Jaeil Ahn

MRE11 deficiency increases sensitivity to poly(ADP-ribose) polymerase inhibition in microsatellite unstable colorectal cancers

Microsatellite instability (MSI) is displayed by approximately 15% of colorectal cancers (CRC). Defective DNA mismatch repair generates mutations at repetitive DNA sequences such as those located in the double strand break (DSB) repair gene MRE11. We assessed the mutational status of MRE11 in a panel of 17 CRC cell lines and 46 primary tumors and found a strong correlation with MSI status in both cell lines and tumors. Therefore, we hypothesized that deficiency in MRE11 may sensitize CRC cells to poly(ADP-ribose) polymerase (PARP-1) inhibition based on the concept of synthetic lethality. We further assessed the activity of the PARP-1 inhibitor, ABT-888, in CRC cell lines and observed preferential cytotoxicity in those MSI cell lines harboring mutations in MRE11 compared with both wild-type cell lines and microsatellite stable (MSS) cell lines. A significant correlation between MRE11 expression levels and cytotoxicity to ABT-888 at 10 μM was observed (R² = 0.915, P < 0.001). Using two experimental approaches, including short hairpin RNA knocking down MRE11 in the wild-type and MSS cell line SW-480 and a second cell line model transfected with mutant MRE11, we experimentally tried to confirm the role of MRE11 in conferring sensitivity to PARP-1 inhibition. Both models led to changes in proliferation in response to ABT-888 at different concentrations, and a drug-response effect was not observed, suggesting a possible contribution of additional genes. We conclude that MSI colorectal tumors deficient in DSB repair secondary to mutation in MRE11 show a higher sensitivity to PARP-1 inhibition. Further clinical investigation of PARP-1 inhibitors is warranted in MSI CRCs.

Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons

Several methods for screening gene-environment interaction have recently been proposed that address the issue of using gene-environment independence in a data-adaptive way. In this report, the authors present a comparative simulation study of power and type I error properties of 3 classes of procedures: 1) the standard 1-step case-control method; 2) the case-only method that requires an assumption of gene-environment independence for the underlying population; and 3) a variety of hybrid methods, including empirical-Bayes, 2-step, and model averaging, that aim at gaining power by exploiting the assumption of gene-environment independence and yet can protect against false positives when the independence assumption is violated. These studies suggest that, although the case-only method generally has maximum power, it has the potential to create substantial false positives in large-scale studies even when a small fraction of markers are associated with the exposure under study in the underlying population. All the hybrid methods perform well in protecting against such false positives and yet can retain substantial power advantages over standard case-control tests. The authors conclude that, for future genome-wide scans for gene-environment interactions, major power gain is possible by using alternatives to standard case-control analysis. Whether a case-only type scan or one of the hybrid methods should be used depends on the strength and direction of gene-environment interaction and association, the level of tolerance for false positives, and the nature of replication strategies.

Tests for Gene-Environment Interaction From Case-Control Data: A Novel Study of Type I Error, Power and Designs

To evaluate the risk of a disease associated with the joint effects of genetic susceptibility and environmental exposures, epidemiologic researchers often test for non‐multiplicative gene‐environment effects from case‐control studies. In this article, we present a comparative study of four alternative tests for interactions: (i) the standard case‐control method; (ii) the case‐only method, which requires an assumption of gene‐environment independence for the underlying population; (iii) a two‐step method that decides between the case‐only and case‐control estimators depending on a statistical test for the gene‐environment independence assumption and (iv) a novel empirical‐Bayes (EB) method that combines the case‐control and case‐only estimators depending on the sample size and strength of the gene‐environment association in the data. We evaluate the methods in terms of integrated Type I error and power, averaged with respect to varying scenarios for gene‐environment association that are likely to appear in practice. These unique studies suggest that the novel EB procedure overall is a promising approach for detection of gene‐environment interactions from case‐control studies. In particular, the EB procedure, unlike the case‐only or two‐step methods, can closely maintain a desired Type I error under realistic scenarios of gene‐environment dependence and yet can be substantially more powerful than the traditional case‐control analysis when the gene‐environment independence assumption is satisfied, exactly or approximately. Our studies also reveal potential utility of some non‐traditional case‐control designs that samples controls at a smaller rate than the cases. Apart from the simulation studies, we also illustrate the different methods by analyzing interactions of two commonly studied genes, N‐acetyl transferase type 2 and glutathione s‐transferase M1, with smoking and dietary exposures, in a large case‐control study of colorectal cancer. Genet. Epidemiol. 2008. Published 2008 Wiley‐Liss, Inc.

