Quantifying the proteome: exploratory associations with multiple myeloma
platform demonstrated high reliability (median intra-plate intra-class correlation [ICC] = 0.91; median inter-plate ICC = 0.88). The study identified 127 significant protein-HCC associations, and we developed a multi-protein risk score with
strong predictive performance (area under the curve [AUC] > 0.9), findings that were replicated in UK Biobank. The score also effectively stratified individuals with liver conditions into higher- and lower- risk groups. For example, among
individuals diagnosed with cirrhosis, the 8-year HCC risk was 20% in the higher-risk group vs 4.0% in the lower-risk group. Given the success of this project, we now propose to implement Phase 2 of our pilot study—evaluating CGR’s capacity to
measure proteins in-house using the Olink Explore HT platform.
The study is expected to identify novel protein associations with MGUS and MM risk, enhance our understanding of the biological mechanisms underlying MGUS progression, and may improve risk prediction models for progression from
MGUS to MM. The addition of proteomics data will further enrich PLCO’s existing biological datasets for MM cases and controls, providing unparalleled phenotypic characterization of participants.
1) Assess the reliability (within and between plate) of the Olink Explore HT platform for proteins measured at the CGR Laboratory, MD.
2) Investigate the associations between circulating proteins and multiple myeloma (MM) risk in a nested case-control study, through the following comparisons:
I) MM cases vs monoclonal gammopathy of undetermined significance (MGUS) negative controls
II) MM cases vs MGUS positive individuals with stable disease which did not progress to MGUS
III) MGUS-positive individuals with stable disease vs MGUS-negative controls
3) Evaluate whether a multi-protein score can predict MGUS progression to MM and assess its predictive performance.
Eleanor Watts (National Cancer Institute)
Vicky Chang (National Cancer Institute)
Li Cheung (National Cancer Institute)
Belynda Hicks (Cancer Genomics Research Laboratory)
Wen-Yi Huang (National Cancer Institute)
Ola Landgren (Miller School of Medicine)
Mitchell Machiela (National Cancer Institute)
Mark Purdue (National Cancer Institute)
Jianxin Shi (National Cancer Institute)
Jonathan Hofmann (National Cancer Institute)
Steven Moore (National Cancer Institute)