Development Of Gene Expression Signatures Research Articles

Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives.

Simple SummaryGene expression signatures were initially developed to take into account tumor biology for adjuvant chemotherapy decision and have become a standard option in hormone receptors-positive/HER2-negative early breast cancer. While recent randomized phase III studies have provided high level evidence to support their use, much more remains to be explored. This prospective review highlights the unsolved issues regarding targeted populations, delineates the best clinical indications and addresses questions that ongoing and future trials will have to meet. Apart from adjuvant chemotherapy indications, we review their potential interest to tailor neoadjuvant systemic treatments, adjuvant radiation therapy, extended adjuvant hormone therapy and CDK4/6 inhibitor adjuvant treatment.The development of gene expression signatures since the early 2000′s has offered standardized assays to evaluate the prognosis of early breast cancer. Five signatures are currently commercially available and recommended by several international guidelines to individualize adjuvant chemotherapy decisions in hormone receptors-positive/HER2-negative early breast cancer. However, many questions remain unanswered about their predictive ability, reproducibility and external validity in specific populations. They also represent a new hope to tailor (neo)adjuvant systemic treatment, adjuvant radiation therapy, hormone therapy duration and to identify a subset of patients who might benefit from CDK4/6 inhibitor adjuvant treatment. This review will highlight these particular issues, address the remaining questions and discuss the ongoing and future trials.

Profile of the SeptiCyte™ LAB gene expression assay to diagnose infection in critically ill patients

ABSTRACTSepsis is a severe and frequently occurring clinical syndrome, caused by the inflammatory response to infections. Recent studies on the human transcriptome during sepsis have yielded several gene-expression assays that might assist physicians during clinical assessment of patients suspected of sepsis. SeptiCyte™ LAB (Immunexpress, Seattle, WA) is the first gene expression assay that was cleared by the FDA in the United States to distinguish infectious from non-infectious causes of systemic inflammation in critically ill patients. The test consists of the simultaneous amplification of four RNA transcripts (CEACAM4, LAMP1, PLAC8, and PLA2G7) in whole blood using a quantitative real-time PCR reaction. This review provides an overview of the challenges in the diagnosis of sepsis, the development of gene expression signatures, and a detailed description of available clinical performance studies evaluating SeptiCyte™ LAB.

Development of gene expression signatures characterizing the tumor-immune interaction.

205 Background: The efficacy of anti-tumor immunity depends on diverse factors, including not just abundance of immune cell populations but also activities of those populations and of tumor cells. Many of these processes are onerous to assay, but all are reflected in a tumor’s gene expression profile. Using a novel method, we develop gene expression signatures measuring a variety of biological processes underlying the tumor-immune interaction. These signatures fall into categories including antigen availability, structural barriers to immune infiltration, inhibitory signaling by both immune and tumor cells, inhibitory metabolism, pro-immune signaling, killing of tumor cells, tumor receptiveness to immune signaling, and tumor proliferation and death. Methods: We develop a method to train signatures of biological processed by synthesizing biological knowledge and large gene expression datasets. For a given process, we use literature searches and expert knowledge to derive lists of candidate genes. We then evaluate the co-expression of these candidate genes in data from The Cancer Genome Atlas (TCGA), discarding genes whose co-expression patterns are incompatible with their measuring their putative biological process. This approach safeguards the interpretability of our signatures: we only report signatures whose genes show evidence for measuring the desired biology. Finally, we further exploit co-expression patterns to obtain optimal weights for each signature gene. Results: We attempted to train signatures of over 30 biological processes involved in immune oncology. Of these, 17 candidate gene sets displayed sufficient evidence for measuring their putative biology. We show these signatures provide granular but intelligible descriptions of both immunotherapy datasets and single samples. We find they improve power in differential expression analyses and in training of predictors of drug response. Conclusions: The signatures we derive convert gene expression data into measurements of biological processes central to immune oncology, and they improve statistical power and interpretation of results in immunotherapy studies. Our training procedure ensures these signatures measure their intended biology.

Impact of Neutron Exposure on Global Gene Expression in a Human Peripheral Blood Model

The detonation of an improvised nuclear device would produce prompt radiation consisting of both photons (gamma rays) and neutrons. While much effort in recent years has gone into the development of radiation biodosimetry methods suitable for mass triage, the possible effect of neutrons on the endpoints studied has remained largely uninvestigated. We have used a novel neutron irradiator with an energy spectrum based on that 1-1.5 km from the epicenter of the Hiroshima blast to begin examining the effect of neutrons on global gene expression, and the impact this may have on the development of gene expression signatures for radiation biodosimetry. We have exposed peripheral blood from healthy human donors to 0.1, 0.3, 0.5 or 1 Gy of neutrons ex vivo using our neutron irradiator, and compared the transcriptomic response 24 h later to that resulting from sham exposure or exposure to 0.1, 0.3, 0.5, 1, 2 or 4 Gy of photons (X rays). We identified 125 genes that responded significantly to both radiation qualities as a function of dose, with the magnitude of response to neutrons generally being greater than that seen after X-ray exposure. Gene ontology analysis suggested broad involvement of the p53 signaling pathway and general DNA damage response functions across all doses of both radiation qualities. Regulation of immune response and chromatin-related functions were implicated only following the highest doses of neutrons, suggesting a physiological impact of greater DNA damage. We also identified several genes that seem to respond primarily as a function of dose, with less effect of radiation quality. We confirmed this pattern of response by quantitative real-time RT-PCR for BAX, TNFRSF10B, ITLN2 and AEN and suggest that gene expression may provide a means to differentiate between total dose and a neutron component.

