Tumour-educated platelets for breast cancer detection: biological and technical insights
Menée à partir d'échantillons sanguins prélevés sur 266 patientes atteintes d'un cancer du sein de stade I à IV et de 212 témoins puis validée sur 37 patientes supplémentaires et 36 témoins, cette étude multicentrique évalue la performance de systèmes de classification, basés sur des profils d'ARN messagers de plaquettes sanguines "éduquées" par la tumeur, pour détecter un cancer du sein
Background : Studies have shown that blood platelets contain tumour-specific mRNA profiles tumour-educated platelets (TEPs). Here, we aim to train a TEP-based breast cancer detection classifier.
Methods : Platelet mRNA was sequenced from 266 women with stage I–IV breast cancer and 212 female controls from 6 hospitals. A particle swarm optimised support vector machine (PSO-SVM) and an elastic net-based classifier (EN) were trained on 71% of the study population. Classifier performance was evaluated in the remainder (29%) of the population, followed by validation in an independent set (37 cases and 36 controls). Potential confounding was assessed in post hoc analyses.
Results : Both classifiers reached an area under the curve (AUC) of 0.85 upon internal validation. Reproducibility in the independent validation set was poor with an AUC of 0.55 and 0.54 for the PSO-SVM and EN classifier, respectively. Post hoc analyses indicated that 19% of the variance in gene expression was associated with hospital. Genes related to platelet activity were differentially expressed between hospitals.
Conclusions : We could not validate two TEP-based breast cancer classifiers in an independent validation cohort. The TEP protocol is sensitive to within-protocol variation and revision might be necessary before TEPs can be reconsidered for breast cancer detection.
British Journal of Cancer , article en libre accès, 2023