Advertisement

pp 1-33 | Cite as

Organotypic Models in Drug Development “Tumor Models and Cancer Systems Biology for the Investigation of Anticancer Drugs and Resistance Development”

  • Érica Aparecida de Oliveira
  • Colin R. Goding
  • Silvya Stuchi Maria-EnglerEmail author
Chapter
Part of the Handbook of Experimental Pharmacology book series

Abstract

The landscape of cancer treatment has improved over the past decades, aiming to reduce systemic toxicity and enhance compatibility with the quality of life of the patient. However, at the therapeutic level, metastatic cancer remains hugely challenging, based on the almost inevitable emergence of therapy resistance. A small subpopulation of cells able to survive drug treatment termed the minimal residual disease may either harbor resistance-associated mutations or be phenotypically resistant, allowing them to regrow and become the dominant population in the therapy-resistant tumor. Characterization of the profile of minimal residual disease represents the key to the identification of resistance drivers that underpin cancer evolution. Therapeutic regimens must, therefore, be dynamic and tailored to take into account the emergence of resistance as tumors evolve within a complex microenvironment in vivo. This requires the adoption of new technologies based on the culture of cancer cells in ways that more accurately reflect the intratumor microenvironment, and their analysis using omics and system-based technologies to enable a new era in the diagnostics, classification, and treatment of many cancer types by applying the concept “from the cell plate to the patient.” In this chapter, we will present and discuss 3D model building and use, and provide comprehensive information on new genomic techniques that are increasing our understanding of drug action and the emergence of resistance.

Keywords

2D- and 3D-culture models Cancer drug resistance Cancer system biology Drug-testing platform 

