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Decoding the JAK-STAT Axis in Colorectal Cancer with AI-HOPE-JAK-STAT: A Conversational Artificial Intelligence

Ei-Wen Yang1, Brigette Waldrup2, Enrique Velazquez-Villarreal2,3

  • 1PolyAgent, San Francisco, CA 94102, USA.

Cancers
|July 29, 2025
PubMed

Insights

This study introduces AI-HOPE-JAK-STAT, an AI platform for exploring Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway alterations in colorectal cancer (CRC). The platform reveals survival advantages in specific patient groups, including early-onset CRC and those treated with FOLFOX.

Area of Science:

  • Oncology
  • Bioinformatics
  • Artificial Intelligence

Background:

  • The Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway is crucial in immune regulation, inflammation, and cancer.
  • Its role in colorectal cancer (CRC) pathogenesis, especially in early-onset CRC (EOCRC), requires further characterization.
  • Molecular heterogeneity and clinical significance of JAK-STAT alterations in CRC are not fully understood across diverse contexts.

Purpose of the Study:

  • To introduce AI-HOPE-JAK-STAT, a novel conversational AI platform for real-time, natural language-driven exploration of JAK/STAT pathway alterations in CRC.
  • To integrate clinical, genomic, and treatment data for dynamic, hypothesis-generating analyses in precision oncology.
  • To enable users to explore JAK/STAT pathway alterations without requiring coding expertise.

Main Methods:

  • Development of AI-HOPE-JAK-STAT, combining large language models (LLMs) and a natural language-to-code engine.
  • Integration of harmonized public CRC datasets from cBioPortal for cohort selection, survival analysis, and mutation profiling.
  • Validation of the platform by replicating known JAK1, JAK3, and STAT3 mutation associations and conducting exploratory analyses on age, treatment, stage, and site.

Main Results:

  • The platform confirmed improved survival for EOCRC patients with JAK/STAT pathway alterations.
  • JAK/STAT-altered tumors in FOLFOX-treated CRC cohorts showed significantly enhanced overall survival (p < 0.0001).
  • Younger patients (age < 50) with JAK/STAT mutations demonstrated survival advantages (p = 0.0379), and STAT5B mutations correlated with favorable trends (p = 0.0000).
  • JAK1 mutations in microsatellite-stable tumors did not impact survival, highlighting the importance of molecular context.
  • JAK3-mutated tumors in Stages I-III had superior survival compared to Stage IV (p = 0.00001), underscoring stage as a key determinant.

Conclusions:

  • AI-HOPE-JAK-STAT sets a new standard for pathway-level analysis in CRC, empowering hypothesis generation and testing.
  • The system improves access to precision oncology analyses, facilitating scalable, real-time discovery of survival trends and treatment-response patterns.
  • It supports the identification of mutational associations and survival trends across stratified patient cohorts without coding expertise.
Keywords:
AIAI-agentsJAK-STAT pathwayLLMartificial intelligencecancercancer geneticscolorectal cancerlarge language modelsprecision oncology

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