Armasu, Sebastian M.

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 Ovarian endometrioid carcinoma (OEC) is the second most common histotype associated with the most favorable prognosis among ovarian carcinomas. Similar to endometrial carcinoma, OEC exhibit significant molecular diversity. We aimed to develop a practical immunohistochemical (IHC) prognosticator by integrating IHC biomarkers with clinical substage, and surgical outcomes. This multi-institutional study included participants with primary invasive OEC from the Alberta Cancer Registry (AOVT), Mayo Clinic, and Disease of the Ovary and their Evaluation Study (DOVE). Pathology review excluded misclassified high-grade serous and mesonephric carcinomas leaving 422 OEC cases (AOVT N=158; DOVE N=143; Mayo N=121) with available tumor biospecimens who did not receive neoadjuvant treatment. Clinical characteristics included age, stage, grade, residual disease and 5-year survival. Clinical risk group was defined by combining stage and residual disease: Low (FIGO stage IA/IB & no macroscopic disease), Intermediate (stage IC-II& no macroscopic disease), High (stage III/IV or macroscopic disease). Tissue microarrays were stained for TP53, PMS2, MSH6, PGR, and CTNNB1 using IHC and were scored by a single pathologist. Tumors were hierarchically categorized as TP53-abnormal (TP53abn)/PGR-loss, mismatch-repair deficient (MMRd, via PMS2 and MSH6), nuclear-CTNNB1 (nCTNNB1), or no-specific-immunohistochemical-profile (NSIP). Overall survival (OS) at 5 years was compared across clinical risk and biomarker groups using Cox regression, adjusted for age and site to generate hazard ratios (HR), and 5-year survival rates (5-YSR). High clinical risk group was associated with worse 5-year OS compared to low & intermediate risk (p<0.0001). Hierarchical IHC biomarker groups were also associated with 5-year OS, adjusted for age and study site (p<0.0001), with consistently worse survival for combined TP53abn/PGR-loss (n=17, HR=6.07, 95% CI 2.78-13.26), PGR-loss only (n=55, HR=4.17, 95% CI 2.19-7.94), TP53abn only (n=29, HR=2.04, 95% CI 0.85-4.90), and better survival for nuclear-CTNNB1 (n=146, HR=0.22, 95% CI 0.07-0.65) compared to NSIP (n=132). Within the low-risk group, MMRd, nCTNNB1, or NSIP had greater than 97% 5-YSR, while OEC with TP53abn/PGR-loss or PR-loss only had less than 75% 5-YSR. Within the intermediate group, only nCTNNB1 exceeded a 5-YSR of greater than 97%. Within the high-risk group, 5YSR for nCTNNB1 OEC was 90.9%, compared to only 16.7% for TP53abn/PGR-loss. This IHC-based algorithm refines prognosis beyond clinical substage. It identifies low-risk patients with unfavorable prognosis (TP53abn/PGRloss, PGR loss only) who may benefit from adjuvant therapy, and also patients with favorable prognosis within the intermediate group for whom adjuvant therapy could be de-escalated. Further patient selection for chemo vs. hormone therapy in the high-risk group may be improved by biomarkers. Citation Format: Gamaliel Taengwa, Hunter J. Atkinson, Bryan M. McCauley, Sebastian M. Armasu, Chen Wang, Jennifer A. Doherty, Holly R. Harris, Ellen L. Goode, Martin Koebel, Stacey J. Winham. IHC-based prognostic sub-stratification of ovarian endometrioid carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1184.

ABSTRACT Introduction Hepatocellular carcinoma (HCC) development in patients with metabolic dysfunction‐associated steatotic liver disease (MASLD) is a growing health concern, but the underlying mechanisms are not fully understood. Epigenetic aging biomarkers, reflecting cellular and tissue aging, have been linked to various age‐related pathologies, but their association with MASLD‐HCC is unknown. We investigated associations between five epigenetic aging biomarkers and MASLD‐HCC risk. Methods We performed whole blood DNA methylation assay (Infinium 850k array) and calculated principal components‐based (PC) versions of HorvathAge, HannumAge, PhenoAge and GrimAge and the DunedinPACE aging rate. We further calculated relative age accelerations for PCHorvathAge, PCHannumAge, PCPhenoAge and PCGrimAge. The aging biomarkers were modelled as continuous variables and categorised into tertiles based on distributions among controls. Associations between each aging biomarker and MASLD‐HCC were examined using logistic regression, calculating odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for covariates. Results Data on 272 MASLD‐HCC cases and 316 cancer‐free MASLD controls recruited from six sites and matched on chronological age, sex and study site were analysed. Higher relative age accelerations of PCPhenoAge (OR T3 vs. T1 = 2.25, 95% CI: 1.45–3.50; OR continuous = 1.04, 95% CI: 1.02–1.07, p = 0.009), PCGrimAge (OR T3 vs. T1 = 3.97, 95% CI: 2.41–6.64; OR continuous = 1.16, 95% CI: 1.10–1.24, p = 8.76 × 10 −07 ) and DunedinPACE (OR T3 vs. T1 = 3.45, 95% CI: 2.17–5.55; OR continuous = 1.72, 95% CI: 1.43–2.10, p = 2.58 × 10 −08 ) were associated with MASLD‐HCC, but not PCHorvathAge or PCHannumAge. Conclusion Higher relative age accelerations of PCPhenoAge, PCGrimAge and DunedinPACE aging rate are associated with risk of MASLD‐HCC. These aging biomarkers could improve HCC risk assessment and facilitate risk stratification in patients with MASLD.

