Peripheral and local predictive immune signatures identified in a phase II trial of ipilimumab with carboplatin/paclitaxel in unresectable stage III or stage IV melanoma
Bibliographic record
Abstract
Background Checkpoint blockade with ipilimumab provides long-term survival to a significant proportion of patients with metastatic melanoma. New approaches to increase survival and to predict which patients will benefit from treatment are needed. This phase II trial combined ipilimumab with carboplatin/paclitaxel (CP) to assess its safety, efficacy, and to search for peripheral and tumor-based predictive biomarkers. Methods Thirty patients with untreated unresectable/metastatic melanoma were treated with ipilimumab and CP. Adverse events (AEs) were monitored and response to treatment was evaluated. Tumor tissue and peripheral blood were collected at specified time points to characterize tumor immune markers by immunohistochemistry and systemic immune activity by multiplex assays and flow cytometry. Results Eighty three percent of patients received all 5 cycles of CP and 93% completed ipilimumab induction. Serious AEs occurred in 13% of patients, and no treatment-related deaths were observed. Best Overall Response Rate (BORR) and Disease Control Rate (DCR) were 27 and 57%, respectively. Median overall survival was 16.2 months. Response to treatment was positively correlated with a higher tumor CD3+ infiltrate (immune score) at baseline. NRAS and BRAF mutations were less frequent in patients who experienced clinical benefit. Assessment of peripheral blood revealed that non-responders had elevated baseline levels of CXCL8 and CCL4, and a higher proportion of circulating late differentiated B cells. Pre-existing high levels of chemokines (CCL3, CCL4 and CXCL8) and advanced B cell differentiation were strongly associated with worse patient overall survival. Elevated proportions of circulating CD8+/PD-1+ T cells during treatment were associated with worse survival. Conclusions The combination of ipilimumab and CP was well tolerated and revealed novel characteristics associated with patients likely to benefit from treatment. A pre-existing systemic inflammatory state characterized by elevation of selected chemokines and advanced B cell differentiation, was strongly associated with poor patient outcomes, revealing potential predictive circulating biomarkers. Trial registration Clinicaltrials.gov, NCT01676649, registered on August 29, 2012.
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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 | 0.000 |
| 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 | 0.001 |
| 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".