Cellular forgetting, desensitisation, stress and ageing in signalling networks. When do cells refuse to learn more?

Recent findings show that single, non-neuronal cells are also able to learn signalling responses developing cellular memory. In cellular learning nodes of signalling networks strengthen their interactions e.g. by the conformational memory of intrinsically disordered proteins, protein translocation, miRNAs, lncRNAs, chromatin memory and signalling cascades. This can be described by a generalized, unicellular Hebbian learning process, where those signalling connections, which participate in learning, become stronger. Here we review those scenarios, where cellular signalling is not only repeated in a few times (when learning occurs), but becomes too frequent, too large, or too complex and overloads the cell. This leads to desensitisation of signalling networks by decoupling signalling components, receptor internalization, and consequent downregulation. These molecular processes are examples of anti-Hebbian learning and 'forgetting' of signalling networks. Stress can be perceived as signalling overload inducing the desensitisation of signalling pathways. Ageing occurs by the summative effects of cumulative stress downregulating signalling. We propose that cellular learning desensitisation, stress and ageing may be placed along the same axis of more and more intensive (prolonged or repeated) signalling. We discuss how cells might discriminate between repeated and unexpected signals, and highlight the Hebbian and anti-Hebbian mechanisms behind the fold-change detection in the NF-κB signalling pathway. We list drug design methods using Hebbian learning (such as chemically-induced proximity) and clinical treatment modalities inducing (cancer, drug allergies) desensitisation or avoiding drug-induced desensitisation. A better discrimination between cellular learning, desensitisation and stress may open novel directions in drug design, e.g. helping to overcome drug resistance.

Protein Kinase D3 (PKD3) Requires Hsp90 for Stability and Promotion of Prostate Cancer Cell Migration.

Prostate cancer metastasis is a significant cause of mortality in men. PKD3 facilitates tumor growth and metastasis, however, its regulation is largely unclear. The Hsp90 chaperone stabilizes an array of signaling client proteins, thus is an enabler of the malignant phenotype. Here, using different prostate cancer cell lines, we report that Hsp90 ensures PKD3 conformational stability and function to promote cancer cell migration. We found that pharmacological inhibition of either PKDs or Hsp90 dose-dependently abrogated the migration of DU145 and PC3 metastatic prostate cancer cells. Hsp90 inhibition by ganetespib caused a dose-dependent depletion of PKD2, PKD3, and Akt, which are all involved in metastasis formation. Proximity ligation assay and immunoprecipitation experiments demonstrated a physical interaction between Hsp90 and PKD3. Inhibition of the chaperone-client interaction induced misfolding and proteasomal degradation of PKD3. PKD3 siRNA combined with ganetespib treatment demonstrated a specific involvement of PKD3 in DU145 and PC3 cell migration, which was entirely dependent on Hsp90. Finally, ectopic expression of PKD3 enhanced migration of non-metastatic LNCaP cells in an Hsp90-dependent manner. Altogether, our findings identify PKD3 as an Hsp90 client and uncover a potential mechanism of Hsp90 in prostate cancer metastasis. The molecular interaction revealed here may regulate other biological and pathological functions.

Translocating proteins compartment-specifically alter the fate of epithelial-mesenchymal transition in a compartmentalized Boolean network model.

Regulation of translocating proteins is crucial in defining cellular behaviour. Epithelial-mesenchymal transition (EMT) is important in cellular processes, such as cancer progression. Several orchestrators of EMT, such as key transcription factors, are known to translocate. We show that translocating proteins become enriched in EMT-signalling. To simulate the compartment-specific functions of translocating proteins we created a compartmentalized Boolean network model. This model successfully reproduced known biological traits of EMT and as a novel feature it also captured organelle-specific functions of proteins. Our results predicted that glycogen synthase kinase-3 beta (GSK3B) compartment-specifically alters the fate of EMT, amongst others the activation of nuclear GSK3B halts transforming growth factor beta-1 (TGFB) induced EMT. Moreover, our results recapitulated that the nuclear activation of glioma associated oncogene transcription factors (GLI) is needed to achieve a complete EMT. Compartmentalized network models will be useful to uncover novel control mechanisms of biological processes. Our algorithmic procedures can be automatically rerun on the https://translocaboole.linkgroup.hu website, which provides a framework for similar future studies.

Screening and monitoring of the BTKC481S mutation in a real-world cohort of patients with relapsed/refractory chronic lymphocytic leukaemia during ibrutinib therapy.