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, increasingly arising in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Epigenetic dysregulation, particularly DNA methylation, has been implicated in MASLD-HCC development, yet the roles that the principal DNA methylation precursor metabolites, S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), play in this association are unclear. Objective: We investigated associations of circulating SAM, SAH, the SAM/SAH ratio, with MASLD-HCC. Methods: In a multi-center pilot case–control study, we evaluated 69 MASLD-HCC cases and 136 cancer-free MASLD controls. Plasma SAM and SAH levels were quantified by liquid chromatography–tandem mass spectrometry. Metabolite levels were categorized as greater than or less than the median based on distribution in controls. Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for age, sex, body mass index, smoking status, and type 2 diabetes. Results: MASLD-HCC cases had significantly higher plasma SAM levels (mean 121 vs. 96 nmol/L; p = 0.001) and SAM/SAH ratios (2.09 vs. 1.48; p = 6.42 × 10−7) than MASLD controls. In multivariable-adjusted models, elevated SAM levels (OR≥median vs. <median = 2.76; 95% CI: 1.38–5.72) and higher SAM/SAH ratio (OR≥median vs. <median = 2.30; 95% CI: 1.15–4.73) were associated with higher odds of MASLD-HCC. SAH alone was associated with MASLD-HCC. Conclusions: Higher plasma SAM levels and SAM/SAH ratios are independently linked to MASLD-HCC development. These metabolites might serve as noninvasive markers for HCC risk stratification in patients with MASLD and improve early detection efforts for MASLD-HCC.

BACKGROUND: Familial hypercholesterolemia (FH) is an autosomal dominant genetic disorder that increases risk for premature coronary artery disease and has accessible and effective interventions. The Dutch lipid clinic network is currently the most used diagnostic criterion; however, genetic sequencing provides a definitive diagnosis of FH. The goals of this study were to determine whether germline genetic screening using exome sequencing could be used to efficiently identify individuals who were genotype positive for FH. METHODS: Participants were recruited from 3 geographically and racially diverse sites in the United States (Rochester, MN; Phoenix, AZ; and Jacksonville, FL). Participants underwent Exome+ sequencing (dba Helix, San Mateo, CA) and return of results for specific genetic findings in APOB , LDLR , or PCSK9 . A chart review was performed to collect demographics, personal, and family cardiovascular history. RESULTS: At the time of the study, 84 413 participants were enrolled in the Tapestry study. Annotation and interpretation of all variants in genes for FH resulted in the identification of 419 likely pathogenic and pathogenic variants (prevalence, 0.50%), which included 116 APOB , 298 LDLR , and 5 PCSK9 . Sixty-six percent were female, the mean body mass index was 27.3, with 12.3% reporting a history of diabetes. Hypertriglyceridemia (≥150 mg/dL) was present in 39.5% and reduced HDL (<50 mg/dL) was present in 56.7% of patients. 27.5% of patients were not on cholesterol-lowering medications, and only 10% of FH carriers were at goal low-density lipoprotein cholesterol levels. A history of coronary artery disease was reported in 22.4% of the cohort. Nearly 90% of these participants were newly diagnosed carriers of FH. Only 30.8% of confirmed genetic diagnoses of FH satisfied clinical (Dutch lipid clinic network) criteria for a diagnosis. CONCLUSIONS: Our results emphasize the need for wider utilization of germline genetic sequencing for enhanced screening and detection of individuals who have familial hypercholesterolemia. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05212428.

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.

PURPOSE The autosomal dominant cancer predisposition disorders hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS) are genetic conditions for which early identification and intervention have a positive effect on the individual and public health. The goals of this study were to determine whether germline genetic screening using exome sequencing could be used to efficiently identify carriers of HBOC and LS. METHODS Participants were recruited from three geographically and racially diverse sites in the United States (Rochester, MN; Phoenix, AZ; Jacksonville, FL). Participants underwent Exome+ sequencing (Helix Inc, San Mateo, CA) and return of results for specific genetic findings: HBOC ( BRCA1 and BRCA1) and LS ( MLH1, MSH2, MSH6, PMS2, and EPCAM). Chart review was performed to collect demographics and personal and family cancer history. RESULTS To date, 44,306 participants have enrolled in Tapestry. Annotation and interpretation of all variants in genes for HBOC and LS resulted in the identification of 550 carriers (prevalence, 1.24%), which included 387 with HBOC (27.2% BRCA1, 42.8% BRCA2) and 163 with LS (12.3% MSH6, 8.8% PMS2, 4.5% MLH1, 3.8% MSH2, and 0.2% EPCAM). More than half of these participants (52.1%) were newly diagnosed carriers with HBOC and LS. In all, 39.2% of HBOC/LS carriers did not satisfy National Comprehensive Cancer Network (NCCN) criteria for genetic evaluation. NCCN criteria were less commonly met in underrepresented minority populations versus self-reported White race (51.5% v 37.5%, P = .028). CONCLUSION Our results emphasize the need for wider utilization of germline genetic sequencing for enhanced screening and detection of individuals who have LS and HBOC cancer predisposition syndromes.