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[Biological network modelling and precision medicine in oncology]
Abstract
Precision medicine in oncology is becoming reality thanks to the next-generation sequencing of tumours and the development of targeted inhibitors enabling tailored therapies. Many clinical trials base their strategy on the identification of mutations to deliver the targeted inhibitor that counteract supposedly the effect of a mutated gene. Recent results have shown that this gene-centered strategy can be successful, but can also fall short in stopping progression. This is due to the many compensation mechanisms, cross-talks and feedback loops that enable the tumoral cell to escape treatment. Taking into account the regulatory network is necessary to establish which inhibitor or combination of inhibitors would achieve the best therapeutic results. Mathematical modelling of biological networks, together with highquality pathway databases collecting our knowledge of the molecular circuitry of normal and tumoral cells, hold the hopes of an enhanced future for precision medicine in oncology.
... Des études ont montré la présence de plusieurs mutations impliquées dans le développement de carcinomes épidermoïdes cutanés dans des tissus de la paupière conservant un fonctionnement physiologique sain . Ou encore, ce programme de recherche ne permet pas de rendre compte du fait que les cellules cancéreuses présentent différents phénotypes aux différents stades de l'évolution de la tumeur (Barillot et al., 2009 ; (Calzone et al., 2014). Cet exemple met en évidence le fait qu'un même gène peut présenter des activités oncogéniques ou suppresseurs de tumeur en fonction du tissu ou de l'organe dans lequel cette mutation se situe (Calzone et al., 2014). ...
... Ou encore, ce programme de recherche ne permet pas de rendre compte du fait que les cellules cancéreuses présentent différents phénotypes aux différents stades de l'évolution de la tumeur (Barillot et al., 2009 ; (Calzone et al., 2014). Cet exemple met en évidence le fait qu'un même gène peut présenter des activités oncogéniques ou suppresseurs de tumeur en fonction du tissu ou de l'organe dans lequel cette mutation se situe (Calzone et al., 2014). Cet échec thérapeutique montre également la trop grande simplification opérée par un programme de recherche génétique qui ignore différents aspects de la maladie et souligne la nécessité de ne pas seulement considérer les mutations individuelles mais également le contexte dans lequel elles prennent place. ...
... Chacun des chapitres présentés est une instanciation non seulement des objectifs évoqués dans la section concernant la contribution de notre thèse, mais également de la méthodologie que nous avons décidé d'adopter. Calzone, L., Kuperstein, I., Cohen, D., Grieco, L., Bonnet, E., Servant, N., Hupé, P., Zinovyev, A., & Barillot, E. (2014). Biological network modelling and precision medicine in oncology. ...
La recherche sur le cancer a longtemps été dominée par un programme de recherche génétique et moléculaire. Cependant, l'accumulation de données expérimentales à partir des années 2000 souligne également l’importance d’un autre facteur dans la carcinogenèse : le microenvironnement tumoral (TME). L'étude du TME s’accompagne de différentes affirmations dans la littérature scientifique : elle serait une opportunité d’améliorer les thérapies et enrichir les connaissances actuelles ou au contraire elle serait un nouveau « paradigme » et permettrait de développer de nouveaux types de thérapies. Notre thèse propose une analyse de l’évolution du domaine de l’oncologie (et de l’hypothèse d’un potentiel changement scientifique) à partir de l’introduction du domaine d’étude du TME. Nous proposons une approche originale de cette question en adoptant deux hypothèses épistémologiques. Premièrement, nous analysons à la fois la communauté scientifique et la communauté médicale. Nous étudions le continuum existant entre recherche fondamentale et pratique clinique, en articulant philosophie de la biologie et philosophie de la médecine. Deuxièmement, nous menons notre analyse philosophique non pas seulement d’un point de vue théorique mais également au plus près des pratiques, des comportements, des méthodes, des organisations de recherche qui participent à ce changement scientifique. Pour cela, nous recourrons à des méthodes peu utilisées en philosophie comme celles des entretiens semi-directifs ou des observations de terrain. Cette approche a pour vocation de produire une analyse philosophique qui contribue à la fois à la littérature philosophique et à la littérature biomédicale.
