Plasma Proteomes Can Be Reidentifiable and Potentially Contain Personally Sensitive and Incidental Findings

Abstract

The goal of clinical proteomics is to identify, quantify, and characterize proteins in body fluids or tissue to assist diagnosis, prognosis, and treatment of patients. In this way, it is similar to more mature omics technologies, such as genomics, that are increasingly applied in biomedicine. We argue that, similar to those fields, proteomics also faces ethical issues related to the kinds of information that is inherently obtained through sample measurement, although their acquisition was not the primary purpose. Specifically, we demonstrate the potential to identify individuals both by their characteristic, individual-specific protein levels and by variant peptides reporting on coding single nucleotide polymorphisms. Furthermore, it is in the nature of blood plasma proteomics profiling that it broadly reports on the health status of an individual-beyond the disease under investigation. Finally, we show that private and potentially sensitive information, such as ethnicity and pregnancy status, can increasingly be derived from proteomics data. Although this is potentially valuable not only to the individual, but also for biomedical research, it raises ethical questions similar to the incidental findings obtained through other omics technologies. We here introduce the necessity of-and argue for the desirability for-ethical and human-rights-related issues to be discussed within the proteomics community. Those thoughts are more fully developed in our accompanying manuscript. Appreciation and discussion of ethical aspects of proteomic research will allow for deeper, better-informed, more diverse, and, most importantly, wiser guidelines for clinical proteomics.

Keywords: alleles; biomarker discovery; clinical proteomics; ethics.

Graphical abstract

Fig. 1

Identifying participants in a longitudinal study by correlation of individual-specific proteins.A, correlation of individual-specific proteins of time points 1 and 2 of individual A. B, seven longitudinal samples of two individuals, A and B, are correlated with each other (Pearson correlation coefficient is color-coded, with color bar below). The comparison displayed in (A) is highlighted by a black frame. C, cross-correlated individual-specific proteins of all samples of the weight loss study. The correlation matrix shown in (B) is highlighted by a black frame (Pearson value coded according to the same color bar). D, identification of individuals by correlating individual-specific proteins. Proteomes of the reference time point were compared with all other time points in turn. The percentage of correctly and incorrectly assigned participants is color-coded.

Fig. 2

Allele information in the plasma proteome.A, the grayscale indicates which peptides were detected across the seven time points (TP) for the 42 individuals in the weight loss study of the 53 variants considered. B, heat map for the number of matching present or absent variant peptides between individuals at the first time point (TP 1) and all individuals on the other time points. The red diagonal lines reflect the high number of matches of the same individual in adjacent time points. C, proportion of correctly identified individuals in the 252 comparisons.

Fig. 3

Disease diagnostics and disease risk assessment relevant proteins.A, comparison of the plasma proteomes of one individual, indicating an infection at time point 4 (TP 4). B, correlation of LDL levels and proteomic measurements of APOB. C, hierarchical clustering illustrating individual-specific responses of APOB levels to weight loss and weight maintenance over seven time points (1, 2, 3, 4, 5, 6, 7). D), intensities of the quantified APOE4 allele determining peptide across individuals.

Fig. 4

Information with the potential to discriminate individuals.A, comparing the plasma proteomes of all individuals and samples in the weight loss study (15). Proteins with elevated levels in women and men are highlighted in red and blue, respectively. B, one-dimensional principal component analysis for plasma samples at one time point. C, proteins and their distribution to the separation in (B). D, vitamin D-binding protein determining alleles quantified by MS-based proteomics.