Multiplex Droplet Digital PCR Quantification of Recurrent Somatic Mutations in Diffuse Large B-Cell and Follicular Lymphoma
Bibliographic record
Abstract
BACKGROUND: A plethora of options to detect mutations in tumor-derived DNA currently exist but each suffers limitations in analytical sensitivity, cost, or scalability. Droplet digital PCR (ddPCR) is an appealing technology for detecting the presence of specific mutations based on a priori knowledge and can be applied to tumor biopsies, including formalin-fixed paraffin embedded (FFPE) tissues. More recently, ddPCR has gained popularity in its utility in quantifying circulating tumor DNA. METHODS: We have developed a suite of novel ddPCR assays for detecting recurrent mutations that are prevalent in common B-cell non-Hodgkin lymphomas (NHLs), including diffuse large B-cell lymphoma, follicular lymphoma, and lymphoplasmacytic lymphoma. These assays allowed the differentiation and counting of mutant and wild-type molecules using one single hydrolysis probe. We also implemented multiplexing that allowed the simultaneous detection of distinct mutations and an "inverted" ddPCR assay design, based on employing probes matching wild-type alleles, capable of detecting the presence of multiple single nucleotide polymorphisms. RESULTS: The assays successfully detected and quantified somatic mutations commonly affecting enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) (Y641) and signal transducer and activator of transcription 6 (STAT6) (D419) hotspots in fresh tumor, FFPE, and liquid biopsies. The "inverted" ddPCR approach effectively reported any single nucleotide variant affecting either of these 2 hotspots as well. Finally, we could effectively multiplex hydrolysis probes targeting 2 additional lymphoma-related hotspots: myeloid differentiation primary response 88 (MYD88; L265P) and cyclin D3 (CCND3; I290R). CONCLUSIONS: Our suite of ddPCR assays provides sufficient analytical sensitivity and specificity for either the invasive or noninvasive detection of multiple recurrent somatic mutations in B-cell NHLs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".