Predicting relapse in patients with medulloblastoma by integrating evidence from clinical and genomic features

Identification of a C2H2 zinc finger-related lncRNA prognostic signature and its association with the immune microenvironment in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the main component of renal cell carcinoma, and advanced ccRCC often predicts a poor prognosis. In recent years, research has revealed the critical role of Cys2His2 zinc finger genes (CHZFs) and long non-coding RNAs (lncRNAs) in the development of cancer. Currently, little is known about the prognostic value of the lncRNAs linked to Cys2His2 (C2H2) zinc finger proteins (ZFPs) in ccRCC. The aim of this study was to construct a prognostic model for C2H2-associated lncRNAs to assist in the selection of clinical therapy.

Methods

RNA-sequencing data, and related clinical and prognostic information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were conducted to identify Cys2His2 zinc finger-associated long non-coding RNAs (CHZFLs) and build prediction signatures. A receiver operating characteristic (ROC) curve analysis was performed to validate the risk model. The prognosis of the groups was analyzed using the Kaplan-Meier method. The independent prognostic significance of these signatures was evaluated by univariate and multivariate Cox regression analyses. The relationship between the CHZFL signature and ccRCC tumor immunity was confirmed by a differential analysis of immune function and immunological checkpoints.

Results

A signature composed of five lncRNAs (AL117336.2, AC026401.3, AC124854.1, DBH-AS1, and LINC02100) was constructed. The results revealed a strong correlation between the CHZFLs signature and the prognosis of ccRCC patients. Prognostic characteristics of CHZFLs are independent prognostic factors in ccRCC patients. The diagnostic efficacy of the predictive signature was higher than that of individual clinicopathologic variables, and it had a ROC area under the curve (AUC) of 0.775. The results of the clinical subgroup analysis showed that the high-risk group had shorter overall survival (OS) than the low-risk group. Common chemotherapy medications, including vinorelbine, cytarabine, epirubicin, and gemcitabine, caused increased sensitivity in the high-risk group. Additionally, the single-sample gene set enrichment analysis (ssGSEA) revealed that the immunological state of the ccRCC patients was substantially linked with the predictive parameters.

Conclusions

The five CHZFL signature can help predict the prognosis of ccRCC patients, and assist in selecting immunotherapy and chemotherapy regimens in clinical practice.

MAGI3 enhances sensitivity to sunitinib in renal cell carcinoma by suppressing the MAS/ERK axis and serves as a prognostic marker

Clear cell renal cell carcinoma (ccRCC) exhibits considerable heterogeneity, with approximately 25% of localized cases susceptible to relapse, highlighting the challenge of the absence of reliable predictive biomarkers for personalized treatment. Meanwhile, metastatic renal cell carcinoma is characterized by unfavorable survival rates, and although Sunitinib offers partial benefits, the clinical advantages are often constrained by drug resistance and adverse side effects. Here, MAGI3 was associate with ccRCC progression, as identified through comprehensive bioinformatics analysis of clinical datasets. A low level of MAGI3 emerged as a high-risk factor for ccRCC, indicating its potential as a prognostic marker. Individuals with MAGI3 expression in middle-to-low levels displayed a significantly poorer survival rate, indicating a need for additional treatment even in the early stages of ccRCC. Furthermore, patients with MAGI3 expression in middle-to-high levels exhibited increased sensitivity to Sunitinib compared to those with lower MAGI3 levels, suggesting that individuals with MAGI3 expression at middle levels may potentially benefit from Sunitinib treatment even in the early stages of ccRCC. Through its interaction with the MAS receptor, MAGI3 has been identified as a regulator of cell proliferation and a determinant of Sunitinib resistance in ccRCC, operating via the Ang-(1-7)/MAS/ERK axis. The loss of MAGI3 expression in ccRCC patients activated the ERK signaling pathway, contributing to both cancer progression and Sunitinib resistance. Therefore, our study not only highlight MAGI3's pivotal role in ccRCC progression and Sunitinib resistance, but also reinforces MAGI3's prospective value as a predictive marker.

Radiomic- and dosiomic-based clustering development for radio-induced neurotoxicity in pediatric medulloblastoma

BACKGROUND

Texture analysis extracts many quantitative image features, offering a valuable, cost-effective, and non-invasive approach for individual medicine. Furthermore, multimodal machine learning could have a large impact for precision medicine, as texture biomarkers can underlie tissue microstructure. This study aims to investigate imaging-based biomarkers of radio-induced neurotoxicity in pediatric patients with metastatic medulloblastoma, using radiomic and dosiomic analysis.

METHODS

This single-center study retrospectively enrolled children diagnosed with metastatic medulloblastoma (MB) and treated with hyperfractionated craniospinal irradiation (CSI). Histological confirmation of medulloblastoma and baseline follow-up magnetic resonance imaging (MRI) were mandatory. Treatment involved helical tomotherapy (HT) delivering a dose of 39 Gray (Gy) to brain and spinal axis and a posterior fossa boost up to 60 Gy. Clinical outcomes, such as local and distant brain control and neurotoxicity, were recorded. Radiomic and dosiomic features were extracted from tumor regions on T1, T2, FLAIR (fluid-attenuated inversion recovery) MRI-maps, and radiotherapy dose distribution. Different machine learning feature selection and reduction approaches were performed for supervised and unsupervised clustering.

RESULTS

Forty-eight metastatic medulloblastoma patients (29 males and 19 females) with a mean age of 12 ± 6 years were enrolled. For each patient, 332 features were extracted. Greater level of abstraction of input data by combining selection of most performing features and dimensionality reduction returns the best performance. The resulting one-component radiomic signature yielded an accuracy of 0.73 with sensitivity, specificity, and precision of 0.83, 0.64, and 0.68, respectively.

CONCLUSIONS

Machine learning radiomic-dosiomic approach effectively stratified pediatric medulloblastoma patients who experienced radio-induced neurotoxicity. Strategy needs further validation in external dataset for its potential clinical use in ab initio management paradigms of medulloblastoma.

LASSO regression and WGCNA-based telomerase-associated lncRNA signaling predicts clear cell renal cell carcinoma prognosis and immunotherapy response

OBJECTIVE

To investigate whether telomerase-associated lncRNA expression affects the prognosis and anti-tumor immunity of patients with renal clear cell carcinoma (ccRCC).

METHODS

A series of analyses were performed to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to assess further the association between immune status, tumor microenvironment, and prognostic risk models.

RESULTS

Eight telomerase-associated lncRNAs associated with prognosis were identified and applied to establish a prognostic risk model. Overall survival was higher in the low-risk group.

CONCLUSION

The established prognostic risk model has a good predictive ability for the prognosis of ccRCC patients and provides a new possible therapeutic target for ccRCC.

Drivers Underlying Metastasis and Relapse in Medulloblastoma and Targeting Strategies

Medulloblastomas comprise a molecularly diverse set of malignant pediatric brain tumors in which patients are stratified according to different prognostic risk groups that span from very good to very poor. Metastasis at diagnosis is most often a marker of poor prognosis and the relapse incidence is higher in these children. Medulloblastoma relapse is almost always fatal and recurring cells have, apart from resistance to standard of care, acquired genetic and epigenetic changes that correlate with an increased dormancy state, cell state reprogramming and immune escape. Here, we review means to carefully study metastasis and relapse in preclinical models, in light of recently described molecular subgroups. We will exemplify how therapy resistance develops at the cellular level, in a specific niche or from therapy-induced secondary mutations. We further describe underlying molecular mechanisms on how tumors acquire the ability to promote leptomeningeal dissemination and discuss how they can establish therapy-resistant cell clones. Finally, we describe some of the ongoing clinical trials of high-risk medulloblastoma and suggest or discuss more individualized treatments that could be of benefit to specific subgroups.

