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Identifying tumor-cell-specific markers and elucidating their epigenetic regulation and spatial heterogeneity provides mechanistic insights into cancer etiology. Here, we perform snRNA-seq and snATAC-seq in 34 and 28 human clear cell renal cell carcinoma (ccRCC) specimens, respectively, with matched bulk proteogenomics data. By identifying 20 tumor-specific markers through a multi-omics tiered approach, we reveal an association between higher ceruloplasmin (CP) expression and reduced survival. CP knockdown, combined with spatial transcriptomics, suggests a role for CP in regulating hyalinized stroma and tumor-stroma interactions in ccRCC. Intratumoral heterogeneity analysis portrays tumor cell-intrinsic inflammation and epithelial-mesenchymal transition (EMT) as two distinguishing features of tumor subpopulations. Finally, BAP1 mutations are associated with widespread reduction of chromatin accessibility, while PBRM1 mutations generally increase accessibility, with the former affecting five times more accessible peaks than the latter. These integrated analyses reveal the cellular architecture of ccRCC, providing insights into key markers and pathways in ccRCC tumorigenesis.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs’ recapitulation of human tumors., Patient-derived xenograft models (PDX) have been extensively used to study the molecular and clinical features of cancers. Here the authors present a cohort of 536 PDX models from 25 cancers, as well as their genomic and evolutionary profiles and their suitability for clinical trials.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

MotivationThe use of single-cell methods is expanding at an ever-increasing rate. While there are established algorithms that address cell classification, they are limited in terms of cross platform compatibility, reliance on the availability of a reference dataset and classification interpretability. Here, we introduce Pollock, a suite of algorithms for cell type identification that is compatible with popular single-cell methods and analysis platforms, provides a set of pretrained human cancer reference models, and reports interpretability scores that identify the genes that drive cell type classifications.ResultsPollock performs comparably to existing classification methods, while offering easily deployable pretrained classification models across a wide variety of tissue and data types. Additionally, it demonstrates utility in immune pan-cancer analysis.Availability and implementationSource code and documentation are available at https://github.com/ding-lab/pollock. Pretrained models and datasets are available for download at https://zenodo.org/record/5895221.Supplementary informationSupplementary data are available at Bioinformatics Advances online.

Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease.

Summary Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

The increased quality and data availability of large-scale transcriptomic, genomic, and proteomic studies require a pan-cancer integrated proteogenomic approach to define tumor molecular signatures accurately and identify new therapeutic targets. We comprehensively investigate more than 1000 samples across 12 cancer types from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) and the International Cancer Proteogenome Consortium (ICPC). The types are comprised of breast (BR), colorectal (CO), and ovarian (OV) cancers, clear cell renal cell (ccRCC), head and neck squamous cell (HNSCC), lung squamous cell (LSCC), hepatitis B virus (HBV)-related hepatocellular (HCC), and endometrial (EC) carcinomas, lung adenocarcinoma (LUAD), pancreatic ductal adenocarcinoma (PDAC), and glioblastoma (GBM). In particular, we examine 8 Tumor Signature Associated Phenotypes (TSAPs), namely aristolochic acid (AA), aging, microsatellite instability (MSI), homologous recombination deficiency (HRD), POLE, APOBEC, smoking, and ultraviolet (UV) light exposure. This study is the first to report proteomic markers associated with these TSAPs on a pan-cancer level. In addition to genetic alterations and mutational signatures, we utilize multi-omics data of high-resolution proteome, phosphoproteome, acetylome, and gene expression to infer expression signatures of TSAPs by defining the most critical changes in the transcriptome and proteome accompanying the transitions to these TSAPs, especially markers that were uniquely found in proteomic data. We consolidated multi-omic data and calculated the novel quantitative Tumor Signature Associated Phenotypes (TSAPs) score to predict the TSAP status. For example, the use of proteomic markers for MSI-TSAP scoring can improve clinical testing of MSI status. We further study environmental exposure-related tumor proteogenomic signatures, immune proteogenomic signatures, and the association between the immune subtypes and TSAPs. Smoking strongly influences the tumor immune microenvironment and disease prognosis. We show that expression signatures can facilitate the prediction of TSAPs and help to uncover their underlying molecular mechanisms. By connecting these findings with druggable databases, we provide a link to actionable therapies, identify putative TSAP-related targets, and offer novel cues to optimize therapeutic options for patients, such as how additional targeting of genes up-regulated in PARP1 inhibitor-treated HRD tumors may overcome resistance. This will promote the identification not only of unique druggable targets, but also to determine putative novel therapeutic targets using integrated approaches. Citation Format: Yize Li, Nadezhda V. Terekhanova, Daniel Cui Zhou, Kelly V. Ruggles, Samuel H. Payne, Michael Wendl, David Fenyő, Li Ding. Pan-cancer proteogenomic signatures associated with HRD, MSI, APOBEC, and smoking [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 845.

