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Abstract 3682: Therapeutic targeting of stem cell self-renewal in childhood medulloblastoma: Strategies for blocking recurrence

2019· article· en· W4230606129 on OpenAlexaff
David Bakhshinyan, Michelle Kameda-Smith, Branavan Manoranjan, Ashley Adile, Chitra Venugopal, Sheila K. Singh

Bibliographic record

VenueTumor Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsBlocking (statistics)MedulloblastomaMedicineStem cellComputer scienceCancer researchBiologyCell biology

Abstract

fetched live from OpenAlex

Introduction: Medulloblastoma (MB) is the most common malignant pediatric brain tumor. Group 3 MB patients face the highest incidence of metastasis and poor overall patient survival. The early onset and highly aggressive nature of MB suggest a stem cell origin, where a highly self-renewing transformed cell of the postnatal cerebellum drives MB tumorigenesis. In this work, we explore how WNT signaling and other essential drivers of self-renewal, BMI1 and MSI1, promote MB progression. We subsequently generate new strategies to therapeutically target mechanisms of MB stem cell self-renewal that drive treatment resistance and relapse in Group 3 MB.Experimental procedures: We apply stem cell assays, patient-derived human-mouse xenograft (PDX) models, and genomic and bioinformatic profiling of recurrent patient-derived MB. Our established brain tumor initiating cell (BTIC) model provides an excellent tool for the examination of developmental pathways implicated in MB.New Unpublished Data: A small molecule Bmi1 inhibitor, PTC-028, induced a remarkable decrease in self-renewal as well as reduction of local and spinal metastatic disease in recurrent MB, which is striking as no prior drug has shown efficacy against recurrent Group 3 MB. Although mouse and human neural stem cells (NSCs) express Bmi1 and are mildly sensitive to Bmi1 inhibitors, no significant toxicity was observed in either mouse or human NSCs upon PTC-028 treatment, at doses that induced efficacious killing of MB cells. Another novel therapeutic paradigm includes activating Wnt signaling in otherwise non-Wnt MB, which abrogates self-renewal and tumorigenicity of these highly aggressive tumors. For safe and non-toxic activation of Wnt in preclinical models, we identified L807mts, a novel inhibitor that functions through a substrate-to-inhibitor conversion mechanism within the catalytic site of GSK. A final therapeutic strategy to target self-renewal lies in the discovery of the targetable MB-specific interactome of the RNA binding protein (RBP) Musashi1, another key regulator of stem cell self-renewal. Msi1 is overexpressed in Group 3 MB compared to normal cerebellum, and is associated with poor patient prognosis. shRNA knockdown of Msi1 decreased the self-renewal capacity of MB stem cells and significantly decreased tumor burden and increased survival in our PDX model. Finally, comparative eCLIP (enhanced cross-linking and immunoprecipitation) of MB stem cells and normal NSCs, combined with mass spectrometry and RNA-sequencing of shMsi1 MB cells, has elucidated novel therapeutic targets in the RBP interactome of Msi1Conclusion: Characterization and therapeutic targeting of self-renewal mechanisms unique to MB BTICs may provide an opportunity to limit treatment-resistant stem cell populations from driving patient relapse in recurrent Group 3 MB, a disease currently lacking any targeted therapies.Note: This abstract was not presented at the meeting.Citation Format: David Bakhshinyan, Michelle Kameda-Smith, Branavan Manoranjan, Ashley Adile, Chitra Venugopal, Sheila Kumari Singh. Therapeutic targeting of stem cell self-renewal in childhood medulloblastoma: Strategies for blocking recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3682.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.263
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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