Estimate mutational signature exposure from sparse clinical sequencing data
Year: 2024
Type: other
Abstract: A typical analysis estimates the presence of known mutational signatures in each sample. However, current approaches rely on a large number of mutations to accurately estimate mutational signature exposure. Making this analysis possible when only sparse mutation data are available, such as data generated from panel sequencing or samples with low mutational burden, requires novel developments in the current methodologies for estimating mutational signature exposures. Here we present our work of assessing signature exposures using a novel predictive modeling approach. Our strategy follows two main steps. First, using a statistical model, we identify relevant signals from cancer mutations based on a mutational signature reference catalogue (e.g., COSMIC). Second, we use these mutational signals to train a predictive model. The model aims to estimate informative regions with respect to mutational signatures from the cancer genome sequence that are being considered when estimating the mutational signature exposure on a single sample.
Authors: Arnab Chakrabarti, Hiroshi Hamano, Lancelot Seillier, Kjong-Van Lehmann
Institution: RWTH Aachen University
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4416502760
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