4 research outputs found

    Early prediction of response to radiotherapy and androgen-deprivation therapy in prostate cancer by repeated functional MRI: a preclinical study

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    <p>Abstract</p> <p>Background</p> <p>In modern cancer medicine, morphological magnetic resonance imaging (MRI) is routinely used in diagnostics, treatment planning and assessment of therapeutic efficacy. During the past decade, functional imaging techniques like diffusion-weighted (DW) MRI and dynamic contrast-enhanced (DCE) MRI have increasingly been included into imaging protocols, allowing extraction of intratumoral information of underlying vascular, molecular and physiological mechanisms, not available in morphological images. Separately, pre-treatment and early changes in functional parameters obtained from DWMRI and DCEMRI have shown potential in predicting therapy response. We hypothesized that the combination of several functional parameters increased the predictive power.</p> <p>Methods</p> <p>We challenged this hypothesis by using an artificial neural network (ANN) approach, exploiting nonlinear relationships between individual variables, which is particularly suitable in treatment response prediction involving complex cancer data. A clinical scenario was elicited by using 32 mice with human prostate carcinoma xenografts receiving combinations of androgen-deprivation therapy and/or radiotherapy. Pre-radiation and on days 1 and 9 following radiation three repeated DWMRI and DCEMRI acquisitions enabled derivation of the apparent diffusion coefficient (ADC) and the vascular biomarker <it>K</it><sup>trans</sup>, which together with tumor volumes and the established biomarker prostate-specific antigen (PSA), were used as inputs to a back propagation neural network, independently and combined, in order to explore their feasibility of predicting individual treatment response measured as 30 days post-RT tumor volumes.</p> <p>Results</p> <p>ADC, volumes and PSA as inputs to the model revealed a correlation coefficient of 0.54 (p < 0.001) between predicted and measured treatment response, while <it>K</it><sup>trans</sup>, volumes and PSA gave a correlation coefficient of 0.66 (p < 0.001). The combination of all parameters (ADC, <it>K</it><sup>trans</sup>, volumes, PSA) successfully predicted treatment response with a correlation coefficient of 0.85 (p < 0.001).</p> <p>Conclusions</p> <p>We have in a preclinical investigation showed that the combination of early changes in several functional MRI parameters provides additional information about therapy response. If such an approach could be clinically validated, it may become a tool to help identifying non-responding patients early in treatment, allowing these patients to be considered for alternative treatment strategies, and, thus, providing a contribution to the development of individualized cancer therapy.</p

    Transcriptional Analysis of Fracture Healing and the Induction of Embryonic Stem Cell–Related Genes

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    Fractures are among the most common human traumas. Fracture healing represents a unique temporarily definable post-natal process in which to study the complex interactions of multiple molecular events that regulate endochondral skeletal tissue formation. Because of the regenerative nature of fracture healing, it is hypothesized that large numbers of post-natal stem cells are recruited and contribute to formation of the multiple cell lineages that contribute to this process. Bayesian modeling was used to generate the temporal profiles of the transcriptome during fracture healing. The temporal relationships between ontologies that are associated with various biologic, metabolic, and regulatory pathways were identified and related to developmental processes associated with skeletogenesis, vasculogenesis, and neurogenesis. The complement of all the expressed BMPs, Wnts, FGFs, and their receptors were related to the subsets of transcription factors that were concurrently expressed during fracture healing. We further defined during fracture healing the temporal patterns of expression for 174 of the 193 genes known to be associated with human genetic skeletal disorders. In order to identify the common regulatory features that might be present in stem cells that are recruited during fracture healing to other types of stem cells, we queried the transcriptome of fracture healing against that seen in embryonic stem cells (ESCs) and mesenchymal stem cells (MSCs). Approximately 300 known genes that are preferentially expressed in ESCs and ∼350 of the known genes that are preferentially expressed in MSCs showed induction during fracture healing. Nanog, one of the central epigenetic regulators associated with ESC stem cell maintenance, was shown to be associated in multiple forms or bone repair as well as MSC differentiation. In summary, these data present the first temporal analysis of the transcriptome of an endochondral bone formation process that takes place during fracture healing. They show that neurogenesis as well as vasculogenesis are predominant components of skeletal tissue formation and suggest common pathways are shared between post-natal stem cells and those seen in ESCs
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