Reproductive and Hormonal Risk Factors for Ductal Carcinoma In situ of the Breast

One-fifth of all newly diagnosed breast cancer cases are ductal carcinoma in situ (DCIS), but little is known about DCIS risk factors. Recent studies suggest that some subtypes of DCIS (high grade, or comedo) share histopathologic and epidemiologic characteristics with invasive disease, while others (medium or low grade, or non-comedo) show different patterns. To investigate whether reproductive and hormonal risk factors differ among comedo and non-comedo types of DCIS and invasive breast cancer, we used a population-based case-control study of 1808 invasive and 446 DCIS breast cancer cases and their age and race frequency-matched controls (1564 invasive and 458 DCIS). Three or more full-term pregnancies showed a strong inverse association with comedo-type DCIS (odds ratio (OR) = 0.53, 95% confidence interval (CI) = 0.30, 0.95) and a weaker inverse association for non-comedo DCIS (OR = 0.73, 95% CI = 0.42, 1.27). Several risk factors (age at first full-term pregnancy, breastfeeding, and age at menopause) demonstrated similar associations for comedo-type DCIS and invasive breast cancer, but different associations for non-comedo DCIS. Ten or more years of oral contraceptive showed a positive association with comedo-type DCIS (OR = 1.31, 05% CI 0.70, 2.47) and invasive breast cancer (OR = 2.33, 95% CI 1.06, 5.09), but an inverse association for noncomedo DCIS (OR = 0.51, 95% CI 0.25-1.04). Our results support the theory that comedo-type DCIS may share hormonal and reproductive risk factors with invasive breast cancer, while the etiology of non-comedo DCIS deserves further investigation

Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas.

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations

Association of germline microRNA SNPs in pre-miRNA flanking region and breast cancer risk and survival: the Carolina Breast Cancer Study

Common germline variation in the 5′ region proximal to precursor (pre-) miRNA gene sequences is evaluated for association with breast cancer risk and survival among African Americans and Caucasians

Genetic variation in TLR genes in Ugandan and South African populations and comparison with HapMap data

Genetic epidemiological studies of complex diseases often rely on data from the International HapMap Consortium for identification of single nucleotide polymorphisms (SNPs), particularly those that tag haplotypes. However, little is known about the relevance of the African populations used to collect HapMap data for study populations conducted elsewhere in Africa. Toll-like receptor (TLR) genes play a key role in susceptibility to various infectious diseases, including tuberculosis. We conducted full-exon sequencing in samples obtained from Uganda (n = 48) and South Africa (n = 48), in four genes in the TLR pathway: TLR2, TLR4, TLR6, and TIRAP. We identified one novel TIRAP SNP (with minor allele frequency [MAF] 3.2%) and a novel TLR6 SNP (MAF 8%) in the Ugandan population, and a TLR6 SNP that is unique to the South African population (MAF 14%). These SNPs were also not present in the 1000 Genomes data. Genotype and haplotype frequencies and linkage disequilibrium patterns in Uganda and South Africa were similar to African populations in the HapMap datasets. Multidimensional scaling analysis of polymorphisms in all four genes suggested broad overlap of all of the examined African populations. Based on these data, we propose that there is enough similarity among African populations represented in the HapMap database to justify initial SNP selection for genetic epidemiological studies in Uganda and South Africa. We also discovered three novel polymorphisms that appear to be population-specific and would only be detected by sequencing efforts

A multi-center population-based case–control study of ovarian cancer in African-American women: the African American Cancer Epidemiology Study (AACES)

Abstract: Background: Ovarian cancer (OVCA) is the leading cause of death from gynecological cancer, with poorer survival for African American (AA) women compared to whites. However, little is known about risk factors for OVCA in AA. To study the epidemiology of OVCA in this population, we started a collaborative effort in 10 sites in the US. Here we describe the study and highlight the challenges of conducting a study of a lethal disease in a minority population. Methods: The African American Cancer Epidemiology Study (AACES) is an ongoing, population-based case–control study of OVCA in AA in 10 geographic locations, aiming to recruit 850 women with invasive epithelial OVCA and 850 controls age- and geographically-matched to cases. Rapid case ascertainment and random-digit-dialing systems are in place to ascertain cases and controls, respectively. A telephone survey focuses on risk factors as well as factors of particular relevance for AAs. Food-frequency questionnaires, follow-up surveys, biospecimens and medical records are also obtained. Results: Current accrual of 403 AA OVCA cases and 639 controls exceeds that of any existing study to date. We observed a high proportion (15%) of deceased non-responders among the cases that in part is explained by advanced stage at diagnosis. A logistic regression model did not support that socio-economic status was a factor in advanced stage at diagnosis. Most risk factor associations were in the expected direction and magnitude. High BMI was associated with ovarian cancer risk, with multivariable adjusted ORs and 95% CIs of 1.50 (0.99-2.27) for obese and 1.27 (0.85- 1.91) for morbidly obese women compared to normal/underweight women. Conclusions: AACES targets a rare tumor in AAs and addresses issues most relevant to this population. The importance of the study is accentuated by the high proportion of OVCA cases ascertained as deceased. Our analyses indicated that obesity, highly prevalent in this population (>60% of the cases), was associated with increased OVCA risk. While these findings need to be replicated, they suggest the potential for an effective intervention on the risk in AAs. Upon completion of enrollment, AACES will be the largest epidemiologic study of OVCA in AA women

