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Deivendran, D.

Publications and source records attributed to Deivendran, D..

2 recordsLinked to original sources

Molecular effects of indoor tanning

BackgroundTanning bed users have a significantly increased risk of melanoma, but it remains unclear how indoor tanning drives melanomagenesis. Tanning bed radiation is often thought of as a substitute for natural UV radiation despite differences in the maximum doses, UV content, body sites exposed, and patterns of melanoma that arise. MethodsTo better understand the epidemiologic trends and etiology of melanoma associated with tanning bed use, we described the patterns of melanoma in patients with quantifiable tanning bed usage and performed exome sequencing of 182 melanocytes from normal skin of a subset of these patients. ResultsTanning bed users were more likely than non-users to have melanoma on body sites with low cumulative levels of sun damage and were more likely to have multiple melanomas. The melanocytes in normal appearing skin from tanning bed users had higher mutation burdens, a higher proportion of melanocytes with pathogenic mutations, and distinct mutational signatures. These differences were most prominent over body sites that experience comparatively less exposure to natural sunlight. ConclusionsWe conclude that tanning bed radiation induces melanoma by increasing the mutation burden of melanocytes and by mutagenizing a broader field of melanocytes than are typically exposed to natural sunlight. The unique signatures of mutations in skin cells of tanning users may be attributable to the distinct spectra of radiation emitted from solariums.

genomics↗

Visualizing somatic alterations in spatial transcriptomics data of skin cancer

Tools to visualize genetic alterations within tissues remain underdeveloped despite the growth of spatial transcriptomic technologies, which measure gene expression in different regions of tissues. Since genetic alterations can be detected in RNA-sequencing data, we explored the feasibility of observing somatic alterations in spatial transcriptomics data. Extracting genetic information from spatial transcriptomic data would illuminate the spatial distribution of clones and allow for correlations with regional changes in gene expression to support genotype-phenotype studies. Recent work demonstrates that copy number alterations can be inferred from spatial transcriptomics data1. Here, we describe new software to further enhance the inference of copy number from spatial transcriptomics data. Moreover, we demonstrate that single nucleotide variants are also detectable in spatial transcriptomic data. We applied these approaches to map the location of point mutations, copy number alterations, and allelic imbalances in spatial transcriptomic data of two cutaneous squamous cell carcinomas. We show that both tumors are dominated by a single clone of cells, suggesting that their regional variations in gene expression2 are likely driven by non-genetic factors. Furthermore, we observe mutant cells in histologically normal tissue surrounding one tumor, which were not discernible upon histopathologic evaluation. Finally, we detected mono-allelic expression of immunoglobulin heavy chains in B-cells, revealing clonal populations of plasma cells surrounding one tumor. In summary, we put forward solutions to add the genetic dimension to spatial transcriptomic datasets, augmenting the potential of this new technology.

genomics↗