Multi-model prognostic evaluation of immunotherapy in advanced biliary tract cancer and development of the bedside biccaps score: A multicenter study.

Y Yichen Zheng (West China Hospital, Sichuan University, Chengdu, Sichuan, China) D Dandan Yuan (College of Pharmacy, Nanjing University of Chinese Medicine, No. 138, Xianlin Road, Nanjing, Jiangsu Province 210023, China) L Li Li Y Yunlu Jia (2Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, hangzhou, China) L Lu Chen X Xiuxue Gu (Department of Oncology, Peking University International Hospital, Beijing, Beijing, China) T Tonghui Ren (West China Hospital, Sichuan University, Chengdu, Sichuan, China) L Luyao Wang J Jian Ruan D Dan Cao J Ji Ma (College of Materials Science and Optoelectronic Technology)

Abstract

e16183 Background: Immune checkpoint inhibitors (ICIs) have improved outcomes for advanced biliary tract cancer (BTC), but survival benefits remain modest and predictive biomarkers to guide clinical practice are lacking. Methods: We analyzed 588 advanced BTC patients receiving ICIs from a multicenter retrospective cohort and two prospective clinical trials. To address substantial multicollinearity among inflammation-, nutrition-, and liver function–related biomarkers, we adopted a rigorous three-stage feature selection pipeline integrating Boruta, LASSO regression, and akaike information criterion (AIC)-based best-subset selection. Cox proportional hazards regression and machine learning survival models were constructed, and an integer-based bedside scoring system (BICCAPS) was derived. Dynamic changes in prognostic nutritional index (PNI) and systemic inflammation response index (SIRI) from baseline to cycle 2–3 were evaluated for additional prognostic value. Results: Seven variables (ICI treatment line, carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), peritoneal metastasis, SIRI, PNI and bilirubin) were incorporated into Cox proportional hazards regression model and random survival forest models, which showed good discrimination and calibration for OS across all cohorts. The BICCAPS score stratified patients into low-, intermediate- and high-risk groups with clearly separated median overall survival (OS) in the training (18.9, 10.5 and 6.8 months) and validation cohorts. Worsening PNI or SIRI during early treatment was associated with significantly shorter OS and progression-free survival. Conclusions: This study provides the first clinically implementable decision-making framework for immunotherapy in BTC. By translating complex statistical modeling into a simple bedside scoring system and incorporating dynamic biomarker assessment, the BICCAPS score offers a practical, low-cost, and accessible tool for individualized ICI management.

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (11)

Y

Yichen Zheng

West China Hospital, Sichuan University, Chengdu, Sichuan, China

D

Dandan Yuan

College of Pharmacy, Nanjing University of Chinese Medicine, No. 138, Xianlin Road, Nanjing, Jiangsu Province 210023, China

L

Li Li

Y

Yunlu Jia

2Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, hangzhou, China

L

Lu Chen

X

Xiuxue Gu

Department of Oncology, Peking University International Hospital, Beijing, Beijing, China

T

Tonghui Ren

West China Hospital, Sichuan University, Chengdu, Sichuan, China

L

Luyao Wang

J

Jian Ruan

D

Dan Cao

J

Ji Ma

College of Materials Science and Optoelectronic Technology