Cancer systems biology

Cancer systems biology encompasses the application of systems biology approaches to cancer research, in order to study the disease as a complex adaptive system with emerging properties at multiple biological scales.[1][2][3] Cancer systems biology represents the application of systems biology approaches to the analysis of how the intracellular networks of normal cells are perturbed during carcinogenesis to develop effective predictive models that can assist scientists and clinicians in the validations of new therapies and drugs. Tumours are characterized by genomic and epigenetic instability that alters the functions of many different molecules and networks in a single cell as well as altering the interactions with the local environment. Cancer systems biology approaches, therefore, are based on the use of computational and mathematical methods to decipher the complexity in tumorigenesis as well as cancer heterogeneity. [4]

Cancer systems biology encompasses concrete applications of systems biology approaches to cancer research, notably (a) the need for better methods to distill insights from large-scale networks, (b) the importance of integrating multiple data types in constructing more realistic models, (c) challenges in translating insights about tumorigenic mechanisms into therapeutic interventions, and (d) the role of the tumor microenvironment, at the physical, cellular, and molecular levels.[5] Cancer systems biology therefore adopts a holistic view of cancer[6] aimed at integrating its many biological scales, including genetics, signaling networks,[7] epigenetics,[8] cellular behavior, mechanical properties,[9] histology, clinical manifestations and epidemiology. Ultimately, cancer properties at one scale, e.g., histology, are explained by properties at a scale below, e.g., cell behavior.

Cancer systems biology merges traditional basic and clinical cancer research with “exact” sciences, such as applied mathematics, engineering, and physics. It incorporates a spectrum of “omics” technologies (genomics, proteomics, epigenomics, etc.) and molecular imaging, to generate computational algorithms and quantitative models[10] that shed light on mechanisms underlying the cancer process and predict response to intervention. Application of cancer systems biology include but are not limited to- elucidating critical cellular and molecular networks underlying cancer risk, initiation and progression; thereby promoting an alternative viewpoint to the traditional reductionist approach which has typically focused on characterizing single molecular aberrations.

History

Cancer systems biology finds its roots in a number of events and realizations in biomedical research, as well as in technological advances. Historically cancer was identified, understood, and treated as a monolithic disease. It was seen as a “foreign” component that grew as a homogenous mass, and was to be best treated by excision. Besides the continued impact of surgical intervention, this simplistic view of cancer has drastically evolved. In parallel with the exploits of molecular biology, cancer research focused on the identification of critical oncogenes or tumor suppressor genes in the etiology of cancer. These breakthroughs revolutionized our understanding of molecular events driving cancer progression. Targeted therapy may be considered the current pinnacle of advances spawned by such insights.

Despite these advances, many unresolved challenges remain, including the dearth of new treatment avenues for many cancer types, or the unexplained treatment failures and inevitable relapse in cancer types where targeted treatment exists.[11] Such mismatch between clinical results and the massive amounts of data acquired by omics technology highlights the existence of basic gaps in our knowledge of cancer fundamentals. Cancer Systems Biology is steadily improving our ability to organize information on cancer, in order to fill these gaps. Key developments include:

  • The generation of comprehensive molecular datasets (genome, transcriptome, epigenomics, proteome, metabolome, etc.)
  • The Cancer Genome Atlas data collection[12]
  • Computational algorithms to extract drivers of cancer progression from existing datasets[13]
  • Statistical and mechanistic modeling of signaling networks[14]
  • Quantitative modeling of cancer evolutionary processes[6]
  • Mathematical modeling of cancer cell population growth[15]
  • Mathematical modeling of cellular responses to therapeutic intervention[16]
  • Mathematical modeling of cancer metabolism[10]

The practice of Cancer Systems Biology requires close physical integration between scientists with diverse backgrounds. Critical large-scale efforts are also underway to train a new workforce fluent in both the languages of biology and applied mathematics. At the translational level, Cancer Systems Biology should engender precision medicine application to cancer treatment.

