Identification of multifunctional cytotoxic T-cell subsets as immune correlates with clinical outcomes in a phase II study of AGS-003, an autologous dendritic cell-based therapy administered to patients with newly diagnosed, metastatic RCC.
Bibliographic record
Abstract
80 Background: AGS-003 is an autologous dendritic cell (DC) immunotherapy prepared from matured monocyte-derived DC co-electroporated with the subject’s own amplified tumor RNA and synthetic CD40L RNA. The mechanism of action (MOA) of AGS-003 was evaluated in combination with sunitinib for treatment of advanced renal cell carcinoma (RCC) in AGS-003-006, an open label phase II trial including subjects with newly diagnosed, unfavorable-risk, metastatic clear cell RCC. The goal of the immune monitoring platform is to identify unique cytotoxic T-cell (CTL) signatures that correlate with clinical outcome in subjects receiving AGS-003 in combination with sunitinib. Methods: Multiparametric flow cytometry was used to identify tumor-reactive CTL subsets induced by AGS-003 based on combinatorial expression patterns of surface markers CD28, CD45RA, CD27 and CCR7. Moreover, further partitioning of each CTL subset identified combinatorial expression patterns of Markers of Immune Function (MIFs) defined as cytokines (IFN-γ TNF-α, IL-2), cytolytic markers (Granzyme b, CD107) and proliferation. Correlates of CTL signatures with clinical outcome were analyzed using an adaptation of a binary tree-structured vector quantization (BTSVQ) approach, originally developed to cluster and visualize large microarray data sets. The BTSVQ approach implements a two-way unsupervised clustering that allows a subject’s CTL signature to be mapped based on both surface marker and MIFs expression patterns to identify unique clustering patterns linked to clinical outcome. Results: Data analysis identified a unique CTL signature (CD28+/CCR7+/CD45RA- phenotype) displaying a broad MIFs profile as a statistically significant correlate to PFS and OS in patients treated with AGS-003. Conclusions: These results support the intended MOA of AGS-003 in vivo, as the induction of anti-tumor central and effector memory CTL responses. These data warrant further immunological evaluation of AGS-003 in the randomized phase III ADAPT study using AGS-003 in combination with standard treatment in RCC subjects.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".