The use of single-cell methods is expanding at an ever-increasing rate. While multiple algorithms address the task of cell classification, they are limited in terms of cross platform compatibility, reliance on the availability of a reference dataset, and classification interpretability. Here, we introduce Pollock, a suite of algorithms for cell type identification that is compatible with popular single cell methods and analysis platforms, provides a series of pretrained human cancer reference models, and reports interpretability scores that identify the genes that drive cell type classifications. Our model combines two important approaches, one each from machine learning and deep learning: a variational autoencoder (VAE) and random forest classifier, to make cell type predictions. Pollock is highly versatile, being available as a command line tool, Python library (with scanpy integration), or R library (with Seurat integration), and can be installed as a conda package, or in containerized form via Docker. To allow for easier pan-disease and pan-tissue analyses, Pollock also ships with a library of pretrained cancer type specific and agnostic modules that were trained on expertly-curated single cell data that are ready to “plug and play” with no additional annotation or training required. Conversely, Pollock also allows for the training of custom classification modules, if an annotated reference single cell dataset is available. These pretrained models were fitted on manually curated and annotated single cell data from eight different cancer types spanning three single cell technologies (scRNA-seq, snRNA-seq, and snATAC-seq). Pollock also provides feature importance scores that allow for cell type classifications to be traced back to the genes influencing a particular cell type classification, further promoting biological interpretability. These scores could allow for new, technology-specific biomarker discovery. We also demonstrate the utility of Pollock by applying it in a pan-cancer single cell immune analysis. Citation Format: Erik Storrs, Daniel Cui Zhou, Michael C. Wendl, Matthew A. Wyczalkowski, Alla Karpova, Liang-Bo Wang, Yize Li, Austin Southard-Smith, Reyka G. Jayasinghe, Lijun Yao, Ruiyang Liu, Yige Wu, Nadezhda V. Terekhanova, Houxiang Zhu, John M. Herndon, Feng Chen, William E. Gillanders, Ryan C. Fields, Li Ding. Pollock: Fishing for cell states [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1932.

Breast cancer is a heterogeneous collection of diseases grouped by hormone receptor status or by expression of key subtype-determining genes. Breast cancer subtypes, in particular basal-like breast cancer and the luminal breast cancer subtypes, differ by hormonal receptor status, proliferation, genomic instability and mutational signatures, treatment response, and prognosis. The highly distinct signatures of basal-like and luminal breast cancer suggest that they may have different cells of origin within the breast duct. As part of the Washington University Human Tumor Atlas Network (WU-HTAN) program, we generated multi-omic data for 53 samples from 37 breast cancer tumors and 4 normal adjacent tissues. Genomic subtyping was applied to both bulk and single-nucleus RNA sequencing. Analysis of single-nucleus gene expression and chromatin accessibility in epithelial cells underscores similarities between basal-like breast cancer and luminal progenitor cells within the breast duct, and between luminal breast cancer and mature luminal ductal cells. This study links distinct breast cancer subtypes to normal breast cell populations and suggests distinct cells of origin for these cancer types. Citation Format: Michael D. Iglesia, Reyka G. Jayasinghe, Daniel Cui Zhou, Nadezhda V. Terekhanova, John Herndon, Alla Karpova, Siqi Chen, Nataly Naser Al Deen, Kazuhito Sato, Feng Chen, Deborah J. Veis, Ryan C. Fields, William E. Gillanders, Li Ding. Multi-omic characterization of transitional cell populations in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2936.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.