The Bruton's tyrosine kinase (BTK) inhibitor ibrutinib has revolutionised the therapeutic landscape of chronic lymphocytic leukaemia (CLL). Acquired mutations emerging at position C481 in the BTK tyrosine kinase domain are the predominant genetic alterations associated with secondary ibrutinib resistance. To assess the correlation between disease progression, and the emergence and temporal dynamics of the most common resistance mutation BTKC481S , sensitive (10-4 ) time-resolved screening was performed in 83 relapsed/refractory CLL patients during single-agent ibrutinib treatment. With a median follow-up time of 40 months, BTKC481S was detected in 48·2% (40/83) of the patients, with 80·0% (32/40) of them showing disease progression during the examined period. In these 32 cases, representing 72·7% (32/44) of all patients experiencing relapse, emergence of the BTKC481S mutation preceded the symptoms of clinical relapse with a median of nine months. Subsequent Bcl-2 inhibition therapy applied in 28/32 patients harbouring BTKC481S and progressing on ibrutinib conferred clinical and molecular remission across the patients. Our study demonstrates the clinical value of sensitive BTKC481S monitoring with the largest longitudinally analysed real-world patient cohort reported to date and validates the feasibility of an early prediction of relapse in the majority of ibrutinib-treated relapsed/refractory CLL patients experiencing disease progression.

Modular Reorganization of Signaling Networks during the Development of Colon Adenoma and Carcinoma.

Network science is an emerging tool in systems biology and oncology, providing novel, system-level insight into the development of cancer. The aim of this project was to study the signaling networks in the process of oncogenesis to explore the adaptive mechanisms taking part in the cancerous transformation of healthy cells. For this purpose, colon cancer proved to be an excellent candidate as the preliminary phase, and adenoma has a long evolution time. In our work, transcriptomic data have been collected from normal colon, colon adenoma, and colon cancer samples to calculating link (i.e., network edge) weights as approximative proxies for protein abundances, and link weights were included in the Human Cancer Signaling Network. Here we show that the adenoma phase clearly differs from the normal and cancer states in terms of a more scattered link weight distribution and enlarged network diameter. Modular analysis shows the rearrangement of the apoptosis- and the cell-cycle-related modules, whose pathway enrichment analysis supports the relevance of targeted therapy. Our work enriches the system-wide assessment of cancer development, showing specific changes for the adenoma state.

Translocatome: a novel resource for the analysis of protein translocation between cellular organelles.

Here we present Translocatome, the first dedicated database of human translocating proteins (URL: http://translocatome.linkgroup.hu). The core of the Translocatome database is the manually curated data set of 213 human translocating proteins listing the source of their experimental validation, several details of their translocation mechanism, their local compartmentalized interactome, as well as their involvement in signalling pathways and disease development. In addition, using the well-established and widely used gradient boosting machine learning tool, XGBoost, Translocatome provides translocation probability values for 13 066 human proteins identifying 1133 and 3268 high- and low-confidence translocating proteins, respectively. The database has user-friendly search options with a UniProt autocomplete quick search and advanced search for proteins filtered by their localization, UniProt identifiers, translocation likelihood or data complexity. Download options of search results, manually curated and predicted translocating protein sets are available on its website. The update of the database is helped by its manual curation framework and connection to the previously published ComPPI compartmentalized protein-protein interaction database (http://comppi.linkgroup.hu). As shown by the application examples of merlin (NF2) and tumor protein 63 (TP63) Translocatome allows a better comprehension of protein translocation as a systems biology phenomenon and can be used as a discovery-tool in the protein translocation field.

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses.

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.

Roles of heat shock factor 1 beyond the heat shock response.

Various stress factors leading to protein damage induce the activation of an evolutionarily conserved cell protective mechanism, the heat shock response (HSR), to maintain protein homeostasis in virtually all eukaryotic cells. Heat shock factor 1 (HSF1) plays a central role in the HSR. HSF1 was initially known as a transcription factor that upregulates genes encoding heat shock proteins (HSPs), also called molecular chaperones, which assist in refolding or degrading injured intracellular proteins. However, recent accumulating evidence indicates multiple additional functions for HSF1 beyond the activation of HSPs. Here, we present a nearly comprehensive list of non-HSP-related target genes of HSF1 identified so far. Through controlling these targets, HSF1 acts in diverse stress-induced cellular processes and molecular mechanisms, including the endoplasmic reticulum unfolded protein response and ubiquitin-proteasome system, multidrug resistance, autophagy, apoptosis, immune response, cell growth arrest, differentiation underlying developmental diapause, chromatin remodelling, cancer development, and ageing. Hence, HSF1 emerges as a major orchestrator of cellular stress response pathways.

