DNA methylation-based patterns for early diagnostic prediction and prognostic evaluation in colorectal cancer patients with high tumor mutation burden

Hao Huang; Weifan Cao; Zhiping Long; Lei Kuang; Xi Li; Yifei Feng; Yuying Wu; Yang Zhao; Yinggang Chen; Peng Sun; Panxin Peng; Jinli Zhang; Lijun Yuan; Tianze Li; Huifang Hu; Gairui Li; Longkun Yang; Xing Zhang; Fulan Hu; Xizhuo Sun; Dongsheng Hu

doi:10.3389/fonc.2022.1030335

DNA methylation-based patterns for early diagnostic prediction and prognostic evaluation in colorectal cancer patients with high tumor mutation burden

Front Oncol. 2023 Jan 13:12:1030335. doi: 10.3389/fonc.2022.1030335. eCollection 2022.

Authors

Hao Huang¹, Weifan Cao¹, Zhiping Long², Lei Kuang³, Xi Li³, Yifei Feng³, Yuying Wu³, Yang Zhao³, Yinggang Chen⁴, Peng Sun⁴, Panxin Peng⁴, Jinli Zhang³, Lijun Yuan³, Tianze Li³, Huifang Hu³, Gairui Li⁵, Longkun Yang⁶, Xing Zhang⁶, Fulan Hu³, Xizhuo Sun¹, Dongsheng Hu^{1

3}

Affiliations

¹ Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, China.
² Department of Epidemiology, Public Health School of Harbin Medical University, Harbin, China.
³ Department of Epidemiology and Health Statistics, School of Public Health, Shenzhen University Health Science Center, Shenzhen, China.
⁴ Department of Gastrointestinal Surgery, Shenzhen Hospital, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
⁵ Department of Chronic Disease Control and Prevention, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China.
⁶ Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, China.

Abstract

Background: Immune checkpoint inhibitor (ICI) therapy has proven to be a promising treatment for colorectal cancer (CRC). We aim to investigate the relationship between DNA methylation and tumor mutation burden (TMB) by integrating genomic and epigenetic profiles to precisely identify clinical benefit populations and to evaluate the effect of ICI therapy.

Methods: A total of 536 CRC tissues from the Cancer Genome Atlas (TCGA) with mutation data were collected and subjected to calculate TMB. 80 CRC patients with high TMB and paired normal tissues were selected as training sets and developed the diagnostic and prognostic methylation models, respectively. In the validation set, the diagnostic model was validated in our in-house 47 CRC tissues and 122 CRC tissues from the Gene Expression Omnibus (GEO) datasets, respectively. And a total of 38 CRC tissues with high TMB from the COLONOMICS dataset verified the prognostic model.

Results: A positive correlation between differential methylation positions and TMB level was observed in TCGA CRC cohort (r=0.45). The diagnostic score that consisted of methylation levels of four genes (ADHFE1, DOK6, GPR75, and MAP3K14-AS1) showed high diagnostic performance in the discovery (AUC=1.000) and two independent validation (AUC=0.946, AUC=0.857) datasets. Additionally, these four genes showed significant positive correlations with NK cells. The prognostic score containing three genes (POU3F3, SYN2, and TMEM178A) had significantly poorer survival in the high-risk TMB samples than those in the low-risk TMB samples (P=0.016). CRC patients with low-risk scores combined with TMB levels represent a favorable survival.

Conclusions: By integrating analyses of methylation and mutation data, it is suggested that DNA methylation patterns combined with TMB serve as a novel potential biomarker for early screening in more high-TMB populations and for evaluating the prognostic effect of CRC patients with ICI therapy.

Keywords: DNA methylation; TCGA; TMB; colorectal cancer; immunotherapy.

Grants and funding

This study was supported by Shenzhen Key Medical Discipline Construction Fund (Grant Number: No.SZXK062), This study was supported by the Natural Science Foundation of China (Grant Number: 81302483), the Science and Technology Development Foundation of Shenzhen (Grant Number: JCYJ20190808111817192, JCYJ20210324093807020), The Scientific Research Initiation foundation for young teachers of Shenzhen University (Grant Number: 2019123, QNJS0160).