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

CREB1-BCL2 drives mitochondrial resilience in RAS GAP-dependent breast cancer chemoresistance

Triple-negative breast cancer (TNBC) is an aggressive and heterogenous breast cancer subtype. RASAL2 is a RAS GTPase-activating protein (GAP) that has been associated with platinum resistance in TNBC, but the underlying mechanism is unknown. Here, we show that RASAL2 is enriched following neoadjuvant chemotherapy in TNBC patients. This enrichment is specific to the tumour compartment compared to adjacent normal tissues, suggesting that RASAL2 upregulation is tumour-selective. Analyses based on 2D/3D cultures and patient-derived xenograft models reveal that RASAL2 confers cross-resistance to common DNA-damaging chemotherapies other than platinum. Mechanistically, we found that apoptotic signalling is significantly downregulated upon RASAL2 expression. This feature is characterised by substantial alterations in the expression of anti-versus pro-apoptotic factors, pointing to heterogeneous mechanisms. In particular, RASAL2 upregulates BCL2 via activation of the oncogenic transcription co-factor YAP. CREB1, a YAP-interacting protein, was identified as the common transcription factor that binds to the promoter regions of RASAL2 and BCL2, driving their collective expression. A subset of RASAL2 colocalises with BCL2 subcellularly. Both proteins decorate mitochondria, where the high levels of mitochondrial RASAL2-induced BCL2 expression render the organelles refractory to apoptosis. Accordingly, mitochondrial outer membrane permeabilisation assay using live mitochondria from RASAL2-high/chemoresistant tumour cells demonstrated attenuated release of death signal, cytochrome c, when exposed to pro-apoptotic factors BAX and tBID. Similarly, these cells were more resilient towards chemotherapy-induced mitochondrial depolarisation. Together, this work reveals a previously undocumented molecular link between RAS GAP and apoptosis regulation, providing a new mechanistic framework for targeting a subset of chemorefractory tumours.

Exome variant prioritization in a large cohort of hearing-impaired individuals indicates IKZF2 to be associated with non-syndromic hearing loss and guides future research of unsolved cases

Although more than 140 genes have been associated with non-syndromic hereditary hearing loss (HL), at least half of the cases remain unexplained in medical genetic testing. One reason is that pathogenic variants are located in 'novel' deafness genes. A variant prioritization approach was used to identify novel (candidate) genes for HL. Exome-wide sequencing data were assessed for subjects with presumed hereditary HL that remained unexplained in medical genetic testing by gene-panel analysis. Cases in group AD had presumed autosomal dominantly inherited HL (n = 124), and in group AR, presumed autosomal recessive HL (n = 337). Variants in known and candidate deafness genes were prioritized based on allele frequencies and predicted effects. Selected variants were tested for their co-segregation with HL. Two cases were solved by variants in recently identified deafness genes (ABHD12, TRRAP). Variant prioritization also revealed potentially causative variants in candidate genes associated with recessive and X-linked HL. Importantly, missense variants in IKZF2 were found to co-segregate with dominantly inherited non-syndromic HL in three families. These variants specifically affected Zn2+-coordinating cysteine or histidine residues of the zinc finger motifs 2 and 3 of the encoded protein Helios. This finding indicates a complex genotype-phenotype correlation for IKZF2 defects, as this gene was previously associated with non-syndromic dysfunction of the immune system and ICHAD syndrome, including HL. The designed strategy for variant prioritization revealed that IKZF2 variants can underlie non-syndromic HL. The large number of candidate genes for HL and variants therein stress the importance of inclusion of family members for variant prioritization.

Rabphilin-3A negatively regulates neuropeptide release, through its SNAP25 interaction

Neuropeptides and neurotrophins are stored in and released from dense core vesicles (DCVs). While DCVs and synaptic vesicles (SVs) share fundamental SNARE/SM proteins for exocytosis, a detailed understanding of DCV exocytosis remains elusive. We recently identified the RAB3-RIM1 pathway to be essential for DCV, but not SV exocytosis, highlighting a significant distinction between the SV and DCV secretory pathways. Whether RIM1 is the only RAB3 effector that is essential for DCV exocytosis is currently unknown. In this study, we show that rabphilin-3A (RPH3A), a known downstream effector of RAB3A, is a negative regulator of DCV exocytosis. Using live-cell imaging at single-vesicle resolution with RPH3A deficient hippocampal mouse neurons, we show that DCV exocytosis increased threefold in the absence of RPH3A. RAB3A-binding deficient RPH3A lost its punctate distribution, but still restored DCV exocytosis to WT levels when re-expressed. SNAP25-binding deficient RPH3A did not rescue DCV exocytosis. In addition, we show that RPH3A did not travel with DCVs, but remained stationary at presynapses. RPH3A null neurons also had longer neurites, which was partly restored when ablating all regulated secretion with tetanus neurotoxin. Taken together, these results show that RPH3A negatively regulates DCV exocytosis, potentially also affecting neuron size. Furthermore, RAB3A interaction is required for the synaptic enrichment of RPH3A, but not for limiting DCV exocytosis. Instead, the interaction of RPH3A with SNAP25 is relevant for inhibiting DCV exocytosis.

