research funding from Novartis and is a founder of Travera. S.R.M. declares that he is a founder of Affinity Biosensors and a founder and scientific adviser of Travera. The other authors declare no competing interests.

Corresponding authors

Correspondence to Scott R. Manalis or David M. Weinstock.