Abbreviations

2D

Two-dimensional space

3D

Three-dimensional space

ABL

Abelson murine leukemia viral oncogene homolog 1

APE1

Apurinic/apyrimidinic endonuclease 1

BCR

The breakpoint cluster region protein

BRAF

v-Raf murine sarcoma viral oncogene homolog B

BRAFi

BRAF inhibitor

BRCA

Breast cancer gene

CAF

Cancer-associated fibroblasts

CancerDR

Cancer drug resistance database

CCLE

Cancer Cell Line Encyclopedia

ChIP-seq

Chromatin immunoprecipitation sequencing

CML

Chronic myeloid leukemia

CRISPR

Clustered regularly interspaced short palindromic repeats

CSC

Cancer stem cells

CTRP

The Cancer Therapeutics Response Portal

DNA

Deoxyribonucleic acid

DNA-seq

DNA sequencing

EC

Endothelial cells

ECM

Extracellular matrix

EGFR

Epidermal growth factor receptor

FDA

Food and Drug Administration

GDSC

Genomics of Drug Sensitivity in Cancer

GEO

Gene Expression Omnibus

HER2

Human epidermal growth factor receptor-type 2

HFL-1

Human lung fibroblast cell line

Hh

Hedgehog signaling pathway

HMVEC

Human dermal blood microvascular endothelial cxells

IC50

The half maximal inhibitory concentration

ICGC

International Cancer Genomics Consortium

IL

Interleukin

LMTK3

Lemur tyrosine kinase 3

lncRNA

Long non-coding RNA

MEK

Mitogen-activated protein kinase kinase

MicroRNA

Small non-coding RNA molecule

MIO

Gold and magnetic iron oxide

miRNA-seq

MicroRNA sequencing

MITF

Microphthalmia-associated transcription factor

MMP

Matrix metallopeptidase

MSC

Mesenchymal stem cells

multi-OMICS

Multiple omics

NCBI

National Center for Biotechnology Information

NIH

National Institute of Health

NLM

National Library of Medicine

NRAS

Neuroblastoma RAS viral oncogene

p53

Tumor protein p53

p-AMPKa

Phospho-5′ adenosine monophosphate-activated protein kinase

Panc-1

Human pancreatic cancer cell line

Par-4

Prostate apoptosis response 4

PARP

Poly ADP-ribose polymerase

PC9

Non-small cell lung cancer

PCR

Polymerase chain reaction

PDMS

Porous polydimethylsiloxane

PDO

Patient-derived organoids

p-ERK

Phospho-extracellular signal-regulated kinase

PKM2/PKM1

Pyruvate kinase isozymes M1/M2

RNA

Ribonucleic acid

RNA-seq

RNA sequencing

RPPA

Reverse-phase protein array

SC

Physiological stem cells

scRNA-seq

Single-cell RNA sequencing

sFRP2

Secreted frizzled-related protein 2

SNP

Single nucleotide polymorphisms

SPCA-1

Human non-small cell lung cancer cell line

TALEN

Transcription activator-like effector nuclease

TAM

Tumor-associated macrophages

TCGA

The Cancer Genome Atlas

TKI

Tyrosine kinase inhibitors

UK

United Kingdom

USA

United States of America

WNT

Wingless-related integration site

References

  1. Acar A, Nichol D, Fernandez-Mateos J, Cresswell GD, Barozzi I, Hong SP, Spiteri I, Stubbs M, Burke R, Stewart A, Vlachogiannis G, Maley CC, Magnani L, Valeri N, Banerj U, Sottoriva A (2020) Exploiting evolutionary herding to control drug resistance in cancer. Nat Commun 11(1):1–4.  https://doi.org/10.1101/566950CrossRefGoogle Scholar
  2. Afghahi A, Sledge GW Jr (2015) Targeted therapy for cancer in the genomic era. Cancer J 21(4):294–298Google Scholar
  3. Agarwal P, Wang H, Sun M, Xu J, Zhao S, Liu Z, Gooch KJ, Zhao Y, Lu X, He X (2017) Microfluidics enabled bottom-up engineering of 3D vascularized tumor for drug discovery. ACS Nano 11(7):6691–6702Google Scholar
  4. Aguirre-Ghiso JA (2007) Models, mechanisms and clinical evidence for cancer dormancy. Nat Rev Cancer 7(11):834–846Google Scholar
  5. Akbani R, Becker KF, Carragher N et al (2014) Realizing the promise of reverse phase protein arrays for clinical, translational, and basic research: a workshop report: the RPPA (Reverse Phase Protein Array) society. Mol Cell Proteomics 13:1625–1643Google Scholar
  6. Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF (2003) Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci U S A 100:3983–3988Google Scholar
  7. Asghar W, El Assal R, Shafiee H, Pitteri S, Paulmurugan R, Demirci U (2015) Engineering cancer microenvironments for in vitro 3-D tumor models. Mater Today (Kidlington) 18(10):539–553.  https://doi.org/10.1016/j.mattod.2015.05.002CrossRefGoogle Scholar
  8. Avvisato CL, Yang X, Shah S, Hoxter B, Li W, Gaynor R, Pestell R, Tozeren A, Byers SW (2007) Mechanical force modulates global gene expression and β-catenin signaling in colon cancer cells. J Cell Sci 120(15):2672–2682Google Scholar
  9. Bai J, Tu TY, Kim C, Thiery JP, Kamm RD (2015) Identification of drugs as single agents or in combination to prevent carcinoma dissemination in a microfluidic 3D environment. Oncotarget 6(34):36603Google Scholar
  10. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136:215–233Google Scholar
  11. Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, Ebright RY, Stewart ML, Ito D, Wang S, Bracha AL, Liefeld T, Wawer M, Gilbert JC, Wilson AJ, Stransky N, Kryukov GV, Dancik V, Barretina J, Garraway LA, Hon CS, Munoz B, Bittker JA, Stockwell BR, Khabele D, Stern AM, Clemons PA, Shamji AF, Schreiber SL (2013) An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154:1151–1161Google Scholar
  12. Bellotti C, Duchi S, Bevilacqua A, Lucarelli E, Piccinini F (2016) Long term morphological characterization of mesenchymal stromal cells 3D spheroids built with a rapid method based on entry-level equipment. Cytotechnology 8(6):2479–2490Google Scholar
  13. Bernards R, Weinberg RA (2002) A progression puzzle. Nature 418:823Google Scholar
  14. Bogorad MI, DeStefano J, Karlsson J, Wong AD, Gerecht S, Searson PC (2015) Review: in vitro microvessel models. Lab Chip 15(22):4242–4255Google Scholar
  15. Brasseur K, Gévry N, Asselin E (2017) Chemoresistance and targeted therapies in ovarian and endometrial cancers. Oncotarget 8(3):4008–4042Google Scholar
  16. Brennan MD, Rexius-Hall ML, Elgass LJ, Eddington DT (2014) Oxygen control with microfluidics. Lab Chip 14(22):4305–4318Google Scholar
