Element contents
Series: Elements in Molecular Oncology
Personalized Drug Screening for Functional Tumor Profiling
Published online by Cambridge University Press: 10 June 2022
Summary
Despite considerable advances in our understanding of the biology that underlies tumor development and progression of cancer and the rapidly evolving field of personalized medicine, cancer is still one of the deadliest diseases. Many cancer patients have benefited from the survival improvements observed with targeted therapies but only a small subset of patients receiving targeted drugs experience an objective response. Because cancer is a complex and heterogeneous disease, the search for effective cancer treatments will need to address not only patient-specific molecular defects but also aspects of the tumor microenvironment. The functional tumor profiling directly measures the cellular phenotype, in particular tumor growth, in response to drugs using patient-derived tumor models and might be the next step toward precision oncology. In this Element, the authors discuss the personalized drug screening as a novel patient stratification strategy for the determination of individualized treatment choices in oncology.
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- Series: Elements in Molecular OncologyOnline ISBN: 9781009037877Publisher: Cambridge University PressPrint publication: 07 July 2022
References
Siegel, RL, Miller, KD and Jemal, A: Cancer statistics, 2020. CA Cancer J. Clin. 70: 7–30, 2020.Google Scholar
Pauli, C, Hopkins, BD, Prandi, D et al.: Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7:462–477, 2017.CrossRefGoogle ScholarPubMed
Bennett, CW, Berchem, G, Kim, YJ et al.: Cell-free DNA and next-generation sequencing in the service of personalized medicine for lung cancer. Oncotarget 7:71013–71035, 2016.Google Scholar
Castro, D Gonzalez de, Clarke, PA, Al-Lazikani, B et al.: Personalized cancer medicine: Molecular diagnostics, predictive biomarkers, and drug resistance. Clin. Pharmacol. Ther. 93:252–259, 2013.Google Scholar
Gerlach, MM, Merz, F, Wichmann, G et al.: Slice cultures from head and neck squamous cell carcinoma: A novel test system for drug susceptibility and mechanisms of resistance. Br. J. Cancer 110:479–488, 2014.Google Scholar
Tredan, O, Wang, Q, Pissaloux, D et al.: Molecular screening program to select molecular-based recommended therapies for metastatic cancer patients: Analysis from the ProfiLER trial. Ann. Oncol. 30:757–765, 2019.Google Scholar
Waud, WR: Murine L1210 and P388 leukemias, in Teicher, B (ed.): Tumor Models in Cancer Research. Totowa, NJ, Humana Press, (2011, pp. 23–41.CrossRefGoogle Scholar
Holbeck, SL, Camalier, R, Crowell, JA et al.: The National Cancer Institute ALMANAC: A comprehensive screening resource for the detection of anticancer drug pairs with enhanced therapeutic activity. Cancer Res. 77:3564–3576, 2017.Google Scholar
Hay, M, Thomas, DW, Craighead, JL et al.: Clinical development success rates for investigational drugs. Nat. Biotechnol. 32:40–51, 2014.Google Scholar
Horvath, P, Aulner, N, Bickle, M et al.: Screening out irrelevant cell-based models of disease. Nat. Rev. Drug Discov. 15: 751–769, 2016.Google Scholar
Wong, CH, KW Siah and AW Lo: Estimation of clinical trial success rates and related parameters. Biostatistics 20: 273–286, 2019.CrossRefGoogle Scholar
Salmon, SE, Hamburger, AW, Soehnlen, B et al.: Quantitation of differential sensitivity of human-tumor stem cells to anticancer drugs. N. Engl. J. Med. 298: 1321–1327, 1978.CrossRefGoogle ScholarPubMed
Bruce, WR, Meeker, BE and Valeriote, FA: Comparison of the sensitivity of normal hematopoietic and transplanted lymphoma colony-forming cells to chemotherapeutic agents administered in vivo. J. Natl. Cancer Inst. 37:233–245, 1966.Google ScholarPubMed
Park, CH, DE Bergsagel and EA McCulloch: Mouse myeloma tumor stem cells: A primary cell culture assay. J. Natl. Cancer Inst. 46: 411–422, 1971.Google Scholar
