Browsing by Subject "Colon cancer"

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Open Access
BO-264: Highly potent TACC3 inhibitor as a novel anticancer drug candidate
(2021-01) Akbulut, Özge
Breast cancer has been consistently ranked to be the most common cancer and the second leading cause of cancer-related death in women worldwide for many years. Despite better understanding of tumor biology and the availability of plethora of anti-cancer therapeutics utilizing different strategies, complete response and/or long-term survival is achieved in only a small fraction of patients with aggressive disease. Since microtubule re-organization is an important step during cell division, drugs that interfere with this process have been a major focus of cancer research. Although anti-microtubule agents are widely used in clinic, cytotoxicity to non-tumorigenic cells and drug resistance are still the main obstacles. Therefore, development of alternative target molecules that selectively and efficiently target cancer cells, but restore normal cells are needed. Transforming acidic coiled-coil containing protein 3 (TACC3) is an important TACC family member, having both mitosis-related roles e.g. regulation of centrosomes and microtubule stability and interphase-related roles e.g. regulation of gene expression and cell migration. Being overexpressed in a broad spectrum of cancers and correlation of its expression with disease progression make TACC3 a highly attractive therapeutic target. Although the oncogenic role of TACC3 has been established albeit mostly in in vitro settings, there is currently no TACC3 inhibitor being tested in clinics. Therefore, by combining rational drug design and screening, we aimed to identify and characterize a novel TACC3 inhibitor hit molecule with high potency and minimum toxicity in in vitro and in vivo systems that is amenable for future drug development. BO-264 was identified as a novel inhibitor targeting TACC3 by direct binding validated by using several biochemical methods, including drug affinity responsive target stability, cellular thermal shift assay, and isothermal titration calorimetry. Compared to two other available TACC3 inhibitors, it showed superior inhibition of mitotic progression and cell viability, especially in aggressive basal and HER2+ breast cancer cell lines. Notably, BO-264 had remarkable cytotoxicity effect on several cancer cell lines in NCI-60 human cancer cell line panel (≥ 90% have less than 1 µM GI50 value) and inhibited the proliferation of FGFR3-TACC3 fusion protein-harboring cells, an oncogenic driver in several malignancies. Importantly, BO-264 did not cause any cytotoxicity to non-cancerous cell lines. Noteworthy, its oral administration significantly suppressed tumor growth in both breast and colon cancer syngeneic and xenograft models, and prolonged survival with no major toxicity. Finally, TACC3 expression level has been identified as a strong independent prognostic factor in breast cancer. Collectively, our preclinical findings suggest that BO-264 is a potent and non-toxic anti-cancer agent targeting TACC3 in breast and colon cancer and can be developed further to obtain better drug-like properties.
Open Access
A combined ULBP2 and SEMA5A expression signature as a prognostic and predictive biomarker for colon cancer
(Ivyspring International Publisher, 2017) Demirkol, S.; Gomceli, I.; Isbilen, M.; Dayanc, B. E.; Tez, M.; Bostanci, E. B.; Turhan, N.; Akoglu, M.; Ozyerli, E.; Durdu, S.; Konu, O.; Nissan, A.; Gonen, M.; Gure, A. O.
Background: Prognostic biomarkers for cancer have the power to change the course of disease if they add value beyond known prognostic factors, if they can help shape treatment protocols, and if they are reliable. The aim of this study was to identify such biomarkers for colon cancer and to understand the molecular mechanisms leading to prognostic stratifications based on these biomarkers. Methods and Findings: We used an in house R based script (SSAT) for the in silico discovery of stage-independent prognostic biomarkers using two cohorts, GSE17536 and GSE17537, that include 177 and 55 colon cancer patients, respectively. This identified 2 genes, ULBP2 and SEMA5A, which when used jointly, could distinguish patients with distinct prognosis. We validated our findings using a third cohort of 48 patients ex vivo. We find that in all cohorts, a combined ULBP2/SEMA5A classification (SU-GIB) can stratify distinct prognostic sub-groups with hazard ratios that range from 2.4 to 4.5 (p=0.01) when overall- or cancer-specific survival is used as an end-measure, independent of confounding prognostic parameters. In addition, our preliminary analyses suggest SU-GIB is comparable to Oncotype DX colon(®) in predicting recurrence in two different cohorts (HR: 1.5-2; p=0.02). SU-GIB has potential as a companion diagnostic for several drugs including the PI3K/mTOR inhibitor BEZ235, which are suitable for the treatment of patients within the bad prognosis group. We show that tumors from patients with worse prognosis have low EGFR autophosphorylation rates, but high caspase 7 activity, and show upregulation of pro-inflammatory cytokines that relate to a relatively mesenchymal phenotype. Conclusions: We describe two novel genes that can be used to prognosticate colon cancer and suggest approaches by which such tumors can be treated. We also describe molecular characteristics of tumors stratified by the SU-GIB signature.
