Skip to main content

Advertisement

SpringerLink
Log in

Complexity and integration. A philosophical analysis of how cancer complexity can be faced in the era of precision medicine

  • Paper in the Philosophy of the Biomedical Sciences
  • Published:
European Journal for Philosophy of Science Aims and scope Submit manuscript

Abstract

Complexity and integration are longstanding widely debated issues in philosophy of science and recent contributions have largely focused on biology and biomedicine. This paper specifically considers some methodological novelties in cancer research, motivated by various features of tumours as complex diseases, and shows how they encourage some rethinking of philosophical discourses on those topics. In particular, we discuss the integrative-cluster approach, and analyse its potential in the epistemology of cancer. We suggest that, far from being the solution to tame cancer complexity, this approach offers a philosophically interesting new manner of considering integration, and show how it can help addressing the apparent contrast between a pluralistic and a unitary account.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. See: https://www.cancer.gov/about-nci/legislative/history/national-cancer-act-1971.

  2. On the rise, growth and success of bioinformatics, especially with respect to the life sciences, see e.g. Perez-Iratxeta et al. (2007); Ouzounis (2012); Mehmood et al. (2014); Ratti (2016).

  3. See e.g. Lemoine (2017).

  4. See e.g. Mitchell (2003, 2009); Bechtel and Richardson (2010); Hooker (2011); Ladyman et al. (2013); Ladyman and Wiesner (forthcoming, 2018).

  5. On integration in biology, along a few of the different dimensions we have recalled, see, e.g., Leonelli (2008, 2016) ch. 6; Brigandt (2010, 2013); O’Malley and Soyer (2012); VV. AA. (2013). On the possible benefits of different ways of conceiving integration, and possible epistemic trade-offs, see also Chang (2012), ch. 5, and Plutynski (2013).

  6. On pathways to the clinic, see also Fagan (2017).

  7. See, https://obamawhitehouse.archives.gov/precision-medicine (Accessed 30 April 2017). On this initiative, see, for example, Ashley (2015); Collins and Varmus (2015); Kohane (2015); Sabatello and Appelbaum (2017). For a first hint on a philosophical analysis, see Tonelli and Shirts (2017).

  8. See the position paper of European Society for Predictive, Preventive and Personalised Medicine (EPMA) by Golubnitschaja et al. (2016).

  9. See, https://ghr.nlm.nih.gov/ Precision Medicine; see also https://www.nih.gov/research-training/allofus-research-program (Accessed 30 April 2017).

  10. See, Nabipour and Assadi (2016); see also Zhang (2015).

  11. See e.g. Xue et al. (2013) and Pu et al. (2016a, b).

  12. See the recent issue of Nature (VV. AA. 2013, issue 501) devoted to it.

  13. Note that there many different ways of thinking cancer that have been proposed along the years. No one is unanimously accepted by researchers and clinicians. For philosophical overviews of the different positions, see Bertolaso (2016) and Plutynski (forthcoming, 2018).

  14. See, for example, Lee (2003); Bracht (2009); Koychev et al. (2011); Negm et al. (2002); Vasan (2006).

  15. See, e.g., Goossens et al. (2015); Mordente et al. (2015); Scatena (2015).

  16. The receptor status is identified by immunohistochemistry, which stains the cells based on the presence (ER+, PR+, HER2+) or the absence (ER-, PR-, HER2-) of the receptor itself.

  17. For some critical remarks over the use of big data, and on limits and drawback of big data science, see, e.g. Boyd and Crawford (2012); Leonelli (2014); Kitchin (2014); Conveney et al. (2016)

  18. A particularly interesting and successful approach proposal on tumour heterogeneity – specifically, in breast cancer - and modes of providing distinctive molecular portraits of each tumour is provided by Perou and Sorlie (see e.g. Perou et al. 2000). As we discuss in the following, what characterizes iCluster with respect to this classification and similar ones is that while these latter are based on molecular features, the former is characterized by both molecular and clinical features.