Adaptation of threespine stickleback to freshwater involves parallel recruitment of freshwater alleles in clusters of closely linked sites, or divergence islands (DIs). However, it remains unclear to what extent the DIs and the alleles that constitute them coincide between populations that underwent adaptation to freshwater independently. We examine threespine sticklebacks from ten freshwater lakes that emerged 500–1500 years ago in the White Sea basin, with the emphasis on repeatability of genomic patterns of adaptation among the lake populations and the role of local recombination rate in the distribution and structure of DIs. The 65 detected DIs are clustered in the genome, forming 12 aggregations, and this clustering cannot be explained by the variation of the recombination rate. Only 21 of the DIs are present in all the freshwater populations, likely being indispensable for successful colonization of freshwater environment by the ancestral marine population. Within most DIs, the same set of single nucleotide polymorphisms (SNPs) distinguish marine and freshwater haplotypes in all the lake populations; however, in some DIs, freshwater alleles differ between populations, suggesting that they could have been established by recruitment of different haplotypes in different populations.

Sturgeons represent a substantial scientific interest due to their high economic value, endangered status and also as the most primitive group of ray-finned fishes. Rapid progress in knowledge of sturgeon immunity was achieved recently with use of RNA sequencing. We report transcriptome sequencing of gill, head kidney, and spleen of bester sturgeon (a hybrid of beluga Huso huso and sterlet Acipenser ruthenus) injected with synthetic double-stranded RNA (polyI:C). The composition of transcriptome and responses to treatment were examined in the context of comparative genomics with focus on immune genes. Sturgeon transcripts matched to 21.5 k different proteins (blastx). With reference to Atlantic salmon, the functional groups and pathways of the immune system were uniformly represented: at average 36.5 ± 0.8% genes were found. Immune genes comprise a significant fraction of transcriptome. Among twenty genes with highest transcription levels, five are specialized immune genes and two encode heme and iron binding proteins (serotransferrin and hemopexin) also known as acute phase proteins. Challenge induced multiple functional groups including apoptosis, cell cycle and a number of metabolic pathways. Treatment stimulated innate antiviral immunity, which is well conserved between sturgeon and salmon, the most responsive genes were mx, rsad2 (viperin), interferon induced protein 44 and protein with tetratricopeptide repeats 5, cd87 and receptor transporting protein 3. Results added to knowledge of immune phylogeny. Gain and loss of genes was assessed by comparison with genomes from different phylogenetic groups. Among differentially expressed genes, percentage of acquired and lost genes was much lower in comparison with genes present in all vertebrates. Innate antiviral immunity was subject to the greatest changes in evolution of jawed vertebrates. A significant fraction of genes (15%) was lost in mammals and only half of genes is annotated in public databases as involved in antiviral responses. Change of function may have an important role in evolution of immunity together with gain and loss of genes.

Threespine sticklebacks adapted to freshwater environments all over the Northern Hemisphere. This adaptation involved parallel recruitment of freshwater alleles in clusters of closely linked sites, or divergence islands (DIs). However, it is unclear to what extent the DIs involved in adaptation and the alleles within them coincide between populations adapting to similar environments. Here, we examine 10 freshwater populations of similar ages from the White Sea basin, and study the repeatability of patterns of adaptation in them. Overall, the 65 detected DIs tend to reside in regions of low recombination, underlining the role of reduced recombination in their establishment. Moreover, the DIs are clustered in the genome to the extent that is not explainable by the recombination rate alone, consistent with the divergence hitchhiking model. 21 out of the 65 DIs are universal; i.e., the frequency of freshwater alleles in them is increased in all analyzed populations. Universal DIs tend to have longer core region shared between populations, and the divergence between the marine and the freshwater haplotypes in them is higher, implying that they are older, also consistently with divergence hitchhiking. Within most DIs, the same set of sites distinguished the marine and the freshwater haplotypes in all populations; however, in some of the DIs, the genetic architecture of the freshwater haplotype differed between populations, suggesting that they could have been established by soft selective sweeps.

