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Circulating cell-free methylated DNA (cfmeDNA) to predict postoperative recurrence in patients with muscle-invasive bladder cancer (MIBC).

2019· article· en· W2922362450 on OpenAlexaff
Pier Vitale Nuzzo, Sándor Spisák, Norbert Solymosi, Ankur Chakravarthy, Shu Yi Shen, Mark M. Pomerantz, Francesco Boccardo, Amin H. Nassar, Gwo‐Shu Mary Lee, Guru Sonpavde, Toni K. Choueiri, Daniel D. De Carvalho, Matthew L. Freedman

Bibliographic record

VenueJournal of Clinical Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMethylationCpG siteDNA methylationMedicineCystectomyBladder cancerBiomarkerBisulfite sequencingMethylated DNA immunoprecipitationInternal medicineCancerOncologyLiquid biopsyDifferentially methylated regionsCell-free fetal DNACancer researchMolecular biologyDNAGeneBiologyGene expressionGenetics

Abstract

fetched live from OpenAlex

454 Background: CfmeDNA is a promising non-invasive biomarker to assess solid tumor burden: i) CpG island methylation changes in cfDNA are stable ii) methylation is tissue- and tumor- specific iii) methylation target size is larger and more sensitively detected than genomic alterations. CfmeDNA Immunoprecipitation and high throughput sequencing (cfMeDIP-seq) is an innovative assay for genome-wide bisulfite-free plasma DNA methylation profiling, that permits CpG enrichment. We tested the feasibility of cfmeDNA to predict recurrence of MIBC post- radical cystectomy (RC). Methods: We selected 12 pts who underwent RC for MIBC: 6 pts who had recurrent disease within 2-3yrs after RC (A) and 6 pts who did not (B). 119 healthy pts without BC were controls. cfDNA isolated from 1ml of plasma samples collected after RC and before recurrence (A) or during follow-up in those who did not recur (B) was analyzed by the cfMeDIP-seq using 10ng cfDNA. The data were analyzed using the MEDIPS program and differentially methylated regions (DMR) between the cohorts were studied. ENCODE ChIP-seq analytical pipeline was used for fastq file processing and peak calling. Results: The average cfDNA isolated from 1ml of plasma was 13.1 ng (6.4-19.7) in A and 17.1 ng (13.6-21.2) in B. The median time from RC to plasma collection were respectively 9.3 mos (3.4-91.3) vs 12.3 mos (2.9-150). Median time from plasma collection to recurrence was 21.9 mos (0.25-141.3). We identified ~137,000 peaks in ≥1 sample. The supervised classification identified 61 DMR (FDR<0.050), predominantly located in intergenic region, which distinguished A from B. Randomized sample tests proved the discriminatory power of the identified set. Supervised analysis comparing the status of the identified DMRs relative to healthy controls showed 28 regions were differentially methylated (logFC > +/- 1, FDR < 0.05). The study is limited by retrospective design and sample size. Conclusions: This is the first study to demonstrate that cfmeDNA can be readily harvested from MIBC pts to detect cancer-specific methylation patterns and predict recurrence post-RC. Prospective validation will enable the selection of suitable pts for adjuvant therapy.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004

Distilled classifier scores by category (both heads)

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

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 head0.050
GPT teacher head0.395
Teacher spread0.345 · 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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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