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Programmatic Precision Oncology Decision Support for Patients With Gastrointestinal Cancer.
Keller, Rachel B; Mazor, Tali; Sholl, Lynette; Aguirre, Andrew J; Singh, Harshabad; Sethi, Nilay; Bass, Adam; Nagaraja, Ankur K; Brais, Lauren K; Hill, Emma; Hennessey, Connor; Cusick, Margaret; Del Vecchio Fitz, Catherine; Zwiesler, Zachary; Siegel, Ethan; Ovalle, Andrea; Trukhanov, Pavel; Hansel, Jason; Shapiro, Geoffrey I; Abrams, Thomas A; Biller, Leah H; Chan, Jennifer A; Cleary, James M; Corsello, Steven M; Enzinger, Andrea C; Enzinger, Peter C; Mayer, Robert J; McCleary, Nadine J; Meyerhardt, Jeffrey A; Ng, Kimmie; Patel, Anuj K; Perez, Kimberley J; Rahma, Osama E; Rubinson, Douglas A; Wisch, Jeffrey S; Yurgelun, Matthew B; Hassett, Michael J; MacConaill, Laura; Schrag, Deborah; Cerami, Ethan; Wolpin, Brian M; Nowak, Jonathan A; Giannakis, Marios.
Afiliação
  • Keller RB; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Mazor T; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.
  • Sholl L; Center for Advanced Molecular Diagnostics, Brigham & Women's Hospital & Harvard Medical School, Boston, MA.
  • Aguirre AJ; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Singh H; Broad Institute of Harvard and MIT, Cambridge, MA.
  • Sethi N; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Bass A; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Nagaraja AK; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Brais LK; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Hill E; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Hennessey C; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Cusick M; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Del Vecchio Fitz C; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Zwiesler Z; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.
  • Siegel E; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.
  • Ovalle A; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.
  • Trukhanov P; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.
  • Hansel J; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.
  • Shapiro GI; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.
  • Abrams TA; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Biller LH; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Chan JA; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Cleary JM; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Corsello SM; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Enzinger AC; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Enzinger PC; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Mayer RJ; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • McCleary NJ; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Meyerhardt JA; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Ng K; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Patel AK; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Perez KJ; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Rahma OE; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Rubinson DA; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Wisch JS; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Yurgelun MB; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Hassett MJ; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • MacConaill L; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Schrag D; Center for Advanced Molecular Diagnostics, Brigham & Women's Hospital & Harvard Medical School, Boston, MA.
  • Cerami E; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Wolpin BM; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA.
  • Nowak JA; Department of Medical Oncology, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA.
  • Giannakis M; Center for Advanced Molecular Diagnostics, Brigham & Women's Hospital & Harvard Medical School, Boston, MA.
JCO Precis Oncol ; 7: e2200342, 2023 01.
Article em En | MEDLINE | ID: mdl-36634297
PURPOSE: With the growing number of available targeted therapeutics and molecular biomarkers, the optimal care of patients with cancer now depends on a comprehensive understanding of the rapidly evolving landscape of precision oncology, which can be challenging for oncologists to navigate alone. METHODS: We developed and implemented a precision oncology decision support system, GI TARGET, (Gastrointestinal Treatment Assistance Regarding Genomic Evaluation of Tumors) within the Gastrointestinal Cancer Center at the Dana-Farber Cancer Institute. With a multidisciplinary team, we systematically reviewed tumor molecular profiling for GI tumors and provided molecularly informed clinical recommendations, which included identifying appropriate clinical trials aided by the computational matching platform MatchMiner, suggesting targeted therapy options on or off the US Food and Drug Administration-approved label, and consideration of additional or orthogonal molecular testing. RESULTS: We reviewed genomic data and provided clinical recommendations for 506 patients with GI cancer who underwent tumor molecular profiling between January and June 2019 and determined follow-up using the electronic health record. Summary reports were provided to 19 medical oncologists for patients with colorectal (n = 198, 39%), pancreatic (n = 124, 24%), esophagogastric (n = 67, 13%), biliary (n = 40, 8%), and other GI cancers. We recommended ≥ 1 precision medicine clinical trial for 80% (406 of 506) of patients, leading to 24 enrollments. We recommended on-label and off-label targeted therapies for 6% (28 of 506) and 25% (125 of 506) of patients, respectively. Recommendations for additional or orthogonal testing were made for 42% (211 of 506) of patients. CONCLUSION: The integration of precision medicine in routine cancer care through a dedicated multidisciplinary molecular tumor board is scalable and sustainable, and implementation of precision oncology recommendations has clinical utility for patients with cancer.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Medicina de Precisão / Neoplasias Gastrointestinais Tipo de estudo: Diagnostic_studies / Guideline / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Medicina de Precisão / Neoplasias Gastrointestinais Tipo de estudo: Diagnostic_studies / Guideline / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article