Publications by authors named "Debra Krupke"

The laboratory mouse has served for decades as an informative animal model system for investigating the genetic and genomic basis of cancer in humans. Although thousands of mouse models have been generated, compiling and aggregating relevant data and knowledge about these models is hampered by a general lack of compliance, in the published literature, with nomenclature and annotation standards for genes, alleles, mouse strains and cancer types. The Mouse Models of Human Cancer database (MMHCdb) is an expertly curated, comprehensive knowledgebase of diverse types of mouse models of human cancer, including inbred mouse strains, genetically engineered mouse models, patient-derived xenografts, and mouse genetic diversity panels such as the Collaborative Cross.

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PDCM Finder (www.cancermodels.org) is a cancer research platform that aggregates clinical, genomic and functional data from patient-derived xenografts, organoids and cell lines.

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Background: Patient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons - ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research.

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Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients' tumors. PDX models are generated and distributed by a diverse group of academic labs, multi-institution consortia and contract research organizations. The distributed nature of PDX repositories and the use of different metadata standards for describing model characteristics presents a significant challenge to identifying PDX models relevant to specific cancer research questions.

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Research using laboratory mice has led to fundamental insights into the molecular genetic processes that govern cancer initiation, progression, and treatment response. Although thousands of scientific articles have been published about mouse models of human cancer, collating information and data for a specific model is hampered by the fact that many authors do not adhere to existing annotation standards when describing models. The interpretation of experimental results in mouse models can also be confounded when researchers do not factor in the effect of genetic background on tumor biology.

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Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models.

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Many mouse models have been created to study hematopoietic cancer types. There are over thirty hematopoietic tumor types and subtypes, both human and mouse, with various origins, characteristics and clinical prognoses. Determining the specific type of hematopoietic lesion produced in a mouse model and identifying mouse models that correspond to the human subtypes of these lesions has been a continuing challenge for the scientific community.

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In recent years, the scientific community has generated an ever-increasing amount of data from a growing number of animal models of human cancers. Much of these data come from genetically engineered mouse models. Identifying appropriate models for skin cancer and related relevant genetic data sets from an expanding pool of widely disseminated data can be a daunting task.

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The laboratory mouse has long been an important tool in the study of the biology and genetics of human cancer. With the advent of genetic engineering techniques, DNA microarray analyses, tissue arrays and other large-scale, high-throughput data generating methods, the amount of data available for mouse models of cancer is growing exponentially. Tools to integrate, locate and visualize these data are crucial to aid researchers in their investigations.

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A substantial effort of the biological community involves the development of model organism databases containing key genomic information concerning specific organisms. This paper describes a developing natural language processing (NLP) tool, which is aimed at assisting curators of the Mouse Tumor Biology (MTB) Database of the Mouse Genome Informatics (MGI) group by helping them quickly find key information in the articles.

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The Mouse Tumor Biology (MTB) database provides access to data about endogenously arising tumors (both spontaneous and induced) in genetically defined mice (inbred, hybrid, mutant and genetically engineered mice). Data include information on the frequency and latency of mouse tumors, pathology reports and images, genomic changes occurring in the tumors, genetic (strain) background and literature or contributor citations. Data are curated from the primary literature or submitted directly from researchers.

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Mice have long been used as models for the study of human cancer. The National Cancer Institute has included among its research areas of extraordinary opportunity the development of new mouse genetic models of human cancer and the exploration of cancer imaging as a research tool. Because of the volume and interconnectedness of relevant data, the creation and maintenance of bioinformatics resources of mouse tumor biology is necessary to facilitate current and future cancer research.

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Histology and immunohistochemistry are important tools in the study of human diseases and their respective animal models. The study of mouse models has been hampered by the absence of a large set of mouse-specific antibodies adapted to paraffin-embedded tissues. A total of 196 antibodies were tested on paraffin-embedded mouse tissues preserved in five different fixatives (Fekete's acid-alcohol-formalin, 10% neutral buffered formalin, 4% paraformaldehyde, IHC Zinc Fixative, and Bouin's fixative).

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Developing genetic mouse models for cancer research has been recognized as an "exceptional opportunity" by the National Cancer Institute. The establishment of bioinformatics resources to facilitate access to published and unpublished data on the genetics and pathology of cancer in different strains of the laboratory mouse is critical to developing and using mouse models of human disease. In this article, we review the Mouse Tumor Biology Database (MTB), a public resource for information on cancer genetics, epidemiology, and pathology in genetically defined mice.

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