Salmon, SE: In vitro assay for sensitivity to anticancer drugs. Hosp. Pract. (Off. Ed.) 20:133–137, 141–142, 145–148, 1985.CrossRefGoogle ScholarPubMed
Hamburger, AW and Salmon, SE: Primary bioassay of human tumor stem cells. Science 197:461–463, 1977.Google Scholar
Hamburger, A and Salmon, SE: Primary bioassay of human myeloma stem cells. J. Clin. Invest. 60:846–854, 1977.Google Scholar
Hoff, DD von, Clark, GM, Stogdill, BJ et al.: Prospective clinical trial of a human tumor cloning system. Cancer Res. 43: 1926–1931, 1983.Google Scholar
Salmon, SE: Human tumor colony assay and chemosensitivity testing. Cancer Treat. Rep. 68: 117–125, 1984.Google ScholarPubMed
Hoff, DD vvon, Casper, J, Bradley, E et al.: Association between human tumor colony-forming assay results and response of an individual patient’s tumor to chemotherapy. Am. J. Med. 70: 1027–1041, 1981.Google Scholar
Mann, BD, Kern, DH, Giuliano, AE et al.: Clinical correlations with drug sensitivities in the clonogenic assay: A retrospective study. Arch. Surg. 117:33–36, 1982.Google Scholar
Meyskens, FL Jr., Moon, TE, Dana, B et al.: Quantitation of drug sensitivity by human metastatic melanoma colony-forming units. Br. J. Cancer 44:787–797, 1981.CrossRefGoogle ScholarPubMed
Alberts, DS, Chen, HS, Salmon, SE et al.: Chemotherapy of ovarian cancer directed by the human tumor stem cell assay. Cancer Chemother. Pharmacol. 6:279–285, 1981.Google Scholar
Link, KH, Kornmann, M, Leder, GH et al.: Regional chemotherapy directed by individual chemosensitivity testing in vitro: A prospective decision-aiding trial. Clin. Cancer Res. 2: 1469–1474, 1996.Google Scholar
Hanauske, AR, Hanauske, U and von Hoff, DD: Recent improvements in the human tumor cloning assay for sensitivity testing of antineoplastic agents. Eur. J. Cancer Clin. Oncol. 23:603–605, 1987.CrossRefGoogle ScholarPubMed
von Hoff, DD, Sandbach, JF, Clark, GM et al.: Selection of cancer chemotherapy for a patient by an in vitro assay versus a clinician. J. Natl. Cancer Inst. 82:110–116, 1990.Google Scholar
Federico, M, Alberts, DS, Garcia, DJ et al.: In vitro drug testing of ovarian cancer using the human tumor colony-forming assay: Comparison of in vitro response and clinical outcome. Gynecol. Oncol. 55:S156–163, 1994.Google Scholar
von Hoff, DD, Forseth, BJ, Turner, JN et al.: Selection of chemotherapy for patient treatment utilizing a radiometric versus a cloning system. Int.J. Cell Cloning 4:16–26, 1986.Google Scholar
von Hoff, DD: Human tumor cloning assays: Applications in clinical oncology and new antineoplastic agent development. Cancer Metastasis Rev. 7: 357–371, 1988.CrossRefGoogle ScholarPubMed
Tanigawa, N, Kern, DH, Hikasa, Y et al.: Rapid assay for evaluating the chemosensitivity of human tumors in soft agar culture. Cancer Res. 42:2159–2164, 1982.Google Scholar
Weisenthal, LM, Marsden, JA, Dill, PL et al.: A novel dye exclusion method for testing in vitro chemosensitivity of human tumors. Cancer Res. 43:749–757, 1983.Google Scholar
Kornmann, M, Butzer, U, Blatter, J et al.: Pre-clinical evaluation of the activity of gemcitabine as a basis for regional chemotherapy of pancreatic and colorectal cancer. Eur. J. Surg. Oncol. 26:583–587, 2000.Google Scholar
Valkenburg, KC, AE de Groot and KJ Pienta: Targeting the tumour stroma to improve cancer therapy. Nat. Rev. Clin. Oncol. 15: 366–381, 2018.CrossRefGoogle Scholar
Baghban, R, Roshangar, L, Jahanban-Esfahlan, R et al.: Tumor microenvironment complexity and therapeutic implications at a glance. Cell. Commun. Signal 18:59, 2020.Google Scholar
van der Steen, SC, Raave, R, Langerak, S et al.: Targeting the extracellular matrix of ovarian cancer using functionalized, drug loaded lyophilisomes. Eur. J. Pharm. Biopharm. 113:229–239, 2017.CrossRefGoogle ScholarPubMed
Loessner, D, Stok, KS, Lutolf, MP et al.: Bioengineered 3D platform to explore cell-ECM interactions and drug resistance of epithelial ovarian cancer cells. Biomaterials 31:8494–8506, 2010.Google Scholar
