Are Fusion Transcripts in Relapsed/ Metastatic Head and Neck Cancer Patients Predictive of Response to Anti-EGFR Therapies?

Prediction of benefit from combined chemotherapy and the antiepidermal growth factor receptor cetuximab is a not yet solved question in head and neck squamous cell carcinoma (HNSCC). In a selected series of 14 long progression-free survival (PFS) and 26 short PFS patients by whole gene and microRNA expression analysis, we developed a model potentially predictive of cetuximab sensitivity. To better decipher the "omics" profile of our patients, we detected transcript fusions by RNA-seq through a Pan-Cancer panel targeting 1385 cancer genes. Twenty-seven different fusion transcripts, involving mRNA and long noncoding RNA (lncRNA), were identified. The majority of fusions (81%) were intrachromosomal, and 24 patients (60%) harbor at least one of them. The presence/absence of fusions and the presence of more than one fusion were not related to outcome, while the lncRNA-containing fusions resulted enriched in long PFS patients (P= 0.0027). The CD274-PDCD1LG2 fusion was present in 7/14 short PFS patients harboring fusions and was absent in long PFS patients (P= 0.0188). Among the short PFS patients, those harboring this fusion had the worst outcome (P= 0.0172) and increased K-RAS activation (P= 0.00147). The associations between HNSCC patient's outcome following cetuximab treatment and lncRNA-containing fusions or the CD274-PDCD1LG2 fusion deserve validation in prospective clinical trials

A ct-based radiomic signature can be prognostic for 10-months overall survival in metastatic tumors treated with nivolumab: An exploratory study

Baseline clinical prognostic factors for recurrent and/or metastatic (RM) head and neck squamous cell carcinoma (HNSCC) treated with immunotherapy are lacking. CT-based radiomics may provide additional prognostic information. A total of 85 patients with RM-HNSCC were enrolled for this study. For each tumor, radiomic features were extracted from the segmentation of the largest tumor mass. A pipeline including different feature selection steps was used to train a radiomic signature prognostic for 10-month overall survival (OS). Features were selected based on their stability to geometrical transformation of the segmentation (intraclass correlation coefficient, ICC > 0.75) and their predictive power (area under the curve, AUC > 0.7). The predictive model was developed using the least absolute shrinkage and selection operator (LASSO) in combination with the support vector machine. The model was developed based on the first 68 enrolled patients and tested on the last 17 patients. Classification performance of the radiomic risk was evaluated accuracy and the AUC. The same metrics were computed for some baseline predictors used in clinical practice (volume of largest lesion, total tumor volume, number of tumor lesions, number of affected organs, performance status). The AUC in the test set was 0.67, while accuracy was 0.82. The performance of the radiomic score was higher than the one obtainable with the clinical variables (largest lesion volume: accuracy 0.59, AUC = 0.55; number of tumoral lesions: accuracy 0.71, AUC 0.36; number of affected organs: accuracy 0.47; AUC 0.42; total tumor volume: accuracy 0.59, AUC 0.53; performance status: accuracy 0.41, AUC = 0.47). Radiomics may provide additional baseline prognostic value compared to the variables used in clinical practice

Tumor biomarkers for the prediction of distant metastasis in head and neck squamous cell carcinoma

19openopenSalvatore Alfieri Andrea Carenzo, Francesca Platini, Mara S Serafini, Federica Perrone, Donata Galbiati, Andrea P Sponghini, Roberta Depenni, Andrea Vingiani, Pasquale Quattrone, Edoardo Marchesi, Maria F Iannó, Arianna Micali, Elisa Mancinelli, Ester Orlandi, Sara Marceglia, Laura D Locati, Lisa Licitra , Paolo Bossi, Loris De CeccoAlfieri Andrea Carenzo, Salvatore; Platini, Francesca; S Serafini, Mara; Perrone, Federica; Galbiati, Donata; P Sponghini, Andrea; Depenni, Roberta; Vingiani, Andrea; Quattrone, Pasquale; Marchesi, Edoardo; F Iannó, Maria; Micali, Arianna; Mancinelli, Elisa; Orlandi, Ester; Marceglia, Sara; D Locati, Laura; Licitra, Lisa; Bossi, Paolo; De Cecco, Lori