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


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.


Cancer subtypes Molecular characteristics Bias Disease classification Aetiological research 



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


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.


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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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