Ortega Michael A, Poirion Olivier, Zhu Xun, Huang Sijia, Wolfgruber Thomas K, Sebra Robert, Garmire Lana X
Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
Department of Molecular Biosciences and Bioengineering, Honolulu, HI, USA.
Clin Transl Med. 2017 Dec 28;6(1):46. doi: 10.1186/s40169-017-0177-y.
It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single-cell multi-omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer treatments.
越来越明显的是，正常组织和癌组织均由异质性群体组成。遗传变异可归因于遗传突变的下游效应、环境因素，或转录和复制中未准确解决的错误。当病变发生在赋予增殖优势的区域时，它可支持克隆扩增、亚克隆变异和肿瘤进展。通过这种方式，肿瘤复杂的异质性微环境增加了血管生成和转移的可能性。下一代测序和计算生物学的最新进展已利用单细胞应用来构建单个细胞的深度图谱，而这些图谱在整体分析中会被掩盖。此外，结合单细胞多组学策略的新技术的发展正在提供对促成细胞身份、功能和生长的因素更精确的理解。单细胞技术和数据计算反卷积的持续进步对于重建患者特异性肿瘤内特征和开发更个性化的癌症治疗至关重要。
