Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell-intrinsic Stemness or Mesenchymality Characteristics.

Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or mesenchymality of tumors based on bulk tumor expression profiling. However, recent studies suggested that noncancerous cells from the microenvironment or macroenvironment heavily influence such signature profiles. Here, we strengthen these findings by investigating 11 published and frequently referenced gene expression signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. By analyses of bulk, single-cell, and pseudobulk expression data, we show that the cell type composition of a tumor sample frequently dominates scores of these EMT-related signatures. A comprehensive, integrated analysis of bulk RNA sequencing (RNA-seq) and single-cell RNA-seq data shows that stromal cells, most often fibroblasts, are the main drivers of EMT-related signature scores. We call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of "stemness subtypes" often result from low cancer cell content in tumor biopsies rather than cancer cell-specific stemness or mesenchymal/EMT characteristics. SIGNIFICANCE: Cancer self-renewal and migratory abilities are often characterized via gene module expression profiles, also called EMT or stemness gene expression signatures. Using published clinical tumor samples, cancer cell lines, and single cancer cells, we highlight the dominating influence of noncancer cells in low cancer cell content biopsies on their scores. We caution on their application for low cancer cell content clinical cancer samples with the intent to assign such characteristics or subtypes.

Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets.

Background: Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches. Results: We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to train random forest classifiers for immune cell type assignment for single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in random forest-based cell type classification. We further demonstrate how our approach helps to avoid bias in downstream statistical analyses by re-analysis of a published IFN stimulation experiment. Discussion and conclusion: We demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for significantly more genes compared to previous cell classification algorithms.

Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi-omics data from cell lines and patients.

Discovering synthetic lethal (SL) gene partners of cancer genes is an important step in developing cancer therapies. However, identification of SL interactions is challenging, due to a large number of possible gene pairs, inherent noise and confounding factors in the observed signal. To discover robust SL interactions, we devised SLIDE-VIP, a novel framework combining eight statistical tests, including a new patient data-based test iSurvLRT. SLIDE-VIP leverages multi-omics data from four different sources: gene inactivation cell line screens, cancer patient data, drug screens and gene pathways. We applied SLIDE-VIP to discover SL interactions between genes involved in DNA damage repair, chromatin remodeling and cell cycle, and their potentially druggable partners. The top 883 ranking SL candidates had strong evidence in cell line and patient data, 250-fold reducing the initial space of 200K pairs. Drug screen and pathway tests provided additional corroboration and insights into these interactions. We rediscovered well-known SL pairs such as RB1 and E2F3 or PRKDC and ATM, and in addition, proposed strong novel SL candidates such as PTEN and PIK3CB. In summary, SLIDE-VIP opens the door to the discovery of SL interactions with clinical potential. All analysis and visualizations are available via the online SLIDE-VIP WebApp.

CONET: copy number event tree model of evolutionary tumor history for single-cell data.

Copy number alterations constitute important phenomena in tumor evolution. Whole genome single-cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient, regularized MCMC procedure to search the space of possible model structures and parameters. We introduce a range of model priors and penalties for efficient regularization. CONET reveals copy number evolution in two breast cancer samples, and outperforms other methods in tree reconstruction, breakpoint identification and copy number calling.

Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines.

Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel interpretability approach, which in addition to the set of modeled features considers also the genes and processes outside of this set. Our approach outperforms simpler matrix factorization models, achieving R [Formula: see text] 0.82 correlation between true and predicted response for the unseen cell lines. The interpretability analysis identifies 67 biological processes that drive the cell line sensitivity to particular compounds. Detailed case studies are shown for PHA-793887, XMD14-99 and Dabrafenib.

RosettaSX: Reliable gene expression signature scoring of cancer models and patients.

