Grant Number:	5R01CA239342-04 Interpret this number
Primary Investigator:	Wang, Wenyi
Organization:	University Of Tx Md Anderson Can Ctr
Project Title:	Statistical Methods and Tools for Cancer Risk Prediction in Families with Germline Mutations in Tp53
Fiscal Year:	2022

Project Summary Sophisticated risk prediction modeling has greatly improved screening and testing for inheritable cancer syndromes such as BRCA1/2 mutations in breast cancer. Such a quantitative risk prediction model is urgently needed for the early detection of the Li-Fraumeni syndrome (LFS) following the demonstration of reduced mortality with surveillance testing for that syndrome. LFS primarily arises from germline mutations in the TP53 tumor suppressor gene and is characterized by cancer occurring relatively early in life, often repeatedly over a lifetime, and affecting multiple sites that overlap with those of other cancer syndromes, in particular the hereditary breast and ovarian cancer syndrome. Our objective is to improve the clinical management of individuals with a family history of early-onset cancers by developing mathematical models to assess 1) germline mutation carrier probability prior to TP53 testing and 2) the absolute lifetime risk of developing cancers in individuals with TP53 mutations. Our rationale is that our advanced models will enable the systematic and comprehensive risk evaluation of families with inherited TP53 mutations so that genetic counselors and physicians can provide more effective counseling and screening of individuals who carry TP53 germline mutations, given the high frequency and varied cancer-type outcomes in these individuals. We will accomplish our research objective through the following Specific Aims. 1) Characterize the onset of specific cancer types for individuals at risk of LFS: a) Estimate the penetrance of TP53 mutation-associated cancers by cancer type, using extended-family data from MD Anderson Cancer Center (MDACC) and from external clinics; b) Develop LFSPROCS to incorporate cancer-type-specific penetrances, and validate these models in predicting future risk; c) Enrich LFSPRO with additional modifiers of cancer risk, such as HER2 status for breast cancer; 2) Characterize the number of primary cancers for individuals at risk of LFS: a) estimate the penetrance for the number of primary cancers using extended-family data; c) Develop LFSPROMP and LFSPROMP+CS to incorporate new penetrances and validate these models; and 3) Develop software and disseminate it among cancer genetic clinics. Our significant contribution will be to develop an advanced quantitative risk assessment tool that will provide more accurate risk quantification, and to provide a general statistical framework for including additional cancer sites and cancer genes for risk assessment in the future. The associated software suite LFSPRO will be quickly disseminated into the MDACC Li-Fraumeni Education and Early Detection (LEAD) screening program, as well as other screening studies in the nation. LFSPRO is already integrated in BayesMendel and CancerGene packages, which are widely used for risk assessment and counseling at high-risk cancer clinics, in particular breast cancer clinics. With the addition of our advanced models, these software tools will continue to bring LFS counseling to new populations under clinical settings and reach more families that are affected by TP53 mutations.




Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data.
Authors: Nguyen N.H. , Dodd-Eaton E.B. , Corredor J.L. , Woodman-Ross J. , Green S. , Gutierrez A.M. , Arun B.K. , Wang W. .
Source: Journal Of Clinical Oncology : Official Journal Of The American Society Of Clinical Oncology, 2024-06-20 00:00:00.0; 42(18), p. 2186-2195.
EPub date: 2024-04-03 00:00:00.0.
PMID: 38569124
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