Abstract 3778: Uncovering the role of glioma stem cells in glioblastoma chemo-resistance and recurrence
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common malignant brain tumor. Despite multimodal aggressive therapies, the median survival after diagnosis is 14 months. The failure of current therapies is due to the presence within the tumor of a subpopulation of cancer cells with stem-like properties, called glioma stem cells (GSCs), which are responsible for chemo-resistance and recurrence. The molecular mechanisms involved in chemo-resistance of this cell subpopulation and its role in recurrence are still largely unknown. In this work, we isolated GSCs from seven recurrent GBM patients and we performed single-cell RNA-sequencing (scRNA-seq) on them. The analysis showed the persistence of a conserved neurodevelopmental hierarchy in recurrent GBM with a glial-like progenitor population at the top. Furthermore, we extended this analysis by sequencing the tumor bulk of seven recurrent GBMs in order to investigate deeply cancer heterogeneity in recurrences. Our data displayed the presence of many different cell populations that recapitulate the multiple steps of the physiological neural differentiation, suggesting that this capability is not affected by standard therapies. Afterwards, we confirmed the stem-like properties of our recurrent GSCs, verifying by immunofluorescence the expression of several stem-ness markers, such as PROM1, MASH1, OLIG2 and SOX2, and assessing the tumorigenicity in vivo of these cells. Standard therapies are able to eliminate only differentiated cancer cells, but they do not eradicate the cancer stem cell subpopulation. In order to defeat GBM and avoid recurrence, GSCs have to become the goal of future therapies. Considering chemo-resistance as an intrinsic and natural property of these cells, we performed a pathways analysis comparing the scRNA-seq datasets of recurrent GBMs with scRNA-seq datasets of primary GBMs and fetal nervous tissues to identify shared molecular pathways. Intriguingly, several pathways involved in DNA repair, cell cycle control and stem cell maintenance, such as BRCA1, ATM, E2F4 and FOXM1 pathways, were uniformly overexpressed by the progenitor cells of the three groups, representing new molecular targets for therapeutic purposes. Afterwards, we tested in vitro an E2F4 inhibitor on GSCs derived from de novo and recurrent GBMs, obtaining stunning results in terms of cell growth inhibition and cell death induction. Furthermore, we pretreated recurrent GCSs with this molecule and we injected them in mice: data showed the ability of the drug to reduce tumor growth sharply and prolong the survival. Currently, we are evaluating the efficacy in vivo of this compound on a recurrent GBM mouse model, administering the drug with intracerebroventricular pumps. This project could seriously help to decipher GSCs chemo-resistance, as well as their role in GBM relapse, also providing the necessary knowledge for the development of new targeted therapies. Citation Format: Gabriele Riva, Charles Couturier, Phuong Uyen Le, Xiaohua Yan, Yu Chang Wang, Marie Christine Guiot, Ioannis Ragoussis, Kevin Petrecca. Uncovering the role of glioma stem cells in glioblastoma chemo-resistance and 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 3778.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".