Abstract 5179: Tumor microenvironment (TME) biomarkers of TIGIT and PD-L1 immune checkpoint blockade in cervical cancer: An exploratory biomarker analysis from SKYSCRAPER-04 (SKY04) study
Bibliographic record
Abstract
Abstract Introduction: Immune checkpoint inhibitors are active in advanced cervical cancer. SKYSCRAPER-04 (NCT04300647) explored the clinical activity of Tiragolumab (T, anti-TIGIT) plus atezolizumab (A, anti-PD-L1) dual blockade (T+A) in patients (pts) with PD-L1+ cervical cancer. This study aimed to evaluate the histological, transcriptomic and genomic TME biomarkers associated with both immune checkpoint blockade (ICB: combined A and A+T arms) and predictive of T clinical outcome. Methods: FFPE tumor biopsies were collected from patients with recurrent/persistent PD-L1+ cervical cancer (n=171). Samples were tested for PD-L1 (high [TAP ≥10%] vs low [TAP 5-9%]), stromal tumor infiltrating lymphocytes (sTIL by H&E, n=168), immune phenotypes (inflamed, excluded, desert) and total CD8 T cells by CD8/PanCK dual IHC (n=163), TIGIT IHC (n=166), tumor DNA NGS panel (FoundationOne CDx, n=95), and RNA sequencing (RNA Access, n=168) for biological pathways. Biomarker association with outcome was performed with chi-squared test for ORR and Cox-regression analysis for PFS and OS. Analyses are not adjusted for multiple testing. Results: Biomarkers associated with PD-L1 and TIGIT biology were evaluated for association to clinical outcome in ICB or T analysis. Pts with PD-L1 high tumors had higher ORR and longer PFS and OS, while IFNg/IFNa pathways (RNA) were associated with higher ORR and TIGIT IHC was only associated with longer ICB OS. In contrast, RNA signatures of highly proliferative tumors were negatively associated with ICB ORR. sTILs (IHC) and PVR (RNA) were numerically linked to higher ORR, PFS and OS in T+A vs A arms. The subgroup of patients analyzed for genomic profile (55% of randomized population) showed that TMB high (≥10 mut/Mb; 26% of pts) had higher ORR for ICB and numerically longer PFS and OS than TMB low (<10 mut/Mb). Mutations (mut) of activated PTEN/PIK3CA/AKT pathway was the most prevalent feature (52%) and pts with PIK3CA-mut had higher ICB ORR, PFS and OS. Conclusions: This exploratory analysis suggests that increased tumor immunity and mutation burden (TMB and PIK3CA-mut) correlates with improved ICB clinical outcomes. No biomarkers were clearly associated with tiragolumab outcome. These results are hypothesis generating and should be confirmed in an independent dataset. Citation Format: Venkatesh Krishnan, Ching-Wei Chang, Elyssa Bader, Ritu Salani, Bradley J. Monk, Yong-Man Kim, Sharad Ghamande, Shaundra L. Hall, Domenica Lorusso, Lisa Barraclough, Lucy Gilbert, Adrián Guzmán Ramírez, Chien-Hsing Lu, Dominique Berton, Nicoletta Colombo, Marcela Castro, Yvonne G. Lin, Mary McCormack, Luciana Molinero. Tumor microenvironment (TME) biomarkers of TIGIT and PD-L1 immune checkpoint blockade in cervical cancer: An exploratory biomarker analysis from SKYSCRAPER-04 (SKY04) study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5179.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".