Solving the genetic aetiology of hereditary gastrointestinal tumour syndromes– a collaborative multicentre endeavour within the project Solve-RD
Bibliographic record
Abstract
BACKGROUND: Patients and families with suspected, but genetically unexplained (unsolved) genetic tumour risk syndromes lack appropriate treatment and prevention, leading to preventable morbidity and mortality. To tackle this problem, patients from the European Reference Network on Genetic Tumour Risk Syndromes (ERN GENTURIS) are analysed in the European Commission's research project "Solving the unsolved rare diseases" (Solve-RD). The aim is to uncover known and novel cancer predisposing genes by reanalysing available whole-exome sequencing (WES) data of large cohorts in a combined manner, and applying a multidimensional omics approach. APPROACH: Around 500 genetically unsolved cases with suspected hereditary gastrointestinal tumour syndromes (polyposis, early-onset/familial colorectal cancer and gastric cancer) from multiple European centres are aimed to be included. Currently, clinical and germline WES data from 294 cases have been analysed. In addition, an extensive molecular profiling of gastrointestinal tumours from these patients is planned and deep learning techniques will be applied. The ambitious, multidisciplinary project is accompanied by a number of methodical, technical, and logistic challenges, which require the development and implementation of new analysis tools, the standardisation of bioinformatics pipelines, and strategies to exchange data and knowledge. RESULTS: and Outlook. The first re-analysis of 229 known and proposed cancer predisposition genes allowed solving 2-3% of previously unsolved GENTURIS cases. The integration of expert knowledge and new technologies will help to identify the genetic basis of additional unsolved cases within the ongoing project. The ERN GENTURIS approach might serve as a model for other genomic initiatives.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".