Karimkhani C, Green AC, Nijsten T, Weinstock M, Dellavalle RP, Naghavi M, et al.The global burden of melanoma: results from the global burden of disease study 2015. Br J Dermatol. 2017; 177(1):134–40.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kunz M. Oncogenes in melanoma: an update. Eur J Cell Biol. 2014; 93(1-2):1–10.
Article
CAS
PubMed
Google Scholar
Omholt K, Platz A, Kanter L, Ringborg U, Hansson J. NRAS and BRAF mutations arise early during melanoma pathogenesis and are preserved throughout tumor progression. Clin Cancer Res. 2003; 9(17):6483–8.
CAS
PubMed
Google Scholar
Aris M, Barrio MM. Combining immunotherapy with oncogene-targeted therapy: a new road for melanoma treatment. Front Immunol. 2015; 6:46.
PubMed
PubMed Central
Google Scholar
Amaral T, Sinnberg T, Meier F, Krepler C, Levesque M, Niessner H, et al.MAPK pathway in melanoma part II—secondary and adaptive resistance mechanisms to BRAF inhibition. Eur J Cancer. 2017; 73:93–101.
Article
CAS
PubMed
Google Scholar
Amaral T, Sinnberg T, Meier F, Krepler C, Levesque M, Niessner H, et al.The mitogen-activated protein kinase pathway in melanoma part I–activation and primary resistance mechanisms to BRAF inhibition. Eur J Cancer. 2017; 73:85–92.
Article
CAS
PubMed
Google Scholar
Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell. 2017; 168(4):707–23.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ugurel S, Röhmel J, Ascierto PA, Flaherty KT, Grob JJ, Hauschild A, et al.Survival of patients with advanced metastatic melanoma: the impact of novel therapies–update 2017. Eur J Cancer. 2017; 83:247–57.
Article
PubMed
Google Scholar
Wolkenhauer O. Why model?Front Physiol. 2014; 5:21.
Article
PubMed
PubMed Central
Google Scholar
Weinberg RA. Coming full circle–from endless complexity to simplicity and back again. Cell. 2014; 157(1):267–71.
Article
CAS
PubMed
Google Scholar
Kolch W, Halasz M, Granovskaya M, Kholodenko BN. The dynamic control of signal transduction networks in cancer cells. Nat Rev Cancer. 2015; 15(9):515.
Article
CAS
PubMed
Google Scholar
Mitchell MJ, Jain RK, Langer R. Engineering and physical sciences in oncology: challenges and opportunities. Nat Rev Cancer. 2017; 17(11):659.
Article
CAS
PubMed
PubMed Central
Google Scholar
Hatzikirou H, Chauviere A, Bauer AL, Leier A, Lewis MT, Macklin P, et al.Integrative physical oncology. Wiley Interdiscip Rev Syst Biol Med. 2012; 4(1):1–14.
Article
PubMed
Google Scholar
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al.Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005; 102(43):15545–50.
Article
CAS
PubMed
PubMed Central
Google Scholar
Akbani R, Akdemir KC, Aksoy BA, Albert M, Ally A, Amin SB, et al.Genomic classification of cutaneous melanoma. Cell. 2015; 161(7):1681–96.
Article
CAS
Google Scholar
Loftus SK. The next generation of melanocyte data: Genetic, epigenetic, and transcriptional resource datasets and analysis tools. Pigment Cell Melanoma Res. 2018; 31(3):442–7.
Article
CAS
PubMed
Google Scholar
Mittal P, Jain M. Proteomics: An indispensable tool for novel biomarker identification in melanoma. J Data Min Genomics Proteomics. 2016; 7(204):2153–0602.
Google Scholar
Liberato T, Pessotti DS, Fukushima I, Kitano ES, Serrano SM, Zelanis A. Signatures of protein expression revealed by secretome analyses of cancer associated fibroblasts and melanoma cell lines. J Proteomics. 2018; 174:1–8.
Article
CAS
PubMed
Google Scholar
Fischer GM, Vashisht Gopal Y, McQuade JL, Peng W, DeBerardinis RJ, Davies MA. Metabolic strategies of melanoma cells: Mechanisms, interactions with the tumor microenvironment, and therapeutic implications. Pigment Cell Melanoma Res. 2018; 31(1):11–30.
Article
PubMed
Google Scholar
Ratnikov BI, Scott DA, Osterman AL, Smith JW, Ronai ZA. Metabolic rewiring in melanoma. Oncogene. 2017; 36(2):147.
Article
CAS
PubMed
Google Scholar
Emmert-Streib F, Glazko G, De Matos Simoes R, et al.Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Front Genet. 2012; 3:8.
Article
PubMed
PubMed Central
Google Scholar
Werner HM, Mills GB, Ram PT. Cancer Systems Biology: a peek into the future of patient care?Nat Rev Clin Oncol. 2014; 11(3):167.
Article
PubMed
PubMed Central
Google Scholar
Mocellin S, Rossi CR. The melanoma molecular map project. LWW. 2008; 18(3):163–165.
Google Scholar
Antonopoulou K, Stefanaki I, Lill CM, Chatzinasiou F, Kypreou KP, Karagianni F, et al.Updated field synopsis and systematic meta-analyses of genetic association studies in cutaneous melanoma: the MelGene database. 135. 2015; 4:1074–9.