A Prognostic Methylation-Driven Two-Gene Signature in Medulloblastoma

Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that one-third of patients will not achieve long-term survival. Conventional prognostic parameters have limited and unreliable correlations with MB outcome, presenting a major challenge for patients' clinical improvement. Acknowledging this issue, our aim was to build a gene signature and evaluate its potential as a new prognostic model for patients with the disease. In this study, we used six datasets totaling 1679 samples including RNA gene expression and DNA methylation data from primary MB as well as control samples from healthy cerebellum. We identified methylation-driven genes (MDGs) in MB, genes whose expression is correlated with their methylation. We employed LASSO regression, incorporating the MDGs as a parameter to develop the prognostic model. Through this approach, we derived a two-gene signature (GS-2) of candidate prognostic biomarkers for MB (CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall survival (OS) with Kaplan-Meier analysis. We evaluated its robustness and accuracy with receiver operating characteristic curves predicting OS at 1, 3, and 5 years in multiple independent datasets. The GS-2 showed highly significant results as an independent prognostic biomarker compared to traditional MB markers. The methylation-regulated GS-2 risk score model can effectively classify patients with MB into high and low-risk, reinforcing the importance of this epigenetic modification in the disease. Such genes stand out as promising prognostic biomarkers with potential application for MB treatment.

Development of a high-throughput screening platform to identify new therapeutic agents for Medulloblastoma Group 3

Pediatric brain tumors (PBTs) represent about 25 % of all pediatric cancers and are the most common solid tumors in children and adolescents. Medulloblastoma (MB) is the most frequently occurring malignant PBT, accounting for almost 10 % of all pediatric cancer deaths. MB Group 3 (MB G3) accounts for 25-30 % of all MB cases and has the worst outcome, particularly when associated with MYC amplification. However, no targeted treatments for this group have been developed so far. Here we describe a unique high throughput screening (HTS) platform specifically designed to identify new therapies for MB G3. The platform incorporates optimized and validated 2D and 3D efficacy and toxicity models, that account for tumor heterogenicity, limited efficacy and unacceptable toxicity from the very early stage of drug discovery. The platform has been validated by conducting a pilot HTS campaign with a 1280 lead-like compound library. Results showed 8 active compounds, targeting MB reported targets and several are currently approved or in clinical trials for pediatric patients with PBTs, including MB. Moreover, hits were combined to avoid tumor resistance, identifying 3 synergistic pairs, one of which is currently under clinical study for recurrent MB and other PBTs.

Upregulated PPP1R14B is connected to cancer progression and immune infiltration in kidney renal clear cell carcinoma

BACKGROUND

Protein phosphatase 1 regulatory subunit 14B (PPP1R14B) is an oncogenic gene found in a variety of tumors, but its role in the prognosis and development of kidney renal clear cell carcinoma (KIRC) remains unknown. Our study aimed to determine whether PPP1R14B could be a prognostic biomarker for KIRC and its role in the development of KIRC.

METHODS

In this work, we used The Cancer Genome Atlas (TCGA) database to explore the expression of PPP1R14B in tumor tissues, its relationship with the prognosis of tumor patients, and its role in tumor occurrence and development. We validated our findings using the International Cancer Genome Consortium (ICGC) cohort, our clinical samples, and in vitro experiments.

RESULTS

PPP1R14B was upregulated in KIRC compared to adjacent normal tissue. Moreover, multivariate analysis revealed that upregulated PPP1R14B expression was an independent risk factor for KIRC progression. High-PPP1R14B groups had shorter overall survival (OS) and disease-free survival (DFS) in TCGA and ICGC cohorts. We used Cell Counting Kit-8 (CCK8) and scratch wound healing assay to explore the proliferation and migration of KIRC cells following PPP1R14B knockdown. Our results indicated that PPP1R14B knockdown significantly reduced the proliferation and migration of KIRC cells in vitro. We also explored the possible cellular mechanisms of PPP1R14B through the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene ontology (GO) analysis, and TISIDB analysis. The function enrich analysis revealed that PPP1R14B-related genes were mainly enriched in purine metabolism and the macromolecule catabolic process. PPP1R14B expression was associated with tumor-infiltrating immune cells (TIICs) in the TCGA cohort, and the results of single-cell RNA-seq (scRNA) further demonstrated that PPP1R14B expression was associated with the enhanced infiltration of CD8 + T lymphocytes.

CONCLUSION

PPP1R14B may serve as a prognostic biomarker in KIRC, affect purine metabolism, activate immune infiltration, and promote KIRC cell migration.

Vitamin B5 supports MYC oncogenic metabolism and tumor progression in breast cancer

Tumors are intrinsically heterogeneous and it is well established that this directs their evolution, hinders their classification and frustrates therapy1-3. Consequently, spatially resolved omics-level analyses are gaining traction4-9. Despite considerable therapeutic interest, tumor metabolism has been lagging behind this development and there is a paucity of data regarding its spatial organization. To address this shortcoming, we set out to study the local metabolic effects of the oncogene c-MYC, a pleiotropic transcription factor that accumulates with tumor progression and influences metabolism10,11. Through correlative mass spectrometry imaging, we show that pantothenic acid (vitamin B5) associates with MYC-high areas within both human and murine mammary tumors, where its conversion to coenzyme A fuels Krebs cycle activity. Mechanistically, we show that this is accomplished by MYC-mediated upregulation of its multivitamin transporter SLC5A6. Notably, we show that SLC5A6 over-expression alone can induce increased cell growth and a shift toward biosynthesis, whereas conversely, dietary restriction of pantothenic acid leads to a reversal of many MYC-mediated metabolic changes and results in hampered tumor growth. Our work thus establishes the availability of vitamins and cofactors as a potential bottleneck in tumor progression, which can be exploited therapeutically. Overall, we show that a spatial understanding of local metabolism facilitates the identification of clinically relevant, tractable metabolic targets.

Association of graph-based spatial features with overall survival status of glioblastoma patients

Glioblastoma is the most common malignant brain tumor with less than 15 months median survival. To aid prognosis, there is a need for decision tools that leverage diagnostic modalities such as MRI to inform survival. In this study, we examine higher-order spatial proximity characteristics from habitats and propose two graph-based methods (minimum spanning tree and graph run-length matrix) to characterize spatial heterogeneity over tumor MRI-derived intensity habitats and assess their relationships with overall survival as well as the immune signature status of patients with glioblastoma. A data set of 74 patients was studied based on the availability of post-contrast T1-weighted and T2-weighted fluid attenuated inversion recovery (FLAIR) image data in The Cancer Image Archive (TCIA). We assessed the predictive value of MST- and GRLM-derived features from 2D images for prediction of 12-month survival status and immune signature status of patients with glioblastoma via a receiver operating characteristic curve analysis. For 12-month survival prediction using MST-based method, sensitivity and specificity were 0.82 and 0.79 respectively. For GRLM-based method, sensitivity and specificity were 0.73 and 0.77 respectively. For immune status, sensitivity and specificity were 0.91 and 0.69, respectively, for the GRLM-based method with an immune effector. Our results show that the proposed MST- and GRLM-derived features are predictive of 12-month survival status as well as the immune signature status of patients with glioblastoma. To our knowledge, this is the first application of MST- and GRLM-based proximity analyses for the study of radiologically-defined tumor habitats in glioblastoma.

[Medulloblastoma: The latest major advances]

Medulloblastoma (MB) is a malignant brain tumor that mainly affects children. It is rarely found in adults. Among the four groups of MB defined today according to molecular characteristics, group 3 is the least favorable with an overall survival rate of 50 %. Current treatments, based on surgery, radiotherapy, and chemotherapy, are not sufficiently adapted to the different characteristics of the four MB groups. However, the use of new cellular and animal models has opened new doors to interesting therapeutic avenues. In this review, we detail recent advances in MB research, with a focus on the genes and pathways that drive tumorigenesis, with particular emphasis on the animal models that have been developed to study tumor biology, as well as advances in new targeted therapies.

The role of CRYAB in tumor prognosis and immune infiltration: A Pan-cancer analysis

Purpose

There is evidence that the Crystallin Alpha B (CRYAB) gene is involved in the regulation of the tumor microenvironment and influences tumor prognosis in some cancers. However, the role of CRYAB gene in prognosis and immunology in pan-cancer is still unclear.

Methods

In this study, we analyzed the transcriptional profiles and survival data of cancer patients from The Cancer Genome Atlas (TCGA) database. CRYAB gene and its relationships with pan-cancer were analyzed using R packages, TIMER2.0, GEPIA2, Sangerbox, UALCAN, cBioPortal, ESTIMATE algorithm, and STRING. Besides, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was utilized to detect CRYAB expression in KIRC and a human KIRC cell line (Caki-1).