  17. Breslin S, O’Driscoll L (2013) Three-dimensional cell culture: the missing link in drug discovery. Drug Discov Today 18(5–6):240–249Google Scholar
  18. Burrell RA, Swanton C (2014) Tumour heterogeneity and the evolution of polyclonal drug resistance. Mol Oncol 8(6):1095–1111Google Scholar
  19. Byrne MB, Leslie MT, Gaskins HR, Kenis PJA (2014) Methods to study the tumor microenvironment under controlled oxygen conditions. Trends Biotechnol 32(11):556–563Google Scholar
  20. Cancer Genome Atlas Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490:61–70Google Scholar
  21. Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45(10):1113–1120Google Scholar
  22. Carletti E, Motta A, Migliaresi C (2011) Scaffolds for tissue engineering and 3D cell culture. Methods Mol Biol 695:17–39Google Scholar
  23. Carpenedo RL, Sargent CY, McDevitt TC (2007) Rotary suspension culture enhances the efficiency, yield, and homogeneity of embryoid body differentiation. Stem Cells 25(9):2224–2234Google Scholar
  24. Cato L, de Tribolet-Hardy J, Lee I, Rottenberg JT, Coleman I, Melchers D, Houtman R, Xiao T, Li W, Uo T, Sun S, Kuznik NC, Göppert B, Ozgun F, van Royen ME, Houtsmuller AB, Vadhi R, Rao PK, Li L, Balk SP, Den RB, Trock BJ, Karnes RJ, Jenkins RB, Klein EA, Davicioni E, Gruhl FJ, Long HW, Liu XS, Cato ACB, Lack NA, Nelson PS, Plymate SR, Groner AC, Brown M (2019) ARv7 represses tumor-suppressor genes in castration-resistant prostate cancer. Cancer Cell 35(3):401–413. pii: S1535-6108(19)30042-XGoogle Scholar
  25. Cha HM, Kim SM, Choi YS, Kim DI (2015) Scaffold-free three-dimensional culture systems for mass production of periosteum-derived progenitor cells. J Biosci Bioeng 120(2):218–222Google Scholar
  26. Chakraborty S, Hosen MI, Ahmed M, Shekhar HU (2018) Onco-multi-OMICS approach: a new frontier in cancer research. Biomed Res Int 2018:9836256Google Scholar
  27. Chang CW, Cheng YJ, Tu M, Chen YH, Peng CC, Liao WH, Tung YC (2014) A polydimethylsiloxane-polycarbonate hybrid microfluidic device capable of generating perpendicular chemical and oxygen gradients for cell culture studies. Lab Chip 14(19):3762–3772Google Scholar
  28. Chatzinikolaidou M (2016) Cell spheroids: the new frontiers in in vitro models for cancer drug validation. Drug Discov Today 21(9):1553–1560Google Scholar
  29. Chin L, Andersen JN, Futreal PA (2011) Cancer genomics: from discovery science to personalized medicine. Nat Med 17:297–303Google Scholar
  30. Chisholm RH, Lorenzi T, Lorz A, Larsen AK, de Almeida LN, Escargueil A, Clairambault J (2015) Emergence of drug tolerance in cancer cell populations: an evolutionary outcome of selection, nongenetic instability, and stress-induced adaptation. Cancer Res 75:930–939Google Scholar
  31. Clough E, Barrett T (2016) The gene expression omnibus database. Methods Mol Biol 1418:93–110Google Scholar
  32. Cokelaer T, Chen E, Iorio F, Menden MP, Lightfoot H, Saez-Rodriguez J, Garnett MJ (2018) GDSCTools for mining pharmacogenomic interactions in cancer. Bioinformatics 34(7):1226–1228Google Scholar
  33. Corre S, Tardif N, Mouchet N, Leclair HM, Boussemart L, Gautron A, Bachelot L, Perrot A, Soshilov A, Rogiers A, Rambow F, Dumontet E, Tarte K, Bessede A, Guillemin GJ, Marine JC, Denison MS, Gilot D, Galibert MD (2018) Sustained activation of the aryl hydrocarbon receptor transcription factor promotes resistance to BRAF-inhibitors in melanoma. Nat Commun 9(4775):1–13Google Scholar
  34. Cruz Rodríguez N, Lineros J, Rodríguez CS, Martínez LM, Rodríguez JA (2019) Establishment of two dimensional (2D) and three-dimensional (3D) melanoma primary cultures as a tool for in vitro drug resistance studies. Methods Mol Biol 1913:119–131Google Scholar
  35. Curry E, Zeller C, Masrour N, Patten DK, Gallon J, Wilhelm-Benartzi CS, Ghaem-Maghami S, Bowtell DD, Brown R (2018) Genes predisposed to DNA hypermethylation during acquired resistance to chemotherapy are identified in ovarian tumors by bivalent chromatin domains at initial diagnosis. Cancer Res 78(6):1383–1391Google Scholar
  36. Curtin N, Szabo C (2013) Therapeutic applications of PARP inhibitors: anticancer therapy and beyond. Mol Asp Med 34(6):1217.  https://doi.org/10.1016/j.mam.2013.01.006CrossRefGoogle Scholar
  37. Daverey A, Drain AP, Kidambi S (2015) Physical intimacy of breast cancer cells with mesenchymal stem cells elicits trastuzumab resistance through src activation. Sci Rep 5:13744Google Scholar
  38. de Groot TE, Veserat KS, Berthier E, Beebe DJ, Theberge AB (2016) Surface-tension driven open microfluidic platform for hanging droplet culture. Lab Chip 16:334–344Google Scholar
  39. de Santiago I, Carroll T (2018) Analysis of ChIP-seq data in R/bioconductor. In: Visa N, Jordán-Pla A (eds) Chromatin immunoprecipitation, Methods in molecular biology, vol 1689. Humana Press, New YorkGoogle Scholar
  40. Demou ZN (2010) Gene expression profiles in 3D tumor analogs indicate compressive strain differentially enhances metastatic potential, Ann. Biomed Eng 38(11):3509–3520Google Scholar
  41. Dereli-Korkut Z, Akaydin HD, Ahmed AH, Jiang X, Wang S (2014) Three dimensional microfluidic cell arrays for ex vivo drug screening with mimicked vascular flow. Anal Chem 86(6):2997–3004Google Scholar
  42. Dubessy C, Merlin JM, Marchal C, Guillemin F (2000) Spheroids in radiobiology and photodynamic therapy. Crit Rev Oncol Hematol 36(2–3):179–192Google Scholar
  43. Ekert JE, Johnson K, Strake B, Pardinas J, Jarantow S, Perkinson R, Colter DC (2014) Three-dimensional lung tumor microenvironment modulates therapeutic compound responsiveness in vitro --implication for drug development. PLoS One 9(3):e92248Google Scholar
  44. Evans CL (2015) Three-dimensional in vitro cancer spheroid models for photodynamic therapy: strengths and opportunities. Front Phys 3:15Google Scholar
  45. Facciabene A, Peng X, Hagemann IS, Balint K, Barchetti A, Wang LP, Gimotty PA, Gilks CB, Lal P, Zhang L, Coukos G (2011) Tumour hypoxia promotes tolerance and angiogenesis via CCL28 and Treg cells. Nature 475(7355):226–230Google Scholar
