Loss of CDKN2A expression is a frequent event in primary invasive melanoma and correlates with sensitivity to the CDK4/6 inhibitor PD0332991 in melanoma cell lines.

We have investigated the potential for the p16-cyclin D-CDK4/6-retinoblastoma protein pathway to be exploited as a therapeutic target in melanoma. In a cohort of 143 patients with primary invasive melanoma, we used fluorescence in situ hybridization to detect gene copy number variations (CNVs) in CDK4, CCND1, and CDKN2A and immunohistochemistry to determine protein expression. CNVs were common in melanoma, with gain of CDK4 or CCND1 in 37 and 18% of cases, respectively, and hemizygous or homozygous loss of CDKN2A in 56%. Three-quarters of all patients demonstrated a CNV in at least one of the three genes. The combination of CCND1 gain with either a gain of CDK4 and/or loss of CDKN2A was associated with poorer melanoma-specific survival. In 47 melanoma cell lines homozygous loss, methylation or mutation of CDKN2A gene or loss of protein (p16(INK) (4A) ) predicted sensitivity to the CDK4/6 inhibitor PD0332991, while RB1 loss predicted resistance.

Synergistic inhibition of ovarian cancer cell growth by combining selective PI3K/mTOR and RAS/ERK pathway inhibitors.

BACKGROUND: Ovarian cancer is the major cause of death from gynaecological malignancy with a 5year survival of only ∼30% due to resistance to platinum and paclitaxel-based first line therapy. Dysregulation of the phosphoinositide 3-kinase/mammalian target of rapamycin (PI3K/mTOR) and RAS/extracellular signal-regulated kinase (ERK) pathways is common in ovarian cancer, providing potential new targets for 2nd line therapy. METHODS: We determined the inhibition of proliferation of an extensive panel of ovarian cancer cell lines, encompassing all the major histotypes, by the dual PI3K/mTOR inhibitor PF-04691502 and a MEK inhibitor, PD-0325901. In addition, we analysed global gene expression, mutation status of key PI3K/mTOR and RAS/ERK pathway members and pathway activation to identify predictors of drug response. RESULTS: PF-04691502 inhibits proliferation of the majority of cell lines with potencies that correlate with the extent of pathway inhibition. Resistant cell lines were characterised by activation of the RAS/ERK pathway as indicated by differential gene expression profiles and pathway activity analysis. PD-0325901 suppressed growth of a subset of cell lines that were characterised by high basal RAS/ERK signalling. Strikingly, using PF-04691502 and PD-0325901 in combination resulted in synergistic growth inhibition in 5/6 of PF-04691502 resistant cell lines and two cell lines resistant to both single agents showed robust synergistic growth arrest. Xenograft studies confirm the utility of combination therapy to synergistically inhibit tumour growth of PF-04691502-resistant tumours in vivo. CONCLUSIONS: These studies identify dual targeted inhibitors of PI3K/mTOR in combination with inhibitors of RAS/ERK signalling as a potentially effective new approach to treating ovarian cancer.

Colorization of CT images to improve tissue contrast for tumor segmentation.

Segmenting tumors from grayscale medical image data can be difficult due to the close intensity values between tumor and healthy tissue. This paper presents a study that demonstrates how colorizing CT images prior to segmentation can address this problem. Colorizing the data a priori accentuates the tissue density differences between tumor and healthy tissue, thereby allowing for easier identification of the tumor tissue(s). The method presented allows pixels representing tumor and healthy tissues to be colorized distinctly in an accurate and efficient manner. The associated segmentation process is then tailored to utilize this color data. It is shown that colorization significantly decreases segmentation time and allows the method to be performed on commodity hardware. To show the effectiveness of the method, a basic segmentation method, thresholding, was implemented with and without colorization. To evaluate the method, False Positives (FP) and False Negatives (FN) were calculated from 10 datasets (476 slices) with tumors of varying size and tissue composition. The colorization method demonstrated statistically significant differences for lower FP in nine out of 10 cases and lower FN in five out of 10 datasets.

Development of a customizable software application for medical imaging analysis and visualization.

Graphics technology has extended medical imaging tools to the hands of surgeons and doctors, beyond the radiology suite. However, a common issue in most medical imaging software is the added complexity for non-radiologists. This paper presents the development of a unique software toolset that is highly customizable and targeted at the general physicians as well as the medical specialists. The core functionality includes features such as viewing medical images in two-and three-dimensional representations, clipping, tissue windowing, and coloring. Additional features can be loaded in the form of 'plug-ins' such as tumor segmentation, tissue deformation, and surgical planning. This allows the software to be lightweight and easy to use while still giving the user the flexibility of adding the necessary features, thus catering to a wide range of user population.

Tumor segmentation from computed tomography image data using a probabilistic pixel selection approach.