Gene expression signatures have proven their potential to characterize important cancer phenomena like oncogenic signaling pathway activities, cellular origins of tumors, or immune cell infiltration into tumor tissues. Large collections of expression signatures provide the basis for their application to data sets, but the applicability of each signature in a new experimental context must be reassessed. We apply a methodology that utilizes the previously developed concept of coherent expression of genes in signatures to identify translatable signatures before scoring their activity in single tumors. We present a web interface (www.rosettasx.com) that applies our methodology to expression data from the Cancer Cell Line Encyclopaedia and The Cancer Genome Atlas. Configurable heat maps visualize per-cancer signature scores for 293 hand-curated literature-derived gene sets representing a wide range of cancer-relevant transcriptional modules and phenomena. The platform allows users to complement heatmaps of signature scores with molecular information on SNVs, CNVs, gene expression, gene dependency, and protein abundance or to analyze own signatures. Clustered heatmaps and further plots to drill-down results support users in studying oncological processes in cancer subtypes, thereby providing a rich resource to explore how mechanisms of cancer interact with each other as demonstrated by exemplary analyses of 2 cancer types.

Female sex and estrogen receptor-beta attenuate cardiac remodeling and apoptosis in pressure overload.

We investigated sex differences and the role of estrogen receptor-beta (ERbeta) on myocardial hypertrophy in a mouse model of pressure overload. We performed transverse aortic constriction (TAC) or sham surgery in male and female wild-type (WT) and ERbeta knockout (ERbeta(-/-)) mice. All mice were characterized by echocardiography and hemodynamic measurements and were killed 9 wk after surgery. Left ventricular (LV) samples were analyzed by microarray profiling, real-time RT-PCR, and histology. After 9 wk, WT males showed more hypertrophy and heart failure signs than WT females. Notably, WT females developed a concentric form of hypertrophy, while males developed eccentric hypertrophy. ERbeta deletion augmented the TAC-induced increase in cardiomyocyte diameter in both sexes. Gene expression profiling revealed that WT male hearts had a stronger induction of matrix-related genes and a stronger repression of mitochondrial genes than WT female hearts. ERbeta(-/-) mice exhibited a different transcriptional response. ERbeta(-/-)/TAC mice of both sexes exhibited induction of proapoptotic genes with a stronger expression in ERbeta(-/-) males. Cardiac fibrosis was more pronounced in male WT/TAC than in female mice. This difference was abolished in ERbeta(-/-) mice. The number of apoptotic nuclei was increased in both sexes of ERbeta(-/-)/TAC mice, most prominent in males. Female sex offers protection against ventricular chamber dilation in the TAC model. Both female sex and ERbeta attenuate the development of fibrosis and apoptosis, thus slowing the progression to heart failure.

Feature selection strategies for drug sensitivity prediction.

Drug sensitivity prediction constitutes one of the main challenges in personalized medicine. Critically, the sensitivity of cancer cells to treatment depends on an unknown subset of a large number of biological features. Here, we compare standard, data-driven feature selection approaches to feature selection driven by prior knowledge of drug targets, target pathways, and gene expression signatures. We asses these methodologies on Genomics of Drug Sensitivity in Cancer (GDSC) dataset, evaluating 2484 unique models. For 23 drugs, better predictive performance is achieved when the features are selected according to prior knowledge of drug targets and pathways. The best correlation of observed and predicted response using the test set is achieved for Linifanib (r = 0.75). Extending the drug-dependent features with gene expression signatures yields the most predictive models for 60 drugs, with the best performing example of Dabrafenib. For many compounds, even a very small subset of drug-related features is highly predictive of drug sensitivity. Small feature sets selected using prior knowledge are more predictive for drugs targeting specific genes and pathways, while models with wider feature sets perform better for drugs affecting general cellular mechanisms. Appropriate feature selection strategies facilitate the development of interpretable models that are indicative for therapy design.

Genetic Interactions and Tissue Specificity Modulate the Association of Mutations with Drug Response.

