Harnessing AI in Biomarker Analysis for Early-Onset CRC: Enrique Velazquez Villarreal, MD, PhD, MPH, MS

The rapid rise of early-onset colorectal cancer (CRC), especially among Hispanic and Latino populations, highlights critical gaps in current genomic research and treatment approaches, explains Enrique Velazquez Villarreal, MD, PhD, MPH, MS, assistant professor in the department of integrative translational sciences at City of Hope. A new study harnesses artificial intelligence (AI) to analyze biomarker pathways—WNT, TGF-β, and PI3K—in more than 2500 cases treated with FOLFOX (folinic acid, fluorouracil, and oxaliplatin), uncovering new insights into treatment resistance and patient survival across diverse ancestries.

The findings were presented at the American Society of Clinical Oncology (ASCO) Gastrointestinal Cancers Symposium.

This transcript was lightly edited for clarity; captions were auto-generated.

Transcript

How did the study utilize AI to enhance precision oncology for patients with early-onset CRC?

The title of this study is “Artificial Intelligence Enhanced Biomarker Analysis of WNY TGF-β and PIK3 Pathway Alterations in FOLFOX-Treated Early-Onset Colorectal Cancers in High-Risk Populations.”

In general, as a big picture, [for] the why, I will say early-onset colorectal cancer, or cancer diagnosed before age 50, is increasing rapidly, and the rise is especially pronounced in Hispanic and Latino populations. However, most genomic studies guiding treatment decisions are still based largely on non-Hispanic White patients. That means we may be missing important biology that affects how younger, diverse patients respond to standard chemotherapy.

About explaining the treatment, FOLFOX immunotherapy is a cornerstone treatment for colorectal cancer. We still don't fully understand how it interacts with key cancer signaling pathways in younger patients or across different ancestries. In this study, we are focused on 3 major pathways, WNT, TGF-β, and PIK3, because they play a central role in tumor growth, treatment resistance, and survival.

Now about the innovation. What makes this work distinct is that we didn't just look at mutations in isolation. We use artificial intelligence–driven platforms to systematically stratify more than 2500 colorectal cancer patients by age of onset, ancestry, and whether they receive FOLFOX. That includes us asking very specific, clinically relevant questions that are nearly impossible to do manually at this scale.