William Schierding

Genome remodelling in a basal-like breast cancer metastasis and xenograft.

Massively parallel DNA sequencing technologies provide an unprecedented ability to screen entire genomes for genetic changes associated with tumour progression. Here we describe the genomic analyses of four DNA samples from an African-American patient with basal-like breast cancer: peripheral blood, the primary tumour, a brain metastasis and a xenograft derived from the primary tumour. The metastasis contained two de novo mutations and a large deletion not present in the primary tumour, and was significantly enriched for 20 shared mutations. The xenograft retained all primary tumour mutations and displayed a mutation enrichment pattern that resembled the metastasis. Two overlapping large deletions, encompassing CTNNA1, were present in all three tumour samples. The differential mutation frequencies and structural variation patterns in metastasis and xenograft compared with the primary tumour indicate that secondary tumours may arise from a minority of cells within the primary tumour.

Whole Genome Analysis Informs Breast Cancer Response to Aromatase Inhibition

To correlate the variable clinical features of oestrogen-receptor-positive breast cancer with somatic alterations, we studied pretreatment tumour biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to haematopoietic disorders. Mutant MAP3K1 was associated with luminal A status, low-grade histology and low proliferation rates, whereas mutant TP53 was associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon aromatase inhibitor treatment. Pathway analysis demonstrated that mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in oestrogen-receptor-positive breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumour biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.

MuSiC: Identifying mutational significance in cancer genomes

Massively parallel sequencing technology and the associated rapidly decreasing sequencing costs have enabled systemic analyses of somatic mutations in large cohorts of cancer cases. Here we introduce a comprehensive mutational analysis pipeline that uses standardized sequence-based inputs along with multiple types of clinical data to establish correlations among mutation sites, affected genes and pathways, and to ultimately separate the commonly abundant passenger mutations from the truly significant events. In other words, we aim to determine the Mutational Significance in Cancer (MuSiC) for these large data sets. The integration of analytical operations in the MuSiC framework is widely applicable to a broad set of tumor types and offers the benefits of automation as well as standardization. Herein, we describe the computational structure and statistical underpinnings of the MuSiC pipeline and demonstrate its performance using 316 ovarian cancer samples from the TCGA ovarian cancer project. MuSiC correctly confirms many expected results, and identifies several potentially novel avenues for discovery.

SciClone: Inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution

The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.

Plasticity of the Systemic Inflammatory Response to Acute Infection during Critical Illness: Development of the Riboleukogram

Background Diagnosis of acute infection in the critically ill remains a challenge. We hypothesized that circulating leukocyte transcriptional profiles can be used to monitor the host response to and recovery from infection complicating critical illness. Methodology/Principal Findings A translational research approach was employed. Fifteen mice underwent intratracheal injections of live P. aeruginosa, P. aeruginosa endotoxin, live S. pneumoniae, or normal saline. At 24 hours after injury, GeneChip microarray analysis of circulating buffy coat RNA identified 219 genes that distinguished between the pulmonary insults and differences in 7-day mortality. Similarly, buffy coat microarray expression profiles were generated from 27 mechanically ventilated patients every two days for up to three weeks. Significant heterogeneity of VAP microarray profiles was observed secondary to patient ethnicity, age, and gender, yet 85 genes were identified with consistent changes in abundance during the seven days bracketing the diagnosis of VAP. Principal components analysis of these 85 genes appeared to differentiate between the responses of subjects who did versus those who did not develop VAP, as defined by a general trajectory (riboleukogram) for the onset and resolution of VAP. As patients recovered from critical illness complicated by acute infection, the riboleukograms converged, consistent with an immune attractor. Conclusions/Significance Here we present the culmination of a mouse pneumonia study, demonstrating for the first time that disease trajectories derived from microarray expression profiles can be used to quantitatively track the clinical course of acute disease and identify a state of immune recovery. These data suggest that the onset of an infection-specific transcriptional program may precede the clinical diagnosis of pneumonia in patients. Moreover, riboleukograms may help explain variance in the host response due to differences in ethnic background, gender, and pathogen. Prospective clinical trials are indicated to validate our results and test the clinical utility of riboleukograms.

Differences in outcome between obese and nonobese patients following severe blunt trauma are not consistent with an early inflammatory genomic response.

Objectives:Obesity has been demonstrated to alter a number of acute and chronic medical conditions. The effect of obesity on severely injured patients, however, remains incompletely defined. We sought to unravel potential physiologic and genomic alterations induced by obesity in severely injured blunt trauma patients. Design:A retrospective review of clinical and genomic information contained in the Inflammation and the Host Response to Injury multicenter trauma-related database examining the relationship between body mass index and the early genomic response from peripheral blood leukocytes to patient outcome following severe blunt trauma was performed. Setting:Multicenter collaboration between university-based academic trauma centers. Patients:Severely injured blunt trauma patients enrolled in the database. Interventions:None. Measurements and Main Results:Univariate analysis of 455 severely injured trauma patients using the National Institutes of Health/World Health Organization body mass index classification system revealed significant increases in morbidity, including longer intensive care unit stays and a greater number of ventilator days, cardiac arrests, episodes of acute renal failure, and patients developing multiple organ failure. Regression modeling identified body mass index class as being independently associated with adverse outcomes and increased morbidity but an inverse relationship with mortality in patients who suffered severe blunt traumatic injury. Initial leukocyte genomic expression patterns between 163 patients in the four different body mass index groupings did not differ; however, analysis of gene differences between body mass index classes occurring over time demonstrated significant changes in 513 probe sets with significant pathway differences being related to cellular metabolism. Conclusions:Increasing body mass index is associated with increased morbidity following severe blunt trauma. The initial blood leukocyte inflammatory response to blunt trauma does not appear to differ significantly between patients despite increasing body mass index. Resolution of the inflammatory response may differ between patients on the basis of body mass index; however, additional work is needed to clarify the potential causality of this finding.

