Innovative Approaches for Risk Assessment in Panel Gene Testing

Abstract

Panel germline tests simultaneously detect multiple pathogenic variants, typically grouped by association with a higher risk of developing certain cancers or syndromes. Next-generation sequencing has made testing for many genes faster and more affordable than ever, but there are nevertheless financial and structural barriers that prevent panel testing from being offered for all patients. Additionally, because clinical guidelines for associations between harmful mutations and heritable diseases are not always well-established, it may not always be beneficial to make panels arbitrarily large. This dissertation explores tools to aid medical decision-making for optimally deploying panel gene testing. Using patient family history and population-level parameter estimates, Mendelian risk prediction models calculate the probability of carrying a pathogenic variant and the future risk of developing cancer. These quantitative risk measures can then be used to develop personalized prevention plans for as further testing or intervention. Existing Mendelian models are widely adopted as part of clinical risk assessment, but are usually limited to specific genes and syndromes. Chapter 1 introduces a generalizable, computationally efficient framework for Mendelian models that incorporates an arbitrary number of genes, cancers, and gene-cancer associations. This more comprehensive approach can flexibly account for new gene-cancer associations as they are discovered, to better reflect the field as it evolves. We rigorously evaluate our approach on simulations and a validation data set. Chapter 2 addresses some of the pragmatic challenges of implementing the framework presented in Chapter 1 by examining Mendelian models that aggregate across genes and cancers. The multi-gene, multi-cancer model proposed in Chapter 1 requires robust estimation of many model parameters, which may be difficult for rare genes and cancers, as well as accurate and detailed patient family history for a large number of cancers. We investigate simplifying modeling assumptions to help overcome uncertainties in parameter estimation and family history reporting. Motivated by the clinical context of pre-screening for a test of any cancer, we apply our aggregate approach to several simulation studies and two high-risk clinical cohorts. In Chapter 3, we continue engaging with the issue of uncertainty in parameter estimation for gene-cancer associations, but switch our attention to the question of determining which genes to include in a panel. We present a utility tool for evaluating the addition and removal of genes in a panel test, based on both quantitative parameter estimates and individualized utility costs. Our approach provides credible intervals to reflect uncertainty in the disease parameter estimates used in its calculation. We illustrate our multi-gene, multi-disease aggregate utility with a hereditary breast cancer panel application and explore of the effects of varying parameter inputs.