The technology is based on gene expression signatures called Patient-derived Gene expression-informed Anticancer drug efficacy (PGA), which the team developed, validated, and tested across different cohorts of patients, encompassing hundreds of samples from patients with lung, breast, and pancreas cancers.

A new study reported a revolutionary technology that could help predict the best drugs for treating patients with non-small cell lung cancer (NSCLC) to enhance progression-free and overall survival. The results, published in Onco, suggested that this “personalized gene signature” could be used to identify most effective cancer drugs that are more likely to eradicate lung tumors.

Precision medicine has saved countless lives but only 20 to 25 percent of patients respond to this cutting-edge treatment as responders. Predicting which drugs would work and benefit the rest non-responders could help these patients and those who may have exhausted other options.

Studying a cohort of 30 patients with NSCLC, the OncoDxRx team first determined the gene expression signatures of individual patients, which showed the specific genes that are constantly turned up. They then mapped and identified the best drug candidates from an existing pool of more than 700 cancer drugs using computational approaches.

The researchers were able to pinpoint top-ranking drugs for each patient according to their unique gene signatures in a single run, with a surprising 5-day turnaround.

The high activities of biomarkers included in the PGA assay which correlated with treatment response is particularly promising as clinicians could use the signature to identify people who would gain the maximum benefit from certain drugs.