Cancer biomarker discovery by in vitro systems biology

This Thesis was made with the intention to mechanistically assess and further develop a multi-stage cell line-based (in vitro) model for oral cancer development. Efforts of establishing additional tumor cell lines for expanding the model were coupled with the application of systems biology technologies for characterization of the three entities of the start-up model, including: 1) normal, 2) immortal and non-tumorigenic, versus 3) immortal and tumorigenic stages. Omics data integration from assessment of cell lines as unique entities, and model-driven in vitro manipulations formed the basis for construction of two bioinformatics-based pipelines for this task. Altered phenotypic and genotypic characteristics and the event of non-functional cell differentiation (a hallmark of cancer development) was analyzed broadly among the transformed stages of the model relative the normal counterpart, testing the overarching hypothesis that thorough analysis of cell line data might contribute clinically useful tumor biomarkers potentially hidden in existing genome-wide assessments of clinical tissue samples. The separate papers forming the Thesis, in order, generated: 1) a review of existing data from the start-up model under a selected standardized serum-free condition, 2) an omics-integrative tumor biomarker discovery pipeline based on the start-up model, 3) a model-driven tumor biomarker discovery pipeline based on assessment of influences of confluency (high cell density and cell-to-cell contact) in the seemingly most differentiation-deficient cell line in the start-up model, 4) a novel tumor cell line applicable to expand the number of serum-free entities of the model, 5) an expanded model-driven tumor biomarker discovery pipeline based on assessment of serum-induced influences of the extended model (now with four entities), and finally, 6) an analysis of the novel cell line under a further expanded omics-integrative tumor biomarker discovery pipeline. The overall results included broad description of the multiple alterations at gene, pathway and ontology levels that coupled with the transformed phenotypes and non-functional cell differentiation in the cell line models. The bioinformatics-driven assessment using overall six different processing tools of differential expression of 44 proteins and thousands of transcripts from these analyses suggested multiple potential biomarker signatures in head and neck squamous cell carcinoma. Overall, five in vitro-based signatures could be validated for clinical significance in independent data from tumor tissue analysis, including multiple oral and non-oral patient data sets as well as body-wide transcriptomics and proteomics expression databases. The taken approaches elucidated basic mechanisms of cell transformation while simultaneously generating paradigms/protocols generally applicable to cancer biomarker discovery. Proving the hypothesis under testing, the results show that the in vitro-derived biomarkers are complementary, often with superior accuracy, to those generated from direct assessment of cancer tissue specimens. Overall, the application of technologies and methods as described possibly generated a first description of an “in vitro systems biology model of oral cancer development” with potential for wide further application in experimental and translational research

Properties and units in the clinical laboratory sciences part XXIV. Properties and units in clinical molecular genetics (IUPAC Technical Report)

This document describes the application of the syntax, semantic rules, and format of the Nomenclature for Properties and Units (NPU) terminology for coded dedicated kinds-of-property in the subject field of clinical molecular genetics. A vocabulary for NPU definitions in this field, based on international terminology and nomenclature, is introduced and examples of actual NPU definitions for different types of investigations are given and explained

MAML1 acts cooperatively with EGR1 to activate EGR1-regulated promoters: implications for nephrogenesis and the development of renal cancer.

Mastermind-like 1 (MAML1) is a transcriptional coregulator of activators in various signaling pathways, such as Notch, p53, myocyte enhancer factor 2C (MEF2C) and beta-catenin. In earlier studies, we demonstrated that MAML1 enhanced p300 acetyltransferase activity, which increased the acetylation of Notch by p300. In this study, we show that MAML1 strongly induced acetylation of the transcription factor early growth response-1 (EGR1) by p300, and increased EGR1 protein expression in embryonic kidney cells. EGR1 mRNA transcripts were also upregulated in the presence of MAML1. We show that MAML1 physically interacted with, and acted cooperatively with EGR1 to increase transcriptional activity of the EGR1 and p300 promoters, which both contain EGR1 binding sites. Bioinformatics assessment revealed a correlation between p300, EGR1 and MAML1 copy number and mRNA alterations in renal clear cell carcinoma and p300, EGR1 and MAML1 gene alterations were associated with increased overall survival. Our findings suggest MAML1 may be a component of the transcriptional networks which regulate EGR1 target genes during nephrogenesis and could also have implications for the development of renal cell carcinoma

CD44 Expression in Oro-Pharyngeal Carcinoma Tissues and Cell Lines

Expression of CD44, a transmembrane hyaluronan-binding glycoprotein, is variably considered to have prognostic significance for different cancers, including oral squamous cell carcinoma. Although unclear at present, tissue-specific expression of particular isoforms of CD44 might underlie the different outcomes in currently available studies. We mined public transcriptomics databases for gene expression data on CD44, and analyzed normal, immortalized and tumour-derived human cell lines for splice variants of CD44 at both the transcript and protein levels. Bioinformatics readouts, from a total of more than 15,000 analyses, implied an increased CD44 expression in head and neck cancer, including increased expression levels relative to many normal and tumor tissue types. Also, meta-analysis of over 260 cell lines and over 4,000 tissue specimens of diverse origins indicated lower CD44 expression levels in cell lines compared to tissue. With minor exceptions, reverse transcribed polymerase chain reaction identified expression of the four main isoforms of CD44 in normal oral keratinocytes, transformed lines termed DT and HaCaT, and a series of paired primary and metastasis-derived cell lines from oral or pharyngeal carcinomas termed HN4/HN12, HN22/HN8 and HN30/HN31. Immunocytochemistry, Western blotting and flow cytometric assessments all confirmed the isoform expression pattern at the protein level. Overall, bioinformatic processing of large numbers of global gene expression analyses demonstrated elevated CD44 expression in head and neck cancer relative to other cancer types, and that the application of standard cell culture protocols might decrease CD44 expression. Additionally, the results show that the many variant CD44 exons are not fundamentally deregulated in a diverse range of cultured normal and transformed keratinocyte lines

Cancer biology, toxicology and alternative methods development go hand-in-hand

Toxicological research faces the challenge of integrating knowledge from diverse fields and novel technological developments generally in the biological and medical sciences. We discuss herein the fact that the multiple facets of cancer research, including discovery related to mechanisms, treatment and diagnosis, overlap many up and coming interest areas in toxicology, including the need for improved methods and analysis tools. Common to both disciplines, in vitro and in silico methods serve as alternative investigation routes to animal studies. Knowledge on cancer development helps in understanding the relevance of chemical toxicity studies in cell models, and many bioinformatics-based cancer biomarker discovery tools are also applicable to computational toxicology. Robotics-aided, cell-based, high-throughput screening, microscale immunostaining techniques and gene expression profiling analyses are common tools in cancer research, and when sequentially combined, form a tiered approach to structured safety evaluation of thousands of environmental agents, novel chemicals or engineered nanomaterials. Comprehensive tumour data collections in databases have been translated into clinically useful data, and this concept serves as template for computer-driven evaluation of toxicity data into meaningful results. Future 'cancer research-inspired knowledge management' of toxicological data will aid the translation of basic discovery results and chemicals- and materials-testing data to information relevant to human health and environmental safet