Low‐depth sequencing for copy number abnormalities in multiple myeloma supersedes fluorescent in situ hybridization in scope and resolution
Bibliographic record
Abstract
Multiple myeloma (MM) is an incurable hematological malignancy that relies on cytogenetic determination of copy number abnormalities (CNAs) for prognosis and management. Low-depth whole genome sequencing (LD-WGS) is a cost-effective alternative to targeted genomics for CNA detection, but its value has yet to be explored in MM. DNA from CD138+ cells from MM patients were sequenced using an Illumina NextSeq at <1x depth (ultralow-depth). Subsampling analysis and window size adjustment were performed for determining sensitivity limits and results compared to fluorescent in-Situ hybridization (FISH). CNA calls made down to 5 million (M) reads were comparable to those at 20 M reads at a window size of 100 kb had a sensitivity and specificity of 93%, 92% and an area under the curve of 0.94. All CNAs detected by FISH on the MM samples were also detected by LD-WGS; the latter detected a further 36 focal CNAs not detected by FISH. Cost per sample of LD-WGS was significantly lower for our organization than FISH testing. LD-WGS for MM is significantly more sensitive than targeted technologies such as FISH in CNA detection and resolution, provides a more cost-effective option for clinical purposes and potential for exploring prognostically relevant and drug discovery targets.
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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 | 0.001 | 0.001 |
| 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".