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Abstract:
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There is growing interest in developing tools for cancer screening and monitoring based on the analysis of DNA sequencing data derived from non-invasive procedures such as blood samples. At early cancer stages, such samples contain DNA from a majority of normal cells and a low fraction of tumor cells. Cancer presence can be assessed measuring allelic imbalance: since a person inherits one allele from each parents, the allele proportion at heterozygous loci is close to 0.5 in normal cells, whereas significant deviations from 0.5 are indicative of the presence of cancer. To efficiently and sensitively detect such deviations, we model the allele proportions over the genome via a novel Bayesian hierarchical Hidden Markov Model. We leverage prior knowledge from population genome databases while borrowing information across multiple samples from the same subject. Hypothesis testing for cancer presence is embedded in the model via a spike and slab prior. We show the performance of our model at different levels of tumor fraction using in-silico mixed data.
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