Removal of, or adjustment for, batch effects or center differences is generally required when such effects are present in data. In particular, when preparing microarray gene expression data from multiple cohorts, array platforms, or batches for later analyses, batch effects can have confounding effects, inducing spurious differences between study groups. Many methods and tools exist for removing batch effects from data. However, when study groups are not evenly distributed across batches, actual group differences may induce apparent batch differences, in which case batch adjustments may bias, usually deflate, group differences. Some tools therefore have the option of preserving the difference between study groups, e.g. using a two-way ANOVA model to simultaneously estimate both group and batch effects. Unfortunately, this approach may systematically induce incorrect group differences in downstream analyses when groups are distributed between the batches in an unbalanced manner. The scientific community seems to be largely unaware of how this approach may lead to false discoveries.

The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at http://hyperbrowser.uio.no.

Robust markers of invasiveness may help reduce the overtreatment of in situ carcinomas. Breast cancer is a heterogeneous disease and biological mechanisms for carcinogenesis vary between subtypes. Stratification by subtype is therefore necessary to identify relevant and robust signatures of invasive disease. We have identified microRNA (miRNA) alterations during breast cancer progression in two separate datasets and used stratification and external validation to strengthen the findings. We analyzed two separate datasets (METABRIC and AHUS) consisting of a total of 186 normal breast tissue samples, 18 ductal carcinoma in situ (DCIS) and 1,338 invasive breast carcinomas. Validation in a separate dataset and stratification by molecular subtypes based on immunohistochemistry, PAM50 and integrated cluster classifications were performed. We propose subtype-specific miRNA signatures of invasive carcinoma and a validated signature of DCIS. miRNAs included in the invasive signatures include downregulation of miR-139-5p in aggressive subtypes and upregulation of miR-29c-5p expression in the luminal subtypes. No miRNAs were differentially expressed in the transition from DCIS to invasive carcinomas on the whole, indicating the need for subtype stratification. A total of 27 miRNAs were included in our proposed DCIS signature. Significant Key words: miRNA, breast cancer invasion, biomarker, subtype, DCIS Abbreviations: DCIS: ductal carcinoma in situ; ER: estrogen receptor; FDR: false discovery rate; HER2: human epidermal growth factor 2; IC: integrated cluster; miRNA: microRNA; PAM50: prediction analysis of microarrays 50; PGR: progesterone receptor Additional Supporting Information may be found in the online version of this article.

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

The development of immune checkpoint inhibitors represents a major breakthrough in cancer therapy. Nevertheless, a substantial number of patients fail to respond to checkpoint pathway blockade. Evidence for WNT/β-catenin signaling-mediated immune evasion is found in a subset of cancers including melanoma. Currently, there are no therapeutic strategies available for targeting WNT/β-catenin signaling. Here we show that a specific small-molecule tankyrase inhibitor, G007-LK, decreases WNT/β-catenin and YAP signaling in the syngeneic murine B16-F10 and Clone M-3 melanoma models and sensitizes the tumors to anti-PD-1 immune checkpoint therapy. Mechanistically, we demonstrate that the synergistic effect of tankyrase and checkpoint inhibitor treatment is dependent on loss of β-catenin in the tumor cells, anti-PD-1-stimulated infiltration of T cells into the tumor and induction of an IFNγ-and CD8 + T cell-mediated anti-tumor immune response. Our study uncovers a combinatorial therapeutical strategy using tankyrase inhibition to overcome β-catenin-mediated resistance to immune checkpoint blockade in melanoma.

The IL-17-producing CD4+ T helper cell (Th17) differentiation is affected by stimulation of the aryl hydrocarbon receptor (AhR) pathway and by hypoxia-inducible factor 1 alpha (HIF-1α). In some cases, Th17 become non-pathogenic and produce IL-10. However, the initiating events triggering this phenotype are yet to be fully understood. Here, we show that such cells may be differentiated at low oxygen and regardless of AhR ligand treatment such as cigarette smoke extract. Hypoxia led to marked alterations of the transcriptome of IL-10-producing Th17 cells affecting genes involved in metabolic, anti-apoptotic, cell cycle, and T cell functional pathways. Moreover, we show that oxygen regulates the expression of CD52, which is a cell surface protein that has been shown to suppress the activation of other T cells upon release. Taken together, these findings suggest a novel ability for Th17 cells to regulate immune responses in vivo in an oxygen-dependent fashion.

ObjectiveThrough the conduct of an individual-based intervention study, the main purpose of this project was to build and evaluate the required infrastructure that may enable routine practice of precision cancer medicine in the public health services of Norway, including modelling of costs.MethodsAn eligible patient had end-stage metastatic disease from a solid tumour. Metastatic tissue was analysed by DNA sequencing, using a 50-gene panel and a study-generated pipeline for analysis of sequence data, supplemented with fluorescence in situ hybridisation to cover relevant biomarkers. Cost estimations compared best supportive care, biomarker-agnostic treatment with a molecularly targeted agent and biomarker-based treatment with such a drug. These included costs for medication, outpatient clinic visits, admission from adverse events and the biomarker-based procedures.ResultsThe diagnostic procedures, which comprised sampling of metastatic tissue, mutation analysis and data interpretation at the Molecular Tumor Board before integration with clinical data at the Clinical Tumor Board, were completed in median 18 (8-39) days for the 22 study patients. The 23 invasive procedures (12 from liver, 6 from lung, 5 from other sites) caused a single adverse event (pneumothorax). Per patient, 0–5 mutations were detected in metastatic tumours; however, no actionable target case was identified for the current single-agent therapy approach. Based on the cost modelling, the biomarker-based approach was 2.5-fold more costly than best supportive care and 2.5-fold less costly than the biomarker-agnostic option.ConclusionsThe first project phase established a comprehensive diagnostic infrastructure for precision cancer medicine, which enabled expedite and safe mutation profiling of metastatic tumours and data interpretation at multidisciplinary tumour boards for patients with end-stage cancer. Furthermore, it prepared for protocol amendments, recently approved by the designated authorities for the second study phase, allowing more comprehensive mutation analysis and opportunities to define therapy targets.

BackgroundThe immune contribution to cancer progression is complex and difficult to characterize. For example in tumors, immune gene expression is detected from the combination of normal, tumor and immune cells in the tumor microenvironment. Profiling the immune component of tumors may facilitate the characterization of the poorly understood roles immunity plays in cancer progression. However, the current approaches to analyze the immune component of a tumor rely on incomplete identification of immune factors.MethodsTo facilitate a more comprehensive approach, we created a ranked immunological relevance score for all human genes, developed using a novel strategy that combines text mining and information theory. We used this score to assign an immunological grade to gene expression profiles, and thereby quantify the immunological component of tumors. This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies. To further characterize immunological relevance for genes, the relevance score was charted against both the human interactome and cancer information, forming an expanded interactome landscape of tumor immunity. We applied this approach to expression profiles in melanomas, thus identifying and grading their immunological components, followed by identification of their associated protein interactions.ResultsThe power of this strategy was demonstrated by the observation of early activation of the adaptive immune response and the diversity of the immune component during melanoma progression. Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival.ConclusionsThe assignment of a ranked immunological relevance score to all human genes extends the content of existing immune gene resources and enriches our understanding of immune involvement in complex biological networks. The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease. In so doing, it stratifies patients according to their immune profiles, which may lead to effective computational prognostic and clinical guides.