한빛사 논문
Minsoo Son1,*, Hongbeom Kim2,*, Dohyun Han3,*, Yoseop Kim1, Iksoo Huh4, Youngmin Han2, Seung-Mo Hong5, Wooil Kwon2, Haeryoung Kim6, Jin-Young Jang2,§, and Youngsoo Kim1,§
1Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea; 2Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea; 3Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Seoul, Korea; 4College of Nursing and Research Institute of Nursing Science, Seoul Nation University, Seoul, Korea; 5Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; 6Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
*These authors equally contributed
§Corresponding authors:
Youngsoo Kim; Department of Biomedical Engineering, Seoul National University College of Medicine, 28 Yongon-Dong, Chongno-Ku, Seoul 110-799, Korea;
and Jin-Young Jang: Department of Surgery, Seoul National University College of Medicine, 103 Daehak-ro, Seoul 03080, Republic of Korea;
Abstract
Purpose: Pancreatic ductal adenocarcinoma (PDAC) subtypes have been identified using various methodologies. However, it is a challenge to develop classification system applicable to routine clinical evaluation. We aimed to identify risk subgroups based on molecular features and develop a classification model that was more suited for clinical applications.
Experimental Design: We collected whole dissected specimens from 225 patients who underwent surgery at Seoul National University Hospital, between October 2009 and February 2018, in Korea. Target proteins with potential relevance to tumor progression or prognosis were quantified with robust quality controls. We used hierarchical clustering analysis to identify risk subgroups. A random forest classification model was developed to predict the identified risk subgroups, and the model was validated using transcriptomic datasets from external cohorts (N = 700), with survival analysis.
Results: We identified 24 protein features that could classify the four risk subgroups associated with patient outcomes: Stable; Exocrine-like; Activated; and Extracellular matrix (ECM)-Remodeling. The "Stable" risk subgroup was characterized by proteins that were associated with differentiation and tumor suppressors. "Exocrine-like" tumors highly expressed pancreatic enzymes. Two high-risk subgroups, "Activated" and "ECM-Remodeling," were enriched in such terms as cell cycle, angiogenesis, immuno-competence, tumor invasion-metastasis, and metabolic reprogramming. The classification model that included these features made prognoses with relative accuracy and precision in multiple cohorts.
Conclusions: We proposed PDAC risk subgroups and developed a classification model that may potentially be useful for routine clinical implementations, at the individual level. This clinical system may improve the accuracy of risk prediction and treatment guidelines.
논문정보
관련 링크
연구자 키워드
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기