Insights for precision healthcare from the 100,000 Genomes Cancer Programme
Bibliographic record
Abstract
Abstract The Cancer Programme of the 100,000 Genomes Project was a transformational UK government initiative that aimed to bring whole genome sequencing (WGS) to cancer patients and evaluate the opportunities for precision cancer care. Genomics England, in partnership with NHS England, generated whole genome analyses for 13,880 solid tumours across 33 different cancer types, and genomic data were linked with real-world health data within a secure national research environment. Here, we report the overall findings of the programme, focusing on clinical actionability and potential wider clinical significance. We found variation between cancer types in the incidence of somatic mutations of different types in genes currently recommended for standard-of-care testing. For example, 94% of glioblastoma multiforme cases had small variants and 54% had copy number aberrations (CNAs) in at least one gene recommended for clinical testing, whereas sarcoma was found to have the highest proportion of actionable structural variants (13%). We confirmed the importance of utilising pan- genomic markers, such as mutational signatures, with 51% of high grade serous ovarian cancer cases showing homologous recombination deficiency, 13% of which were associated with pathogenic germline variants, indicating the value of combined somatic and germline analyses. We also observed a significant co-occurrence of somatic small variants and CNAs in several known oncogenes including EGFR, GNAS, BRAF, and KRAS. Our findings demonstrate the value of combining genomic testing with real world clinical and treatment data to inform clinical recommendations for genomic testing in cancer, to enable survival analysis and improve understanding of the long-term effects of clinical cancer genomics on patient outcomes.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".