Intergraft Variability in Nonhematopoietic Immunoregulatory Cell Number and Expression of Immune Checkpoint Inhibitor Receptors and Ligands in Both Allo- and Autografts: Potential Target for Intervention
Bibliographic record
Abstract
Abstract Intergraft variability in nonhematopoietic immunoregulatory cell number and expression of immune checkpoint inhibitor receptors and ligands in both allo- and autografts: potential target for intervention Qingdong Guan,1-3 Scott Gilpin,3 James Doerksen,3 Lauren Bath,3 Tracey Lam,3 Kristjan Paulson,4 Pascal Lambert,4 Yun Li,1,3 Donna A.Wall1-4 1, Department of Pediatrics and Child Health, 2, Immunology, University of Manitoba; 3, Manitoba Center for Advanced Cell and Tissue Therapy; 4, CancerCare Manitoba The number of CD34+ hematopoietic stem/progenitor cells (HSC) in HSC products is the main and often sole characterization of the graft used in HSCT. However CD34+ cells make up only 0.3-5% of the graft with the rest of the cells being lymphocytes and immature myeloid and granulocytic cells, including myeloid-derived suppressor cells (MDSC). We examined a cohort of HSC products collected from 2010-2014. Filgrastim and chemotherapy was used to mobilize 60 multiple myeloma and 34 lymphoma patients. Filgrastim-mobilized healthy donor products used in allografts (N=68) was a comparator. Aliquots stored in liquid nitrogen were analyzed for cell phenotype with a focus on immunoregulatory populations. We found CD33+CD15-CD14+HLA-DR-/low monocytic (M-MDSC) ranged from 0-59% in the infused graft. Similarly CD3+T lymphocyte ranged from 2-80% in the graft. There were 10-50 fold more M-MDSC than CD34+ cells with the infused M-MDSC cell dose ranging from 0-600×106/kg (Fig 1). Similarly CD3+T cell dose ranged from 4-670×106/kg (Fig1). M-MDSC were functional as they could suppress T cell proliferation and IFN-γ secretion, but promote regulatory T cell development in vitro. We examined receptor-ligand relations between M-MDSC and T cells and markers of T exhaustion. M-MDSC expressed variable PD-L1 (19.3±13.9% for MM, 10.4±4.4% for lymphoma and 7.0±4.8% for allografts), and CD86 (48.3±17.1% for MM, 59.9±15.4% for lymphoma and 57.8±17.0% for allografts), the ligands for PD-1 and CTLA-4, respectively. Blocking PD-L1-PD-1 signaling pathway using anti-PD-L1 or anti-PD1 partially reversed the suppressive functions of M-MDSC. Compared to allografts, CD4+T and CD8+T cells in the autografts showed poor proliferation, decreased the secretion of IFN-γ and/or granzyme B, and increased inhibitory receptors PD-1 and CTLA-4 on their surface - markers of T cell exhaustion. Levels of PD-L1 and CD86 on M-MDSC were correlated with expression of inhibitory receptors PD-1 and CTLA-4 on T cells, respectively. Taken together, our pilot data showed variable numbers of M-MDSC are infused with HSC grafts. These cells have strong immune regulatory function in vitro. T cells in autografts have high levels of T cell exhaustion markers and are less functional. It indicated immune function may be enhanced by interfering with PD1/PDL1 or CTLA-4. The numbers of M-MDSC and T cells are in the range of a cellular therapy product and may be targeted for enhance/inactivation pre- or peri-transplant immune function. Figure 1. The infusion cell dose of CD34+ stem cells, M-MDSC and CD3+T. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".