According to new research, 88% of trials initially matched to patients were eventually classified as false positives when manually screened by a provider.
Automated algorithms that match patients with cancer to clinical trials based on their next-generation sequencing (NGS) testing results carries poor predictive value, claim researchers, who say efforts are needed to alleviate this burden put on the provider.
According to the researchers, 88% of trials initially matched to patients were eventually classified as false positives when manually screened by a provider in their prospective study.
Typically, when a patient gets their NGS report with clinical trial recommendations based on their diagnosis and identified biomarkers, physicians review the report to eliminate any false-positive trials to best determine appropriate trials for their patients. Data has indicated that research nurses can spend between 4 to 9 hours determining a patient’s eligibility for a clinical trial.
“As the cost of sequencing decreases and the clinical focus moves from limited gene-panel testing to whole-exon sequencing, RNA sequencing, and ultimately to whole-genome sequencing, the pool of potentially matching trials—that requires manual review—will expand,” reflected the researchers. “Informatics tools that assist in refining the trials that need to be manually reviewed or suggest potential matches based on the patient’s clinical and biomarker profile may reduce the manual burden of this process and reduce the barrier to trial enrollment.”
Across the 82 patients included in the study, the algorithms curated 808 trials, which were then filtered to 755. Of these, just 87 were considered matched trials following the manual prescreening.
The high rate of false positives identified by the researchers in the study stem from several factors, most often due to administrative details of the trials. For example, patients were often matched to a clinical trial that was either closed to enrollment, did not have openings, was never opened at the study site, or was halted while other arms of the trial continued.
On a global level, data is sometimes not readily be available to the local institution in real time, particularly for phase 1 trials.
“it is important to uncover the factors that result in a high rate of false positives while generating clinical trial recommendations,” explain the researchers. “Such studies can help to drive efficiency and improve the design for future trial matching efforts. Furthermore, such studies should be designed such that trial recommendations are sent to physicians in real time to maximize patient impact and generate enough learnings to inform design of a larger pragmatic study.”
Other factors influencing the false-positive rate included the presence of multiple malignancies.
Jain N, Culley A, Micheel C, Osterman T, Levy M. Learnings from precision clinical trial matching for oncology patients who received NGS testing. JCO Clin Cancer Inform. Published online February 24, 2021. doi:10.1200/CCI.20.00142