| 1. |
- Jerhammar, Fredrik, 1979-
(författare)
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Predictive Markers of Treatment Resistance in Head and Neck Squamous Cell Carcinoma
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Head and neck cancer is a common malignancy with approximately 600 000 new cases yearly. Disappointingly, the overall survival has not increased over the last decades. The concept of personalized medicine, i.e. to treat every patient with an individually planned treatment regime has gathered increased interest, but requires the establishment of novel biomarkers that can predict treatment response.The aim of this thesis is to propose novel predictive single markers or combinations of markers of response to radiation, cisplatin and cetuximab. The general methodology is to evaluate common differences of cell lines resistant to radiation, cisplatin or cetuximab compared to sensitive counterparts.In paper I, we analysed the expression of 14 proteins involved in growth control and/or apoptosis by western blot and related them to intrinsic radiosensitivity (IR) in nine cell lines. No factor had a significant correlation to IR on its own. A combination of EGFR, survivin, Bak, Smad4, and Hsp70 had the best correlation to IR (R=0.886, p=0.001). Additionally, we analysed the presence of p53 mutations in the cell lines. All cell lines had at least one missense, splice site or loss of transcript mutation. To be able to combine protein expression and presence of p53 mutations we created a system designated the number of negative points (NNP). With this system we could extract that expression of EGFR, survivin, and p53 missense or splice site mutations had the best correlation to IR (R=0.990, p<0.001).In paper II we conducted a gene expression microarray analysis of three cell lines, from which common deregulations in two cisplatin resistant cell lines was compared to a cisplatin sensitive cell line. From a bioinformatic approach of gene ontology and molecular network analysis, we defined a transcriptional profile of 20 genes. Finally, key findings were analysed in a larger panel of cell lines, where high MMP-7 expression correlated with higher cisplatin resistance.Paper III compared 4 cell lines with high IR to a radiosensitive equivalent. Using a similar bioinformatic approach as paper II, we established a transcriptional profile of 14 genes. Analysis in a larger panel of cell lines revealed that FN1 expression predicts higher IR.Paper IV establishes the cetuximab sensitivity of 35 cell lines of which 12 were resistant and five were sensitive to cetuximab. After whole genome gene copy number analysis of five cetuximab resistant and five cetuximab sensitive cell lines, and verification of key findings in a larger cell line panel, the results show that the amplification of the YAP1 gene is coupled to cetuximab resistance.In summary, this thesis proposes a number of novel markers of resistance to radiation, cisplatin, and cetuximab which could influence treatment choice in the future, following verifications in primary tumor material.
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| 2. |
- Lampa, Samuel, 1983-
(författare)
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Reproducible Data Analysis in Drug Discovery with Scientific Workflows and the Semantic Web
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The pharmaceutical industry is facing a research and development productivity crisis. At the same time we have access to more biological data than ever from recent advancements in high-throughput experimental methods. One suggested explanation for this apparent paradox has been that a crisis in reproducibility has affected also the reliability of datasets providing the basis for drug development. Advanced computing infrastructures can to some extent aid in this situation but also come with their own challenges, including increased technical debt and opaqueness from the many layers of technology required to perform computations and manage data. In this thesis, a number of approaches and methods for dealing with data and computations in early drug discovery in a reproducible way are developed. This has been done while striving for a high level of simplicity in their implementations, to improve understandability of the research done using them. Based on identified problems with existing tools, two workflow tools have been developed with the aim to make writing complex workflows particularly in predictive modelling more agile and flexible. One of the tools is based on the Luigi workflow framework, while the other is written from scratch in the Go language. We have applied these tools on predictive modelling problems in early drug discovery to create reproducible workflows for building predictive models, including for prediction of off-target binding in drug discovery. We have also developed a set of practical tools for working with linked data in a collaborative way, and publishing large-scale datasets in a semantic, machine-readable format on the web. These tools were applied on demonstrator use cases, and used for publishing large-scale chemical data. It is our hope that the developed tools and approaches will contribute towards practical, reproducible and understandable handling of data and computations in early drug discovery.
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| 3. |
- Martens, Marvin, et al.
(författare)
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ELIXIR and Toxicology : a community in development
- 2021
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Ingår i: F1000 Research. - : F1000 Research Ltd. - 2046-1402. ; 10, s. 1129-1129
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Tidskriftsartikel (refereegranskat)abstract
- Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.
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