Clinical use of precision oncology decision support

Purpose Precision oncology is hindered by the lack of decision support for determining the functional and therapeutic significance of genomic alterations in tumors and relevant clinically available options. To bridge this knowledge gap, we established a Precision Oncology Decision Support team that provides annotations at the alteration level and subsequently determined whether clinical decision making was influenced. Methods Genomic alterations were annotated to determine actionability on the basis of a variant's known or potential functional and/or therapeutic significance.Themedical records of a subset of patients annotated in 2015 were manually reviewed to assess trial enrollment. A Webbased survey was implemented to capture the reasons genotype-matched therapies were not pursued. ResultsThePrecisionOncologyDecision Supportteamprocessed 1,669 requests for annotation of 4,084 alterations (2,254 unique) across 49 tumor types for 1,197 patients. A total of 2,444 annotations for 669 patients included an actionable variant call: 32.5% actionable, 9.4% potentially actionable, 29.7% unknown, and 28.4% nonactionable. Sixty-six percent of patients had at least one actionable/potentially actionable alteration, and 20.6% of patients (110 of 535) annotated enrolled in a genotype-matched trial. Trial enrollment was significantly higher for patients with actionable/potentially actionable alterations (92 of 333; 27.6%) than for those with unknown (16 of 136; 11.8%) and nonactionable (2 of 66; 3%) alterations (P < .001). Actionable alterations in PTEN, PIK3CA, and ERBB2 most frequently led to enrollment in genotypematched trials. Clinicians cited a variety of reasons that patients with actionable alterations did not enroll in trials. Conclusion Over half of alterations annotated were of unknown significance or nonactionable. Physicians were more likely to enroll a patient in a genotype-matched trial when an annotation supported actionability. Future studies are needed to demonstrate the impact of decision support on trial enrollment and oncologic outcomes.