Error-prone repair of stalled replication forks drives mutagenesis and loss of heterozygosity in haploinsufficient BRCA1 cells.

Germline mutations in the BRCA genes are associated with a higher risk of carcinogenesis, which is linked to an increased mutation rate and loss of the second unaffected BRCA allele (loss of heterozygosity, LOH). However, the mechanisms triggering mutagenesis are not clearly understood. The BRCA genes contain high numbers of repetitive DNA sequences. We detected replication forks stalling, DNA breaks, and deletions at these sites in haploinsufficient BRCA cells, thus identifying the BRCA genes as fragile sites. Next, we found that stalled forks are repaired by error-prone pathways, such as microhomology-mediated break-induced replication (MMBIR) in haploinsufficient BRCA1 breast epithelial cells. We detected MMBIR mutations in BRCA1 tumor cells and noticed deletions-insertions (>50 bp) at the BRCA1 genes in BRCA1 patients. Altogether, these results suggest that under stress, error-prone repair of stalled forks is upregulated and induces mutations, including complex genomic rearrangements at the BRCA genes (LOH), in haploinsufficient BRCA1 cells.

Germline Testing Data Validate Inferences of Mutational Status for Variants Detected From Tumor-Only Sequencing.

Pathogenic germline variants (PGVs) in cancer susceptibility genes are usually identified through germline testing of DNA from blood or saliva: their detection can affect treatment options and potential risk-reduction strategies for patient relatives. PGV can also be identified in tumor sequencing assays, which, when performed without patient-matched normal specimens, render determination of variants' germline or somatic origin critical. METHODS: Tumor-only sequencing data from 1,608 patients were retrospectively analyzed to infer germline versus somatic status of variants using an information-theoretic, gene-independent approach. Loss of heterozygosity was also determined. Predicted mutational models were compared with clinical germline testing results. Statistical measures were computed to evaluate performance. RESULTS: Tumor-only sequencing detected 3,988 variants across 70 cancer susceptibility genes for which germline testing data were available. We imputed germline versus somatic status for > 75% of all detected variants, with a sensitivity of 65%, specificity of 88%, and overall accuracy of 86% for pathogenic variants. False omission rate was 3%, signifying minimal error in misclassifying true PGV. A higher portion of PGV in known hereditary tumor suppressors were found to be retained with loss of heterozygosity in the tumor specimens (72%) compared with variants of uncertain significance (58%). CONCLUSION: Analyzing tumor-only data in the context of specimens' tumor cell content allows precise, systematic exclusion of somatic variants and suggests a balance between type 1 and 2 errors for identification of patients with candidate PGV for standard germline testing. Although technical or systematic errors in measuring variant allele frequency could result in incorrect inference, misestimation of specimen purity could result in inferring somatic variants as germline in somatically mutated tumor suppressor genes. A user-friendly bioinformatics application facilitates objective analysis of tumor-only data in clinical settings.

Wearable sensors for the monitoring of movement disorders.

This paper offers a review of the implementation of current wearable sensing technologies in monitoring the movement and activity of patients suffering from movement disorders. Recent literature has focused on incorporating simple and reliable wearable technologies for the continuous and objective monitoring of patient movement during normal daily activities. However, the use of such wearable sensing technologies has yet to find its way to clinical practice. In the following, the basic elements of such monitoring systems and their applications are introduced, and a discussion regarding current clinical applications is presented.

Activity Recognition Using Complex Network Analysis.

In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a random forest classifier, and when considering a monitoring system composed of only two modules positioned at the neck and thigh of the subject's body.

Detection of Levodopa Induced Dyskinesia in Parkinson's Disease patients based on activity classification.

In this paper, we present an activity classification-based algorithm for the automatic detection of Levodopa Induced Dyskinesia in Parkinson's Disease (PD) patients. Two PD patients experiencing motor fluctuations related to chronic Levodopa therapy performed a protocol of simple daily life activities on at least two different occasions. A Random Forest classifier was able to classify the performed activities by the patients with an overall accuracy of 86%. Based on the detected activity, a K Nearest Neighbor classifier detected the presence of dyskinesia with accuracy ranging from 75% to 88%.