Variation in cancer risk among families with genetic susceptibility

Huang, Theodore; Braun, Danielle; Lynch, Henry T.; Parmigiani, Giovanni

doi:10.1002/gepi.22366

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…This is further supported by the identification of keywords closely linked to genetic aspects, emphasizing the pivotal role of genetic determinants in GN pathogenesis by affecting key physiological processes such as genomic stability and cellular proliferation[ 33 ]. The genetic underpinnings of many GNs indicate that mutations and genetic predispositions significantly contribute to cancer etiology[ 34 ]. Conditions such as familial adenomatous polyposis and HNPCC notably increase GN risk, with mutations in genes such as MLH1 , MSH2 , and APC being closely linked to these neoplasms[ 35 - 37 ].…”

Section: Discussionmentioning

confidence: 99%

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

Fu,

Ma,

Niu

et al. 2024

World J Gastrointest Oncol

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BACKGROUND Gastrointestinal neoplasm (GN) significantly impact the global cancer burden and mortality, necessitating early detection and treatment. Understanding the evolution and current state of research in this field is vital. AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research, focusing on key contributors, institutions, and thematic evolution. METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the "bibliometrix" R package, VOSviewer, and CiteSpace. The analysis focused on the distribution of publications, contributions by institutions and countries, and trends in keywords. The methods included data synthesis, network analysis, and visualization of international collaboration networks. RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration. It highlights the United States' critical role in advancing this field, with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute. The last five years, substantial advancements have been made, representing nearly 45% of the examined literature. Publication rates have dramatically increased, from 20 articles in 2002 to 112 in 2022, reflecting intensified research efforts. This study underscores a growing trend toward interdisciplinary and international collaboration, with the Journal of Clinical Oncology standing out as a key publication outlet. This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks. CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis. This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy, ultimately enhancing worldwide patient care.

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Section: Discussionmentioning

confidence: 99%

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

Fu,

Ma,

Niu

et al. 2024

World J Gastrointest Oncol

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“…This justifies looking at calibration across varying family sizes. However, this result relies on model assumptions whose degree of realism may begin to deteriorate as the family size increases (Huang et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Statistical methods for Mendelian models with multiple genes and cancers

Liang

Idos

Hong

et al. 2022

Genetic Epidemiology

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Risk evaluation to identify individuals who are at greater risk of cancer as a result of heritable pathogenic variants is a valuable component of individualized clinical management. Using principles of Mendelian genetics, Bayesian probability theory, and variant‐specific knowledge, Mendelian models derive the probability of carrying a pathogenic variant and developing cancer in the future, based on family history. Existing Mendelian models are widely employed, but are generally limited to specific genes and syndromes. However, the upsurge of multigene panel germline testing has spurred the discovery of many new gene–cancer associations that are not presently accounted for in these models. We have developed PanelPRO, a flexible, efficient Mendelian risk prediction framework that can incorporate an arbitrary number of genes and cancers, overcoming the computational challenges that arise because of the increased model complexity. We implement an 11‐gene, 11‐cancer model, the largest Mendelian model created thus far, based on this framework. Using simulations and a clinical cohort with germline panel testing data, we evaluate model performance, validate the reverse‐compatibility of our approach with existing Mendelian models, and illustrate its usage. Our implementation is freely available for research use in the PanelPRO R package.

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“…Using Bayes’ theorem, The prior P ( G 1 ) (= P ( G 1 | U, Y ) = P ( G 1 | Y )) is the population-level probability of genotypes and is ethnicity-specific and not sex-specific, and P ( H 1 , …, H n | G 1 ) can be calculated as follows: The last equality assumes each family member’s cancer history is conditionally independent given the geno-type. Relaxation of this assumption is discussed by Huang et al [23]. Assuming Hardy-Weinberg equilibrium [24], P ( G 2 , …, G n | G 1 ) can be calculated based on Mendelian laws of inheritance, and subsequently marginalized depending on which gene is considered dominant or recessive.…”

Section: Methodsmentioning

confidence: 99%

“…The last equality assumes each family member's cancer history is conditionally independent given the genotype. Relaxation of this assumption is discussed by Huang et al [23]. Assuming Hardy-Weinberg equilibrium [24], P (G 2 , .…”

Section: Mendelian Risk Prediction Modelsmentioning

confidence: 99%

Variant-specific Mendelian Risk Prediction Model

Bae

Dias

Huang

et al. 2023

Preprint

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Many pathogenic sequence variants (PSVs) have been associated with increased risk of cancers. Mendelian risk prediction models use Mendelian laws of inheritance to predict the probability of having a PSV based on family history, as well as specified PSV frequency and penetrance (age-specific probability of developing cancer given genotype). Most existing models assume penetrance is the same for any PSVs in a certain gene. However, for some genes (for example, BRCA1/2), cancer risk does vary by PSV. We propose an extension of Mendelian risk prediction models to relax the assumption that risk is the same for any PSVs in a certain gene by incorporating variant-specific penetrances and illustrating these extensions on two existing Mendelian risk prediction models, BRCAPRO and PanelPRO. Our proposed BRCAPRO-variant and PanelPRO-variant models incorporate variant-specific BRCA1/2 PSVs through the region classifications. Due to the sparsity of the variant information we classify BRCA1/2 PSVs into three regions; the breast cancer clustering region (BCCR), the ovarian cancer clustering region (OCCR), and an other region. Simulations were conducted to evaluate the performance of the proposed BRCAPRO-variant model compared to the existing BRCAPRO model which assumes the penetrance is the same for any PSVs in BRCA1 (and respectively BRCA2). Simulation results showed that the BRCAPRO-variant model was well calibrated to predict region-specific BRCA1/2 carrier status with high discrimination and accuracy on the region-specific level. In addition, we showed that the BRCAPRO-variant model achieved performance gains over the existing risk prediction models in terms of calibration without loss in discrimination and accuracy. We also evaluated the performance of the two proposed models, BRCAPRO-variant and PanelPRO-variant, on a cohort of 1,961 families from the Cancer Genetics Network (CGN). We showed that our proposed models provide region-specific PSV carrier probabilities with high accuracy, while the calibration, discrimination and accuracy of gene-specific PSV carrier probabilities were comparable to the existing gene-specific models. As more variant-specific PSV penetrances become available, we have shown that Mendelian risk prediction models can be extended to integrate the additional information, providing precise variant or region-specific PSV carrier probabilities and improving future cancer risk predictions.

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Variation in cancer risk among families with genetic susceptibility

Cited by 3 publications

References 26 publications

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

Statistical methods for Mendelian models with multiple genes and cancers

Variant-specific Mendelian Risk Prediction Model

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