Neighbours of cancer-related proteins have key influence on pathogenesis and could increase the drug target space for anticancer therapies.

Even targeted chemotherapies against solid cancers show a moderate success increasing the need to novel targeting strategies. To address this problem, we designed a systems-level approach investigating the neighbourhood of mutated or differentially expressed cancer-related proteins in four major solid cancers (colon, breast, liver and lung). Using signalling and protein-protein interaction network resources integrated with mutational and expression datasets, we analysed the properties of the direct and indirect interactors (first and second neighbours) of cancer-related proteins, not found previously related to the given cancer type. We found that first neighbours have at least as high degree, betweenness centrality and clustering coefficient as cancer-related proteins themselves, indicating a previously unknown central network position. We identified a complementary strategy for mutated and differentially expressed proteins, where the affect of differentially expressed proteins having smaller network centrality is compensated with high centrality first neighbours. These first neighbours can be considered as key, so far hidden, components in cancer rewiring, with similar importance as mutated proteins. These observations strikingly suggest targeting first neighbours as a novel strategy for disrupting cancer-specific networks. Remarkably, our survey revealed 223 marketed drugs already targeting first neighbour proteins but applied mostly outside oncology, providing a potential list for drug repurposing against solid cancers. For the very central first neighbours, whose direct targeting would cause several side effects, we suggest a cancer-mimicking strategy by targeting their interactors (second neighbours of cancer-related proteins, having a central protein affecting position, similarly to the cancer-related proteins). Hence, we propose to include first neighbours to network medicine based approaches for (but not limited to) anticancer therapies.

Are rapidly growing cancers more lethal?

The view, that rapidly growing tumours are more likely than slow-growing tumours to metastasize and become lethal, has remained almost axiomatic for decades. Unaware of any solid evidence supporting this view, we undertook an exhaustive system-level analysis of intra- and intercellular signalling networks. This analysis indicated that rapid growth and metastasis are often different outcomes of complex integrated molecular events. Evidence from humans can be derived chiefly from screening interventions because interval cancers that surface clinically shortly after a negative screening test are, on average, more rapidly growing than cancers not detected by screening. We reviewed all available data limited to cancers of the breast, cervix and large bowel. The evidence from humans provides no support for the theory that rapidly growing cancers are more prone to metastasize. These findings indicate that the prevailing view should be reconsidered, as should the impact of length-biased sampling in cancer screening, and the findings provide no support for treating interval cancers more aggressively than non-interval cancers.

Identification of critical paralog groups with indispensable roles in the regulation of signaling flow.

Extensive cross-talk between signaling pathways is required to integrate the myriad of extracellular signal combinations at the cellular level. Gene duplication events may lead to the emergence of novel functions, leaving groups of similar genes - termed paralogs - in the genome. To distinguish critical paralog groups (CPGs) from other paralogs in human signaling networks, we developed a signaling network-based method using cross-talk annotation and tissue-specific signaling flow analysis. 75 CPGs were found with higher degree, betweenness centrality, closeness, and 'bowtieness' when compared to other paralogs or other proteins in the signaling network. CPGs had higher diversity in all these measures, with more varied biological functions and more specific post-transcriptional regulation than non-critical paralog groups (non-CPG). Using TGF-beta, Notch and MAPK pathways as examples, SMAD2/3, NOTCH1/2/3 and MEK3/6-p38 CPGs were found to regulate the signaling flow of their respective pathways. Additionally, CPGs showed a higher mutation rate in both inherited diseases and cancer, and were enriched in drug targets. In conclusion, the results revealed two distinct types of paralog groups in the signaling network: CPGs and non-CPGs. Thus highlighting the importance of CPGs as compared to non-CPGs in drug discovery and disease pathogenesis.

Intracellular and intercellular signaling networks in cancer initiation, development and precision anti-cancer therapy: RAS acts as contextual signaling hub.

