Abstract 5724: Revelation of shared transcriptional gradients in glioblastoma tumors and cultured glioma stem cells
Bibliographic record
Abstract
Abstract The brain tumor Glioblastoma (GBM) is a virtual death sentence for anyone who is diagnosed, with a median survival time of 12-15 months with standard treatments1 and a 5 year survival rate of under 10% 2. There is a strong body of evidence supporting the existence of stem like cells, termed glioma stem cells (GSCs), that can repopulate the tumor after removal and therapy application3-6. Thus, GSCs present a tantalizing target for potential therapies against GBM. However, studies of GSCs have shown heterogeneity at the level of the transcriptome and drug response5,7, suggesting that there is significant biological variation that translates into differential sensitivity to various drugs. A full characterization of biological heterogeneity may aid in the search for targeted therapies. Here, we profile the transcriptomes of 72 patient derived GSC cultures, and obtain scRNA-seq on 29 cultures from 26 patients (> 69,000 cells) plus 5 GBM tumors (> 14,000 cells). With this data, we find two anticorrelated transcriptional programs in the GSC cultures, one associated with immune or injury response related pathways and the other with neural developmental pathways. We then compare the GSC cultures to patient tumors in the scRNA-seq data, and find that a portion of GBM tumor cells are similar to GSC cultures. We find that a gradient between GSC and astrocyte programs separates cells with stemness properties from those that are not stem-like, and that within stem-like tumor cells and non stem-like tumor cells a gradient between the neural developmental and immune related programs exists as was seen for the cultured GSCs. In GSCs, we also find epigenetic variation in DNA methylation associated with the developmental and immune related programs. Overall these data suggest variation between two biological programs manifests at the level of gene expression and epigenetic regulation in GSCs in tumors. 1. Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987-996 (2005). 2. Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462-477 (2013). 3. Singh, S. K. et al. Identification of human brain tumour initiating cells. Nature 432, 396-401 (2004). 4. Chen, J. et al. A restricted cell population propagates glioblastoma growth after chemotherapy. Nature 488, 522-526 (2012). 5. Lan, X. et al. Fate mapping of human glioblastoma reveals an invariant stem cell hierarchy. Nature 549, 227-232 (2017). 6. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396-1401 (2014). 7. Meyer, M. et al. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc. Natl. Acad. Sci. U. S. A. 112, 851-856 (2015). Citation Format: Owen K. Whitley, Laura M. Richards, Florence Cavalli, Paul Guilhamon, Fiona Coutinho, Michelle Kushida, H. Artee Luchman, Samuel Weiss, Mathieu Lupien, Peter Dirks, Trevor Pugh, Gary Bader. Revelation of shared transcriptional gradients in glioblastoma tumors and cultured glioma stem cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5724.
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 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 | 0.001 |
| 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".