Detection of Recurrent Mutations, Immunoglobulin Rearrangements and Copy Number Changes in Cell-Free DNA and Bone Marrow on Patients with Waldenstrom's Macroglobulinemia over a Course of Treatment
Bibliographic record
Abstract
Background & Methods We developed a targeted-gene next generation sequencing (NGS) panel including immunoglobulin VDJ probes for use in Waldenstrom's Macroglobulinemia (WM) to characterize genomic alterations in treatment-naïve patients and follow genomic alterations over time in bone marrow (BM) and peripheral blood (PB) cell free DNA (cfDNA). Shallow whole genome sequencing (sWGS) is used to characterize copy number alterations. This panel was piloted on PB from 44 patients with a clinical diagnosis of WM, 8/44 with paired BM and is now being utilized to follow patients on the “Bendamustine-Rituximab in combination with Acalabrutinib in Waldenstrom's Macroglobulinemia (BRAWM)” study. We aim to characterize the mutation profile of treatment-naïve WM, follow genomic changes over time, explore minimal residual disease (MRD) analysis and compare PB to BM. Results In the pilot study, 5/8 paired PB/BM samples were positive for MYD88 L265P by clinical PCR and NGS on BM samples (100% concordant), however only 3/5 of the known MYD88 L265P mutations were detected in PB (60% concordant). CXCR4 mutations were identified in 3/5 MYD88 mutated samples. Chromosome 6 changes were detected in 2 samples, both of which were MYD88 mutated, and one of which also had a less common CXCR4 mutation (K331Rfs*12). In 36 cfDNA samples without paired BM, 5/36 were concordantly positive for MYD88 L265P compared to clinical PCR, 13/36 were concordantly negative, 13/36 were negative by NGS but clinical PCR positive and 5/36 were NGS positive but clinical PCR negative. Of the 13 discordant clinical PCR positive and NGS negative samples, 4/13 PB samples had insufficient DNA for NGS (<83ng), 4/13 had PB taken after treatment for WM, and there was not a clear explanation for remaining 5/13. On the BRAWM study, 14 paired BM/cfDNA screening samples and 4 cycle 7 samples were sequenced. 2/14 failed trial screening and did not have clinical data available. 11/12 remaining patients were MYD88 L265P mutated by clinical PCR. Concordance using targeted NGS was 100% for BM and 82% for cfDNA for pre-treatment samples. CXCR4 mutations were detected in 7/12 samples by clinical PCR and in 9/12 by NGS. 5/7 patients identified as CXCR4 mutated by clinical PCR had concordant results with NGS. The majority of CXCR4 mutations were S338*, however additional mutations were discovered by NGS. Only 3/9 CXCR4 mutations found in BM were detected in PB by NGS. Other mutations were identified in ARID1A, ATM, KMT2D, TBL1XR1, KMT2D, TP53 and CD79B genes. One sample showed an acquired PLCG2 mutation at cycle 7. VDJ rearrangements were found in all screening BM and PB samples. For MRD assessments at cycle 7, 2/4 were positive by VDJ and MYD88 analysis, 1/4 was positive by VDJ only and 1/4 was negative. sWGS identified 6q losses in 3/12 samples, an 11q loss in 1/12 and a chromosome 3 gain in 1/12 patients. Clinical cytogenetic information was not presently available for comparison. Conclusions: A custom targeted-capture panel NGS approach in WM with VDJ and gene probes can detect mutations and immunoglobulin rearrangements in WM in BM and PB, and can be used to follow somatic mutations that change with treatment and time, while sWGS can identify copy number alterations. Both mutation and VDJ sequencing can be used for MRD analysis. Targeted-capture sequencing may detect less common mutations within CXCR4 compared to PCR-based assays. Sensitivity in cfDNA is reduced compared to BM-derived genomic DNA. cfDNA analysis may not be sensitive in low disease burden states, including after treatment and for MRD analysis. Because of the potential clinical value in detecting molecular changes from cfDNA, further experiments to improve sensitivity in PB are being explored.
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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 | 0.000 | 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".