Resources

High-throughput technologies enable comprehensive genomic analyses of mutations, rearrangements, copy number variations, and methylation at the cellular and tissue levels, as well as robust analysis of RNA and microRNA expression data, protein levels and metabolite levels.[17][18][19][20][21][22]

List of High-Throughput Technologies and the Data they generated, with representative databases and publications

TechnologyExperimental dataRepresentative database
DNA-seq, NGSDNA sequences, exome sequences, genomes, genesTCGA,[23] GenBank,[24] DDBJ,[25] Ensembl [26]
Microarray, RNA-seqGene expression levels, microRNA levels, transcriptsGEO,[27] Expression Atlas [28]
MS, iTRAQProtein concentration, phosphorylationsGPMdb,[29] PRIDE,[30] Human Protein Atlas [31]
C-MS, GC-MS, NMRMetabolite levelsHMDB [32]
ChIP-chip, ChIP-seqProtein-DNA interactions, transcript factor binding sitesGEO,[27] TRANSFAC,[33] JASPAR,[34] ENCODE [35]
CLIP-seq, PAR-CLIP, iCLIPMicroRNA-mRNA regulationsStarBase,[36] miRTarBase [37]
Y2H, AP/MS, MaMTH, maPPITProtein-protein interactionsHPRD,[38] BioGRID [39]
Protein microarrayKinase–substrate interactionsTCGA,[23] PhosphoPOINT [40]
SGA, E-MAP, RNAiGenetic interactionsHPRD,[41] BioGRID [42]
SNP genotyping arrayGWAS loci, eQTL, aberrant SNPsGWAS Catalog,[43] dbGAP,[44] dbSNP [45]
LUMIER, data integrationSignaling pathways, metabolic pathways, molecular signaturesTCGA,[23] KEGG,[46] Reactome [47]

Approaches

The computational approaches used in cancer systems biology include new mathematical and computational algorithms that reflect the dynamic interplay between experimental biology and the quantitative sciences.[48] A cancer systems biology approach can be applied at different levels, from an individual cell to a tissue, a patient with a primary tumour and possible metastases, or to any combination of these situations. This approach can integrate the molecular characteristics of tumours at different levels (DNA, RNA, protein, epigenetic, imaging)[49] and different intervals (seconds versus days) with multidisciplinary analysis.[50] One of the major challenges to its success, besides the challenge posed by the heterogeneity of cancer per se, resides in acquiring high-quality data that describe clinical characteristics, pathology, treatment, and outcomes and integrating the data into robust predictive models [51][19][20][21][22][52][53]

Applications
  • Modelling Cancer Growth and Development

Mathematical modeling can provide useful context for the rational design, validation and prioritization of novel cancer drug targets and their combinations. Network-based modeling and multi-scale modeling have begun to show promise in facilitating the process of effective cancer drug discovery. Using a systems network modeling approach, Schoerberl et al.[54] identified a previously unknown, complementary and potentially superior mechanism of inhibiting the ErbB receptor signaling network. ErbB3 was found to be the most sensitive node, leading to Akt activation; Akt regulates many biological processes, such as proliferation, apoptosis and growth, which are all relevant to tumor progression.[55] This target driven modelling has paved way for first of its kind clinical trials. Bekkal et al. presented a nonlinear model of the dynamics of a cell population divided into proliferative and quiescent compartments. The proliferative phase represents the complete cell cycle (G (1)-S-G (2)-M) of a population committed to divide at its end. The asymptotic behavior of solutions of the nonlinear model is analysed in two cases, exhibiting tissue homeostasis or tumor exponential growth. The model is simulated and its analytic predictions are confirmed numerically.[56] Furthermore, advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET-CT and single-photon emission CT (SPECT-CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET-MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools.[57]

  • Cancer Genomics
  • Statistical and mechanistic modelling of cancer progression and development
  • Clinical response models / Modelling cellular response to therapeutic interventions
  • Sub-typing in Cancer.
  • Systems Oncology - Clinical application of Cancer Systems Biology