  46. Faião-Flores F, Alves-Fernandes DK, Pennacchi PC, Sandri S, Vicente AL, Scapulatempo-Neto C, Vazquez VL, Reis RM, Chauhan J, Goding CR, Smalley KS, Maria-Engler SS (2017) Targeting the hedgehog transcription factors GLI1 and GLI2 restores sensitivity to vemurafenib-resistant human melanoma cells. Oncogene 36(13):1849–1861Google Scholar
  47. Farazi TA, Hoell JI, Morozov P, Tuschl T (2011) MicroRNAs in human cancer. J Pathol 223:102–105Google Scholar
  48. Fennema E, Rivron N, Rouwkema J, van Blitterswijk C, De Boer J (2013) Spheroid culture as a tool for creating 3D complex tissues. Trends Biotechnol 31:108–115Google Scholar
  49. Fornecker LM, Muller L, Bertrand F, Paul N, Pichot A, Herbrecht R, Chenard MP, Mauvieux L, Vallat L, Bahram S, Cianférani S, Carapito R, Carapito C (2019) Multi-omics dataset to decipher the complexity of drug resistance in diffuse large B-cell lymphoma. Sci Rep 9(1):895Google Scholar
  50. Frey O, Misun PM, Fluri DA, Hengstler JG, Hierlemann A (2014) Reconfigurable microfluidic hanging drop network for multi-tissue interaction and analysis. Nat Commun 5:4250Google Scholar
  51. Frith JE, Thomson B, Genever PG (2010) Dynamic three-dimensional culture methods enhance mesenchymal stem cell properties and increase therapeutic potential. Tissue Eng Part C Methods 16(4):735–749Google Scholar
  52. García-Jiménez C, Goding CR (2019) Starvation and pseudo-starvation as drivers of cancer metastasis through translation reprogramming. Cell Metab 29(2):254–267Google Scholar
  53. Gautam A, Chaudhary K, Kumar R, Gupta S, Singh H, Raghava GPS (2016) Managing drug resistance in cancer: role of cancer informatics. In: Rueff J, Rodrigues A (eds) Cancer drug resistance, Methods in molecular biology, vol 1395. Humana Press, New YorkGoogle Scholar
  54. Gentles AJ, Gallahan D (2011) Systems biology: confronting the complexity of cancer. Cancer Res 71:5961–5964Google Scholar
  55. GEO – Gene Expression Omnibus (2019) GEO overview. https://www.ncbi.nlm.nih.gov/geo/info/overview.html. Accessed 13 July 2019
  56. Girotti MR, Saturno G, Lorigan P, Marais R (2014) No longer an untreatable disease: how targeted and immunotherapies have changed the management of melanoma patients. Mol Oncol 8(6):1140–1158Google Scholar
  57. Goel S, Duda DG, Xu L, Munn LL, Boucher Y, Fukumura D, Jain RK (2011) Normalization of the vasculature for treatment of cancer and other diseases. Physiol Rev 91(3):1071–1121Google Scholar
  58. Griffith LG, Swartz MA (2006) Capturing complex 3D tissue physiology in vitro. Nat Rev Mol Cell Biol 7:211–224Google Scholar
  59. Grimes DR, Kelly C, Bloch K, Partridge M (2014) A method for estimating the oxygen consumption rate in multicellular tumour spheroids. J R Soc Interface 11:20131124Google Scholar
  60. Gröbner SN, Worst BC, Weischenfeldt J, Buchhalter I, Kleinheinz K, Rudneva VA, Johann PD, Balasubramanian GP, Segura-Wang M, Brabetz S, Bender S, Hutter B, Sturm D, Pfaff E, Hübschmann D, Zipprich G, Heinold M, Eils J, Lawerenz C, Erkek S, Lambo S, Waszak S, Blattmann C, Borkhardt A, Kuhlen M, Eggert A, Fulda S, Gessler M, Wegert J, Kappler R, Baumhoer D, Burdach S, Kirschner-Schwabe R, Kontny U, Kulozik AE, Lohmann D, Hettmer S, Eckert C, Bielack S, Nathrath M, Niemeyer C, Richter GH, Schulte J, Siebert R, Westermann F, Molenaar JJ, Vassal G, Witt H, ICGC PedBrain-Seq Project, ICGC MMML-Seq Project, Burkhardt B, Kratz CP, Witt O, van Tilburg CM, Kramm CM, Fleischhack G, Dirksen U, Rutkowski S, Frühwald M, von Hoff K, Wolf S, Klingebiel T, Koscielniak E, Landgraf P, Koster J, Resnick AC, Zhang J, Liu Y, Zhou X, Waanders AJ, Zwijnenburg DA, Raman P, Brors B, Weber UD, Northcott PA, Pajtler KW, Kool M, Piro RM, Korbel JO, Schlesner M, Eils R, Jones DTW, Lichter P, Chavez L, Zapatka M, Pfister SM (2018) The landscape of genomic alterations across childhood cancers. Nature 555(7696):321–327Google Scholar
  61. Guang MHZ, McCann A, Bianchi G, Zhang L, Dowling P, Bazou D, O’Gorman P, Anderson KC (2018) Overcoming multiple myeloma drug resistance in the era of cancer ‘omics’. Leuk Lymphoma 59(3):542–561Google Scholar
  62. Guhathakurta D, Sheikh NA, Meagher TC, Letarte S, Trager JB (2013) Applications of systems biology in cancer immunotherapy: from target discovery to biomarkers of clinical outcome. Expert Rev Clin Pharmacol 6(4):387–401Google Scholar
  63. Gunaratne PH, Coarfa C, Soibam B, Tandon A (2012) miRNA data analysis: next-gene sequencing. Methods Mol Biol 822:273–288Google Scholar
  64. Gupta PB, Onder TT, Jiang G, Tao K, Kuperwasser C, Weinberg RA, Lander ES (2009) Identification of selective inhibitors of cancer stem cells by high-throughput screening. Cell 138:645–659Google Scholar
  65. Gupta N, Liu JR, Patel B, Solomon DE, Vaidya B, Gupta V (2016) Microfluidics-based 3D cell culture models: utility in novel drug discovery and delivery research. Bioeng Transl Med 1(1):63–81Google Scholar
  66. Hagiwara M, Koh I (2020) Engineering approaches to control and design the in vitro environment towards the reconstruction of organs. Develop Growth Differ.  https://doi.org/10.1111/dgd.12647
  67. Han J, Puri RK (2018) Analysis of the cancer genome atlas (TCGA) database identifies an inverse relationship between interleukin-13 receptor α1 and α2 gene expression and poor prognosis and drug resistance in subjects with glioblastoma multiforme. J Neuro-Oncol 136(3):463–474Google Scholar
  68. Herter S, Morra L, Schlenker R, Sulcova J, Fahrni L, Waldhauer I, Lehmann S, Reisländer T, Agarkova I, Kelm JM, Klein C, Umana P, Bacac M (2017) A novel three-dimensional heterotypic spheroid model for the assessment of the activity of cancer immunotherapy agents. Cancer Immunol Immunother 66(1):129–140Google Scholar
  69. Hoarau-Véchot J, Rafii A, Touboul C, Pasquier J (2018) Halfway between 2D and animal models: are 3D cultures the ideal tool to study cancer-microenvironment interactions? Int J Mol Sci 19(1):E181Google Scholar
