The Effect of Sunitinib on Immune Subsets in Metastatic Clear Cell Renal Cancer
Bibliographic record
Abstract
BACKGROUND: Sunitinib is standard first-line therapy for metastatic clear cell renal cancer (MCRC). It is associated with leucopenia; however, its effects on specific immune cell subsets are unclear. Alterations in immune cell subsets may contribute to tumour progression. METHODS: Lymphocyte subsets (CD3, 4, 8, 19 and 56) were measured in 43 untreated MCRC patients who received sunitinib. The protocol included a structured treatment interruption of 5 weeks. Cell populations were measured at specific time points during sunitinib treatment and the treatment break. RESULTS: Sunitinib was associated with significant declines in total leucocyte (-48%), neutrophil (-62%), CD3 total T cell (-31%) and CD4 counts (32%; p < 0.05). There was no significant change in CD19 B lymphocyte, CD8 or CD56 natural killer cells. During the sunitinib-free interval, all parameters recovered to baseline. No patients developed opportunistic infections or neutropenic sepsis. The level of specific immune subsets at presentation or occurrence of a fall in specific counts had an effect on progression-free survival (p > 0.05). CONCLUSIONS: Sunitinib is associated with reversible inhibition of specific lymphocyte subsets which has implications for the immunological control of MCRC and its use in combination with other agents. Despite suppressive effects, there was no evidence of predisposition to immune suppressive-related infection.
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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 | 0.001 |
| 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".