The chapters that comprised this thesis cover a broad range of subjects from analytical method development to clinical application of metabolic profiling. They are united by the facts that all of these studies aimed at analysis of biological fluids and that the presented methods and approaches may ultimately become parts of a robust metabolomics workflow that might be used in a future personalized medicine.
On nuclear magnetic resonance, metabolomics and "metabolic individuality"Metabolomics is an attractive methodology for clinical research as it is the most accurate reflection of the actual physiological and biochemical state of the organism.The dynamic and highly "individualized" nature of the metabolome is a strong indication that it could provide the means to make personalized medicine go all the way from an "elusive dream" 11 , via "proof-of-principle", to real application.
<p>RT-PCR analysis of 20 transcription factors in four tamoxifen-associated endometrial tumors and Ishikawa cells. Corresponding primers can be found in supplementary table S6.</p>
<p>Supplementary method, Supplementary figures and supplementary figure legends: Supplementary Figure 1. Viral VBIM integration site in MAML3 gene and equal sensitivity to cisplatin of parental and SD3.23 cells. Supplementary Figure 2. Overexpression of MAML3, but not MAML1 and MAML2, mediates resistance to retinoic acid in multiple neuroblastoma cell lines. Supplementary Figure 3. Induction of neuronal markers upon RA treatment is decreased in MAML3 overexpressing cells. Supplementary Figure 4. MAML3 binds to RXR. Supplementary Figure 5. IGF2 is upregulated in MAML3 overexpressing cells and results in hyperproliferation, but not RA resistance.</p>
206 Background: In prostate cancer, homologous recombination deficiency (HRD) is associated with poor prognosis, and sensitivity to DNA damaging agents and DNA damage repair (DDR) inhibitors. As new classes of DDR inhibitors become available, identifying patients with HRD will be critical for treatment selection. Here, we present machine learning (ML)-based models trained to predict HRD status directly from hematoxylin and eosin (H&E) whole slide images (WSI). Methods: ML models were trained to predict and segment cells and tissue regions within the tumor microenvironment (TME) using annotated (N=91,021 annotations) WSI of H&E-stained resections from the cancer genome atlas prostate adenocarcinoma (TCGA PRAD) dataset (N=401) and needle core biopsies from a proprietary dataset (N=1,000). Quantified Human Interpretable Features (HIFs) that describe the TME composition were extracted. Three models were trained to predict HRD status using 373 WSI with known HRD score (TCGA PRAD; train N=259, validation N=76, and test N=38). Two models used input from the TME model: An HIF multivariate logistic regression model, and a graph neural network (GNN) where predictions are based on the complex spatial relationships within the TME. An end-to-end (E2E) multiple instance learning model predicted directly from the WSI. Two cutoffs for HRD were defined using Gaussian Mixture Models, resulting in 99 WSI (train N=72, validation N=18, and test N=9) positive for the Genomic Instability (>16 events) cutoff, and 58 WSI (train N=44, validation N=10, test N=4) positive for the Genomic Instability (>22 events) cutoff. An independent validation set of 45 biopsies and 16 resections from a biobank of metastatic castration resistant prostate cancer with HRD status determined by whole-exome sequencing was compared to ML model H&E-based HRD prediction. Results: In the TCGA test set of resection samples, all three models moderately or strongly predicted HRD status, with the HIF model showing the best performance (AUROC 0.87, sensitivity 0.88, specificity 0.62). The same HIF model performed equally well (AUROC 0.85, Sensitivity 0.93, specificity 0.67) in the resection samples from the independent validation set. However, the model performance went down (AUROC 0.69, sensitivity 0.91, specificity: 0.3) when both resection and needle biopsy samples were included, highlighting the importance of a representative training set to achieve robust performance in a real world setting. Further model training and validation with a more diverse dataset is required to accurately assess the performance of the model on needle biopsies. Conclusions: ML models trained on resection prostate cancer samples performed well in predicting HRD status when applied to the same sample type, demonstrating the potential of ML models to predict genomic biomarkers status in surgical specimens for treatment decision.
Gas Chromatography (GC)-Mass Spectrometry (MS) with Atmospheric Pressure (AP) interface was introduced more than 30 years ago but never became a mainstream technique, mainly because of technical difficulties and cost of instrumentation. A recently introduced multipurpose AP source created the opportunity to reconsider the importance of AP ionization for GC. Here, we present an analytical evaluation of GC/APCI-MS showing the benefits of soft atmospheric pressure chemical ionization for GC in combination with a Time of Flight (TOF) mass analyzer. During this study, the complete analytical procedure was optimized and evaluated with respect to characteristic analytical parameters, such as repeatability, reproducibility, linearity, and detection limits. Limits of detection (LOD) were found within the range from 11.8 to 72.5 nM depending on the type of compound. The intraday and interday repeatability tests demonstrate relative standard deviations (RSDs) of peak areas between 0.7%-2.1% and 3.8%-6.4% correspondingly. Finally, we applied the developed method to the analysis of human cerebrospinal fluid (CSF) samples to check the potential of this new analytical combination for metabolic profiling.
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