BRCA gene mutations in cancer

This study assesses oncogenetics knowledge among healthcare workers, finding gaps in breast cancer-specific knowledge and gender differences.

The paper identifies how the BRCA1-Y1853ter mutation alters protein interactions and cellular processes, suggesting gain-of-function effects in cancer.

The paper validates a digital tool for assessing germline cancer genetic testing eligibility based on patient and family history, showing high agreement with genetic counselors.

The paper identifies two founder BRCA variants in Orkney and Shetland populations, accounting for 93.5% of pathogenic BRCA alleles.

This study compares digital and in-person genetic counseling for germline testing in breast cancer patients, finding higher uptake with digital information without compromising other outcomes.

The paper develops a Nextflow pipeline for BRCA1/2 variant detection in Chilean breast cancer patients using targeted sequencing and variant callers, identifying one pathogenic variant among 16 samples.

CAL-Leiden is a co-segregation model for BRCA1, BRCA2, and PALB2 variant classification, integrating population data and cancer types to improve pathogenicity assessment.

The paper functionally tests most CHEK2 SNVs using a yeast complementation assay, finding 770 damaging and 2,417 tolerated variants, aiding in pathogenicity prediction.

The paper refines ACMG/AMP variant classification criteria for BRCA1 and BRCA2 by calibrating evidence strength and improving classification consistency through expert panel review.

This study evaluates 140 breast cancer PRS models in a Thai population, identifying PGS004688 as the best-performing model with significant risk stratification.

The paper updates breast cancer risk estimates for BRCA2 carriers aged 60 and older using data from two large studies.

This study evaluates the accuracy of next-generation sequencing in capturing allele frequencies for polygenic risk score loci in breast cancer, finding significant deviations affecting risk prediction.

This paper performs a meta-analysis of breast cancer risk associated with PALB2 pathogenic variants, finding an overall risk of 12.80% by age 50 and 48.47% by age 80.

This study evaluates deep learning models for classifying pathogenic germline variants in hereditary breast cancer using UK Biobank data, finding limited clinical utility for variants of uncertain significance.

This study identifies seven novel genetic loci associated with high-grade serous ovarian cancer risk using a large-scale GWAS and develops polygenic scores for improved risk prediction.

The paper creates a functional map of all possible missense variants in CHK2 to assess their impact on cancer risk.

The paper evaluates a next-generation sequencing panel for hereditary breast cancer in a Brazilian population, identifying two pathogenic variants and one novel candidate pathogenic variant in BRCA2.

The paper identifies how germline and somatic mutations interact to cause resistance to CDK4/6 inhibitors in breast cancer and proposes a sequential treatment strategy using PARP inhibitors.

This study evaluates genetic screening approaches for breast cancer risk, finding that combining rare and common variants improves risk stratification.

The paper develops AVENGERS, a method using CRISPR-Cas9 to classify BRCA2 variants as functional or nonfunctional, improving the interpretation of variants of uncertain significance.

The paper functionally characterizes BRCA2 variants using saturation genome editing and classifies them clinically, finding functionally abnormal variants are associated with increased cancer risk.

This study evaluates genetic counselling strategies for genome sequencing-based tumour diagnostics, finding that post-test strategies reduce unnecessary referrals while maintaining PGV detection rates.

The paper develops methods to classify homologous recombination deficiency in breast cancer using mutational and transcriptional signatures, showing heterogeneity and single-cell microenvironment interactions.

The paper analyzes BRCA1 exon 11 genetic variations in Libyan breast cancer patients with a family history of cancer, identifying 12 variants, three of which are novel.

The paper examines how family history affects cancer penetrance in individuals with pathogenic variants, finding higher risks with family history for BRCA1/2 and Lynch syndrome.

The paper identifies prenatal BRCA1 epimutations as a cause of approximately 20% of triple-negative breast cancers and low-ER expression breast cancers.

This paper evaluates an educational program for healthcare providers on polygenic risk scores, finding it improves their attitudes, confidence, knowledge, and preparedness for using PRS in clinical practice.

HRD-One provides a reproducible HRD score with high concordance to myChoice(R) and identifies HRD in ovarian cancer samples with and without BRCA1/2 mutations.

The paper identifies a BRCA1 founder variant in Orcadians and estimates its high prevalence, suggesting population screening for this variant.

The paper extends Mendelian risk prediction models to incorporate variant-specific penetrances, improving cancer risk predictions for BRCA1/2 pathogenic variants.