Abstract Despite the crucial role of phenotypic and genetic intratumoral heterogeneity in understanding and predicting clinical outcomes for patients with cancer, computational pathology studies have yet to make substantial steps in this area. The major limiting factor has been the bulk gene–sequencing practice that results in loss of spatial information of gene status, making the study of intratumoral heterogeneity difficult. In this issue of Cancer Research, Acosta and colleagues used deep learning to study if localized gene mutation status can be predicted from localized tumor morphology for clear cell renal cell carcinoma. The algorithm was developed using curated sets of matched hematoxylin and eosin and IHC images, which represent spatially resolved morphology and genotype, respectively. This study confirms the existence of a strong link between morphology and underlying genetics on a regional level, paving the way for further investigations into intratumoral heterogeneity. See related article by Acosta et al., p. 2792
Intratumoral Heterogeneity Genetic Intratumoral Heterogeneity IHC Images Related Article Regional Level Deep Learning Information Of Status Gene Mutation Loss Of Information Phenotypic Heterogeneity
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Round-ups are the summaries of handpicked papers around trending topics published every week. These would enable you to scan through a collection of papers and decide if the paper is relevant to you before actually investing time into reading it.
Climate change Research Articles published between Sep 12, 2022 to Sep 18, 2022
Sep 19, 2022
Articles Included: 5
Rainfall projections from the Coupled Model Intercomparison Project (CMIP) models are strongly tied to projected sea surface temperature (SST) spatial...Read More
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