新的空间分子技术有望改变我们对泌尿系统癌症的理解和治疗。通过绘制肿瘤的空间分子结构，这些平台揭示了个体恶性肿瘤内部和周围的复杂异质性，为疾病的发展、进展、诊断和治疗提供了新的见解。它们能够在肿瘤微环境中原位跟踪克隆系统发育和免疫细胞相互作用。整个转录组/基因组/蛋白质组水平的空间分析是假设的生成，特别是在风险分层和精准医学领域。当前的挑战包括试剂成本、协议的协调和计算需求。尽管如此，不断发展的技术格局和不断发展的机器学习应用有可能克服这些障碍，利用详细的空间细胞和分子数据推动个性化癌症治疗的未来。患者小结：肿瘤很复杂，包含许多不同的成分。尽管我们已经能够在显微镜下观察到其中一些差异，但直到最近，我们还无法观察到支持癌症发展的遗传变化。科学家现在能够使用“空间转录组学”和“空间蛋白质组学”等方法探索分子/遗传差异，这使他们能够在不破坏组织结构的情况下观察正常组织和癌组织区域的遗传和细胞变异。目前，这些技术受到高相关成本以及对强大而复杂的计算分析工作流程的需求的限制。这些新技术的未来进步和结果可能会帮助患者及其医生做出治疗癌症的决定。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

New spatial molecular technologies are poised to transform our understanding and treatment of urological cancers. By mapping the spatial molecular architecture of tumours, these platforms uncover the complex heterogeneity within and around individual malignancies, offering novel insights into disease development, progression, diagnosis, and treatment. They enable tracking of clonal phylogenetics in situ and immune-cell interactions in the tumour microenvironment. A whole transcriptome/genome/proteome-level spatial analysis is hypothesis generating, particularly in the areas of risk stratification and precision medicine. Current challenges include reagent costs, harmonisation of protocols, and computational demands. Nonetheless, the evolving landscape of the technology and evolving machine learning applications have the potential to overcome these barriers, pushing towards a future of personalised cancer therapy, leveraging detailed spatial cellular and molecular data. PATIENT SUMMARY: Tumours are complex and contain many different components. Although we have been able to observe some of these differences visually under the microscope, until recently, we have not been able to observe the genetic changes that underpin cancer development. Scientists are now able to explore molecular/genetic differences using approaches such as "spatial transcriptomics" and "spatial proteomics", which allow them to see genetic and cellular variation across a region of normal and cancerous tissue without destroying the tissue architecture. Currently, these technologies are limited by high associated costs, and a need for powerful and complex computational analysis workflows. Future advancements and results through these new technologies may assist patients and their doctors as they make decisions about treating their cancer.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.