Google Scholar
Trevarton AJ, Mann M, Knapp C, Araki H, Wren JD, Stones-Havas S, et al.MelanomaDB: a web tool for integrative analysis of melanoma genomic information to identify disease-associated molecular pathways. Front Oncol. 2013; 3:184.
Article
PubMed
PubMed Central
Google Scholar
Zhang D, Zhu R, Zhang H, Zheng CH, Xia J. MGDB: a comprehensive database of genes involved in melanoma. Database. 2015;:2015. https://doi.org/10.1093/database/bav097.
Reinhold WC. Commentary on “MelanomaDB: a web tool for integrative analysis of melanoma genomic information to identify disease-associated molecular pathways”. Front Genet. 2013; 4:156.
Article
PubMed
PubMed Central
CAS
Google Scholar
Ciriello G, Cerami E, Sander C, Schultz N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 2012; 22(2):398–406.
Article
CAS
PubMed
PubMed Central
Google Scholar
Guan J, Gupta R, Filipp FV. Cancer systems biology of TCGA SKCM: efficient detection of genomic drivers in melanoma. Sci Rep. 2015; 5:7857.
Article
CAS
PubMed
PubMed Central
Google Scholar
Marzese DM, Scolyer RA, Roqué M, Vargas-Roig LM, Huynh JL, Wilmott JS, et al.DNA methylation and gene deletion analysis of brain metastases in melanoma patients identifies mutually exclusive molecular alterations. Neuro-oncology. 2014; 16(11):1499–1509.
Article
CAS
PubMed
PubMed Central
Google Scholar
Barter RL, Schramm SJ, Mann GJ, Yang YH. Network-based biomarkers enhance classical approaches to prognostic gene expression signatures. BMC Syst Biol. 2014; 8(4):S5.
Article
PubMed
PubMed Central
Google Scholar
Wang L, Hurley DG, Watkins W, Araki H, Tamada Y, Muthukaruppan A, et al.Cell cycle gene networks are associated with melanoma prognosis. PloS ONE. 2012; 7(4):e34247.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kaushik A, Bhatia Y, Ali S, Gupta D. Gene network rewiring to study melanoma stage progression and elements essential for driving melanoma. PloS ONE. 2015; 10(11):e0142443.
Article
PubMed
PubMed Central
CAS
Google Scholar
Dang CV, Reddy EP, Shokat KM, Soucek L. Drugging the’undruggable’cancer targets. Nat Rev Cancer. 2017; 17(8):502.
Article
CAS
PubMed
PubMed Central
Google Scholar
Saez-Rodriguez J, MacNamara A, Cook S. Modeling signaling networks to advance new cancer therapies. Annu Rev Biomed Eng. 2015; 17:143–63.
Article
CAS
PubMed
Google Scholar
Passante E, Würstle ML, Hellwig CT, Leverkus M, Rehm M. Systems analysis of apoptosis protein expression allows the case-specific prediction of cell death responsiveness of melanoma cells. Cell Death Differ. 2013; 20(11):1521.
Article
CAS
PubMed
PubMed Central
Google Scholar
Fallahi-Sichani M, Moerke NJ, Niepel M, Zhang T, Gray NS, Sorger PK. Systematic analysis of BRAFV600E melanomas reveals a role for JNK/c-Jun pathway in adaptive resistance to drug-induced apoptosis. Mol Syst Biol. 2015; 11(3):797.
Article
PubMed
CAS
Google Scholar
Fallahi-Sichani M, Becker V, Izar B, Baker GJ, Lin JR, Boswell SA, et al.Adaptive resistance of melanoma cells to RAF inhibition via reversible induction of a slowly dividing de-differentiated state. Mol Syst Biol. 2017; 13(1):905.
Article
PubMed
PubMed Central
CAS
Google Scholar
Bernardo-Faura M, Massen S, Falk CS, Brady NR, Eils R. Data-derived modeling characterizes plasticity of MAPK signaling in melanoma. PLoS Comput Biol. 2014; 10(9):e1003795.
Article
PubMed
PubMed Central
CAS
Google Scholar
Del Mistro G, Lucarelli P, Müller I, De Landtsheer S, Zinoveva A, Hutt M, et al.Systemic network analysis identifies XIAP and I κBα as potential drug targets in TRAIL resistant BRAF mutated melanoma. NPJ Syst Biol Appl. 2018; 4(1):39.
Article
PubMed
PubMed Central
Google Scholar
Korkut A, Wang W, Demir E, Aksoy BA, Jing X, Molinelli EJ, et al.Perturbation biology nominates upstream–downstream drug combinations in RAF inhibitor resistant melanoma cells. Elife. 2015; 4:e04640.
Article
PubMed Central
Google Scholar
Antoniewicz MR. Methods and advances in metabolic flux analysis: a mini-review. J Ind Microbiol Biotechnol. 2015; 42(3):317– 25.
Article
CAS
PubMed
Google Scholar
Scott DA, Richardson AD, Filipp FV, Knutzen CA, Chiang GG, Ze’ev AR, et al.Comparative metabolic flux profiling of melanoma cell lines beyond the warburg effect. J Biol Chem. 2011; 286(49):42626–34.
Article
CAS
PubMed
PubMed Central
Google Scholar
Koppenol WH, Bounds PL, Dang CV. Otto Warburg’s contributions to current concepts of cancer metabolism. Nat Rev Cancer. 2011; 11(5):325.
