Proteomic Analysis of Sera from Individuals with Diffuse Cutaneous Systemic Sclerosis Reveals a Multianalyte Signature Associated with Clinical Improvement during Imatinib Mesylate Treatment
Bibliographic record
Abstract
OBJECTIVE: Imatinib has been investigated for the treatment of systemic sclerosis (SSc) because of its ability to inhibit the platelet-derived growth factor receptor and transforming growth factor-β signaling pathways, which have been implicated in SSc pathogenesis. In a 12-month open-label clinical trial assessing the safety and efficacy of imatinib in the treatment of diffuse cutaneous SSc (dcSSc), significant improvements in skin thickening were observed. Here, we report our analysis of sera collected during the clinical trial. METHODS: We measured the levels of 46 cytokines, chemokines, and growth factors in the sera of individuals with dcSSc using Luminex and ELISA. Autoantigen microarrays were used to measure immunoglobulin G reactivity to 28 autoantigens. Elastic net regularization was used to identify a signature that was predictive of clinical improvement (reduction in the modified Rodnan skin score ≥ 5) during treatment with imatinib. The signature was also tested using sera from a clinical trial of nilotinib, a tyrosine kinase inhibitor that is structurally related to imatinib, in dcSSc. RESULTS: The elastic net algorithm identified a signature, based on levels of CD40 ligand, chemokine (C-X-C motif) ligand 4 (CXCL4), and anti-PM/Scl-100, that was significantly higher in individuals who experienced clinical improvement than in those who did not (p = 0.0011). The signature was validated using samples from a clinical trial of nilotinib. CONCLUSION: Identification of patients with SSc with the greatest probability of benefit from treatment with imatinib has the potential to guide individualized treatment. Validation of the signature will require testing in randomized, placebo-controlled studies. Clinicaltrials.gov NCT00555581 and NCT01166139.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".