Results

We found that CRYAB expression was different in tumors and adjacent tumors in human cancers, affecting patients' prognosis in 15 cancer types. Additionally, CRYAB expression significantly correlated with tumor microenvironment (TME), immune checkpoints (ICP), tumor mutational burden (TMB), and microsatellite instability (MSI) in human cancers. Besides, CRYAB expression was positively associated with the immune infiltration of cancer-associated fibroblasts (CAFs) and endothelial cells in most human cancers. Based on enrichment analysis, the most prevalent CRYAB gene mechanism in malignant tumors may be through anti-apoptotic activity. Moreover, some FDA-approved drugs were found to be associated with CRYAB and might be potential cancer therapeutic candidates.

Conclusions

CRYAB is a crucial component of the TME and influences immune cell infiltration, making it a promising biomarker to assess immune infiltration and prognosis in many malignancies.

TLR3 serves as a novel diagnostic and prognostic biomarker and is closely correlated with immune microenvironment in three types of cancer

Background: Toll-like receptor 3 (TLR3) plays an important role in both innate and adaptive immunity, but the prognostic value of TLR3 in heterogeneous tumors and the correlations between TLR3 expression and immune infiltration of heterogeneous tumors remain unclear. Methods: We investigated the expression of TLR3 in a variety of tumors and focused on the diagnostic and prognostic values of TLR3 in kidney renal clear cell carcinoma (KIRC), pancreatic adenocarcinoma (PAAD) and brain lower grade glioma (LGG) by GEPIA, DriverDBv3, UALCAN, TIMER, LinkedOmics, STRING, GeneMANIA and FunRich, as well as the possible mechanisms of TLR3 affecting tumor prognosis were discussed. Additionally, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to validate TLR3 expression in early KIRC. We also compared the expression of TLR3 in the plasma of early KIRC patients and normal controls by enzyme linked immunosorbent assay (ELISA). Results: TLR3 expression was significantly different in multiple tumors compared with paracancerous nontumor tissues. Elevated expression of TLR3 contributed to the prolonged survival outcome in KIRC patients. Suppressed expression of TLR3 contributed to the prolonged survival outcome in LGG and PAAD patients. Moreover, TLR3 was significantly elevated in stage1, grade1 and N0 of KIRC. The expression and function of TLR3 in KIRC, LGG and PAAD were closely related to tumor immune microenvironment. TRAF6 was a key gene in the interactions between TLR3 and its interacting genes. Finally, the results of RT-qPCR and ELISA indicated that TLR3 expression levels were significantly raised in renal tissue and plasma of early KIRC patients. Conclusion: TLR3 has the potential to be a diagnostic biomarker of KIRC, LGG and PAAD as well as a biomarker for evaluating the prognosis of KIRC, LGG and PAAD, particularly for the early diagnosis of KIRC. TLR3 affects tumors mainly by acting on the immune microenvironment of KIRC, LGG and PAAD. These findings could lead to new insights into the immunotherapeutic targets for KIRC, LGG, and PAAD.

A new CCCH-type zinc finger-related lncRNA signature predicts the prognosis of clear cell renal cell carcinoma patients

Background: Clear cell renal cell carcinoma (ccRCC) is the main component of renal cell carcinoma (RCC), and advanced ccRCC frequently indicates a poor prognosis. The significance of the CCCH-type zinc finger (CTZF) gene in cancer has been increasingly demonstrated during the past few years. According to studies, targeted radical therapy for cancer treatment may be a revolutionary therapeutic approach. Both lncRNAs and CCCH-type zinc finger genes are essential in ccRCC. However, the predictive role of long non-coding RNA (lncRNA) associated with the CCCH-type zinc finger gene in ccRCC needs further elucidation. This study aims to predict patient prognosis and investigate the immunological profile of ccRCC patients using CCCH-type zinc finger-associated lncRNAs (CTZFLs).

Methods: From the Cancer Genome Atlas database, RNA-seq and corresponding clinical and prognostic data of ccRCC patients were downloaded. Univariate and multivariate Cox regression analyses were conducted to acquire CTZFLs for constructing prediction models. The risk model was verified using receiver operating characteristic curve analysis. The Kaplan-Meier method was used to analyze the overall survival (OS) of high-risk and low-risk groups. Multivariate Cox and stratified analyses were used to assess the prognostic value of the predictive feature in the entire cohort and different subgroups. In addition, the relationship between risk scores, immunological status, and treatment response was studied.

Results: We constructed a signature consisting of eight CTZFLs (LINC02100, AC002451.1, DBH-AS1, AC105105.3, AL357140.2, LINC00460, DLGAP1-AS2, AL162377.1). The results demonstrated that the prognosis of ccRCC patients was independently predicted by CTZFLs signature and that the prognosis of high-risk groups was poorer than that of the lower group. CTZFLs markers had the highest diagnostic adequacy compared to single clinicopathologic factors, and their AUC (area under the receiver operating characteristic curve) was 0.806. The overall survival of high-risk groups was shorter than that of low-risk groups when patients were divided into groups based on several clinicopathologic factors. There were substantial differences in immunological function, immune cell score, and immune checkpoint expression between high- and low-risk groups. Additionally, Four agents, including ABT737, WIKI4, afuresertib, and GNE 317, were more sensitive in the high-risk group.

Conclusion: The Eight-CTZFLs prognostic signature may be a helpful prognostic indicator and may help with medication selection for clear cell renal cell carcinoma.

Radiomics signature for the prediction of progression-free survival and radiotherapeutic benefits in pediatric medulloblastoma

PURPOSE

To develop and validate a radiomics signature for progression-free survival (PFS) and radiotherapeutic benefits in pediatric medulloblastoma.

MATERIALS AND METHODS

We retrospectively enrolled 253 consecutive children with medulloblastoma from two hospitals. A total of 1294 radiomic features were extracted from the region of tumor on the T1-weighted and contrast-enhanced T1-weighted (CE-T1w) MRI. Radiomic feature selection and machine learning modelling were performed to build radiomics signature for the prediction of PFS on the training set. Moreover, the prognostic performance of the clinical parameters was investigated for PFS. The Concordance index (a value of 0.5 indicates no predictive discrimination, and a value of 1 indicates perfect predictive discrimination) was used to measure and compare the prognostic performance of these models.

RESULTS

The radiomics signature for the prediction of the PFS yielded Concordance indices of 0.711, 0.707, and 0.717 on the training and held-out test sets 1 and 2, respectively. The radiomics nomogram integrating the radiomics signature, age, and metastasis performed better than the nomogram incorporating only clinicopathological factors (C-index, 0.723 vs. 0.665 and 0.722 vs. 0.677 on the held-out test sets 1 and 2, respectively), which was also validated by the good calibration and decision curve analysis. Further analysis demonstrated that patients with lower value of radiomics signature were associated with better clinical outcomes after postoperative radiotherapy (p < 0.001).

CONCLUSION

The radiomics signature and nomogram performed well for the prediction of PFS and could stratify patients underwent postoperative radiotherapy into the high- and low-risk groups with significantly different clinical outcomes.

Elevated Kir2.1/nuclear N2ICD defines a highly malignant subtype of non-WNT/SHH medulloblastomas

Medulloblastoma (MB) is one of the most common childhood malignant brain tumors (WHO grade IV), traditionally divided into WNT, SHH, Group 3, and Group 4 subgroups based on the transcription profiles, somatic DNA alterations, and clinical outcomes. Unlike WNT and SHH subgroup MBs, Group 3 and Group 4 MBs have similar transcriptomes and lack clearly specific drivers and targeted therapeutic options. The recently revised WHO Classification of CNS Tumors has assigned Group 3 and 4 to a provisional non-WNT/SHH entity. In the present study, we demonstrate that Kir2.1, an inwardly-rectifying potassium channel, is highly expressed in non-WNT/SHH MBs, which promotes tumor cell invasion and metastasis by recruiting Adam10 to enhance S2 cleavage of Notch2 thereby activating the Notch2 signaling pathway. Disruption of the Notch2 pathway markedly inhibited the growth and metastasis of Kir2.1-overexpressing MB cell-derived xenograft tumors in mice. Moreover, Kir2.1^high/nuclear N2ICD^high MBs are associated with the significantly shorter lifespan of the patients. Thus, Kir2.1^high/nuclear N2ICD^high can be used as a biomarker to define a novel subtype of non-WNT/SHH MBs. Our findings are important for the modification of treatment regimens and the development of novel-targeted therapies for non-WNT/SHH MBs.

