Grant Details
Grant Number: |
5R03CA242562-02 Interpret this number |
Primary Investigator: |
Biswas, Swati |
Organization: |
University Of Texas Dallas |
Project Title: |
A Bayesian Meta-Analysis Approach for Estimation of Penetrance and Its Application to Palb2 Gene for Breast Cancer Risk |
Fiscal Year: |
2021 |
Abstract
Project Abstract
Due to the rapid drop in the cost of DNA sequencing, a panel of multiple (25 to 125) cancer susceptibility
genes can be now tested at a fraction of cost and time of what was needed earlier for testing only a handful
of well-known cancer genes such as BRCA1 and BRCA2. As multi-gene panel tests are becoming accessible to
increasingly broader population, more and more patients carrying pathogenic germline mutations of various
genes are being identified. This trend is creating a major paradigm shift in hereditary cancer risk assessment
because the newly identified mutation carriers need advise about approporiate management strategies such as
targeted surveillance and preventive options. The counseling hinges crucially on accurate quantitative estimates
of age-specific risks of developing cancers associated with a specific gene whose pathogenic mutation a patient
carries, i.e., penetrance estimates. For several gene-cancer associations, a substantial amount of literature on risk
estimation is available and new studies are also becoming available at a fast pace. Yet a synthesis of evidence
from all relevant studies in the form of a robust meta-analysis of penetrance is generally lacking. A case in point
is PALB2-breast cancer association for which several studies report risk measures, however, there is no meta-
analysis of penetrance estimates. Thus there is an urgent need for meta-analysis of penetrances so that mutation
carriers in PALB2 and many other genes can be counseled appropriately in light of their age-specific risks of
developing various cancers. A major challenge in this task is that studies typically vary in design (e.g., family-
or population-based) and hence type of risk measures reported (e.g., penetrance, relative risk, or odds ratio).
Synthesis of such heterogeneous risk measures is not possible using the standard meta-analysis approaches.
Moreover, for an accurate and robust estimation, the meta-analysis model should properly take into account
various sources of uncertainties that arise in such kind of synthesis. To fill this gap, we propose a Bayesian
hiererchical model for meta-analysis as it allows seamless integration of results of different types by borrowing
information across the studies. At the same time, it accounts for uncertainties in a formal manner through
hierarchical priors. Our first specific aim is to develop this Bayesian meta-analysis methodology, investigate its
properties, and compare it with existing approaches through simulations. The next specific aim is to apply this
method to estimate the penetrance for PALB2 gene for breast cancer. Our final aim is to develop an R package
that implements the proposed Bayesian methodology and integrate it into the clinical decision support tool
ASK2ME for immediate clinical use.
Publications
Meta-analysis of breast cancer risk for individuals with PALB2 pathogenic variants.
Authors: Ruberu T.L.M.
, Braun D.
, Parmigiani G.
, Biswas S.
.
Source: Genetic Epidemiology, 2024-04-23 00:00:00.0; , .
EPub date: 2024-04-23 00:00:00.0.
PMID: 38654400
Related Citations
Bayesian meta-analysis of penetrance for cancer risk.
Authors: Ruberu T.L.M.
, Braun D.
, Parmigiani G.
, Biswas S.
.
Source: Biometrics, 2024-03-27 00:00:00.0; 80(2), .
PMID: 38819308
Related Citations
Rejoinder to the discussion on "Bayesian meta-analysis of penetrance for cancer risk".
Authors: Ruberu T.L.M.
, Braun D.
, Parmigiani G.
, Biswas S.
.
Source: Biometrics, 2024-03-27 00:00:00.0; 80(2), .
PMID: 38819314
Related Citations
Meta-Analysis of Breast Cancer Risk for Individuals with PALB2 Pathogenic Variants.
Authors: Ruberu T.L.M.
, Braun D.
, Parmigiani G.
, Biswas S.
.
Source: Medrxiv : The Preprint Server For Health Sciences, 2023-06-04 00:00:00.0; , .
EPub date: 2023-06-04 00:00:00.0.
PMID: 37398422
Related Citations