... 1 Interactions among biological macromolecules are central not only to study biological functions of living cells but also to elucidate the molecular mechanism of disease progression on a network level. 2 To detect protein-protein interactions (PPIs) in a cell, screening methods such as yeast two-hybrid, 3 bimolecular fluorescence complementation 4 and tandem affinity purification 5 were developed and applied in different organisms. In contrast, to detect protein-DNA interactions, chromatin immunoprecipitation with microarray (ChIP-chip) or massively parallel DNA sequencing (ChIP-seq) was applied to study the DNA binding regions of transcription factors. ...
... To address this question, a priorknowledge based method is applied to evaluate the relevance of each GO level to the studied disease by using the known disease-associated genes. Particularly, the most relevant GO level is selected by the optimization of formula (2), which aims to identify one level, where the resulting sub-networks have the best enrichment significance with the known diseaseassociated genes. The formulation is represented as follows: ...
... The GO terms associated with one disease and corresponding to function-enriched sub-networks could be extracted by solving formula (1) and formula (2). However, in order to gain biological insight into the molecular mechanisms of diseases based on the extracted result, we quantified the activation value of the selected GO terms corresponding to function-enriched sub-networks. ...
A major challenge of systems biology is to capture rewiring of biological functions (e.g. signaling pathways) in a molecular network. To address this problem, we proposed a novel computational framework, namely Network Stratification Analysis (NetSA), to stratify the whole biological network into various function-specific network layers corresponding to particular functions (e.g. KEGG pathways), which transform the network analysis from the gene level to the functional level by integrating expression data, gene/protein network and gene ontology information together. The application of NetSA in yeast and its comparison with traditional network-partition both suggest that NetSA can more effectively reveal functional implications of network rewiring and extract significant phenotype-related biological processes. Furthermore, for time-series or stage-wise data, the function-specific network layer obtained by NetSA is also shown to be able to characterize the disease progression in a dynamical manner. In particular, when applying NetSA to hepatocellular carcinoma and type 1 diabetes, we can derive functional spectrums of the progression of cancer, and capture active biological functions (i.e. active pathways) in different disease stages. The additional comparison between NetSA and SPIA illustrate again NetSA could discover more complete biological functions in disease progression. In all, NetSA provides a general framework to stratify a network into various layers of function-specific sub-networks, which can not only analyze biological network on function level but also investigate genes’ rewired relationship involved in biological processes.
... Taking into account the information about biological signaling machinery in cells may help to better interpret the patterns observed in omics data of tumours. This will allow rationalized medicine approach for patients stratification, drug response prediction and treatment assignment , Calzone et al., 2014). ...
The knowledge of cell molecular mechanisms implicated in human diseases is expanding and should be converted into guidelines for deciphering pathological cell signaling and suggesting appropriate treatment. The basic assumption is that during a pathological transformation, the cell does not create new signaling mechanisms, but rather it hijacks the existing molecular programs. This affects not only intracellular functions, but also a crosstalk between different cell types resulting in a new, yet pathological status of the system. There is a certain combination of molecular characteristics dictating specific cell signaling states that sustains the pathological disease status. Identifying and manipulating the key molecular players controlling these cell signaling states, and shifting the pathological status toward the desired healthy phenotype, are the major challenge for molecular biology of human diseases.
http://arxiv.org/abs/1512.05234
The knowledge of cell molecular mechanisms implicated in human diseases is expanding and should be converted into guidelines for deciphering pathological cell signaling and suggesting appropriate treatment. The basic assumption is that during a pathological transformation, the cell does not create new signaling mechanisms, but rather it hijacks the existing molecular programs. This affects not only intracellular functions, but also a crosstalk between different cell types resulting in a new, yet pathological status of the system. There is a certain combination of molecular characteristics dictating specific cell signaling states that sustains the pathological disease status. Identifying and manipulating the key molecular players controlling these cell signaling states, and shifting the pathological status toward the desired healthy phenotype, are the major challenge for molecular biology of human diseases.
