Use of an orthovoltage X-ray treatment unit as a radiation research system in a small-animal cancer model

<p>Abstract</p> <p>Background</p> <p>We explore the use of a clinical orthovoltage X-ray treatment unit as a small-animal radiation therapy system in a tumoral model of cervical cancer.</p> <p>Methods</p> <p>Nude mice were subcutaneously inoculated with 5 × 10⁶HeLa cells in both lower limbs. When tumor volume approximated 200 mm³treatment was initiated. Animals received four 2 mg/kg intraperitoneal cycles (1/week) of cisplatin and/or 6.25 mg/kg of gemcitabine, concomitant with radiotherapy. Tumors were exposed to 2.5 Gy/day nominal surface doses (20 days) of 150 kV X-rays. Lead collimators with circular apertures (0.5 to 1.5 cm diameter) were manufactured and mounted on the applicator cone to restrict the X-ray beam onto tumors. X-ray penetration and conformality were evaluated by measuring dose at the surface and behind the tumor lobe by using HS GafChromic film. Relative changes in tumor volume (RTV) and a clonogenic assay were used to evaluate the therapeutic response of the tumor, and relative weight loss was used to assess toxicity of the treatments.</p> <p>Results</p> <p>No measurable dose was delivered outside of the collimator apertures. The analysis suggests that dose inhomogeneities in the tumor reach up to ± 11.5% around the mean tumor dose value, which was estimated as 2.2 Gy/day. Evaluation of the RTV showed a significant reduction of the tumor volume as consequence of the chemoradiotherapy treatment; results also show that toxicity was well tolerated by the animals.</p> <p>Conclusion</p> <p>Results and procedures described in the present work have shown the usefulness and convenience of the orthovoltage X-ray system for animal model radiotherapy protocols.</p

n silico dekonvolutionsmetod för bestämning av haltencancerceller genom transkriptomik

In this work we suggest a deconvolution method, based on a convex optimization problem, to calculate the cancer amount from heterogeneous cell type gene expression profiles generated in silico. Expression profiling is a technique for identifying global expression patterns within cellular groups, its multiple purposes may include the identification of disease biomarkers and the basic understanding of cellular processes. Given the necessity for understanding complex biological processes such as development and carcinogenesis, it is of main importance to distinguish between contributions to gene expression profiles from either regulation processes or abundance of cellular groups. Unfortunately, many biological samples contain mixtures of cell-types. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. We describe a model to estimate the proportions of cell types in a given test data set based on a gene expression profile derived from transcriptomics. Our model is based on least squares estimation and the solution of a convex optimization problem. The technical aim is to solve an undetermined system of linear equations, which must satisfy several constraints and under a particular sparsity assumption. Cell type mixtures were simulated in silico using a special procedure based on mean and standard deviations. Variable selection was performed by Analysis of Variance (ANOVA) using “cell type” as main factor and genes were ranked by F-statistics. We tested our model in breast and liver tissues, employing four cell types (three normal and one cancerous). We also performed a bootstrap procedure to test the robustness of our method concluding that our method is stable and accurate enough to estimate cancer portions of at least 10%.I denna studie föreslås en in silico dekonvolutionsmetod baserad på ett konvext optimeringsproblem för att bestämma mängden cancerceller genom genuttrycksprofilering av en heterogen blandning av celltyper. Profilering av genuttryck är en metod som används for identifieringen av genuttrycksmönster inom olika cellgrupper. Metoden kan till exempel användas för att identifiera biomarkörer för sjukdomar och för att studera cellprocesser. En svårighet är att biologiska prover innehåller många olika celltyper, vilket har hittills begränsat metodens användbarhet för att studera genuttryck i en specifik celltyp.   För att uppskatta mängden av olika celltyper från test data föreslås en modell baserat på transkriptomik. I modellen används minsta kvadratmetoden och lösningen av ett konvext optimeringsproblem för att lösa ett underbestämt system av linjära ekvationer. Test data med blandade celltyper simulerats in silico, baserat på medelvärden och standardavvikelser. Selektion av variabler gjordes med hjälp av variansanalys (ANOVA), och F-statistik användes för att ordna av gener. Modellen tillämpades på simulerade vävnadsprover från bröst och lever, innehållande tre normala celltyper och en typ av cancercell. Modellens tillförlitlighet testades med hjälp av en bootstrapping metod. Vi fann att den föreslagna modellen är stabil och tillräckligt noggrann för att bestämma cancerhalt av minst 10%