scholarly journals MBRS-06. Gli3 INDUCES NEURONAL DIFFERENTIATION IN WNT- AND SHH- ACTIVATED MEDULLOBLASTOMA

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii399-iii400
Author(s):  
Manabu Natsumeda ◽  
Hiroaki Miyahara ◽  
Junichi Yoshimura ◽  
Yoshihiro Tsukamoto ◽  
Makoto Oishi ◽  
...  
Keyword(s):  
Neuronal Differentiation ◽  
Sonic Hedgehog ◽  
Molecular Classification ◽  
Main Function ◽  
Molecular Subgroups ◽  
Exact Test ◽  
Downstream Target ◽  
Group 4 ◽  
Public Data ◽  
Group 3

Abstract BACKGROUND We have previously investigated the expression of Gli3, a downstream target of the Sonic Hedgehog pathway, which main function is to suppress Gli1/2 in medulloblastomas. We found that Gli3 is associated with neuronal and glial differentiation in desmoplastic / nodular (D/N) type medulloblastomas (Miyahara et al., Neuropathology, 2013). In the present study, we investigated the expression of Gli3 in molecular subgroups. METHOD Thirty-one medulloblastomas treated at Niigata University between 1982 and 2013 were studied. Molecular classification into 4 subgroups (WNT-activated, SHH-activated, Group 3 and Group 4) using Nanostring and immunohistochemistry was performed. Furthermore, Gli3 and Gli1 expression in molecular subgroups was assessed using public data bases. RESULTS Nanostring was considered reliable (confidence > 0.9) in 28 cases. Four cases were classified as WNT-, 5 cases as SHH-activated, 4 cases as Group 3 and 16 cases as Group 4. Gli3 was positive in 7 out of 9 (78%) WNT-/SHH- cases, but positive in only 8 out of 19 (42.1%) non-WNT-/SHH- subgroup cases (p = 0.1145, Fisher’s exact test). R2 database analysis confirmed that Gli3 was significantly elevated in WNT- and SHH-activated medulloblastoma. Gli1 was elevated in SHH-activated cases but suppressed in WNT-activated cases. IHC analysis revealed that Gli3 was elevated inside nodules showing neuronal differentiation in D/N type medulloblastoma. Results of single cell RNA analyses were consistent with those of IHC, Nanostring and R2. CONCLUSION These results suggest that Gli3 is elevated inside the nodules of SHH-activated medulloblastoma, whereas in WNT-activated cases, Gli3 diffusely suppresses HH signaling.

scholarly journals IMG-19. RADIOMICS AND SUPERVISED DEEP LEARNING TO PREDICT MOLECULAR SUBGROUPS IN MEDULLOBLASTOMA BASED ON WHOLE TUMOR VOLUME LABELING: A SINGLE CENTER MULTIPARAMETRIC MR ANALYSIS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii358-iii359
Author(s):  
Ioan Paul Voicu ◽  
Piero Chiacchiaretta ◽  
Massimo Caulo ◽  
Evelina Miele ◽  
Alessia Carboni ◽  
...  
Keyword(s):  
Random Forest ◽  
Intraclass Correlation ◽  
Major Axis ◽  
Molecular Classification ◽  
Molecular Subgroups ◽  
Molecular Subgroup ◽  
First Order ◽  
Group 4 ◽  
Contrast Enhanced ◽  
Group 3

Abstract PURPOSE Medulloblastoma (MB) is a complex pathology. Four molecular subgroups have been unveiled (Wingless-WNT, Sonic Hedgehog-SHH, Group 3-G3 and Group 4-G4), characterized by significant differences in patient clinical outcome. We investigated the utility of a radiomic analysis to predict molecular subgroups in patients with MB. MATERIALS AND METHODS We retrospectively evaluated 42 patients with histological diagnosis of MB, known molecular subgroup, and diagnostic MRI scan performed in our Institution on a 3 Tesla magnet. For each patient, FLAIR, ADC, T2 and contrast-enhanced MPRAGE sequences were analysed. Solid tumor volumes were segmented semiautomatically. 107 features were extracted for each sequence (Pyradiomics, Python). Features were tested for stability against labelling variations, selecting those presenting Intraclass Correlation Coefficient (ICC)>0.9 across all labelling variations and all sequences. Among the remaining features, relevant features were selected with an all-relevant wrapper algorithm (Boruta, R). Remaining features were used to predict MB subgroup with a Random Forest algorithm(R). The most relevant features were ranked based on Gini index (R). RESULTS 83/107 features presented ICC >0.9 for all sequences. Boruta selected 10 features. Classification analysis yielded an out-of-bag (OOB) error rate of 0.6%, (99.4% accuracy). The most relevant features for classification were “simple” first-order features such as volume, major axis or shape. CONCLUSION This radiomic study yielded robust features, which showed high accuracy in predicting the molecular MB subgroups. Random forest algorithms are ideal for multiclass classification (eg. MB subgroups) and are intrinsically suited against overfitting. The most relevant for molecular classification were first-order features.