Niches have traditionally been characterized as signalling microenvironments that allow stem cells to maintain their fate. This definition implicitly assumes that the various niche signals are integrated towards a binary fate decision between stemness and differentiation. However, observations in multiple systems have demonstrated that stem cell properties such as proliferation and self renewal can be uncoupled at the level of niche signalling input, which is incompatible with this simplified view. We have studied the role of the transcriptional regulator Zfh1, a shared target of the Hedgehog and Jak/Stat niche signalling pathways, in the somatic stem cells of the Drosophila testis. We found that Zfh1 binds and downregulates salvador and kibra, two tumour suppressor genes of the Hippo/Wts/Yki pathway, thereby restricting Yki activation and proliferation to the Zfh1 positive stem cells. These observations provide an unbroken link from niche signal input to an individual aspect of stem cell behaviour that does not, at any step, involve a fate decision. We discuss the relevance of these findings for an overall concept of stemness and niche function.

Niches have traditionally been characterised as signalling microenvironments that allow stem cells to maintain their fate. This definition implicitly assumes that the various niche signals are integrated towards a binary fate decision between stemness and differentiation. However, observations in multiple systems have demonstrated that stem cell properties, such as proliferation and self-renewal, can be uncoupled at the level of niche signalling input, which is incompatible with this simplified view. We have studied the role of the transcriptional regulator Zfh1, a shared target of the Hedgehog and Jak/Stat niche signalling pathways, in the somatic stem cells of the

Niches have traditionally been characterized as signalling microenvironments that allow stem cells to maintain their fate. This definition implicitly assumes that the various niche signals are integrated towards a binary fate decision between stemness and differentiation. However, observations in multiple systems have demonstrated that stem cell properties such as proliferation and self renewal can be uncoupled at the level of niche signalling input, which is incompatible with this simplified view. We have studied the role of the transcriptional regulator Zfh1, a shared target of the Hedgehog and Jak/Stat niche signalling pathways, in the somatic stem cells of theDrosophilatestis. We found that Zfh1 binds and downregulatessalvadorandkibra, two tumour suppressor genes of the Hippo/Wts/Yki pathway, thereby restricting Yki activation and proliferation to the Zfh1 positive stem cells. These observations provide an unbroken link from niche signal input to an individual aspect of stem cell behaviour that does not, at any step, involve a fate decision. We discuss the relevance of our observations and other reports in the literature for an overall concept of stemness and niche function.Summary statementWe demonstrate that the fly testis niche controls stem cell proliferation by repressing Hippo pathway genes independent of a binary cell fate decision between stemness and proliferation.

Mutation rate varies along the human genome, and part of this variation is explainable by measurable local properties of the DNA molecule. Moreover, mutation rates differ between orthologous genomic regions of different species, but the drivers of this change are unclear. Here, we use data on human divergence from chimpanzee, human rare polymorphism, and human de novo mutations to predict the substitution rate at orthologous regions of non-human mammals. We show that the local mutation rates are very similar between human and apes, implying that their variation has a strong underlying cryptic component not explainable by the known genomic features. Mutation rates become progressively less similar in more distant species, and these changes are partially explainable by changes in the local genomic features of orthologous regions, most importantly, in the recombination rate. However, they are much more rapid, implying that the cryptic component underlying the mutation rate is more ephemeral than the known genomic features. These findings shed light on the determinants of mutation rate evolution. Key words: local mutation rate, molecular evolution, recombination rate.

Evolution of sequences mostly involves independent changes at different sites. However, substitutions at neighboring sites may co-occur as multinucleotide replacement events (MNRs). Here, we compare noncoding sequences of several species of primates, and of three species of Drosophila fruit flies, in a phylogenetic analysis of the replacements that occurred between species at nearby nucleotide sites. Both in primates and in Drosophila, the frequency of single-nucleotide replacements is substantially elevated within 10 nucleotides from other replacements that occurred on the same lineage but not on another lineage. The data imply that dinucleotide replacements (DNRs) affecting sites at distances of up to 10 nucleotides from each other are responsible for 2.3% of single-nucleotide replacements in primate genomes and for 5.6% in Drosophila genomes. Among these DNRs, 26% and 69%, respectively, are in fact parts of replacements of three or more trinucleotide replacements (TNRs). The plurality of MNRs affect nearby nucleotides, so that at least six times as many DNRs affect two adjacent nucleotide sites than sites 10 nucleotides apart. Still, approximately 60% of DNRs, and approximately 90% of TNRs, span distances more than two (or three) nucleotides. MNRs make a major contribution to the observed clustering of substitutions: In the human–chimpanzee comparison, DNRs are responsible for 50% of cases when two nearby replacements are observed on the human lineage, and TNRs are responsible for 83% of cases when three replacements at three immediately adjacent sites are observed on the human lineage. The prevalence of MNRs matches that is observed in data on de novo mutations and is also observed in the regions with the lowest sequence conservation, suggesting that MNRs mainly have mutational origin; however, epistatic selection and/or gene conversion may also play a role.