  19. Of course, this is not the right place to enter technical details on the statistical algorithms that are used. They could be easily retrieved in textbooks on cluster theory, or in the scientific papers adopting statistical models based on it. Our current focus is on the epistemological impact of the adoption of iCluster in dealing with cancer complexity, especially from a classificatory and a prognostic standpoint.

  20. Against the reductionism concerning molecular mechanisms and in favour of a more holist approach based on pathways, see Boniolo and Campaner (2018).

  21. https://www.cruk.cam.ac.uk/research-groups/caldas-group. See Curtis et al. (2012); Ali et al. (2014); Bruna et al. (2016); Pereira et al. (2016); Russnes et al. (2017).

  22. The group obtained about 1000 frozen breast cancer samples from five tumor biobanks in the UK and Canada. It should be noted that “Nearly all oestrogen receptor (ER)-positive and/or lymph node (LN)-negative patients did not receive chemotherapy, whereas ER-negative and LN-positive patients did. Additionally, none of the HER21patients received trastuzumab. As such, the treatments were homogeneous with respect to clinically relevant groupings.” (Curtis et al. 2012, p. 346).

  23. The SNPs are the most common type of genetic variation among people. Each SNP represents a difference in a single DNA nucleotide. CNV is a repetition of sections of the genome the number of repetition varies among people. CNA is a repetition of sections of the genome that has arisen in somatic tissue. Cis module and trasn module are stretches of DNA that affect the expression, respectively, of nearby and distant genes

  24. PAM50 (Prosigna®) is a tumour profiling test that helps determine the benefit of using chemotherapy in addition to hormone therapy for some oestrogen receptor-positive (ER-positive) and HER2-negative breast cancers.

  25. An analogous figure, but contemplating also a column explicitly dedicated to prognosis, is Table II in Dawson et al. (2013).

  26. A tumour is said to have had a pathological Complete Response (i.e. a pCR) if, after surgery, no residual cancer cells remain.

  27. See, respectively, Ross-Adams (2015); Weddell et al. (2015); Guinney et al. (2015); Robertson et al. (2017); Cancer Genome Atlas Network (2015).

  28. Mutational processes molding the genomes of 21 breast cancers. See Nik-Zainal et al. (2012, 2016); Morganella et al. (2016).

  29. This is what is happening inside the Personalised Breast Cancer Project! See, https://crukcambridgecentre.org.uk/news/personalised-breast-cancer-program-launches-cambridge.

  30. On measures and evaluation of the usefulness of clusters for particular tasks, and for a catalogue of clustering problems, see e.g. Von Luxburg et al. (2012).

  31. For instance, in discussing IntCluster3, Dawson et al. (2013) state: “The excellent prognosis of this subtype emphasizes the importance of identifying this cluster within the previously defined luminal A intrinsic subtype, as these individuals represent a distinct group that could potentially be spared treatment with systemic chemotherapy” (p. 622), while InCluster4 provides hints as to specific immunological responses, to be exploited for future therapies.

  32. See e.g. Hennig (2017).

  33. For some theoretical reflections on the relations between clusters and ways of conceiving natural kinds, reality and truth, see Hennig (2015). Hennig discusses context- and aims-dependence of clustering methods, comparisons and choices among them, and related impacts in practice.

  34. On problems for cancer classification see also Song et al. (2015).

References

  • Ali, R. H., et al. (2014). Genome-driven integrated classification of breast cancer validated in over 7,500 samples. Genome Biology, 15, 431.

    Google Scholar 

  • Ashley, E. A. (2015). The precision medicine initiative: A new national effort. JAMA, 313, 2119–2120.

    Google Scholar 

  • Bechtel, W., & Richardson, R. (2010). Discovering complexity. Decomposition and localization as strategies in scientific research. Cambridge: MIT Press.

    Google Scholar 

  • Bertolaso, M. (2016). Philosophy of cancer. A dynamic and relational view. Dordrecht: Springer.

    Google Scholar 

  • Boniolo, G. (2007). On scientific representation. From Kant to a new philosophy of science. Houndmills: Palgrave Macmillan chapter 1.

    Google Scholar 

  • Boniolo, G. (2017). Patchwork narratives for tumour heterogeneity. In H. Leitgeb, I. Niiniluoto, E. Sober, & P. Seppälä (Eds.), Logic, methodology and philosophy of science – Proceedings of the 15th international congress (pp. 311–324). London: College Publications.

    Google Scholar 

  • Boniolo, G., & Campaner, R. (2018). Molecular pathways and the contextual explanation of molecular functions. Biology and Philosophy, 33, 24. https://doi.org/10.1007/s10539-018-9634-2.

    Article  Google Scholar 

  • Boniolo, G., & Nathan, M. J. (Eds.). (2017). Philosophy of molecular medicine. London: Routledge.

    Google Scholar 

  • Boyd, D., & Crawford, K. (2012). Provocations for a cultural, technological, and scholarly phenomenon. Information. Communications Society, 15(5), 662–679.

    Google Scholar 

  • Bracht, K. (2009). Biomarker: Indikatoren für Diagnose und Therapie. Pharmazeutische Zeitung http://www.pharmazeutische-zeitung.de/index.php?id=29346 (Accessed 30 April 2017.

  • Brigandt, I. (2010). Beyond reduction and pluralism: Toward an epistemology of explanatory integration in biology. Erkenntnis, 73, 295–311.

    Google Scholar 

  • Brigandt, I. (2013). Integration in biology: Philosophical perspectives on the dynamics of interdisciplinarity. Studies in History and Philosophy of Biological and Biomedical Sciences, 44, 461–465.

    Google Scholar 

  • Bruna, A., Rueda, O. M., Greenwood, W., Batra, A. S., Callari, M., Batra, R. N., et al. (2016). A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell, 167, 260–274.

    Google Scholar 

  • Burrell, R. A., McGranahan, N., Bartek, J., & Swanton, C. (2013). The causes and consequences of genetic heterogeneity in cancer evolution. Nature, 501, 338–345.

    Google Scholar 

  • Cancer Genome Atlas Network. (2015). Genomic classification of cutaneous melanoma. Cell, 161, 1681–1696. https://doi.org/10.1016/j.cell.2015.05.044.

    Article  Google Scholar 

  • Chang, H. (2012). Is water H 2 O? Evidence, realism and pluralism. Dordrecht: Springer.

  • Collins, F. S., & Varmus, H. (2015). A new initiative on precision medicine. New England Journal of Medicine, 372, 793–795.

    Google Scholar 

  • Conveney, P. V., Dougherty, E., & Highfield, R. (2016). Big data need big theory too. Philosophical Transactions of the Royal Society A, 374, 20160153 https://doi.org/10.1098/rsta.2016.0153.

    Google Scholar 

  • Craver, C. (2007). Explaining the brain. Oxford: OUP.

    Google Scholar 

  • Curtis, C., et al. (2012). The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature, 18(486), 346–352.

    Google Scholar 

  • Dawson, S.-J., Rueda, O. M., Aparicio, S., & Caldas, C. (2013). A new genome-driven integrated classification of breast cancer and its implications. The EMBO Journal, 32, 617–628.

    Google Scholar 

  • Fagan, M. B. (2017). In Boniolo and Nathan (Ed.), Pathways to the clinic: Cancer stem cells and challenges for translational research (pp. 165–191).

    Google Scholar 

  • Geyer, F. C., Weigelt, B., Natrajan, R., Lambros, M. B. K., de Biase, D., Vatcheva, R., et al. (2010). Molecular analysis reveals a genetic basis for the phenotypic diversity of metaplastic breast carcinomas. The Journal of Pathology, 220, 562–573.

    Google Scholar 

  • Ghasemi, M., Nabipour, I., Omrani, A., Alipour, Z., & Assadi, M. (2016). Precision medicine and molecular imaging: New targeted approaches toward cancer therapeutic and diagnosis. American Journal of Nuclear Medicine and Molecular Imaging, 6(6), 310–327.

    Google Scholar 

  • Golubnitschaja, O., et al. (2016). Medicine in the early twenty-first century: Paradigm and anticipation - EPMA position paper 2016. EMPA Journal, 7, 23. https://doi.org/10.1186/s13167-016-0072-4.

    Article  Google Scholar 

  • Goossens, N., Nakagawa, S., Sun, X., & Hoshida, Y. (2015). Cancer biomarker discovery and validation. Translational Cancer Research, 4, 256–269.

    Google Scholar 

  • Guinney, J., Rodrigo, D., Xin, W., de Reyniès, A., Andreas, S., Charlotte, S., et al. (2015). The consensus molecular subtypes of colorectal cancer. Nature Medicine, 21, 1350–1356.

  • Hastie, T., Tibshirani, R., & Jerome, J. F. (2008). The elements of statistical learning. Data mining, inference, and prediction (pp. 502–503). New York: Springer.

    Google Scholar 

  • Hennig, C. (2015). What are true clusters? Pattern Recognition Letters, 64, 53–62.

    Google Scholar 

  • Hennig, C. (2017). Cluster validation by measurement of clustering characteristics relevant to the user, arXiv:1703.09282v1[stat.ME].

  • Hooker, C. (2011). Philosophy of complex systems. Amsterdam: Elsevier.

    Google Scholar 

  • Kaiser, M. (2013). Complexity. In W. Dubitzky, O. Wolkenhauer, K.-H. Cho, & H. Yokota (Eds.), Encyclopedia of systems biology (pp. 456–460). New York: Springer.

    Google Scholar 

  • Kaiser, J. (2015). Obama gives east room rollout to precision medicine initiative. Science. https://doi.org/10.1126/science.aaa6436 (Jan. 30 2015).

  • Kitchin, R. (2014). Big data: New epistemologies and paradigm shifts. Big Data & Society, 1(1), 1–12.

    Google Scholar 

  • Kohane, I. S. (2015). Ten things we have to do to achieve precision medicine. Science, 349, 37–38.

    Google Scholar 

  • Koychev, I., Barkus, E., Ettinger, U., Killcross, S., Roiser, J. P., Wilkinson, L., & Deakin, B. (2011). Evaluation of state and trait biomarkers in healthy volunteers for the development of novel drug treatments in schizophrenia. Journal of Psychopharmacology, 25, 1207–1225.

    Google Scholar 

  • Ladyman, James, and Karoline Wiesner. (Forthcoming, 2018). What is a complex system? Princeton: Princeton University Press.

  • Ladyman, J., Lambert, J., & Wiesner, K. (2013). What is a complex system? European Journal for the Philosophy of Science, 3, 33–67.

    Google Scholar 

  • Lee, J. J. (2003). Statistical methods for biomarker analysis for head and neck carcinogenesis and prevention. In J. F. Ensley, J. Silvio Gutkind, J. R. Jacobs, & S. M. Lippman (Eds.), Head and neck cancer (pp. 287–304). San Diego: Academic Press.

    Google Scholar 

  • Lemoine, M. (2017). Molecular complexity: Why has psychiatry not been revolutionised by genomics (yet)? In Philosophy of molecular medicine, eds Giovanni Boniolo and Marco J. Nathan, ch. 4, London: Taylor and Francis.

  • Leonelli, S. (2008). Bio-ontologies as tools for integration in biology. Biological Theory, 3, 8–11.

    Google Scholar 

  • Leonelli, S. (2014). What difference does quantity make? On the epistemology of big data in biology. Big Data & Society, 1, 1–11.

    Google Scholar 

  • Leonelli, S. (2016). Data-centric biology: A philosophical study. Chicago: University of Chicago Press.

    Google Scholar 

  • Martelotto, L. G., Ng, C. K. Y., Piscuoglio, S., Weigelt, B., & Reis-Filho, J. S. (2014). Breast cancer intra-tumour heterogeneity. Breast Cancer Research, 16, 210. https://doi.org/10.1186/bcr3658.

    Article  Google Scholar 

  • Mehmood, M. A., Sehar, U., & Ahmad, N. (2014). Use of bioinformatics in different spheres of life sciences. Data Mining in Genomics and Proteomics, 5(2), 1000158.

    Google Scholar 

  • Mitchell, S. (2003). Biological complexity and integrative pluralism. Cambridge: Cambridge University Press.

    Google Scholar 

  • Mitchell, S. (2009). Unsimple truths. Science, complexity and policy. Chicago and London: The University of Chicago Press.

    Google Scholar 

  • Mordente, A., Meucci, E., Martorana, G. E., & Silvestrini, A. (2015). Cancer biomarkers discovery and validation: State of the art, problems and future perspectives. Advances in Experimental Medicine and Biology, 867, 9–26.

    Google Scholar 

  • Morganella, S., Alexandrov, L. B., Glodzik, D., Zou, X., Davies, H., Staaf, J., et al. (2016). The topography of mutational processes in breast cancer genomes. Nature Communications, 7, 11383.

  • Nabipour, I., & Assadi, M. (2016). Precision medicine, an approach for development of the future medicine technologies. ISMJ, 19, 167–184.

    Google Scholar 

  • Negm, R. S., Verma, M., & Srivastava, S. (2002). The promise of biomarkers in cancer screening and detection. Trends in Molecular Medicine, 8, 288–293.

    Google Scholar 

  • Nik-Zainal, S., van Loo, P., Wedge, D. C., Alexandrov, L. B., Greenman, C. D., Lau, K. W., et al. (2012). The life history of 21 breast cancers. Cell, 149, 994–1007.

  • Nik-Zainal, S., Davies, H., Staaf, J., Ramakrishna, M., Glodzik, D., Zou, X., et al. (2016). Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature, 534, 47–54.

  • O’Malley, M. A., & Soyer, O. (2012). The roles of integration in molecular systems biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43, 58–68.

    Google Scholar 

  • Ouzounis, C. (2012). Rise and demise of bioinformatics? Promise and Progress. PLoS Computational Biology, 8(4), e1002487.

    Google Scholar 

  • Pereira, B., Chin, S.-F., Rueda, O. M., Vollan, H.-K. M., Provenzano, E., Bardwell, H. et al. (2016). The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes. Nature Communications, 7, 11479. https://doi.org/10.1038/ncomms11479.

  • Perez-Iratxeta, C., Andrade-Navarro, M., & Wren, J. (2007). Evolving research trends in bioinformatics. Briefings in Bioinformatics, 8(2), 88–95.

    Google Scholar 

  • Perou, C. M., Sørlie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., Rees, C. A., et al. (2000). Molecular portraits of human breast tumours. Nature, 406, 747–752.

    Google Scholar 

  • Plutynski, A. (2013). Cancer and the goals of integration. Studies in History and Philosophy of Biological and Biomedical Sciences, 44, 266–276.

    Google Scholar 

  • Plutynski, Anya. (Forthcoming, 2018). Explaining cancer: Finding order in disorder. Oxford: Oxford University Press.

  • Pu, F., Xue, J. Q., Patel, A., & Jenny, J. (2016a). Towards the molecular imaging of prostate cancer biomarkers using protein-based MRI contrast agents. Current Protein & Peptide Science, 17(6), 519–533.

    Google Scholar 

  • Pu, F., Xue, S., & Yang, J. J. (2016b). ProCA1.GRPR: A new imaging agent in cancer detection. Biomarkers in Medicine, 10(5), 449–452.

    Google Scholar 

  • Ratti, E. (2016). The end of ‘small biology’? Some thoughts about biomedicine and big science. Big Data & Society., 3, 205395171667843. https://doi.org/10.1177/2053951716678430.

    Article  Google Scholar 

  • Robertson, A. G., Kim, J., al-Ahmadie, H., Bellmunt, J., Guo, G., Cherniack, A. D., et al. (2017). Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell, 171, 540–556 e25.

  • Ross-Adams, H. (2015). Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study. EBioMedicine, 2, 1133–1144.

    Google Scholar 

  • Russnes, H. G., Lingjærde, O. C., Børresen-Dale, A. L., & Caldas, C. (2017). Breast cancer molecular stratification: From intrinsic subtypes to integrative clusters. The American Journal of Pathology, 187, 2152–2162.

    Google Scholar 

  • Sabatello, M., & Appelbaum, P. S. (2017). The precision medicine nation. Hastings Center Report, 47(4), 19–29.

