Core CRUK funding: MD, AGL, ADL.

Academy of Medical Sciences Clinical Lecturer Starter Grant SGCL11 (prinicipal funder of this work): ADL

Boutros, P

Dunning, MJ

Lalonde, E

Lamb, AD

Lynch, AG

Mills, IG

Ross-Adams, H

Vowler, SL

EBioMedicine

PubMed

Dunning, Mark J.

Vowler, Sarah L.

Lalonde, Emilie

Ross-Adams, Helen

Boutros, Paul

Mills, Ian G.

Lynch, Andy G.

Lamb, Alastair D.

University of St. Andrews - Pure

Mining human prostate cancer datasets:the “camcAPP” shiny app

Mark J Dunning

Sarah L Vowler

Emilie Lalonde

Helen Ross-Adams

Ian G Mills

Andy G Lynch

Alastair D Lamb

Crossref

Mining human prostate cancer datasets: The “camcAPP” shiny app

Apollo (Cambridge)

EBioMedicine 17 (2017) 5–6Contents lists available at ScienceDirectEBioMedicinej ourna l homepage: www.eb iomed ic ine.comIn FocusMining Human Prostate Cancer Datasets: The “camcAPP” Shiny App☆Mark J. Dunning a, Sarah L. Vowler a, Emilie Lalonde b,c, Helen Ross-Adams d, Paul Boutros b,c, Ian G. Mills e,Andy G. Lynch a, Alastair D. Lamb a,f,g,⁎a Cancer Research UK Cambridge Institute, Cambridge Biomedical Campus, UKb Department of Medical Biophysics, University of Toronto, Canadac Informatics and Bio-Computing Program, Ontario Institute for Cancer Research, Toronto, Canadad Bioinformatics Unit, Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University London, UKe Centre for Cancer Research and Cell Biology (CCRCB), Queen's University of Belfast, 97 Lisburn Road, Belfast, UKf Department of Genito-urinary Oncology, Peter MacCallum Cancer Centre, Melbourne, Australiag Academic Urology Group, Department of Surgery, University of Cambridge, UK☆ Web address: bioinformatics.cruk.cam.ac.uk/apps/cam⁎ Corresponding author.E-mail address: Alastair.Lamb@cruk.cam.ac.uk (A.D. Lahttp://dx.doi.org/10.1016/j.ebiom.2017.02.0222352-3964/© 2017 The Authors. Published by Elsevier B.Va r t i c l e i n f oArticle history:Received 22 February 2017Accepted 22 February 2017Available online 2 March 2017Keywords:cluded a number of prominent publicly available sets with both geneexpression and copy number data leading to a cohort of almost 500men (Ross-Adams et al., 2015; Taylor et al., 2010; Grasso et al., 2012).We also included a small landmark series of expression data(Varambally et al., 2005). These studies are summarised in Table 1.Prostate cancerGene signaturePrognosticationOnline interfaceBig dataObtaining access to robust, well-annotated human genomic datasetsis an important step in demonstrating the relevance of experimentalﬁndings and, often, in generating the hypotheses that led to those ex-periments being conducted in the ﬁrst place. We recently publisheddata from the CamCaP Study Group which comprised two cohorts ofmen with prostate cancer who had undergone prostatectomy in Cam-bridge, UK and Stockholm, Sweden (Ross-Adams et al., 2015). We con-sidered how we might best share our output with those who wish tointerrogate the data with their own ideas, gene lists and clinical ques-tions.We recognised thatﬁnding, down-loading, pre-processing and as-similating any suchdataset into a usable format is daunting andmay putoff many researchers. We also felt that interrogation tools generated todate (e.g. cBioPortal) lack functionality as they either cover too manyorgan types, or are limited in the extent, precision and tumour-sitespeciﬁcity of their clinical annotation. We therefore determined toproduce an accessible web-based platform that would permitstraightforward interrogation of these datasets with individualgene identiﬁers or gene sets. Furthermore, we decided to include ad-ditional ‘publicly-accessible’ human prostate cancer sets in order tocAPP/.mb).. This is an open access article underincrease the number of samples available and provide a degree of vali-dation of any observations made across independent cohorts. We in-We plan to include additional studies in the app as well-annotateddatasets become publicly available.An important ﬁnding in our recent study was that prostate cancercould be divided into ﬁve distinct molecular subgroups based on strati-ﬁcation with a small