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Record W4417007176 · doi:10.1182/blood-2025-2217

Novel method of minimal residual disease testing in myeloma: Liquid biopsies to enumerate and 3D telomere-profiling of circulating tumor cells

2025· article· en· W4417007176 on OpenAlexaff
Yulia Shifrin, Asieh Alikhah, Zahabiya Husain, Jack Khouri, Louis O. Williams, Christy Samaras, Shahzad Raza, Faiz Answer, Jason Valent, Beth Faiman, Emmet Samsa, Jessica Jones, Kelly Shepperd, Sabine Mai, Sandra Mazzoni

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of ManitobaOntario Genomics
Fundersnot available
KeywordsMinimal residual diseaseCirculating tumor cellLiquid biopsyImmunophenotypingBone marrowBiopsyMultiple myelomaDisease

Abstract

fetched live from OpenAlex

Abstract Introduction: Novel therapeutic approaches, especially cellular therapies, have significantly prolonged the survival of patients with multiple myeloma (MM). However, a cure is yet to be discovered, and the natural course of myeloma remains a series of disease relapses that become treatment refractory. Minimal residual disease (MRD), characterized by the presence of detectable clonal plasma cells in the bone marrow (BM) during or following therapy, is a critical prognostic and treatment-monitoring biomarker. Current approved MRD assessment technologies require invasive BM aspiration, which limits the ability to monitor the disease progression over time. Furthermore, the testing requires a baseline sample to identify tumor-specific sequences that do not predict the biological behavior of tumor cells and cannot account for the inherent variability of MM nor the development of treatment-resistant clones. In contrast to BM aspiration, liquid biopsy and evaluation of circulating tumor cells (CTCs) from peripheral blood (PB) offer a non-invasive, reproducible alternative that not only provides a comprehensive picture of the whole disease burden but also enables continuous monitoring of patients. However, due to the heterogeneity of MM, CTC enumeration alone cannot give a precise indication of the level of genomic instability related to MRD stability/progression. We recently demonstrated that the 3-dimensional (3D) profiles of telomeres, a marker of genomic instability, can predict disease progression in patients with smoldering multiple myeloma. Here, we describe a new method for MRD evaluation that combines the enumeration and immunophenotyping of individual MM CTCs in liquid biopsy with 3D telomere profiling to characterize the residual MM cells or clones, and determine MRD negativity or positivity, enabling continuous non-invasive follow-up. Methods: Intact CTCs from the PB of 14 MM patients were isolated at the point of diagnosis and at the time of disease relapse, with subsequent enumeration and immunophenotyping using CD56 and CD138 markers combined with 3D telomere profiling using the TeloView® software platform. Results: We consistently identified and enumerated CTCs in all patient samples with high sensitivity (1 in 107). Using the 6 parameters of quantitative 3D telomere measurements provided by the TeloView®, we compared the 3D telomere profiles of CD56+/Cd138+ MM CTCs and normal lymphocytes of the same patient. We demonstrate that MM cells have higher nuclear volume and a/c ratio (a measure of cell cycle progression/division), abnormal spatial telomere distribution within the nuclear space, and lower average telomere length. Conclusions: The novel workflow we present here successfully identifies and enumerates detectable CTCs not only at the point of diagnosis, but also at various times in the disease course: pre and post-ASCT, pre and post CAR-T and at disease progression. 3D telomere analysis of the isolated CTCs demonstrates 3D telomere profiles characteristic of MM and distinct from those of lymphocytes of the same patient. The proposed unique workflow allows for longitudinal and minimally invasive monitoring of MRD in multiple myeloma patients from the time of treatment. Unlike conventional approaches, this platform does not require a baseline sample and yields functionally and biologically actionable data on CTCs. It provides insights into disease stability or progression beyond simple enumeration, while avoiding the need for repeated bone marrow biopsies.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.035
GPT teacher head0.330
Teacher spread0.296 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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