Relating mutational signature exposures to clinical data in cancers via signeR 2.0.

BACKGROUND: Cancer is a collection of diseases caused by the deregulation of cell processes, which is triggered by somatic mutations. The search for patterns in somatic mutations, known as mutational signatures, is a growing field of study that has already become a useful tool in oncology. Several algorithms have been proposed to perform one or both the following two tasks: (1) de novo estimation of signatures and their exposures, (2) estimation of the exposures of each one of a set of pre-defined signatures. RESULTS: Our group developed signeR, a Bayesian approach to both of these tasks. Here we present a new version of the software, signeR 2.0, which extends the possibilities of previous analyses to explore the relation of signature exposures to other data of clinical relevance. signeR 2.0 includes a user-friendly interface developed using the R-Shiny framework and improvements in performance. This version allows the analysis of submitted data or public TCGA data, which is embedded in the package for easy access. CONCLUSION: signeR 2.0 is a valuable tool to generate and explore exposure data, both from de novo or fitting analyses and is an open-source R package available through the Bioconductor project at ( https://doi.org/10.18129/B9.bioc.signeR ).

Family-based whole-exome sequencing identifies rare variants potentially related to cutaneous melanoma predisposition in Brazilian melanoma-prone families.

Genetic predisposition accounts for nearly 10% of all melanoma cases and has been associated with a dozen moderate- to high-penetrance genes, including CDKN2A, CDK4, POT1 and BAP1. However, in most melanoma-prone families, the genetic etiology of cancer predisposition remains undetermined. The goal of this study was to identify rare genomic variants associated with cutaneous melanoma susceptibility in melanoma-prone families. Whole-exome sequencing was performed in 2 affected individuals of 5 melanoma-prone families negative for mutations in CDKN2A and CDK4, the major cutaneous melanoma risk genes. A total of 288 rare coding variants shared by the affected relatives of each family were identified, including 7 loss-of-function variants. By performing in silico analyses of gene function, biological pathways, and variant pathogenicity prediction, we underscored the putative role of several genes for melanoma risk, including previously described genes such as MYO7A and WRN, as well as new putative candidates, such as SERPINB4, HRNR, and NOP10. In conclusion, our data revealed rare germline variants in melanoma-prone families contributing with a novel set of potential candidate genes to be further investigated in future studies.

Mutational Signatures Driven by Epigenetic Determinants Enable the Stratification of Patients with Gastric Cancer for Therapeutic Intervention.

DNA mismatch repair deficiency (dMMR) is associated with the microsatellite instability (MSI) phenotype and leads to increased mutation load, which in turn may impact anti-tumor immune responses and treatment effectiveness. Various mutational signatures directly linked to dMMR have been described for primary cancers. To investigate which mutational signatures are associated with prognosis in gastric cancer, we performed a de novo extraction of mutational signatures in a cohort of 787 patients. We detected three dMMR-related signatures, one of which clearly discriminates tumors with MLH1 gene silencing caused by promoter hypermethylation (area under the curve = 98%). We then demonstrated that samples with the highest exposure of this signature share features related to better prognosis, encompassing clinical and molecular aspects and altered immune infiltrate composition. Overall, the assessment of the prognostic value and of the impact of modifications in MMR-related genes on shaping specific dMMR mutational signatures provides evidence that classification based on mutational signature exposure enables prognosis stratification.

Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer.

DNA repair deficiency (DRD) is an important driver of carcinogenesis and an efficient target for anti-tumor therapies to improve patient survival. Thus, detection of DRD in tumors is paramount. Currently, determination of DRD in tumors is dependent on wet-lab assays. Here we describe an efficient machine learning algorithm which can predict DRD from histopathological images. The utility of this algorithm is demonstrated with data obtained from 1445 cancer patients. Our method performs rather well when trained on breast cancer specimens with homologous recombination deficiency (HRD), AUC (area under curve) = 0.80. Results for an independent breast cancer cohort achieved an AUC = 0.70. The utility of our method was further shown by considering the detection of mismatch repair deficiency (MMRD) in gastric cancer, yielding an AUC = 0.81. Our results demonstrate the capacity of our learning-base system as a low-cost tool for DRD detection.

