Network-based stratification of tumor mutations.

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence

Typing tumors using pathways selected by somatic evolution.

Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient's tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

'Big data' approaches for novel anti-cancer drug discovery

Introduction: The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we review how recent advances in platform technologies and the increasing availability of biological ‘big data’ are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. We then discuss how these discoveries may be amenable to therapeutic interventions. Areas covered: We discuss the current approaches that use ‘big data’ to identify cancer drivers. These approaches include genomic sequencing, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. We review how big data is being used to assess the tractability of potential drug targets and how systems biology is being utilised to identify novel drug targets. We finish the review with an overview of available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs

Network Approaches to the Study of Genomic Variation in Cancer

Advances in genomic sequencing technologies opened the door for a wider study of cancer etiology. By analyzing datasets with thousands of exomes (or genomes), researchers gained a better understanding of the genomic alterations that confer a selective advantage towards cancerous growth. A predominant narrative in the field has been based on a dichotomy of alterations that confer a strong selective advantage, called cancer drivers, and the bulk of other alterations assumed to have a neutral effect, called passengers. Yet, a series of studies questioned this narrative and assigned potential roles to passengers, be it in terms of facilitating tumorigenesis or countering the effect of drivers. Consequently, the passenger mutational landscape received a higher level of attention in attempt to prioritize the possible effects of its alterations and to identify new therapeutic targets. In this dissertation, we introduce interpretable network approaches to the study of genomic variation in cancer. We rely on two types of networks, namely functional biological networks and artificial neural nets. In the first chapter, we describe a propagation method that prioritizes 230 infrequently mutated genes with respect to their potential contribution to cancer development. In the second chapter, we further transcend the driver-passenger dichotomy and demonstrate a gradient of cancer relevance across human genes. In the last two chapters, we present methods that simplify neural network models to render them more interpretable with a focus on functional genomic applications in cancer and beyond

Overexpressed Somatic Alleles are Enriched in Functional Elements in Breast Cancer.

Asymmetric allele content in the transcriptome can be indicative of functional and selective features of the underlying genetic variants. Yet, imbalanced alleles, especially from diploid genome regions, are poorly explored in cancer. Here we systematically quantify and integrate the variant allele fraction from corresponding RNA and DNA sequence data from patients with breast cancer acquired through The Cancer Genome Atlas (TCGA). We test for correlation between allele prevalence and functionality in known cancer-implicated genes from the Cancer Gene Census (CGC). We document significant allele-preferential expression of functional variants in CGC genes and across the entire dataset. Notably, we find frequent allele-specific overexpression of variants in tumor-suppressor genes. We also report a list of over-expressed variants from non-CGC genes. Overall, our analysis presents an integrated set of features of somatic allele expression and points to the vast information content of the asymmetric alleles in the cancer transcriptome

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

Computational Cancer Research: Network-based analysis of cancer data disentangles clinically relevant alterations from molecular measurements

