Electronically ascertained extended pedigrees in breast cancer genetic counseling

Stefánsdóttir, Vigdís; Skirton, Heather; Jóhannsson, Óskar T.; Ólafsdóttir, Halla Sif; Olafsdottir, Gudridur H; Tryggvadóttír, Laufey; Jónsson, Jón J.

doi:10.1007/s10689-018-0105-3

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…In June 2006, a formal cancer GC unit was established at Landspitali University Hospital LUH [13,14] The unit's current workflow in cancer GC for individuals with cancer family history varies slightly depending on knowledge of a PV within the family (Fig. 1).…”

Section: Cancer Genetic Counselling Workflowmentioning

confidence: 99%

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Web-based return of BRCA2 research results: one-year genetic counselling experience in Iceland

et al. 2020

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There is an increased pressure to return results from research studies. In Iceland, deCODE Genetics has emphasised the importance of returning results to research participants, particularly the founder pathogenic BRCA2 variant; NM_000059.3: c.771_775del. To do so, they opened the website www.arfgerd.is. Individuals who received positive results via the website were offered genetic counselling (GC) at Landspitali in Reykjavik. At the end of May 2019, over 46.000 (19% of adults of Icelandic origin) had registered at the website and 352 (0.77%) received text message informing them about their positive results. Of those, 195 (55%) contacted the GC unit. Additionally, 129 relatives asked for GC and confirmatory testing, a total of 324 individuals. Various information such as gender and age, prior knowledge of the variant and perceived emotional impact, was collected. Of the BRCA2 positive individuals from the website, 74 (38%) had prior knowledge of the pathogenic variant (PV) in the family. The majority initially stated worries, anxiety or other negative emotion but later in the process many communicated gratitude for the knowledge gained. Males represented 41% of counsellees as opposed to less than 30% in the regular hereditary breast and ovarian (HBOC) clinic. It appears that counselling in clinical settings was more reassuring for worried counsellees. In this article, we describe one-year experience of the GC service to those who received positive results via the website. This experience offers a unique opportunity to study the public response of a successful method of the return of genetic results from research.

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Section: Cancer Genetic Counselling Workflowmentioning

confidence: 99%

“…Normal time for return of results is 7-10 days for a founder PV in the BRCA genes and 4-6 weeks for a cancer panel. Results are mostly given by phone, followed by an information leaflet about the PV, inheritance and ment and the counsellee is referred to the appropriate surveillance program [13,15].…”

Section: Cancer Genetic Counselling Workflowmentioning

confidence: 99%

Web-based return of BRCA2 research results: one-year genetic counselling experience in Iceland

et al. 2020

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“…Though sample sizes are currently limited for most diseases, in recent years, extensive progress has been made to improve and expand family health history collection, including growing efforts in systematic data collection by research consortia [54,78,71] and genetic testing companies [44], the development of a wide array of electronic patient-facing family history tools [99], which allow patients to gather family history information outside the clinic and therefore overcome the time constraints of traditional approaches where practitioners record family history during clinical visits, and the implementation of technology allowing for communication between family history tools and electronic health records [67]. Also, electronic genealogical databases are rapidly expanding and there are continuing efforts to link them with clinical data to generate pedigrees [85,86,1,91]. These developments will lead to increased opportunities to refine NN models for hereditary cancer and to train NN models for other hereditary diseases.…”

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confidence: 99%

Prediction of Hereditary Cancers Using Neural Networks

Guan¹,

Parmigiani²,

Braun³

et al. 2021

Preprint

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Family history is a major risk factor for many types of cancer. Mendelian risk prediction models translate family histories into cancer risk predictions based on knowledge of cancer susceptibility genes. These models are widely used in clinical practice to help identify high-risk individuals. Mendelian models leverage the entire family history, but they rely on many assumptions about cancer susceptibility genes that are either unrealistic or challenging to validate due to low mutation prevalence. Training more flexible models, such as neural networks, on large databases of pedigrees can potentially lead to accuracy gains. In this paper, we develop a framework to apply neural networks to family history data and investigate their ability to learn inherited susceptibility to cancer. While there is an extensive literature on neural networks and their state-of-the-art performance in many tasks, there is little work applying them to family history data. We propose adaptations of fully-connected neural networks and convolutional neural networks to pedigrees. In data simulated under Mendelian inheritance, we demonstrate that our proposed neural network models are able to achieve nearly optimal prediction performance. Moreover, when the observed family history includes misreported cancer diagnoses, neural networks are able to outperform the Mendelian BRCAPRO model embedding the correct inheritance laws. Using a large dataset of over 200,000 family histories, the Risk Service cohort, we train prediction models for future risk of breast cancer. We validate the models using data from the Cancer Genetics Network.

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