Validation of a prognostic multi-gene signature in high-risk neuroblastoma using the high throughput digital NanoString nCounter™ system

Implementation of a High-Accuracy Targeted Gene Expression Panel for Clinical Care

This study describes the validation of a clinical RNA expression panel with evaluation of concordance between gene copy gain by a next-generation sequencing (NGS) assay and high gene expression by an RNA expression panel. The RNA Salah Targeted Expression Panel (RNA STEP) was designed with input from oncologists to include 204 genes with utility for clinical trial prescreening and therapy selection. RNA STEP was validated with the nanoString platform using remnant formalin-fixed, paraffin-embedded-derived RNA from 102 patients previously tested with a validated clinical NGS panel. The repeatability, reproducibility, and concordance of RNA STEP results with NGS results were evaluated. RNA STEP demonstrated high repeatability and reproducibility, with excellent correlation (r > 0.97, P < 0.0001) for all comparisons. Comparison of RNA STEP high gene expression (log2 ratio ≥ 2) versus NGS DNA-based gene copy number gain (copies ≥ 5) for 38 mutually covered genes revealed an accuracy of 93.0% with a positive percentage agreement of 69.4% and negative percentage agreement of 93.8%. Moderate correlation was observed between platforms (r = 0.53, P < 0.0001). Concordance between high gene expression and gene copy number gain varied by specific gene, and some genes had higher accuracy between assays. Clinical implementation of RNA STEP provides gene expression data complementary to NGS and offers a tool for prescreening patients for clinical trials.

Development and validation of a 21-gene prognostic signature in neuroblastoma

Survival outcomes for patients with neuroblastoma vary markedly and reliable prognostic markers and risk stratification tools are lacking. We sought to identify and validate a transcriptomic signature capable of predicting risk of mortality in patients with neuroblastoma. The TARGET NBL dataset (n = 243) was used to develop the model and two independent cohorts, E-MTAB-179 (n = 478) and GSE85047 (n = 240) were used as validation sets. EFS was the primary outcome and OS was the secondary outcome of interest for all analysis. We identified a 21-gene signature capable of stratifying neuroblastoma patients into high and low risk groups in the E-MTAB-179 (HR 5.87 [3.83-9.01], p < 0.0001, 5 year AUC 0.827) and GSE85047 (HR 3.74 [2.36-5.92], p < 0.0001, 5 year AUC 0.815) validation cohorts. Moreover, the signature remained independent of known clinicopathological variables, and remained prognostic within clinically important subgroups. Further, the signature was effectively incorporated into a risk model with clinicopathological variables to improve prognostic performance across validation cohorts (Pooled Validation HR 6.93 [4.89-9.83], p < 0.0001, 5 year AUC 0.839). Similar prognostic utility was also demonstrated with OS. The identified signature is a robust independent predictor of EFS and OS outcomes in neuroblastoma patients and can be combined with clinically utilized clinicopathological variables to improve prognostic performance.

Nutlin-3a Enhances Natural Killer Cell-Mediated Killing of Neuroblastoma by Restoring p53-Dependent Expression of Ligands for NKG2D and DNAM-1 Receptors

In this study, we explored whether Nutlin-3a, a well-known, nontoxic small-molecule compound antagonizing the inhibitory interaction of MDM2 with the tumor suppressor p53, may restore ligands for natural killer (NK) cell-activating receptors (NK-AR) on neuroblastoma cells to enhance the NK cell-mediated killing. Neuroblastoma cell lines were treated with Nutlin-3a, and the expression of ligands for NKG2D and DNAM-1 NK-ARs and the neuroblastoma susceptibility to NK cells were evaluated. Adoptive transfer of human NK cells in a xenograft neuroblastoma-bearing NSG murine model was assessed. Two data sets of neuroblastoma patients were explored to correlate p53 expression with ligand expression. Luciferase assays and chromatin immunoprecipitation analysis of p53 functional binding on PVR promoter were performed. Primary neuroblastoma cells were also treated with Nutlin-3a, and neuroblastoma spheroids obtained from one high-risk patient were assayed for NK-cell cytotoxicity. We provide evidence showing that the Nutlin-3a-dependent rescue of p53 function in neuroblastoma cells resulted in (i) increased surface expression of ligands for NK-ARs, thus rendering neuroblastoma cell lines significantly more susceptible to NK cell-mediated killing; (ii) shrinkage of human neuroblastoma tumor masses that correlated with overall survival upon adoptive transfer of NK cells in neuroblastoma-bearing mice; (iii) and increased expression of ligands in primary neuroblastoma cells and boosting of NK cell-mediated disaggregation of neuroblastoma spheroids. We also found that p53 was a direct transcription factor regulating the expression of PVR ligand recognized by DNAM-1. Our findings demonstrated an immunomodulatory role of Nutlin-3a, which might be prospectively used for a novel NK cell-based immunotherapy for neuroblastoma.

Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma

Tumor-infiltrating lymphocytes play an essential role in improving clinical outcome of neuroblastoma (NB) patients, but their relationship with other tumor-infiltrating immune cells in the T cell-inflamed tumors remains poorly investigated. Here we show that dendritic cells (DCs) and natural killer (NK) cells are positively correlated with T-cell infiltration in human NB, both at transcriptional and protein levels, and associate with a favorable prognosis. Multiplex imaging displays DC/NK/T cell conjugates in the tumor microenvironment of low-risk NB. Remarkably, this connection is further strengthened by the identification of gene signatures related to DCs and NK cells able to predict survival of NB patients and strongly correlate with the expression of PD-1 and PD-L1. In summary, our findings unveil a key prognostic role of DCs and NK cells and indicate their related gene signatures as promising tools for the identification of clinical biomarkers to better define risk stratification and survival of NB patients.

Tumour-infiltrating lymphocytes play a crucial role in neuroblastoma, but their relationship to other immune cells is poorly understood. Here the authors identify the cellular and gene signatures of intratumoural dendritic cells and natural killer cells that predict the clinical outcome of neuroblastoma.

Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies ERG as the strongest independent predictor of recurrence

BACKGROUND

As a major cause of morbidity and mortality among men, prostate cancer is a heterogenous disease, with a vast heterogeneity in the biology of the disease and in clinical outcome. While it often runs an indolent course, local progression or metastasis may eventually develop, even among patients considered "low risk" at diagnosis. Therefore, biomarkers that can discriminate aggressive from indolent disease at an early stage would greatly benefit patients. We hypothesized that tissue specimens from early stage prostate cancers may harbor predictive signatures for disease progression.

METHODS

We used a cohort of radical prostatectomy patients with longitudinal follow-up, who had tumors with low grade and stage that revealed no signs of future disease progression at surgery. During the follow-up period, some patients either remained indolent (non-BCR) or progressed to biochemical recurrence (BCR). Total RNA was extracted from tumor, and adjacent normal epithelium of formalin-fixed-paraffin-embedded (FFPE) specimens. Differential gene expression in tumors, and in tumor versus normal tissues between BCR and non-BCR patients were analyzed by NanoString using a customized CodeSet of 151 probes.

RESULTS

After controlling for false discovery rates, we identified a panel of eight genes (ERG, GGT1, HDAC1, KLK2, MYO6, PLA2G7, BICD1 and CACNAID) that distinguished BCR from non-BCR patients. We found a clear association of ERG expression with non-BCR, which was further corroborated by quantitative RT-PCR and immunohistochemistry assays.

CONCLUSIONS

Our results identified ERG as the strongest predictor for BCR and showed that potential prognostic prostate cancer biomarkers can be identified from FFPE tumor specimens.

Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes

BACKGROUND

Gene expression profiling of rare cancers has proven challenging due to limited access to patient materials and requirement of intact, non-degraded RNA for next-generation sequencing. We customized a gene expression panel compatible with degraded RNA from formalin-fixed, paraffin-embedded (FFPE) patient cancer samples and investigated its utility in pathway activity profiling in patients with metaplastic breast cancer (MpBC).

METHODS

Activity of various biological pathways was profiled in samples from nineteen patients with MpBC and 8 patients with invasive ductal carcinoma with triple negative breast cancer (TNBC) phenotype using a custom gene expression-based assay of 345 genes.

RESULTS

MpBC samples of mesenchymal (chondroid and/or osteoid) histology demonstrated increased SNAI1 and BCL2L11 pathway activity compared to samples with non-mesenchymal histology. Additionally, late cornified envelope and keratinization genes were downregulated in MpBC compared to TNBC, and epithelial-to-mesenchymal transition (EMT) and collagen genes were upregulated in MpBC. Patients with high activity of an invasiveness gene expression signature, as well as high expression of the mesenchymal marker and extracellular matrix glycoprotein gene SPARC, experienced worse outcomes than those with low invasiveness activity and low SPARC expression.

CONCLUSIONS

This study demonstrates the utility of gene expression profiling of metaplastic breast cancer FFPE samples with a custom counts-based assay. Gene expression patterns identified by this assay suggest that, although often histologically triple negative, patients with MpBC have distinct pathway activation compared to patients with invasive ductal TNBC. Incorporation of targeted therapies may lead to improved outcome for MpBC patients, especially in those patients expressing increased activity of invasiveness pathways.

The challenge of defining "ultra-high-risk" neuroblastoma

Given the biological and clinical heterogeneity of neuroblastoma, risk stratification is vital to determining appropriate treatment. Historically, most patients with high-risk neuroblastoma (HR-NBL) have been treated uniformly without further stratification. Attempts have been made to identify factors that can be used to risk stratify these patients and to characterize an "ultra-high-risk" (UHR) subpopulation with particularly poor outcome. However, among published data, there is a lack of consensus in the definition of the UHR population and heterogeneity in the endpoints and statistical methods used. This review summarizes our current understanding of stratification of HR-NBL and discusses the complex issues in defining UHR neuroblastoma.

