Innate lymphoid cells in the spotlight: from biomarkers to blueprint for innovative immunotherapy
Bibliographic record
Abstract
Since their discovery, innate lymphoid cells (ILCs) have emerged as key players in immune regulation, tissue homeostasis, and disease pathogenesis. Early research focused on defining ILC subsets, including ILC1s, ILC2s, ILC3s, and lymphoid tissue inducer (LTi) cells, by distinguishing their development, transcriptional profiles, and effector functions relative to T cells. Subsequent studies characterized the tissue-resident nature of ILCs and mapped their context-dependent phenotypes across diverse organs. In parallel, increasing evidence linked ILC subset imbalances to the pathogenesis of autoimmune diseases and various cancers. Recent work has leveraged circulating ILC frequencies and phenotypes as potential biomarkers for disease severity and progression. Notably, the immunomodulatory, tissue-reparative, and cytotoxic functions of helper ILCs have attracted interest as novel therapeutic avenues. Current strategies to harness ILCs for therapy include ex vivo expansion of autologous or allogeneic ILCs, derivation of ILC-like cells from umbilical cord blood or pluripotent stem cells (PSCs), and engineering of ILCs with chimeric antigen receptors (CARs) to enhance antigen specificity. Additionally, cytokine modulation and immune checkpoint blockade are being explored to sustain or redirect ILC function in disease contexts. This review synthesizes recent advances in understanding the functional diversity, plasticity, and tissue residency of ILC subsets, emphasizing their interactions with other immune and stromal cells, and their roles as predictive, diagnostic, and therapeutic targets in autoimmune diseases and cancers. Key translational challenges, including subset heterogeneity, plasticity, tissue-restricted residency, and limited scalability, remain barriers to clinical application. However, emerging multi-omic technologies, single-cell atlases, and synthetic biology approaches are accelerating efforts to map ILC states with unprecedented resolution and guide rational therapeutic design. Looking forward, integration of ILC-based therapies with regenerative medicine, cellular engineering, and immuno-oncology platforms holds promise for developing next-generation precision immunotherapies. By bridging fundamental biology with translational innovation, this field is poised to expand the therapeutic landscape for both autoimmune and malignant diseases.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".