Transcriptome Analysis in Vulvar Squamous Cell Cancer

To date, therapeutic strategies in vulvar squamous cell carcinoma (VSCC) are lacking molecular pathological information and targeted therapy hasn’t been approved in the treatment of VSCC, yet. Two etiological pathways are widely accepted: HPV induced vs. HPV independent, associated with chronic skin disease, often harboring TP53 mutations (mut). The aim of this analysis was to analyze the RNA expression patterns for subtype stratification on VSCC samples that can be integrated into the previously performed whole exome sequencing data for the detection of prognostic markers and potential therapeutic targets. We performed multiplex gene expression analysis (NanoString) with 770 genes in 24 prior next generation sequenced samples. An integrative data analysis was performed. Here, 98 genes were differentially expressed in TP53mut vs. HPV+ VSCC, in the TP53mut cohort, where 56 genes were upregulated and 42 were downregulated in comparison to the HPV+ tumors. Aberrant expression was primarily observed in cell cycle regulation, especially in HPV+ disease. Within the TP53mut group, a distinct cluster was identified that was correlated to a significantly worse overall survival (p = 0.017). The RNA expression profiles showed distinct patterns with regard to the known VSCC subtypes and could potentially enable further subclassification in the TP53mut groups

DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost

Detection of somatic mutation in whole-exome sequencing data can help elucidate the mechanism of tumor progression. Most computational approaches require exome sequencing for both tumor and normal samples. However, it is more common to sequence exomes for tumor samples only without the paired normal samples. To include these types of data for extensive studies on the process of tumorigenesis, it is necessary to develop an approach for identifying somatic mutations using tumor exome sequencing data only. In this study, we designed a machine learning approach using Deep Neural Network (DNN) and XGBoost to identify somatic mutations in tumor-only exome sequencing data and we integrated this into a pipeline called DNN-Boost. The XGBoost algorithm is used to extract the features from the results of variant callers and these features are then fed into the DNN model as input. The XGBoost algorithm resolves issues of missing values and overfitting. We evaluated our proposed model and compared its performance with other existing benchmark methods. We noted that the DNN-Boost classification model outperformed the benchmark method in classifying somatic mutations from paired tumor-normal exome data and tumor-only exome data.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Secondary findings from whole-exome sequencing data in families with familial combined hyperlipidemia (FCHL)

Background During the interpretation of genome sequencing data, some types of secondary findings are identified that are located in genes that do not appear to be related to the causes of the primary disease. Although these are not the primary targets for evaluation, they have a high risk for some diseases different from the primary disease. Therefore, they can be vital for preventing and intervention from such disease. Results Here, we analyzed secondary findings obtained from WES in 6 families with FCHL disease who had an autosomal-dominant pattern based on their pedigrees. These finding are found in CDKAL1, ITGA2, FAM111A, WNK4, PTGIS, SCN10, TBX20, DCHS1, ANK2 and ABCA1 genes. Conclusions Secondary findings are very important and must be considered different variants from sequencing results in a diagnostic setting. Although we have considered these variants as secondary findings, some of them may be related to the primary disease.

Machine-learning algorithms predict breast cancer patient survival from UK Biobank whole-exome sequencing data

Aim: We tested whether machine-learning algorithm could find biomarkers predicting overall survival in breast cancer patients using blood-based whole-exome sequencing data. Materials & methods: Whole-exome sequencing data derived from 1181 female breast cancer patients within the UK Biobank was collected. We found feature genes (n = 50) regarding total mutation burden using the long short-term memory model. Then, we developed the XGBoost survival model with selected feature genes. Results: The XGBoost survival model performed acceptably, with a concordance index of 0.75 and a scaled Brier score of 0.146 in terms of overall survival prediction. The high-mutation group exhibited inferior overall survival compared with the low-mutation group in patients ≥56 years (log-rank test, p = 0.042). Conclusion: We showed that machine-learning algorithms can be used to predict overall survival in breast cancer patients from blood-based whole-exome sequencing data.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole exome sequencing data of about 4,000 SARS-CoV-2-positive individuals were used to define an interpretable machine learning model for predicting COVID-19 severity. Firstly, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthly, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data

Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.

Identification and prioritization of myeloid malignancy germline variants in a large cohort of adult AML patients

Inherited predisposition to myeloid malignancies is more common than previously appreciated. We analyzed the whole-exome sequencing data of paired leukemia and skin biopsy samples from 391 adult patients from the Beat AML 1.0 consortium. Using the 2015 ACMG guidelines for variant interpretation, we curated 1,547 unique variants from 228 genes. The pathogenic/likely pathogenic (P/LP) germline variants were identified in 53 AML patients (13.6%) in 34 genes. 41% of variants were in DNA damage response genes, and the most frequently mutated genes were CHEK2 (8 patients) and DDX41 (7 patients). 44% of the pathogenic germline variants were in genes considered clinically actionable (tier 1). Pathogenic germline variants were also found in new candidate genes (DNAH5, DNAH9, DNMT3A, SUZ12). No strong correlation was found between the germline mutational rate and age of AML onset. Among 49 patients who have a reported history of at least one family member affected with hematological malignancies, six patients harbored known P/LP germline variants and the remaining patients had at least one variant of uncertain significance, suggesting a need for further functional validation studies. Using CHEK2 as an example, we show that three-dimensional protein modeling can be one of the effective methodologies to prioritize variants of unknown significance for functional studies. Further, we evaluated an in-silico approach that applies ACMG/AMP curation in an automated manner using the tool for assessment and prioritization in exome studies (TAPES), which can minimize manual curation time for variants. Overall, our findings suggest a need to comprehensively understand the predisposition potential of many germline variants in order to enable closer monitoring for disease management and treatment interventions for affected patients and families.

A mosaic PIK3CA variant in a young adult with diffuse gastric cancer: case report

Hereditary diffuse gastric cancer (HDGC) is associated with germline deleterious variants in CDH1 and CTNNA1. The majority of HDGC-suspected patients are still genetically unresolved, raising the need for identification of novel HDGC predisposing genes. Under the collaborative environment of the SOLVE-RD consortium, re-analysis of whole-exome sequencing data from unresolved gastric cancer cases (n = 83) identified a mosaic missense variant in PIK3CA in a 25-year-old female with diffuse gastric cancer (DGC) without familial history for cancer. The variant, c.3140A>G p.(His1047Arg), a known cancer-related somatic hotspot, was present at a low variant allele frequency (18%) in leukocyte-derived DNA. Somatic variants in PIK3CA are usually associated with overgrowth, a phenotype that was not observed in this patient. This report highlights mosaicism as a potential, and understudied, mechanism in the etiology of DGC.