Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 144

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Molecular tumour boards — current and future considerations for precision oncology

Tsimberidou, Apostolia M., Kahle, Michael, Vo, Henry Hiep, Baysal, Mehmet A., Johnson, Amber, Meric-Bernstam, Funda·2023·Nature Reviews Clinical Oncology

Abstract

Over the past 15 years, rapid progress has been made in developmental therapeutics, especially regarding the use of matched targeted therapies against specific oncogenic molecular alterations across cancer types. Molecular tumour boards (MTBs) are panels of expert physicians, scientists, health-care providers and patient advocates who review and interpret molecular-profiling results for individual patients with cancer and match each patient to available therapies, which can include investigational drugs. Interpretation of the molecular alterations found in each patient is a complicated task that requires an understanding of their contextual functional effects and their correlations with sensitivity or resistance to specific treatments. The criteria for determining the actionability of molecular alterations and selecting matched treatments are constantly evolving. Therefore, MTBs have an increasingly necessary role in optimizing the allocation of biomarker-directed therapies and the implementation of precision oncology. Ultimately, increased MTB availability, accessibility and performance are likely to improve patient care. The challenges faced by MTBs are increasing, owing to the plethora of identifiable molecular alterations and immune markers in tumours of individual patients and their evolving clinical significance as more and more data on patient outcomes and results from clinical trials become available. Beyond next-generation sequencing, broader biomarker analyses can p…

Overview

Integrity signals

Registry

Crossref · 10.1038/s41571-023-00824-4

Cites

28 of 28 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Research works printed but not linked: 167

