An integrated genomic analysis of anaplastic meningioma identifies prognostic molecular signatures.

Anaplastic meningioma is a rare and aggressive brain tumor characterised by intractable recurrences and dismal outcomes. Here, we present an integrated analysis of the whole genome, transcriptome and methylation profiles of primary and recurrent anaplastic meningioma. A key finding was the delineation of distinct molecular subgroups that were associated with diametrically opposed survival outcomes. Relative to lower grade meningiomas, anaplastic tumors harbored frequent driver mutations in SWI/SNF complex genes, which were confined to the poor prognosis subgroup. Aggressive disease was further characterised by transcriptional evidence of increased PRC2 activity, stemness and epithelial-to-mesenchymal transition. Our analyses discern biologically distinct variants of anaplastic meningioma with prognostic and therapeutic significance

Multi-omics molecular profiling of lung tumours

Lung Cancer (LC) is one of the most common malignancies and is the leading cause of cancer death worldwide among both men and women. Current LC classifications are based on histopathological features which poorly reflect the molecular diversity of these tumours. Consequently, primary and secondary drug resistance are very frequent, and a high mortality is usual in LC patients. Despite the fact that LC has been intensively studied, there is a lack of effective biomarkers for early detection, stratification and prognosis. Integration of omics data is a powerful approach that can be used to identify molecular subgroups relevant in the clinical setting. This thesis addresses this challenge by characterising the molecular alterations accompanying LC at the genetic and DNA methylation level, using a combination of Whole-Exome Sequencing (WES), Targeted Capture Sequencing (TCS), Single Nucleotide Polymorphism (SNP) genotyping, Whole-Genome Bisulfite Sequencing and RNA-sequencing. The integration of different types of omics data first validated previous molecular alterations in frequently diagnosed LC tumours. This allowed comparison of the genomic and epigenomic landscapes between these common and rarer LC subtypes. Next, novel molecular subgroups of Non-Small Cell Lung Cancer (NSCLC) tumours with bad prognostic, as well as subgroups of Lung Carcinoids (L-CDs, an understudied LC subtype) have been identified and their molecular alterations and signatures characterised. Significant associations with histological features and gene expression programmes have been found by using several bioinformatic tools. These results show the value of multi-omics approaches to better understand the molecular mechanisms underlying LC and to identify new biomarkers. Importantly, some of these findings may be translatable and are likely to improve the detection, monitoring and stratification for targeted therapies in LC patients.Open Acces

An integrated genomic analysis of anaplastic meningioma identifies prognostic molecular signatures

Anaplastic meningioma is a rare and aggressive brain tumor characterised by intractable recurrences and dismal outcomes. Here, we present an integrated analysis of the whole genome, transcriptome and methylation profiles of primary and recurrent anaplastic meningioma. A key finding was the delineation of distinct molecular subgroups that were associated with diametrically opposed survival outcomes. Relative to lower grade meningiomas, anaplastic tumors harbored frequent driver mutations in SWI/SNF complex genes, which were confined to the poor prognosis subgroup. Aggressive disease was further characterised by transcriptional evidence of increased PRC2 activity, stemness and epithelial-to-mesenchymal transition. Our analyses discern biologically distinct variants of anaplastic meningioma with prognostic and therapeutic significance

Next-generation sequencing identifies mechanisms of tumourigenesis caused by loss of SMARCB1 in Malignant Rhabdoid Tumours

