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Abstract B030: Whole genome sequencing provides practice-changing benefits when performed on consecutive, unselected children with suspected cancer

2024· article· en· W4402266839 on OpenAlexaboutno aff
Angus Hodder, Sarah M. Leiter, Jonathan Kennedy, Dilys Addy, Munaza Ahmed, Thankamma Ajithkumar, Kieren Allinson, Phil Ancliff, Shivani Bailey, Gemma Barnard, G. A. Amos Burke, Charlotte Burns, Julian Cano-Flanagan, Jane Chalker, Nicholas Coleman, Danny Cheng, Yasmin Clinch, C Dryden, Sara Ghorashian, Blanche Griffin, Gail Horan, Michael Hubank, Phillippa May, Joanna McDerra, Rajvi Nagrecha, James C. Nicholson, David O’Connor, Vesna Pavasovic, Annelies Quaegebeur, Anupama Rao, Thomas C. Roberts, Sujith Samarasinghe, Iryna Stasevich, John A. Tadross, Claire Trayers, Jamie Trotman, Ajay Vora, James Watkins, Lyn S. Chitty, Sarah Bowdin, Ruth Armstrong, Matthew J. Murray, C. Elizabeth Hook, Patrick Tarpey, Aditi Vedi, Jack Bartram, Sam Behjati

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsCancerMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Whilst large scale paediatric whole genome sequencing (WGS) projects have been performed, these studies have been limited to children with high-risk disease, non-consecutive patients, and/or assessing potential benefits, rather than determining the actual impact on patient care. In England, where children with suspected cancer have universal access to WGS, we set out to assess the real-world impact of WGS on consecutive, unselected patients. Methods To assess this, we collected WGS reports, clinical and diagnostic information from children who presented to two large paediatric centres in England, where WGS became routine. We evaluated the overlap between standard of care (SOC) genetic tests and WGS, and the actual impact WGS results had on clinical practice, only counting those instances where findings were solely attributable to WGS. Results We studied 281 children (282 tumours), across 75 diagnoses – 130 solid malignancies (from Cambridge University Hospitals), and 152 haematological malignancies (from Great Ormond Street Hospital). Both centres performed extensive SOC tests, including targeted DNA and/or RNA sequencing panels on many diagnoses. Median turn-around time (TAT), from DNA availability to the release of Genomic England reports was 20.3 days (range 9-71). We found that consecutive WGS was feasible, and faithfully reproduced all 738 SOC genetic tests. Beyond this, in 108 instances for 83 (29%) children, WGS provided additional disease-relevant (diagnostic, risk-defining, mutation-target or germline) information. In 80 instances, from 69 (24%) cases, WGS provided direct clinical benefit above SOC. In 40 (14%) cases it provided accelerated genetic findings compared to SOC, leading to a modified or clarified diagnosis but without a substantive change to management. In 20 (7%) of cases WGS findings made children eligible for mutation-targeting medication during treatment/relapse. Our key finding was that in 20 (7%) cases, unique WGS findings directly changed the clinical care that children received. This was through the discovery of tumour findings that changed the diagnosis or risk category of patients and their management, or through unexpected germline findings that led to cascade testing and/or predisposition-directed therapy. ConclusionsOur study demonstrates that WGS can be delivered as part of routine clinical care to children with cancer and reproduces SOC genetic tests, providing opportunities for assay consolidation. Beyond this we evidence that WGS can provide clinical benefit, and in a substantial minority change the care children receive. Citation Format: Angus Hodder, Sarah M. Leiter, Jonathan Kennedy, Dilys Addy, Munaza Ahmed, Thankamma Ajithkumar, Kieren Allinson, Phil Ancliff, Shivani Bailey, Gemma Barnard, GA Amos Burke, Charlotte Burns, Julian Cano-Flanagan, Jane Chalker, Nicholas Coleman, Danny Cheng, Yasmin Clinch, Caryl Dryden, Sara Ghorashian, Blanche Griffin, Gail Horan, Michael Hubank, Phillippa May, Joanna McDerra, Rajvi Nagrecha, James Nicholson, David O'Connor, Vesna Pavasovic, Annelies Quaegebeur, Anupama Rao, Thomas Roberts, Sujith Samarasinghe, Iryna Stasevich, John A. Tadross, Claire Trayers, Jamie Trotman, Ajay Vora, James Watkins, Lyn S Chitty, Sarah Bowdin, Ruth Armstrong, Matthew J. Murray, Catherine E. Hook, Patrick Tarpey, Aditi Vedi, Jack Bartram, Sam Behjati. Whole genome sequencing provides practice-changing benefits when performed on consecutive, unselected children with suspected cancer [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 B030.

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)0.006
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.339
Teacher spread0.302 · 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 designObservational
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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