Search

Objective: The aim of this study was to validate noninvasive prenatal testing (NIPT) for fetal aneuploidies by whole-genome massively parallel sequencing (MPS)., Methods: MPS was performed on cell-free DNA (cfDNA) isolated from maternal plasma in two groups: a first set of 186 euploid samples and a second set of 195 samples enriched of aneuploid cases (n = 69); digital PCR for fetal fraction (FF) assessment was performed on 178/381 samples. Cases with <10 × 10 6 reads (n = 54) were excluded for downstream data analysis. Follow-up data (invasive testing results or neonatal information) were available for all samples. Performances in terms of specificity/sensitivity and Z-score distributions were evaluated., Results: All positive samples for trisomy 21 (T21) (n = 43), trisomy 18 (T18) (n = 6) and trisomy 13 (T13) (n = 7) were correctly identified (sensitivity: 99.9%); 5 false positive results were reported: 3 for T21 (specificity = 98.9%) and 2 for T13 (specificity = 99.4%). Besides FF, total cfDNA concentration seems another important parameter for MPS, since it influences the number of reads., Conclusions: The overall test accuracy allowed us introducing NIPT for T21, T18 and T13 as a clinical service for pregnant women after 10 + 4 weeks of gestation. Sex chromosome aneuploidy assessment needs further validation due to the limited number of aneuploid cases in this study.

Loss of androgen receptor (AR) signaling dependence occurs in approximately 15%-20% of advanced treatment-resistant prostate cancers, and this may manifest clinically as transformation from a prostate adenocarcinoma histology to a castration-resistant neuroendocrine prostate cancer (CRPC-NE). The diagnosis of CRPC-NE currently relies on a metastatic tumor biopsy, which is invasive for patients and sometimes challenging to diagnose due to morphologic heterogeneity. By studying whole-exome sequencing and whole-genome bisulfite sequencing of cell free DNA (cfDNA) and of matched metastatic tumor biopsies from patients with metastatic prostate adenocarcinoma and CRPC-NE, we identified CRPC-NE features detectable in the circulation. Overall, there was markedly higher concordance between cfDNA and biopsy tissue genomic alterations in patients with CRPC-NE compared with castration-resistant adenocarcinoma, supporting greater intraindividual genomic consistency across metastases. Allele-specific copy number and serial sampling analyses allowed for the detection and tracking of clonal and subclonal tumor cell populations. cfDNA methylation was indicative of circulating tumor content fraction, reflective of methylation patterns observed in biopsy tissues, and was capable of detecting CRPCNE-associated epigenetic changes (e.g., hypermethylation of ASXL3 and SPDEF; hypomethylation of INSM1 and CDH2). A targeted set combining genomic (TP53, RB1, CYLD, AR) and epigenomic (hypo- and hypermethylation of 20 differential sites) alterations applied to ctDNA was capable of identifying patients with CRPC-NE., Introduction Prostate cancer is a leading cause of cancer death for men worldwide (1). Despite significant advances in therapy for patients with metastatic disease, the emergence of treatment resistance remains [...]

Schwannomatosis is characterized by the development of multiple non-vestibular, non-intradermal schwannomas. Constitutional inactivating variants in two genes, SMARCB1 and, very recently, LZTR1, have been reported. We performed exome sequencing of 13 schwannomatosis patients from 11 families without SMARCB1 deleterious variants. We identified four individuals with heterozygous loss-of-function variants in LZTR1. Sequencing of the germline of 60 additional patients identified 18 additional heterozygous variants in LZTR1. We identified LZTR1 variants in 43% and 30% of familial (three of the seven families) and sporadic patients, respectively. In addition, we tested LZTR1 protein immunostaining in 22 tumors from nine unrelated patients with and without LZTR1 deleterious variants. Tumors from individuals with LZTR1 variants lost the protein expression in at least a subset of tumor cells, consistent with a tumor suppressor mechanism. In conclusion, our study demonstrates that molecular analysis of LZTR1 may contribute to the molecular characterization of schwannomatosis patients, in addition to NF2 mutational analysis and the detection of chromosome 22 losses in tumor tissue. It will be especially useful in differentiating schwannomatosis from mosaic Neurofibromatosis type 2 (NF2). However, the role of LZTR1 in the pathogenesis of schwannomatosis needs further elucidation.

DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice. [ABSTRACT FROM AUTHOR]

Motivation: The advent of high-throughput sequencing technologies is revolutionizing our ability in discovering and genotyping DNA copy number variants (CNVs). Read count-based approaches are able to detect CNV regions with an unprecedented resolution. Although this computational strategy has been recently introduced in literature, much work has been already done for the preparation, normalization and analysis of this kind of data.Results: Here we face the many aspects that cover the detection of CNVs by using read count approach. We first study the characteristics and systematic biases of read count distributions, focusing on the normalization methods designed for removing these biases. Subsequently, we compare the algorithms designed to detect the boundaries of CNVs and we investigate the ability of read count data to predict the exact number of DNA copy. Finally, we review the tools publicly available for analysing read count data. To better understand the state of the art of read count approaches, we compare the performance of the three most widely used sequencing technologies (Illumina Genome Analyzer, Roche 454 and Life Technologies SOLiD) in all the analyses that we perform.Contact: albertomagi@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Array comparative genomic hybridization (aCGH) is a microarray technology that allows one to detect and map genomic alterations. The goal of aCGH analysis is to identify the boundaries of the regions where the number of DNA copies changes (breakpoint identification) and then to label each region as loss, neutral, or gain (calling). In this paper, we introduce a new algorithm, based on the shifting level model (SLM), with the aim of locating regions with different means of the log2 ratio in genomic profiles obtained from aCGH data. We combine the SLM algorithm with the CGHcall calling procedure and compare their performances with 5 state-of-the-art methods. When dealing with synthetic data, our method outperforms the other 5 algorithms in detecting the change in the number of DNA copies in the most challenging situations. For real aCGH data, SLM is able to locate all the cytogenetically mapped aberrations giving a smaller number of false-positive breakpoints than the compared methods. The application of the SLM algorithm is not limited to aCGH data. Our approach can also be used for the analysis of several emerging experimental strategies such as high-resolution tiling array. [ABSTRACT FROM PUBLISHER]

The tumor suppressor protein retinoblastoma (RB) is mechanistically linked to suppression of transcription factor E2F1-mediated cell cycle regulation. For multiple tumor types, loss of RB function is associated with poor clinical outcome. RB action is abrogated either by direct depletion or through inactivation of RB function; however, the basis for this selectivity is unknown. Here, analysis of tumor samples and cell-free DNA from patients with advanced prostate cancer showed that direct RB loss was the preferred pathway of disruption in human disease. While RB loss was associated with lethal disease, RB-deficient tumors had no proliferative advantage and exhibited downstream effects distinct from cell cycle control. Mechanistically, RB loss led to E2F1 cistrome expansion and different binding specificity, alterations distinct from those observed after functional RB inactivation. Additionally, identification of protumorigenic transcriptional networks specific to RB loss that were validated in clinical samples demonstrated the ability of RB loss to differentially reprogram E2F1 in human cancers. Together, these findings not only identify tumor-suppressive functions of RB that are distinct from cell cycle control, but also demonstrate that the molecular consequence of RB loss is distinct from RB inactivation. Thus, these studies provide insight into how RB loss promotes disease progression, and identify new nodes for therapeutic intervention. [ABSTRACT FROM AUTHOR]