Factors Associated with Early Versus Late Development of Breast and Ovarian Cancer in BRCA1 and BRCA2 Positive Women

Hereditary breast and ovarian cancer (HBOC) is caused by a mutation in the BRCA1 or BRCA2 genes. Women with a BRCA1/2 mutation are at increased risks for breast and ovarian cancer and often develop cancer at an earlier age than the general population. However, some women with a BRCA1/2 mutation do not develop breast or ovarian cancer under the age of 50 years. There have been no specific studies on BRCA positive women with no cancer prior to age 50, therefore this study sought to investigate factors within these women with no cancer under age 50 with respect to reproductive risk factors, BMI, tumor pathology, screening history, risk-reducing surgeries, and family history. 241 women were diagnosed with cancer prior to age 50, 92 with cancer at age 50 or older, and 20 women were over age 50 with no cancer. Data were stratified based on BRCA1 and BRCA2 mutation status. Within the cohorts we investigated differences between women who developed cancer prior to age 50 and those who developed cancer at age 50 or older. We also investigated the differences between women who developed cancer at age 50 or older and those who were age 50 or older with no cancer. Of the 92 women with a BRCA1/2 mutation who developed cancer at age 50 or older, 46 developed ovarian cancer first, 45 developed breast cancer, and one had breast and ovarian cancer diagnosed synchronously. BRCA2 carriers diagnosed age 50 or older were more likely to have ER/PR negative breast tumors when compared to BRCA2 carriers who were diagnosed before age 50. This is consistent with one other study that has been performed. Ashkenazi Jewish women with a BRCA1 mutation were more likely to be diagnosed age 50 or older than other ethnicities. Hispanic women with a BRCA2 mutation were more likely to be diagnosed prior to age 50 when compared to other ethnicities. No differences in reproductive factors or BMI were observed. Further characterization of BRCA positive women with no cancer prior to age 50 may aid in finding factors important in the development of breast or ovarian cancer.

Hazardous air pollutants and childhood lymphohematopoietic cancer in Southeast Texas, 1995--2004

Southeast Texas, including Houston, has a large presence of industrial facilities and has been documented to have poorer air quality and significantly higher cancer rates than the remainder of Texas. Given citizens’ concerns in this 4th largest city in the U.S., Mayor Bill White recently partnered with the UT School of Public Health to determine methods to evaluate the health risks of hazardous air pollutants (HAPs). Sexton et al. (2007) published a report that strongly encouraged analytic studies linking these pollutants with health outcomes. In response, we set out to complete the following aims: 1. determine the optimal exposure assessment strategy to assess the association between childhood cancer rates and increased ambient levels of benzene and 1,3-butadiene (in an ecologic setting) and 2. evaluate whether census tracts with the highest levels of benzene or 1,3-butadiene have higher incidence of childhood lymphohematopoietic cancer compared with census tracts with the lowest levels of benzene or 1,3-butadiene, using Poisson regression. The first aim was achieved by evaluating the usefulness of four data sources: geographic information systems (GIS) to identify proximity to point sources of industrial air pollution, industrial emission data from the U.S. EPA’s Toxic Release Inventory (TRI), routine monitoring data from the U.S. EPA Air Quality System (AQS) from 1999-2000 and modeled ambient air levels from the U.S. EPA’s 1999 National Air Toxic Assessment Project (NATA) ASPEN model. Further, once these four data sources were evaluated, we narrowed them down to two: the routine monitoring data from the AQS for the years 1998-2000 and the 1999 U.S. EPA NATA ASPEN modeled data. We applied kriging (spatial interpolation) methodology to the monitoring data and compared the kriged values to the ASPEN modeled data. Our results indicated poor agreement between the two methods. Relative to the U.S. EPA ASPEN modeled estimates, relying on kriging to classify census tracts into exposure groups would have caused a great deal of misclassification. To address the second aim, we additionally obtained childhood lymphohematopoietic cancer data for 1995-2004 from the Texas Cancer Registry. The U.S. EPA ASPEN modeled data were used to estimate ambient levels of benzene and 1,3-butadiene in separate Poisson regression analyses. All data were analyzed at the census tract level. We found that census tracts with the highest benzene levels had elevated rates of all leukemia (rate ratio (RR) = 1.37; 95% confidence interval (CI), 1.05-1.78). Among census tracts with the highest 1,3-butadiene levels, we observed RRs of 1.40 (95% CI, 1.07-1.81) for all leukemia. We detected no associations between benzene or 1,3-butadiene levels and childhood lymphoma incidence. This study is the first to examine this association in Harris and surrounding counties in Texas and is among the first to correlate monitored levels of HAPs with childhood lymphohematopoietic cancer incidence, evaluating several analytic methods in an effort to determine the most appropriate approach to test this association. Despite recognized weakness of ecologic analyses, our analysis suggests an association between childhood leukemia and hazardous air pollution.^

