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Record W4224216547 · doi:10.3390/curroncol29050242

Enrichment-Free Single-Cell Detection and Morphogenomic Profiling of Myeloma Patient Samples to Delineate Circulating Rare Plasma Cell Clones

2022· article· en· W4224216547 on OpenAlexvenueno aff
Libere J. Ndacayisaba, Kate Rappard, Stephanie N. Shishido, Carmen Ruiz Velasco, Nicholas Matsumoto, Rafael Navarez, Guilin Tang, Pei Lin, Sonia Maryam Setayesh, Amin Naghdloo, Ching-Ju Hsu, Carlisle Maney, David E. Symer, Kelly Bethel, Kevin R. Kelly, Akil Merchant, Robert Z. Orlowski, James Hicks, Jeremy Mason, Elisabeth E. Manasanch, Peter Kühn

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsnot available
FundersNational Cancer InstituteDr. Miriam and Sheldon G. Adelson Medical Research FoundationNational Center for Advancing Translational SciencesLeukemia and Lymphoma Society
KeywordsBone marrowMultiple myelomaPlasma cellPlasma cell leukemiaPathologyMonoclonal gammopathy of undetermined significanceMalignancyPlasma cell neoplasmMedicineCirculating tumor cellCancer researchBiologyMonoclonalCancerAntibodyImmunologyMonoclonal antibodyMetastasisInternal medicinePlasmacytoma

Abstract

fetched live from OpenAlex

Multiple myeloma is an incurable malignancy that initiates from a bone marrow resident clonal plasma cell and acquires successive mutational changes and genomic alterations, eventually resulting in tumor burden accumulation and end-organ damage. It has been recently recognized that myeloma secondary genomic events result in extensive sub-clonal heterogeneity both in localized bone marrow areas and circulating peripheral blood plasma cells. Rare genomic subclones, including myeloma initiating cells, could be the drivers of disease progression and recurrence. Additionally, evaluation of rare myeloma cells in blood for disease monitoring has numerous advantages over invasive bone marrow biopsies. To this end, an unbiased method for detecting rare cells and delineating their genomic makeup enables disease detection and monitoring in conditions with low abundant cancer cells. In this study, we applied an enrichment-free four-plex (CD138, CD56, CD45, DAPI) immunofluorescence assay and single-cell DNA sequencing for morphogenomic characterization of plasma cells to detect and delineate common and rare plasma cells and discriminate between normal and malignant plasma cells in paired blood and bone marrow aspirates from five patients with newly diagnosed myeloma (N = 4) and monoclonal gammopathy of undetermined significance (n = 1). Morphological analysis confirms CD138+CD56+ cells in the peripheral blood carry genomic alterations that are clonally identical to those in the bone marrow. A subset of altered CD138+CD56- cells are also found in the peripheral blood consistent with the known variability in CD56 expression as a marker of plasma cell malignancy. Bone marrow tumor clinical cytogenetics is highly correlated with the single-cell copy number alterations of the liquid biopsy rare cells. A subset of rare cells harbors genetic alterations not detected by standard clinical diagnostic methods of random localized bone marrow biopsies. This enrichment-free morphogenomic approach detects and characterizes rare cell populations derived from the liquid biopsies that are consistent with clinical diagnosis and have the potential to extend our understanding of subclonality at the single-cell level in this disease. Assay validation in larger patient cohorts has the potential to offer liquid biopsy for disease monitoring with similar or improved disease detection as traditional blind 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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.093
GPT teacher head0.337
Teacher spread0.244 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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