Nilotinib Effects in Parkinson’s disease and Dementia with Lewy bodies

Background: We evaluated the effects of low doses of the tyrosine kinase Abelson (Abl) inhibitor Nilotinib, on safety and pharmacokinetics in Parkinson’s disease dementia or dementia with Lewy bodies. Objectives: The primary outcomes of this study were safety and tolerability; pharmacokinetics and target engagement were secondary, while clinical outcomes were exploratory. Methods: Twelve subjects were randomized into 150 mg (n = 5) or 300 mg (n = 7) groups and received Nilotinib orally every day for 24 weeks. Results: This study shows that 150 mg and 300 mg doses of Nilotinib appear to be safe and tolerated in subjects with advanced Parkinson’s disease. Nilotinib is detectable in the cerebrospinal fluid (CSF) and seems to engage the target Abl. Motor and cognitive outcomes suggest a possible beneficial effect on clinical outcomes. The CSF levels of homovanillic acid are significantly increased between baseline and 24 weeks of treatment. Exploratory CSF biomarkers were measured. Conclusions: This small proof-of-concept study lacks a placebo group and participants were not homogenous, resulting in baseline differences between and within groups. This limits the interpretations of the biomarker and clinical data, and any conclusions should be drawn cautiously. Nonetheless, the collective observations suggest that it is warranted to evaluate the safety and efficacy of Nilotinib in larger randomized, double-blind, placebo-controlled trials.

Transcriptome profiling identifies HMGA2 as a biomarker of melanoma progression and prognosis.

The genetic alterations contributing to melanoma pathogenesis are incompletely defined, and few independent prognostic features have been identified beyond the clinicopathological characteristics of the primary tumor. We used transcriptome profiling of 46 primary melanomas, 12 melanoma metastases, and 16 normal skin (N) samples to find genes associated with melanoma development and progression. Results were confirmed using immunohistochemistry and real-time PCR and replicated in an independent set of 330 melanomas using AQUA analysis of tissue microarray (TMA). Transcriptome profiling revealed that transcription factor HMGA2, previously unrecognized in melanoma pathogenesis, is significantly upregulated in primary melanoma and metastases (P-values=1.2 × 10(-7) and 9 × 10(-5)) compared with N. HMGA2 overexpression is associated with BRAF/NRAS mutations (P=0.0002). Cox proportional hazard regression model and log-rank test showed that HMGA2 is independently associated with disease-free survival (hazard ratio (HR)=6.3, 95% confidence interval (CI)=1.8-22.3, P=0.004), overall survival (OS) (stratified log-rank P=0.008), and distant metastases-free survival (HR=6.4, 95% CI=1.4-29.7, P=0.018) after adjusting for American Joint Committee on Cancer (AJCC) stage and age at diagnosis. Survival analysis in an independent replication TMA of 330 melanomas confirmed the association of HMGA2 expression with OS (P=0.0211). Our study implicates HMGA2 in melanoma progression and demonstrates that HMGA2 overexpression can serve as an independent predictor of survival in melanoma.

DeMix: deconvolution for mixed cancer transcriptomes using raw measured data

MOTIVATION Tissue samples of tumor cells mixed with stromal cells cause underdetection of gene expression signatures associated with cancer prognosis or response to treatment. In silico dissection of mixed cell samples is essential for analyzing expression data generated in cancer studies. Currently, a systematic approach is lacking to address three challenges in computational deconvolution: (i) violation of linear addition of expression levels from multiple tissues when log-transformed microarray data are used; (ii) estimation of both tumor proportion and tumor-specific expression, when neither is known a priori; and (iii) estimation of expression profiles for individual patients. RESULTS We have developed a statistical method for deconvolving mixed cancer transcriptomes, DeMix, which addresses the aforementioned issues in array-based expression data. We demonstrate the performance of our model in synthetic and real, publicly available, datasets. DeMix can be applied to ongoing biomarker-based clinical studies and to the vast expression datasets previously generated from mixed tumor and stromal cell samples. AVAILABILITY All codes are written in C and integrated into an R function, which is available at http://odin.mdacc.tmc.edu/∼wwang7/DeMix.html. CONTACT wwang7@mdanderson.org SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Risk of colorectal cancer in self-reported inflammatory bowel disease and modification of risk by statin and NSAID use

Statins and nonsteroidal anti‐inflammatory drugs (NSAIDs) are associated with reduced risk of colorectal cancer (CRC) in some studies. The objective of this study was to quantify the relative risk of inflammatory bowel disease (IBD) as a risk factor for CRC and to estimate whether this risk may be modified by long‐term use of NSAIDs or statins.

