Abstract PR001: Inherited genetic variants in known cancer predisposition genes: A survey of the largest European cohort of patients under the age of 25 with whole genome sequencing data
Bibliographic record
Abstract
Abstract Since 2020 whole genome sequencing has been offered as standard of care for patients in the United Kingdom diagnosed with cancer before age of 25 (1). Where patients have consented for their data to be used by researchers, this can be accessed via collaboration with Genomics England. We have used Genomics England data to investigate the impact pathogenic or likely pathogenic (P/LP) germline genetic variants within genes included on cancer susceptibility panels (2) have on cancer risk in this population. We identified a cohort of 1,572 participants diagnosed with a hematological or solid cancer < age 25 and 30,680 controls, sequenced on the same platform. Controls were recruited to the rare disease arm of the 100,000 genomes project (3). Controls are relatives of probands with a rare disease not known to be linked to a predisposition to tumors. 7.12% of patients diagnosed with cancer before age 25 carried a pathogenic or likely pathogenic germline variant compared to 0.09% of controls (chi-sq = 1727.9, p-value < 2.2 x 10-16). A further 7.44% of cases carried a variant of unknown significance (VUS) in one of these genes that was present at a frequency of < 1% in the control group of participants. The genes that contained multiple P/LP variants that were significantly enriched in participants with cancer diagnosed before age 25 compared to the control group were mainly DNA repair genes, including TP53, DICER1, MSH6, MLH1, PMS2 and FANG, along with the tumor predisposition gene, NF1. Other significantly enriched genes containing multiple P/LPs included those with functions associated with metabolism, G6PC3, or cytotoxic immune responses, PRF1. Our results highlight that a significant proportion of participants diagnosed with cancer < age 25 possess a P/LP and VUS in a known cancer predisposition gene. Germline findings have implications for the care of cancer patients and their families to ensure that appropriate early detection screening is offered to those at high risk of developing tumors early in life. References 1. Trotman et al., (2022) The NHS England 100,000 Genomes Project: feasibility and utility of centralised genome sequencing for children with cancer. British Journal of Cancer, 127:137–144). 2. https://panelapp.genomicsengland.co.uk/ 3. The 100,000 Genomes Project – Current Rare Disease List v 1.9.0, https://files.genomicsengland.co.uk/forms/List-of-rare-diseases.pdf Citation Format: David James Barnes, Bea Storer-Prescott, Aditi Vedi, Claire Palles. Inherited genetic variants in known cancer predisposition genes: A survey of the largest European cohort of patients under the age of 25 with whole genome sequencing data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr PR001.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".