Karolina Edlund

Tissue-based map of the human proteome

Protein expression across human tissues Sequencing the human genome gave new insights into human biology and disease. However, the ultimate goal is to understand the dynamic expression of each of the approximately 20,000 protein-coding genes and the function of each protein. Uhlén et al. now present a map of protein expression across 32 human tissues. They not only measured expression at an RNA level, but also used antibody profiling to precisely localize the corresponding proteins. An interactive website allows exploration of expression patterns across the human body. Science, this issue 10.1126/science.1260419 Transcriptomics and immunohistochemistry map protein expression across 32 human tissues. INTRODUCTION Resolving the molecular details of proteome variation in the different tissues and organs of the human body would greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on quantitative transcriptomics on a tissue and organ level combined with protein profiling using microarray-based immunohistochemistry to achieve spatial localization of proteins down to the single-cell level. We provide a global analysis of the secreted and membrane proteins, as well as an analysis of the expression profiles for all proteins targeted by pharmaceutical drugs and proteins implicated in cancer. RATIONALE We have used an integrative omics approach to study the spatial human proteome. Samples representing all major tissues and organs (n = 44) in the human body have been analyzed based on 24,028 antibodies corresponding to 16,975 protein-encoding genes, complemented with RNA-sequencing data for 32 of the tissues. The antibodies have been used to produce more than 13 million tissue-based immunohistochemistry images, each annotated by pathologists for all sampled tissues. To facilitate integration with other biological resources, all data are available for download and cross-referencing. RESULTS We report a genome-wide analysis of the tissue specificity of RNA and protein expression covering more than 90% of the putative protein-coding genes, complemented with analyses of various subproteomes, such as predicted secreted proteins (n = 3171) and membrane-bound proteins (n = 5570). The analysis shows that almost half of the genes are expressed in all analyzed tissues, which suggests that the gene products are needed in all cells to maintain “housekeeping” functions such as cell growth, energy generation, and basic metabolism. Furthermore, there is enrichment in metabolism among these genes, as 60% of all metabolic enzymes are expressed in all analyzed tissues. The largest number of tissue-enriched genes is found in the testis, followed by the brain and the liver. Analysis of the 618 proteins targeted by clinically approved drugs unexpectedly showed that 30% are expressed in all analyzed tissues. An analysis of metabolic activity based on genome-scale metabolic models (GEMS) revealed liver as the most metabolically active tissue, followed by adipose tissue and skeletal muscle. CONCLUSIONS A freely available interactive resource is presented as part of the Human Protein Atlas portal (www.proteinatlas.org), offering the possibility to explore the tissue-elevated proteomes in tissues and organs and to analyze tissue profiles for specific protein classes. Comprehensive lists of proteins expressed at elevated levels in the different tissues have been compiled to provide a spatial context with localization of the proteins in the subcompartments of each tissue and organ down to the single-cell level. The human tissue–enriched proteins. All tissue-enriched proteins are shown for 13 representative tissues or groups of tissues, stratified according to their predicted subcellular localization. Enriched proteins are mainly intracellular in testis, mainly membrane bound in brain and kidney, and mainly secreted in pancreas and liver. Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray–based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics.

Global classification of the human proteins with regards to spatial expression patterns across organs and tissues is important for studies of human biology and disease. Here, we used a quantitative transcriptomics analysis (RNA-Seq) to classify the tissue-specific expression of genes across a representative set of all major human organs and tissues and combined this analysis with antibody-based profiling of the same tissues. To present the data, we launch a new version of the Human Protein Atlas that integrates RNA and protein expression data corresponding to ∼80% of the human protein-coding genes with access to the primary data for both the RNA and the protein analysis on an individual gene level. We present a classification of all human protein-coding genes with regards to tissue-specificity and spatial expression pattern. The integrative human expression map can be used as a starting point to explore the molecular constituents of the human body.

A Comprehensive Analysis of Human Gene Expression Profiles Identifies Stromal Immunoglobulin κ C as a Compatible Prognostic Marker in Human Solid Tumors