Development of an automated 3D high content cell screening platform for organoid phenotyping

The use of organoid models in biomedical research has grown substantially since their inception. As they gain popularity among scientists seeking more complex and biologically relevant systems, there is a direct need to expand and clarify potential uses of such systems in diverse experimental contexts. Herein we outline a high-content screening (HCS) platform that allows researchers to screen drugs or other compounds against three-dimensional (3D) cell culture systems in a multi-well format (384-well). Furthermore, we compare the quality of robotic liquid handling with manual pipetting and characterize and contrast the phenotypic effects detected by confocal imaging and biochemical assays in response to drug treatment. We show that robotic liquid handling is more consistent and amendable to high throughput experimental designs when compared to manual pipetting due to improved precision and automated randomization capabilities. We also show that image-based techniques are more sensitive to detecting phenotypic changes within organoid cultures than traditional biochemical assays that evaluate cell viability, supporting their integration into organoid screening workflows. Finally, we highlight the enhanced capabilities of confocal imaging in this organoid screening platform as they relate to discerning organoid drug responses in single-well co-cultures of organoids derived from primary human biopsies and patient-derived xenograft (PDX) models. Altogether, this platform enables automated, imaging-based HCS of 3D cellular models in a non-destructive manner, opening the path to complementary analysis through integrated downstream methods.

Cancer research is not correlated with driver gene mutation burdens

BACKGROUND

Cancer research is pursued with the goal of positively impacting patients with cancer. Decisions regarding how to allocate research funds reflect a complex balancing of priorities and factors. Even though these are subjective decisions, they should be made with consideration of all available objective facts. An accurate estimate of the affected cancer patient population by mutation is one variable that has only recently become available to inform funding decisions.

METHODS

We compared the overall incident burden of mutations within each cancer-associated gene with two measures of cancer research efforts: research grant funding amounts and numbers of academic manuscripts. We ask to what degree the aggregate set of cancer research efforts reflects the relative burdens of the different cancer genetic drivers. We thoroughly investigate the design of our queries to ensure that the presented results are robust and conclusions are well justified.

FINDINGS

We find cancer research is generally not correlated with the relative burden of mutation within the different genetic drivers of cancer.

CONCLUSIONS

We suggest that cancer research would benefit from incorporating, among other factors, an epidemiologically informed mutation-estimate baseline into a larger framework for funding and research allocation decisions.

FUNDING

This work was supported in part by the National Institutes of Health (NIH) P30CA014195 and NIH DP2AT011327.

Web-Based Resources to Investigate Protease Function

In 2001, the release of the first draft of the human genome marked the beginning of the Big Data era for biological sciences. Since then, the complexity of datasets generated by laboratories worldwide has increased exponentially. Public repositories such as the Protein Data Bank, which has exceeded the 200000 entries in 2023, have been instrumental not only to collect, organize, and distill this enormous research output but also to promote further research enterprises. The achievements of artificial intelligence programs such as AlphaFold would not have been possible without the collective efforts of countless researchers who made their work publicly available. Here, I provide a practical, but far from exhaustive, list of resources useful to investigate protease function.

Effectively utilizing publicly available databases for cancer target evaluation

The majority of compounds designed against cancer drug targets do not progress to become approved drugs, mainly due to lack of efficacy and/or unmanageable toxicity. Robust target evaluation is therefore required before progressing through the drug discovery process to reduce the high attrition rate. There are a wealth of publicly available databases that can be mined to generate data as part of a target evaluation. It can, however, be challenging to learn what databases are available, how and when they should be used, and to understand the associated limitations. Here, we have compiled and present key, freely accessible and easy-to-use databases that house informative datasets from in vitro, in vivo and clinical studies. We also highlight comprehensive target review databases that aim to bring together information from multiple sources into one-stop portals. In the post-genomics era, a key objective is to exploit the extensive cell, animal and patient characterization datasets in order to deliver precision medicine on a patient-specific basis. Effective utilization of the highlighted databases will go some way towards supporting the cancer research community achieve these aims.

