New hints towards a precision medicine strategy for IDH wild-type glioblastoma

White, Kirsten A.; Connor, Kate; Clerkin, James; Murphy, Brona M.; Salvucci, Manuela; O’Farrell, Alice C.; Rehm, Markus; O'Brien, David; Prehn, Jochen H M; Niclou, Simone P.; Lamfers, Martine L.M.; Verreault, Maïté; Idbaïh, Ahmed; Verhaak, Roel G.W.; Golebiewska, Anna; Byrne, Annette T.

doi:10.1016/j.annonc.2020.08.2336

Cited by 37 publications

(30 citation statements)

References 122 publications

(153 reference statements)

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“…Glioblastoma (GBM) is the deadliest primary brain tumor. 70 Research on the role of METTL3 in GBM has produced conflicting conclusions. The different phenotype associated with METTL3 can be illustrated by the different dependence and genetic heterogeneity of m6A modified RNA in different types of GBM cells.…”

Section: Main Textmentioning

confidence: 99%

RNA m6A Modification in Cancers: Molecular Mechanisms and Potential Clinical Applications

Shi

Dai

et al. 2020

The Innovation

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N 6 -Methyladenosine (m 6 A) RNA modification brings a new dawn for RNA modification researches in recent years. This posttranscriptional RNA modification is dynamic and reversible, and is regulated by methylases (“writers”), demethylases (“erasers”), and proteins that preferentially recognize m 6 A modifications (“readers”). The change of RNA m 6 A modification regulates RNA metabolism in eucaryon, including translation, splicing, exporting, decay, and processing. Thereby the dysregulation of m 6 A may lead to tumorigenesis and progression. Given the tumorigenic role of abnormal m 6 A expression, m 6 A regulators may function as potential clinical therapeutic targets for cancers. In this review, we emphasize on the underlying mechanisms of m 6 A modifications in tumorigenesis and further introduce the potential m 6 A regulators-associated therapeutic targets for tumor therapy.

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Section: Main Textmentioning

confidence: 99%

RNA m6A Modification in Cancers: Molecular Mechanisms and Potential Clinical Applications

Shi

Dai

et al. 2020

The Innovation

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“…Despite substantial efforts to characterize molecular signatures of brain tumor, there remain a gap between immune heterogeneity and clinical behaviors (8, 21,22). This study represents an attempt to identi ed new subtypes of brain tumor based on distinct immune in ltration signature, to spanning the previous classi cation systems that are mainly de ned on histology and genome (3).…”

Section: Discussionmentioning

confidence: 99%

Transcriptomic Analysis Identified two Subtypes of Brain Tumor Characterized by Distinct Immune Infiltration and Prognosis

Shen

Feng

et al. 2021

Preprint

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Background Brain tumor ranks the most devastating cancer type. The complex tumor immune microenvironment prevents brain tumor from therapeutic benefits. The purpose of this study was to stratify brain tumors based on their distinct immune infiltration signatures to facilitate better clinical decision making and prognosis prediction.Methods We developed a deep learning model to characterize immune infiltration from transcriptome. The developed model was applied to distill expression signatures of transcriptome of brain tumor samples. We performed molecular subtyping with the extracted expression signatures to unveil brain tumor subtypes. Computational methods including gene set enrichment analysis, Kaplan-Meier survival and multivariate Cox regression analyses were employed.Results We identified two distinctive subtypes (i.e. C1/2) of brain tumor featured by distinct immune infiltration signatures. The C1 subtype is characterized by protective immune infiltration signatures, including high infiltration of CD8+ T cells and activation of CX3CL1. The C2 subtype has an extensive infiltration of tumor-associated macrophages and microglia, and was enriched with immune suppressive, wound healing and angiogenic signatures. The C1 subtype had significantly better prognosis as compared with C2 (Log-rank test, HR: 2.5, 95% CI: 2.2 – 2.7; P = 8.2e-78). This difference remained statistically significant (multivariate Cox model, HR: 2.2, 95% CI: 1.7 – 2.9; P = 3.7e-10) by taking into account age, gender, recurrent/secondary status at sampling time, tumor grade, histology, radio-chemotherapy, IDH mutation, MGMT methylation and co-deletion of 1p and 19q. This finding was validated in 6 datasets. The C2 subtype of glioblastoma patients with IDH mutation has poor survival analogous to those without IDH mutation (Log-rank test, adjusted P = 0.8), while C1 has favorable prognosis as compared with glioblastoma of C2 subtype with IDH mutation (Log-rank test, adjusted P = 1.2e-3) or without IDH mutation (Log-rank test, adjusted P = 1.3e-6).Conclusions We identified two distinctive subtypes of brain tumor with different immune infiltration signatures and prognosis. Our finding is helpful for better understanding of brain tumor and has potential clinical utilities.

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“…Despite marked variation in treatment responsiveness and outcomes, nearly all patients with newly diagnosed GBM still receive the same standard radiation and temozolomide-based therapy, underscoring a lack of personalized treatment in GBM relative to many other solid tumors 37 . Moreover, interpatient heterogenetiy in both tumor biology and clinical characteristics has made it di cult to accurately determine the e cacy of experimental treatments, particularly in newly diagnosed GBM trials with progression-free survival (PFS) or OS endpoints 38,39 .…”

Section: Discussionmentioning

confidence: 99%

Multi-Omic Prediction of Overall Survival in Patients With Glioblastoma: Additive and Synergistic Value of Clinical Measures, Radiomics, and Genomics

Kazerooni

Saxena

Toorens

et al. 2021

Preprint

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Background. Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in newly diagnosed, treatment-naïve, IDH-wildtype GBM patients, by combining conventional and deep learning methods.Methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. SVM classifiers were trained on the discovery cohort (n=404) to categorize patient groups of high-risk (OS<6 months) vs all, and low-risk (OS≥18 months) vs all. The trained patient stratification model was independently tested in the replication cohort (n=112) and a patient-wise survival prediction index (SPIradiomics) was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information.Results. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed AUCs of 0.78 (95%CI:0.70–0.85)/0.75 (95%CI:0.64–0.79) and 0.75 (95%CI: 0.65–0.84)/0.63 (95%CI: 0.52–0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95%CI:0.6–0.7) for clinical data, 0.70 (95%CI:0.65–0.75) for clinical and radiomics, 0.72 (95%CI:0.68–0.77) for clinical, MGMT methylation, and radiomics, and 0.75 (95%CI:0.72–0.79) for the combination of all omics, i.e., clinical, MGMT methylation, radiomics, and genomics.Conclusions. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM. Our multi-omic survival prediction tool is easily scalable and can be used for more effective clinical trial stratification.

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New hints towards a precision medicine strategy for IDH wild-type glioblastoma

Cited by 37 publications

References 122 publications

RNA m6A Modification in Cancers: Molecular Mechanisms and Potential Clinical Applications

RNA m6A Modification in Cancers: Molecular Mechanisms and Potential Clinical Applications

Transcriptomic Analysis Identified two Subtypes of Brain Tumor Characterized by Distinct Immune Infiltration and Prognosis

Multi-Omic Prediction of Overall Survival in Patients With Glioblastoma: Additive and Synergistic Value of Clinical Measures, Radiomics, and Genomics

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