Stratified Medicine - Applications and Case Studies
Stratified Medicine –Opportunities for Business 24th January 2013
Session 3 – Applications and Case Studies 14:00 Systems Biology in Cancer – Dr Andrei Zinovyev, Institut Curie, Paris 14:20 Single Molecule Imaging Technology – Professor George Fraser, University of Leicester 14:35 Knowledge Engineering for Biomedical Research – Dr Jonathan Tedds, University of Leicester 14:50 Applications in an SME Environment – Dr Kevin Slater, PetScreen Ltd
Systems Biology in Cancer Dr Andrei Zinovyev Institut Curie, Paris
Systems Biology of Cancer Andrei Zinovyev Institut Curie - INSERM U900 / Mines ParisTech Computational Systems Biology of Cancer Stratified Medicine - Opportunities for Business Leicester - 23 January 2013
Institut Curie, Bioinformatics and Systems Biology of Cancer DepartmentInstitut Curie • Created in 1909 by Marie Curie • From fundamental research to innovative treatment • Comprehensive Cancer Center • 2 cancer hospitals, focus on breast cancer, pediatric tumors, uveal melanoma • 15 research departments • 3,000 staffComputational Systems Biology of Cancer group (http://sysbio.curie.fr)• 15 people (physicists, mathematicians, biologists)• Cancer data analysis• Mathematical modeling of cancer processes• Collaborations with pharmaceutical companies
Example of Stratified/Personalized Medicine: SHIVA clinical trial at Institut Curie InformedPatients with refractory consentcancer (all tumor types) signed Tumor biopsy + Blood sample High Throughput Sequencing Therapy based on molecular profiling - Approved molecularly targeted agent Informed Molecular profiling consent signed R Conventional therapy based on Molecular oncologist’s choice Prospective Eligible biology cohort patient board Cross-over Specific NO therapy YES available
One of the problems of personalized medicine: existence of complex feedbacks in a cancer cellAn example of «paradoxal» answer to treatment (Prahallad et al, Nature 2012)
HOW SYSTEMS BIOLOGY CAN HELP? (what is systems biology?) can it be a support for rationaldecision-making in stratified medicine?
Two “systems biologies” 2001 2002…studying biological systems by …studying structure and dynamicssystematically disturbing them of cellular and organismaland monitoring the gene, protein function, rather than theand informational pathway characteristics of isolated parts of aresponses and integrating these cell, with particular emphasis ondata in mathematical models emerging system properties such as robustness…Danger: high-throughput stamp Danger: creating fruitlesscollection abstractions
Computational Systems Biology of Cancer Specific flavor of systems biologyObject: cancer and cancer treatmentTools:1) High-throughput data withparticular emphasis on individualgenomic data,2) Statistical analysis in large dimensions3) Mathematical modeling (“what if”questions)Objective: prediction of cancertreatment success in a concrete patient(virtual tumour in virtual patient?)
Computational Systems Biology of Cancer group at Institut CurieObjective of our group: based on existing knowledge anddata, be able to explain why certain mutations of normalgenome can lead to tumorigenesis, and how to reversetheir effect?Tools: Formal representation of biological knowledge (map of cancer) Mathematical modeling (“animation”) of biological diagrams Mechanistic models of epistasy (genetic interactions)
Cancer: hallmarks, networks and maps Task: assemble this network at its full complexity Problems: What language to use? How to navigate? How to maintain? Hanahan and Weinberg, 2011, Cell How to use?