  1. Tsimberidou, A. M. et al. Initiative for Molecular Profiling and Advanced Cancer Therapy (IMPACT): an MD Anderson Precision Medicine Study. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00002 (2017). · DOI
  2. Christofyllakis, K. et al. Cost-effectiveness of precision cancer medicine — current challenges in the use of next generation sequencing for comprehensive tumour genomic profiling and the role of clinical utility frameworks (Review). Mol. Clin. Oncol. 16, 21 (2022). · DOI
  3. 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). · DOI
  4. 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). · DOI
  5. Tsimberidou, A. M. 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). · DOI
  6. Tsimberidou, A. M. et al. Long-term overall survival and prognostic score predicting survival: the IMPACT study in precision medicine. J. Hematol. Oncol. 12, 145 (2019). · DOI
  7. Larson, K. L. et al. Clinical outcomes of molecular tumor boards: a systematic review. JCO Precis. Oncol. https://doi.org/10.1200/PO.20.00495 (2021). · DOI
  8. Luthra, R. et al. A targeted high-throughput next-generation sequencing panel for clinical screening of mutations, gene amplifications, and fusions in solid tumors. J. Mol. Diagn. 19, 255–264 (2017). · DOI
  9. Lih, C. J. et al. Analytical validation of the next-generation sequencing assay for a nationwide signal-finding clinical trial: molecular analysis for therapy choice clinical trial. J. Mol. Diagn. 19, 313–327 (2017). · DOI
  10. Goswami, R. S. et al. Identification of factors affecting the success of next-generation sequencing testing in solid tumors. Am. J. Clin. Pathol. 145, 222–237 (2016). · DOI
  11. Ramani, N. S. et al. Factors impacting clinically relevant RNA fusion assays using next-generation sequencing. Arch. Pathol. Lab. Med. 145, 1405–1412 (2021). · DOI
  12. Jennings, L. J. et al. Guidelines for validation of next-generation sequencing-based oncology panels: a Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists. J. Mol. Diagn. 19, 341–365 (2017). · DOI
  13. 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). · DOI
  14. 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). · DOI
  15. Galuppini, F. et al. Tumor mutation burden: from comprehensive mutational screening to the clinic. Cancer Cell Int. 19, 209 (2019). · DOI
  16. Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019). · DOI · Paper page
  17. Gelsomino, F., Barbolini, M., Spallanzani, A., Pugliese, G. & Cascinu, S. The evolving role of microsatellite instability in colorectal cancer: a review. Cancer Treat. Rev. 51, 19–26 (2016). · DOI
  18. Le, D. T. et al. Phase II open-label study of pembrolizumab in treatment-refractory, microsatellite instability-high/mismatch repair-deficient metastatic colorectal cancer: KEYNOTE-164. J. Clin. Oncol. 38, 11–19 (2020). · DOI · Paper page
  19. Le, D. T. et al. Pembrolizumab for previously treated, microsatellite instability-high/mismatch repair-deficient advanced colorectal cancer: final analysis of KEYNOTE-164. Eur. J. Cancer 186, 185–195 (2023). · DOI
  20. O’Malley, D. M. et al. Pembrolizumab in patients with microsatellite instability-high advanced endometrial cancer: results from the KEYNOTE-158 study. J. Clin. Oncol. 40, 752–761 (2022). · DOI
  21. Yoshino, T. et al. Pembrolizumab in Asian patients with microsatellite-instability-high/mismatch-repair-deficient colorectal cancer. Cancer Sci. 114, 1026–1036 (2023). · DOI
  22. Oaknin, A. et al. Safety and antitumor activity of dostarlimab in patients with advanced or recurrent DNA mismatch repair deficient/microsatellite instability-high (dMMR/MSI-H) or proficient/stable (MMRp/MSS) endometrial cancer: interim results from GARNET-a phase I, single-arm study. J. Immunother. Cancer 10, e003777 (2022). · DOI
  23. Oaknin, A. et al. Clinical activity and safety of the anti-programmed death 1 monoclonal antibody dostarlimab for patients with recurrent or advanced mismatch repair-deficient endometrial cancer: a nonrandomized phase 1 clinical trial. JAMA Oncol. 6, 1766–1772 (2020). · DOI
  24. Oaknin, A. et al. Safety, efficacy, and biomarker analyses of dostarlimab in patients with endometrial cancer: interim results of the phase I GARNET study. Clin. Cancer Res. https://doi.org/10.1158/1078-0432.CCR-22-3915 (2023). · DOI
