Signatures of copy number alterations in human cancer
Bibliographic record
Abstract
ABSTRACT The gains and losses of DNA that emerge as a consequence of mitotic errors and chromosomal instability are prevalent in cancer. These copy number alterations contribute to cancer initiaition, progression and therapeutic resistance. Here, we present a conceptual framework for examining the patterns of copy number alterations in human cancer using whole-genome sequencing, whole-exome sequencing, and SNP6 microarray data making it widely applicable to diverse datasets. Deploying this framework to 9,873 cancers representing 33 human cancer types from the TCGA project revealed a set of 19 copy number signatures that explain the copy number patterns of 93% of TCGA samples. 15 copy number signatures were attributed to biological processes of whole-genome doubling, aneuploidy, loss of heterozygosity, homologous recombination deficiency, and chromothripsis. The aetiology of four copy number signatures are unexplained and some cancer types have unique patterns of amplicon signatures associated with extrachromosomal DNA, disease-specific survival, and gains of proto-oncogenes such as MDM2 . In contrast to base-scale mutational signatures, no copy number signature associated with known cancer risk factors. The results provide a foundation for exploring patterns of copy number changes in cancer genomes and synthesise the global landscape of copy number alterations in human cancer by revealing a diversity of mutational processes giving rise to copy number changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".