CanRisk-Prostate: A Comprehensive, Externally Validated Risk Model for the Prediction of Future Prostate Cancer.
JOURNAL OF CLINICAL ONCOLOGY(2023)
Univ Cambridge | Inst Canc Res | Univ Manchester
Abstract
PURPOSE:Prostate cancer (PCa) is highly heritable. No validated PCa risk model currently exists. We therefore sought to develop a genetic risk model that can provide personalized predicted PCa risks on the basis of known moderate- to high-risk pathogenic variants, low-risk common genetic variants, and explicit cancer family history, and to externally validate the model in an independent prospective cohort. MATERIALS AND METHODS:We developed a risk model using a kin-cohort comprising individuals from 16,633 PCa families ascertained in the United Kingdom from 1993 to 2017 from the UK Genetic Prostate Cancer Study, and complex segregation analysis adjusting for ascertainment. The model was externally validated in 170,850 unaffected men (7,624 incident PCas) recruited from 2006 to 2010 to the independent UK Biobank prospective cohort study. RESULTS:The most parsimonious model included the effects of pathogenic variants in BRCA2, HOXB13, and BRCA1, and a polygenic score on the basis of 268 common low-risk variants. Residual familial risk was modeled by a hypothetical recessively inherited variant and a polygenic component whose standard deviation decreased log-linearly with age. The model predicted familial risks that were consistent with those reported in previous observational studies. In the validation cohort, the model discriminated well between unaffected men and men with incident PCas within 5 years (C-index, 0.790; 95% CI, 0.783 to 0.797) and 10 years (C-index, 0.772; 95% CI, 0.768 to 0.777). The 50% of men with highest predicted risks captured 86.3% of PCa cases within 10 years. CONCLUSION:To our knowledge, this is the first validated risk model offering personalized PCa risks. The model will assist in counseling men concerned about their risk and can facilitate future risk-stratified population screening approaches.
MoreTranslated text
Key words
Cancer Risk,cancer susceptibility,Prostate Cancer,Polygenic Risk Scores,Genetic Testing
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2010
被引用45 | 浏览
2001
被引用123 | 浏览
2010
被引用71 | 浏览
2008
被引用475 | 浏览
2011
被引用135 | 浏览
2012
被引用126 | 浏览
2009
被引用48 | 浏览
2013
被引用25 | 浏览
2017
被引用2033 | 浏览
2018
被引用86 | 浏览
2019
被引用52 | 浏览
2019
被引用151 | 浏览
2019
被引用167 | 浏览
2020
被引用244 | 浏览
2021
被引用324 | 浏览
2021
被引用35 | 浏览
2021
被引用36 | 浏览
2021
被引用22 | 浏览
2022
被引用16 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest