Published online Nov 26, 2018. doi: 10.4252/wjsc.v10.i11.160
Peer-review started: August 30, 2018
First decision: October 16, 2018
Revised: October 18, 2018
Accepted: October 23, 2018
Article in press: October 23, 2018
Published online: November 26, 2018
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.
Core tip: Extensive heterogeneity in cancer cells negatively influences treatment efficacy and survival of patients. The existing molecular methods for biomarker discovery of cancer cells and cancer stem cells are often unsuited to capture the heterogeneous nature of cell populations. Recent advances in single-cell based profiling approaches allowed the detection of molecular changes in individual cancer cells. Therefore, single-cell analysis is leading to build a complete landscape of cell types within tumor cells and facilitating the study of complex molecular heterogeneity in cancer cell populations. This will improve the investigation of more specific biomarkers to identify and target cancer stem cells.