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution

Glioblastoma (GBM) is a prototypical heterogeneous brain tumor refractory to conventional therapy. A small residual population of cells escapes surgery and chemoradiation, resulting in a typically fatal tumor recurrence ~7 mo after diagnosis. Understanding the molecular architecture of this residual population is critical for the development of successful therapies. We used whole-genome sequencing and whole-exome sequencing of multiple sectors from primary and paired recurrent GBM tumors to reconstruct the genomic profile of residual, therapy resistant tumor initiating cells. We found that genetic alteration of the p53 pathway is a primary molecular event predictive of a high number of subclonal mutations in glioblastoma. The genomic road leading to recurrence is highly idiosyncratic but can be broadly classified into linear recurrences that share extensive genetic similarity with the primary tumor and can be directly traced to one of its specific sectors, and divergent recurrences that share few genetic alterations with the primary tumor and originate from cells that branched off early during tumorigenesis. Our study provides mechanistic insights into how genetic alterations in primary tumors impact the ensuing evolution of tumor cells and the emergence of subclonal heterogeneity

Genetic Ancestry, Self-Reported Race and Ethnicity in African Americans and European Americans in the PCaP Cohort

Family history and African-American race are important risk factors for both prostate cancer (CaP) incidence and aggressiveness. When studying complex diseases such as CaP that have a heritable component, chances of finding true disease susceptibility alleles can be increased by accounting for genetic ancestry within the population investigated. Race, ethnicity and ancestry were studied in a geographically diverse cohort of men with newly diagnosed CaP.Individual ancestry (IA) was estimated in the population-based North Carolina and Louisiana Prostate Cancer Project (PCaP), a cohort of 2,106 incident CaP cases (2063 with complete ethnicity information) comprising roughly equal numbers of research subjects reporting as Black/African American (AA) or European American/Caucasian/Caucasian American/White (EA) from North Carolina or Louisiana. Mean genome wide individual ancestry estimates of percent African, European and Asian were obtained and tested for differences by state and ethnicity (Cajun and/or Creole and Hispanic/Latino) using multivariate analysis of variance models. Principal components (PC) were compared to assess differences in genetic composition by self-reported race and ethnicity between and within states.Mean individual ancestries differed by state for self-reporting AA (p = 0.03) and EA (p = 0.001). This geographic difference attenuated for AAs who answered "no" to all ethnicity membership questions (non-ethnic research subjects; p = 0.78) but not EA research subjects, p = 0.002. Mean ancestry estimates of self-identified AA Louisiana research subjects for each ethnic group; Cajun only, Creole only and both Cajun and Creole differed significantly from self-identified non-ethnic AA Louisiana research subjects. These ethnicity differences were not seen in those who self-identified as EA.Mean IA differed by race between states, elucidating a potential contributing factor to these differences in AA research participants: self-reported ethnicity. Accurately accounting for genetic admixture in this cohort is essential for future analyses of the genetic and environmental contributions to CaP

Genetic variation in estrogen and progesterone pathway genes and breast cancer risk: an exploration of tumor subtype-specific effects

To determine whether associations between estrogen pathway-related single nucleotide polymorphisms (SNPs) and breast cancer risk differ by molecular subtype, we evaluated associations between SNPs in cytochrome P450 family 19 subfamily A polypeptide 1 (CYP19A1), estrogen receptor (ESR1), 3-beta hydroxysteroid dehydrogenase type I (HSD3B1), 17-beta hydroxysteroid dehydrogenase type II (HSD17B2), progesterone receptor (PGR), and sex hormone-binding globulin (SHBG) and breast cancer risk in a case-control study in North Carolina

Common genetic variation in adiponectin, leptin, and leptin receptor and association with breast cancer subtypes

Adipocytokines are produced by visceral fat, and levels may be associated with breast cancer risk. We investigated whether single nucleotide polymorphisms (SNPs) in adipocytokine genes adiponectin (ADIPOQ), leptin (LEP), and the leptin receptor (LEPR) were associated with basal-like or luminal A breast cancer subtypes. 104 candidate and tag SNPs were genotyped in 1776 of 2022 controls and 1972 (200 basal-like, 679 luminal A) of 2311 cases from the Carolina Breast Cancer Study (CBCS), a population-based case–control study of whites and African Americans. Breast cancer molecular subtypes were determined by immunohistochemistry. Genotype odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression. Haplotype ORs and 95% CIs were estimated using Hapstat. Interactions with waist-hip ratio were evaluated using a multiplicative interaction term. Ancestry was estimated from 144 ancestry informative markers (AIMs), and included in models to control for population stratification. Candidate SNPs LEPR K109R (rs1137100) and LEPR Q223R (rs1137101) were positively associated with luminal A breast cancer, whereas ADIPOQ +45 T/G (rs2241766), ADIPOQ +276 G/T (rs1501299), and LEPR K656N (rs8129183) were not associated with either subtype. Few patterns were observed among tag SNPs, with the exception of 3 LEPR SNPs (rs17412175, rs9436746, and rs9436748) that were in moderate LD and inversely associated with basal-like breast cancer. However, no SNP associations were statistically significant after adjustment for multiple comparisons. Haplotypes in LEP and LEPR were associated with both basal-like and luminal A subtypes. There was no evidence of interaction with waist-hip ratio. Data suggest associations between LEPR candidate SNPs and luminal A breast cancer in the CBCS and LEPR intron 2 tag SNPs and basal-like breast cancer. Replication in additional studies where breast cancer subtypes have been defined is necessary to confirm these potential associations