Overcoming immunotherapy resistance in hepatocellular carcinoma by targeting myeloid IL-8/CXCR2 signaling
Bibliographic record
Abstract
Durable responses to immune checkpoint blockade (ICB) in hepatocellular carcinoma (HCC) are limited to a minority of patients, yet reliable biomarkers are still lacking. Inflammatory cytokines such as interleukin-8 (IL-8) are associated with HCC progression, and IL-8 is known as the chemoattractant for immunosuppressive myeloid cells. Therefore, we aim to elucidate the ICB resistance mechanisms mediated by the activation of the IL-8/CXCR2 pathway. Single-cell RNA sequencing (scRNA-seq) was performed in advanced HCC patients with baseline and on-treatment biopsy after pembrolizumab in a phase 2 clinical trial cohort. Our data revealed that IL-8 and its receptor, CXCR2, mainly derived from immunosuppressive myeloid-derived suppressor cells (MDSCs). In particular, the high circulating IL-8 level was strongly associated with poor ICB response. This myeloid IL-8/CXCR2 pathway was further elucidated in our ICB-resistant orthotopic mouse model using AZD5069, a clinically available CXCR2 antagonist. Suppression of the IL-8/CXCR2 pathway significantly abrogated MDSC trafficking and immunosuppressive activity, which sensitized the anti-PD-L1 blockade to reduce tumor growth and prolong survival. The association between myeloid IL-8 and ICB therapeutic outcomes also extended to multiple cancer types. Collectively, our study not only suggests a potential non-invasive biomarker for patient stratification and monitoring of ICB response but it also provides a proof of concept for combinational immunotherapy to benefit patients who are non-responsive to ICB monotherapy.
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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".