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Akt and mTORC1 signaling as predictive biomarkers for the EGFR antibody nimotuzumab in glioblastoma
(2018)
Glioblastoma (GB) is the most frequent primary brain tumor in adults with a dismal prognosis despite aggressive treatment including surgical resection, radiotherapy and chemotherapy with the alkylating agent temozolomide. Thus far, the successful implementation of the concept of targeted therapy where a drug targets a selective alteration in cancer cells was mainly limited to model diseases with identified genetic drivers. One of the most commonly altered oncogenic drivers of GB and therefore plausible therapeutic target is the epidermal growth factor receptor (EGFR). Trials targeting this signaling cascade, however, have been negative, including the phase III OSAG 101-BSA-05 trial. This highlights the need for further patient selection to identify subgroups of GB with true EGFR-dependency. In this retrospective analysis of treatment-naïve samples of the OSAG 101-BSA-05 trial cohort, we identify the EGFR signaling activity markers phosphorylated PRAS40 and phosphorylated ribosomal protein S6 as predictive markers for treatment efficacy of the EGFR-blocking antibody nimotuzumab in MGMT promoter unmethylated GBs. Considering the total trial population irrespective of MGMT status, a clear trend towards a survival benefit from nimotuzumab was already detectable when tumors had above median levels of phosphorylated ribosomal protein S6. These results could constitute a basis for further investigations of nimotuzumab or other EGFR- and downstream signaling inhibitors in selected patient cohorts using the reported criteria as candidate predictive biomarkers.
Comparative proteomics reveals a diagnostic signature for pulmonary head‐and‐neck cancer metastasis
(2018)
Patients with head‐and‐neck cancer can develop both lung metastasis and primary lung cancer during the course of their disease. Despite the clinical importance of discrimination, reliable diagnostic biomarkers are still lacking. Here, we have characterised a cohort of squamous cell lung (SQCLC) and head‐and‐neck (HNSCC) carcinomas by quantitative proteomics. In a training cohort, we quantified 4,957 proteins in 44 SQCLC and 30 HNSCC tumours. A total of 518 proteins were found to be differentially expressed between SQCLC and HNSCC, and some of these were identified as genetic dependencies in either of the two tumour types. Using supervised machine learning, we inferred a proteomic signature for the classification of squamous cell carcinomas as either SQCLC or HNSCC, with diagnostic accuracies of 90.5% and 86.8% in cross‐ and independent validations, respectively. Furthermore, application of this signature to a cohort of pulmonary squamous cell carcinomas of unknown origin leads to a significant prognostic separation. This study not only provides a diagnostic proteomic signature for classification of secondary lung tumours in HNSCC patients, but also represents a proteomic resource for HNSCC and SQCLC.