New platform optimizes selection of combination cancer therapies (Free interactive website for anyone to use—no registration needed)

Medical Xpress / University of Texas M. D. Anderson Cancer Center / Cancer Discovery ^ | Apr. 12, 2022 | Anil Korkut, Ph.D. et al

[ConservativeMind]

Researchers have developed a new bioinformatics platform that predicts optimal treatment combinations for a given group of patients based on co-occurring tumor alterations. In retrospective validation studies, the tool selected combinations that resulted in improved patient outcomes across both pre-clinical and clinical studies.

The platform, called REcurrent Features LEveraged for Combination Therapy (REFLECT), integrates machine learning and cancer informatics algorithms to analyze biological tumor features and identify frequent co-occurring alterations that could be targeted by multiple drugs.

"Our ultimate goal is to make precision oncology more effective and create meaningful patient benefit," Korkut said. "We believe REFLECT may be one of the tools that can help overcome some of the current challenges in the field by facilitating both the discovery and the selection of combination therapies matched to the molecular composition of tumors."

Cancer cells frequently rely on co-occurring alterations, such as mutations in two signaling pathways, to drive tumor progression. Increasing evidence suggests that identifying and targeting both alterations simultaneously could increase durable responses, Korkut explained.

Korkut built and used the REFLECT tool to develop a systematic and unbiased approach to match patients with optimal combination therapies.

Within each cohort, the team generated REFLECT signatures of additional alterations that may be actionable therapeutic targets, thus pointing to sub-cohorts that may benefit from specific combination therapies.

In pre-clinical trials with PDX models, REFLECT-matched combinations had a 34.5% decrease in median tumor volume, while non-matched combinations had a 5.1% increase. Similarly, progression-free survival (PFS) was higher with matched combinations.

…Patients in this trial that received combinations predicted by REFLECT to be most beneficial had significantly longer PFS and overall survival compared to other combinations.

The researchers plan to expand their study to better address and predict toxicity from matched drug combinations.

[ConservativeMind]

It’s a pretty technical site, but for those willing to experiment with it, it has downloadable R code for some nice output.

The site should be useful to assure your cancer specialists are taking a better approach, rather than a standard “one size fits all.”