Next-Generation Sequencing:Application in Liver Cancer-Past, Present and Future?

Hepatocellular Carcinoma (HCC) is the third most deadly malignancy worldwide characterized by phenotypic and molecular heterogeneity. In the past two decades, advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. More recently, improvements of sophisticated next-generation sequencing (NGS) technologies have enabled complete and cost-efficient analyses of cancer genomes at a single nucleotide resolution and advanced into valuable tools in translational medicine. Although the use of NGS in human liver cancer is still in its infancy, great promise rests in the systematic integration of different molecular analyses obtained by these methodologies, i.e., genomics, transcriptomics and epigenomics. This strategy is likely to be helpful in identifying relevant and recurrent pathophysiological hallmarks thereby elucidating our limited understanding of liver cancer. Beside tumor heterogeneity, progress in translational oncology is challenged by the amount of biological information and considerable “noise” in the data obtained from different NGS platforms. Nevertheless, the following review aims to provide an overview of the current status of next-generation approaches in liver cancer, and outline the prospects of these technologies in diagnosis, patient classification, and prediction of outcome. Further, the potential of NGS to identify novel applications for concept clinical trials and to accelerate the development of new cancer therapies will be summarized

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

Signaling Network Assessment of Mutations and Copy Number Variations Predicts Breast Cancer Subtype-specific Drug Targets

Individual cancer cells carry a bewildering number of distinct genomic alterations i.e., copy number variations and mutations, making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here we performed exome-sequencing on several breast cancer cell lines which represent two subtypes, luminal and basal. We integrated this sequencing data, and functional RNAi screening data (i.e., for identifying genes which are essential for cell proliferation and survival), onto a human signaling network. Two subtype-specific networks were identified, which potentially represent core-signaling mechanisms underlying tumorigenesis. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes based on genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.Comment: 4 figs, more related papers at http://www.cancer-systemsbiology.org, appears in Cell Reports, 201

Analyzing the genomic variation of microbial cell factories in the era of “New Biotechnology”

The application of genome-scale technologies, both experimental and in silico, to industrial biotechnology has allowed improving the conversion of biomass-derived feedstocks to chemicals, materials and fuels through microbial fermentation. In particular, due to rapidly decreasing costs and its suitability for identifying the genetic determinants of a phenotypic trait of interest, whole genome sequencing is expected to be one of the major driving forces in industrial biotechnology in the coming years. We present some of the recent studies that have successfully applied high-throughput sequencing technologies for finding the underlying molecular mechanisms for (a) improved carbon source utilization, (b) increased product formation, and (c) stress tolerance. We also discuss the strengths and weaknesses of different strategies for mapping industrially relevant genotype-to-phenotype links including exploiting natural diversity in natural isolates or crosses between isolates, classical mutagenesis and evolutionary engineering

Molecular Subtypes of Melanoma. Biological and Clinical Significance.

Cutaneous malignant melanoma (CMM) is the most lethal form of skin cancer and its incidence has increased faster than that of any other cancer, rendering it a major public health problem worldwide. High-throughput screenings have opened the door to a new scientific world, which enables molecular-based characterization of large cancer cohort collections. The aim of the research presented in this thesis was to explore the molecular landscapes of melanoma tumors on a genomic and transcriptomic level and subsequently correlate certain molecular features with patient survival, treatment response and tumor evolutionary patterns. In Paper I, it was concluded that metastatic melanoma could be divided into transcriptomic subtypes (gene expression (GEX) phenotypes) possessing diverse biological and clinical features. Patients harboring melanomas infiltrated by immune cells, i.e. the high-immune subtype, showed a superior survival, whereas highly proliferative melanomas, i.e. the proliferative subtype, was correlated to a poor survival outcome and resistance to targeted therapies. Moreover, it was also shown that, irrespectively of the GEX phenotypes, melanomas could be divided into genomic subtypes based on genetic aberrations in the mitogen-activated protein kinase (MAPK) signaling pathway. In Paper II, it was found that mutations in the tumor suppressor gene neurofibromin 1 (NF1), was linked to inferior survival. Today, it is well accepted that most tumors possess some level of intratumor heterogeneity (ITH), i.e. subclonality, influencing disease progression. In Papers III and IV, the evolutionary aspects of melanoma were considered by analyzing ITH, as well as disease progression on a molecular basis. When analyzing multiple metastatic lesions from individual patients, we found that most tumors were genetically different, with a common stem of genetic aberrations and the addition of new “private” ones, thus pointing to continued evolution during progression. Moreover, the GEX proliferative phenotype appeared to be correlated to a later disease course. From multiregional biopsies from single tumors, it was found that mutations in the MAPK signaling pathway appeared to be early events in tumorigenesis. Heterogeneous somatic mutations were found in the range of 3-38%, thus highlighting different levels of subclonality in melanoma. A high degree of mutational heterogeneity was associated with a more aggressive disease progression. In conclusion, melanoma is a complex molecular disease that can be characterized by genomic and transcriptomic signatures with clinical implications. However, a single biopsy might not reflect the true tumor complexity, and subclonality may be one reason behind resistance development

Assessing Tumor Genomic Profiling Reports for Genetic Counseling Referral Indications

Our purpose is two-fold: (1) to identify patients who may benefit from referrals to cancer genetic counseling by characterizing the alterations reported in tumor profiling reports without matched germline control, and (2) to assess the utility of public database ClinVar in providing further information on these alterations. We assessed 160 reports across 66 tumor types. 127 (79%) reports had a mutation in 1 of 86 selected cancer predisposition genes. Of these, 29 (23%) did not meet ACMG/NSGC criteria for referral based on tumor type alone. 19% of mutations in selected genes were found in ClinVar, with 15% reported as both germline and on the pathogenic spectrum. 31% of VUSs in selected genes were found in ClinVar, with 2% reported as both germline and on the pathogenic spectrum. Our results highlight a potential to miss patients at increased risk of cancer predisposition syndromes based on tumor type alone

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

Integrative methods for analysing 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