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Sem. "Uczenie maszynowe"


Tumoroscope: a probabilistic graphical model for mapping tumor clones in cancerous tissue

Prelegent: Shadi Shafighi

2022-06-09 12:15

Tumor cell populations are highly heterogeneous and form clones with
different genotypes. Geographically distinct parts of the tumor have
different genetic and phenotypic compositions. Elucidating tumor
heterogeneity is hampered by the fact that there is no technology
available that would directly identify the localization of different
clones in the tumor tissue. Here, we propose a probabilistic approach
accounting for the spatial resolution of tumor heterogeneity. We first infer
the clones and their corresponding genotype using existing methods for
tumor phylogeny reconstruction from bulk DNA sequencing data. Second, we
infer their location from spatial transcriptomics, which consists of
mini-bulk RNA-sequencing measurements in multiple spots of the tumor
tissue. We investigate the variants of the RNA sequences in each spot on
the tumor sample. The model maps variants found in each spot to the
variants existing in the genotype of the clones and finds the most
likely clonal structure of each spot. The model efficiently combines
information contained in spatial transcriptomics and bulk DNA sequencing
measurements and is a step forward in constructing a tool for mapping 
tumor subclones in the tumor tissue.