How to understand the cell by breaking it: network analysis of gene perturbation screens

Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current state-of-the-art in analyzing perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.Comment: Review based on ISMB 2009 tutorial; after two rounds of revisio

Structure Learning in Nested Effects Models

Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g. the effects showing in gene expression profiles or as morphological features of the perturbed cell. In this paper we expand the statistical basis of NEMs in four directions: First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show that the new formulation of the likelihood allows to efficiently traverse model space. Fourth, we incorporate prior knowledge and an automated variable selection criterion to decrease the influence of noise in the data

You Are Not Working for Me; I Am Working with You.

Since 2009, I have led a cancer research group at the University of Cambridge; the current group includes ten scientists (five postdocs, five PhD students). In the following, I will share with you some of the lessons I learned over the years and some of the leadership strategies that work well for me. Key topics will be the integration of new lab members and the communication in the lab (in particular, how to make expectations explicit).I would like to acknowledge the support of The University of Cambridge, Cancer Research UK (core grant C14303/A17197), and Hutchison Whampoa Limited. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLoS via http://dx.doi.org/ 10.1371/journal.pcbi.100438

A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes

The influence of DNA cis-regulatory elements on a gene's expression has been intensively studied. However, little is known about expressions driven by trans-acting DNA hotspots. DNA hotspots harboring copy number aberrations are recognized to be important in cancer as they influence multiple genes on a global scale. The challenge in detecting trans-effects is mainly due to the computational difficulty in detecting weak and sparse trans-acting signals amidst co-occuring passenger events. We propose an integrative approach to learn a sparse interaction network of DNA copy-number regions with their downstream targets in a breast cancer dataset. Information from this network helps distinguish copy-number driven from copy-number independent expression changes on a global scale. Our result further delineates cis- and trans-effects in a breast cancer dataset, for which important oncogenes such as ESR1 and ERBB2 appear to be highly copy-number dependent. Further, our model is shown to be efficient and in terms of goodness of fit no worse than other state-of the art predictors and network reconstruction models using both simulated and real data.Comment: Accepted at IEEE International Conference on Bioinformatics & Biomedicine (BIBM 2010

SANTA: quantifying the functional content of molecular networks.

Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the S. cerevisiae genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations, and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release/bioc/html/SANTA.html.We acknowledge support by the University of Cambridge, Cancer Research UK, and Hutchison Whampoa Limited.This is the final published version. It was first published by PLOS here: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003808

BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies.

Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, we present BitPhylogenyBitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways. Using a full Bayesian approach, we jointly estimate the number and composition of clones in the sample as well as the most likely tree connecting them. We validate our approach in the controlled setting of a simulation study and compare it against several competing methods. In two case studies, we demonstrate how BitPhylogeny BitPhylogeny reconstructs tumor phylogenies from methylation patterns in colon cancer and from single-cell exomes in myeloproliferative neoplasm.KY and FM would like to acknowledge the support of the University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited.This is the final published version. It first appeared at http://genomebiology.com/2015/16/1/36

Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study

$\textbf{Background}$: Immune infiltration of breast tumours is associated with clinical outcome. However, past work has not accounted for the diversity of functionally distinct cell types that make up the immune response. The aim of this study was to determine whether differences in the cellular composition of the immune infiltrate in breast tumours influence survival and treatment response, and whether these effects differ by molecular subtype. $\textbf{Methods and Findings}$: We applied an established computational approach (CIBERSORT) to bulk gene expression profiles of almost 11,000 tumours to infer the proportions of 22 subsets of immune cells. We investigated associations between each cell type and survival and response to chemotherapy, modelling cellular proportions as quartiles. We found that tumours with little or no immune infiltration were associated with different survival patterns according to oestrogen receptor (ER) status. In ER-negative disease, tumours lacking immune infiltration were associated with the poorest prognosis, whereas in ER-positive disease, they were associated with intermediate prognosis. Of the cell subsets investigated, T regulatory cells and M0 and M2 macrophages emerged as the most strongly associated with poor outcome, regardless of ER status. Among ER-negative tumours, CD8+ T cells (hazard ratio [HR] = 0.89, 95% CI 0.80-0.98; $p$ = 0.02) and activated memory T cells (HR 0.88, 95% CI 0.80-0.97; $p$ = 0.01) were associated with favourable outcome. T follicular helper cells (odds ratio [OR] = 1.34, 95% CI 1.14-1.57; $p$ < 0.001) and memory B cells (OR = 1.18, 95% CI 1.0-1.39; $p$ = 0.04) were associated with pathological complete response to neoadjuvant chemotherapy in ER-negative disease, suggesting a role for humoral immunity in mediating response to cytotoxic therapy. Unsupervised clustering analysis using immune cell proportions revealed eight subgroups of tumours, largely defined by the balance between M0, M1, and M2 macrophages, with distinct survival patterns by ER status and associations with patient age at diagnosis. The main limitations of this study are the use of diverse platforms for measuring gene expression, including some not previously used with CIBERSORT, and the combined analysis of different forms of follow-up across studies. $\textbf{Conclusions}$: Large differences in the cellular composition of the immune infiltrate in breast tumours appear to exist, and these differences are likely to be important determinants of both prognosis and response to treatment. In particular, macrophages emerge as a possible target for novel therapies. Detailed analysis of the cellular immune response in tumours has the potential to enhance clinical prediction and to identify candidates for immunotherapy.HRA is an NIHR Academic Clinical Lecturer and was a recipient of a Career Development Fellowship from The Pathological Society of GB and N Ireland, and a Starter Grant for Clinical Lecturers from the Academy of Medical Sciences. LC, CC, and FM received funding from the CRUK & EPSRC Cancer Imaging Centre in Cambridge & Manchester (grant C197/A16465)

Keyword search over relational tables and streams

Relational keyword search (R-KWS) provides an intuitive way to query relational data without requiring SQL, or knowledge of the underlying schema. In this paper we describe a comprehensive framework for R-KWS covering snapshot queries on conventional tables and continuous queries on relational streams. Our contributions are summarized as follows: (i) we provide formal semantics, addressing the temporal validity and order of results, spanning uniformly over tables and streams; (ii) we investigate two general methodologies for query processing, graph based and operator based that resolve several problems of previous approaches; and (iii) we develop a range of algorithms and optimizations covering both methodologies. We demonstrate the effectiveness of R-KWS, as well as the significant performance benefits of the proposed techniques, through extensive experiments with static and streaming datasets

Differential C3NET reveals disease networks of direct physical interactions.

BACKGROUND: Genes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour. RESULTS: C3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis. CONCLUSIONS: Our approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are