Towards an Atlas of Cancer Signaling Networks Atlas of Cancer Signalling NetworksRB/E2F-Cell Cycle DNA repair-Cell Cycle • CellDesigner tool (Diagram editor for signaling networks representation) • Systems Biology Graphical Notation (SBGN) visual syntax Calzone et al, Kuperstein et al, Mol Syst Bio 2008 unpublishedCell Survival Cell death-energy metabolism • Coming: maps of EMT, motility, Cohen et al, Fourquet et al, unpublished unpublished polarity, immune response
NaviCell: Navigation and curation of Atlas of Cancer Signaling Networks Atlas of Cancer Signalling Networks NaviCell = Google map + Semantic zoom + BlogGoogle map Blog Semantic zoomNaviCell: a web tool for navigation, curation and maintenance of molecular interaction maps. http://navicell.curie.frKuperstein I, Pook S, Cohen DPA, Calzone L, Barillot E and Zinovyev A (submitted) email@example.com
Pathway “staining” and Anna Karenina’s principle 506, G1, T1, noninvasive 1533-1, G3, T4, invasive 2307, G2, T2, invasive 870-1, normal 3721-10, normal 915-1, normal
Using the maps: finding alternative routesAll path of length <30 from Through ROS formation by thesuccinate to DNA damage respiratory chain Through transfer of the reductive equivalents of succinate to NADPH and thioredoxin, then ROS detoxification or RNR activity and DNA repair Through reduction of ubiquinone, the oxidative equivalents of which are necessary for pyrimidine biosynthesis and DNA repair (see Khutornenko AA et al., PNAS, 2010,107,12828)
Example: Cell fate decision mechanism fragilities utilized by cancers (Calzone et al, 2010) Ewing’s Lung cancers, sarcoma, cervical cancers, lung cancer, oesophageal squamous neuroblastomas Lymphomas cell carcinomas Colorectal Lymphomas, tumors breast cancer
Compute phenotype probabilities using state transition graphs Asynchronous state transition graphInfluence graph = The probability to reach a final state from an initial state = probability of observing a phenotype in experiment Apoptosis Necrosis Survival
Validate the model with mutants TNF=1Example : Caspase 8 deletion• ≈ 85% survival (NFkB)• ≈ 15% necrosis• No apoptosisQualitatively consistent with the literature“TNF-induced apoptosis is blocked though not necrosis”[Kawahara, Ohsawa et al., J Cell Biol 1998](Jurkat cells, C8-/-) Naïve NFkB apoptosis necrosis survival survival
Synthetic lethality and cancer treatment: hot topic in new anticancer drug development If gene A is already mutated in cancer cells,Gene A Gene B targeting B will specifically kill cancer cells leaving normal cells intactGene A Gene B Example: BRCA1+PARP synthetic lethal pair (PARP inhibitors, Helleday, Carcinogenesis, 2010)Gene A Gene B If gene A is amplified in cancer, then one should look for synthetic dosage lethality There is a big promise here for stratified medicine
Example: Metastases in mouse model of colon cancerExperimental system: p53-null mouse Colon cancer is associated with: Mutations in APC gene (b-catenin/WNT pathway) Mutations in RAS gene Less frequent mutations in many other pathways (Notch, MLH, PTEN, SMAD, etc.)Question: what combination of mutations in these pathwayslead to rapid metastatic tumorigenesis?
Epithelial-Mesenchymal Transition (EMT):a necessary condition to appearance of metastasesFrom Friedl and Alexander, Cell, 2011
Synthetic interaction between p53 and overexpression of NICD leads to EMT in a mouse model of metastasizing colon cancer p53 is downNICD NICD NICD is up NICD is up and p53 is down
Take home messageImplementing Personalized (Stratified) medicine has a numberof obstacles, including complex response of cancer cells totreatmentUnderstanding and predicting this response requires either “try and fail” approach or / and more intelligent guess (systems biology)Use of synthetic interactions (synthetic lethality) is a newparadigm of individualized cancer treatment
AcknowledgementsCurie - INSERM U900 Funding MAE MOST-FI P2R / Mines ParisTech ANR SITCON Ligue contre le cancer EC FP7 APO-SYSComputational Systems ANR CALAMARBiology of Cancer team INCA SYBEWINGEmmanuel Barillot Curie-Servier Alliance Institut des Systèmes ComplexesValentina Boeva Collaborators EC FP7 ASSETEric Bonnet INCA IVOIRESLaurence Calzone Daniel Louvard (Institut Curie) INCA Breast cancer predispositionDavid Cohen Sylvie Robine (Institut Curie) Investissements d’avenir Bio- QuickTime™ et un décompresseur sont requis pour visionner cette image.Simon Fourquet Boris Zhivotovsky (Karolinska) informatique ABS4NGSInna Kuperstein Wolf-Dietrich Heyer (UC Davis) EC FP7 RAIDLoredana Martignetti Alexander Gorban (Leicester, UK) Cancéropole IDF Data integrationTatiana Popova ITMO cancer SystemsDaniel Rovera Biology INVADEMeriem Sefta PIC Computational SystemsGautier Stoll Biology of CancerBruno TessonPaola Vera-Licona
Single Molecule Imaging Technology Professor George Fraser University of Leicester
A Physical Analysis of Microarray Data G.W. Fraser Space Research Centre, Department of Physics and Astronomy,Michael Atiyah Building, University of Leicester, Leicester LEI 7RH, UK.