National funding efforts

In 2004, the US National Cancer Institute launched a program effort on Integrative Cancer Systems Biology[58] to establish Centers for Cancer Systems Biology that focus on the analysis of cancer as a complex biological system. The integration of experimental biology with mathematical modeling will result in new insights in the biology and new approaches to the management of cancer. The program brings clinical and basic cancer researchers together with researchers from mathematics, physics, engineering, information technology, imaging sciences, and computer science to work on unraveling fundamental questions in the biology of cancer.[59]

See also

References
  1. Wang, Edwin. Cancer Systems Biology. Chapman & Hall, 2010
  2. Liu & Lauffenburger. Systems Biomedicine: Concepts and Perspectives. Academic Press, 2009.
  3. Barillot, Emmanuel; Calzone, Laurence; Hupe, Philippe; Vert, Jean-Philippe; Zinovyev, Andrei (2012). Computational Systems Biology of Cancer. Chapman & Hall/CRC Mathematical & Computational Biology. p. 461. ISBN 978-1439831441.
  4. Werner, HM; Mills, GB; Ram, PT (March 2014). "Cancer Systems Biology: a peek into the future of patient care?". Nature Reviews. Clinical Oncology. 11 (3): 167–76. doi:10.1038/nrclinonc.2014.6. PMC 4321721. PMID 24492837.
  5. Gentles, AJ; Gallahan, D (15 September 2011). "Systems biology: confronting the complexity of cancer". Cancer Research. 71 (18): 5961–4. doi:10.1158/0008-5472.CAN-11-1569. PMC 3174325. PMID 21896642.
  6. Anderson, AR; Quaranta (2008). "Integrative mathematical oncology". Nat Rev Cancer. 8 (3): 227–234. doi:10.1038/nrc2329. PMID 18273038.
  7. Kreeger, PK; Lauffenburger (2010). "Cancer systems biology: A network modeling perspective". Carcinogenesis. 31 (1): 2–8. doi:10.1093/carcin/bgp261. PMC 2802670. PMID 19861649.
  8. Huang, YW; Kuo, Stoner; Huang, Wang (2011). "An overview of epigenetics and chemoprevention". FEBS Lett. 585 (13): 2129–2136. doi:10.1016/j.febslet.2010.11.002. PMC 3071863. PMID 21056563.
  9. Spill, Fabian; Bakal, Chris; Mak, Michael (2018). "Mechanical and Systems Biology of Cancer". Computational and Structural Biotechnology Journal. 16: 237–245. arXiv:1807.08990. Bibcode:2018arXiv180708990S. doi:10.1016/j.csbj.2018.07.002. PMC 6077126. PMID 30105089.
  10. Lewis, NE; Abdel-Haleem, AM (2013). "The evolution of genome-scale models of cancer metabolism". Front. Physiol. 4: 237. doi:10.3389/fphys.2013.00237. PMC 3759783. PMID 24027532.
  11. Garraway; Jänne (2012). "Circumventing cancer drug resistance in the era of personalized medicine". Cancer Discovery. 2 (3): 214–226. doi:10.1158/2159-8290.CD-12-0012. PMID 22585993.
  12. Collins; Barker (2007). "Mapping the cancer genome. Pinpointing the genes involved in cancer will help chart a new course across the complex landscape of human malignancies". Sci Am. 296 (3): 50–57. doi:10.1038/scientificamerican0307-50. PMID 17348159.
  13. Pe'er, Dana; Nir Hacohen (2011). "Principles and Strategies for Developing Network Models in Cancer". Cell. 144 (6): 864–873. doi:10.1016/j.cell.2011.03.001. PMC 3082135. PMID 21414479.
