Comprehensive immune profiling of patients with advanced urothelial or renal cell carcinoma receiving immune checkpoint blockade
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICI) are used in the treatment of urothelial and renal cell cancers. While some patients may have exceptional responses, better predictive biomarkers are needed. We profiled the circulating immune compartment of patients receiving ICI to identify possible immune markers associated with immunotherapy response or resistance. Peripheral blood samples were collected prior to, and 3 weeks after initiation of ICI. Using mass cytometry, 26 distinct immune populations were identified. Responders to immune checkpoint inhibitors had higher frequencies of naïve CD4+ T-cells, and lower frequencies of CD161+ Th17 cells and CCR4+ Th2 cells. Non-responders had a higher frequency of circulating PD1+ T-cells at baseline; there was a subsequent decrease in frequency with exposure to ICI with a concomitant increase in Ki67 expression. Flow cytometry for cytokines and chemokine receptors showed that CD4+ T cells of non-responder patients expressed less CXCR4 and CCR7. In addition, their PD1- CD4+ T cells had higher TNFα and higher CCR4 expression, while their PD1+ CD4+ T cells had higher interferon γ and lower CCR4 expression. The role of γ/δ T-cells was also explored. In responders, these cells had higher levels of interferon γ, TNFα and CCR5. One patient with a complete response had markedly higher frequency of γ/δ T-cells at baseline, and an expansion of these cells after treatment. This case was analyzed using single-cell gene expression profiling. The bulk of the γ/δ T cells consisted of a single clone of Vγ9/Vδ2 cells both before and after expansion, although the expansion was polyclonal. Gene expression analysis showed that exposure to an ICI led to a more activated phenotype of the γ/δ T cells. In this study, the circulating immune compartment was shown to have potential for biomarker discovery. Its dynamic changes during treatment may be used to assess response before radiographic changes are apparent, and these changes may help us delineate mechanisms that underpin both response and resistance to ICI. It also hypothesizes a potential role for γ/δ T cells as effector cells in some cases.
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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".