Automatic segmentation of tumors is a complicated and difficult process as most tumors are rarely clearly delineated from healthy tissues. A new method for probabilistic segmentation to efficiently segment tumors within CT data and to improve the use of digital medical data in diagnosis has been developed. Image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied. To initialize the segmentation process, a user places a seed point within the object of interest and defines a search region for segmentation. Based on the pixels' spatial and intensity properties, a probabilistic selection criterion is used to extract pixels with a high probability of belonging to the object. To facilitate the segmentation of multiple slices, an automatic seed selection algorithm was developed to keep the seeds in the object as its shape and/or location changes between consecutive slices. The seed selection algorithm performs a greedy search by searching for pixels with matching intensity close to the location of the original seed point. A total of ten CT datasets were used as test cases, each with varying difficulty in terms of automatic segmentation. Five test cases had mean false positive error rates less than 10%, and four test cases had mean false negative error rates less than 10% when compared to manual segmentation of those tumors by radiologists.

Both the p33 and p55 subunits of the Helicobacter pylori VacA toxin are targeted to mammalian mitochondria.

Helicobacter pylori infection causes peptic ulcers and gastric cancer. A major toxin secreted by H. pylori is the bipartite vacuolating cytotoxin A, VacA. The toxin is believed to enter host cells as two subunits: the p55 subunit (55 kDa) and the p33 subunit (33 kDa). At the biochemical level, it has been shown that VacA forms through the assembly of large multimeric pores composed of both the p33 subunit and the p55 subunit in biological membranes. One of the major target organelles of VacA is the mitochondria. Since only the p33 subunit has been reported to be translocated into mitochondria and the p55 subunit is not imported, it has been contentious as to whether VacA assembles into pores in a mitochondrial membrane. Here we show the p55 protein is imported into the mitochondria along with the p33 protein subunit. The p33 subunit integrally associates with the mitochondrial inner membrane, and both the p33 subunit and the p55 subunit are exposed to the mitochondrial intermembrane space. Their colocalization suggests that they could reassemble and form a pore in the inner mitochondrial membrane.

Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system.

A new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI's spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system. With a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected as tumor. This process is automatically repeated for every subsequent slice in the CT set without further user input, as the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image. Test cases success in segmenting the tumor from seven of the 10 CT datasets with <10% false positive errors and five test cases with <10% false negative errors. The consistency of the segmentation results statistics also showed a high repeatability factor, with low values of inter- and intra-user variability for both methods.

A collaborative interaction and visualization multi-modal environment for surgical planning.

The proliferation of virtual reality visualization and interaction technologies has changed the way medical image data is analyzed and processed. This paper presents a multi-modal environment that combines a virtual reality application with a desktop application for collaborative surgical planning. Both visualization applications can function independently but can also be synced over a network connection for collaborative work. Any changes to either application is immediately synced and updated to the other. This is an efficient collaboration tool that allows multiple teams of doctors with only an internet connection to visualize and interact with the same patient data simultaneously. With this multi-modal environment framework, one team working in the VR environment and another team from a remote location working on a desktop machine can both collaborate in the examination and discussion for procedures such as diagnosis, surgical planning, teaching and tele-mentoring.

A virtual reality environment for patient data visualization and endoscopic surgical planning.

Visualizing patient data in a three-dimensional (3D) representation can be an effective surgical planning tool.As medical imaging technologies improve with faster and higher resolution scans, the use of virtual reality for interacting with medical images adds another level of realism to a 3D representation. The software framework presented in this paper is designed to load and display any DICOM/PACS-compatible 3D image data for visualization and interaction in an immersive virtual environment. In "examiner" mode, the surgeon can interact with a 3D virtual model of the patient by using an intuitive set of controls designed to allow slicing, coloring,and windowing of the image to show different tissue densities and enhance important structures. In the simulated"endoscopic camera" mode, the surgeon can see through the point of view of a virtual endoscopic camera to navigate inside the patient. These tools allow the surgeon to perform virtual endoscopy on any suitable structure.The software is highly scalable, as it can be used on a single desktop computer to a cluster of computers in an immersive multiprojection virtual environment. By wearing a pair of stereo glasses, a surgeon becomes immersed within the model itself, thus providing a sense of realism, as if the surgeon is "inside" the patient.

A framework for interactive examination of automatic segmented tumors in a virtual environment.

An immersive virtual environment for viewing and interacting with three-dimensional representations of medical image data is presented. Using a newly developed automatic segmentation method, a segmented object (e.g., tumor or organ) can also be viewed in the context of the original patient data. Real time interaction is established using joystick movements and button presses on a wireless gamepad. Several open-source platforms have been utilized, such as DCMTK for processing of DICOM formatted data, Coin3D for scenegraph management, SimVoleon for volume rendering, and VRJuggler to handle the immersive visualization. The application allows the user to manipulate representations with features such as fast pseudo-coloring to highlight details of the patient data, windowing to select a range of tissue densities for display, and multiple clipping planes to allow the user to slice into the patient.