Back

Soluble Immune Checkpoint Protein and Lipid Network Associations with All-Cause Mortality Risk: Trans-Omics for Precision Medicine (TOPMed) Program

Rodriguez, A.; Yang, C.; Gan, W.; Karlinsey, K.; Zhou, B.; Rich, S. S. S.; Taylor, K. D.; Guo, X.; Rotter, J. I. I.; Johnson, W. C.; Cornell, E.; Tracy, R. P.; Durda, P.; Gerszten, R. E.; Clish, C. B.; Blackwell, T. W.; Papanicolaou, G. J.; Lin, H.; Raffield, L. M.; Vargas, J. D.; Vasan, R. S.; Manichaikul, A. W.

2025-01-09 cardiovascular medicine
10.1101/2025.01.08.25320225 medRxiv
Show abstract

Adverse cardiovascular events are emerging with the use of immune checkpoint therapies in oncology. Using datasets in the Trans-Omics for Precision Medicine program (Multi-Ethnic Study of Atherosclerosis, Jackson Heart Study [JHS], and Framingham Heart Study), we examined the association of immune checkpoint plasma proteins with each other, their associated protein network with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C), and the association of HDL-C- and LDL-C-associated protein networks with all-cause mortality risk. Plasma levels of LAG3 and HAVCR2 showed statistically significant associations with mortality risk. Colocalization analysis using genome wide-association studies of HDL-C or LDL-C and protein quantitative trait loci from JHS and the Atherosclerosis Risk in Communities identified TFF3 rs60467699 and CD36 rs3211938 variants as significantly colocalized with HDL-C; in contrast, none colocalized with LDL-C. The measurement of plasma LAG3, HAVCR2, and associated proteins plus targeted genotyping may identify patients at increased mortality risk.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.