Article
CAS
PubMed
Google Scholar
Lee HS, Goh MJ, Kim J, Choi TJ, Lee HK, Na YJ, et al.A systems-biological study on the identification of safe and effective molecular targets for the reduction of ultraviolet B-induced skin pigmentation. Sci Rep. 2015; 5:10305.
Article
CAS
PubMed
PubMed Central
Google Scholar
Pappalardo F, Russo G, Candido S, Pennisi M, Cavalieri S, Motta S, et al.Computational modeling of PI3K/AKT and MAPK signaling pathways in melanoma cancer. PLoS ONE. 2016; 11(3):e0152104.
Article
PubMed
PubMed Central
CAS
Google Scholar
Brown KS, Hill CC, Calero GA, Myers CR, Lee KH, Sethna JP, et al.The statistical mechanics of complex signaling networks: nerve growth factor signaling. Phys Biol. 2004; 1(3):184.
Article
CAS
PubMed
Google Scholar
Flach EH, Rebecca VW, Herlyn M, Smalley KS, Anderson AR. Fibroblasts contribute to melanoma tumor growth and drug resistance. Mol Pharm. 2011; 8(6):2039–49.
Article
CAS
PubMed
PubMed Central
Google Scholar
Hirata E, Girotti MR, Viros A, Hooper S, Spencer-Dene B, Matsuda M, et al.Intravital imaging reveals how BRAF inhibition generates drug-tolerant microenvironments with high integrin β1/FAK signaling. Cancer Cell. 2015; 27(4):574–88.
Article
CAS
PubMed
PubMed Central
Google Scholar
Fedorenko IV, Abel EV, Koomen JM, Fang B, Wood ER, Chen YA, et al.Fibronectin induction abrogates the BRAF inhibitor response of BRAF V600E/PTEN-null melanoma cells. Oncogene. 2016; 35(10):1225.
Article
CAS
PubMed
Google Scholar
Picco N, Sahai E, Maini PK, Anderson AR. Integrating models to quantify environment-mediated drug resistance. Cancer Res. 2017; 77(19):5409–18.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kim E, Rebecca VW, Smalley KS, Anderson AR. Phase i trials in melanoma: A framework to translate preclinical findings to the clinic. Eur J Cancer. 2016; 67:213–22.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sun X, Bao J, Shao Y. Mathematical modeling of therapy-induced cancer drug resistance: connecting cancer mechanisms to population survival rates. Sci Rep. 2016; 6:22498.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ouellet D, Gibiansky E, Leonowens C, O’Hagan A, Haney P, Switzky J, et al.Population pharmacokinetics of dabrafenib, a BRAF inhibitor: effect of dose, time, covariates, and relationship with its metabolites. J Clin Pharmacol. 2014; 54(6):696–706.
Article
CAS
PubMed
Google Scholar
Herzberg B, Fisher DE. Metastatic melanoma and immunotherapy. Clin Immunol. 2016; 172:105–10.
Article
CAS
PubMed
PubMed Central
Google Scholar
Hodi FS, O’day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, et al.Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med. 2010; 363(8):711–23.
Article
CAS
PubMed
PubMed Central
Google Scholar
Andtbacka R, Kaufman HL, Collichio F, Amatruda T, Senzer N, Chesney J, et al.Talimogene laherparepvec improves durable response rate in patients with advanced melanoma. J Clin Oncol. 2015; 33(25):2780–8.
Article
CAS
PubMed
Google Scholar
Kogan Y, Agur Z, Elishmereni M. A mathematical model for the immunotherapeutic control of the Th1/Th2 imbalance in melanoma. Discret Contin Dyn Syst Ser B. 2013; 18(4):1017–30.
Google Scholar
Cappuccio A, Elishmereni M, Agur Z. Cancer immunotherapy by interleukin-21: potential treatment strategies evaluated in a mathematical model. Cancer Res. 2006; 66(14):7293–300.
Article
CAS
PubMed
Google Scholar
den Breems NY, Eftimie R. The re-polarisation of M2 and M1 macrophages and its role on cancer outcomes. J Theor Biol. 2016; 390:23–39.
Article
CAS
PubMed
Google Scholar
Eftimie R, Hamam H. Modelling and investigation of the CD4+ T cells–Macrophages paradox in melanoma immunotherapies. J Theor Biol. 2017; 420:82–104.
Article
CAS
PubMed
Google Scholar
DePillis L, Gallegos A, Radunskaya A. A model of dendritic cell therapy for melanoma. Front Oncol. 2013; 3:6. https://doi.org/10.3389/fonc.2013.00056.
Article
Google Scholar
Pizzurro GA, Barrio MM. Dendritic cell-based vaccine efficacy: aiming for hot spots. Front Immunol. 2015; 6:91.
Article
PubMed
PubMed Central
CAS
Google Scholar
Santos G, Nikolov S, Lai X, Eberhardt M, Dreyer FS, Paul S, et al.Model-based genotype-phenotype mapping used to investigate gene signatures of immune sensitivity and resistance in melanoma micrometastasis. Sci Rep. 2016; 6:24967.
Article
CAS
PubMed
PubMed Central
Google Scholar
Pappalardo F, Forero IM, Pennisi M, Palazon A, Melero I, Motta S. SimB16: modeling induced immune system response against B16-melanoma. PloS ONE. 2011; 6(10):e26523.
Article
CAS
PubMed
PubMed Central
Google Scholar
Altrock PM, Liu LL, Michor F. The mathematics of cancer: integrating quantitative models. Nat Rev Cancer. 2015; 15(12):730.
