Comprehensive genomic characterization defines human glioblastoma genes and core pathways.

Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas (TCGA) pilot project aims to assess the value of large-scale multi-dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas--the most common type of adult brain cancer--and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol-3-OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer

Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors

<p>Abstract</p> <p>Background</p> <p>Assays of multiple tumor samples frequently reveal recurrent genomic aberrations, including point mutations and copy-number alterations, that affect individual genes. Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known.</p> <p>Methods</p> <p>We present a method that identifies functional modules without any information other than patterns of recurrent and mutually exclusive aberrations (RME patterns) that arise due to positive selection for key cancer phenotypes. Our algorithm efficiently constructs and searches networks of potential interactions and identifies significant modules (RME modules) by using the algorithmic significance test.</p> <p>Results</p> <p>We apply the method to the TCGA collection of 145 glioblastoma samples, resulting in extension of known pathways and discovery of new functional modules. The method predicts a role for <it>EP300 </it>that was previously unknown in glioblastoma. We demonstrate the clinical relevance of these results by validating that expression of <it>EP300 </it>is prognostic, predicting survival independent of age at diagnosis and tumor grade.</p> <p>Conclusions</p> <p>We have developed a sensitive, simple, and fast method for automatically detecting functional modules in tumors based solely on patterns of recurrent genomic aberration. Due to its ability to analyze very large amounts of diverse data, we expect it to be increasingly useful when applied to the many tumor panels scheduled to be assayed in the near future.</p

Understanding genomic alterations in cancer genomes using an integrative network approach

In recent years, cancer genome sequencing and other high-throughput studies of cancer genomes have generated many notable discoveries. In this review, Novel genomic alteration mechanisms, such as chromothripsis (chromosomal crisis) and kataegis (mutation storms), and their implications for cancer are discussed. Genomic alterations spur cancer genome evolution. Thus, the relationship between cancer clonal evolution and cancer stems cells is commented. The key question in cancer biology concerns how these genomic alterations support cancer development and metastasis in the context of biological functioning. Thus far, efforts such as pathway analysis have improved the understanding of the functional contributions of genetic mutations and DNA copy number variations to cancer development, progression and metastasis. However, the known pathways correspond to a small fraction, plausibly 5-10%, of somatic mutations and genes with an altered copy number. To develop a comprehensive understanding of the function of these genomic alterations in cancer, an integrative network framework is proposed and discussed. Finally, the challenges and the directions of studying cancer omic data using an integrative network approach are commented.Comment: 2 figs, more related papers at http://www.cancer-systemsbiology.org. appears in Cancer Letter, 201

From Parts to Mechanisms: Research Heuristics for Addressing Heterogeneity in Cancer Genetics

A major approach to cancer research in the late 20th century was to search for genes that, when altered, initiated the development of a cell into a cancerous state (oncogenes) or failed to stop this development (tumor suppressor genes). But as researchers acquired the capacity to sequence tumors and incorporated the resulting data into databases, it became apparent that for many tumors no genes were frequently altered and that the genes altered in different tumors in the same tissue type were often distinct. To address this heterogeneity problem, many researchers looked to a higher level of organization—to mechanisms in which gene products (proteins) participated. They proposed to reduce heterogeneity by recognizing that multiple gene alterations affect the same mechanism and that it is the altered mechanism that is responsible for the cell developing one or more hallmarks of cancer. I examine how mechanisms figure in this research and focus on two heuristics researchers use to integrate proteins into mechanisms, one focusing on pathways and one focusing on clusters in networks

The role of network science in glioblastoma

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.This work was partially supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references CEECINST/00102/2018, CEECIND/00072/2018 and PD/BDE/143154/2019, UIDB/04516/2020, UIDB/00297/2020, UIDB/50021/2020, UIDB/50022/2020, UIDB/50026/2020, UIDP/50026/2020, NORTE-01-0145-FEDER-000013, and NORTE-01-0145-FEDER000023 and projects PTDC/CCI-BIO/4180/2020 and DSAIPA/DS/0026/2019. This project has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 951970 (OLISSIPO project)

Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma

This dissertation reviews the development and implementation of integrative, systems biology methods designed to parse driver mutations from high- throughput array data derived from human patients. The analysis of vast amounts of genomic and genetic data in the context of complex human genetic diseases such as Glioblastoma is a daunting task. Mutations exist by the hundreds, if not thousands, and only an unknown handful will contribute to the disease in a significant way. The goal of this project was to develop novel computational methods to identify candidate mutations from these data that drive the molecular differentiation of glioblastoma into the mesenchymal subtype, the most aggressive, poorest-prognosis tumors associated with glioblastoma

Network-based method for inferring cancer progression at the pathway level from cross-sectional mutation data

Large-scale cancer genomics projects are providing a wealth of somatic mutation data from a large number of cancer patients. However, it is difficult to obtain several samples with a temporal order from one patient in evaluating the cancer progression. Therefore, one of the most challenging problems arising from the data is to infer the temporal order of mutations across many patients. To solve the problem efficiently, we present a Network-based method (NetInf) to Infer cancer progression at the pathway level from cross-sectional data across many patients, leveraging on the exclusive property of driver mutations within a pathway and the property of linear progression between pathways. To assess the robustness of NetInf, we apply it on simulated data with the addition of different levels of noise. To verify the performance of NetInf, we apply it to analyze somatic mutation data from three real cancer studies with large number of samples. Experimental results reveal that the pathways detected by NetInf show significant enrichment. Our method reduces computational complexity by constructing gene networks without assigning the number of pathways, which also provides new insights on the temporal order of somatic mutations at the pathway level rather than at the gene level

Genome-scale Precision Proteomics Identifies Cancer Signaling Networks and Therapeutic Vulnerabilities

