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Record W4206098478 · doi:10.1073/pnas.2113476118

Cell-free DNA profiling informs all major complications of hematopoietic cell transplantation

2022· article· en· W4206098478 on OpenAlexfundno aff
Alexandre Pellan Cheng, Matthew P. Cheng, Conor Loy, Joan Sesing Lenz, Kaiwen Chen, Sami Smalling, Philip Burnham, Kaitlyn Marie Timblin, J. Orejas, Emily A. Silverman, Paz Polak, Francisco M. Marty, Jerome Ritz, Iwijn De Vlaminck

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsHematopoietic cellCell-free fetal DNAHematopoietic stem cell transplantationDiseaseTransplantationMedicineCellDNAGraft-versus-host diseaseHaematopoiesisOncologyImmunologyBiologyInternal medicineStem cellGenetics

Abstract

fetched live from OpenAlex

Significance Hematopoietic cell transplantation is the gold standard treatment for many blood disorders, including blood cancers. Nonetheless, frequent post-transplant complications limit the long-term benefit of HCT. Here, we find that circulating cell-free DNA is a highly versatile analyte for monitoring of the most important complications of HCT: Graft-Versus-Host Disease, infection, graft failure and disease relapse. We show that these different therapeutic complications can be informed from a single cell-free DNA sequencing assay followed by disease-specific bioinformatic analyses. This test requires only low coverage DNA sequencing and is compatible with small volumes of blood. Cell-free DNA may improve the care of allogeneic HCT recipients by enabling earlier detection and better prediction of the complex array of complications that occur after HCT.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.270
Teacher spread0.250 · 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

Citations49
Published2022
Admission routes1
Has abstractyes

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