scholarly journals Family History Assessment Significantly Enhances Delivery of Precision Medicine in the Genomics Era

2020 ◽  
Author(s):  
Yasmin Bylstra ◽  
Weng Khong Lim ◽  
Sylvia Kam ◽  
Koei Wan Tham ◽  
R. Ryanne Wu ◽  
...  
Keyword(s):  
Family History ◽  
Cancer Risk ◽  
Clinical Care ◽  
Fold Increase ◽  
Genomic Sequencing ◽  
Cancer Genes ◽  
Screening Programs ◽  
Family History Assessment ◽  
Potential Impact ◽  
Group 2

AbstractBackgroundFamily history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs, with less emphasis on family history assessment. We evaluated the utility of family history for genomic sequencing selection.MethodsWe analysed whole genome sequences of 1750 healthy research participants, with and without preselection based on standardised family history collection, screening 95 cancer genes.ResultsThe frequency of likely pathogenic/ pathogenic (LP/P) variants in 884 participants with no family history available (FH not available group) (2%) versus 866 participants with family history available (FH available group) (3.1%) was not significant (p=0.158). However, within the FH available group, amongst 73 participants with an increased family history cancer risk (increased FH risk), 1 in 7 participants carried a LP/P variant inferring a six-fold increase compared with 1 in 47 participants assessed at average family history cancer risk (average FH risk) and a seven-fold increase compared to the FH not available group. The enrichment was further pronounced (up to 18-fold) when assessing the 25 cancer genes in the ACMG 59-gene panel. Furthermore, 63 participants had an increased family history cancer risk in absence of an apparent LP/P variant.ConclusionOur findings show that systematic family history collection remains critical for health risk assessment, providing important actionable data and augmenting the yield from genomic data. Family history also highlights the potential impact of additional hereditary, environmental and behavioural influences not reflected by genomic sequencing.

scholarly journals Family history assessment significantly enhances delivery of precision medicine in the genomics era

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yasmin Bylstra ◽  
Weng Khong Lim ◽  
Sylvia Kam ◽  
Koei Wan Tham ◽  
R. Ryanne Wu ◽  
...  
Keyword(s):  
Family History ◽  
Cancer Risk ◽  
Clinical Care ◽  
Genomic Analysis ◽  
Population Level ◽  
Cancer Genes ◽  
Family History Information ◽  
Screening Programs ◽  
Family History Assessment ◽  
Risk Of Cancer

Abstract Background Family history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs rendering the value of family history unknown. We evaluated the utility of incorporating family history information for genomic sequencing selection. Methods To ascertain the relationship between family histories on such population-level initiatives, we analysed whole genome sequences of 1750 research participants with no known pre-existing conditions, of which half received comprehensive family history assessment of up to four generations, focusing on 95 cancer genes. Results Amongst the 1750 participants, 866 (49.5%) had high-quality standardised family history available. Within this group, 73 (8.4%) participants had an increased family history risk of cancer (increased FH risk cohort) and 1 in 7 participants (n = 10/73) carried a clinically actionable variant inferring a sixfold increase compared with 1 in 47 participants (n = 17/793) assessed at average family history cancer risk (average FH risk cohort) (p = 0.00001) and a sevenfold increase compared to 1 in 52 participants (n = 17/884) where family history was not available (FH not available cohort) (p = 0.00001). The enrichment was further pronounced (up to 18-fold) when assessing only the 25 cancer genes in the American College of Medical Genetics (ACMG) Secondary Findings (SF) genes. Furthermore, 63 (7.3%) participants had an increased family history cancer risk in the absence of an apparent clinically actionable variant. Conclusions These findings demonstrate that the collection and analysis of comprehensive family history and genomic data are complementary and in combination can prioritise individuals for genomic analysis. Thus, family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs. Trial registration ClinicalTrials.gov NCT02791152. Retrospectively registered on May 31, 2016.

scholarly journals Secondary findings from clinical genomic sequencing: prevalence, patient perspectives, family history assessment, and health-care costs from a multisite study

2018 ◽  
Vol 21 (5) ◽  
pp. 1100-1110 ◽  
Author(s):  
M. Ragan Hart ◽  
Barbara B. Biesecker ◽  
Carrie L. Blout ◽  
Kurt D. Christensen ◽  
Laura M. Amendola ◽  
...  
Keyword(s):  
Health Care ◽  
Family History ◽  
Health Care Costs ◽  
Patient Perspectives ◽  
Genomic Sequencing ◽  
Secondary Findings ◽  
Family History Assessment ◽  
Care Costs ◽  
Clinical Genomic

Demographic variation and risk factors regarding breast cancer among female in Southern Punjab, Pakistan

2021 ◽  
pp. 1-16
Author(s):  
Waheed Ahmad ◽  
Sabika Firasat ◽  
Muhammad Sohail Akhtar ◽  
Kiran Afshan ◽  
Kaukab Jabeen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Logistic Regression ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Conditional Logistic Regression ◽  
Positive Family History ◽  
Study Cohort ◽  
Screening Programs

