Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

Borisov, Nicolas; Sorokin, Maxim; Tkachev, Victor; Garazha, Andrew; Buzdin, Anton

doi:10.1186/s12920-020-00759-0

Cited by 19 publications

(15 citation statements)

References 80 publications

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“…Temozolomide has been considered to be the first-line chemotherapeutic agent in the treatment of glioma. According to the date from TCGA, glioma patients treated with temozolomide, as a previous study has selected [ 38 ], were divided into effective group (CR: complete response; PR: partial response; SD: stable disease) and ineffective group (PD: progressive disease). The risk score of each group was calculated and verified to be statistically significant between two groups ( Figure 9 A).…”

Section: Resultsmentioning

confidence: 99%

Identification and Validation of an Immune-Associated RNA-Binding Proteins Signature to Predict Clinical Outcomes and Therapeutic Responses in Glioma Patients

Tian

Liu

et al. 2021

Cancers

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The prognosis of patients with glioma is largely related to both the tumor-infiltrating immune cells and the expression of RNA-binding proteins (RBPs) that are able to regulate various pro-inflammatory and oncogenic mediators. However, immune-associated RBPs in glioma remain unexplored. In this study, we captured patient data from The Cancer Genome Atlas (TCGA) and divided them into two immune subtype groups according to the difference in infiltration of immune cells. After differential expression and co-expression analysis, we identified 216 RBPs defined as immune-associated RBPs. After narrowing down processes, eight RBPs were selected out to construct a risk signature that proven to be a novel and independent prognostic factor. The patients were divided into high- and low-risk groups on the basis of risk score. Higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints such as PD1 and CTLA4. In addition, analyses of pathway enrichment, somatic mutation, copy number variations and immuno-/chemotherapeutic response prediction were performed in high- and low-risk groups and compared with each other. For the first time, we demonstrated a novel signature composed of eight immune-associated RBPs that was valuable in predicting the survival of glioma patients and directing immunotherapy and chemotherapy.

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Section: Resultsmentioning

confidence: 99%

Identification and Validation of an Immune-Associated RNA-Binding Proteins Signature to Predict Clinical Outcomes and Therapeutic Responses in Glioma Patients

Tian

Liu

et al. 2021

Cancers

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“…The area under the ROC curve (AUC) is frequently used for scoring molecular biomarkers in oncology [38][39][40][41]. It reflects biomarker robustness and depends on its sensitivity and specificity [42]. It varies between 0.5 and 1, AUC less 0.7 reflects no biomarker ability to discriminate patients by condition, and 0.7 to 0.8 threshold is considered acceptable in diagnostic test assessment, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding [43][44][45].…”

Section: Reconstruction Of Frem2 Molecular Pathwaymentioning

confidence: 99%

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas

Zolotovskaia

Tkachev

Sorokin

et al. 2021

Cancers

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Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level itself. For all relevant datasets, it could robustly discriminate GBM and LGG (p < 1.63 × 10−13, AUC > 0.74). High FREM2 pathway activation level was associated with poor OS in LGG (p < 0.001), and low PFS in LGG (p < 0.001) and GBM (p < 0.05). FREM2 pathway activation level was poor prognosis biomarker for OS (p < 0.05) and PFS (p < 0.05) in LGG with IDH mutation, for PFS in LGG with wild type IDH (p < 0.001) and mutant IDH with 1p/19q codeletion(p < 0.05), in GBM with unmethylated MGMT (p < 0.05), and in GBM with wild type IDH (p < 0.05). Thus, we conclude that the activation level of the FREM2 pathway is a potent new-generation diagnostic and prognostic biomarker for multiple molecular subtypes of GBM and LGG.

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“…We then built the ROC curve and calculated the AUC metric for it. ROC AUC is widely used to assess the performance of biomarkers in oncology (41)(42)(43), and it depends on their sensitivity and specificity (44). It varies between 0.5 and 1, and the robustness criterion of biomarkers is typically AUC greater than 0.7 (45).…”

Section: Modeling Of Wes-rnaseq-tmb Correlation On Tcga Dataset With Matched Normal Wes Controlsmentioning

confidence: 99%

RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples

et al. 2021

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Tumor mutation burden (TMB) is a well-known efficacy predictor for checkpoint inhibitor immunotherapies. Currently, TMB assessment relies on DNA sequencing data. Gene expression profiling by RNA sequencing (RNAseq) is another type of analysis that can inform clinical decision-making and including TMB estimation may strongly benefit this approach, especially for the formalin-fixed, paraffin-embedded (FFPE) tissue samples. Here, we for the first time compared TMB levels deduced from whole exome sequencing (WES) and RNAseq profiles of the same FFPE biosamples in single-sample mode. We took TCGA project data with mean sequencing depth 23 million gene-mapped reads (MGMRs) and found 0.46 (Pearson)–0.59 (Spearman) correlation with standard mutation calling pipelines. This was converted into low (<10) and high (>10) TMB per megabase classifier with area under the curve (AUC) 0.757, and application of machine learning increased AUC till 0.854. We then compared 73 experimental pairs of WES and RNAseq profiles with lower (mean 11 MGMRs) and higher (mean 68 MGMRs) RNA sequencing depths. For higher depth, we observed ~1 AUC for the high/low TMB classifier and 0.85 (Pearson)–0.95 (Spearman) correlation with standard mutation calling pipelines. For the lower depth, the AUC was below the high-quality threshold of 0.7. Thus, we conclude that using RNA sequencing of tumor materials from FFPE blocks with enough coverage can afford for high-quality discrimination of tumors with high and low TMB levels in a single-sample mode.

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Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

Cited by 19 publications

References 80 publications

Identification and Validation of an Immune-Associated RNA-Binding Proteins Signature to Predict Clinical Outcomes and Therapeutic Responses in Glioma Patients

Identification and Validation of an Immune-Associated RNA-Binding Proteins Signature to Predict Clinical Outcomes and Therapeutic Responses in Glioma Patients

Algorithmically Deduced FREM2 Molecular Pathway Is a Potent Grade and Survival Biomarker of Human Gliomas

RNA Sequencing Data for FFPE Tumor Blocks Can Be Used for Robust Estimation of Tumor Mutation Burden in Individual Biosamples

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