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Abstract A39: Concordance of 5-hydroxymethylcytosine-modified genes from circulating cell-free DNA and positron emission tomography in multiple myeloma

2020· article· en· W3033918338 on OpenAlexaff
Benjamin A. Derman, Zhou Zhang, Jason Karpus, Chang Zeng, Elizabeth Stepniak, Diana C. West-Szymanski, Rudy Chiu, John Spinelli, Chuan He, Jagoda Jasielec, Andrzej Jakubowiak, Wei Zhang, Brian C.‐H. Chiu

Bibliographic record

VenueClinical Cancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsConcordanceMedicinePositron emission tomographyDigital polymerase chain reactionEpigeneticsCell-free fetal DNAOncologyInternal medicineNuclear medicinePathologyGenePolymerase chain reactionBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Background: Positron emission tomography (PET) is one of the current standard-of-care imaging techniques for evaluating extramedullary disease in multiple myeloma (MM); hypermetabolic focal lesions at diagnosis are associated with poor prognosis. Molecular analysis of circulating cell-free DNA (cfDNA) has potential to not only be a noninvasive test for measuring extramedullary disease to complement imaging, but also provide pathobiologic insights. We previously discovered that signatures of the epigenetic modification 5-hydroxyethylcytosine (5hmC) in cfDNA differ between MM and its precursor conditions and predict relapse risk for MM patients at the time of diagnosis. We evaluated differentially modified 5hmC genes between PET-negative and PET-positive patients at the time of MM diagnosis and assessed concordance of the cfDNA 5hmC profiles with PET. Methods: We prospectively enrolled patients with newly diagnosed MM at The University of Chicago Medical Center from 2010 to 2017. Patients enrolled in the study who underwent standard-of-care PET imaging within 30 days of a corresponding blood draw were evaluated. Blood samples were collected and processed immediately to separate plasma. We profiled 5hmC with DNA extracted from ~2 mL of plasma to construct the 5hmC-seal libraries using the nano-hmC-Seal technology, which then underwent next-generation sequencing. 5hmC sequencing data were mapped to the human genome and annotated to ~22,000 gene bodies. We compared genome-wide 5hmC loci between PET-positive and PET-negative patients with MM. We developed an eight gene-based weighted prognostic score (wp-score) for predicting overall survival by applying the elastic net regularization on Cox proportional hazards model. Next, we evaluated concordance between PET results and wp-score. Results: A total of 71 MM patients (age, 61.2±10.65 year; males n=42) had at least one PET scan performed and had cfDNA sequencing data available; 29 (36%) patients had an initial PET positive for MM and the remaining 52 had a negative PET. We found 14 differentially modified 5hmC genes between PET-positive and PET-negative patients at baseline (p<0.005). PET positivity at baseline is associated with poor prognosis for MM. We also found that 9 out of 19 (47%) patients with a positive PET had a high wp-score (i.e., worse survival), while 11 out of 32 patients (34%) with a negative PET had a low-risk wp-score. Conclusions: Differential enrichment of 5hmC-modifications on gene body regions identified from 5hmC profiling of plasma cfDNA at the time of MM diagnosis differentiates patterns of PET imaging and adds complementary prognostic information to PET. These novel findings support the investigation of 5hmC in cfDNA as part of a multimodal approach to enhance prognostication in MM and potentially guide initial therapy. Citation Format: Benjamin Derman, Zhou Zhang, Jason Karpus, Chang Zeng, Elizabeth Stepniak, Diana West-Szymanski, Rudy Chiu, John Spinelli, Chuan He, Jagoda Jasielec, Andrzej Jakubowiak, Wei Zhang, Brian Chiu. Concordance of 5-hydroxymethylcytosine-modified genes from circulating cell-free DNA and positron emission tomography in multiple myeloma [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A39.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.104
GPT teacher head0.402
Teacher spread0.298 · 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
Published2020
Admission routes1
Has abstractyes

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