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.

BAYESIAN PHASE I DOSE FINDING IN CANCER TRIALS

This dissertation explores phase I dose-finding designs in cancer trials from three perspectives: the alternative Bayesian dose-escalation rules, a design based on a time-to-dose-limiting toxicity (DLT) model, and a design based on a discrete-time multi-state (DTMS) model. We list alternative Bayesian dose-escalation rules and perform a simulation study for the intra-rule and inter-rule comparisons based on two statistical models to identify the most appropriate rule under certain scenarios. We provide evidence that all the Bayesian rules outperform the traditional ``3+3'' design in the allocation of patients and selection of the maximum tolerated dose. The design based on a time-to-DLT model uses patients' DLT information over multiple treatment cycles in estimating the probability of DLT at the end of treatment cycle 1. Dose-escalation decisions are made whenever a cycle-1 DLT occurs, or two months after the previous check point. Compared to the design based on a logistic regression model, the new design shows more safety benefits for trials in which more late-onset toxicities are expected. As a trade-off, the new design requires more patients on average. The design based on a discrete-time multi-state (DTMS) model has three important attributes: (1) Toxicities are categorized over a distribution of severity levels, (2) Early toxicity may inform dose escalation, and (3) No suspension is required between accrual cohorts. The proposed model accounts for the difference in the importance of the toxicity severity levels and for transitions between toxicity levels. We compare the operating characteristics of the proposed design with those from a similar design based on a fully-evaluated model that directly models the maximum observed toxicity level within the patients' entire assessment window. We describe settings in which, under comparable power, the proposed design shortens the trial. The proposed design offers more benefit compared to the alternative design as patient accrual becomes slower.

Evidence for the involvement of proximal chromosome 3P loci in the progression of Von Hippel-Lindau related tumors

Von Hippel-Lindau (VHL) disease is an autosomal dominant disorder characterized by the development of retinal and central nervous system hemangioblastoma, renal cell carcinoma (RCC), pheochromocytoma and pancreatic islet cell tumors (PICT). The VHL gene maps to chromosome 3p25 and has been shown to be mutated in 57% of sporadic cases of RCC, implicating VHL in the genesis of RCC. We report a multigeneration VHL kindred in which four affected female siblings developed PICT at early ages. Analysis of the three coding exons of the VHL gene in this family revealed a single, missense mutation in codon 238. Inheritance of the 238 mutation has been reported to correlate with a 62% risk of pheochromocytoma development. In this kindred, all affected individuals carried the mutation as well as one additional sibling who showed no evidence of disease. Clinical screening of this individual indicated small ($<$1 cm) pancreatic and kidney tumors. Results suggest that inheritance of the codon 238 mutation does not correlate with early onset pheochromocytoma. Rather, the only individual in the pedigree with pheochromocytoma was the proband's mother who developed bilateral pheochromocytoma at the age of 62. Thus, the VHL codon 238 mutation may predispose to late onset pheochromocytoma in this family; however, it does not explain the preponderance of PICT in the third generation since this mutation has not been reported to increase the risk of developing pancreatic lesions. This suggests that inheritance of the codon 238 mutation and subsequent somatic inactivation of the wild type allele of the VHL gene may not be sufficient to explain the initiation and subsequent progression to malignancy in VHL-associated neoplasms. Since the two tumor types that most frequently progress to malignancy are RCC and PICT, we asked whether loss of heterozygosity (LOH) could be detected proximal to the VHL gene on chromosome 3 in distinct regions of 3p previously implicated by LOH and cytogenetic studies to contain tumor suppressor loci for RCC. LOH was performed on high molecular weight DNA isolated from peripheral blood and frozen tumor tissue of family members using microsatellite markers spanning 3p. Results indicated LOH for all informative 3p loci in tumor tissue from affected individuals with PICT. LOH was detected along the entire length of the chromosome arm and included the proximal region of 3p13-14.2 implicated in the hereditary form of renal cell carcinoma.^ If 3p LOH were a critical event in pancreatic islet cell tumorigenesis, then it should be expected that LOH in sporadic islet cell tumors would also be observed. We expanded LOH studies to include sporadic cases of PICT. Consistent LOH was observed on 3p with a highest frequency LOH in the region 3p21.2. This is the first evidence for an association between chromosome 3 loci and pancreatic islet cell tumorigenesis. (Abstract shortened by UMI.) ^

Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.

Bayesian adaptive designs in early phase clinical trials

There are two practical challenges in the phase I clinical trial conduct: lack of transparency to physicians, and the late onset toxicity. In my dissertation, Bayesian approaches are used to address these two problems in clinical trial designs. The proposed simple optimal designs cast the dose finding problem as a decision making process for dose escalation and deescalation. The proposed designs minimize the incorrect decision error rate to find the maximum tolerated dose (MTD). For the late onset toxicity problem, a Bayesian adaptive dose-finding design for drug combination is proposed. The dose-toxicity relationship is modeled using the Finney model. The unobserved delayed toxicity outcomes are treated as missing data and Bayesian data augment is employed to handle the resulting missing data. Extensive simulation studies have been conducted to examine the operating characteristics of the proposed designs and demonstrated the designs' good performances in various practical scenarios.^