Toll-Like Receptors Serve as Biomarkers for Early Diagnosis and Prognosis Assessment of Kidney Renal Clear Cell Carcinoma by Influencing the Immune Microenvironment: Comprehensive Bioinformatics Analysis Combined With Experimental Validation

Background: Toll-like receptors (TLRs) are important initiators of innate and acquired immune responses. However, its role in kidney renal clear cell carcinoma (KIRC) remains unclear.

Methods: TLRs and their relationships with KIRC were studied in detail by ONCOMINE, UALCAN, GEPIA, cBioPortal, GeneMANIA, FunRich, LinkedOmics, TIMER and TRRUST. Moreover, we used clinical samples to verify the expressions of TLR3 and TLR4 in early stage of KIRC by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), flow cytometry (FC) and immunohistochemistry (IHC).

Results: The expression levels of TLRs in KIRC were generally different compared with adjacent normal tissues. Moreover, the expressions of TLR3 and TLR4 elevated significantly in the early stage of KIRC. Overexpressions of TLR1, TLR3, TLR4 and TLR8 in KIRC patients were associated with longer overall survival (OS), while inhibition of TLR9 expression was related to longer OS. Additionally, overexpressions of TLR1, TLR3 and TLR4 in KIRC patients were associated with longer disease free survival (DFS). There were general genetic alterations and obvious co-expression correlation of TLRs in KIRC. The PPI network between TLRs was rather complex, and the key gene connecting the TLRs interaction was MYD88. The GO analysis and KEGG pathway analysis indicated that TLRs were closely related to adaptive immunity, innate immunity and other immune-related processes. RELA, NFKB1, IRF8, IRF3 and HIF1A were key transcription factors regulating the expressions of TLRs. What’s more, the expression levels of all TLRs in KIRC were positively correlated with the infiltration levels of dendritic cells, macrophages, neutrophils, B cells, CD4⁺ T cells and CD8⁺ T cells. Finally, the results of RT-qPCR, FC and IHC confirmed that TLR3 and TLR4 were significantly elevated in the early stage of KIRC.

Conclusion: The occurrence and development of KIRC are closely related to TLRs, and TLRs have the potential to be early diagnostic biomarkers of KIRC and biomarkers for judging the prognosis and immune status of KIRC. This study may provide new insights into the selection of KIRC immunotherapy targets.

CYP2J2 Is a Diagnostic and Prognostic Biomarker Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma

Cytochrome P450 family 2 subfamily J member 2 (CYP2J2), a member of the monooxygenase cytochrome P450 (CYP) family and the only member of the human CYP2J subfamily, has many functions, including regulation of oxidative stress, inflammation, apoptosis, and immune responses. However, its role in cancer development has not been clearly elucidated. In this study, expression levels of CYP2J2 in various cancer types were determined using the Oncomine, the Gene Expression Profiling Interactive Analysis (GEIPA), DriverDBv3, UALCAN, and Tumor Immune Estimation Resource (TIMER) databases. The prognostic value of CYP2J2 for KIRC was analyzed using GEPIA, UALCAN, OSkirc, and DriverDBv3 databases. We evaluated the expression levels of CYP2J2 transcript, protein, and promoter methylation at different clinical characteristics in KIRC through the UALCAN database. Simultaneously, CYP2J2 network-related functions were evaluated using the GeneMANIA interactive tool while the biological processes involved in CYP2J2 and its interactive genes were investigated through Metascape and FunRich. Then, we used TIMER to determine the correlation between CYP2J2 expression levels and immune infiltration levels in KIRC. In KIRC, the CYP2J2 gene, RNA, and protein were found to be overexpressed. However, the methylation level of CYP2J2 promoter in KIRC was lower than in normal tissues. Surprisingly, elevated expression levels of CYP2J2 exhibited better prognostic outcomes in KIRC. Evaluation of protein-protein interaction networks and biological processes revealed that CYP2J2 was principally involved in immune responses, apoptosis, and other metabolic processes. Moreover, we found that the expression levels of CYP2J2 were positively correlated with infiltration levels of B cells, CD8 + T cells, neutrophils, and dendritic cells in KIRC. Therefore, we speculated that the overexpression of CYP2J2 prolonged the survival outcome of KIRC patients, which may be related to the change of tumor immune microenvironment. Moreover, all these new understandings of CYP2J2 may provide important value for the early diagnosis and new targeted drug therapy of KIRC.

Medulloblastoma: clinicopathological parameters, risk stratification, and survival analysis of immunohistochemically validated molecular subgroups

BACKGROUND

Medulloblastoma (MB) is a heterogeneous disease, displaying distinct genetic profiles with specific molecular subgroups. This study aimed to validate MB molecular subgrouping using surrogate immunohistochemistry and associate molecular subgroups, histopathological types, and available clinicopathological parameters with overall survival (OS) and progression-free survival (PFS) of MB patients. This study included 40 MBs; immunohistochemical staining, using β-catenin and GRB2-Associated Binding Protein 1 (GAB1) antibodies, was used to classify MB cases into wingless signaling activated (WNT), sonic hedgehog (SHH), and non-WNT/SHH molecular subgroups. Nuclear morphometric analysis (for assessment of degree of anaplasia) and Kaplan-Meier survival curves were done.

RESULTS

MB cases were classified into WNT (10%), SHH (30%), and non-WNT/SHH (60%) subgroups. Histopathological types differed significantly according to tumor location (p< 0.001), degree of anaplasia (p = 0.014), molecular subgroups (p < 0.001), and risk stratification (p = 0.008). Molecular subgroups differed significantly in age distribution (p = 0.031), tumor location (p< 0.001), histopathological variants (p < 0.001), and risk stratification (p < 0.001). OS was 77.5% and 50% after 1 and 2 years, while PFS was 65% and 27.5% after 1 and 2 years, respectively. OS and PFS were associated significantly with histopathological variants (p < 0.001 and 0.001), molecular subgroups (p = 0.012 and 0.005), and risk stratification (p < 0.001 and < 0.001), respectively.

CONCLUSIONS

Medulloblastoma classification based on molecular subgroups, together with clinicopathological indicators, mainly histopathological types; accurately risk stratifies MB patients and predicts their survival.

Role of FUT8 expression in clinicopathology and patient survival for various malignant tumor types: a systematic review and meta-analysis

Dysregulation of α(1,6)-fucosyltransferase (FUT8) plays significant roles in development of a variety of malignant tumor types. We collected as many relevant articles and microarray datasets as possible to assess the prognostic value of FUT8 expression in malignant tumors. For this purpose, we systematically searched PubMed, Embase, Web of Science, Springer, Chinese National Knowledge Infrastructure (CNKI), and Wan Fang, and eventually identified 7 articles and 35 microarray datasets (involving 6124 patients and 10 tumor types) for inclusion in meta-analysis. In each tumor type, FUT8 expression showed significant (p< 0.05) correlation with one or more clinicopathological parameters; these included patient gender, molecular subgroup, histological grade, TNM stage, estrogen receptor, progesterone receptor, and recurrence status. In regard to survival prognosis, FUT8 expression level was associated with overall survival in non-small cell lung cancer (NSCLC), breast cancer, diffuse large B cell lymphoma, gastric cancer, and glioma. FUT8 expression was also correlated with disease-free survival in NSCLC, breast cancer, and colorectal cancer, and with relapse-free survival in pancreatic ductal adenocarcinoma. For most tumor types, survival prognosis of patients with high FUT8 expression was related primarily to clinical features such as gender, tumor stage, age, and pathological category. Our systematic review and meta-analysis confirmed the association of FUT8 with clinicopathological features and patient survival rates for numerous malignant tumor types. Verification of prognostic value of FUT8 in these tumor types will require a large-scale study using standardized methods of detection and analysis.