  25. Lebofsky, R. et al. Circulating tumor DNA as a non-invasive substitute to metastasis biopsy for tumor genotyping and personalized medicine in a prospective trial across all tumor types. Mol. Oncol. 9, 783–790 (2015). · DOI
  26. Normanno, N., Cervantes, A., Ciardiello, F., De Luca, A. & Pinto, C. The liquid biopsy in the management of colorectal cancer patients: current applications and future scenarios. Cancer Treat. Rev. 70, 1–8 (2018). · DOI
  27. Polivka, J. Jr., Pesta, M. & Janku, F. Testing for oncogenic molecular aberrations in cell-free DNA-based liquid biopsies in the clinic: are we there yet. Expert Rev. Mol. Diagn. 15, 1631–1644 (2015). · DOI
  28. Fleischhacker, M. & Schmidt, B. Circulating nucleic acids (CNAs) and cancer — a survey. Biochim. Biophys. Acta 1775, 181–232 (2007).
  29. Suraj, S., Dhar, C. & Srivastava, S. Circulating nucleic acids: an analysis of their occurrence in malignancies. Biomed. Rep. 6, 8–14 (2017). · DOI
  30. Janku, F. et al. Development and validation of an ultradeep next-generation sequencing assay for testing of plasma cell-free DNA from patients with advanced cancer. Clin. Cancer Res. 23, 5648–5656 (2017). · DOI
  31. Said, R., Guibert, N., Oxnard, G. R. & Tsimberidou, A. M. Circulating tumor DNA analysis in the era of precision oncology. Oncotarget 11, 188–211 (2020). · DOI
  32. Sanchez, N. S. et al. Identification of actionable genomic alterations using circulating cell-free DNA. JCO Precis. Oncol. https://doi.org/10.1200/PO.19.00017 (2019). · DOI
  33. Laufer-Geva, S. et al. The clinical impact of comprehensive genomic testing of circulating cell-free DNA in advanced lung cancer. J. Thorac. Oncol. 13, 1705–1716 (2018). · DOI
  34. Coombs, C. C. et al. Identification of clonal hematopoiesis mutations in solid tumor patients undergoing unpaired next-generation sequencing assays. Clin. Cancer Res. 24, 5918–5924 (2018). · DOI
  35. Bauml, J. & Levy, B. Clonal hematopoiesis: a new layer in the liquid biopsy story in lung cancer. Clin. Cancer Res. 24, 4352–4354 (2018). · DOI
  36. Jensen, K. et al. Association of clonal hematopoiesis in DNA repair genes with prostate cancer plasma cell-free DNA testing interference. JAMA Oncol. 7, 107–110 (2021). · DOI
  37. Merker, J. D. et al. Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. J. Clin. Oncol. 36, 1631–1641 (2018). · DOI · Paper page
  38. Coombs, C. C. et al. Therapy-related clonal hematopoiesis in patients with non-hematologic cancers is common and associated with adverse clinical outcomes. Cell Stem Cell 21, 374–382.e4 (2017). · DOI · Paper page
  39. Chan, H. T., Chin, Y. M., Nakamura, Y. & Low, S. K. Clonal hematopoiesis in liquid biopsy: from biological noise to valuable clinical implications. Cancers 12, 2277 (2020). · DOI
  40. Razavi, P. et al. High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants. Nat. Med. 25, 1928–1937 (2019). · DOI
  41. Moore, K. et al. Maintenance olaparib in patients with newly diagnosed advanced ovarian cancer. N. Engl. J. Med. 379, 2495–2505 (2018). · DOI · Paper page
  42. DiSilvestro, P. et al. Overall survival with maintenance olaparib at a 7-year follow-up in patients with newly diagnosed advanced ovarian cancer and a BRCA mutation: the SOLO1/GOG 3004 trial. J. Clin. Oncol. 41, 609–617 (2023). · DOI · Paper page
  43. Banerjee, S. et al. Maintenance olaparib for patients with newly diagnosed advanced ovarian cancer and a BRCA mutation (SOLO1/GOG 3004): 5-year follow-up of a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 22, 1721–1731 (2021). · DOI
  44. Pujade-Lauraine, E. et al. Olaparib tablets as maintenance therapy in patients with platinum-sensitive, relapsed ovarian cancer and a BRCA1/2 mutation (SOLO2/ENGOT-Ov21): a double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Oncol. 18, 1274–1284 (2017). · DOI
  45. Poveda, A. et al. Olaparib tablets as maintenance therapy in patients with platinum-sensitive relapsed ovarian cancer and a BRCA1/2 mutation (SOLO2/ENGOT-Ov21): a final analysis of a double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Oncol. 22, 620–631 (2021). · DOI
  46. Yamauchi, H. et al. Adjuvant olaparib in the subset of patients from Japan with BRCA1- or BRCA2-mutated high-risk early breast cancer from the phase 3 OlympiA trial. Breast Cancer 30, 596–605 (2023). · DOI · Paper page
