Implications of Increasing College Attainment for Aging in General Equilibrium.

We develop an overlapping generations general equilibrium model of the U.S. economy with heterogeneous consumers who face idiosyncratic earnings and health risk to study the implications of increasing college attainment, decreasing fertility, and increasing longevity (2005-2100). While all three trends contribute to a higher old age dependency ratio, increasing college attainment has different implications because it increases labor productivity. Decreasing fertility and increasing longevity require the government to increase the average labor tax rate from 33.5 to 47.1 percent. Increasing college attainment lowers the required tax increase by 12.0 percentage points. The labor tax rate required to balance the government budget is higher under general equilibrium than in a small open economy with a constant interest rate, because the reduction in the interest rate lowers capital income tax revenues.

Dysregulation of MITF Leads to Transformation in MC1R-Defective Melanocytes.

The MC1R/cAMP/MITF pathway is a key determinant for growth, differentiation, and survival of melanocytes and melanoma. MITF-M is the melanocyte-specific isoform of Microphthalmia-associated Transcription Factor (MITF) in human melanoma. Here we use two melanocyte cell lines to show that forced expression of hemagglutinin (HA) -tagged MITF-M through lentiviral transduction represents an oncogenic insult leading to consistent cell transformation of the immortalized melanocyte cell line Hermes 4C, being a melanocortin-1 receptor (MC1R) compound heterozygote, while not causing transformation of the MC1R wild type cell line Hermes 3C. The transformed HA-tagged MITF-M transduced Hermes 4C cells form colonies in soft agar and tumors in mice. Further, Hermes 4C cells display increased MITF chromatin binding, and transcriptional reprogramming consistent with an invasive melanoma phenotype. Mechanistically, forced expression of MITF-M drives the upregulation of the AXL tyrosine receptor kinase (AXL), with concomitant downregulation of phosphatase and tensin homolog (PTEN), leading to increased activation of the PI3K/AKT pathway. Treatment with AXL inhibitors reduces growth of the transformed cells by reverting AKT activation. In conclusion, we present a model system of melanoma development, driven by MITF-M in the context of MC1R loss of function, and independent of UV exposure. This model provides a basis for further studies of critical changes in the melanocyte transformation process.

Tankyrase inhibition sensitizes melanoma to PD-1 immune checkpoint blockade in syngeneic mouse models.

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/ß-catenin signaling-mediated immune evasion is found in a subset of cancers including melanoma. Currently, there are no therapeutic strategies available for targeting WNT/ß-catenin signaling. Here we show that a specific small-molecule tankyrase inhibitor, G007-LK, decreases WNT/ß-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 ß-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 ß-catenin-mediated resistance to immune checkpoint blockade in melanoma.

No association to sudden infant death syndrome detected by targeted amplicon sequencing of 24 genes.

AIM: The aim was to identify genetic variants associated with sudden infant death syndrome (SIDS) that can cause disease or introduce vulnerability. Genes reported in a previous SIDS study to have altered messenger ribonucleic acid (mRNA) expression in SIDS were investigated. METHODS: Samples from 81 SIDS (56 male/28 female) with a median age of 4 months (range 0.75-9 months) were analysed using Illumina TruSeq custom amplicon for 24 selected genes. Samples were collected from autopsy at Oslo university hospital from children whom died suddenly and unexpectedly from 1988 to 2006. The controls were the germline variation database, Norgene (no description of cases available). RESULTS: After filtering for rare variants, there were a total of 38 variants in the 81 SIDS cases and 462 variants in the 789 controls. After the filtration and curation steps, we found 36 rare variants. The overall occurrence of rare variants for all the SIDS samples was lower than for the Norgene population. CONCLUSION: There was no association between rare variants in the included genes and SIDS. Although not statistically significant, two of the SIDS cases had a rare variant in the MyD88 gene: rs746651350, rs200424253.

Molecularly matched therapy in the context of sensitivity, resistance, and safety; patient outcomes in end-stage cancer - the MetAction study.

Background: In precision cancer medicine, the challenge is to prioritize DNA driver events, account for resistance markers, and procure sufficient information for treatment that maintains patient safety. The MetAction project, exploring how tumor molecular vulnerabilities predict therapy response, first established the required workflow for DNA sequencing and data interpretation (2014-2015). Here, we employed it to identify molecularly matched therapy and recorded outcome in end-stage cancer (2016-2019).Material and methods: Metastatic tissue from 26 patients (16 colorectal cancer cases) was sequenced by the Oncomine assay. The study tumor boards interpreted called variants with respect to sensitivity or resistance to matched therapy and recommended single-agent or combination treatment if considered tolerable. The primary endpoint was the rate of progression-free survival 1.3-fold longer than for the most recent systemic therapy. The objective response rate and overall survival were secondary endpoints.Results: Both common and rare actionable alterations were identified. Thirteen patients were found eligible for therapy following review of tumor sensitivity and resistance variants and patient tolerability. The interventions were inhibitors of ALK/ROS1-, BRAF-, EGFR-, FGFR-, mTOR-, PARP-, or PD-1-mediated signaling for 2-3 cases each. Among 10 patients who received treatment until radiologic evaluation, 6 (46% of the eligible cases) met the primary endpoint. Four colorectal cancer patients (15% of the total study cohort) had objective response. The only serious adverse event was a transient colitis, which appeared in 1 of the 2 patients given PD-1 inhibitor with complete response. Apart from those two, overall survival was similar for patients who did and did not receive study treatment.Conclusions: The systematic MetAction approach may point forward to a refined framework for how to interpret the complexity of sensitivity versus resistance and patient safety that resides in tumor sequence data, for the possibly improved outcome of precision cancer medicine in future studies. ClinicalTrials.gov, identifier: NCT02142036.

