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Causation and melanoma classification

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Abstract

In this article, I begin by giving a brief history of melanoma causation. I then discuss the current manner in which malignant melanoma is classified. In general, these systems of classification do not take account of the manner of tumour causation. Instead, they are based on phenomenological features of the tumour, such as size, spread, and morphology. I go on to suggest that misclassification of melanoma is a major problem in clinical practice. I therefore outline an alternative means of classifying these tumours based on causal factors. By analogy with similar systems that have recently emerged for other cancers, I suggest that this causal classification is likely to be both workable and helpful, even in the absence of a full causal-mechanistic understanding of the aetiology of the tumour.

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Notes

  1. ‘Melanotic sarcoma’ is Rolleston's terminology.

  2. Constitutional risk factors include fair skin, number of moles (melanocytic naevi), tendency to freckle, family history of melanoma or atypical naevi, Xeroderma pigmentosa, and increasing age. The main environmental factor is sun exposure, with no clear pattern of risk from other purported risk factors, including sunlamp use (either UVA 320–400 nm or UVB 290–320 nm), smoking, hair dyes, fluorescent lighting, hormone replacement therapy, or stress. Sunscreen use seems to positively correlate with melanoma risk, but the research is heavily confounded and controversial (see e.g. [13, 14]). One author notes, ‘there have been no studies in humans, to date, that clearly demonstrate that the use of sunscreens alone can reduce the risk of melanoma’ [12].

  3. Cutaneous melanomas have high frequencies of BRAF/N-RAS mutations, while both acral and mucosal melanomas tend to show mutations in the CDK4 (cyclin-dependent kinase 4) or CCND1 (cyclin D1) pathways.

  4. Clark [19] confusingly presents two systems of classification. I have dubbed this first system, based on gross tumour appearance, ‘Clark-II’, while the other, more commonly encountered system is known as the ‘Clark level’, or ‘Clark-I’ as I have dubbed it. See Textbox 1 for details.

  5. See Textbox 1 for details.

  6. Imatinib is a recently developed chemotherapeutic agent which has become widely used in the treatment of cancers, including chronic myelogenous leukaemia (CML). It acts via a specific inhibition of certain types of tyrosine kinase enzymes (TK), which play a vital role in cell signalling. The types of TK inhibited by imatinib include the cytokine receptor c-KIT, which appears to play an important role in melanoma aetiology. I discuss the significance of this for imatinib as a possible therapeutic agent for melanoma in the ‘Causal classification of melanoma’ section.

  7. As far as I can see, this clinical trial was otherwise methodologically sound.

  8. A range of further biomarkers are currently employed in research contexts; see e.g., [38].

  9. There might also be a direct benefit from thinking causally, in that one can then develop specific targeted treatments more easily.

  10. This is generally talked about as operator dependency.

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Acknowledgments

This paper was originally presented at the Philosophy of Medicine Roundtable event at the Erasmus Institute for Philosophy and Economics (EIPE) in Rotterdam on October 19, 2009. I am grateful to all those whose penetrating questions—in particular, of Raffaella Campaner, Jan Vandenbroucke, and three anonymous reviewers—helped to shape this paper.

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  1. Department of Science and Technology Studies, University College London, Gower Street, London, WC1E 6BT, UK

    Brendan Clarke

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Clarke, B. Causation and melanoma classification. Theor Med Bioeth 32, 19–32 (2011). https://doi.org/10.1007/s11017-010-9168-3

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