Project description:Translational Relevance Historically, African Americans have been underrepresented in clinical cancer research. Diversity helps to ensure equal access to new cancer therapies and better treatment for everyone. Cancer research is increasingly focused on classifying patients according to molecular profiles for particular groups. We provide a detailed molecular analysis from paired NSCLC tissues that identified differential coding and noncoding RNA expression in NSCLC from African Americans (AA) and European Americans (EA). Similar to other tumor types, we determined that race-enriched gene and microRNA expression signatures suggest a more aggressive disease in African Americans. Based on predicted drug resistance to adjuvant chemotherapies, AA may not equally benefit from the same range of clinical drugs as EA. Our findings provide a rationale for integrating coding and noncoding transcriptome profiles, along with clinical, demographic, and genomic data, when determining treatment options. Abstract Purpose: To determine if racial differences in gene and microRNA expression translates to differences in lung tumor biology with clinical relevance in African Americans (AA) and European Americans (EA). Experimental Design: The NCI-Maryland Case Control Study includes seven Baltimore City hospitals and is overrepresented with AA patients (~40%). Patients that underwent curative NSCLC surgery between 1998 and 2014 were enrolled. Comparative molecular profiling used mRNA (n = 22 AAs and n = 19 EAs) and microRNA (n = 42 AAs and n = 55 EAs) expression arrays to track differences in paired fresh frozen normal tissues and lung tumor specimens from AA and EA. Pathway enrichment, predicted drug response, tumor microenvironment infiltration, cancer immunotherapy antigen profiling, and microRNA target enrichment were assessed. Results: AA-enriched differential gene expression was characterized by stem-cell and invasion pathways. Differential gene expression in lung tumors from EA were primarily characterized by cell proliferation pathways. Population-specific gene expression was partly driven by population-specific miRNA expression profiles. Drug susceptibility predictions revealed a strong inverse correlation between AA resistance and EA sensitivity to the same panel of drugs. Statistically significant differences in M1 and M2 macrophage infiltration was observed in AA (P <0.05), however, PD-L1, PD-L2 expression was similar between both. Conclusions: Comparative transcriptomic profiling revealed clear differences in lung tumor biology between AA and EA. Increased participation by AA in lung cancer clinical trials are needed to integrate, and leverage, transcriptomic differences with other clinical information to maximize therapeutic benefit in both AA and EA. GSEA, Connectivity Map, CIBERSORT, cancer immunotherapy antigen profiling, and hypergeometric testing for overlapping miRNA targets were performed.

Project description:Translational Relevance Historically, African Americans have been underrepresented in clinical cancer research. Diversity helps to ensure equal access to new cancer therapies and better treatment for everyone. Cancer research is increasingly focused on classifying patients according to molecular profiles for particular groups. We provide a detailed molecular analysis from paired NSCLC tissues that identified differential coding and noncoding RNA expression in NSCLC from African Americans (AA) and European Americans (EA). Similar to other tumor types, we determined that race-enriched gene and microRNA expression signatures suggest a more aggressive disease in African Americans. Based on predicted drug resistance to adjuvant chemotherapies, AA may not equally benefit from the same range of clinical drugs as EA. Our findings provide a rationale for integrating coding and noncoding transcriptome profiles, along with clinical, demographic, and genomic data, when determining treatment options. Abstract Purpose: To determine if racial differences in gene and microRNA expression translates to differences in lung tumor biology with clinical relevance in African Americans (AA) and European Americans (EA). Experimental Design: The NCI-Maryland Case Control Study includes seven Baltimore City hospitals and is overrepresented with AA patients (~40%). Patients that underwent curative NSCLC surgery between 1998 and 2014 were enrolled. Comparative molecular profiling used mRNA (n = 22 AAs and n = 19 EAs) and microRNA (n = 42 AAs and n = 55 EAs) expression arrays to track differences in paired fresh frozen normal tissues and lung tumor specimens from AA and EA. Pathway enrichment, predicted drug response, tumor microenvironment infiltration, cancer immunotherapy antigen profiling, and microRNA target enrichment were assessed. Results: AA-enriched differential gene expression was characterized by stem-cell and invasion pathways. Differential gene expression in lung tumors from EA were primarily characterized by cell proliferation pathways. Population-specific gene expression was partly driven by population-specific miRNA expression profiles. Drug susceptibility predictions revealed a strong inverse correlation between AA resistance and EA sensitivity to the same panel of drugs. Statistically significant differences in M1 and M2 macrophage infiltration was observed in AA (P <0.05), however, PD-L1, PD-L2 expression was similar between both. Conclusions: Comparative transcriptomic profiling revealed clear differences in lung tumor biology between AA and EA. Increased participation by AA in lung cancer clinical trials are needed to integrate, and leverage, transcriptomic differences with other clinical information to maximize therapeutic benefit in both AA and EA.

Project description:ObjectiveWe performed single-variant and gene-based association analyses of plasma amyloid-β (aβ) concentrations using whole exome sequence from 1,414 African and European Americans. Our goal was to identify genes that influence plasma aβ42 concentrations and aβ42:aβ40 ratios in late middle age (mean = 59 years), old age (mean = 77 years), or change over time (mean = 18 years).MethodsPlasma aβ measures were linearly regressed onto age, gender, APOE ε4 carrier status, and time elapsed between visits (fold-changes only) separately by race. Following inverse normal transformation of the residuals, seqMeta was used to conduct race-specific single-variant and gene-based association tests while adjusting for population structure. Linear regression models were fit on autosomal variants with minor allele frequencies (MAF)≥1%. T5 burden and Sequence Kernel Association (SKAT) gene-based tests assessed functional variants with MAF≤5%. Cross-race fixed effects meta-analyses were Bonferroni-corrected for the number of variants or genes tested.ResultsSeven genes were associated with aβ in late middle age or change over time; no associations were identified in old age. Single variants in KLKB1 (rs3733402; p = 4.33x10-10) and F12 (rs1801020; p = 3.89x10-8) were significantly associated with midlife aβ42 levels through cross-race meta-analysis; the KLKB1 variant replicated internally using 1,014 additional participants with exome chip. ITPRIP, PLIN2, and TSPAN18 were associated with the midlife aβ42:aβ40 ratio via the T5 test; TSPAN18 was significant via the cross-race meta-analysis, whereas ITPRIP and PLIN2 were European American-specific. NCOA1 and NT5C3B were associated with the midlife aβ42:aβ40 ratio and the fold-change in aβ42, respectively, via SKAT in African Americans. No associations replicated externally (N = 725).ConclusionWe discovered age-dependent genetic effects, established associations between vascular-related genes (KLKB1, F12, PLIN2) and midlife plasma aβ levels, and identified a plausible Alzheimer's Disease candidate gene (ITPRIP) influencing cell death. Plasma aβ concentrations may have dynamic biological determinants across the lifespan; plasma aβ study designs or analyses must consider age.

Dataset Information

Mild test anxiety influences neurocognitive performance among African Americans and European Americans: identifying interfering and facilitating sources.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets