Accurate parent-of-origin variant assignment for multiple hereditary cancer syndromes using proband-only blood sample analysis.
Bibliographic record
Abstract
10516 Background: Predicting which side of the family a germline variant comes from is a critical gap in current clinical practice, vital for risk management, variant curation, and cascade genetic testing. Assignment of any autosomal variant to either parent with 99% accuracy is now possible with only a blood sample from the proband. Parent-of-Origin-Aware genomic analysis (POAga) is achieved by combining methylation and sequence data from Oxford Nanopore long-read sequencing with chromosome-length haplotypes generated from Strand-seq to infer parent of origin of any variant along the length of a chromosome, due to accurate phasing of imprinted differentially methylated regions that occur on each autosome. We sought to validate POAga in common, high penetrant hereditary cancer conditions such as hereditary breast and ovarian cancer (HBOC) and Lynch syndrome, rarer syndromes with parent-of-origin-effects and other genes predisposing to breast cancer and gastrointestinal malignancies that are associated with genes across multiple chromosomes. Methods: Blood samples from carriers of pathogenic variants in ATM, BRCA1, BRCA2, CDH1, MLH1, MSH2, MSH6, PMS2, EPCAM, PALB2, SDHD and SDHAF2, with known parental segregation, are currently being ascertained to determine the analytic validity of POAga in real world samples of differing age, sex, ethnicity, and cancer status. Samples are undergoing whole genome analysis by long and short read sequencing. Parent-of-origin of the pathogenic or likely pathogenic variant or variant of uncertain significance is predicted according to previously described methods (Akbari V, Hanlon VCT, et al. Cell Genom. 2022 Dec 21;3(1):100233.) under an REB approved protocol. Results: To date, analysis is complete for 100 individuals with a total of 107 rare or pathogenic germline variants with known or presumed parental segregation. Germline variants are in SDHD (n = 18), BRCA2 (n = 17), BRCA1 (n = 14), MLH1 (n = 10), PALB2 (n = 9), PMS2 (n = 8), MSH2 (n = 9), MSH6 (n = 7), ATM (n = 5), CDH1 (n = 5), SDHAF2 (n = 1), DICER1 (n = 1), MUTYH (n = 1), RET (n = 1), and EPCAM (n = 1). Of variants able to be assigned a parent-of-origin (n = 104 of 107, 97%), there was complete concordance between the predicted parent-of-origin and known clinical segregation (n = 104, 100%). Conclusions: Results to date support the ability of POAga to accurately infer parent-of-origin of rare or pathogenic variants with known parental segregation using only a blood sample from carriers of diverse hereditary cancer syndromes. Ongoing validation of POAga will continue to test its feasibility in real-world samples and inform its path towards clinically translation. Parent-of-Origin-Aware genomic analysis is a powerful technology that could improve our understanding of hereditary cancer syndromes and transform our ability to conduct genetic cancer risk assessments for patients and families.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".