In oncology, biomarkers are widely used to predict subgroups of patients that respond to a given drug. Although clinical decisions often rely on single gene biomarkers, machine learning approaches tend to generate complex multi-gene biomarkers that are hard to interpret. Models predicting drug response based on multiple altered genes often assume that the effects of single alterations are independent. We asked whether the association of cancer driver mutations with drug response is modulated by other driver mutations or the tissue of origin. We developed an analytic framework based on linear regression to study interactions in pharmacogenomic data from two large cancer cell line panels. Starting from a model with only covariates, we included additional variables only if they significantly improved simpler models. This allows to systematically assess interactions in small, easily interpretable models. Our results show that including mutation-mutation interactions in drug response prediction models tends to improve model performance and robustness. For example, we found that TP53 mutations decrease sensitivity to BRAF inhibitors in BRAF-mutated cell lines and patient tumors, suggesting a therapeutic benefit of combining inhibition of oncogenic BRAF with reactivation of the tumor suppressor TP53. Moreover, we identified tissue-specific mutation-drug associations and synthetic lethal triplets where the simultaneous mutation of two genes sensitizes cells to a drug. In summary, our interaction-based approach contributes to a holistic view on the determining factors of drug response.

The novel EPTP repeat defines a superfamily of proteins implicated in epileptic disorders.

Recent studies suggest that mutations in the LGI1/Epitempin gene cause autosomal dominant lateral temporal epilepsy. This gene encodes a protein of unknown function, which we postulate is secreted. The LGI1 protein has leucine-rich repeats in the N-terminal sequence and a tandem repeat (which we named EPTP) in its C-terminal region. A redefinition of the C-terminal repeat and the application of sensitive sequence analysis methods enabled us to define a new superfamily of proteins carrying varying numbers of the novel EPTP repeats in combination with various extracellular domains. Genes encoding proteins of this family are located in genomic regions associated with epilepsy and other neurological disorders.

Activating and deactivating mutations in the receptor interaction site of GDF5 cause symphalangism or brachydactyly type A2.

Here we describe 2 mutations in growth and differentiation factor 5 (GDF5) that alter receptor-binding affinities. They cause brachydactyly type A2 (L441P) and symphalangism (R438L), conditions previously associated with mutations in the GDF5 receptor bone morphogenetic protein receptor type 1b (BMPR1B) and the BMP antagonist NOGGIN, respectively. We expressed the mutant proteins in limb bud micromass culture and treated ATDC5 and C2C12 cells with recombinant GDF5. Our results indicated that the L441P mutant is almost inactive. The R438L mutant, in contrast, showed increased biological activity when compared with WT GDF5. Biosensor interaction analyses revealed loss of binding to BMPR1A and BMPR1B ectodomains for the L441P mutant, whereas the R438L mutant showed normal binding to BMPR1B but increased binding to BMPR1A, the receptor normally activated by BMP2. The binding to NOGGIN was normal for both mutants. Thus, the brachydactyly type A2 phenotype (L441P) is caused by inhibition of the ligand-receptor interaction, whereas the symphalangism phenotype (R438L) is caused by a loss of receptor-binding specificity, resulting in a gain of function by the acquisition of BMP2-like properties. The presented experiments have identified some of the main determinants of GDF5 receptor-binding specificity in vivo and open new prospects for generating antagonists and superagonists of GDF5.

Genome-wide expression patterns of invasion front, inner tumor mass and surrounding normal epithelium of colorectal tumors.

Colorectal tumors have characteristic genome-wide expression patterns that allow their distinction from normal colon epithelia and facilitate clinical prognosis. The expression heterogeneity within a primary colorectal tumor has not been studied on a genome scale yet. Here we investigated three compartments of colorectal tumors, the invasion front, the inner tumor mass, and surrounding normal epithelial tissue by microdissection and microarray-based expression profiling. In both tumor compartments many genes were differentially expressed when compared to normal epithelium. The sets of significantly deregulated genes in both compartments overlapped to a large extent and revealed various interesting known and novel pathways that could have contributed to tumorigenesis. Cells from the invasion front and inner tumor mass, however, did not show significant differences in their expression profile, neither on the single gene level nor on the pathway level. Instead, gene expression differences between individuals are more pronounced as all patient-matched tumor samples clustered in close proximity to each other. With respect to invasion front and inner tumor mass we conclude that the specific tumor cell micro-environment does not have a strong influence on expression patterns: largely similar genome-wide expression programs operate in the invasion front and interior compartment of a colorectal tumor.

The SYSTERS Protein Family Database in 2005.