Targeted delivery of siRNA to cell death proteins in sepsis

Immune suppression is a major cause of morbidity and mortality in the patients with sepsis. Apoptotic loss of immune effector cells such as CD4 T and B cells is a key component in the loss of immune competence in sepsis. Inhibition of lymphocyte apoptosis has led to improved survival in animal models of sepsis. Using quantitative real-time polymerase chain reaction of isolated splenic CD4 T and B cells, we determined that Bim and PUMA, two key cell death proteins, are markedly upregulated during sepsis. Lymphocytes have been notoriously difficult to transfect with small interfering RNA (siRNA). Consequently a novel, cyclodextrin polymer-based, transferrin receptor-targeted, delivery vehicle was used to coadminister siRNA to Bim and PUMA to mice immediately after cecal ligation and puncture. Antiapoptotic siRNA-based therapy markedly decreased lymphocyte apoptosis and prevented the loss of splenic CD4 T and B cells. Flow cytometry confirmed in vivo delivery of siRNA to CD4 T and B cells and also demonstrated decreases in intracellular Bim and PUMA protein. In conclusion, Bim and PUMA are two critical mediators of immune cell death in sepsis. Use of a novel cyclodextrin polymer-based, transferrin receptor-targeted siRNA delivery vehicle enables effective administration of antiapoptotic siRNAs to lymphocytes and reverses the immune cell depletion that is a hallmark of this highly lethal disorder.

Genome Modeling System: A Knowledge Management Platform for Genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

Estimating Sparse Gene Regulatory Networks Using a Bayesian Linear Regression

In this paper, we propose a gene regulatory network (GRN) estimation method, which assumes that such networks are typically sparse, using time-series microarray datasets. We represent the regulatory relationships between the genes using weights, with the “net” regulation influence on a genes expression being the summation of the independent regulatory inputs. We estimate the weights using a Bayesian linear regression method for sparse parameter vectors. We apply our proposed method to the extraction of differential gene expression software selected genes of a human buffy-coat microarray expression profile dataset of ventilator-associated pneumonia (VAP), and compare the estimation result with the GRNs estimated using both a correlation coefficient method and a database-based method ingenuity pathway analysis. A biological analysis of the resulting consensus network that is derived using the GRNs, estimated with both our and the correlation-coefficient methods results in four biologically meaningful subnetworks. Also, our method performs either better than or competitively with the existing well-established GRN estimation methods. Moreover, it performs comparatively with respect to: 1) the ground-truth GRNs for the in silico 50- and 100-gene datasets reported recently in the DREAM3 challenge and 2) the GRN estimated using a mutual information-based method for the top-ranked Bayesian analysis of time series (a Bayesian user-friendly software for analyzing time-series microarray experiments) selected genes of the VAP dataset.

Preliminary Evidence for Leukocyte Transcriptional Signatures for Pediatric Ventilator-Associated Pneumonia

Objective: Ventilator-associated pneumonia (VAP) is a significant contributor to intensive care unit (ICU) morbidity and mortality and presents a significant diagnostic challenge. Our hypothesis was that blood RNA expression profiles can be used to track the response to VAP in children, using the same methods that proved informational in adults. Design: A pilot, nonrandomized, repeated measures case-control study of changes in the abundance of total RNA in buffy coat and clinical scores for VAP. Setting: A large, multispecialty university-based pediatric ICU and cardiac ICU. Patients: Seven children requiring intubation and mechanical ventilation. Interventions: Blood samples were drawn at time of enrollment and every 48 hours for a maximum of 11 samples (21 days). Patients ranged in age from 1 to 18 months (mean 8 months). All patients survived to the end of the study. Of the 7 patients studied, 4 developed VAP. Measurements and Main Results: Statistical analysis of the Affymetrix Human Genome Focus GeneChip signal was conducted on normalized expression values of 8793 probe sets using analysis of variance (ANOVA) with a false discovery rate of 0.10. The expression patterns of 48 genes appeared to discriminate between the 2 classes of ventilated children: those with and those without pneumonia. Gene expression network analysis revealed several gene ontologies of interest, including cell proliferation, differentiation, growth, and apoptosis, as well as genes not previously implicated in sepsis. Conclusions: These preliminary data are the first in critically ill children supporting the hypothesis that there is a detectable VAP signal in gene expression profiles. Larger studies are needed to validate these preliminary findings and test the diagnostic value of longitudinal changes in leukocyte RNA signatures.