number of copy number features which were alsoassociated with RNA-expression change. These groups had differentclinical outcomes.Wewanted the app to allow researchers to determinethe mean RNA-expression level or copy number status of a single geneor gene-set in prostates from men divided either according to clinicalcategories (Gleason score, biochemical relapse status or tumour type)or according to molecular subgroups. These subgroups could either bepre-deﬁned molecular groups published in the relevant papers, or denovo subgroups generated by hierarchical clustering based on anuploaded geneset.We searched for other web-tools that are already available for thispurpose. Although no such site exists for assessment of subgroup pat-terns or combined expression and copy number proﬁles, the MemorialSloane Kettering Cancer Centre (MSKCC) and Michigan data (Table 1)can be analysed as part of cBioPortal (cBioPortal for Cancer Genomics,n.d.) along with the recently published prostate TCGA dataset(Robinson et al., 2015).Here we introduce the camcAPP (http://bioinformatics.cruk.cam.ac.uk/apps/camcAPP/); a bespoke web interface to multiple prostatecancer genomics datasets. The interface was created with Shiny(https://www.rstudio.com/products/shiny/), and allows the non-specialist Bioinformatician to create publication-ready ﬁgures and ta-bles through an intuitive interface to the underlying computer code.After selecting a dataset of interest, and uploading a list of genes, thefollowing analyses can be performed:1) Boxplots and analysis of variance for expression of genes of interestgrouped by clinical group, sample type, Gleason grade of copy-the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Table 1Summary of studies included in the camcAPP at initial release. Primary Tumours = tissue taken from radical prostatectomy specimens in men with conﬁrmed organ-conﬁned disease.Advanced Tumours = tissue from channel transurethral resection of the prostate (chTURP) or prostatectomy in men with metastatic disease.Dataset Paper Platform: gene expression Platform: copy number Primary tumours Advanced tumours Clinical covariatesMichigan 2005 Varambally et al. (2005)GSE3325Affymetrix U133 2.0 N/A 7 6 Sample GroupMSKCC 2010 Taylor et al. (2010)GSE21032Affymetrix Human 1.0 ST Agilent 244k 109 19 Gleason, Copy Number ClusterMichigan 2012 Grasso et al. (2012)GSE35988Agilent Whole Human 44k Agilent 105k/244k 59 32 Sample GroupStockholm 2015 Ross-Adams et al. (2015)GSE70770Illumina HT12 Affymetrix SNP 6.0 101 N/A iCluster, Sample GroupCambridge 2015 Ross-Adams et al. (2015)GSE70770Illumina HT12 Illumina Omni 2.5 125 19 iCluster, Gleason, Sample Group6 M.J. Dunning et al. / EBioMedicine 17 (2017) 5–6number cluster (N.B. not all covariates available for all data sets) (seeSupplementary Fig. 2).2) A recursive partitioning-based survival analysis and Kaplan-Meierplots on a gene-by-gene basis (Supplementary Fig. 3).3) Pairwise-correlations of gene expression across whole studies andwithin clinical subgroups.4) Clustering andheatmaps of gene expression data,with options to in-terrogate associations between clinical covariates and newly-derived clusters (Supplementary Fig. 4).5) Tabulating the number of copy-number ampliﬁcations and deletionsobserved across whole studies or within a particular clinical covari-ate, and making a heatmap of copy-number calls (SupplementaryFig. 5) (Lalonde et al., 2014).One of the challenges in constructing such a tool is delivering an out-put format that is readily transferable to slides for presentations orpanels of a ﬁgure for publication. We recognise that this is, in part, amatter of axis typesetting and plot conﬁguration but also of deliveringan output ﬁle which permits further adjustment of the ﬁgure in, for ex-ample, Adobe Illustrator™. To this end, all plots can be exported as PDFor PNG ﬁles with conﬁgurable dimensions. Furthermore, for those thatare well-versed in R, the code to produce a particular plot can bedownloaded and modiﬁed as required. A further challenge that weseek to address with this interface is merging datasets for