Distillation of the clinical algorithm improves prognosis by multi-task deep learning in high-risk Neuroblastoma

We introduce the CDRP (Concatenated Diagnostic-Relapse Prognostic) architecture for multi-task deep learning that incorporates a clinical algorithm, e.g., a risk stratification schema to improve prognostic profiling. We present the first application to survival prediction in High-Risk (HR) Neuroblastoma from transcriptomics data, a task that studies from the MAQC consortium have shown to remain the hardest among multiple diagnostic and prognostic endpoints predictable from the same dataset. To obtain a more accurate risk stratification needed for appropriate treatment strategies, CDRP combines a first component (CDRP-A) synthesizing a diagnostic task and a second component (CDRP-N) dedicated to one or more prognostic tasks. The approach leverages the advent of semi-supervised deep learning structures that can flexibly integrate multimodal data or internally create multiple processing paths. CDRP-A is an autoencoder trained on gene expression on the HR/non-HR risk stratification by the Children’s Oncology Group, obtaining a 64-node representation in the bottleneck layer. CDRP-N is a multi-task classifier for two prognostic endpoints, i.e., Event-Free Survival (EFS) and Overall Survival (OS). CDRP-A provides the HR embedding input to the CDRP-N shared layer, from which two branches depart to model EFS and OS, respectively. To control for selection bias, CDRP is trained and evaluated using a Data Analysis Protocol (DAP) developed within the MAQC initiative. CDRP was applied on Illumina RNA-Seq of 498 Neuroblastoma patients (HR: 176) from the SEQC study (12,464 Entrez genes) and on Affymetrix Human Exon Array expression profiles (17,450 genes) of 247 primary diagnostic Neuroblastoma of the TARGET NBL cohort. On the SEQC HR patients, CDRP achieves Matthews Correlation Coefficient (MCC) 0.38 for EFS and MCC = 0.19 for OS in external validation, improving over published SEQC models. We show that a CDRP-N embedding is indeed parametrically associated to increasing severity and the embedding can be used to better stratify patients’ survival.

Risk stratification of high-risk metastatic neuroblastoma: A report from the HR-NBL-1/SIOPEN study

BACKGROUND

Risk stratification is crucial to treatment decision-making in neuroblastoma. This study aimed to explore factors present at diagnosis affecting outcome in patients aged ≥18 months with metastatic neuroblastoma and to develop a simple risk score for prognostication.

PROCEDURE

Data were derived from the European high-risk neuroblastoma 1 (HR-NBL1)/International Society for Paediatric Oncology European Neuroblastoma (SIOPEN) trial with analysis restricted to patients aged ≥18 months with metastatic disease and treated prior to the introduction of immunotherapy. Primary endpoint was 5-year event-free survival (EFS). Prognostic factors assessed were sex, age, tumour MYCN amplification (MNA) status, serum lactate dehydrogenase (LDH)/ferritin, primary tumour and metastatic sites. Factors significant in univariate analysis were incorporated into a multi-variable model and an additive scoring system developed based on estimated log-cumulative hazard ratios.

RESULTS

The cohort included 1053 patients with median follow-up 5.5 years and EFS 27 ± 1%. In univariate analyses, age; serum LDH and ferritin; involvement of bone marrow, bone, liver or lung; and >1 metastatic system/compartment were associated with worse EFS. Tumour MNA was not associated with worse EFS. A multi-variable model and risk score incorporating age (>5 years, 2 points), serum LDH (>1250 U/L, 1 point) and number of metastatic systems (>1, 2 points) were developed. EFS was significantly correlated with risk score: EFS 52 ± 9% for score = 0 versus 6 ± 3% for score = 5 (P < 0.0001).

CONCLUSIONS

A simple score can identify an "ultra-high risk" (UHR) cohort (score = 5) comprising 8% of patients with 5-year EFS <10%. These patients appear not to benefit from induction therapy and could potentially be directed earlier to alternative experimental therapies in future trials.

65 YEARS OF THE DOUBLE HELIX: Classification of endocrine tumors in the age of integrated genomics

The classification of human cancers represents one of the cornerstones of modern pathology. Over the last century, surgical pathologists established the current taxonomy of neoplasia using traditional histopathological parameters, which include tumor architecture, cytological features and cellular proliferation. This morphological classification is efficient and robust with high reproducibility and has served patients and health care providers well. The most recent decade has witnessed an explosion of genome-wide molecular genetic and epigenetic data for most cancers, including tumors of endocrine organs. The availability of this expansive multi-dimensional genomic data, collectively termed the cancer genome, has catalyzed a re-examination of the classification of endocrine tumors. Here, recent cancer genome studies of various endocrine tumors, including those of the thyroid, pituitary and adrenal glands, pancreas, small bowel, lung and skin, are presented with special emphasis on how genomic insights are impacting endocrine tumor classification.

Too many targets, not enough patients: rethinking neuroblastoma clinical trials

Neuroblastoma is a rare solid tumour of infancy and early childhood with a disproportionate contribution to paediatric cancer mortality and morbidity. Combination chemotherapy, radiation therapy and immunotherapy remains the standard approach to treat high-risk disease, with few recurrent, actionable genetic aberrations identified at diagnosis. However, recent studies indicate that actionable aberrations are far more common in relapsed neuroblastoma, possibly as a result of clonal expansion. In addition, although the major validated disease driver, MYCN, is not currently directly targetable, multiple promising approaches to target MYCN indirectly are in development. We propose that clinical trial design needs to be rethought in order to meet the challenge of providing rigorous, evidence-based assessment of these new approaches within a fairly small patient population and that experimental therapies need to be assessed at diagnosis in very-high-risk patients rather than in relapsed and refractory patients.

