Molecular mechanisms final


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Molecular mechanisms final

  1. 1. Molecular Mechanisms in Urothelial Cancer – Invasiveness: Systems Approach By Akshay Bhat
  2. 2. Today’s Menu...! ●  Background ●  ECM and its components ●  Proteases ●  Network Biology - Interactome ●  Its applications in clinical diagnostics
  3. 3. Bladder Carcinoma ●  Cancers of urinary bladder present as –  Urothelial carcinomas (UC) or Transitional cell Carcinoma (TCC) –  Squamous cell carcinoma –  Adenocarcinoma ●  Two clinical staging types –  Non-invasive – Papillary Carcinoma (Ta) & Tis –  Invasive – T1 – T4 (infiltrating tumour) ●  All these transformations are due altering molecular pathways –  Low-Grade – Papillary tumours •  Ras-MAPK pathways (FGFR3 and HRAS) –  High-Grade – Ta tumours •  Homozygous deletions (p16 gene) –  CIS and Invasive tumours •  p53 and Rb pathways (TP53 and RB gene) ●  Moving from non-invasive to invasive (Occasionally) –  Seen to show molecules that effect p53 pathway ●  Alterations in Cadherins, MMPs, VEGF, TSP-1 (Used to remodel ECM and promote angiogenesis) –  Common in muscle invasive (T2 - T4) Bladder cancer stages. Superficial cancers that have not yet invaded muscle include stages Tis (Cis), Ta and T1 while invasive tumours are stages T2, T3 and T4. (Fig. Downloaded from “Challenges of using mass spectrometry as a bladder cancer biomarker discovery platform, Eric Schiffer, Harald Mischak, Dan Theodorescu, Antonia Vlahou: World J Urol (2008) 26:67–74”)
  4. 4. Alterations in Molecular pathways ●  Strong evidence that malignant transformation of bladder urothelium is caused by the alteration in molecular pathways mainly; –  Intrinsic processes ●  Cell cycle regulation ●  Cell death - Apoptosis ●  Cell growth ●  Signal transduction ●  Gene regulation –  Extrinsic processes ●  Tumour angiogenesis ●  Tumour invasion Here we focus on the invasive phenotype of bladder carcinoma mainly looking at pathways like cell- cycle regulation; cell death; angiogenesis and muscle infiltration (invasion)
  5. 5. Alterations in a Urothelial carcinoma cell – Not Biomarkers Fig. downloaded with permission from Molecular Pathogenesis and Diagnostics of Bladder Cancer; Anirban P. Mitra and Richard J. Cote1; Annu Rev Pathol. 2009;4:251-85. doi: 10.1146/annurev.pathol.4.110807.092230; PMID:18840072
  6. 6. Controlled by p53 and Rb pathways – in turn are associated with apoptotic,signal transduction and gene regulation processes. P53 pathway §  Located on Chromosome 17, TP53 gene encodes p53 → central protein for the pathway §  Normal process p53 → inhibits G1-S transition (cell-cycle progression) and controls activation of p21 §  P53 has a short half-life hence preventing its accumulation in the nucleus. During TP53 mutation → high intra-nuclear accumulation. §  P21 → chromosome 6 and encodes a CDK inhibitor p21 → regulated by p53 → involved in tumour progression. §  MDM2 → chromosome 12 and involves in auto-regulatory feedback loop with p53. Increased levels of p53 → activates Mdm2 → upregulates p53 → proteasomal degradation of p53 (reduces level) hence reducing Mdm2 causing increasing tumour stage and grade. §  Mdm2 is inhibited by p14 → derived from CDKN2A (chromosome 9) → induced by E2F. §  P14 → Plays a leading role in both pathways (p53 and Rb) → deregulates them. §  Other CDKN2A → p16 acts like a CDKI and inhibits Cyclin D1 → involved in Rb pathway. Cell cycle regulation Rb Pathway RB gene → located on chromosome 13 → encodes for pRb pRb → interacts with multiple regulatory proteins in G1-S transition Rb (dephosphorylated) binds to E2F (transcription factor). When pRb is phosphorylated by cyclin-dependent CDKs, E2F is withdrawn from the bond (Rb-E2F) → transcription of genes in DNA synthesis. Inactivation of Rb mutation leads to loss of protein expression → shown to be in all stages and grades of UCs. High levels of Rb expression is due to loss of p16 expression (inhibits CDK4/6) and/or cyclin D1 over-expression. Negative regulation of CDKs |-- by CDKIs ( p21, p16, p27) → these are tumour suppressors too. Key features/markers playing in p53 and Rb pathways.
