Enhancer somatic mutations with functional impact in lung cancers identified by leveraging the tissue-specific enhancer-target genes regulatory network
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE228832 | GEO | 2023/09/27
REPOSITORIES: GEO
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Project description:Enhancers are non-coding genomic features that regulate the transcriptional output of their target genes. Their dysregulation has been associated with various diseases including cancer. However, identification and characterisation of non-coding mutations that are relevant for tumorigenesis and prognosis remain a major challenge. Here we present the detection and characterisation of enhancer mutations in a genome-wide analysis of three lung cancer subtypes. We carefully define lung tissue-specific enhancers across the genome by integrating novel experimental data and reference public datasets of epigenomic signatures from eight lung cell lines and primary tissue. We observe that in lung cancer, the mutation burden at enhancers and exons is similar, whereas their corresponding mutation signatures show stark differences. Our results indicate that recurrent mutations in individual enhancers significantly impact the expression of target genes with potential relevance for prognosis. We also demonstrate that the genes enriched for mutated enhancers recurrently converge on the regulation of key biological process and pathways.
2023-09-27 | GSE228831 | GEO
Project description:Enhancers are non-coding genomic features that regulate the transcriptional output of their target genes. Their dysregulation has been associated with various diseases including cancer. However, identification and characterisation of non-coding mutations that are relevant for tumorigenesis and prognosis remain a major challenge. Here we present the detection and characterisation of enhancer mutations in a genome-wide analysis of three lung cancer subtypes. We carefully define lung tissue-specific enhancers across the genome by integrating novel experimental data and reference public datasets of epigenomic signatures from eight lung cell lines and primary tissue. We observe that in lung cancer, the mutation burden at enhancers and exons is similar, whereas their corresponding mutation signatures show stark differences. Our results indicate that recurrent mutations in individual enhancers significantly impact the expression of target genes with potential relevance for prognosis. We also demonstrate that the genes enriched for mutated enhancers recurrently converge on the regulation of key biological process and pathways.
2023-09-27 | GSE228830 | GEO
Project description:Recent technological advances in sequencing have flooded the field of cancer research with knowledge about somatic mutations for many different cancer types. Most cancer genomics studies focus on mutations that alter the amino acid sequence, ignoring the potential impact of synonymous mutations. However, accumulating experimental evidence has demonstrated clear consequences for gene function, leading to a widespread recognition of the functional role of synonymous mutations and their causal connection to various diseases. Here, we review the evidence supporting the direct impact of synonymous mutations on gene function via gene splicing; mRNA stability, folding, and translation; protein folding; and miRNA-based regulation of expression. These results highlight the functional contribution of synonymous mutations to oncogenesis and the need to further investigate their detection and prioritization for experimental assessment.
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Project description:A number of large-scale cancer somatic genome sequencing projects are now identifying genetic alterations in cancers. Evaluation of the effects of these mutations is essential for understanding their contribution to tumorigenesis. We have used SNPs3D, a software suite originally developed for analyzing nonsynonymous germ-line variants, to identify single-base mutations with a high impact on protein structure and function. Two machine learning methods are used: one identifying mutations that destabilize protein three-dimensional structure and the other utilizing sequence conservation and detecting all types of effects on in vivo protein function. Incorporation of detailed structure information into the analysis allows detailed interpretation of the functional effects of mutations in specific cases. Data from a set of breast and colorectal tumors were analyzed. In known cancer genes, mutations approaching 100% of mutations are found to impact protein function, supporting the view that these methods are appropriate for identifying driver mutations. Overall, 50-60% of all somatic missense mutations are predicted to have a high impact on structural stability or to more generally affect the function of the corresponding proteins. This value is similar to the fraction of all possible missense mutations that have a high impact and is much higher than the corresponding one for human population single-nucleotide polymorphisms, at about 30%. The majority of mutations in tumor suppressors destabilize protein structure, while mutations in oncogenes operate in more varied ways, including destabilization of less active conformational states. The set of high-impact mutations encompasses the possible drivers.
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Project description:Serine/Threonine Kinase 11 (STK11) encodes an important tumor suppressor that is frequently mutated in lung adenocarcinoma. Clinical studies have shown that mutations in STK11 resulting in loss of function correlate with resistance to anti-PD-1 monoclonal antibody therapy in KRAS-driven non-small cell lung cancer (NSCLC), but the molecular mechanisms responsible remain unclear. Despite this uncertainty, STK11 functional status is emerging as a reliable biomarker for predicting non-response to anti-PD-1 therapy in NSCLC patients. The clinical utility of this biomarker ultimately depends upon accurate classification of STK11 variants. For nonsense variants occurring early in the STK11 coding region, this assessment is straightforward. However, rigorously demonstrating the functional impact of missense variants remains an unmet challenge. Here we present data characterizing four STK11 splice-site variants by analyzing tumor mRNA, and 28 STK11 missense variants using an in vitro kinase assay combined with a cell-based p53-dependent luciferase reporter assay. The variants we report were identified in primary human NSCLC biopsies in collaboration with the University of Vermont Genomic Medicine group. Additionally, we compare our experimental results with data from 22 in silico predictive algorithms. Our work highlights the power, utility and necessity of functional variant assessment and will aid STK11 variant curation, provide a platform to assess novel STK11 variants and help guide anti-PD-1 therapy utilization in KRAS-driven NSCLCs.
