86 research outputs found

    Towards Leveraging Inhibition State of the Kinome for Precision Oncology

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    Protein phosphorylation forms the most common method of regulation in eukaryotes, and kinases are enzymes that chiefly enable its application. Due to their central role in physiology, dysregulation of the kinome is implicated in a myriad of diseases, particularly cancer. This dissertation demonstrates that the measured inhibition of the kinome (the kinome inhibition state) by cancer targeted therapies can be predictive of cell line and patient-derived xenograft (PDX) tumor responses to treatment by that therapy using interpretable machine learning models. The predictive capability of kinome inhibition states with currently used baseline genomics for monotherapy cancer cell line responses across diverse cancer types is demonstrated first using multi-dose kinome inhibition states, and second using multi-assay single-dose data. Then, the predictive value of kinome inhibition states is extended to kinase inhibitor combination therapies, demonstrating that combined kinome inhibition states can accurately predict cancer cell line sensitivity and synergy to combination treatments, providing the basis for rational kinome-informed drug combination selection. Finally, the predictive capacity of kinome inhibition states is demonstrated for PDX tumor responses in five common solid tumor types, confirming the generalizability of kinome inhibition-based prediction models in a preclinical setting, and emphasizing their potential for clinical translation and application in precision oncology. Overall, this dissertation provides compelling evidence that integrating kinome inhibition states in machine learning models can enhance the prediction of cancer cell line and PDX tumor responses. This work shows that kinome inhibition data has potential to be included in precision oncology platforms alongside baseline genomic profiling, aiding in the identification of effective therapeutic strategies and ultimately improving patient outcomes.Doctor of Philosoph

    Development and application of a platform for harmonisation and integration of metabolomics data

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    Integrating diverse metabolomics data for molecular epidemiology analyses provides both opportuni- ties and challenges in the field of human health research. Combining patient cohorts may improve power and sensitivity of analyses but is challenging due to significant technical and analytical vari- ability. Additionally, current systems for the storage and analysis of metabolomics data suffer from scalability, query-ability, and integration issues that limit their adoption for molecular epidemiological research. Here, a novel platform for integrative metabolomics is developed, which addresses issues of storage, harmonisation, querying, scaling, and analysis of large-scale metabolomics data. Its use is demonstrated through an investigation of molecular trends of ageing in an integrated four-cohort dataset where the advantages and disadvantages of combining balanced and unbalanced cohorts are explored, and robust metabolite trends are successfully identified and shown to be concordant with previous studies.Open Acces

    Computational modeling of biological nanopores

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    Throughout our history, we, humans, have sought to better control and understand our environment. To this end, we have extended our natural senses with a host of sensors-tools that enable us to detect both the very large, such as the merging of two black holes at a distance of 1.3 billion light-years from Earth, and the very small, such as the identification of individual viral particles from a complex mixture. This dissertation is devoted to studying the physical mechanisms that govern a tiny, yet highly versatile sensor: the biological nanopore. Biological nanopores are protein molecules that form nanometer-sized apertures in lipid membranes. When an individual molecule passes through this aperture (i.e., "translocates"), the temporary disturbance of the ionic current caused by its passage reveals valuable information on its identity and properties. Despite this seemingly straightforward sensing principle, the complexity of the interactions between the nanopore and the translocating molecule implies that it is often very challenging to unambiguously link the changes in the ionic current with the precise physical phenomena that cause them. It is here that the computational methods employed in this dissertation have the potential to shine, as they are capable of modeling nearly all aspects of the sensing process with near atomistic precision. Beyond familiarizing the reader with the concepts and state-of-the-art of the nanopore field, the primary goals of this dissertation are fourfold: (1) Develop methodologies for accurate modeling of biological nanopores; (2) Investigate the equilibrium electrostatics of biological nanopores; (3) Elucidate the trapping behavior of a protein inside a biological nanopore; and (4) Mapping the transport properties of a biological nanopore. In the first results chapter of this thesis (Chapter 3), we used 3D equilibrium simulations [...]Comment: PhD thesis, 306 pages. Source code available at https://github.com/willemsk/phdthesis-tex