  7. 7. Cell Death Pathways Apoptosis Extrinsic apoptotic pathway §  Members of TNFR superfamily include death receptors → Fas §  Interaction of ligands with Fas → FADD protein and recruits Caspase 8 and 10 (initiators) §  Caspase 8 induces apoptosis by activating Caspase -3, 6, and 7 (effector) §  Caspase 10 activates Bid (protein) which activates pro-apoptotic protein Bax Apoptosis (programmed cell death) is a key process in cancer development and progression. Ability of tumour cells to avoid apoptosis and continue to proliferate is a fundamental hallmark in UC development. Apoptosis can be initiated by two pathways: Extrinsic pathways → activation of death receptors on cell surface Intrinsic pathways → mediated by mitochondria Both pathways activate Caspases → cleave cellular substrates and lead to morphological changes Intrinsic Apoptotic pathways §  Bcl-2 family of proteins → Bcl-2 and Bcl-XL (anti-apoptotic members) → Bax, Bid and Bad (pro-apoptotic) §  Mitochondrial megapores open & close rapidly due to cellular stress → directs Bax to the site §  This alters the membrane potential and releases Cytochrome c and calcium → activating Caspases. §  Bax expression is controlled by p53 and JNK (c-jun N-terminal kinase) §  In cytoplasm, Cytochrome c binds → Apaf-1 → activated complex binds to ATP → triggers procaspase-9 → then activates Caspase-9 → activates downstream effector (caspase-3) → apoptosis. §  Inhibitor of apoptosis (IAP), protein cIAP bind and inhibits to caspases and may be involved in their degradation.
  8. 8. Tumour AngiogensisAll tissues develop vascular networks that provide cells with nutrients and oxygen which enable them to eliminate metabolic wastes. Angiogenesis – Can be understood as follows; §  A cell activated by a lack of oxygen releases angiogeneic molecules that attract inflammatory and endothelial cells to promote proliferation §  During their migration, inflammatory cells also secrete molecules that intensify angiogenic stimuli. §  Endothelial cells that form blood vessels respond to the angiogenic call by differentiating and secreting MMPs which digest blood-vessel walls (BVW) enabling them to escape and migrate towards the site of angiogenic stimuli. §  Several protein fragments produced by the digestion of the BVW intensify the proliferation and migration of the activity of endothelial cells which then form a capillary tube by altering the arrangement of their adherence-membrane proteins. Angiogenesis is measured by micro-vessel density (MVD) In UC, §  Hypoxia-inducible factors (HIF-1 and HIF-2) bind to DNA-regulating genes. §  HIF-1alpha is in conjunction with altered p53 expression → induces VEGF downstream §  VEGF over-expression in Ta is associated with early recurrence and progression to invasive phenotype §  High expression in VEGF in serum → associated with high grade and stage UC, vascular invasion, CIS and metastasis. §  Enzyme TP promotes IL-8 production and MMPs. §  IL-8 → enhances angiogenesis via endothelial cells (found in cell line invasion) §  MMPs activate basic and acidic growth factors (bFGF and aFGF) → re-stimulates MMPs → endothelial migration. §  MMPs activates SF → stimulates angiogenesis.VEGF induces formation of uPA → generates plasmin → stimulates bFGF, aFGF and SF. §  uPA degrades ECM leading to endothelial migration and invasion §  Urine bFGF → high recurrence; Urine aFGF → invasive phenotype §  p53 upregulates TSP-1 expression → angiogenesis inhibitor Fig downloaded from “The extracellular matrix: a dynamic niche in cancer progression; Lu P, Weaver VM, Werb Z; doi: 10.1083/jcb.201102147; J Cell Biol. 2012 February 20; PMID: 22351925”
  9. 9. Tumour Invasion Potential Process of invasion is a key feature in malignant growths. In UCs, the process contributes towards invasion of tumour cells into vasculature and lymphatics in addition to spreading to adjacent and distant sites. Invasive tumour cells can migrate as single cells or collectively in files, clusters and sheets. Many adhesion and signaling molecules, including integrins, CD44, cadherins and IgCAMs have been implicated in tumour migration and invaison. Cadherins are prime mediators of intercellular adhesion and are localized to adherens junctions in addition to binding to adjoining cells. E-cadherin plays a key role in epithelial cell-cell adhesion → decreased expression correlates to increased tumour recurrence and progression Integrin signalling is critical for cell migration/invasion by FAK/ SRC signaling and the activity of RHO family GTPases. Tumours ability to degrade the matrix and invade is facilitated by proteases like MMPs and uPA's High levels of MMP-2 and MMP-9 → associated with increasing stage and grade. (Shown with clinical proof further) Tissue inhibitors of metalloproteinases (TIMPs), proteins produced, antagonize MMP function → inhibiting tumour cell invasion. (doubt is whether TIMP1 or TIMP2) Created using PathViso (“”)
  10. 10. Extracellular Matrix (ECM) “Spare the rod, Spoil the child” → The Holy Bible The local microenvironment (niche) of a cancer cell plays important roles in cancer development And the major component → ECM What happens outside the house makes an effect inside!
