Value of the loss of heterozygosity to BRCA1 variant classification.
Elizabeth Santana Dos SantosAmanda B SpurdleDirce M CarraroAdrien BriauxMelissa C SoutheyGiovana Tardin TorrezanAmbre PetitalotRaphael LemanPhilippe Lafittenull nullDidier MeseureKeltouma DriouchLucy SideCarole BrewerSarah BeckAthalie MelvilleAlison CallawayFrançoise RevillionMaria A A Koike FolgueiraMichael T ParsonsHeather ThorneAnne-Vincent SalomonDominique Stoppa-LyonnetIvan BiecheSandrine M CaputoEtienne RouleauPublished in: NPJ breast cancer (2022)
At least 10% of the BRCA1/2 tests identify variants of uncertain significance (VUS) while the distinction between pathogenic variants (PV) and benign variants (BV) remains particularly challenging. As a typical tumor suppressor gene, the inactivation of the second wild-type (WT) BRCA1 allele is expected to trigger cancer initiation. Loss of heterozygosity (LOH) of the WT allele is the most frequent mechanism for the BRCA1 biallelic inactivation. To evaluate if LOH can be an effective predictor of BRCA1 variant pathogenicity, we carried out LOH analysis on DNA extracted from 90 breast and seven ovary tumors diagnosed in 27 benign and 55 pathogenic variant carriers. Further analyses were conducted in tumors with PVs yet without loss of the WT allele: BRCA1 promoter hypermethylation, next-generation sequencing (NGS) of BRCA1/2, and BRCAness score. Ninety-seven tumor samples were analyzed from 26 different BRCA1 variants. A relatively stable pattern of LOH (65.4%) of WT allele for PV tumors was observed, while the allelic balance (63%) or loss of variant allele (15%) was generally seen for carriers of BV. LOH data is a useful complementary argument for BRCA1 variant classification.
Keyphrases
- copy number
- breast cancer risk
- machine learning
- deep learning
- gene expression
- squamous cell carcinoma
- dna methylation
- genome wide
- circulating tumor
- pseudomonas aeruginosa
- staphylococcus aureus
- lps induced
- cell free
- transcription factor
- cystic fibrosis
- single molecule
- artificial intelligence
- big data
- genome wide identification