Lee, DW, Choi, YS, Seo, YJ et al.: High-throughput screening (HTS) of anticancer drug efficacy on a micropillar/microwell chip platform. Anal. Chem. 86:535–542, 2014.CrossRefGoogle ScholarPubMed
Imamura, Y, Mukohara, T, Shimono, Y et al.: Comparison of 2D- and 3D-culture models as drug-testing platforms in breast cancer. Oncol. Rep. 33: 1837–1843, 2015.CrossRefGoogle ScholarPubMed
Riedl, A, Schlederer, M, Pudelko, K et al.: Comparison of cancer cells in 2D vs 3D culture reveals differences in AKT-mTOR-S6 K signaling and drug responses. J. Cell. Sci. 130:203–218, 2017.Google Scholar
Halfter, K, Hoffmann, O, Ditsch, N et al.: Testing chemotherapy efficacy in HER2 negative breast cancer using patient-derived spheroids. J. Transl. Med. 14:112, 2016.CrossRefGoogle ScholarPubMed
Gunness, P, Mueller, D, Shevchenko, V et al.: 3D organotypic cultures of human HepaRG cells: A tool for in vitro toxicity studies. Toxicol. Sci. 133:67–78, 2013.Google Scholar
Jabs, J, Zickgraf, FM, Park, J et al.: Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations. Mol. Syst. Biol. 13:955, 2017.CrossRefGoogle ScholarPubMed
Hall, MD, Martin, C, Ferguson, DJ et al.: Comparative efficacy of novel platinum(IV) compounds with established chemotherapeutic drugs in solid tumour models. Biochem. Pharmacol. 67:17–30, 2004.Google Scholar
Sutherland, RM: Cell and environment interactions in tumor microregions: The multicell spheroid model. Science 240: 177–184, 1988.Google Scholar
Hwang, CI, Boj, SF, Clevers, H et al.: Preclinical models of pancreatic ductal adenocarcinoma. J. Pathol. 238:197–204, 2016.Google Scholar
Godoy, P, Hewitt, NJ, Albrecht, U et al.: Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch. Toxicol. 87: 1315–1530, 2013.Google Scholar
Wu, XZ, D Chen and GR Xie: Extracellular matrix remodeling in hepatocellular carcinoma: Effects of soil on seed? Med. Hypotheses 66: 1115–1120, 2006.Google Scholar
Fang, Y and Eglen, RM: Three-dimensional cell cultures in drug discovery and development. SLAS Discov. 22: 456–472, 2017.CrossRefGoogle ScholarPubMed
Tentler, JJ, Tan, AC, Weekes, CD et al.: Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9:338–350, 2012.Google Scholar
Hidalgo, M, Amant, F, Biankin, AV et al.: Patient-derived xenograft models: An emerging platform for translational cancer research. Cancer Discov. 4:998–1013, 2014.Google Scholar
Cho, SY: Patient-derived xenografts as compatible models for precision oncology. Lab. Anim. Res. 36: 14, 2020.CrossRefGoogle ScholarPubMed
Houghton, JA, Houghton, PJ and Green, AA: Chemotherapy of childhood rhabdomyosarcomas growing as xenografts in immune-deprived mice. Cancer Res. 42:535–539, 1982.Google ScholarPubMed
Fiebig, HH, Neumann, HA, Henss, H et al.: Development of three human small cell lung cancer models in nude mice. Recent Results Cancer Res. 97:77–86, 1985.Google Scholar
Rosfjord, E, Lucas, J, Li, G et al.: Advances in patient-derived tumor xenografts: From target identification to predicting clinical response rates in oncology. Biochem. Pharmacol. 91:135–143, 2014.Google Scholar
Hidalgo, M, Bruckheimer, E, Rajeshkumar, NV et al.: A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol. Cancer Ther. 10: 1311–1316, 2011.Google Scholar
Thompson, J, George, EO, Poquette, CA et al.: Synergy of topotecan in combination with vincristine for treatment of pediatric solid tumor xenografts Clin. Cancer Res. 5:3617–3631, 1999s.Google Scholar
Hoff, DD von, Ramanathan, RK, Borad, MJ et al.: Gemcitabine plus nab-paclitaxel is an active regimen in patients with advanced pancreatic cancer: A phase I/II trial. J. Clin. Oncol. 29: 4548–4554, 2011.Google Scholar
von Hoff, DD, Ervin, T, Arena, FP et al.: Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N. Engl. J. Med. 369: 1691–1703, 2013.Google Scholar
Girotti, MR, Lopes, F, Preece, N et al.: Paradox-breaking RAF inhibitors that also target SRC are effective in drug-resistant BRAF mutant melanoma. Cancer Cell 27:85–96, 2015.Google Scholar
