Dataset Information

Molecular analysis of Chinese oesophageal squamous cell carcinoma identifies novel subtypes associated with distinct clinical outcomes.

ABSTRACT:

Background

Oesophageal squamous cell carcinoma (ESCC) is a highly heterogeneous cancer with a distinct incidence and prognosis. Molecular events driving ESCC subtypes and prognosis have not been established, and little is known regarding Chinese ESCC patients in Xinjiang, China.

Methods

Here, we first integrated the genomic and transcriptomic data of 125 Chinese ESCC patients from Xinjiang Tumor Hospital (Urumqi, China). Two independent datasets of GSE53624 and The Cancer Genome Atlas (TCGA) ESCC were used to confirm the results of this study. DNA mutation and overall survival (OS) were analysed independently in the Chinese ESCC cohorts.

Findings

Genomic analyses revealed a consistent mutation signatures and discordance among mutated genes across the different ESCC cohorts. In addition, transcriptomic profiling identified three Chinese ESCC subtypes associated with clinical and molecular attributes, including patient survival, lymph node status and genetic profile. Moreover, Chinese ESCC subtypes have distinct metabolic, inflammatory, metastatic, and cell proliferation features and unique potential therapeutics. Furthermore, the expression of cell cycle- and/or cell proliferation-related genes was higher in cyclin D1 (CCND1)-amplified tumours than in CCND1-normal tumours from Chinese ESCC patients, suggesting that CCND1 amplification promoted cell proliferation.

Interpretation

Our findings provide a framework to facilitate the rational categorization of ESCC in Chinese patients and a foundation for new therapies.

Funding

This study was supported by the Research Fund of Key Laboratory of Xinjiang oncology (Grant no.2017D04006) and the Outstanding Youth Science and technology training project fund of Xinjiang, China (Grant no. 2017Q058).

SUBMITTER: Liu M

PROVIDER: S-EPMC7317223 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe 5-year survival rate of oesophageal squamous cell carcinoma (ESCC) is approximately 20%. The prognosis and drug response exhibit substantial heterogeneity in ESCC, impeding progress in survival outcomes. Our goal is to identify a signature for tumour subtype classification, enabling precise clinical treatments.MethodsUtilising pre-treatment multi-omics data from an ESCC dataset (n = 310), an enhancer methylation-eRNA-target gene regulation network was constructed and validated by in vitro experiments. Four machine learning methods collectively identified core target genes, establishing an Enhancer Demethylation-Regulated Gene Score (EDRGS) model for classification. The molecular function of EDRGS subtyping was explored in scRNA-seq (n = 60) and bulk-seq (n = 310), and the EDRGS's potential to predict treatment response was assessed in datasets of various cancer types.FindingsEDRGS stratified ESCCs into EDRGS-high/low subtypes, with EDRGS-high signifying a less favourable prognosis in ESCC and nine additional cancer types. EDRGS-high exhibited an immune-hot but immune-suppressive phenotype with elevated immune checkpoint expression, increased T cell infiltration, and IFNγ signalling in ESCC, suggesting a better response to immunotherapy. Notably, EDRGS outperformed PD-L1 in predicting anti-PD-1/L1 therapy effectiveness in ESCC (n = 42), kidney renal clear cell carcinoma (KIRC, n = 181), and bladder urothelial carcinoma (BLCA, n = 348) cohorts. EDRGS-low showed a cell cycle-activated phenotype with higher CDK4 and/or CDK6 expression, demonstrating a superior response to the CDK4/6 inhibitor palbociclib, validated in ESCC (n = 26), melanoma (n = 18), prostate cancer (n = 15) cells, and PDX models derived from patients with pancreatic cancer (n = 30).InterpretationIdentification of EDRGS subtypes enlightens ESCC categorisation, offering clinical insights for patient management in immunotherapy (anti-PD-1/L1) and CDK4/6 inhibitor therapy across cancer types.FundingThis study was supported by funding from the National Key R&D Program of China (2021YFC2501000, 2020YFA0803300), the National Natural Science Foundation of China (82030089, 82188102), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-018, 2022-I2M-2-001, 2021-I2M-1-067), the Fundamental Research Funds for the Central Universities (3332021091).