Ig Gene Clonality Analysis Using Next-Generation Sequencing for Improved Minimal Residual Disease Detection with Significant Prognostic Value in Multiple Myeloma Patients
Bibliographic record
Abstract
Next-generation sequencing (NGS) of rearranged Ig genes is an effective technology for identifying pathologic clonal cells in multiple myeloma (MM) and tracking minimal residual disease. The clinical effect of implementing NGS in Ig gene clonality analysis was evaluated via a retrospective chart review. A total of 312 patients diagnosed with MM were enrolled in the study. Ig gene clonality was determined by fragment analysis using BIOMED-2 multiplex PCR assays and by NGS using the LymphoTrack IGH FR1 Assay and LymphoTrack IGK Assay. The clonality detection rates in diagnostic samples obtained using fragment analysis and NGS were 96.7% and 95.4%, respectively (statistically nonsignificant difference; P = 0.772). Among samples of patients in complete remission, the clonality detection rates obtained using fragment analysis and NGS were 33.3% and 60.3%, respectively (statistically significant difference; P = 0.034). Progression-free survival was significantly longer in negative than positive patients by NGS analysis (P = 0.03). Clonality detection by NGS-based methods using IGH FR1 and IGK assays in routine clinical practice is feasible, provides good clonality detection rates in diagnostic samples, and allows monitoring of samples in MM patients with significant prognostic value.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".