Article
CAS
PubMed
Google Scholar
Balois T, Amar MB. Morphology of melanocytic lesions in situ. Sci Rep. 2014; 4:3622.
Article
CAS
PubMed
PubMed Central
Google Scholar
Mendes AI, Nogueira C, Pereira J, Fonseca-Pinto R. On the geometric modulation of skin lesion growth: a mathematical model for melanoma. Res Biomed Eng. 2016; 32(1):44–54.
Article
Google Scholar
Lee H, Kwon K. A mathematical analysis of the ABCD criteria for diagnosing malignant melanoma. Phys Med Biol. 2017; 62(5):1865.
Article
PubMed
Google Scholar
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al.Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542(7639):115.
Article
CAS
PubMed
PubMed Central
Google Scholar
Haenssle H, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al.Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018; 29(8):1836–42.
Article
CAS
PubMed
Google Scholar
Marchetti MA, Codella NC, Dusza SW, Gutman DA, Helba B, Kalloo A, et al.Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol. 2018; 78(2):270–7.
Article
PubMed
Google Scholar
Satheesha T, Satyanarayana D, Prasad MG, Dhruve KD. Melanoma is skin deep: a 3D reconstruction technique for computerized dermoscopic skin lesion classification. IEEE J Transl Eng Health Med. 2017; 5:1–17.
Article
Google Scholar
Xu H, Wang H, Berendt R, Jha N, Mandal M. Computerized measurement of melanoma depth of invasion in skin biopsy images. In: 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Orlando: IEEE: 2017. p. 17–20.
Google Scholar
Eikenberry S, Thalhauser C, Kuang Y. Tumor-immune interaction, surgical treatment, and cancer recurrence in a mathematical model of melanoma. PLoS Comput Biol. 2009; 5(4):e1000362.
Article
PubMed
PubMed Central
CAS
Google Scholar
Payan Y. Soft tissue biomechanical modeling for computer assisted surgery. vol. 11. Berlin Heidelberg: Springer; 2012.
Book
Google Scholar
Shain AH, Bastian BC. From melanocytes to melanomas. 16. 2016; 6:345.
Google Scholar
Treloar KK, Simpson MJ, Binder BJ, McElwain DS, Baker RE. Assessing the role of spatial correlations during collective cell spreading. Sci Rep. 2014; 4:5713.
Article
CAS
PubMed
PubMed Central
Google Scholar
Treloar KK, Simpson MJ, Haridas P, Manton KJ, Leavesley DI, McElwain DS, et al.Multiple types of data are required to identify the mechanisms influencing the spatial expansion of melanoma cell colonies. BMC Syst Biol. 2013; 7(1):137.
Article
PubMed
PubMed Central
Google Scholar
Vo BN, Drovandi CC, Pettitt AN, Pettet GJ. Melanoma cell colony expansion parameters revealed by approximate Bayesian computation. PLoS Comput Biol. 2015; 11(12):e1004635.
Article
PubMed
PubMed Central
CAS
Google Scholar
Haridas P, Penington CJ, McGovern JA, McElwain DS, Simpson MJ. Quantifying rates of cell migration and cell proliferation in co-culture barrier assays reveals how skin and melanoma cells interact during melanoma spreading and invasion. J Theor Biol. 2017; 423:13–25.
Article
PubMed
Google Scholar
La Porta CA, Ghilardi A, Pasini M, Laurson L, Alava MJ, Zapperi S, et al.Osmotic stress affects functional properties of human melanoma cell lines. Eur Phys J Plus. 2015; 130(4):64.
Article
CAS
Google Scholar
Barton DL, Henkes S, Weijer CJ, Sknepnek R. Active vertex model for cell-resolution description of epithelial tissue mechanics. PLoS Comput Biol. 2017; 13(6):e1005569.
Article
PubMed
PubMed Central
CAS
Google Scholar
Stichel D, Middleton AM, Müller BF, Depner S, Klingmüller U, Breuhahn K, et al.An individual-based model for collective cancer cell migration explains speed dynamics and phenotype variability in response to growth factors. NPJ Syst Biol Appl. 2017; 3(1):5.
Article
PubMed
PubMed Central
Google Scholar
Breitkreutz D, Koxholt I, Thiemann K, Nischt R. Skin basement membrane: the foundation of epidermal integrity—BM functions and diverse roles of bridging molecules nidogen and perlecan. BioMed Res Int. 2013; 2013:179784. https://doi.org/10.1155/2013/179784.
Article
PubMed
PubMed Central
CAS
Google Scholar
Evans ND, Oreffo RO, Healy E, Thurner PJ, Man YH. Epithelial mechanobiology, skin wound healing, and the stem cell niche. J Mech Behav Biomed Mater. 2013; 28:397–409.
Article
PubMed
Google Scholar
Nia HT, Liu H, Seano G, Datta M, Jones D, Rahbari N, et al.Solid stress and elastic energy as measures of tumour mechanopathology. Nat Biomed Eng. 2016; 1:0004.
Article
PubMed
PubMed Central
CAS
Google Scholar
Tozluoğlu M, Tournier AL, Jenkins RP, Hooper S, Bates PA, Sahai E. Matrix geometry determines optimal cancer cell migration strategy and modulates response to interventions. Nat Cell Biol. 2013; 15(7):751.
Article
PubMed
CAS
Google Scholar
Paul CD, Mistriotis P, Konstantopoulos K. Cancer cell motility: lessons from migration in confined spaces. 17. 2017; 2:131.
