Pathology - Theses
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ItemUnderstanding mammographic density and breast cancer risk: from histology to genomics( 2012)This thesis describes a novel multi-disciplinary strategy that advances our understanding of mammographic density (MD) biology and genetics and its relationship to breast cancer risk. Studies have indicated that women with high MD are at 4 to 6 fold increased risk of getting breast cancer. MD is also highly heritable. Hence, a greater understanding of MD biology and genetics would improve our knowledge about breast biology and potentially identify new cancer predisposition genes. Nonetheless, MD biology remains controversial and much is unknown about the genetic basis of this trait. This thesis revisits MD histopathology using a novel methodology of sampling high and low MD tissue within the breast of an individual. This method samples tissue guided by its true MD, as determined by real-time mammography imaging - in contrast to previous studies that examined MD histopathology relying on “random” sampling of breast tissue. Studies have suggested that dense regions of the breast are potentially associated with breast cancer risk on the basis that percent MD (PMD) adjusted for body mass index (BMI) and age is a better measure of breast cancer risk. Hence, this method also allows assessment of tissue most pertinent to MD associated breast cancer risk. Furthermore, the use of sampling within an individual allows control of all potential confounders that helps improve the power of analysis. The histopathological analysis revealed that high MD regions were significantly associated with greater composition of dense connective tissue stroma and lesser composition of adipose tissue while no difference in glandular areas between high and low MD tissue was observed. However, it was noted that high MD tissue tended to have smaller and lower complexity glands compared to low MD tissue. This raises the possibility of high MD regions being associated with stem-like cells and their niche compared to low MD regions. Whole-genome expression profiling of accrued high and low MD tissue were interrogated to further our understanding of the genetic and biological bases of MD. Currently, only two other studies have investigated gene expression in MD tissue and these studies have generally failed to account for all potential unwanted variation in the data that could impact on the validity of the analysis outcomes. The present work differs from previous studies in that careful quality assessments and analyses were performed to improve sensitivity and power of the study. The use of precisely sampled tissue also allowed a better representation of the MD expression profile. Both single-gene and gene-set based analyses of adjusted MD expression dataset concurred with the histopathological correlates of high versus low MD tissue. Interestingly, these analyses also showed that high MD regions correlated with a cancer-signature and a CD24 (i.e. luminal epithelial)-signature; while the observed anti-correlation with the CD44-signature was postulated to be of stromal origin. The association of high versus low MD tissue with a cancer-signature agrees with the general direction of MD associated breast cancer risk. Furthermore, immunohistochemical examination of the tissue uncovered a trend of potential “stemness” in high versus low MD tissue that suggests the CD24-signature being of progenitor origin. This supports the hypothesis of high MD tissue being associated with “stemness” (and their niche), as proposed from the initial histopathological examination of MD. To further understand MD biology and its genetic basis, both univariate and pathway-based analyses of genome-wide association (GWA) study data were performed. One major issue with such pathway-based analysis is the assignment of SNPs to their respective gene(s). Increasingly, studies suggest that conventional assignment of SNPs to their nearest gene may be inaccurate. This was the impetus to develop a novel framework using expression profiling analysis of high and low MD tissue to guide mapping of SNPs to their genes. The methodology helped improve the biological relevance and reproducibility of the significant pathways, and also identified the mitogen-activated protein kinase (MAPK) signalling pathway as the most significantly associated with the MD trait. This outcome corresponds to the results obtained from a recent study of breast cancer GWA studies. Taken together, this multi-disciplinary approach has given potential insight into the biological and genetic basis of MD, allowing inference to be made about the increased risk associated with high MD.
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ItemGenetic determinants of mammographic density as a risk factor for breast cancer( 2010)Background: Mammographic density (MD) for age and BMI is a strong risk factor (up to a 6 fold increase across extreme quantiles) for breast cancer. More than 60% of MD variation is estimated to be due to heritable (genetic) factors. Taking two different approaches, this work aimed to determine some of these genetic factors. The recently identified common genetic variants associated with small gradients in breast cancer risk and candidate common genetic variants identified using a genome wide association study (GWAS) of MD extremes were tested for an association with MD. Methods: Germline DNA extracted from peripheral blood samples from 497 monozygotic (MZ) and 330 dizygotic (DZ) twin pairs, and 634 of their sisters from 903 families were genotyped for 22 independent variants (12 from associations with breast cancer and 10 from GWAS of MD extremes). Mammographic dense area, percent dense area and non-dense area were measured by three observers using a computer thresholding technique. Associations with MD measures adjusted for age, BMI and other determinants were estimated: (a) cross-sectionally using a multivariate normal model for pedigree analysis (P-values reported by Px), and (b) between-sibships and (c) within-sibships using orthogonal transformations of outcomes and exposures. A combined test of association (P-values reported by Pc) was derived using the independent estimates from (b) and (c). The distributions of P-values across variants were tested for a deviation from the uniform distribution (P-values reported by Pu). Results: For the breast cancer associated common genetic variants tested, for dense area and percent dense area, the distributions of Pc-values deviated from the uniform distribution (both Pu<0.007), providing strong evidence that at least one genetic variant is associated with these MD measures. Consistent with their breast cancer associations, rs3817198 (LSP1) and rs13281615 (8q) were associated with dense area and percent dense area (all Px and Pc<0.05), and rs889312 (MAP3K1), rs2107425 (H19) and rs17468277 (CASP8) were marginally associated with dense area (some Px or Pc <0.05). For the candidate genetic variants from the GWAS of MD extremes the distributions of Pc-values deviated from the uniform distribution for dense area and percent dense area (Pu=0.07 and 0.009). One variant, rs10827227 (NRP1) showed strong evidence for an association with both dense area and percent dense area (Pc<0.009). For both approach, all associations were independent of menopausal status. Conclusion: At least two common breast cancer susceptibility variants and one common variant identified through a GWAS for MD extremes were associated with MD measures that predict breast cancer. Together these variants explain about 1% of the variation in MD.