Open Access
Constrained Delaunay triangulation for diagnosis and grading of colon cancer
(2009) Erdoğan, Süleyman Tuncer
In our century, the increasing rate of cancer incidents makes it inevitable to employ computerized tools that aim to help pathologists more accurately diagnose and grade cancerous tissues. These mathematical tools offer more stable and objective frameworks, which cause a reduced rate of intra- and inter-observer variability. There has been a large set of studies on the subject of automated cancer diagnosis/grading, especially based on textural and/or structural tissue analysis. Although the previous structural approaches show promising results for different types of tissues, they are still unable to make use of the potential information that is provided by tissue components rather than cell nuclei. However, this additional information is one of the major information sources for the tissue types with differentiated components including luminal regions being useful to describe glands in a colon tissue. This thesis introduces a novel structural approach, a new type of constrained Delaunay triangulation, for the utilization of non-nuclei tissue components. This structural approach first defines two sets of nodes on cell nuclei and luminal regions. It then constructs a constrained Delaunay triangulation on the nucleus nodes with the lumen nodes forming its constraints. Finally, it classifies the tissue samples using the features extracted from this newly introduced constrained Delaunay triangulation. Working with 213 colon tissues taken from 58 patients, our experiments demonstrate that the constrained Delaunay triangulation approach leads to higher accuracies of 87.83 percent and 85.71 percent for the training and test sets, respectively. The experiments also show that the introduction of this new structural representation, which allows definition of new features, provides a more robust graph-based methodology for the examination of cancerous tissues and better performance than its predecessors.
Open Access
Differential expression of colon cancer associated transcript1 (CCAT1) along the colonic adenoma-carcinoma sequence
(BioMed Central, 2013) Alaiyan, B.; Ilyayev, N.; Stojadinovic, A.; Izadjoo, M.; Roistacher, M.; Pavlov, V.; Tzivin, V.; Halle, D.; Pan, H.; Trink, B.; Gure, A. O.; Nissan, A.
Background: The transition from normal epithelium to adenoma and, to invasive carcinoma in the human colon is associated with acquired molecular events taking 5-10 years for malignant transformation. We discovered CCAT1, a non-coding RNA over-expressed in colon cancer (CC), but not in normal tissues, thereby making it a potential disease-specific biomarker. We aimed to define and validate CCAT1 as a CC-specific biomarker, and to study CCAT1 expression across the adenoma-carcinoma sequence of CC tumorigenesis.Methods: Tissue samples were obtained from patients undergoing resection for colonic adenoma(s) or carcinoma. Normal colonic tissue (n = 10), adenomatous polyps (n = 18), primary tumor tissue (n = 22), normal mucosa adjacent to primary tumor (n = 16), and lymph node(s) (n = 20), liver (n = 8), and peritoneal metastases (n = 19) were studied. RNA was extracted from all tissue samples, and CCAT1 expression was analyzed using quantitative real time-PCR (qRT-PCR) with confirmatory in-situ hybridization (ISH).Results: Borderline expression of CCAT1 was identified in normal tissue obtained from patients with benign conditions [mean Relative Quantity (RQ) = 5.9]. Significant relative CCAT1 up-regulation was observed in adenomatous polyps (RQ = 178.6 ± 157.0; p = 0.0012); primary tumor tissue (RQ = 64.9 ± 56.9; p = 0.0048); normal mucosa adjacent to primary tumor (RQ = 17.7 ± 21.5; p = 0.09); lymph node, liver and peritoneal metastases (RQ = 11,414.5 ± 12,672.9; 119.2 ± 138.9; 816.3 ± 2,736.1; p = 0.0001, respectively). qRT-PCR results were confirmed by ISH, demonstrating significant correlation between CCAT1 up-regulation measured using these two methods.Conclusion: CCAT1 is up-regulated across the colon adenoma-carcinoma sequence. This up-regulation is evident in pre-malignant conditions and through all disease stages, including advanced metastatic disease suggesting a role in both tumorigenesis and the metastatic process. © 2013 Alaiyan et al.; licensee BioMed Central Ltd.