combinedanalysis. We hope to offer this option in due course, as we include fur-ther datasets that include samples analysed on compatible platforms.Strategies to address the Big Data problem have focussed onmakingthe ever-increasing volume of genomic data accessible to scientists andon opening up the possibility of engaging non-specialists (Keener,2015). This approach embodies a responsible attitude to science bothin terms of patient input and ﬁnancial resource and we believe thattools such as this are an important step to maximising the value ofthese landmark studies.We take pleasure inmaking this platform avail-able to the prostate cancer community bymeans of this ‘In Focus’ articlein EBioMedicine, a journal that we believe champions this responsibleapproach to genomic data both in cancer genomics (Kerns et al., 2016)and further aﬁeld (Taudien et al., 2016).FundingCore CRUK funding: MD, AGL, ADL.Academy of Medical Sciences Clinical Lecturer Starter Grant SGCL11(prinicipal funder of this work): ADL.No other funding source to declare. The funding bodies have notbeen involved in any way in the design, completion or writing of theproject.Authors' contributionStudy concept: MD, AGL, ADL.Study design: MD, AGL, ADL.CamCaP Study Group leads: ADL, HRA.Data programming: MD, SV, AGL.Programme contributions: EL.Beta testing: IGM, EL, PB.Oversight: AGL, ADL.DisclosureThe authors have no conﬂicts of interest to declare.AcknowledgementsThe authors would like to thank Ola Bratt, Christof Kastner, RajeshNair and Declan Murphy for their critical and creative advice in reviewof the manuscript and/or design. We also acknowledge David Neal asthe lead and principal investigator for the CamCaP Study Group.Appendix A. Supplementary dataSupplementary data to this article can be found online at http://dx.doi.org/10.1016/j.ebiom.2017.02.022. This includes a ‘manual’ for theShiny App which can also be downloaded from the app itself.ReferencescBioPortal for Cancer Genomics. Memorial Sloane Kettering Cancer Centre, www.cbioportal.org, (Accessed: 23/03/2016).Grasso, C.S., Wu, Y.M., Robinson, D.R., Cao, X., Dhanasekaran, S.M., Khan, A.P., et al., 2012.The mutational landscape of lethal castration-resistant prostate cancer. Nature 487(7406), 239–243.Keener, Amanda B., July 8, 2015. The Scientist http://www.the-scientist.com/?articles.view/articleNo/43483/title/Big-Data-Problem/ (Accessed: 23/3/16).Kerns, S.L., Dorling, L., Fachal, L., Bentzen, S., Pharoah, P.D., Barnes, D.R., et al., 2016. Meta-analysis of genome wide association studies identiﬁes genetic markers of late toxicityfollowing radiotherapy for prostate cancer: radiogenomics consortium. EBioMedicine10:150–163. http://dx.doi.org/10.1016/j.ebiom.2016.07.022.Lalonde, E., Ishkanian, A.S., Sykes, J., Fraser, M., Ross-Adams, H., Erho, N., et al., 2014. Tu-mour genomic and microenvironmental heterogeneity for integrated prediction of5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lan-cet Oncol. 15 (13), 1521–1532.Robinson, D., Van Allen, E.M., Wu, Y.M., Schultz, N., Lonigro, R.J., Mosquera, J.M., et al.,2015. Integrative clinical genomics of advanced prostate cancer. Cell 161 (5),1215–1228.Ross-Adams, H., Lamb, A.D., Dunning, M.J., Halim, S., Lindberg, J., Massie, C.M., et al., 2015.Integration of copy number and transcriptomics provides risk stratiﬁcation in pros-tate cancer: a discovery and validation cohort study. EBioMedicine 2 (9), 1133–1144.Taudien, S., Lausser, L., Giamarellos-Bourboulis, E.J., Sponholz, C., Schöneweck, F., Felder,M., et al., 2016. Genetic factors of the disease course after sepsis: rare deleterious var-iants are predictive. EBioMedicine 12:227–238. http://dx.doi.org/10.1016/j.ebiom.2016.08.037.Taylor, B.S., Schultz, N., Hieronymus, H., Gopalan, A., Xiao, Y., Carver, B.S., et al., 2010. In-tegrative genomic proﬁling of human prostate cancer. Cancer Cell 18 (1), 11–22.Varambally, S., Yu, J., Laxman, B., Rhodes, D.R., Mehra, R., Tomlins, S.A., et al., 2005. Inte-grative genomic and proteomic analysis of prostate cancer reveals signatures of met-astatic progression. Cancer Cell 8 (5), 393–406.

