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.

BackgroundPhosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha, PIK3CA, is one of the most frequently mutated genes in breast cancer, and the mutation status of PIK3CA has clinical relevance related to response to therapy.The aim of our study was to investigate the mutation status of PIK3CA gene and to evaluate the concordance between NGS and SGS for the most important hotspot regions in exon 9 and 20, to investigate additional hotspots outside of these exons using NGS, and to correlate the PIK3CA mutation status with the clinicopathological characteristics of the cohort.MethodsIn the current study, next-generation sequencing (NGS) and Sanger Sequencing (SGS) was used for the mutational analysis of PIK3CA in 186 breast carcinomas.ResultsAltogether, 64 tumors had PIK3CA mutations, 55 of these mutations occurred in exons 9 and 20. Out of these 55 mutations, 52 could also be detected by Sanger sequencing resulting in a concordance of 98.4 % between the two sequencing methods. The three mutations missed by SGS had low variant frequencies below 10 %. Additionally, 4.8 % of the tumors had mutations in exons 1, 4, 7, and 13 of PIK3CA that were not detected by SGS. PIK3CA mutation status was significantly associated with hormone receptor-positivity, HER2-negativity, tumor grade, and lymph node involvement. However, there was no statistically significant association between the PIK3CA mutation status and overall survival.ConclusionsBased on our study, NGS is recommended as follows: 1) for correctly assessing the mutation status of PIK3CA in breast cancer, especially for cases with low tumor content, 2) for the detection of subclonal mutations, and 3) for simultaneous mutation detection in multiple exons.Electronic supplementary materialThe online version of this article (doi:10.1186/s12907-015-0020-6) contains supplementary material, which is available to authorized users.

Platinum-based drugs, in particular cisplatin (cis-diamminedichloridoplatinum(II), CDDP), are used for treatment of squamous cell carcinoma of the head and neck (SCCHN). Despite initial responses, CDDP treatment often results in chemoresistance, leading to therapeutic failure. The role of primary resistance at subclonal level and treatment-induced clonal selection in the development of CDDP resistance remains unknown. By applying targeted next-generation sequencing, fluorescence hybridization, microarray-based transcriptome, and mass spectrometry-based phosphoproteome analysis to the CDDP-sensitive SCCHN cell line FaDu, a CDDP-resistant subline, and single-cell derived subclones, the molecular basis of CDDP resistance was elucidated. The causal relationship between molecular features and resistant phenotypes was determined by siRNA-based gene silencing. The clinical relevance of molecular findings was validated in patients with SCCHN with recurrence after CDDP-based chemoradiation and the TCGA SCCHN dataset. Evidence of primary resistance at clonal level and clonal selection by long-term CDDP treatment was established in the FaDu model. Resistance was associated with aneuploidy of chromosome 17, increased copy-numbers and overexpression of the gain-of-function (GOF) mutant variant p53 siRNA-mediated knockdown established a causal relationship between mutant p53 and CDDP resistance. Resistant clones were also characterized by increased activity of the PI3K-AKT-mTOR pathway. The poor prognostic value of GOF variants and mTOR pathway upregulation was confirmed in the TCGA SCCHN cohort. Our study demonstrates a link of intratumoral heterogeneity and clonal evolution as important mechanisms of drug resistance in SCCHN and establishes mutant GOF variants and the PI3K/mTOR pathway as molecular targets for treatment optimization..