Genetic variation and protein expression of metastasis-related genes as markers of tumor aggressiveness, recurrence, and survival among breast cancer patients
Roberts, Michelle R.
MetadataShow full item record
Lymph node (LN) metastases and tumor characteristics predict breast cancer prognosis but correlate imperfectly with likelihood of metastatic relapse. Discovery of biomarkers that are predictive of metastasis may improve identification of patients requiring aggressive adjuvant therapy to prevent recurrence. In this dissertation, two types of markers were explored. First, inherited genetic variation could potentially contribute to inter-patient variability in successful metastatic dissemination and colonization. To investigate this possibility, relationships between single nucleotide polymorphisms (SNPs) in 12 metastasis-related genes ( BRMS1, CDH1, CD82/KAI1, CTNNB1, KISS1, MTA1, MTA2, MTA3, NME1, SATB1, SIPA1, and SNAI1 ), prognosis, and tumor characteristics that predispose patients to poorer prognosis, such as high grade and aggressive tumor subtypes, were analyzed in two separate study populations: the DataBank and BioRepository (DBBR) at Roswell Park Cancer Institute (N=859) and the Women’s Circle of Health Study (WCHS; N=2,671), a case-control study of European-American (EA) and African-American (AA) women. In the DBBR dataset, seven variants in the BRMS1 (rs11537993, rs3116068, and rs1052566) and SIPA1 (rs75894763, rs746429, rs3741378, and rs2306364) genes were measured using Sequenom ® iPLEX Gold and Taqman ® real-time PCR assays. Logistic and Cox proportional hazards regressions were used to estimate odds ratios (OR) and hazard ratios (HR), respectively, for associations with tumor characteristics and prognosis. When the variants were combined into a risk allele score, the presence of eight or more risk alleles was associated with an increased likelihood of having a node positive tumor (OR=2.14, 95% CI 1.18-3.36, P trend = 0.002), although there were no significant associations with survival. In the WCHS dataset, 154 variants in the 12 aforementioned genes were measured using Illumina GoldenGate assays. Logistic regression, Haploview and PLINK were used to investigate single-SNP and haplotype associations between variants and risk of breast cancer, stratified by estrogen receptor (ER) status and LN status, and likelihood of ER negative and LN positive tumors. Pathway analyses, using the adaptive rank truncated product (ARTP) method, were conducted to assess the combined effect of SNPs with these outcomes. For significant genes and/or pathways (p?0.10), multiallelic risk scores were created using SNPs in the significant gene(s). To explore the contribution of each SNP in the significant gene(s) identified via ARTP, the elastic net form of penalized logistic regression was used. P-values for single-SNP, haplotype, and risk score analyses were corrected using the false discovery rate method. None of the single-SNP or haplotype associations remained significant after p-value correction. However, several genes were significant in the ARTP analysis [AA – CDH1 (risk of node positive disease, p=0.10) and SIPA1 (likelihood of ER negative tumor, p=0.09); EA – MTA2 (risk of ER positive, ER negative, and node negative breast cancer), SNAI1/CD82/NME1/CTNNB1 (risk of node positive breast cancer), SATB1 (risk of ER negative tumors, p=0.03)]. Multiallelic risk scores computed from the SNPs in these significant genes were also strongly associated with their respective outcomes. Second, proteins involved in metastasis-related processes, such as the epithelial-mesenchymal transition (EMT), may serve as useful biomarkers of recurrence for breast cancer patients. To investigate this hypothesis, expression of three EMT-related proteins, E-cadherin, N-cadherin, and SATB1, was measured in primary breast tumors and matched lymph node (LN) metastases in 151 women who received surgery at RPCI. Protein expression was measured in tissue microarrays using immunohistochemistry. Semi-quantitative and quantitative assessments were made, using 4-category intensity/extent scores and Aperio’s Positive Pixel Count algorithm (Aperio, v9). Unconditional and multinomial logistic regression was used to examine associations between primary tumor expression and tumor grade and subtype. Expression in primary tumors and metastases was compared using paired t-tests and McNemar’s test. Time to recurrence and disease-free survival analyses were conducted using Kaplan-Meier and Cox proportional hazards regression techniques. Positive N-cadherin expression was associated with increased likelihood of having a high grade tumor as well as the HER2-enriched and triple negative tumor subtypes. Reduced E-cadherin expression was also associated with increased likelihood of having the HER2-enriched and triple negative tumor subtypes. Higher average SATB1 pixel intensity, indicating weaker expression, was associated with reduced likelihood of the HER2-enriched (OR=0.91, 95% CI 0.84-0.98) and triple negative (OR=0.91, 95% CI 0.84-0.99) subtypes. Mean differences in expression variables between primary tumor and LN metastases were generally significant for E-cadherin and SATB1, but there were no significant differences for N-cadherin. Patients with negative E-cadherin expression in primary tumors had poorer disease-free survival (log-rank test, p=0.04; HR=2.30, 95% CI 1.01-5.23), compared to those with positive expression. Patients with positive N-cadherin expression in LN metastases were at higher risk of recurrence compared to those negative for this marker (HR=2.93, 95% CI 1.01-8.49). SATB1 expression in LN metastases was also associated with increased risk of recurrence (HR=2.69, 95% CI 1.14-6.36) and poorer disease-free survival (HR=2.27, 95% CI 1.10-4.70). In conclusion, our results suggest that genetic variants in and expression of metastasis-related genes may be related to tumor characteristics associated with more aggressive behavior, as well as prognostic outcomes, although larger studies are needed to confirm these findings. Further investigation of metastasis-related genes is necessary to better understand the biology of metastasis as well as to identify additional biomarkers that can accurately stratify patients by risk of recurrence.