Insights Into the Somatic Mutation Burden of Hepatoblastomas From Brazilian Patients.

Hepatoblastoma is a very rare embryonal liver cancer supposed to arise from the impairment of hepatocyte differentiation during embryogenesis. In this study, we investigated by exome sequencing the burden of somatic mutations in a cohort of 10 hepatoblastomas, including a congenital case. Our data disclosed a low mutational background and pointed out to a novel set of candidate genes for hepatoblastoma biology, which were shown to impact gene expression levels. Only three recurrently mutated genes were detected: CTNNB1 and two novel candidates, CX3CL1 and CEP164. A relevant finding was the identification of a recurrent mutation (A235G) in two hepatoblastomas at the CX3CL1 gene; evaluation of RNA and protein expression revealed upregulation of CX3CL1 in tumors. The analysis was replicated in two independents cohorts, substantiating that an activation of the CX3CL1/CX3CR1 pathway occurs in hepatoblastomas. In inflammatory regions of hepatoblastomas, CX3CL1/CX3CR1 were not detected in the infiltrated lymphocytes, in which they should be expressed in normal conditions, whereas necrotic regions exhibited negative labeling in tumor cells, but strongly positive infiltrated lymphocytes. Altogether, these data suggested that CX3CL1/CX3CR1 upregulation may be a common feature of hepatoblastomas, potentially related to chemotherapy response and progression. In addition, three mutational signatures were identified in hepatoblastomas, two of them with predominance of either the COSMIC signatures 1 and 6, found in all cancer types, or the COSMIC signature 29, mostly related to tobacco chewing habit; a third novel mutational signature presented an unspecific pattern with an increase of C>A mutations. Overall, we present here novel candidate genes for hepatoblastoma, with evidence that CX3CL1/CX3CR1 chemokine signaling pathway is likely involved with progression, besides reporting specific mutational signatures.

APOBEC-mediated DNA alterations: A possible new mechanism of carcinogenesis in EBV-positive gastric cancer.

Mechanisms of viral oncogenesis are diverse and include the off-target activity of enzymes expressed by the infected cells, which evolved to target viral genomes for controlling their infection. Among these enzymes, the single-strand DNA editing capability of APOBECs represent a well-conserved viral infection response that can also cause untoward mutations in the host DNA. Here we show, after evaluating somatic single-nucleotide variations and transcriptome data in 240 gastric cancer samples, a positive correlation between APOBEC3s mRNA-expression and the APOBEC-mutation signature, both increased in EBV+ tumors. The correlation was reinforced by the observation of APOBEC mutations preferentially occurring in the genomic loci of the most active transcripts. This EBV infection and APOBEC3 mutation-signature axis were confirmed in a validation cohort of 112 gastric cancer patients. Our findings suggest that APOBEC3 upregulation in EBV+ cancer may boost the mutation load, providing further clues to the mechanisms of EBV-induced gastric carcinogenesis. After further validation, this EBV-APOBEC axis may prove to be a secondary driving force in the mutational evolution of EBV+ gastric tumors, whose consequences in terms of prognosis and treatment implications should be vetted.

The impact of HER2 overexpression on the miRNA and circRNA transcriptomes in two breast cell lines and their vesicles.

HER2 upregulation is related with poor outcome in many tumor types. Whereas anti-HER2 treatment is the standard approach as adjuvant therapy in HER2-overexpressing breast cancer, the frequent relapses reinforce the need for alternative treatments. Here we used next-generation sequencing (NGS) to evaluate miRNAs and circRNAs in the cell-lines HB4a and C5.2, where the latter is a HER2-overexpressing clone of the former, and also from two different populations of their secreted extracellular vesicles. Whereas circRNA-levels were stable, we found at least 16 miRNAs apparently modulated by HER2-expression. The miR223-3p, miR-421 and miR-21-5p were validated in an independent cohort of 431 breast cancer patients from The Cancer Genome Atlas (TCGA). The consistent modulation of these molecules and their possible involvement in the HER2-axis makes them promising new targets to overcome HER2-activation.