External Validation of a Nomogram and Risk Grouping System for Predicting Individual Prognosis of Patients With Medulloblastoma

Background: Medulloblastoma (MB) is one of the most malignant neuroepithelial tumors in the central nervous system. This study aimed to establish an effective prognostic nomogram and risk grouping system for predicting overall survival (OS) of patients with MB.

Materials and Methods: The nomogram was constructed based on data from the database of Surveillance, Epidemiology, and End Results (SEER). This database consisted of 2,824 patients with medulloblastoma and was used as the training cohort. The data of another additional 161 patients treated at the Sun Yat-sen University Cancer Center (SYSUCC) were used as the external validation cohort. Cox regression analysis was used to select independent prognostic factors. Concordance index (C-index) and calibration curve were used to predict the prognostic effect of the nomogram for overall survival.

Results: In the training cohort, Cox regression analyses showed that the prognostic factors included histopathology, surgery, radiotherapy, chemotherapy, tumor size, dissemination, and age at diagnosis. The internal and external validated C-indexes were 0.681 and 0.644, respectively. Calibration curves showed that the nomogram was able to predict 1-, 3-, and 5-year OS for patients with MB precisely. Using the training cohort, a risk grouping system was built, which could perfectly classify patients into four risk nomogroups with a 5-year survival rate of 83.9%, 76.5%, 64.5%, and 46.8%, respectively.

Conclusion: We built and validated a nomogram and risk grouping system that can provide individual prediction of OS and distinguish MB patients from different risk groups. This nomogram and risk grouping system could help clinicians making better treatment plan and prognostic assessment.

Molecular stratifications, biomarker candidates and new therapeutic options in current medulloblastoma treatment approaches

Medulloblastoma (MB) is the most common malignant childhood tumor of the brain. Multimodal treatment consisting of surgery, radiation therapy, and chemotherapy reduced cumulative incidence of late mortality but increased the incidence of subsequent neoplasms and severe, incapacitating chronic health conditions. Present treatment strategies fail to recognize heterogeneity within patients despite wide divergence in individual responses. The persistent mortality rates and serious side effects of non-targeted cytotoxic therapies indicate a need for more refined therapeutic approaches. Advanced genomic research has led to the accumulation of an enormous amount of genetic information and resulted in a consensus distinguishing four molecular subgroups, WNT-activated, SHH-activated, and Group 3 and 4 medulloblastomas. These have distinct origin, demographics, molecular alterations, and clinical outcomes. Although subgroup affiliation does not predict response to therapy, new subgroup-specific markers of prognosis can enable a more layered risk stratification with additional subtypes within each primary subgroup. Here, we summarize subgroup-specific genetic alterations and their utility in current treatment strategies. The transition toward molecularly targeted interventions for newly diagnosed MBs remains slow, and prospective trials are needed to confirm stratifications based on molecular alterations. At the same time, numerous studies focus at fine-tuning the intensity of invasive radio- and chemotherapies to reduce intervention-related long-term morbidity. There are an increasing number of immunotherapy-based treatment strategies including immune checkpoint-inhibitors, oncolytic viruses, CAR-T therapy, and NK cells in recurrent and refractory MBs. Although most trials are in early phase, there is hope for therapeutic breakthroughs for advanced MBs within the next decade.

Construction and Validation of a 13-Gene Signature for Prognosis Prediction in Medulloblastoma

Background: Recent studies have identified several molecular subgroups of medulloblastoma associated with distinct clinical outcomes; however, no robust gene signature has been established for prognosis prediction. Our objective was to construct a robust gene signature-based model to predict the prognosis of patients with medulloblastoma.

Methods: Expression data of medulloblastomas were acquired from the Gene Expression Omnibus (GSE85217, n = 763; GSE37418, n = 76). To identify genes associated with overall survival (OS), we performed univariate survival analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. A risk score model was constructed based on selected genes and was validated using multiple datasets. Differentially expressed genes (DEGs) between the risk groups were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and protein–protein interaction (PPI) analyses were performed. Network modules and hub genes were identified using Cytoscape. Furthermore, tumor microenvironment (TME) was evaluated using ESTIMATE algorithm. Tumor-infiltrating immune cells (TIICs) were inferred using CIBERSORTx.

Results: A 13-gene model was constructed and validated. Patients classified as high-risk group had significantly worse OS than those as low-risk group (Training set: p < 0.0001; Validation set 1: p < 0.0001; Validation set 2: p = 0.00052). The area under the curve (AUC) of the receiver operating characteristic (ROC) analysis indicated a good performance in predicting 1-, 3-, and 5-year OS in all datasets. Multivariate analysis integrating clinical factors demonstrated that the risk score was an independent predictor for the OS (validation set 1: p = 0.001, validation set 2: p = 0.004). We then identified 265 DEGs between risk groups and PPI analysis predicted modules that were highly related to central nervous system and embryonic development. The risk score was significantly correlated with programmed death-ligand 1 (PD-L1) expression (p < 0.001), as well as immune score (p = 0.035), stromal score (p = 0.010), and tumor purity (p = 0.010) in Group 4 medulloblastomas. Correlations between the 13-gene signature and the TIICs in Sonic hedgehog and Group 4 medulloblastomas were revealed.

Conclusion: Our study constructed and validated a robust 13-gene signature model estimating the prognosis of medulloblastoma patients. We also revealed genes and pathways that may be related to the development and prognosis of medulloblastoma, which might provide candidate targets for future investigation.

High expression of the transcriptional coactivator TAZ is associated with a worse prognosis and affects cell proliferation in patients with medulloblastoma

The transcriptional coactivator tafazzin (TAZ) serves pivotal roles in organ development, tumor initiation and tumor progression. However, to the best of our knowledge, the expression of TAZ and its clinical significance in human medulloblastoma have not been defined. The present study aimed to clarify the clinical and biological significance of TAZ expression in human medulloblastoma. Immunohistochemical staining for TAZ was performed with 72 medulloblastoma and three normal brain tissue samples. A high expression level of TAZ was detected in 65.28% of medulloblastoma tissues, whereas low expression was identified in the normal brain tissues. TAZ expression was significantly associated with medulloblastoma recurrence. However, the expression of TAZ was not associated with sex, age, tumor location, tumor maximal diameter and tumor histology. Furthermore, both the overall survival and tumor-free survival rate of patients with high levels of expression of TAZ were shorter compared with those of patients with tumors expressing low levels of TAZ. In univariate and multivariate Cox regression analyses, TAZ expression was identified as a significant prognostic factor for patients with medulloblastoma. Functionally, downregulation of TAZ inhibited the proliferation and tumor formation of medulloblastoma cells and the expression of cell-cycle associated proteins in Daoy cells. In conclusion, high expression of TAZ may serve as a prognostic marker for patients with medulloblastoma and TAZ may be a potential target for medulloblastoma therapy.

Decomposing Oncogenic Transcriptional Signatures to Generate Maps of Divergent Cellular States

The systematic sequencing of the cancer genome has led to the identification of numerous genetic alterations in cancer. However, a deeper understanding of the functional consequences of these alterations is necessary to guide appropriate therapeutic strategies. Here, we describe Onco-GPS (OncoGenic Positioning System), a data-driven analysis framework to organize individual tumor samples with shared oncogenic alterations onto a reference map defined by their underlying cellular states. We applied the methodology to the RAS pathway and identified nine distinct components that reflect transcriptional activities downstream of RAS and defined several functional states associated with patterns of transcriptional component activation that associates with genomic hallmarks and response to genetic and pharmacological perturbations. These results show that the Onco-GPS is an effective approach to explore the complex landscape of oncogenic cellular states across cancers, and an analytic framework to summarize knowledge, establish relationships, and generate more effective disease models for research or as part of individualized precision medicine paradigms.

Molecular markers and potential therapeutic targets in non-WNT/non-SHH (group 3 and group 4) medulloblastomas

Childhood medulloblastomas (MB) are heterogeneous and are divided into four molecular subgroups. The provisional non-wingless-activated (WNT)/non-sonic hedgehog-activated (SHH) category combining group 3 and group 4 represents over two thirds of all MBs, coupled with the highest rates of metastases and least understood pathology. The molecular era expanded our knowledge about molecular aberrations involved in MB tumorigenesis, and here, we review processes leading to non-WNT/non-SHH MB formations.

