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Record W2767646803 · doi:10.1093/neuonc/nox168.1041

TMOD-02. IDENTIFICATION OF NOVEL MARKERS OF TREATMENT-REFRACTORY RECURRENT GLIOBLASTOMA

2017· article· en· W2767646803 on OpenAlexaff
Nicolas Yelle, Chirayu Chokshi, Parvez Vora, Chitra Venugopal, Sheila K. Singh

Bibliographic record

VenueNeuro-Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRadiation therapyCancer stem cellChemoradiotherapyCancer researchStem cellGlioblastomaTranscriptomeTemozolomideBrain tumorMedicineChemotherapyOncologyCancerBiologyInternal medicinePathologyGeneGene expression

Abstract

fetched live from OpenAlex

Glioblastoma (GBM) is a very aggressive and invasive tumor that relapses within nine months of diagnosis and remains incurable despite advances in multimodal therapy including surgical resection, chemotherapy and radiation. Poor patient outcome has been linked to both marker expression of brain tumor initiating cells (BTICs) and intratumoral heterogeneity (ITH), which have been associated with treatment resistance and tumor recurrence. ITH can be explained at the cellular level by the existence of multiple populations of cancer cells, including some which have acquired stemness properties like self-renewal, proliferation, and multilineage differentiation, also known as cancer stem cells (CSCs). In brain tumors, CSCs or BTICs, have been shown to be resistant to both chemotherapy and radiation treatment, allowing them to escape therapy and consequently allowing for tumor recurrence. As a result, therapies that focus on targeting the BTIC compartment within the bulk GBM tumor would provide better treatment and prognosis for patients. To profile ITH as it evolves through therapy delivery, we have developed a novel and dynamic BTIC patient-derived xenograft (PDX) model of human GBM recurrence, which allows for multimodal profiling of GBM BTICs at engraftment, after chemoradiotherapy delivery in a phase we have termed “minimal residual disease” (MRD), and at tumor recurrence. In this study, we present the profiling of the transcriptome and the cell-surface proteome at each of these stages, including validation of targets, novel and exclusive to recurrent treatment-refractory GBM, by CRISPR/Cas9 knockout and subsequent functional stem cell assays. Despite the fact that recurrent GBM is what ultimately leads to patient demise, it remains a largely unknown landscape. Virtually all of the current genomic, transcriptomic, and proteomic data is based on primary GBM. Hence, our study provides a unique therapeutic window into the often-overlooked elephant in the room: recurrent glioblastoma.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.337
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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