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Perier, C.

Publications and source records attributed to Perier, C..

2 recordsLinked to original sources

Identification of distinct pathological signatures induced by patient-derived α-synuclein structures in non-human primates

AO_SCPLOWBSTRACTC_SCPLOWDopaminergic neuronal cell death, associated with intracellular -synuclein (-syn)-rich protein aggregates (termed Lewy bodies), is a well-established characteristic of Parkinsons disease. Much evidence, accumulated from multiple experimental models has suggested that -syn plays a role in PD pathogenesis, not only as a trigger of pathology but also as a mediator of disease progression through pathological spreading. Here we have used a machine learning-based approach to identify unique signatures of neurodegeneration in monkeys induced by distinct -syn pathogenic structures derived from PD patients. Unexpectedly, our results show that, in non-human primates, a small amount of singular -syn aggregates is as toxic as larger amyloid fibrils present in the LBs, thus reinforcing the need for preclinical research in this species. Furthermore, our results provide evidence supporting the true multifactorial nature of PD as multiple causes can induce similar outcome regarding dopaminergic neurodegeneration.

neuroscience↗

Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer

PurposeFor patients with early-stage breast cancer, prediction of the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (e.g. Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for the time to metastatic relapse. MethodsThe data consisted of 642 patients with 21 clinicopathological variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter ) and dissemination (parameter ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of 5 covariates with best predictive power. These were further considered to individually predict the model parameters, by using a backward selection approach. Predictive performances were compared to classical Cox regression and machine learning algorithms. ResultsThe mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with (p=0.001) and EGFR with (p=0.009). Achieving a c-index of 0.65 (0.60-0.71), the model had similar predictive performance as the random survival forest (c-index 0.66-0.69) and Cox regression (c-index 0.62 - 0.67), as well as machine learning classification algorithms. ConclusionBy providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool of help for routine management of breast cancer patients.

cancer biology↗