Immunological biomarkers of response and resistance to treatment with cabozantinib and nivolumab in recurrent endometrial cancer
Bibliographic record
Abstract
BACKGROUND: Antiangiogenics combined with immune checkpoint blockade have become standard of care for recurrent endometrial cancer after standard platinum-based chemotherapy. To dissect mechanisms and define biomarkers associated with clinical outcomes to these combinations, we applied multidimensional immune monitoring to peripheral blood specimens collected from a randomized phase 2 trial of nivolumab with or without cabozantinib in 75 evaluable patients with recurrent endometrial cancer (NCI ETCTN 10104, NCT03367741). This trial demonstrated superiority of the combination to nivolumab alone. METHODS AND RESULTS: Using Olink proteomics, mass cytometry, tumor antigen-specific ELISA, and whole exome tumor sequencing, we identified longitudinal immune signatures specific to cabozantinib use, including an increase in plasma HO-1 and reduction in plasma vascular endothelial growth factor receptor 2, interleukin-12, and circulating plasmacytoid dendritic cells. Prior exposure to immunotherapy and carcinosarcoma histology had no adverse impact on clinical benefit or biomarkers, and copy-number high tumors were associated with increased plasma granzymes on combination treatment. Higher baseline plasma levels of myeloid-related markers (chemokine ligand 23/CCL23, colony-stimulating factor-1/macrophage colony-stimulating factor/CSF1) were associated with poor overall and progression-free survival, and lack of clinical benefit (defined as progressive or stable disease <6 months) following combination treatment (Kaplan-Meier, multivariate Cox, false discover rate <0.05). Patients with favorable outcomes had higher levels of activated T-cell markers (plasma ICOS-L, CD28) and exhibited spontaneous autoantibody titers to tumor antigen NY-ESO-1. Patients experiencing severe adverse events from the combination therapy had higher baseline levels of neutrophil-derived markers (CXCL1). CONCLUSIONS: Overall, this study highlights potential resistance and response mechanisms to nivolumab+cabozantinib and suggests prioritizing combination treatment in patients with activated T-cell immunogenicity profiles while exploring future combinatorial therapies targeting myeloid populations to overcome resistance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".