Combinatorial immune biomarkers of efficacy on buparlisib and paclitaxel in patients with SCCHN: BERIL-1 sub-analysis.
Bibliographic record
Abstract
e18030 Background: Previous analysis of the BERIL-1 Phase 2 clinical trial (Soulieres et al, CCR, 2018), evaluating the efficacy of Buparlisib + Paclitaxel, was one of the largest genomic landscape analyses to date in metastatic SCCHN. With the recent approval of PD-1 in this indication, we sought to expand the analysis, focusing on combinatorial biomarkers of immune infiltration in addition to specific genomic alterations. The goal is to evaluate hierarchically prognostic and predictive biomarkers to select predefined analysis in the current Phase 3 clinical trial, BURAN (NCT04338399) post immune modulatory therapy. Methods: The results of this analysis focused on combination of genomic alterations in both tumor and plasma samples. Alterations found in tumor versus plasma revealed over 50 differentially altered genes. Combination of these sample types is more comprehensive of the patients’ true mutational and copy number status; no significant differences in alteration frequency between treatment and placebo treatment arms were identified. We evaluated correlations between select biomarkers and improvement in efficacy end points of Progression Free Survival (PFS) and Overall Survival (OS) between the treatment and control arms and within the treatment arm. Results: The most frequently altered genes were, TP53 (49%), NOTCH1 (21%), PIK3CA (19%), FAT1 (15%), and CDKN2A (13%). Previous analysis demonstrated that TP53 mutations as well as high immune infiltration (> 10% intratumoral or stromal TILs) showed improvement in PFS & OS. Further improvement to OS was observed in subjects with high immune infiltration and TP53 (p = .031, HR = 0.52), PIK3CA (p = .005, HR = 0.34), NOTCH1(p = .046, HR = 0.13) or FAT1(p = .007; HR = 0.28) alterations when compared to non-altered subjects in the treatment arm. Further improvement to PFS was observed in subjects with high immune infiltration and TP53(p = .031, HR = 0.52) or FAT1(p = .006, HR = 0.23) alterations when compared to non-altered subjects in the treatment arm; NOTCH1 alterations showed strong trends in improved PFS. Additionally, high tumor mutational burden (TMB) combined with alterations in the TP53 pathway showed significant OS improvement (p = .052, HR = 0.44) compared with the placebo arm. Conclusions: This analysis highlights unique insights into the mechanism of Buparlisib and Paclitaxel in of metastatic SCCHN patients; a significant improvement in efficacy observed when specific alterations are considered in the context of immune infiltration. These observations will be further explored in the ongoing Phase 3 clinical trial, BURAN. Additional analysis to be presented at the meeting include prognostic and predictive value of each biomarker and combination, differences between tumor and plasma samples, and comparison with end of treatment samples.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".