Cancer initiation and development are increasingly perceived as systems-level phenomena, where intra- and inter-cellular signaling networks of the ecosystem of cancer and stromal cells offer efficient methodologies for outcome prediction and intervention design. Within this framework, RAS emerges as a 'contextual signaling hub', i.e. the final result of RAS activation or inhibition is determined by the signaling network context. Current therapies often 'train' cancer cells shifting them to a novel attractor, which has increased metastatic potential and drug resistance. The few therapy-surviving cancer cells are surrounded by massive cell death triggering a primordial adaptive and reparative general wound healing response. Overall, dynamic analysis of patient- and disease-stage specific intracellular and intercellular signaling networks may open new areas of anticancer therapy using multitarget drugs, drugs combinations, edgetic drugs, as well as help design 'gentler', differentiation and maintenance therapies.

Oncogenic KRAS signaling and YAP1/ß-catenin: Similar cell cycle control in tumor initiation.

Why are YAP1 and c-Myc often overexpressed (or activated) in KRAS-driven cancers and drug resistance? Here, we propose that there are two independent pathways in tumor proliferation: one includes MAPK/ERK and PI3K/A kt/mTOR; and the other consists of pathways leading to the expression (or activation) of YAP1 and c-Myc. KRAS contributes through the first. MYC is regulated by e.g. ß-catenin, Notch and Hedgehog. We propose that YAP1 and ERK accomplish similar roles in cell cycle control, as do ß-catenin and PI3K. This point is compelling, since the question of how YAP1 rescues K-Ras or B-Raf ablation has recently captured much attention, as well as the mechanism of resistance to PI3K inhibitors. The similarity in cell cycle actions of ß-catenin and PI3K can also clarify the increased aggressiveness of lung cancer when both K-Ras and ß-catenin operate. Thus, we propose that the two pathways can substitute one another - or together amplify each other - in promoting proliferation. This new understanding of the independence and correspondence of the two pathways in cancer - MAPK/ERK and PI3K/Akt/mTOR; and YAP1 and c-Myc - provide a coherent and significant picture of signaling-driven oncogenic proliferation and may help in judicious, pathway-based drug discovery.

Confrontation of fibroblasts with cancer cells in vitro: gene network analysis of transcriptome changes and differential capacity to inhibit tumor growth.

BACKGROUND: There is growing evidence that emerging malignancies in solid tissues might be kept under control by physical intercellular contacts with normal fibroblasts. METHODS: Here we characterize transcriptional landscapes of fibroblasts that confronted cancer cells. We studied four pairs of in vitro and ex vivo fibroblast lines which, within each pair, differed in their capacity to inhibit cancer cells. The natural process was modeled in vitro by confronting the fibroblasts with PC-3 cancer cells. Fibroblast transcriptomes were recorded by Affymetrix microarrays and then investigated using network analysis. RESULTS: The network enrichment analysis allowed us to separate confrontation- and inhibition-specific components of the fibroblast transcriptional response. Confrontation-specific differences were stronger and were characterized by changes in a number of pathways, including Rho, the YAP/TAZ cascade, NF-kB, and TGF-beta signaling, as well as the transcription factor RELA. Inhibition-specific differences were more subtle and characterized by involvement of Rho signaling at the pathway level and by potential individual regulators such as IL6, MAPK8, MAP2K4, PRKCA, JUN, STAT3, and STAT5A. CONCLUSIONS: We investigated the interaction between cancer cells and fibroblasts in order to shed light on the potential mechanisms and explain the differential inhibitory capacity of the latter, which enabled both a holistic view on the process and details at the gene/protein level. The combination of our methods pointed to proteins, such as members of the Rho pathway, pro-inflammatory signature and the YAP1/TAZ cascade, that warrant further investigation via tools of experimental perturbation. We also demonstrated functional congruence between the in vitro and ex vivo models. The microarray data are made available via the Gene Expression Omnibus as GSE57199.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations.

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Multitarget network strategies to influence memory and forgetting: the Ras/MAPK pathway as a novel option.

The Ras/mitogen activated protein kinase (MAPK) pathway has key importance in development, cell differentiation and senescence, tumorigenesis, learning and memory. The clinical manifestations associated with this highly conserved pathway are called RASopathies. Phenotypic features are diverse and overlapping, but cognitive impairment is a common symptom. Here, we propose an approach based on molecular networks that link learning, memory and forgetting to the RASopathies and various neurodegenerative and neurodevelopmental diseases such as Alzheimer's disease, Parkinson's disease and autism spectrum disorders. We demonstrate the cross-talks of the molecular pathways in RASopathies and memory and the role of compartmentalization in these processes. The approved drugs are also overviewed, and C. elegans is proposed as a viable model system for experimental exploration and compound target prediction.n.