  47. Robson, M. et al. Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N. Engl. J. Med. 377, 523–533 (2017). · DOI · Paper page
  48. Im, S. A. et al. Olaparib monotherapy for Asian patients with a germline BRCA mutation and HER2-negative metastatic breast cancer: OlympiAD randomized trial subgroup analysis. Sci. Rep. 10, 8753 (2020). · DOI
  49. Hussain, M. et al. Survival with olaparib in metastatic castration-resistant prostate cancer. N. Engl. J. Med. 383, 2345–2357 (2020). · DOI · Paper page
  50. Matsubara, N. et al. Olaparib efficacy in patients with metastatic castration-resistant prostate cancer and BRCA1, BRCA2, or ATM alterations identified by testing circulating tumor DNA. Clin. Cancer Res. 29, 92–99 (2023). · DOI
  51. Matsubara, N. et al. Olaparib in patients with mCRPC with homologous recombination repair gene alterations: PROfound Asian subset analysis. Jpn. J. Clin. Oncol. 52, 441–448 (2022). · DOI
  52. Mirza, M. R. et al. Niraparib maintenance therapy in platinum-sensitive, recurrent ovarian cancer. N. Engl. J. Med. 375, 2154–2164 (2016). · DOI
  53. Del Campo, J. M. et al. Niraparib maintenance therapy in patients with recurrent ovarian cancer after a partial response to the last platinum-based chemotherapy in the ENGOT-OV16/NOVA trial. J. Clin. Oncol. 37, 2968–2973 (2019). · DOI
  54. Coleman, R. L. et al. Rucaparib maintenance treatment for recurrent ovarian carcinoma after response to platinum therapy (ARIEL3): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 390, 1949–1961 (2017). · DOI
  55. O’Malley, D. M. et al. Clinical and molecular characteristics of ARIEL3 patients who derived exceptional benefit from rucaparib maintenance treatment for high-grade ovarian carcinoma. Gynecol. Oncol. 167, 404–413 (2022). · DOI
  56. Ledermann, J. A. et al. Rucaparib for patients with platinum-sensitive, recurrent ovarian carcinoma (ARIEL3): post-progression outcomes and updated safety results from a randomised, placebo-controlled, phase 3 trial. Lancet Oncol. 21, 710–722 (2020). · DOI
  57. Abida, W. et al. Rucaparib for the treatment of metastatic castration-resistant prostate cancer associated with a DNA damage repair gene alteration: final results from the phase 2 TRITON2 study. Eur. Urol. 84, 321–330 (2023). · DOI
  58. Chan, H. T. et al. Clinical significance of clonal hematopoiesis in the interpretation of blood liquid biopsy. Mol. Oncol. 14, 1719–1730 (2020). · DOI
  59. Koch, A. et al. Analysis of DNA methylation in cancer: location revisited. Nat. Rev. Clin. Oncol. 15, 459–466 (2018). · DOI · Paper page
  60. Xu, L. et al. Long-read sequencing identifies novel structural variations in colorectal cancer. PLoS Genet. 19, e1010514 (2023). · DOI
  61. Fisher, C. L., Dillon, R., Anguita, E., Morris-Rosendahl, D. J. & Awan, A. R. A novel bead-capture nanopore sequencing method for large structural rearrangement detection in cancer. J. Mol. Diagn. 24, 1264–1278 (2022). · DOI
  62. Chen, Y. et al. Gene fusion detection and characterization in long-read cancer transcriptome sequencing data with FusionSeeker. Cancer Res. 83, 28–33 (2023). · DOI
  63. von Bubnoff, N., Schneller, F., Peschel, C. & Duyster, J. BCR-ABL gene mutations in relation to clinical resistance of Philadelphia-chromosome-positive leukaemia to STI571: a prospective study. Lancet 359, 487–491 (2002). · DOI
  64. Goyal, L. et al. Polyclonal secondary FGFR2 mutations drive acquired resistance to FGFR inhibition in patients with FGFR2 fusion-positive cholangiocarcinoma. Cancer Discov. 7, 252–263 (2017). · DOI
  65. Katayama, R. et al. Mechanisms of acquired crizotinib resistance in ALK-rearranged lung cancers. Sci. Transl. Med. 4, 120ra117 (2012). · DOI
  66. Kong, L. L. et al. Structural pharmacological studies on EGFR T790M/C797S. Biochem. Biophys. Res. Commun. 488, 266–272 (2017). · DOI
  67. Uchibori, K. et al. Brigatinib combined with anti-EGFR antibody overcomes osimertinib resistance in EGFR-mutated non-small-cell lung cancer. Nat. Commun. 8, 14768 (2017). · DOI
  68. Yu, H. A. et al. Acquired resistance of EGFR-mutant lung cancer to a T790M-specific EGFR inhibitor: emergence of a third mutation (C797S) in the EGFR tyrosine kinase domain. JAMA Oncol. 1, 982–984 (2015). · DOI