DIETARY RELATIONSHIPS TO FATAL BREAST CANCER AMONG SEVENTH-DAY ADVENTISTS

Laboratory experiments in animals, correlational and migrant studies in humans suggest a role for diet in the etiology of breast cancer. Data gathered from individuals via case-control studies are less consistent. Seventh-day Adventist women experience lower mortality from breast cancer than comparable U.S. populations and this decrease is thought to be, at least in part, related to dietary practices (half are vegetarian). In 1960, 25,000 California Seventh-day Adventists completed a questionnaire which included a 21 item food frequency section. Cancer mortality in this population was monitored between 1960 and 1980 and the relationship of high fat food intake and fatal breast cancer was evaluated. Although established risk factors for breast cancer were observed in this population (e.g. age at menarche, age at first pregnancy, age at menopause and obesity) consumption of high fat foods were not observed to exert a strong influence on fatal breast cancer risk. Odds ratios (O.R.) for fatal breast cancer among non-vegetarians was 1.2. Increasing meat consumption bore little relation to risk; O.R. = 1.0, 1.2, 1.1 for consumption categories of none/occasional, 1-3 days/week and 4+ days/week respectively. Nor did the consumption of other high fat foods of animal origin (e.g. butter, cheese, milk, eggs) show any relationship to risk. These results remained unchanged after simultaneously controlling for the effect of other, potentially confounding variables (menstrual characteristics, obesity) via logistic regression analysis. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

EVIDENCE OF HUMAN ENDOGENOUS RETROVIRUS K INVOLVEMENT IN HUMAN CANCER

Many lines of clinical and experimental evidence indicate a viral role in carcinogenesis (1-6). Our access to patient plasma, serum, and tissue samples from invasive breast cancer (N=19), ductal carcinoma in situ (N=13), malignant ovarian cancer (N=12), and benign ovarian tumors (N=9), via IRB-approved and informed consent protocols through M.D. Anderson Cancer Center, as well as normal donor plasmas purchased from Gulf Coast Regional Blood Center (N=6), has allowed us to survey primary patient blood and tissue samples, healthy donor blood from the general population, as well as commercially available human cell lines for the presence of human endogenous retrovirus K (HERV-K) Env viral RNA (vRNA), protein, and viral particles. We hypothesize that HERV-K proteins are tumor-associated antigens and as such can be profiled and targeted in patients for diagnostic and therapeutic purposes. To test this hypothesis, we employed isopycnic ultracentrifugation, a microplate-based reverse transcriptase enzyme activity assay, reverse transcription – polymerase chain reaction (RT-PCR), cDNA sequencing, SDS-PAGE and western blotting, immunofluorescent staining, confocal microscopy, and transmission electron microscopy to evaluate v HERV-K activation in cancer. Data from large numbers of patients tested by reverse transcriptase activity assay were analyzed statistically by t-test to determine the potential use of this assay as a diagnostic tool for cancer. Significant reverse transcriptase enzyme activity was detected in 75% of ovarian cancer patients, 53.8% of ductal carcinoma in situ patient, and 42.1% of invasive breast cancer patient samples. Only 11.1% of benign ovarian patient and 16.7% of normal donor samples tested positive. HERV-K Env vRNA, or Env SU were detected in the majority of cancer types screened, as demonstrated by the results shown herein, and were largely absent in normal controls. These findings support our hypothesis that the presence of HERV-K in patient blood circulation is an indicator of cancer or pre-malignancy in vivo, that the presence of HERV-K Env on tumor cell surfaces is indicative of malignant phenotype, and that HERV-K Env is a tumor-associated antigen useful not only as a diagnostic screening tool to predict patient disease status, but also as an exploitable therapeutic target for various novel antibody-based immunotherapies.

