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Ligand Expression Predicts Vectibix Benefit in Colon Cancer, Roche and U of Leeds Study Says

NEW YORK – High levels of the EGFR ligands AREG and EREG may help predict which metastatic colorectal cancer patients benefit from the anti-EGFR agent panitumumab (Amgen's Vectibix), according to research out of Roche Diagnostics and the University of Leeds.

The research, published Thursday in the journal Clinical Cancer Research, details an immunohistochemistry (IHC)- and artificial intelligence-based approach to measuring AREG and EREG expression levels. Researchers, including the University of Leeds' Christopher Williams, used the test to retrospectively assess the relationship between AREG/EREG expression and survival outcomes among 274 advanced RAS-wildtype colorectal cancer patients treated in the Phase III PICCOLO trial evaluating panitumumab plus chemotherapy versus chemo alone.

Using the IHC-AI approach, the researchers found that high expression of the ligands coincided with improved progression-free survival with the panitumumab-chemotherapy combination versus chemotherapy alone. Specifically, patients with high AREG-EREG expression lived a median of 8 months without their cancers progressing with the addition of panitumumab versus 3.2 months with chemo alone. Patients with low AREG-EREG expression, on the other hand, lived a median of 3.4 months with panitumumab plus chemo versus 4.4 months with chemo alone.

The researchers further found that their results were consistent after they adjusted for patients' BRAF mutation status and the location of their primary tumors. They repeated their analysis considering patients' response rates and overall survival rates and found that the AREG/EREG IHC test likewise predicted for improved response rates and overall survival times. The overall survival benefit, of note, was improved but not to a statistically significant extent among the AREG/EREG-high expressers.

"IHC is a practical assay that may be of use in routine practice," Williams and colleagues wrote in their paper, noting that issues with analytical precision have hindered the routine adoption of ligand RNA assays.

For this reason, the researchers developed the AI aspect of the approach, whereby whole-slide analysis algorithms — developed using machine learning and computer vision techniques — allowed pathologists to compute the percentage of positively stained tumor cells within the tumor areas for AREG and EREG.