A Novel Single-Tube Next Generation Sequencing Assay for B-Cell Receptor Clonality Testing
Bibliographic record
Abstract
B-cell neoplasms possess clonal B-cell receptor rearrangements (BCR clonotype lineages) that can be identified by sequencing the B-cell repertoire for use in diagnostics, risk stratification, and high-sensitivity monitoring. BCR somatic hypermutation (SHM) can result in clonality detection failure from point mutations in PCR primer binding regions, often necessitating splitting samples into multiple reactions which increases test costs, turnaround times, and sample requirements. We evaluated the Oncomine BCR Pan-Clonality Assay, a novel single-tube PCR reaction that simultaneously amplifies all BCR loci for next-generation DNA sequencing, using neoplastic B-cell lines and clinical research samples from multiple myeloma (MM) patients, a plasma cell neoplasm associated with high SHM levels. The assay showed a linear detection range down to 1 ng of clonal DNA input, sensitivity to 10−6 in a polyclonal background, and high reproducibility. Clonotype lineages were identified in 42/45 (93%) MM samples. Ion Reporter software packaged with the assay permitted straightforward identification of MM subgroups. As expected, SHM was identified in 94% of MM cases, but several unexpected subgroups were identified including biased IGHV3-11 or IGHV4-34 usage in 20% of MM samples, and two cases with very low levels of SHM. Evidence of intraclonal diversity/ongoing SHM was identified in 18% of samples, suggesting a possible germinal center origin for some MM cases. The single-tube Oncomine BCR Pan-Clonality assay efficiently detects BCR clonotype lineages at rates comparable to existing multiple reaction assays and permits their characterization for cell of origin studies and lymphoma classification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".