Shifting the focus: Addressing breast cancer disparities at the health care organization level

The purpose of this study was to understand the scope of breast cancer disparities within the Texas Medical Center. The goal was to increase the awareness of breast cancer disparities at the health care organization level, and to foster the development of organizational interventions to reduce breast cancer disparities. The study seeks to answer the following questions: 1. Are hospitals in the Texas Medical Center implementing interventions to reduce breast cancer disparities? 2. What are their interventions for reducing the effects of non clinical factors on breast cancer treatment disparities? 3. What are their measures for monitoring, continuously improving, and evaluating the success of their interventions? ^ This research project was designed as a mixed methods case study. Quantitative breast cancer data for the years 2000-2009 was obtained from the Texas Cancer Registry (TCR). Qualitative data collection and analysis was done by conducting a total of 20 semi-structured interviews of administrators, physicians and nurses at five hospitals (A, B, C, D and E) in the Texas Medical Center (TMC). For quantitative analysis, the study was limited to early stage breast cancer patients: local and regional. The dependent variable was receipt of standard treatment: Surgery (Yes/No), BCS vs Mastectomy, Chemotherapy (Yes/No) and Radiation after BCS (Yes/No). The main independent variable was race: non-Hispanic White (NHW) , non-Hispanic Black (NHB), and Hispanic. Other covariates included age at diagnosis, diagnosis date, percent poverty, grade, stage, and regional nodes. Multivariate logistic regression was used to test the adjusted association between receipt of standard care and race. Qualitative data was analyzed with the Atlas.ti7 software (ATLAS.ti GmbH, Berlin). ^ Though there were significant differences by race for all dependent variables when the data was analyzed as a single group of all hospitals; at the level of the individual hospitals the results were not consistent by race/ethnicity across all dependent variables for hospitals A, B, and E. There were no racial differences in adjusted analysis for receipt of chemotherapy for the individual hospitals of interest in this study. For hospitals C and D, no racial disparities in treatment was observed in adjusted multivariable analysis. All organizations in this study were aware of the body of research which shows that there are disparities in breast cancer outcomes for patient population groups. However, qualitative data analysis found that there were differences in interest among hospitals in addressing breast cancer disparities in their patient population groups. Some organizations were actively implementing directed measures to reduce the breast cancer disparity gap in outcomes for patients, and others were not. Despite the differences in levels of interest, quantitative data analysis showed that organizations in the Texas Medical Center were making progress in reducing the burden of breast cancer disparities in the patient populations being served.^

A Bayesian analysis for spatial processes with application to mapping

In geographical epidemiology, maps of disease rates and disease risk provide a spatial perspective for researching disease etiology. For rare diseases or when the population base is small, the rate and risk estimates may be unstable. Empirical Bayesian (EB) methods have been used to spatially smooth the estimates by permitting an area estimate to "borrow strength" from its neighbors. Such EB methods include the use of a Gamma model, of a James-Stein estimator, and of a conditional autoregressive (CAR) process. A fully Bayesian analysis of the CAR process is proposed. One advantage of this fully Bayesian analysis is that it can be implemented simply by using repeated sampling from the posterior densities. Use of a Markov chain Monte Carlo technique such as Gibbs sampler was not necessary. Direct resampling from the posterior densities provides exact small sample inferences instead of the approximate asymptotic analyses of maximum likelihood methods (Clayton & Kaldor, 1987). Further, the proposed CAR model provides for covariates to be included in the model. A simulation demonstrates the effect of sample size on the fully Bayesian analysis of the CAR process. The methods are applied to lip cancer data from Scotland, and the results are compared. ^

Age -period -cohort analysis: An extension of Cox's lifetable regression with time -dependent covariates

It is well known that an identification problem exists in the analysis of age-period-cohort data because of the relationship among the three factors (date of birth + age at death = date of death). There are numerous suggestions about how to analyze the data. No one solution has been satisfactory. The purpose of this study is to provide another analytic method by extending the Cox's lifetable regression model with time-dependent covariates. The new approach contains the following features: (1) It is based on the conditional maximum likelihood procedure using a proportional hazard function described by Cox (1972), treating the age factor as the underlying hazard to estimate the parameters for the cohort and period factors. (2) The model is flexible so that both the cohort and period factors can be treated as dummy or continuous variables, and the parameter estimations can be obtained for numerous combinations of variables as in a regression analysis. (3) The model is applicable even when the time period is unequally spaced.^ Two specific models are considered to illustrate the new approach and applied to the U.S. prostate cancer data. We find that there are significant differences between all cohorts and there is a significant period effect for both whites and nonwhites. The underlying hazard increases exponentially with age indicating that old people have much higher risk than young people. A log transformation of relative risk shows that the prostate cancer risk declined in recent cohorts for both models. However, prostate cancer risk declined 5 cohorts (25 years) earlier for whites than for nonwhites under the period factor model (0 0 0 1 1 1 1). These latter results are similar to the previous study by Holford (1983).^ The new approach offers a general method to analyze the age-period-cohort data without using any arbitrary constraint in the model. ^