  11. 11. The ECM Stable structure → Maintains tissue morphology ECM → essential milieu of a cell, dynamic, versatile and regulates almost all cellular processes directly or indirectly. Multiple regulatory mechanisms in its dynamics → production, degradation, remodelling → organ development and function. Disruption to such controlled mechanisms deregulates and disorganizes ECM → abnormal behaviours of cell residing in the “Niche” → Ultimately failure of organ homeostasis and function. ECM → composed of biochemically distinct components → proteins, glycoproteins, proteoglycans and polysaccharides. All these components make up; u  Basement Membrane (BM) – epithelium, endothelium, stromal cells u  Interstitial Matrix (IM) – Stromal cells BM → compact, less porous → Collagen IV, Laminins, Fibronectin Nidogen and Entactin → Linker proteins (connect collagens to other proteins) IM → Fibrillar collages, proteoglycans and glycoproteins (tenascin C and fibronectin) → tensile strength of tissues When put together in order, ECM → confers unique physical, biochemical and bio-mechanical properties essential for regulating cell behavior
  12. 12. ECM during cell invasion ➔  Abnormal ECM compromises BM as physical barrier → promotes epithelial-mesenchymal transition (EMT) → facilitates invasion of cancer cells. ➔  Over expression of MMPs → removes basement membrane. ➔  Tumour/stromal/immune cells bearing MMPs → exit or enter BM with changes in ECM ➔  Stiffening and linearisation of collagen fibres → common in active tumour invasion. ➔  Deregulation of ECM dynamics → cellular dedifferentiation, disruption of tissue polarity and promotion of tissue invasion with direct affects to epithelial cells. Epithelial-Mesenchymal transition (EMT) – is a natural cellular process where individual epithelial cells lose their gene expression pattern and behaviour to gain phenotypic traits of mesenchymal cells. In doing so, they lose adhesion and apical-basal polarity to migrate and invade through the extracellular matrix. EMT is specially important during embryonic development, but also its seen in tumour types especially in invasive, inflammation and metastasis phenotypes. Fig downloaded with permission from BerGenBio AS (
  13. 13. Matrix MetalloProteinases (MMPs) MMPs are a class of 25 zinc-dependent endogenous proteases involved in degradation of all the components in the ECM. Increased MMP activity → several pathological conditions and cancers phenotypes including invasion. MMPs are regulated by u  Transcription factors u  Endogenous inhibitors u  Pro-enzymes MMPs are involved in several physiological and tumour supporting cellular processes → loss of cell- adhesion, tumour angiogenesis, cell poliferation, apoptosis and EMT. → Fig Fig downloaded with permission from “Matrix metalloproteinases and their clinical relevance in urinary bladder cancer; Szarvas, T. et al. Nat. Rev. Urol. 8, 241–254 (2011); published online 12April 2011; doi:10.1038/nrurol.2011.44” Activating other MMPs Can process non-bound proteins
  14. 14. MMPs in Urothelial cancer - Invasion ➔  There are many MMPs studied in bladder cancer biology but only few are extensively studies; mainly MMP-1, MMP-2, MMP-3, MMP-7, MMP-9, TIMP-1 and TIMP-2 ➔  MMP-2 and MMP-9 are known to be playing roles in invasive phenotypes – Recently MMP14 has been also involved by triggering MMP-2 as for TIMP-1. ➔  Growth factors and cytokines (e.g. bFGF) → upregulates MMP-2 and 9 → enhances invasiveness. EMMPRIN (ECM MMP inducer) – represses MMP-2 and -9 → decreasing proliferation, migration and invasion 42 individuals (11 healthy controls, 31 patients with bladder cancer of different stages) – Sample type Serum. Patients with metastatic disease also had increased MMP-9, MMP-2 and TIMP-2 mRNA levels Detection and molecular staging of bladder cancer using real-time RT-PCR for gelatinases (MMP-2, MMP-9) and TIMP-2 in peripheral blood. Actas Urol Esp. 2011 Mar;35(3):127-36. doi: 10.1016/ j.acuro.2010.10.006. Epub 2011 Feb 18. Angulo JC, Ferruelo A, Rodríguez-Barbero JM, Núñez C, de Fata FR, González J. PMID: 21334102 Matrix metalloproteinase-9 measured in urine from bladder cancer patients is an independent prognostic marker of poor survival. Offersen BV, Knap MM, Horsman MR, Verheijen J, Hanemaaijer R, Overgaard J. Acta Oncol. 2010 Nov;49(8):1283-7. doi: 10.3109/0284186X.2010.509109. Epub 2010 Sep 15. PMID: 20843171 188 patients diagnosed with bladder cancer (Cystectomy and TUR) Sample – Voided Urine Result- Poor survival – 175 died after 13 year follow-up → MMP9 highly activated in invasive phenotypes
