Molecular Alterations and Buparlisib Efficacy in Patients with Squamous Cell Carcinoma of the Head and Neck: Biomarker Analysis from BERIL-1
Bibliographic record
Abstract
Abstract Purpose: The preplanned exploratory analysis of the BERIL-1 trial presented here aimed to identify biomarkers of response to the combination of buparlisib and paclitaxel. Patients and Methods: BERIL-1 was a multicenter, randomized, double-blind, placebo-controlled phase II study. Patients with recurrent or metastatic squamous cell carcinoma of the head and neck (SCCHN) progressing on/after one previous platinum-based chemotherapy regimen in the recurrent or metastatic setting were treated with either buparlisib plus paclitaxel or placebo plus paclitaxel. Archival tumor tissue and ctDNA samples were analyzed for molecular alterations and immune infiltration using next-generation sequencing or immunohistochemistry. Results: Biomarker analyses were performed in randomized patients (n = 158) with available biomarker data. The most frequently (>5%) mutated genes were TP53, FAT1, TET2, KMT2D, PIK3CA, NOTCH1, NFE2L2, NOTCH2, CCND1, and CDKN2A. Patients with SCCHN tumors (from various primary sites) having HPV-negative status (HR = 0.51), TP53 alterations (HR = 0.55) or low mutational load (HR = 0.57) derived overall survival (OS) benefit with the combination of buparlisib and paclitaxel. OS benefit with this combination was also increased in patients with presence of intratumoral TILs ≥10% (HR = 0.51), stromal TILs ≥15% (HR = 0.53), intratumoral CD8-positive cells ≥5% (HR = 0.45), stromal CD8-positive cells ≥10% (HR = 0.47), or CD8-positive cells in invasive margins >25% (HR = 0.37). A trend for improved progression-free survival with the combination of buparlisib and paclitaxel was also observed in these patients. Conclusions: The BERIL-1 biomarker analyses showed that patients with TP53 alterations, HPV-negative status, low mutational load, or high infiltration of TILs or CD8-positive cells derived survival benefit with the combination of buparlisib and paclitaxel. Clin Cancer Res; 24(11); 2505–16. ©2018 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".