Affiliations
- 1State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
- 2Lymphoma Genomic Translational Research Laboratory, Cellular and Molecular Research, National Cancer Centre Singapore, Singapore.
- 3ONCO-ACP, Duke-NUS Medical School, Singapore.
- 4Division of Medical Oncology, National Cancer Centre Singapore, Singapore.
- 5Department of Anatomic Pathology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
- 6Chang Gung University, Taoyuan, Taiwan.
- 7Division of Hematology-Oncology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
- 8Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
- 9Laboratory of Cancer Epigenome, Division of Medical Sciences, National Cancer Centre Singapore, Singapore.
- 10Guangdong Provincial People’s Hospital, Guangdong, China.
- 11Guangdong Academy of Medical Sciences, Guangdong, China.
- 12Department of Haematology-Oncology, National University Cancer Institute of Singapore, National University Health System, Singapore.
- 13Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore.
- 14Cancer Science Institute of Singapore, National University of Singapore, Singapore City, Singapore.
- 15Department of Haematology-Oncology, National University Health System, Singapore City, Singapore.
- 16Lymphoma Genomic Translational Research Laboratory, Division of Medical Oncology, National Cancer Centre Singapore, Singapore City, Singapore.
- 17Department of Pathology, Singapore General Hospital, Singapore City, Singapore.
- 18Department of Haematology, Singapore General Hospital, Singapore City, Singapore.
- 19Singapore Immunology?Network (SIgN), A*STAR (Agency for Science, Technology and Research), Singapore City, Singapore.
- 20Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics,National Research Center for Translational Medicine at Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- 21Genome Institute of Singapore, Singapore City, Singapore.
- 22Director’s office, National Cancer Centre, Singapore City, Singapore.
- 23Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
- 24Institut D’Investigacions Biomediques August Pi I Sunyer, Hospital Clinic, University of Barcelona, Barcelona, Spain.
- 25Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore City, Singapore.
- 26Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Singapore City, Singapore.
- 27Translational Cell and Tissue Research Lab, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
- 28Department of Pathology, UZ Leuven, Leuven, Belgium.
- 29Centre for Computational Biology, Duke-NUS Medical School, Singapore City, Singapore.
- 30Office of Education, Duke-NUS Medical School, Singapore City, Singapore.
- 31Cancer and Stem Cell Biology, Duke-NUS Graduate Medical School, Singapore City, Singapore.
Abstract
With lowering costs of sequencing and genetic profiling techniques, genetic drivers can now be detected readily in tumors but current prognostic models for Natural-killer/T cell lymphoma (NKTCL) have yet to fully leverage on them for prognosticating patients. Here, we used next-generation sequencing to sequence 260 NKTCL tumors, and trained a genomic prognostic model (GPM) with the genomic mutations and survival data from this retrospective cohort of patients using LASSO Cox regression. The GPM is defined by the mutational status of 13 prognostic genes and is weakly correlated with the risk-features in International Prognostic Index (IPI), Prognostic Index for Natural-Killer cell lymphoma (PINK), and PINK-Epstein-Barr virus (PINK-E). Cox-proportional hazard multivariate regression also showed that the new GPM is independent and significant for both progression-free survival (PFS, HR: 3.73, 95% CI 2.07-6.73; p < .001) and overall survival (OS, HR: 5.23, 95% CI 2.57-10.65; p = .001) with known risk-features of these indices. When we assign an additional risk-score to samples, which are mutant for the GPM, the Harrell’s C-indices of GPM-augmented IPI, PINK, and PINK-E improved significantly (p < .001, ?2 test) for both PFS and OS. Thus, we report on how genomic mutational information could steer toward better prognostication of NKTCL patients.
PMID: 35726449 DOI: 10.1002/ajh.26636