Cell-of-origin of diffuse large B-cell lymphomas determined by the Lymph2Cx assay: better prognostic indicator than Hans algorithm

Diffuse large B-cell lymphomas (DLBCLs) are clinically heterogeneous and need a biomarker that can predict the outcome of treatments accurately. To assess the prognostic significance of the cell-of-origin type for DLBCLs, we applied the Lymph2Cx assay using a NanoString gene expression platform on formalin-fixed paraffin wax-embedded pretreatment tissues obtained from 82 patients with de novo DLBCL, not otherwise specified. All patients were treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) as the first line of chemotherapy. Based on the expression levels of Bcl-6, CD10, and MUM-1 measured by immunohistochemistry, cases were subdivided into germinal center B-cell (GCB) and non-GCB types according to the Hans algorithm. NanoString assay was performed on 82 cases. The Lymph2Cx assay successfully classified 82 cases into three categories: activated B-cell (ABC), GCB, and unclassified types. The concordance rate between the Lymph2Cx assay and the Hans algorithm was 73.6%. The Lymph2Cx-defined ABC type had significantly poorer outcomes compared with the GCB type (5-year overall survival, GCB vs. ABC, 96.6% vs. 77.1%, P = 0.020; 5-year disease-free survival, GCB vs. ABC, 96.6% vs. 79.2%, P = 0.018). In contrast, no significant differences were observed in survival between the two patient subgroups with DLBCL types classified by the Hans algorithm. The Lymph2Cx assay is a robust, reliable method for predicting the outcome of patients with DLBCL treated with R-CHOP chemotherapy.

Incorporating genomic, transcriptomic and clinical data: a prognostic and stem cell-like MYC and PRC imbalance in high-risk neuroblastoma

BACKGROUND

Previous studies suggested that cancer cells possess traits reminiscent of the biological mechanisms ascribed to normal embryonic stem cells (ESCs) regulated by MYC and Polycomb repressive complex 2 (PRC2). Several poorly differentiated adult tumors showed preferentially high expression levels in targets of MYC, coincident with low expression levels in targets of PRC2. This paper will reveal this ESC-like cancer signature in high-risk neuroblastoma (HR-NB), the most common extracranial solid tumor in children.

METHODS

We systematically assembled genomic variants, gene expression changes, priori knowledge of gene functions, and clinical outcomes to identify prognostic multigene signatures. First, we assigned a new, individualized prognostic index using the relative expressions between the poor- and good-outcome signature genes. We then characterized HR-NB aggressiveness beyond these prognostic multigene signatures through the imbalanced effects of MYC and PRC2 signaling. We further analyzed Retinoic acid (RA)-induced HR-NB cells to model tumor cell differentiation. Finally, we performed in vitro validation on ZFHX3, a cell differentiation marker silenced by PRC2, and compared cell morphology changes before and after blocking PRC2 in HR-NB cells.

RESULTS

A significant concurrence existed between exons with verified variants and genes showing MYCN-dependent expression in HR-NB. From these biomarker candidates, we identified two novel prognostic gene-set pairs with multi-scale oncogenic defects. Intriguingly, MYC targets over-represented an unfavorable component of the identified prognostic signatures while PRC2 targets over-represented a favorable component. The cell cycle arrest and neuronal differentiation marker ZFHX3 was identified as one of PRC2-silenced tumor suppressor candidates. Blocking PRC2 reduced tumor cell growth and increased the mRNA expression levels of ZFHX3 in an early treatment stage. This hypothesis-driven systems bioinformatics work offered novel insights into the PRC2-mediated tumor cell growth and differentiation in neuroblastoma, which may exert oncogenic effects together with MYC regulation.

CONCLUSION

Our results propose a prognostic effect of imbalanced MYC and PRC2 moderations in pediatric HR-NB for the first time. This study demonstrates an incorporation of genomic landscapes and transcriptomic profiles into the hypothesis-driven precision prognosis and biomarker discovery. The application of this approach to neuroblastoma, as well as other cancer more broadly, could contribute to reduced relapse and mortality rates in the long term.

Artificial neural network classifier predicts neuroblastoma patients' outcome

Background

More 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.

Methods

Multi-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.

Results

We 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.

Conclusions

We 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 material

The online version of this article (doi:10.1186/s12859-016-1194-3) contains supplementary material, which is available to authorized users.

