Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets.
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ABSTRACT:
SUBMITTER: Gershanov S
PROVIDER: S-EPMC8223061 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials.
| S-EPMC6163053 | biostudies-literature
Project description:PurposeMedulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication.Patients and methodsMolecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models.ResultsSubgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas.ConclusionCombining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.
| S-EPMC3948094 | biostudies-literature
Project description:BACKGROUND AND PURPOSE:Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma. MATERIALS AND METHODS:In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance. RESULTS:Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma. CONCLUSIONS:This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.
| S-EPMC6330121 | biostudies-literature
Project description:Systemic therapy has improved osteosarcoma event-free and overall survival, but 30-50% of patients originally diagnosed will have progressive or recurrent disease, which is difficult to cure. Osteosarcoma has a complex karyotype, with loss of p53 in the vast majority of cases and an absence of recurrent, targetable pathways. In this study, we explored 54 agents that are clinically approved for other oncologic indications, agents in active clinical development, and others with promising preclinical data in osteosarcoma at clinically achievable concentrations in 5 osteosarcoma cell lines. We found significant single-agent activity of multiple agents and tested 10 drugs in all permutations of two-drug combinations to define synergistic combinations by Chou and Talalay analysis. We then evaluated order of addition to choose the combinations that may be best to translate to the clinic. We conclude that the repurposing of chemotherapeutics in osteosarcoma by using an in vitro system may define novel drug combinations with significant in vivo activity. In particular, combinations of proteasome inhibitors with histone deacetylase inhibitors and ixabepilone and MK1775 demonstrated excellent activity in our assays.
| S-EPMC4658502 | biostudies-literature
Project description:PurposeSystemic therapy has improved rhabdomyosarcoma event-free and overall survival; however, approximately 40 % of patients will have progressive or recurrent disease which is difficult to cure and remains a considerable challenge. Minimal progress has been made in improving outcomes for metastatic or relapsed RMS due to a lack of effective therapeutic agents. Targeted therapies are likely to be incorporated into regimens which rely on conventional cytotoxic chemotherapy. A system to evaluate novel combinations of interest is needed.MethodsIn this study, we explored 8 agents, 5 that are routinely used or similar to agents used in the clinical management of RMS and 3 biologically targeted agents with novel mechanisms of action, the Wee1 inhibitor AZD1775, the tyrosine kinase inhibitor cabozantinib, and the proteasome inhibitor bortezomib. All were tested individually at clinically achievable concentrations for activity in 4 RMS cell lines and then for potential synergy in two-drug combinations.ResultsWe found single-agent activity in five of the agents (or their active metabolites) that constitute the standard of care in RMS and for AZD1775 with mean IC50 values of 207 ng/ml, well below clinically achievable levels. In addition, the combination of individual cytotoxic chemotherapeutics currently used for RMS demonstrated largely synergistic activity with higher, but clinically achievable concentrations of AZD1775 in our assays.ConclusionsPrioritization of chemotherapeutics in RMS is possible using an in vitro system that can define novel drug combinations worthy of future investigation. AZD1775 exhibits single-agent activity, as well as synergy with conventional cytotoxic chemotherapy, and is a novel targeted agent that warrants further study in RMS.
| S-EPMC4965487 | biostudies-literature
Project description:PurposeMicroarray studies indicate medulloblastoma comprises distinct molecular disease subgroups, which offer potential for improved clinical management.Experimental designMinimal mRNA expression signatures diagnostic for the Wnt/Wingless (WNT) and Sonic Hedgehog (SHH) subgroups were developed, validated, and used to assign subgroup affiliation in 173 tumors from four independent cohorts, alongside a systematic investigation of subgroup clinical and molecular characteristics.ResultsWNT tumors [12% (21/173)] were diagnosed >5 years of age (peak, 10 years), displayed classic histology, CTNNB1 mutation (19/20), and associated chromosome 6 loss, and have previously been associated with favorable prognosis. SHH cases [24% (42/173)] predominated in infants (<3 years) and showed an age-dependent relationship to desmoplastic/nodular pathology; all infant desmoplastic/nodular cases (previously associated with a good outcome) were SHH-positive, but these relationships broke down in noninfants. PTCH1 mutations were common [34% (11/32)], but PTCH1 exon1c hypermethylation, chromosome 9q and REN (KCTD11) genetic loss were not SHH associated, and SMO or SUFU mutation, PTCH1 exon1a or SUFU hypermethylation did not play a role, indicating novel activating mechanisms in the majority of SHH cases. SHH tumors were associated with an absence of COL1A2 methylation. WNT/SHH-independent medulloblastomas [64% (110/173)] showed all histologies, peaked at 3 and 6 years, and were exclusively associated with chromosome 17p loss.ConclusionsMedulloblastoma subgroups are characterized by distinct genomic, epigenomic and clinicopathologic features, and clinical outcomes. Validated array-independent gene expression assays for the rapid assessment of subgroup affiliation in small biopsies provide a basis for their routine clinical application, in strategies including molecular disease-risk stratification and delivery of targeted therapeutics.