  14. Tyson, J.J.; Baumann, W.T.; Chen, C.; Verdugo, A.; Tavassoly, I.; Wang, Y.; Weiner, L.M.; Clarke, R. (2011). "Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells". Nat. Rev. Cancer. 11 (7): 523–532. doi:10.1038/nrc3081. PMC 3294292. PMID 21677677.
  15. Tyson, D.R.; Garbett, S.P.; Frick, P.L.; Quaranta, V (2012). "Fractional proliferation: a method to deconvolve cell population dynamics from single-cell data". Nat. Methods. 9 (9): 923–928. doi:10.1038/nmeth.2138. PMC 3459330. PMID 22886092.
  16. Traina, Tiffany A.; U. Dugan; B. Higgins; K. Kolinsky; M. Theodoulou; C. A. Hudis; Larry Norton (2010). "Optimizing Chemotherapy Dose and Schedule by Norton-Simon Mathematical Modeling". Breast Disease. 31 (1): 7–18. doi:10.3233/BD-2009-0290. PMC 3228251. PMID 20519801.
  17. Cancer Genome Atlas Research, Network. (4 July 2013). "Comprehensive molecular characterization of clear cell renal cell carcinoma". Nature. 499 (7456): 43–9. Bibcode:2013Natur.499...43T. doi:10.1038/nature12222. PMC 3771322. PMID 23792563.
  18. Cancer Genome Atlas Research, Network.; Kandoth, C; Schultz, N; Cherniack, AD; Akbani, R; Liu, Y; Shen, H; Robertson, AG; Pashtan, I; Shen, R; Benz, CC; Yau, C; Laird, PW; Ding, L; Zhang, W; Mills, GB; Kucherlapati, R; Mardis, ER; Levine, DA (2 May 2013). "Integrated genomic characterization of endometrial carcinoma". Nature. 497 (7447): 67–73. Bibcode:2013Natur.497...67T. doi:10.1038/nature12113. PMC 3704730. PMID 23636398.
  19. Sumazin, P; Yang, X; Chiu, HS; Chung, WJ; Iyer, A; Llobet-Navas, D; Rajbhandari, P; Bansal, M; Guarnieri, P; Silva, J; Califano, A (14 October 2011). "An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma". Cell. 147 (2): 370–81. doi:10.1016/j.cell.2011.09.041. PMC 3214599. PMID 22000015.
  20. Tentner, AR; Lee, MJ; Ostheimer, GJ; Samson, LD; Lauffenburger, DA; Yaffe, MB (31 January 2012). "Combined experimental and computational analysis of DNA damage signaling reveals context-dependent roles for Erk in apoptosis and G1/S arrest after genotoxic stress". Molecular Systems Biology. 8: 568. doi:10.1038/msb.2012.1. PMC 3296916. PMID 22294094.
  21. Bozic, I; Antal, T; Ohtsuki, H; Carter, H; Kim, D; Chen, S; Karchin, R; Kinzler, KW; Vogelstein, B; Nowak, MA (26 October 2010). "Accumulation of driver and passenger mutations during tumor progression". Proceedings of the National Academy of Sciences of the United States of America. 107 (43): 18545–50. arXiv:0912.1627. Bibcode:2010PNAS..10718545B. doi:10.1073/pnas.1010978107. PMC 2972991. PMID 20876136.
