MétaCan
Menu
← Back to cohort
Record W4393093059 · doi:10.1158/1538-7445.am2024-2427

Abstract 2427: The development of a tissue-agnostic genome-wide methylome enrichment MRD assay for applications across the cancer care continuum for head and neck malignancies

2024· article· en· W4393093059 on OpenAlexaff
Geoffrey Liu, Jun Won Min, Yarong Wang, Justin Burgener, Ben Brown, Karen Budhraja, Junjun Zhang, Owen Hall, Shu Shen, Martha Pienkowski, Shao Hui Huang, Laurie Ailles, Katrina Rey‐McIntyre, Jeremy Provance, Eduardo V. Sosa, Cynthia Frye, Scott V. Bratman, Brian Allen, Joshua T. Jones, Abel Licon, Jing Zhang, Anne‐Renee Hartman, Daniel D. De Carvalho

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsAssociation of Canadian Map Libraries and ArchivesUniversity Health Network
Fundersnot available
KeywordsHead and neckHead and neck cancerDNA methylationCancerOncologyBiologyMedicineComputational biologyInternal medicineGeneticsSurgeryGene

Abstract

fetched live from OpenAlex

Abstract Background: Plasma-based tests to quantify circulating cell-free DNA cancer signal have emerged as viable applications across the cancer continuum, from early detection to optimal disease management. Here we demonstrate the feasibility of a tissue-agnostic, genome-wide methylome enrichment platform based on cell-free methylated DNA immunoprecipitation and high throughput sequencing (cfMEDIP-seq) for cancer detection, cancer signal quantification, and prognostication in head and neck cancer (HNC). Methods: Pre-treatment plasma samples from individuals with newly diagnosed stage I-IV HPV+ or HPV- HNC were analyzed with a bisulfite-free, non-degradative, genome-wide methylome enrichment platform using 5-10 ng of cell-free DNA. For cancer detection, a machine learning classifier used differentially methylated regions to distinguish cancers from non-cancer controls. The area under the receiver operating characteristic curve (AUC) and 95% confidence intervals were calculated. Cancer signals were quantified from average normalized counts across informative methylated regions and a 95% specificity threshold. For prognostication, events were defined as recurrence, progression, or death due to HNC, whichever occurred earliest. Time to event was compared for samples with cancer signal quantities above versus below the threshold. Post-treatment and longitudinal plasma samples from individuals with Stage I-IVB HNC (HPV+ and HPV- included) will be analyzed for recurrence prediction and detection of relapse. More than 100 patients and 300 samples will be analyzed. Results: For cancer detection, 92 pre-treatment plasma samples from HNC cases were distinguished from 674 controls with an AUC of 0.96 (0.94, 0.98). The AUC was 0.93 (0.86, 1.0) for Stage I, 0.93 (0.83, 1.0) for Stage II, 0.96 (0.94, 0.99) for Stage III, and 0.97 (0.96, 0.99) for Stage IV. For prognostication, 91 pre-treatment samples were included (7 stage I, 16 stage II, 23 stage III, 45 stage IV). Median follow-up time was 50.6 months with 27 events. Likelihood of recurrence or progression was significantly higher in samples with cancer signal above the threshold [hazard ratio 5.4 (95% CI 2.25, 12.95), log-rank P<0.001]. In the upcoming analysis, data will be reported on the ability to predict recurrence and relapse in post-treatment samples. Conclusions: The cfMeDIP-seq approach demonstrated robust detection of HNC, across all stages and subtypes, and the ability to predict recurrence and progression from pre-treatment samples. We will report training data with cross validation to predict recurrence and relapse using post-treatment and longitudinal sampling. Collectively, data from these studies indicate that genome wide methylome enrichment has multiple use cases across the care continuum for patients with HNC. Citation Format: Geoffrey Liu, Jun Min, Yarong Wang, Justin Burgener, Ben Brown, Karen Budhraja, Junjun Zhang, Owen Hall, Shu Yi Shen, Martha Pienkowski, Shao Hui Huang, Laurie Ailles, Katrina Rey-McIntyre, Jeremy B. Provance, Eduardo Sosa, Cynthia Frye, Scott Bratman, Brian Allen, Joshua T. Jones, Abel Licon, Jing Zhang, Anne-Renee Hartman, Daniel D. De Carvalho. The development of a tissue-agnostic genome-wide methylome enrichment MRD assay for applications across the cancer care continuum for head and neck malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2427.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.057
GPT teacher head0.457
Teacher spread0.400 · 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 designNot applicable
Domainnot available
GenreOther

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

Explore more

Same venueCancer Research→Same topicRadiomics and Machine Learning in Medical Imaging→French-language works237,207→