iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes

Ensenyat-Mendez, Miquel; Íñiguez-Muñoz, Sandra; Sesé, Borja; Marzese, Diego M.

doi:10.1186/s13040-021-00273-8

Cited by 11 publications

(10 citation statements)

References 96 publications

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“…Ensenyat-Mendez and co-workers constructed the iGlioSub classifier using machine learning, computational biology algorithms and prioritization of highly informative transcriptomic and epigenomic features which, based on gene expression and DNA methylation profiles, classified GBMs into classical, mesenchymal and proneural subtypes ( Figure 1 , Supplementary Table S1 ). This classifier showed better performance in stratifying patients compared to stratification based on gene expression profiles only [ 48 ].…”

Section: Molecular Gbm Subtypesmentioning

confidence: 99%

Current Opportunities for Targeting Dysregulated Neurodevelopmental Signaling Pathways in Glioblastoma

Drakulić

Schwirtlich

Petrović

et al. 2022

Cells

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Glioblastoma (GBM) is the most common and highly lethal type of brain tumor, with poor survival despite advances in understanding its complexity. After current standard therapeutic treatment, including tumor resection, radiotherapy and concomitant chemotherapy with temozolomide, the median overall survival of patients with this type of tumor is less than 15 months. Thus, there is an urgent need for new insights into GBM molecular characteristics and progress in targeted therapy in order to improve clinical outcomes. The literature data revealed that a number of different signaling pathways are dysregulated in GBM. In this review, we intended to summarize and discuss current literature data and therapeutic modalities focused on targeting dysregulated signaling pathways in GBM. A better understanding of opportunities for targeting signaling pathways that influences malignant behavior of GBM cells might open the way for the development of novel GBM-targeted therapies.

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Section: Molecular Gbm Subtypesmentioning

confidence: 99%

Current Opportunities for Targeting Dysregulated Neurodevelopmental Signaling Pathways in Glioblastoma

Drakulić

Schwirtlich

Petrović

et al. 2022

Cells

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“…The R/VarSelRF v0.7-8 27 package was used to identify the best gene signatures to stratify responder and non-responder patients in the training cohort. We employed VarSelRF to remove the least important features in each iteration, selecting the combination of features with the highest predictive potential, as we have previously shown [28][29][30] . This process was iterated 1,000 times to improve the classifiers, and, in each iteration, the combination of genes with the highest Area Under the Curve (AUC) was selected.…”

Section: Construction Of a Random Forest-based Classifiermentioning

confidence: 99%

Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer

Ensenyat-Mendez

Orozco²,

Llinàs‐Arias³

et al. 2022

Preprint

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Background: Immune checkpoint inhibitors (ICI) improve clinical outcomes in triple-negative breast cancer (TNBC) patients. However, a subset of patients does not respond to treatment. Biomarkers that show ICI predictive potential in other solid tumors, such as levels of PD-L1 and the tumor mutational burden, among others, show a modest predictive performance in patients with TNBC. Methods: We built machine learning models based on pre-ICI treatment gene expression profiles, to construct gene expression classifiers to identify primary TNBC ICI-responder patients. This study involved 132 ICI-naïve and 721 specimens treated with ICI plus chemotherapy, including TNBC tumors, HR+/HER2- breast tumors, and other solid non-breast tumors. Results: A 37-gene TNBC ICI predictive (TNBC-ICI) classifier showed a high performance in predicting pathological complete response (pCR) to ICI plus chemotherapy on an independent TNBC validation cohort (AUC=0.86). The TNBC-ICI classifier showed better performance than other previously established molecular signatures, including PD-1 (PDCD1) and PD-L1 (CD274) gene expression (AUC=0.67). Integrating molecular signatures did not improve the efficiency of the classifier (AUC=0.75). TNBC-ICI displayed a modest accuracy in predicting ICI response in two different cohorts of HR+/HER2- breast cancer patients (AUC: 0.72-0.76). Evaluation of six cohorts of patients with non-breast solid tumors treated with ICI plus chemotherapy showed overall poor performance (median AUC=0.67). Conclusion: TNBC-ICI predicts pCR to ICI plus chemotherapy in patients with primary TNBC. The study provides a guide to implementing the TNBC-ICI classifier in clinical studies. Further validations will consolidate a novel predictive panel to improve the treatment decision-making for patients with TNBC.

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“…Following publication of the original article [ 1 ], the authors identified an error in the Funding section.…”

mentioning

confidence: 99%

Correction to: iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes

et al. 2021

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iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes

Cited by 11 publications

References 96 publications

Current Opportunities for Targeting Dysregulated Neurodevelopmental Signaling Pathways in Glioblastoma

Current Opportunities for Targeting Dysregulated Neurodevelopmental Signaling Pathways in Glioblastoma

Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer

Correction to: iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes

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