Kinase Gene Expression Profiling of Metastatic Clear Cell Renal Cell Carcinoma Tissue Identifies Potential New Therapeutic Targets

Kinases are therapeutically actionable targets. Kinase inhibitors targeting vascular endothelial growth factor receptors (VEGFR) and mammalian target of rapamycin (mTOR) improve outcomes in metastatic clear cell renal cell carcinoma (ccRCC), but are not curative. Metastatic tumor tissue has not been comprehensively studied for kinase gene expression. Paired intra-patient kinase gene expression analysis in primary tumor (T), matched normal kidney (N) and metastatic tumor tissue (M) may assist in identifying drivers of metastasis and prioritizing therapeutic targets. We compared the expression of 519 kinase genes using NanoString in T, N and M in 35 patients to discover genes over-expressed in M compared to T and N tissue. RNA-seq data derived from ccRCC tumors in The Cancer Genome Atlas (TCGA) were used to demonstrate differential expression of genes in primary tumor tissue from patients that had metastasis at baseline (n = 79) compared to those that did not develop metastasis for at least 2 years (n = 187). Functional analysis was conducted to identify key signaling pathways by using Ingenuity Pathway Analysis. Of 10 kinase genes overexpressed in metastases compared to primary tumor in the discovery cohort, 9 genes were also differentially expressed in TCGA primary tumors with metastasis at baseline compared to primary tumors without metastasis for at least 2 years: EPHB2, AURKA, GSG2, IKBKE, MELK, CSK, CHEK2, CDC7 and MAP3K8; p<0.001). The top pathways overexpressed in M tissue were pyridoxal 5'-phosphate salvage, salvage pathways of pyrimidine ribonucleotides, NF-kB signaling, NGF signaling and cell cycle control of chromosomal replication. The 9 kinase genes validated to be over-expressed in metastatic ccRCC may represent currently unrecognized but potentially actionable therapeutic targets that warrant functional validation.

Transcript signatures that predict outcome and identify targetable pathways in MYCN-amplified neuroblastoma

BACKGROUND

In the pediatric cancer neuroblastoma (NB), patients are stratified into low, intermediate or high-risk subsets based in part on MYCN amplification status. While MYCN amplification in general predicts unfavorable outcome, no clinical or genomic factors have been identified that predict outcome within these cohorts of high-risk patients. In particular, it is currently not possible at diagnosis to determine which high-risk neuroblastoma patients will ultimately fail upfront therapy.

EXPERIMENTAL DESIGN

We analyzed the prognostic potential of most published gene expression signatures for NB and developed a new prognostic signature to predict outcome for patients with MYCN amplification. Network and pathway analyses identified candidate therapeutic targets for this MYCN-amplified patient subset with poor outcome.

RESULTS

Most signatures have a high capacity to predict outcome of unselected NB patients. However, the majority of published signatures, as well as most randomly generated signatures, are highly confounded by MYCN amplification, and fail to predict outcome in subpopulations of high-risk patients with MYCN-amplified NB. We identify a MYCN module signature that predicts patient outcome for those with MYCN-amplified tumors, that also predicts potential tractable therapeutic signaling pathways and targets including the DNA repair enzyme Poly [ADP-ribose] polymerase 1 (PARP1).

CONCLUSION

Many prognostic signatures for NB are confounded by MYCN amplification and fail to predict outcome for the subset of high-risk patients with MYCN amplification. We report a MYCN module signature that is associated with distinct patient outcomes, and predicts candidate therapeutic targets in DNA repair pathways, including PARP1 in MYCN-amplified NB.

An Update on the Role of Immunotherapy and Vaccine Strategies for Primary Brain Tumors

Existing therapies for glioblastoma (GBM), the most common malignant primary brain tumor in adults, have fallen short of improving the dismal patient outcomes, with an average 14-16-month median overall survival. The biological complexity and adaptability of GBM, redundancy of dysregulated signaling pathways, and poor penetration of therapies through the blood-brain barrier contribute to poor therapeutic progress. The current standard of care for newly diagnosed GBM consists of maximal safe resection, followed by fractionated radiotherapy combined with concurrent temozolomide (TMZ) and 6-12 cycles of adjuvant TMZ. At progression, bevacizumab with or without additional chemotherapy is an option for salvage therapy. The recent FDA approval of sipuleucel-T for prostate cancer and ipilumimab, nivolumab, and pembrolizumab for select solid tumors and the ongoing trials showing clinical efficacy and response durability herald a new era of cancer treatment with the potential to change standard-of-care treatment across multiple cancers. The evaluation of various immunotherapeutics is advancing for GBM, putting into question the dogma of the CNS as an immuno-privileged site. While the field is yet young, both active immunotherapy involving vaccine strategies and cellular therapy as well as reversal of GBM-induced global immune-suppression through immune checkpoint blockade are showing promising results and revealing essential immunological insights regarding kinetics of the immune response, immune evasion, and correlative biomarkers. The future holds exciting promise in establishing new treatment options for GBM that harness the patients' own immune system by activating it with immune checkpoint inhibitors, providing specificity using vaccine therapy, and allowing for modulation and enhancement by combinatorial approaches.

Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays

Environmental studies increasingly identify the presence of both contaminants of emerging concern (CECs) and legacy contaminants in aquatic environments; however, the biological effects of these compounds on resident fishes remain largely unknown. High throughput methodologies were employed to establish partial transcriptomes for three wild-caught, non-model fish species; smallmouth bass (Micropterus dolomieu), white sucker (Catostomus commersonii) and brown bullhead (Ameiurus nebulosus). Sequences from these transcriptome databases were utilized in the development of a custom nCounter CodeSet that allowed for direct multiplexed measurement of 50 transcript abundance endpoints in liver tissue. Sequence information was also utilized in the development of quantitative real-time PCR (qPCR) primers. Cross-species hybridization allowed the smallmouth bass nCounter CodeSet to be used for quantitative transcript abundance analysis of an additional non-model species, largemouth bass (Micropterus salmoides). We validated the nCounter analysis data system with qPCR for a subset of genes and confirmed concordant results. Changes in transcript abundance biomarkers between sexes and seasons were evaluated to provide baseline data on transcript modulation for each species of interest.