The future of patient-derived xenografts in prostate cancer research

Patient-derived xenografts (PDXs) are generated by engrafting human tumours into mice. Serially transplantable PDXs are used to study tumour biology and test therapeutics, linking the laboratory to the clinic. Although few prostate cancer PDXs are available in large repositories, over 330 prostate cancer PDXs have been established, spanning broad clinical stages, genotypes and phenotypes. Nevertheless, more PDXs are needed to reflect patient diversity, and to study new treatments and emerging mechanisms of resistance. We can maximize the use of PDXs by exchanging models and datasets, and by depositing PDXs into biorepositories, but we must address the impediments to accessing PDXs, such as institutional, ethical and legal agreements. Through collaboration, researchers will gain greater access to PDXs representing diverse features of prostate cancer.

Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis

Many enhancers exist as clusters in the genome and control cell identity and disease genes; however, the underlying mechanism remains largely unknown. Here, we introduce an algorithm, eNet, to build enhancer networks by integrating single-cell chromatin accessibility and gene expression profiles. The complexity of enhancer networks is assessed by two metrics: the number of enhancers and the frequency of predicted enhancer interactions (PEIs) based on chromatin co-accessibility. We apply eNet algorithm to a human blood dataset and find cell identity and disease genes tend to be regulated by complex enhancer networks. The network hub enhancers (enhancers with frequent PEIs) are the most functionally important. Compared with super-enhancers, enhancer networks show better performance in predicting cell identity and disease genes. eNet is robust and widely applicable in various human or mouse tissues datasets. Thus, we propose a model of enhancer networks containing three modes: Simple, Multiple and Complex, which are distinguished by their complexity in regulating gene expression. Taken together, our work provides an unsupervised approach to simultaneously identify key cell identity and disease genes and explore the underlying regulatory relationships among enhancers in single cells.

A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research.

SIGNIFICANCE

Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

Early expression of mature αβ TCR in CD4-CD8- T cell progenitors enables MHC to drive development of T-ALL bearing NOTCH mutations

T cell development and immune responses are directed by major histocompatibility complex:T cell antigen receptor (MHC:TCR) signaling, but aberrant signals can cause T cell tumors to form. We show that in mice and humans, a low-frequency progenitor cell population expresses early αβ TCR while coreceptor double-negative (EADN), and these EADN cells can transform to thymic leukemia. Mouse models showed that EADN cells did not require MHC to develop but when presented with MHC they could respond with high sensitivity. Transformation to leukemia occurred and required MHC, although with extended tumor growth this requirement could be lost. Thus, MHC:TCR signaling can initiate a leukemia phenotype from an understudied developmental state that appears to be represented in the mouse and human disease spectrum.

During normal T cell development in mouse and human, a low-frequency population of immature CD4⁻CD8⁻ double-negative (DN) thymocytes expresses early, mature αβ T cell antigen receptor (TCR). We report that these early αβ TCR+ DN (EADN) cells are DN3b-DN4 stage and require CD3δ but not major histocompatibility complex (MHC) for their generation/detection. When MHC - is present, however, EADN cells can respond to it, displaying a degree of coreceptor-independent MHC reactivity not typical of mature, conventional αβ T cells. We found these data to be connected with observations that EADN cells were susceptible to T cell acute lymphoblastic leukemia (T-ALL) transformation in both humans and mice. Using the OT-1 TCR transgenic system to model EADN-stage αβ TCR expression, we found that EADN leukemogenesis required MHC to induce development of T-ALL bearing NOTCH1 mutations. This leukemia-driving MHC requirement could be lost, however, upon passaging the tumors in vivo, even when matching MHC was continuously present in recipient animals and on the tumor cells themselves. These data demonstrate that MHC:TCR signaling can be required to initiate a cancer phenotype from an understudied developmental state that appears to be represented in the mouse and human disease spectrum.

Programmable RNA-Guided Large DNA Transgenesis by CRISPR/Cas9 and Site-Specific Integrase Bxb1

The inability to insert large DNA constructs into the genome efficiently and precisely is a key challenge in genomic engineering. Random transgenesis, which is widely used, lacks precision, and comes with a slew of drawbacks. Lentiviral and adeno-associated viral methods are plagued by, respectively, DNA toxicity and a payload capacity of less than 5 kb. Homology-directed repair (HDR) techniques based on CRISPR-Cas9 can be effective, but only in the 1-5 kb range. In addition, long homology arms-DNA sequences that permit construct insertion-of lengths ranging from 0.5 to 5 kb are required by currently known HDR-based techniques. A potential new method that uses Cas9-guided transposases to insert DNA structures up to 10 kb in length works well in bacteria, but only in bacteria. Surmounting these roadblocks, a new toolkit has recently been developed that combines RNA-guided Cas9 and the site-specific integrase Bxb1 to integrate DNA constructs ranging in length from 5 to 43 kb into mouse zygotes with germline transmission and into human cells. This ground-breaking toolkit will give researchers a valuable resource for developing novel, urgently needed mouse and human induced pluripotent stem cell (hiPSC) models of cancer and other genetic diseases, as well as therapeutic gene integration and biopharmaceutical applications, such as the development of stable cell lines to produce therapeutic protein products.

