The brain is a common site of metastatic disease in patients with breast cancer, which has few therapeutic options and dismal outcomes. The purpose of our study was to identify common and rare events that underlie breast cancer brain metastasis. We performed deep genomic profiling, which integrated gene copy number, gene expression and DNA methylation datasets on a collection of breast brain metastases. We identified frequent large chromosomal gains in 1q, 5p, 8q, 11q, and 20q and frequent broad-level deletions involving 8p, 17p, 21p and Xq. Frequently amplified and overexpressed genes included ATAD2, BRAF, DERL1, DNMTRB and NEK2A. The ATM, CRYAB and HSPB2 genes were commonly deleted and underexpressed. Knowledge mining revealed enrichment in cell cycle and G2/M transition pathways, which contained AURKA, AURKB and FOXM1. Using the PAM50 breast cancer intrinsic classifier, Luminal B, Her2+/ER negative, and basal-like tumors were identified as the most commonly represented breast cancer subtypes in our brain metastasis cohort. While overall methylation levels were increased in breast cancer brain metastasis, basal-like brain metastases were associated with significantly lower levels of methylation. Integrating DNA methylation data with gene expression revealed defects in cell migration and adhesion due to hypermethylation and downregulation of PENK, EDN3, and ITGAM. Hypomethylation and upregulation of KRT8 likely affects adhesion and permeability. Genomic and epigenomic profiling of breast brain metastasis has provided insight into the somatic events underlying this disease, which have potential in forming the basis of future therapeutic strategies.

BackgroundA number of clinico-pathological criteria and molecular profiles have been used to stratify patients into high- and low-risk groups. Currently, there are still no effective methods to determine which patients harbor micrometastatic disease after standard breast cancer therapy and who will eventually develop local or distant recurrence. The purpose of our study was to identify circulating DNA methylation changes that can be used for prediction of metastatic breast cancer (MBC).ResultsDifferential methylation analysis revealed ~5.0 × 106 differentially methylated CpG loci in MBC compared with healthy individuals (H) or disease-free survivors (DFS). In contrast, there was a strong degree of similarity between H and DFS. Overall, MBC demonstrated global hypomethylation and focal CpG island (CPGI) hypermethylation. Data analysis identified 21 novel hotspots, within CpG islands, that differed most dramatically in MBC compared with H or DFS.ConclusionsThis unbiased analysis of cell-free (cf) DNA identified 21 DNA hypermethylation hotspots associated with MBC and demonstrated the ability to distinguish tumor-specific changes from normal-derived signals at the whole-genome level. This signature is a potential blood-based biomarker that could be advantageous at the time of surgery and/or after the completion of chemotherapy to indicate patients with micrometastatic disease who are at a high risk of recurrence and who could benefit from additional therapy.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-015-0135-8) contains supplementary material, which is available to authorized users.

Racism is now widely recognized as a fundamental cause of health inequali­ties in the United States. As such, health scholars have rightly turned their attention toward examining the role of struc­tural racism in fostering morbidity and mortality. However, to date, much of the empirical structural racism-health dispari­ties literature limits the operationalization of structural racism to a single domain or orients the construct around a White/ Black racial frame. This operationaliza­tion approach is incomprehensive and overlooks the heterogeneity of historical and lived experiences among other racial and ethnic groups.To address this gap, we present a theoreti­cally grounded framework that illumi­nates core mutually reinforcing domains of structural racism that have stratified opportunities for health in the United States. We catalog instances of structural discrimination that were particularly con­straining (or advantageous) to the health of racial and ethnic groups from the late 1400s to present. We then illustrate the utility of this framework by applying it to American Indians or Alaska Natives and discuss the framework’s broader implica­tions for empirical health research. This framework should help future scholars across disciplines as they identify and interrogate important laws, policies, and norms that have differentially constrained opportunities for health among racial and ethnic groups.Ethn Dis. 2021;31(Suppl 1):301-310; doi:10.18865/ed.31.S1.301

Despite repeated calls by scholars to critically engage with the concepts of race and ethnicity in US epidemiologic research, the incorporation of these social constructs in scholarship may be suboptimal. This study characterizes the conceptualization, operationalization, and utilization of race and ethnicity in US research published in leading journals whose publications shape discourse and norms around race, ethnicity, and health within the field of epidemiology. We systematically reviewed randomly selected articles from prominent epidemiology journals across five periods: 1995-99, 2000-04, 2005-09, 2010-14, 2015-18. All original human-subjects research conducted in the US was eligible for review. Information on definitions, measurement, coding, and use in analysis was extracted. We reviewed 1050 articles, including 414 (39%) in analyses. Four studies explicitly defined race and/or ethnicity. Authors rarely made clear delineations between race and ethnicity, often adopting an ethno-racial construct. In the majority of studies across time periods, authors did not state how race and/or ethnicity was measured. Top coding schemes included “Black, White” (race), “Hispanic, Non-Hispanic” (ethnicity), and “Black, White, Hispanic” (ethno-racial). Most often, race and ethnicity were deemed “not of interest” in analyses (e.g., control variable). Broadly, disciplinary practices have remained largely the same between 1995-2018 and are in need of improvement.

Background The dearth of relevant tumor models reflecting the heterogeneity of human central nervous system metastasis (CM) has hindered development of novel therapies. Methods We established 39 CM patient-derived xenograft (PDX) models representing the histological spectrum, and performed phenotypic and multi-omic characterization of PDXs and their original patient tumors. PDX clonal evolution was also reconstructed using allele-specific copy number and somatic variants. Results PDXs retained their metastatic potential, with flank-implanted PDXs forming spontaneous metastases in multiple organs, including brain, and CM subsequent to intracardiac injection. PDXs also retained the histological and molecular profiles of the original patient tumors, including retention of genomic aberrations and signaling pathways. Novel modes of clonal evolution involving rapid expansion by a minor clone were identified in 2 PDXs, including CM13, which was highly aggressive in vivo forming multiple spontaneous metastases, including to brain. These PDXs had little molecular resemblance to the patient donor tumor, including reversion to a copy number neutral genome, no shared nonsynonymous mutations, and no correlation by gene expression. Conclusions We generated a diverse and novel repertoire of PDXs that provides a new set of tools to enhance our knowledge of CM biology and improve preclinical testing. Furthermore, our study suggests that minor clone succession may confer tumor aggressiveness and potentiate brain metastasis.