Expression Patterns of Immune Genes Reveal Heterogeneous Subtypes of High-Risk Neuroblastoma

The immune landscape of solid pediatric tumors

Background

Large immunogenomic analyses have demonstrated the prognostic role of the functional orientation of the tumor microenvironment in adult solid tumors, this variable has been poorly explored in the pediatric counterpart.

Methods

We performed a systematic analysis of public RNAseq data (TARGET) for five pediatric tumor types (408 patients): Wilms tumor (WLM), neuroblastoma (NBL), osteosarcoma (OS), clear cell sarcoma of the kidney (CCSK) and rhabdoid tumor of the kidney (RT). We assessed the performance of the Immunologic Constant of Rejection (ICR), which captures an active Th1/cytotoxic response. We also performed gene set enrichment analysis (ssGSEA) and clustered more than 100 well characterized immune traits to define immune subtypes and compared their outcome.

Results

A higher ICR score was associated with better survival in OS and high risk NBL without MYCN amplification but with poorer survival in WLM. Clustering of immune traits revealed the same five principal modules previously described in adult tumors (TCGA). These modules divided pediatric patients into six immune subtypes (S1-S6) with distinct survival outcomes. The S2 cluster showed the best overall survival, characterized by low enrichment of the wound healing signature, high Th1, and low Th2 infiltration, while the reverse was observed in S4. Upregulation of the WNT/Beta-catenin pathway was associated with unfavorable outcomes and decreased T-cell infiltration in OS.

Conclusions

We demonstrated that extracranial pediatric tumors could be classified according to their immune disposition, unveiling similarities with adults’ tumors. Immunological parameters might be explored to refine diagnostic and prognostic biomarkers and to identify potential immune-responsive tumors.

A signature based on five immune-related genes to predict the survival and immune characteristics of neuroblastoma

BACKGROUND

MYCN amplification (MNA) has been proved to be related to poor prognosis in neuroblastoma (NBL), but the MYCN-related immune signatures and genes remain unclear.

METHODS

Enrichment analysis was used to identify the significant enrichment pathways of differentially expressed immune-related genes (DEIRGs). Weight gene coexpression network analysis (WGCNA) was applied to reveal the correlation between these DEIRGs and MYCN status. Univariate and multivariate Cox analyses were used to construct risk model. The relevant fractions of immune cells were evaluated by CIBERSORT and single-sample gene set enrichment analysis (ssGSEA).

RESULTS

Five genes, including CHGA, PTGER1, SHC3, PLXNC1, and TRIM55 were enrolled into the risk model. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve showed that our model performed well in predicting the outcomes of NBL (3-years AUC = 0.720, 5-year AUC = 0.775, 10-years AUC = 0.782), which has been validated in the GSE49711 dataset and the E-MTAB-8248 dataset. By comparing with the tumor immune dysfunction and exclusion (TIDE) and tumor inflammation signature (TIS), we further proved that our model is reliable. Univariate and multivariate Cox regression analyses indicated that the risk score, age, and MYCN can serve as independent prognostic factors in the E-MATB-8248. Functional enrichment analysis showed the DEIRGs were enriched in leukocyte adhesion-related signaling pathways. Gene set enrichment analysis (GSEA) revealed the significantly enriched pathways of the five MYCN-related DEIRGs. The risk score was negatively correlated with the immune checkpoint CD274 (PD-L1) but no significant difference with the TMB. We also confirmed the prognostic value of our model in predicting immunotherapeutics.

CONCLUSION

We constructed and verified a signature based on DEIRG that related to MNA and predicted the survival of NBL based on relevant immune signatures. These findings could provide help for predicting prognosis and developing immunotherapy in NBL.

Asphericity of tumor [123 I]mIBG uptake as a prognostic factor in high-risk neuroblastoma

BACKGROUND

In recent years, many research groups have attempted to identify a subgroup of "ultra-high risk" patients within the high-risk neuroblastoma (NB) category. The aim of our study was to evaluate the prognostic significance of parameters derived from pretherapeutic ¹²³ I-meta-iodobenzylguanidine ([¹²³ I]mIBG) integrated single photon emission computed tomography and computed tomography in high-risk patients with NB.

METHODS

The established parameters metabolic tumor volume (MTV), maximal standardized uptake value (SUV_max ) and the novel parameter tumor asphericity as well as clinical (age, stage) and genetic factors (1p/11q deletions and MYCN amplification) were analyzed in this single-center retrospective study of high-risk patients with newly diagnosed NB. Univariate/multivariable Cox regression and propensity score matching were performed for clinical and radiological parameters.

RESULTS

Twenty-eight high-risk patients with NB were included (14 males, median age 28.8 (11.3-41.0), range 3-74 months). Multivariable analysis of "full" cohort identified high asphericity (≥65%, adjusted hazard ratio [HR] 5.32, 95% confidence interval [CI]: 1.18-24.07, p = .03) and MTV (≥50 ml, adjusted HR 4.31, 95% CI: 1.18-15.80, p = .027) as the only factors associated with worse event-free survival. In matched cohort, tumor asphericity was a significant predictor of relapse/progression (HR 3.83, 95% CI: 1.03-14.26, p = .046).

CONCLUSION

In this exploratory study, imaging parameters related to tumor metabolic activity, tumor asphericity and MTV, provided prognostic value for event-free survival in high-risk NB patients. Asphericity ≥65% and MTV ≥50 ml may serve as additional prognostic factors to those already used.

Adrenal ganglioneuroma: Prognostic factors (Review)

Ganglioneuroma, a rare neural crest-derived tumor, exhibits a benign profile in contrast to other neuroblastic tumors (neuroblastoma/ganglioneuroblastoma). Ganglioneuromas can be found anywhere autonomic ganglia are located, mostly abdominal/pelvic sites followed by the adrenal glands (one-third of cases), mediastinum/thorax and cervical area. Affecting especially children more than 10 years of age, Ganglioneuroma is either asymptomatic or may cause local compressive effects; rarely inducing nonspecific abdominal complains or arterial hypertension related to oversecretion of epinephrine/norepinephrine/dopamine. Despite a good prognosis, adrenalectomy is necessary in order to rule out a malignancy. Open procedure represents the standard therapeutic option; alternatively, centers with large laparoscopic pediatric experience and good stratification protocols have reported successful procedures. High uptake of I¹²³-MIBG is associated with a more severe outcome in cases with increased mitotic index. In neuroblastic tumors, neuron-specific enolase >33 ng/ml, age at diagnosis <49 months, and blood vessel invasion indicate a poor prognosis. Concurrent extra-adrenal/adrenal ganglioneuroma is associated with a more severe prognosis; post-surgical complications are more frequent in non-adrenal vs. adrenal ganglioneuroma. Exceptionally, immune-mediated paraneoplastic neurologic syndromes have been reported: anti-N-methyl-D-aspartate receptor encephalitis and opsoclonus-myoclonus-ataxia syndrome. ROHHAD syndrome is the underlying cause in 40-56% of cases of neuroendocrine tumors including ganglioneuroma; 70% of tumors are diagnosed within the first 24 months after hypothalamic obesity onset, associated with a severe prognosis due to hypoventilation, sleep apnea, and dysautonomia. Recently, the PKB/AKT/mTOR/S6 pathway was identified as a tumorigenic pathway in pediatric ganglioneuroma, not in neuroblastoma; mTOR inhibitors are a potential option for pre-operatory tumor shrinkage. Pediatric adrenal ganglioneuroma has a good prognosis if adequately treated; its recognition requires adrenalectomy. Further development of specific biomarkers is needed. In the present article, we aimed to introduce a review of the literature involving adrenal ganglioneuroma based on a practical, multidisciplinary perspective of prognostic factors.

Establishment and Validation of a Prognostic Immune Signature in Neuroblastoma

BACKGROUND

Neuroblastoma (NBL) is the most common extracranial solid tumor in childhood, and patients with high-risk neuroblastoma had a relatively poor prognosis despite multimodal treatment. To improve immunotherapy efficacy in neuroblastoma, systematic profiling of the immune landscape in neuroblastoma is an urgent need.

METHODS

RNA-seq and according clinical information of neuroblastoma were downloaded from the TARGET database and GEO database (GSE62564). With an immune-related-gene set obtained from the ImmPort database, Immune-related Prognostic Gene Pairs for Neuroblastoma (IPGPN) for overall survival (OS) were established with the TARGET-NBL cohort and then verified with the GEO-NBL cohort. Immune cell infiltration analysis was subsequently performed. The integrated model was established with IPGPN and clinicopathological parameters. Immune cell infiltration was analyzed with the XCELL algorithm. Functional enrichment analysis was performed with clusterProfiler package in R.

RESULTS

Immune-related Prognostic Gene Pairs for Neuroblastoma was successfully established with seven immune-related gene pairs (IGPs) involving 13 unique genes in the training cohort. In the training cohort, IPGPN successfully stratified neuroblastoma patients into a high and low immune-risk groups with different OS (HR=3.92, P = 2 × 10^-8) and event-free survival (HR=3.66, P=2 × 10^-8). ROC curve analysis confirmed its predictive power. Consistently, high IPGPN also predicted worse OS (HR=1.84, P = .002) and EFS in validation cohort (HR=1.38, P = .06) Moreover, higher activated dendritic cells, M1 macrophage, Th1 CD4⁺, and Th2 CD4⁺ T cell enrichment were evident in low immune-risk group. Further integrating IPGPN with age and stage demonstrated improved predictive performance than IPGPN alone.

CONCLUSION

Herein, we presented an immune landscape with IPGPN for prognosis prediction in neuroblastoma, which complements the present understanding of the immune signature in neuroblastoma.

