In this study, bidirectional MR and genetic colocalization analyses were performed to identify whether circulating proteins causally impact MM risk. There was evidence using two protein GWAS that higher levels of four circulating proteins may increase MM risk and higher levels of three circulating proteins may decrease MM risk, however none of these results were supported by genetic colocalization, possibly indicative of low power or that estimates may not be robust to horizontal pleiotropy. A single protein, KIAA1161, measured only by SomaLogic, with evidence of an increasing effect on MM risk was supported by evidence from genetic colocalization.
Two previous MR studies have explored the effect of circulating proteins on MM risk. The first focused on inflammatory proteins alone [26], whereas the second used GWAS data from protein levels measured by the SomaScan in a smaller sample (3,301 participants) from the INTERVAL study [57]. Four of the 13 proteins with evidence for a causal relationship with MM risk by Wang et al. were also instrumented in our analysis. Our MR evidence (also using SomaScan) only supported a causal relationship for one of these proteins and MM risk (follistatin-related protein 1, FSTL1). We did not find evidence for a causal effect for the other three proteins, this may be due to the use of trans SNPs by Wang et al. which likely included pleiotropic pathways [57].
All seven proteins with consistent evidence across our two protein datasets have limited evidence in the literature of having previously been implicated in the pathogenesis or progression to MM [58]. The strongest evidence for an effect was with dermatopontin on MM risk, where higher levels were associated with an increase in MM risk. DPT is an extracellular matrix protein and has been shown to promote adherence of whole bone marrow to ECM proteins in mice [59]. As this protein may have a role in the bone marrow microenvironment, it is possible that dysregulation of this protein could contribute to the MM pathology. The involvement of DPT in MM pathology needs to be further characterised, such as through mouse models of MM and by exploring whether DPT is dysregulated in the bone marrow in patients with MM. In the current study, MR evidence suggested that higher levels of KDR (VEGFR2) increased risk of MM, and there was no evidence for an effect in the reverse direction (MM risk on levels of VEGFR2). VEGFR2 is involved in endothelial migration and proliferation and is implicated in liver, renal and thyroid cancers, where it is now exploited as a drug target [60]. The role of VEGFR2 in the progression from healthy, through the precursors of MM (MGUS and smouldering myeloma), and to MM, should be further characterised, for instance, by generating proteomic data on patient samples. MR evidence suggested that higher levels of GCLM may result in a decrease in MM risk. GCLM is a subunit of an enzyme involved in the cellular glutathione (GSH) biosynthetic pathway, which is critical to cell survival. Treating MM cells with a proteasome inhibitor, bortezomib (an approved MM treatment), has been shown to lead to higher levels of GCLM. This is directionally consistent with the MR results, where higher levels of GCLM had a lowering effect on MM risk [61]. In addition, there was one protein with evidence of genetic colocalization: uncharacterized family 31 glucosidase KIAA1161, it is unclear how this protein might be involved in MM risk; MR analysis should be replicated using a large independent protein GWAS using SomaScan to investigate this further.
Our results point towards putative causal relationships between circulating proteins and MM risk. However, there are limitations to these analyses that need to be considered and results should be interpreted with caution. Firstly, we did not adjust for multiple testing in each individual MR analyses. As we attempted to perform a discovery and replication approach, we believe that adjusting for multiple testing would be too conservative, especially given that the MM GWAS used are not highly powered. Suitable genetic instruments were also not available for all proteins, therefore some potentially important protein-MM or MM-protein effects will inevitably be missed. Additionally, perturbations in one protein do not occur in isolation. Effects of a single protein may be because of its role in one or more pathways, and therefore it is likely that there is a much more complex interaction between circulating proteins and risk of MM, rather than one or a few proteins being solely responsible for the change in risk. Currently, exploring the contribution of proteins together (as opposed to performing univariable analyses) to MM risk remains a challenge. These analyses were performed in participants only of European ancestry living in the UK, Iceland and Finland, therefore findings may not be generalisable to participants of other ancestries or in other contexts. More highly powered GWAS are required in non-European ancestries in order to evaluate the role of circulating proteins in MM risk more broadly. Another possible limitation is that there may be some misclassification, where participants who were deemed as controls could include those with undetected MGUS or smouldering myeloma, and this may lead to estimates being biased (towards or away from the null).
We identified seven proteins which have consistent MR evidence across two proteomic datasets for a role in MM risk. Some of these proteins have previously been implicated in the other cancer types (VEGFR2 and GCLM), however relatively little is known about these seven proteins in relation to MM risk. Generating proteomic data from patients with myeloma (or its precursor conditions) and characterising these proteins further represent important steps in further understanding MM development.