Computational Hybrid Systems for Identifying Prognostic Gene Markers of Lung Cancer

Lung cancer is the most fatal cancer around the world. Current lung cancer prognosis and treatment is based on tumor stage population statistics and could not reliably assess the risk for developing recurrence in individual patients. Biomarkers enable treatment options to be tailored to individual patients based on their tumor molecular characteristics. To date, there is no clinically applied molecular prognostic model for lung cancer. Statistics and feature selection methods identify gene candidates by ranking the association between gene expression and disease outcome, but do not account for the interactions among genes. Computational network methods could model interactions, but have not been used for gene selection due to computational inefficiency. Moreover, the curse of dimensionality in human genome data imposes more computational challenges to these methods.;We proposed two hybrid systems for the identification of prognostic gene signatures for lung cancer using gene expressions measured with DNA microarray. The first hybrid system combined t-tests, Statistical Analysis of Microarray (SAM), and Relief feature selections in multiple gene filtering layers. This combinatorial system identified a 12-gene signature with better prognostic performance than published signatures in treatment selection for stage I and II patients (log-rank P\u3c0.04, Kaplan-Meier analyses). The 12-gene signature is a more significant prognostic factor (hazard ratio=4.19, 95% CI: [2.08, 8.46], P\u3c0.00006) than other clinical covariates. The signature genes were found to be involved in tumorigenesis in functional pathway analyses.;The second proposed system employed a novel computational network model, i.e., implication networks based on prediction logic. This network-based system utilizes gene coexpression networks and concurrent coregulation with signaling pathways for biomarker identification. The first application of the system modeled disease-mediated genome-wide coexpression networks. The entire genomic space were extensively explored and 21 gene signatures were discovered with better prognostic performance than all published signatures in stage I patients not receiving chemotherapy (hazard ratio\u3e1, CPE\u3e0.5, P \u3c 0.05). These signatures could potentially be used for selecting patients for adjuvant chemotherapy. The second application of the system modeled the smoking-mediated coexpression networks and identified a smoking-associated 7-gene signature. The 7-gene signature generated significant prognostication specific to smoking lung cancer patients (log-rank P\u3c0.05, Kaplan-Meier analyses), with implications in diagnostic screening of lung cancer risk in smokers (overall accuracy=74%, P\u3c0.006). The coexpression patterns derived from the implication networks in both applications were successfully validated with molecular interactions reported in the literature (FDR\u3c0.1).;Our studies demonstrated that hybrid systems with multiple gene selection layers outperform traditional methods. Moreover, implication networks could efficiently model genome-scale disease-mediated coexpression networks and crosstalk with signaling pathways, leading to the identification of clinically important gene signatures

Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer.

The cancer stem cell hypothesis, that a small population of tumour cells are responsible for tumorigenesis and cancer progression, is becoming widely accepted and recent evidence has suggested a prognostic and predictive role for such cells. Intra-tumour heterogeneity, the diversity of the cancer cell population within the tumour of an individual patient, is related to cancer stem cells and is also considered a potential prognostic indicator in oncology. The measurement of cancer stem cell abundance and intra-tumour heterogeneity in a clinically relevant manner however, currently presents a challenge. Here we propose signalling entropy, a measure of signalling pathway promiscuity derived from a sample's genome-wide gene expression profile, as an estimate of the stemness of a tumour sample. By considering over 500 mixtures of diverse cellular expression profiles, we reveal that signalling entropy also associates with intra-tumour heterogeneity. By analysing 3668 breast cancer and 1692 lung adenocarcinoma samples, we further demonstrate that signalling entropy correlates negatively with survival, outperforming leading clinical gene expression based prognostic tools. Signalling entropy is found to be a general prognostic measure, valid in different breast cancer clinical subgroups, as well as within stage I lung adenocarcinoma. We find that its prognostic power is driven by genes involved in cancer stem cells and treatment resistance. In summary, by approximating both stemness and intra-tumour heterogeneity, signalling entropy provides a powerful prognostic measure across different epithelial cancers

