Innate tumor killers in colorectal cancer

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.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.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.Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response. 

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

Abstract Cancer genome sequencing enables accurate classification of tumours and tumour sub-types. 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 entities 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. 

/pdf/mutation-attention-muat-deep-representation-learning-of-efozp5qk.pdf

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

Re: Association of Germline Variants in the APOBEC3 Region with Cancer Risk and Enrichment with APOBEC-Signature Mutations in Tumors

1 Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, China, 2 National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, 3 School of Medicine, Xiamen University, Xiamen, China, 4 Department of Hematology, Key Laboratory of Xiamen for Diagnosis and Treatment of Hematological Malignancy, Xiamen, China, 5 Laboratory of Surgical Oncology, Peking University People’s Hospital, Beijing, China, 6 Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Beijing, China

/pdf/editorial-the-molecular-basis-of-somatic-evolution-bv8vdek8.pdf

Editorial: The Molecular Basis of Somatic Evolution

The somatic landscape of the cancer genome results from different mutational processes represented by distinct "mutational signatures". Although several mutagenic mechanisms are known to cause specific mutational signatures in cell lines, the variation of somatic mutational activities in patients, which is mostly attributed to somatic selection, is still poorly explained. Here we introduce a quantitative trait, mutational propensity (MP), and describe an integrated method to infer genetic determinants of variations in the mutational processes in 3,566 cancers with specific underlying mechanisms. As a result, we report 2,314 candidate determinants with both significant germline and somatic effects on somatic selection of mutational processes, of which 485 act via cancer gene expression and 1,427 act through the tumor immune microenvironment. These data demonstrate that the genetic determinants of MPs provide complementary information to known cancer driver genes, clonal evolution, and clinical biomarkers.

/pdf/genetic-determinants-of-somatic-selection-of-mutational-1v9b6w8ndt.pdf

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

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