Gallbladder cancer is a rare malignancy of the biliary tract with a poor prognosis, frequently presenting at an advanced stage. While rare in the United States overall, gallbladder cancer has an elevated incidence in geographically distinct locations of the globe including Chile, North India, Korea, Japan and the state of New Mexico in the United States. People with Native American ancestry have a much elevated incidence of gallbladder cancer compared to Hispanic and non-Hispanic white populations of New Mexico. Gallbladder cancer is also one of the few bi-gendered cancers with an elevated female incidence compared to men. Similar to other gastrointestinal cancers, gallbladder cancer etiology is likely multi-factorial involving a combination of genomic, immunological, and environmental factors. Understanding the interplay of these unique epidemiological factors is crucial in improving the prevention, early detection, and treatment of this lethal disease. Previous studies have failed to identify a distinct genomic mutational profile in gallbladder cancers, however, work to identify promising clinically actionable targets is this form of cancer is ongoing. Examples include, interest in the HER2/Neu signaling pathway and the recognition that chronic inflammation plays a crucial role in gallbladder cancer pathogenesis. In this review, we provide a comprehensive overview of gallbladder cancer epidemiology, risk factors, pathogenesis, and treatment with a specific focus on the rural and Native American populations of New Mexico. We conclude this review by discussing future research directions with the goal of improving clinical outcomes for patients of this lethal malignancy.

The Human Genome Project (HGP) provided the initial draft of mankind's DNA sequence in 2001. The HGP was produced by 23 collaborating laboratories using Sanger sequencing of mapped regions as well as shotgun sequencing techniques in a process that occupied 13 years at a cost of ~$3 billion. Today, Next Generation Sequencing (NGS) techniques represent the next phase in the evolution of DNA sequencing technology at dramatically reduced cost compared to traditional Sanger sequencing. A single laboratory today can sequence the entire human genome in a few days for a few thousand dollars in reagents and staff time. Routine whole exome or even whole genome sequencing of clinical patients is well within the realm of affordability for many academic institutions across the country. This paper reviews current sequencing technology methods and upcoming advancements in sequencing technology as well as challenges associated with data generation, data manipulation and data storage. Implementation of routine NGS data in cancer genomics is discussed along with potential pitfalls in the interpretation of the NGS data. The overarching importance of bioinformatics in the clinical implementation of NGS is emphasized.[7] We also review the issue of physician education which also is an important consideration for the successful implementation of NGS in the clinical workplace. NGS technologies represent a golden opportunity for the next generation of pathologists to be at the leading edge of the personalized medicine approaches coming our way. Often under-emphasized issues of data access and control as well as potential ethical implications of whole genome NGS sequencing are also discussed. Despite some challenges, it's hard not to be optimistic about the future of personalized genome sequencing and its potential impact on patient care and the advancement of knowledge of human biology and disease in the near future.

Context.-Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched.Objective.-To investigate the use of DL for nonneoplastic gastric biopsies.Design.-Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion.Results.-For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] ¼ 99.7%) and H pylori (AUC ¼ 100%), and 40% for reactive gastropathy (AUC ¼ 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/ specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%).Conclusions.-A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

Abstract/Synopsis Recent technological advances in Next Generation Sequencing (NGS) methods have substantially reduced cost and operational complexity leading to the production of bench top sequencers and commercial software solutions for implementation in small research and clinical laboratories. This chapter summarizes requirements and hurdles to the successful implementation of these systems including 1) calibration, validation and optimization of the instrumentation, experimental paradigm and primary readout, 2) secure transfer, storage and secondary processing of the data, 3) implementation of software tools for targeted analysis, and 4) training of research and clinical personnel to evaluate data fidelity and interpret the molecular significance of the genomic output. In light of the commercial and technological impetus to bring NGS technology into the clinical domain, it is critical that novel tests incorporate rigid protocols with built-in calibration standards and that data transfer and processing occur under exacting security measures for interpretation by clinicians with specialized training in molecular diagnostics.