The Future of Biology is the Detection of Light• Spin-off company since 2002 based on ESA/ESTEC optical STJ detector technology• Disruptive hyperspectral imaging of unequalled sensitivity• Operation at 0.3 K• Hardware entry point to studies of basic fluorophore response and microarray analysis Self-quenching 1.5 Texas Red 5 Comparison of measured and tabulated emission spectra 4 Counts/10nm/second Alexa 488 1 3 S(n) 2 Fluorescein-EX 0.5 Alexa 546 1 0 0 450 500 550 600 650 700 750 800 0 5 10 15 20 Wavelength (nm) n , Fluorophores/molecule
The Microarray as a Two-Dimensional Electronic Imaging Device Microarrays exhibit a number of “confounding factors” familiar to the detector physicist :• Spatial non-uniformity (imperfect flat-field and fixed-pattern noise)• Temporal variability (photobleaching)• Integral Non-linearity (output not linearly dependent on input)• Digital divide errors and preferred locations *• Differential Non-linearity (non-uniform sensitivity) * Data from: (a) two-colour Red/Green Cy3,Cy5 spotted arrays (SMD Blader3932 and Willert wnt3a) (b) Affymetrix Genepix (TDF458 SMD) (c) Quantile data (courtesy Dr J Luo, MRC Toxicology Unit / Tas Gohir)
Knowledge Engineering from Biomedical Research Dr Jonathan Tedds University of Leicester
BRISSKit:Biomedical Research Infrastructure Software Service KitA vision for cloud-based open source research applications#BRISSKithttp://www.brisskit.le.ac.uk
BRISSKit context: The I4Health goal of applying knowledge engineering to close the ‘ICT gap’ between research and healthcare (Beck, T. et al 2012) Data as a public good & research efficiencies = strategic priority for government, NHS, funders (e.g. MRC, Wellcome, CRUK)
Overview of BRISSKit• Developing “software as a service” data management infrastructure based on open- source applications• More efficient & easier for researchers• Offers significant savings in research database and IT support costs• Development funded by HEFCE• University of Leicester in partnership with the University Hospitals Leicester Trust and the Cardiovascular BRU
BRISSkit USPs Integrated support for core research processes Well-established mature open source applications as protoyped in Cardiovascular: fully UK customised A platform for seamless management and integration between applications An API allows integration with existing clinical systems Easy set up, use and administration through browser (including on mobile devices) Capability of being hosted in any compliant cloud provider including UHL (NHS information governance)
BRISSkit components = web services CiviCRMEnables end-to-endcontact managementfor volunteers andresearch participants,tracking approaches,contact, responses,recruitment,exclusions.CiviCRM was designedfor the civic sectorand has an objectmodel that reflectscommunity buildingand non-profitrelationships.
