Genetic Determinants of Somatic Selection of Mutational Processes in 3,566 Human Cancers

Guo, Jin-Tao; Zhou, Ying; Xu, Chaoqun; Chen, Qinwei; Sztupinszki, Zsófia; Börcsök, Judit; Xu, Canqiang; Ye, Feng; Tang, Weiwei; Kang, Jiapeng; Yang, Lu; Zhong, Jiaxin; Zhong, Taoling; Hu, Tianhui; Yu, Rongshan; Szállási, Zoltán; Deng, Xianming; Li, Qiyuan

doi:10.1158/0008-5472.can-21-0086

Cited by 9 publications

(8 citation statements)

References 66 publications

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“…Inspired by recent advances in cancer epigenetic reprogramming and our previous findings [ 39 ], we believe that revealing the underlying biological processes of methylation signatures is important. Here, we performed an integrated analysis to identify possible deterministic genes of the methylation signatures ( Methods ).…”

Section: Resultsmentioning

confidence: 99%

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Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

Qin,

Zhou,

Guo

et al. 2024

Genome Med

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Background Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes. Methods We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts. Results We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment. Conclusions Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

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Section: Resultsmentioning

confidence: 99%

“…Instrumental variable (IV) analysis was employed to identify the driver genes affecting the methylation signatures, as previously described [ 39 ]. IV analysis was performed using the screened genes via the R package “ivpack” [version 1.2].…”

Section: Methodsmentioning

confidence: 99%

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

Qin,

Zhou,

Guo

et al. 2024

Genome Med

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“…We have demonstrated how deep representation learning can in large cancer datasets yield features which are useful beyond labelled data, for instance in tumour subtyping. Our method, MuAt, can be extended to incorporate additional data on somatic mutations, such as epigenetics, potentially enabling scrutiny of the role of epigenetic interactions in somatic mutagenesis [59, 60, 61, 13, 62]. MuAt is already able to contribute to multiomics data integration to drive biological discovery and clinical applications by providing informative representations of somatic mutation catalogues of tumours.…”

Section: Discussionmentioning

confidence: 99%

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

Sanjaya

Waszak

Stegle

et al. 2022

Preprint

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Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumor types with low somatic mutation burden such as many pediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown. We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. MuAt achieved prediction accuracy of 89% for whole genomes (24 tumour types) and 64% for whole exomes (20 types), and a top-5 accuracy of 97% and 90%, respectively. Tumour representations learnt by MuAt included tumour entitites such as acral melanoma, SHH-activated medulloblastoma, SPOP-associated prostate cancer, microsatellite instability, and MUTYH-associated pancreatic endocrine tumours although these tumour subtypes and subgroups were not used as training labels. Integrated representations of somatic alterations hold significant potential to drive discovery of novel tumour entities and clinical application.

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“…Germline mutations of BRCA1 and BRCA2 are the main mechanisms involved in homologous recombination deficiency (HRD). Moreover, other mechanisms, such as germline and/or somatic mutations in the other HR repair genes and epigenetic modifications, generate a characteristic mutational signature ( Guo et al, 2018 ; Alexandrov et al, 2020 ; Guo et al, 2021 ) and also play a certain role in HRD. It is well-known that patients with HRD exhibit specific clinical features and excellent responses to platinum-based chemotherapy and poly (ADP-ribose) polymerase (PARP) inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Deciphering the expression patterns of homologous recombination-related lncRNAs identifies new molecular subtypes and emerging therapeutic opportunities in epithelial ovarian cancer

Hua

Zhang²,

Wang³

et al. 2022

Front. Genet.

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Epithelial ovarian cancer (EOC) is the leading killer among women with gynecologic malignancies. Homologous recombination deficiency (HRD) has attracted increasing attention due to its significant implication in the prediction of prognosis and response to treatments. In addition to the germline and somatic mutations of homologous recombination (HR) repair genes, to widely and deeply understand the molecular characteristics of HRD, we sought to screen the long non-coding RNAs (lncRNAs) with regard to HR repair genes and to establish a prognostic risk model for EOC. Herein, we retrieved the transcriptome data from the Genotype-Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA) databases. HR-related lncRNAs (HRRlncRNAs) associated with prognosis were identified by co-expression and univariate Cox regression analyses. The least absolute shrinkage and selection operator (LASSO) and multivariate stepwise Cox regression were performed to construct an HRRlncRNA risk model containing AC138904.1, AP001001.1, AL603832.1, AC138932.1, and AC040169.1. Next, Kaplan−Meier analysis, time-dependent receiver operating characteristics (ROC), nomogram, calibration, and DCA curves were made to verify and evaluate the model. Gene set enrichment analysis (GSEA), immune analysis, and prediction of the half-maximal inhibitory concentration (IC50) in the risk groups were also analyzed. The calibration plots showed a good concordance with the prognosis prediction. ROCs of 1-, 3-, and 5-year survival confirmed the well-predictive efficacy of this model in EOC. The risk score was used to divide the patients into high-risk and low-risk subgroups. The low-risk group patients tended to exhibit a lower immune infiltration status and a higher HRD score. Furthermore, consensus clustering analysis was employed to divide patients with EOC into three clusters based on the expression of the five HRRlncRNAs, which exhibited a significant difference in checkpoints’ expression levels and the tumor microenvironment (TME) status. Taken together, the results of this project supported that the five HRRlncRNA models might function as a biomarker and prognostic indicator with respect to predicting the PARP inhibitor and immune treatment in EOC.

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Genetic Determinants of Somatic Selection of Mutational Processes in 3,566 Human Cancers

Cited by 9 publications

References 66 publications

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

Deciphering the expression patterns of homologous recombination-related lncRNAs identifies new molecular subtypes and emerging therapeutic opportunities in epithelial ovarian cancer

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