scholarly journals Immunohistochemical and nanoString-Based Subgrouping of Clinical Medulloblastoma Samples

2020 ◽  
Vol 79 (4) ◽  
pp. 437-447 ◽  
Author(s):  
Colleen E D’Arcy ◽  
Liana Figueiredo Nobre ◽  
Anthony Arnaldo ◽  
Vijay Ramaswamy ◽  
Michael D Taylor ◽  
...  
Keyword(s):  
Sonic Hedgehog ◽  
Expression Profiling ◽  
Ffpe Tissue ◽  
Filamin A ◽  
Study Materials ◽  
Group 4 ◽  
P53 Antibodies ◽  
Group 3 ◽  
Highly Correlated

Abstract The diagnosis of medulloblastoma incorporates the histologic and molecular subclassification of clinical medulloblastoma samples into wingless (WNT)-activated, sonic hedgehog (SHH)-activated, group 3 and group 4 subgroups. Accurate medulloblastoma subclassification has important prognostic and treatment implications. Immunohistochemistry (IHC)-based and nanoString-based subgrouping methodologies have been independently described as options for medulloblastoma subgrouping, however have not previously been directly compared. We describe our experience with nanoString-based subgrouping in a clinical setting and compare this with our IHC-based results. Study materials included FFPE tissue from 160 medulloblastomas. Clinical data and tumor histology were reviewed. Immunohistochemical-based subgrouping using β-catenin, filamin A and p53 antibodies and nanoString-based gene expression profiling were performed. The sensitivity and specificity of IHC-based subgrouping of WNT and SHH-activated medulloblastomas was 91.5% and 99.54%, respectively. Filamin A immunopositivity highly correlated with SHH/WNT-activated subgroups (sensitivity 100%, specificity 92.7%, p < 0.001). Nuclear β-catenin immunopositivity had a sensitivity of 76.2% and specificity of 99.23% for detection of WNT-activated tumors. Approximately 23.8% of WNT cases would have been missed using an IHC-based subgrouping method alone. nanoString could confidently predict medulloblastoma subgroup in 93% of cases and could distinguish group 3/4 subgroups in 96.3% of cases. nanoString-based subgrouping allows for a more prognostically useful classification of clinical medulloblastoma samples.

Clinical Characteristic and Molecular Subgroups of Thai Pediatric Medulloblastomas at Queen Sirikit National Institute of Child Health

2021 ◽  
Vol 104 (10) ◽  
pp. 1648-1657
Keyword(s):  
Child Health ◽  
Signaling Pathway ◽  
Survival Rates ◽  
Large Cell ◽  
Disease Classification ◽  
Molecular Subgroups ◽  
Molecular Subgroup ◽  
Group 4 ◽  
Group 3 ◽  
Clinical Records

Objective: To determine the correlation between clinical characteristics and molecular subgroups of medulloblastoma (MB) in Thai pediatric patients at the Queen Sirikit National Institute of Child Health (QSNICH), Thailand. Materials and Methods: MB specimens operated between 2004 and 2018 were classified by Nanostring into four molecular subgroups, including Wingless signaling pathway (WNT), Sonic Hedgehog signaling pathway (SHH), Group 3, and Group 4. For the present cases, the clinical records were retrospectively analyzed. Results: Twenty-two MB cases with complete clinical records were analyzed. Group 4 was the most common molecular subgroup (31.82%), followed by WNT (27.27%), SHH (22.73%), and Group 3 (18.18%). The histologic subtypes included 18, three, and one cases of classic MB, MB with extensive nodularity (MBEN), and large cell MB, respectively. All SHH MBs were found in infants. All MBENs belonged to SHH subgroup, and the large cell MB was Group 3. All six WNT MB cases did not experience tumor recurrence. Five-year cause specific survival rates were 100% in WNT, 60% in SHH, 57.1% in Group 4, and 0% in Group 3. Five-year recurrence-free survival rates were 100% in WNT, 42.9% in Group 4, and 0% in SHH and Group 3. Conclusion: MB is a heterogeneous disease. Classification of MB, especially at the molecular subtype, is helpful for the management and prognostication. Keywords: Medulloblastoma; Molecular subgroup