Comparison of multiple comparison methods for identifying differential gene expression in simulated and real papillary thyroid cancer microarray data

The difficulty of detecting differential gene expression in microarray data has existed for many years. Several correction procedures try to avoid the family-wise error rate in multiple comparison process, including the Bonferroni and Sidak single-step p-value adjustments, Holm's step-down correction method, and Benjamini and Hochberg's false discovery rate (FDR) correction procedure. Each multiple comparison technique has its advantages and weaknesses. We studied each multiple comparison method through numerical studies (simulations) and applied the methods to the real exploratory DNA microarray data, which detect of molecular signatures in papillary thyroid cancer (PTC) patients. According to our results of simulation studies, Benjamini and Hochberg step-up FDR controlling procedure is the best process among these multiple comparison methods and we discovered 1277 potential biomarkers among 54675 probe sets after applying the Benjamini and Hochberg's method to PTC microarray data.^

High-resolution microarray analysis of chromosome 20q in human colon cancer metastasis model systems

Amplification of human chromosome 20q DNA is the most frequently occurring chromosomal abnormality detected in sporadic colorectal carcinomas and shows significant correlation with liver metastases. Through comprehensive high-resolution microarray comparative genomic hybridization and microarray gene expression profiling, we have characterized chromosome 20q amplicon genes associated with human colorectal cancer metastasis in two in vitro metastasis model systems. The results revealed increasing complexity of the 20q genomic profile from the primary tumor-derived cell lines to the lymph node and liver metastasis derived cell lines. Expression analysis of chromosome 20q revealed a subset of over expressed genes residing within the regions of genomic copy number gain in all the tumor cell lines, suggesting these are Chromosome 20q copy number responsive genes. Bases on their preferential expression levels in the model system cell lines and known biological function, four of the over expressed genes mapping to the common intervals of genomic copy gain were considered the most promising candidate colorectal metastasis-associated genes. Validation of genomic copy number and expression array data was carried out on these genes, with one gene, DNMT3B, standing out as expressed at a relatively higher levels in the metastasis-derived cell lines compared with their primary-derived counterparts in both the models systems analyzed. The data provide evidence for the role of chromosome 20q genes with low copy gain and elevated expression in the clonal evolution of metastatic cells and suggests that such genes may serve as early biomarkers of metastatic potential. The data also support the utility of the combined microarray comparative genomic hybridization and expression array analysis for identifying copy number responsive genes in areas of low DNA copy gain in cancer cells. ^

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

DOWNREGULATION OF PAX2 SUPPRESSES OVARIAN CANCER CELL GROWTH

PAX2 is one of nine PAX genes regulating tissue development and cellular differentiation in embryos. PAX2 promotes cell proliferation, oncogenic transformation, cell-lineage specification, migration, and survival. Unattenuated PAX2 has been found in several cancer types. We therefore sought to elucidate the role of PAX2 in ovarian carcinomas. We found that PAX2 was expressed in low-grade serous, clear cell, endometrioid and mucinous cell ovarian carcinomas, which are relatively chemoresistant compared to high grade serous ovarian carcinomas. Four ovarian cancer cell lines, RMUGL (mucinous), TOV21G (clear cell), MDAH-2774 (endometrioid) and IGROV1 (endometrioid), which express high-levels of PAX2, were used to study the function of PAX2. Lentiviral shRNAs targeting PAX2 were used to knock down PAX2 expression in these cell lines. Cellular proliferation and motility assays subsequently showed that PAX2 stable knockdown had slower growth and migration rates. Microarray gene expression profile analysis further identified genes that were affected by PAX2 including the tumor suppressor gene G0S2. Reverse phase protein array (RPPA) data showed that PAX2 knockdown affected several genes that are involved in apoptosis, which supports the fact that downregulation of PAX2 in PAX2-expressing ovarian cancer cells inhibits cell growth. We hypothesize that this growth inhibition is due to upregulation of the tumor suppressor gene G0S2 via induction of apoptosis. PAX2 represents a potential therapeutic target for chemoresistant PAX2-expressing ovarian carcinomas.

