Final outcomes analysis of the cell product SQZ‐PBMC‐HPV Phase 1 trial in incurable HPV16+ solid tumors shows improved overall survival in patients with increased CD8+ T cell tumor infiltration
Bibliographic record
Abstract
Cancer vaccines strive to induce robust, antigen-targeted, T-cell-mediated immune responses but have struggled to produce meaningful regression in solid tumors. An autologous cell vaccine, SQZ-PBMC-HPV, was developed by SQZ Biotechnologies using microfluidic squeezing technology to load PBMCs with HPV16 E6 and E7 antigens in HLA-A*02+ patients. The SQZ-PBMC-HPV-101 Phase 1 trial (NCT04084951) enrolled patients with incurable HPV16+ cancers. Here, we present a post hoc analysis of the relationship between Posttreatment CD8+ T cell infiltration and patient outcomes. SQZ-PBMC-HPV was administered as monotherapy every 3 weeks. Tumor samples were collected pre-dose and post-dose 4 weeks after treatment start. Biomarkers including CD8, MHC-I, E6, E7, GZMB, and Ki67 were evaluated by immunohistochemistry, immunofluorescence, and RNA in situ hybridization, and were correlated with clinical response, survival, and drug product composition. Eighteen patients had paired pre- and post-dose biopsies. Six (33%) had an increase in CD8+ T cell density in tumor parenchyma between screening and C2D8. Patients with increased CD8+ T cell density had improved disease control rate (66.7% vs 16.7%) and median overall survival (606.5 days vs 170.0 days, p = 0.0078). Drug product was significantly enriched for higher T cells and lower monocytes in the increased CD8+ T cell density group. In patients with incurable HPV16+ solid tumors treated with SQZ-PBMC-HPV, an increase in CD8+ T cell density within the tumor parenchyma was associated with superior disease control rate and overall survival. The product composition for patients with increased CD8+ T cell density was enriched for T cells.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".