Gao, H, Korn, JM, Ferretti, S et al.: High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21: 1318–1325, 2015.CrossRefGoogle ScholarPubMed
Townsend, EC, Murakami, MA, Christodoulou, A et al.: The Public Repository of Xenografts enables discovery and randomized phase II-like trials in mice. Cancer Cell 29:574–586, 2016.CrossRefGoogle ScholarPubMed
Goto, T: Patient-derived tumor xenograft models: Toward the establishment of precision cancer medicine. J. Pers. Med. 10: 64, 2020.Google Scholar
Malaney, P, Nicosia, SV and Dave, V: One mouse, one patient paradigm: New avatars of personalized cancer therapy. Cancer Lett. 344:1–12, 2014.Google Scholar
Clohessy, JG and Pandolfi, PP: Mouse hospital and co-clinical trial project: From bench to bedside. Nat. Rev. Clin. Oncol. 12:491–498, 2015.Google Scholar
Stebbing, J, Paz, K, Schwartz, GK et al.: Patient-derived xenografts for individualized care in advanced sarcoma. Cancer 120:2006–2015, 2014.Google Scholar
Kurmasheva, RT and Houghton, PJ: Identifying novel therapeutic agents using xenograft models of pediatric cancer. Cancer Chemother. Pharmacol. 78:221–232, 2016.Google Scholar
Allen, TM, Brehm, MA, Bridges, S et al.: Humanized immune system mouse models: Progress, challenges and opportunities. Nat. Immunol. 20:770–774, 2019.Google Scholar
Meijer, TG, Naipal, KA, Jager, A et al.: Ex vivo tumor culture systems for functional drug testing and therapy response prediction. Future Sci. OA 3:FSO190, 2017.Google Scholar
Horowitz, LF, Rodriguez, AD, Dereli-Korkut, Z et al.: Multiplexed drug testing of tumor slices using a microfluidic platform. NPJ Precis. Oncol. 4:12, 2020.CrossRefGoogle ScholarPubMed
Koerfer, J, Kallendrusch, S, Merz, F et al.: Organotypic slice cultures of human gastric and esophagogastric junction cancer. Cancer Med. 5:1444–1453, 2016.CrossRefGoogle ScholarPubMed
Vaira, V, Fedele, G, Pyne, S et al.: Preclinical model of organotypic culture for pharmacodynamic profiling of human tumors. Proc. Natl. Acad. Sci. USA 107:8352–8356, 2010.CrossRefGoogle Scholar
Naipal, KA, Verkaik, NS, Sanchez, H et al.: Tumor slice culture system to assess drug response of primary breast cancer. BMC Cancer 16:78, 2016.Google Scholar
Sivakumar, R, Chan, M, Shin, JS et al.: Organotypic tumor slice cultures provide a versatile platform for immuno-oncology and drug discovery. Oncoimmunology 8:e1670019, 2019.Google Scholar
Kondo, J, Ekawa, T, Endo, H et al.: High-throughput screening in colorectal cancer tissue-originated spheroids. Cancer Sci. 110:345–355, 2019.Google Scholar
Zanoni, M, Cortesi, M, Zamagni, A et al.: Modeling neoplastic disease with spheroids and organoids. J. Hematol. Oncol. 13:97, 2020.Google Scholar
Foglietta, F, Canaparo, R, Muccioli, G et al.: Methodological aspects and pharmacological applications of three-dimensional cancer cell cultures and organoids. Life Sci. 254:117784, 2020.Google Scholar
Weeber, F, van de Wetering, M, Hoogstraat, M et al.: Preserved genetic diversity in organoids cultured from biopsies of human colorectal cancer metastases. Proc. Natl. Acad. Sci. USA 1 12:13308–13311, 2015.CrossRefGoogle Scholar
van de Wetering, M, Francies, HE, Francis, JM et al.: Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161:933–945, 2015.CrossRefGoogle ScholarPubMed
Kiyohara, Y, Yoshino, K, Kubota, S et al.: Drug screening and grouping by sensitivity with a panel of primary cultured cancer spheroids derived from endometrial cancer. Cancer Sci. 107:452–460, 2016.CrossRefGoogle ScholarPubMed
Kondo, J and Inoue, M: Application of cancer organoid model for drug screening and personalized therapy. Cells 8:470, 2019.Google Scholar
Liu, T, Delavaux, C and Zhang, YS: 3D bioprinting for oncology applications. J. 3D Print. Med. 3:55–58, 2019.CrossRefGoogle ScholarPubMed
Mao, S, Pang, Y, Liu, T et al.: Bioprinting of in vitro tumor models for personalized cancer treatment: A review. Biofabrication 12:042001, 2020.Google Scholar