Purpose: Although the central role of the immune system for tumor prognosis is generally accepted, a single robust marker is not yet available. Experimental Design: On the basis of receiver operating characteristic analyses, robust markers were identified from a 60-gene B cell–derived metagene and analyzed in gene expression profiles of 1,810 breast cancer; 1,056 non–small cell lung carcinoma (NSCLC); 513 colorectal; and 426 ovarian cancer patients. Protein and RNA levels were examined in paraffin-embedded tissue of 330 breast cancer patients. The cell types were identified with immunohistochemical costaining and confocal fluorescence microscopy. Results: We identified immunoglobulin κ C (IGKC) which as a single marker is similarly predictive and prognostic as the entire B-cell metagene. IGKC was consistently associated with metastasis-free survival across different molecular subtypes in node-negative breast cancer (n = 965) and predicted response to anthracycline-based neoadjuvant chemotherapy (n = 845; P < 0.001). In addition, IGKC gene expression was prognostic in NSCLC and colorectal cancer. No association was observed in ovarian cancer. IGKC protein expression was significantly associated with survival in paraffin-embedded tissues of 330 breast cancer patients. Tumor-infiltrating plasma cells were identified as the source of IGKC expression. Conclusion: Our findings provide IGKC as a novel diagnostic marker for risk stratification in human cancer and support concepts to exploit the humoral immune response for anticancer therapy. It could be validated in several independent cohorts and carried out similarly well in RNA from fresh frozen as well as from paraffin tissue and on protein level by immunostaining. Clin Cancer Res; 18(9); 2695–703. ©2012 AACR.

Biomarker Discovery in Non–Small Cell Lung Cancer: Integrating Gene Expression Profiling, Meta-analysis, and Tissue Microarray Validation

Purpose: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multigene signatures in clinical practice is unclear, and the biologic importance of individual genes is difficult to assess, as the published signatures virtually do not overlap. Experimental Design: Here, we describe a novel single institute cohort, including 196 non–small lung cancers (NSCLC) with clinical information and long-term follow-up. Gene expression array data were used as a training set to screen for single genes with prognostic impact. The top 450 probe sets identified using a univariate Cox regression model (significance level P < 0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n = 860). Results: The meta-analysis revealed 14 genes that were significantly associated with survival (P < 0.001) with a false discovery rate <1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on tissue microarrays from 2 independent NSCLC cohorts, altogether including 617 NSCLC samples. Low CADM1 protein expression was significantly associated with shorter survival, with particular influence in the adenocarcinoma patient subgroup. Conclusions: Using a novel NSCLC cohort together with a meta-analysis validation approach, we have identified a set of single genes with independent prognostic impact. One of these genes, CADM1, was further established as an immunohistochemical marker with a potential application in clinical diagnostics. Clin Cancer Res; 19(1); 194–204. ©2012 AACR.

Targeted resequencing of candidate genes using Selector Probes

Targeted genome enrichment is a powerful tool for making use of the massive throughput of novel DNA-sequencing instruments. We herein present a simple and scalable protocol for multiplex amplification of target regions based on the Selector technique. The updated version exhibits improved coverage and compatibility with next-generation-sequencing (NGS) library-construction procedures for shotgun sequencing with NGS platforms. To demonstrate the performance of the technique, all 501 exons from 28 genes frequently involved in cancer were enriched for and sequenced in specimens derived from cell lines and tumor biopsies. DNA from both fresh frozen and formalin-fixed paraffin-embedded biopsies were analyzed and 94% specificity and 98% coverage of the targeted region was achieved. Reproducibility between replicates was high (R2 = 0, 98) and readily enabled detection of copy-number variations. The procedure can be carried out in <24 h and does not require any dedicated instrumentation.

Forkhead box F1 regulates tumor-promoting properties of cancer-associated fibroblasts in lung cancer.

Cancer-associated fibroblasts (CAF) attract increasing attention as potential cancer drug targets due to their ability to stimulate, for example, tumor growth, invasion, angiogenesis, and metastasis. However, the molecular mechanisms causing the tumor-promoting properties of CAFs remain poorly understood. Forkhead box F1 (FoxF1) is a mesenchymal target of hedgehog signaling, known to regulate mesenchymal-epithelial interactions during lung development. Studies with FoxF1 gain- and loss-of-function fibroblasts revealed that FoxF1 regulates the contractility of fibroblasts, their production of hepatocyte growth factor and fibroblast growth factor-2, and their stimulation of lung cancer cell migration. FoxF1 status of fibroblasts was also shown to control the ability of fibroblasts to stimulate xenografted tumor growth. FoxF1 was expressed in CAFs of human lung cancer and associated with activation of hedgehog signaling. These observations suggest that hedgehog-dependent FoxF1 is a clinically relevant lung CAF-inducing factor, and support experimentally the general concept that CAF properties can be induced by activation of developmentally important transcription factors.

Impact of thawing on RNA integrity and gene expression analysis in fresh frozen tissue

Biobanks of fresh, unfixed human tissue represent a valuable source for gene expression analysis in translational research and molecular pathology. The aim of this study was to evaluate the impact of thawing on RNA integrity and gene expression in fresh frozen tissue specimens. Portions of snap frozen tonsil tissue, unfixed or immersed in RNAlater, were thawed at room temperature for 0 minute, 5 minutes, 30 minutes, 45 minutes, 1 hour, 3 hours, 6 hours, and 16 hours before RNA extraction. Additionally, tonsil tissue underwent repetitive freezing and thawing cycles. RNA integrity was analyzed by microchip gel electrophoresis and gene expression by quantitative real-time polymerase chain reaction for selected genes (FOS, TGFB1, HIF1A, BCL2, and PCNA). Minimal RNA degradation was detected after 30 minutes of thawing in unfixed samples. This degradation was accompanied by relevant changes in gene expression for FOS and BCL2 at 45 minutes. Modified primer design or the use of different housekeeping genes could not rectify the changes for FOS. Repetitive thawing cycles had similar effects on RNA integrity. The incubation of the tissue in RNAlater efficiently prevented RNA degradation. In conclusion, degradation of RNA in frozen tissue occurs first after several minutes of thawing. Already minimal decrease in RNA quality may result in significant changes in gene expression patterns in clinical tissue samples.

