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Terenzi, F.

Publications and source records attributed to Terenzi, F..

2 recordsLinked to original sources

Extrachromosomal DNA driven oncogene spatial heterogeneity and evolution in glioblastoma

Oncogene amplification on extrachromosomal DNA (ecDNA) is strongly associated with treatment resistance and shorter survival for patients with cancer, including patients with glioblastoma. The non-chromosomal inheritance of ecDNA during cell division is a major contributor to intratumoral genetic heterogeneity. At present, the spatial dynamics of ecDNA, and the impact on tumor evolutionary trajectories, are not well understood. Here, we investigate the spatial-temporal evolution of ecDNA and its clinical impact by analyzing tumor samples from 94 treatment-naive human IDH-wildtype glioblastoma patients. We developed a spatial-temporal computational model of ecDNA positive tumors ( SPECIES) that integrates whole-genome sequencing, multi-region DNA FISH, and nascent RNAscope, to provide unique insight into the spatial dynamics of ecDNA evolution. Random segregation in combination with positive selection of ecDNAs induce large, predictable spatial patterns of cell-to-cell ecDNA copy number variation that are highly dependent on the oncogene encoded on the circular DNA. EGFR ecDNAs often reach high mean copy number (mean of 50 copies per tumor cell), are under strong positive selection (mean selection coefficient, s > 2) and do not co-amplify other oncogenes on the same ecDNA particles. In contrast, PDGFRA ecDNAs have lower mean copy number (mean of 15 copies per cell), are under weaker positive selection and frequently co-amplify other oncogenes on the same ecDNA. Evolutionary modeling suggests that EGFR ecDNAs often accumulate prior to clonal expansion. EGFR structural variants, including vIII and c-terminal deletions are under strong positive selection, are found exclusively on ecDNA, and are intermixed with wild-type EGFR ecDNAs. Simulations show EGFRvIII ecDNA likely arises after ecDNA formation in a cell with high wild-type EGFR copy number (> 10) before the onset of the most recent clonal expansion. This remains true even in cases of co-selection and co-amplification of multiple oncogenic ecDNA species in a subset of patients. Overall, our results suggest a potential time window in which early ecDNA detection may provide an opportunity for more effective intervention. HighlightsO_LIecDNA is the most common mechanism of focal oncogene amplification in IDHwt glioblastoma. C_LIO_LIEGFR and its variants on ecDNA are particularly potent, likely arising early in tumor development, providing a strong oncogenic stimulus to drive tumorigenesis. C_LIO_LIWild-type and variant EGFR ecDNA heteroplasmy (co-occurrence) is common with EGFRvIII or c-terminal deletions being derived from EGFR wild-type ecDNA prior to the most recent clonal expansion. C_LIO_LITumors with ecDNA amplified EGFR versus PDGFRA exhibit different evolutionary trajectories. C_LIO_LISPECIES model can infer spatial evolutionary dynamics of ecDNA in cancer. C_LIO_LIA delay between ecDNA accumulation and subsequent oncogenic mutation may give a therapeutic window for early intervention. C_LI

cancer biology↗

The dynamic fitness landscape of ageing haematopoiesis through clonal competition

Clonal haematopoiesis (CH) - the existence of large mutant clones in blood - is a prime example of somatic evolution. Yet how evolution shapes CH with age remains to be understood. Here, we show that clonal competition can explain the complex dynamics observed in vivo. In this paradigm, numerous fit clones continually appear and compete, driving an evolving fitness landscape of stem cells. This naturally explains shrinking expanded clones, varying driver efficacy across individuals, and transitions of site frequency spectra with age. Inferences of evolutionary parameters from variant trajectories and site frequency spectra converge to nearly identical estimates of a non-exponential fitness distribution with mean 0.08, and an arrival rate of 2-20 advantageous clones per year. Inferring innate fitnesses from single trajectories, we find that 80% of the variance on identical mutations is explained by clonal competition, with fitness estimates of most common drivers between 0.14 and 0.18. Strikingly, we find clones with much higher fitness to occur only later in life, with an arrival time distribution well-described by a multi-step model of clonal evolution. Overall, a quantitative clonal competition model predicts many aspects of ageing haematopoiesis and allows a personalized identification of high-risk clones potentially important for patient stratification.

evolutionary biology↗