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Preliminary results of 3D telomeres profiling for myeloma MRD and evaluation of concordance between blood and marrow.

2025· article· en· W4410804862 on OpenAlexaff
Rayan Kaedbey, Hans Knecht, Kenneth C. Anderson, Sabine Mai, Sherif Louis

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversity of ManitobaOntario GenomicsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineConcordanceMultiple myelomaBone marrowOncologyProfiling (computer programming)Internal medicinePathology

Abstract

fetched live from OpenAlex

e19560 Background: Recent meta-analyses have demonstrated the importance of minimal residual disease (MRD) as a prognostic factor in multiple myeloma (MM). As a result, the FDA has approved MRD as an accelerated end point in clinical trials of MM. Current MRD technologies recognized by the International Myeloma Working Group include next generation sequencing and next generation flow cytometry. These technologies are focused on detection and enumeration of MRD. Each of these technologies has its technical limitations that prevent its broader applicability to all MM patients. Furthermore, these technologies are predominantly applicable to bone marrow specimens, which compromises the ability to monitor patients repeatedly over time. Since MM is a patchy disease, sampling of one area of the bone marrow may not detect the true burden of the disease, whereas a blood-based assay may be more representative of the tumor burden. Given the heterogeneity of MM there is a need for technologies that go beyond enumeration to characterize residual MM clones and classify MRD positive cases as aggressive or MGUS-like. Genomic instability (GI) is an accepted sensitive indicator of disease progression in cancer. Telomere dysfunction is an early event in GI. The 3-dimensional (3D) profiling of telomeres was shown to inform on GI and predict disease progression in cancer including hematological disorders. Here we present a comparative analysis of the 3D telomere profiles from blood vs marrow of 8 transplant eligible MM patients enrolled in our MRD clinical trial at baseline. Methods: We developed a technology that allows for enumeration of myeloma MRD cells combined with 3D telomere profiling using the TeloView platform. 3D co-immuno-telomere FISH is conducted on myeloma plasma cells isolated from marrow samples or circulating myeloma plasma cells isolated from blood. MRD enumeration is conducted based on immunophenotyping of the MM plasma cells followed by 3D telomere profiling using TeloView. At least 2 independent samples (from marrow & from blood) were included in the analysis. Statistics was conducted to calculate standard error, standard deviation and co-efficient of variation (CV). Concordance was considered if the CV was less than 20%. Results: The assay was successfully conducted on marrow and blood equally. We report concordance between blood and marrow in 5 out of 6 telomere parameters quantified by TeloView across all 8 patients (>80%), and concordance of all 6 parameters in 6 out of the 8 patients. Conclusions: These results show that at time of diagnosis, the clones in the marrow can be identified in the blood thus allowing for the assessment of GI in the plasma cells which may then predict risk of relapse. As patients will enter MRD negative status, we will demonstrate if this assay compares to the current standard of care assays, and potentially show an assessment of the residual clones in those that remain MRD positive. Clinical trial information: NCT05530096 .

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.143
GPT teacher head0.479
Teacher spread0.336 · 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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Citations2
Published2025
Admission routes1
Has abstractyes

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