Clinical Relevance of CD4 Cytotoxic T Cells in High-Risk Neuroblastoma

Neuroblastoma is the most common extracranial childhood solid tumor. The majority of high-risk neuroblastoma is resistant/refractory to the current high intensity therapy, and the survival of these patients remains poor for the last three decades. To effectively treat these extremely unfavorable neuroblastomas, innovative immunotherapy approaches would be the most promising. In this article, we discuss the identity of tumor-infiltrating effector cells and immunosuppressive cells in high-risk neuroblastoma. Neuroblastoma is unique in that it expresses little or no classical HLA Class I and II. In contrast, high-risk neuroblastomas express the stress-responsive non-classical Class I, HLA-E molecule. HLA-E is the ligand of activating receptors NKG2C/E that are expressed on memory NK cells, CD8+T cells and CD4 CTLs. By examining a comprehensive RNA-seq gene expression dataset, we detected relatively high levels of CD4 expression in high-risk neuroblastoma tissues. The majority of CD4+ cells were CD3+, and thus they were likely tumor-associated CD4+T cells. In addition, high-level of both CD4 and NKG2C/E expression was associated with prolonged survival of the high-risk neuroblastoma patients, but CD8 levels were not, further suggesting that the CD4+ NKG2C/E+ T cells or CD4 CTL conferred cytotoxicity against the neuroblastoma cells. However, this T cell mediated- "protective effect" declined over time, in part due to the progressive formation of immunosuppressive tumor microenvironment. These observations suggest that to improve survival of high-risk neuroblastoma patients, it is essential to gain insights into how to enhance CD4 CTL cytotoxicity and control the immunosuppressive tumor microenvironment during the course of the disease.

Prognostic Signature of Immune Genes and Immune-Related LncRNAs in Neuroblastoma: A Study Based on GEO and TARGET Datasets

Background

The prognostic value of immune-related genes and lncRNAs in neuroblastoma has not been elucidated, especially in subgroups with different outcomes. This study aimed to explore immune-related prognostic signatures.

Materials and Methods

Immune-related prognostic genes and lncRNAs were identified by univariate Cox regression analysis in the training set. The top 20 C-index genes and 17 immune-related lncRNAs were included in prognostic model construction, and random forest and the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms were employed to select features. The risk score model was constructed and assessed using the Kaplan-Meier plot and the receiver operating characteristic curve. Functional enrichment analysis of the immune-related lncRNAs was conducted using the STRING database.

Results

In GSE49710, five immune genes (CDK4, PIK3R1, THRA, MAP2K2, and ULBP2) were included in the risk score five genes (RS5_G) signature, and eleven immune-related lncRNAs (LINC00260, FAM13A1OS, AGPAT4-IT1, DUBR, MIAT, TSC22D1-AS1, DANCR, MIR137HG, ERC2-IT1, LINC01184, LINC00667) were brought into risk score LncRNAs (RS_Lnc) signature. Patients were divided into high/low-risk score groups by the median. Overall survival and event/progression-free survival time were shortened in patients with high scores, both in training and validation cohorts. The same results were found in subgroups. In grouping ability assessment, the area under the curves (AUCs) in distinguishing different groups ranged from 0.737 to 0.94, better in discriminating MYCN status and high risk in training cohort (higher than 0.9). Multivariate Cox analysis demonstrated that RS5_G and RS_Lnc were the independent risk factors for overall and event/progression-free survival (all p-values <0.001). Correlation analysis showed that RS5_G and RS_Lnc were negatively associated with aDC, CD8+ T cells, but positively correlated with Th2 cells. Functional enrichment analyzes demonstrated that immune-related lncRNAs are mainly enriched in cancer-related pathways and immune-related pathways.

Conclusion

We identified the immune-related prognostic signature RS5_G and RS_Lnc. The predicting and grouping ability is close to being even better than those reported in other studies, especially in subgroups. This study provided prognostic signatures that may help clinicians to choose optimal treatment strategies and showed a new insight for NB treatment. These results need further biological experiments and clinical validation.

Identification of novel prognostic markers of survival time in high-risk neuroblastoma using gene expression profiles

Neuroblastoma is the most common extracranial solid tumor in childhood. Patients in high-risk group often have poor outcomes with low survival rates despite several treatment options. This study aimed to identify a genetic signature from gene expression profiles that can serve as prognostic indicators of survival time in patients of high-risk neuroblastoma, and that could be potential therapeutic targets. RNA-seq count data was downloaded from UCSC Xena browser and samples grouped into Short Survival (SS) and Long Survival (LS) groups. Differential gene expression (DGE) analysis, enrichment analyses, regulatory network analysis and machine learning (ML) prediction of survival group were performed. Forty differentially expressed genes (DEGs) were identified including genes involved in molecular function activities essential for tumor proliferation. DEGs used as features for prediction of survival groups included EVX2, NHLH2, PRSS12, POU6F2, HOXD10, MAPK15, RTL1, LGR5, CYP17A1, OR10AB1P, MYH14, LRRTM3, GRIN3A, HS3ST5, CRYAB and NXPH3. An accuracy score of 82% was obtained by the ML classification models. SMIM28 was revealed to possibly have a role in tumor proliferation and aggressiveness. Our results indicate that these DEGs can serve as prognostic indicators of survival in high-risk neuroblastoma patients and will assist clinicians in making better therapeutic and patient management decisions.