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Abstract
In this review, we propose a recension of biological observations on plasticity in cancer cell populations and discuss theoretical considerations about their mechanisms.
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Affiliation(s)
- Shensi Shen
- Inserm U981, Institut Gustave Roussy, Université Paris-Saclay, Villejuif, France
| | - Jean Clairambault
- Sorbonne Université, CNRS, Université de Paris, Laboratoire Jacques-Louis Lions (LJLL), & Inria Mamba team, Paris, France
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2
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Wang G, Yuan R, Zhu X, Ao P. Endogenous Molecular-Cellular Network Cancer Theory: A Systems Biology Approach. Methods Mol Biol 2018; 1702:215-245. [PMID: 29119508 DOI: 10.1007/978-1-4939-7456-6_11] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
In light of ever apparent limitation of the current dominant cancer mutation theory, a quantitative hypothesis for cancer genesis and progression, endogenous molecular-cellular network hypothesis has been proposed from the systems biology perspective, now for more than 10 years. It was intended to include both the genetic and epigenetic causes to understand cancer. Its development enters the stage of meaningful interaction with experimental and clinical data and the limitation of the traditional cancer mutation theory becomes more evident. Under this endogenous network hypothesis, we established a core working network of hepatocellular carcinoma (HCC) according to the hypothesis and quantified the working network by a nonlinear dynamical system. We showed that the two stable states of the working network reproduce the main known features of normal liver and HCC at both the modular and molecular levels. Using endogenous network hypothesis and validated working network, we explored genetic mutation pattern in cancer and potential strategies to cure or relieve HCC from a totally new perspective. Patterns of genetic mutations have been traditionally analyzed by posteriori statistical association approaches in light of traditional cancer mutation theory. One may wonder the possibility of a priori determination of any mutation regularity. Here, we found that based on the endogenous network theory the features of genetic mutations in cancers may be predicted without any prior knowledge of mutation propensities. Normal hepatocyte and cancerous hepatocyte stable states, specified by distinct patterns of expressions or activities of proteins in the network, provide means to directly identify a set of most probable genetic mutations and their effects in HCC. As the key proteins and main interactions in the network are conserved through cell types in an organism, similar mutational features may also be found in other cancers. This analysis yielded straightforward and testable predictions on an accumulated and preferred mutation spectrum in normal tissue. The validation of predicted cancer state mutation patterns demonstrates the usefulness and potential of a causal dynamical framework to understand and predict genetic mutations in cancer. We also obtained the following implication related to HCC therapy, (1) specific positive feedback loops are responsible for the maintenance of normal liver and HCC; (2) inhibiting proliferation and inflammation-related positive feedback loops, and simultaneously inducing liver-specific positive feedback loop is predicated as the potential strategy to cure or relieve HCC; (3) the genesis and regression of HCC is asymmetric. In light of the characteristic property of the nonlinear dynamical system, we demonstrate that positive feedback loops must be existed as a simple and general molecular basis for the maintenance of phenotypes such as normal liver and HCC, and regulating the positive feedback loops directly or indirectly provides potential strategies to cure or relieve HCC.
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Affiliation(s)
- Gaowei Wang
- Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Pathology, University of California, San Diego, La Jolla, CA, 92093-0864, USA
| | - Ruoshi Yuan
- Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Systems Biology, Harvard University, Boston, MA, USA
| | - Xiaomei Zhu
- Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Quantitative Life Sciences and Physics Department, Shanghai University, Shanghai, China
| | - Ping Ao
- Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.
- Shanghai Center for Quantitative Life Sciences and Physics Department, Shanghai University, Shanghai, China.