Integrative transcriptomics in smoking related lung diseases

Chronic lung diseases including Chronic Obstructive Pulmonary Disease (COPD), Idiopathic Pulmonary Fibrosis (IPF) and lung cancer are major causes of morbidity and mortality in the United States due to high incidence and limited therapeutic options. In order to address this critical issue, I have leveraged RNA sequencing and integrative genomics to define disease-associated transcriptomic changes which could be potentially targeted to lead to new therapeutics. We sequenced the lung transcriptome of subjects with IPF (n=19), emphysema (n=19, a subtype of COPD), or neither (n=20). The expression levels of 1770 genes differed between IPF and control lung, and 220 genes differed between emphysema and control lung (p<0.001). Upregulated genes in both emphysema and IPF were enriched for the p53/hypoxia pathway. These results were validated by immunohistochemistry of select p53/hypoxia proteins and by GSEA analysis of independent expression microarray experiments. To identify regulatory events, I constructed an integrative miRNA target prediction and anticorrelation miRNA-mRNA network, which highlighted several miRNA whose expression levels were the opposite of genes differentially expressed in both IPF and emphysema. MiR-96 was a highly connected hub in this network and was subsequently overexpressed in cell lines to validate several potential regulatory connections. Building upon these successful experiments, I next sought to define gene expression changes and the miRNA-mRNA regulatory network in never smoker lung cancer. Large and small RNA was sequenced from matched lung adenocarcinoma tumor and adjacent normal lung tissue obtained from 22 subjects (8 never, 14 current and former smokers). I identified 120 genes whose expression was modified uniquely in never smoker lung tumors. Using a repository of gene-expression profiles associated with small bioactive molecules, several compounds which counter the never smoker tumor signature were identified in silico. Leveraging differential expression information, I again constructed an mRNA-miRNA regulatory network, and subsequently identified a potential never smoker oncomir has-mir-424 and its transcription factor target FOXP2. In this thesis, I have identified genes, pathways and the miRNA-mRNA regulatory network that is altered in COPD, IPF, and lung adenocarcinoma among never smokers. My findings may ultimately lead to improved treatment options by identifying targetable pathways, regulators, and therapeutic drug candidates.2017-02-01T00:00:00

Multi-omic biomarker discovery and network analyses to elucidate the molecular mechanisms of lung cancer premalignancy

Lung cancer (LC) is the leading cause of cancer death in the US, claiming over 160,000 lives annually. Although CT screening has been shown to be efficacious in reducing mortality, the limited access to screening programs among high-risk individuals and the high number of false positives contribute to low survival rates and increased healthcare costs. As a result, there is an urgent need for preventative therapeutics and novel interception biomarkers that would enhance current methods for detection of early-stage LC. This thesis addresses this challenge by examining the hypothesis that transcriptomic changes preceding the onset of LC can be identified by studying bronchial premalignant lesions (PMLs) and the normal-appearing airway epithelial cells altered in their presence (i.e., the PML-associated airway field of injury). PMLs are the presumed precursors of lung squamous cell carcinoma (SCC) whose presence indicates an increased risk of developing SCC and other subtypes of LC. Here, I leverage high-throughput mRNA and miRNA sequencing data from bronchial brushings and lesion biopsies to develop biomarkers of PML presence and progression, and to understand regulatory mechanisms driving early carcinogenesis. First, I utilized mRNA sequencing data from normal-appearing airway brushings to build a biomarker predictive of PML presence. After verifying the power of the 200-gene biomarker to detect the presence of PMLs, I evaluated its capacity to predict PML progression and detect presence of LC (Aim 1). Next, I identified likely regulatory mechanisms associated with PML severity and progression, by evaluating miRNA expression and gene coexpression modules containing their targets in bronchial lesion biopsies (Aim2). Lastly, I investigated the preservation of the PML-associated miRNAs and gene modules in the airway field of injury, highlighting an emergent link between the airway field and the PMLs (Aim 3). Overall, this thesis suggests a multi-faceted utility of PML-associated genomic signatures as markers for stratification of high-risk smokers in chemoprevention trials, markers for early detection of lung cancer, and novel chemopreventive targets, and yields valuable insights into early lung carcinogenesis by characterizing mRNA and miRNA expression alterations that contribute to premalignant disease progression towards LC.2020-01-2

