Genome-wide association study of panic disorder reveals genetic overlap with neuroticism and depression

Panic disorder (PD) has a lifetime prevalence of 2-4% and heritability estimates of 40%. The contributory genetic variants remain largely unknown, with few and inconsistent loci having been reported. The present report describes the largest genome-wide association study (GWAS) of PD to date comprising genome-wide genotype data of 2248 clinically well-characterized PD patients and 7992 ethnically matched controls. The samples originated from four European countries (Denmark, Estonia, Germany, and Sweden). Standard GWAS quality control procedures were conducted on each individual dataset, and imputation was performed using the 1000 Genomes Project reference panel. A meta-analysis was then performed using the Ricopili pipeline. No genome-wide significant locus was identified. Leave-one-out analyses generated highly significant polygenic risk scores (PRS) (explained variance of up to 2.6%). Linkage disequilibrium (LD) score regression analysis of the GWAS data showed that the estimated heritability for PD was 28.0-34.2%. After correction for multiple testing, a significant genetic correlation was found between PD and major depressive disorder, depressive symptoms, and neuroticism. A total of 255 single-nucleotide polymorphisms (SNPs) with p < 1 × 10-4 were followed up in an independent sample of 2408 PD patients and 228,470 controls from Denmark, Iceland and the Netherlands. In the combined analysis, SNP rs144783209 showed the strongest association with PD (pcomb = 3.10  × 10-7). Sign tests revealed a significant enrichment of SNPs with a discovery p-value of <0.0001 in the combined follow up cohort (p = 0.048). The present integrative analysis represents a major step towards the elucidation of the genetic susceptibility to PD

A combination of the immunohistochemical markers CK7 and SATB2 is highly sensitive and specific for distinguishing primary ovarian mucinous tumors from colorectal and appendiceal metastases

This study is supported by research funds from Cancer Research Society of Canada (19319). NSM is supported by the NSW Ministry of Health and UNSW Sydney under the NSW Health PhD Scholarship Program, and the Translational Cancer Research Network, a translational cancer research center program funded by the Cancer Institute NSW. The Gynaecological Oncology Biobank at Westmead was funded by Cancer Institute NSW (12/RIG/1–17 and 15/RIG/1–16) and the National Health and Medical Research Council of Australia (ID310670, ID628903). FM is funded by University of Pittsburgh School of Medicine Dean's Faculty Advancement Award. The HOPE study is funded by: US National Cancer Institute (K07-CA80668, P50-CA159981, R01CA095023), US Army Medical Research and Materiel Command (DAMD17–02–1–0669) and NIH/National Center for Research Resources/General Clinical Research Center (MO1- RR000056). KS is funded by the Swedish Cancer foundation. The Generations Study thank Breast Cancer Now, the Institute of Cancer Research and Ovarian Cancer Action for support and funding. The ICR acknowledge NHS funding to the NIHR Biomedical Research Centre. Tissue samples for GER were provided by the tissue bank of the National Center for Tumor Diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of the University of Heidelberg. The Health Science Alliance (HSA) Biobank acknowledges the UNSW Biorepository, UNSW Sydney, Australia. We thank Shuhong Liu, Young Ou, and Deon Richards for immunohistochemical stains, and Thomas Kryton, BFA, digital imaging specialist for Alberta Public Lab for creating the figures. We especially thank all the study participants, health care staff and data providers internationally who have made this research possible

Acute Abdomen in 8-year Old Girl due to Bilateral Ovarian Burkitt’s Lymphoma, Mwanza Tanzania: A Case Report

Burkitt’s lymphoma is a highly aggressive lymphoma composed of monomorphic medium size B cells. It is endemic in Equatorial Africa presenting as a jaw or orbital tumor unlike sporadic Burkitt’s lymphoma which commonly presents with an abdominal tumor usually in the ovary. The presentation of the abdominal tumor sometimes can lead to challenges in clinical diagnosis, especially when it presents like an acute abdominal condition. This is a case report of an 8-year old female who was admitted at Bugando Medical Center with history of on and off abdominal pain and fever for two weeks. Physical examination revealed asymmetrical distended abdomen with a tender mass in the right iliac fossa and mild pallor. Presumptive diagnosis of appendicular mass was made with differential diagnosis of ovarian mass. Pelvic scan showed bilateral iliac masses suggestive of inflammatory process. At Laparotomy both ovary had a mass and bilateral salphingo-oophorectomy was done. The histopathology results showed ovarian Burkitt’s lymphoma. The patient had stage IV disease and chemotherapy treatment was started, however she died after the third course of chemotherapy. Although ovarian Burkitt’s lymphoma is common, presentation with signs of acute abdomen with involvement of both ovaries is not a usual mode of presentation.Key words: Acute abdomen, bilateral ovarian Burkitt‟s lymphom

An Immunohistochemical Algorithm for Ovarian Carcinoma Typing

There are 5 major histotypes of ovarian carcinomas. Diagnostic typing criteria have evolved over time, and past cohorts may be misclassified by current standards. Our objective was to reclassify the recently assembled Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts using immunohistochemical (IHC) biomarkers and to develop an IHC algorithm for ovarian carcinoma histotyping. A total of 1626 ovarian carcinoma samples from the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type were subjected to a reclassification by comparing the original with the predicted histotype. Histotype prediction was derived from a nominal logistic regression modeling using a previously reclassified cohort (N=784) with the binary input of 8 IHC markers. Cases with discordant original or predicted histotypes were subjected to arbitration. After reclassification, 1762 cases from all cohorts were subjected to prediction models (χ 2 Automatic Interaction Detection, recursive partitioning, and nominal logistic regression) with a variable IHC marker input. The histologic type was confirmed in 1521/1626 (93.5%) cases of the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts. The highest misclassification occurred in the endometrioid type, where most of the changes involved reclassification from endometrioid to high-grade serous carcinoma, which was additionally supported by mutational data and outcome. Using the reclassified histotype as the endpoint, a 4-marker prediction model correctly classified 88%, a 6-marker 91%, and an 8-marker 93% of the 1762 cases. This study provides statistically validated, inexpensive IHC algorithms, which have versatile applications in research, clinical practice, and clinical trials