HER2 overexpression/amplification is linked with poor prognosis in early breast cancer. Co-expression of HER2 and HER3 is associated with endocrine and chemotherapy resistance, driven not simply by expression but by signalling via HER2:HER3 or HER2:HER2 dimers. Proximity ligation assays (PLAs) detect protein-protein complexes at a single-molecule level and allow study of signalling pathways in situ. A cohort of 100 tumours was analyzed by PLA, IHC and FISH. HER complexes were analyzed by PLA in a further 321 tumours from the BR9601 trial comparing cyclophosphamide, methotrexate and fluorouracil (CMF) with epirubicin followed by CMF (epi-CMF). The relationships between HER dimer expression and RFS and OS were investigated, and multivariate regression analysis identified factors influencing patient prognosis. PLA successfully and reproducibly detected HER2:HER2 and HER2:HER3 protein complexes in vivo. A significant association (P < 0.00001) was identified between HER2 homodimerization and HER2 gene amplification. Following a minimum p value approach high levels of HER2:HER2 dimers were significantly associated with reduced relapse-free (RFS; hazard ratio = 1.72, 95% confidence interval 1.15-2.56, P = 0.008) and overall survival (OS HR = 1.69 95% CI = 1.09-2.62, P = 0.019). Similarly, high levels of HER2:HER3 dimers were associated with reduced RFS (HR = 2.18, 95% CI = 1.46-3.26, P = 0.00016) and OS (HR = 2.21, 95% CI = 1.41-3.47, P = 0.001). This study demonstrates that in situ detection of HER2 and HER2:3 protein:protein complexes can be performed robustly and reproducibly in clinical specimens, provides novel prognostic information and opens a significant novel opportunity to probe the clinical impact of cellular signalling processes.

BackgroundA key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison.ResultsIn this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded tissues; we tested our algorithm with both deep targeted and whole-exome sequencing data. Our algorithm correctly classified between 95 and 98% of somatic mutations with F1-measure ranges from 75.9 to 98.6% depending on the tumor type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues).ConclusionsIn this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0446-9) contains supplementary material, which is available to authorized users.

Background:Protein tyrosine kinase 6 (PTK6; breast tumour kinase) is overexpressed in up to 86% of the invasive breast cancers, and its association with the oncoprotein human epidermal growth factor receptor 2 (HER2) was shown in vitro by co-precipitation. Furthermore, expression of PTK6 in tumours is linked with the expression of HER2.Method and results:In this study, we used the proximity ligation assay (PLA) technique on formalin-fixed paraffin sections from eighty invasive breast carcinoma tissue specimens to locate PTK6–HER2 protein–protein complexes. Proximity ligation assay signals from protein complexes were assessed quantitatively, and expression levels showed a statistically significant association with tumour size (P=0.015) and course of the cancer disease (P=0.012).Conclusion:Protein tyrosine kinase 6 forms protein complexes with HER2 in primary breast cancer tissues, which can be visualised by use of the PLA technique. Human epidermal growth factor receptor 2–PTK6 complexes are of prognostic relevance.