  69. Song, H. N. et al. Acquired C797S mutation upon treatment with a T790M-specific third-generation EGFR inhibitor (HM61713) in non-small cell lung cancer. J. Thorac. Oncol. 11, e45–47 (2016). · DOI
  70. Weigelt, B. et al. Diverse BRCA1 and BRCA2 reversion mutations in circulating cell-free DNA of therapy-resistant breast or ovarian cancer. Clin. Cancer Res. 23, 6708–6720 (2017). · DOI
  71. Pettitt, S. J. et al. Clinical BRCA1/2 reversion analysis identifies hotspot mutations and predicted neoantigens associated with therapy resistance. Cancer Discov. 10, 1475–1488 (2020). · DOI
  72. Lin, K. K. et al. BRCA reversion mutations in circulating tumor DNA predict primary and acquired resistance to the PARP inhibitor rucaparib in high-grade ovarian carcinoma. Cancer Discov. 9, 210–219 (2019). · DOI
  73. Shroff, R. T. et al. Rucaparib monotherapy in patients with pancreatic cancer and a known deleterious BRCA mutation. JCO Precis. Oncol. 2018, 00316 (2018).
  74. Afghahi, A. et al. Tumor BRCA1 reversion mutation arising during neoadjuvant platinum-based chemotherapy in triple-negative breast cancer is associated with therapy resistance. Clin. Cancer Res. 23, 3365–3370 (2017). · DOI
  75. Tobalina, L., Armenia, J., Irving, E., O’Connor, M. J. & Forment, J. V. A meta-analysis of reversion mutations in BRCA genes identifies signatures of DNA end-joining repair mechanisms driving therapy resistance. Ann. Oncol. 32, 103–112 (2021). · DOI
  76. Zong, H. et al. Comprehensive analysis of somatic reversion mutations in homologous recombination repair (HRR) genes in a large cohort of Chinese pan-cancer patients. J. Cancer 13, 1119–1129 (2022). · DOI
  77. Hilton, J. et al. Results of the phase I CCTG IND.231 trial of CX-5461 in patients with advanced solid tumors enriched for DNA-repair deficiencies. Nat. Commun. 13, 3607 (2022). · DOI · Paper page
  78. Leonetti, A. et al. Resistance mechanisms to osimertinib in EGFR-mutated non-small cell lung cancer. Br. J. Cancer 121, 725–737 (2019). · DOI
  79. Tsimberidou, A. M. et al. Phase 1 study of M2698, a p70S6K/AKT dual inhibitor, in patients with advanced cancer. J. Hematol. Oncol. 14, 127 (2021). · DOI
  80. Meric-Bernstam, F. et al. A decision support framework for genomically informed investigational cancer therapy. J. Natl Cancer Inst. 107, djv098 (2015). · DOI
  81. Johnson, A. et al. Clinical use of precision oncology decision support. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00036 (2017). · DOI
  82. Kurnit, K. C. et al. ‘Personalized cancer therapy’: a publicly available precision oncology resource. Cancer Res. 77, e123–e126 (2017). · DOI
  83. Zeng, J. et al. OCTANE: oncology clinical trial annotation engine. JCO Clin. Cancer Inf. 3, 1–11 (2019).
  84. Zeng, J. et al. Operationalization of next-generation sequencing and decision support for precision oncology. JCO Clin. Cancer Inf. 3, 1–12 (2019).
  85. Mangat, P. K. et al. Rationale and design of the Targeted Agent and Profiling Utilization Registry (TAPUR) study. JCO Precis. Oncol. https://doi.org/10.1200/PO.18.00122 (2018). · DOI
  86. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00011 (2017). · DOI
  87. Patterson, S. E., Statz, C. M., Yin, T. & Mockus, S. M. Utility of the JAX Clinical Knowledgebase in capture and assessment of complex genomic cancer data. npj Precis. Oncol. 3, 2 (2019). · DOI
  88. Tamborero, D. et al. The Molecular Tumor Board Portal supports clinical decisions and automated reporting for precision oncology. Nat. Cancer 3, 251–261 (2022). · DOI
  89. 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). · DOI
  90. Van Allen, E. M. 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). · DOI
  91. Andre, F. et al. Prioritizing targets for precision cancer medicine. Ann. Oncol. 25, 2295–2303 (2014). · DOI
  92. Mateo, J. et al. A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT). Ann. Oncol. 29, 1895–1902 (2018). · DOI
  93. Leichsenring, J. et al. Variant classification in precision oncology. Int. J. Cancer 145, 2996–3010 (2019). · DOI
  94. Sicklick, J. K. et al. Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nat. Med. 25, 744–750 (2019). · DOI
