Characterization of Hypersensitive Response Related Genes of Arabidopsis thaliana

Like most complex living organisms, plants have many mechanisms to prevent disease by microbial pathogens. One of the most important and well developed defense systems that involve recognition, identification and systematic response is the hypersensitive response. The hypersensitive response is a complex, early defense response against pathogens that causes necrosis and cell death at the site of infection to restrict the spread of pathogen. Hypersensitive response is a type of programmed cell death, and its activation usually happens when the plant recognizes a pathogen through an elicitor. This recognition triggers a series of signal transductions events which end in the expression of several defense-associated genes. The result of those responses causes death of plant cells and the formation of local lesions, creating barriers and hostile environment to inhibit the spread of infection. The work in our laboratory has identified several hundred Arabidopsis genes that are differentially expressed in response to pathogen infection. In addition to regulating defense against pathogens, some of these genes are likely to be involved in regulating other plant physiological processes. To determine the role of these genes in regulating defense against pathogens and other biotic and abiotic stresses, I have analyzed responses to knockout mutants of several of these genes to a variety of biotic and abiotic stresses. My results suggest that many of the pathogen defence-related genes are also involved in regulating other biotic and abiotic stresses

Profiling Critical Cancer Gene Mutations in Clinical Tumor Samples

Background: Detection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; however, high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting. Methodology: We developed and implemented an optimized mutation profiling platform (“OncoMap”) to interrogate ∼400 mutations in 33 known oncogenes and tumor suppressors, many of which are known to predict response or resistance to targeted therapies. The performance of OncoMap was analyzed using DNA derived from both frozen and FFPE clinical material in a diverse set of cancer types. A subsequent in-depth analysis was conducted on histologically and clinically annotated pediatric gliomas. The sensitivity and specificity of OncoMap were 93.8% and 100% in fresh frozen tissue; and 89.3% and 99.4% in FFPE-derived DNA. We detected known mutations at the expected frequencies in common cancers, as well as novel mutations in adult and pediatric cancers that are likely to predict heightened response or resistance to existing or developmental cancer therapies. OncoMap profiles also support a new molecular stratification of pediatric low-grade gliomas based on BRAF mutations that may have immediate clinical impact. Conclusions: Our results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of “actionable” cancer gene mutations. In the future, this type of approach may be incorporated into both cancer epidemiologic studies and clinical decision making to specify the use of many targeted anticancer agents

Profiling Critical Cancer Gene Mutations in Clinical Tumor Samples

BACKGROUND: Detection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; however, high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting. METHODOLOGY: We developed and implemented an optimized mutation profiling platform ("OncoMap") to interrogate approximately 400 mutations in 33 known oncogenes and tumor suppressors, many of which are known to predict response or resistance to targeted therapies. The performance of OncoMap was analyzed using DNA derived from both frozen and FFPE clinical material in a diverse set of cancer types. A subsequent in-depth analysis was conducted on histologically and clinically annotated pediatric gliomas. The sensitivity and specificity of OncoMap were 93.8% and 100% in fresh frozen tissue; and 89.3% and 99.4% in FFPE-derived DNA. We detected known mutations at the expected frequencies in common cancers, as well as novel mutations in adult and pediatric cancers that are likely to predict heightened response or resistance to existing or developmental cancer therapies. OncoMap profiles also support a new molecular stratification of pediatric low-grade gliomas based on BRAF mutations that may have immediate clinical impact. CONCLUSIONS: Our results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of "actionable" cancer gene mutations. In the future, this type of approach may be incorporated into both cancer epidemiologic studies and clinical decision making to specify the use of many targeted anticancer agents

A comprehensive study of telecytology using robotic digital microscope and single Z-stack digital scan for fine-needle aspiration-rapid on-site evaluation

Background: The current technology for remote assessment of fine-needle aspiration-rapid on-site evaluation (FNA-ROSE) is limited. Recent advances may provide solutions. This study compared the performance of VisionTek digital microscope (VDM) (Sakura, Japan) and Hamamatsu NanoZoomer C9600-12 single Z-stack digital scan (SZDS) to conventional light microscopy (CLM) for FNA-ROSE. Methods: We assembled sixty FNA cases from the thyroid (n = 16), lymph node (n = 16), pancreas (n = 9), head and neck (n = 9), salivary gland (n = 5), lung (n = 4), and rectum (n = 1) based on a single institution's routine workflow. One Diff-Quik-stained slide was selected for each case. Two board-certified cytopathologists independently evaluated the cases using VDM, SZDS, and CLM. A “washout” period of at least 14 days was placed between the reviews. The results were categorized into satisfactory versus unsatisfactory for adequacy assessment (AA) and unsatisfactory, benign, atypical, suspicious, and malignant for preliminary diagnosis (PD). Results: For AA, the Cohen's kappa statistics (CKS) scores of intermodality agreement (IMA) were 0.74–0.94 (CLM vs. VDM) and 0.86–1 (CLM vs. SZDS). The discordant rates of IMA were 3.3% (4/120) for VDM versus CLM, and 1.7% (2/120) for SZDS versus CLM. For PD, the CKS scores of IMA ranged 0.7–0.93. The overall discordant rates of IMA were 15% (18/120) for CLM versus VDM and 10.8% (13/120) for CLM versus SZDS. The discordant rates of IMA with 2 or higher degrees were 5.8% (7/120) for CLM versus VDM and 1.7% (2/120) for CLM versus SZDS. The average time spent per slide was 270 s for VDM, significantly longer than that for CLM (113 s) or for SZDS (122 s). Conclusions: Our data demonstrate that both VDM and SZDS are suitable to provide AA and reasonable PD evaluation. VDM, however, has a significantly longer turnaround time and worse diagnostic performance. The study demonstrates both the potentials and challenges of using VDM and SZDS for FNA-ROSE

Current applications and challenges of digital pathology in cytopathology

Cytopathology stands to gain a multitude of benefits from the movement toward digitization. The increased demand for rapid on-site evaluation (ROSE), primary diagnosis, education, and biomarker quantification can be met by the use of telecytology, whole slide imaging (WSI), and digital image analysis solutions. In this review, we will focus on currently available clinical applications as well as challenges in digital cytopathology

A Study of Thyroid Fine Needle Aspiration of Follicular Adenoma in the “Atypia of Undetermined Significance” Bethesda Category Using Digital Image Analysis

Background: Originally designed for computerized image analysis, ThinPrep is underutilized in that role outside gynecological cytology. It can be used to address the inter/intra-observer variability in the evaluation of thyroid fine needle aspiration (TFNA) biopsy and help pathologists to gain additional insight into thyroid cytomorphology. Methods: We designed and validated a feature engineering and supervised machine learning-based digital image analysis method using ImageJ and Python scikit-learn. The method was trained and validated from 400 low power (100x) and 400 high power (400x) images generated from 40 TFNA cases. Result: The area under the curve (AUC) for receiver operating characteristics (ROC) is 0.75 (0.74–0.82) for model based from low-power images and 0.74 (0.69–0.79) for the model based from high-power images. Cytomorphologic features were synthesized using feature engineering and when performed in isolation, they achieved AUC of 0.71 (0.64–0.77) for chromatin, 0.70 (0.64–0.73) for cellularity, 0.65 (0.60–0.69) for cytoarchitecture, 0.57 (0.51–0.61) for nuclear size, and 0.63 (0.57–0.68) for nuclear shape. Conclusion: Our study proves that ThinPrep is an excellent preparation method for digital image analysis of thyroid cytomorphology. It can be used to quantitatively harvest morphologic information for diagnostic purpose