Background:
The SHIVA trial is a multicentric randomised proof-of-concept phase II trial comparing molecularly targeted therapy based on tumour molecular profiling vs conventional therapy in patients with any type of refractory cancer. Results of the feasibility study on the first 100 enrolled patients are presented.
Methods:
Adult patients with any type of metastatic cancer who failed standard therapy were eligible for the study. The molecular profile was performed on a mandatory biopsy, and included mutations and gene copy number alteration analyses using high-throughput technologies, as well as the determination of oestrogen, progesterone, and androgen receptors by immunohistochemistry (IHC).
Results:
Biopsy was safely performed in 95 of the first 100 included patients. Median time between the biopsy and the therapeutic decision taken during a weekly molecular biology board was 26 days. Mutations, gene copy number alterations, and IHC analyses were successful in 63 (66%), 65 (68%), and 87 (92%) patients, respectively. A druggable molecular abnormality was present in 38 patients (40%).
Conclusions:
The establishment of a comprehensive tumour molecular profile was safe, feasible, and compatible with clinical practice in refractory cancer patients.
Treatment of BRAF(V600E) mutant melanoma by small molecule drugs that target the BRAF or MEK kinases can be effective, but resistance develops invariably1, 2. In contrast, colon cancers that harbour the same BRAF(V600E) mutation are intrinsically resistant to BRAF inhibitors, due to feedback activation of the epidermal growth factor receptor (EGFR)3, 4. Here we show that 6 out of 16 melanoma tumours analysed acquired EGFR expression after the development of resistance to BRAF or MEK inhibitors. Using a chromatin-regulator-focused short hairpin RNA (shRNA) library, we find that suppression of sex determining region Y-box 10 (SOX10) in melanoma causes activation of TGF-β signalling, thus leading to upregulation of EGFR and platelet-derived growth factor receptor-β (PDGFRB), which confer resistance to BRAF and MEK inhibitors. Expression of EGFR in melanoma or treatment with TGF-β results in a slow-growth phenotype with cells displaying hallmarks of oncogene-induced senescence. However, EGFR expression or exposure to TGF-β becomes beneficial for proliferation in the presence of BRAF or MEK inhibitors. In a heterogeneous population of melanoma cells having varying levels of SOX10 suppression, cells with low SOX10 and consequently high EGFR expression are rapidly enriched in the presence of drug, but this is reversed when the drug treatment is discontinued. We find evidence for SOX10 loss and/or activation of TGF-β signalling in 4 of the 6 EGFR-positive drug-resistant melanoma patient samples. Our findings provide a rationale for why some BRAF or MEK inhibitor-resistant melanoma patients may regain sensitivity to these drugs after a ‘drug holiday’ and identify patients with EGFR-positive melanoma as a group that may benefit from re-treatment after a drug holiday.
Reactome (http://www.reactome.org) is a manually curated open-source open-data resource of human pathways and reactions. The current version 46 describes 7088
human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107
research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase
speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due
to infectious agents and to mutation.
Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.
A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns - attractors - dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication.
Background
Public repositories of biological pathways and networks have greatly expanded in recent years. Such databases contain many pathways that facilitate the analysis of high-throughput experimental work and the formulation of new biological hypotheses to be tested, a fundamental principle of the systems biology approach. However, large-scale molecular maps are not always easy to mine and interpret.
Results
We have developed BiNoM (Biological Network Manager), a Cytoscape plugin, which provides functions for the import-export of some standard systems biology file formats (import from CellDesigner, BioPAX Level 3 and CSML; export to SBML, CellDesigner and BioPAX Level 3), and a set of algorithms to analyze and reduce the complexity of biological networks. BiNoM can be used to import and analyze files created with the CellDesigner software. BiNoM provides a set of functions allowing to import BioPAX files, but also to search and edit their content. As such, BiNoM is able to efficiently manage large BioPAX files such as whole pathway databases (e.g. Reactome). BiNoM also implements a collection of powerful graph-based functions and algorithms such as path analysis, decomposition by involvement of an entity or cyclic decomposition, subnetworks clustering and decomposition of a large network in modules.