  22. Greenman, C; Stephens, P; Smith, R; Dalgliesh, GL; Hunter, C; Bignell, G; Davies, H; Teague, J; Butler, A; Stevens, C; Edkins, S; O'Meara, S; Vastrik, I; Schmidt, EE; Avis, T; Barthorpe, S; Bhamra, G; Buck, G; Choudhury, B; Clements, J; Cole, J; Dicks, E; Forbes, S; Gray, K; Halliday, K; Harrison, R; Hills, K; Hinton, J; Jenkinson, A; Jones, D; Menzies, A; Mironenko, T; Perry, J; Raine, K; Richardson, D; Shepherd, R; Small, A; Tofts, C; Varian, J; Webb, T; West, S; Widaa, S; Yates, A; Cahill, DP; Louis, DN; Goldstraw, P; Nicholson, AG; Brasseur, F; Looijenga, L; Weber, BL; Chiew, YE; DeFazio, A; Greaves, MF; Green, AR; Campbell, P; Birney, E; Easton, DF; Chenevix-Trench, G; Tan, MH; Khoo, SK; Teh, BT; Yuen, ST; Leung, SY; Wooster, R; Futreal, PA; Stratton, MR (8 March 2007). "Patterns of somatic mutation in human cancer genomes". Nature. 446 (7132): 153–8. Bibcode:2007Natur.446..153G. doi:10.1038/nature05610. PMC 2712719. PMID 17344846.
  23. "The Cancer Genome Atlas Home Page". The Cancer Genome Atlas - National Cancer Institute. 2018-06-13.
  24. Benson, DA; Clark, K; Karsch-Mizrachi, I; Lipman, DJ; Ostell, J; Sayers, EW (January 2014). "GenBank". Nucleic Acids Research. 42 (Database issue): D32–7. doi:10.1093/nar/gkt1030. PMC 3965104. PMID 24217914.
  25. Kodama, Y; Mashima, J; Kosuge, T; Katayama, T; Fujisawa, T; Kaminuma, E; Ogasawara, O; Okubo, K; Takagi, T; Nakamura, Y (January 2015). "The DDBJ Japanese Genotype-phenotype Archive for genetic and phenotypic human data". Nucleic Acids Research. 43 (Database issue): D18–22. doi:10.1093/nar/gku1120. PMC 4383935. PMID 25477381.
  26. Cunningham, F; Amode, MR; Barrell, D; Beal, K; Billis, K; Brent, S; Carvalho-Silva, D; Clapham, P; Coates, G; Fitzgerald, S; Gil, L; Girón, CG; Gordon, L; Hourlier, T; Hunt, SE; Janacek, SH; Johnson, N; Juettemann, T; Kähäri, AK; Keenan, S; Martin, FJ; Maurel, T; McLaren, W; Murphy, DN; Nag, R; Overduin, B; Parker, A; Patricio, M; Perry, E; Pignatelli, M; Riat, HS; Sheppard, D; Taylor, K; Thormann, A; Vullo, A; Wilder, SP; Zadissa, A; Aken, BL; Birney, E; Harrow, J; Kinsella, R; Muffato, M; Ruffier, M; Searle, SM; Spudich, G; Trevanion, SJ; Yates, A; Zerbino, DR; Flicek, P (January 2015). "Ensembl 2015". Nucleic Acids Research. 43 (Database issue): D662–9. doi:10.1093/nar/gku1010. PMC 4383879. PMID 25352552.
  27. Edgar, R; Domrachev, M; Lash, AE (1 January 2002). "Gene Expression Omnibus: NCBI gene expression and hybridization array data repository". Nucleic Acids Research. 30 (1): 207–10. doi:10.1093/nar/30.1.207. PMC 99122. PMID 11752295.
  28. Petryszak, R; Burdett, T; Fiorelli, B; Fonseca, NA; Gonzalez-Porta, M; Hastings, E; Huber, W; Jupp, S; Keays, M; Kryvych, N; McMurry, J; Marioni, JC; Malone, J; Megy, K; Rustici, G; Tang, AY; Taubert, J; Williams, E; Mannion, O; Parkinson, HE; Brazma, A (January 2014). "Expression Atlas update--a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments". Nucleic Acids Research. 42 (Database issue): D926–32. doi:10.1093/nar/gkt1270. PMC 3964963. PMID 24304889.
  29. "GPMDB Proteome Database".
  30. "PRIDE Archive".
  31. Uhlen, M; Oksvold, P; Fagerberg, L; Lundberg, E; Jonasson, K; Forsberg, M; Zwahlen, M; Kampf, C; Wester, K; Hober, S; Wernerus, H; Björling, L; Ponten, F (December 2010). "Towards a knowledge-based Human Protein Atlas". Nature Biotechnology. 28 (12): 1248–50. doi:10.1038/nbt1210-1248. PMID 21139605.
  32. "Human Metabolome Database".
  33. "Gene Regulation". www.gene-regulation.com.
