Novel Single Panel Method for Minimal Residual Disease Detection for Plasma Cells By Flow Cytometry
Bibliographic record
Abstract
Introduction: Advancement in myeloma therapy has significantly improved outcomes including minimal residual disease (MRD) negativity that may be a surrogate for overall survival. We describe an assay using a single 10-colour panel to detect minimal residual disease (MRD) in plasma cell neoplasms to a level < 10-5. Methodology: Bone marrow aspirate specimens for this MRD assay must be from the first pull of 1.0 to 1.5 mL of bone marrow. A 1 in 10 dilution of the bone marrow is prepared and run on the instrument using a WBC count from the following calculation to determine the volume of specimen to process: For the MRD method, a corrective factor of 4 has been empirically determined to provide a sufficient number of cells to achieve the goal of 10-13 million viable cells during analysis. The required volume of bone marrow is added directly to Versalyse (Beckman Coulter) with 10% bovine serum albumin (BSA) in a bulk RBC lysis step. The cells are incubated for 15 minutes at room temperature while rocking. The cells are centrifuged and the following antibodies are added (Table 1): The cells and surface antibodies are incubated for a total of 20 minutes, specimen is gently vortexed at 10 minutes. Intracellular staining is achieved using the IntraPrep Kit (Beckman Coulter) using our standardized laboratory process. Cells are suspended in approximately 2mL of RPMI 1640 with 10% FCS. The specimen is loaded on to the Navios EX flow cytometer (Beckman Coulter)and data is acquired at approximately 5000 to 10000 events per second. The Navios EX is not capable of collecting more 1 700 000 events at acquisition when all 10 fluorescent detectors are in use plus light scatter detectors, so the specimen is repeatedly reloaded a total of 7 or 8 times. All data files are opened in Kaluza (Beckman Coulter) and merged in to a single file. This large data file is then imported in to the analysis template and analyzed for plasma cells. Analysis: A pilot of 20 specimens from patients with varying plasma cell disorders have been analyzed. Half of these specimens contained populations of monoclonal plasma cells. Ranked in order of smallest to largest (Figure 1): The smallest clone detected at 10x10E-4 is comprised of 1311 events. If a theoretical lower limit of quantitation of 50 events or 5x10E-6 in 10 000 000 total cells analyzed is required, this method will meet this criteria .Notably, all bone marrow specimens of adequate quality (not clotted, non-hemodilute) required less than 1.5mL of bone marrow to achieve > 10 000 000 nucleated cells in the final analysis. Analysis is complex using several dozen plots. Plasma cells are identified using CD38 and CD138. Gated plasma cells are analyzed for the immunophenotype of CD56, CD117, CD27, CD45, CD81 and cytoplasmic light chains simultaneously using n-dimensional radar plots (Figures 2, 3a, 3b): Qualitative results can be calculated from adjusted gates (Table 2): Conclusion: This rapid, high-sensitivity assay for immunophenotypically abnormal and clonal plasma cells requires low volumes of bone marrow. Results are ready in approximately 4 hours which is a distinct advantage and sensitivity can be shown to reach 5 X10-6. Disclosures Foley: Amgen,CelgeneJanssen: Honoraria. Mian:Takeda: Consultancy, Honoraria; Sanofi: Consultancy; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Celgene: Consultancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".