Google Scholar
Deakin NO, Turner CE. Distinct roles for paxillin and Hic-5 in regulating breast cancer cell morphology, invasion, and metastasis. Mol Biol Cell. 2011; 22(3):327–341.
Article
CAS
PubMed
PubMed Central
Google Scholar
Leight JL, Tokuda EY, Jones CE, Lin AJ, Anseth KS. Multifunctional bioscaffolds for 3D culture of melanoma cells reveal increased MMP activity and migration with BRAF kinase inhibition. Proc Natl Acad Sci. 2015; 112(17):5366–71.
Article
CAS
PubMed
PubMed Central
Google Scholar
Holle AW, Young JL, Spatz JP. In vitro cancer cell–ECM interactions inform in vivo cancer treatment. Adv Drug Deliv Rev. 2016; 97:270–9.
Article
CAS
PubMed
Google Scholar
Hutchenreuther J, Leask A. Why target the tumor stroma in melanoma?J Cell Commun Signal. 2018; 12(1):113–18.
Article
PubMed
Google Scholar
Ju RJ, Stehbens SJ, Haass NK. The Role of Melanoma Cell-Stroma Interaction in Cell Motility, Invasion, and Metastasis. Front Med (Lausanne). 2018; 5:307. https://doi.org/10.3389/fmed.2018.00307.
Article
Google Scholar
Lanir Y. Multi-scale structural modeling of soft tissues mechanics and mechanobiology. J Elast. 2017; 129(1-2):7–48.
Article
Google Scholar
Shao Y, Aplin AE. Akt3-mediated resistance to apoptosis in B-RAF–targeted melanoma cells. Cancer Res. 2010; 70(16):6670–81. https://doi.org/10.1158/0008-5472.CAN-09-4471.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ambrosi D, Pezzuto S, Riccobelli D, Stylianopoulos T, Ciarletta P. Solid tumors are poroelastic solids with a chemo-mechanical feedback on growth. J Elast. 2017; 129(1-2):107–24.
Article
CAS
PubMed
PubMed Central
Google Scholar
Levesque MP, Cheng PF, Raaijmakers MI, Saltari A, Dummer R. Metastatic melanoma moves on: translational science in the era of personalized medicine. Cancer Metastasis Rev. 2017; 36(1):7–21.
Article
PubMed
Google Scholar
Hoek KS, Eichhoff OM, Schlegel NC, Döbbeling U, Kobert N, Schaerer L, et al.In vivo switching of human melanoma cells between proliferative and invasive states. Cancer Res. 2008; 68(3):650–656.
Article
CAS
PubMed
Google Scholar
Izar B, Joyce CE, Goff S, Cho NL, Shah PM, Sharma G, et al.Bidirectional cross talk between patient-derived melanoma and cancer-associated fibroblasts promotes invasion and proliferation. Pigment Cell Melanoma Res. 2016; 29(6):656–68.
Article
CAS
PubMed
Google Scholar
Paszek MJ, Zahir N, Johnson KR, Lakins JN, Rozenberg GI, Gefen A, et al.Tensional homeostasis and the malignant phenotype. Cancer Cell. 2005; 8(3):241–54.
Article
CAS
PubMed
Google Scholar
Halder G, Dupont S, Piccolo S. Transduction of mechanical and cytoskeletal cues by YAP and TAZ. Nat Rev Mol Cell Biol. 2012; 13(9):591.
Article
CAS
PubMed
Google Scholar
Kim MH, Kim J, Hong H, Lee SH, Lee JK, Jung E, et al.Actin remodeling confers BRAF inhibitor resistance to melanoma cells through YAP/TAZ activation. EMBO J. 2016; 35(5):462–78.
Article
CAS
PubMed
Google Scholar
Feng X, Degese MS, Iglesias-Bartolome R, Vaque JP, Molinolo AA, Rodrigues M, et al.Hippo-independent activation of YAP by the GNAQ uveal melanoma oncogene through a trio-regulated rho GTPase signaling circuitry. Cancer Cell. 2014; 25(6):831–45.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sanchez IM, Aplin AE. Hippo: hungry, hungry for melanoma invasion. J Investig Dermatol. 2014; 134(1):14–16.
Article
CAS
PubMed
Google Scholar
Nallet-Staub F, Marsaud V, Li L, Gilbert C, Dodier S, Bataille V, et al.Pro-invasive activity of the Hippo pathway effectors YAP and TAZ in cutaneous melanoma. J Investig Dermatol. 2014; 134(1):123–32.
Article
CAS
PubMed
Google Scholar
Seip K, Fleten KG, Barkovskaya A, Nygaard V, Haugen MH, Engesæter BØ, et al.Fibroblast-induced switching to the mesenchymal-like phenotype and PI3K/mTOR signaling protects melanoma cells from BRAF inhibitors. Oncotarget. 2016; 7(15):19997.
Article
PubMed
PubMed Central
Google Scholar
Weder G, Hendriks-Balk MC, Smajda R, Rimoldi D, Liley M, Heinzelmann H, et al.Increased plasticity of the stiffness of melanoma cells correlates with their acquisition of metastatic properties. Nanomedicine. 2014; 10(1):141–148.
Article
CAS
PubMed
Google Scholar
Wei SC, Yang J. Forcing through tumor metastasis: the interplay between tissue rigidity and epithelial–mesenchymal transition. Trends Cell Biol. 2016; 26(2):111–20.
