Therapeutic and Diagnostic Guidelines
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Stem Cells. Aug 26, 2020; 12(8): 706-720
Published online Aug 26, 2020. doi: 10.4252/wjsc.v12.i8.706
Hunting down the dominating subclone of cancer stem cells as a potential new therapeutic target in multiple myeloma: An artificial intelligence perspective
Lisa X Lee, Shengwen Calvin Li
Lisa X Lee, Division of Hematology/Oncology, Department of Medicine, Chao Family Comprehensive Cancer Center, UCI Health, Orange, CA 92868, United States
Shengwen Calvin Li, Neuro-oncology and Stem Cell Research Laboratory, CHOC Children's Research Institute, Children's Hospital of Orange County, Orange, CA 92868, United States
Shengwen Calvin Li, Department of Neurology, University of California-Irvine School of Medicine, Orange, CA 92868, United States
Author contributions: Lee LX and Li SC conceived and wrote the manuscript; both revised the manuscript; and all authors approved the final version submitted.
Supported by the CHOC-UCI Joint Research Award (in part).
Conflict-of-interest statement: The authors declare no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Shengwen Calvin Li, PhD, Professor, Neuro-oncology and Stem Cell Research Laboratory, CHOC Children's Research Institute, Children's Hospital of Orange County, 1201 W La Veta Ave., Orange, CA 92868, United States. shengwel@uci.edu
Received: May 18, 2020
Peer-review started: May 18, 2020
First decision: June 5, 2020
Revised: July 8, 2020
Accepted: August 14, 2020
Article in press: August 14, 2020
Published online: August 26, 2020
Abstract

The development of single-cell subclones, which can rapidly switch from dormant to dominant subclones, occur in the natural pathophysiology of multiple myeloma (MM) but is often "pressed" by the standard treatment of MM. These emerging subclones present a challenge, providing reservoirs for chemoresistant mutations. Technological advancement is required to track MM subclonal changes, as understanding MM's mechanism of evolution at the cellular level can prompt the development of new targeted ways of treating this disease. Current methods to study the evolution of subclones in MM rely on technologies capable of phenotypically and genotypically characterizing plasma cells, which include immunohistochemistry, flow cytometry, or cytogenetics. Still, all of these technologies may be limited by the sensitivity for picking up rare events. In contrast, more incisive methods such as RNA sequencing, comparative genomic hybridization, or whole-genome sequencing are not yet commonly used in clinical practice. Here we introduce the epidemiological diagnosis and prognosis of MM and review current methods for evaluating MM subclone evolution, such as minimal residual disease/multiparametric flow cytometry/next-generation sequencing, and their respective advantages and disadvantages. In addition, we propose our new single-cell method of evaluation to understand MM's mechanism of evolution at the molecular and cellular level and to prompt the development of new targeted ways of treating this disease, which has a broad prospect.

Keywords: Multiple myeloma, Single cells, Single-cell transcriptome, Subclonal evolution, Cancer stem cells, Systemic tracking of single-cell landscape, Artificial intelligence medicine

Core tip: Current methods for determining prognosis in multiple myeloma are limited. The prototype device called Multi-Phase Laser-cavitation Single Cell Analyzer can perform reverse transcriptase polymerase chain reaction (RT-PCR) on single cells in a one-step microfluidics chip platform. The ability of the microfluidics chip platform to enrich plasma cell content by depleting CD45+ white blood cells has been demonstrated. Further studies will need to combine single-cell selection with RT-PCR to further enhance the diagnostic capabilities of this technology. This platform has the potential to be used for clinical risk stratification in multiple myeloma as well as minimal residual disease monitoring and selection of therapies to modulate the development of resistance.