OBiBa OnyxRecords participantconsent, questionnairedata and primaryspecimen IDs.Web-based, securedata entry by researchstaff. E.g. used for allpatient recruits inLCBRU – mobilecomputing on wardsand outpatient clinic inTMF.Await significant newrelease…
Market: who is BRISSkit for?Modular approaches and scalable tools with opensource licenses make good investments• Individual researchers and associates • enterprise-level tools without the IT overheads• Research themes and departments • stand-alone instances of required tools to accelerate research• Research units and centres • integrated toolkit with clinical data loading services, or jigsaw pieces to complement existing provision
Applications in an SME Environment Dr Kevin Slater PetScreen Ltd
Dogs, Cancer and MathematicsAn SME Perspective on University Collaboration. Kevin Slater Ilias Alexandrakis, Renu Tuli Alexander Gorban, Evgeny Mirkes
Why Dogs? Why Lymphoma? USA dog population = 78 Million Canine Lymphoma - Incidence- 20% of all canine tumours are lymphoma cases- 0.1% of older dogs will develop lymphoma- Very high incidence in some breeds, e.g. Golden Retrievers 25% in USA Canine Lymphoma - Symptoms• Lymphadenopathy• Lethargy• Weakness• Fever• Anorexia• Pu/Pd
Canine Lymphoma – Treatment• Predominantly treated with chemotherapy• Diverse range of treatment protocols• Initially responds well to treatment Canine Lymphoma – Prognosis B-cell lymphoma favorable to T-cell lymphoma Clinical stage (Stage V has poorer prognosis against Stage I) Dogs treated with chemotherapy experience a greater survival time Recurrence almost inevitable Presents a good model for Non-Hodgkin’s Lymphoma in humans
Canine Lymphoma Diagnosis Cytology Histology Immunophenotyping (T or B cell) • Generally invasive procedures • FNA prone to no diagnostic samples • Not suitable to treatment monitoring
Serum Biomarkers Serum easily accessible Potential for picking up circulating biomarkers Diverse array of cancers whereby potential biomarkers identified Prostate cancer Breast cancer Melanoma Developed a serum biomarker approach to assist with detection of canine lymphoma
Multi Vs Single Biomarker tests in Human Testing
Data Processing 79 peaks identified on first pass. Greater than 30 peaks with P<0.05 (Mann Whitney U- test) between the two populations Manual triage resulted in19 candidate peaks for CART analysis Final algorithm focuses on two key biomarkers, 1 up regulated and 1 down regulated
Classification and Regression TreeBreiman L, Friedman JH, Olshen RA, Stone CJ.Classification and Regression Trees.Chapman & Hall (Wadsworth, Inc.): New York, 1984.
Bioinformatic model generation - CARTInitial Training Sample Set (Biomarker Identification):Samples used to develop model (n=21) randomly selected: 10 non-lymphoma 11 lymphomaInitial Test Sample Set (Biomarker Verification):Samples used as independent test set (n= 158): 82 Non-lymphoma 76 Lymphoma These samples were blind to the algorithm
First Collaboration:Charles W. Gehrke Proteomics Centre University of Missouri
Summary of Biomarker Identification Studies• Protein sequence analysis identified 3 different biomarkers• Limited information in the literature about the function of 2 biomarkers and their involvement in lymphoma• Third biomarker identified as Haptoglobin, know to be unregulated in canine lymphoma.• No antibodies available to the unique biomarkers, therefore had to work with human antibodies with poor cross reactivity to the canine proteins.