Article
CAS
PubMed
Google Scholar
Ringer P, Colo G, Faessler R, Grashoff C. Sensing the mechano-chemical properties of the extracellular matrix. Matrix Biol. 2017; 64:6–16.
Article
CAS
PubMed
Google Scholar
Northey JJ, Przybyla L, Weaver VM. Tissue force programs cell fate and tumor aggression. Cancer Discov. 2017; 7(11):1224–37.
Article
CAS
PubMed
PubMed Central
Google Scholar
Charras G, Yap AS. Tensile forces and mechanotransduction at cell–cell junctions. Curr Biol. 2018; 28(8):R445–R457.
Article
CAS
PubMed
Google Scholar
Querleux B. Computational biophysics of the skin. vol. 10. Stanford: Jenny Stanford Publishing; 2014.
Google Scholar
Ciarletta P, Foret L, Ben Amar M. The radial growth phase of malignant melanoma: multi-phase modelling, numerical simulations and linear stability analysis. J R Soc Interface. 2010; 8(56):345–68.
Article
PubMed
PubMed Central
Google Scholar
Sciumè G, Santagiuliana R, Ferrari M, Decuzzi P, Schrefler B. A tumor growth model with deformable ECM. Phys Biol. 2014; 11(6):065004.
Article
PubMed
PubMed Central
Google Scholar
Albrecht M, Sciumè G, Lucarelli P, Sauter T. Thermodynamically constrained averaging theory for cancer growth modelling. IFAC-PapersOnLine. 2016; 49(26):289–94.
Article
Google Scholar
Taloni A, Alemi AA, Ciusani E, Sethna JP, Zapperi S. La Porta CA. Mechanical properties of growing melanocytic nevi and the progression to melanoma. PloS ONE. 2014; 9(4):e94229.
Article
PubMed
PubMed Central
CAS
Google Scholar
Pritchard RH, Huang YYS, Terentjev EM. Mechanics of biological networks: from the cell cytoskeleton to connective tissue. Soft matter. 2014; 10(12):1864–84.
Article
CAS
PubMed
Google Scholar
Silk D, Kirk PD, Barnes CP, Toni T, Stumpf MP. Model selection in systems biology depends on experimental design. PLoS Comput Biol. 2014; 10(6):e1003650.
Article
PubMed
PubMed Central
CAS
Google Scholar
Stanford NJ, Wolstencroft K, Golebiewski M, Kania R, Juty N, Tomlinson C, et al.The evolution of standards and data management practices in systems biology. Mol Syst Biol. 2015; 11(12):851.
Article
PubMed
PubMed Central
Google Scholar
Weiswald LB, Bellet D, Dangles-Marie V. Spherical cancer models in tumor biology. Neoplasia. 2015; 17(1):1–15.
Article
PubMed
PubMed Central
Google Scholar
Kulms D, Meier F. In vitro models of melanoma. In: Skin Tissue Models. Amsterdam: Elsevier: 2018. p. 57–75.
Google Scholar
Vörsmann H, Groeber F, Walles H, Busch S, Beissert S, Walczak H, et al.Development of a human three-dimensional organotypic skin-melanoma spheroid model for in vitro drug testing. Cell death Dis. 2013; 4(7):e719.
Article
PubMed
PubMed Central
CAS
Google Scholar
Halfter W, Oertle P, Monnier CA, Camenzind L, Reyes-Lua M, Hu H, et al.New concepts in basement membrane biology. FEBS J. 2015; 282(23):4466–79.
Article
CAS
PubMed
Google Scholar
Soofi SS, Last JA, Liliensiek SJ, Nealey PF, Murphy CJ. The elastic modulus of Matrigel™ as determined by atomic force microscopy. J Struct Biol. 2009; 167(3):216–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Chaudhuri O, Gu L, Klumpers D, Darnell M, Bencherif SA, Weaver JC, et al.Hydrogels with tunable stress relaxation regulate stem cell fate and activity. Nat Mater. 2016; 326(3).
Caliari SR, Burdick JA. A practical guide to hydrogels for cell culture. Nat Methods. 2016; 13(5):405–14.
Article
CAS
PubMed
PubMed Central
Google Scholar
Rimann M, Angres B, Patocchi-Tenzer I, Braum S, Graf-Hausner U. Automation of 3D cell culture using chemically defined hydrogels. J Lab Autom. 2014; 19(2):191–7.
Article
CAS
PubMed
Google Scholar
Verjans ET, Doijen J, Luyten W, Landuyt B, Schoofs L. Three-dimensional cell culture models for anticancer drug screening: Worth the effort?J Cell Physiol. 2018; 233(4):2993–3003.
Article
CAS
PubMed
Google Scholar
Derler S, Gerhardt LC. Tribology of skin: review and analysis of experimental results for the friction coefficient of human skin. Tribol Lett. 2012; 45(1):1–27.
Article
Google Scholar
Grashoff C, Hoffman BD, Brenner MD, Zhou R, Parsons M, Yang MT, et al.Measuring mechanical tension across vinculin reveals regulation of focal adhesion dynamics. Nature. 2010; 466(7303):263.
Article
CAS
PubMed
PubMed Central
Google Scholar
Campàs O, Mammoto T, Hasso S, Sperling RA, O’connell D, Bischof AG, et al.Quantifying cell-generated mechanical forces within living embryonic tissues. Nat Methods. 2014; 11(2):183.
