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Personalised cancer treatment depends on identification of therapeutically relevant biological subgroups of patients for assessing effect of treatment and to discover new therapeutic options. By analyses in heterogeneous patient populations, the effects may be lost in noise. Squamous cell carcinoma of the lung is a major killer worldwide. Despite recent advances, mortality is high and response to therapies varies greatly from patient to patient. Target search in biologically relevant subgroups may identify treatment options not so far discovered. A total of 198 patients undergoing surgery for squamous cell carcinomas of the lung were included in the study. The tumours were analysed for copy number alterations (n = 152) and gene expression from tumour (n = 188) and normal lung (n = 21), with both data levels present in 140 patients. We studied alterations in tumours harbouring mutations in TP53 and in previously published gene expression subtypes. Genes with consistent alterations in both genomic levels were identified as putative biomarkers. Results were validated in TCGA. The most convincing biomarker in TP53 mutated squamous cell carcinomas of the lung was BIRC5 with amplification in 36% of mutated samples, 5% in wild-type samples and a 17%-fold change of expression between TP53 mutated tumours and normal lung tissue. BIRC5 was significantly altered in the classical and primitive subtypes. We suggest BIRC5 as a putative predictive biomarker and putative druggable target in squamous cell lung carcinomas harbouring TP53 mutation or classified as classical and primitive subtypes., (© 2020 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancer types. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research, and provide a foundation for future development of cancer-specific compendia.

Background: The introduction of immune check-point inhibition in non-small cell lung cancer (NSCLC) therapy represents improved prospects for the patients. The response rates to check-point inhibitors are approximately 20% in unselected NSCLC patients. Increasing levels of tumor PD-L1 expression are associated with higher response rates. However, patients with low PD-L1 levels may also have durable responses, and improved strategies for patient stratification are needed., Material and Methods: In this study, we investigated circulating microRNAs aiming to identify circulating predictive biomarkers associated with increased overall survival after immune check-point treatment. Using next generation sequencing, we performed microRNA profiling in serum from NSCLC patients (n = 20) treated with nivolumab. Serum samples from 31 patients were used for validation using qPCR assays. Serum samples were collected prior to immune therapy initiation., Results: Based on multivariate regression analysis, we identified a signature of seven microRNAs (miR-215-5p, miR-411-3p, miR-493-5p, miR-494-3p, miR-495-3p, miR-548j-5p and miR-93-3p) significantly associated with overall survival (OS) > 6 months in discovery cohort (p = .0003). We further validated this in another similar set of samples (n = 31) and the model was significantly associated with overall survival (OS) > 6 months (p = .001) with sensitivity and specificity of 71% and 90%, respectively., Conclusions: In this study of circulating microRNAs, we have identified a 7-miR signature associated with survival in nivolumab-treated NSCLC patients. This signature may lead to better treatment options for patients with NSCLC, but a validation in an independent cohort is needed to confirm the predicted potential.

It has been hypothesized based on accumulated data that a class of small noncoding RNAs, termed microRNAs, are key factors in intercellular communication. Here, microRNAs present in interstitial breast tumor fluids have been analyzed to identify relevant markers for a diagnosis of breast cancer and to elucidate the cross-talk that exists among cells in a tumor microenvironment. Matched tumor interstitial fluid samples (TIF, n = 60), normal interstitial fluid samples (NIF, n = 51), corresponding tumor tissue specimens (n = 54), and serum samples (n = 27) were collected from patients with breast cancer, and detectable microRNAs were analyzed and compared. In addition, serum data from 32 patients with breast cancer and 22 healthy controls were obtained for a validation study. To identify potential serum biomarkers of breast cancer, first the microRNA profiles of TIF and NIF samples were compared. A total of 266 microRNAs were present at higher level in the TIF samples as compared to normal counterparts. Sixty-one of these microRNAs were present in > 75% of the serum samples and were subsequently tested in a validation set. Seven of the 61 microRNAs were associated with poor survival, while 23 were associated with the presence of immune cells and adipocytes. To our knowledge, these data demonstrate for the first time that profiling of microRNAs in TIF can identify novel biomarkers for the prognostic classification and detection of breast cancer. In addition, the present findings demonstrate that microRNAs may represent the cross-talk that occurs between tumor cells and their surrounding stroma., (© 2016 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.)