P803 iGenoMed-MTT: A prospective multiomic study of response to molecularly targeted therapies in IBD
Bibliographic record
Abstract
Abstract Background No tools exist to individualize treatment selection in Inflammatory bowel disease (IBD) to optimize outcomes. We prospectively recruited patients prior to initiation of molecularly targeted (advanced) therapy (MTT) to identify predictive biomarkers of response and define pathways impacted by successful treatment. Methods Serum was collected before initiation of treatment in all patients, and at visit 1 (V1~16 wks) for a subset of patients. Serum samples were analyzed for >140 protein biomarkers (OLINK) and >1200 lipid metabolites. Whole exome sequencing and GWAS data was generated for all participants. Initial data outline proteomic data. Results Of the first 166 IBD patients analyzed, therapies initiated included TNF antagonists (n=85), ustekinumab (n=43), vedolizumab (n=28) and janus kinase inhibitors (n=10). Bio-naïve (n=96) and patients using baseline corticosteroids (n=77) were included. Sixty-three patients achieved remission. In TNF-antagonist initiations, serum TNF concentrations increased between baseline and V1 (p=4E-3), while it decreased for other MTTs (p=0.07). In bio-naïve patients, serum TNF concentration had a strong correlation with CXCL10 (r=0.58, P=3E-10). From the Principal Component Analysis of analytes correlated to TNF in bio-naïve patients, we built a proxy for the level of active TNF. At baseline, compared to TNF antagonist naïve patients, significantly higher serum TNF concentrations existed in patients with prior TNF-antagonist exposure >1-month post-cessation and a markedly high TNF concentration with cessation <1 month prior to baseline (p< 2E-16). In patients with prior TNF-antagonist exposure, serum TNF concentrations did not correlate with other cytokines (incl. CXCL10). In these patients, elevated serum TNF were found at V1, suggesting accumulation of inactive (complexed) TNF with treatment. A similar pattern was observed for IL-12 after ustekinumab treatment (elevated serum IL-12 with recent ustekinumab (p= 8E-13), large increase in IL-12 after ustekinumab (p=9E-5)). Reduced IL-12 concentrations existed in current corticosteroid users, compared to previous users and those who never used (p=4E-8). In baseline samples in bio-naive patients prior to therapy initiation, serum concentrations of six analytes provided a discriminant model of corticosteroid use, whith an area under (AUC) the receiver operation characteristic (ROC) curve showed excellent discrimination (AUC=0.91). Conclusion These data uniquely characterize proteomic data in patients with various advanced therapies and will enable development of predictive panels. Unique availability of multiple therapies linked to longitudinal proteomic data may allow personalized panels for treatment selection.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".