Article
PubMed
CAS
Google Scholar
Alessandri K, Sarangi BR, Gurchenkov VV, Sinha B, Kießling TR, Fetler L, et al.Cellular capsules as a tool for multicellular spheroid production and for investigating the mechanics of tumor progression in vitro. Proc Natl Acad Sci. 2013; 110(37):14843–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Otto O, Rosendahl P, Mietke A, Golfier S, Herold C, Klaue D, et al.Real-time deformability cytometry: on-the-fly cell mechanical phenotyping. Nat Methods. 2015; 12(3):199.
Article
CAS
PubMed
Google Scholar
Swift J, Ivanovska IL, Buxboim A, Harada T, Dingal PDP, Pinter J, et al.Nuclear lamin-A scales with tissue stiffness and enhances matrix-directed differentiation. Science. 2013; 341(6149):1240104.
Article
PubMed
PubMed Central
CAS
Google Scholar
Kim W, Ferguson VL, Borden M, Neu CP. Application of elastography for the noninvasive assessment of biomechanics in engineered biomaterials and tissues. Ann Biomed Eng. 2016; 44(3):705–24.
Article
PubMed
PubMed Central
Google Scholar
Jid CB, Bolboacă SD, Cosgarea R, Şenilă S, Rogojan L, Lenghel M, et al.Doppler ultrasound and strain elastography in the assessment of cutaneous melanoma: preliminary results. Med Ultrason. 2015; 17(4):509–14.
Google Scholar
Peela N, Truong D, Saini H, Chu H, Mashaghi S, Ham SL, et al.Advanced biomaterials and microengineering technologies to recapitulate the stepwise process of cancer metastasis. Biomaterials. 2017; 133:176–207.
Article
CAS
PubMed
Google Scholar
Stücker M, Struk A, Altmeyer P, Herde M, Baumgärtl H, Lübbers DW. The cutaneous uptake of atmospheric oxygen contributes significantly to the oxygen supply of human dermis and epidermis. J Physiol. 2002; 538(3):985–94.
Article
PubMed
PubMed Central
Google Scholar
Wang W, Winlove C, Michel C. Oxygen partial pressure in outer layers of skin of human finger nail folds. J Physiol. 2003; 549(3):855–63.
Article
CAS
PubMed
PubMed Central
Google Scholar
Horikoshi T, Balin AK, Carter DM. Effects of oxygen tension on the growth and pigmentation of normal human melanocytes. J Investig Dermatol. 1990; 96(6):841–4.
Article
Google Scholar
Hanna SC, Krishnan B, Bailey ST, Moschos SJ, Kuan PF, Shimamura T, et al.HIF1 α and HIF2 α independently activate SRC to promote melanoma metastases. J Clin Invest. 2013; 123(5):2078–93.
Article
CAS
PubMed
PubMed Central
Google Scholar
Carreau A, Hafny-Rahbi BE, Matejuk A, Grillon C, Kieda C. Why is the partial oxygen pressure of human tissues a crucial parameter? Small molecules and hypoxia. J Cell Mol Med. 2011; 15(6):1239–53.
Article
CAS
PubMed
PubMed Central
Google Scholar
Dmitriev RI, Borisov SM, Düssmann H, Sun S, Müller BJ, Prehn J, et al.Versatile conjugated polymer nanoparticles for high-resolution O2 imaging in cells and 3D tissue models. ACS Nano. 2015; 9(5):5275–88.
Article
CAS
PubMed
Google Scholar
Erapaneedi R, Belousov VV, Schäfers M, Kiefer F. A novel family of fluorescent hypoxia sensors reveal strong heterogeneity in tumor hypoxia at the cellular level. EMBO J. 2016; 35(1):102–13.
Article
CAS
PubMed
Google Scholar
Brand MD, Nicholls DG. Assessing mitochondrial dysfunction in cells. Biochem J. 2011; 435(2):297–312.
Article
CAS
PubMed
Google Scholar
Hall A, Meyle KD, Lange MK, Klima M, Sanderhoff M, Dahl C, et al.Dysfunctional oxidative phosphorylation makes malignant melanoma cells addicted to glycolysis driven by the V600EBRAF oncogene. Oncotarget. 2013; 4(4):584.
Article
PubMed
PubMed Central
Google Scholar
Chwalek K, Tsurkan MV, Freudenberg U, Werner C. Glycosaminoglycan-based hydrogels to modulate heterocellular communication in in vitro angiogenesis models. Sci Rep. 2014; 4:4414.
Article
PubMed
PubMed Central
CAS
Google Scholar
Balcioglu HE, Van De Water B, Danen EH. Tumor-induced remote ECM network orientation steers angiogenesis. Sci Rep. 2016; 6:22580.
Article
CAS
PubMed
PubMed Central
Google Scholar
Scianna M, Bell C, Preziosi L. A review of mathematical models for the formation of vascular networks. J Theor Biol. 2013; 333:174–209.
Article
CAS
PubMed
Google Scholar
Welter M, Rieger H. Physical determinants of vascular network remodeling during tumor growth. Eur Phys J E. 2010; 33(2):149–63.
Article
CAS
PubMed
Google Scholar
Döme B, Paku S, Somlai B, Tímár J. Vascularization of cutaneous melanoma involves vessel co-option and has clinical significance. J Pathol. 2002; 197(3):355–62.
Article
PubMed
Google Scholar
Wang J, Zhang L, Jing C, Ye G, Wu H, Miao H, et al.Multi-scale agent-based modeling on melanoma and its related angiogenesis analysis. Theor Biol Med Model. 2013; 10(1):41.