NanoString expression profiling identifies candidate biomarkers of RAD001 response in metastatic gastric cancer

Background

Gene expression profiling has contributed greatly to cancer research. However, expression-driven biomarker discovery in metastatic gastric cancer (mGC) remains unclear. A gene expression profile predicting RAD001 response in refractory GC was explored in this study.

Methods

Total RNA isolated from 54 tumour specimens from patients with mGC, prior to RAD001 treatment, was analysed via the NanoString nCounter gene expression assay. This assay targeted 477 genes representing 10 different GC-related oncogenic signalling and molecular subtype-specific expression signatures. Gene expression profiles were correlated with patient clinicopathological variables.

Results

NanoString data confirmed similar gene expression profiles previously identified by microarray analysis. Signature I with 3 GC subtypes (mesenchymal, metabolic and proliferative) showed approximately 90% concordance where the mesenchymal and proliferative subtypes were significantly associated with signet ring cell carcinoma and the WHO classified tubular adenocarcinoma GC, respectively (p=0.042). Single-gene-level correlations with patient clinicopathological variables showed strong associations between FHL1 expression (mesenchymal subtype) and signet ring cell carcinoma, and NEK2, OIP5, PRC1, TPX2 expression (proliferative subtype) with tubular adenocarcinoma (adjusted p<0.05). Increased BRCA2 (p=0.040) and MMP9 (p=0.045) expression was significantly associated with RAD001 good response and longer progression-free survival outcome (BRCA2, p=0.012, HR 0.370 95% CI (0.171 to 0.800); MMP9, p=0.010, HR 0.359 95% CI (0.166 to 0.779)). In contrast, increased BTC (p=0.035) expression was significantly associated with RAD001 poor response and poor progression-free survival (p=0.031, HR 2.336 95% CI (1.079 to 5.059) by univariate Cox regression analysis.

Conclusions

Microarray results are highly reproducible with NanoString nCounter gene expression profiling. Additionally, BRCA2 and MMP9 expression are potential predictive biomarkers for good response in RAD001-treated mGC.

Genetic discoveries and treatment advances in neuroblastoma

PURPOSE OF REVIEW

Major advances in our understanding of the genetic basis of neuroblastoma, and the role somatic alterations play in driving tumor growth, have led to improvements in risk-stratified therapy and have provided the rationale for targeted therapies. In this review, we highlight current risk-based treatment approaches and discuss the opportunities and challenges of translating recent genomic discoveries into the clinic.

RECENT FINDINGS

Significant progress in the treatment of neuroblastoma has been realized using risk-based treatment strategies. Outcome has improved for all patients, including those classified as high-risk, although survival remains poor for this cohort. Integration of whole-genome DNA copy number and comprehensive molecular profiles into neuroblastoma classification systems will allow more precise prognostication and refined treatment assignment. Promising treatments that include targeted systemic radiotherapy, pathway-targeted small molecules, and therapy targeted at cell surface molecules are being evaluated in clinical trials, and recent genomic discoveries in relapsed tumor samples have led to the identification of new actionable mutations.

SUMMARY

The integration of refined treatment stratification based on whole-genome profiles with therapeutics that target the molecular drivers of malignant behavior in neuroblastoma has the potential to dramatically improve survival, with decreased toxicity.

Leveraging global gene expression patterns to predict expression of unmeasured genes

Background

Large collections of paraffin-embedded tissue represent a rich resource to test hypotheses based on gene expression patterns; however, measurement of genome-wide expression is cost-prohibitive on a large scale. Using the known expression correlation structure within a given disease type (in this case, high grade serous ovarian cancer; HGSC), we sought to identify reduced sets of directly measured (DM) genes which could accurately predict the expression of a maximized number of unmeasured genes.

Results

We developed a greedy gene set selection (GGS) algorithm which returns a DM set of user specified size based on a specific correlation threshold (|r_P|) and minimum number of DM genes that must be correlated to an unmeasured gene in order to infer the value of the unmeasured gene (redundancy). We evaluated GGS in the Cancer Genome Atlas (TCGA) HGSC data across 144 combinations of DM size, redundancy (1–3), and |r_P| (0.60, 0.65, 0.70). Across the parameter sweep, GGS allows on average 9 times more gene expression information to be captured compared to the DM set alone. GGS successfully augments prognostic HGSC gene sets; the addition of 20 GGS selected genes more than doubles the number of genes whose expression is predictable. Moreover, the expression prediction is highly accurate. After training regression models for the predictable gene set using 2/3 of the TCGA data, the average accuracy (ranked correlation of true and predicted values) in the 1/3 testing partition and four independent populations is above 0.65 and approaches 0.8 for conservative parameter sets. We observe similar accuracies in the TCGA HGSC RNA-sequencing data. Specifically, the prediction accuracy increases with increasing redundancy and increasing |r_P|.

