Influence of T Cell-Mediated Immune Surveillance on Somatic Mutation Occurrences in Melanoma

BackgroundNeoantigens are presented on the cancer cell surface by peptide-restricted human leukocyte antigen (HLA) proteins and can subsequently activate cognate T cells. It has been hypothesized that the observed somatic mutations in tumors are shaped by immunosurveillance.MethodsWe investigated all somatic mutations identified in The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) samples. By applying a computational algorithm, we calculated the binding affinity of the resulting neo-peptides and their corresponding wild-type peptides with the major histocompatibility complex (MHC) Class I complex. We then examined the relationship between binding affinity alterations and mutation frequency.ResultsOur results show that neoantigens derived from recurrent mutations tend to have lower binding affinities with the MHC Class I complex compared to peptides from non-recurrent mutations. Tumor samples harboring recurrent SKCM mutations exhibited lower immune infiltration levels, indicating a relatively colder immune microenvironment.ConclusionsThese results suggested that the occurrences of somatic mutations in melanoma have been shaped by immunosurveillance. Mutations that lead to neoantigens with high MHC class I binding affinity are more likely to be eliminated and thus are less likely to be present in tumors.

A Predictor Combining Clinical and Genetic Factors for AML1-ETO Leukemia Patients

Core Binding Factor (CBF)-AML is one of the most common somatic mutations in acute myeloid leukemia (AML). t(8;21)/AML1-ETO-positive acute myeloid leukemia accounts for 5-10% of all AMLs. In this study, we consecutively included 254 AML1-ETO patients diagnosed and treated at our institute from December 2009 to March 2020, and evaluated molecular mutations by 185-gene NGS platform to explore genetic co-occurrences with clinical outcomes. Our results showed that high KIT VAF(≥15%) correlated with shortened overall survival compared to other cases with no KIT mutation (3-year OS rate 26.6% vs 59.0% vs 69.6%, HR 1.50, 95%CI 0.78-2.89, P=0.0005). However, no difference was found in patients’ OS whether they have KIT mutation in two or three sites. Additionally, we constructed a risk model by combining clinical and molecular factors; this model was validated in other independent cohorts. In summary, our study showed that c-kit other than any other mutations would influence the OS in AML1-ETO patients. A proposed predictor combining both clinical and genetic factors is applicable to prognostic prediction in AML1-ETO patients.

Comprehensive Analysis of Co-Mutations Identifies Cooperating Mechanisms of Tumorigenesis

Somatic mutations are one of the most important factors in tumorigenesis and are the focus of most cancer-sequencing efforts. The co-occurrence of multiple mutations in one tumor has gained increasing attention as a means of identifying cooperating mutations or pathways that contribute to cancer. Using multi-omics, phenotypical, and clinical data from 29,559 cancer subjects and 1747 cancer cell lines covering 78 distinct cancer types, we show that co-mutations are associated with prognosis, drug sensitivity, and disparities in sex, age, and race. Some co-mutation combinations displayed stronger effects than their corresponding single mutations. For example, co-mutation TP53:KRAS in pancreatic adenocarcinoma is significantly associated with disease specific survival (hazard ratio = 2.87, adjusted p-value = 0.0003) and its prognostic predictive power is greater than either TP53 or KRAS as individually mutated genes. Functional analyses revealed that co-mutations with higher prognostic values have higher potential impact and cause greater dysregulation of gene expression. Furthermore, many of the prognostically significant co-mutations caused gains or losses of binding sequences of RNA binding proteins or micro RNAs with known cancer associations. Thus, detailed analyses of co-mutations can identify mechanisms that cooperate in tumorigenesis.