  34. "JASPAR 2018: An open-access database of transcription factor binding profiles".
  35. "ENCODE: Encyclopedia of DNA Elements – ENCODE".
  36. Li, JH; Liu, S; Zhou, H; Qu, LH; Yang, JH (January 2014). "starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data". Nucleic Acids Research. 42 (Database issue): D92–7. doi:10.1093/nar/gkt1248. PMC 3964941. PMID 24297251.
  37. "MiRTarBase: The experimentally validated microRNA-target interactions database".
  38. Keshava Prasad, TS; Goel, R; Kandasamy, K; Keerthikumar, S; Kumar, S; Mathivanan, S; Telikicherla, D; Raju, R; Shafreen, B; Venugopal, A; Balakrishnan, L; Marimuthu, A; Banerjee, S; Somanathan, DS; Sebastian, A; Rani, S; Ray, S; Harrys Kishore, CJ; Kanth, S; Ahmed, M; Kashyap, MK; Mohmood, R; Ramachandra, YL; Krishna, V; Rahiman, BA; Mohan, S; Ranganathan, P; Ramabadran, S; Chaerkady, R; Pandey, A (January 2009). "Human Protein Reference Database--2009 update". Nucleic Acids Research. 37 (Database issue): D767–72. doi:10.1093/nar/gkn892. PMC 2686490. PMID 18988627.
  39. Chatr-Aryamontri, A; Breitkreutz, BJ; Oughtred, R; Boucher, L; Heinicke, S; Chen, D; Stark, C; Breitkreutz, A; Kolas, N; O'Donnell, L; Reguly, T; Nixon, J; Ramage, L; Winter, A; Sellam, A; Chang, C; Hirschman, J; Theesfeld, C; Rust, J; Livstone, MS; Dolinski, K; Tyers, M (January 2015). "The BioGRID interaction database: 2015 update". Nucleic Acids Research. 43 (Database issue): D470–8. doi:10.1093/nar/gku1204. PMC 4383984. PMID 25428363.
  40. "PhosphoSitePlus".
  41. Keshava Prasad, TS; Goel, R; Kandasamy, K; Keerthikumar, S; Kumar, S; Mathivanan, S; Telikicherla, D; Raju, R; Shafreen, B; Venugopal, A; Balakrishnan, L; Marimuthu, A; Banerjee, S; Somanathan, DS; Sebastian, A; Rani, S; Ray, S; Harrys Kishore, CJ; Kanth, S; Ahmed, M; Kashyap, MK; Mohmood, R; Ramachandra, YL; Krishna, V; Rahiman, BA; Mohan, S; Ranganathan, P; Ramabadran, S; Chaerkady, R; Pandey, A (January 2009). "Human Protein Reference Database--2009 update". Nucleic Acids Research. 37 (Database issue): D767–72. doi:10.1093/nar/gkn892. PMC 2686490. PMID 18988627.
  42. Chatr-Aryamontri, A; Breitkreutz, BJ; Oughtred, R; Boucher, L; Heinicke, S; Chen, D; Stark, C; Breitkreutz, A; Kolas, N; O'Donnell, L; Reguly, T; Nixon, J; Ramage, L; Winter, A; Sellam, A; Chang, C; Hirschman, J; Theesfeld, C; Rust, J; Livstone, MS; Dolinski, K; Tyers, M (January 2015). "The BioGRID interaction database: 2015 update". Nucleic Acids Research. 43 (Database issue): D470–8. doi:10.1093/nar/gku1204. PMC 4383984. PMID 25428363.
  43. NHGRI, Tony Burdett, Emma Hastings, Dani Welter, SPOT, EMBL-EBI. "GWAS Catalog". www.ebi.ac.uk.
  44. "Home - dbGaP - NCBI". www.ncbi.nlm.nih.gov.
  45. "dbSNP Home Page". www.ncbi.nlm.nih.gov.
  46. "KEGG PATHWAY Database". www.genome.jp.
  47. "Home - Reactome Pathway Database". reactome.org.
  48. Carro, MS; Lim, WK; Alvarez, MJ; Bollo, RJ; Zhao, X; Snyder, EY; Sulman, EP; Anne, SL; Doetsch, F; Colman, H; Lasorella, A; Aldape, K; Califano, A; Iavarone, A (21 January 2010). "The transcriptional network for mesenchymal transformation of brain tumours". Nature. 463 (7279): 318–25. Bibcode:2010Natur.463..318C. doi:10.1038/nature08712. PMC 4011561. PMID 20032975.
