Prevalence of mutations in common tumour types in Northern England and comparable utility of national and international Trial Finders
Bibliographic record
Abstract
PURPOSE: Tumour genomic profiling is of increasing importance in early phase trials to match patients to targeted therapeutics. Mutations vary by demographic group; however, regional differences are not characterised. This was investigated by comparing mutation prevalence for common cancers presenting to Newcastle Experimental Cancer Medicine Centre (ECMC) to The Cancer Genome Atlas (TCGA) and utility of trial matching modalities. METHODS: Detailed clinicogenomic data were obtained for patients presenting September 2017-December 2020. Prevalence of mutations in lung, colorectal, breast and prostate cancer was compared to TCGA GDC Data Portal. Experimental Cancer (EC) Trial Finder utility in matching trials was compared to a Molecular Tumour Board (MTB) and commercial sequencing reports. RESULTS: Of 311 patients with advanced cancer, this consisted of lung (n = 131, 42.1%), colorectal (n = 44, 14.1%), breast (n = 36, 11.6%) and prostate (n = 18, 5.6%). More than one mutation was identified in the majority (n = 260, 84%). Significant prevalence differences compared to TCGA were identified, including a high prevalence of EGFR in lung (P = 0.001); RB1 in breast (P = 0.0002); and multiple mutations in prostate cancer. EC Trial Finder demonstrated significantly different utility than sequencing reports in identifying trials (P = 0.007). CONCLUSIONS: Regional differences in mutations may exist with advanced stage accounting for prevalence of specific mutations. A national Trial Finder shows utility in finding targeted trials whilst commercial sequencing reports may over-report 'actionable' mutations. Understanding local prevalence and trial availability could increase enrolment onto matched early phase trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".