MétaCan
Menu
← Back to cohort
Record W4405038949 · doi:10.1182/blood-2024-208740

Cell-Free DNA Whole Genome Sequencing for Non-Invasive MRD Detection in Multiple Myeloma

2024· article· en· W4405038949 on OpenAlexaffabout
Dor Abelman, Jenna Eagles, Aimée A. Wong, Saumil Shah, Stephanie Pedersen, Sarah Bridges, Cecília Bonolo de Campos, Darrell White, Irwindeep Sandhu, Kevin Song, Zac McDonald, Abir Khaled, Liqiang Yang, Alli Murugesan, Tony Reiman, Suzanne Trudel, Trevor J. Pugh

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsOntario Institute for Cancer ResearchUniversity of British ColumbiaQueen Elizabeth II Health Sciences CentreDalhousie UniversityHealth Sciences CentreUniversity Health NetworkUniversity of TorontoUniversity of New BrunswickPrincess Margaret Cancer CentreVancouver General HospitalSaint John Regional HospitalBC Cancer Agency
Fundersnot available
KeywordsMultiple myelomaCell-free fetal DNABiologyGenomeWhole genome sequencingDNA sequencingDNAComputational biologyGeneticsCancer researchGeneImmunology

Abstract

fetched live from OpenAlex

Introduction Accurate detection of minimal residual disease (MRD) is crucial for evaluating treatment efficacy in multiple myeloma (MM), yet current methods are invasive and often limited by bone marrow (BM) sample quality. We therefore compared standard MRD detection methods to whole-genome sequencing (WGS) of peripheral blood plasma cell-free DNA (cfDNA) to provide less invasive monitoring options. Methods The MM Molecular Monitoring (M4) prospective cohort study included 45 newly diagnosed transplant-eligible MM patients uniformly treated with standard of care frontline therapy at 8 Canadian sites. MRD testing was performed at 100 days post-autologous stem cell transplant (ASCT) (n=39) and/or after one year of lenalidomide (len) maintenance (n=33). We analyzed 43 patients by multiparameter flow cytometry (MFC) (71 samples, CytoQuest Technologies), 39 patients using EasyM (57 samples, Rapid Novor), 28 patients using clonoSEQ (Adaptive Technologies), and 18 patients using PET/CT imaging. We also performed 30-40X WGS on CD138+ selected BM cells pre-treatment initiation to identify somatic mutations (n=11) and tracked these mutations by 30-40X WGS in longitudinal peripheral blood cfDNA samples (cfWGS, n=12). Results MRD-negative rates at 100 days post-ASCT were lowest for EasyM at 0% (n=30), compared to 20% for cfWGS (n=5), 45% for clonoSEQ (n=11), and 49% for MFC (n=39). This trend persisted after one year of len maintenance, with MRD-negative rates of 22% for EasyM (n=27), 29% for cfWGS (n=7), 41% for clonoSEQ (n=17), 59% for MFC (n=32), and 83% for PET (n=18). Notably, among the EasyM-positive samples at 100 days post-transplant, only 21/27 remained positive after one year of maintenance therapy, likely due to delayed clearance of the M-protein. As of July 2024, 12/45 patients had relapsed, with an average time to relapse of 714 days (SD=375) after initiating len. At the 100 days post-ASCT timepoint, all samples from patients who later relapsed were positive by EasyM (n=6) and clonoSEQ (n=2), with 67% of samples (n=6/9) positive by MFC (mean proportion aberrant cells 0.006%, limit of detection (LOD) range 0.00038%-3.4%) and 50% (n=1/2) by cfWGS. After one year of len maintenance, all samples from relapsed patients were positive by EasyM (n=8), with 67% of samples positive by clonoSEQ (n=4/6), 78% by MFC (n=7/9, LOD 0.00035%-0.38%), 100% by cfWGS (n=3), and 17% by PET (n=1/6). All samples from patients who later relapsed which were negative by MFC and cfWGS showed detectable disease below the LOD, indicating the presence of potential subclinical residual disease. None of the patients who were EasyM-negative relapsed within two years of their sample collection (n=6). In contrast, relapse within two years occurred in 17% of clonoSEQ-negative (n=2/12), 5% of MFC-negative (n=2/38), 33% of cfWGS-negative (n=1/3), and 33% of PET-negative (n=5/15) samples. cfWGS demonstrated 82% concordance with EasyM (n=11), 67% with PET (n=6), 50% with MFC (n=12), and 25% with clonoSEQ (n=8). The lower concordance rates with MFC and clonoSEQ were primarily due to cfWGS detecting additional positive cases missed by these methods but identified as positive by EasyM. For example, cfWGS identified four clonoSEQ-negative cases as positive, with one case leading to relapse within a year. Conversely, two clonoSEQ-positive cases were below the LOD for cfWGS, highlighting the need for further method optimization. Conclusions Overall, cfWGS is a promising MRD testing alternative which offers less invasive monitoring than MFC and clonoSEQ. It demonstrated superior sensitivity to MFC and clonoSEQ, identifying residual disease missed by these methods but detected by EasyM. cfWGS may be particularly beneficial for non-secretory and some light chain only patients where EasyM is not currently feasible. Additionally, it provides information on clonal dynamics at progression that is not offered by any of the other technologies. However, these findings are based on preliminary data from a small cohort, requiring further validation in larger studies. Future research will focus on enhancing sensitivity and validating these findings in a broader patient population.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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
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

Citations3
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueBlood→Same topicMultiple Myeloma Research and Treatments→French-language works237,207→