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Mutation signature analysis identifies increased mutation caused by tobacco smoke associated DNA adducts in larynx squamous cell carcinoma compared with oral cavity and oropharynx.

Squamous cell carcinomas of the head and neck (HNSCC) arise from mucosal keratinocytes of the upper aero-digestive tract. Despite a common cell of origin and similar driver-gene mutations which divert cell fate from differentiation to proliferation, HNSCC are considered a heterogeneous group of tumors categorized by site of origin within the aero-digestive mucosa, and the presence or absence of HPV infection. Tobacco use is a major driver of carcinogenesis in HNSCC and is a poor prognosticator that has previously been associated with poor immune cell infiltration and higher mutation numbers. Here, we study patterns of mutations in HNSCC that are derived from the specific nucleotide changes and their surrounding nucleotide context (also known as mutation signatures). We identify that mutations linked to DNA adducts associated with tobacco smoke exposure are predominantly found in the larynx. Presence of this class of mutation, termed COSMIC signature 4, is responsible for the increased burden of mutation in this anatomical sub-site. In addition, we show that another mutation pattern, COSMIC signature 5, is positively associated with age in HNSCC from non-smokers and that larynx SCC from non-smokers have a greater number of signature 5 mutations compared with other HNSCC sub-sites. Immunohistochemistry demonstrates a significantly lower Ki-67 proliferation index in size matched larynx SCC compared with oral cavity SCC and oropharynx SCC. Collectively, these observations support a model where larynx SCC are characterized by slower growth and increased susceptibility to mutations from tobacco carcinogen DNA adducts

Genomic and Transcriptomic Alterations Associated with STAT3 Activation in Head and Neck Cancer.

BackgroundHyperactivation of STAT3 via constitutive phosphorylation of tyrosine 705 (Y705) is common in most human cancers, including head and neck squamous carcinoma (HNSCC). STAT3 is rarely mutated in cancer and the (epi)genetic alterations that lead to STAT3 activation are incompletely understood. Here we used an unbiased approach to identify genomic and epigenomic changes associated with pSTAT3(Y705) expression using data generated by The Cancer Genome Atlas (TCGA).Methods and findingsMutation, mRNA expression, promoter methylation, and copy number alteration data were extracted from TCGA and examined in the context of pSTAT3(Y705) protein expression. mRNA expression levels of 1279 genes were found to be associated with pSTAT3(705) expression. Association of pSTAT3(Y705) expression with caspase-8 mRNA expression was validated by immunoblot analysis in HNSCC cells. Mutation, promoter hypermethylation, and copy number alteration of any gene were not significantly associated with increased pSTAT3(Y705) protein expression.ConclusionsThese cumulative results suggest that unbiased approaches may be useful in identifying the molecular underpinnings of oncogenic signaling, including STAT3 activation, in HNSCC. Larger datasets will likely be necessary to elucidate signaling consequences of infrequent alterations

Global Epidemiology of Lung Cancer.

