Genomic characterization of inter- and intra-tumour heterogeneity in primary adult diffuse gliomas and associated disease models
Bibliographic record
Abstract
Adult diffuse gliomas are deadly tumours that are characterized by extensive molecular (e.g. genetic, transcriptomic, epigenetic, proteomic) and cellular (e.g. microenvironmental) heterogeneity. This has become increasingly apparent especially with the advent of single-cell profiling technologies that allow the dissection of molecular heterogeneity at the level of individual cells. However, this heterogeneity is difficult to recapitulate in model systems, which has hindered our understanding of glioma biology and our ability to develop and test novel therapeutics. To further characterize cellular and molecular heterogeneity in adult diffuse gliomas and the extent to which it can be replicated in selected disease models, I analysed genetic and transcriptomic profiles of primary glioma samples and of representative cell line- and organoid-based models. I first investigated the function of Capicua (CIC), a transcriptional repressor that is frequently mutated in a subtype of lower-grade glioma. I found that the transcriptional consequences of CIC loss tended to converge onto dysregulated expression of genes involved in mitogen-activated protein kinase (MAPK) signalling and mitotic regulation. Analyses of single-cell genome profiles also revealed that loss of CIC may be associated with an increase in genomic instability and aneuploidy, possibly contributing to CIC’s function as a tumour suppressor. Secondly, I explored heterogeneity in glioblastoma (GBM), the most common and aggressive subtype of glioma, and novel patient-derived organoid (PDO) models of GBM. To do this, I used single-cell genome and transcriptome profiles of primary GBM samples and of cell lines and PDOs derived from them. I found that PDOs largely retained the genetic characteristics of the tumour from which they were derived and tended to display comparable transcriptomic heterogeneity, whereas cell lines were enriched for cells in a more uniform transcriptional state. Finally, I also evaluated a novel method for single-cell transcriptome profiling that provides exciting opportunities for the characterization of full transcripts in heterogeneous populations. Overall, the research presented in this thesis constitutes a step forward in our collective understanding of cellular and molecular heterogeneity in adult diffuse gliomas and models derived from them, providing a valuable resource to help understand how best these models can be deployed in future research studies.
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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 | 0.001 | 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 | 0.001 |
| 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".