The heterogeneous group 3 and group 4 MBs frequently harbor rare individual genetic alterations, yet the emerging profiles suggest that infrequent events converge on common, potentially targetable signaling pathways. A mutual theme is the altered epigenetic regulation, and in vitro approaches targeting epigenetic machinery are promising. Growing evidence indicates the presence of an intermediate, mixed signature group along group 3 and group 4, and future clarifications are imperative for concordant classification, as misidentifying patient samples has serious implications for therapy and clinical trials.

To subdue the high MB mortality, we need to discern mechanisms of disease spread and recurrence. Current preclinical models do not represent the full scale of group 3 and group 4 heterogeneity: all of existing group 3 cell lines are MYC-amplified and most mouse models resemble MYC-activated MBs. Clinical samples provide a wealth of information about the genetic divergence between primary tumors and metastatic clones, but recurrent MBs are rarely resected. Molecularly stratified treatment options are limited, and targeted therapies are still in preclinical development. Attacking these aggressive tumors at multiple frontiers will be needed to improve stagnant survival rates.

Bioinformatics Analysis of Key Genes and Pathways for Medulloblastoma as a Therapeutic Target

Introduction:

One of the major challenges in cancer treatment is the lack of specific and accurate treatment in cancer. Data analysis can help to understand the underlying molecular mechanism that leads to better treatment. Increasing availability and reliability of DNA microarray data leads to increase the use of these data in a variety of cancers. This study aimed at applying and evaluating microarray data analyzing, identification of important pathways and gene network for medulloblastoma patients to improve treatment approaches especially target therapy.

Methods:

In the current study, Microarray gene expression data (GSE50161) were extracted from Geo datasets and then analyzed by the affylmGUI package to predict and investigate upregulated and downregulated genes in medulloblastoma. Then, the important pathways were determined by using software and gene enrichment analyses. Pathways visualization and network analyses were performed by Cytoscape.

Results:

A total number of 249 differentially expressed genes (DEGs) were identified in medulloblastoma compared to normal samples. Cell cycle, p53, and FoxO signaling pathways were indicated in medulloblastoma, and CDK1, CCNB1, CDK2, and WEE1 were identified as some of the important genes in the medulloblastoma.

Conclusion:

Identification of critical and specific pathway in any disease, in our case medulloblastoma, can lead us to better clinical management and accurate treatment and target therapy.

Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups

There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma. To address this challenge, we quantitatively profiled global proteomes and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels. We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies.

Long non-coding RNA GAS5 and ZFAS1 are prognostic markers involved in translation targeted by miR-940 in prostate cancer

Identification of prognostic biomarkers helps facilitate the prediction of patient outcomes as well as guide treatments. Accumulating evidence now suggests that long non-coding RNAs (lncRNAs) play key roles in tumor progression with diagnostic and prognostic values. However, little is known about the biological functions of lncRNAs and how they contribute to the pathogenesis of cancer. Herein, we performed weighted correlation network analysis (WGCNA) on 380 RNA-seq samples from prostate cancer patients to create networks comprising of microRNAs, lncRNAs, and protein-coding genes. Our analysis revealed expression modules that associated with pathological parameters. More importantly, we identified a gene module that is involved in protein translation and is associated with patient survival. In this gene module, we explored the regulation axis involving GAS5, ZFAS1, and miR-940. We show that GAS5, ZFAS1, and miR-940 are up-regulated in tumors relative to normal prostate tissues, and high expression of either lncRNA is an indicator of poor patient outcome. Finally, we constructed a co-expression network involving GAS5, ZFAS1, and miR-940, as well as the targets of miR-940. Our results show that GAS5 and ZFAS1 are targeted by miR-940 via NAA10 and RPL28. Taken together, co-expression analysis of gene expression profiling from RNA-seq can accelerate the identification and functional characterization of novel prognostic markers in prostate cancer.

Brain cancer genomics and epigenomics

Classically, brain cancers have been graded and diagnosed based on histology and risk stratified by clinical criteria. Recent advances in genomics and epigenomics have ushered in an era of defining cancers based on molecular criteria. These advances have increased our precision of identifying oncogenic driving events and, most importantly, increased our precision at predicting clinical outcome. For the first time in its history, the 2016 revision of the WHO Classification of Tumors of the Central Nervous System included molecular features as tumor classification criteria. Brain tumors can develop in the context of genetic cancer predisposition syndromes, such as Li-Fraumeni or Gorlin syndrome, but by far most commonly arise through the acquisition of somatic mutations and chromosome changes in the malignant cells. By taking a survey across this cancer landscape, certain themes emerge as being common events to drive cancer: DNA damage repair, genomic instability, mechanistic target of rapamycin pathway, sonic hedgehog pathway, hypoxia, and epigenetic dysfunction. Understanding these mechanisms is of paramount importance for improving targeted therapies, and for identifying the right patients for those therapies.

Clustering of samples and variables with mixed-type data

Analysis of data measured on different scales is a relevant challenge. Biomedical studies often focus on high-throughput datasets of, e.g., quantitative measurements. However, the need for integration of other features possibly measured on different scales, e.g. clinical or cytogenetic factors, becomes increasingly important. The analysis results (e.g. a selection of relevant genes) are then visualized, while adding further information, like clinical factors, on top. However, a more integrative approach is desirable, where all available data are analyzed jointly, and where also in the visualization different data sources are combined in a more natural way. Here we specifically target integrative visualization and present a heatmap-style graphic display. To this end, we develop and explore methods for clustering mixed-type data, with special focus on clustering variables. Clustering of variables does not receive as much attention in the literature as does clustering of samples. We extend the variables clustering methodology by two new approaches, one based on the combination of different association measures and the other on distance correlation. With simulation studies we evaluate and compare different clustering strategies. Applying specific methods for mixed-type data proves to be comparable and in many cases beneficial as compared to standard approaches applied to corresponding quantitative or binarized data. Our two novel approaches for mixed-type variables show similar or better performance than the existing methods ClustOfVar and bias-corrected mutual information. Further, in contrast to ClustOfVar, our methods provide dissimilarity matrices, which is an advantage, especially for the purpose of visualization. Real data examples aim to give an impression of various kinds of potential applications for the integrative heatmap and other graphical displays based on dissimilarity matrices. We demonstrate that the presented integrative heatmap provides more information than common data displays about the relationship among variables and samples. The described clustering and visualization methods are implemented in our R package CluMix available from https://cran.r-project.org/web/packages/CluMix.

Low neighbor of Brca1 gene expression predicts poor clinical outcome and resistance of sunitinib in clear cell renal cell carcinoma

Objective

To study the expression of Neighbor of Brca1 gene (NBR1) in clear cell renal cell carcinoma (ccRCC), renal cancer cells and the chemoresistance cells and to elucidate its clinical prognostic and chemoresistance value.

Materials and Methods

We screened the NBR1 mRNA in ccRCC from The Cancer Genome Atlas (TCGA) database and examined expression levels of NBR1 mRNA in 48 cases of ccRCC tissues, renal cancer cell lines and chemoresistance cells by qRT-PCR. Then, we extended two additional data sets in oncomine datebase (https://www.oncomine.org) to further confirm the results of the TCGA database. Immunohistochemistry (IHC) assay data performed in ccRCC tissues and normal tissues were downloaded from The Human Protein Atlas.

Results

The mRNA levels of NBR1 were downregulated in TCGA-KIRC database (n = 533) and ccRCC patient samples (n=48) as well as in RCC cell lines and their chemoresistance cells. Similarly, the protein levels of NBR1 were lower in ccRCC patient samples. NBR1 level was associated with the clinical pathological stage and could discriminate metastasis, recurrence and prognosis in ccRCC patients. Low level of NBR1 mRNA showed a significance poor prognostic of overall survival (OS), disease–free survival (DFS) with univariate and multivariate analyses in ccRCC patients and sunitinib resistance.

Conclusions

Taken together, our results suggest that low level of NBR1 can predict poor clinical outcome and resistance of sunitinib in patients with ccRCC.

