한빛사논문
Whijae Roh # 1, Yifat Geffen # 1 2, Hongui Cha # 3, Mendy Miller 1, Shankara Anand 1, Jaegil Kim 4, David I Heiman 1, Justin F Gainor 5 6, Peter W Laird 7, Andrew D Cherniack 1, Chan-Young Ock 8, Se-Hoon Lee 3 9, Gad Getz 1 2 6, National Cancer Institute Center for Cancer Genomics Tumor Molecular Pathology (TMP) Analysis Working Group
1Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts.
2Cancer Center and Dept. of Pathology, Massachusetts General Hospital, Boston, Massachusetts.
3Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
4GSK inc. 196 Broadway, Cambridge, Massachusetts.
5Center for Thoracic Cancers, Massachusetts General Hospital, Boston, Massachusetts.
6Harvard Medical School, Boston, Massachusetts.
7Van Andel Institute, Grand Rapids, Michigan.
8Lunit, Seoul, Republic of Korea.
9Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea.
#Contributed equally.
*Corresponding Authors: Gad Getz
Abstract
Lung adenocarcinoma (LUAD) is one of the most common cancer types and has various treatment options. Better biomarkers to predict therapeutic response are needed to guide choice of treatment modality and to improve precision medicine. Here, we used a consensus hierarchical clustering approach on 509 LUAD cases from The Cancer Genome Atlas to identify five robust LUAD expression subtypes. Genomic and proteomic data from patient samples and cell lines was then integrated to help define biomarkers of response to targeted therapies and immunotherapies. This approach defined subtypes with unique proteogenomic and dependency profiles. Subtype 4 (S4)-associated cell lines exhibited specific vulnerability to loss of CDK6 and CDK6-cyclin D3 complex gene (CCND3). Subtype 3 (S3) was characterized by dependency on CDK4, immune-related expression patterns, and altered MET signaling. Experimental validation showed that S3-associated cell lines responded to MET inhibitors, leading to increased expression of programmed death-ligand 1 (PD-L1). In an independent real-world patient dataset, patients with S3 tumors were enriched with responders to immune checkpoint blockade. Genomic features in S3 and S4 were further identified as biomarkers for enabling clinical diagnosis of these subtypes. Overall, our consensus hierarchical clustering approach identified robust tumor expression subtypes, and our subsequent integrative analysis of genomics, proteomics, and CRISPR screening data revealed subtype-specific biology and vulnerabilities. These LUAD expression subtypes and their biomarkers could help identify patients likely to respond to CDK4/6, MET, or PD-L1 inhibitors, potentially improving patient outcome.
논문정보
관련 링크
연구자 키워드
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기