Tumor mutational burden standardization initiatives: Recommendations for consistent tumor mutational burden assessment in clinical samples to guide immunotherapy treatment decisions.

Characterization of tumors utilizing next-generation sequencing methods, including assessment of the number of somatic mutations (tumor mutational burden [TMB]), is currently at the forefront of the field of personalized medicine. Recent clinical studies have associated high TMB with improved patient response rates and survival benefit from immune checkpoint inhibitors; hence, TMB is emerging as a biomarker of response for these immunotherapy agents. However, variability in current methods for TMB estimation and reporting is evident, demonstrating a need for standardization and harmonization of TMB assessment methodology across assays and centers. Two uniquely placed organizations, Friends of Cancer Research (Friends) and the Quality Assurance Initiative Pathology (QuIP), have collaborated to coordinate efforts for international multistakeholder initiatives to address this need. Friends and QuIP, who have partnered with several academic centers, pharmaceutical organizations, and diagnostic companies, have adopted complementary, multidisciplinary approaches toward the goal of proposing evidence-based recommendations for achieving consistent TMB estimation and reporting in clinical samples across assays and centers. Many factors influence TMB assessment, including preanalytical factors, choice of assay, and methods of reporting. Preliminary analyses highlight the importance of targeted gene panel size and composition, and bioinformatic parameters for reliable TMB estimation. Herein, Friends and QuIP propose recommendations toward consistent TMB estimation and reporting methods in clinical samples across assays and centers. These recommendations should be followed to minimize variability in TMB estimation and reporting, which will ensure reliable and reproducible identification of patients who are likely to benefit from immune checkpoint inhibitors.

Probe mapping across multiple microarray platforms.

Access to gene expression data has become increasingly common in recent years; however, analysis has become more difficult as it is often desirable to integrate data from different platforms. Probe mapping across microarray platforms is the first and most crucial step for data integration. In this article, we systematically review and compare different approaches to map probes across seven platforms from different vendors: U95A, U133A and U133 Plus 2.0 from Affymetrix, Inc.; HT-12 v1, HT-12v2 and HT-12v3 from Illumina, Inc.; and 4112A from Agilent, Inc. We use a unique data set, which contains 56 lung cancer cell line samples-each of which has been measured by two different microarray platforms-to evaluate the consistency of expression measurement across platforms using different approaches. Based on the evaluation from the empirical data set, the BLAST alignment of the probe sequences to a recent revision of the Transcriptome generated better results than using annotations provided by Vendors or from Bioconductor's Annotate package. However, a combination of all three methods (deemed the 'Consensus Annotation') yielded the most consistent expression measurement across platforms. To facilitate data integration across microarray platforms for the research community, we develop a user-friendly web-based tool, an API and an R package to map data across different microarray platforms from Affymetrix, Illumina and Agilent. Information on all three can be found at http://qbrc.swmed.edu/software/probemapper/.

Lycopene inhibits IGF-I signal transduction and growth in normal prostate epithelial cells by decreasing DHT-modulated IGF-I production in co-cultured reactive stromal cells.

Prostate stromal and epithelial cell communication is important in prostate functioning and cancer development. Primary human stromal cells from normal prostate stromal cells (PRSC) maintain a smooth muscle phenotype, whereas those from prostate cancer (6S) display reactive and fibroblastic characteristics. Dihydrotestosterone (DHT) stimulates insulin-like growth factor-I (IGF-I) production by 6S but not PSRC cells. Effects of reactive versus normal stroma on normal human prostate epithelial (NPE or PREC) cells are poorly understood. We co-cultured NPE plus 6S or PRSC cells to compare influences of different stromal cells on normal epithelium. Because NPE and PREC cells lose androgen receptor (AR) expression in culture, DHT effects must be modulated by associated stromal cells. When treated with camptothecin (CM), NPE cells, alone and in stromal co-cultures, displayed a dose-dependent increase in DNA fragmentation. NPE/6S co-cultures exhibited reduced CM-induced cell death with exposure to DHT, whereas NPE/PRSC co-cultures exhibited CM-induced cell death regardless of DHT treatment. DHT blocked CM-induced, IGF-I-mediated, NPE death in co-cultured NPE/6S cells without, but not with, added anti-IGF-I and anti-IGF-R antibodies. Lycopene consumption is inversely related to human prostate cancer risk and inhibits IGF-I and androgen signaling in rat prostate cancer. In this study, lycopene, in dietary concentrations, reversed DHT effects of 6S cells on NPE cell death, decreased 6S cell IGF-I production by reducing AR and beta-catenin nuclear localization and inhibited IGF-I-stimulated NPE and PREC growth, perhaps by attenuating IGF-I's effects on serine phosphorylation of Akt and GSK3beta and tyrosine phosphorylation of GSK3. This study expands the understanding of the preventive mechanisms of lycopene in prostate cancer.

Ensemble-based network aggregation improves the accuracy of gene network reconstruction.

Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies - producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled "ENA", accessible on CRAN (http://cran.r-project.org/web/packages/ENA/).

Developing precision medicine in a global world.

Advances in understanding the biology of cancer, as well as advances in diagnostic technologies, such as the advent of affordable high-resolution DNA sequencing, have had a major impact on the approach to identification of specific alterations in a given patient's cancer that could be used as a basis for treatment selection, and hence the development of companion diagnostics. Although there are now several examples of successful development of companion diagnostics that allow identification of patients who will achieve the greatest benefit from a new therapeutic, the path to coapproval of a diagnostic test along with a new therapeutic is complex and often inefficient. This review and the accompanying articles examine the current state of companion diagnostic development in the United States and Europe from academic, industry, regulatory, and economic perspectives. See all articles in this CCR Focus section, "The Precision Medicine Conundrum: Approaches to Companion Diagnostic Co-development."

Comparing statistical methods for constructing large scale gene networks.

The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structure; (2) GeneNet and SPACE (Sparse PArtial Correlation Estimation) performed well in identifying a few connections with high specificity.

Model-Based Background Correction (MBCB): R Methods and GUI for Illumina Bead-array Data.

SUMMARY: Illumina BeadArray platform (Illumina Inc.) is playing an increasing role in cancer research. MBCB, an R package designed for use on Illumina Bead-Array data, allows for microarray data to be pre-processed through various model-based statistical methods. These model-based background-correction methods have proven to be a significant improvement over the traditional methods provided by Illumina in their BeadStudio software. MBCB accepts the summarized bead-type data; the data can then be normalized and background-corrected in a statistically-efficient manner. When compared to the popular Robust multi-array (RMA) background correction approach and the default, Illumina-provided background-correction method, MBCB has shown to lead to more precise determination of gene expression and better biological interpretation of Illumina BeadArray data. The software developed will facilitate molecular biomedical - especially cancer - research. AVAILABILITY: This package will soon be available from Bioconductor. Instructions for use are included with the package.

Androgen receptor or estrogen receptor-beta blockade alters DHEA-, DHT-, and E(2)-induced proliferation and PSA production in human prostate cancer cells.

BACKGROUND: Dehydroepiandrosterone (DHEA) is an endogenous steroid that is metabolized to androgens and/or estrogens in the human prostate. DHEA levels decline with age, and use of DHEA supplements to retard the aging process is of unproved effectiveness and safety. LNCaP and LAPC-4 prostate cancer cells were used to determine whether DHEA-modulated proliferation and prostate specific antigen (PSA) production were mediated via the androgen receptor (AR) and/or ERbeta. METHODS: Cells were treated with DHEA, DHT, or E(2) and antagonists to AR (Casodex-bicalutamide) or ER (ICI 182,780) or siRNA to the respective receptors. Proliferation was assessed by MTT assay and PSA mRNA and protein secretion were measured by quantitative real-time PCR and ELISA. Associations of AR and ERbeta were analyzed by co-immunoprecipitation studies and fluorescent confocal microscopy. RESULTS: DHEA-, T-, and E(2)-induced proliferation of LNCaP cells was blunted by Casodex but not by ICI treatment. In LNCaP cells, Casodex and ICI suppressed hormone-induced PSA production. In LAPC-4 cells, DHT-stimulated PSA mRNA was inhibited by Casodex and ICI, and the minimal stimulation by DHEA was inhibited by ICI. Use of siRNAs confirmed involvement of AR and ERbeta in hormone-induced PSA production while AR-ERbeta co-association was suggested by immunoprecipitation and nuclear co-localization. CONCLUSIONS: These findings support involvement of both AR and ERbeta in mediating DHEA-, DHT-, and E(2)-induced PSA expression in prostate cancer cells.

American College Of Radiology/Society of Breast Imaging curriculum for resident and fellow education in breast imaging.

The ACR and the Society of Breast Imaging have revised the curriculum for resident and fellow education in breast imaging on the basis of substantial changes in breast imaging practice since the initial curriculum was published in 2000. This curriculum provides guidance to academic chairs, residency program directors, and academic section chiefs in assessing and improving their residency and fellowship training programs and indicates to residents and breast imaging fellows the topics they need to learn and the experience they should try to acquire during their training. Radiologists already in practice also may find the curriculum useful in outlining the material they need to know to remain up to date in the practice of breast imaging.