Chronic lymphocytic leukemia: A study of the changing patterns of the natural history, clinical factors associated with prognosis, and risk of second cancers

A retrospective cohort study was conducted among 1542 patients diagnosed with CLL between 1970 and 2001 at the M. D. Anderson Cancer Center (MDACC). Changes in clinical characteristics and the impact of CLL on life expectancy were assessed across three decades (1970–2001) and the role of clinical factors on prognosis of CLL were evaluated among patients diagnosed between 1985 and 2001 using Kaplan-Meier and Cox proportional hazards method. Among 1485 CLL patients diagnosed from 1970 to 2001, patients in the recent cohort (1985–2001) were diagnosed at a younger age and an earlier stage compared to the earliest cohort (1970–1984). There was a 44% reduction in mortality among patients diagnosed in 1985–1995 compared to those diagnosed in 1970–1984 after adjusting for age, sex and Rai stage among patients who ever received treatment. There was an overall 11 years (5 years for stage 0) loss of life expectancy among 1485 patients compared with the expected life expectancy based on the age-, sex- and race-matched US general population, with a 43% decrease in the 10-year survival rate. Abnormal cytogenetics was associated with shorter progression-free (PF) survival after adjusting for age, sex, Rai stage and beta-2 microglobulin (beta-2M); whereas, older age, abnormal cytogenetics and a higher beta-2M level were adverse predictors for overall survival. No increased risk of second cancer overall was observed, however, patients who received treatment for CLL had an elevated risk of developing AML and HD. Two out of three patients who developed AML were treated with alkylating agents. In conclusion, CLL patients had improved survival over time. The identification of clinical predictors of PF/overall survival has important clinical significance. Close surveillance of the development of second cancer is critical to improve the quality of life of long-term survivors. ^

Natural history study of arthrogryposis multiplex congenita, amyoplasia type

Arthrogryposis or Arthrogrypsosis Multiplex Congenita (AMC) are terms used to describe the clinical finding of multiple congenital contractures. There are more than 300 distinct disorders associated with arthrogryposis. Amyoplasia is the most common type of arthrogryposis and is often referred to as the “classic” type. There is no known cause of amyoplasia and no risk factors have been identified. Moreover, there is no established diagnostic criteria, which has led to inconsistency and confusion in the medical literature. The purpose of this study was to describe the natural history of amyoplasia, to determine if there are any identifiable risk factors and develop a list of diagnostic criteria. A retrospective chart review of 59 children with arthrogryposis ascertained at the Shriners Hospitals for Children in Houston, Texas was performed and included the following information: prenatal, birth, and family histories, and phenotypic descriptions. Forty-four children were identified with amyoplasia and 15 children with other multiple congenital contractures and other anomalies (MCC) were used as a comparison group. With the exception of abnormal amniotic fluid levels during pregnancy, there were no significant demographic or prenatal risk factors identified. However, we found common features that discriminate amyoplasia from other types of arthrogryposis and developed a diagnostic checklist. This checklist can be used as diagnostic criteria for discriminating amyoplasia from isolated and multiple contracture conditions.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.