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Grant Details

Grant Number: 5R21CA177233-02 Interpret this number
Primary Investigator: Parmigiani, Giovanni
Organization: Dana-Farber Cancer Inst
Project Title: Novel Tools for Familial Risk Prediction
Fiscal Year: 2014


DESCRIPTION (provided by applicant): Challenges: The vast majority of individuals in the developed world have a family history of at least one type of cancer. Aside from major cancer syndromes where family histories point clearly towards a specific cancer site, family history information is not systematically used for the purpose of managing risk, of wisely using genetic testing, and of improving prevention practices. Strong evidence is emerging that syndromes once thought to be distinct, are overlapping in terms of the cancer site, and that several genetic factors increase the risk of multiple cancers. This opens important opportunities for screening and management of risk across clinical disciplines. A critical obstacle is the lack of software infrastructures and analytical approaches for capturing family history information across a large number of disease sites, for assessing whether the occurrence of multiple cancers in a family is likely to be random or hereditary; and for translating family history across multiple disease sites data into useful clinical decision tools. Aims: Investigators in this proposal have developed the most detailed, accurate, and widely used tools for the breast-ovarian, colorectal, pancreatic, and skin cancer syndromes and the most widely used clinical tools to implement them, including CancerGene and HRA. All are freely available for research. The overall goal of this proposal is to lay the informatics and statistical foundations for both model implementation and clinical application of more comprehensive approaches. This cannot simply be addressed by juxtaposing software and algorithms that have been successful for single-syndrome models, but it requires novel strategies. Specifically, AIM 1 Is to develop software, including a) a general purpose open source risk calculator that can cover simultaneously an arbitrary number of cancer sites and, at the individual level, cancer-specific biomarkers, preventative interventions, and covariates; and b) tools for the implementation of the calculator in both primary and high risk clinical environments. AIM 2 is to develop statistical methods to estimate the population parameters required by the general purpose calculator. AIM 3 is to develop a proof-of-principle model covering about 10 disease sites, based on a comprehensive literature review of penetrance, interventions, and cancer markers. This will allow testing and troubleshooting of the clinical implementation and permit quantification of the benefits of clinical approaches using information across clinical disciplines. Impact: This research will have a direct impact by generating freely available computational and methodological resources for developing and implementing models that consider multiple syndromes. The hypothesis behind this proposal is that making these tools available can have a significant effect on: what data is collected; what use is made of this data across disease-specific programs; and whether individuals at increased risk receive appropriate attention in both early detection and treatment.


Simpson's paradox in the integrated discrimination improvement.
Authors: Chipman J. , Braun D. .
Source: Statistics in medicine, 2017-12-10; 36(28), p. 4468-4481.
EPub date: 2016-01-05.
PMID: 29160558
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Penetrance of ATM Gene Mutations in Breast Cancer: A Meta-Analysis of Different Measures of Risk.
Authors: Marabelli M. , Cheng S.C. , Parmigiani G. .
Source: Genetic epidemiology, 2016 07; 40(5), p. 425-31.
EPub date: 2016-04-25.
PMID: 27112364
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Recent BRCAPRO upgrades significantly improve calibration.
Authors: Mazzola E. , Chipman J. , Cheng S.C. , Parmigiani G. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2014 Aug; 23(8), p. 1689-95.
EPub date: 2014-06-02.
PMID: 24891549
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Frailty Models for Familial Risk with Application to Breast Cancer.
Authors: Gorfine M. , Hsu L. , Parmigiani G. .
Source: Journal of the American Statistical Association, 2013-12-01; 108(504), p. 1205-1215.
PMID: 24678132
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