Translational Oncogenomics and Human Cancer Interactome Networks

An overview of translational, human oncogenomics, transcriptomics and cancer interactomic networks is presented together with basic concepts and potential, new applications to Oncology and Integrative Cancer Biology. Novel translational oncogenomics research is rapidly expanding through the application of advanced technology, research findings and computational tools/models to both pharmaceutical and clinical problems. A self-contained presentation is adopted that covers both fundamental concepts and the most recent biomedical, as well as clinical, applications. Sample analyses in recent clinical studies have shown that gene expression data can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general, ‘personalized medicine’. Several cancer detection techniques are currently under development both in the direction of improved detection sensitivity and increased time resolution of cellular events, with the limits of single molecule detection and picosecond time resolution already reached. The urgency for the complete mapping of a human cancer interactome with the help of such novel, high-efficiency / low-cost and ultra-sensitive techniques is also pointed out

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice

Chapter Integrative Systems Biology Resources and Approaches in Disease Analytics

Currently, our analytical competences are struggling to keep-up the pace of in-deep analysis of all generated large-scale data resultant of high-throughput omics platforms. While, a substantial effort was spent on methods enhancement regarding technical aspects across many detection omics platforms, the development of integrative down-stream approaches is still challenging. Systems biology has an immense applicability in the biomedical and pharmacological areas since the main goal of those focuses in the translation of measured outputs into potential markers of a Human ailment and/or to provide new compound leads for drug discovery. This approach would become more straightforward and realistic to use in standard analysis workflows if the collation of all available information of every component of a biological system was ensured into a single database framework, instead of search and fetch a single component at time across a scatter of databases resources. Here, we will describe several database resources, standalone and web-based tools applied in disease analytics workflows based in data-driven integration of outputs of multi-omic detection platforms

Identification of new candidate biomarkers for prostate cancer by affinity proteomics

Prostate cancer (PCA) is a complex malignancy that needs to be more thoroughly studied and understood at a molecular level to fill the current knowledge gap, and optimize diagnosis and to treatment. Prostate specific antigen (PSA) showed to be not specific for PCA, therefore a demand for novel specific biomarkers exists. The aim of our work was to identify new specific candidate biomarkers for PCA in tissue and plasma samples by means of affinity proteomics approaches such as reverse phase protein array and antigen arrays. Tissue samples are an invaluable source of biomarkers for cancer, but very limited in amount and requiring invasive procedures for collection. Still, they allow to directly profiling the molecular status of tumor itself. Beside tissue, a screening procedure on biological fluid such as plasma would be highly desirable, thanks to the less invasiveness and low-costs of samples collection. Among the biomarkers detectable in plasma are the autoantibodies. The first part of this thesis summarizes the current status of PCA epidemiology, treatment, and biomarkers research. Beside this, an overview of the affinity proteomics platforms available for biomarkers research, and the critical variables to consider in the biomarkers validation process are presented. The second part of the thesis reports the main results of two original studies where the author of the thesis is the main contributor. Paper I is based on the profiling of PCA tissue samples using RPPA. Our results indicate the feasibility of combining laser capture microdissection (LCM) and RPPA for evaluating the molecular architecture and cross-talking of epithelial and stromal compartments. Paper II is based on profiling the autoimmune response to PCA patients, comparing early and late stage of the disease. The authors identified and characterized the IgG reactivity toward a novel epitope for the candidate biomarker prostein. The data presented in this thesis provide two robust frameworks based on affinity proteomics platforms applied for protein profiling in tissue, and autoantibodies profiling in plasma in the context of PCA biomarkers discovery.Co-supervisors: Peter Nilsson (Biotechnology Department, KTH-Royal Institute of Technology, Stockholm, Sweden), Mariaelena Pierobon (Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA)openDottorato di ricerca in Medicina cellulare e molecolareopenPin, Elis

Discovery and validation of clinically relevant long non-coding RNAs in colorectal cancer

Colorectal cancer (CRC) is the third most prevalent cancer worldwide, with nearly two million newly diagnosed cases each year. The survival of patients with CRC greatly depends on the cancer stage at the time of diagnosis, with worse prognosis for more advanced cases. Consequently, considerable effort has been directed towards improving population screening programs for early diagnosis and identifying prognostic markers that can better inform treatment strategies. In recent years, long non-coding RNAs (lncRNAs) have been recognized as promising molecules, with diagnostic and prognostic potential in many cancers, including CRC. Although large-scale genome and transcriptome sequencing surveys have identified many lncRNAs that are altered in CRC, most of their roles in disease onset and progression remain poorly understood. Here, we critically review the variety of detection methods and types of supporting evidence for the involvement of lncRNAs in CRC. In addition, we provide a reference catalog that features the most clinically relevant lncRNAs in CRC. These lncRNAs were selected based on recent studies sorted by stringent criteria for both supporting experimental evidence and reproducibility.This research was funded by the Spanish Ministry of Science and Innovation with grant PGC2018-099921-B-I00, cofounded by European Regional Development Fund (ERDF); by the Catalan Research Agency (AGAUR) SGR423; by the European Union’s Horizon 2020 research and innovation programme (Grant ERC-2016-724173); by TRANSCOLONCAN COST action network (CA17118); by the Gordon and Betty Moore Foundation (Grant GBMF9742); by the “La Caixa” foundation (Grant LCF/PR/HR21/00737), and by the Instituto de Salud Carlos III (IMPACT grant IMP/00019) and CIBERINFEC (grant CB21/13/00061-ISCIII-SGEFI/ERDF). This research was made possible by the Fulbright U.S. Student Grant Program, sponsored by the U.S. Department of State, Bureau of Education and Cultural Affairs.Peer ReviewedPostprint (published version

Systems analysis of host-parasite interactions.

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug-resistant parasites necessitates that the research community take an active role in understanding host-parasite infection biology in order to develop improved therapeutics. Recent advances in next-generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host-parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high-throughput -omic data will undoubtedly generate extraordinary insight into host-parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host-parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies

Ranked prediction of p53 targets using hidden variable dynamic modeling

Full exploitation of microarray data requires hidden information that cannot be extracted using current analysis methodologies. We present a new approach, hidden variable dynamic modeling (HVDM), which derives the hidden profile of a transcription factor from time series microarray data, and generates a ranked list of predicted targets. We applied HVDM to the p53 network, validating predictions experimentally using small interfering RNA. HVDM can be applied in many systems biology contexts to predict regulation of gene activity quantitatively

Ovarian cancer: can proteomics give new insights for therapy and diagnosis?

The study of the ovarian proteomic profile represents a new frontier in ovarian cancer research, since this approach is able to enlighten the wide variety of post-translational events (such as glycosylation and phosphorylation). Due to the possibility of analyzing thousands of proteins, which could be simultaneously altered, comparative proteomics represent a promising model of possible biomarker discovery for ovarian cancer detection and monitoring. Moreover, defining signaling pathways in ovarian cancer cells through proteomic analysis offers the opportunity to design novel drugs and to optimize the use of molecularly targeted agents against crucial and biologically active pathways. Proteomic techniques provide more information about different histological types of ovarian cancer, cell growth and progression, genes related to tumor microenvironment and specific molecular targets predictive of response to chemotherapy than sequencing or microarrays. Estimates of specificity with proteomics are less consistent, but suggest a new role for combinations of biomarkers in early ovarian cancer diagnosis, such as the OVA1 test. Finally, the definition of the proteomic profiles in ovarian cancer would be accurate and effective in identifying which pathways are differentially altered, defining the most effective therapeutic regimen and eventually improving health outcomes

Clinical proteomics for precision medicine: the bladder cancer case

Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management