Systems Biology for Personalised Medicine? Marc Wilkins Topics of this lecture: 1) What is systems biology 2) Systems biology and disease 3) Case study – patient classification by „network type‟ 4) Confounding factors for personalised medicine 1
What is Systems Biology?* The study of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions, which together give rise to life. Systems Biology differs from previous biological approaches as it: - does not focus on individual components - focuses on the interactions of all components and how they work as a system Systems biology has become possible through the rise of the „omics‟. * summarised from www.systemsbiology.org Why is Systems Biology Important? There are properties of biology which cannot be discovered by analysing individual components. These are known as emergent properties. These are said to be irreducible (cannot be broken down). This engine has many parts. The power and torque produced by the engine are emergent properties. Are there others? These would be difficult to discover without studying it as a system. 2
Systems Biology and Disease Some diseases are monogenic - mutations in a single gene lead to disease - e.g. cystic fibrosis, retinoblastoma, Huntington‟s BUT… Most human diseases are complex and/or polygenic - many cancers - diabetes - cardiovascular disease Many also have environmental influences. Study of genes and proteins as part of a system should help understand the basis of complex disease, and to personalise therapies. Figure: Peltonen and McKusick (2001) Science 291, 1224- 29. 3
Systems Biology for Personalised Medicine genomics, transcriptomics, proteomics, kinomics, metabolomics, lipidomics, glycomics PPI networks metabolic pathways signaling networks patient classification? therapy? Patient Classification by „Network Type‟ 4
Sarah-Jane Schramm Cancer cells show dysregulation, as compared to normal cells. Following Taylor et al. (Nat. Biotech. 2009. 27:199-204) do networks of „bad outcome‟ melanoma (surviving < 1 year) show dysregulation compared to „good outcome‟ melanoma (surviving > 4 years)?? Does this reveal proteins of interest? Prognostic or possible therapeutic value… 5
Some considerations… - The correlation of expression of interacting proteins can be measured. This can be done for hubs…. Why hubs…??? - But….. - Every expression set from every melanoma sample is genetically different … - Human network data is sparse and of medium to low quality… there is no gold standard… - How can we define a hub in a human cell? Data Preparation Select 4 melanoma gene expression microarray datasets (REMARK compliant) Partition datasets by 65 „good‟ outcome 93 „bad‟ outcome patient outcome Prepare 4 human iRefWeb, BioGRID, protein-protein MetaCore, HPRD interaction networks Identify hubs from >5 interactions or networks using 2 top 15% of hubs approaches Undertake hub analysis 6
Hub Analysis Aim: identify hubs with patterns of gene co-expression that are different between good and bad outcome patients bad outcome good outcome Correlation of expression between hub and partner Is the above hub of interest….. Why? Results Edges show correlation with hub. Network topology HDAC1 HDAC1 preserved. Experiment done 32 times, concordance measured. 7
Results • 32 hubs showed dysregulation in bad versus good outcome patients in 5 or more of the 32 experiments • 21 of 32 hubs were of interest (REDISCOVERED) here – 4 hubs are known correlates of melanoma prognosis ( CCNA2 ), progression ( HIF1A ), or tumour thickness ( TNF and SMAD2 ) – 9 hubs are already drug targets ( AKT1 , HDAC1 , HIF1A , IKBKB , JAK1 , PIM1 , PTPN11 , TNF , and TGM2 ) – 8 are causally implicated in other cancers ( AKT1 , CIITA , CREBBP , FANCG , JAK1 , NF2 , PIM1 , and PTPN11 ) Hanahan- Weinberg „cancer hallmarks‟: functional significance of the 32 hubs 8
Classification of Patients by Hubs 32-hub expression signature, K-nearest neighbour classification Cohort: Mann Bogunovic Jonsson John Sample size 47 (25;22) 33 (23;10) 54 (7;47) 24 (10;14) (n good outcome ; n poor outcome ) survival >4yr with time taken to no sign of relapse survival ≥ 1.5yr tumor progression Outcomes or <1yr after overall or<1.5yr since from stage III to compared surgical resection survival metastasis stage IV disease of stage III ≥2yr or <2yr disease Good / bad prediction error 0.33 0.24 0.20 0.29 (LOOCV under KNN) Prediction error by clinical 0.56 * tumor-positive lymph nodes, tumor burden at the time of parameters* staging, presence or absence of primary tumor ulceration, and thickness of the primary melanoma (Balch et al. 2009). Conclusions….. Networks showed reproducible, survival-associated differences Hubs with correlative differences were functionally relevant Hub expression signatures could classify patients (why?) and Implications Systems-based approaches have been successful in achieving one aspect of personalised medicine…. And suggesting a number of novel protein candidates of interest. But….. classification was not by network …. despite efforts to do so…..!! 9
Network Analysis Formalised: VAN: an R-package for identifying biologically perturbed networks via differential variability analysis. Is network dysregulation widespread? V. Jayaswal, SJ Schramm, G Mann, MR Wilkins, J Yang. submitted. Confounding Factors for Systems-based Personalised Medicine? Inter-cellular noise 10
Single Cell Proteomics: yeast Newman et al. 2006 Nature 441: 840-6. GFP chromosome-based fusions of 4159 proteins, measured cell by cell with FACS Noise is related to protein function: - high noise proteins include stress- response, amino acid biosynthesis, and heat shock - low noise proteins include translation initiation, ribosomal and degradation Noise is related to localisation: - High noise include mitochondria and peroxisome proteins - Low noise include Golgi proteins Noise or Variance in Networks Gene expression variance calculated from 270 yeast microarray experiments. A: proteasome regulatory lid B: mediator complex C: SAGA complex D: SWR1 complex Data: Komurov & White 2007 Mol Syst Biol. 3: 110. static dynamic 11
Single Cell Proteomics: human cell line Cohen et al. (2008) Science 322, 1511-16. Response of human lung carcinoma cell line to camptothecin: - Cell line double-transformed with mCherry (nuclear) then YPF fusions - 1020 proteins studied - Video microscopy and image analysis Proteins showed cell to cell variability: - standard deviation of 10% to 60% of mean - 20% of this variability due to cell-cycle - r emainder due to stochastic processes. 12
Relationship between average expression level in single cells ( μ , x axis) and standard deviation ( σ , y axis) for 6,313 genes. “We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts… Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. ” Confounding Factors for Systems-based Personalised Medicine? Inherited noise? 13
Cancer Biomarkers have Different „Normal‟ Ranges Gene expression data: - B cells - 233 individuals, 14 families, 3 generations per family PPA2, CDNK1A, CD44 show small to large family-associated differences in median and variance of expression Little, Williams, Wilkins (2009) Trends Biotech, 27: 5-10. Approved and In-Clinic Biomarkers have Lower Inter-Individual Variation 1,261 „biomarker‟ genes. 9 are FDA approved. 32 in clinical use. Approved & clinical biomarkers have statistically lower inter-individual variation. 14
Final Comments - Integration of networks with expression (or variance) data is a powerful experimental paradigm - Hubs can be a biologically relevant focus - although be aware of biases - Much is still to be learned about networks at different levels - per individual - per tissue - per cell - Incompleteness of human networks remains a significant constraint Acknowledgements Marc Wilkins Graham Mann Jean Yang Apurv Goel Sarah-Jane Schramm Simone Li Anna Campain Chi Nam Ignatius Pang Vivek Jayaswal David Fung Richard Scolyer 15
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