Open Access
Histopathological image classification using salient point patterns
(2011) Çığır, Celal
Over the last decade, computer aided diagnosis (CAD) systems have gained great importance to help pathologists improve the interpretation of histopathological tissue images for cancer detection. These systems offer valuable opportunities to reduce and eliminate the inter- and intra-observer variations in diagnosis, which is very common in the current practice of histopathological examination. Many studies have been dedicated to develop such systems for cancer diagnosis and grading, especially based on textural and structural tissue image analysis. Although the recent textural and structural approaches yield promising results for different types of tissues, they are still unable to make use of the potential biological information carried by different tissue components. However, these tissue components help better represent a tissue, and hence, they help better quantify the tissue changes caused by cancer. This thesis introduces a new textural approach, called Salient Point Patterns (SPP), for the utilization of tissue components in order to represent colon biopsy images. This textural approach first defines a set of salient points that correspond to nuclear, stromal, and luminal components of a colon tissue. Then, it extracts some features around these salient points to quantify the images. Finally, it classifies the tissue samples by using the extracted features. Working with 3236 colon biopsy samples that are taken from 258 different patients, our experiments demonstrate that Salient Point Patterns approach improves the classification accuracy, compared to its counterparts, which do not make use of tissue components in defining their texture descriptors. These experiments also show that different set of features can be used within the SPP approach for better representation of a tissue image.
Open Access
Kelime histogram modeli ile histopatolojik görüntü sınıflandırılması
(IEEE, 2011-04) Özdemir, Erdem; Sökmensüer, C.; Gündüz-Demir, Çiğdem
Colon cancer, which is one of the most common cancer type, could be cured with its early diagnosis. In the current practice of medicine, there are many screening techniques such as colonoscopy, sigmoidoscopy, and stool test, however the most effective and most widely used method for cancer diagnosis is to take tissue sections with biopsy and examine them under a microscope. As this examination is based on visual interpretation, it may lead to subjective decisions and diagnostic differences among pathologists. The need of reducing inter-variability in cancer diagnosis has led to studies for extraction of features from biopsy images and development of algorithms that give objective results. In this paper, we propose a method for the automated classification of a colon tissue image with the features extracted from a histogram that models the existence of image regions determined in an unsupervised way. Experiments on colon tissue images show that the proposed method leads to more successful results compared to its counterparts. Moreover, the proposed method, which uses color intensities for feature extraction, has the potential of giving better results with the use of additional features. © 2011 IEEE.
Open Access
Local object patterns for representation and classification of colon tissue images
(Institute of Electrical and Electronics Engineers, 2014-07) Olgun, G.; Sokmensuer, C.; Gunduz Demir, C.
This paper presents a new approach for the effective representation and classification of images of histopathological colon tissues stained with hematoxylin and eosin. In this approach, we propose to decompose a tissue image into its histological components and introduce a set of new texture descriptors, which we call local object patterns, on these components to model their composition within a tissue. We define these descriptors using the idea of local binary patterns, which quantify a pixel by constructing a binary string based on relative intensities of its neighbors. However, as opposed to pixel-level local binary patterns, we define our local object pattern descriptors at the component level to quantify a component. To this end, we specify neighborhoods with different locality ranges and encode spatial arrangements of the components within the specified local neighborhoods by generating strings. We then extract our texture descriptors from these strings to characterize histological components and construct the bag-of-words representation of an image from the characterized components. Working on microscopic images of colon tissues, our experiments reveal that the use of these component-level texture descriptors results in higher classification accuracies than the previous textural approaches. © 2013 IEEE.
Open Access
Local object patterns for tissue image representation and cancer classification
(2013) Olgun, Gülden
Histopathological examination of a tissue is the routine practice for diagnosis and grading of cancer. However, this examination is subjective since it requires visual interpretation of a pathologist, which mainly depends on his/her experience and expertise. In order to minimize the subjectivity level, it has been proposed to use automated cancer diagnosis and grading systems that represent a tissue image with quantitative features and use these features for classifying and grading the tissue. In this thesis, we present a new approach for effective representation and classification of histopathological tissue images. In this approach, we propose to decompose a tissue image into its histological components and introduce a set of new texture descriptors, which we call local object patterns, on these components to model their composition within a tissue. We define these descriptors using the idea of local binary patterns. However, we define our local object pattern descriptors at the component-level to quantify a component, as opposed to pixel-level local binary patterns, which quantify a pixel by constructing a binary string based on relative intensities of its neighbors. To this end, we specify neighborhoods with different locality ranges and encode spatial arrangements of the components within the specified local neighborhoods by generating strings. We then extract our texture descriptors from these strings to characterize histological components and construct the bag-of-words representation of an image from the characterized components. In this thesis, we use two approaches for the selection of the components: The first approach uses all components to construct a bag-ofwords representation whereas the second one uses graph walking to select multiple subsets of the components and constructs multiple bag-of-words representations from these subsets. Working with microscopic images of histopathological colon tissues, our experiments show that the proposed component-level texture descriptors lead to higher classification accuracies than the previous textural approaches.