Mining Human Prostate Cancer Datasets: The “camcAPP” Shiny App

Obtaining access to robust,well-annotated human genomic datasets is an important step in demonstrating the relevance of experimental findings and, often, in generating the hypotheses that led to those experiments being conducted in the first place. We recently published data from the CamCaP Study Group which comprised two cohorts of men with prostate cancer who had undergone prostatectomy in Cambridge, UK and Stockholm, Sweden (Ross-Adams et al., 2015).We considered how we might best share our output with those who wish to interrogate the data with their own ideas, gene lists and clinical questions. Werecognised that finding, down-loading, pre-processing and assimilating any such dataset into a usable format is daunting andmay put off many researchers.We also felt that interrogation tools generated to date (e.g. cBioPortal) lack functionality as they either cover too many organ types, or are limited in the extent, precision and tumour-site specificity of their clinical annotation. We therefore determined to produce an accessible web-based platform that would permit  straightforward interrogation of these datasets with individual gene identifiers or gene sets. Furthermore, we decided to include additional ‘publicly-accessible’ human prostate cancer sets in order to increase the number of samples available and provide a degree of validation of any observations made across independent cohorts. We included a number of prominent publicly available sets with both gene expression and copy number data leading to a cohort of almost 500 men (Ross-Adams et al., 2015; Taylor et al., 2010; Grasso et al., 2012). We also included a small landmark series of expression data (Varambally et al., 2005). These studies are summarised in Table 1. We plan to include additional studies in the app as well-annotated datasets become publicly available