Responsiveness to PD-1 Blockade in End-Stage Colon Cancer with Gene Locus 9p24.1 Copy-Number Gain.

Most patients whose large bowel cancer has spread to other organs do not respond to immune therapy. We detected a rare gene mutation, termed 9p24.1 copy-number gain (CNG), in an otherwise incurable colorectal cancer that provoked an immune therapy response. We identified this gene mutation by gene-panel sequencing of DNA from a liver metastasis biopsy from a patient who had disease refractory to standard therapies. Following immune checkpoint blockade (ICB) with pembrolizumab (anti-PD-1), the patient experienced conversion of the tumor phenotype from one with epithelial features to that of an inflamed microenvironment, detected by high-resolution RNA sequencing. Circulating tumor DNA disappeared over the first weeks of therapy. As assessed by standard radiographic measurement, the patient had a partial response that was durable. This patient's response may support the use of histology-agnostic ICB in solid tumors that carry the rare 9p24.1 CNG.

Implementing precision cancer medicine in the public health services of Norway: the diagnostic infrastructure and a cost estimate.

OBJECTIVE: Through 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. METHODS: An 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. RESULTS: The 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. CONCLUSIONS: The 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.

Th17 Polarization under Hypoxia Results in Increased IL-10 Production in a Pathogen-Independent Manner.

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.

Subtype-specific micro-RNA expression signatures in breast cancer progression.

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 alterations of expression included upregulation of miR-21-5p and the miR-200 family and downregulation of let-7 family members in DCIS samples. The signatures proposed here can form the basis for studies exploring DCIS samples with increased invasive potential and serum biomarkers for in situ and invasive breast cancer.

Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses.

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.

Identifying pathogenic processes by integrating microarray data with prior knowledge.

BACKGROUND: It is of great importance to identify molecular processes and pathways that are involved in disease etiology. Although there has been an extensive use of various high-throughput methods for this task, pathogenic pathways are still not completely understood. Often the set of genes or proteins identified as altered in genome-wide screens show a poor overlap with canonical disease pathways. These findings are difficult to interpret, yet crucial in order to improve the understanding of the molecular processes underlying the disease progression. We present a novel method for identifying groups of connected molecules from a set of differentially expressed genes. These groups represent functional modules sharing common cellular function and involve signaling and regulatory events. Specifically, our method makes use of Bayesian statistics to identify groups of co-regulated genes based on the microarray data, where external information about molecular interactions and connections are used as priors in the group assignments. Markov chain Monte Carlo sampling is used to search for the most reliable grouping. RESULTS: Simulation results showed that the method improved the ability of identifying correct groups compared to traditional clustering, especially for small sample sizes. Applied to a microarray heart failure dataset the method found one large cluster with several genes important for the structure of the extracellular matrix and a smaller group with many genes involved in carbohydrate metabolism. The method was also applied to a microarray dataset on melanoma cancer patients with or without metastasis, where the main cluster was dominated by genes related to keratinocyte differentiation. CONCLUSION: Our method found clusters overlapping with known pathogenic processes, but also pointed to new connections extending beyond the classical pathways.

The Genomic HyperBrowser: an analysis web server for genome-scale data.

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 differential disease regulome.

BACKGROUND: Transcription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information. RESULTS: We here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format.The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF.The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available. CONCLUSION: We have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at http://hyperbrowser.uio.no.

Immunological network signatures of cancer progression and survival.

BACKGROUND: The 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. METHODS: To 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. RESULTS: The 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. CONCLUSIONS: The 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.

The Genomic HyperBrowser: inferential genomics at the sequence level.

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.

FigSearch: a figure legend indexing and classification system.

UNLABELLED: FigSearch is a prototype text-mining and classification system for figures from any corpus of full-text biological papers. The system allows users to search for figures that contain genes of interest and illustrate protein interactions. The retrieved figures are ranked by a score representing the likelihood to be of a certain type, in this case, schematic illustrations of protein interactions and signaling events. The system contains a Web interface for search, a module for classification of figures based on vector representations of figure legends and a module for indexing gene names. In a preliminary validation, the FigSearch system showed satisfactory performance according to domain experts in providing the most relevant graphical representations. This strategy may be easily extended to other figure types. Moreover, as more full-text data become available, such a system will find increased usefulness in identifying and presenting compressed biological knowledge. AVAILABILITY: A searchable Web interface, FigSearch, is accessible via http://pubgeneserver.uio.no/figsearch/ for all figures from the available corpus.