癌症在动态的生态系统中进化。因此，表征癌症的生态动力学对于理解癌症进化至关重要，并且可以发现新的生物标志物来预测疾病进展。导管原位癌（DCIS）是一种早期乳腺癌，其特征是乳管内异常上皮细胞生长。尽管对乳腺癌发生的遗传和表观遗传原因进行了广泛的研究，但这些研究均未成功鉴定出 DCIS 进展和/或升级的生物标志物。在这项研究中，我们表明，缺氧和酸中毒生物标志物的生态栖息地分析可以显着改善 DCIS 升级的预测。首先，我们开发了一种新颖的生态进化设计方法，根据 84 名 DCIS 患者队列中的氧扩散距离来定义肿瘤导管内微环境中的栖息地。然后，我们鉴定了具有归因于其栖息地条件的代谢表型的癌细胞，例如表明缺氧反应表型的 CA9 的表达，以及表明缺氧诱导的酸适应的 LAMP2b 的表达。传统上，这些标记物对 DCIS 升级（如果有）的预测能力有限。然而，从生态角度分析时，它们区分惰性 DCIS 和抢风头的 DCIS 的能力显着增强。其次，利用生态进化引导的计算和数字病理学技术，我们发现了这些生物标志物的独特空间模式，并利用这些模式的分布来预测患者的分期。这些模式的特征是细胞特征和空间特征。通过对活检队列进行 5 倍验证，我们训练了一个随机森林分类器，以实现 0.74 的曲线下面积 (AUC)。我们的结果通过证明生态进化动力学在预测癌症进展中的作用，肯定了在数字病理学时代使用生态进化设计的方法在生物标志物发现研究中的重要性。

Cancers evolve in a dynamic ecosystem. Thus, characterizing cancer's ecological dynamics is crucial to understanding cancer evolution and can lead to discovering novel biomarkers to predict disease progression. Ductal carcinoma in situ (DCIS) is an early-stage breast cancer characterized by abnormal epithelial cell growth confined within the milk ducts. Although there has been extensive research on genetic and epigenetic causes of breast carcinogenesis, none of these studies have successfully identified a biomarker for the progression and/or upstaging of DCIS. In this study, we show that ecological habitat analysis of hypoxia and acidosis biomarkers can significantly improve prediction of DCIS upstaging. First, we developed a novel eco-evolutionary designed approach to define habitats in the tumor intra-ductal microenvironment based on oxygen diffusion distance in our DCIS cohort of 84 patients. Then, we identify cancer cells with metabolic phenotypes attributed to their habitat conditions, such as the expression of CA9 indicating hypoxia responding phenotype, and LAMP2b indicating a hypoxia-induced acid adaptation. Traditionally these markers have shown limited predictive capabilities for DCIS upstaging, if any. However, when analyzed from an ecological perspective, their power to differentiate between indolent and upstaged DCIS increased significantly. Second, using eco-evolutionary guided computational and digital pathology techniques, we discovered distinct spatial patterns of these biomarkers and used the distribution of such patterns to predict patient upstaging. The patterns were characterized by both cellular features and spatial features. With a 5-fold validation on the biopsy cohort, we trained a random forest classifier to achieve the area under curve(AUC) of 0.74. Our results affirm the importance of using eco-evolutionary-designed approaches in biomarkers discovery studies in the era of digital pathology by demonstrating the role of eco-evolution dynamics in predicting cancer progression.