Developing a relational framework for biomarkers

In this sub-section, I shall propose a relational framework capable of casting light on the particularity of the function that biomarkers play in research. My intuition is built on Leonelli’s data relational framework (2015). Investigating the nature of data, Leonelli proposed:

“[…] to view data as any product of research activities, ranging from artifacts such as photographs to symbols such as letters or numbers, which is collected, stored, and disseminated in order to be used as evidence for knowledge claims. […] Hence, any object can be considered as a datum as long as (1) it is treated as potential evidence for one or more claims about phenomena and (2) it is possible to circulate it [the object] among individuals […]. Within this relational framework, it is meaningless to ask what objects count as data in the abstract, because data are defined in terms of their function within specific processes of inquiry, rather than in terms of intrinsic properties. The question ‘what is data?’ can only be answered with reference to concrete research situations, in which investigators make specific decisions about what can be used as evidence for which claims.” (Leonelli, 2015, pp. 817–818)

According to Leonelli’s framework, the definition of a datum is always associated with a particular purpose: any object produced by human interactions with research instruments can be considered a datum, but only if it is examined within specific research circumstances. The same consideration, I suggest, can be applied to biomarkers. Not all measures count as biomarkers in every particular study, as well as not all products of research activities count as data in a specific situation.

This interpretation is in line with the process of biomarkers identification described in section 6.4.1. First, data produced through several data-collection techniques are collected (in general relying on causal knowledge and hypotheses); then, scientists have to apply

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measurement procedures if the data obtained are not numeric, and next they have to understand whether such measures count as biomarkers or not. The only way to establish if biological measures count as biomarkers is by considering the specific research circumstances.

To give an example, in a recent paper Vineis et al. (2013, p. 465) described how biomarkers were selected in a study of breast cancer. Researchers examined 96 cases of breast cancer and the genome-wide methylation profile associated with each patient: considering casual hypotheses and statistical analyses, they collected data about 10 CpG sites (regions of DNA where a cytosine nucleotide and a guanine nucleotide appear consecutively on the same strand of nucleic acid) and measured the methylation fractions, that were already known to be strongly associated with breast cancer onset. Next, they observed the associations between such methylation fractions and hundreds of diverse exposure items, looking for candidates exhibiting statistical associations only with reproductive factors (in particular, ever having breastfed and the age at menopause, two well-known risk factors). Their decision was motivated by the hypothesis according to which such types of exposure can trigger processes that, through the methylation fractions of some sites, lead to breast cancer. Only the methylation fractions of 4 of the 10 CpG sites were finally identified as possible biomarkers linking reproductive factors and the development of breast cancer. It should be considered that, from this study, it does not follow that the methylation fractions of the other 6 CpG sites cannot be used to find biomarkers in different situations. Furthermore, it might be plausible that the biomarkers selected by Vineis et al. are used also as biomarkers of other health phenomena, such as other forms of cancer.

My proposal is that a measure can act as a biomarker of one causal phenomenon or of a variety of causal phenomena; and that anything that counts as a biomarker requires someone able to articulate the relationship between that biomarker and the causal process responsible for the phenomenon under study. To clarify this point, let us consider again Figure 18: my argument is that a measure can be said to be a biomarker of a particular phenomenon only if scientists can identify, among the five possibilities illustrated in Figure 18, which one properly represents the link between the biomarker and the phenomenon. In other words, biological measures become biomarkers when researchers develop causal hypotheses that allow them to locate the markers in specific positions in

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relation to the causal processes responsible for the phenomena17_{. As recognised by}

Henderson et al. (1989), indeed:

“The term ‘biomarker’ refers to the use made of a piece of information, rather than to a specific type of information” (Henderson et al., 1989, p. 65)

For instance, in the study conducted by Vineis et al. (2013, p. 465), the methylation fractions of the 4 CpG sites selected were hypothesised to be the right biomarkers directly linking reproductive factors and the development of breast cancer, in a situation similar to Figure 18(a).

The discussion on this feature can help in the development of an exhaustive definition of biomarkers. My first suggestion is that we can call a ‘biomarker’ any measure of a biological entity, quality or event that is used to obtain information about a specific biological process linking the cause A to the causal effect B. Any biomarker, in order to be used as such, must have the following characteristics: 1) can be objectively measurable, and 2) is linked to the causal process under study either because is directly involved in the process, or because is caused by the same causal factor A, or is caused by another unmeasurable biomarker involved in the process or is a background condition of the causal process (as illustrated in Figure 18).

This definition would solve some of the limitations characterising the two official definitions of biomarkers currently used that have been discussed in section 6.4.1. Indeed, the definition clarifies what can count as biomarkers, and makes explicit the relationship the can exist between a causal process and the biomarker associated with it. It remains to understand, however, what kind of information biomarkers can provide. In the next section I shall address this question.