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

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.





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