Abstract 2563: Development of clinical assay to mitigate daratumumab, an IgG1κ monoclonal antibody, interference with serum immunofixation (IFE) and clinical assessment of M-protein response in multiple myeloma
Bibliographic record
Abstract
Abstract Daratumumab is a fully human anti-CD38 IgG1κ monoclonal antibody currently in clinical development for the treatment of multiple myeloma (MM) (PMID:21187443). Novel agents like daratumumab can potentially result in partial or complete clearance of the M-protein or monoclonal immunoglobulin produced by multiple myeloma cells. Monoclonal paraprotein clearance by serum electrophoresis and immunofixation (IFE) is part of the International Multiple Myeloma Working Group (IMWG) criteria for complete response and followed routinely in clinical practice to assess treatment response. The current limit of detection of many serum IFE assays is approximately 150 μg/ml M-protein, which is below the serum concentration of most monoclonal antibodies dosed in the therapeutic range. Indeed, recent studies demonstrated that therapeutic monoclonal antibodies in clinical development for treatment of MM are readily detected on serum IFE and can interfere with the detection and monitoring of endogenous M-protein (PMID: 20940329, 21521182). Therefore, mitigation strategies will need to be put in place to remove this interference to ensure valid clinical response calls that meet IMWG criteria for complete response and stringent complete response (negative serum IFE). We first confirmed daratumumab's interference on serum IFE by spiking various concentrations of antibody into PBS and normal healthy serum. We were able to detect daratumumab in PBS and serum at 50 μg/ml. We then utilized commercially-sourced MM serum samples to determine the M-protein profiles that may be observed in patients undergoing treatment with daratumumab and project the percentage of subjects that may demonstrate interference by serum IFE. Daratumumab was spiked into MM sera at clinically relevant doses (200 μg/ml) and the interference was noted in approximately 50% of IgG kappa samples where daratumumab and endogenous M-protein co-migrated. In remaining IgG kappa samples, the endogenous M-protein migrated in a different position than daratumumab and was easily identifiable. To mitigate interference, we utilized a mouse anti-daratumumab antibody to bind daratumumab and shift the complex migration on IFE. In addition, labeling the anti-idiotype antibody with either biotin or Alexa-fluor tags provided further shift of the daratumumab-complex allowing for additional distinction between the therapeutic antibody and endogenous M-protein. Patients with suspected daratumumab interference can be monitored with this additional reflex serum IFE utilizing the mitigation strategy described above. After clinically validating this assay, we intend to incorporate this mitigation strategy in clinical trials with daratumumab to ensure accurate assessment of clinical M-protein response, a critical evaluation for clinical trial endpoints and disease assessment. Citation Format: Amy E. Axel, Christopher R. McCudden, Hong Xie, Brett M. Hall, A. Kate Sasser. Development of clinical assay to mitigate daratumumab, an IgG1κ monoclonal antibody, interference with serum immunofixation (IFE) and clinical assessment of M-protein response in multiple myeloma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2563. doi:10.1158/1538-7445.AM2014-2563
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".