A p53 Drug Response Signature Identifies Prognostic Genes in High-Risk Neuroblastoma

Barbieri, Eveline; Preter, Katleen De; Capasso, Mario; Johansson, Peter; Man, Tsz-Kwong; Chen, Zaowen; Stowers, Paris N.; Tonini, Gian Paolo; Speleman, Franki; Shohet, Jason M.

doi:10.1371/journal.pone.0079843

Cited by 39 publications

(34 citation statements)

References 56 publications

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“…Several groups have used gene expression-based approaches to stratify neuroblastoma patients and prognostic gene signatures have been described [11, 13–22]. The performance of our NB-hypo classifier is comparable with that of the other prognostic gene expression signatures proposed for neuroblastoma [68].…”

Section: Discussionmentioning

confidence: 84%

Artificial neural network classifier predicts neuroblastoma patients’ outcome

Cangelosi¹,

Pelassa²,

Morini³

et al. 2016

BMC Bioinformatics

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BackgroundMore than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients’ outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease.MethodsMulti-layer perceptron (MLP) was trained on the expression values of the 62 probe sets constituting NB-hypo signature to develop a predictive model for neuroblastoma patients’ outcome. We utilized the expression data of 100 tumors in a leave-one-out analysis to select and construct the classifier and the expression data of the remaining 82 tumors to test the classifier performance in an external dataset. We utilized the Gene set enrichment analysis (GSEA) to evaluate the enrichment of hypoxia related gene sets in patients predicted with “Poor” or “Good” outcome.ResultsWe utilized the expression of the 62 probe sets of the NB-Hypo signature in 182 neuroblastoma tumors to develop a MLP classifier predicting patients’ outcome (NB-hypo classifier). We trained and validated the classifier in a leave-one-out cross-validation analysis on 100 tumor gene expression profiles. We externally tested the resulting NB-hypo classifier on an independent 82 tumors’ set. The NB-hypo classifier predicted the patients’ outcome with the remarkable accuracy of 87 %. NB-hypo classifier prediction resulted in 2 % classification error when applied to clinically defined low-intermediate risk neuroblastoma patients. The prediction was 100 % accurate in assessing the death of five low/intermediated risk patients. GSEA of tumor gene expression profile demonstrated the hypoxic status of the tumor in patients with poor prognosis.ConclusionsWe developed a robust classifier predicting neuroblastoma patients’ outcome with a very low error rate and we provided independent evidence that the poor outcome patients had hypoxic tumors, supporting the potential of using hypoxia as target for neuroblastoma treatment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1194-3) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 84%

Artificial neural network classifier predicts neuroblastoma patients’ outcome

Cangelosi¹,

Pelassa²,

Morini³

et al. 2016

BMC Bioinformatics

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“…For p53 shRNA, second-generation lentiviruses expressing shp53 and shLuc control were used as described in ref. (29). Briefly, 293T cells were transfected with pLSLPw construct along with packaging plasmids, pVSVG, and pLV-CMV-delta 8.2 by using lipofectamine 2000.…”

Section: Methodsmentioning

confidence: 99%

p53 Nongenotoxic Activation and mTORC1 Inhibition Lead to Effective Combination for Neuroblastoma Therapy

Moreno‐Smith

Lakoma

Chen

et al. 2017

Clinical Cancer Research

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Purpose mTORC1 inhibitors are promising agents for neuroblastoma therapy; however, they have shown limited clinical activity as monotherapy, thus rational drug combinations need to be explored to improve efficacy. Importantly, neuroblastoma maintains both an active p53 and an aberrant mTOR signaling. Experimental Design Using an orthotopic xenograft model and modulating p53 levels, we investigated the antitumor effects of the mTORC1 inhibitor temsirolimus in neuroblastoma expressing normal, decreased, or mutant p53, both as single agent and in combination with first- and second-generation MDM2 inhibitors to reactivate p53. Results Nongenotoxic p53 activation suppresses mTOR activity. Moreover, p53 reactivation via RG7388, a second-generation MDM2 inhibitor, strongly enhances the in vivo antitumor activity of temsirolimus. Single-agent temsirolimus does not elicit apoptosis, and tumors rapidly regrow after treatment suspension. In contrast, our combination therapy triggers a potent apoptotic response in wild-type p53 xenografts and efficiently blocks tumor regrowth after treatment completion. We also found that this combination uniquely led to p53-dependent suppression of survivin whose ectopic expression is sufficient to rescue the apoptosis induced by our combination. Conclusions Our study supports a novel highly effective strategy that combines RG7388 and temsirolimus in wild-type p53 neuroblastoma, which warrants testing in early-phase clinical trials.

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“…Despite these heavy treatment regimens, ∼50% of these patients still succumb to the disease . Although many gene‐expression based studies have also focused on the prognostics of high‐risk patients, independence and significant contribution of prognostic signatures within high‐risk patients defined as stage 4 diagnosed over 18 months and/or MYCN ‐amplified is rarely described . Recently, however, studies have indeed managed to further stratify these high‐risk patients, while still relying on cross‐sample normalization and varying cutoffs, which may limit their clinical applicability (discussed below).…”

Section: Discussionmentioning

confidence: 99%

Individual patient risk stratification of high-risk neuroblastomas using a two-gene score suited for clinical use

et al. 2015

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Several gene expression-based prognostic signatures have been described in neuroblastoma, but none have successfully been applied in the clinic. Here we have developed a clinically applicable prognostic gene signature, both with regards to number of genes and analysis platform. Importantly, it does not require comparison between patients and is applicable amongst highrisk patients. The signature is based on a two-gene score (R-score) with prognostic power in high-stage tumours (stage 4 and/or MYCN-amplified diagnosed after 18 months of age). QPCR-based and array-based analyses of matched cDNAs confirmed cross platform (array-qPCR) transferability. We also defined a fixed cut-off value identifying prognostically differing subsets of high-risk patients on an individual patient basis. This gene expression signature independently contributes to the current neuroblastoma classification system, and if prospectively validated could provide further stratification of high-risk patients, and potential upfront identification of a group of patients that are in need of new/additional treatment regimens.

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A p53 Drug Response Signature Identifies Prognostic Genes in High-Risk Neuroblastoma

Cited by 39 publications

References 56 publications

Artificial neural network classifier predicts neuroblastoma patients’ outcome

Artificial neural network classifier predicts neuroblastoma patients’ outcome

p53 Nongenotoxic Activation and mTORC1 Inhibition Lead to Effective Combination for Neuroblastoma Therapy

Individual patient risk stratification of high-risk neuroblastomas using a two-gene score suited for clinical use

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