- State Key Laboratory for Oncogenes and Related Genes, Shanghai Cancer Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Potential landscape of high dimensional nonlinear stochastic dynamics with large noise. Sci Rep 2017; 7:15762. [PMID: 29150680 PMCID: PMC5693902 DOI: 10.1038/s41598-017-15889-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 10/02/2017] [Indexed: 12/14/2022] Open
Abstract
Quantifying stochastic processes is essential to understand many natural phenomena, particularly in biology, including the cell-fate decision in developmental processes as well as the genesis and progression of cancers. While various attempts have been made to construct potential landscape in high dimensional systems and to estimate transition rates, they are practically limited to the cases where either noise is small or detailed balance condition holds. A general and practical approach to investigate real-world nonequilibrium systems, which are typically high-dimensional and subject to large multiplicative noise and the breakdown of detailed balance, remains elusive. Here, we formulate a computational framework that can directly compute the relative probabilities between locally stable states of such systems based on a least action method, without the necessity of simulating the steady-state distribution. The method can be applied to systems with arbitrary noise intensities through A-type stochastic integration, which preserves the dynamical structure of the deterministic counterpart dynamics. We demonstrate our approach in a numerically accurate manner through solvable examples. We further apply the method to investigate the role of noise on tumor heterogeneity in a 38-dimensional network model for prostate cancer, and provide a new strategy on controlling cell populations by manipulating noise strength.
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Yuan R, Zhang S, Yu J, Huang Y, Lu D, Cheng R, Huang S, Ao P, Zheng S, Hood L, Zhu X. Beyond cancer genes: colorectal cancer as robust intrinsic states formed by molecular interactions. Open Biol 2017; 7:rsob.170169. [PMID: 29118272 PMCID: PMC5717345 DOI: 10.1098/rsob.170169] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 10/06/2017] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) has complex pathological features that defy the linear-additive reasoning prevailing in current biomedicine studies. In pursuing a mechanistic understanding behind such complexity, we constructed a core molecular–cellular interaction network underlying CRC and investigated its nonlinear dynamical properties. The hypothesis and modelling method has been developed previously and tested in various cancer studies. The network dynamics reveal a landscape of several attractive basins corresponding to both normal intestinal phenotype and robust tumour subtypes, identified by their different molecular signatures. Comparison between the modelling results and gene expression profiles from patients collected at the second affiliated hospital of Zhejiang University is presented as validation. The numerical ‘driving’ experiment suggests that CRC pathogenesis may depend on pathways involved in gastrointestinal track development and molecules associated with mesenchymal lineage differentiation, such as Stat5, BMP, retinoic acid signalling pathways, Runx and Hox transcription families. We show that the multi-faceted response to immune stimulation and therapies, as well as different carcinogenesis and metastasis routes, can be straightforwardly understood and analysed under such a framework.
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Affiliation(s)
- Ruoshi Yuan
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Suzhan Zhang
- Key Laboratory of Cancer Prevention and Intervention, Chinese Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Hangzhou, Zhejiang Province 310009, People's Republic of China.,Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, People's Republic of China
| | - Jiekai Yu
- Key Laboratory of Cancer Prevention and Intervention, Chinese Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Hangzhou, Zhejiang Province 310009, People's Republic of China.,Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, People's Republic of China
| | - Yanqin Huang
- Key Laboratory of Cancer Prevention and Intervention, Chinese Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Hangzhou, Zhejiang Province 310009, People's Republic of China.,Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, People's Republic of China
| | - Demin Lu
- Key Laboratory of Cancer Prevention and Intervention, Chinese Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Hangzhou, Zhejiang Province 310009, People's Republic of China.,Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, People's Republic of China
| | - Runtan Cheng
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
| | - Sui Huang
- Institute for Systems Biology, 401 Terry Ave. N., Seattle, WA 98109-5234, USA
| | - Ping Ao
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China .,Shanghai Center of Quantitative Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China
| | - Shu Zheng
- Key Laboratory of Cancer Prevention and Intervention, Chinese Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Hangzhou, Zhejiang Province 310009, People's Republic of China.,Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, People's Republic of China
| | - Leroy Hood
- Institute for Systems Biology, 401 Terry Ave. N., Seattle, WA 98109-5234, USA
| | - Xiaomei Zhu
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China .,Shanghai Center of Quantitative Life Sciences, Shanghai University, Shanghai 200444, People's Republic of China
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Abstract
A decade ago mainstream molecular biologists regarded it impossible or biologically ill-motivated to understand the dynamics of complex biological phenomena, such as cancer genesis and progression, from a network perspective. Indeed, there are numerical difficulties even for those who were determined to explore along this direction. Undeterred, seven years ago a group of Chinese scientists started a program aiming to obtain quantitative connections between tumors and network dynamics. Many interesting results have been obtained. In this paper we wish to test such idea from a different angle: the connection between a normal biological process and the network dynamics. We have taken early myelopoiesis as our biological model. A standard roadmap for the cell-fate diversification during hematopoiesis has already been well established experimentally, yet little was known for its underpinning dynamical mechanisms. Compounding this difficulty there were additional experimental challenges, such as the seemingly conflicting hematopoietic roadmaps and the cell-fate inter-conversion events. With early myeloid cell-fate determination in mind, we constructed a core molecular endogenous network from well-documented gene regulation and signal transduction knowledge. Turning the network into a set of dynamical equations, we found computationally several structurally robust states. Those states nicely correspond to known cell phenotypes. We also found the states connecting those stable states. They reveal the developmental routes-how one stable state would most likely turn into another stable state. Such interconnected network among stable states enabled a natural organization of cell-fates into a multi-stable state landscape. Accordingly, both the myeloid cell phenotypes and the standard roadmap were explained mechanistically in a straightforward manner. Furthermore, recent challenging observations were also explained naturally. Moreover, the landscape visually enables a prediction of a pool of additional cell states and developmental routes, including the non-sequential and cross-branch transitions, which are testable by future experiments. In summary, the endogenous network dynamics provide an integrated quantitative framework to understand the heterogeneity and lineage commitment in myeloid progenitors.