Article
CAS
PubMed
PubMed Central
Google Scholar
Dzwinel W, Kłusek A, Vasilyev OV. Supermodeling in simulation of melanoma progression. Procedia Comput Sci. 2016; 80:999–1010.
Article
Google Scholar
Łoś M, Paszyński M, Kłusek A, Dzwinel W. Application of fast isogeometric L2 projection solver for tumor growth simulations. Comput Methods Appl Mech Eng. 2017; 316:1257–69.
Article
Google Scholar
Duane GS, Grabow C, Selten F, Ghil M. Introduction to focus issue: Synchronization in large networks and continuous media—data, models, and supermodels. Melville: AIP Publishing; 2017.
Google Scholar
Kłusek A, Łoś M, Paszyński M, Dzwinel W. Efficient model of tumor dynamics simulated in multi-GPU environment. Int J High Perform Comput Appl. 2019; 33(3):489–506.
Article
Google Scholar
Panuszewska M, Minch B, Wcisło R, Dzwinel W. PAM: Discrete 3-D Model of Tumor Dynamics in the Presence of Anti-tumor Treatment. In: International Conference on Cellular Automata. Como: Springer: 2018. p. 42–54.
Google Scholar
Gaustad JV, Simonsen TG, Leinaas MN, Rofstad EK. Sunitinib treatment does not improve blood supply but induces hypoxia in human melanoma xenografts. BMC Cancer. 2012; 12(1):388.
Article
CAS
PubMed
PubMed Central
Google Scholar
Schweitzer AD, Rakesh V, Revskaya E, Datta A, Casadevall A, Dadachova E. Computational model predicts effective delivery of 188-Re-labeled melanin-binding antibody to metastatic melanoma tumors with wide range of melanin concentrations. Melanoma Res. 2007; 17(5):291–303.
Article
CAS
PubMed
Google Scholar
Liu J, Ding W, Ruan R, Zou L, Chen M, Wei P, et al.A theoretical study on inhibition of melanoma with controlled and targeted delivery of siRNA via skin using SPACE-EGF. Ann Biomed Eng. 2017; 45(6):1407–19.
Article
PubMed
Google Scholar
Stylianopoulos T, Martin JD, Chauhan VP, Jain SR, Diop-Frimpong B, Bardeesy N, et al.Causes, consequences, and remedies for growth-induced solid stress in murine and human tumors. Proc Natl Acad Sci. 2012; 109(38):15101–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ramírez-Torres A, Rodríguez-Ramos R, Merodio J, Penta R, Bravo-Castillero J, Guinovart-Díaz R, et al.The influence of anisotropic growth and geometry on the stress of solid tumors. Int J Eng Sci. 2017; 119:40–49.
Article
Google Scholar
Elder DE, Bastian BC, Cree IA, Massi D, Scolyer RA. The 2018 World Health Organization Classification of Cutaneous, Mucosal, and Uveal Melanoma: Detailed Analysis of 9 Distinct Subtypes Defined by Their Evolutionary Pathway. 2018; 144(4):500–522. https://doi.org/10.5858/arpa.2019-0561-RA.
Howard J. Quantitative cell biology: the essential role of theory. Mol Biol Cell. 2014; 25(22):3438–40.
Article
CAS
PubMed
PubMed Central
Google Scholar
Waltemath D, Wolkenhauer O. How modeling standards, software, and initiatives support reproducibility in systems biology and systems medicine. IEEE Trans Biomed Eng. 2016; 63(10):1999–2006.
Article
PubMed
Google Scholar
Begley CG, Ioannidis JP. Reproducibility in science: improving the standard for basic and preclinical research. Circ Res. 2015; 116(1):116–26.
Article
CAS
PubMed
Google Scholar
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011; 144(5):646–74.
Article
CAS
PubMed
Google Scholar
Shain AH, Yeh I, Kovalyshyn I, Sriharan A, Talevich E, Gagnon A, et al.The genetic evolution of melanoma from precursor lesions. Engl J Med. 2015; 373(20):1926–36.
Article
CAS
Google Scholar
Pacheco MP, Sauter T. The FASTCORE family: for the fast reconstruction of compact context-specific metabolic networks models. In: In: Metabolic Network Reconstruction and Modeling. Berlin Heidelberg: Springer: 2018. p. 101–10.
Google Scholar
Brodland GW. How computational models can help unlock biological systems. vol. 47. In: In: Seminars in cell & developmental biology. Amsterdam: Elsevier: 2015. p. 62–73.
Google Scholar
Cristini V, Koay E, Wang Z. An Introduction to physical oncology: how mechanistic mathematical modeling can improve cancer therapy outcomes. Boca Raton: CRC Press; 2017.
Book
Google Scholar
Konstorum A, Vella AT, Adler AJ, Laubenbacher RC. Addressing current challenges in cancer immunotherapy with mathematical and computational modelling. J R Soc Interface. 2017; 14(131):20170150.
Article
PubMed
PubMed Central
CAS
Google Scholar
Chatzinasiou F, Lill CM, Kypreou K, Stefanaki I, Nicolaou V, Spyrou G, et al.Comprehensive field synopsis and systematic meta-analyses of genetic association studies in cutaneous melanoma. J Natl Cancer Inst. 2011; 103(16):1227–35.
Article
CAS
PubMed
PubMed Central
Google Scholar