Alejandro Araya1, Jia Zeng2, Amber Johnson2, Md Abu Shufean3, Jordi Rodon2, Funda Meric-Bernstam2, Elmer V Bernstam4. 1. The University of Texas School of Biomedical Informatics, Houston, TX, USA. 2. Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 3. Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 4. The University of Texas School of Biomedical Informatics, Houston, TX, USA; Division of General Internal Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, TX, USA. Electronic address: Elmer.V.Bernstam@uth.tmc.edu.
Abstract
PURPOSE: Genomic analysis of individual patients is now affordable, and therapies targeting specific molecular aberrations are being tested in clinical trials. Genomically-informed therapy is relevant to many clinical domains, but is particularly applicable to cancer treatment. However, even specialized clinicians need help to interpret genomic data, to navigate the complicated space of clinical trials, and to keep up with the rapidly expanding biomedical literature. To quantitate the cognitive load on treating clinicians, we attempt to quantitate the rate of change in potential treatment options for patients considering genomically-relevant and genomically-selected therapy for cancer. MATERIALS AND METHODS: To this end, we analyzed patient-specific reports generated by a precision oncology decision support team (PODS) at a large academic cancer center. Two types of potential treatment options were analyzed: FDA-approved genomically-relevant and genomically-selected therapies and therapies available via clinical trials. We focused on two clinically-actionable alterations: ERBB2 (Her2/neu; amplified vs. non-amplified) and BRAF mutation (V600 vs. non-V600). To determine changes in available treatment options, we grouped patients into similar groups by disease site (ERBB2: breast, gastric and "other"; BRAF: melanoma, non-melanoma). RESULTS: A total of 2927 reports for 2366 unique patients were generated 8/2016-12/2018. Reports included 9902 gene variants and 150 disease classifications. BRAF mutation and ERBB2 amplification were annotated with therapeutic options in 270 reports (225 unique patients). The median survival time of a therapeutic option was nine months. CONCLUSION: When compared to "traditional" clinical practice guideline recommendations, treatment options for personalized cancer therapy change seven times more rapidly; partly due to change in knowledge and partly due to logistics such as clinical trial availability.
PURPOSE: Genomic analysis of individual patients is now affordable, and therapies targeting specific molecular aberrations are being tested in clinical trials. Genomically-informed therapy is relevant to many clinical domains, but is particularly applicable to cancer treatment. However, even specialized clinicians need help to interpret genomic data, to navigate the complicated space of clinical trials, and to keep up with the rapidly expanding biomedical literature. To quantitate the cognitive load on treating clinicians, we attempt to quantitate the rate of change in potential treatment options for patients considering genomically-relevant and genomically-selected therapy for cancer. MATERIALS AND METHODS: To this end, we analyzed patient-specific reports generated by a precision oncology decision support team (PODS) at a large academic cancer center. Two types of potential treatment options were analyzed: FDA-approved genomically-relevant and genomically-selected therapies and therapies available via clinical trials. We focused on two clinically-actionable alterations: ERBB2 (Her2/neu; amplified vs. non-amplified) and BRAF mutation (V600 vs. non-V600). To determine changes in available treatment options, we grouped patients into similar groups by disease site (ERBB2: breast, gastric and "other"; BRAF: melanoma, non-melanoma). RESULTS: A total of 2927 reports for 2366 unique patients were generated 8/2016-12/2018. Reports included 9902 gene variants and 150 disease classifications. BRAF mutation and ERBB2 amplification were annotated with therapeutic options in 270 reports (225 unique patients). The median survival time of a therapeutic option was nine months. CONCLUSION: When compared to "traditional" clinical practice guideline recommendations, treatment options for personalized cancer therapy change seven times more rapidly; partly due to change in knowledge and partly due to logistics such as clinical trial availability.
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