Mutation rate varies along the human genome, and part of this variation is explainable by measurable local properties of the DNA molecule. Moreover, mutation rates differ between orthologous genomic regions of different species, but the drivers of this change are unclear. Here, we compare the local mutation rates of several species. We show that these rates are very similar between human and apes, implying that their variation has a strong underlying cryptic component not explainable by the known genomic features. Mutation rates become progressively less similar in more distant species, and these changes are partially explainable by changes in the local genomic features of orthologous regions, most importantly, in the recombination rate. However, they are much more rapid, implying that the cryptic component underlying the mutation rate is more ephemeral than the known genomic features. These findings shed light on the determinants of mutation rate evolution.

// Elena Alexandrova 1,2 , Giovanni Nassa 1 , Giacomo Corleone 1 , Anton Buzdin 3,4 , Alexander M. Aliper 3,4 , Nadezhda Terekhanova 4 , Denis Shepelin 4,5 , Alexander Zhavoronkov 6 , Michael Tamm 7 , Luciano Milanesi 8 , Nicola Miglino 7,* , Alessandro Weisz 1,9 and Pieter Borger 7,* 1 Laboratory of Molecular Medicine and Genomics, Department of Medicine and Surgery, University of Salerno, Baronissi (SA), Italy 2 Genomix4Life Srl, Campus of Medicine, University of Salerno, Baronissi (SA), Italy 3 Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia 4 Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR 5 Group for Genomic Regulation of Cell Signalling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia 6 Insilico Medicine, Inc, ETC, Johns Hopkins University, Baltimore, MD, USA 7 Department of Biomedicine, University Hospital Basel, Basel, Switzerland 8 Institute of Biomedical Technologies, National Research Council, Segregate, (MI), Italy 9 Molecular Pathology and Medical Genomics Unit, ‘SS. Giovanni di Dio e Ruggi d’Aragona - Schola Medica Salernitana’ University Hospital, Salerno, (SA), Italy * These authors have contributed equally to this work Correspondence to: Pieter Borger, email: // Alessandro Weisz, email: // Keywords : asthma, smooth muscle cells, signalling pathways, CAGE Received : December 07, 2015 Accepted : January 26, 2016 Published : February 05, 2016 Abstract Background: Bronchial smooth muscle (BSM) cells from asthmatic patients maintain in vitro a distinct hyper-reactive (“primed”) phenotype, characterized by increased release of pro-inflammatory factors and mediators, as well as hyperplasia and/or hypertrophy. This “primed” phenotype helps to understand pathogenesis of asthma, as changes in BSM function are essential for manifestation of allergic and inflammatory responses and airway wall remodelling. Objective: To identify signalling pathways in cultured primary BSMs of asthma patients and non-asthmatic subjects by genome wide profiling of differentially expressed mRNAs and activated intracellular signalling pathways (ISPs). Methods: Transcriptome profiling by cap-analysis-of-gene-expression (CAGE), which permits selection of preferentially capped mRNAs most likely to be translated into proteins, was performed in human BSM cells from asthmatic (n=8) and non-asthmatic (n=6) subjects and OncoFinder tool were then exploited for identification of ISP deregulations. Results: CAGE revealed >600 RNAs differentially expressed in asthma vs control cells (p≤0.005), with asthma samples showing a high degree of similarity among them. Comprehensive ISP activation analysis revealed that among 269 pathways analysed, 145 (p

