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

Proteogenomic characterization of hepatocellular carcinoma

We performed a proteogenomic analysis of hepatocellular carcinomas (HCCs) across clinical stages and etiologies. We identified pathways differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. These pathways are involved in the organization of cellular components, cell cycle control, signaling pathways, transcriptional and translational control and metabolism. Analyses of CNA-mRNA and mRNA-protein correlations identified candidate driver genes involved in epithelial-to-mesenchymal transition, the Wnt-β- catenin pathway, transcriptional control, cholesterol biosynthesis and sphingolipid metabolism. The activity of targetable kinases aurora kinase A and CDKs was upregulated. We found that CTNNB1 mutations are associated with altered phosphorylation of proteins involved in actin filament organization, whereas TP53 mutations are associated with elevated CDK1/2/5 activity and altered phosphorylation of proteins involved in lipid and mRNA metabolism. Integrative clustering identified HCC subgroups with distinct regulation of biological processes, metabolic reprogramming and kinase activation. Our analysis provides insights into the molecular processes underlying HCCs

Characterization of inter- and intratumoral heterogeneity and the differential immune microenvironment during malignant progression in meningiomas

Meningiomas are thought to arise from the arachnoid cells of the leptomeninx and make up the most common primary intracranial tumor in adults. They are usually benign, however in about 20 % of cases, tumors present with an aggressive phenotype and higher risk of recurrence. Risk stratification thereby remains challenging especially for NF2-mutated meningiomas, which make up about two thirds of all cases, as they can occur at the full spectrum of WHO grades in meningioma from 1 to 3. Recently, molecular profiling has gained importance for prognosis in meningioma with several classification systems that have been established mostly based on the DNA methylation of the tumors. However, the DNA methylation-based classification has not been extensively linked to phenotypic traits of the tumor. Nor have meningiomas been investigated regarding intratumoral subpopulations that may exist in parallel and may have different characteristics, especially regarding the stage of progression and ability to recur. In addition, the role of the immune microenvironment in meningiomas is poorly understood, despite the identification of an immune-enriched meningioma subgroup with beneficial outcome in two independent DNA methylation classification systems. In this dissertation, I investigated the consistency of subgroups initially defined on epigenomic level across molecular levels by comparison to transcriptomic and proteomic data. Further, I leveraged single nuclei transcriptomic profiling to dissect intertumoral differences in the expression profile specific to the tumor cell population depending on the tumor subgroup, and to investigate the abundance and phenotype of intratumoral tumor cell subpopulations across samples. Similarly, I analyzed the single nuclei transcriptomic data to characterize tumor-infiltrating immune cells with respect to their abundance and activation status. I furthermore correlated the differences in immune infiltration with the progression-free survival of patients by deconvoluting DNA methylation array data according to their cellular composition. These analyses underlined the coherence of epigenomic meningioma subgroups across transcriptome and proteome. Moreover, I identified six tumor cell subpopulations that were defined by distinct expression profiles und could be identified across samples at varying abundancies depending on the stage of progression. Similarly, I observed profound differences in infiltrating immune cells between tumor subgroups, with a significant enrichment of tumor-associated macrophages in a benign subgroup of NF2-mutated meningiomas as compared to more progressed tumors. In parallel to their abundancy, macrophages changed in activation between benign and malignant cases from an anti- to a pro-tumorigenic phenotype. The evaluation of progression-free survival data revealed a positive correlation to the proportion of infiltrating immune cells as estimated from epigenomic profiles. Altogether, these results highlight the role of multi-level molecular profiling for tumor grading in a paradigmatic, epidemiologically relevant tumor type. They further indicate an important role of tumor-infiltrating macrophages during meningioma progression with possible consequences for risk prediction as well as therapeutic targets in meningioma

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodologyâ sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying responseâ predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two transâ hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32â 33, which is associated with chemoresistance in ovarian cancer.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135396/1/gepi22018.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135396/2/gepi22018_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135396/3/gepi22018-sup-0001-SuppMat.pd

Genomic landscape of lung adenocarcinoma in East Asians

Lung cancer is the world’s leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305), we found that East Asian LUADs had more stable genomes characterized by fewer mutations and fewer copy number alterations than LUADs from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new EAS-specific LUAD subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts

Attractor Metafeatures and Their Application in Biomolecular Data Analysis

This dissertation proposes a family of algorithms for deriving signatures of mutually associated features, to which we refer as attractor metafeatures, or simply attractors. Specifically, we present multi-cancer attractor derivation algorithms, identifying correlated features in signatures from multiple biological data sets in one analysis, as well as the groups of samples or cells that exclusively express these signatures. Our results demonstrate that these signatures can be used, in proper combinations, as biomarkers that predict a patient’s survival rate, based on the transcriptome of the tumor sample. They can also be used as features to analyze the composition of the tumor. Through analyzing large data sets of 18 cancer types and three high-throughput platforms from The Cancer Genome Atlas (TCGA) PanCanAtlas Project and multiple single-cell RNA-seq data sets, we identified novel cancer attractor signatures and elucidated the identity of the cells that express these signatures. Using these signatures, we developed a prognostic biomarker for breast cancer called the Breast Cancer Attractor Metagenes (BCAM) biomarker as well as a software platform to analyze the tumor sample, called Analysis of the Single-Cell Omics for Tumor (ASCOT)

Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas