Unsupervised clustering of multi-omics molecular layers reveals consensus molecular subtypes showing potential therapeutic opportunities for pancreatic cancer

Sung Hwan LEE; Jiyeon Park; Hye Jung CHO; Incheon KANG; Sung Hoon CHOI; Sunyoung LEE; Ju-Seog LEE

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자료유형: 학술대회자료

저자정보: Sung Hwan LEE (CHA University School of Medicine) Jiyeon Park (McMaster University) Hye Jung CHO (CHA University School of Medicine) Incheon KANG (CHA University School of Medicine) Sung Hoon CHOI (CHA University School of Medicine) Sunyoung LEE (University of Texas MD Anderson Cancer Center) Ju-Seog LEE (University of Texas MD Anderson Cancer Center)

저널정보: 대한종양외과학회 대한임상종양학회 학술대회지 대한종양외과학회 2021 Seoul International Symposium of Surgical Oncology [초록집]

발행연도: 2021.6

수록면: 177 - 177 (1page)

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이 논문의 연구 히스토리 (3)

2021

Integrative multi-omics profiling of resectable pancreatic cancer reveals clinically relevant molecular subtypes with precision strategies beyond the clinical staging system

Sung Hwan LEE , Hye Jung CHO , Incheon KANG 외 3명 Annals of Hepato-Biliary-Pancreatic Surgery 2021.12 학술저널

Unsupervised clustering of multi-omics molecular layers reveals consensus molecular subtypes showing potential therapeutic opportunities for pancreatic cancer

Sung Hwan LEE , Jiyeon PARK , Hye Jung CHO 외 4명 Annals of Hepato-Biliary-Pancreatic Surgery 2021.12 학술저널

Unsupervised clustering of multi-omics molecular layers reveals consensus molecular subtypes showing potential therapeutic opportunities for pancreatic cancer

Sung Hwan LEE , Jiyeon Pa , k 외 5명 대한임상종양학회 학술대회지 2021.06 학술대회자료

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Introduction:
Pancreatic cancer is a lethal disease showing dismal prognosis and therapeutic resistance. Previous molecular subtypes from genome or transcriptome did not show clinical relevance regarding precision strategy for optimal therapeutic options followed by precise patient classification. This study aims to uncover consensus molecular subtypes from cancer-specific multi-omics data showing clinically relevant therapeutic opportunities.

Methods:
We performed comprehensive analyses using the dataset from the cancer dependency map (DepMap) project, including cancer-specific molecular characterization with multi-omics data, genome-wide loss-of-function screening using the CRISPR-Cas9 system, and cancer drug sensitivity. The subtype-specific molecular signatures were validated in independent translational cohorts (TCGA-PAAD; n=150, ICGC-PACA-AU; n=461, ICGC-PACA-CA; n=317).

Results:
Integrative profiling of multi-omics molecular layers (Mutational signature, Copy number alteration, Transcriptome, MicroRNA, Chromatic profile, Proteome, an ... 전체 초록 보기

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