Conclusions

GGS-selected genes, which maximize expression information about unmeasured genes, can be combined with candidate gene sets as a cost effective way to increase the amount of gene expression information obtained in large studies. This method can be applied to any organism, model system, disease, or tissue type for which whole genome gene expression data exists.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2250-5) contains supplementary material, which is available to authorized users.

Clinical Application of Prognostic Gene Expression Signature in Fusion Gene-Negative Rhabdomyosarcoma: A Report from the Children's Oncology Group

PURPOSE

Pediatric rhabdomyosarcoma (RMS) has two common histologic subtypes: embryonal (ERMS) and alveolar (ARMS). PAX-FOXO1 fusion gene status is a more reliable prognostic marker than alveolar histology, whereas fusion gene-negative (FN) ARMS patients are clinically similar to ERMS patients. A five-gene expression signature (MG5) previously identified two diverse risk groups within the fusion gene-negative RMS (FN-RMS) patients, but this has not been independently validated. The goal of this study was to test whether expression of the MG5 metagene, measured using a technical platform that can be applied to routine pathology material, would correlate with outcome in a new cohort of patients with FN-RMS.

EXPERIMENTAL DESIGN

Cases were taken from the Children's Oncology Group (COG) D9803 study of children with intermediate-risk RMS, and gene expression profiling for the MG5 genes was performed using the nCounter assay. The MG5 score was correlated with clinical and pathologic characteristics as well as overall and event-free survival.

RESULTS

MG5 standardized score showed no significant association with any of the available clinicopathologic variables. The MG5 signature score showed a significant correlation with overall (N = 57; HR, 7.3; 95% CI, 1.9-27.0; P = 0.003) and failure-free survival (N = 57; HR, 6.1; 95% CI, 1.9-19.7; P = 0.002).

CONCLUSIONS

This represents the first, validated molecular prognostic signature for children with FN-RMS who otherwise have intermediate-risk disease. The capacity to measure the expression of a small number of genes in routine pathology material and apply a simple mathematical formula to calculate the MG5 metagene score provides a clear path toward better risk stratification in future prospective clinical trials.

MYCN concurrence with SAHA-induced cell death in human neuroblastoma cells

BACKGROUND

In the past, the HDAC inhibitor suberoylanilide hydroxamic acid (SAHA) has been shown to induce apoptosis in several human tumor types, including neuroblastomas. Amplification and over-expression of the MYCN oncogene is a diagnostic hallmark and a poor prognostic indicator in high-risk neuroblastomas. Here, we studied the relationship between MYCN amplification and over-expression and the anti-tumor effect of SAHA to assess whether this drug may serve as a treatment option for high-risk neuroblastomas.

METHODS

Different human neuroblastoma cell lines, over-expressing or not over-expressing MYCN, were used in this study. Targeted knockdown and exogenous over-expression of MYCN were employed to examine correlations between MYCN expression levels and SAHA responses. After various time periods and concentration exposures to the drug, cell viability was measured by MTS assay, and variations in MYCN mRNA and protein levels were assessed by qPCR and Western blotting, respectively.

RESULTS

We found that SAHA decreased cell viability in all cell lines tested through apoptosis induction, and that SAHA had a stronger effect on cell lines carrying an amplified MYCN gene. A decrease in MYCN mRNA and protein levels was observed in the SAHA treated cell lines. Subsequent silencing and exogenous over-expression of MYCN changed the proliferation rate of the cells, but did not have any significant impact on the effect of SAHA on the viability of the cells. We also found that SAHA blocked the expression of MYCN and, by doing so, reduced the effects mediated by this protein.

CONCLUSIONS

Our results suggest that SAHA may be used as a single-drug treatment option for neuroblastomas with an amplified MYCN gene, and as an adjuvant treatment option for all neuroblastomas.

Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples

Analysis of clinical trial specimens such as formalin-fixed paraffin-embedded (FFPE) tissue for molecular mechanisms of disease progression or drug response is often challenging and limited to a few markers at a time. This has led to the increasing importance of highly multiplexed assays that enable profiling of many biomarkers within a single assay. Methods for gene expression analysis have undergone major advances in biomedical research, but obtaining a robust dataset from low-quality RNA samples, such as those isolated from FFPE tissue, remains a challenge. Here, we provide a detailed evaluation of the NanoString Technologies nCounter platform, which provides a direct digital readout of up to 800 mRNA targets simultaneously. We tested this system by examining a broad set of human clinical tissues for a range of technical variables, including sensitivity and limit of detection to varying RNA quantity and quality, reagent performance over time, variability between instruments, the impact of the number of fields of view sampled, and differences between probe sequence locations and overlapping genes across CodeSets. This study demonstrates that Nanostring offers several key advantages, including sensitivity, reproducibility, technical robustness, and utility for clinical application.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Background

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.

Results

We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.

Conclusions

We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0694-1) contains supplementary material, which is available to authorized users.