THE MECHANISM OF TUMORIGENESIS IN THE IMMORTALIZED HUMAN PANCREATIC CELL LINES: CELL CULTURE MODELS OF HUMAN PANCREATIC CANCER

The mechanism of tumorigenesis in the immortalized human pancreatic cell lines: cell culture models of human pancreatic cancer Pancreatic ductal adenocarcinoma (PDAC) is the most lethal cancer in the world. The most common genetic lesions identified in PDAC include activation of K-ras (90%) and Her2 (70%), loss of p16 (95%) and p14 (40%), inactivation p53 (50-75%) and Smad4 (55%). However, the role of these signature gene alterations in PDAC is still not well understood, especially, how these genetic lesions individually or in combination contribute mechanistically to human pancreatic oncogenesis is still elusive. Moreover, a cell culture transformation model with sequential accumulation of signature genetic alterations in human pancreatic ductal cells that resembles the multiple-step human pancreatic carcinogenesis is still not established. In the present study, through the stepwise introduction of the signature genetic alterations in PDAC into the HPV16-E6E7 immortalized human pancreatic duct epithelial (HPDE) cell line and the hTERT immortalized human pancreatic ductal HPNE cell line, we developed the novel experimental cell culture transformation models with the most frequent gene alterations in PDAC and further dissected the molecular mechanism of transformation. We demonstrated that the combination of activation of K-ras and Her2, inactivation of p16/p14 and Smad4, or K-ras mutation plus p16 inactivation, was sufficient for the tumorigenic transformation of HPDE or HPNE cells respectively. We found that these transformed cells exhibited enhanced cell proliferation, anchorage-independent growth in soft agar, and grew tumors with PDAC histopathological features in orthotopic mouse model. Molecular analysis showed that the activation of K-ras and Her2 downstream effector pathways –MAPK, RalA, FAK, together with upregulation of cyclins and c-myc were involved in the malignant transformation. We discovered that MDM2, BMP7 and Bmi-1 were overexpressed in the tumorigenic HPDE cells, and that Smad4 played important roles in regulation of BMP7 and Bmi-1 gene expression and the tumorigenic transformation of HPDE cells. IPA signaling pathway analysis of microarray data revealed that abnormal signaling pathways are involved in transformation. This study is the first complete transformation model of human pancreatic ductal cells with the most common gene alterations in PDAC. Altogether, these novel transformation models more closely recapitulate the human pancreatic carcinogenesis from the cell origin, gene lesion, and activation of specific signaling pathway and histopathological features.

LMW-E MEDIATES MAMMARY TUMORIGENESIS BY DEREGULATING ACINAR MORPHOGENESIS & GENERATING CANCER STEM CELLS

Cyclin E is the regulatory subunit of the cyclin E/CDK2 complex that mediates the G1-S phase transition. N-terminal cleavage of cyclin E by elastase in breast cancer generates two low molecular weight (LMW) isoforms that exhibit both enhanced kinase activity and resistance to p21 and p27 inhibition compared to fulllength cyclin E. Clinically, approximately 27% of breast cancer patients overexpress LMW-E and associate with poor survival. Therefore, we hypothesize that LMW-E disrupts normal mammary acinar morphogenesis and serves as the initial route into breast tumor development. We first demonstrate that LMW-E overexpression in non-tumorigenic hMECs is sufficient to induce tumor formation in athymic mice significantly more than overexpression of full-length cyclin E and requires CDK2- associated kinase activity. Further in vivo passaging of these tumors augments LMW-E expression and tumorigenic potential. When subjected to acinar morphogenesis in vitro, LMW-E mediates significant morphological disruption by generating hyperproliferative and multi-acinar complexes. Proteomic analysis of patient tissues and tumor cells with high LMW-E expression reveals that the activation of the b-Raf-ERK1/2-mTOR pathway in concert with high LMW-E expression predicts poor patient survival. Combination treatment using roscovitine (CDK inhibitor) plus either rapamycin (mTOR inhibitor) or sorafenib (b-raf inhibitor) effectively prevented aberrant acinar formation in LMW-E-expressing cells by inducing the G1/S cell cycle arrest. In addition, the LMW-E-expressing tumor cells exhibit phenotypes characteristic of the EMT and enhanced cellular invasiveness. These tumor cells also enrich for cells with CSC phenotypes such as increased CD44hi/CD24lo population, enhanced mammosphere formation, and upregulation of ALDH expression and enzymatic activity. Furthermore, the CD44hi/CD24lo population also shows positive correlation with LMW-E expression in both the tumor cell line model and breast cancer patient samples (p<0.0001 & p=0.0435, respectively). Combination treatment using doxorubicin and salinomycin demonstrates synergistic cytotoxic effects in cells with LMW-E expression but not in those with full-length cyclin E expression. Finally, ProtoArray microarray identifies Hbo1 as a novel substrate of the cyclin E/CDK2 complex and its overexpression results in enrichment for CSCs. Collectively, these data emphasize the strong oncogenic potential of LMW-E in mammary tumorigenesis and suggest possible therapeutic strategies to treat breast cancer patients with high LMW-E expression.