Inherited Susceptibility to Cancer: Past, Present and Future
Bibliographic record
Abstract
Germline pathogenic variants (GPVs, 'mutations') causing inherited susceptibility to certain cancers (cancer susceptibility genes, CSGs) broadly belong to one of two main classes-loss of function variants in tumour suppressor genes (TSGs) or gain of function variants in proto-oncogenes (an over-simplification). Genomic analyses of tumours identify 'driver mutations' promoting tumour growth and somatic variants which contribute to 'mutation signatures' which, with histopathology, can be used to subclassify cancers with implications for causality and treatment. The identification of susceptible individuals is important, as they and their relatives may be at elevated risk of tumours, and this can influence optimal cancer treatment. Classically, cancer risk assessment utilises family history, lifestyle/environment factors, and any non-neoplastic clinical findings, followed by genetic testing of high/moderate penetrance CSGs. In cancer cases not caused by highly penetrant CSGs, multiple variants conferring relatively small risks play a major role. These were discovered by genome-wide association (GWAS) studies. The utility of polygenic risk scores (PRS) derived from multiple such variants for clinical risk profiling is being assessed. Access to genetic tests is improved by widening eligibility criteria for testing and empowering non-genetic clinicians to identify CSG GPVs and manage carriers. This will contribute to expanding programmes of screening, prevention and early detection (SPED), with personalised surveillance and prophylactic interventions, and exploit knowledge of the molecular mechanisms of cancer susceptibility to develop novel cancer therapies. In some jurisdictions, population testing is being considered, but GPV penetrance in this setting can be unclear, and the public health implications are complex.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".