Personalized risk stratification in colorectal cancer via PIANOS system

Jul 16, 2025·
Du CAI
Du CAI
1st Author
Hao-Ning QI
Hao-Ning QI
Co-1st Author
,
Qiuxia Yang
Co-1st Author
,
Huayu Li
Co-1st Author
Cheng-Hang LI
Cheng-Hang LI
Chu-Ling HU
Chu-Ling HU
Bao-Wen GAI
Bao-Wen GAI
,
Xu Zhang
,
Yize Mao
Co-corresponding Author
Feng GAO
Feng GAO
Co-corresponding Author
,
Xiaojian Wu
Corresponding Author
· 0 min read
Abstract

Background: Colorectal cancer (CRC) is the third-most common malignancy and the second-leading cause of cancer-related death worldwide. Despite considerable progress in therapeutic approaches for CRC, patient outcomes remain unsatisfactory, with a general five-year survival rate of ~65%. A robust, accurate, and clinically actionable risk stratification system provides a foundation for further drug discovery to improve CRC patient outcomes.

Methods: We present PIANOS, a robust, platform-agnostic classifier for stratifying CRC patient risk. We analyzed data from 24 cohorts across ten countries comprising 5439 patients. We used the single-sample gene set enrichment analysis (ssGSEA) algorithm to compute enrichment scores for 22,596 pathways from the MSigDB database for each patient. PIANOS was developed using k-TSP and resampling algorithms, training with 364 patients.

Results: PIANOS stratified patients into high- or low-risk categories based on gene expression profiles, designating those scoring >17 as high-risk. Multivariate analysis for disease-free survival (DFS) revealed that PIANOS stratification operates independently of TNM staging and MSI status. DFS in high-risk patients was considerably shorter compared to those in the low-risk group across all examined cohorts. PIANOS suggests that low-risk patients may benefit more from chemotherapy and immunotherapy, whereas high-risk patients may exhibit activated angiogenic features.

Conclusion: This study underscores the potential of PIANOS to enhance personalized clinical decision-making in CRC through robust, platform-agnostic risk stratification that identifies distinct treatment sensitivities.

Type
Publication
Nature Communications
publication
Du CAI
Authors
Postdoc
I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.
Hao-Ning QI
Authors
Postdoc
I am a postdoctoral researcher working on AI for colorectal cancer with a focus on clinically actionable models.
Cheng-Hang LI
Authors
Research Assistant
I am a research assistant focusing on deep learning, multimodal feature fusion, and medical AI system development.
Chu-Ling HU
Authors
PhD Student
I am a PhD student focusing on AI-driven colorectal cancer research and clinically useful model development.
Bao-Wen GAI
Authors
PhD Student
I am a PhD student working on AI methods for colorectal cancer diagnosis and prognosis.
Feng GAO
Authors
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.