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See detailMathematical Histopathology and Systems Pharmacology of Melanoma
Albrecht, Marco UL

Doctoral thesis (2019)

Treated metastatic melanoma often becomes resistant and relapses whereby resistance mechanisms can be found at the level of biochemical, histological, and pharmacological data. By using this data in a ... [more ▼]

Treated metastatic melanoma often becomes resistant and relapses whereby resistance mechanisms can be found at the level of biochemical, histological, and pharmacological data. By using this data in a mathematical form, an integrative understanding of tumour progression can be gained that reveal the functionality of more complex and hidden recurrence mechanisms. The aims of this thesis were - to investigate how a new engineering concept on tumour growth, based on porous media theory, can be leveraged to support medicine and cancer biology research, - to identify suitable tests for cancer growth model validation, - to study how elements of biochemical cancer pathways are linked to the elements of physical growth, and - to establish a pharmacokinetics module for the melanoma cancer drug dabrafenib. The studied engineering concept is qualitatively suitable to represent late-stage metastatic melanoma in irregular fibrous tissue types, whereby all equations are tested for biological relevance and parametrisation. The framework allows modelling of tissue-specific growth, and the thesis shows that the simulated tumour can shift between compact growth with ECM displacement and invasive growth with ECM circumvention as a consequence of cell plasticity/viscosity change. This is unique among continuous models of tumour growth. However, the investigation also shows that the pressure-saturation relationships are not biologically motivated and can be replaced by a swelling polymer model which captures the water absorbing effect of glycans. The thesis addresses a biologically and computationally reasonable strategy to validate the tumour growth model as complete as possible. A suitable way to validate a part of the tumour growth model is using time course data of spheroid growth in hydrogels of different stiffness values. Spheroids generated from the LU451 melanoma cell line mainly grow due to ECM degradation, have a time-variant growth rate increasing with gel rigidity, and the confined environment renders the melanoma cell line drug-resistant upon dabrafenib dose escalation. This setting reveals the interplay between mechanical and biochemical development over time. The dependency between biological elements of cancer pathways and the mechanical elements of the engineering concept on tumour growth were clarified. Therefore, the literature on mechanoregulation has been reviewed and serves as a computational link between systems biology and physical oncology. Finally, the thesis provides preliminary steps and a concept toward a serious interdisciplinary methodology to understand tumour growth, although this cannot be considered a final model for any of the known melanoma growth settings. Additionally, the thesis provides a novel quantitative systems pharmacology approach to consider liver-enzyme-induction and drug-drug-interaction. The finding is that the potent dabrafenib metabolite desmethyl-dabrafenib accumulates with consequential efficacy loss in a confined tumour environment. [less ▲]

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See detail"Stroma-induced phenotypic plasticity offers phenotype-specific targeting to improve melanoma treatment".
Seip, Kotryna; Jorgensen, Kjetil; Haselager, Marco Vincent et al

in Cancer letters (2018)

Cancer cells' phenotypic plasticity, promoted by stromal cells, contributes to intra-tumoral heterogeneity and affects response to therapy. We have disclosed an association between fibroblast-stimulated ... [more ▼]

Cancer cells' phenotypic plasticity, promoted by stromal cells, contributes to intra-tumoral heterogeneity and affects response to therapy. We have disclosed an association between fibroblast-stimulated phenotype switching and resistance to the clinically used BRAF inhibitor (BRAFi) vemurafenib in malignant melanoma, revealing a challenge in targeting the fibroblast-induced phenotype. Here we compared molecular features and drug sensitivity in melanoma cells grown as co-cultures with fibroblasts versus mono-cultures. In the presence of fibroblasts, melanoma cells switched to the dedifferentiated, mesenchymal-like, inflammatory phenotype that showed reduced sensitivity to the most of 275 tested cancer drugs. Fibroblasts, however, sensitized melanoma cells to PI3K inhibitors (PI3Ki) and particularly the inhibitor of GSK3, AR-A014418 (GSK3i), that showed superior efficacy in co-cultures. The proteome changes induced by the BRAFi+GSK3i combination mimicked changes induced by BRAFi in mono-cultures, and GSK3i in co-cultures. This suggests that the single drug drives the response to the combination treatment, depending on fibroblast presence or absence, consequently, phenotype. We propose that the BRAFi and GSK3i (or PI3Ki) combination exemplifies phenotype-specific combinatorial treatment that should be beneficial in phenotypically heterogeneous tumors rich in stromal interactions. [less ▲]