  15. 15. Integrins: masters and slaves of transport Integrins are family of adhesion molecules involved in interactions between the cell and ECM. Integrins control many cellular processes that occur during development. If altered → promotes tumour progression, invasion and metastasis How? → By conveying outside signals inside “Outside -in signaling” or vice versa “inside-out signaling”. Integrins act as receptors for ECM proteins such as laminin and collagen VII Integrin trafficking on cell migration could be controlled by Rho GTPase signaling or by altering receptors i.e. EGFR, VEGFR → contributes to polymerization of fibronectin (ECM component) causing cell progression. α6β4 integrin → well studied in bladder tumourigenesis. → normal process α6β4 integrin has close relationship with collagen VII and restricts cell migration of laminin. Loss of α6β4 integrin has been seen in Ta and muscle invasive phenotypes. Disruption of ECM affects integrin expression and hence disturbs its normal function.
  16. 16. Invasion → Inflammation → Metastasis Cancer Progression – Invasion to Inflammation to Metastasis. A.  ECM remodelling is tightly controlled for organ homeostasis and functions. ECM dynamics are essential for maintaining tissue integrity and overall healthy microenvironment. B.  With age or under pathological conditions, tissue can enter tumourigenic events; Stage I – generation of activated fibroblasts (CAFs) – contributes to abnormal ECM build-up Stage II – deregulated expression of ECM modelling enzymes – promotes epithelial cellular transformation and hyperplasia (Stage III) C.  In high stages tumours, immune cells are recruited to tumour site → promotes progression (Stage IV); (Stage V) deregulated ECM affects vascular biology → promotes tumour angiogenesis; This in turn facilitates tumour invasion and metastasis (Stage VI) D.  At distant sites, tumour cells leave the circulation and take hold of local tissues creating a local metastatic niche. Abnormal ECM → extravasation, survival and proliferation of cancer cells (Stage VII) and also turns into angiogenic switch (Stage VIII) Fig downloaded from “The extracellular matrix: a dynamic niche in cancer progression; Lu P, Weaver VM, Werb Z; doi: 10.1083/jcb.201102147; J Cell Biol. 2012 February 20; PMID: 22351925”
  17. 17. Molecular Features - Summary Bca-associated feature Molecular pathway Normal function Regulation No. of cohort p53 p53 Inhibits G1-S progression Down 341/692 p21 p53 Cyclin-dependent kinase inhibitor Down 36/56 Mdm2 p53 Mediates proteasomal degradation of p53 Down 73/164 p16 p53 Cyclin-dependent kinase inhibitor Down 25/39 p14 Rb Inhibits MDM2-gene Down 20/64 Rb Rb Sequesters E2F; inhibits cell-cycle progression Down 39/80 CDK4 Rb Complexes with cyclin-D1; involved in G1-S transition Down 15/26 p27 Rb Cyclin-dependent kinase inhibitor Down 46/99 Cell-cycle regulation
  18. 18. Molecular Features - Summary Bca-associated feature Molecular pathway Normal function Regulation No. of cohort HIF Transcribes genes responsible for angiogenesis Up 35/92 VEGF Ras-MAPK;PI3K- Akt Promotes angiogenesis → (NO) synthase Up 156/171 TP Promotes VEGF, IL8 secretion; induces MMP Up 29/71 uPA Degrades ECM Up 46/100 bFGF Ras-MAPK Growth factor stimulating angiogenesis Up 161/204 aFGF Ras-MAPK Growth factor stimulating angiogenesis Up 17/40 SF Growth factor stimulating angiogenesis Up 57/60 TSP-1 p53 Inhibits angiogenesis Up 128/204 Tumour Angiogenesis
