Measurement of Bone Marrow Tumour Burden and Minimal Residual Disease in Waldenstrom’s Macroglobulinemia through Cell-free Whole Genome Sequencing
Bibliographic record
Abstract
Abstract Purpose We assessed the utility of blood cell-free DNA (cfDNA) whole genome sequencing (cfWGS) for minimal residual disease (MRD) monitoring in Waldenstrom’s macroglobulinemia (WM) by comparing this to 1) targeted panel sequencing of 27 genes of interest in WM and targeted capture of immunoglobulin gene rearrangements in blood and bone marrow 2) Multiplex-PCR of immunoglobulin loci followed by Illumina sequencing (clonoSEQ). Experimental design Samples were collected from 7 patients on a clinical trial who were treated uniformly with chemoimmunotherapy and Bruton’s Tyrosine Kinase inhibitor (BTKi). Samples were collected prior to starting treatment and at clinical timepoints up to 18 months. MRD detection technologies were compared across all timepoints. Results cfWGS was superior to both in-house targeted panel sequencing on cfDNA and clinical NGS in peripheral blood (PB) cells, using clinical bone marrow (BM) NGS as a standard. Tumor burden measured by cfWGS reflected MRD counts by clonoSEQ in BM. Conclusions cfWGS may be a valuable non-invasive alternative to bone marrow testing in WM patients who require close follow up and provides greater sensitivity than targeted panel sequencing of cfDNA. Statement of Translational Relevance Whole-genome sequencing in cell-free DNA (cfWGS) is a highly sensitive marker of minimal residual disease that has application as a biomarker in clinical trials. cfWGS more accurately reflects bone marrow tumor burden than other available non-invasive measures to date. Further exploration is warranted to determine its full potential for use in cancer diagnostics and research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".