  70. Hoek K, Goding CR (2010) Cancer stem cells versus phenotype switching in melanoma. Pigment Cell Melanoma Res 23:746–759Google Scholar
  71. Horvath P, Aulner N, Bickle M, Davies AM, Nery ED, Ebner D, Montoya MC, Östling P, Pietiäinen V, Price LS, Shorte SL, Turcatti G, von Schantz C, Carragher NO (2016) Screening out irrelevant cell-based models of disease. Nat Rev Drug Discov 15(11):751–769Google Scholar
  72. Huang L, Brunell D, Stephan C, Mancuso J, He B, Thompson TC, Zinner R, Kim J, Davies P, Wong STC (2019) Driver network as a biomarker: systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction. Bioinformatics 35:btz109Google Scholar
  73. Huanga B, Gao JQ (2018) Application of 3D cultured multicellular spheroid tumor models in tumor-targeted drug delivery system research. Bull Cancer 270:246–259Google Scholar
  74. ICGC – International Cancer Genome Consortium (2019). https://icgc.org/. Accessed 15 July 2019
  75. Imamura Y, Mukohara T, Shimono Y, Funakoshi Y, Chayahara N, Toyoda M, Kiyota N, Takao S, Kono S, Nakatsura T, Minami H (2015) Comparison of 2D-and 3D-culture models as drug-testing platforms in breast cancer. Oncol Rep 33(4):1837–1843Google Scholar
  76. Imura Y, Sato K, Yoshimura E (2010) Micro total bioassay system for ingested substances: assessment of intestinal absorption, hepatic metabolism, and bioactivity. Anal Chem 82(24):9983–9988Google Scholar
  77. Imura Y, Yoshimura E, Sato K (2012) Micro total bioassay system for oral drugs: evaluation of gastrointestinal degradation, intestinal absorption, hepatic metabolism, and bioactivity. Anal Sci 28(3):197–199Google Scholar
  78. Ishimoto T, Sawayama H, Sugihara H, Baba H (2014) Interaction between gastric cancer stem cells and the tumor microenvironment. J Gastroenterol 49:1111–1120Google Scholar
  79. Ivanov DP, Parker TL, Walker DA, Alexander C, Ashford MB, Gellert PR, Garnett MC (2014) Multiplexing spheroid volume, resazurin and acid phosphatase viability assays for high-throughput screening of tumour spheroids and stem cell neurospheres. PLoS One 9(8):e103817Google Scholar
  80. Kapałczyńska M, Kolenda T, Przybyła W, Zajączkowska M, Teresiak A, Filas V, Ibbs M, Bliźniak R, Łuczewski Ł, Lamperska K (2016) 2D and 3D cell cultures – a comparison of different types of cancer cell cultures. Arch Med Sci 14(4):910–919Google Scholar
  81. Kaur A, Webster MR, Marchbank K, Behera R, Ndoye A, Kugel CH, Dang VM, Appleton J, O’Connell MP, Cheng P, Valiga AA, Morissette R, McDonnell NB, Ferrucci L, Kossenkov AV, Meeth K, Tang HY, Yin X, Wood WH, Lehrmann E, Becker KG, Flaherty KT, Frederick DT, Wargo JA, Cooper ZA, Tetzlaff MT, Hudgens C, Aird KM, Zhang R, Xu X, Liu Q, Bartlett E, Karakousis G, Eroglu Z, Lo RS, Chan M, Menzies AM, Long GV, Johnson DB, Sosman J, Schilling B, Schadendorf D, Speicher DW, Bosenberg M, Ribas A, Weeraratna AT (2016) sFRP2 in the aged microenvironment drives melanoma metastasis and therapy resistance. Nature 532(7598):250–254Google Scholar
  82. Keibler MA, Wasylenko TM, Kelleher JK, Iliopoulos O, Vander Heiden MG, Stephanopoulos G (2016) Metabolic requirements for cancer cell proliferation. Cancer Metab 4:16Google Scholar
  83. Khan DH, Roberts SA, Cressman J, Agrawal N (2017) Microfluidic generation of physiological oxygen gradients in vitro. In: Healthcare innovations and point of care technologies (HI-POCT), 2017 IEEE. IEEE, BethesdaGoogle Scholar
  84. Kieninger J, Weltin A, Flamm H, Urban GA (2018) Microsensor systems for cell metabolism – from 2D culture to organ-on-chip. Lab Chip 18:1274Google Scholar
  85. Kim TH, Mount CW, Gombotz WR, Pun SH (2010) The delivery of doxorubicin to 3-D multicellular spheroids and tumors in a murine xenograft model using tumor-penetrating triblock polymeric micelles. Biomaterials 31(28):7386–7397Google Scholar
  86. Kim JY, Fluri DA, Kelm JM, Hierlemann A, Frey O (2015) 96-well format-based microfluidic platform for parallel interconnection of multiple multicellular spheroids. J Lab Autom 20:274–282Google Scholar
  87. Kloss S, Chambron N, Gardlowski T, Weil S, Koch J, Esser R, Pogge von Strandmann E, Morgan MA, Arseniev L, Seitz O, Kohl U (2015) Cetuximab reconstitutes pro-inflammatory cytokine secretions and tumor-infiltrating capabilities of sMICA-inhibited NK cells in HNSCC tumor spheroids. Front Immunol 6:543Google Scholar
  88. Knight E, Przyborski S (2015) Advances in 3D cell culture technologies enabling tissue-like structures to be created in vitro. J Anat 227(6):746–756Google Scholar
  89. Kukurba KR, Montgomery SB (2015) RNA sequencing and analysis. Cold Spring Harb Protoc 2015(11):951–969Google Scholar
  90. Kumar R, Chaudhary K, Gupta S, Singh H, Kumar S, Gautam A, Kapoor P, Raghava GP (2013) CancerDR: cancer drug resistance database. Sci Rep 3:1445Google Scholar
  91. Kwapiszewska K, Michalczuk A, Rybka M, Kwapiszewski R, Brzózka Z (2014) A microfluidic-based platform for tumour spheroid culture, monitoring and drug screening. Lab Chip 14:2096–2104Google Scholar
  92. LaBarbera DV, Reid BG, Yoo BH (2012) The multicellular tumor spheroid model for high-throughput cancer drug discovery. Expert Opin Drug Discov 7(9):819–830Google Scholar
  93. Lamfers ML, Hemminki A (2004) Multicellular tumor spheroids in gene therapy and oncolytic virus therapy. Curr Opin Mol Ther 6(4):403–411Google Scholar
  94. Langhans SA (2018) Three-dimensional in vitro cell culture models in drug discovery and drug repositioning. Front Pharmacol 9:6Google Scholar
  95. Lee SY, Meier R, Furuta S, Lenburg ME, Kenny PA, Xu R, Bissell MJ (2012) FAM83A confers EGFR-TKI resistance in breast cancer cells and in mice. J Clin Invest 122(9):3211–3220Google Scholar
  96. Lefebvre C, Rieckhof G, Califano A (2012) Reverse-engineering human regulatory networks. Wiley Interdiscip Rev Syst Biol Med 4:311–325Google Scholar
  97. Li L, Xie T (2005) Stem cell niche: structure and function. Annu Rev Cell Dev Biol 21:605–631Google Scholar
