CRYOVATE: A Pilot Study of Lung Cancer Cryoactivation in Combination With Immunotherapy in Advanced NSCLC
Bibliographic record
Abstract
Introduction NSCLC is the leading cause of cancer-related mortality. Although immune-checkpoint inhibitors (ICIs) have improved survival in patients with advanced NSCLC, treatment resistance remains a challenge. Cryoactivation, a technique inducing cell death by cycles of freezing and thawing, has the potential to augment tumor responses when combined with ICIs. Methods This single-arm phase 1 clinical trial enrolled patients with previously untreated advanced NSCLC and 50% or higher programmed cell death ligand-1 (PD-L1). Patients underwent cryoactivation followed by ICI monotherapy initiated 5 days later. The primary end point was the objective response rate. Co-secondary end points included the safety and feasibility of the procedure and overall survival. Immune cell infiltration by immunohistochemistry was performed on paired pre- and post-treatment samples, with patients dichotomized according to clinical benefit (CB) rate (CB versus no CB [NCB]). Results Eight patients were enrolled. Two patients achieved a partial response, yielding an objective response rate of 25%. Median progression-free survival and overall survival were 3.8 and 13.0 months, respectively. The cryoactivation procedure was well tolerated, without grade 3 to 4 adverse events. Post-hoc analysis reported a CB rate of 50%. Immunohistochemistry analysis reported a numerical difference in the cluster of differentiation 8–positive (CD8 + ) T cell infiltration in CB versus NCB in the pre- and post-treatment biopsies ( p = 0.09) and an increase in CD8 + T cells in the post-treatment biopsies of CB versus NCB ( p = 0.03). Conclusions Although cryoactivation combined with pembrolizumab was safe and well tolerated in patients with NSCLC, therapeutic benefits were not evident compared with historical cohorts of ICI monotherapy. Correlative analyses validated CD8 + T cell recruitment in patients deriving CB.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".