Pervasive Chromosomal Instability Drives the Karyotypic Evolution of Hypodiploid Tumours
Bibliographic record
Abstract
BACKGROUND: Tumours frequently exhibit extreme levels of aneuploidy. While increases in ploidy are well-characterised, the opposite phenomenon-extensive chromosome loss leading to hypodiploidy-remains underexplored. METHODS: Here, we analyse over 17,000 cancer genomes from 34 cancer types and perform a pan-cancer analysis of karyotypic evolution in hypodiploid tumours. We develop methods to identify current and former hypodiploid tumours, analyse predictors of chromosome loss patterns across tissues, and characterise the relationship between hypodiploidy and other forms of chromosomal instability. RESULTS: We find that hypodiploidy is widespread and associated with a generalised chromosomal instability phenotype, marked by significantly elevated rates of genome doubling, intrachromosomal copy number alterations, chromoanagenesis, and intra-tumour heterogeneity. These tumours are hypoxic and strongly enriched for TP53 mutations. However, we also identify a subset of cancers-acute lymphoblastic leukaemia (ALL), kidney chromophobe, and adrenocortical carcinoma-that exhibit stable hypodiploidy, with stereotyped chromosome loss patterns, low chromosomal instability, and distinct evolutionary origins. We exploit this stability to develop a simple method of distinguishing poor-prognosis masked hypodiploid from good-prognosis hyperdiploid ALL using only cytogenetic data, enabling more precise risk stratification. Finally, we show that unstable hypodiploidy predicts poor prognosis across cancers. Genome doubling does not confer a fitness advantage in hypodiploid tumours, nor do these tumours evolve to avoid loss of dosage-sensitive genes. CONCLUSIONS: Together, these findings provide the first pan-cancer characterization of hypodiploidy as a widespread and clinically relevant phenomenon often driven by pervasive chromosomal instability, and illustrate the remarkable ability of cancer cells to tolerate and evolve under extreme dosage imbalance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".