  95. Jain, N. M., Culley, A., Micheel, C. M., Osterman, T. J. & Levy, M. A. Learnings from precision clinical trial matching for oncology patients who received NGS testing. JCO Clin. Cancer Inf. 5, 231–238 (2021). · DOI
  96. Meropol, N. J. et al. Barriers to clinical trial participation as perceived by oncologists and patients. J. Natl Compr. Cancer Netw. 5, 655–664 (2007).
  97. Mills, E. J. et al. Barriers to participation in clinical trials of cancer: a meta-analysis and systematic review of patient-reported factors. Lancet Oncol. 7, 141–148 (2006). · DOI
  98. St Germain, D. C. & McCaskill-Stevens, W. Use of a clinical trial screening tool to enhance patient accrual. Cancer 127, 1630–1637 (2021). · DOI
  99. Unger, J. M., Cook, E., Tai, E. & Bleyer, A. The role of clinical trial participation in cancer research: barriers, evidence, and strategies. Am. Soc. Clin. Oncol. Educ. Book. 35, 185–198 (2016). · DOI · Paper page
  100. Unger, J. M. et al. ‘When offered to participate’: a systematic review and meta-analysis of patient agreement to participate in cancer clinical trials. J. Natl Cancer Inst. 113, 244–257 (2021). · DOI
  101. Fountzilas, E., Said, R. & Tsimberidou, A. M. Expanded access to investigational drugs: balancing patient safety with potential therapeutic benefits. Expert Opin. Investig. Drugs 27, 155–162 (2018). · DOI
  102. Hull, R. et al. Cervical cancer in low and middle-income countries. Oncol. Lett. 20, 2058–2074 (2020). · DOI
  103. Knaul, F. M., Bhadelia, A., Atun, R. & Frenk, J. Achieving effective universal health coverage and diagonal approaches to care for chronic illnesses. Health Aff. 34, 1514–1522 (2015). · DOI
  104. Bharadwaj, M., Vallurupalli, M. & Huang, F. W. Global precision oncology: a call to action on expanding access to targeted cancer therapies. Oncologist 26, 353–355 (2021). · DOI
  105. Nelson, A. M., Milner, D. A., Rebbeck, T. R. & Iliyasu, Y. Oncologic care and pathology resources in Africa: survey and recommendations. J. Clin. Oncol. 34, 20–26 (2016). · DOI
  106. Lopez, M. S. et al. Cervical cancer prevention and treatment in Latin America. J. Surg. Oncol. 115, 615–618 (2017). · DOI
  107. Brandao, M. et al. Survival impact and cost–effectiveness of a multidisciplinary tumor board for breast cancer in Mozambique, Sub-Saharan Africa. Oncologist 26, e996–e1008 (2021). · DOI
  108. Incorvaia, L., Russo, A. & Cinieri, S. The molecular tumor board: a tool for the governance of precision oncology in the real world. Tumori 108, 288–290 (2022). · DOI
  109. Erdmann, J. All aboard: will molecular tumor boards help cancer patients? Nat. Med. 21, 655–656 (2015). · DOI
  110. Mosele, F. et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. Ann. Oncol. 31, 1491–1505 (2020). · DOI
  111. Alva, A. S. et al. Pembrolizumab in patients with metastatic breast cancer with high tumor mutational burden: results from the Targeted Agent and Profiling Utilization Registry (TAPUR) Study. J. Clin. Oncol. 39, 2443–2451 (2021). · DOI
  112. Ahn, E. R. et al. Palbociclib in patients with non-small-cell lung cancer with CDKN2A alterations: results from the targeted agent and profiling utilization registry study. JCO Precis. Oncol. 4, 757–766 (2020). · DOI
  113. Song, I. W. et al. Precision oncology: evolving clinical trials across tumor types. Cancers 15, 1967 (2023). · DOI
  114. Vo, H. H. et al. Challenges and opportunities associated with the MD Anderson IMPACT2 randomized study in precision oncology. npj Precis. Oncol. 6, 78 (2022). · DOI
  115. Tsimberidou, A. M. et al. Precision medicine: preliminary results from the Initiative for Molecular Profiling and Advanced Cancer Therapy 2 (IMPACT2) study. npj Precis. Oncol. 5, 21 (2021). · DOI
  116. Tredan, O. et al. Molecular screening program to select molecular-based recommended therapies for metastatic cancer patients: analysis from the ProfiLER trial. Ann. Oncol. 30, 757–765 (2019). · DOI
  117. Bonneville-Levard, A. et al. Molecular profile to guide personalized medicine in adult patients with primary brain tumors: results from the ProfiLER trial. Med. Oncol. 39, 4 (2021). · DOI
  118. Varnier, R. et al. Actionable molecular alterations in advanced gynaecologic malignancies: updated results from the ProfiLER programme. Eur. J. Cancer 118, 156–165 (2019). · DOI