Fitting stratified proportional odds models by amalgamating conditional likelihoods

Classical methods for fitting a varying intercept logistic regression model to stratified data are based on the conditional likelihood principle to eliminate the stratum-specific nuisance parameters. When the outcome variable has multiple ordered categories, a natural choice for the outcome model is a stratified proportional odds or cumulative logit model. However, classical conditioning techniques do not apply to the general K-category cumulative logit model (K>2) with varying stratum-specific intercepts as there is no reduction due to sufficiency; the nuisance parameters remain in the conditional likelihood. We propose a methodology to fit stratified proportional odds model by amalgamating conditional likelihoods obtained from all possible binary collapsings of the ordinal scale. The method allows for categorical and continuous covariates in a general regression framework. We provide a robust sandwich estimate of the variance of the proposed estimator. For binary exposures, we show equivalence of our approach to the estimators already proposed in the literature. The proposed recipe can be implemented very easily in standard software. We illustrate the methods via three real data examples related to biomedical research. Simulation results comparing the proposed method with a random effects model on the stratification parameters are also furnished.

Case-control studies of gene-environment interaction: Bayesian design and analysis.

With increasing frequency, epidemiologic studies are addressing hypotheses regarding gene-environment interaction. In many well-studied candidate genes and for standard dietary and behavioral epidemiologic exposures, there is often substantial prior information available that may be used to analyze current data as well as for designing a new study. In this article, first, we propose a proper full Bayesian approach for analyzing studies of gene-environment interaction. The Bayesian approach provides a natural way to incorporate uncertainties around the assumption of gene-environment independence, often used in such an analysis. We then consider Bayesian sample size determination criteria for both estimation and hypothesis testing regarding the multiplicative gene-environment interaction parameter. We illustrate our proposed methods using data from a large ongoing case-control study of colorectal cancer investigating the interaction of N-acetyl transferase type 2 (NAT2) with smoking and red meat consumption. We use the existing data to elicit a design prior and show how to use this information in allocating cases and controls in planning a future study that investigates the same interaction parameters. The Bayesian design and analysis strategies are compared with their corresponding frequentist counterparts.

Bayesian inference for the stereotype regression model: Application to a case-control study of prostate cancer.

The stereotype regression model for categorical outcomes, proposed by Anderson (J. Roy. Statist. Soc. B. 1984; 46:1-30) is nested between the baseline-category logits and adjacent category logits model with proportional odds structure. The stereotype model is more parsimonious than the ordinary baseline-category (or multinomial logistic) model due to a product representation of the log-odds-ratios in terms of a common parameter corresponding to each predictor and category-specific scores. The model could be used for both ordered and unordered outcomes. For ordered outcomes, the stereotype model allows more flexibility than the popular proportional odds model in capturing highly subjective ordinal scaling which does not result from categorization of a single latent variable, but are inherently multi-dimensional in nature. As pointed out by Greenland (Statist. Med. 1994; 13:1665-1677), an additional advantage of the stereotype model is that it provides unbiased and valid inference under outcome-stratified sampling as in case-control studies. In addition, for matched case-control studies, the stereotype model is amenable to classical conditional likelihood principle, whereas there is no reduction due to sufficiency under the proportional odds model. In spite of these attractive features, the model has been applied less, as there are issues with maximum likelihood estimation and likelihood-based testing approaches due to non-linearity and lack of identifiability of the parameters. We present comprehensive Bayesian inference and model comparison procedure for this class of models as an alternative to the classical frequentist approach. We illustrate our methodology by analyzing data from The Flint Mens Health Study, a case-control study of prostate cancer in African-American men aged 40-79 years. We use clinical staging of prostate cancer in terms of Tumors, Nodes and Metastasis as the categorical response of interest.