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Accurate parent-of-origin variant assignment for multiple hereditary cancer syndromes using proband-only blood sample analysis.

2024· article· en· W4399281554 on OpenAlexaff
Kasmintan A. Schrader, Vahid Akbari, Vincent C. T. Hanlon, Tiffany Leung, Katherine Dixon, Kieran O’Neill, Alexandra Roston, Lílian Córdova, Karen Wong, Alexandra Fok, David F. Schaeffer, Daniel J. Renouf, Dean A. Regier, Alice Virani, Fabio Feldman, Marco A. Marra, Sophie Sun, Stephen Yip, Peter M. Lansdorp, Steven J.M. Jones

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsBC Children's HospitalVancouver General HospitalUniversity of British ColumbiaCanada's Michael Smith Genome Sciences CentreTerry Fox Research InstituteBC Cancer Agency
Fundersnot available
KeywordsProbandMedicineCancerGeneticsBlood cancerSample (material)OncologyInternal medicineMutationGeneBiology

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.183
GPT teacher head0.490
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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