Dataset Information

Clinically Relevant Biomarker Discovery in High-Risk Recurrent Neuroblastoma.

ABSTRACT: Neuroblastoma is a pediatric cancer of the developing sympathetic nervous system. High-risk neuroblastoma patients typically undergo an initial remission in response to treatment, followed by recurrence of aggressive tumors that have become refractory to further treatment. The need for biomarkers that can select patients not responding well to therapy in an early phase is therefore needed. In this study, we used next generation sequencing technology to determine the expression profiles in high-risk neuroblastoma cell lines established before and after therapy. Using partial least squares-discriminant analysis (PLS-DA) with least absolute shrinkage and selection operator (LASSO) and leave-one-out cross-validation, we identified a panel of 55 messenger RNAs and 17 long non-coding RNAs (lncRNAs) which were significantly altered in the expression between cell lines isolated from primary and recurrent tumors. From a neuroblastoma patient cohort, we found 20 of the 55 protein-coding genes to be differentially expressed in patients with unfavorable compared with favorable outcome. We further found a twofold increase or decrease in hazard ratios in these genes when comparing patients with unfavorable and favorable outcome. Gene set enrichment analysis (GSEA) revealed that these genes were involved in proliferation, differentiation and regulated by Polycomb group (PcG) proteins. Of the 17 lncRNAs, 3 upregulated (NEAT1, SH3BP5-AS1, NORAD) and 3 downregulated lncRNAs (DUBR, MEG3, DHRS4-AS1) were also found to be differentially expressed in favorable compared with unfavorable outcome. Moreover, using expression profiles on both miRNAs and mRNAs in the same cohort of cell lines, we found 13 downregulated and 18 upregulated experimentally observed miRNA target genes targeted by miR-21, -424 and -30e, -29b, -138, -494, -181a, -34a, -29b, respectively. The advantage of analyzing biomarkers in a clinically relevant neuroblastoma model system enables further studies on the effect of individual genes upon gene perturbation. In summary, this study identified several genes, which may aid in the prediction of response to therapy and tumor recurrence.

SUBMITTER: Utnes P

PROVIDER: S-EPMC6413431 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Clinically Relevant Biomarker Discovery in High-Risk Recurrent Neuroblastoma.

Utnes Peter P Løkke Cecilie C Flægstad Trond T Einvik Christer C

Cancer informatics 20190311

Neuroblastoma is a pediatric cancer of the developing sympathetic nervous system. High-risk neuroblastoma patients typically undergo an initial remission in response to treatment, followed by recurrence of aggressive tumors that have become refractory to further treatment. The need for biomarkers that can select patients not responding well to therapy in an early phase is therefore needed. In this study, we used next generation sequencing technology to determine the expression profiles in high-r ...[more]

PMID: 30886518

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Project description:Neuroblastoma is a pediatric cancer of the developing sympathetic nervous system. High-risk neuroblastoma patients typically undergo an initial remission in response to treatment, followed by recurrence of aggressive tumors that have become refractory to further treatment. The need for biomarkers that can select patients not responding well to therapy in an early phase is therefore needed. In this study, we used next generation sequencing technology to determine the expression profiles in high-risk neuroblastoma cell lines established before and after therapy. Using partial least squares-discriminant analysis (PLS-DA) with least absolute shrinkage and selection operator (LASSO) and leave-one-out cross-validation, we identified a panel of 55 messenger RNAs and 17 long non-coding RNAs (lncRNAs) which were significantly altered in the expression between cell lines isolated from primary and recurrent tumors. From a neuroblastoma patient cohort, we found 20 of the 55 protein-coding genes to be differentially expressed in patients with unfavorable compared with favorable outcome. We further found a twofold increase or decrease in hazard ratios in these genes when comparing patients with unfavorable and favorable outcome. Gene set enrichment analysis (GSEA) revealed that these genes were involved in proliferation, differentiation and regulated by Polycomb group (PcG) proteins. Of the 17 lncRNAs, 3 upregulated (NEAT1, SH3BP5-AS1, NORAD) and 3 downregulated lncRNAs (DUBR, MEG3, DHRS4-AS1) were also found to be differentially expressed in favorable compared with unfavorable outcome. Moreover, using expression profiles on both miRNAs and mRNAs in the same cohort of cell lines, we found 13 downregulated and 18 upregulated experimentally observed miRNA target genes targeted by miR-21, -424 and -30e, -29b, -138, -494, -181a, -34a, -29b, respectively. The advantage of analyzing biomarkers in a clinically relevant neuroblastoma model system enables further studies on the effect of individual genes upon gene perturbation. In summary, this study identified several genes, which may aid in the prediction of response to therapy and tumor recurrence.