  119. Tredan O, P. D. et al. Increasing targeted therapy options for patients with relapsed cancer with broader somatic gene panel analysis from the primary tumor: the Profiler02 randomized phase II trial. J. Clin. Oncol. 40, 1104659 (2022). · DOI
  120. Flaherty, K. T. et al. The molecular analysis for therapy choice (NCI-MATCH) trial: lessons for genomic trial design. J. Natl Cancer Inst. 112, 1021–1029 (2020). · DOI
  121. Basse, C. et al. Relevance of a molecular tumour board (MTB) for patients’ enrolment in clinical trials: experience of the Institut Curie. ESMO Open. 3, e000339 (2018). · DOI
  122. Tao, J. J. et al. Real-world outcomes of an automated physician support system for genome-driven oncology. JCO Precis. Oncol. https://doi.org/10.1200/PO.19.00066 (2019). · DOI
  123. Pagel, K. A. et al. Integrated informatics analysis of cancer-related variants. JCO Clin. Cancer Inf. 4, 310–317 (2020). · DOI
  124. Reardon, B. et al. Integrating molecular profiles into clinical frameworks through the molecular oncology almanac to prospectively guide precision oncology. Nat. Cancer 2, 1102–1112 (2021). · DOI
  125. Farhangfar, C. J. et al. Impact of a clinical genomics program on trial accrual for targeted treatments: practical approach overcoming barriers to accrual for underserved patients. JCO Clin. Cancer Inf. 6, e2200011 (2022).
  126. 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). · DOI
  127. Rodon, J. et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nat. Med. 25, 751–758 (2019). · DOI
  128. Lamping, M. et al. Support of a molecular tumour board by an evidence-based decision management system for precision oncology. Eur. J. Cancer 127, 41–51 (2020). · DOI
  129. Moore, D. A. et al. Prospective analysis of 895 patients on a UK Genomics Review Board. ESMO Open 4, e000469 (2019). · DOI
  130. Rothwell, D. G. et al. Utility of ctDNA to support patient selection for early phase clinical trials: the TARGET study. Nat. Med. 25, 738–743 (2019). · DOI
  131. Righi, M. L. V., Martinez, P., Silva, A., Umpierrez, C. & Rodriguez, R. Oncotherapy: a decision support system to validate oncological treatments. Stud. Health Technol. Inf. 264, 1799–1800 (2019).
  132. Tamborero, D. et al. Support systems to guide clinical decision-making in precision oncology: the Cancer Core Europe Molecular Tumor Board Portal. Nat. Med. 26, 992–994 (2020). · DOI
  133. Andre, F. et al. Genomics to select treatment for patients with metastatic breast cancer. Nature 610, 343–348 (2022). · DOI
  134. Sadik, H. et al. Impact of clinical practice gaps on the implementation of personalized medicine in advanced non-small-cell lung cancer. JCO Precis. Oncol. 6, e2200246 (2022). · DOI
  135. Kim, E. S. et al. Continuing to broaden eligibility criteria to make clinical trials more representative and inclusive: ASCO-Friends of Cancer Research Joint Research Statement. Clin. Cancer Res. 27, 2394–2399 (2021). · DOI
  136. Love, T. M., Anaya, D. A., Prime, M. S., Ardolino, L. & Ekinci, O. Development and validation of ACTE-MTB: a tool to systematically assess the maturity of molecular tumor boards. PLoS ONE 17, e0268477 (2022). · DOI
  137. Zeng, J. & Shufean, M. A. Molecular-based precision oncology clinical decision making augmented by artificial intelligence. Emerg. Top. Life Sci. 5, 757–764 (2021). · DOI
  138. Ballester, P. J. & Carmona, J. Artificial intelligence for the next generation of precision oncology. npj Precis. Oncol. 5, 79 (2021). · DOI
  139. Azuaje, F. Artificial intelligence for precision oncology: beyond patient stratification. npj Precis. Oncol. 3, 6 (2019). · DOI
  140. Bhalla, S. & Lagana, A. Artificial intelligence for precision oncology. Adv. Exp. Med. Biol. 1361, 249–268 (2022). · DOI
  141. Hernandez-Boussard, T. et al. Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nat. Med. 27, 2065–2066 (2021). · DOI · Paper page
  142. Bodenmiller, B. Multiplexed epitope-based tissue imaging for discovery and healthcare applications. Cell Syst. 2, 225–238 (2016). · DOI
  143. Mateo, J. et al. Delivering precision oncology to patients with cancer. Nat. Med. 28, 658–665 (2022). · DOI
  144. O’Regan, P. et al. Digital ECMT cancer trial matching tool: an open source research application to support oncologists in the identification of precision medicine clinical trials. JCO Clin. Cancer Inf. 7, e2200137 (2023).