Cancer is a very complex genetic disease driven by combinations of mutated genes. This complexity strongly complicates the identification of driver genes and puts enormous challenges to reveal how they influence cancerogenesis, prognosis or therapy response. Thousands of molecular profiles of the major human types of cancer have been measured over the last years. Apart from well-studied frequently mutated genes, still only little is known about the role of rarely mutated genes in cancer or the interplay of mutated genes in individual cancers. Gene expression and mutation profiles can be measured routinely, but computational methods for the identification of driver candidates along with the prediction of their potential impacts on downstream targets and clinically relevant characteristics only rarely exist. Instead of only focusing on frequently mutated genes, each cancer patient should better be analyzed by using the full information in its cancer-specific molecular profiles to improve the understanding of cancerogenesis and to more precisely predict prognosis and therapy response of individual patients. This requires novel computational methods for the integrative analysis of molecular cancer data. A promising way to realize this is to consider cancer as a disease of cellular networks. Therefore, I have developed a novel network-based approach for the integrative analysis of molecular cancer data over the last years. This approach directly learns gene regulatory networks form gene expression and copy number data and further enables to quantify impacts of altered genes on clinically relevant downstream targets using network propagation. This habilitation thesis summarizes the results of seven of my publications. All publications have a focus on the integrative analysis of molecular cancer data with an overarching connection to the newly developed network-based approach. In the first three publications, networks were learned to identify major regulators that distinguish characteristic gene expression signatures with applications to astrocytomas, oligodendrogliomas, and acute myeloid leukemia. Next, the central publication of this habilitation thesis, which combines network inference with network propagation, is introduced. The great value of this approach is demonstrated by quantifying potential direct and indirect impacts of rare and frequent gene copy number alterations on patient survival. Further, the publication of the corresponding user-friendly R package regNet is introduced. Finally, two additional publications that also strongly highlight the value of the developed network-based approach are presented with the aims to predict cancer gene candidates within the region of the 1p/19q co-deletion of oligodendrogliomas and to determine driver candidates associated with radioresistance and relapse of prostate cancer. All seven publications are embedded into a brief introduction that motivates the scientific background and the major objectives of this thesis. The background is briefly going from the hallmarks of cancer over the complexity of cancer genomes down to the importance of networks in cancer. This includes a short introduction of the mathematical concepts that underlie the developed network inference and network propagation algorithms. Further, I briefly motivate and summarize my studies before the original publications are presented. The habilitation thesis is completed with a general discussion of the major results with a specific focus on the utilized network-based data analysis strategies. Major biologically and clinically relevant findings of each publication are also briefly summarized

The role of intra-tumoural heterogeneity in resistance to neoadjuvant chemotherapy in breast cancer

Breast cancer is a heterogeneous disease and accumulating evidence suggests that treatment failure may be driven by intra-tumour heterogeneity (ITH). Utilising the current protocol for neoadjuvant (pre-surgery) chemotherapy (NAC) provides the opportunity to study molecular genetic changes between pre- and post-therapy by assessing pre-therapy biopsies and post-therapy surgical resections. Whole exome sequencing was performed on matched pre- and post-treatment cancer cells from 6 patients with oestrogen receptor positive breast cancers that showed partial responses to the chemotherapeutic combination epirubicin/cyclophosphamide. Data analysis was performed to determine differences in genetic aberrations between pre- and post-NAC, and in particular to identify evidence of consistent selection by therapy of aberrations that therefore may define chemotherapy resistance or sensitivity. There were extensive differences in the range of genetic aberrations between pre- and post-NAC. 48 genes were identified for further study based on evidence of mutations conferring a selective advantage or disadvantage during chemotherapeutic response. The relevance of these was screened using siRNA knock-down and assessment of response to epirubicin using cell viability assays in vitro. Two genes were taken forward. Potential loss-of-function mutations in MUC17 were selected against during therapy in patients, and in accordance with this MUC17 knock-down was associated with increased sensitivity in vitro. Potential loss-of-function mutations in PCNX1 were selected for during therapy in patients, and in accordance with this PCNX1 knock-down was associated with resistance. Further work was performed to investigate mechanisms by which these genes modify chemotherapy response, by examining drug loading and ABC transporter expression levels. Data indicate that both genes impact on drug loading, potentially through modulating ABC transporter expression. Also, MUC17 or PCNX1 protein levels were tested as prognostic and predictive markers for breast cancer clinical outcomes using tissue taken from cohorts of patients who received adjuvant chemotherapy or neoadjuvant chemotherapy. Kaplan-Meier survival analyses revealed that low MUC17 expression after neoadjuvant chemotherapy was significantly associated with longer disease free survival, which was in agreement with the selection of MUC17 mutations seen after therapy in the initial patient group, and with the in vitro siRNA findings concerning drug sensitivity. I concluded that MUC17 and PCNX1 are potential markers of response to chemotherapy in breast cancer, and that therapeutic modulation of their activities could enhance chemotherapy responses