Abstract 967: Single-cell transcriptomics uncovers clonal heterogeneity linked to drug response and cellular phenotype in adult brain tumor stem cells
Bibliographic record
Abstract
Abstract Brain tumours remain a largely incurable disease, with glioblastoma multiforme (GBM) representing the predominant form of malignancy. GBMs contain a rare population of cells, termed brain tumour stem cells (BTSCs) that drive tumour growth and disease relapse. BTSCs can be isolated and expanded in culture from primary GBMs and retain self-renewal and proliferative capacity in vitro. However, the functional diversity within the BTSC fraction, as well as relationship to the tumour bulk remains unknown. To investigate genotypic and phenotypic states within the stem-like compartment of GBM, we profiled 54,442 patient-derived BTSCs isolated from 25 GBMs using single cell RNA-sequencing (10X Genomics Chromium). Within each BTSC population, we observed diverse transcriptional clonotypes ranging from 1-7 subpopulations per sample. BTSCs grown as adherent monolayers displayed greater heterogeneity (median 4 clusters, range 2-7) compared to sphere-based cultures (median 2 clusters, range 1-3). Across samples, clusters were commonly associated with cell cycling, glial cell development and DNA replication pathways. Within multiple BTSC cultures, we identified transcriptional subclones with variable stemness properties, such as expression of ASCL1, suggesting a hierarchy of differentiation within the tumor-initiating fraction of GBM. Additionally, inference of CNVs from scRNA-seq data revealed subclonal somatic CNVs within BTSCs correspond to distinct transcriptional clonotypes that may partially explain stemness phenotypes and the formation of subpopulations during culture. To nominate targeted and combination therapies against BTSC populations in GBM, we next mapped transcriptomic features of BTSC subpopulations to high-throughput drug-screening data from central nervous system cell lines aggregated by the PharmacoDB platform.This approach nominated FDA-approved therapies that may be effective against subpopulations within each BTSC culture. In some cases, subpopulations were predicted to be sensitive to different drugs, opening a path for testing combination therapies nominated from single cell RNA-seq in vitro. Overall, these data illustrate the extent to which BTSC cultures functionally recapitulate the stem fraction in bulk primary tumours, as well as define networks of self-renewal, therapeutic resistance and targetable vulnerabilities in glioblastoma. Ongoing efforts include single nuclei RNA-seq of primary GBM tumours to enable comparison of transcriptional programs of BTSC cultures with cellular populations found in primary tissues. Citation Format: Laura M. Richards, Owen Whitley, Florence M. Cavalli, Zhaleh Safikhani, Fiona Coutinho, H. Artee Luchman, Benjamin Haibe-Kains, Samuel Weiss, Peter Dirks, Gary Bader, Trevor J. Pugh. Single-cell transcriptomics uncovers clonal heterogeneity linked to drug response and cellular phenotype in adult brain tumor stem cells [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 967.
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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) | 0.001 | 0.000 |
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".