  49. Huang, SS; Clarke, DC; Gosline, SJ; Labadorf, A; Chouinard, CR; Gordon, W; Lauffenburger, DA; Fraenkel, E (2013). "Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling". PLoS Computational Biology. 9 (2): e1002887. Bibcode:2013PLSCB...9E2887H. doi:10.1371/journal.pcbi.1002887. PMC 3567149. PMID 23408876.
  50. Pascal, J; Bearer, EL; Wang, Z; Koay, EJ; Curley, SA; Cristini, V (27 August 2013). "Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements". Proceedings of the National Academy of Sciences of the United States of America. 110 (35): 14266–71. Bibcode:2013PNAS..11014266P. doi:10.1073/pnas.1300619110. PMC 3761643. PMID 23940372.
  51. Hill, SM; Lu, Y; Molina, J; Heiser, LM; Spellman, PT; Speed, TP; Gray, JW; Mills, GB; Mukherjee, S (1 November 2012). "Bayesian inference of signaling network topology in a cancer cell line". Bioinformatics. 28 (21): 2804–10. doi:10.1093/bioinformatics/bts514. PMC 3476330. PMID 22923301.
  52. Mills, GB (February 2012). "An emerging toolkit for targeted cancer therapies". Genome Research. 22 (2): 177–82. doi:10.1101/gr.136044.111. PMC 3266025. PMID 22301131.
  53. Metzcar, John; Wang, Yafei; Heiland, Randy; Macklin, Paul (2019-02-04). "A Review of Cell-Based Computational Modeling in Cancer Biology". JCO Clinical Cancer Informatics. 3 (3): 1–13. doi:10.1200/CCI.18.00069. PMC 6584763. PMID 30715927.
  54. Schoeberl, B; Kudla, A; Masson, K; Kalra, A; Curley, M; Finn, G; Pace, E; Harms, B; Kim, J; Kearns, J; Fulgham, A; Burenkova, O; Grantcharova, V; Yarar, D; Paragas, V; Fitzgerald, J; Wainszelbaum, M; West, K; Mathews, S; Nering, R; Adiwijaya, B; Garcia, G; Kubasek, B; Moyo, V; Czibere, A; Nielsen, UB; MacBeath, G (2017). "Systems biology driving drug development: from design to the clinical testing of the anti-ErbB3 antibody seribantumab (MM-121)". NPJ Systems Biology and Applications. 3: 16034. doi:10.1038/npjsba.2016.34. PMC 5516865. PMID 28725482.
  55. Wang, Z; Deisboeck, TS (February 2014). "Mathematical modeling in cancer drug discovery". Drug Discovery Today. 19 (2): 145–50. doi:10.1016/j.drudis.2013.06.015. PMID 23831857.
  56. Bekkal Brikci, F; Clairambault, J; Ribba, B; Perthame, B (July 2008). "An age-and-cyclin-structured cell population model for healthy and tumoral tissues". Journal of Mathematical Biology. 57 (1): 91–110. doi:10.1007/s00285-007-0147-x. PMID 18064465.
  57. Yankeelov, TE; Abramson, RG; Quarles, CC (November 2014). "Quantitative multimodality imaging in cancer research and therapy". Nature Reviews. Clinical Oncology. 11 (11): 670–80. doi:10.1038/nrclinonc.2014.134. PMC 4909117. PMID 25113842.
  58. NCI Cancer Bulletin. Feb 24, 2004. V1, 8. p5-6
  59. Gentles; Gallahan (2011). "Systems biology: confronting the complexity of cancer". Cancer Res. 71 (18): 5961–5964. doi:10.1158/0008-5472.CAN-11-1569. PMC 3174325. PMID 21896642.
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.