Frank, Jacob A.

Abstract Rationale Idiopathic Pulmonary Fibrosis (IPF) is characterized by chronic progressive pulmonary fibrosis and high mortality. Genetic markers, summarized into a polygenic risk score (PRS), associate with IPF in well-phenotyped research cohorts. Objectives To evaluate the performance of the PRS using real-world data from routinely captured electronic healthcare records. Methods We conducted an observational study evaluating the association of a PRS for IPF with electronic healthcare record diagnosis of IPF as well as lung transplant-free survival in four independent cohorts; the Mass General Brigham Biobank (MGBB), Mayo Clinic Biobank (MCBB), Mayo Clinic Tapestry Cohort (Tapestry), and U.K. Biobank (UKBB). We used multivariable logistic regression and multivariable Cox proportional hazards models adjusting for age, gender, and principal components of ancestry. The cohorts then underwent fixed and random effects meta-analysis. Measurements and Main Results Of 37,709; 44,195; 43,202; and 447,422 participants from MGBB, MCBB, Tapestry and UKBB respectively, 1,015 (2.7%) 2,879 (6.5%), 1,310 (3.0%), and 2,742 (0.6%) participants had an IPF diagnosis. Meta-analysis demonstrated a high-risk PRS associated with IPF diagnosis, OR 2.88 (95%CI 2.41-3.44) compared to all other individuals. A high-risk PRS also associated with the composite endpoint of mortality or lung transplant among those with an IPF diagnosis, HR 1.23(95%CI 1.11-1.35) compared to all other individuals. Conclusions A PRS can identify those at risk for an IPF diagnosis and mortality in biobank-scale data, which may have implications for clinical decisions. Further work is necessary to evaluate the utility of adding genetics in clinical settings.

Abstract Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a fast-rising global public health problem. About 10% of patients with MASLD will develop hepatocellular carcinoma (HCC), but molecular markers for predicting future risk of HCC development in patients with MASLD are lacking. We investigated whether protein biomarkers in pre-malignant plasma samples can predict susceptibility to HCC in the setting of MASLD. Methods: A nested case-control study was performed among patients with MASLD who later developed HCC (cases) and MASLD patients who never progressed to HCC (controls), matched on age at MASLD diagnosis (± 1-year), sex, the year of blood sample collection, and length of follow-up (± 1-year). MASLD at baseline was determined by the presence of hepatic steatosis (assessed by imaging or biopsy) plus at least one cardiometabolic condition (obesity, diabetes mellitus, hypertension, or dyslipidemia). Archival baseline plasma samples stored at -80oC were used to perform the SomaScan 7k v4.0 assay. We also performed genotyping of the well-established HCC susceptibility variant, PNPLA3-rs738409 (I148M), among the cases and controls. The study sample was split into discovery (70%) and validation (30%) sets, and we selected promising protein biomarkers in the discovery set using differential expression analysis with volcano plots and Bonferroni-corrected p-values. A final set of significant proteins was used for prediction modeling in the validation sample, calculating area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. Results: The study sample included 54 MASLD to HCC cases and 54 MASLD controls, split into discovery and validation sets. We identified five significantly differentially expressed proteins in the discovery set, four overexpressed (TSP2, GPR56, MIC-1, TIMP-1) and one under-expressed (IGFALS) in the cases versus controls. Fitting the five proteins in the validation sample yielded an AUC=0.88, sensitivity=0.87, specificity=0.75, and accuracy=0.81. Adding PNPLA3-rs738409 to the model improved the model’s specificity and accuracy (AUC=0.88, sensitivity=0.87; specificity=0.81, accuracy=0.84). Conclusions: Our findings show that a five-protein biomarker panel can robustly predict future HCC development in patients with MASLD. Adding a well-established HCC genetic risk variant, PNPLA3-rs738409, slightly improved the model’s performance. These initial findings provide evidence of HCC prediction with baseline plasma biomarkers and genetic risk variants, toward improving risk stratification and enhancing early detection of HCC in the setting of metabolic perturbations. Citation Format: Ampem Darko Jnr Siaw, Sebastian M. Armasu, Jacob A. Frank, Irene K. Yan, Kirk J. Wangensteen, Tushar Patel, Samuel O. Antwi. Protein biomarkers of HCC development in patients with metabolic liver disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2350.

Abstract Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects about 32% of adults worldwide and is projected to increase in many parts of the world. Liver cancer is a recognized complication of end-stage MASLD. An estimated 20% of patients with MASLD will progress to cirrhosis, a premalignant state in liver cancer evolution. Because MASLD is usually asymptomatic, its progression to cirrhosis can be difficult to detect early for timely intervention. There are currently no known biological markers for predicting cirrhosis risk in patients with MASLD, making it challenging to identify patients at risk of disease progression. The aim of this study was to identify plasma protein biomarkers that can predict future cirrhosis development in patients with MASLD. Methods: We performed a nested case-control study among patients with MASLD at baseline who later progressed to cirrhosis and control patients with MASLD at baseline who never progressed to cirrhosis, matched on age at MASLD diagnosis (± 1-year), sex, race, year of blood sample collection, and length of follow-up (± 1-year). An aptamer-based array (SOMAscan™) platform was used to measure over 7000 proteins in baseline plasma samples obtained from the participants. The study sample was split 70/30 for training and validation analyses. We identified plasma protein biomarkers significantly differentially expressed in the training sample, adjusting for multiple testing with Bonferroni-corrected p-values. The significant set of proteins from the training set was used for prediction modeling in the validation sample, computing prediction metrics (area under the curve [AUC], sensitivity, specificity, and accuracy). Results: A total of 64 MASLD to cirrhosis cases matched to 64 MASLD controls were included in the analysis. Six protein biomarkers (PTPRU, ATL2, MFAP4, C7, CSTN2, and COLEC11) were overexpressed in cases, compared to controls, in the training data. Prediction modeling of the six-protein biomarker panel in the validation sample yielded an AUC = 0.85, sensitivity = 0.72, specificity = 0.78, and accuracy = 0.75. Conclusions: We have identified a panel of six plasma protein biomarkers that predict risk of cirrhosis development in patients with MASLD. These proteins could serve as candidates for future mechanistic studies on MASLD progression to advanced liver disease. If validated in larger studies and with assays suitable for clinical laboratory testing (e.g., ELISA), these protein biomarkers could be useful clinically for cirrhosis risk prediction in MASLD patients, and hence those at risk of liver cancer, enabling timely surveillance or interventions to prevent disease progression. Citation Format: Pasang Sherpa, Sebastian M. Armasu, Jacob A. Frank, Irene K. Yan, Kirk J. Wangensteen, Tushar Patel, Samuel O. Antwi. Predicting cirrhosis risk in patients with metabolic dysfunction-associated steatotic liver disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2335.