Linking genes to diseases: it's all in the data

Genome-wide association analyses on large patient cohorts are generating large sets of candidate disease genes. This is coupled with the availability of ever-increasing genomic databases and a rapidly expanding repository of biomedical literature. Computational approaches to disease-gene association attempt to harness these data sources to identify the most likely disease gene candidates for further empirical analysis by translational researchers, resulting in efficient identification of genes of diagnostic, prognostic and therapeutic value. Existing computational methods analyze gene structure and sequence, functional annotation of candidate genes, characteristics of known disease genes, gene regulatory networks, protein-protein interactions, data from animal models and disease phenotype. To date, a few studies have successfully applied computational analysis of clinical phenotype data for specific diseases and shown genetic associations. In the near future, computational strategies will be facilitated by improved integration of clinical and computational research, and by increased availability of clinical phenotype data in a format accessible to computational approaches

A novel integrative risk index of papillary thyroid cancer progression combining genomic alterations and clinical factors.

Although the majority of papillary thyroid cancer (PTC) is indolent, a subset of PTC behaves aggressively despite the best available treatment. A major clinical challenge is to reliably distinguish early on between those patients who need aggressive treatment from those who do not. Using a large cohort of PTC samples obtained from The Cancer Genome Atlas (TCGA), we analyzed the association between disease progression and multiple forms of genomic data, such as transcriptome, somatic mutations, and somatic copy number alterations, and found that genes related to FOXM1 signaling pathway were significantly associated with PTC progression. Integrative genomic modeling was performed, controlling for demographic and clinical characteristics, which included patient age, gender, TNM stages, histological subtypes, and history of other malignancy, using a leave-one-out elastic net model and 10-fold cross validation. For each subject, the model from the remaining subjects was used to determine the risk index, defined as a linear combination of the clinical and genomic variables from the elastic net model, and the stability of the risk index distribution was assessed through 2,000 bootstrap resampling. We developed a novel approach to combine genomic alterations and patient-related clinical factors that delineates the subset of patients who have more aggressive disease from those whose tumors are indolent and likely will require less aggressive treatment and surveillance (p = 4.62 × 10-10, log-rank test). Our results suggest that risk index modeling that combines genomic alterations with current staging systems provides an opportunity for more effective anticipation of disease prognosis and therefore enhanced precision management of PTC

Chromosomal instability in untreated primary prostate cancer as an indicator of metastatic potential.

BackgroundMetastatic prostate cancer (PC) is highly lethal. The ability to identify primary tumors capable of dissemination is an unmet need in the quest to understand lethal biology and improve patient outcomes. Previous studies have linked chromosomal instability (CIN), which generates aneuploidy following chromosomal missegregation during mitosis, to PC progression. Evidence of CIN includes broad copy number alterations (CNAs) spanning > 300 base pairs of DNA, which may also be measured via RNA expression signatures associated with CNA frequency. Signatures of CIN in metastatic PC, however, have not been interrogated or well defined. We examined a published 70-gene CIN signature (CIN70) in untreated and castration-resistant prostate cancer (CRPC) cohorts from The Cancer Genome Atlas (TCGA) and previously published reports. We also performed transcriptome and CNA analysis in a unique cohort of untreated primary tumors collected from diagnostic prostate needle biopsies (PNBX) of localized (M0) and metastatic (M1) cases to determine if CIN was linked to clinical stage and outcome.MethodsPNBX were collected from 99 patients treated in the VA Greater Los Angeles (GLA-VA) Healthcare System between 2000 and 2016. Total RNA was extracted from high-grade cancer areas in PNBX cores, followed by RNA sequencing and/or copy number analysis using OncoScan. Multivariate logistic regression analyses permitted calculation of odds ratios for CIN status (high versus low) in an expanded GLA-VA PNBX cohort (n = 121).ResultsThe CIN70 signature was significantly enriched in primary tumors and CRPC metastases from M1 PC cases. An intersection of gene signatures comprised of differentially expressed genes (DEGs) generated through comparison of M1 versus M0 PNBX and primary CRPC tumors versus metastases revealed a 157-gene "metastasis" signature that was further distilled to 7-genes (PC-CIN) regulating centrosomes, chromosomal segregation, and mitotic spindle assembly. High PC-CIN scores correlated with CRPC, PC-death and all-cause mortality in the expanded GLA-VA PNBX cohort. Interestingly, approximately 1/3 of M1 PNBX cases exhibited low CIN, illuminating differential pathways of lethal PC progression.ConclusionsMeasuring CIN in PNBX by transcriptome profiling is feasible, and the PC-CIN signature may identify patients with a high risk of lethal progression at the time of diagnosis

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

The correlation between the mutation of protein kinase genes and the clinical characteristics of breast cancer progression

