In a groundbreaking study published in Onco (www.mdpi.com/2673-7523/4/3/12), scientists at OncoDxRx leveraged a gene-drug mapping technology to tackle one of the biggest challenges facing cancer researchers: predicting which drugs will most likely to benefit those patients unresponsive to precision medicine.

While scientists recognize that a tumor's genetic composition heavily influences its specific drug response, the vast multitude of mutations found within tumors has made prediction of drug efficacy a challenging prospect.

The new technology, PGA (Patient-derived Gene expression-informed Anticancer drug efficacy), overcomes this barrier by exploring how gene activation pattern collectively influence a tumor's reaction to anticancer drugs. Specifically, they tested their approach on lung cancer tumors, successfully forecasting drug efficacy for each individual patient. The assay was able to identify effective drugs at most likelihood for positive treatment responses.

"Clinicians were previously aware of a few individual mutations that are associated with treatment resistance, but these isolated mutations tended to lack significant predictive value. The reason is that a much larger number of mutations can shape a tumor's treatment response than previously appreciated," OncoDxRx explained. "PGA bridges that gap in our understanding, enabling us to screen a complex array of hundreds of drugs at once."

One of the challenges in understanding how tumors respond to drugs is the inherent complexity of signaling pathways — a mechanism targeted by numerous cancer drugs.

OncoDxRx’s researchers focused on the core set of 750 key genes involved in major cancer pathways, for gene activation profiling. After validating it with publicly accessible clinical data, the assay pinpointed dozens of molecular assemblies — clusters of functionality — where gene over-activation influence drug efficacy. The researchers put it to the test in lung cancer. PGA was able to accurately identify drugs the patients will be responsive to, which were associated with improved patient outcomes. The test also effectively pinpointed likely to resist treatment.

Further still, beyond forecasting drug response/efficacy, PGA helped shed light on its decision-making process by identifying the functional genomics assemblies driving positive treatment response in lung cancer.

"Unraveling PGA's decision-making process is crucial, sometimes as important as the prediction itself," said the company. "PGA's modularity is one of its strengths, first because it builds trust in the technology, and second because each of these molecular assemblies we’ve identified becomes a potential new target for new-generation therapy. We’re optimistic that PGA will have broad applications in not only enhancing current cancer treatment, but also in pioneering new ones."