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Abstract B014: Increasing the clinical utility of transcriptome analysis in high-risk childhood precision oncology

2024· article· en· W4402267281 on OpenAlexaboutno aff
Chelsea Mayoh, Paulette Barahona, Angela Lin, Lujing Cui, Pamela Ajuyah, Ann Altekoester, Loretta M. S. Lau, Dong‐Anh Khuong‐Quang, Vanessa Tyrrell, Michelle Haber, Marie Wong, Paul G. Ekert, Mark J. Cowley

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsPrecision oncologyTranscriptomeMedicineOncologyClinical OncologyInternal medicineComputational biologyBioinformaticsCancerBiologyGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION Utilizing whole genome (WGS) and transcriptome sequencing (RNA-seq) to identify the molecular features of individual cancers sits at the heart of pediatric cancer precision medicine. Whilst the utility of WGS for mutation detection is well established, most precision medicine programs utilize RNA-seq for fusion detection, which underutilizes the potential of RNA-seq information. In ZERO (Australia’s national precision medicine program for children with high-risk cancer), we showed that combining WGS with RNA-seq identified the molecular driver of their cancer in 94% of patients (n=247). We have sought to measure the added utility integrating a comprehensive RNA-seq pipeline provides in a precision medicine program (n=477). METHODS We developed a comprehensive RNA-seq pipeline (Carbonite) to identify expressed single nucleotide variants (SNV), small insertion and deletions (InDels) and structural variant (SV) events. SVs extend beyond fusions to include intragenic deletions, duplications, inversions and insertions, previously only identifiable through WGS. Fusion detection was expanded beyond gold-standard algorithms by incorporating a reference-free WGS guided approach that identifies cryptic SVs and intragenic alternate splicing events. The application of RNA-seq measured gene abundance was applied to identify enhancer hijacking events and biallelic deletions resulting from an SV. SNV and InDel analysis reports the zygosity in comparison to germline and tumor DNA. RESULTS A total of 218 SVs and 537 SNVs/InDels were identified as either pathogenic or likely pathogenic in our cohort of 477 patients. Carbonite identified 96% of SVs in comparison to 54% identified using gold-standard fusion detection algorithms only. In addition, Carbonite resolved the expressed chimeric transcript from complex chromosome shattering events, multi-hop complex rearrangements and SVs occurring in homologous and difficult to sequence regions in 33 patients. This information also aided the functional interpretation of SVs through accurate frame calling of potential activating oncogenic SV events, and disruption of key tumor suppressor genes. Of the driver SNV/InDels identified in WGS, Carbonite corroborated 92%. Those not validated resulted in either non-sense mediated decay or the gene was not expressed. Importantly, in 22% of somatic SNV/InDels, RNA-seq altered the assessment of pathogenicity, principally through resolving allele specific expression (ASE) and confirmation of the transcriptional consequence of splice site mutations. Germline SNVs/InDels exhibited ASE in the tumor RNA in 48%, aiding in interpretation of penetrance and oncogenicity. CONCLUSIONS Our results show that the enhanced RNA-seq pipeline has clinical applicability in precision medicine beyond fusion detection, providing functional insights derived from the transcriptional consequences of SVs and SNVs/InDels. We propose that RNA-seq is an essential tool in interpretation of the pathogenicity prediction of molecular drivers of cancer. Citation Format: Chelsea Mayoh, Paulette Barahona, Angela Lin, Lujing Cui, Pamela Ajuyah, Ann Altekoester, Loretta MS Lau, Dong-Anh Khuong-Quang, Vanessa Tyrrell, Michelle Haber, Marie Wong-Erasmus, Paul G. Ekert, Mark J. Cowley. Increasing the clinical utility of transcriptome analysis in high-risk childhood precision oncology [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 B014.

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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)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.059
GPT teacher head0.439
Teacher spread0.381 · 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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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