Concordance of parent-of-origin predictions for hereditary cancer variants using proband-only analysis.
Bibliographic record
Abstract
10595 Background: Determining the parental origin of germline variants is a critical gap in clinical genetics, essential for risk management, variant classification, and cascade genetic testing. Traditional methods rely on testing family members, which can be time-consuming and impractical when relatives are unavailable, deceased, or unwilling to participate. Parent-of-Origin-Aware Genomic Analysis (POAga) offers a transformative solution by enabling accurate assignment of any autosomal variant to either parent with 99% accuracy using only a blood sample from the proband. This method integrates methylation and sequence data from Oxford Nanopore long-read sequencing with chromosome-length haplotypes generated from Strand-seq, leveraging the accurate phasing of imprinted differentially methylated regions (iDMRs) that occur on each autosome to infer the parent of origin (PofO) of variants across the genome. This study aims to validate POAga across multiple hereditary cancer syndromes, including high-penetrance conditions such as hereditary breast and ovarian cancer (HBOC) and Lynch syndrome, as well as rarer syndromes with PofO effects and other genes associated with breast and gastrointestinal malignancies. Methods: Blood samples from carriers of pathogenic variants in ATM , BRCA1 , BRCA2 , CDH1 , MLH1 , MSH2 , MSH6 , PMS2 , EPCAM , PALB2 , SDHD , SDHAF2 and TP53 with known parental segregation, are currently being ascertained and undergoing whole-genome analysis to determine the analytic validity of POAga. These samples span diverse demographics, including variations in age, sex, ethnicity, and cancer status. PofO predictions are made 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, 188 individuals carrying 189 pathogenic variants with known parental segregation have been analyzed. The distribution of variants includes BRCA2 (n=31), MLH1 (n=23), MSH2 (n=22), BRCA1 (n=22), SDHD (n=21), MSH6 (n=20), PALB2 (n=14), PMS2 (n=13), ATM (n=9), CDH1 (n=9), SDHAF2 (n=2), EPCAM (n=2) and TP53 (n=1). PofO assignment was successful for 172 of 189 (91%) variants. Only one sample with an MLH1 variant was misassigned, while all other cases demonstrated concordance between the predicted and known parental origin (188 of 189, 99.5% accuracy). Conclusions: These results support the ability of POAga to accurately infer the parental origin of pathogenic variants in diverse hereditary cancer syndromes using only blood sample from the proband. Ongoing validation will further assess its feasibility in real-world clinical settings and refine its clinical translation. POAga represents a powerful advancement in hereditary cancer genetics, with the potential transform how we 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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".