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Intertumoral Heterogeneity within Medulloblastoma Subgroups.

ABSTRACT: 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.

SUBMITTER: Cavalli FMG

PROVIDER: S-EPMC6163053 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Intertumoral Heterogeneity within Medulloblastoma Subgroups.

Cavalli Florence M G FMG Remke Marc M Rampasek Ladislav L Peacock John J Shih David J H DJH Luu Betty B Garzia Livia L Torchia Jonathon J Nor Carolina C Morrissy A Sorana AS Agnihotri Sameer S Thompson Yuan Yao YY Kuzan-Fischer Claudia M CM Farooq Hamza H Isaev Keren K Daniels Craig C Cho Byung-Kyu BK Kim Seung-Ki SK Wang Kyu-Chang KC Lee Ji Yeoun JY Grajkowska Wieslawa A WA Perek-Polnik Marta M Vasiljevic Alexandre A Faure-Conter Cecile C Jouvet Anne A Giannini Caterina C Nageswara Rao Amulya A AA Li Kay Ka Wai KKW Ng Ho-Keung HK Eberhart Charles G CG Pollack Ian F IF Hamilton Ronald L RL Gillespie G Yancey GY Olson James M JM Leary Sarah S Weiss William A WA Lach Boleslaw B Chambless Lola B LB Thompson Reid C RC Cooper Michael K MK Vibhakar Rajeev R Hauser Peter P van Veelen Marie-Lise C MC Kros Johan M JM French Pim J PJ Ra Young Shin YS Kumabe Toshihiro T López-Aguilar Enrique E Zitterbart Karel K Sterba Jaroslav J Finocchiaro Gaetano G Massimino Maura M Van Meir Erwin G EG Osuka Satoru S Shofuda Tomoko T Klekner Almos A Zollo Massimo M Leonard Jeffrey R JR Rubin Joshua B JB Jabado Nada N Albrecht Steffen S Mora Jaume J Van Meter Timothy E TE Jung Shin S Moore Andrew S AS Hallahan Andrew R AR Chan Jennifer A JA Tirapelli Daniela P C DPC Carlotti Carlos G CG Fouladi Maryam M Pimentel José J Faria Claudia C CC Saad Ali G AG Massimi Luca L Liau Linda M LM Wheeler Helen H Nakamura Hideo H Elbabaa Samer K SK Perezpeña-Diazconti Mario M Chico Ponce de León Fernando F Robinson Shenandoah S Zapotocky Michal M Lassaletta Alvaro A Huang Annie A Hawkins Cynthia E CE Tabori Uri U Bouffet Eric E Bartels Ute U Dirks Peter B PB Rutka James T JT Bader Gary D GD Reimand Jüri J Goldenberg Anna A Ramaswamy Vijay V Taylor Michael D MD

Cancer cell 20170601 6

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 ...[more]

PMID: 28609654

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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.

Project description:BackgroundA major barrier to effective treatment of glioblastoma (GBM) is the large intertumoral heterogeneity at the genetic and cellular level. In early phase clinical trials, patient heterogeneity in response to therapy is commonly observed; however, how tumor heterogeneity is reflected in individual drug sensitivities in the treatment-naïve glioblastoma stem cells (GSC) is unclear.MethodsWe cultured 12 patient-derived primary GBMs as tumorspheres and validated tumor stem cell properties by functional assays. Using automated high-throughput screening (HTS), we evaluated sensitivity to 461 anticancer drugs in a collection covering most FDA-approved anticancer drugs and investigational compounds with a broad range of molecular targets. Statistical analyses were performed using one-way ANOVA and Spearman correlation.ResultsAlthough tumor stem cell properties were confirmed in GSC cultures, their in vitro and in vivo morphology and behavior displayed considerable tumor-to-tumor variability. Drug screening revealed significant differences in the sensitivity to anticancer drugs (p < 0.0001). The patient-specific vulnerabilities to anticancer drugs displayed a heterogeneous pattern. They represented a variety of mechanistic drug classes, including apoptotic modulators, conventional chemotherapies, and inhibitors of histone deacetylases, heat shock proteins, proteasomes and different kinases. However, the individual GSC cultures displayed high biological consistency in drug sensitivity patterns within a class of drugs. An independent laboratory confirmed individual drug responses.ConclusionsThis study demonstrates that patient-derived and treatment-naïve GSC cultures maintain patient-specific traits and display intertumoral heterogeneity in drug sensitivity to anticancer drugs. The heterogeneity in patient-specific drug responses highlights the difficulty in applying targeted treatment strategies at the population level to GBM patients. However, HTS can be applied to uncover patient-specific drug sensitivities for functional precision medicine.

Dataset Information

Intertumoral Heterogeneity within Medulloblastoma Subgroups.

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Intertumoral Heterogeneity within Medulloblastoma Subgroups.

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