  98. Li J, Zhao W, Akbani R, Liu W, Ju Z, Ling S, Vellano CP, Roebuck P, Yu Q, Eterovic AK, Byers LA, Davies MA, Deng W, Gopal YN, Chen G, von Euw EM, Slamon D, Conklin D, Heymach JV, Gazdar AF, Minna JD, Myers JN, Lu Y, Mills GB, Liang H (2017) Characterization of human cancer cell lines by reverse-phase protein arrays. Cancer Cell 31(2):225–239Google Scholar
  99. Liu H, Zhang W, Jia Y, Yu Q, Grau GE, Peng L, Ran Y, Yang Z, Deng H, Lou J (2013) Single-cell clones of liver cancer stem cells have the potential of differentiating into different types of tumor cells. Cell Death Dis 4:e857Google Scholar
  100. Lovitt CJ, Shelper TB, Avery VM (2018) Doxorubicin resistance in breast cancer cells is mediated by extracellular matrix proteins. BMC Cancer 18(1):41Google Scholar
  101. Lu T, Li Y, Chen T (2013) Techniques for fabrication and construction of three-dimensional scaffolds for tissue engineering. Int J Nanomedicine 8:337–350Google Scholar
  102. Mao Y, Keller ET, Garfield DH, Shen K, Wang J (2013) Stromal cells in tumor microenvironment and breast cancer. Cancer Metastasis Rev 32(1–2):303–315Google Scholar
  103. Mehta G, Hsiao AY, Ingram M, Luker GD, Takayama S (2012) Opportunities and challenges for use of tumor spheroids as models to test drug delivery and efficacy. J Control Release 164(2):192–204Google Scholar
  104. Min D, Lee W, Bae IH, Lee TR, Croce P, Yoo SS (2018) Bioprinting of biomimetic skin containing melanocytes. Exp Dermatol 27:453–459Google Scholar
  105. Mitchel MJ, King MR (2013) Computational and experimental models of cancer cell response to fluid shear stress. Front Oncol 3:44Google Scholar
  106. Moreira AF, Dias DR, Correia IJ (2016) Stimuli-responsive mesoporous silica nanoparticles for cancer therapy: a review. Microporous Mesoporous Mater 236:141–157Google Scholar
  107. Motta S, Pappalardo F (2013) Mathematical modeling of biological systems. Brief Bioinform 14(4):411–422Google Scholar
  108. Nabavi S (2016) Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data. BMC Genomics 17(1):638Google Scholar
  109. Nagy JA, Chang SH, Dvorak AM, Dvorak HF (2009) Why are tumour blood vessels abnormal and why is it important to know? Br J Cancer 100:865–869Google Scholar
  110. Nami B, Wang Z (2018) Genetics and expression profile of the tubulin gene superfamily in breast cancer subtypes and its relation to taxane resistance. Cancers 10(8):E274Google Scholar
  111. Nath S, Devi GR (2016) Three-dimensional culture systems in cancer research: focus on tumor spheroid model. Pharmacol Ther 163:94–108Google Scholar
  112. Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, Romero IL, Carey MS, Mills GB, Hotamisligil GS, Yamada SD, Peter ME, Gwin K, Lengyel E (2011) Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat Med 17:1498–1503Google Scholar
  113. Niu N, Wang L (2015) In vitro human cell line models to predict clinical response to anticancer drugs. Pharmacogenomics 16(3):273–285Google Scholar
  114. Nunes AS, Barros AS, Costa EC, Moreira AF, Correia IJ (2019) 3D tumor spheroids as in vitro models to mimic in vivo human solid tumor resistance to therapeutic drugs. Biotechnol Bioeng 116:206–226Google Scholar
  115. Oliveira ÉA, Lima DS, Cardozo LE (2017) et al Toxicogenomic and bioinformatics platforms to identify key molecular mechanisms of a curcumin-analogue DM-1 toxicity in melanoma cells. Pharmacol Res (Pt B):178–187.  https://doi.org/10.1016/j.phrs.2017.08.018
  116. Pampaloni F, Reynaud EG, Stelzer EHK (2007) The third dimension bridges the gap between cell culture and live tissue. Nat Rev Mol Cell Biol 8:839–845Google Scholar
  117. Pandya HJ, Dhingra K, Prabhakar D, Chandrasekar V, Natarajan SK, Vasan AS, Kulkarni A, Shafiee H (2017) A microfluidic platform for drug screening in a 3D cancer microenvironment. Biosens Bioelectron 94:632–642Google Scholar
  118. Patel NR, Aryasomayajula B, Abouzeid AH, Torchilin VP (2015) Cancer cell spheroids for screening of chemotherapeutics and drug-delivery systems. Ther Deliv 6(4):509–520Google Scholar
  119. Peeper DS (2014) Cancer drug resistance: old concept, novel solutions required. Mol Oncol 8:1064–1066Google Scholar
  120. Penfornis P, Vallabhaneni KC, Janorkar AV, Pochampally RR (2017) Three dimensional tumor models for cancer studies. Front Biosci 9:162–173Google Scholar
  121. Pennacchi PC, de Almeida ME, Gomes OL, Faião-Flores F, de Araújo Crepaldi MC, Dos Santos MF, de Moraes Barros SB, Maria-Engler SS (2015) Glycated reconstructed human skin as a platform to study the pathogenesis of skin aging. Tissue Eng Part A 21(17–18):2417–2425Google Scholar
  122. Phung YT, Barbone D, Broaddus VC, Ho M (2011) Rapid generation of in vitro multicellular spheroids for the study of monoclonal antibody therapy. J Cancer 2:507–514Google Scholar
  123. Pozdeyev N, Yoo M, Mackie R, Schweppe RE, Tan AC, Haugen BR (2016) Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies. Oncotarget 7(32):51619–51625Google Scholar
  124. Raghavan S, Mehta P, Horst EN, Ward MR, Rowley KR, Mehta G (2016) Comparative analysis of tumor spheroid generation techniques for differential in vitro drug toxicity. Oncotarget 7(13):16948–16961Google Scholar
  125. Rambow F, Rogiers A, Marin-Bejar O, Aibar S, Femel J, Dewaele M, Karras P, Brown D, Chang YH, Debiec-Rychter M, Adriaens C, Radaelli E, Wolter P, Bechter O, Dummer R, Levesque M, Piris A, Frederick DT, Boland G, Flaherty KT, van den Oord J, Voet T, Aerts S, Lund AW, Marine JC (2018) Toward minimal residual disease-directed therapy in melanoma. Cell 174(4):843–855.e19Google Scholar
  126. Rambow F, Marine JC, Goding CR (2019) Melanoma plasticity and phenotypic diversity: therapeutic barriers and opportunities. Genes Dev 33(19–20):1295–1318Google Scholar
  127. Randall MJ, Jüngel A, Rimann M, Wuertz-Kozak K (2018) Advances in the biofabrication of 3D skin in vitro: healthy and pathological models. Front Bioeng Biotechnol 6:154Google Scholar