Project description:Traditional approaches to genome-wide marker discovery often follow a common top-down strategy, where a large scale 'omics' investigation is followed by the analysis of functional pathways involved, to narrow down the list of identified putative biomarkers, and to deconvolute gene expression networks, or to obtain an insight into genetic alterations observed in cancer. We set out to investigate whether a reverse approach would allow full or partial reconstruction of the transcriptional programs and biological pathways specific to a given cancer and whether the full or substantially expanded list of putative markers could thus be identified by starting with the partial knowledge of a few disease-specific markers. To this end, we used 10 well-documented differentially expressed markers of colorectal cancer (CRC), analyzed their transcription factor networks and biological pathways, and predicted the existence of 193 new putative markers. Incredibly, the use of a validation marker set of 10 other completely different known CRC markers and the same procedure resulted in a very similar set of 143 predicted markers. Of these, 138 were identical to those found using the training set, confirming our main hypothesis that a much-expanded set of disease markers can be predicted by starting with just a small subset of validated markers. Further to this, we validated the expression of 42 out of 138 top-ranked predicted markers experimentally using qPCR in surgically removed CRC tissues. We showed that 41 out of 42 mRNAs tested have significantly altered levels of mRNA expression in surgically excised CRC tissues. Of the markers tested, 36 have been reported to be associated with aspects of CRC in the past, whilst only limited published evidence exists for another three genes (BCL2, PDGFRB and TSC2), and no published evidence directly linking genes to CRC was found for CCNA1, SHC1 and TGFB3. Whilst we used CRC to test and validate our marker discovery strategy, the reported procedures apply more generally to cancer marker discovery.

Project description:BackgroundPediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions.ResultsOur Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a "known fusion list" prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient's medical record, both known and novel fusions provided medically meaningful information.ConclusionsThe EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

Project description:Purpose: High-risk neuroblastoma is an aggressive disease. DNA sequencing studies have revealed a paucity of actionable genomic alterations and a low mutation burden, posing challenges to develop effective novel therapies. We used RNA sequencing (RNA-seq) to investigate the biology of this disease, including a focus on tumor-infiltrating lymphocytes (TIL).Experimental Design: We performed deep RNA-seq on pretreatment diagnostic tumors from 129 high-risk and 21 low- or intermediate-risk patients with neuroblastomas. We used single-sample gene set enrichment analysis to detect gene expression signatures of TILs in tumors and examined their association with clinical and molecular parameters, including patient outcome. The expression profiles of 190 additional pretreatment diagnostic neuroblastomas, a neuroblastoma tissue microarray, and T-cell receptor (TCR) sequencing were used to validate our findings.Results: We found that MYCN-not-amplified (MYCN-NA) tumors had significantly higher cytotoxic TIL signatures compared with MYCN-amplified (MYCN-A) tumors. A reported MYCN activation signature was significantly associated with poor outcome for high-risk patients with MYCN-NA tumors; however, a subgroup of these patients who had elevated activated natural killer (NK) cells, CD8+ T cells, and cytolytic signatures showed improved outcome and expansion of infiltrating TCR clones. Furthermore, we observed upregulation of immune exhaustion marker genes, indicating an immune-suppressive microenvironment in these neuroblastomas.Conclusions: This study provides evidence that RNA signatures of cytotoxic TIL are associated with the presence of activated NK/T cells and improved outcomes in high-risk neuroblastoma patients harboring MYCN-NA tumors. Our findings suggest that these high-risk patients with MYCN-NA neuroblastoma may benefit from additional immunotherapies incorporated into the current therapeutic strategies. Clin Cancer Res; 24(22); 5673-84. ©2018 AACR.

Dataset Information

Clinically Relevant Biomarker Discovery in High-Risk Recurrent Neuroblastoma.

Publications

Clinically Relevant Biomarker Discovery in High-Risk Recurrent Neuroblastoma.

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