Whole Slide Image Analysis Quantification using Aperio Digital Imaging in a Mouse Lung Metastasis Model

poster abstractDigital whole slide imaging is the technique of digitizing a microscope slide at the highest resolution to produce a “digital virtual microscope slide”. This digital image can be viewed in three or four fields, from low to high power, which can be commonly used to evaluate the tissue. Many of these systems have whole slide software image analysis capability. The goal of this study was to determine if the Aperio positive pixel algorithm (image analysis) could effectively quantitate metastatic mouse lung tumors in a lung section using a H&E stain. Lung sections from a mouse lung metastasis model of 8 mice per group were evaluated: control, 50mg/kg, and 75mg/kg carboplatin. H&E and Ki67 immunostain slides were scanned using the Aperio whole slide scanning system (Scanscope CS). A single field of view from each slide representing a whole lung lobe with multiple lung metastases was selected for image analysis. The standard positive pixel algorithm was altered to read the H&E slides. Various histology slides were used to validate the altered algorithm. The immunostain (Ki67) was generated using the standard positive pixel algorithm analysis. The Aperio automated positive pixel count for a Ki67 immunostain was consistent with the H&E image analysis. The values decreased with a dose dependent treatment (control vs. 50mg/kg and 75mg/kg carboplatin) and were (H&E) 37%, 28%, and 22%, and (Ki67) 9%, 5%, and 3%. The analysis had decreasing values for both the H&E and Ki67 analysis on a dose dependent drug treatment. The metastases decreased in both treatment groups compared to controls with both the H&E and Ki67 analyses. The Aperio Image Analysis positive pixel algorithm allows large areas of the lung tissue section to be examined and not just a single 25x or 40x field like many common image analyses systems

NQO1-Bioactivatable Therapeutics as Radiosensitizers for Cancer Treatment

Developing cancer therapeutics that radiosensitize in a tumor-selective manner remains an ideal. We developed a novel means of radiosensitization, exploiting NAD(P)H:Quinone Oxidoreductase 1 (NQO1) overexpression, and lowered catalase expression in solid human tumors using NQO1-bioactivatable drugs. Non-small cell lung (NSCLC), pancreatic (PDAC), prostate, and breast cancers overexpress NQO1. Ionizing radiation (IR) creates a spectrum of DNA lesions, including lethal DNA double-strand breaks (DSBs), and mutagenic but rarely lethal altered DNA bases and DNA single-strand breaks (SSBs). NQO1-bioactivatable drugs (e.g., β-lapachone and deoxynyboquiones) also promote abasic DNA lesions and SSBs. These hyperactivate poly (ADP-ribose) polymerase 1 (PARP1) and dramatically increase calcium release from the endoplasm reticulum (ER). Exposure of human cancer cells overexpressing NQO1 to NQO1-bioactivatable drugs immediately following IR, therefore, hyperactivates PARP1 synergistically, which in turn depletes NAD+ and ATP, inhibiting DSB repair. Ultimately, this leads to cell death. Combining IR with NQO1-bioactivatable drugs allows for a reduction in drug dose. Similarly, a lower IR dose can be used in combination with the drug, reducing the effects of IR on normal tissue. The combination treatment is effective in preclinical animal models with NSCLC, prostate, and head and neck xenografts, indicating that clinical trials are warranted

Deep imitation learning for 3D navigation tasks

Deep learning techniques have shown success in learning from raw high dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: Deep-Q-networks (DQN) and Asynchronous actor critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an e�ective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples

Longitudinal Bioluminescence Imaging of Primary Versus Abdominal Metastatic Tumor Growth in Orthotopic Pancreatic Tumor Models in NSG Mice

Objectives: The purpose of the present study was to develop and validate noninvasive bioluminescence imaging methods for differentially monitoring primary and abdominal metastatic tumor growth in mouse orthotopic models of pancreatic cancer. Methods: A semiautomated maximum entropy segmentation method was implemented for the primary tumor region of interest, and a rule-based method for manually drawing a region of interest for the abdominal metastatic region was developed for monitoring tumor growth in orthotopic models of pancreatic cancer. The 2 region-of-interest methods were validated by having 2 observers independently segment Panc-1 tumors, and the results were compared with the number of mesenteric lymph node nodules and histopathologic assessment of liver metastases. The findings were extended to orthotopic tumors of the more metastatic MIA PaCa-2 and AsPC-1 cells where separate groups of animals were implanted with different numbers of cells. Results: The results demonstrated that the segmentation methods were highly reliable, reproducible, and robust and allowed statistically significant discrimination in the growth rates of primary and abdominal metastatic tumors of different cell lines implanted with different numbers of cells. Conclusions: The present results demonstrate that primary tumors and abdominal metastatic foci in orthotopic pancreatic cancer models can be reliably quantified separately and noninvasively over time with bioluminescence imaging