The SYSTERS project aims to provide a meaningful partitioning of the whole protein sequence space by a fully automatic procedure. A refined two-step algorithm assigns each protein to a family and a superfamily. The sequence data underlying SYSTERS release 4 now comprise several protein sequence databases derived from completely sequenced genomes (ENSEMBL, TAIR, SGD and GeneDB), in addition to the comprehensive Swiss-Prot/TrEMBL databases. The SYSTERS web server (http://systers.molgen.mpg.de) provides access to 158 153 SYSTERS protein families. To augment the automatically derived results, information from external databases like Pfam and Gene Ontology are added to the web server. Furthermore, users can retrieve pre-processed analyses of families like multiple alignments and phylogenetic trees. New query options comprise a batch retrieval tool for functional inference about families based on automatic keyword extraction from sequence annotations. A new access point, PhyloMatrix, allows the retrieval of phylogenetic profiles of SYSTERS families across organisms with completely sequenced genomes.

A genome-wide map of aberrantly expressed chromosomal islands in colorectal cancer.

BACKGROUND: Cancer development is accompanied by genetic phenomena like deletion and amplification of chromosome parts or alterations of chromatin structure. It is expected that these mechanisms have a strong effect on regional gene expression. RESULTS: We investigated genome-wide gene expression in colorectal carcinoma (CRC) and normal epithelial tissues from 25 patients using oligonucleotide arrays. This allowed us to identify 81 distinct chromosomal islands with aberrant gene expression. Of these, 38 islands show a gain in expression and 43 a loss of expression. In total, 7.892 genes (25.3% of all human genes) are located in aberrantly expressed islands. Many chromosomal regions that are linked to hereditary colorectal cancer show deregulated expression. Also, many known tumor genes localize to chromosomal islands of misregulated expression in CRC. CONCLUSION: An extensive comparison with published CGH data suggests that chromosomal regions known for frequent deletions in colon cancer tend to show reduced expression. In contrast, regions that are often amplified in colorectal tumors exhibit heterogeneous expression patterns: even show a decrease of mRNA expression. Because for several islands of deregulated expression chromosomal aberrations have never been observed, we speculate that additional mechanisms (like abnormal states of regional chromatin) also have a substantial impact on the formation of co-expression islands in colorectal carcinoma.

About the interrelation of evolutionary rate and protein age.

Evolutionary rate and gene age are interrelated when the age of a gene is assessed by the taxonomic distribution in the gene family. This is because homology detection by sequence comparison is depending on sequence similarity. We estimate family specific rates of protein evolution for orthologous families with representatives from man, fugu, fly, and worm. In fact, we observe that younger proteins tend to evolve faster than older ones. We estimate time points of duplication events that gave rise to novel protein functions and show that younger proteins were duplicated more recently than older ones.

The death-domain fold of the ASC PYRIN domain, presenting a basis for PYRIN/PYRIN recognition.

The PYRIN domain is a conserved sequence motif identified in more than 20 human proteins with putative functions in apoptotic and inflammatory signalling pathways. The three-dimensional structure of the PYRIN domain from human ASC was determined by NMR spectroscopy. The structure determination reveals close structural similarity to death domains, death effector domains, and caspase activation and recruitment domains, although the structural alignment with these other members of the death-domain superfamily differs from previously predicted amino acid sequence alignments. Two highly positively and negatively charged surfaces in the PYRIN domain of ASC result in a strong electrostatic dipole moment that is predicted to be present also in related PYRIN domains. These results suggest that electrostatic interactions play an important role for the binding between PYRIN domains. Consequently, the previously reported binding between the PYRIN domains of ASC and ASC2/POP1 or between the zebrafish PYRIN domains of zAsc and Caspy is proposed to involve interactions between helices 2 and 3 of one PYRIN domain with helices 1 and 4 of the other PYRIN domain, in analogy to previously reported homophilic interactions between caspase activation and recruitment domains.

The human LAPTM4b transcript is upregulated in various types of solid tumours and seems to play a dual functional role during tumour progression.