PhD ThesisIntroduction: Malignant Rhabdoid Tumours (MRT) are unique malignancies caused by biallelic inactivation of a single gene (SMARCB1). SMARCB1 encodes for a protein that is part of the SWI/SNF chromatin remodelling complex, responsible for the regulation of hundreds of downstream genes/pathways. Despite the simple biology of these tumours, no studies have identified the critical pathways involved in tumourigenesis. The understanding of downstream effects is essential to identifying therapeutic targets that can improve the outcome of MRT patients. Methods: RNA-seq and 450K-methylation analyses have been performed in MRT human primary malignancies (n > 39) and in 4 MRT cell lines in which lentivirus was used to re-express SMARCB1 (G401, A204, CHLA-266, and STA-WT1). The MRT cell lines were treated with 5-aza-2 -deoxycytidine followed by global gene transcription analysis (RNA-seq and 450K-methylation) to investigate how changes in methylation lead to tumourigenesis. Results: We show that primary Malignant Rhabdoid Tumours present a unique and distinct expression/methylation profile which confirms that MRT broadly constitute a single and different tumour type from other paediatric malignancies. However, despite their common cause MRT can be can sub-group by location (i.e. CNS or kidney). We observe that re-expression of SMARCB1 in MRT cell lines determines activation/inactivation of specific downstream pathways such as IL-6/TGF beta. We also observe a direct correlation between alterations in methylation and gene expression in CD44, GLI2, GLI3, CDKN1A, CDKN2A and JARID after SMARB1 re-expression. Loss of SMARCB1 also promotes expression of aberrant isoforms and novel transcripts and causes genome-wide changes in SWI/SNF binding. Conclusion: Next generation transcriptome and methylome analysis in primary MRT and in functional models give us detailed downstream effects of SMARCB1 loss in Malignant Rhabdoid Tumours. The integration of data from both primary and functional models has provided, for the first time, a genome-wide catalogue of SMARCB1 tumourigenic changes (validated using systems biology). Here we show how a single V deletion of SMARCB1 is responsible for deregulation of expression, methylation status and binding at the promoter regions of potent tumour-suppressor genes. The genes, pathways and biological mechanisms indicated as key in tumour development may ultimately be targetable therapeutically and will lead to better treatments for what is currently one of the most lethal paediatric cancers.NECCR, Children with cancer UK, Brain Trust, Love Oliver, CCLG, Karen and Iain Wark, The Smiley Ridley Fund, whose financial support made this project possible

INTEGRATIVE BIOINFORMATICS APPROACHES TO ELUCIDATING PROSTATE CANCER CELL HETEROGENEITY, PLASTICITY, AND TREATMENT RESPONSE

Prostate cancer (PCa) is the most common non-cutaneous tumor in American men, and the second leading cause of cancer-related deaths. PCa-related deaths can be attributed to heterogeneous tumors containing metastatic and therapy-resistant cancer cells. Cancer stem cells (CSC) are an important contributor to this tumor heterogeneity, which are present in primary tumors and become enriched in castration resistant PCa (CRPC). Our lab has demonstrated that the prostate cancer stem cells (PCSCs) are enriched in the phenotypically undifferentiated PCa cell population that lacks the expression of differentiation marker prostate-specific antigen (PSA). Our work has also demonstrated that PCa cells manifest significant plasticity such that phenotypically differentiated PSA+ PCa cells can be reprogrammed to the castrationresistant, stem-like state by chronic castration or overexpression of the stemness factor NANOG. Therefore, my overarching hypothesis is that PSA-/lo cells possess intrinsic molecular and epigenetic features that regulate their aggressiveness and stemness and contribute to tumor progression and therapy resistance, and that these properties can be gained through epigenetic reprogramming of more differentiated PSA+ PCa cell population. Throughout my Ph.D. thesis research, I employed an integrative bioinformatics approach to test this hypothesis

Mutations in regulators of the epigenome and their effects on the DNA methylome

Genome-wide profiling for genetic alterations in cancer has identified mutations in genes that are associated with epigenetic programming of genomes for DNA methylation patterns, histone modifications patterns and the positioning of nucleosomes. Here a systematic evaluation of the available cancer genome profiling data established by large international consortia, in order to identify recurrently mutated genes or pathways was described. Using curated list of approximately 700 epigenetic regulators and currently available genome-wide datasets on genetic and epigenetic alterations in cancers, the distribution of alterations in epigenetic regulators was described. Epigenetic genes were classified as potential oncogenic or those with tumor-suppressor function based on the location of mutations relative to functional domains and their frequencies. A panel of 50 epigenetic genes, including: DNMTs, histones (H3F3A, HIST1H3B), histone editors (KDM5C, KDM6A) and writers (MLLs, SETD2, EZH2, ATM) that can promote epigenetic changes in cancer was identified. Using correlative analysis of publicly available methylation data with information on deregulated epigenetic driver genes, many identified subtype-specific methylation clusters were correlated with groups of up to 3 epigenetic regulators. This analysis provides a source for the identification and link between methylation groups and deregulated epigenetic genes. Major cancer specific methylation changes have been observed in promoters and gene bodies. Tissue-specific cancer methylation differences have been located in enhancers and regulatory regions of non-coding RNAs. Based on identified results, the major mechanism of non-coding RNA deregulation in cancer has been investigated on independent data cohort. Using integrative analysis of non-coding RNA in early-onset prostate cancer, non-coding RNAs were classified as tumor-suppressive and oncogenic. About 120 novel prostate cancer specific non-coding RNAs that have been epigenetically deregulated have been identified. Our study on the defects in regulators of the epigenome will help to understand mechanisms leading to distinct epigenetic patterns and will allow the molecular validation of defined correlations in experimental settings

