Discrepancies in Serum Free Light Chain Assays and Immunofixation for Response Evaluation and Prognostication in Plasma Cell Proliferative Disorders
Bibliographic record
Abstract
Abstract Abstract 5099 Plasma cell proliferative disorders are monitored by a variety of methods. Serum protein electrophoresis (SPE), M-spike quantitation and Immunoelectrophoresis (IFE) are commonly assessed in patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and multiple myeloma (MM) to determine disease progression, response, or relapse. sFLC quantitation provides a rapid indicator of response, detects the rare occurrence of FLC escape, and also allows disease monitoring in the absence of a measurable serum or urine M-spike. To improve sensitivity of response assessment in MM, the International Myeloma Working Group (IMWG) has recently introduced the stringent CR category. However, no formal studies have validated this criterion. Indeed, the role of the sFLC assay has recently been questioned because of the presence of discordant abnormal sFLC ratios in a significant proportion of patients in CR. This could be at least partly explained by the presence of oligoclonal bands in response to therapy, potentially leading to false-positive results. Accordingly it has been recommended that the serum M-spike be used to monitor disease, and that FLC quantitation be used only if there is no measurable disease by electrophoresis and if the monoclonal sFLC concentration is greater than 10 mg/dL in the context of an abnormal FLC ratio. By analyzing serial samples in our patient population we aim to help usefully interpret the sFLC results and in the long run validate the prognostic impact of attaining CR versus CR plus normal sFLC ratio (stringent CR) after therapy in MM. From a total of 566 samples submitted for FLC analysis over 24-month period at our institution, 94 cases were monoclonal (abnormal FLC ratio) with kappa being the involved chain in 63 and lambda in 31 cases. Serial data from 35 multiple myeloma patients were identified by the availability of 3 serum test results (SPE/IFE/sFLC) in at least 3 serial samples that were obtained 3 months to 6 months apart along with treatment outcome details. Kappa and lambda FLC were quantitated using a Siemens BNII® nephelometer and Freelite® reagent sets from The Binding Site, Birmingham, UK; M-spikes were quantitated Capillarys® system and reagent sets (Sebia Electrophoresis, Norcross, GA). The FLC data was analyzed as the involved FLC concentration (iFLC), the difference between the involved and uninvolved FLC concentration (dFLC), and the FLC K/L ratio (rFLC). Treatment modalities included allogeneic, or autologous stem cell transplantation, conventional, bortezomib or lenalidomide containing chemotherapy. There were 16 (45%) cases in which discordance was observed between the three techniques (sFLC/SPE/IFE) during the follow-up. 11/16 (68%) patients were found to have abnormal sFLC with both abnormal FLC ratio and involved chain (FLCi), while no M-band was detected by SPE/IFE. In two cases (13%) the pattern of discrepancy was opposite with IFE found to be positive while rFLC results were within normal range of 0.26 – 1.65 mg/l. In three cases (19%) abnormal FLC ratio was detected with SPE/IFE being normal but the sFLC ratio did not match the myeloma isotype (sFLC ratio <0.26 for kappa and >1.65 for lambda isotype). In a subgroup of patients (n = 4) who relapsed during follow-up from complete remission sequential monitoring of immunofixation and free light chain assays revealed normalization of SPE/IFE with only faint/ doubtful band detected in one while FLC results were abnormal. The variability of the serum M-spike, IFE and FLC measurements confirm the IMWG recommendations for patient monitoring. The free light chain assay ratio is widely reported as a useful marker for a faster detection of remission or progression in myeloma patients. These techniques in reality complement each other and the FLC results need to be interpreted with caution in context of the electrophoresis results in order to determine the status of remission. More sensitive methods such as multiparametric imunophenotyping analysis for minimal residual disease by multiparametric flowcytometry or molecular primer assays may be useful to determine the depth of complete remission as choosing the type of screening test will likely have a relevant impact in clinical decision making in MM. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".