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Yuan R, Zhu X, Wang G, Li S, Ao P. Cancer as robust intrinsic state shaped by evolution: a key issues review. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2017; 80:042701. [PMID: 28212112 DOI: 10.1088/1361-6633/aa538e] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Cancer is a complex disease: its pathology cannot be properly understood in terms of independent players-genes, proteins, molecular pathways, or their simple combinations. This is similar to many-body physics of a condensed phase that many important properties are not determined by a single atom or molecule. The rapidly accumulating large 'omics' data also require a new mechanistic and global underpinning to organize for rationalizing cancer complexity. A unifying and quantitative theory was proposed by some of the present authors that cancer is a robust state formed by the endogenous molecular-cellular network, which is evolutionarily built for the developmental processes and physiological functions. Cancer state is not optimized for the whole organism. The discovery of crucial players in cancer, together with their developmental and physiological roles, in turn, suggests the existence of a hierarchical structure within molecular biology systems. Such a structure enables a decision network to be constructed from experimental knowledge. By examining the nonlinear stochastic dynamics of the network, robust states corresponding to normal physiological and abnormal pathological phenotypes, including cancer, emerge naturally. The nonlinear dynamical model of the network leads to a more encompassing understanding than the prevailing linear-additive thinking in cancer research. So far, this theory has been applied to prostate, hepatocellular, gastric cancers and acute promyelocytic leukemia with initial success. It may offer an example of carrying physics inquiring spirit beyond its traditional domain: while quantitative approaches can address individual cases, however there must be general rules/laws to be discovered in biology and medicine.
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Affiliation(s)
- Ruoshi Yuan
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
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From molecular interaction to acute promyelocytic leukemia: Calculating leukemogenesis and remission from endogenous molecular-cellular network. Sci Rep 2016; 6:24307. [PMID: 27098097 PMCID: PMC4838884 DOI: 10.1038/srep24307] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 03/10/2016] [Indexed: 12/24/2022] Open
Abstract
Acute promyelocytic leukemia (APL) remains the best example of a malignancy that can be cured clinically by differentiation therapy. We demonstrate that APL may emerge from a dynamical endogenous molecular-cellular network obtained from normal, non-cancerous molecular interactions such as signal transduction and translational regulation under physiological conditions. This unifying framework, which reproduces APL, normal progenitor, and differentiated granulocytic phenotypes as different robust states from the network dynamics, has the advantage to study transition between these states, i.e. critical drivers for leukemogenesis and targets for differentiation. The simulation results quantitatively reproduce microarray profiles of NB4 and HL60 cell lines in response to treatment and normal neutrophil differentiation, and lead to new findings such as biomarkers for APL and additional molecular targets for arsenic trioxide therapy. The modeling shows APL and normal states mutually suppress each other, both in "wiring" and in dynamical cooperation. Leukemogenesis and recovery under treatment may be a consequence of spontaneous or induced transitions between robust states, through "passes" or "dragging" by drug effects. Our approach rationalizes leukemic complexity and constructs a platform towards extending differentiation therapy by performing "dry" molecular biology experiments.
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