Conclusions
Here, we provide an in-depth overview of the BiNoM functions, and we also detail novel aspects such as the support of the BioPAX Level 3 format and the implementation of a new algorithm for the quantification of pathways for influence networks. At last, we illustrate some of the BiNoM functions on a detailed biological case study of a network representing the G1/S transition of the cell cycle, a crucial cellular process disturbed in most human tumors.
Tumor development is characterized by a compromised balance between cell
life and death decision mechanisms, which are tighly regulated in normal
cells. Understanding this process provides insights for developing new
treatments for fighting with cancer. We present a study of a
mathematical model describing cellular choice between survival and two
alternative cell death modalities: apoptosis and necrosis. The model is
implemented in discrete modeling formalism and allows to predict
probabilities of having a particular cellular phenotype in response to
engagement of cell death receptors. Using an original parameter
sensitivity analysis developed for discrete dynamic systems, we
determine the critical parameters affecting cellular fate decision
variables that appear to be critical in the cellular fate decision and
discuss how they are exploited by existing cancer therapies.
Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone
towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable
progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and
associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized
pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality
data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored
and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version
9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information,
to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one
model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed
in their networks.
Personalized medicine is defined by the National Cancer Institute as "a form of medicine that uses information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease." In oncology, the term "personalized medicine" arose with the emergence of molecularly targeted agents. The prescription of approved molecularly targeted agents to cancer patients currently relies on the primary tumor location and histological subtype. Predictive biomarkers of efficacy of these modern agents have been exclusively validated in specific tumor types. A major concern today is to determine whether the prescription of molecularly targeted therapies based on tumor molecular abnormalities, independently of primary tumor location and histology, would improve the outcome of cancer patients. This new paradigm requires prospective validation before being implemented in clinical practice. In this paper, we will first review different designs, including observational cohorts, as well as nonrandomized and randomized clinical trials, that have been recently proposed to evaluate the relevance of this approach, and further discuss their advantages and drawbacks. The design of the SHIVA trial, a randomized proof-of-concept phase II trial comparing therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer will be detailed. Finally, we will discuss the multiple challenges associated with the implementation of personalized medicine in oncology, as well as perspectives for the future.
Inhibition of the BRAF(V600E) oncoprotein by the small-molecule drug PLX4032 (vemurafenib) is highly effective in the treatment of melanoma. However, colon cancer patients harbouring the same BRAF(V600E) oncogenic lesion have poor prognosis and show only a very limited response to this drug. To investigate the cause of the limited therapeutic effect of PLX4032 in BRAF(V600E) mutant colon tumours, here we performed an RNA-interference-based genetic screen in human cells to search for kinases whose knockdown synergizes with BRAF(V600E) inhibition. We report that blockade of the epidermal growth factor receptor (EGFR) shows strong synergy with BRAF(V600E) inhibition. We find in multiple BRAF(V600E) mutant colon cancers that inhibition of EGFR by the antibody drug cetuximab or the small-molecule drugs gefitinib or erlotinib is strongly synergistic with BRAF(V600E) inhibition, both in vitro and in vivo. Mechanistically, we find that BRAF(V600E) inhibition causes a rapid feedback activation of EGFR, which supports continued proliferation in the presence of BRAF(V600E) inhibition. Melanoma cells express low levels of EGFR and are therefore not subject to this feedback activation. Consistent with this, we find that ectopic expression of EGFR in melanoma cells is sufficient to cause resistance to PLX4032. Our data suggest that BRAF(V600E) mutant colon cancers (approximately 8-10% of all colon cancers), for which there are currently no targeted treatment options available, might benefit from combination therapy consisting of BRAF and EGFR inhibitors.
Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ or http://www.kegg.jp/) is a database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs
from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem.
Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing
experimental knowledge in computable forms; namely, in the forms of KEGG pathway maps, BRITE functional hierarchies and KEGG
modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking
genomes to the molecular networks through the KEGG Orthology system. Here we report KEGG Mapper, a collection of tools for
KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets. We also report a
variant of the KEGG mapping procedure to extend the knowledge base, where different types of data and knowledge, such as disease
genes and drug targets, are integrated as part of the KEGG molecular networks. Finally, we describe recent enhancements to
the KEGG content, especially the incorporation of disease and drug information used in practice and in society, to support
translational bioinformatics.
Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.
The rapid accumulation of knowledge on biological signaling pathways and their regulatory mechanisms has highlighted the need
for specific repositories that can store, organize and allow retrieval of pathway information in a way that will be useful
for the research community. SPIKE (Signaling Pathways Integrated Knowledge Engine; http://www.cs.tau.ac.il/&~spike/) is a database for achieving this goal, containing highly curated interactions for particular human pathways, along with
literature-referenced information on the nature of each interaction. To make database population and pathway comprehension
straightforward, a simple yet informative data model is used, and pathways are laid out as maps that reflect the curator’s
understanding and make the utilization of the pathways easy. The database currently focuses primarily on pathways describing
DNA damage response, cell cycle, programmed cell death and hearing related pathways. Pathways are regularly updated, and additional
pathways are gradually added. The complete database and the individual maps are freely exportable in several formats. The
database is accompanied by a stand-alone software tool for analysis and dynamic visualization of pathways.
Pathway Commons (http://www.pathwaycommons.org) is a collection of publicly available pathway data from multiple organisms. Pathway Commons provides a web-based interface
that enables biologists to browse and search a comprehensive collection of pathways from multiple sources represented in a
common language, a download site that provides integrated bulk sets of pathway information in standard or convenient formats
and a web service that software developers can use to conveniently query and access all data. Database providers can share
their pathway data via a common repository. Pathways include biochemical reactions, complex assembly, transport and catalysis
events and physical interactions involving proteins, DNA, RNA, small molecules and complexes. Pathway Commons aims to collect
and integrate all public pathway data available in standard formats. Pathway Commons currently contains data from nine databases
with over 1400 pathways and 687 000 interactions and will be continually expanded and updated.
To compare the progression-free survival (PFS) using a treatment regimen selected by molecular profiling (MP) of a patient's tumor with the PFS for the most recent regimen on which the patient had experienced progression (ie, patient as his own control).
Patients with refractory metastatic cancer had tissue samples submitted for MP in two formats including formalin-fixed tissue for immunohistochemistry and fluorescent in situ hybridization assays and immediately frozen tissue for oligonucleotide microarray (MA) gene expression assays (all performed in a Clinical Laboratory Improvement Amendments [CLIA]-certified laboratory). The MP approach was deemed of clinical benefit for the individual patient who had a PFS ratio (PFS on MP-selected therapy/PFS on prior therapy) of ≥ 1.3.
In 86 patients who had MP attempted, there was a molecular target detected in 84 (98%). Sixty-six of the 84 patients were treated according to MP results. Eighteen (27%) of 66 patients had a PFS ratio of ≥ 1.3 (95% CI, 17% to 38%; one-sided, one-sample P = .007). Therefore, the null hypothesis (that ≤ 15% of this patient population would have a PFS ratio of ≥ 1.3) was rejected.
It is possible to identify molecular targets in patients' tumors from nine different centers across the United States. In 27% of patients, the MP approach resulted in a longer PFS on an MP-suggested regimen than on the regimen on which the patient had just experienced progression. Issues to be considered in interpretation of this study include limited prior experience with patients as their own controls as a study end point and overall patient attrition.
In breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo resistance to this antibody is still a serious issue, requiring the identification of additional targets to overcome resistance. In this study, we have combined computational simulations, experimental testing of simulation results, and finally reverse engineering of a protein interaction network to define potential therapeutic strategies for de novo trastuzumab resistant breast cancer.