Mass spectrometry (MS) based-proteomics technology has been emerging as an indispensable tool for biomedical research. But the highly diverse physical and chemical properties of the protein building blocks and the dramatic human proteome complexity largely limited proteomic profiling depth. Moreover, there was a lack of high-throughput quantitative strategies that were both precise and parallel to in-depth proteomic techniques. To solve these grand challenges, a high resolution liquid chromatography (LC) system that coupled with an advanced mass spectrometer was developed to allow genome-scale human proteome identification. Using the combination of pre-MS peptide fractionation, MS2-based interference detection and post-MS computational interference correction, we enabled precise proteome quantification with isobaric labeling. We then applied these advanced proteomics tools for cancer proteome analyses on high grade gliomas (HGG) and rhabdomyosarcomas (RMS). Using systems biology approaches, we demonstrated that these newly developed proteomic analysis pipelines are able to (i) define human proteotypes that link oncogenotypes to cancer phenotypes in HGG and to (ii) identify therapeutic vulnerabilities in RMS. Development of high resolution liquid chromatography is essential for improving the sensitivity and throughput of mass spectrometry-based proteomics to genome-scale. Here we present systematic optimization of a long gradient LC-MS/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse phase long column (100 µm x 150 cm, 5 µm C18 beads) coupled with Q Exactive MS. The column was capable of achieving a peak capacity of approximately 700 in a 720 min gradient of 10-45% acetonitrile. The optimal loading amount was about 6 micrograms of peptides, although the column allowed loading as many as 20 micrograms. Gas phase fractionation of peptide ions further increased the number of peptides identified by ~10%. Moreover, the combination of basic pH LC pre-fractionation with the long gradient LC-MS/MS platform enabled the identification of 96,127 peptides and 10,544 proteins at 1% protein false discovery rate in a postmortem brain sample of Alzheimer’s disease. As deep RNA sequencing of the same specimen suggested that ~16,000 genes were expressed, current analysis covered more than 60% of the expressed proteome. Isobaric labeling quantification by mass spectrometry has emerged as a powerful technology for multiplexed large-scale protein profiling, but measurement accuracy in complex mixtures is confounded by the interference from co-isolated ions, resulting in ratio compression. Here we report that the ratio compression can be essentially resolved by the combination of pre-MS peptide fractionation, MS2-based interference detection and post-MS computational interference correction. To recapitulate the complexity of biological samples, we pooled tandem mass tag (TMT) labeled E. coli peptides at 1 : 3 : 10 ratios, and added in ~20-fold more rat peptides as background, followed by the analysis of two dimensional liquid chromatography-MS/MS. Systematic investigation indicated that the quantitative interference was impacted by LC fractionation depth, MS isolation window and peptide loading amount. Exhaustive fractionation (320 x 4 h) can nearly eliminate the interference and achieve results comparable to the MS3-based method. Importantly, the interference in MS2 scans can be estimated by the intensity of contaminated y1 product ions, and we thus developed an algorithm to correct reporter ion ratios of tryptic peptides. Our data indicated that intermediate fractionation (40 x 2 h) and y1 ion-based correction allowed accurate and deep TMT protein profiling, which represents a straightforward and affordable strategy in isobaric labeling proteomics High throughput omics approaches provide an unprecedented opportunity for dissecting molecular mechanisms in cancer biology. Here we present deep profiling of whole proteome, phosphoproteome and transcriptome in two high-grade glioma mouse models driven by mutated receptor tyrosine kinase (RTK) oncogenes, platelet-derived growth factor receptor alpha (PDGFRA) and neurotrophic receptor tyrosine kinase 1 (NTRK1), analyzing 13,860 proteins (11,941 genes) and 30,431 phosphosites by mass spectrometry. Systems biology approaches identified numerous functional modules and master regulators, including 41 kinases and 26 transcription factors. Pathway activity computation and mouse survival curves indicate the NTRK1 mutation induces a higher activation of AKT targets, drives a positive feedback loop to up-regulate multiple other RTKs, and shows higher oncogenic potency than the PDGFRA mutation. Further integration of the mouse data with human HGG transcriptome data determines shared regulators of invasion and stemness. Thus, multi-omics integrative profiling is a powerful avenue to characterize oncogenic activity. There is growing emphasis on personalizing cancer therapy based on somatic mutations identified in patient’s tumors. Among pediatric solid tumors, RAS pathway mutations in rhabdomyosarcoma are the most common potentially actionable lesions. Recent success targeting CDK4/6 and MEK in RAS mutant adult cancers led our collaborator Dr. Dyer’s group to test this approach for rhabdomyosarcoma. They achieved synergistic killing of RAS mutant rhabdomyosarcoma tumor cells by combining MEK and CDK4/6 inhibitors in culture but failed to achieve efficacy in vivo using orthotopic patient derived xenografts (O-PDXs). To determine how rhabdomyosarcomas evade targeting of CDK4/6 and MEK, we collaborated to perform large-scale deep proteomic, phosphoproteomic, and epigenomic profiling of RMS tumors. Integrative analysis of these omics data detected that RMS tumor cells rapidly compensate and overcome CDK4/6 and MEK combination therapy through 6 myogenic signal transduction pathways including WNT, HH, BMP, Adenyl Cyclase, P38/MAPK and PI3K. While it is not feasible to target each of these signal transduction pathways simultaneously in RMS, we discovered that they require the HSP90 chaperone to sustain the complex developmental signal transduction milieu. We achieved specific and synergistic killing of RMS cells using sub-therapeutic concentrations of an HSP90 inhibitor (ganetespib) in combination with conventional chemotherapy used for recurrent RMS. These effects were seen in the most aggressive recurrent RMS orthotopic patient derived xenografts irrespective of RAS pathway perturbations, histologic or molecular classification. Thus, multi-omics integrative cancer profiling using our newly developed tools is powerful to identify core signaling transduction networks, tumor vulnerability (master regulators) for novel cancer therapy