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European Journal of Epidemiology

, Volume 32, Issue 5, pp 353–361 | Cite as

Cancer subtypes in aetiological research

  • Lorenzo RichiardiEmail author
  • Francesco Barone-Adesi
  • Neil Pearce
ESSAY

Abstract

Researchers often attempt to categorize tumors into more homogeneous subtypes to better predict prognosis or understand pathogenic mechanisms. In clinical research, typically the focus is on prognosis: the tumor subtypes are intended to be associated with specific responses to treatment and/or different clinical outcomes. In aetiological research, the focus is on identifying distinct pathogenic mechanisms, which may involve different risk factors. We used directed acyclic graphs to present a framework for considering potential biases arising in aetiological research of tumor subtypes, when there is incomplete correspondence between the identified subtypes and the underlying pathogenic mechanisms. We identified two main scenarios: (1) weak effect, when the tumor subtypes are identified through combinations of characteristics and some of these characteristics are affected by factors that are unrelated with the underlying pathogenic mechanisms; and (2) lack of causality, when the set of characteristics corresponds with a mechanism that is actually not a cause of the tumor of interest. Examples of the magnitude of bias that can be introduced in these situations are provided. Although categorization of tumors into homogenous subtypes may have important implications for aetiological research and identification of risk factors, the characteristics used to classify tumors into subtypes should be as close as possible to the actual pathogenic mechanisms to avoid interpretative biases. Whenever our knowledge of these mechanisms is limited, research into risk factors for tumor subtypes should first aim to causally link the characteristics to the pathogenic mechanisms.

Keywords

Cancer subtypes Molecular characteristics Bias Disease classification Aetiological research 

Notes

Acknowledgements

We would like to thank Dr. Andreas Pettersson for helpful comments on earlier versions of this paper.

Funding

Lorenzo Richiardi was partially supported by a Fulbright Research Scholar fellowship when working on this paper. The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement No. 668954.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–5.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Muraro A, Lemanske RF Jr, Hellings PW, Akdis CA, Bieber T, Casale TB, et al. Precision medicine in patients with allergic diseases: airway diseases and atopic dermatitis-PRACTALL document of the European Academy of Allergy and Clinical Immunology and the American Academy of Allergy, Asthma and Immunology. J Allergy Clin Immunol. 2016;137(5):1347–58.CrossRefPubMedGoogle Scholar
  3. 3.
    Pitt GS. Cardiovascular precision medicine: hope or hype? Eur Heart J. 2015;36(29):1842–3.CrossRefPubMedGoogle Scholar
  4. 4.
    Pearson ER. Personalized medicine in diabetes: the role of ‘omics’ and biomarkers. Diabet Med. 2016;33(6):712–7.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Blows FM, Driver KE, Schmidt MK, Broeks A, van Leeuwen FE, Wesseling J, et al. Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: a collaborative analysis of data for 10,159 cases from 12 studies. PLoS Med. 2010;7(5):e1000279.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Network Cancer Genome Atlas. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.CrossRefGoogle Scholar
  7. 7.
    Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24(9):2206–23.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Yang XR, Chang-Claude J, Goode EL, Couch FJ, Nevanlinna H, Milne RL, et al. Associations of breast cancer risk factors with tumor subtypes: a pooled analysis from the Breast Cancer Association Consortium studies. J Natl Cancer Inst. 2011;103(3):250–63.CrossRefPubMedGoogle Scholar
  9. 9.
    WHO/IARC Classification of tumours, http://publications.iarc.fr/Book-And-Report-Series/Who-Iarc-Classification-Of-Tumours. Accessed 28 Jul 2016.
  10. 10.
    Song Q, Merajver SD, Li JZ. Cancer classification in the genomic era: five contemporary problems. Hum Genom. 2015;9:27.CrossRefGoogle Scholar
  11. 11.
    Blows FM, Driver KE, Schmidt MK, Broeks A, van Leeuwen FE, Wesseling J, et al. Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: a collaborative analysis of data for 10,159 cases from 12 studies. PLoS Med. 2010;7(5):e1000279.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10):997–1003.CrossRefPubMedGoogle Scholar
  13. 13.
    Rapkins RW, Wang F, Nguyen HN, Cloughesy TF, Lai A, Ha W, et al. The MGMT promoter SNP rs16906252 is a risk factor for MGMT methylation in glioblastoma and is predictive of response to temozolomide. Neuro Oncol. 2015;17(12):1589–98.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Morton LM, Slager SL, Cerhan JR, Wang SS, Vajdic CM, Skibola CF, et al. Etiologic heterogeneity among non-Hodgkin lymphoma subtypes: the InterLymph non-Hodgkin lymphoma subtypes project. J Natl Cancer Inst Monogr. 2014;2014(48):130–44.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Zellmer VR, Zhang S. Evolving concepts of tumor heterogeneity. Cell Biosci. 2014;4:69.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Ogino S, Stampfer M. Lifestyle factors and microsatellite instability in colorectal cancer: the evolving field of molecular pathological epidemiology. J Natl Cancer Inst. 2010;102(6):365–7.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Ogino S, Lochhead P, Chan AT, Nishihara R, Cho E, Wolpin BM, et al. Molecular pathological epidemiology of epigenetics: emerging integrative science to analyze environment, host, and disease. Mod Pathol. 2013;26(4):465–84.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Ikram MA. Molecular pathological epidemiology: the role of epidemiology in the omics-era. Eur J Epidemiol. 2015;30(10):1077–8.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Hamada T, Keum N, Nishihara R, Ogino S. Molecular pathological epidemiology: new developing frontiers of big data science to study etiologies and pathogenesis. J Gastroenterol. 2016 Oct 13.Google Scholar
  20. 20.
    Ogino S, Nishihara R, VanderWeele TJ, Wang M, Nishi A, Lochhead P. Review article: the role of molecular pathological epidemiology in the study of neoplastic and non-neoplastic diseases in the era of precision medicine. Epidemiology. 2016;27(4):602–11.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Nishihara R, VanderWeele TJ, Shibuya K, Mittleman MA, Wang M, Field AE, et al. Molecular pathological epidemiology gives clues to paradoxical findings. Eur J Epidemiol. 2015;30(10):1129–35.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Porta M, Vineis P, Bolúmar F. The current deconstruction of paradoxes: one sign of the ongoing methodological “revolution”. Eur J Epidemiol. 2015;30(10):1079–87.CrossRefPubMedGoogle Scholar
  23. 23.
    Martinez FD, Wright AL, Taussig LM, Holberg CJ, Halonen M, Morgan WJ. Asthma and wheezing in the first six years of life. The Group Health Medical Associates. N Engl J Med. 1995;332(3):133–8.CrossRefPubMedGoogle Scholar
  24. 24.
    Wenzel SE. Asthma phenotypes: the evolution from clinical to molecular approaches. Nat Med. 2012;185:716–25.CrossRefGoogle Scholar
  25. 25.
    Siroux V, Basagaña X, Boudier A, Pin I, Garcia-Aymerich J, Vesin A, et al. Identifying adult asthma phenotypes using a clustering approach. Eur Respir J. 2011;38(2):310–7.CrossRefPubMedGoogle Scholar
  26. 26.
    Wang M, Spiegelman D, Kuchiba A, Lochhead P, Kim S, Chan AT, et al. Statistical methods for studying disease subtype heterogeneity. Stat Med. 2016;35(5):782–800.CrossRefPubMedGoogle Scholar
  27. 27.
    Laible M, Schlombs K, Kaiser K, Veltrup E, Herlein S, Lakis S, Stöhr R, Eidt S, Hartmann A, Wirtz RM, Sahin U. Technical validation of an RT-qPCR in vitro diagnostic test system for the determination of breast cancer molecular subtypes by quantification of ERBB2, ESR1, PGR and MKI67 mRNA levels from formalin-fixed paraffin-embedded breast tumor specimens. BMC Cancer. 2016;7(16):398.CrossRefGoogle Scholar
  28. 28.
    Tamimi RM, Colditz GA, Hazra A, Baer HJ, Hankinson SE, Rosner B, et al. Traditional breast cancer risk factors in relation to molecular subtypes of breast cancer. Breast Cancer Res Treat. 2012;131(1):159–67.CrossRefPubMedGoogle Scholar
  29. 29.
    Heng YJ, Lester SC, Tse GM, Factor RE, Allison KH, Collins LC, et al. The molecular basis of breast cancer pathological phenotypes. J Pathol. 2017;241(3):375–91.CrossRefPubMedGoogle Scholar
  30. 30.
    Denkert C, Liedtke C, Tutt A, von Minckwitz G. Molecular alterations in triple-negative breast cancer-the road to new treatment strategies. Lancet. 2016. doi: 10.1016/S0140-6736(16)32454-0.
  31. 31.
    Anderson WF, Rosenberg PS, Prat A, Perou CM, Sherman ME. How many etiological subtypes of breast cancer: two, three, four, or more? J Natl Cancer Inst. 2014;106(8):dju165.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Pesch B, Kendzia B, Gustavsson P, Jöckel KH, Johnen G, Pohlabeln H, et al. Cigarette smoking and lung cancer–relative risk estimates for the major histological types from a pooled analysis of case-control studies. Int J Cancer. 2012;131(5):1210–9.CrossRefPubMedGoogle Scholar
  33. 33.
    Kadara H, Scheet P, Wistuba II, Spira AE. Early events in the molecular pathogenesis of lung cancer. Cancer Prev Res. 2016;9(7):518–27.CrossRefGoogle Scholar
  34. 34.
    Sung H, Yang HH, Zhang H, Yang Q, Hu N, Tang ZZ, et al. Common genetic variants in epigenetic machinery genes and risk of upper gastrointestinal cancers. Int J Epidemiol. 2015;44(4):1341–52.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Cancer Epidemiology Unit, Department of Medical SciencesUniversity of Turin and CPO-PiemonteTurinItaly
  2. 2.Harvard T.H. Chan School of Public HealthBostonUSA
  3. 3.Department of Pharmaceutical SciencesUniversity of Eastern PiedmontNovaraItaly
  4. 4.Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonUK
  5. 5.Centre for Public Health ResearchMassey UniversityWellingtonNew Zealand

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