TMOD-02. IDENTIFICATION OF NOVEL MARKERS OF TREATMENT-REFRACTORY RECURRENT GLIOBLASTOMA
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".