It is accepted that breast cancer (BC) is a heterogeneous disease. In order to investigate BC as a group of disease sub-types, the varying clinical characteristics of BC patients must be considered. In this project a series of clinical, pathological, genetic and genomic data, retrieved from multiple data repositories, will be reviewed for selection in a large-scale meta-analysis and then categorised into 5 sub-groups (Luminal A, Luminal B, Basal, HER2 and Normal). The meta-analysis is primarily designed to ascertain if a correlation exists between the mutation of protein kinase (PK) genes and BC progression. As PK genes play important roles in regulating most cellular processes (e.g. cell proliferation, differentiation and apoptosis), it is no surprise that deregulated PK activity is a frequent cause of disease, and that PK genes are often oncogenes. The meta-analysis objectives are two-fold: 1. To conduct an integrative meta-analysis of the differential gene expression of the PK gene family between clinical categories of BC progression (low vs high proliferation; luminal vs basal tissue; and grade 1 vs grade 3 tumours). Results from the meta-analysis will generate a ranked list of PK gene expression profiles observed in BC progression. 2. Through the use of powerful bioinformatics tools and sequence analysis interfaces the ranked PK list will be used to direct investigations into the correlations between: codon usage bias; aberrant epigenetic factors; somatic mutations; and observed structural/functional changes of deregulated PK genes in different BC progression categories. To address these objectives a series of in silico bioinformatics experiments have been designed. A software program (MYGEO) has been specifically written for: multiple dataset download; calculation of p-values between BC progression groups; finding Q-values to control for the false discovery rate over multiple dataset comparisons; and to perform permutation testing on the ranked PK gene list; and 2D/3D sequence analysis functions for the analysis of structure/function relationships in significantly differentiated PK genes in BC progression. This project will benefit our understanding of the complex system of BC biology by identifying significantly deregulated PK genes in BC progression. The results will identify BC biomarkers and structural/functional locations within PK genes not yet elucidated, thus providing new directions for the development of PK inhibitors and improving the effectiveness of current BC treatment strategies

Complete Genome Sequence and Comparative Metabolic Profiling of the Prototypical Enteroaggregative Escherichia coli Strain 042

Background \ud Escherichia coli can experience a multifaceted life, in some cases acting as a commensal while in other cases causing intestinal and/or extraintestinal disease. Several studies suggest enteroaggregative E. coli are the predominant cause of E. coli-mediated diarrhea in the developed world and are second only to Campylobacter sp. as a cause of bacterial-mediated diarrhea. Furthermore, enteroaggregative E. coli are a predominant cause of persistent diarrhea in the developing world where infection has been associated with malnourishment and growth retardation. \ud \ud Methods \ud In this study we determined the complete genomic sequence of E. coli 042, the prototypical member of the enteroaggregative E. coli, which has been shown to cause disease in volunteer studies. We performed genomic and phylogenetic comparisons with other E. coli strains revealing previously uncharacterised virulence factors including a variety of secreted proteins and a capsular polysaccharide biosynthetic locus. In addition, by using Biolog™ Phenotype Microarrays we have provided a full metabolic profiling of E. coli 042 and the non-pathogenic lab strain E. coli K-12. We have highlighted the genetic basis for many of the metabolic differences between E. coli 042 and E. coli K-12. \ud \ud Conclusion \ud This study provides a genetic context for the vast amount of experimental and epidemiological data published thus far and provides a template for future diagnostic and intervention strategies

Adenoid cystic carcinoma: emerging role of translocations and gene fusions.

Adenoid cystic carcinoma (ACC), the second most common salivary gland malignancy, is notorious for poor prognosis, which reflects the propensity of ACC to progress to clinically advanced metastatic disease. Due to high long-term mortality and lack of effective systemic treatment, the slow-growing but aggressive ACC poses a particular challenge in head and neck oncology. Despite the advancements in cancer genomics, up until recently relatively few genetic alterations critical to the ACC development have been recognized. Although the specific chromosomal translocations resulting in MYB-NFIB fusions provide insight into the ACC pathogenesis and represent attractive diagnostic and therapeutic targets, their clinical significance is unclear, and a substantial subset of ACCs do not harbor the MYB-NFIB translocation. Strategies based on detection of newly described genetic events (such as MYB activating super-enhancer translocations and alterations affecting another member of MYB transcription factor family-MYBL1) offer new hope for improved risk assessment, therapeutic intervention and tumor surveillance. However, the impact of these approaches is still limited by an incomplete understanding of the ACC biology, and the manner by which these alterations initiate and drive ACC remains to be delineated. This manuscript summarizes the current status of gene fusions and other driver genetic alterations in ACC pathogenesis and discusses new therapeutic strategies stemming from the current research