First, we employed Boolean logic to model regulatory interactions and simulated single and multiple protein loss-of-functions. Then, our simulation results were tested experimentally by producing single and double knockdowns of the network components and measuring their effects on G1/S transition during cell cycle progression. Combinatorial targeting of ERBB2 and EGFR did not affect the response to trastuzumab in de novo resistant cells, which might be due to decoupling of receptor activation and cell cycle progression. Furthermore, examination of c-MYC in resistant as well as in sensitive cell lines, using a specific chemical inhibitor of c-MYC (alone or in combination with trastuzumab), demonstrated that both trastuzumab sensitive and resistant cells responded to c-MYC perturbation.
In this study, we connected ERBB signaling with G1/S transition of the cell cycle via two major cell signaling pathways and two key transcription factors, to model an interaction network that allows for the identification of novel targets in the treatment of trastuzumab resistant breast cancer. Applying this new strategy, we found that, in contrast to trastuzumab sensitive breast cancer cells, combinatorial targeting of ERBB receptors or of key signaling intermediates does not have potential for treatment of de novo trastuzumab resistant cells. Instead, c-MYC was identified as a novel potential target protein in breast cancer cells.
Human Protein Reference Database (HPRD--http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system--Human Proteinpedia (http://www.humanproteinpedia.org/)--through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15,000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.
Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
Cancer is a complex and heterogeneous disease that exhibits high levels of robustness against various therapeutic interventions. It is a constellation of diverse and evolving disorders that are manifested by the uncontrolled proliferation of cells that may eventually lead to fatal dysfunction of the host system. Although some of the cancer subtypes can be cured by early diagnosis and specific treatment, no effective treatment is yet established for a significant portion of cancer subtypes.
In industrial countries where the average life expectancy is high, cancer is one of the major causes of death. Any contribution to an in-depth understanding of cancer shall eventually lead to better care and treatment for patients. Due to the complex, heterogeneous, and evolving nature of cancer, it is essential for a system-oriented view to be adopted for an in-depth understanding. The question is how to achieve an in-depth yet realistic understanding of cancer dynamics. Although large-scale experiments are now being deployed, there are practical limitations of how much they do to convey the reality of cancer pathology and progression within the patient’s body. Computational approaches with system-oriented thinking may complement the limitations of an experimental approach. Computational studies not only provide us with new insights from large-scale experimental data, but also enable us to perceive what are the conceivable characteristics of cancer under certain assumptions. It is an engine of thoughts and proving grounds of various hypotheses on how cancer may behave as well as how molecular mechanisms work within anomalous conditions. It is not just computing that helps us fight against cancer, but a computational approach has to be combined with a proper theoretical framework that enables us to perceive “cancer” as complex dynamical and evolvable systems that entail a robust yet fragile nature. This recognition shifts our attention from the magic bullet approach of anti-cancer drugs to a more systematic control of cancer as complex dynamical phenomena. This leads to the view that a complex system has to be controlled by complex interventions. To understand such a system and design complex interventions, it is essential that we combine experimental and computational approaches. Thus, computational systems biology of cancer is an essential discipline for cancer biology and is expected to have major impacts for clinical decision-making.
This is the first book specifically focused on computational systems biology of cancer with a coherent and proper vision on how to tackle this formidable challenge.
Book web-site:http://www.cancer-systems-biology.net/
The extensive molecular characterization of tumors with high throughput technologies has led to the segmentation of different tumors into very small molecularly defined subgroups. Many ongoing clinical trials are conducted only when specific molecular alterations are identified in tumor samples. In this review, we will describe the implementation of genome analysis in the clinical setting as it has expanded over the last four years in our Precision Medicine Program. This manuscript will also highlight the main limitations and challenges related to the development of broader and deeper genome analysis.