Oxford University Research Archive

EBioMedicine 17 (2017) 5–6Contents lists available at ScienceDirectEBioMedicinej ourna l homepage: www.eb iomed ic ine.comIn FocusMining Human Prostate Cancer Datasets: The “camcAPP” Shiny App☆Mark J. Dunning a, Sarah L. Vowler a, Emilie Lalonde b,c, Helen Ross-Adams d, Paul Boutros b,c, Ian G. Mills e,Andy G. Lynch a, Alastair D. Lamb a,f,g,⁎a Cancer Research UK Cambridge Institute, Cambridge Biomedical Campus, UKb Department of Medical Biophysics, University of Toronto, Canadac Informatics and Bio-Computing Program, Ontario Institute for Cancer Research, Toronto, Canadad Bioinformatics Unit, Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University London, UKe Centre for Cancer Research and Cell Biology (CCRCB), Queen's University of Belfast, 97 Lisburn Road, Belfast, UKf Department of Genito-urinary Oncology, Peter MacCallum Cancer Centre, Melbourne, Australiag Academic Urology Group, Department of Surgery, University of Cambridge, UK☆ Web address: bioinformatics.cruk.cam.ac.uk/apps/cam⁎ Corresponding author.E-mail address: Alastair.Lamb@cruk.cam.ac.uk (A.D. Lahttp://dx.doi.org/10.1016/j.ebiom.2017.02.0222352-3964/© 2017 The Authors. Published by Elsevier B.Va r t i c l e i n f oArticle history:Received 22 February 2017Accepted 22 February 2017Available online 2 March 2017Keywords:cluded a number of prominent publicly available sets with both geneexpression and copy number data leading to a cohort of almost 500men (Ross-Adams et al., 2015; Taylor et al., 2010; Grasso et al., 2012).We also included a small landmark series of expression data(Varambally et al., 2005). These studies are summarised in Table 1.Prostate cancerGene signaturePrognosticationOnline interfaceBig dataObtaining access to robust, well-annotated human genomic datasetsis an important step in demonstrating the relevance of experimentalfindings and, often, in generating the hypotheses that led to those ex-periments being conducted in the first place. We recently publisheddata from the CamCaP Study Group which comprised two cohorts ofmen with prostate cancer who had undergone prostatectomy in Cam-bridge, UK and Stockholm, Sweden (Ross-Adams et al., 2015). We con-sidered how we might best share our output with those who wish tointerrogate the data with their own ideas, gene lists and clinical ques-tions.We recognised thatfinding, down-loading, pre-processing and as-similating any suchdataset into a usable format is daunting andmay putoff many researchers. We also felt that interrogation tools generated todate (e.g. cBioPortal) lack functionality as they either cover too manyorgan types, or are limited in the extent, precision and tumour-sitespecificity of their clinical annotation. We therefore determined toproduce an accessible web-based platform that would permitstraightforward interrogation of these datasets with individualgene identifiers or gene sets. Furthermore, we decided to include ad-ditional ‘publicly-accessible’ human prostate cancer sets in order tocAPP/.mb).. This is an open access article underincrease the number of samples available and provide a degree of vali-dation of any observations made across independent cohorts. We in-We plan to include additional studies in the app as well-annotateddatasets become publicly available.An important finding in our recent study was that prostate cancercould be divided into five distinct molecular subgroups based on strati-fication with a small number of copy number features which were alsoassociated with RNA-expression change. These groups had differentclinical outcomes.Wewanted the app to allow researchers to determinethe mean RNA-expression level or copy number status of a single geneor gene-set in prostates from men divided either according to clinicalcategories (Gleason score, biochemical relapse status or tumour type)or according to molecular subgroups. These subgroups could either bepre-defined molecular groups published in the relevant papers, or denovo subgroups generated by hierarchical clustering based on anuploaded geneset.We searched for other web-tools that are already available for thispurpose. Although no such site exists for assessment of subgroup pat-terns or combined expression and copy number profiles, the MemorialSloane Kettering Cancer Centre (MSKCC) and Michigan data (Table 1)can be analysed as part of cBioPortal (cBioPortal for Cancer Genomics,n.d.) along with the recently published prostate TCGA dataset(Robinson et al., 2015).Here we introduce the camcAPP (http://bioinformatics.cruk.cam.ac.uk/apps/camcAPP/); a bespoke web interface to multiple prostatecancer genomics datasets. The interface was created with Shiny(https://www.rstudio.com/products/shiny/), and allows the non-specialist Bioinformatician to create publication-ready figures and ta-bles through an intuitive interface to the underlying computer code.After selecting a dataset of interest, and uploading a list of genes, thefollowing analyses can be performed:1) Boxplots and analysis of variance for expression of genes of interestgrouped by clinical group, sample type, Gleason grade of copy-the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Table 1Summary of studies included in the camcAPP at initial release. Primary Tumours = tissue taken from radical prostatectomy specimens in men with confirmed organ-confined disease.Advanced Tumours = tissue from channel transurethral resection of the prostate (chTURP) or prostatectomy in men with metastatic disease.Dataset Paper Platform: gene expression Platform: copy number Primary tumours Advanced tumours Clinical covariatesMichigan 2005 Varambally et al. (2005)GSE3325Affymetrix U133 2.0 N/A 7 6 Sample GroupMSKCC 2010 Taylor et al. (2010)GSE21032Affymetrix Human 1.0 ST Agilent 244k 109 19 Gleason, Copy Number ClusterMichigan 2012 Grasso et al. (2012)GSE35988Agilent Whole Human 44k Agilent 105k/244k 59 32 Sample GroupStockholm 2015 Ross-Adams et al. (2015)GSE70770Illumina HT12 Affymetrix SNP 6.0 101 N/A iCluster, Sample GroupCambridge 2015 Ross-Adams et al. (2015)GSE70770Illumina HT12 Illumina Omni 2.5 125 19 iCluster, Gleason, Sample Group6 M.J. Dunning et al. / EBioMedicine 17 (2017) 5–6number cluster (N.B. not all covariates available for all data sets) (seeSupplementary Fig. 2).2) A recursive partitioning-based survival analysis and Kaplan-Meierplots on a gene-by-gene basis (Supplementary Fig. 3).3) Pairwise-correlations of gene expression across whole studies andwithin clinical subgroups.4) Clustering andheatmaps of gene expression data,with options to in-terrogate associations between clinical covariates and newly-derived clusters (Supplementary Fig. 4).5) Tabulating the number of copy-number amplifications and deletionsobserved across whole studies or within a particular clinical covari-ate, and making a heatmap of copy-number calls (SupplementaryFig. 