While lung cancer has been the leading cause of cancer-related deaths for many years in the United States, incidence and mortality statistics - among other measures - vary widely worldwide. The aim of this study was to review the evidence on lung cancer epidemiology, including data of international scope with comparisons of economically, socially, and biologically different patient groups. In industrialized nations, evolving social and cultural smoking patterns have led to rising or plateauing rates of lung cancer in women, lagging the long-declining smoking and cancer incidence rates in men. In contrast, emerging economies vary widely in smoking practices and cancer incidence but commonly also harbor risks from environmental exposures, particularly widespread air pollution. Recent research has also revealed clinical, radiologic, and pathologic correlates, leading to greater knowledge in molecular profiling and targeted therapeutics, as well as an emphasis on the rising incidence of adenocarcinoma histology. Furthermore, emergent evidence about the benefits of lung cancer screening has led to efforts to identify high-risk smokers and development of prediction tools. This review also includes a discussion on the epidemiologic characteristics of special groups including women and nonsmokers. Varying trends in smoking largely dictate international patterns in lung cancer incidence and mortality. With declining smoking rates in developed countries and knowledge gains made through molecular profiling of tumors, the emergence of new risk factors and disease features will lead to changes in the landscape of lung cancer epidemiology

Short-hairpin RNA library: identification of therapeutic partners for gefitinib-resistant non-small cell lung cancer.

Somatic mutations of the epidermal growth factor receptor often cause resistance to therapy with tyrosine kinase inhibitor in non-small cell lung cancer (NSCLC). In this study, we aimed to identify partner drugs and pathways that can induce cell death in combination with gefitinib in NSCLC cells. We undertook a genome-wide RNAi screen to identify synthetic lethality with gefitinib in tyrosine kinase inhibitor resistant cells. The screening data were utilized in different approaches. Firstly, we identified PRKCSH as a candidate gene, silencing of which induces apoptosis of NSCLC cells treated with gefitinib. Next, in an in silico gene signature pathway analysis of shRNA library data, a strong correlation of genes involved in the CD27 signaling cascade was observed. We showed that the combination of dasatinib (NF-κB pathway inhibitor) with gefitinib synergistically inhibited the growth of NSCLC cells. Lastly, utilizing the Connectivity Map, thioridazine was identified as a top pharmaceutical perturbagen. In our experiments, it synergized with gefitinib to reduce p-Akt levels and to induce apoptosis in NSCLC cells. Taken together, a pooled short-hairpin library screen identified several potential pathways and drugs that can be therapeutic targets for gefitinib resistant NSCLC

microRNA pattern in tumoral lung tissues from former- and non-smokers: a possible footprint for environmental and genetic risk factors evaluation.

For the last decade the interest of microRNA as a possible early biomarker for lung-cancer has increased. Less attention has been focused in using microRNA as a biomarker to study the contribution of environmental exposure to air pollutants in non-smokers lung cancer patients. The aim of this thesis is the identification of environmental related microRNA pattern in collected tissues from a total of 64 formal- and non- smoker patients recruited in a three-year observational study. The identified patterns may be used as early predictors of lung cancer, as well as environmental-related footprints. Through microRNA-chip array analysis of lung tissue, it was studied the expression of 2549 microRNAs. A differential analysis between healthy and tumoral tissue showed the presence of 273 microRNA differentially regulated, 222 were down-regulated and 51 up-regulated. Differential analysis was also applied to identify environmental pollution related microRNAs and finding microRNA deregulation in Passive Smoking at home (n=8), Passive smoking at work (n=1), Vehicle traffic at home (n=53), home distance from Etna Volcano (n=21), and home Type radon risk (n=19) exposures. A second biomarker, Benzo(a)Pyrene-DNA adducts levels in blood, was also studied to understand its correlation to the above-mentioned environmental factors. The specificity of this biomarker was minor than microRNA pattern biomarker, but it was strongly correlated to vehicle traffic pollution. The analysis of the microRNA environmental signatures indicates the contribution of environmental factors to the analysed lung cancers in the following decreasing rank: (a) vehicle traffic, (b) passive smoke, (c) radon, and (d) volcano ashes. These results provide evidence that microRNA analysis can be used to investigate the contribution of environmental factors in human lung cancer occurring in non-smoker