| Abstract |
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.
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