Many genome-scale studies in molecular biology deliver results in the form of a ranked list of gene names, accordingly to some
scoring method. There is always the question how many top-ranked genes to consider for further analysis, for example, in order
creating a diagnostic or predictive gene signature for a disease. This question is usually approached from a statistical point of view,
without considering any biological properties of top-ranked genes or how they are related to each other functionally. Here we
suggest a new method for selecting a number of genes in a ranked gene list such that this set forms the Optimally Functionally
Enriched Network (OFTEN), formed by known physical interactions between genes or their products. The method allows
associating a network with the gene list, providing easier interpretation of the results and classifying the genes or proteins
accordingly to their position in the resulting network. We demonstrate the method on four breast cancer datasets and show that 1)
the resulting gene signatures are more reproducible from one dataset to another compared to standard statistical procedures and 2)
the overlap of these signatures has significant prognostic potential. The method is implemented in BiNoM Cytoscape plugin
(http://binom.curie.fr).
Tumor development is characterized by a compromised balance between cell life and death decision mechanisms, which are tightly regulated in normal cells. Understanding this process provides insights for developing new treatments for fighting with cancer. We present a study of a mathematical model describing cellular choice between survival and two alternative cell death modalities: apoptosis and necrosis. The model is implemented in discrete modeling formalism and allows to predict probabilities of having a particular cellular phenotype in response to engagement of cell death receptors. Using an original parameter sensitivity analysis developed for discrete dynamic systems, we determine variables that appear to be critical in the cellular fate decision and discuss how they are exploited by existing cancer therapies.
An increased understanding of the molecular etiology of cancer has enabled the development of novel therapies that are collectively referred to as molecular targeted agents. Unlike the drugs used in conventional chemotherapy, these agents are designed to specifically interfere with key molecular events that are responsible for the malignant phenotype. They hold great promise for widening the therapeutic window, which would provide more effective treatment options as compared with cytotoxic therapies. In addition, the targeted approach that is characteristic of these drugs provides unique opportunities for combination therapies with other anticancer agents that have non-overlapping toxicities. Targeted agents are therefore primed to become invaluable therapeutic tools in the multimodal treatment of cancer. The challenges associated with these novel targeted therapies are distinct from those faced in conventional chemotherapy. These unique challenges include the need to select appropriate pharmacodynamic markers to guide dose and schedule and to identify biomarkers that enable selection of patient populations that are most likely to benefit from the treatment. In addition, although the emergence of resistance to targeted therapies is a problem frequently faced in the clinic, the molecular characterization of resistance mechanisms provides the opportunity to design second-generation therapies or combination therapies aimed at preventing resistance or restoring response. The development of the tyrosine kinase inhibitor imatinib has revolutionized the treatment of chronic myeloid leukemia (CML). In this article, we discuss the lessons learned from the application of imatinib and other targeted agents in clinical practice and discuss how these insights may guide the development of novel targeted therapies.
BiNoM (Biological Network Manager) is a new bioinformatics software that significantly facilitates the usage and the analysis
of biological networks in standard systems biology formats (SBML, SBGN, BioPAX). BiNoM implements a full-featured BioPAX editor
and a method of ‘interfaces’ for accessing BioPAX content. BiNoM is able to work with huge BioPAX files such as whole pathway
databases. In addition, BiNoM allows the analysis of networks created with CellDesigner software and their conversion into
BioPAX format. BiNoM comes as a library and as a Cytoscape plugin which adds a rich set of operations to Cytoscape such as
path and cycle analysis, clustering sub-networks, decomposition of network into modules, clipboard operations and others.
Availability: Last version of BiNoM distributed under the LGPL licence together with documentation, source code and API are available at
http://bioinfo.curie.fr/projects/binom
Contact: andrei.zinovyev{at}curie.fr
Precision oncology: an overview
- L A Garraway
- J Verweij
- K V Ballman
Garraway LA, Verweij J, Ballman KV. Precision oncology: an
overview. J Clin Oncol Off J Am Soc Clin Oncol 2013 ; 31 : 1803-5.