Data-driven unbiased curation of the TP53 tumor suppressor gene mutation database and validation by ultradeep sequencing of human tumors

Cancer mutation databases are expected to play central roles in personalized medicine by providing targets for drug development and biomarkers to tailor treatments to each patient. The accuracy of reported mutations is a critical issue that is commonly overlooked, which leads to mutation databases that include a sizable number of spurious mutations, either sequencing errors or passenger mutations. Here we report an analysis of the latest version of the TP53 mutation database, including 34,453 mutations. By using several data-driven methods on multiple independent quality criteria, we obtained a quality score for each report contributing to the database. This score can now be used to filter for high-confidence mutations and reports within the database. Sequencing the entire TP53 gene from various types of cancer using next-generation sequencing with ultradeep coverage validated our approach for curation. In summary, 9.7% of all collected studies, mostly comprising numerous tumors with multiple infrequent TP53 mutations, should be excluded when analyzing TP53 mutations. Thus, by combining statistical and experimental analyses, we provide a curated mutation database for TP53 mutations and a framework for mutation database analysis.

The prognostic relevance of tumour-infiltrating plasma cells and immunoglobulin kappa C indicates an important role of the humoral immune response in non-small cell lung cancer

A prognostic impact of immunoglobulin kappa C (IGKC) expression has been described in cancer. We analysed the influence of B-cell and plasma cell markers, as well as IGKC expression, in non-small lung cancer (NSCLC) using immunohistochemistry on a tissue microarray. IGKC protein expression was independently associated with longer survival, with particular impact in the adenocarcinoma subgroup. Moreover, a correlation was seen with CD138+ cells, but not with CD20. CD138 expression revealed a comparable association with survival. In conclusion, IGKC expression in stroma-infiltrating plasma cells is a prognostic marker in NSCLC, supporting emerging treatment concepts that exploit the humoral immune response.

KRAS analysis in colorectal carcinoma: Analytical aspects of Pyrosequencing and allele-specific PCR in clinical practice

BackgroundEpidermal growth factor receptor inhibitor therapy is now approved for treatment of metastatic colorectal carcinomas (CRC) in patients with tumors lacking KRAS mutations. Several procedures to detect KRAS mutations have been developed. However, the analytical sensitivity and specificity of these assays on routine clinical samples are not yet fully characterised.MethodsThe practical aspects and clinical applicability of a KRAS-assay based on Pyrosequencing were evaluated in a series of 314 consecutive CRC cases submitted for diagnostic KRAS analysis. The performance of Pyrosequencing compared to allele-specific, real-time PCR was then explored by a direct comparison of CE-IVD-marked versions of Pyrosequencing and TheraScreen (DxS) KRAS assays for a consecutive subset (n = 100) of the 314 clinical CRC samples.ResultsUsing Pyrosequencing, 39% of the 314 CRC samples were found KRAS-mutated and several of the mutations (8%) were located in codon 61. To explore the analytical sensitivity of the Pyrosequencing assay, mutated patient DNA was serially diluted with wild-type patient DNA. Dilutions corresponding to 1.25-2.5% tumor cells still revealed detectable mutation signals. In clinical practice, our algorithm for KRAS analysis includes a reanalysis of samples with low tumor cell content (< 10%, n = 56) using an independent assay (allele-specific PCR, DxS). All mutations identified by Pyrosequencing were then confirmed and, in addition, one more mutated sample was identified in this subset of 56 samples. Finally, a direct comparison of the two technologies was done by re-analysis of a subset (n = 100) of the clinical samples using CE-IVD-marked versions of Pyrosequencing and TheraScreen KRAS assays in a single blinded fashion. The number of samples for which the KRAS codon 12/13 mutation status could be defined using the Pyrosequencing or the TheraScreen assay was 94 and 91, respectively, and both assays detected the same number of codon 12 and 13 mutations.ConclusionsKRAS mutation detection using Pyrosequencing was evaluated on a consecutive set of clinical CRC samples. Pyrosequencing provided sufficient analytical sensitivity and specificity to assess the mutation status in routine formalin-fixed CRC samples, even in tissues with a low tumor cell content.