Genes associated with histopathologic features of triple negative breast tumors predict molecular subtypes.

Distinct subtypes of triple negative (TN) breast cancer have been identified by tumor expression profiling. However, little is known about the relationship between histopathologic features of TN tumors, which reflect aspects of both tumor behavior and tumor microenvironment, and molecular TN subtypes. The histopathologic features of TN tumors were assessed by central review and 593 TN tumors were subjected to whole genome expression profiling using the Illumina Whole Genome DASL array. TN molecular subtypes were defined based on gene expression data associated with histopathologic features of TN tumors. Gene expression analysis yielded signatures for four TN subtypes (basal-like, androgen receptor positive, immune, and stromal) consistent with previous studies. Expression analysis also identified genes significantly associated with the 12 histological features of TN tumors. Development of signatures using these markers of histopathological features resulted in six distinct TN subtype signatures, including an additional basal-like and stromal signature. The additional basal-like subtype was distinguished by elevated expression of cell motility and glucose metabolism genes and reduced expression of immune signaling genes, whereas the additional stromal subtype was distinguished by elevated expression of immunomodulatory pathway genes. Histopathologic features that reflect heterogeneity in tumor architecture, cell structure, and tumor microenvironment are related to TN subtype. Accounting for histopathologic features in the development of gene expression signatures, six major subtypes of TN breast cancer were identified.

Bayesian probit regression model for the diagnosis of pulmonary fibrosis: proof-of-principle

BackgroundThe accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is a major clinical challenge. We developed a model to diagnose IPF by applying Bayesian probit regression (BPR) modelling to gene expression profiles of whole lung tissue.MethodsWhole lung tissue was obtained from patients with idiopathic pulmonary fibrosis (IPF) undergoing surgical lung biopsy or lung transplantation. Controls were obtained from normal organ donors. We performed cluster analyses to explore differences in our dataset. No significant difference was found between samples obtained from different lobes of the same patient. A significant difference was found between samples obtained at biopsy versus explant. Following preliminary analysis of the complete dataset, we selected three subsets for the development of diagnostic gene signatures: the first signature was developed from all IPF samples (as compared to controls); the second signature was developed from the subset of IPF samples obtained at biopsy; the third signature was developed from IPF explants. To assess the validity of each signature, we used an independent cohort of IPF and normal samples. Each signature was used to predict phenotype (IPF versus normal) in samples from the validation cohort. We compared the models' predictions to the true phenotype of each validation sample, and then calculated sensitivity, specificity and accuracy.ResultsSurprisingly, we found that all three signatures were reasonably valid predictors of diagnosis, with small differences in test sensitivity, specificity and overall accuracy.ConclusionsThis study represents the first use of BPR on whole lung tissue; previously, BPR was primarily used to develop predictive models for cancer. This also represents the first report of an independently validated IPF gene expression signature. In summary, BPR is a promising tool for the development of gene expression signatures from non-neoplastic lung tissue. In the future, BPR might be used to develop definitive diagnostic gene signatures for IPF, prognostic gene signatures for IPF or gene signatures for other non-neoplastic lung disorders such as bronchiolitis obliterans.

Development of Gene Expression Signatures for Practical Radiation Biodosimetry

In a large-scale radiologic emergency, estimates of exposure doses and radiation injury would be required for individuals without physical dosimeters. Current methods are inadequate for the task, so we are developing gene expression profiles for radiation biodosimetry. This approach could provide both an estimate of physical radiation dose and an indication of the extent of individual injury or future risk. We used whole genome microarray expression profiling as a discovery platform to identify genes with the potential to predict radiation dose across an exposure range relevant for medical decision making in a radiologic emergency. Human peripheral blood from 10 healthy donors was irradiated ex vivo, and global gene expression was measured both 6 and 24 h after exposure. A 74-gene signature was identified that distinguishes between four radiation doses (0.5, 2, 5, and 8 Gy) and controls. More than one third of these genes are regulated by TP53. A nearest centroid classifier using these same 74 genes correctly predicted 98% of samples taken either 6 h or 24 h after treatment as unexposed, exposed to 0.5, 2, or > or =5 Gy. Expression patterns of five genes (CDKN1A, FDXR, SESN1, BBC3, and PHPT1) from this signature were also confirmed by real-time polymerase chain reaction. The ability of a single gene set to predict radiation dose throughout a window of time without need for individual pre-exposure controls represents an important advance in the development of gene expression for biodosimetry.

Gene-Expression Signatures in Oncology Diagnostics

The advent of DNA microarray technologies has enabled the development of gene expression signatures that can be used for prognostic and predictive purposes. This new information can change the paradigm of how medicine is practiced, coupling physical examination, pathology and clinical tests with new molecular information. However, many unanswered questions regarding sample acquisition, platform development, signature validation and clinical trial design will need to be addressed before this new medical content will have an impact on the clinical setting. This article will examine some of these issues in greater detail, focusing on tissue type, platform comparison, biospecimen collection and signature validation.