Role of genomic variants in the response to biologics targeting common autoimmune disorders
Bibliographic record
Abstract
Autoimmune diseases (AID) are common chronic inflammatory conditions initiated by the loss of the immunological tolerance to self-antigens.Chronic immune response and uncontrolled inflammation provoke diverse clinical manifestations, causing impairment of various tissues, organs or organ systems.To avoid disability and death, AID must be managed in clinical practice over long periods with complex and closely controlled medication regimens.The anti-tumor necrosis factor biologics (aTNFs) are targeted therapeutic drugs used for AID management.However, in spite of being very successful therapeutics, aTNFs are not able to induce remission in one third of AID phenotypes.In our research, we investigated genomic variability of AID phenotypes in order to explain unpredictable lack of response to aTNFs.Our hypothesis is that key genetic factors, responsible for the aTNFs unresponsiveness, are positioned at the crossroads between aTNF therapeutic processes that generate remission and pathogenic or disease processes that lead to AID phenotypes expression.In order to find these key genetic factors at the intersection of the curative and the disease pathways, we combined genomic variation data collected from publicly available curated AID genome wide association studies (AID GWAS) for each disease.Using collected data, we performed prioritization of genes and other genomic structures, defined the key disease pathways and networks, and related the results with the known data by the bioinformatics approaches.We queried the AID results against known data about the aTNFs interventional pathways.Our findings allowed us to infer potential genetic factors and pathways responsible for the aTNF therapeutic effects in AID.A multitude of publicly available bioinformatics tools and databases allowed us to extract the knowledge, analyse it and provide orthogonal evidence for the results.The results support existence of at least two different sets of pathways responsible for AID pathology, most probably reflecting subtypes of AID.Only one set of pathways might be influenced by aTNFs, offering an explanation why aTNF therapy is not always effective.Additionally, our results narrow down the complex common genomic variability responsible for aTNF unresponsiveness that could be tested in future.If our results are confirmed by functional assays and/or clinical trials, then the lack of response could be predicted ahead of aTNF therapy, leading to better patient selection and improved prognostic outcomes.The extremely high cost of the standard aTNFs therapy can easily cover the cost of genotyping ahead of medication.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".