Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models

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

Bioinformatic Analysis for the Validation of Novel Biomarkers for Cancer Diagnosis and Drug Sensitivity

Background: The genetic control of tumour progression presents the opportunity for bioinformatics and gene expression data to be used as a basis for tumour grading. The development of a genetic signature based on microarray data allows for the development of personalised chemotherapeutic regimes. Method: ONCOMINE was utilised to create a genetic signature for ovarian serous adenocarcinoma and to compare the expression of genes between normal ovarian and cancerous cells. Ingenuity Pathways Analysis was also utilised to develop molecular pathways and observe interactions with exogenous molecules. Results: The gene signature demonstrated 98.6% predictive capability for the differentiation between borderline ovarian serous neoplasm and ovarian serous adenocarcinoma. The data demonstrated that many genes were related to angiogenesis. Thymidylate synthase, GLUT-3 and HSP90AA1 were related to tanespimycin sensitivity (p=0.005). Conclusions: Genetic profiling with the gene signature demonstrated potential for clinical use. The use of tanespimycin alongside overexpression of thymidylate synthase, GLUT-3 and HSP90AA1 is a novel consideration for ovarian cancer treatment

Pathway-Based Multi-Omics Data Integration for Breast Cancer Diagnosis and Prognosis.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Public data and open source tools for multi-assay genomic investigation of disease

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Hunting a Silent Killer. Biomolecular Approaches in Ovarian Cancer

Ovarian cancer is a heterogeneous disease and recent advances in improving patient outcome havebeen limited. It is estimated that a woman’s risk of developing ovarian cancer during her lifetime is about 1 in 70, making it a frequently occurring cancer type in women.This thesis investigated biological events in ovarian cancer, which were translated into clinically relevant observations using different biomolecular approaches.Study I investigated the use of sex steroid hormone receptor expression as a prognostic marker in ovarian cancer. We evaluated the expression of estrogen receptor (ER)α, ERβ, progesterone receptor (PR) and androgen receptor (AR) in a cohort of serous and endometrioid cancers. We found that expression of PR and AR was associated with favorable outcome, and co-expression of AR and PR granted an additional prognostic effect. Although we were unable to detect any association between mRNA expression and a favorable outcome in an independent data set, molecular subtypes in the data set differentially expressed PGR and ESR1. Whether this effect accounted for the reported improved outcome in some of the subgroups remains to be investigated.Study II characterized ovarian clear cell carcinomas (OCCC) with the purpose of identifying potential treatment candidates. OCCC presents a distinct molecular subtype of ovarian cancer, with high chemoresistance. Through integrative bioinformatics we evaluated combined gene expression data, DNA sequencing data and protein expression data from OCCC tumors. The collective data suggested Rho GTPases as a potential treatment candidate in OCCC.Study III evaluated the effect of targeting Rho GTPases in OCCC using simvastatin and CID-1067700 in OCCC cell lines. All OCCC cell lines were more sensitive to simvastatin as compared to conventional platinum-based chemotherapy. Both of the drugs we evaluated were found to disorganize the cytoskeleton and inhibit migration. The cellular response mechanisms differed between cell lines, however a potential effect on both the PI3K/AKT/mTOR and RAS/ERK pathways was suggested.Study IV aimed at evaluating the effects of screening prediagnostic liquid based vaginal samples for TP53 mutations, an approach applied for early detection of ovarian cancer. We identified 8 women with somatic TP53 mutations in high-grade serous ovarian cancer (HGSOC) and analyzed both prediagnostic (presymptomatic) and diagnostic vaginal samples. We used ultrasensitive droplet digital PCR (ddPCR) (IBSAFE™) and found mutations in diagnostic samples from 75% (6/8) of the patients; however, no mutations were detected in the prediagnostic samples. Despite this ddPCR was able to analyze samples with very limited DNA, where other methods would fail. This provides a basis for the further evaluation of IBSAFE™ in a larger cohort of patients.In conclusion, these studies further characterized ovarian cancer biology and heterogeniety, and have provided the basis for future studies in ovarian cancer with the potential of improving outcome