  128. Rathe SK, Moriarity BS, Stoltenberg CB, Kurata M, Aumann NK, Rahrmann EP, Bailey NJ, Melrose EG, Beckmann DA, Liska CR, Largaespada DA (2014) Using RNA-seq and targeted nucleases to identify mechanisms of drug resistance in acute myeloid leukemia. Sci Rep 4:6048Google Scholar
  129. Riahi R, Yang YL, Kim H, Jiang L, Wong PK, Zohar Y (2014) A microfluidic model for organ-specific extravasation of circulating tumor cells. Biomicrofluidics 8(2):024103Google Scholar
  130. Rodenhizer D, Gaude E, Cojocari D, Mahadevan R, Frezza C, Wouters BG, McGuigan AP (2016) A three-dimensional engineered tumour for spatial snapshot analysis of cell metabolism and phenotype in hypoxic gradients. Nat Mater 15(2):227–234Google Scholar
  131. Rothbauer M, Zirath H, Ertl P (2018) Recent advances in microfluidic technologies for cell-to-cell interaction studies. Lab Chip 18:249Google Scholar
  132. Ryabaya O, Prokofieva A, Akasov R, Khochenkov D, Emelyanova M, Burov S, Markvicheva E, Inshakov A, Stepanova E (2019) Metformin increases antitumor activity of MEK inhibitor binimetinib in 2D and 3D models of human metastatic melanoma cells. Biomed Pharmacother 109(2019):2548–2560Google Scholar
  133. Sá PHCG, Guimarães LC, Graças DA, Veras AAO, Barh D, Azevedo V, Rommel ALCS, Ramos TJ (2018) Next-generation sequencing and data analysis. In: Omics technologies and bio-engineering, towards improving quality of life. Academic Press, New York, pp 191–207Google Scholar
  134. Sadanandam A, Lyssiotis CA, Homicsko K, Collisson EA, Gibb WJ, Wullschleger S, Ostos LC, Lannon WA, Grotzinger C, Del Rio M, Lhermitte B, Olshen AB, Wiedenmann B, Cantley LC, Gray JW, Hanahan D (2013) A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat Med 19:619–625Google Scholar
  135. Sáez-Ayala M, Montenegro MF, Sánchez-Del-Campo L, Fernández-Pérez MP, Chazarra S, Freter R, Middleton M, Piñero-Madrona A, Cabezas-Herrera J, Goding CR et al (2013) Directed phenotype switching as an effective antimelanoma strategy. Cancer Cell 24:105–119Google Scholar
  136. Samur MK, Yan Z, Wang X, Cao Q, Munshi NC, Li C, Shah PK (2013) canEvolve: a web portal for integrative oncogenomics. PLoS One 8:e56228Google Scholar
  137. Sandhu S, Garzon R (2011) Potential applications of microRNAs in cancer diagnosis, prognosis, and treatment. Semin Oncol 38:781–787Google Scholar
  138. Sandri S, Faião-Flores F, Tiago M, Pennacchi PC, Massaro RR, Alves-Fernandes DK, Berardinelli GN, Evangelista AF, de Lima Vazquez V, Reis RM, Maria-Engler SS (2016) Vemurafenib resistance increases melanoma invasiveness and modulates the tumor microenvironment by MMP-2 upregulation. Pharmacol Res 111:523–533Google Scholar
  139. Schmidt F, Efferth T (2016) Tumor heterogeneity, single-cell sequencing, and drug resistance. Pharmaceuticals 9(2):E33Google Scholar
  140. Schwachöfer JH (1990) Multicellular tumor spheroids in radiotherapy research (review). Anticancer Res 10(4):963–969Google Scholar
  141. Sciarrillo R, Wojtuszkiewicz A, Kooi IE, Gómez VE, Boggi U, Jansen G, Kaspers GJ, Cloos J, Giovannetti E (2016) Using RNA-sequencing to detect novel splice variants related to drug resistance in in vitro cancer models. J Vis Exp 118:54714Google Scholar
  142. Seth S, Li CY, Ho IL, Corti D, Loponte S, Sapio L, Del Poggetto E, Yen EY, Robinson FS, Peoples M, Karpinets T, Deem AK, Kumar T, Song X, Jiang S, Kang Y, Fleming J, Kim M, Zhang J, Maitra A, Heffernan TP, Giuliani V, Genovese G, Futreal A, Draetta GF, Carugo A, Viale A (2019) Pre-existing functional heterogeneity of tumorigenic compartment as the origin of chemoresistance in pancreatic tumors. Cell Rep 26(6):1518–1532.e9Google Scholar
  143. Shaffer SM, Dunagin MC, Torborg SR, Torre EA, Emert B, Krepler C, Beqiri M, Sproesser K, Brafford PA, Xiao M, Eggan E, Anastopoulos IN, Vargas-Garcia CA, Singh A, Nathanson KL, Herlyn M, Raj A (2017) Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546(7658):431–435Google Scholar
  144. Shang M, Soon RH, Lim CT, Khoo BL, Han J (2019) Microfluidic modelling of the tumor microenvironment for anti-cancer drug development. Lab Chip 19:369–386Google Scholar
  145. Shin Y, Han S, Jeon JS, Yamamoto K, Zervantonakis IK, Sudo R, Kamm RD, Chung S (2012) Microfluidic assay for simultaneous culture of multiple cell types on surfaces or within hydrogels. Nat Protoc 7(7):1247–1259Google Scholar
  146. Simian M, Bissell MJ (2017) Organoids: a historical perspective of thinking in three dimensions. Cell Biol 216:31–40Google Scholar
  147. Somaweera H, Ibraguimov A, Pappas D (2016) A review of chemical gradient systems for cell analysis. Anal Chim Acta 907:7–17Google Scholar
  148. Song JW, Munn LL (2011) Fluid forces control endothelial sprouting. Proc Natl Acad Sci U S A 108(37):15342–15347Google Scholar
  149. Souza GR, Molina JR, Raphael RM, Ozawa MG, Stark DJ, Levin CS, Bronk LF, Ananta JS, Mandelin J, Georgescu MM, Bankson JA, Gelovani JG, Killian TC, Arap W, Pasqualini R (2010) Three-dimensional tissue culture based on magnetic cell levitation. Nat Nanotechnol 5:291–296Google Scholar
  150. Spurrier B, Ramalingam S, Nishizuka S (2008) Reverse-phase protein microarrays for cell signaling analysis. Nat Protoc 3:1796–1808Google Scholar
  151. Stanislaus R, Carey M, Deus HF, Coombes K, Hennessy BT, Mills GB, Almeida JS (2008) RPPAML/RIMS: a metadata format and an information management system for reverse phase protein arrays. BMC Bioinformatics 9:555Google Scholar
  152. Stebbing J, Shah K, Lit LC, Gagliano T, Ditsiou A, Wang T, Wendler F, Simon T, Szabó KS, O'Hanlon T, Dean M, Roslani AC, Cheah SH, Lee SC, Giamas G (2018) LMTK3 confers chemo-resistance in breast cancer. Oncogene 37(23):3113–3130Google Scholar
  153. Stratton MR, Campbell PJ, Futreal PA (2009) The cancer genome. Nature 458:719–724Google Scholar
  154. Stylianopoulos T, Martin JD, Chauhan VP, Jain SR, Diop-Frimpong B, Bardeesy N, Smith BL, Ferrone CR, Hornicek FJ, Boucher Y, Munn LL, Jain RK (2012) Causes, consequences, and remedies for growth-induced solid stress in murine and human tumors. Proc Natl Acad Sci U S A 109(38):15101–15108Google Scholar
