Kinetics of protein-based in vivo Imaging tracers for positron emission tomography

Within the framework of the “Sel-tag imaging project”, a novel method was used to rapidly label protein tracers and the in vivo targeting abilities of these tracers were studied in animal models of cancer using a preclinical positron emission tomography (PET) camera. To first evaluate and optimize preclinically the use of PET tracers can facilitate their translation to and implementation in human patient studies. The ultimate goal of the different projects within the Sel-tag imaging project was to find imaging biomarkers that could potentially be used for individualizing cancer treatment and thereby improve the therapeutic results. This thesis focuses on methods employed to describe the distribution of these protein-based tracers in human xenografts. Many of the techniques used had been developed for other imaging circumstances. Therefore verification for these imaging applications was an important aspect of these papers. Paper I examined the distribution in a tumour of a medium-sized AnnexinA5-based tracer that targeted phosphatidylserine externalised during cell death in tumours in two cases; first, with no pre-treatment (baseline) and, second, after pre-treatment with a chemotherapeutic agent. Small differences between tracer uptakes in the two cases required a macro parameter analysis method for quantifications. Evaluations of the influence of the enhanced permeability and retention effect by using a size-matched control were introduced. The AnnexinA5 results were compared to those of the metabolic tracer [18F]FDG and complemented with circulating serum markers to increase sensitivity. Paper II extended the analysis in paper I to incorporate more verifications that were also more thorough. The choice of input (blood or reference tissue) and the statistical significance of intergroup comparisons when using conventional uptake measurements and the more involved macro parameter analyses like in paper I were compared. We also proposed that distribution volume ratio was a more appropriate quantification parameter concept for these protein-based tracers with relatively large non-specific uptake. Paper III assessed the smaller Affibody™ tracer ZHER2:342 as an imaging biomarker for human epidermal growth factor 2 (HER2), whose overexpressions are associated with a poor prognosis for breast cancer patients. In order to demonstrate specific binding to HER2, pre-treatment of the tumour with unlabelled protein and uptake in xenografts with low HER2 expression was evaluated. Ex vivo immunohistochemistry of expression levels supported the imaging results. Paper IV examined a radiopharmaceutical that targeted the epidermal growth factor receptor (EGFR), whose overexposure in tumours is associated with a negative prognosis. Again an Affibody™ molecule, (ZEGFR:2377), was used and, as in in paper I, a size-matched control was also used to estimate the non-specific uptake. Uptakes, quantified by conventional uptake methods, varied in tumours with different EGFR expression levels. Ex vivo analyses of expression levels were also performed. Paper V addressed the non-uniform (heterogeneous) uptake of different tracers in a tumour tissue. An algorithm was written that aimed at incorporating all relevant aspects that will influence non-uniformity. Histograms were generated that visualized how the frequency and spread of deviations contributed to the heterogeneity. These aspects could not always be attended in a direct manner, but instead had to be handled in an indirect way. The effect of varying imaging parameters was examined as part of the validation procedure. The method developed is a robust, user-friendly tool for comparing heterogeneity in similar volume preclinical tumor tissues

Combining [(11)C]-AnxA5 PET imaging with serum biomarkers for improved detection in live mice of modest cell death in human solid tumor xenografts

BACKGROUND: In vivo imaging using Annexin A5-based radioligands is a powerful technique for visualizing massive cell death, but has been less successful in monitoring the modest cell death typically seen in solid tumors after chemotherapy. Here we combined dynamic positron emission tomography (PET) imaging using Annexin A5 with a serum-based apoptosis marker, for improved sensitivity and specificity in assessment of chemotherapy-induced cell death in a solid tumor model. METHODOLOGY/PRINCIPAL FINDINGS: Modest cell death was induced by doxorubicin in a mouse xenograft model with human FaDu head and neck cancer cells. PET imaging was based on (11)C-labeled Sel-tagged Annexin A5 ([(11)C]-AnxA5-ST) and a size-matched control. 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]-FDG) was utilized as a tracer of tissue metabolism. Serum biomarkers for cell death were ccK18 and K18 (M30 Apoptosense® and M65). Apoptosis in tissue sections was verified ex vivo for validation. Both PET imaging using [(11)C]-AnxA5-ST and serum ccK18/K18 levels revealed treatment-induced cell death, with ccK18 displaying the highest detection sensitivity. [(18)F]-FDG uptake was not affected by this treatment in this tumor model. [(11)C]-AnxA5-ST gave robust imaging readouts at one hour and its short half-life made it possible to perform paired scans in the same animal in one imaging session. CONCLUSIONS/SIGNIFICANCE: The combined use of dynamic PET with [(11)C]-AnxA5-ST, showing specific increases in tumor binding potential upon therapy, with ccK18/K18 serum measurements, as highly sensitive markers for cell death, enabled effective assessment of modest therapy-induced cell death in this mouse xenograft model of solid human tumors.VetenskapsrådetPublishe

Assimilating remote sensing data with forest growth models

As we are entering an era of increased supply of remote sensing data, we believe that dataassimilation that combines growth forecasts of previous estimates with new observations of thecurrent state has a large potential for keeping forest stand registers up to date (Ehlers et al. 2013).The data assimilation will update a forest model e in an optimal way based on the uncertainties inthe forecast and the observations, each time new data becomes available. These forecasting andupdating steps can be repeated with new available observations to get improved estimations. In thisstudy we present the first practical results from data assimilation of mean tree height, basal area andgrowing stock. The remote sensing data used were canopy height models obtained from matching ofdigital aerial photos over the test site Remningstorp in Sweden. The photos were acquired 2003,2005, 2007, 2009, 2010 and 2012 and normalized with a DEM from airborne laser scanning.The procedure for the data assimilation was as follows: mean tree height, basal area and growingstock were predicted on 18 m × 18 m raster cells using the area based method. Ten meter radiussample plots were used as field calibration data. For each photo year, the field data were adjustedfor growth to have the same state year as each acquisition year of the photos. Growth models wereconstructed from National Forest Inventory plot data. Data assimilation could then be performed onraster cell level by initially start with the estimates from 2003 year´s photos. This prediction was thenforecasted to year 2005 by calculating the growth for the raster cell. This forecasted value is thenblended with the new remote sensing estimation collected 2005. The process was then repeated forthe following years where new measurements were available. In this study, extended Kalmanfiltering was used to blend the forecasted values with the new remote sensing measurements.Validation was done for 40 m radius field plots. Further, the results were also compared with twoalternative approaches: the first was to forecast the first remote sensing estimate to the endpointand the second was to use remote sensing data acquired at the endpoint only.The preliminary results for the eight forest stands show that the variances were lower when usingassimilation of new estimates and there were less fluctuation compared to only using remote sensingdata from the endpoint. However, the mean deviation from the measured value 2011 was lowerwhen only data from the endpoint were used. The assimilated values 2011 were consistently closerto the validation data compared to only forecasting the starting estimate from 2003 to 2011