hereditary cancer
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Author(s):  
Su-Ying Fang ◽  
Ling-Ling Hsieh ◽  
Chen-Fang Hung ◽  
Fei-Hung Hung ◽  
Hung-Pin Peng ◽  
...  

2022 ◽  
Vol 11 ◽  
Author(s):  
Van Thuan Tran ◽  
Sao Trung Nguyen ◽  
Xuan Dung Pham ◽  
Thanh Hai Phan ◽  
Van Chu Nguyen ◽  
...  

BackgroundHereditary cancer syndromes (HCS) are responsible for 5-10% of cancer cases. Genetic testing to identify pathogenic variants associated with cancer predisposition has not been routinely available in Vietnam. Consequently, the prevalence and genetic landscape of HCS remain unknown.Methods1165 Vietnamese individuals enrolled in genetic testing at our laboratory in 2020. We performed analysis of germline mutations in 17 high- and moderate- penetrance genes associated with HCS by next generation sequencing.ResultsA total of 41 pathogenic variants in 11 genes were detected in 3.2% individuals. The carrier frequency was 4.2% in people with family or personal history of cancer and 2.6% in those without history. The percentage of mutation carriers for hereditary colorectal cancer syndromes was 1.3% and for hereditary breast and ovarian cancer syndrome was 1.6%. BRCA1 and BRCA2 mutations were the most prevalent with the positive rate of 1.3% in the general cohort and 5.1% in breast or ovarian cancer patients. Most of BRCA1 mutations located at the BRCA C-terminus domains and the top recurrent mutation was NM_007294.3:c.5251C>T (p.Arg1751Ter). One novel variant NM_000038.6(APC):c.6665C>A (p.Pro2222His) was found in a breast cancer patient with a strong family history of cancer. A case study of hereditary cancer syndrome was illustrated to highlight the importance of genetic testing.ConclusionThis is the first largest analysis of carrier frequency and mutation spectrum of HCS in Vietnam. The findings demonstrate the clinical significance of multigene panel testing to identify carriers and their at-risk relatives for better cancer surveillance and management strategies.


2022 ◽  
Author(s):  
Amanda Bartenbaker Thompson ◽  
Erin G. Sutcliffe ◽  
Kevin Arvai ◽  
Maegan E. Roberts ◽  
Lisa R. Susswein ◽  
...  

Author(s):  
Chethan Ramamurthy ◽  
Eric W. Stutz ◽  
Martin Goros ◽  
Jonathan Gelfond ◽  
Teresa L. Johnson-Pais ◽  
...  

2021 ◽  
pp. 1-9
Author(s):  
Jordon B. Ritchie ◽  
Brandon M. Welch ◽  
Caitlin G. Allen ◽  
Lewis J. Frey ◽  
Heath Morrison ◽  
...  

<b><i>Introduction:</i></b> Primary care providers (PCPs) and oncologists lack time and training to appropriately identify patients at increased risk for hereditary cancer using family health history (FHx) and clinical practice guideline (CPG) criteria. We built a tool, “ItRunsInMyFamily” (ItRuns) that automates FHx collection and risk assessment using CPGs. The purpose of this study was to evaluate ItRuns by measuring the level of concordance in referral patterns for genetic counseling/testing (GC/GT) between the CPGs as applied by the tool and genetic counselors (GCs), in comparison to oncologists and PCPs. The extent to which non-GCs are discordant with CPGs is a gap that health information technology, such as ItRuns, can help close to facilitate the identification of individuals at risk for hereditary cancer. <b><i>Methods:</i></b> We curated 18 FHx cases and surveyed GCs and non-GCs (oncologists and PCPs) to assess concordance with ItRuns CPG criteria for referring patients for GC/GT. Percent agreement was used to describe concordance, and logistic regression to compare providers and the tool’s concordance with CPG criteria. <b><i>Results:</i></b> GCs had the best overall concordance with the CPGs used in ItRuns at 82.2%, followed by oncologists with 66.0% and PCPs with 60.6%. GCs were significantly more likely to concur with CPGs (OR = 4.04, 95% CI = 3.35–4.89) than non-GCs. All providers had higher concordance with CPGs for FHx cases that met the criteria for genetic counseling/testing than for cases that did not. <b><i>Discussion/Conclusion:</i></b> The risk assessment provided by ItRuns was highly concordant with that of GC’s, particularly for at-risk individuals. The use of such technology-based tools improves efficiency and can lead to greater numbers of at-risk individuals accessing genetic counseling, testing, and mutation-based interventions to improve health.


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