Biomarkers for glioma immunotherapy: the next generation

The term "biomarker" historically refers to a single parameter, such as the expression level of a gene or a radiographic pattern, used to indicate a broader biological state. Molecular indicators have been applied to several aspects of cancer therapy: to describe the genotypic and phenotypic state of neoplastic tissue for prognosis, to predict susceptibility to anti-proliferative agents, to validate the presence of specific drug targets, and to evaluate responsiveness to therapy. For glioblastoma (GBM), immunohistochemical and radiographic biomarkers accessible to the clinical lab have informed traditional regimens, but while immunotherapies have emerged as potentially disruptive weapons against this diffusely infiltrating, heterogeneous tumor, biomarkers with strong predictive power have not been fully established. The cancer immunotherapy field, through the recently accelerated expansion of trials, is currently leveraging this wealth of clinical and biological data to define and revise the use of biomarkers for improving prognostic accuracy, personalization of therapy, and evaluation of responses across the wide variety of tumors. Technological advancements in DNA sequencing, cytometry, and microscopy have facilitated the exploration of more integrated, high-dimensional profiling of the disease system-incorporating both immune and tumor parameters-rather than single metrics, as biomarkers for therapeutic sensitivity. Here we discuss the utility of traditional GBM biomarkers in immunotherapy and how the impending transformation of the biomarker paradigm-from single markers to integrated profiles-may offer the key to bringing predictive, personalized immunotherapy to GBM patients.

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

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 high-risk 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.

Neuroblastoma: paradigm for precision medicine

Neuroblastoma (NB) is the third most common pediatric cancer. Although NB accounts for 7% of pediatric malignancies, it is responsible for more than 10% of childhood cancer-related mortality. Prognosis and treatment are determined by clinical and biological risk factors. Estimated 5-year survival rates for patients with non-high-risk and high-risk NB are more than 90% and less than 50%, respectively. Recent clinical trials have continued to reduce therapy for patients with non-high-risk NB, including the most favorable subsets who are often followed with observation approaches. In contrast, high-risk patients are treated aggressively with chemotherapy, radiation, surgery, and myeloablative and immunotherapies.

Gene Expression Profiling of Ewing Sarcoma Tumors Reveals the Prognostic Importance of Tumor-Stromal Interactions: A Report from the Children's Oncology Group

Relapse of Ewing sarcoma (ES) can occur months or years after initial remission, and salvage therapy for relapsed disease is usually ineffective. Thus, there is great need to develop biomarkers that can predict which patients are at risk for relapse so that therapy and post‐therapy evaluation can be adjusted accordingly. For this study, we performed whole genome expression profiling on two independent cohorts of clinically annotated ES tumours in an effort to identify and validate prognostic gene signatures. ES specimens were obtained from the Children's Oncology Group and whole genome expression profiling performed using Affymetrix Human Exon 1.0 ST arrays. Lists of differentially expressed genes between survivors and non‐survivors were used to identify prognostic gene signatures. An independent cohort of tumours from the Euro‐Ewing cooperative group was similarly analysed as a validation cohort. Unsupervised clustering of gene expression data failed to segregate tumours based on outcome. Supervised analysis of survivors versus non‐survivors revealed a small number of differentially expressed genes and several statistically significant gene signatures. Gene‐specific enrichment analysis demonstrated that integrin and chemokine genes were associated with survival in tumours where stromal contamination was present. Tumours that did not harbour stromal contamination showed no association of any genes or pathways with clinical outcome. Our results reflect the challenges of performing RNA‐based assays on archived bone tumour specimens. In addition, they reveal a key role for tumour stroma in determining ES prognosis. Future biological and clinical investigations should focus on elucidating the contribution of tumour:micro‐environment interactions on ES progression and response to therapy.

Validation of a prognostic multi-gene signature in high-risk neuroblastoma using the high throughput digital NanoString nCounter™ system

Microarray-based molecular signatures have not been widely integrated into neuroblastoma diagnostic classification systems due to the complexities of the assay and requirement for high-quality RNA. New digital technologies that accurately quantify gene expression using RNA isolated from formalin-fixed paraffin embedded (FFPE) tissues are now available. In this study, we describe the first use of a high-throughput digital system to assay the expression of genes in an "ultra-high risk" microarray classifier in FFPE high-risk neuroblastoma tumors. Customized probes corresponding to the 42 genes in a published multi-gene neuroblastoma signature were hybridized to RNA isolated from 107 FFPE high-risk neuroblastoma samples using the NanoString nCounter™ Analysis System. For classification of each patient, the Pearson's correlation coefficient was calculated between the standardized nCounter™ data and the molecular signature from the microarray data. We demonstrate that the nCounter™ 42-gene panel sub-stratified the high-risk cohort into two subsets with statistically significantly different overall survival (p = 0.0027) and event-free survival (p = 0.028). In contrast, none of the established prognostic risk markers (age, stage, tumor histology, MYCN status, and ploidy) were significantly associated with survival. We conclude that the nCounter™ System can reproducibly quantify expression levels of signature genes in FFPE tumor samples. Validation of this microarray signature in our high-risk patient cohort using a completely different technology emphasizes the prognostic relevance of this classifier. Prospective studies testing the prognostic value of molecular signatures in high-risk neuroblastoma patients using FFPE tumor samples and the nCounter™ System are warranted.