What question did this study set out to answer?

This research aims to explore how JAK-STAT, MAPK, and RTK-RAS pathways differ in early-onset colorectal cancer across various ancestries and treatments.

May 30, 2026

Artificial intelligence–integrated profiling of JAK-STAT, MAPK, and RTK–RAS signaling alterations in chemotherapy-treated early-onset colorectal cancer among disproportionately affected populations.

Key Points

This research aims to explore how JAK-STAT, MAPK, and RTK-RAS pathways differ in early-onset colorectal cancer across various ancestries and treatments.
Analyzed somatic mutation and clinical data from 2,515 colorectal cancer patients across public datasets.
Stratified patients by age at diagnosis (early-onset vs. late-onset), ancestry, and FOLFOX exposure.
Used conversational AI platforms for cohort construction and multi-parameter analysis.
JAK-STAT pathway alterations were more frequent in untreated Hispanic/Latino early-onset colorectal cancer compared to treated.
MAPK alterations showed variability, with specific mutations enriched in untreated Hispanic/Latino tumors.
Survival analysis indicated pathway-specific associations, particularly among non-Hispanic white patients.

Abstract

e15510 Background: Early-onset colorectal cancer (EOCRC; < 50 years) is increasing rapidly, with disproportionate burden among Hispanic/Latino (H/L) populations that remain underrepresented in molecular studies. FOLFOX (folinic acid, fluorouracil, oxaliplatin) is a standard chemotherapy backbone in CRC, yet how major oncogenic signaling pathways respond to treatment across ancestry and age remains incompletely characterized. The JAK-STAT, MAPK, and RTK–RAS pathways are central regulators of tumor growth, survival, and therapeutic response. We conducted an artificial intelligence (AI)–enabled, multi-pathway analysis. Methods: Somatic mutation and clinical data from 2,515 CRC patients were analyzed across public datasets. Patients were stratified by age at diagnosis (EOCRC vs. late-onset CRC LOCRC), ancestry, and FOLFOX exposure. JAK-STAT, MAPK, and RTK–RAS pathway alterations were defined using curated gene sets and analyzed categorically (altered vs. wild-type). Mutation frequencies were compared using Fisher’s exact or chi-square tests. Overall survival (OS) was assessed using Kaplan–Meier analyses. All analyses were exploratory and unadjusted for multiple testing. AI-HOPE, AI-HOPE-JAK-STAT, AI-HOPE-MAPK, and AI-HOPE-RTK–RAS conversational AI platforms enabled natural language–driven cohort construction, stratification, and multi-parameter analysis. Results: Pathway alterations demonstrated substantial heterogeneity across ancestry, age, and treatment context. JAK-STAT alterations were more frequent in FOLFOX-untreated H/L EOCRC compared with treated counterparts and enriched in untreated EO H/L relative to NHW patients. MAPK pathway alterations varied by subgroup, with FGFR3, NF1, and RPS6KA6 enriched in untreated EO H/L tumors, while PDGFRB alterations were more frequent in FOLFOX-treated EO H/L disease. RTK–RAS alterations also showed treatment-linked shifts, including reduced ERBB2 and NF1 frequencies in FOLFOX-treated EO H/L patients and distinct patterns among NHW strata. Survival analyses revealed pathway-specific OS associations in selected subgroups, particularly among NHW patients, though signals in H/L EOCRC were limited by small mutation-positive sample sizes. Conclusions: This AI-integrated analysis highlights context-dependent variation in JAK-STAT, MAPK, and RTK–RAS pathway alterations across ancestry, age, and FOLFOX treatment status in CRC. The findings underscore the biological heterogeneity of EOCRC and emphasize the importance of ancestry- and treatment-aware biomarker evaluation. Conversational AI platforms facilitated rapid, reproducible multi-pathway discovery and support hypothesis generation for future validation studies aimed at advancing equitable precision oncology.

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