The Non-Hematopoietic Content in Paediatric Autologous Hematopoietic Stem Cell Grafts Is Skewed Towards Regulatory Myeloid and Exhausted Phenotypes
Bibliographic record
Abstract
Autologous hematopoietic stem cell transplant (aHSCT) is a central component of paediatric brain tumours and high-risk neuroblastoma management. Standardly, chemotherapy and G-CSF are used to mobilize the CD34+ hematopoietic stem/progenitor cells (HSC), which are used to support hematopoietic recovery following dose chemotherapy. Clinically, the graft infused is defined by solely by the CD34+ content. The non-hematopoietic passenger cells makeup most of the graft and are a mixture of myeloid and lymphoid cells that are potentially biologically active in the post-transplant setting. Here we study the content of the graft, focusing on the phenotypes of the cell populations, to begin to look at how the non-hematopoietic cells may facilitate anti-tumor activity, affect the tumour microenvironment, and/or immune recovery post-transplant. High parameter mass cytometry, with a custom 40-marker panel, was used to characterize 43 autologous cryopreserved grafts from children (median age 39 months, range 14-255 months) undergoing apheresis following chemotherapy and G-CSF mobilization. Healthy G-CSF-mobilized allogeneic donors (allograft) were used as a comparator group (n=10). Young children frequently have robust mobilization (peripheral blood CD34 count range from 100-1800 CD34/µL on day of collection) and generally one day collection will support multiple cycles of high dose therapy (target collections 20 x10 6 CD34+ cells/kg and often over collecting in 2-3 blood volume processed). This results in a product that is rich in CD34+ cells, with intergraft heterogeneity (median 7%; range 0.7-31%), compared to the CD34+ content of allografts (median 1.1%; range 0.3-2.4%) (autograft vs allograft median CD34 difference p<0.00005; Wilcoxon unpaired). There was variability in the non-hematopoietic component in the autografts (median T cells 10.8%; range 0.6 - 51.7%, and median monocytes 18.8%; range 4.1 - 51.4%). The allogeneic products were found to have less variability (T cells: 19.9 - 56%; monocytes: 7.3 - 19.6%). Autografts had increased expression of T cell exhaustion markers, with a median 30.8% (range 14.7 - 88.1%) of T cells expressing TIGIT and 35.1% (range 13.1 - 75.9%) expressing PD1 compared to the allografts (median TIGIT: 15.7%; range 9.3 - 28.7% and median PD1: 11%; range 4.2 - 19.7%). When grafts from high mobilizers (N= 21; >7% CD34+ cells) were analyzed by the actual number of cells infused they had had fewer regulatory cells: monocytic-myeloid derived-suppressor cells (M-MDSCs) (3.6x10 7 cells/kg; range 0.5 - 73.8x10 7; p< 0.0003), non-classical monocytes (0.9x10 7 cells/kg; range 0.5 - 21.3x10 7 cells/kg; p< 0.002) compared to the grafts from lower mobilizers (N = 22; <7.1% CD34+ cells) M-MDSC: median 7.7x10 7/kg; range 0.6 - 21.3x10 7 cells/kg, non-classical monocytes: 1.7x10 7 cells/kg; range 0.05 - 3.7x10 7 cells/kg). T cells and NK cells did not differ between high and low mobilizers. We are seeing significant differences in the regulatory cell populations infused in paediatric aHSCT based on the how robustly a patient mobilizes CD34+ cells after chemotherapy and G-CSF. It is not known whether these differences are clinically significant or affect anti-tumour immunity, tumour microenvironment perturbation, or immune recovery. With this high parameter mass cytometry strategy, we will be able to correlate engraftment kinetics and transplant outcomes (survival, relapse), which will allow us to re-envision how grafts should be optimally manufactured. Controllable variables include the timing of collection (earlier or later in the clinical course), alternative growth factor usage, enrichment or depletion of cell populations in the graft, or post-transplant growth factor administration. We suggest that the aHSCT graft can be thought of as an immunotherapy tool and not just for hematopoietic recovery.
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 | 0.000 | 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 | 0.000 |
| 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".