5) (Lalonde et al., 2014).One of the challenges in constructing such a tool is delivering an out-put format that is readily transferable to slides for presentations orpanels of a figure for publication. We recognise that this is, in part, amatter of axis typesetting and plot configuration but also of deliveringan output file which permits further adjustment of the figure in, for ex-ample, Adobe Illustrator™. To this end, all plots can be exported as PDFor PNG files with configurable dimensions. Furthermore, for those thatare well-versed in R, the code to produce a particular plot can bedownloaded and modified as required. A further challenge that weseek to address with this interface is merging datasets for combinedanalysis. We hope to offer this option in due course, as we include fur-ther datasets that include samples analysed on compatible platforms.Strategies to address the Big Data problem have focussed onmakingthe ever-increasing volume of genomic data accessible to scientists andon opening up the possibility of engaging non-specialists (Keener,2015). This approach embodies a responsible attitude to science bothin terms of patient input and financial resource and we believe thattools such as this are an important step to maximising the value ofthese landmark studies.We take pleasure inmaking this platform avail-able to the prostate cancer community bymeans of this ‘In Focus’ articlein EBioMedicine, a journal that we believe champions this responsibleapproach to genomic data both in cancer genomics (Kerns et al., 2016)and further afield (Taudien et al., 2016).FundingCore CRUK funding: MD, AGL, ADL.Academy of Medical Sciences Clinical Lecturer Starter Grant SGCL11(prinicipal funder of this work): ADL.No other funding source to declare. The funding bodies have notbeen involved in any way in the design, completion or writing of theproject.Authors' contributionStudy concept: MD, AGL, ADL.Study design: MD, AGL, ADL.CamCaP Study Group leads: ADL, HRA.Data programming: MD, SV, AGL.Programme contributions: EL.Beta testing: IGM, EL, PB.Oversight: AGL, ADL.DisclosureThe authors have no conflicts of interest to declare.AcknowledgementsThe authors would like to thank Ola Bratt, Christof Kastner, RajeshNair and Declan Murphy for their critical and creative advice in reviewof the manuscript and/or design. We also acknowledge David Neal asthe lead and principal investigator for the CamCaP Study Group.Appendix A. Supplementary dataSupplementary data to this article can be found online at http://dx.doi.org/10.1016/j.ebiom.2017.02.022. This includes a ‘manual’ for theShiny App which can also be downloaded from the app itself.ReferencescBioPortal for Cancer Genomics. Memorial Sloane Kettering Cancer Centre, www.cbioportal.org, (Accessed: 23/03/2016).Grasso, C.S., Wu, Y.M., Robinson, D.R., Cao, X., Dhanasekaran, S.M., Khan, A.P., et al., 2012.The mutational landscape of lethal castration-resistant prostate cancer. Nature 487(7406), 239–243.Keener, Amanda B., July 8, 2015. The Scientist http://www.the-scientist.com/?articles.view/articleNo/43483/title/Big-Data-Problem/ (Accessed: 23/3/16).Kerns, S.L., Dorling, L., Fachal, L., Bentzen, S., Pharoah, P.D., Barnes, D.R., et al., 2016. Meta-analysis of genome wide association studies identifies genetic markers of late toxicityfollowing radiotherapy for prostate cancer: radiogenomics consortium. EBioMedicine10:150–163. http://dx.doi.org/10.1016/j.ebiom.2016.07.022.Lalonde, E., Ishkanian, A.S., Sykes, J., Fraser, M., Ross-Adams, H., Erho, N., et al., 2014. Tu-mour genomic and microenvironmental heterogeneity for integrated prediction of5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lan-cet Oncol. 15 (13), 1521–1532.Robinson, D., Van Allen, E.M., Wu, Y.M., Schultz, N., Lonigro, R.J., Mosquera, J.M., et al.,2015. Integrative clinical genomics of advanced prostate cancer. Cell 161 (5),1215–1228.Ross-Adams, H., Lamb, A.D., Dunning, M.J., Halim, S., Lindberg, J., Massie, C.M., et al., 2015.Integration of copy number and transcriptomics provides risk stratification in pros-tate cancer: a discovery and validation cohort study. EBioMedicine 2 (9), 1133–1144.Taudien, S., Lausser, L., Giamarellos-Bourboulis, E.J., Sponholz, C., Schöneweck, F., Felder,M., et al., 2016. Genetic factors of the disease course after sepsis: rare deleterious var-iants are predictive. EBioMedicine 12:227–238. http://dx.doi.org/10.1016/j.ebiom.2016.08.037.Taylor, B.S., Schultz, N., Hieronymus, H., Gopalan, A., Xiao, Y., Carver, B.S., et al., 2010. In-tegrative genomic profiling of human prostate cancer. Cancer Cell 18 (1), 11–22.Varambally, S., Yu, J., Laxman, B., Rhodes, D.R., Mehra, R., Tomlins, S.A., et al., 2005. Inte-grative genomic and proteomic analysis of prostate cancer reveals signatures of met-astatic progression. Cancer Cell 8 (5), 393–406.

English

Mining human prostate cancer datasets: The "camcAPP" shiny app.

Mark J. Dunning

Sarah L. Vowler

Paul Boutros

Ian G. Mills

Andy G. Lynch

Alastair D. Lamb

Core CRUK funding: MD, AGL, ADL.

Academy of Medical Sciences Clinical Lecturer Starter Grant SGCL11 (prinicipal funder of this work): ADL

Queen Mary Research Online

Mining Human Prostate Cancer Datasets: The "camcAPP" Shiny App

http://bioinformatics.cruk.cam.ac.uk/apps/camcAPP/

Mining Human Prostate Cancer Datasets: The “camcAPP” Shiny App

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