Identification of Copy Number Variation-Driven Molecular Subtypes Informative for Prognosis and Treatment in Pancreatic Adenocarcinoma of a Chinese Cohort
EBIOMEDICINE(2021)
Shanghai Jiao Tong Univ
Abstract
BACKGROUND:Pancreatic adenocarcinoma (PAAD) is one of the most lethal carcinomas, and the current histopathological classifications are of limited use in clinical decision-making. There is an unmet need to identify new biomarkers for prognosis-informative molecular subtyping and ultimately for precision medicine.METHODS:We profiled genomic alterations for 608 PAAD patients in a Chinese cohort, including somatic mutations, pathogenic germline variants and copy number variations (CNV). Using the CNV information, we performed unsupervised consensus clustering of these patients, differential CNV analysis and functional/pathway enrichment analysis. Cox regression was conducted for progression-free survival analysis, the elastic net algorithm used for prognostic model construction, and rank-based gene set enrichment analysis for exploring tumor microenvironments.FINDINGS:Our data did not support prognostic value of point mutations in either highly mutated genes (such as KRAS, TP53, CDKN2A and SMAD4) or homologous recombination repair genes. Instead, associated with worse prognosis were amplified genes involved in DNA repair and receptor tyrosine kinase (RTK) related signalings. Motivated by this observation, we categorized patients into four molecular subtypes (namely repair-deficient, proliferation-active, repair-proficient and repair-enhanced) that differed in prognosis, and also constructed a prognostic model that can stratify patients with low or high risk of relapse. Finally, we analyzed publicly available datasets, not only reinforcing the prognostic value of our identified genes in DNA repair and RTK related signalings, but also identifying tumor microenvironment correlates with prognostic risks.INTERPRETATION:Together with the evidence from genomic footprint analysis, we suggest that repair-deficient and proliferation-active subtypes are better suited for DNA damage therapies, while immunotherapy is highly recommended for repair-proficient and repair-enhanced subtypes. Our results represent a significant step in molecular subtyping, diagnosis and management for PAAD patients.FUNDING:This work was supported by the National Natural Science Foundation of China (grant numbers 81470894, 81502695, 81672325, 81871906, 82073326, 82103482 and 32170663), the Shanghai Sailing Program (grant number 20YF1426900), and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (awarded to H.F.).
MoreTranslated text
Key words
Pancreatic adenocarcinoma,Molecular subtyping,Copy number variations,DNA repair,Receptor tyrosine kinase signaling
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined