Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data

BACKGROUND: An increasing number of studies have profiled tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most intriguing yet challenging tasks is to develop robust statistical models to integrate the findings. RESULTS: By applying a two-stage Bayesian mixture modeling strategy, we were able to assimilate and analyze four independent microarray studies to derive an inter-study validated "meta-signature" associated with breast cancer prognosis. Combining multiple studies (n = 305 samples) on a common probability scale, we developed a 90-gene meta-signature, which strongly associated with survival in breast cancer patients. Given the set of independent studies using different microarray platforms which included spotted cDNAs, Affymetrix GeneChip, and inkjet oligonucleotides, the individually identified classifiers yielded gene sets predictive of survival in each study cohort. The study-specific gene signatures, however, had minimal overlap with each other, and performed poorly in pairwise cross-validation. The meta-signature, on the other hand, accommodated such heterogeneity and achieved comparable or better prognostic performance when compared with the individual signatures. Further by comparing to a global standardization method, the mixture model based data transformation demonstrated superior properties for data integration and provided solid basis for building classifiers at the second stage. Functional annotation revealed that genes involved in cell cycle and signal transduction activities were over-represented in the meta-signature. CONCLUSION: The mixture modeling approach unifies disparate gene expression data on a common probability scale allowing for robust, inter-study validated prognostic signatures to be obtained. With the emerging utility of microarrays for cancer prognosis, it will be important to establish paradigms to meta-analyze disparate gene expression data for prognostic signatures of potential clinical use

Variance prior specification for a basket trial design using Bayesian hierarchical modeling

Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. Methods: A common method used to capture the correlated endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a basket trial and investigate two popular prior specifications: an inverse-gamma prior on the basket-level variance and a uniform prior on the basket-level standard deviation. Results: From our simulation study, we see the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero (\u3c0.5), this can lead to unacceptably high false positive rates (\u3e40%) in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that moves the mass of the variance parameter away from zero, such as the uniform prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior must be larger than 1. Conclusion: Based on our results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a large density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior on the standard deviation

Modeling intra-tumor protein expression heterogeneity in tissue microarray experiments

Tissue microarrays (TMAs) measure tumor-specific protein expression via high-density immunohistochemical staining assays. They provide a proteomic platform for validating cancer biomarkers emerging from large-scale DNA microarray studies. Repeated observations within each tumor result in substantial biological and experimental variability. This variability is usually ignored when associating the TMA expression data with patient survival outcome. It generates biased estimates of hazard ratio in proportional hazards models. We propose a Latent Expression Index (LEI) as a surrogate protein expression estimate in a two-stage analysis. Several estimators of LEI are compared: an empirical Bayes, a full Bayes, and a varying replicate number estimator. In addition, we jointly model survival and TMA expression data via a shared random effects model. Bayesian estimation is carried out using a Markov chain Monte Carlo method. Simulation studies were conducted to compare the two-stage methods and the joint analysis in estimating the Cox regression coefficient. We show that the two-stage methods reduce bias relative to the naive approach, but still lead to under-estimated hazard ratios. The joint model consistently outperforms the two-stage methods in terms of both bias and coverage property in various simulation scenarios. In case studies using prostate cancer TMA data sets, the two-stage methods yield a good approximation in one data set whereas an insufficient one in the other. A general advice is to use the joint model inference whenever results differ between the two-stage methods and the joint analysis. Copyright © 2008 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58565/1/3217_ftp.pd

A Latent Variable Approach for Meta-Analysis of Gene Expression Data from Multiple Microarray Experiments

<p>Abstract</p> <p>Background</p> <p>With the explosion in data generated using microarray technology by different investigators working on similar experiments, it is of interest to combine results across multiple studies.</p> <p>Results</p> <p>In this article, we describe a general probabilistic framework for combining high-throughput genomic data from several related microarray experiments using mixture models. A key feature of the model is the use of latent variables that represent quantities that can be combined across diverse platforms. We consider two methods for estimation of an index termed the probability of expression (POE). The first, reported in previous work by the authors, involves Markov Chain Monte Carlo (MCMC) techniques. The second method is a faster algorithm based on the expectation-maximization (EM) algorithm. The methods are illustrated with application to a meta-analysis of datasets for metastatic cancer.</p> <p>Conclusion</p> <p>The statistical methods described in the paper are available as an R package, metaArray 1.8.1, which is at Bioconductor, whose URL is <url>http://www.bioconductor.org/</url>.</p

Sex-specific survival and tumor mutational burden in early stage melanoma

Introduction Tumor mutational burden (TMB) is a promising biomarker of clinical response to immune checkpoint inhibitors in metastatic cancers, and melanoma-specific survival. There are also significant gender-specific differences in TMB with men having consistently higher TMB than women. This relationship is provocative given the well-documented female melanoma survival advantage, and has not been investigated in early-stage primary tumors naïve to treatment. Approach Here we present preliminary findings on sex, survival, and tumor mutational burden from Stages II and III primary melanoma tumors, none of which have received immunotherapy using the MSK IMPACT™ next generation sequencing assay. Our team evaluated survival in 581 primary melanoma tumors procured by the parent P01 grant; 251 from patients who died with melanoma within five years (median survival, 2.4 years), and 330 from individuals who have lived at least five years (median follow up 8.5 years). Preliminary Results In the full dataset, we found the expected female survival advantage (log rank test P=0.049). After controlling for multiple comparisons using maximally selected ranked statistics7 the protective effect of high TMB on survival disappeared (HR=0.43, 95% CI=0.19 to 0.97, P=0.037). When stratified by sex, high TMB was associated with significantly improved melanoma specific survival among men (p=0.024), but not women (P=0.9). Broader Impacts Our study is the first to investigate the relationship between sex, tumor mutational burden, and mortality in an early stage primary cohort that has not received immunotherapy. In our small sample, we observed the expected protective effect of TMB on survival, but no evidence of gender differences in TMB or survival, despite the robust, consistent, and well-documented female survival advantage 5,6. Our results are an important first step to increasing our understanding of the relationship between mutational burden, survival, and biological sex. Limitations These results are exploratory and have not been adjusted for potential confounding factors such as stage, Breslow score, gender, or age

Internet-based profiler system as integrative framework to support translational research

BACKGROUND: Translational research requires taking basic science observations and developing them into clinically useful tests and therapeutics. We have developed a process to develop molecular biomarkers for diagnosis and prognosis by integrating tissue microarray (TMA) technology and an internet-database tool, Profiler. TMA technology allows investigators to study hundreds of patient samples on a single glass slide resulting in the conservation of tissue and the reduction in inter-experimental variability. The Profiler system allows investigator to reliably track, store, and evaluate TMA experiments. Here within we describe the process that has evolved through an empirical basis over the past 5 years at two academic institutions. RESULTS: The generic design of this system makes it compatible with multiple organ system (e.g., prostate, breast, lung, renal, and hematopoietic system,). Studies and folders are restricted to authorized users as required. Over the past 5 years, investigators at 2 academic institutions have scanned 656 TMA experiments and collected 63,311 digital images of these tissue samples. 68 pathologists from 12 major user groups have accessed the system. Two groups directly link clinical data from over 500 patients for immediate access and the remaining groups choose to maintain clinical and pathology data on separate systems. Profiler currently has 170 K data points such as staining intensity, tumor grade, and nuclear size. Due to the relational database structure, analysis can be easily performed on single or multiple TMA experimental results. The TMA module of Profiler can maintain images acquired from multiple systems. CONCLUSION: We have developed a robust process to develop molecular biomarkers using TMA technology and an internet-based database system to track all steps of this process. This system is extendable to other types of molecular data as separate modules and is freely available to academic institutions for licensing

Prognostic Impact of KRAS Mutation Subtypes in 677 Patients with Metastatic Lung Adenocarcinomas

BackgroundWe previously demonstrated that patients with metastatic KRAS mutant lung cancers have a shorter survival compared with patients with KRAS wild-type cancers. Recent reports have suggested different clinical outcomes and distinct activated signaling pathways depending on KRAS mutation subtype. To better understand the impact of KRAS mutation subtype, we analyzed data from 677 patients with KRAS mutant metastatic lung cancer.MethodsWe reviewed all patients with metastatic or recurrent lung cancers found to have KRAS mutations over a 6-year time period. We evaluated the associations among KRAS mutation type, clinical factors, and overall survival in univariate and multivariate analyses. Any significant findings were validated in an external multi-institution patient dataset.ResultsAmong 677 patients with KRAS mutant lung cancers (53 at codon 13, 624 at codon 12), there was no difference in overall survival for patients when comparing KRAS transition versus transversion mutations (p = 0.99), smoking status (p = 0.33), or when comparing specific amino acid substitutions (p = 0.20). In our dataset, patients with KRAS codon 13 mutant tumors (n = 53) had shorter overall survival compared with patients with codon 12 mutant tumors (n = 624) (1.1 versus 1.3 years, respectively; p = 0.009), and the findings were confirmed in a multivariate Cox model controlling for age, sex, and smoking status (hazard ratio: 1.52, 95% confidence interval: 1.11–2.08; p = 0.008). In an independent validation set of tumors from 682 patients with stage IV KRAS mutant lung cancers, there was no difference in survival between patients with KRAS codon 13 versus codon 12 mutations (1.0 versus 1.1 years, respectively; p = 0.41).ConclusionsAmong individuals with KRAS mutant metastatic lung cancers treated with conventional therapy, there are no apparent differences in outcome based on KRAS mutation subtype

Distinct Clinical Course of EGFR-Mutant Resected Lung Cancers: Results of Testing of 1118 Surgical Specimens and Effects of Adjuvant Gefitinib and Erlotinib

Background:EGFR and KRAS mutations are mutually exclusive and predict outcomes with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment in patients with stage IV lung cancers. The clinical significance of these mutations in patients with resected stage I–III lung cancers is unclear.Methods:At our institution, resection specimens from patients with stage I–III lung adenocarcinomas are tested for the presence of EGFR or KRAS mutations during routine pathology analysis such that the results are available before consideration of adjuvant therapy. In a cohort of 1118 patients tested over 8 years, overall survival was analyzed using multivariate analysis to control for potential confounders, including age, sex, stage, and smoking history. The impact of adjuvant erlotinib or gefitinib was examined in an independent data set of patients exclusively with EGFR mutation, in which date of recurrence was recorded.Results:In the overall population, we identified 227 KRAS (25%) and 222 EGFR (20%) mutations. Patients with EGFR-mutant lung cancers had a lower risk of death compared with those without EGFR mutations, overall survival (OS) HR 0.51 (95% confidence interval [CI]: 0.34–0.76, p < 0.001). Patients with KRAS-mutant lung cancers had similar outcomes compared with individuals with KRAS wild-type tumors, OS HR 1.17 (95% CI: 0.87–1.57, p = 0.30). A separate data set includes only patients with EGFR-mutant lung cancers identified over 10 years (n = 286). In patients with resected lung cancers and EGFR mutation, treatment with adjuvant erlotinib or gefitinib was associated with a lower risk of recurrence or death, disease-free survival HR 0.43 (95% CI: 0.26–0.72, p = 0.001), and a trend toward improved OS.Conclusions:Patients with resected stage I–III lung cancers and EGFR mutation have a lower risk of death compared with patients without EGFR mutation. This may be because of treatment with EGFR TKIs. Patients with, and without KRAS mutation have similar OS. These data support reflex testing of resected lung adenocarcinomas for EGFR mutation to provide prognostic information and identify patients for enrollment on prospective clinical trials of adjuvant EGFR TKIs