Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.

During the course of disease, many cancer patients eventually present with metastatic disease including peritoneal or pleural spread. In this context, cytology specimens derived from ascites or pleural effusion may help to differentiate malignant from benign conditions and sometimes yield diagnosis of a malignancy. However, even when supported by immunohistochemistry, cytological interpretation can be challenging, especially if tumor cellularity is low. Here, we investigated whether targeted deep sequencing of formalin-fixed and paraffin embedded (FFPE) cytology specimens of cancer patients is feasible, and has diagnostic and clinical impact. To this end, a cohort of 20 matched pairs was compiled, each comprising a cytology sample (FFPE cell block) and at least one biopsy/surgical resection specimen serving as benchmark. In addition, 5 non-malignant effusions were sequenced serving as negative-controls. All samples yielded sufficient libraries and were successfully subjected to targeted sequencing employing a semiconductor based next-generation sequencing platform. Using gene panels of different size and composition, including the Oncomine Comprehensive Assay, for targeted sequencing, somatic mutations were detected in the tissue of all 20 cases. Of these, 15 (75%) harbored mutations that were also detected in the corresponding cytology samples. In four of these cases (20%), additional private mutations were detected in either cytology or tissue samples, reflecting spatiotemporal tumor evolution. Of the five remaining cases, three (15%) showed wild type alleles in cytology material whereas tumor tissue had mutations in interrogated genes. Two cases were discordant, showing different private mutations in the cytology and in the tissue sample, respectively. In summary, sequencing of cytology specimens (FFPE cell block) reflecting spatiotemporal tumor evolution is feasible and yields adjunct genetic information that may be exploitable for diagnostics and therapy.

Histomorphology and immunohistochemistry are the most common ways of cancer classification in routine cancer diagnostics, but often reach their limits in determining the organ origin in metastasis. These cancers of unknown primary, which are mostly adenocarcinomas or squamous cell carcinomas, therefore require more sophisticated methodologies of classification. Here, we report a multiplex protein profiling-based approach for the classification of fresh frozen and formalin-fixed paraffin-embedded (FFPE) cancer tissue samples using the digital western blot technique DigiWest. A DigiWest-compatible FFPE extraction protocol was developed, and a total of 634 antibodies were tested in an initial set of 16 FFPE samples covering tumors from different origins. Of the 303 detected antibodies, 102 yielded significant correlation of signals in 25 pairs of fresh frozen and FFPE primary tumor samples, including head and neck squamous cell carcinomas (HNSC), lung squamous cell carcinomas (LUSC), lung adenocarcinomas (LUAD), colorectal adenocarcinomas (COAD), and pancreatic adenocarcinomas (PAAD). For this signature of 102 analytes (covering 88 total proteins and 14 phosphoproteins), a support vector machine (SVM) algorithm was developed. This allowed for the classification of the tissue of origin for all five tumor types studied here with high overall accuracies in both fresh frozen (90.4%) and FFPE (77.6%) samples. In addition, the SVM classifier reached an overall accuracy of 88% in an independent validation cohort of 25 FFPE tumor samples. Our results indicate that DigiWest-based protein profiling represents a valuable method for cancer classification, yielding conclusive and decisive data not only from fresh frozen specimens but also FFPE samples, thus making this approach attractive for routine clinical applications.