New Technology Based on Gene Expression Could Identify Effective Drugs for Non-Responders in Cancer

Study finds patient-derived gene expression signature to be predictive of drug efficacy. End Quote

(1888PressRelease) September 08, 2024 - Using transcriptomic profile of the blood samples from cancer patients, a group of OncoDxRx investigators has developed a predictive platform of anticancer drug response. The technology is capable of mapping out the most effective drugs with high accuracy by analyzing patient-unique genetic signatures.

The study, published in Onco (https://www.mdpi.com/2673-7523/4/3/12), represents a major advance in the drug response prediction field.

“This technology may become a focused tool to help clinicians select the best treatment options for each individual patient who may respond to,” explains OncoDxRx. “This allows clinicians to pivot treatment to the growing myriad of options being introduced to the field. Further studies will reveal whether this technology can be used dynamically to reflect the changes in patients as they may develop acquired resistance, increasing its utility.”

The management of cancer has changed in recent years, with targeted therapy and immunotherapy gradually complementing chemotherapy. “In average, about 20-30% of cancer patients are candidates for targeted therapy or immunotherapy,” the company says. “You’ve got one really good shot with these precision treatment, but if a patient doesn’t respond, you’ve got to figure out what else to do with them.”

Approximately 70-80% of cancer patients will be disqualified for or fail to respond to precision medicine, and the lack of a predictive tool of drug response remains a significant unmet need in this population. Studies have proposed neural network deep learning, machine learning or AI models as potential predictors of drug response. However, none of these in silico attempts have been clinically validated.

“Other than doing imaging, performing a physical exam, and clinically monitoring the patient, there is no effective companion tool to tell us who will or will not do well,” OncoDxRx says.

OncoDxRx’s team developed a predictive platform of drug response prediction using the patient’s transcriptomic profile. The PGA (Patient-derived Gene expression-informed Anticancer drug efficacy) technology successfully identified effective drugs in 30 NSCLC patients, resulting in longer survival.

According to OncoDxRx, the implications of these findings could be vast — not only in predicting drug response, but also in the potential therapeutic targeting. “We discovered multiple potential targetable pathways that are relatively unique, so we want to leverage their uniqueness to go right after them therapeutically,” the company says. “If we can understand the mechanisms that make these tumor cells tick, we can go directly after them to change the course of therapy.”

OncoDxRx notes that several important questions remain to be answered on this path of discovery, including whether these tumor cells cause resistance without any therapy, get generated after treatment, or are part of the resistance mechanism after a patient loses their response.

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