    Google Scholar 

  • Scatena, R. (2015). Advances in cancer biomarkers. From biochemistry to clinic for a critical revision. Heidelberg: Springer.

    Google Scholar 

  • Shyr, D., & Liu, Q. (2013). Next generation sequencing in cancer research and clinical application. Biol Procedures Online, 15, 4. https://doi.org/10.1186/1480-9222-15-4.

    Article  Google Scholar 

  • Song, Q., Merajver, S. D., & Li, J. Z. (2015). Cancer classification in the genomic era: Five contemporary problems. Human Genomics, 9, 27. https://doi.org/10.1186/s40246-015-0049-8.

    Article  Google Scholar 

  • Strasser, B. J. (2017). The “data-deluge”: Turning private data into public archives. In L. Daston (Ed.), Science in the archives. Pasts, presents, futures (pp. 185–202). Chicago: Chicago University Press.

    Google Scholar 

  • Tonelli, M. R., & Shirts, B. H. (2017). Knowledge for precision medicine. Mechanistic reasoning and methodological pluralism. Jama, 18, 1649–1650.

    Google Scholar 

  • Torres, L., et al. (2006). Intratumour genomic heterogeneity in breast cancer with clonal divergence between primary carcinomas and lymph node metastases. Breast Cancer Research and Treatment, 102, 143–155.

    Google Scholar 

  • Van Smeden, M., Harrell, F., & Dahly, D. (2018). Novel diabetes subtypes. The Lancet, 6, 439–440.

    Google Scholar 

  • Vasan, R. S. (2006). Biomarkers of cardiovascular disease molecular basis and practical considerations. Circulation, 113, 2335–2362.

    Google Scholar 

  • Von Luxburg, U., Williamson, R. C., & Guyon, I. (2012). Clustering: Science or art? JMLR, Workshop and Conference Proceedings, 27, 65–79.

    Google Scholar 

  • VV. AA. (2013). Integration in biology: Philosophical perspectives on the dynamics of interdisciplinarity. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44, 461–562.

    Google Scholar 

  • Weddell, N., et al. (2015). Whole genomes redefine the mutational landscape of pancreatic cancer. Nature, 518(26), 495–501.

    Google Scholar 

  • Weinberg, R. A. (2014). Coming full circle—From endless complexity to simplicity and back again. Cell, 57, 267–271.

    Google Scholar 

  • Wimsatt, W. (2007). Re-engineering philosophy for limited beings. Piecewise approximation to reality. Cambridge: Harvard University Press.

    Google Scholar 

  • Xue, S., Qiao, J., Pu, F., Cameron, M., & Yang, J. (2013). Design of a novel class of protein-based magnetic resonance imaging contrast agents for the molecular imaging of cancer biomarkers. Wiley interdisciplinary reviews Nanomedicine and nanobiotechnology, 5(2), 163–179. https://doi.org/10.1002/wnan.1205.

    Article  Google Scholar 

  • Zhang, X. D. D. (2015). Precision medicine, personalized medicine, omics and big data: Concepts and relationships. Journal of Pharmacogenomics and Pharmacoproteomics, 6(2).

Download references

Acknowledgements

We would like to thanks the four anonymous referees, whose comments have been very useful to improve early versions of the manuscript.

Author information

Author notes

  1. Giovanni Boniolo and Raffaella Campaner contributed equally to this work.

Authors and Affiliations

  1. Dipartimento di Scienze Biomediche e Chirurgico Specialistiche, Università di Ferrara, Via Fossato di Mortara, 64a, 44121, Ferrara, Italy

    Giovanni Boniolo

  2. Department of Philosophy and Communication, University of Bologna, Via Zamboni, 38 40126, Bologna, Italy

    Raffaella Campaner

Authors
  1. Giovanni Boniolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Raffaella Campaner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giovanni Boniolo.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boniolo, G., Campaner, R. Complexity and integration. A philosophical analysis of how cancer complexity can be faced in the era of precision medicine. Euro Jnl Phil Sci 9, 34 (2019). https://doi.org/10.1007/s13194-019-0257-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13194-019-0257-5

Keywords

Search

Navigation