Objective: Breast cancer is a second major cause of female death worldwide. This study aimed to explore epidemiology, clinical profiles and contribution of reproductive and non-reproductive risk factors in breast cancer development among females from South Punjab, Pakistan. Methods: Data was collected through hospitals between October 2017 and March 2018 and study got approval by Bioethical Committee of Quaid-i-Azam University in September, 2017. A total of 163 cases and 163 age-matched controls were recruited through non-probability consecutive sampling method. All histologically confirmed patients irrespective of age, family history, clinical presentation and histopathological type were included in the study as cases. Patients, who were not willing to participate were excluded from the study. Details regarding socio-demographic characteristics, family history of cancer, reproductive health and lifestyle factors were recorded using a structured questionnaire. Conditional logistic regression was performed to calculate odds ratios at 95% confidence intervals for breast cancer by menstrual and reproductive factors after adjustment of potential confounders. Conditional logistic regression was also applied for various demographic and medical risk factors/exposures. Results: We found positive family history and hypertension significantly linked to an increased breast cancer risk (adjusted O.R >1.5, 95% CI, P<0.05) whereas, intense physical activity, increased anthropometric measurements and breastfeeding per child in months were inversely associated with breast cancer risk (adjusted O.R <1.0, 95% CI, P<0.05) in our study cohort. Conclusion: Our study reaffirms contribution of established risk factors for breast cancer, highlights protective factors and necessitates awareness/screening programs to reduce breast cancer burden in upcoming generations. Continuous...

scholarly journals Disease family history and modification of breast cancer risk in common BRCA2 variants

2008 ◽  
Author(s):  
Ian Seymour ◽  
Silvia Casadei ◽  
Valentina Zampiga ◽  
Simonetta Rosato ◽  
Rita Danesi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Disease Family

Limitations of direct-to-consumer (DTC) genetic testing for hereditary breast and ovarian cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10515-10515
Author(s):  
Neelam Vijay Desai ◽  
Elizabeth Dominic Barrows ◽  
Sarah M. Nielsen ◽  
Kathryn E. Hatchell ◽  
Edward D. Esplin ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Genetic Testing ◽  
Cancer Risk ◽  
Clinical Indication ◽  
Ovarian Cancer Risk ◽  
Cancer Genes ◽  
Primary Analysis ◽  
Risk Genes ◽  
Group 3 ◽  
Group 5

10515 Background: With the advent of DTC genetic testing, individuals have access to genetic testing without input from a healthcare professional. DTC testing now exists for the 3 Ashkenazi Jewish (AJ) BRCA1/2 founder variants. DTC testing may provide false reassurance to individuals that they do not carry a pathogenic or likely pathogenic variant (PLPV) in BRCA1/2 or other cancer-risk genes. Methods: Multi-panel genetic testing was performed in 348,692 individuals for a clinical indication of hereditary breast/ovarian cancer (Clinical cohort) and 7,636 self-referred ostensibly healthy individuals (Healthy cohort) by a clinical testing laboratory. The primary analysis evaluated PLPVs for Group 1 genes: BRCA1/2 AJ founder variants and Group 2: full sequence BRCA1/2. Secondary analyses assessed PLPVs in Group 3: high-risk breast cancer genes ( BRCA1/2, CDH1, PALB2, PTEN, STK11, TP53), Group 4: all breast or ovarian cancer-risk genes (Group 3 genes plus ATM, BARD1, BRIP1, truncating CHEK2, EPCAM, MLH1, MSH2/6, NF1, PMS2, RAD51C/D) and Group 5: 41 cancer-risk genes; these analyses were limited to participants who tested for all 41 genes. Potentially mosaic variants were excluded. Results: Table illustrates PLPVs found in both cohorts. The BRCA1/2 AJ founder variants account for only ̃11% (1513/13,987) and ̃30% (19/64) of the BRCA PLPVs in the Clinical and Healthy cohorts, respectively. Even among AJ individuals, testing only for the 3 founder variants will miss ̃10% (52/513) of all BRCA1/2 PLPVs. Evaluating only the BRCA AJ founder variants missed a higher percentage of PLPVs in other cancer-risk genes. Conclusions: The 3 BRCA1/2 AJ founder variants analyzed by DTC testing account for a small fraction of PLPVs in cancer-risk genes in the general population, and miss 10% of BRCA PLPVs even among AJ individuals. Greater public education is needed to dispel the misconception that DTC tests are equivalent to clinical assessment and comprehensive genetic testing. PLPVs identified in Clinical and Healthy Cohorts.[Table: see text]

scholarly journals Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Johanna Tolksdorf ◽  
Michael W. Kattan ◽  
Stephen A. Boorjian ◽  
Stephen J. Freedland ◽  
Karim Saba ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Family History ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prostate Biopsy ◽  
Prostate Cancer Risk ◽  
High Grade ◽  
Prediction Tools ◽  
History Of

Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). Conclusions We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

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