Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA 02139, USA.
Cell. 2020 Apr 16;181(2):236-249. doi: 10.1016/j.cell.2020.03.053.
Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.
癌症中的关键转变——包括肿瘤起始、局部扩张、转移和治疗抵抗——涉及动态肿瘤生态系统中细胞之间的复杂相互作用。变革性的单细胞基因组学技术和空间多重原位方法现在提供了一个前所未有的分辨率来研究这种复杂性的机会。作为国家癌症研究所（NCI）癌症登月计划的一部分，人类肿瘤图谱网络（HTAN）将建立一个临床、实验、计算和组织框架，为一系列不同的肿瘤类型生成信息丰富且易于访问的癌症转变三维图谱。这项工作既是对正在进行的健康器官图谱绘制工作的补充，也是对之前集中在单一时间点进行批量测序的大型癌症基因组学方法的补充。生成单细胞、多参数、纵向图谱，并将其与临床结果相结合，应该有助于识别新的预测性生物标志物和特征，以及治疗相关的细胞类型、细胞状态和细胞间相互作用，贯穿整个转变过程。由此产生的肿瘤图谱将对我们对癌症生物学的理解产生深远的影响，并有可能改善癌症检测、预防和治疗发现，为癌症患者和癌症高危人群提供更好的精准医疗治疗。