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    3D genomics::form and function of chromatin

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    Development of novel anticancer agents targeting G protein coupled receptor: GPR120

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    The G-protein coupled receptor, GPR120, has ubiquitous expression and multifaceted roles in modulating metabolic and anti-inflammatory processes. GPR120 - also known as Free Fatty Acid Receptor 4 (FFAR4) is classified as a free fatty acid receptor of the Class A GPCR family. GPR120 has recently been implicated as a novel target for cancer management. GPR120 gene knockdown in breast cancer studies revealed a role of GPR120-induced chemoresistance in epirubicin and cisplatin-induced DNA damage in tumour cells. Higher expression and activation levels of GPR120 is also reported to promote tumour angiogenesis and cell migration in colorectal cancer. A number of agonists targeting GPR120 have been reported, such as TUG891 and Compound39, but to date development of small-molecule inhibitors of GPR120 is limited. This research applied a rational drug discovery approach to discover and design novel anticancer agents targeting the GPR120 receptor. A homology model of GPR120 (short isoform) was generated to identify potential anticancer compounds using a combined in silico docking-based virtual screening (DBVS), molecular dynamics (MD) assisted pharmacophore screenings, structure–activity relationships (SAR) and in vitro screening approach. A pharmacophore hypothesis was derived from analysis of 300 ns all-atomic MD simulations on apo, TUG891-bound and Compound39-bound GPR120 (short isoform) receptor models and was used to screen for ligands interacting with Trp277 and Asn313 of GPR120. Comparative analysis of 100 ns all-atomic MD simulations of 9 selected compounds predicted the effects of ligand binding on the stability of the “ionic lock” – a characteristic of Class A GPCRs activation and inactivation. The “ionic lock” between TM3(Arg136) and TM6(Asp) is known to prevent G-protein recruitment while GPCR agonist binding is coupled to outward movement of TM6 breaking the “ionic lock” which facilitates G-protein recruitment. The MD-assisted pharmacophore hypothesis predicted Cpd 9, (2-hydroxy-N-{4-[(6-hydroxy-2-methylpyrimidin-4-yl) amino] phenyl} benzamide) to act as a GPR120S antagonist which can be evaluated and characterised in future studies. Additionally, DBVS of a small molecule database (~350,000 synthetic chemical compounds) against the developed GPR120 (short isoform) model led to selection of the 13 hit molecules which were then tested in vitro to evaluate their cytotoxic, colony forming and cell migration activities against SW480 – human CRC cell line expressing GPR120. Two of the DBVS hit molecules showed significant (\u3e 90%) inhibitory effects on cell growth with micromolar affinities (at 100 μM) - AK-968/12713190 (dihydrospiro(benzo[h]quinazoline-5,1′-cyclopentane)-4(3H)-one) and AG-690/40104520 (fluoren-9-one). SAR analysis of these two test compounds led to the identification of more active compounds in cell-based cytotoxicity assays – AL-281/36997031 (IC50 = 5.89–6.715 μM), AL-281/36997034 (IC50 = 6.789 to 7.502 μM) and AP-845/40876799 (IC50 = 14.16-18.02 μM). In addition, AL-281/36997031 and AP-845/40876799 were found to be significantly target-specific during comparative cytotoxicity profiling in GPR120-silenced and GPR120-expressing SW480 cells. In wound healing assays, AL-281/36997031 was found to be the most active at 3 μM (IC25) and prevented cell migration. As well as in the assessment of the proliferation ability of a single cell to survive and form colonies through clonogenic assays, AL-281/36997031 was found to be the most potent of all three test compounds with the survival rate of ~ 30% at 3 μM. The inter-disciplinary approach applied in this work identified potential chemical scaffolds –spiral benzo-quinazoline and fluorenone, targeting GPR120 which can be further explored for designing anti-cancer drug development studies
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