The oncology clinical space has witnessed a rapid rise in the use of next-generation sequencing in an effort to target specific mutations in tumors. Trials like TAPUR and NCI-MATCH
have been designed to mine this information so patients can be matched to specific trial arms based on these biomarkers. However, identifying “actionable” mutations is a big task and requires decision support tools that clinicians can utilize at the point of care.
A new paper in JCO Precision Oncology
evaluated the impact of the support provided by a decision support team on clinical decision making and trial enrollment. The idea of creating a Precision Oncology Decision Support system (PODS) at the University of Texas MD Anderson Cancer Center came from the absence of access to decision-making tools, in addition to the following factors:
Clinicians are unlikely to use resources that need longer time to retrieve data
Limited information on publicly available resources
The rapid rate of discoveries being made in oncogenomics.
The researchers compiled annotation requests from genomic testing panels that were primarily Clinical Laboratory Improvement Amendments (CLIA)–validated (90.2%) or research panels (9.8%). Genomic alterations were annotated to identify actionable alterations based on knowledge of a gene variant’s known or potential function or therapeutic significance. A Web-based survey was developed that queried the reasons for the absence of genotype-matched therapies.
Overall, the PODS team processed 1669 requests for annotation of 4084 alterations across 49 tumor types for 1197 patients. The team identified 2444 annotations for 669 patients for an actionable variant call: 32.5% actionable, 9.4% potentially actionable, 29.7% unknown, and 28.4% nonactionable. About 66% of the patients in the study were identified as having at least 1 actionable, or potentially actionable, mutation, and 20.6% of patients who were annotated enrolled in a genotype-matched trial.
A significantly greater number (P
<.001) of patients with actionable mutations (27.6%; 92/333) enrolled in clinical trials compared with the number of patients with unknown mutations (11.8%; 16/136) or mutations of nonactionable consequences (3%; 2/66).
The most common genetic alterations that led to enrollment in genotype-matched trials were those in PTEN
(n = 20), PIK3CA
(n = 11), and ERBB2
(n = 10). Further, the researchers found that physicians did not need decision support for evaluating the most common genetic alterations and they acted on well-known oncogenic variants without an interpretation by PODS.
The researchers observed a 20.6% enrollment (110/535) in genotype-matched trials, which they attribute to:
Proactive genotype-matched trials provided by PODS in select reports
A wider portfolio of genotype-matched trials being offered at MD Anderson Cancer Center
E-mail alerts to physicians, which raised their awareness on potential genotype-matched trials for patients with specific alterations.
A decision-support system that marks patients’ molecular profiles was used widely at their research institute, the authors conclude. They add that the information provided in PODS reports yielded more patients enrolled in genotype-matched trials with actionable or potentially actionable alterations than those with unknown or not actionable information.
Johnson A, Khotskaya YB, Brusco L, et al. Clinical use of precision oncology decision support [published online September 13, 2017]. JCO Precision Oncology
. 2017. doi: 10.1200/PO.17.00036.