CD71<sup>+</sup> erythroid cells suppress T-cell effector functions and predict immunotherapy outcomes in patients with virus-associated solid tumors
Bibliographic record
Abstract
Background Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of cancer. However, only a portion of patients respond to such treatments. Therefore, it remains a prevailing clinical need to identify factors associated with acquired resistance or lack of response to ICIs. We hypothesized that the immunosuppressive CD71 + erythroid cells (CECs) within the tumor and/or distant ‘out-of-field’ may impair antitumor response. Methods We studied 38 patients with cancer through a phase II clinical trial investigating the effects of oral valproate combined with avelumab (anti-programmed death-ligand 1 (PD-L1)) in virus-associated solid tumors (VASTs). We quantified the frequency/functionality of CECs in blood and biopsies of patients. Also, we established an animal model of melanoma (B16-F10) to investigate the possible effects of erythropoietin (EPO) treatment on anti-PD-L1 therapy. Results We found a substantial expansion of CECs in the blood of patients with VAST compared with healthy controls. We noted that the frequency of CECs in circulation was significantly higher at the baseline and throughout the study in non-responders versus responders to PD-L1 therapy. Moreover, we observed that CECs in a dose-dependent manner suppress effector functions of autologous T cells in vitro. The subpopulation of CD45 + CECs appears to have a more robust immunosuppressive property compared with their CD45 − counterparts. This was illustrated by a stronger expression of reactive oxygen species, PD-L1/PD-L2, and V-domain Ig suppressor of T-cell activation in this subpopulation. Lastly, we found a higher frequency of CECs in the blood circulation at the later cancer stage and their abundance was associated with anemia, and a poor response to immunotherapy. Finally, we report the expansion of CECs in the spleen and tumor microenvironment of mice with melanoma. We found that although CECs in tumor-bearing mice secret artemin, this was not the case for VAST-derived CECs in humans. Notably, our results imply that EPO, a frequently used drug for anemia treatment in patients with cancer, may promote the generation of CECs and subsequently abrogates the therapeutic effects of ICIs (eg, anti-PD-L1). Conclusions Our results demonstrate that anemia by the expansion of CECs may enhance cancer progression. Notably, measuring the frequency of CECs may serve as a valuable biomarker to predict immunotherapy outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".