| S-EPMC3071325 | biostudies-literature
Project description:Medulloblastoma is a malignant childhood brain tumour comprising four discrete subgroups. To identify mutations that drive medulloblastoma we sequenced the entire genomes of 37 tumours and matched normal blood. One hundred and thirty-six genes harbouring somatic mutations in this discovery set were sequenced in an additional 56 medulloblastomas. Recurrent mutations were detected in 41 genes not yet implicated in medulloblastoma: several target distinct components of the epigenetic machinery in different disease subgroups, e.g., regulators of H3K27 and H3K4 trimethylation in subgroup-3 and 4 (e.g., KDM6A and ZMYM3), and CTNNB1-associated chromatin remodellers in WNT-subgroup tumours (e.g., SMARCA4 and CREBBP). Modelling of mutations in mouse lower rhombic lip progenitors that generate WNT-subgroup tumours, identified genes that maintain this cell lineage (DDX3X) as well as mutated genes that initiate (CDH1) or cooperate (PIK3CA) in tumourigenesis. These data provide important new insights into the pathogenesis of medulloblastoma subgroups and highlight targets for therapeutic development. A total of 76 pediatric medulloblastoma samples were analyzed, representing 4 expression classes
| S-ECPF-GEOD-37418 | biostudies-other
Project description:Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.
| S-EPMC8193683 | biostudies-literature
Project description:Recently identified molecular subgroups of medulloblastoma have shown potential for improved risk stratification. We hypothesized that distinct MR imaging features can predict these subgroups.All patients with a diagnosis of medulloblastoma at one institution, with both pretherapy MR imaging and surgical tissue, served as the discovery cohort (n = 47). MR imaging features were assessed by 3 blinded neuroradiologists. NanoString-based assay of tumor tissues was conducted to classify the tumors into the 4 established molecular subgroups (wingless, sonic hedgehog, group 3, and group 4). A second pediatric medulloblastoma cohort (n = 52) from an independent institution was used for validation of the MR imaging features predictive of the molecular subtypes.Logistic regression analysis within the discovery cohort revealed tumor location (P < .001) and enhancement pattern (P = .001) to be significant predictors of medulloblastoma subgroups. Stereospecific computational analyses confirmed that group 3 and 4 tumors predominated within the midline fourth ventricle (100%, P = .007), wingless tumors were localized to the cerebellar peduncle/cerebellopontine angle cistern with a positive predictive value of 100% (95% CI, 30%-100%), and sonic hedgehog tumors arose in the cerebellar hemispheres with a positive predictive value of 100% (95% CI, 59%-100%). Midline group 4 tumors presented with minimal/no enhancement with a positive predictive value of 91% (95% CI, 59%-98%). When we used the MR imaging feature-based regression model, 66% of medulloblastomas were correctly predicted in the discovery cohort, and 65%, in the validation cohort.Tumor location and enhancement pattern were predictive of molecular subgroups of pediatric medulloblastoma and may potentially serve as a surrogate for genomic testing.
| S-EPMC4819007 | biostudies-literature
Project description:Medulloblastoma is a malignant childhood brain tumour comprising four discrete subgroups. Here, to identify mutations that drive medulloblastoma, we sequenced the entire genomes of 37 tumours and matched normal blood. One-hundred and thirty-six genes harbouring somatic mutations in this discovery set were sequenced in an additional 56 medulloblastomas. Recurrent mutations were detected in 41 genes not yet implicated in medulloblastoma; several target distinct components of the epigenetic machinery in different disease subgroups, such as regulators of H3K27 and H3K4 trimethylation in subgroups 3 and 4 (for example, KDM6A and ZMYM3), and CTNNB1-associated chromatin re-modellers in WNT-subgroup tumours (for example, SMARCA4 and CREBBP). Modelling of mutations in mouse lower rhombic lip progenitors that generate WNT-subgroup tumours identified genes that maintain this cell lineage (DDX3X), as well as mutated genes that initiate (CDH1) or cooperate (PIK3CA) in tumorigenesis. These data provide important new insights into the pathogenesis of medulloblastoma subgroups and highlight targets for therapeutic development.
| S-EPMC3412905 | biostudies-literature