Autophagy Regulatory Network - a systems-level bioinformatics resource for studying the mechanism and regulation of autophagy.

Autophagy is a complex cellular process having multiple roles, depending on tissue, physiological, or pathological conditions. Major post-translational regulators of autophagy are well known, however, they have not yet been collected comprehensively. The precise and context-dependent regulation of autophagy necessitates additional regulators, including transcriptional and post-transcriptional components that are listed in various datasets. Prompted by the lack of systems-level autophagy-related information, we manually collected the literature and integrated external resources to gain a high coverage autophagy database. We developed an online resource, Autophagy Regulatory Network (ARN; http://autophagy-regulation.org), to provide an integrated and systems-level database for autophagy research. ARN contains manually curated, imported, and predicted interactions of autophagy components (1,485 proteins with 4,013 interactions) in humans. We listed 413 transcription factors and 386 miRNAs that could regulate autophagy components or their protein regulators. We also connected the above-mentioned autophagy components and regulators with signaling pathways from the SignaLink 2 resource. The user-friendly website of ARN allows researchers without computational background to search, browse, and download the database. The database can be downloaded in SQL, CSV, BioPAX, SBML, PSI-MI, and in a Cytoscape CYS file formats. ARN has the potential to facilitate the experimental validation of novel autophagy components and regulators. In addition, ARN helps the investigation of transcription factors, miRNAs and signaling pathways implicated in the control of the autophagic pathway. The list of such known and predicted regulators could be important in pharmacological attempts against cancer and neurodegenerative diseases.

Cancer stem cells display extremely large evolvability: alternating plastic and rigid networks as a potential Mechanism: network models, novel therapeutic target strategies, and the contributions of hypoxia, inflammation and cellular senescence.

Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: "what does not kill me makes me stronger".

Attractor Structures of Signaling Networks: Consequences of Different Conformational Barcode Dynamics and Their Relations to Network-Based Drug Design.

Conformational barcodes tag functional sites of proteins and are decoded by interacting molecules transmitting the incoming signal. Conformational barcodes are modified by all co-occurring allosteric events induced by post-translational modifications, pathogen, drug binding, etc. We argue that fuzziness (plasticity) of conformational barcodes may be increased by disordered protein structures, by integrative plasticity of multi-phosphorylation events, by increased intracellular water content (decreased molecular crowding) and by increased action of molecular chaperones. This leads to increased plasticity of signaling and cellular networks. Increased plasticity is both substantiated by and inducing an increased noise level. Using the versatile network dynamics tool, Turbine (www.turbine.linkgroup.hu), here we show that the 10 % noise level expected in cellular systems shifts a cancer-related signaling network of human cells from its proliferative attractors to its largest, apoptotic attractor representing their health-preserving response in the carcinogen containing and tumor suppressor deficient environment modeled in our study. Thus, fuzzy conformational barcodes may not only make the cellular system more plastic, and therefore more adaptable, but may also stabilize the complex system allowing better access to its largest attractor.

Cancer-related networks: a help to understand, predict and change malignant transformation.

Cancer is increasingly described as a systems-level, network phenomenon. Genetic methods, such as next generation sequencing and RNA interference uncovered the complexity tumor-specific mutation-induced effects and the identification of multiple target sets. Network analysis of cancer-specific metabolic and signaling pathways highlighted the structural features of cancer-related proteins and their complexes to develop next-generation protein kinase inhibitors, as well as the modulation of inflammatory and autophagic pathways in anti-cancer therapies. Importantly, malignant transformation can be described as a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of tumor development. Late-stage tumors should be attacked by an indirect network influence strategy. On the contrary, the attack of early-stage tumors may target central network nodes. Cancer stem cells need special diagnosis and targeting, since they potentially have an extremely high ability to change the rigidity/plasticity of their networks. The early warning signals of the activation of fast growing tumor cell clones are important in personalized diagnosis and therapy. Multi-target attacks are needed to perturb cancer-specific networks to exit from cancer attractors and re-enter a normal attractor. However, the dynamic non-genetic heterogeneity of cancer cell population induces the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors. The development of drug resistance is further complicated by interactions of tumor clones and their microenvironment. Network analysis of intercellular cooperation using game theory approaches may open new areas of understanding tumor complexity. In conclusion, the above applications of the network approach open up new, and highly promising avenues in anti-cancer drug design.