LAPTM4b (lysosome associated protein transmembrane 4 beta) was recently identified as a gene overexpressed in human hepatocellular carcinoma and belongs to the mammalian LAPTM family. By analysing genome-wide expression profiles of microdissected solid tumour samples by the means of Affymetrix GenChip hybridisation, we found LAPTM4b to be upregulated in 88% (23/26) of lung and in 67% (18/27) of colon carcinoma patients. Northern blots revealed additionally an overexpression of LAPTM4b in the majority of carcinomas of the uterus (30/44), breast (27/53) and ovary (11/16). Other members of the LAPTM family were not overexpressed in the investigated tumour samples according to GeneChip hybridisation data. Northen blot and quantitative RT-PCR on different normal tissues, detected highest levels of LAPTM4b mRNA in uterus, heart and skeletal muscle. Due to sequence analysis of bilaterian LAPTM proteins we suggests the presence of four transmembrane helices per protein, which are probably packed together by hydrophobic forces that are excerted by several evolutionary conserved aromatic residues within the alpha-helices. We discuss an active role for LAPTM4b during disease progression of malignant cells and conclude that its putative dual functional involvement in tumour cell proliferation as well as in multidrug-resistance may represent LAPTM4b as a target suitable for development of novel therapeutic agents.

Systematic identification of immunoreceptor tyrosine-based inhibitory motifs in the human proteome.

Immunoreceptor tyrosine-based inhibitory motifs (ITIMs) are short sequences of the consensus (ILV)-x-x-Y-x-(LV) in the cytoplasmic tail of immune receptors. The phosphorylation of tyrosines in ITIMs is known to be an important signalling mechanism regulating the activation of immune cells. The shortness of the motif makes it difficult to predict ITIMs in large protein databases. Simple pattern searches find ITIMs in approximately 30% of the protein sequences in the RefSeq database. The majority are false positive predictions. We propose a new database search strategy for ITIM-bearing transmembrane receptors based on the use of sequence context, i.e. the predictions of signal peptides, transmembrane helices (TMs) and protein domains. Our new protocol allowed us to narrow down the number of potential human ITIM receptors to 109 proteins (0.7% of RefPep). Of these, 36 have been described as ITIM receptors in the literature before. Many ITIMs are conserved between orthologous human and mouse proteins which represent novel ITIM receptor candidates. Publicly available DNA array expression data revealed that ITIM receptors are not exclusively expressed in blood cells. We hypothesise that ITIM signalling is not restricted to immune cells, but also functions in diverse solid organs of mouse and man.

Different structural organization of the encephalopsin gene in man and mouse.

Encephalopsin, also called Panopsin, is a recently discovered extraretinal photoreceptor, which may play a role in non-visual photic processes such as the entrainment of circadian rhythm or the regulation of pineal melatonin production. Based on RT-PCR data and comparative genomic sequence analysis, we show that the human OPN3 gene consists of six exons and expresses various splice variants, while the murine homologue contains four exons and produces just one splice form. Furthermore, the human OPN3 gene overlaps with the neighboring KMO gene on a genomic as well as on an RNA level, whereas the corresponding genes in mouse lie close together but do not overlap. This finding is of particular interest, since differences in gene organization between man and mouse, that have been reported so far, occur within gene clusters, i.e. the number of genes within a certain cluster may differ between man and mouse. OPN3 provides an exception to this rule, since it is positionally uncoupled from other genes of the opsin family.

A novel repeat in the melanoma-associated chondroitin sulfate proteoglycan defines a new protein family.

The human melanoma-associated chondroitin sulfate proteoglycan (MCSP) and its rat ortholog NG2 are thought to play important roles in angiogenesis-dependent processes like wound healing and tumor growth. Based on electron microscopy studies, the highly glycosylated ectodomain of NG2 has been subdivided into the globular N-terminus, a flexible rod-like central region and a C-terminal portion in globular conformation. We identified a novel repeat named CSPG in the central ectodomain of NG2, MCSP and other proteins from fly, worm, human, sea urchin and a cyanobacterium which shows similarity to cadherin repeats. As earlier electron microscopy studies indicate, the folding of the tandem repeats compresses the length of the proposed repeat region by a factor of approximately 10 compared to the fully extended peptide chain. We identified two conserved negatively charged residues which might govern the binding properties of CSPG repeats. The phyletic distribution of CSPG repeats suggests that horizontal gene transfer contributed to their evolutionary history.