  155. Su R, Liu X, Wei L, Zou Q (2019) Deep-resp-forest: a deep forest model to predict anti-cancer drug response. Methods 166:91–102. pii:S1046-2023(18)30323-2Google Scholar
  156. Sultana N, Hassan MI, Lim MM (2015) Scaffold fabrication protocols. In: Composite synthetic scaffolds for tissue engineering and regenerative medicine. Springer, Berlin, pp 13–24Google Scholar
  157. Swartz MA, Lund AW (2012) Lymphatic and interstitial flow in the tumour microenvironment: linking mechanobiology with immunity. Nat Rev Cancer 12(3):210–219Google Scholar
  158. Tang B, Wang Y, Zhu J, Zhao W (2015) Web resources for model organism studies. Genomics Proteomics Bioinformatics 13:64–68Google Scholar
  159. Tatusova T (2016) Update on genomic databases and resources at the national center for biotechnology information. In: Carugo O, Eisenhaber F (eds) Data mining techniques for the life sciences, Methods in molecular biology, vol 1415. Humana Press, New YorkGoogle Scholar
  160. TCGA – The Cancer Genome Atlas homepage (2019). http://cancergenome.nih.gov/abouttcga. Accessed 15 July 2019
  161. Tiago M, de Oliveira EM, Brohem CA, Pennacchi PC, Paes RD, Haga RB, Campa A, de Moraes Barros SB, Smalley KS, Maria-Engler SS (2014) Fibroblasts protect melanoma cells from the cytotoxic effects of doxorubicin. Tissue Eng Part A 20(17–18):2412–2421Google Scholar
  162. Tseng H, Gage JA, Raphael RM, Moore RH, Killian TC, Grande-Allen KJ, Souza GR (2013) Assembly of a three-dimensional multitype bronchiole coculture model using magnetic levitation. Tissue Eng Part C Methods 19:665–675Google Scholar
  163. Tung YC, Hsiao AY, Allen SG, Torisawa YS, Ho M, Takayama S (2011) High throughput 3D spheroid culture and drug testing using a 384 hanging drop array. Analyst 136(3):473–478Google Scholar
  164. Vaupel P, Mayer A, Hockel M (2004) Tumor hypoxia and malignant progression. Methods Enzymol 381:335–354Google Scholar
  165. Viale A, Draetta GF (2016) Metabolic features of cancer treatment resistance. Recent Results Cancer Res 207:135–156Google Scholar
  166. Vlachogiannis G, Hedayat S, Vatsiou A et al (2018) Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359(6378):920–926.  https://doi.org/10.1126/science.aao2774CrossRefGoogle Scholar
  167. Vörsmann H, Groeber F, Walles H, Busch S, Beissert S, Walczak H, Kulms D (2013) Development of a human three-dimensional organotypic skin-melanoma spheroid model for in vitro drug testing. Cell Death Dis 4:e719Google Scholar
  168. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63Google Scholar
  169. Wang Z, Jensen MA, Zenklusen JC (2016) A practical guide to the cancer genome atlas (TCGA). In: Mathé E, Davis S (eds) Statistical genomics, Methods in molecular biology, vol 1418. Humana Press, New YorkGoogle Scholar
  170. Werner HMJ, Mills GB, Ram PT (2014) Cancer systems biology: a peak into the future of patient care? Nat Rev Clin Oncol 11(3):167–176Google Scholar
  171. Whitesides GM (2006) The origins and the future of microfluidics. Nature 442:368–373Google Scholar
  172. Wilson WR, Hay MP (2011) Targeting hypoxia in cancer therapy. Nat Rev Cancer 11(6):393Google Scholar
  173. Xiao L, Guo J (2018) Single-cell in situ RNA analysis with switchable fluorescent oligonucleotides. Front Cell Dev Biol 6:42Google Scholar
  174. Xiao Z, Hansen CB, Allen TM, Miller GG, Moore RB (2005) Distribution of photosensitizers in bladder cancer spheroids: implications for intravesical instillation of photosensitizers for photodynamic therapy of bladder cancer. J Pharm Pharm Sci 8(3):536–543Google Scholar
  175. Xu Z, Gao Y, Hao Y, Li E, Wang Y, Zhang J, Wang W, Gao Z, Wang Q (2013) Application of a microfluidic chip-based 3D co-culture to test drug sensitivity for individualized treatment of lung cancer. Biomaterials 34:4109–4117Google Scholar
  176. Yagi K, Tsuda K, Serada M, Yamada C, Kondoh A, Miura Y (1993) Rapid formation of multicellular spheroids of adult rat hepatocytes by rotation culture and their immobilization within calcium alginate. Artif Organs 17(11):929–934Google Scholar
  177. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, Ramaswamy S, Futreal PA, Haber DA, Stratton MR, Benes C, McDermott U, Garnett MJ (2013) Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 41(Database issue):D955–D961Google Scholar
  178. Yap YS, Singh AP, Lim JHC, Ahn JH, Jung KH, Kim J, Dent RA, Ng RCH, Kim SB, Chiang DY (2018) Elucidating therapeutic molecular targets in premenopausal Asian women with recurrent breast cancers. NPJ Breast Cancer 4:19Google Scholar
  179. Youn BS, Sen A, Kallos MS, Behie LA, Girgis-Gabardo A, Kurpios N, Barcelon M, Hassell JA (2005) Large-scale expansion of mammary epithelial stem cell aggregates in suspension bioreactors. Biotechnol Prog 21(3):984–993Google Scholar
  180. Yu Y, Wang X, Li Q, Zhang M, Xu P, Chen Y, Yan Y, Zhang L (2018) Bioinformatics analysis of gene expression alterations conferring drug resistance in tumor samples from melanoma patients with EGFR-activating BRAF mutations. Oncol Lett 15(1):635–641Google Scholar
  181. Zhu S, Qing T, Zheng Y, Jin L, Shi L (2017) Advances in single-cell RNA sequencing and its applications in cancer research. Oncotarget 8(32):53763–53779Google Scholar
  182. Zips D, Thames HD, Baumann M (2005) New anticancer agents: in vitro and in vivo evaluation. In Vivo 19(1):1–7Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Érica Aparecida de Oliveira
    • 1
  • Colin R. Goding
    • 2
  • Silvya Stuchi Maria-Engler
    • 1
    Email author
  1. 1.Skin Biology and Melanoma Lab, Department of Clinical Chemistry and Toxicology, School of Pharmaceutical SciencesUniversity of São PauloSão PauloBrazil
  2. 2.Ludwig Institute for Cancer Research, Nuffield Department of Clinical MedicineUniversity of OxfordOxfordUK

Personalised recommendations