Radpour Ramin, Forouharkhou Farzad
Tumor Immunology, Department for BioMedical Research (DBMR), University of Bern, Bern 3008, Switzerland.
Department for Bioinformatics, Persian Bioinformatics System, Tehran 14166, Iran.
World J Stem Cells. 2018 Nov 26;10(11):160-171. doi: 10.4252/wjsc.v10.i11.160.
Biomarker-driven individualized treatment in oncology has made tremendous progress through technological developments, new therapeutic modalities and a deeper understanding of the molecular biology for tumors, cancer stem cells and tumor-infiltrating immune cells. Recent technical developments have led to the establishment of a variety of cancer-related diagnostic, prognostic and predictive biomarkers. In this regard, different modern OMICs approaches were assessed in order to categorize and classify prognostically different forms of neoplasia. Despite those technical advancements, the extent of molecular heterogeneity at the individual cell level in human tumors remains largely uncharacterized. Each tumor consists of a mixture of heterogeneous cell types. Therefore, it is important to quantify the dynamic cellular variations in order to predict clinical parameters, such as a response to treatment and or potential for disease recurrence. Recently, single-cell based methods have been developed to characterize the heterogeneity in seemingly homogenous cancer cell populations prior to and during treatment. In this review, we highlight the recent advances for single-cell analysis and discuss the challenges and prospects for molecular characterization of cancer cells, cancer stem cells and tumor-infiltrating immune cells.
通过技术发展、新的治疗方式以及对肿瘤、癌症干细胞和肿瘤浸润免疫细胞分子生物学的更深入理解，肿瘤学中基于生物标志物的个体化治疗取得了巨大进展。最近的技术发展促使人们建立了多种与癌症相关的诊断、预后和预测生物标志物。在这方面，人们评估了不同的现代组学方法，以便对预后不同的肿瘤形成形式进行分类。尽管有这些技术进步，但人类肿瘤在单个细胞水平上的分子异质性程度在很大程度上仍未得到充分描述。每个肿瘤都由异质细胞类型混合而成。因此，量化动态细胞变化对于预测临床参数（如对治疗的反应和疾病复发的可能性）很重要。最近，基于单细胞的方法已被开发出来，用于在治疗前和治疗期间表征看似同质的癌细胞群体中的异质性。在这篇综述中，我们重点介绍单细胞分析的最新进展，并讨论癌细胞、癌症干细胞和肿瘤浸润免疫细胞分子表征面临的挑战和前景。
