Abstract PO-070: New patient-derived head and neck cancer xenograft (PDX) panel for drug development, immuno-oncology and translational research
Bibliographic record
Abstract
Abstract Head and neck squamous cell carcinomas (HNSCC) represent a heterogeneous group of epithelial cell malignancies arising from the upper aerodigestive tract. Despite of improved therapies, HNSCC remain a devastating disease. Oropharynx cancers are mainly attributed to infection with human papillomavirus (HPV). Since 2017, HNSCC are classified into HPV-negative and HPV-positive HNSCC. Therapies in the clinic are surgery and/or radiotherapy (RT)/radiochemotherapy (RCT) with a recurrence of about 50%. Therefore, new approaches are needed to improve long-term remission and patient survival. Recent advances in high-throughput molecular profiling have helped to identify genetic dispositions (TP53, FAT1, CDKNA2, NOTCH1 etc.) for HNSCC. We have generated and characterized a diverse panel (n = 85) of patient-derived xenografts (PDX) of HNSCC for preclinical research and immuno-oncological approaches. We successfully established 85 HNSCC PDX from fresh surgery tissue of primary and recurrent carcinomas by direct subcutaneous transplantation into immune-deficient mice. 14 HNSCC PDX were derived from HPV-positive tumors. We treated 28 HNSCC PDX with radiotherapy alone or radiochemotherapy. Heterogeneous individual responses to treatments resemble the clinical situation. For characterization, the established HNSCC PDX were treated with standard of cares (SoC) such as docetaxel, platinum compounds, cetuximab, 5-fluorouracil and investigational drugs. No correlation was observed to mutations and drug response. Molecular subtypes were determined based on gene expression data. Cetuximab response was associated with basal subtype and inflamed/mesenchymal subtype was negative predictive for cetuximab response. Further, 30 HNSCC PDX were analyzed for PD-L1 expression as suitable candidates for the evaluation of checkpoints inhibitors such as nivolumab and pembrolizumab and other novel immunotherapy approaches on humanized mice. To gain a deeper insight into the molecular biology, RNA sequencing was performed for 46 HNSCC models. Based on RNASeq data, an individual Human Leucocyte Antigen (HLA) profile of each PDX was determined and a comprehensive HLA matching analysis of the HNSCC models and 9 peripheral blood mononuclear cell (PBMC) donors was performed to enable personalized, preclinical immuno-oncology studies. Our newly and comprehensively characterized HNSCC PDX panel enables the evaluation of new targeted and immunological therapies in preclinical phase II studies. It provides an exceptional platform for the identification and validation of new targets and enables the preclinical screening of new combinations in translational research. Citation Format: Bernadette Brzezicha, Theresia Conrad, Maria Stecklum, Michael Becker, Konrad Klinghammer, Jens Hoffmann. New patient-derived head and neck cancer xenograft (PDX) panel for drug development, immuno-oncology and translational research [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-070.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".