Proteogenomic profiling predicts outcomes of adjuvant chemotherapy in extrahepatic cholangiocarcinoma.

Background & Aims: Cholangiocarcinoma is a heterogeneous disease, and its molecular characteristics and biomarkers and not yet fully understood. Here we performed comprehensive proteogenomic analyses to investigate the molecular landscape of extrahepatic cholangiocarcinoma (EH-CCA).

Methods: To identify potential biomarkers, prespecified exploratory analyses were conducted within the STAMP trial-a randomized phase 2 trial of adjuvant capecitabine or gemcitabine plus cisplatin (GemCis) for patients with resected EH-CCA. Among 101 patients in the intention-to-treat population, 89 were included in the biomarker analysis (45 treated with GemCis, 44 with capecitabine). Surgical specimens were assessed by whole-exome sequencing and proteomics analyses. We performed correlative prognostic and predictive biomarker analyses for disease-free survival (DFS).

Results: Somatic mutations were most frequently detected in TP53 (63%), SMAD4 (20%), and KRAS (18%). Homologous recombination deficiency (HRD) was present in 10 patients (11%), microsatellite instability in one patient (1%), and actionable alterations, according to ESCAT classification, in 13 patients (15%). PIK3CA and FBXW7 mutations were significantly associated with poor DFS, and 11q13.3 amplification with favorable DFS. Some genomic alterations-including 8q24.21, 3q26.1, and 4p16.3 amplifications, 8p23.1 deletion, and HRD-showed significant interactions with the adjuvant treatment regimen, favoring GemCis. To integrate the impacts of these potentially predictive multiomic biomarkers, we developed a machine-learning prediction model to calculate the conditional average treatment effect (CATE), and categorize patients into three groups: CATE-highest (favoring GemCis), middle (no predicted difference), and lowest (favoring capecitabine). The model showed significant interaction with adjuvant treatment, and was significantly associated with DFS.

Conclusions: Here we identified several prognostic and predictive biomarkers that may be useful for guiding adjuvant chemotherapy selection in resected EH-CCA. Further validation is required.

Gov Identifier: NCT03079427 IMPACT AND IMPLICATIONS: Here we performed comprehensive proteogenomic analyses within a prospective randomized trial of patients with extrahepatic cholangiocarcinoma (EH-CCA). The findings highlighted specific genomic and proteomic alterations that were associated with differential benefit from adjuvant chemotherapy regimens. The integration of multiomics data into a machine learning model provides a practical framework for stratifying patients and guiding treatment decisions, which may inform future clinical trials and biomarker-driven therapeutic strategies.