Blockade of the CD6-ALCAM pathway modulates effector T cell function
Bibliographic record
Abstract
Abstract CD6 is a T-cell costimulatory receptor predominantly expressed on T cells and promotes immune synapse formation, T-cell activation, and T-cell migration via interaction with activated leukocyte cell adhesion molecule (ALCAM). The CD6-ALCAM pathway has been linked to the pathogenesis of multiple autoimmune and inflammatory diseases. While the contribution of CD6 to T cell activation has been well described, less is known regarding the expression levels and role of CD6 on effector and memory T cells which contribute to ongoing disease. Consequently, the aim of this study was to determine the role of CD6 specifically on Th1, Th2 and Th17 effector T cells (Teff). Blockade of the CD6 pathway using itolizumab, a humanized mAb currently being tested in the clinic, during restimulation of fully differentiated Th1, Th2 or Th17 Teff cells in the presence of ALCAM inhibited multiple effector functions, including proliferation and changes in cell size. An average 40% decrease in proliferation was observed across multiple donors. Furthermore, treatment of Teff cells with itolizumab resulted in a significant decrease in relevant cytokines such as IFNγ and TNFα for Th1, IL-4 and IL-13 for Th2, TNFα and IL-17 for Th17, as well as in expression levels of T cell markers of activation and exhaustion such as CD69, CD25, and PD-1. This effect was exclusively observed in the presence of ALCAM, indicating that the effect was specific to blockade of the CD6-ALCAM pathway. This study is the first to characterize the CD6-ALCAM pathway as a key regulator of effector Teff cell function. As these are the cells that drive disease pathogenicity, these data support targeting the CD6-ALCAM pathway to inhibit effector Teff cell populations in autoimmune and inflammatory diseases.
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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 | 0.001 |
| 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".