Single-cell RNA-seq Reveals Diverse Molecular Signatures Associated with Methotrexate Resistance in Primary Central Nervous System Lymphoma Cells

Overview

Journal J Neurooncol

Publisher Springer

Date 2024 Dec 5

PMID 39636551

Authors

Ryosuke Osako

Azusa Hayano

Atsushi Kawaguchi

Ryuya Yamanaka

Affiliations

Soon will be listed here.

Abstract

Purpose: Methotrexate is one of the most essential single agents in patients with primary central nervous system lymphoma (PCNSL). However, 25-50% result in relapse with a poor prognosis. Therefore, studies on methotrexate resistance are warranted to explore salvage chemotherapy for recurrent PCNSL. Single-cell sequence analysis enables the characterization of novel cell types and provides a precise understanding of cancer biology.

Methods: Single-cell sequence analysis of parental and methotrexate-resistant PCNSL cells was performed. We used a Weighted Gene Co-expression Network Analysis to identify groups of significantly connected genes.

Results: We identified consensus modules in both the HKBML and TK datasets. HLA-DRβ1, HLA-DQβ1,and SNRPG were hub genes those detected in both datasets revealed by network analysis. Cyclosporine A was selected as the candidate drug for treating methotrexate-resistant cells.

Conclusion: The results of the present study characterized the methotrexate resistance-related signaling pathways in cultured PCNSL cells. Overall, these results may account for variations in treatment responses and lead potential novel therapeutic strategies for patients with PCNSL.

References

Yu J, Du H, Ye X, Zhang L, Xiao H . High-dose methotrexate-based regimens and post-remission consolidation for treatment of newly diagnosed primary CNS lymphoma: meta-analysis of clinical trials. Sci Rep. 2021; 11(1):2125. PMC: 7822904. DOI: 10.1038/s41598-020-80724-0. View

Ferreri A, Calimeri T, Cwynarski K, Dietrich J, Grommes C, Hoang-Xuan K . Primary central nervous system lymphoma. Nat Rev Dis Primers. 2023; 9(1):29. PMC: 10637780. DOI: 10.1038/s41572-023-00439-0. View

Wong C, Siah K, Lo A . Estimation of clinical trial success rates and related parameters. Biostatistics. 2018; 20(2):273-286. PMC: 6409418. DOI: 10.1093/biostatistics/kxx069. View

McGranahan N, Swanton C . Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell. 2017; 168(4):613-628. DOI: 10.1016/j.cell.2017.01.018. View

Burrell R, McGranahan N, Bartek J, Swanton C . The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013; 501(7467):338-45. DOI: 10.1038/nature12625. View

Rosati D, Giordano A . Single-cell RNA sequencing and bioinformatics as tools to decipher cancer heterogenicity and mechanisms of drug resistance. Biochem Pharmacol. 2021; 195:114811. DOI: 10.1016/j.bcp.2021.114811. View

Miyoshi I, Kubonishi I, Yoshimoto S, Hikita T, Dabasaki H, Tanaka T . Characteristics of a brain lymphoma cell line derived from primary intracranial lymphoma. Cancer. 1982; 49(3):456-9. DOI: 10.1002/1097-0142(19820201)49:3<456::aid-cncr2820490311>3.0.co;2-k. View

Hayano A, Takashima Y, Yamanaka R . Cell-type-specific sensitivity of bortezomib in the methotrexate-resistant primary central nervous system lymphoma cells. Int J Clin Oncol. 2019; 24(9):1020-1029. DOI: 10.1007/s10147-019-01451-9. View

Takashima Y, Hayano A, Yamanaka R . Metabolome Analysis Reveals Excessive Glycolysis via PI3K/AKT/mTOR and RAS/MAPK Signaling in Methotrexate-Resistant Primary CNS Lymphoma-Derived Cells. Clin Cancer Res. 2020; 26(11):2754-2766. DOI: 10.1158/1078-0432.CCR-18-3851. View

10.

Hao Y, Hao S, Andersen-Nissen E, Mauck 3rd W, Zheng S, Butler A . Integrated analysis of multimodal single-cell data. Cell. 2021; 184(13):3573-3587.e29. PMC: 8238499. DOI: 10.1016/j.cell.2021.04.048. View

11.

Livak K, Schmittgen T . Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2002; 25(4):402-8. DOI: 10.1006/meth.2001.1262. View

12.

Mansoori B, Mohammadi A, Davudian S, Shirjang S, Baradaran B . The Different Mechanisms of Cancer Drug Resistance: A Brief Review. Adv Pharm Bull. 2017; 7(3):339-348. PMC: 5651054. DOI: 10.15171/apb.2017.041. View

13.

Skums P, Tsyvina V, Zelikovsky A . Inference of clonal selection in cancer populations using single-cell sequencing data. Bioinformatics. 2019; 35(14):i398-i407. PMC: 6612866. DOI: 10.1093/bioinformatics/btz392. View

14.

Raj A, van Oudenaarden A . Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008; 135(2):216-26. PMC: 3118044. DOI: 10.1016/j.cell.2008.09.050. View

15.

Hayford C, Tyson D, Robbins 3rd C, Frick P, Quaranta V, Harris L . An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability. PLoS Biol. 2021; 19(6):e3000797. PMC: 8195356. DOI: 10.1371/journal.pbio.3000797. View

16.

Lathia J, Liu H . Overview of Cancer Stem Cells and Stemness for Community Oncologists. Target Oncol. 2017; 12(4):387-399. PMC: 5524873. DOI: 10.1007/s11523-017-0508-3. View

17.

Karaayvaz M, Cristea S, Gillespie S, Patel A, Mylvaganam R, Luo C . Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq. Nat Commun. 2018; 9(1):3588. PMC: 6123496. DOI: 10.1038/s41467-018-06052-0. View

18.

Kim K, Lee H, Lee H, Kim S, Seo Y, Chung W . Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 2015; 16:127. PMC: 4506401. DOI: 10.1186/s13059-015-0692-3. View

19.

Mitra A, Mukherjee U, Harding T, Jang J, Stessman H, Li Y . Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors. Leukemia. 2015; 30(5):1094-102. DOI: 10.1038/leu.2015.361. View

20.

Yang L, Zhang X, Hou Q, Huang M, Zhang H, Jiang Z . Single-cell RNA-seq of esophageal squamous cell carcinoma cell line with fractionated irradiation reveals radioresistant gene expression patterns. BMC Genomics. 2019; 20(1):611. PMC: 6659267. DOI: 10.1186/s12864-019-5970-0. View