Prediction of Hereditary Cancers Using Neural Networks

TL;DR

This paper develops neural network models to predict hereditary cancer risk from family history data, demonstrating improved accuracy over traditional Mendelian models especially with noisy data, using simulated and real datasets.

Contribution

It introduces neural network frameworks adapted for pedigree data, showing they can outperform Mendelian models in predicting inherited cancer susceptibility.

Findings

Neural networks achieve near-optimal prediction in simulated Mendelian inheritance.

They outperform Mendelian models when family history data is misreported.

Models trained on large datasets effectively predict future breast cancer risk.

Abstract

Family history is a major risk factor for many types of cancer. Mendelian risk prediction models translate family histories into cancer risk predictions based on knowledge of cancer susceptibility genes. These models are widely used in clinical practice to help identify high-risk individuals. Mendelian models leverage the entire family history, but they rely on many assumptions about cancer susceptibility genes that are either unrealistic or challenging to validate due to low mutation prevalence. Training more flexible models, such as neural networks, on large databases of pedigrees can potentially lead to accuracy gains. In this paper, we develop a framework to apply neural networks to family history data and investigate their ability to learn inherited susceptibility to cancer. While there is an extensive literature on neural networks and their state-of-the-art performance in many tasks, there is little work applying them to family history data. We propose adaptations of fully-connected neural networks and convolutional neural networks to pedigrees. In data simulated under Mendelian inheritance, we demonstrate that our proposed neural network models are able to achieve nearly optimal prediction performance. Moreover, when the observed family history includes misreported cancer diagnoses, neural networks are able to outperform the Mendelian BRCAPRO model embedding the correct inheritance laws. Using a large dataset of over 200,000 family histories, the Risk Service cohort, we train prediction models for future risk of breast cancer. We validate the models using data from the Cancer Genetics Network.