Biology subjects

Gourmet, L.

Publications and source records attributed to Gourmet, L..

3 recordsLinked to original sources

bioRxiv2024

The temporal evolution of cancer hallmarks

Cancer hallmarks describe key physiological characteristics that distinguish cancers from normal tissues. The temporal order in which these hallmarks appear during cancer pathogenesis is of interest from both evolutionary and clinical perspectives but has not been investigated before. Here, we order hallmarks based on the allele frequency and selective advantage of mutations in cancer hallmark genes across >10k untreated primary tumors and >8K healthy tissues. Using this novel approach, we identified a common evolutionary trajectory for 27 of 32 cancer types with genomic instability as the first and immune evasion as the last hallmark. We demonstrated widespread positive selection in cancer and strong negative selection in normal tissues for all hallmarks. Notable exceptions to the hallmark ordering in tumours were melanomas (uveal and skin) suggesting that strong environmental factors could disrupt common evolutionary paths. Clustering of hallmark trajectories across patients revealed 2 clusters defined by early or late genomic instability, with differential prognosis. Our study is the first to identify the temporal order of cancer hallmarks during tumorigenesis and demonstrate a prognostic value that could be exploited for early detection and risk stratification across multiple cancer types.

cancer biology↗

bioRxiv2023

Physics-informed deep generative learning for quantitative assessment of the retina

Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks that requires no human input and out-performs human labelling. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care.

biophysics↗

bioRxiv2022

Immune evasion impacts the selective landscape of driver genes during tumorigenesis

Carcinogenesis is an evolutionary process fueled by the interplay of somatic mutations and the local microenvironment. In recent years, hundreds of cancer related genes have been discovered using cancer cohorts. However, these cohorts are heterogenous mixtures of different molecular phenotypes, which hampers the identification of driver genes associated to a specific cancer hallmark or microenvironment. Here, we compared the landscape of positively selected somatic mutations in immune-escaped (escape+) versus non-escaped (escape-) tumors. We applied the ratio of non-synonymous to synonymous mutations (dN/dS) to 9896 individuals from 31 primary tumor tissues from the Cancer Genome Atlas (TCGA) separated by escape status. Altogether, we found 85 driver genes, including 27 and 16 novel driver genes in escape- and escape+ tumors, respectively. Overall, driver dN/dS of escape+ tumors (dN/dS=1.23) was significantly lower and closer to neutrality than driver dN/dS of escape-tumors (dN/dS=1.62), suggesting a relaxation of positive selection in driver genes, a relaxation of negative selection on immunogenic driver sites, or a combination of both fueled by immune escape. We also found that the proportion of unique sites mutated in escape+ tumors is almost double than in escape-tumors, and that immune evasion allows for a more diverse repertoire of mutational signatures. We also identified that strong immunoediting in the absence of escape leads to a better overall survival in tumors enriched by an inflamed phenotype. Ultimately, our findings reveal differences in the evolutionary strategies used by cancer cells to establish tumorigenesis and highlight the need for better patient stratification to develop tailored treatments based on molecular targets.

genomics↗