Establishment and characterization of patient-derived head and neck cancer models from surgical specimens and endoscopic biopsies

Abstract

Abstract Background Head and neck squamous cell carcinoma (HNSCC) is heterogeneous in etiology, phenotype and biology. Patient-derived xenografts (PDX) maintain morphology and molecular profiling of the original tumors and have become a standard “Avatar” model for human cancer research. However, restricted availability of tumor samples hindered the widespread use of PDX. Most PDX-projects include only surgical specimens because reliable engraftment from biopsies is missing. Therefore, sample collection is limited and excludes recurrent and metastatic, non-resectable cancer from preclinical models as well as future personalized medicine. Methods This study compares the PDX-take rate, -growth, histopathology, and molecular characteristics of endoscopic specimens with surgical specimens. HNSCC samples (n = 55) were collected ad hoc, fresh frozen and implanted into NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ mice. Results Engraftment was successful in both sample types. However, engraftment rate was lower (21 vs. 52%) and growth delayed (11.2 vs. 6.7 weeks) for endoscopic biopsies. Following engraftment, growth kinetic was similar. Comparisons of primary tumors and corresponding PDX models confirmed preservation of histomorphology (HE histology) and molecular profile (Illumina Cancer Hotspot Panel) of the patients’ tumors. Accompanying flow cytometry on primary tumor specimens revealed a heterogeneous tumor microenvironment among individual cases and identified M2-like macrophages as positive predictors for engraftment. Vice versa, a high PD-L1 expression (combined positive score on tumor/immune cells) predicted PDX rejection. Conclusion Including biopsy samples from locally advanced or metastatic lesions from patients with non-surgical treatment strategies, increases the availability of PDX for basic and translational research. This facilitates (pre-) clinical studies for individual response prediction based on immunological biomarkers.