Editorial: cancer models

Editorial on the Research Topic
Cancer Models

Cancer is still a major concern for public health and is a cause of death while being psychologically the most dreaded disease. Until recent times, the diagnosis of a progressed form of cancer invariably meant that there was little chance for long-term survival. This notion has changed recently thanks to the tremendous efforts in cancer research and development (compared to other diseases) supported by enormous public and private financing. To illustrate this, just consider the “ moonshot initiative” announced by US President Obama in 2016 ( 1 ).

Cancer research, like research in other diseases, highly depends on representative and reliable model systems. However, cancer is not the single molecule-defined tumor, but rather a heterogenic and highly variable complex of different individual diseases. This makes research on cancer highly difficult, expensive, and explains the enormous resources needed.

Looking back on more than 100 years of cancer research, the models used have changed constantly and extended permanently to all the new stages of drug discovery including target identification, lead structure optimization, tolerability, toxicity, biomarker discovery, and individual patient prediction. Today, the selection of the most appropriate model to best reflect the given tumor entity is one of the major challenges for the cancer researchers.

From the available volumes on the research topic “ Cancer Models,” we collected original papers and review articles that addressed the topic of tumor modeling from perspectives such as molecular biology, biochemistry, microorganisms, cells, and organoids, fishes, animals, and xenografts, up to computational cancer models and patient data analysis.

Next-generation sequencing-based methods have recently revealed complex patterns of chromosomal aberrations, which are beyond explanation by classical models of karyotypic evolution. The term chromothripsis has been introduced to describe the phenomenon of temporarily and spatially confined genomic instability. This results in dramatic chromosomal rearrangements of segments of one or a few chromosomes. Simultaneously, misrepaired DNA double-strand breaks are causing another phenomenon called chromoplexy, which is characterized by the presence of chained translocations and interlinking deletion bridges in chromosomes.

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3. Dimri GP, Lee X, Basile G, Acosta M, Scott G, Roskelley C, et al. A biomarker that identifies senescent human cells in culture and in aging skin in vivo . Proc Natl Acad Sci USA.(1995) 92: 9363–7. doi: 10. 1073/pnas. 92. 20. 9363

4. de Keizer PL. The fountain of youth by targeting senescent cells? Trends Mol Med.(2017) 23: 6–17. doi: 10. 1016/j. molmed. 2016. 11. 006

5. Zhu Y, Tchkonia T, Pirtskhalava T, Gower AC, Ding H, Giorgadze N, et al. The Achilles’ heel of senescent cells: from transcriptome to senolytic drugs. Aging Cell (2015) 4: 644–58. doi: 10. 1111/acel. 12344

6. Baar MP, Brandt RMC, Putavet DA, Klein JDD, Derks KWJ, Bourgeois BRM, et al. Targeted apoptosis of senescent cells restores tissue homeostasis in response to chemotoxicity and aging. Cell (2017) 169: 132–47. doi: 10. 1016/j. cell. 2017. 02. 031

7. Collado M, Serrano M. The power and the promise of oncogene-induced senescence markers. Nat Rev Cancer (2006) 6: 472–6. doi: 10. 1038/nrc1884

8. Campisi J. Senescent cells, tumor suppression, and organismal aging: good citizens, bad neighbors. Cell (2005) 120: 513–22. doi: 10. 1016/j. cell. 2005. 02. 003

9. Ehrlich P, Apolant H. Beobachtungen über maligne Mäusetumoren. Berliner Klinische Wochenschrift (1905) 42: 871–4.

10. Klinghammer K, Walther W, Hoffmann J. Choosing wisely-Preclinical test models in the era of precision medicine. Cancer Treat Rev.(2017) 55: 36–45. doi: 10. 1016/j. ctrv. 2017. 02. 009

11. Kaufman CK, Mosimann C, Fan ZP, Yang S, Thomas AJ, Ablain J, et al. A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation. Science (2016) 351: 6272. doi: 10. 1126/science. aad2197

12. Ridges S, Heaton WL, Joshi D, Choi H, Eiring A, Batchelor L, et al. Zebrafish screen identifies novel compound with selective toxicity against leukemia. Blood (2012) 119: 5621–31. doi: 10. 1182/blood-2011-12-398818

13. Behrens D, Rolff J, Hoffmann J. Predictive In Vivo Models for Oncology. Handb Exp Pharmacol (2016) 232: 203–21. doi: 10. 1007/164_2015_29

14. Barlas S. The white house launches a cancer moonshot: despite funding questions, the progress appears promising. P & T (2016) 41: 290–5.

15. Warburg O. On respiratory impairment in cancer cells. Science (1956) 124: 269–70.

PubMed Abstract

16. Angermueller C, Pärnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol.(2016) 12: 878. doi: 10. 15252/msb. 20156651

17. Schork NJ. Personalized medicine: time for one-person trials. Nature (2015) 520: 609–11. doi: 10. 1038/520609a