"Jumping" DNA: A Tool for Finding Cancer Genes

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

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High likelihood of actionable pathogenic variant detection in breast cancer genes in women with very early onset breast cancer

Journal of Medical Genetics ◽

10.1136/jmedgenet-2020-107347 ◽

2021 ◽

pp. jmedgenet-2020-107347

Author(s):

D Gareth Evans ◽

Elke Maria van Veen ◽

Helen J Byers ◽

Sarah J Evans ◽

George J Burghel ◽

...

Keyword(s):

Breast Cancer ◽

Early Onset ◽

Ductal Carcinoma ◽

Cancer Genes ◽

Early Onset Breast Cancer ◽

Younger Women ◽

Pathogenic Variants ◽

Younger Age ◽

Predisposition Genes

BackgroundWhile the likelihood of identifying constitutional breast cancer-associated BRCA1, BRCA2 and TP53 pathogenic variants (PVs) increases with earlier diagnosis age, little is known about the correlation with age at diagnosis in other predisposition genes. Here, we assessed the contribution of known breast cancer-associated genes to very early onset disease.MethodsSequencing of BRCA1, BRCA2, TP53 and CHEK2 c.1100delC was undertaken in women with breast cancer diagnosed ≤30 years. Those testing negative were screened for PVs in a minimum of eight additional breast cancer-associated genes. Rates of PVs were compared with cases ≤30 years from the Prospective study of Outcomes in Sporadic vs Hereditary breast cancer (POSH) study.ResultsTesting 379 women with breast cancer aged ≤30 years identified 75 PVs (19.7%) in BRCA1, 35 (9.2%) in BRCA2, 22 (5.8%) in TP53 and 2 (0.5%) CHEK2 c.1100delC. Extended screening of 184 PV negative women only identified eight additional actionable PVs. BRCA1/2 PVs were more common in women aged 26–30 years than in younger women (p=0.0083) although the younger age group had rates more similar to those in the POSH cohort. Out of 26 women with ductal carcinoma in situ (DCIS) alone, most were high-grade and 11/26 (42.3%) had a PV (TP53=6, BRCA2=2, BRCA1=2, PALB2=1). This PV yield is similar to the 61 (48.8%) BRCA1/2 PVs identified in 125 women with triple-negative breast cancer. The POSH cohort specifically excluded pure DCIS which may explain lower TP53 PV rates in this group (1.7%).ConclusionThe rates of BRCA1, BRCA2 and TP53 PVs are high in very early onset breast cancer, with limited benefit from testing of additional breast cancer-associated genes.

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