Novel Diagnostic Biomarkers in Colorectal Cancer

Colorectal cancer (CRC) is still a leading cause of cancer death worldwide. Less than half of cases are diagnosed when the cancer is locally advanced. CRC is a heterogenous disease associated with a number of genetic or somatic mutations. Diagnostic markers are used for risk stratification and early detection, which might prolong overall survival. Nowadays, the widespread use of semi-invasive endoscopic methods and feacal blood tests characterised by suboptimal accuracy of diagnostic results has led to the detection of cases at later stages. New molecular noninvasive tests based on the detection of CRC alterations seem to be more sensitive and specific then the current methods. Therefore, research aiming at identifying molecular markers, such as DNA, RNA and proteins, would improve survival rates and contribute to the development of personalized medicine. The identification of “ideal” diagnostic biomarkers, having high sensitivity and specificity, being safe, cheap and easy to measure, remains a challenge. The purpose of this review is to discuss recent advances in novel diagnostic biomarkers for tumor tissue, blood and stool samples in CRC patients.

Self-controlled Sequencing Suggests that Somatic Mutations of Signaling, Transcription and Tumor Suppression Genes are a Precondition for AML Transformation in MDS

Background The transformation biology of secondary AML from MDS is still not fully understood. Here, we performed a large cohort of paired self-controlled sequences including target, whole-exome and single cell sequencing to search AML transformation-related mutations (TRMs). Methods 39 target genes from paired samples from 72 patients with MDS who had undergone AML transformation were analyzed by next generation target sequencing. Whole exome and single-cell RNA sequencing were used to verify the dynamics of transformation. Results The target sequencing results showed that sixty-four out of the 72 (88.9%) patients presented presumptive TRMs involving activated signaling, transcription factors, or tumor suppressors. Of the 64 patients, most of TRMs (62.5%, 40 cases) emerged at the leukemia transformation point. All three of the remaining eight patients analyzed by paired whole exome sequencing showed TRMs which are not included in the reference targets. No patient with MDS developed into AML only by acquiring mutations involved in epigenetic modulation or RNA splicing. Single-cell sequencing in one pair sample indicated that the activated cell signaling route was related to TRMs which take place prior to phenotypic development. Of note, target sequencing defined TRMs were limited to a small set of seven genes (in the order: NRAS/KRAS, CEBPA, TP53, FLT3, CBL, PTPN11 and RUNX1, accounted for nearly 90.0% of the TRMs). Conclusions Somatic mutations involving in signaling, transcription factors, or tumor suppressors appeared to be a precondition for AML transformation from MDS. The TRMs may be considered as new therapy targets.

The Complicated Nature of Somatic mtDNA Mutations in Aging

Mitochondria are the main source of energy used to maintain cellular homeostasis. This aspect of mitochondrial biology underlies their putative role in age-associated tissue dysfunction. Proper functioning of the electron transport chain (ETC), which is partially encoded by the extra-nuclear mitochondrial genome (mtDNA), is key to maintaining this energy production. The acquisition of de novo somatic mutations that interrupt the function of the ETC have long been associated with aging and common diseases of the elderly. Yet, despite over 30 years of study, the exact role(s) mtDNA mutations play in driving aging and its associated pathologies remains under considerable debate. Furthermore, even fundamental aspects of age-related mtDNA mutagenesis, such as when mutations arise during aging, where and how often they occur across tissues, and the specific mechanisms that give rise to them, remain poorly understood. In this review, we address the current understanding of the somatic mtDNA mutations, with an emphasis of when, where, and how these mutations arise during aging. Additionally, we highlight current limitations in our knowledge and critically evaluate the controversies stemming from these limitations. Lastly, we highlight new and emerging technologies that offer potential ways forward in increasing our understanding of somatic mtDNA mutagenesis in the aging process.

Somatic Mutations in Cardiovascular Disease

Advances in population-scale genomic sequencing have greatly expanded the understanding of the inherited basis of cardiovascular disease (CVD). Reanalysis of these genomic datasets identified an unexpected risk factor for CVD, somatically acquired DNA mutations. In this review, we provide an overview of somatic mutations and their contributions to CVD. We focus on the most common and well-described manifestation, clonal hematopoiesis of indeterminate potential. We also review the currently available data regarding how somatic mutations lead to tissue mosaicism in various forms of CVD, including atrial fibrillation and aortic aneurism associated with Marfan Syndrome. Finally, we highlight future research directions given current knowledge gaps and consider how technological advances will enhance the discovery of somatic mutations in CVD and management of patients with somatic mutations.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Background Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. Results In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. Conclusions The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions