Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma.

Hao Lian,Wei Meng,Yang Zhao,Jian Yang,Yu‐Chao Zhang,Jie Ma,Bao‐Cheng Wang,Wei‐Wei Mao,Jia‐Jia Wang,Qin‐Hua Wang,Yi‐Peng Han,Qi‐Feng Li,Shan Yan

doi:10.1002/1878-0261.12557

Abstract

Most human cancers develop from stem and progenitor cell populations through the sequential accumulation of various genetic and epigenetic alterations. Cancer stem cells have been identified from medulloblastoma (MB), but a comprehensive understanding of MB stemness, including the interactions between the tumor immune microenvironment and MB stemness, is lacking. Here, we employed a trained stemness index model based on an existent one‐class logistic regression (OCLR) machine‐learning method to score MB samples; we then obtained two stemness indices, a gene expression‐based stemness index (mRNAsi) and a DNA methylation‐based stemness index (mDNAsi), to perform an integrated analysis of MB stemness in a cohort of primary cancer samples (n = 763). We observed an inverse trend between mRNAsi and mDNAsi for MB subgroup and metastatic status. By applying the univariable Cox regression analysis, we found that mRNAsi significantly correlated with overall survival (OS) for all MB patients, whereas mDNAsi had no significant association with OS for all MB patients. In addition, by combining the Lasso‐penalized Cox regression machine‐learning approach with univariate and multivariate Cox regression analyses, we identified a stemness‐related gene expression signature that accurately predicted survival in patients with Sonic hedgehog (SHH) MB. Furthermore, positive correlations between mRNAsi and prognostic copy number aberrations in SHH MB, including MYCN amplifications and GLI2 amplifications, were detected. Analyses of the immune microenvironment revealed unanticipated correlations of MB stemness with infiltrating immune cells. Lastly, using the Connectivity Map, we identified potential drugs targeting the MB stemness signature. Our findings based on stemness indices might advance the development of objective diagnostic tools for quantitating MB stemness and lead to novel biomarkers that predict the survival of patients with MB or the efficacy of strategies targeting MB stem cells.

Highlights

Medulloblastoma (MB) is the most commonly diagnosed embryonal tumor of the central nervous system (CNS) in children
By using the univariable Cox regression analyses, we found that gene expression-based stemness index (mRNAsi) had a statistically significant effect on overall survival (OS) for MB (HR, 11.43; 95% CI, 2.79–46.76; P = 7.03 9 10À4), whereas DNA methylation-based stemness index (mDNAsi) had no significant association with OS for MB (Table 1)
With regard to the association between stemness indices and prognosis in MB patients, we showed that mRNAsi had a positive correlation with MB subgroup and a significant association with OS, while mDNAsi had a negative correlation with MB subgroup and no significant association with OS, suggesting that mRNAsi could recapitulate prognostic molecular subgroups of MB

Summary

Introduction

Medulloblastoma (MB) is the most commonly diagnosed embryonal tumor of the central nervous system (CNS) in children. Despite being initially characterized based on histological features, it is clearly accepted that MB mainly comprises four distinct molecular subgroups: wingless (WNT)-activated, Sonic hedgehog (SHH)-activated, group 3, and group 4, as reflected in the 2016 World Health Organization (WHO) classification of tumors of the CNS (Louis et al, 2016; Ramaswamy et al, 2016a,b). These four subgroups have divergent transcriptional profiles, somatic mutations, copy number aberrations, and clinical outcomes (Morrissy et al, 2016; Northcott et al, 2012; Ramaswamy et al, 2013, 2016a,b). It is essential to define the mechanisms of MB growth, metastasis, and recurrence to develop tailored therapies to selectively eradicate tumor cells responsible for MB expansion, metastasis, and relapse while sparing the developing brain (Vanner et al, 2014)

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Discussion

Conclusion