Protein Expression for Novel Prognostic Markers (Cyclins D1, D2, D3, B1, B2, ITGβ7, FGFR3, PAX5) Correlate With Previously Reported Gene Expression Profile Patterns in Plasma Cell Myeloma
Bibliographic record
Abstract
Among plasma cell myeloma (PCM) patients, gene expression profiling (GEP)-based molecular classification has proven to be an independent predictor of survival, after autologous stem cell transplantation. However, GEP has limited routine clinical applicability given its complex methodology, high cost, and limited availability in clinical laboratories. In this study, we have evaluated biomarkers identified from GEP discoveries, utilizing immunohistochemistry (IHC) platform in a cohort of PCM patients. IHC staining for cyclins B1, B2, D1, D2, D3, FGFR3, PAX5, and integrin β7 (ITGβ7) was performed on the bone marrow biopsies of 93 newly diagnosed PCM patients. Expression of FGFR3 was noted in 10 (11%) samples correlating completely with t(4;14)(p16;q32) results (P<0.001); however, the association between FGFR3 and cyclin D2 expression was not significant (P=0.14). ITGβ7 expression was present in 9/93 (9%) patients and all these samples also demonstrated upregulated expression of cyclin D2 (P=0.014). Expression of cyclins D1, D2, and D3 was variable in this cohort. Positive protein expression of cyclin D1 was noted in 30/93 (32%), D2 in 17/93 (18%), and D3 in 5/93 (5%) samples. Coexpression of cyclins D1 and D2 was observed in 13/93 (14%) samples, whereas 28 (30%) samples were negative for all the 3 cyclin D proteins. Cyclin B1 was not expressed in any sample, despite adequate staining in positive controls. Cyclin B2 was expressed in 33/93 (35%) and PAX5 protein was noted in 7/93 (8%) samples. In summary, we have demonstrated that mRNA-based prognostic markers can be detected by routine IHC in decalcified bone marrow samples. This approach may provide a useful tool for the wider adoption of prognostic makers for risk stratification of PCM patients. We anticipate that such an approach might allow patients with high-risk immunoprofiles to be considered for other potential novel therapeutic agents, potentially sparing some patients the toxicity of stem cell transplant.
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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 | 0.000 |
| 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) | 0.001 | 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".