Characterization of cell type-specific molecular heterogeneity in cancer using multi-omic approaches

Tumors are composed of heterogeneous cell types each with its own unique molecular profiles. Recent advances in single cell genomics technologies have begun to increase our understanding of the molecular heterogeneity that exists in tumors with particular focus on gene expression and chromatin accessibility profiles. However, due to limitations in methods for certain sample types and high cost for single cell genomics, bulk tumor molecular profiling has been and remains widely used. In addition, other facets of single cell epigenomic profiling, particularly methylation and hydroxymethylation, remains underexplored. Thus, investigations to understand the cell type specific epigenetic heterogeneity and the cooperation among various molecular layers to regulate tumorigenesis are needed. In this thesis, I utilize a multi-omic approach integrating DNA methylation, hydroxymethylation, chromatin accessibility, and gene expression profiles to investigate unique single cell type-specific features in 1) epithelial-to-mesenchymal transition and in 2) pediatric central nervous system tumors. First, I demonstrate the shared and distinct epigenetic profiles that are associated with single cells undergoing epithelial-to-mesenchymal transition. With a multi-omic approach, I identify increased hydroxymethylation in binding motifs of transcription factors critical in regulating epithelial-to-mesenchymal transition. Then, I shift my focus to characterize the cellular heterogeneity in pediatric central nervous system tumors and transcriptomic alterations associated with these tumors, while accounting for cell type composition, with single nuclei gene expression data. I detect novel pediatric central nervous system tumor associated genes that are differentially expressed. Finally, I illustrate the cytosine modification alterations that occur predominantly in the progenitorlike cell types of pediatric central nervous system tumors with a multi-omic approach. I determine associations between cell type-specific hydroxymethylation alterations with cell type-specific gene expression changes. Together, these findings emphasize the need for consideration of cellular identity to determine molecular heterogeneity that exist in various cancer contexts. Moreover, these works collectively suggest the utility of multiomic approaches to uncover novel insights in underlying tumor biology

An intelligent management of integrated biomedical data for digital health via Network Medicine and its application to different human diseases

Personalized medicine aims to tailor the health care to each person’s unique signature leading to better distinguish an individual patient from the others with similar clinical manifestation. Many different biomedical data types contribute to define this patient’s unique signature, such as omics data produced trough next generation sequencing technologies. The integration of single-omics data, in a sequential or simultaneous manner, could help to understand the interplay of the different molecules thus helping to bridge the gap between genotype and phenotype. To this end, Network Medicine offers a promising formalism for multi-omics data integration by providing a holistic approach that look at the whole system at once rather than focusing on the single entities. This thesis regards the integration of various omics data following two different procedures within the framework of Network Medicine: A procedural multi-omics data integration, where a single omics was first selected to perform the main analysis, and then the other omics were used in cascade to molecularly characterize the results obtained in the main analysis. A parallel multi-omics data integration, where the result was given by the intersection of the results of each single-omics. The procedural multi-omics data integration was leveraged to study Colorectal and Breast Cancer. In the Colorectal Cancer case study, we defined the molecular signatures of a new subgroup of Colorectal Cancer possibly eligible for immune-checkpoint inhibitors therapy. Moreover, in the Breast Cancer case study we defined 11 prognostic biomarkers specific for the Basal-like subtype of Breast Cancer. Instead, the parallel multi-omics data integration was exploited to study COVID-19 and Chronic Obstructive Pulmonary Disease. In the COVID-19 case study, we defined a pool of drugs potentially repurposable for COVID-19. Whereas, in the Chronic Obstructive Pulmonary Disease case study, we discovered a group of differentially expressed and methylated genes that have a considerable biological specificity and could be related to the inflammatory pathological mechanism of Chronic Obstructive Pulmonary Disease