scholarly journals Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas

2012 ◽  
Vol 123 (4) ◽  
pp. 473-484 ◽  
Author(s):  
Marcel Kool ◽  
Andrey Korshunov ◽  
Marc Remke ◽  
David T. W. Jones ◽  
Maria Schlanstein ◽  
...  
Keyword(s):  
Clinical Data ◽  
Meta Analysis ◽  
Molecular Subgroups ◽  
Genetic Aberrations ◽  
Group 4 ◽  
Group 3

scholarly journals IMG-01. DWI RATIO OF HISTOLOGICAL MOLECULAR SUBTYPES OF PAEDIATRIC MEDULLOBLASTOMAS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii354-iii354
Author(s):  
Phua Hwee Tang ◽  
Sharon Low ◽  
Enrica Tan ◽  
Kenneth Chang
Keyword(s):  
Sonic Hedgehog ◽  
Molecular Subtypes ◽  
Region Of Interest ◽  
Cross Sectional ◽  
Post Contrast ◽  
Group 4 ◽  
Single Operator ◽  
Radiological Information System ◽  
Cerebellar Tissue ◽  
Group 3

Abstract AIM To evaluate if diffusion weighted imaging (DWI) ratio on MRI is able to distinguish between the histological molecular subtypes of paediatric medulloblastomas. MATERIALS AND METHODS From 2002 to 2017, 38 cases of medulloblastoma with preoperative MRI available had histological subtyping performed with NanoString nCounter technology. The medulloblastomas were classified into 4 subtypes. There were 3 Sonic Hedgehog (SHH), 9 Wingless (WNT), 12 Group 3 and 14 Group 4 subtypes. Single operator manually outlined solid non-haemorrhagic component of the tumour on DWI images with largest axial tumour cross sectional diameter, correlating with the other MRI images (T1 pre and post contrast, SWI/GRE, FLAIR) to identify areas of haemorrhage. The same operator also drew region of interest to identify normal cerebellar tissue on the same axial images on which the tumour was outlined. All MRI images were obtained from the department’s Radiological Information System Picture Archiving and Communicating System (RIS PACS). DWI ratio for each case was obtained by dividing the values obtained from tumour by normal cerebellar tissue seen on the same axial image. RESULTS DWI ratio of all medullloblastomas is 1.34 +/- 0.18. DWI ratio of SHH subtype is 1.43 +/- 0.07. DWI ratio of WNT subtype is 1.40 +/- 0.07. DWI ratio of Group 3 subtype is 1.31 +/- 0.25. DWI ratio of Group 4 subtype is 1.30 +/- 0.17. There is no significant statistical differences in the DWI ratio between the various subtypes. CONCLUSION DWI ratio of medulloblastoma is unable to distinguish between the 4 medulloblastoma subtypes.

scholarly journals Identification of Let-7 miRNA Activity as a Prognostic Biomarker of SHH Medulloblastoma

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 139
Author(s):  
Maximillian S. Westphal ◽  
Eunjee Lee ◽  
Eric E. Schadt ◽  
Giselle S. Sholler ◽  
Jun Zhu
Keyword(s):  
Sonic Hedgehog ◽  
Prognostic Biomarker ◽  
Treatment Options ◽  
Critical Role ◽  
Group 4 ◽  
High Let ◽  
Embryonal Brain ◽  
Mycn Expression ◽  
Group 3 ◽  
Embryonal Brain Tumor

Medulloblastoma (MB) is the most common pediatric embryonal brain tumor. The current consensus classifies MB into four molecular subgroups: sonic hedgehog-activated (SHH), wingless-activated (WNT), Group 3, and Group 4. MYCN and let-7 play a critical role in MB. Thus, we inferred the activity of miRNAs in MB by using the ActMiR procedure. SHH-MB has higher MYCN expression than the other subgroups. We showed that high MYCN expression with high let-7 activity is significantly associated with worse overall survival, and this association was validated in an independent MB dataset. Altogether, our results suggest that let-7 activity and MYCN can further categorize heterogeneous SHH tumors into more and less-favorable prognostic subtypes, which provide critical information for personalizing treatment options for SHH-MB. Comparing the expression differences between the two SHH-MB prognostic subtypes with compound perturbation profiles, we identified FGFR inhibitors as one potential treatment option for SHH-MB patients with the less-favorable prognostic subtype.