// Nikolay M. Borisov 1, 2 , Nadezhda V. Terekhanova 1, 3 , Alexander M. Aliper 1, 3 , Larisa S. Venkova 1, 2 , Philip Yu Smirnov 1, 2 , Sergey Roumiantsev 1, 3 , Mikhail B. Korzinkin 1, 2 , Alex A. Zhavoronkov 1, 3, 4 , Anton A. Buzdin 1, 3, 4 1 Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR 2 Laboratory of Systems Biology, A.I. Burnasyan Federal Medical Biophysical Center, Moscow, 123182, Russia 3 Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198, Russia 4 Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia Correspondence to: Anton Buzdin, e-mail: bu3din@mail.ru Key words: Cancer, Intracellular signaling pathway activation, Gene expression, Transcriptome profiling, Molecular markers, AUC Received: July 16, 2014 Accepted: August 16, 2014 Published: October 13, 2014 ABSTRACT Identification of reliable and accurate molecular markers remains one of the major challenges of contemporary biomedicine. We developed a new bioinformatic technique termed OncoFinder that for the first time enables to quantatively measure activation of intracellular signaling pathways basing on transcriptomic data. Signaling pathways regulate all major cellular events in health and disease. Here, we showed that the Pathway Activation Strength (PAS) value itself may serve as the biomarker for cancer, and compared it with the “traditional” molecular markers based on the expression of individual genes. We applied OncoFinder to profile gene expression datasets for the nine human cancer types including bladder cancer, basal cell carcinoma, glioblastoma, hepatocellular carcinoma, lung adenocarcinoma, oral tongue squamous cell carcinoma, primary melanoma, prostate cancer and renal cancer, totally 292 cancer and 128 normal tissue samples taken from the Gene expression omnibus (GEO) repository. We profiled activation of 82 signaling pathways that involve ~2700 gene products. For 9/9 of the cancer types tested, the PAS values showed better area-under-the-curve (AUC) scores compared to the individual genes enclosing each of the pathways. These results evidence that the PAS values can be used as a new type of cancer biomarkers, superior to the traditional gene expression biomarkers.

Adaptation is driven by natural selection; however, many adaptations are caused by weak selection acting over large timescales, complicating its study. Therefore, it is rarely possible to study selection comprehensively in natural environments. The threespine stickleback (Gasterosteus aculeatus) is a well-studied model organism with a short generation time, small genome size, and many genetic and genomic tools available. Within this originally marine species, populations have recurrently adapted to freshwater all over its range. This evolution involved extensive parallelism: pre-existing alleles that adapt sticklebacks to freshwater habitats, but are also present at low frequencies in marine populations, have been recruited repeatedly. While a number of genomic regions responsible for this adaptation have been identified, the details of selection remain poorly understood. Using whole-genome resequencing, we compare pooled genomic samples from marine and freshwater populations of the White Sea basin, and identify 19 short genomic regions that are highly divergent between them, including three known inversions. 17 of these regions overlap protein-coding genes, including a number of genes with predicted functions that are relevant for adaptation to the freshwater environment. We then analyze four additional independently derived young freshwater populations of known ages, two natural and two artificially established, and use the observed shifts of allelic frequencies to estimate the strength of positive selection. Adaptation turns out to be quite rapid, indicating strong selection acting simultaneously at multiple regions of the genome, with selection coefficients of up to 0.27. High divergence between marine and freshwater genotypes, lack of reduction in polymorphism in regions responsible for adaptation, and high frequencies of freshwater alleles observed even in young freshwater populations are all consistent with rapid assembly of G. aculeatus freshwater genotypes from pre-existing genomic regions of adaptive variation, with strong selection that favors this assembly acting simultaneously at multiple loci., Author Summary Adaptation to novel environments is a keystone of evolution. There is only a handful of natural and experimental systems in which the process of adaptation has been studied in detail, and each studied system brings its own surprises with regard to the number of loci involved, dynamics of adaptation, extent of interactions between loci and of parallelism between different adapting populations. The threespine stickleback is an excellent model organism for evolutionary studies. Marine-derived freshwater populations of this species have consistently acquired a specific set of morphological, physiological and behavioral traits allowing them to reside in freshwater for their whole lifespan. Previous studies identified several genomic regions responsible for this adaptation. Here, using whole-genome sequencing, we compare the allele frequencies at such regions in four derived freshwater populations of known ages: two natural, and two artificially established in 1978. Knowledge of population ages allows us to infer the strength of selection that acted at these loci. Adaptation of threespine stickleback to freshwater is typically fast, and is driven by strong selection favoring pre-existing alleles that are likely present in the ancestral marine population at low frequencies; however, some of the adaptation may also be due to young population-specific alleles.