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Project description:BackgroundMalignant transformation requires the interaction of cancer cells with their microenvironment, including infiltrating leukocytes. However, somatic mutational studies have focused on alterations in cancer cells, assuming that the microenvironment is genetically normal. Because we hypothesized that this might not be a valid assumption, we performed exome sequencing and targeted sequencing to investigate for the presence of pathogenic mutations in tumor-associated leukocytes in breast cancers.MethodsWe used targeted sequencing and exome sequencing to evaluate the presence of mutations in sorted tumor-infiltrating CD45-positive cells from primary untreated breast cancers. We used high-depth sequencing to determine the presence/absence of the mutations we identified in breast cancer-infiltrating leukocytes in purified tumor cells and in circulating blood cells.ResultsCapture-based sequencing of 15 paired tumor-infiltrating leukocytes and matched germline DNA identified variants in known cancer genes in all 15 primary breast cancer patients in our cohort. We validated the presence of mutations identified by targeted sequencing in infiltrating leukocytes through orthogonal exome sequencing. Ten patients harbored alterations previously reported as somatically acquired variants, including in known leukemia genes (DNTM3A, TET2, and BCOR). One of the mutations observed in the tumor-infiltrating leukocytes was also detected in the circulating leukocytes of the same patients at a lower allele frequency than observed in the tumor-infiltrating cells.ConclusionsHere we show that somatic mutations, including mutations in known cancer genes, are present in the leukocytes infiltrating a subset of primary breast cancers. This observation allows for the possibility that the cancer cells interact with mutant infiltrating leukocytes, which has many potential clinical implications.
| S-EPMC5515194 | biostudies-literature
Project description:Somatic mtDNA mutations have been reported in some human tumors, but their spectrum in different malignancies and their role in cancer development remain incompletely understood. Here, we describe the breadth of somatic and inherited mutations across the mitochondrial genome by sequence analyses of paired tumor and normal tissue samples from 226 individuals with five types of cancer using whole-genome data generated by The Cancer Genome Atlas Research Network. The frequencies of deleterious tumor-specific somatic mutations found in mtDNA varied across tumor types, ranging from 13% of glioblastomas to 63% of rectal adenocarcinomas. Compared with inherited mtDNA variants, somatic mtDNA mutations were enriched for nonsynonymous vs. synonymous changes (93 vs. 15; P < 2.2E-16) and were predicted to functionally impact the encoded protein. Somatic missense mutations in tumors were distributed uniformly among the mitochondrial protein genes, but 65% of somatic truncating mutations occurred in NADH dehydrogenase 5. Analysis of staging data in colon and rectal cancers revealed that the frequency of damaging mitochondrial mutations is the same in stages I and IV tumors. In summary, these data suggest that damaging somatic mtDNA mutations occur frequently (13-63%) in these five tumor types and likely confer a selective advantage in oncogenesis.
| S-EPMC3435197 | biostudies-literature
Project description:It is unknown whether the human immune system frequently mounts a T cell response against mutations expressed by common epithelial cancers. Using a next-generation sequencing approach combined with high-throughput immunologic screening, we demonstrated that tumor-infiltrating lymphocytes (TILs) from 9 out of 10 patients with metastatic gastrointestinal cancers contained CD4(+) and/or CD8(+) T cells that recognized one to three neo-epitopes derived from somatic mutations expressed by the patient's own tumor. There were no immunogenic epitopes shared between these patients. However, we identified in one patient a human leukocyte antigen-C*08:02-restricted T cell receptor from CD8(+) TILs that targeted the KRAS(G12D) hotspot driver mutation found in many human cancers. Thus, a high frequency of patients with common gastrointestinal cancers harbor immunogenic mutations that can potentially be exploited for the development of highly personalized immunotherapies.
| S-EPMC7445892 | biostudies-literature
Project description:A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer-target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the hierarchy of structural domains which define chromatin three-dimensional (3D) architecture. Here we introduce a change of perspective based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work (i) we develop a computational and statistical framework to reconstruct a comprehensive map of ETG pairs leveraging functional genomics data; (ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy and (iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research.
| S-EPMC8464068 | biostudies-literature
Project description:BackgroundWith the advent of whole-genome analysis for profiling tumor tissue, a pressing need has emerged for principled methods of organizing the large amounts of resulting genomic information. We propose the concept of multiplicity measures on cancer and gene networks to organize the information in a clinically meaningful manner. Multiplicity applied in this context extends Fearon and Vogelstein's multi-hit genetic model of colorectal carcinoma across multiple cancers.MethodsUsing the Catalogue of Somatic Mutations in Cancer (COSMIC), we construct networks of interacting cancers and genes. Multiplicity is calculated by evaluating the number of cancers and genes linked by the measurement of a somatic mutation. The Kamada-Kawai algorithm is used to find a two-dimensional minimum energy solution with multiplicity as an input similarity measure. Cancers and genes are positioned in two dimensions according to this similarity. A third dimension is added to the network by assigning a maximal multiplicity to each cancer or gene. Hierarchical clustering within this three-dimensional network is used to identify similar clusters in somatic mutation patterns across cancer types.ResultsThe clustering of genes in a three-dimensional network reveals a similarity in acquired mutations across different cancer types. Surprisingly, the clusters separate known causal mutations. The multiplicity clustering technique identifies a set of causal genes with an area under the ROC curve of 0.84 versus 0.57 when clustering on gene mutation rate alone. The cluster multiplicity value and number of causal genes are positively correlated via Spearman's Rank Order correlation (rs(8) = 0.894, Spearman's t = 17.48, p < 0.05). A clustering analysis of cancer types segregates different types of cancer. All blood tumors cluster together, and the cluster multiplicity values differ significantly (Kruskal-Wallis, H = 16.98, df = 2, p < 0.05).ConclusionWe demonstrate the principle of multiplicity for organizing somatic mutations and cancers in clinically relevant clusters. These clusters of cancers and mutations provide representations that identify segregations of cancer and genes driving cancer progression.
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