STRprofiler: efficient comparisons of short tandem repeat profiles for biomedical model authentication.

Summary: Short tandem repeat (STR) profiling is commonly performed for authentication of biomedical models of human origin, yet no tools exist to easily compare sets of STR profiles to each other or an existing database in a high-throughput manner. Here, we present STRprofiler, a Python package, command line tool, and Shiny application providing methods for STR profile comparison and cross-contamination detection. STRprofiler can be run with custom databases or used to query against the Cellosaurus cell line database.

A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response.

The Mouse Models of Human Cancer database (MMHCdb).

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.

The Mouse Tumor Biology Database: A Comprehensive Resource for Mouse Models of Human Cancer.

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.

PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models.

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.

Finding mouse models of human lymphomas and leukemia's using the Jackson laboratory mouse tumor biology database.

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.

Mouse Tumor Biology (MTB): a database of mouse models for human cancer.

The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.

Identifying mouse models for skin cancer using the Mouse Tumor Biology Database.

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.