DNA methylome and transcriptome landscapes of cancer-associated fibroblasts reveal a smoking-associated malignancy index
Sheng-Fang Su, … , Chao-Chi Ho, Ker-Chau Li
Sheng-Fang Su, … , Chao-Chi Ho, Ker-Chau Li
Published July 6, 2021
Citation Information: J Clin Invest. 2021;131(16):e139552. https://doi.org/10.1172/JCI139552.
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Research Article Genetics Oncology

DNA methylome and transcriptome landscapes of cancer-associated fibroblasts reveal a smoking-associated malignancy index

Abstract

Unlike the better-studied aberrant epigenome in the tumor, the clinicopathologic impact of DNA methylation in the tumor microenvironment (TME), especially the contribution from cancer-associated fibroblasts (CAFs), remains elusive. CAFs exhibit profound patient-to-patient tumorigenic heterogeneity. We asked whether such heterogeneity may be exploited to quantify the level of TME malignancy. We developed a robust and efficient methylome/transcriptome co-analytical system for CAFs and paired normal fibroblasts (NFs) from non–small-cell lung cancer patients. We found 14,781 CpG sites of CAF/NF differential methylation, of which 3,707 sites showed higher methylation changes in ever-smokers than in nonsmokers. Concomitant CAF/NF differential gene expression analysis pointed to a subset of 54 smoking-associated CpG sites with strong methylation-regulated gene expression. A methylation index that summarizes the β values of these CpGs was built for NF/CAF discrimination (MIND) with high sensitivity and specificity. The potential of MIND in detecting premalignancy across individual patients was shown. MIND succeeded in predicting tumor recurrence in multiple lung cancer cohorts without reliance on patient survival data, suggesting that the malignancy level of TME may be effectively graded by this index. Precision TME grading may provide additional pathological information to guide cancer prognosis and open up more options in personalized medicine.

Authors

Sheng-Fang Su, Hao Ho, Jia-Hua Li, Ming-Fang Wu, Hsu-Chieh Wang, Hsiang-Yuan Yeh, Shuenn-Wen Kuo, Huei-Wen Chen, Chao-Chi Ho, Ker-Chau Li

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Figure 2

Identification of differential gene expression between lung NFs and CAFs.

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Identification of differential gene expression between lung NFs and CAFs...
(A) Volcano plot of the differentially expressed (DE) probes between NFs and CAFs. A probe is significant if fold change > 1.5 and Q < 0.1. (B) Heatmap of 614 DE probes on 25 pairs of primarily cultured NF/CAF samples from NSCLC patients. The standardized log2 expression values are displayed. Rows represent probes and columns represent samples. The clinical characteristics are encoded in the bottom and the significantly (P < 0.05 by Welch’s t test; for age, Pearson’s correlation coefficient was tested using the t test) correlated DNA methylation data, clinical variables, and related pathways are indicated in the right columns. (C and D) Analysis of DE genes, upregulated (C) or downregulated (D) in CAFs, in NF/CAF pairs (n = 19 or n = 9 per gene) by quantitative real-time PCR (qPCR). Actin was used as the internal control. Data presented as mean ± SD; symbols represent individual samples. *P < 0.05, **P < 0.01 by Mann-Whitney U test. Box plots display the median, first and third quartiles with whiskers as maximum and minimum values. (E) Using DAVID analysis, 7 KEGG pathways were significantly enriched in the DE genes at the false discovery rate of 0.1. (F) GSEA enrichment plot for ECM-receptor interaction pathway in comparing CAFs (red) to NFs (blue). The log2 fold changes of the core genes are shown in the right panel. (G) UpSet plot for the intersections among the 5 sets of the DE probes correlated (P < 0.05 by Welch’s t test; for age, Pearson’s correlation coefficient was tested using the t test) with tumor histology, stage, age, sex, and patients’ smoking status. The set of smoking-correlated DE probes overwhelmingly outnumbered other variables.