  19. 19. Bca-associated feature Molecular pathway Normal function Regulation No. of cohort Fas Extrinsic apoptosis Promotes apoptosis; activates death-inducing signalling complex Down 28/54 Bcl-2 Intrinsic apoptosis Inhibits caspase activation Up 73/226 Bax Intrinsic apoptosis Releases cytochrome c → mitochondria; promotes apoptosis Down 25/41 Caspase-3 Common apoptosis effector Promotes apoptosis Down 120/226 Cell death - Apoptosis Tumour Invasion Bca-associated feature Molecular pathway Normal function Regulation No. of cohort E-Cadherin Cadherin Mediates intercellular adhesion Down 33/94 β-catenin Wnt/β-catenin Links cadherins to actin cytoskeleton Down - α6β4-integrin Cytoskeletal signalling Links collagen VII to actin cytoskeleton; traduces regulatory signals Down 21/57 MMP-2 Degrades ECM Up 45/50 MMP-9 Degrades ECM Up 39/60 TIMP-1 Antagonizes MMP function Up 18/33 TIMP-2 Antagonizes MMP function Up 38/84
  20. 20. Interactome - An old adage says: “Show me your friends, and I’ll know who you are.” u  Protein-Protein Interactions (PPIs) are fundamental to all biological processes. u  To understand an integrated tumor cell system – a comprehensive determination of all possible PPIs that take place in a tumour cell is necessary. u  By knowing their fold change and p-values, we scientists can further group these proteins based on whether certain features are up- or down-regulated. u  This specific/comprehensive network would then help us in determining how certain proteins/molecules behave if their expression is perturbed and how it could be used in clinics to develop strategies for disease classifying in diagnosis and treatment. u  On the other hand, many features still lack knowledge of their biological role. u  A network cluster could help overcome this limitation by predicting their functions through their interactors and then validating them using IHC, ELISA etc. u  96 proteins à specific to urothelial invasiveness à collected from Literature mined databases à Web of Science, PubMed, Google Scholars. u  Omics databases à GEO and Array Express (In process) u  MeSH terms à Bladder/Urothelial, neoplasm/tumor, clinc*, invas*, molecular Invasive urothelial carcinoma features (96 literature mined) with their physical neighbours (First order interactions) - Performed using Cytoscape and MiMI plugin (“” “http://”)
  21. 21. Interactome for ECM components ➔  Our main goal is to scavenge for new features that interact with our core protein list. ➔  These molecules could also be the role players in the ECM disruption and the cause for invasion and inflammation phenotypes. I. Existing intermolecular interactions ECM - Features II. 1st group node extension III. 2Nd group node extension Based on Public Interactome database results
  22. 22. How do you know they are the Players? Linking back to the paper Further, read literature on omics profiling and find for regulation on tissue, urine, cell line, serum and plasma.
  23. 23. ➔  It is easier to predict the function of a module than a function of a gene symbol. ➔  Many functions of proteins are still unknown → predicting functional role of a module contains sufficient number of proteins of known functions. ➔  Such enrichment analysis builds on the assumption that a function of protein can be assigned a functional category such as Gene Ontology (GO) ➔  The question whether the no. of proteins with functional annotation in a given protein module is higher than expected? ➔  Can be determined by statistical tests such as χ2 or Fisher's exact test. Modules and Pathways This is the visualisation of the selected terms in a functionally grouped annotation network that reflects the relationships between the terms based on the similarity of their associated gene symbols. The size of the node reflects the statistical significance of the terms. The degree of connectivity between terms (edges) is calculated using kappa statistics. The calculated kappa score is also used for defining functional groups. Advantage working with Network clusters rather than Individual proteins..!