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See detailEpigenetically Regulated Chromosome 14q32 miRNA Cluster Induces Metastasis and Predicts Poor Prognosis in Lung Adenocarcinoma Patients.
Gonzalez-Vallinas, Margarita; Rodriguez-Paredes, Manuel; Albrecht, Marco UL et al

in Molecular cancer research : MCR (2018)

Most lung cancer deaths are related to metastases, which indicates the necessity of detecting and inhibiting tumor cell dissemination. Here, we aimed to identify microRNAs (miRNAs) involved in metastasis ... [more ▼]

Most lung cancer deaths are related to metastases, which indicates the necessity of detecting and inhibiting tumor cell dissemination. Here, we aimed to identify microRNAs (miRNAs) involved in metastasis of lung adenocarcinoma as prognostic biomarkers and therapeutic targets. To that end, lymph node metastasis-associated miRNAs were identified in The Cancer Genome Atlas (TCGA) lung adenocarcinoma patient cohort (sequencing data; n=449) and subsequently validated by RT-qPCR in an independent clinical cohort (n=108). Overexpression of miRNAs located on chromosome 14q32 were associated with metastasis in lung adenocarcinoma patients. Importantly, Kaplan-Meier analysis and log-rank test revealed that higher expression levels of individual 14q32 miRNAs (mir-539, mir-323b, and mir-487a) associated with worse disease-free survival of never-smoker patients. Epigenetic analysis including DNA methylation microarray data and bisulfite sequencing validation demonstrated that the induction of 14q32 cluster correlated with genomic hypomethylation of the 14q32 locus. CRISPR activation technology, applied for the first time to functionally study the increase of clustered miRNA levels in a coordinated manner, showed that simultaneous overexpression of 14q32 miRNAs promoted tumor cell migratory and invasive properties. Analysis of individual miRNAs by mimic transfection further illustrated that miR-323b-3p, miR-487a-3p, and miR-539-5p significantly contributed to the invasive phenotype through the indirect regulation of different target genes. In conclusion, overexpression of 14q32 miRNAs, associated with the respective genomic hypomethylation, promotes metastasis and correlates with poor patient prognosis in lung adenocarcinoma. IMPLICATIONS: This study points to chromosome 14q32 miRNAs as promising targets to inhibit tumor cell dissemination and to predict patient prognosis in lung adenocarcinoma. [less ▲]

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See detailTTCA: an R package for the identification of differentially expressed genes in time course microarray data
Albrecht, Marco UL; Stichel, Damian; Müller, Benedikt et al

in BMC Bioinformatics (2017), 18(1), 33

Background: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes ... [more ▼]

Background: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. Results: The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Conclusion: Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1. [less ▲]

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See detailThermodynamically constrained averaging theory for cancer growth modelling
Albrecht, Marco UL; Sciumè, Giuseppe; Lucarelli, Philippe UL et al

in IFAC-PapersOnLine (2016), 49(26), 289-294

In Systems Biology, network models are often used to describe intracellular mechanisms at the cellular level. The obtained results are difficult to translate into three-dimensional biological systems of ... [more ▼]

In Systems Biology, network models are often used to describe intracellular mechanisms at the cellular level. The obtained results are difficult to translate into three-dimensional biological systems of higher order. The multiplicity and time dependency of cellular system boundaries, mechanical phenomena and spatial concentration gradients affect the intercellular relations and communication of biochemical networks. These environmental effects can be integrated with our promising cancer modelling environment, that is based on thermodynamically constrained averaging theory (TCAT). Especially, the TCAT parameter viscosity can be used as critical player in tumour evolution. Strong cell-cell contacts and a high degree of differentiation make cancer cells viscous and support compact tumour growth with high tumour cell density and accompanied displacement of the extracellular material. In contrast, dedifferentiation and losing of cell-cell contacts make cancer cells more fluid and lead to an infiltrating tumour growth behaviour without resistance due to the ECM. The fast expanding tumour front of the invasive type consumes oxygen and the limited oxygen availability behind the invasive front results automatically in a much smaller average tumour cell density in the tumour core. The proposed modelling technique is most suitable for tumour growth phenomena in stiff tissues like skin or bone with high content of extracellular matrix. [less ▲]

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