Clinical Tier Grading of Cancer Stem Cells According to Clinical Characteristics for Immune Checkpoint Inhibitors Guided by mRNA stemness index
Main Article Content
Cancer stems cells are cells in tumors that have self-renewing capabilities and proliferation, and are partly responsible for tumor growth, metastasis and drug resistance, and have been associated with multidrug resistance and epithelial-mesenchymal transition. mRNA stemness index or mRNAsi is a machine learning tool that uses the application of algorithms to find associations between cancer stemness and tumor prognostic signatures. mRNAsi predicts gene mutation status and identifies tumor signaling pathways. Clinical tier grading is a common feature for stratifying the presenting features and symptoms of patients in several diseases. This study is a review article that summarizes studies in lung cancer, gastric cancer, hepatocellular carcinoma and glioblastoma that use mRNA stemness index machine learning tools to identify differentially expressed genes, characterize the tumor microenvironment and tumor mutational burden, and determine clinical endpoints. A prognostic signature is shown in this paper as determined by mRNAsi high and low values, and a clinical tier grading system is proposed that categorizes cancer stemness presenting characteristics. This clinical grading tier system demonstrates a relationship between cancer stemness and immune checkpoint inhibitor efficacy. This type of tiered system for cancer patients and the accompanying workflow proposed may prove useful to oncologists, and has not been performed before, and is unique in the literature.
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
2. López-Lázaro M. The Stem Cell Division Theory of Cancer. Crit Rev Oncol Hematol (2018) 123:95–113.
3. Lathia J, Liu H, Matei D. The Clinical Impact of Cancer Stem Cells. Oncologist. (2020) 25(2):123–31.
4. Malta TM, Sokolov A, Gentles AJ, Burzykowski T et al. Machine learning identifies stemness features associated with oncogenic differentiation. 2018; Cell 173, 338-354.
5. Li N, Li Y, Zheng P, Zhan Z. Cancer Stemness-Based Prognostic Immune-Related Gene Signatures in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma. Front. Endo. 2021.
6. Mao D, Zhou Z, Song S, Li D, He Y, Wei Z, Zhang C. Identification of Stemness Characteristics Associated With the Immune Microenvironment and Prognosis in Gastric Cancer. Front Onc 2021.
7. Xu Q, Xu H, Chen S and Huang W. Immunological Value of Prognostic Signature Based on Cancer Stem Cell Characteristics in Hepatocellular Carcinoma. Front Cell Dev Biol. 2021;9:7102-7.
8. Wang Z, Wang Y, Yang T, Xing H, et al. Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients. Brief Bion 22(5) 2021; 1-20.
9. Shi X, Liu Y, Cheng, S, Hu H, Zhang J, Wei M, Lin Zhao L, Xin S. Cancer Stemness Associate With Prognosis and the Efficacy of Immunotherapy in Adrenocortical Carcinoma. Front Onc. 2021,
10. Santoro, S. Clinical Phenotype and Management Data in Down Syndrome Regression Disorder. American College of Medical Genetics and Genomics Annual Meeting 2022.
11. Heng WS, Gosens R, Kruyt FAE. Lung Cancer Stem Cells: Origin, Features, Maintenance Mechanisms and Therapeutic Targeting. Biochem Pharmacol. (2019) 160:121–33.
12. Yang YC, Wang SW, Hung HY, Chang CC, Wu IC, Huang YL, et al. Isolation and characterization of human gastric cell lines with stem cell phenotypes. J Gastroenterol Hepatol ((2007) 22:1460–8.
13. Chen XL, Chen XZ, Wang YG, He D, Lu ZH, Liu K, et al. Clinical significance of putative markers of cancer stem cells in gastric cancer: A retrospective cohort study. Oncotarget (2016) 7:62049–69.
14. Pietrantonio F, Miceli R, Raimondi A, Kim YW, Kang WK, Langley RE, et al. Individual Patient Data Meta-Analysis of the Value of Microsatellite Instability As a Biomarker in Gastric Cancer. J Clin Oncol (2019) 37:3392–400.
15. Nishino M, Ramaiya N, Hatabu H, and Hodi R. (2017). Monitoring immune checkpoint blockade response evaluation and biomarker development. Nat Rev Clin Oncol 14, 655-668.
16. El-Khoueiry A, Sangro B, Yau T and Crocenzi T. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase ½ dose escalation and expansion trial. Lanc. 2017;389:2492-2502.