Abstract. Colorectal cancer (CRC) is one of the most frequent malignant diseases in the world. Metastatic spread of the cancer to the lymph nodes is a crucial factor for progression and therapeutic management of the disease. We analysed gene expression profiles of CRC patiens by lowdensity cancer-focused oligonucleotide microarrays to identify new predictive markers of the extent of the disease and for better understanding of CRC progression. Relative expression levels of 440 genes known to be involved in cancer biology were obtained by low-density oligonucleotide microarrays from 20 tumor samples. Statistical analysis of gene expression data identified 3 genes (HSP110, HYOU1 and TCTP) significantly up-regulated in primary tumors of patients who developed lymph node metastasis. We have shown, for the first time, that up-regulation HSP110 and HYOU1 expression is associated with lymph node involvement in CRC. We validated the differences in HSP110 expression in an independent group of 30 patients of all clinical stages by real-time PCR. We identified significant up-regulation of HSP110 expression in colorectal tumors compared to adjacent non-tumoral tissue (p<0.0003). We observed significant differences of HSP110 gene expression between metastatic and localized disease (p=0.031) and negative trend of HSP110 gene expression and overall survival of CRC patients. We suggest that HSP110 gene is a promising molecular predictor in CRC. IntroductionColorectal cancer (CRC) is one of the most frequent malignant diseases in the world. With the incidence rate ~78 per 100,000 people, the Czech Republic is a country with one of the highest incidence of CRC in the world and the highest in Europe (1). The prognosis of these patients depends largely on the extent of the disease and a possibility of curative surgical intervention which is feasible only in patients with disease limited to the primary tumor and regional lymph nodes. Spread of the cancer to lymph nodes has been considered a crucial factor for progression and further therapeutic management of the disease. While in the case of clinical stage I and IV, the prognostic significance of the TNM classification is evident, for the group of patients diagnosed at stages II (without dissemination to regional lymph nodes) and III (with dissemination to lymph nodes) the prognostic information of clinical stage is much lower. The fact, that 30% of patients with clinical stage II of CRC will progress within five years and only 45% of the patients with clinical stage III will reach a 5-year survival, although undergoing radical surgery, implies that recent staging of CRC based on TNM classification is not optimal and failed for a significant proportion of non-advanced CRC patients. This situation is caused by two factors: firstly, understaging of regional lymph node status for the reason of insufficient number of resected and/or examined lymph nodes, secondly, the TNM classification does not include biological characteristics and predictors of tumor behaviour. Multicentric studies ba...
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
In this paper, we present an easy-to-follow procedure for the analysis of tissue sections from 3D cell cultures (spheroids) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and laser scanning confocal microscopy (LSCM). MALDI MSI was chosen to detect the distribution of the drug of interest, while fluorescence immunohistochemistry (IHC) followed by LSCM was used to localize the cells featuring specific markers of viability, proliferation, apoptosis and metastasis. The overlay of the mass spectrometry (MS) and IHC spheroid images, typically without any morphological features, required fiducial-based coregistration. The MALDI MSI protocol was optimized in terms of fiducial composition and antigen epitope preservation to allow MALDI MSI to be performed and directly followed by IHC analysis on exactly the same spheroid section. Once MS and IHC images were coregistered, the quantification of the MS and IHC signals was performed by an algorithm evaluating signal intensities along equidistant layers from the spheroid boundary to its center. This accurate colocalization of MS and IHC signals showed limited penetration of the clinically tested drug perifosine into spheroids during a 24 h period, revealing the fraction of proliferating and promigratory/proinvasive cells present in the perifosine-free areas, decrease of their abundance in the perifosine-positive regions, and distinguishing between apoptosis resulting from hypoxia/nutrient deprivation and drug exposure.
Colorectal cancer (CRC) is a disease with constantly increasing incidence and high mortality. The treatment efficacy could be curtailed by drug resistance resulting from poor drug penetration into tumor tissue and the tumor-specific microenvironment, such as hypoxia and acidosis. Furthermore, CRC tumors can be exposed to different pH depending on the position in the intestinal tract. CRC tumors often share upregulation of the Akt signaling pathway. In this study, we investigated the role of external pH in control of cytotoxicity of perifosine, the Akt signaling pathway inhibitor, to CRC cells using 2D and 3D tumor models. In 3D settings, we employed an innovative strategy for simultaneous detection of spatial drug distribution and biological markers of proliferation/apoptosis using a combination of mass spectrometry imaging and immunohistochemistry. In 3D conditions, low and heterogeneous penetration of perifosine into the inner parts of the spheroids was observed. The depth of penetration depended on the treatment duration but not on the external pH. However, pH alteration in the tumor microenvironment affected the distribution of proliferation- and apoptosis-specific markers in the perifosine-treated spheroid. Accurate co-registration of perifosine distribution and biological response in the same spheroid section revealed dynamic changes in apoptotic and proliferative markers occurring not only in the perifosine-exposed cells, but also in the perifosine-free regions. Cytotoxicity of perifosine to both 2D and 3D cultures decreased in an acidic environment below pH 6.7. External pH affects cytotoxicity of the other Akt inhibitor, MK-2206, in a similar way. Our innovative approach for accurate determination of drug efficiency in 3D tumor tissue revealed that cytotoxicity of Akt inhibitors to CRC cells is strongly dependent on pH of the tumor microenvironment. Therefore, the effect of pH should be considered during the design and pre-clinical/clinical testing of the Akt-targeted cancer therapy.
Spheroids—three-dimensional aggregates of cells grown from a cancer cell line—represent a model of living tissue for chemotherapy investigation. Distribution of chemotherapeutics in spheroid sections was determined using the matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). Proliferating or apoptotic cells were immunohistochemically labeled and visualized by laser scanning confocal fluorescence microscopy (LSCM). Drug efficacy was evaluated by comparing coregistered MALDI MSI and LSCM data of drug-treated spheroids with LSCM only data of untreated control spheroids. We developed a fiducial-based workflow for coregistration of low-resolution MALDI MS with high-resolution LSCM images. To allow comparison of drug and cell distribution between the drug-treated and untreated spheroids of different shapes or diameters, we introduced a common diffusion-related coordinate, the distance from the spheroid boundary. In a procedure referred to as “peeling”, we correlated average drug distribution at a certain distance with the average reduction in the affected cells between the untreated and the treated spheroids. This novel approach makes it possible to differentiate between peripheral cells that died due to therapy and the innermost cells which died naturally. Two novel algorithms—for MALDI MS image denoising and for weighting of MALDI MSI and LSCM data by the presence of cell nuclei—are also presented.
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