Open Access
Prediction of prognosis and chemosensitivity in gastrointestinal cancers
(2017-12) Demirkol, Seçil
Colon and gastric cancers are the third and fifth cancer types with the poorest survival. Surgery is considered the primary treatment option, which can be curative. However the decision as to whether chemotherapy administration after surgery is needed, is critical for especially stage 2 colon cancer; since a group of patients who receive chemotherapy do not have significantly improved clinical outcome. For this purpose, clinical risk factors are currently evaluated to determine patients with a high risk of progression. However this is not standardized by guidelines yet. Therefore, in this thesis my first aim was to ex vivo validate two novel independent mRNA based biomarkers, ULBP2 and SEMA5A, which have been previously identified in our lab, and to generate a prognostic signature which could stratify colon cancer patients with differential prognostic profiles using the best stratification method. I showed that a 3-group prognostic signature, SU-GIB, based on expression of these two genes is associated with cancer-specific, disease-free, and overall survival independent of clinical confounding factors in colon cancer. I performed in silico analysis in order to understand the biological details of the prognostic distinction, and revealed that patients with poorer prognosis show higher expression of pro-inflammatory cytokines and a more mesenchymal profile. Patients with better clinical outcome exhibit a more epithelial profile with higher levels of phosphorylated EGFR and Shc proteins. Analysis of high-throughput drug cytotoxicity databases also showed that colon cancer cell lines with „Bad‟ SU-GIB signature are more sensitive to a dual PI3K-MTOR inhibitor, BEZ235. In this thesis, my second goal was to define prognostic and molecular sub-groups for gastric cancer and characterize the specific biology of each sub-group. Utilizing an unsupervised approach on publicly available microarray data, I identified 3 biological groups, which harbor differential characteristics related to ECM involvement, EMT, proliferation and cell cycle. Moreover I identified distinct prognostic groups which are related to this molecular classification which can further stratify patients with known pathologic subtypes (diffuse and intestinal). I found that EMT was an important parameter for molecular and prognostic classifications for both types of cancers. I therefore, studied high-throughput drug cytotoxicity databases in order to identify selective compounds with selective growth inhibition on epithelial or mesenchymal cancer cells. I identified EGFR inhibitors as being significantly more effective on epithelial cancer cells regardless of the tissue type. My future plans include the large scale validation of SU-GIB, ex vivo validation of the gastric prognostic signature, and in vitro studies that would demonstrate effectivity of EGFR inhibitors on epithelial cancer cells and combination of EGFR inhibitors with MET inducers.
Open Access
Two-tier tissue decomposition for histopathological image representation and classification
(Institute of Electrical and Electronics Engineers, 2015) Gultekin, T.; Koyuncu, C. F.; Sokmensuer, C.; Gunduz Demir, C.
In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly employ color information to define their objects, which approximately represent histological tissue components in an image, and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this paper, our aim is to design a classification system for histopathological images. Towards this end, we present a new model for effective representation of these images that will be used by the classification system. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining texture, shape, and size information and they may correspond to individual histological tissue components as well as local tissue subregions of different characteristics. As its second contribution, it defines a new metric, which we call dominant blob scale, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.
Open Access
Unsupervised feature extraction via deep learning for histopathological classification of colon tissue images
(Institute of Electrical and Electronics Engineers, 2019) Sarı, Can Taylan; Gündüz-Demir, Çiğdem
Histopathological examination is today’s gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these methods mainly rely on features that they use, and thus, their success strictly depends on the ability of these features successfully quantifying the histopathology domain. With this motivation, this paper presents a new unsupervised feature extractor for effective representation and classification of histopathological tissue images. This feature extractor has three main contributions: First, it proposes to identify salient subregions in an image, based on domain-specific prior knowledge, and to quantify the image by employing only the characteristics of these subregions instead of considering the characteristics of all image locations. Second, it introduces a new deep learning based technique that quantizes the salient subregions by extracting a set of features directly learned on image data and uses the distribution of these quantizations for image representation and classification. To this end, the proposed deep learning based technique constructs a deep belief network of restricted Boltzmann machines (RBMs), defines the activation values of the hidden unit nodes in the final RBM as the features, and learns the quantizations by clustering these features in an unsupervised way. Third, this extractor is the first example of successfully using restricted Boltzmann machines in the domain of histopathological image analysis. Our experiments on microscopic colon tissue images reveal that the proposed feature extractor is effective to obtain more accurate classification results compared to its counterparts. IEEE