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

A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma

<p>Abstract</p> <p>Background</p> <p>Lung cancer is the leading cause of cancer deaths in the world. The most common type of lung cancer is lung adenocarcinoma (AC). The genetic mechanisms of the early stages and lung AC progression steps are poorly understood. There is currently no clinically applicable gene test for the early diagnosis and AC aggressiveness. Among the major reasons for the lack of reliable diagnostic biomarkers are the extraordinary heterogeneity of the cancer cells, complex and poorly understudied interactions of the AC cells with adjacent tissue and immune system, gene variation across patient cohorts, measurement variability, small sample sizes and sub-optimal analytical methods. We suggest that gene expression profiling of the primary tumours and adjacent tissues (PT-AT) handled with a rational statistical and bioinformatics strategy of biomarker prediction and validation could provide significant progress in the identification of clinical biomarkers of AC. To minimise sample-to-sample variability, repeated multivariate measurements in the same object (organ or tissue, e.g. PT-AT in lung) across patients should be designed, but prediction and validation on the genome scale with small sample size is a great methodical challenge.</p> <p>Results</p> <p>To analyse PT-AT relationships efficiently in the statistical modelling, we propose an Extreme Class Discrimination (ECD) feature selection method that identifies a sub-set of the most discriminative variables (e.g. expressed genes). Our method consists of a paired Cross-normalization (CN) step followed by a modified sign Wilcoxon test with multivariate adjustment carried out for each variable. Using an Affymetrix U133A microarray paired dataset of 27 AC patients, we reviewed the global reprogramming of the transcriptome in human lung AC tissue versus normal lung tissue, which is associated with about 2,300 genes discriminating the tissues with 100% accuracy. Cluster analysis applied to these genes resulted in four distinct gene groups which we classified as associated with (i) up-regulated genes in the mitotic cell cycle lung AC, (ii) silenced/suppressed gene specific for normal lung tissue, (iii) cell communication and cell motility and (iv) the immune system features. The genes related to mutagenesis, specific lung cancers, early stage of AC development, tumour aggressiveness and metabolic pathway alterations and adaptations of cancer cells are strongly enriched in the AC PT-AT discriminative gene set. Two AC diagnostic biomarkers SPP1 and CENPA were successfully validated on RT-RCR tissue array. ECD method was systematically compared to several alternative methods and proved to be of better performance and as well as it was validated by comparison of the predicted gene set with literature meta-signature.</p> <p>Conclusions</p> <p>We developed a method that identifies and selects highly discriminative variables from high dimensional data spaces of potential biomarkers based on a statistical analysis of paired samples when the number of samples is small. This method provides superior selection in comparison to conventional methods and can be widely used in different applications. Our method revealed at least 23 hundreds patho-biologically essential genes associated with the global transcriptional reprogramming of human lung epithelium cells and lung AC aggressiveness. This gene set includes many previously published AC biomarkers reflecting inherent disease complexity and specifies the mechanisms of carcinogenesis in the lung AC. SPP1, CENPA and many other PT-AT discriminative genes could be considered as the prospective diagnostic and prognostic biomarkers of lung AC.</p

Doctor of Philosophy

dissertationEwing sarcoma is a devastating pediatric tumor that is particularly aggressive and highly metastatic. The current treatment regimen for this malignancy is aggressive, invasive and toxic, but with limited success. Treatment of metastatic or relapsed Ewing sarcoma is even more dismal with a low 5-year patient survival. My research is focused on identifying new mechanisms that could explain early and extensive metastasis in Ewing sarcoma potentially enabling better disease outcome in the future. In the majority of Ewing sarcoma cases, the causal oncoprotein EWS/FLI develops from the reciprocal chromosomal translocation t(11;22)(q24;q12). For this dissertation, I analyzed the effect of EWS/FLI oncoprotein on the cellular behavior of multiple patient-derived Ewing sarcoma cell lines, and discovered that EWS/FLI compromises the cytoskeletal framework of cells. As a result, Ewing sarcoma cells display small round cell morphology and low cellular adhesion. I further show that these EWS/FLI-dependent changes in cell behavior in vitro are pertinent to tumor cell behavior in vivo and could provide relevant insight into the cell of origin for Ewing sarcoma. Based on these observations, I suggest a new model where the inciting oncogenic event could affect tumor cell adhesion to permit early dissemination of tumor cells or increased colonization of tumor cells at a secondary site, to explain early metastasis of tumor cells. In this dissertation the analysis of the EWS/FLI transcriptome, revealed that focal adhesion proteins, extracellular matrix components, and actin cytoskeletal modulators were most downregulated by EWS/FLI expression. Two focal adhesion proteins namely iv zyxin and the fibronectin receptor α5 integrin were shown to play vital roles in modulating Ewing sarcoma cell morphology, cytoskeletal structure, spreading and adhesion. To adequately study the tumor progression and metastasis of Ewing sarcoma, I developed an intratibial orthotopic mouse model. The Ewing sarcoma tumors grew aggressively in mice and spontaneously metastasized from the tibia to lungs and other bones. This model system recapitulates key features pertinent to the human disease. Taken together, the work described in this dissertation reveals the important role of EWS/FLI mediated downregulation of adhesion proteins in modulating essential cellular features, such as cell adhesion and cytoskeletal structure, to govern oncogenic transformation and metastasis

