准确估计患者在诊断时的生存情况对于规划有效的治疗方案至关重要。我们需要一种简单且多功能的工具，以推动精确医学的时代进一步发展。利用干细胞和癌细胞中广泛高表达的CD10特性，我们评估了侵袭性癌细胞的分子特性。我们使用上皮原代细胞开发了一种基于乳腺癌干细胞的渐进模型。我们通过大规模实体癌分析评估了早期转化孤立分子指数的优越性。BMP2驱动的细胞转化增加了CD10表达，这保留了干细胞的特性。我们的模型确定了一组在G2/M细胞周期阶段和纺锤体组装复合物中富集的159个基因。使用预定于转化的样本，我们确认了与CD10相关的早期肿瘤指数（ENI10）用于鉴别人体组织的癌前状态的价值。使用分层Cox模型，大规模分析（> 10,000个样本，TCGA全癌症）验证了高ENI10水平的强风险梯度（风险比达到HR = 5.15（95％CI：4.00-6.64））。通过不同数据库，Cox回归模型分析在50％以上经过测试的癌症亚型中突出了ENI10与疾病进展期间无进展时间（PFI）的关联，以及ENI10预测药物疗效的潜力。ENI10指数构成了一个强大的工具，用于检测转化前组织并识别诊断阶段的高风险患者。由于其与难治癌干细胞的生物学联系，ENI10指数构成了识别有效治疗以改善临床护理的独特途径。

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.