Potentiation of Carboplatin-Mediated DNA Damage by the Mdm2 Modulator Nutlin-3a in a Humanized Orthotopic Breast-to-Lung Metastatic Model

Triple-negative breast cancers (TNBC) are typically resistant to treatment, and strategies that build upon frontline therapy are needed. Targeting the murine double minute 2 (Mdm2) protein is an attractive approach, as Mdm2 levels are elevated in many therapy-refractive breast cancers. The Mdm2 protein-protein interaction inhibitor Nutlin-3a blocks the binding of Mdm2 to key signaling molecules such as p53 and p73α and can result in activation of cell death signaling pathways. In the present study, the therapeutic potential of carboplatin and Nutlin-3a to treat TNBC was investigated, as carboplatin is under evaluation in clinical trials for TNBC. In mutant p53 TMD231 TNBC cells, carboplatin and Nutlin-3a led to increased Mdm2 and was strongly synergistic in promoting cell death in vitro. Furthermore, sensitivity of TNBC cells to combination treatment was dependent on p73α. Following combination treatment, γH2AX increased and Mdm2 localized to a larger degree to chromatin compared with single-agent treatment, consistent with previous observations that Mdm2 binds to the Mre11/Rad50/Nbs1 complex associated with DNA and inhibits the DNA damage response. In vivo efficacy studies were conducted in the TMD231 orthotopic mammary fat pad model in NOD.Cg-Prkdc(scid)Il2rg(tm1Wjl)/SzJ (NSG) mice. Using an intermittent dosing schedule of combined carboplatin and Nutlin-3a, there was a significant reduction in primary tumor growth and lung metastases compared with vehicle and single-agent treatments. In addition, there was minimal toxicity to the bone marrow and normal tissues. These studies demonstrate that Mdm2 holds promise as a therapeutic target in combination with conventional therapy and may lead to new clinical therapies for TNBC

Dichloroacetate reverses the hypoxic adaptation to bevacizumab and enhances its antitumor effects in mouse xenografts.

Inhibition of vascular endothelial growth factor increases response rates to chemotherapy and progression-free survival in glioblastoma. However, resistance invariably occurs, prompting the urgent need for identification of synergizing agents. One possible strategy is to understand tumor adaptation to microenvironmental changes induced by antiangiogenic drugs and test agents that exploit this process. We used an in vivo glioblastoma-derived xenograft model of tumor escape in presence of continuous treatment with bevacizumab. U87-MG or U118-MG cells were subcutaneously implanted into either BALB/c SCID or athymic nude mice. Bevacizumab was given by intraperitoneal injection every 3 days (2.5 mg/kg/dose) and/or dichloroacetate (DCA) was administered by oral gavage twice daily (50 mg/kg/dose) when tumor volumes reached 0.3 cm(3) and continued until tumors reached approximately 1.5-2.0 cm(3). Microarray analysis of resistant U87 tumors revealed coordinated changes at the level of metabolic genes, in particular, a widening gap between glycolysis and mitochondrial respiration. There was a highly significant difference between U87-MG-implanted athymic nude mice 1 week after drug treatment. By 2 weeks of treatment, bevacizumab and DCA together dramatically blocked tumor growth compared to either drug alone. Similar results were seen in athymic nude mice implanted with U118-MG cells. We demonstrate for the first time that reversal of the bevacizumab-induced shift in metabolism using DCA is detrimental to neoplastic growth in vivo. As DCA is viewed as a promising agent targeting tumor metabolism, our data establish the timely proof of concept that combining it with antiangiogenic therapy represents a potent antineoplastic strategy

Dysregulation of neuronal iron homeostasis as an alternative unifying effect of mutations causing familial Alzheimer's disease

The overwhelming majority of dominant mutations causing early onset familial Alzheimer's disease (EOfAD) occur in only three genes, PSEN1, PSEN2, and APP. An effect-in-common of these mutations is alteration of production of the APP-derived peptide, amyloid ß (Aß). It is this key fact that underlies the authority of the Amyloid Hypothesis that has informed Alzheimer's disease research for over two decades. Any challenge to this authority must offer an alternative explanation for the relationship between the PSEN genes and APP. In this paper, we explore one possible alternative relationship - the dysregulation of cellular iron homeostasis as a common effect of EOfAD mutations in these genes. This idea is attractive since it provides clear connections between EOfAD mutations and major characteristics of Alzheimer's disease such as dysfunctional mitochondria, vascular risk factors/hypoxia, energy metabolism, and inflammation. We combine our ideas with observations by others to describe a "Stress Threshold Change of State" model of Alzheimer's disease that may begin to explain the existence of both EOfAD and late onset sporadic (LOsAD) forms of the disease. Directing research to investigate the role of dysregulation of iron homeostasis in EOfAD may be a profitable way forward in our struggle to understand this form of dementia