A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants.

The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing an appropriate software from such a broad range of tools can be challenging; therefore, systematic benchmarking with high-quality, independent datasets is critical. Using three independent benchmarking datasets compiled from the ClinVar database, we evaluated the performance of ten widely used prediction algorithms with missense variants from 21 clinically relevant genes, including BRCA1 and BRCA2. A fourth dataset consisting of 1053 missense variants was also used to investigate the impact of type 1 circularity on their performance. The performance of the prediction algorithms varied widely across datasets. Based on Matthews Correlation Coefficient and Area Under the Curve, SNPs&GO and PMut consistently displayed an overall above-average performance across the datasets. Most of the tools demonstrated greater sensitivity and negative predictive values at the expense of lower specificity and positive predictive values. We also demonstrated that type 1 circularity significantly impacts the performance of these tools and, if not accounted for, may confound the selection of the best performing algorithms.

BC-Monitor: Towards a Routinely Accessible Circulating Tumor DNA-Based Tool for Real-Time Monitoring Breast Cancer Progression and Treatment Effectiveness.

Circulating tumor DNA (ctDNA) is increasingly employed in the screening, follow-up, and monitoring of the continuously evolving tumor; however, most ctDNA assays validated for clinical use cannot maintain the right balance between sensitivity, coverage, sample requirements, time, and cost. Here, we report our BC-monitor, a simple, well-balanced ctDNA diagnostic approach using a gene panel significant in breast cancer and an optimized multiplex PCR-based NGS protocol capable of identifying allele variant frequencies below 1% in cell-free plasma DNA. We monitored a cohort of 45 breast cancer patients prospectively enrolled into our study receiving neoadjuvant chemotherapy or endocrine therapy or palliative therapy for metastatic diseases. Their tumor mutation status was examined in the archived tumor samples and plasma samples collected before and continuously during therapy. Traceable mutations of the used 38-plex NGS assay were found in approximately two-thirds of the patients. Importantly, we detected new pathogenic variants in follow-up plasma samples that were not detected in the primary tumor and baseline plasma samples. We proved that the BC-monitor can pre-indicate disease progression four-six months earlier than conventional methods. Our study highlights the need for well-designed ctDNA monitoring during treatment and follow-up, integrated into a real-time treatment assessment, which could provide information on the active tumor DNA released into the blood.

The Combination of Single-Cell and Next-Generation Sequencing Can Reveal Mosaicism for BRCA2 Mutations and the Fine Molecular Details of Tumorigenesis.

Germline mutations in the BRCA1 and BRCA2 genes are responsible for hereditary breast and ovarian cancer syndrome. Germline and somatic BRCA1/2 mutations may define therapeutic targets and refine cancer treatment options. However, routine BRCA diagnostic approaches cannot reveal the exact time and origin of BRCA1/2 mutation formation, and thus, the fine details of their contribution to tumor progression remain less clear. Here, we establish a diagnostic pipeline using high-resolution microscopy and laser microcapture microscopy to test for BRCA1/2 mutations in the tumor at the single-cell level, followed by deep next-generation sequencing of various tissues from the patient. To demonstrate the power of our approach, here, we describe a detailed single-cell-level analysis of an ovarian cancer patient we found to exhibit constitutional somatic mosaicism of a pathogenic BRCA2 mutation. Employing next-generation sequencing, BRCA2 c.7795G>T, p.(Glu2599Ter) was detected in 78% of reads in DNA extracted from ovarian cancer tissue and 25% of reads in DNA derived from peripheral blood, which differs significantly from the expected 50% of a hereditary mutation. The BRCA2 mutation was subsequently observed at 17-20% levels in the normal ovarian and buccal tissue of the patient. Together, our findings suggest that this mutation occurred early in embryonic development. Characterization of the mosaic mutation at the single-cell level contributes to a better understanding of BRCA mutation formation and supports the concept that the combination of single-cell and next-generation sequencing methods is advantageous over traditional mutational analysis methods. This study is the first to characterize constitutional mosaicism down to the single-cell level, and it demonstrates that BRCA2 mosaicism occurring early during embryogenesis can drive tumorigenesis in ovarian cancer.

Simultaneous detection of BRCA mutations and large genomic rearrangements in germline DNA and FFPE tumor samples.

The development of breast and ovarian cancer is strongly connected to the inactivation of the BRCA1 and BRCA2 genes by different germline and somatic alterations, and their diagnosis has great significance in targeted tumor therapy, since recently approved PARP inhibitors show high efficiency in the treatment of BRCA-deficient tumors. This raises the need for new diagnostic methods that are capable of performing an integrative mutation analysis of the BRCA genes not only from germline DNA but also from formalin-fixed and paraffin-embedded (FFPE) tumor samples. Here we describe the development of such a methodology based on next-generation sequencing and a new bioinformatics software for data analysis. The diagnostic method was initially developed on an Illumina MiSeq NGS platform using germline-mutated stem cell lines and then adapted for the Ion Torrent PGM NGS platform as well. We also investigated the usability of NGS coverage data for the detection of copy number variations and exon deletions as a replacement of the conventional MLPA technique. Finally, we tested the developed workflow on FFPE samples from breast and ovarian cancer patients. Our method meets the sensitivity and specificity requirements for the genetic diagnosis of breast and ovarian cancers both from germline and FFPE samples.