Novel proteomic characterization of multiple myeloma bone marrow interstitial fluid links prognosis to coagulation pathways
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Bibliographic record
Abstract
BACKGROUND: Multiple myeloma (MM), the second most prevalent hematological malignancy, carries high morbidity with variability in clinical progression among patients. This necessitates accurate risk stratification for effective therapy and life planning. While extensively genomically and transcriptomically characterized, MM remains modestly studied from a proteomic perspective. As proteomics is a closer measure of phenotype than genomic and transcriptomic assessments, addressing this gap in the literature may yield new insights into disease biology and novel biomarkers. METHODS: Herein, we applied a new sample preparation approach for mass-spectrometry based proteomics to bone marrow interstitial fluid (BMIF) from patients with MM or its precursors. RESULTS: We achieved deep coverage of the proteome, identifying > 11,000 protein groups (PGs) across our cohort, with an average of ~ 8900 PGs per sample. Of these, 194 PGs were significantly associated with overall survival (OS). These survival-associated PGs were enriched for those involved in coagulation, and clustering newly diagnosed MM (NDMM) based on coagulation-related proteins revealed three distinct groups characterised by globally high, medium, and low intensity of coagulation-related proteins. The group with low intensity of coagulation-related PGs had significantly reduced OS (log-rank p = 0.00078). Clustering was independent of measured clinical covariates, including chemotherapeutic regimens used, Revised International Staging System (R-ISS stage), International Normalised Ratio (INR), and age, among others. CONCLUSION: Our findings support the value of fluid-based proteomic assessment of MM and suggest that coagulation-related PGs could serve as valuable novel biomarkers for risk stratification in multiple myeloma, warranting further investigation into this area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 it