Identification of DNA mutations in gastric washes from gastric adenocarcinoma patients: Possible implications for liquid biopsies and patient follow-up.

Whereas cancer patients have benefited from liquid biopsies, the scenario for gastric adenocarcinoma (GAC) is still dismal. We used next-generation deep sequencing of TP53-a highly mutated and informative gene in GAC-to assess mutations in tumor biopsies, plasma (PL) and stomach fluids (gastric wash-GW). We evaluated their potential to reveal tumor-derived mutations, useful for monitoring mutational dynamics at diagnosis, progression and treatment. Exon-capture libraries were constructed from 46 patients including tumor biopsies, GW and PL pre and post-treatment (196 samples), with high vertical coverage >8,000×. At diagnosis, we detected TP53 mutations in 15/46 biopsies (32.6%), 7/46 GW- (15.2%) and 6/46 PL-samples (13%). Biopsies and GW were concordant in 38/46 cases (82.6%) for the presence/absence of mutations and, furthermore, four GW-exclusive mutations were identified, suggesting tumor heterogeneity. Considering the combined analysis of GW and PL, TP53 mutations found in biopsies were also identified in 9/15 (60%) of cases, the highest detection level reported for GAC. Our study indicates that GW could be useful to track DNA alterations, especially if anchored to a comprehensive gene-panel designed for this malignancy.

Genomics and epidemiology for gastric adenocarcinomas (GE4GAC): a Brazilian initiative to study gastric cancer

Gastric cancer (GC) is the fifth most common type of cancer worldwide with high incidences in Asia, Central, and South American countries. This patchy distribution means that GC studies are neglected by large research centers from developed countries. The need for further understanding of this complex disease, including the local importance of epidemiological factors and the rich ancestral admixture found in Brazil, stimulated the implementation of the GE4GAC project. GE4GAC aims to embrace epidemiological, clinical, molecular and microbiological data from Brazilian controls and patients with malignant and pre-malignant gastric disease. In this letter, we summarize the main goals of the project, including subject and sample accrual and current findings

Complex Landscape of Germline Variants in Brazilian Patients With Hereditary and Early Onset Breast Cancer.

Pathogenic variants in known breast cancer (BC) predisposing genes explain only about 30% of Hereditary Breast Cancer (HBC) cases, whereas the underlying genetic factors for most families remain unknown. Here, we used whole-exome sequencing (WES) to identify genetic variants associated to HBC in 17 patients of Brazil with familial BC and negative for causal variants in major BC risk genes (BRCA1/2, TP53, and CHEK2 c.1100delC). First, we searched for rare variants in 27 known HBC genes and identified two patients harboring truncating pathogenic variants in ATM and BARD1. For the remaining 15 negative patients, we found a substantial vast number of rare genetic variants. Thus, for selecting the most promising variants we used functional-based variant prioritization, followed by NGS validation, analysis in a control group, cosegregation analysis in one family and comparison with previous WES studies, shrinking our list to 23 novel BC candidate genes, which were evaluated in an independent cohort of 42 high-risk BC patients. Rare and possibly damaging variants were identified in 12 candidate genes in this cohort, including variants in DNA repair genes (ERCC1 and SXL4) and other cancer-related genes (NOTCH2, ERBB2, MST1R, and RAF1). Overall, this is the first WES study applied for identifying novel genes associated to HBC in Brazilian patients, in which we provide a set of putative BC predisposing genes. We also underpin the value of using WES for assessing the complex landscape of HBC susceptibility, especially in less characterized populations.

A total transcriptome profiling method for plasma-derived extracellular vesicles: applications for liquid biopsies.

