Tumor Signature Analysis Implicates Hereditary Cancer Genes in Endometrial Cancer Development.
Olga KondrashovaJannah ShamsaniTracy A O'MaraFelicity NewellAmy E McCart ReedSunil R LakhaniJudy KirkJohn V PearsonNicola WaddellAmanda B SpurdlePublished in: Cancers (2021)
Risk of endometrial cancer (EC) is increased ~2-fold for women with a family history of cancer, partly due to inherited pathogenic variants in mismatch repair (MMR) genes. We explored the role of additional genes as explanation for familial EC presentation by investigating germline and EC tumor sequence data from The Cancer Genome Atlas (n = 539; 308 European ancestry), and germline data from 33 suspected familial European ancestry EC patients demonstrating immunohistochemistry-detected tumor MMR proficiency. Germline variants in MMR and 26 other known/candidate EC risk genes were annotated for pathogenicity in the two EC datasets, and also for European ancestry individuals from gnomAD as a population reference set (n = 59,095). Ancestry-matched case-control comparisons of germline variant frequency and/or sequence data from suspected familial EC cases highlighted ATM, PALB2, RAD51C, MUTYH and NBN as candidates for large-scale risk association studies. Tumor mutational signature analysis identified a microsatellite-high signature for all cases with a germline pathogenic MMR gene variant. Signature analysis also indicated that germline loss-of-function variants in homologous recombination (BRCA1, PALB2, RAD51C) or base excision (NTHL1, MUTYH) repair genes can contribute to EC development in some individuals with germline variants in these genes. These findings have implications for expanded therapeutic options for EC cases.
Keyphrases
- dna repair
- genome wide
- endometrial cancer
- dna damage
- genome wide identification
- copy number
- papillary thyroid
- bioinformatics analysis
- dna damage response
- case control
- dna methylation
- genome wide analysis
- electronic health record
- early onset
- squamous cell
- gene expression
- squamous cell carcinoma
- big data
- newly diagnosed
- machine learning
- ejection fraction
- pseudomonas aeruginosa
- prognostic factors
- data analysis
- cystic fibrosis
- single cell
- lymph node metastasis
- biofilm formation
- artificial intelligence