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Cancer subtypes in aetiological research.

Lorenzo Richiardi1,2, Francesco Barone-Adesi3, Neil Pearce4,5

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Categorizing tumors into subtypes aids prognosis and etiological research. However, biases can arise if subtypes don't perfectly match underlying mechanisms, impacting risk factor identification.

Keywords:
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Area of Science:

  • Oncology
  • Epidemiology
  • Biostatistics

Background:

  • Tumor subtypes are crucial for predicting prognosis and understanding disease mechanisms.
  • Clinical research often uses subtypes for treatment response and outcome prediction.
  • Etiological research aims to identify distinct pathogenic mechanisms and associated risk factors for tumor subtypes.

Purpose of the Study:

  • To present a framework using directed acyclic graphs for potential biases in etiological research of tumor subtypes.
  • To identify scenarios where tumor subtypes incompletely correspond with underlying pathogenic mechanisms.
  • To guide researchers in avoiding interpretative biases when studying tumor subtypes and risk factors.

Main Methods:

  • Utilized directed acyclic graphs (DAGs) to model relationships between tumor characteristics, pathogenic mechanisms, and risk factors.
  • Defined and analyzed two primary bias scenarios: weak effect and lack of causality.
  • Provided examples to illustrate the magnitude of bias in etiological research.

Main Results:

  • Identified 'weak effect' bias when subtype characteristics are influenced by unrelated factors.
  • Identified 'lack of causality' bias when subtype characteristics are linked to non-causal mechanisms.
  • Demonstrated that incomplete correspondence between subtypes and mechanisms can introduce significant bias.

Conclusions:

  • Categorizing tumors into homogeneous subtypes has implications for etiological research and risk factor identification.
  • Characteristics used for tumor subtyping should closely align with actual pathogenic mechanisms to prevent bias.
  • In cases of limited mechanistic knowledge, research should prioritize establishing causal links between characteristics and mechanisms.