  145. Xu, Q. et al. OncoPubMiner: a platform for mining oncology publications. Brief. Bioinform. 23, bbac383 (2022). · DOI
  146. Bosserman, L. D. et al. Pyramidal decision support framework leverages subspecialty expertise across enterprise to achieve superior cancer outcomes and personalized, precision care plans. J. Clin. Med. 11, 6738 (2022). · DOI
  147. Kurz, N. S. et al. Identifying actionable variants in cancer — the dual web and batch processing tool MTB-Report. Stud. Health Technol. Inf. 296, 73–80 (2022).
  148. de Andrade, K. C. et al. The TP53 database: transition from the International Agency for Research on Cancer to the US National Cancer Institute. Cell Death Differ. 29, 1071–1073 (2022). · DOI
  149. UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021). · DOI · Paper page
  150. Chunn, L. M. et al. Mastermind: a comprehensive genomic association search engine for empirical evidence curation and genetic variant interpretation. Front. Genet. 11, 577152 (2020). · DOI
  151. Xu, Q. et al. OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level. BMC Cancer 20, 740 (2020). · DOI
  152. Gudmundsson, S. et al. Variant interpretation using population databases: lessons from gnomAD. Hum. Mutat. 43, 1012–1030 (2022). · DOI
  153. Cline, M. S. et al. BRCA challenge: BRCA exchange as a global resource for variants in BRCA1 and BRCA2. PLoS Genet. 14, e1007752 (2018). · DOI
  154. Patterson, S. E. et al. The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies. Hum. Genomics 10, 4 (2016). · DOI
  155. Bayle, A. et al. Clinical utility of circulating tumor DNA sequencing with a large panel: a National Center for Precision Medicine (PRISM) study. Ann. Oncol. 34, 389–396 (2023). · DOI
  156. Hlevnjak, M. et al. CATCH: a prospective precision oncology trial in metastatic breast cancer. JCO Precis. Oncol. https://doi.org/10.1200/PO.20.00248 (2021). · DOI
  157. Irmisch, A. et al. The tumor profiler study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell 39, 288–293 (2021). · DOI
  158. Conley, B. A. & Doroshow, J. H. Molecular analysis for therapy choice: NCI MATCH. Semin. Oncol. 41, 297–299 (2014). · DOI
  159. Keller, R. B. et al. Programmatic precision oncology decision support for patients with gastrointestinal cancer. JCO Precis. Oncol. 7, e2200342 (2023). · DOI
  160. Cannon, T. L. et al. Patient attendance at molecular tumor board: a new means of shared decision making. Curr. Probl. Cancer 46, 100860 (2022). · DOI
  161. Le Tourneau, C. et al. An approach to solving the complex clinicogenomic data landscape in precision oncology: learnings from the design of WAYFIND-R, a global precision oncology registry. JCO Precis. Oncol. 6, e2200019 (2022). · DOI
  162. Heinrich, K. et al. Lessons learned: the first consecutive 1000 patients of the CCCMunich(LMU) Molecular Tumor Board. J. Cancer Res. Clin. Oncol. 149, 1905–1915 (2023). · DOI
  163. Thouvenin, J. et al. PRECISION: the Belgian molecular profiling program of metastatic cancer for clinical decision and treatment assignment. ESMO Open. 7, 100524 (2022). · DOI
  164. Park, K. H. et al. Genomic landscape and clinical utility in Korean advanced pan-cancer patients from prospective clinical sequencing: K-MASTER program. Cancer Discov. 12, 938–948 (2022). · DOI
  165. Malani, D. et al. Implementing a functional precision medicine tumor board for acute myeloid leukemia. Cancer Discov. 12, 388–401 (2022). · DOI · Paper page
  166. Lin, F. P. et al. Criteria-based curation of a therapy-focused compendium to support treatment recommendations in precision oncology. npj Precis. Oncol. 5, 58 (2021). · DOI
  167. Jain, N. M. et al. Framework for implementing and tracking a molecular tumor board at a National Cancer Institute-Designated Comprehensive Cancer Center. Oncologist 26, e1962–e1970 (2021). · DOI

Cited by

See all 144

Metadata

Authors
Tsimberidou, A. M., Kahle, M., Vo, H. H., Baysal, M. A., Johnson, A., & Meric-Bernstam, F.
Venue
Nature Reviews Clinical Oncology
Published
2023-10-16
Volume
20
Issue
12
Pages
843-863
ISSN
  • 1759-4774
  • 1759-4782
Cited by
144
References
28
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.