Sample size for detecting differentially expressed genes in microarray experiments

BACKGROUND: Microarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human). RESULTS: We hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile. CONCLUSIONS: Factors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects

Lack of sufficiently strong informative features limits the potential of gene expression analysis as predictive tool for many clinical classification problems

<p>Abstract</p> <p>Background</p> <p>Our goal was to examine how various aspects of a gene signature influence the success of developing multi-gene prediction models. We inserted gene signatures into three real data sets by altering the expression level of existing probe sets. We varied the number of probe sets perturbed (signature size), the fold increase of mean probe set expression in perturbed compared to unperturbed data (signature strength) and the number of samples perturbed. Prediction models were trained to identify which cases had been perturbed. Performance was estimated using Monte-Carlo cross validation.</p> <p>Results</p> <p>Signature strength had the greatest influence on predictor performance. It was possible to develop almost perfect predictors with as few as 10 features if the fold difference in mean expression values were > 2 even when the spiked samples represented 10% of all samples. We also assessed the gene signature set size and strength for 9 real clinical prediction problems in six different breast cancer data sets.</p> <p>Conclusions</p> <p>We found sufficiently large and strong predictive signatures only for distinguishing ER-positive from ER-negative cancers, there were no strong signatures for more subtle prediction problems. Current statistical methods efficiently identify highly informative features in gene expression data if such features exist and accurate models can be built with as few as 10 highly informative features. Features can be considered highly informative if at least 2-fold expression difference exists between comparison groups but such features do not appear to be common for many clinically relevant prediction problems in human data sets.</p

What Is the Best Reference RNA? And Other Questions Regarding the Design and Analysis of Two-Color Microarray Experiments

The reference design is a practical and popular choice for microarray studies using two-color platforms. In the reference design, the reference RNA uses half of all array resources, leading investigators to ask: What is the best reference RNA? We propose a novel method for evaluating reference RNAs and present the results of an experiment that was specially designed to evaluate three common choices of reference RNA. We found no compelling evidence in favor of any particular reference. In particular, a commercial reference showed no advantage in our data. Our experimental design also enabled a new way to test the effectiveness of pre-processing methods for two-color arrays. Our results favor using an intensity-normalization and foregoing background-subtraction. Finally, we evaluate the sensitivity and specificity of data quality filters, and propose a new filter that can be applied to any experimental design and does not rely on replicate hybridizations

Left Ventricular Ejection Fraction in Patients With Ovarian Cancer Treated With Avelumab, Pegylated Liposomal Doxorubicin, or Both

In the phase III JAVELIN Ovarian 200 trial, 566 patients with platinum-resistant/refractory ovarian cancer were randomized 1:1:1 to receive avelumab alone, avelumab plus pegylated liposomal doxorubicin (PLD), or PLD alone. Cardiac monitoring was included for all patients. We report left ventricular ejection fraction (LVEF) data from the trial. Grade ≥3 cardiac adverse events (AEs) occurred in 4 (2.1%), 1 (0.5%), and 0 patients in the avelumab, combination, and PLD arms, respectively. LVEF decreases of ≥10% to below institutional lower limit of normal at any time during treatment were observed in 1 (0.8%), 3 (1.9%), and 2 (1.5%) patients, respectively; 4 had subsequent assessments, and these showed transient decreases. No patient had a cardiovascular AE related to LVEF decrease. This analysis is, to our knowledge, the first analysis of LVEF in patients receiving immune checkpoint inhibitors. CLINICALTRIALS.GOV IDENTIFIER: NCT02580058

LMW-E/CDK2 Deregulates Acinar Morphogenesis, Induces Tumorigenesis, and Associates with the Activated b-Raf-ERK1/2-mTOR Pathway in Breast Cancer Patients

Elastase-mediated cleavage of cyclin E generates low molecular weight cyclin E (LMW-E) isoforms exhibiting enhanced CDK2–associated kinase activity and resistance to inhibition by CDK inhibitors p21 and p27. Approximately 27% of breast cancers express high LMW-E protein levels, which significantly correlates with poor survival. The objective of this study was to identify the signaling pathway(s) deregulated by LMW-E expression in breast cancer patients and to identify pharmaceutical agents to effectively target this pathway. Ectopic LMW-E expression in nontumorigenic human mammary epithelial cells (hMECs) was sufficient to generate xenografts with greater tumorigenic potential than full-length cyclin E, and the tumorigenicity was augmented by in vivo passaging. However, cyclin E mutants unable to interact with CDK2 protected hMECs from tumor development. When hMECs were cultured on Matrigel, LMW-E mediated aberrant acinar morphogenesis, including enlargement of acinar structures and formation of multi-acinar complexes, as denoted by reduced BIM and elevated Ki67 expression. Similarly, inducible expression of LMW-E in transgenic mice generated hyper-proliferative terminal end buds resulting in enhanced mammary tumor development. Reverse-phase protein array assay of 276 breast tumor patient samples and cells cultured on monolayer and in three-dimensional Matrigel demonstrated that, in terms of protein expression profile, hMECs cultured in Matrigel more closely resembled patient tissues than did cells cultured on monolayer. Additionally, the b-Raf-ERK1/2-mTOR pathway was activated in LMW-E–expressing patient samples, and activation of this pathway was associated with poor disease-specific survival. Combination treatment using roscovitine (CDK inhibitor) plus either rapamycin (mTOR inhibitor) or sorafenib (a pan kinase inhibitor targeting b-Raf) effectively prevented aberrant acinar formation in LMW-E–expressing cells by inducing G1/S cell cycle arrest. LMW-E requires CDK2–associated kinase activity to induce mammary tumor formation by disrupting acinar development. The b-Raf-ERK1/2-mTOR signaling pathway is aberrantly activated in breast cancer and can be suppressed by combination treatment with roscovitine plus either rapamycin or sorafenib

Epithelial to mesenchymal transition is associated with rapamycin resistance

Rapamycin analogues have antitumor efficacy in several tumor types, however few patients demonstrate tumor regression. Thus, there is a pressing need for markers of intrinsic response/resistance and rational combination therapies. We hypothesized that epithelial-to-mesenchymal transition (EMT) confers rapamycin resistance. We found that the epithelial marker E-cadherin protein is higher in rapamycin sensitive (RS) cells and mesenchymal breast cancer cell lines selected by transcriptional EMT signatures are less sensitive to rapamycin. MCF7 cells, transfected with constitutively active mutant Snail, had increased rapamycin resistance (RR) compared to cells transfected with wild-type Snail. Conversely, we transfected two RR mesenchymal cell lines—ACHN and MDA-MB-231—with miR-200b/c or ZEB1 siRNA to promote mesenchymal-to-epithelial transition. This induced E-cadherin expression in both cell lines, and ACHN demonstrated a significant increase in RS. Treatment of ACHN and MDA-MB-231 with trametinib modulated EMT in ACHN cells in vitro. Treatment of MDA-MB-231 and ACHN xenografts with trametinib in combination with rapamycin resulted in significant growth inhibition in both but without an apparent effect on EMT. Future studies are needed to determine whether EMT status is predictive of sensitivity to rapalogs and to determine whether combination therapy with EMT modulating agents can enhance antitumor effects of PI3K/mTOR inhibitors

Avelumab Alone or in Combination With Chemotherapy Versus Chemotherapy Alone in Platinum-Resistant or Platinum-Refractory Ovarian Cancer (JAVELIN Ovarian 200): An Open-Label, Three-Arm, Randomised, Phase 3 Study

The majority of patients with ovarian cancer will experience relapse and develop platinum-resistant disease after being treated with frontline platinum-based chemotherapy. Treatment options for platinum-resistance or platinum-refractory disease are very limited, usually involving nonplatinum chemotherapy, and they are associated with poor objective response rates and life expectancy

Bayesian modeling of combined endpoints for sequentially adaptive design and comfirmatory trial planning

Recent scientific advances in biomedical research have rapidly increased the number of promising new cancer treatments available for clinical evaluation. Yet, current drug development strategies have proved to be quite inefficient. Agents are screened one-at-a-time in a sequential process that is characterized by low cost effectiveness and low specificity, as it often fails to identify those agents for which further development should be stopped. The vast majority of failures occurs late in the drug development process, phase III trials. The high failure rate in phase III may be due to the deficiency of phase II with improper endpoints. My dissertation is to explore multi-arm clinical trial design for phase II that uses Bayesian hierarchical modeling to discern and leverage the relationships between short-term tumor response and long-term survival endpoints. We evaluate the extent to which combining endpoints may help facilitating simultaneous sequential screening of multiple competing therapies for the purpose of efficiently and effectively identifying the most promising therapies for subsequent confirmatory evaluation in phase III. We explain how to use phase II data to properly predict the probability of success in a future phase III as a function of the planned sample size, as well as how to use predictive probability of success and predicted utility to decide whether to conduct a phase III trial. Simulation studies demonstrate that the proposed design greatly improves the efficiency of the trial by reducing total sample size by 40% to 50%, and achieves significantly greater power to identify the efficacious arm(s) while maintaining comparable family wise type I error rate when compared to the conventional sequential two-armed designs. The reduction in total sample size is more profound when there is(are) efficacious treatment arm(s). Our proposed design also outperforms a comparator Bayesian multi-arm design without ordering of the long-term survival for the ordinal short-term response categories by achieving significant higher posterior probability to identify the superior experimental arm(s) and significantly higher predictive probability of success in future phase III. When using expected utility and predictive probability of success combining both endpoints we enhance/optimize the expected payoff of a future phase III for a given sample size and utility