  24. 24. Gene Ontology - GO GO – Is a method to group molecules based on their cellular component, biological processes and molecular pathways that they play in. The GO analysis was performed on the feature list by using another Cytoscape plugin – ClueGO to achieve this graph. (“Bindea G, Galon J, Mlecnik B, CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013, 29(5):661-3.”) The histogram chart presents the specific terms for the core proteins and the information related to their associated genes. The bars represent the number of the gene symbols from the analysed cluster found associated with the term, and the label displayed on the bar is the percentage of found proteins compared to all the proteins associated with the term. ➔  In total, 1294 cellular pathways and 96 potential molecular features were included in this study from BioCarta, Kegg, GO, Reactome. ➔  Criteria for accepting a marker was occurrence in 50 or more samples/cohorts in a study. ➔  406 cellular pathways were enriched using the 96 features in ClueGO. ➔  Of which 14 fundamental pathways that were associated to invasion phenotype ➔  From this, we understand that the invasive phenotype of bladder carcinoma involves certain signalling pathways that trigger the known molecular pathways like (p53, Rb, RAS-MAPK, etc.) through the degradation of the extracellular matrix components especially done by MMPs leading to tumour inflammation and metastasis.
  25. 25. Applications of Network Modules – Disease Diagnosis & Treatment How can network modules help facilitate approaches in clinical diagnosis and treatment? Traditional approach (Clinical Disease Diagnosis) ➔  Based on pathological analysis & existing knowledge on disease. ➔  Prone to “Errors” Current network approach ➔  Knowledge of dys-regulated pathways → used to subtype disease. ➔  Helps to develop relevant treatments for individual disease subgroups. ➔  For e.g network clusters → used to predict u  Patient survival u  Inflammation u  Metastasis u  Drug response for various cancers and their phenotypes
  26. 26. Network modules – Clinical diagnosis & Treatment Disease Classification ➔  Starts with set of samples with known partition into disease subtypes (Invasive or non) ➔  Attempts to identify classifying principle using molecular features. Strategy: ➔  Search for subnetwork markers (whose activity best discriminates two disease subtypes) ➔  Network markers → distinguishes “Some” but not all disease cases → leads to multiple subnetworks. ➔  Among selected candidate network markers → “Best” markers are selected (based on training samples) Some markers take unsupervised approaches → subclasses and features are discovered without knowing training set. Developed network based approaches for classifying cancer subtypes by identifying densely connected subnetworks and randomized algorithms
  27. 27. Disease Similarity ➔  Overlaps of dys-regulated network clusters explains why complex diseases share similar phenotypic traits. ➔  Various tools support similarity network modules e.g iRefscape, PanGIA, DisGeNET (Cytoscape plugins), PathBlast, etc. Network modules – Clinical diagnosis & Treatment This explains why... some drugs can treat many different diseases! Several dys-regulated modules were found common Analyses disease similarity by comparing expression patterns of various disease modules (subnetworks)
  28. 28. Response to treatment ➔  Network modules may help determine whether a given patient responds to a particular drug → Valuable for treatment design ➔  Understanding molecular differences b/w responder & non-responder → development of alternative treatments. Network modules – Clinical diagnosis & Treatment Comparing result subnetworks helped shed light on the mechanisms leading to apoptosis and to identify potential drug targets
  29. 29. Pictures adaped from: A. vandenBerg, L. Ringrose, First annual meeting of the EpiGeneSys Network of Excellence: moving epigenetics towards systems biology. BioEssays : news and reviews in molecular, cellular and developmental biology34, 620 (Jul, 2012). SYSTEMS BIOLOGY Using information about parts of a system (left) → to build a working biological system. (right) u  Models are built on mathematical descriptions of interconnected parts. u  Can be changed AND/OR rebuilt infinitely → explore behavior of the system and incorporate new experimental information u  Predict outcome of changes to a system → then tested experimentally.
  30. 30. SYSTEMS BIOLOGY Proteomics Metabolomics Theoretical biology Functional genomics Bioinformatics Computational biology Transcriptomics Synthetic biology Structural genomics You name it.... We've got it...!!