Recent Developments in Cancer Systems Biology

This ebook includes original research articles and reviews to update readers on the state of the art systems approach to not only discover novel diagnostic and prognostic biomarkers for several cancer types, but also evaluate methodologies to map out important genomic signatures. In addition, therapeutic targets and drug repurposing have been emphasized for a variety of cancer types. In particular, new and established researchers who desire to learn about cancer systems biology and why it is possibly the leading front to a personalized medicine approach will enjoy reading this book

Modulating Tumor Microenvironment: A Review on STK11 Immune Properties and Predictive vs Prognostic Role for Non-small-cell Lung Cancer Immunotherapy

The quest for immunotherapy (IT) biomarkers is an element of highest clinical interest in both solid and hematologic tumors. In non-small-cell lung cancer (NSCLC) patients, besides PD-L1 expression evaluation with its intrinsic limitations, tissue and circulating parameters, likely portraying the tumor and its stromal/immune counterparts, have been proposed as potential predictors of IT responsiveness. STK11 mutations have been globally labeled as markers of IT resistance. After a thorough literature review, STK11 mutations condition the prognosis of NSCLC patients receiving ICI-containing regimens, implying a relevant biological and clinical significance. On the other hand, waiting for prospective and solid data, the putative negative predictive value of STK11 inactivation towards IT is sustained by less evidence. The physiologic regulation of multiple cellular pathways performed by STK11 likely explains the multifaceted modifications in tumor cells, stroma, and tumor immune microenvironment (TIME) observed in STK11 mutant lung cancer, particularly explored in the molecular subgroup of KRAS co-mutation. IT approaches available thus far in NSCLC, mainly represented by anti-PD-1/PD-L1 inhibitors, are not promising in the case of STK11 inactivation. Perceptive strategies aimed at modulating the TIME, regardless of STK11 status or specifically addressed to STK11-mutated cases, will hopefully provide valid therapeutic options to be adopted in the clinical practice

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Gene Expression Analyses in Non Muscle Invasive Bladder Cancer Reveals a Role for Alternative Splicing and Tp53 Status

Non-muscle invasive bladder cancer (NMIBC) represents a crucial problem for the national health care systems due to its high rates of recurrence and the consequent need of frequent follow-ups. Here, gene expression analyses in patients diagnosed as NMIBC were performed to determine those molecular pathways involved in tumor initiation, finding that both MYC and E2F are up regulated and helps to tumor initiation and progression. Our results also support an important involvement of alternative splicing events, modifying key pathways to favour bladder tumor evolution. Finally, since MDM2 showed differential exon usage, mutations in TP53 and its protein expression have been also studied in the same patients. Our data support that recurrence is epigenetically mediated and favoured by an increase protein expression of TP53, which appears more frequently mutated in advanced stages and grades, being associated to a worse prognosis. Therefore, TP53 mutational status could be used as a potential biomarker in the first stages of NMIBC to predict recurrence and prognosis.We express our deepest acknowledgement to patients and their families. The authors also acknowledge the computing resources and technical support provided by Abel Paz-Gallardo and Alfonso Pardo from Extremadura Research Centre for Advanced Technologies (CETA−CIEMAT). This work was supported FEDER cofounded MINECO grant SAF2015-66015-R, ISCIII-RETIC RD12/0036/0009, and PIE15/00076 and CB/16/00228 (to J.M. Paramio); MMF was supported by a Jose Castillejo Fellowship (CAS16/00115)