Background 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. Recent studies have shown that cancer stemness is capable of being targeted by immunotherapies.1
mRNAsi, or mRNA stemness index, is a tool that has been developed to analyze prognostic significance for immunotherapy response for cancer stemness in lung adenocarcinoma, adrenocortical carcinoma and gastric cancer, among other carcinomas2,3,4 This abstract proposes to apply the prognostic signatures as determined by mRNAsi to create a clinical tier grading system that categorizes cancer stemness presenting characteristics based on studies by for ICI (nivolumab, (anti-PD-1) ipilumumab (anti-CTLA-4), pembrolizumab (anti-PD-L1), atezolizumab (anti-PD-L1)) treatment. 5
Methods A literature search will be conducted using the keywords “cancer stem cells” OR “cancer stemness” AND “immunotherapies” AND “mRNAsi” AND “immune checkpoint inhibitors efficacy”. Additional keywords include “prognostic signatures” AND “metastasis” AND “clinical features” AND “clinical presentation.”
Results mRNAsi-guided tools determined differentially expressed genes in tumors and generated prognostic signatures which in turn reflected clinical characteristics that could be grouped into a tiered list, creating grading categories for ICI treatment efficacy (table 1). Low-risk and high-risk survival groups, tumor mutational burden, TNM pathological stages, overall survival were generated from prognostic gene signatures through mRNAsi. They also could predict 1-year, 3-year, and 5-year overall survival in certain cancers. Based on this clinical presentation, guidance for ICI therapy could be developed.
Conclusions A clinical tier grading list may be an effective way to guide oncologists in applying mRNAsi tools to cancer stemness for treatment through ICIs. Future studies could focus on stratifying patients through the prognostic signatures generated by these tools.
Qin P, Li Q, Liu S, Ning N, Zhang X, Xu Y, Chang AE, Wicha MS. Targeting cancer stem cells using immunologic approaches. Stem Cells. 2015 July ; 33(7): 2085–2092.
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.
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.
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,
Santoro S. Clinical phenotype and management data in down syndrom regression disorder. American College of Medical Genetics and Genomics Annual Meeting 2022.
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