👤 Angela Abdollahi

Lp(a) (lipoprotein[a]) is a known cardiovascular risk factor; however, its role in cardiac remodeling and functional changes over time across diverse racial and ethnic groups remains underexplored. MESA is a prospective multi-ethnic cohort study of individuals without a history of cardiovascular disease on enrollment (2000-2002), conducted across 6 sites in the United States. Participants with baseline Lp(a) measurements and cardiac magnetic resonance imaging at both baseline and 10-year follow-up exam were included. Lp(a) was treated as both a log-transformed continuous variable (per SD log) and a categorical variable based on data-driven Lp(a) terciles. Multivariable regression models adjusted for sociodemographic, and cardiovascular risk factors, including coronary artery calcium and interim myocardial infarction, were used to assess associations between Lp(a) and longitudinal changes in left ventricular and atrial structure and function over a decade across different racial/ethnic groups. A total of 2366 participants were included. The average age at baseline was 60±9 with 53% women, 43% White, 24% Black, 21% Hispanic, and 12% Chinese. Each 1-SD increase in log-transformed Lp(a) was associated with an increase in left ventricular end-systolic volume index (β, 0.60 [95% CI, 0.02-1.18]), and left atrial minimum volume index (β, 0.81 [95% CI, 0.09-1.52]), and a decline in left ventricular ejection fraction (β, -0.75 [95% CI, -1.34 to -0.17]), and total left atrial emptying fraction (β, -1.17 [95% CI, -2.09 to -0.24]) in Hispanic subjects over a decade. No significant associations were seen in White, Black, or Chinese participants. The observed findings persisted after adjusting for coronary artery calcium, interim myocardial infarction, and atrioventricular decoupling, and when Lp(a) was treated as a categorical variable with race-specific terciles. Elevated Lp(a) levels were independently associated with maladaptive left ventricular and left atrial remodeling in Hispanic adults over a decade, while no statistically significant relationships were observed in White, Black, and Chinese participants. This suggests a unique susceptibility of Hispanic individuals to Lp(a)-mediated cardiovascular remodeling, independent of ischemic pathways. Show less

KRAS mutations are high prevalence oncogenic drivers for multiple cancers. With the advent of new classes of KRAS inhibitors that are showing meaningful clinical activity, research is now turning to questions of optimal combinations of therapies for specific indications, as many patients with KRAS G12C mutations do not respond and/or develop resistance to single-agent treatment. Here, we investigate combination therapies that may overcome resistance to KRAS G12C inhibitors. We found that pemigatinib, a potent and selective FGFR1-3 inhibitor, had a significantly high Bliss synergy score in combination with KRAS G12C inhibitors, and FGFR1 activity was shown to decrease KRAS G12C-dependency conferring inherent resistance in mesenchymal-like cell lines. Knockdown experiments verified the importance of FGFR1, but not FGFR2-4, for the synergistic effect with KRAS G12C inhibitors. Additionally, human lung cancer xenograft and patient-derived xenograft models with a mesenchymal phenotype and high FGFR1 expression were sensitive to the combination of G12C inhibitors and pemigatinib. In short, we demonstrate that pemigatinib and KRAS G12C inhibitors are promising agents for combination therapy in non-small cell lung cancer with a mesenchymal-like phenotype harboring high FGFR1 expression and KRAS G12C mutations to broaden patient response. Show less

Triple negative breast cancer (TNBC) is the most aggressive subtype and disproportionately affects African American women. The development of breast cancer is highly associated with interactions between tumor cells and the extracellular matrix (ECM), and recent research suggests that cellular components of the ECM vary between racial groups. This pilot study aimed to evaluate gene expression in TNBC samples from patients who identified as African American and Caucasian using traditional statistical methods and emerging Machine Learning (ML) approaches. ML enables the analysis of complex datasets and the extraction of useful information from small datasets. We selected four regions of interest from tumor biopsy samples and used laser microdissection to extract tissue for gene expression characterization via RT-qPCR. Both parametric and non-parametric statistical analyses identified genes differentially expressed between the two ethnic groups. Out of 40 genes analyzed, 4 were differentially expressed in the edge of tumor (ET) region and 8 in the ECM adjacent to the tumor (ECMT) region. In addition to statistical approach, ML was used to generate decision trees (DT) for a broader analysis of gene expression and ethnicity. Our DT models achieved 83.33 % accuracy and identified the most significant genes, including Show less