A team at the University of Leeds collaborated with researchers at Roche Diagnostics to develop the technique, which could help doctors and patients to decide on the best treatment options.
They used samples from a previous trial funded by Cancer Research UK to look at the levels of two proteins, known as tumor amphiregulin (AREG) and epiregulin (EREG), which are produced by some colorectal cancers.
Algorithms driven by AI enabled the researchers to show that patients with higher levels of these proteins received significant benefit from a treatment which inhibits a different protein involved in cancer cell growth, known as EGFR. Of equal importance, patients with low levels of the proteins did not benefit from the treatment.
Currently, anti-EGFR treatments are only given to patients with advanced, incurable bowel cancers. The researchers hope their methodology could be used in the future to identify patients in the earlier stages of illness who could also benefit from the drugs.
The study
The study was funded by Innovate UK and Roche Diagnostics as well as Yorkshire Cancer Research. It was part of a program of work in this field being conducted by the National Pathology Imaging Co-operative.
The team relied on artificial intelligence-assisted immunohistochemical (IHC) evaluation of AREG and EREG expression as a combined predictive biomarker for panitumumab (Pan) therapy benefit in RAS wild-type (wt) metastatic colorectal cancer (mCRC).
According to their study, high tumor mRNA levels of the EGFR ligands, AREG and EREG are associated with anti-EGFR agent response in patients with RAS-wt mCRC, regardless of tumor location.