  31. 31. Reference u  Molecular pathogenesis and diagnostics of bladder cancer. Anirban P. Mitra and Richard J. Cote; Annual Review of Pathology: Mechanisms of Disease. doi: 10.1146/annurev.pathol.4.110807.092230. 2009 u  Prognostic value of cell-cycle regulation biomarkers in bladder cancer. Anirban P. Mitra, Donna E. Hansel, Richard J. Cote; Seminars in Oncology; doi: 10.1053/j.seminoncol.2012.08.008; Volume 39, Issue 5, October 2012, Pages 524– 533 u  Normal and neoplastic urothelial stem cells: getting to the root of the problem. Philip Levy Ho, Antonina Kurtova, and Keith Syson Chan; Nat Rev Urol. 2012 October; 9(10): 583–594; doi: 10.1038/nrurol.2012.142. u  Molecular pathways in invasive bladder cancer: new insights into mechanisms, progression, and target identification. Anirban P. Mitra, Ram H. Datar and Richard J. Cote; J Clin Oncol. 2006 Dec 10;24(35):5552-64; u  Molecular biology of bladder cancer: prognostic and clinical implications. Anirban P. Mitra, Henry Lin, Ram H. Datar, Richard J. Cote; Clinical Genitourinary Cancer; Volume 5, Issue 1, June 2006, Pages 67–77 u  Bladder cancer or bladder cancers? Genetically distinct malignant conditions of the urothelium. Peter J. Goebell, Margaret A. Knowles; Urologic Oncology: Seminars and Original Investigations; Volume 28, Issue 4, July–August 2010, Pages 409–428 u  E-cadherin expression in plasmacytoid, signet ring cell and micropapillary variants of urothelial carcinoma: comparison with usual-type high-grade urothelial carcinoma. Matthew G Lim, N Volkan Adsay, David J Grignon and Adeboye.O.Osunkoya; Modern Pathology (2011) 24, 241–247; doi:10.1038/modpathol.2010.187; published online 3 September 2010. u  Mechanics, malignancy, and metastasis: The force journey of a tumor cell. Sanjay Kumar and Valerie M. Weaver; Cancer Metastasis Rev. 2009 June ; 28(1-2): 113–127. doi:10.1007/s10555-008-9173-4. u  Epithelial-Mesenchymal Transition: At the Crossroads of Development and Tumor Metastasis. Jing Yang and Robert A. Weinberg, Developmental Cell, Volume 14, Issue 6, 10 June 2008, Pages 818–829; doi 10.1016/j.devcel.2008.05.009. u  ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, et al. Bioinformatics. 2009, 25(8):1091-3. u  Chapter 5: Network Biology Approach to Complex Diseases. Dong-Yeon Cho; Yoo-Ah Kim; Teresa M. Przytycka. PLoS Comput Biol. 2012;8(12):e1002820. doi: 10.1371/journal.pcbi.1002820. Epub 2012 Dec 27. u  Inferring pathway activity toward precise disease classification. Lee E, Chuang HY, Kim JW, Ideker T, Lee D. PLoS Comput Biol. 2008 Nov;4(11):e1000217. doi: 10.1371/journal.pcbi.1000217. Epub 2008 Nov 7. u  Inferring cancer subnetwork markers using density-constrained biclustering. Dao P, Colak R, Salari R, Moser F, Davicioni E, Schönhuth A, Ester M. Bioinformatics. 2010 Sep 15;26(18):i625-31. doi: 10.1093/bioinformatics/btq393. u  Optimally discriminative subnetwork markers predict response to chemotherapy. Dao P, Wang K, Collins C, Ester M, Lapuk A, Sahinalp SC. Bioinformatics. 2011 Jul 1;27(13):i205-13. doi: 10.1093/bioinformatics/btr245. u  Identification of coordinately dysregulated subnetworks in complex phenotypes. Chowdhury SA, Koyutürk M. Pac Symp Biocomput. 2010:133-44. u  Module cover - a new approach to genotype-phenotype studies. Kim YA, Salari R, Wuchty S, Przytycka TM. Pac Symp Biocomput. 2013:135-46. u  Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets. Chu LH, Chen BS. BMC Syst Biol. 2008 Jun 30;2:56. doi: 10.1186/1752-0509-2-56. u  Integrins: masters and slaves of endocytic transport. Nature Reviews Molecular Cell Biology 10, 843-853 (December 2009), doi:10.1038/nrm2799. u  Cadherins and cancer: how does cadherin dysfunction promote tumor progression?. A Jeanes, CJ Gottardi, and AS Yap; Oncogene. 