Medulloblastoma: Molecular Classification-Based Personal Therapeutics

Recent advances in cancer genomics have revealed 4 distinct subgroups of medulloblastomas, each with unique transcription profiles, DNA alterations and clinical outcome. Molecular classification of medulloblastomas improves predictions of clinical outcome, allowing more accurate matching of intensity of conventional treatments with chemotherapy and radiation to overall prognosis and setting the stage for the introduction of targeted therapies.

Low level of PDZ domain containing 1 (PDZK1) predicts poor clinical outcome in patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most lethal neoplasm of the urologic system. Clinical therapeutic effect varies greatly between individual ccRCC patients, so there is an urgent need to develop prognostic molecular biomarkers to help clinicians identify patients in need of early aggressive management. In this study, samples from primary ccRCC tumor and their corresponding nontumor adjacent tissues (n=18) were analyzed by quantitative proteomic assay. Proteins downregulated in tumors were studied by GO and KEGG pathways enrichment analyses. Six proteins were found both downregulated and annotated with cell proliferation in ccRCC patients. Of these proteins, PDZK1 and FABP1 were also involved in the lipid metabolism pathway. The downregulation of PDZK1 was further validated in TCGA_KIRC dataset (n=532) and independent set (n=202). PDZK1 could discriminate recurrence, metastasis and prognosis between ccRCC patients. Low level of PDZK1 in both mRNA and protein was associated with reduced overall survival (OS) and disease-free survival (DFS) in two independent sets. In univariate and multivariate analyses, PDZK1 was defined as an independent prognostic factor for both OS and DFS. These findings indicated that low level of PDZK1 could predict poor clinical outcome in patients with ccRCC.

A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds

The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.

DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with In Silico Analysis to Identify Novel Therapeutic Targets

PURPOSE

We used human stem and progenitor cells to develop a genetically accurate novel model of MYC-driven Group 3 medulloblastoma. We also developed a new informatics method, Disease-model Signature versus Compound-Variety Enriched Response ("DiSCoVER"), to identify novel therapeutics that target this specific disease subtype.

EXPERIMENTAL DESIGN

Human neural stem and progenitor cells derived from the cerebellar anlage were transduced with oncogenic elements associated with aggressive medulloblastoma. An in silico analysis method for screening drug sensitivity databases (DiSCoVER) was used in multiple drug sensitivity datasets. We validated the top hits from this analysis in vitro and in vivo

RESULTS

Human neural stem and progenitor cells transformed with c-MYC, dominant-negative p53, constitutively active AKT and hTERT formed tumors in mice that recapitulated Group 3 medulloblastoma in terms of pathology and expression profile. DiSCoVER analysis predicted that aggressive MYC-driven Group 3 medulloblastoma would be sensitive to cyclin-dependent kinase (CDK) inhibitors. The CDK 4/6 inhibitor palbociclib decreased proliferation, increased apoptosis, and significantly extended the survival of mice with orthotopic medulloblastoma xenografts.

CONCLUSIONS

We present a new method to generate genetically accurate models of rare tumors, and a companion computational methodology to find therapeutic interventions that target them. We validated our human neural stem cell model of MYC-driven Group 3 medulloblastoma and showed that CDK 4/6 inhibitors are active against this subgroup. Our results suggest that palbociclib is a potential effective treatment for poor prognosis MYC-driven Group 3 medulloblastoma tumors in carefully selected patients. Clin Cancer Res; 22(15); 3903-14. ©2016 AACR.

Risk stratification of childhood medulloblastoma in the molecular era: the current consensus

Historical risk stratification criteria for medulloblastoma rely primarily on clinicopathological variables pertaining to age, presence of metastases, extent of resection, histological subtypes and in some instances individual genetic aberrations such as MYC and MYCN amplification. In 2010, an international panel of experts established consensus defining four main subgroups of medulloblastoma (WNT, SHH, Group 3 and Group 4) delineated by transcriptional profiling. This has led to the current generation of biomarker-driven clinical trials assigning WNT tumors to a favorable prognosis group in addition to clinicopathological criteria including MYC and MYCN gene amplifications. However, outcome prediction of non-WNT subgroups is a challenge due to inconsistent survival reports. In 2015, a consensus conference was convened in Heidelberg with the objective to further refine the risk stratification in the context of subgroups and agree on a definition of risk groups of non-infant, childhood medulloblastoma (ages 3-17). Published and unpublished data over the past 5 years were reviewed, and a consensus was reached regarding the level of evidence for currently available biomarkers. The following risk groups were defined based on current survival rates: low risk (>90 % survival), average (standard) risk (75-90 % survival), high risk (50-75 % survival) and very high risk (<50 % survival) disease. The WNT subgroup and non-metastatic Group 4 tumors with whole chromosome 11 loss or whole chromosome 17 gain were recognized as low-risk tumors that may qualify for reduced therapy. High-risk strata were defined as patients with metastatic SHH or Group 4 tumors, or MYCN-amplified SHH medulloblastomas. Very high-risk patients are Group 3 with metastases or SHH with TP53 mutation. In addition, a number of consensus points were reached that should be standardized across future clinical trials. Although we anticipate new data will emerge from currently ongoing and recently completed clinical trials, this consensus can serve as an outline for prioritization of certain molecular subsets of tumors to define and validate risk groups as a basis for future clinical trials.

Medulloblastoma development: tumor biology informs treatment decisions

Medulloblastoma is the most common malignant pediatric brain tumor. Current treatments including surgery, craniospinal radiation and high-dose chemotherapy have led to improvement in survival. However, the risk for recurrence as well as significant long-term neurocognitive and endocrine sequelae associated with current treatment modalities underscore the urgent need for novel tumor-specific, normal brain-sparing therapies. It has also provided the impetus for research focused on providing a better understanding of medulloblastoma biology. The expectation is that such studies will lead to the identification of new therapeutic targets and eventually to an increase in personalized treatment approaches.

Medulloblastoma: recurrence and metastasis

Medulloblastoma is the most common malignant brain tumor of childhood. Although there is now long-term survival or cure for the majority of children, the survivors bear a significant burden of complications due, at least in part, to the intense therapies given to ensure eradication of the tumor. Significant efforts have been made over the years to be able to distinguish between patients who do and do not need intensive therapies. This review summarizes the history and current state of clinical risk stratification, pathologic diagnosis and genetics. Recent developments in correlation between genetics and pathology, genome-wide association studies and the biology of medulloblastoma metastasis are discussed in detail. The current state of clinical treatment trials are reviewed and placed into the perspective of potential novel therapies in the near term.

Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework

BACKGROUND

Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies.

RESULTS

A supervised multiclass pathway activity inference method using optimisation techniques is reported. For each pathway expression dataset, patterns of its constituent genes are summarised into one composite feature, termed pathway activity, and a novel mathematical programming model is proposed to infer this feature as a weighted linear summation of expression of its constituent genes. Gene weights are determined by the optimisation model, in a way that the resulting pathway activity has the optimal discriminative power with regards to disease phenotypes. Classification is then performed on the resulting low-dimensional pathway activity profile.

CONCLUSIONS

The model was evaluated through a variety of published gene expression profiles that cover different types of disease. We show that not only does it improve classification accuracy, but it can also perform well in multiclass disease datasets, a limitation of other approaches from the literature. Desirable features of the model include the ability to control the maximum number of genes that may participate in determining pathway activity, which may be pre-specified by the user. Overall, this work highlights the potential of building pathway-based multi-phenotype classifiers for accurate disease diagnosis and prognosis problems.

