Diagnostic Evaluation of t[4;14] in Multiple Myeloma.
Bibliographic record
Abstract
Abstract The t[4;14] simultaneously deregulates fibroblast growth factor receptor 3 (FGFR3) and MMSET in 15% of patients (pts) with Multiple Myeloma (MM). Although oncogenic, FGFR3 expression is lost in ~25% of t[4;14] pts on gene expression profiling while MMSET is always retained. The t[4;14] is highly associated with poor prognosis, furthermore FGFR3 kinase inhibitors are in Phase I trials, thus detection of t[4;14] pts is increasingly important. The Multiple Myeloma Research Consortium (MMRC) therefore evaluated 4 competing methodologies for the diagnostic detection of t[4;14]. Bone marrow (BM) samples were collected in uniform fashion from 85 pts. FGFR3 immunocytochemistry (ICC) and flow cytometry (FC) were performed at the retrieval site, while split samples were shipped to the MMRC tissue bank and processed under GLP conditions. Processed samples were analyzed by cIg-FISH and quantitative IgH-MMSET RT-PCR (QPCR) on unsorted BM and blood. Of 60 samples with sufficient plasma cells for analysis, 8 were FISH +ve [13.3%]. Low % BM plasma cells prevented successful FISH in 25% of samples reflecting large volume harvests and hemodilution. Analysis was conducted in a blinded fashion. With FISH as the gold standard for detection of t[4;14] the sensitivity and specificity of the other diagnostic tests are: BM QPCR is most sensitive (7 of 8 FISH positive detected) and specific (42/43 negatives correctly identified). QPCR on peripheral blood is ongoing. FC is more sensitive to the detection of FGFR3 protein than ICC. Correlation between FC and ICC was only 0.46. Interestingly, 6 of 7 evaluable pts here and 13/14 (92%) in an expanded analysis of FISH +ve patients also expressed the FGFR3 protein. Given the discrepancy between this finding and previously reported loss of FGFR3 expression in 25% of pts we further explored t[4;14] stability. Clonal selection/heterogeneity was determined by the % plasma cells in each pt. with an unbalanced translocation (loss of one der chromosome). In 42 t[4;14] pts, 13 (31%) had a balanced translocation(>75% of cells with a double fusion), 14 (33%) had an unbalanced translocation (>75% cells with only one signal), and 15 (36%) had a chimeric picture. This heterogeneous pattern is suggestive of an evolution towards an unbalanced translocation. Despite this survival did not differ between pts with balanced or unbalanced translocation. Conclusions: These results indicate that the t[4;14] is highly heterogeneous with respect to balanced versus unbalanced translocations, which likely evolve over time. For detection, cIg-FISH continues to be the gold standard diagnostic methodology but may be limited by low plasma cell numbers in dilute BM or during MM remission. QPCR and FC appear most sensitive and specific for presence of t[4;14] and FGFR3 protein expression respectively. Surprisingly (given prior evidence of loss of FGFR3 expression in 25% of pts) 92% of t[4;14] pts in this series expressed FGFR3 protein. # Analyzed % Positive Sensitivity Specificity FISH 60 13 - - QPCR 65 14 87.5 97.6 Flow 82 15 85.7 91 ICC 85 15 62.5 92
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".