Extracellular vesicles (EVs) are key mediators of intercellular communication. Part of their biological effects can be attributed to the transfer of cargos of diverse types of RNAs, which are promising diagnostic and prognostic biomarkers. EVs found in human biofluids are a valuable source for the development of minimally invasive assays. However, the total transcriptional landscape of EVs is still largely unknown. Here we develop a new method for total transcriptome profiling of plasma-derived EVs by next generation sequencing (NGS) from limited quantities of patient-derived clinical samples, which enables the unbiased characterization of the complete RNA cargo, including both small- and long-RNAs, in a single library preparation step. This approach was applied to RNA extracted from EVs isolated by ultracentrifugation from the plasma of five healthy volunteers. Among the most abundant RNAs identified we found small RNAs such as tRNAs, miRNAs and miscellaneous RNAs, which have largely unknown functions. We also identified protein-coding and long noncoding transcripts, as well as circular RNA species that were also experimentally validated. This method enables, for the first time, the full spectrum of transcriptome data to be obtained from minute patient-derived samples, and will therefore potentially allow the identification of cell-to-cell communication mechanisms and biomarkers.

signeR: an empirical Bayesian approach to mutational signature discovery.

MOTIVATION: Mutational signatures can be used to understand cancer origins and provide a unique opportunity to group tumor types that share the same origins and result from similar processes. These signatures have been identified from high throughput sequencing data generated from cancer genomes by using non-negative matrix factorisation (NMF) techniques. Current methods based on optimization techniques are strongly sensitive to initial conditions due to high dimensionality and nonconvexity of the NMF paradigm. In this context, an important question consists in the determination of the actual number of signatures that best represent the data. The extraction of mutational signatures from high-throughput data still remains a daunting task. RESULTS: Here we present a new method for the statistical estimation of mutational signatures based on an empirical Bayesian treatment of the NMF model. While requiring minimal intervention from the user, our method addresses the determination of the number of signatures directly as a model selection problem. In addition, we introduce two new concepts of significant clinical relevance for evaluating the mutational profile. The advantages brought by our approach are shown by the analysis of real and synthetic data. The later is used to compare our approach against two alternative methods mostly used in the literature and with the same NMF parametrization as the one considered here. Our approach is robust to initial conditions and more accurate than competing alternatives. It also estimates the correct number of signatures even when other methods fail. Results on real data agree well with current knowledge. AVAILABILITY AND IMPLEMENTATION: signeR is implemented in R and C ++, and is available as a R package at http://bioconductor.org/packages/signeR CONTACT: itojal@cipe.accamargo.org.brSupplementary information: Supplementary data are available at Bioinformatics online.

Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c+ or CD141+ DCs.

In humans, conventional dendritic cells (cDCs) exist as two unique populations characterized by expression of CD1c and CD141. cDCs arise from increasingly restricted but well-defined bone marrow progenitors that include the common DC progenitor that differentiates into the pre-cDC, which is the direct precursor of cDCs. In this study, we show that pre-cDCs in humans are heterogeneous, consisting of two distinct populations of precursors that are precommitted to become either CD1c+ or CD141+ cDCs. The two groups of lineage-primed precursors can be distinguished based on differential expression of CD172a. Both subpopulations of pre-cDCs arise in the adult bone marrow and can be found in cord blood and adult peripheral blood. Gene expression analysis revealed that CD172a+ and CD172a- pre-cDCs represent developmentally discrete populations that differentially express lineage-restricted transcription factors. A clinical trial of Flt3L injection revealed that this cytokine increases the number of both CD172a- and CD172a+ pre-cDCs in human peripheral blood.

S-score: a scoring system for the identification and prioritization of predicted cancer genes.

A new method, which allows for the identification and prioritization of predicted cancer genes for future analysis, is presented. This method generates a gene-specific score called the "S-Score" by incorporating data from different types of analysis including mutation screening, methylation status, copy-number variation and expression profiling. The method was applied to the data from The Cancer Genome Atlas and allowed the identification of known and potentially new oncogenes and tumor suppressors associated with different clinical features including shortest term of survival in ovarian cancer patients and hormonal subtypes in breast cancer patients. Furthermore, for the first time a genome-wide search for genes that behave as oncogenes and tumor suppressors in different tumor types was performed. We envisage that the S-score can be used as a standard method for the identification and prioritization of cancer genes for follow-up studies.