An early neoplasia index (ENI10), based on molecular identity of CD10 cells and associated stemness biomarkers, is a predictor of patient outcome in many cancers.

An accurate estimate of patient survival at diagnosis is critical to plan efficient therapeutic options. A simple and multi-application tool is needed to move forward the precision medicine era. Taking advantage of the broad and high CD10 expression in stem and cancers cells, we evaluated the molecular identity of aggressive cancer cells. We used epithelial primary cells and developed a breast cancer stem cells-based progressive model. The superiority of the early-transformed isolated molecular index was evaluated by large-scale analysis in solid cancers. BMP2-driven cell transformation increases CD10-expression which preserves stemness properties. Our model identified a unique set of 159 genes enriched in G2/M cell cycle phases and spindle assembly complex. Using samples pre-disposed to transformation, we confirmed the value of an Early Neoplasia Index associated to CD10 (ENI10) to discriminate pre-malignant status of a human tissue. Using a stratified Cox model, a large-scale analysis (>10,000 samples, TCGA Pan-Cancer) validated a strong risk gradient (hazard ratios reaching HR = 5.15 (95% CI: 4.00-6.64) for high ENI10 levels. Through different databases, Cox regression model analyses highlighted an association between ENI10 and poor progression-free intervals (PFI) for more than 50% of cancer subtypes tested, and the potential of ENI10 to predict drug efficacy. The ENI10 index constitutes a robust tool to detect pre-transformed tissues and identify high-risk patients at diagnosis. Owing to its biological link with refractory cancer stem cells, the ENI10 index constitutes a unique way of identifying effective treatments to improve clinical care.