Abstract 3058: Data and resource sharing: Advancing our understanding of glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma multiforme (GBM), characterized by an aggressive clinical course, therapeutic resistance, and striking molecular heterogeneity, remains incurable. GBM has a median survival of only 12-15 months post-surgery in adults even with standard of care chemotherapy and radiation. Through a collaborative pan-Canadian Stand up to Cancer (SU2C) Stem Cell Dream Team effort, our group is focusing on brain tumour stem cells (BTSCs), a subpopulation of cells within tumours, which are hypothesized to drive tumour growth, relapse, and resistance to conventional chemotherapies. We are completing large-scale phenotypic cell biology, tumourigenicity, drug screening, and multi-platform ‘omic studies (genomics, bulk and single cell transcriptomics, epigenomics, metabolomics, proteomics, ATACseq) on a large number of adult GBM patient-derived BTSC cultures. Using both in vitro and in vivo approaches and the above multi-platform approaches, we are investigating the unique growth and stem cell characteristics of the BTSCs. In addition, we are using BTSC cultures to investigate drug response and genetic targeting approaches to identify new therapeutic strategies for clinical translation and understand mechanisms of resistance and recurrence. The wealth of information derived from this project is being integrated using systems biology approaches and will be shared in the public domain via data visualization and big data sharing platforms. Contemporaneously, we are depositing a number of well-characterized BTSC cultures in the American Type Culture Collection repository for unrestricted access to the scientific community. Our cross-Canada multi-disciplinary Cancer Stem Cell Dream Team, comprised of scientists, clinicians and patient advocates supports the ideal of data and resource sharing to help improve patient outcome for this devastating disease. Our goal is to provide the broader community of scientists, clinicians and patient groups an enduring resource and data sharing model to advance our understanding of glioblastoma. Citation Format: Hema A. Luchman, Fiona Coutinho, Samuel Weiss, Stand Up to Cancer Stem Cell Dream Team Consortium, Peter Dirks. Data and resource sharing: Advancing our understanding of glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3058.
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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 | 0.026 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.035 |
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".