Strategies to mitigate economic hardship among family dairy farms of Central Mexico

2021 ◽  
pp. 1-10
Author(s):  
Carlos Galdino Martínez-García ◽  
Claire Clugston ◽  
Carlos Manuel Arriaga-Jordán ◽  
Jesús Olmos-Colmenero ◽  
Michel André Wattiaux
Keyword(s):  
Dairy Farms ◽  
Economic Hardship ◽  
Herd Size ◽  
Milk Composition ◽  
Dairy Farmers ◽  
Exact Test ◽  
Group 4 ◽  
Common Strategy ◽  
Group 3 ◽  
Group 2

Abstract The economic hardship of dairy producers has worsened in the last decade because of increasing costs of production. A field survey with 51 dairy farmers was conducted to explore strategies to mitigate economic hardship. Factor and cluster analyses were conducted to characterize the farmers and their farms. Differences among groups regarding changes adopted to increase incomes, to reduce costs, and to pay bills were tested using Fisher’s exact test. Four factors explained 76.2% of the cumulative variance and four groups were identified: “stagnant farms” were in group 1, with the lowest daily income over concentrate feed cost (DIOCFC) and the least number of changes, “effectively management farms” were in group 2, with the highest DIOCFC and the highest number of income-increasing changes, the “cost reducing farms” were in group 3, with the smallest in size with a focus on cutting cost, and the “mixed strategy farms” were in group 4, with the largest herd size. Most prevalent income-increasing strategies included attempts to improve cow nutritional balance and milk composition, whereas the most prevalent cost-reducing strategies included reductions in input purchases of inputs (concentrates and fertilizers) and selected household expenses. Selling cows was a common strategy to generate cash in acute hardship situations. In conclusion, responses to economic hardship varied substantially among groups of farms, cost-reducing strategies were linked to lower cow productivity and lower technological levels, but income-increasing strategies were linked to higher cow productivity and higher DIOCFC. Our findings may contribute to the design of extension initiatives to promote useful strategies to help mitigate economic hardship on dairy farms.

scholarly journals Magnetic resonance radiomics features and prognosticators in different molecular subtypes of pediatric Medulloblastoma

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255500
Author(s):  
Feng-Chi Chang ◽  
Tai-Tong Wong ◽  
Kuo-Sheng Wu ◽  
Chia-Feng Lu ◽  
Ting-Wei Weng ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Prediction Models ◽  
Gray Level ◽  
Resonance Imaging ◽  
Molecular Subgroups ◽  
Characteristic Analysis ◽  
Group 4 ◽  
Pediatric Medulloblastoma ◽  
Group 3

Purpose Medulloblastoma (MB) is a highly malignant pediatric brain tumor. In the latest classification, medulloblastoma is divided into four distinct groups: wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We analyzed the magnetic resonance imaging radiomics features to find the imaging surrogates of the 4 molecular subgroups of MB. Material and methods Frozen tissue, imaging data, and clinical data of 38 patients with medulloblastoma were included from Taipei Medical University Hospital and Taipei Veterans General Hospital. Molecular clustering was performed based on the gene expression level of 22 subgroup-specific signature genes. A total 253 magnetic resonance imaging radiomic features were generated from each subject for comparison between different molecular subgroups. Results Our cohort consisted of 7 (18.4%) patients with WNT medulloblastoma, 12 (31.6%) with SHH tumor, 8 (21.1%) with Group 3 tumor, and 11 (28.9%) with Group 4 tumor. 8 radiomics gray-level co-occurrence matrix texture (GLCM) features were significantly different between 4 molecular subgroups of MB. In addition, for tumors with higher values in a gray-level run length matrix feature—Short Run Low Gray-Level Emphasis, patients have shorter survival times than patients with low values of this feature (p = 0.04). The receiver operating characteristic analysis revealed optimal performance of the preliminary prediction model based on GLCM features for predicting WNT, Group 3, and Group 4 MB (area under the curve = 0.82, 0.72, and 0.78, respectively). Conclusion The preliminary result revealed that 8 contrast-enhanced T1-weighted imaging texture features were significantly different between 4 molecular subgroups of MB. Together with the prediction models, the radiomics features may provide suggestions for stratifying patients with MB into different risk groups.

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