Acute Phase Protein Response in DogsInfection Inflammation Monocyte - Macrophage IL-1 IL-6 TNF-α C-RP Haptoglobin SAA AGP
APP in Malignant LymphomaSig Diff fromcontrol P <0.0001 P <0.0001;<0.0001; <0.001; <0.02 >20 31.6 >100 224 136 18 90 Outside values Outside values C-reactive protein (mg/L) Haptoglobin (g/L) Far outside values 16 Far outside values 80 70 14 60 12 50 10 40 8 30 6 20 4 10 2 0 0 Lymphoma CLL Control ALL Myeloma CLL myeloma control lymphoma ALL C-reactive protein Haptoglobin lymphoma (n=16), acute lymphoblastic leukaemia (ALL) (n=11), chronic lymphocytic leukaemia (CLL) (n=7) and multiple myeloma (n=9) Control (n=25) Mischke et al Vet J 2006 174:188-92
From MS to ELISA Development Use ofMore than 19 Further of a multi Biomarkerprotein peaks investigation marker test Patternidentified as in order to Identification using Acute Software tosignificantly characterise of Haptoglobin Phase Proteins create uniquedifferent on and identify (Haptoglobin, algorithmsMS the proteins CRP) A unique new method of quickly and accurately diagnosing canine lymphoma . The combination of two Acute Phase Protein Assays, Haptoglobin and a specific canine CRP, combined with a unique Diagnostic Algorithm provide a diagnostic system
Tri-Screen Assay Development• Serum samples collected from dogs with lymphoma, healthy dogs and dogs with other diseases (many with similar presentation to lymphoma). Positive samples were confirmed by either FNA or excisional biopsy. Non lymphoma dogs were confirmed to be free of the disease at a minimum of six months after providing the serum sample• Samples were tested in batches using HAPT & CRP assay kits• Ciphergen Biomarker Pattern Software was used to generate a series of algorithms using the Classification and Regression Tree (CART) procedure. Through an iterative process, the software uses the training set of data to build trees to a point when optimal differentiation between the populations is achieved.• Blinded sample test performed.
Classification and Regression TreeBreiman L, Friedman JH, Olshen RA, Stone CJ.Classification and Regression Trees.Chapman & Hall (Wadsworth, Inc.): New York, 1984.
Developments with The University of Leicester Two cohortsDatabase Lymphoma – 97, Other disease – 135 Healthy – 71 Clinically suspected HealthyProblems Differential diagnosis Screening Challenge: The Estimation of Lymphoma Risk
Methodologies Risk mapsK nearest neighbours• Classic kNN with k from 1 to 30• kNN with Fisher’s distance transformations• kNN with adaptive distance transformationsDecision tree• Information gain (C4.5)• Gini gain (CART)• DKMProbability density function estimation• Radial-basis function (statistics kernel)• Three random values (Lymphoma, Other diseases, Healthy) x-axis CRP, y-axis Hapt
Software tools Database maintenance • Add new data • Delete old data Microsoft Excel Selection of the best methods for each problem and input data set. Best solutions are exportedCanine lymphoma software to the applet Providing access for practitioner vets to the diagnosis applet
Summary• MS and other proteomic work confirmed already known findings that APP levels are increased in canine lymphoma• Application of CART algorithms is able to confer improved specificity over previously non-specific APP assays.• Facilitated the development of a useful test kit to aid in the differential diagnosis of lymphoma in dogs.• The delivery and performance of this test has been dramatically enhanced through working with the Dept of Mathematics at the University of Leicester.• We have so far been unable to produce a reliable canine ELISAs for the two previously unknown biomarkers discovered in the MS work.• However, we have very good ELISA’s for these markers in human blood.• Now embarking on a study of these markers in human NHL
Comparative ResearchFrom 2 Legs to 4 and Back Again
From Visualisation to Prediction using Data Professor Jeremy Levesley Department of Mathematics University of Leicester Stratified Medicine, January 2013www.le.ac.uk
Data Mining: Confluence of Multiple Disciplines Database Statistics Technology Machine Learning Data Mining Visualization Information Other Science Disciplines2
What do we have • Practical experience in Data Mining for Medical Datasets (~40 expert and diagnostic systems, main technique: Neural Networks, Cluster Analysis, Visualization) • New algorithms for Data Approximation and Visualization • Fast algorithms for Neural Networks3
Growing principal tree: branching data distribution Iris data set4 Together with A. Zinovyev Toy data set (Curie, Paris)
The process• Data consolidation and preparation• Data selection and preprocessing• Data mining tasks and methods• Automated exploration and discovery• Prediction and classification• Interpretation and evaluation• Visualization tools can be very helpful