Langer, EM, Allen-Petersen, BL, King, SM et al.: Modeling tumor phenotypes in vitro with three-dimensional bioprinting. Cell Rep. 26:608–623.e6, 2019.Google Scholar
Yi, HG, Jeong, YH, Kim, Y et al.: A bioprinted human-glioblastoma-on-a-chip for the identification of patient-specific responses to chemoradiotherapy. Nat. Biomed. Eng. 3:509–519, 2019.Google Scholar
Knowlton, S, Onal, S, Yu, CH et al.: Bioprinting for cancer research. Trends Biotechnol. 33:504–513, 2015.Google Scholar
Peng, W, Datta, P, Ayan, B et al.: 3D bioprinting for drug discovery and development in pharmaceutics. Acta Biomater. 57:26–46, 2017.Google Scholar
Matai, I, Kaur, G, Seyedsalehi, A et al.: Progress in 3D bioprinting technology for tissue/organ regenerative engineering. Biomaterials 226:119536, 2020.Google Scholar
Datta, P, Dey, M, Ataie, Z et al.: 3D bioprinting for reconstituting the cancer microenvironment. NPJ Precis. Oncol. 4:18, 2020.Google Scholar
Rijal, G and Li, W: A versatile 3D tissue matrix scaffold system for tumor modeling and drug screening. Sci. Adv. 3:e1700764, 2017.Google Scholar
Heinrich, MA, Bansal, R, Lammers, T et al.: 3D-bioprinted mini-brain: A glioblastoma model to study cellular interactions and therapeutics. Adv. Mater. 31:e1806590, 2019.Google Scholar
Mao, S, He, J, Zhao, Y et al.: Bioprinting of patient-derived in vitro intrahepatic cholangiocarcinoma tumor model: Establishment, evaluation and anti-cancer drug testing. Biofabrication 12:045014, 2020.Google Scholar
Tang, M, Xie, Q, Gimple, RC et al.: Three-dimensional bioprinted glioblastoma microenvironments model cellular dependencies and immune interactions. Cell Res., 30:833–853, 2020.Google Scholar
Zhao, Y, Yao, R, Ouyang, L et al.: Three-dimensional printing of Hela cells for cervical tumor model in vitro. Biofabrication 6:035001, 2014.Google Scholar
Doh, I, Kwon, YJ, Ku, B et al.: Drug efficacy comparison of 3D forming and preforming sphere models with a micropillar and microwell chip platform. SLAS Discov. 24:476–483, 2019.Google Scholar
Gorshkov, K, Chen, CZ, Marshall, RE et al.: Advancing precision medicine with personalized drug screening. Drug Discov. Today 24:272–278, 2019.Google Scholar
Turanli, B, Altay, O, Boren, J et al.: Systems biology based drug repositioning for development of cancer therapy. Semin. Cancer Biol. 68:47–58, 2021.Google Scholar
Nowak-Sliwinska, P, Scapozza, L and Ruiz, IAA: Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer. Biochim. Biophys. Acta Rev. Cancer 1871:434–454, 2019.Google Scholar
Law, GL, Tisoncik-Go, J, Korth, MJ et al.: Drug repurposing: A better approach for infectious disease drug discovery? Curr. Opin. Immunol. 25:588–592, 2013.CrossRefGoogle ScholarPubMed
Yuan, H, Myers, S, Wang, J et al.: Use of reprogrammed cells to identify therapy for respiratory papillomatosis. N. Engl. J. Med. 367: 1220–1227, 2012.Google Scholar
Alvarez, MJ, Shen, Y, Giorgi, FM et al.: Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48:838–847, 2016.CrossRefGoogle ScholarPubMed
Alvarez, MJ, Subramaniam, PS, Tang, LH et al.: A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors. Nat. Genet. 50:979–989, 2018.Google Scholar
Califano, A and Alvarez, MJ: The recurrent architecture of tumour initiation, progression and drug sensitivity. Nat. Rev. Cancer 17:116–130, 2017.Google Scholar
Chari, A, Vogl, DT, Gavriatopoulou, M et al.: Oral selinexor-dexamethasone for triple-class refractory multiple myeloma. N. Engl. J. Med. 381:727–738, 2019.Google Scholar
Weeber, F, Ooft, SN, Dijkstra, KK et al.: Tumor organoids as a pre-clinical cancer model for drug discovery. Cell Chem. Biol. 24: 1092–1100, 2017.Google Scholar
Jia, Z, Wang, Y, Cao, L et al.: First-line treatment selection with organoids of an EGFRm + TP53 m stage IA1 patient with early metastatic recurrence after radical surgery and follow-up. J. Thorac. Dis. 12: 3764–3773, 2020.CrossRefGoogle Scholar
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