2008 November 24; 27(55): 6920–6929; doi:10.1038/onc.2008.343. u  Extracellular Matrix Degradation and Remodeling in Development and Disease. Pengfei Lu1, Ken Takai, Valerie M. Weaver, and Zena Werb; Cold Spring Harb Perspect Biol. ; 3(12): . doi:10.1101/cshperspect.a005058. u  Matrix metalloproteinases and their clinical relevance in urinary bladder cancer. Szarvas, T. et al. Nat. Rev. Urol. 8, 241–254 (2011); published online 12April 2011; doi:10.1038/nrurol.2011.44 u  Molecular pathways of urothelial development and bladder tumorigenesis. Carlos Cordon-Cardo et. al; Urologic Oncology: Seminars and Original Investigations 28 (2010) 401– 408; doi:10.1016/j.urolonc.2009.04.019. u  The extracellular matrix at a glance. Frantz C, Stewart KM, Weaver VM; J Cell Sci. 2010 Dec 15;123(Pt 24):4195-200. doi: 10.1242/jcs.023820. u  The extracellular matrix: A dynamic niche in cancer progression. Lu et al; J Cell Biol. 2012 Feb 20;196(4):395-406. doi: 10.1083/jcb.201102147. u  Metastasis mechanisms. Geiger TR, Peeper DS; Biochim Biophys Acta. 2009 Dec;1796(2):293-308. doi: 10.1016/j.bbcan. 2009.07.006. Epub 2009 Aug 14. u  Integrating and annotating the interactome using the MiMI plugin for cytoscape. Gao J, Ade AS, Tarcea VG, Weymouth TE, Mirel BR, Jagadish HV, States DJ. Bioinformatics. 2009 Jan 1;25(1):137-8. doi: 10.1093/bioinformatics/btn501. Epub 2008 Sep 23. u  Bindea G, Galon J, Mlecnik B, CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013, 29(5):661-3 u  iRefScape. A Cytoscape plug-in for visualization and data mining of protein interaction data from iRefIndex. Razick S, Mora A, Michalickova K, Boddie P, Donaldson IM. BMC Bioinformatics. 2011 Oct 5;12:388. doi: 10.1186/1471-2105-12-388. u  An introduction to effective use of enrichment analysis software. Hannah Tipney; Lawrence Hunter. Hum Genomics. 2010 Feb; 4(3):202-6. u  Network-based classification of breast cancer metastasis. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Mol Syst Biol. 2007;3:140. Epub 2007 Oct 16. u  Subnetwork state functions define dysregulated subnetworks in cancer. Chowdhury SA, Nibbe RK, Chance MR, Koyutürk M. J Comput Biol. 2011 Mar;18(3):263-81. doi: 10.1089/cmb.2010.0269. u  Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Kelley BP, Sharan R, Karp RM, Sittler T, Root DE, Stockwell BR, Ideker T. Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11394-9. Epub 2003 Sep 22. u  Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. Suthram S, Dudley JT, Chiang AP, Chen R, Hastie TJ, Butte AJ. PLoS Comput Biol. 2010 Feb 5;6(2):e1000662. doi: 10.1371/journal.pcbi.1000662. u  Immunohistochemical biomarkers for bladder cancer prognosis. Matsushita K, Cha EK, Matsumoto K, Baba S, Chromecki TF, Fajkovic H, Sun M, Karakiewicz PI, Scherr DS, Shariat SF. Int J Urol. 2011 Sep;18(9):616-29. doi: 10.1111/j. 1442-2042.2011.02809.x. Epub 2011 Jul 20.
  32. 32. Acknowledgement u  Prof. Dr. Dr. Harald Mischak u  Mr. Joachim Conrads u  Dr. Jochen Metzger u  Justyna Siwy u  Dr. Petra Zürbig u  Dr. Mohammed Dakna Daniel Kolbe; Clemens Rumpf; Igor Golovko; Thomas Soeffing; Janosh Hoffmann u  Dr. Holger Husi u  Dr. William Mullen u  Dr. Angelique Stalmach u  Dr. Bernd Mayer u  Dr Paul Perco u  Andreas Heinzel Dr. Joost-Peter Schanstra u  Dr. Antonia Vlahou u  Maria Frantzi u  Agnieszka Latosińska
  33. 33. My goal is to publish a number of papers…. And my Boss must be happy to know about this…! - Akshay Bhat