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template – used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

α5-GABAA receptors negatively regulate MYC-amplified medulloblastoma growth

Neural tumors often express neurotransmitter receptors as markers of their developmental lineage. Although these receptors have been well characterized in electrophysiological, developmental and pharmacological settings, their importance in the maintenance and progression of brain tumors and, importantly, the effect of their targeting in brain cancers remains obscure. Here, we demonstrate high levels of GABRA5, which encodes the α5-subunit of the GABAA receptor complex, in aggressive MYC-driven, "Group 3" medulloblastomas. We hypothesized that modulation of α5-GABAA receptors alters medulloblastoma cell survival and monitored biological and electrophysiological responses of GABRA5-expressing medulloblastoma cells upon pharmacological targeting of the GABAA receptor. While antagonists, inverse agonists and non-specific positive allosteric modulators had limited effects on medulloblastoma cells, a highly specific and potent α5-GABAA receptor agonist, QHii066, resulted in marked membrane depolarization and a significant decrease in cell survival. This effect was GABRA5 dependent and mediated through the induction of apoptosis as well as accumulation of cells in S and G2 phases of the cell cycle. Chemical genomic profiling of QHii066-treated medulloblastoma cells confirmed inhibition of MYC-related transcriptional activity and revealed an enrichment of HOXA5 target gene expression. siRNA-mediated knockdown of HOXA5 markedly blunted the response of medulloblastoma cells to QHii066. Furthermore, QHii066 sensitized GABRA5 positive medulloblastoma cells to radiation and chemotherapy consistent with the role of HOXA5 in directly regulating p53 expression and inducing apoptosis. Thus, our results provide novel insights into the synthetic lethal nature of α5-GABAA receptor activation in MYC-driven/Group 3 medulloblastomas and propose its targeting as a novel strategy for the management of this highly aggressive tumor.

Diagnostic application of high resolution single nucleotide polymorphism array analysis for children with brain tumors

Single nucleotide polymorphism (SNP) array analysis is currently used as a first tier test for pediatric brain tumors at The Children's Hospital of Philadelphia. The results from 100 consecutive patients are summarized in the present report. Eighty-seven percent of the tumors had at least one pathogenic copy number alteration. Nineteen of 56 low grade gliomas (LGGs) demonstrated a duplication in 7q34, which resulted in a KIAA1549-BRAF fusion. Chromosome band 7q34 deletions, which resulted in a FAM131B-BRAF fusion, were identified in one pilocytic astrocytoma (PA) and one dysembryoplastic neuroepithelial tumor (DNT). One ganglioglioma (GG) demonstrated a 6q23.3q26 deletion that was predicted to result in a MYB-QKI fusion. Gains of chromosomes 5, 6, 7, 11, and 20 were seen in a subset of LGGs. Monosomy 6, deletion of 9q and 10q, and an i(17)(q10) were each detected in the medulloblastomas (MBs). Deletions and regions of loss of heterozygosity that encompassed TP53, RB1, CDKN2A/B, CHEK2, NF1, and NF2 were identified in a variety of tumors, which led to a recommendation for germline testing. A BRAF p.Thr599dup or p.V600E mutation was identified by Sanger sequencing in one and five gliomas, respectively, and a somatic TP53 mutation was identified in a fibrillary astrocytoma. No TP53 hot-spot mutations were detected in the MBs. SNP array analysis of pediatric brain tumors can be combined with pathologic examination and molecular analyses to further refine diagnoses, offer more accurate prognostic assessments, and identify patients who should be referred for cancer risk assessment.

Gene signatures related to B-cell proliferation predict influenza vaccine-induced antibody response

Vaccines are very effective at preventing infectious disease but not all recipients mount a protective immune response to vaccination. Recently, gene expression profiles of PBMC samples in vaccinated individuals have been used to predict the development of protective immunity. However, the magnitude of change in gene expression that separates vaccine responders and nonresponders is likely to be small and distributed across networks of genes, making the selection of predictive and biologically relevant genes difficult. Here we apply a new approach to predicting vaccine response based on coordinated upregulation of sets of biologically informative genes in postvaccination gene expression profiles. We found that enrichment of gene sets related to proliferation and immunoglobulin genes accurately segregated high responders to influenza vaccination from low responders and achieved a prediction accuracy of 88% in an independent clinical trial. Many of the genes in these gene sets would not have been identified using conventional, single-gene level approaches because of their subtle upregulation in vaccine responders. Our results demonstrate that gene set enrichment method can capture subtle transcriptional changes and may be a generally useful approach for developing and interpreting predictive models of the human immune response.

Medulloblastoma biology in the post-genomic era

Medulloblastomas, the most common malignant pediatric brain tumors, are comprised of four molecularly distinct subtypes. However, treatment has yet to exploit these molecular vulnerabilities. Three recent studies sequenced a total of 310 primary tumors and identified that two of the four medulloblastoma subtypes are concomitantly associated with subtype-specific mutations as previously characterized. In contrast, the overwhelming majority of mutations occurred only once in the entire cohort and just 12 genes were recurrently mutated with statistical significance. Perturbations in epigenetic regulation are emerging as a unifying theme in cancer and similarly recurring mutations in epigenetic mechanisms were distributed across all subtypes in medulloblastoma. Designing targeted therapies to such a molecularly diverse disease in the post-genomic era presents new challenges. This will require novel methods to link these nonrecurrent mutations into pathways, and preclinical models that faithfully recapitulate patient driver events. Presently, medulloblastoma reinforces epigenetic mechanisms as a tantalizing therapeutic target in cancers.

Aberrant patterns of H3K4 and H3K27 histone lysine methylation occur across subgroups in medulloblastoma

Recent sequencing efforts have described the mutational landscape of the pediatric brain tumor medulloblastoma. Although MLL2 is among the most frequent somatic single nucleotide variants (SNV), the clinical and biological significance of these mutations remains uncharacterized. Through targeted re-sequencing, we identified mutations of MLL2 in 8 % (14/175) of MBs, the majority of which were loss of function. Notably, we also report mutations affecting the MLL2-binding partner KDM6A, in 4 % (7/175) of tumors. While MLL2 mutations were independent of age, gender, histological subtype, M-stage or molecular subgroup, KDM6A mutations were most commonly identified in Group 4 MBs, and were mutually exclusive with MLL2 mutations. Immunohistochemical staining for H3K4me3 and H3K27me3, the chromatin effectors of MLL2 and KDM6A activity, respectively, demonstrated alterations of the histone code in 24 % (53/220) of MBs across all subgroups. Correlating these MLL2- and KDM6A-driven histone marks with prognosis, we identified populations of MB with improved (K4+/K27-) and dismal (K4-/K27-) outcomes, observed primarily within Group 3 and 4 MBs. Group 3 and 4 MBs demonstrate somatic copy number aberrations, and transcriptional profiles that converge on modifiers of H3K27-methylation (EZH2, KDM6A, KDM6B), leading to silencing of PRC2-target genes. As PRC2-mediated aberrant methylation of H3K27 has recently been targeted for therapy in other diseases, it represents an actionable target for a substantial percentage of medulloblastoma patients with aggressive forms of the disease.

Histological variants of medulloblastoma are the most powerful clinical prognostic indicators

BACKGROUND

Medulloblastoma histological classification has gained in importance and newer treatment protocols will include histology stratification. We centrally reviewed medulloblastoma cases from past 10 years reassessing their histology to ascertain its prognostic significance.

METHODS

Samples from 125 consecutive patients (99 males; 10 under age 3 years) were reviewed according to the two WHO classifications of 2000/2007.

RESULTS

Eighty-two patients did not have metastases, the primary tumor was completely resected in 101. The median follow-up was 96 months. Treatment was: our institutional protocol, that is, hyperfractionated accelerated radiotherapy (HART), for 39 non-metastatic cases up to 2003; according to the European PNET IV protocol in 31 cases; a HART-based strategy in 39 metastatic cases; tailored to the age below 3 years and based on high-dose chemotherapy in 10; and tailored to the patients conditions in 7. The 5-year PFS/OS rates were 76% and 81%, respectively. Histology was classic in 93 cases, nodular/desmoplastic in 20, anaplastic/large-cell in 9, and with extensive nodularity (MBEN) in 3. Stratification by residual disease after resection, metastases, age, or protocols was not prognostic. Histology suggested 5-year PFS rates of 82% for the desmoplastic and MBEN variants, 78% for classic medulloblastoma, 44% for the anaplastic/large-cell variants (P = 0.01). Multivariable analysis demonstrated statistically significant difference in PFS by histology (P = 0.02), due to the poor prognosis of anaplastic/large-cell medulloblastoma.

CONCLUSIONS

Tailoring treatments to known risk factors cancelled all prognostic differences, except for anaplasia (not considered as such within previous trials) which proved the most powerful prognostic factor, warranting appropriate treatment intensification.