Lung Cancer Computational Biology and Resources

Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer. Clinically, cancer registry and publicly available historical clinical trial data enable retrospective analyses to examine how socioeconomic factors, patient demographics, and cancer characteristics affect treatment and outcome. Pathologically, digital pathology and artificial intelligence are revolutionizing histopathological image analyses, not only with improved efficiency and accuracy, but also by extracting additional information for prognostication and tumor microenvironment characterization. Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies and translation of the findings into the clinic. In this review, we provide a list of resources and tools potentially valuable for lung cancer basic and translational research. Importantly, we point out pitfalls and caveats when performing computational analyses of these data sets and provide a vision of future computational biology developments that will aid lung cancer translational research.

Research-Relevant Conditions and Pathology of Laboratory Mice, Rats, Gerbils, Guinea Pigs, Hamsters, Naked Mole Rats, and Rabbits

Animals are valuable resources in biomedical research in investigations of biological processes, disease pathogenesis, therapeutic interventions, safety, toxicity, and carcinogenicity. Interpretation of data from animals requires knowledge not only of the processes or diseases (pathophysiology) under study but also recognition of spontaneous conditions and background lesions (pathology) that can influence or confound the study results. Species, strain/stock, sex, age, anatomy, physiology, spontaneous diseases (noninfectious and infectious), and neoplasia impact experimental results and interpretation as well as animal welfare. This review and the references selected aim to provide a pathology resource for researchers, pathologists, and veterinary personnel who strive to achieve research rigor and validity and must understand the spectrum of "normal" and expected conditions to accurately identify research-relevant experimental phenotypes as well as unusual illness, pathology, or other conditions that can compromise studies involving laboratory mice, rats, gerbils, guinea pigs, hamsters, naked mole rats, and rabbits.

OUP accepted manuscript

Conflicts between transcription and replication machinery are a potent source of replication stress and genome instability; however, no technique currently exists to identify endogenous genomic locations prone to transcription–replication interactions. Here, we report a novel method to identify genomic loci prone to transcription–replication interactions termed transcription–replication immunoprecipitation on nascent DNA sequencing, TRIPn-Seq. TRIPn-Seq employs the sequential immunoprecipitation of RNA polymerase 2 phosphorylated at serine 5 (RNAP2s5) followed by enrichment of nascent DNA previously labeled with bromodeoxyuridine. Using TRIPn-Seq, we mapped 1009 unique transcription–replication interactions (TRIs) in mouse primary B cells characterized by a bimodal pattern of RNAP2s5, bidirectional transcription, an enrichment of RNA:DNA hybrids, and a high probability of forming G-quadruplexes. TRIs are highly enriched at transcription start sites and map to early replicating regions. TRIs exhibit enhanced Replication Protein A association and TRI-associated genes exhibit higher replication fork termination than control transcription start sites, two marks of replication stress. TRIs colocalize with double-strand DNA breaks, are enriched for deletions, and accumulate mutations in tumors. We propose that replication stress at TRIs induces mutations potentially contributing to age-related disease, as well as tumor formation and development.

Challenges in genomic analysis of model systems and primary tumors of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is characterized by aggressive tumor behavior and poor prognosis. Recent next-generation sequencing (NGS)-based genomic studies have provided novel treatment modes for pancreatic cancer via the identification of cancer driver variants and molecular subtypes in PDAC. Genome-wide approaches have been extended to model systems such as patient-derived xenografts (PDXs), organoids, and cell lines for pre-clinical purposes. However, the genomic characteristics vary in the model systems, which is mainly attributed to the clonal evolution of cancer cells during their construction and culture. Moreover, fundamental limitations such as low tumor cellularity and the complex tumor microenvironment of PDAC hinder the confirmation of genomic features in the primary tumor and model systems. The occurrence of these phenomena and their associated complexities may lead to false insights into the understanding of mechanisms and dynamics in tumor tissues of patients. In this review, we describe various model systems and discuss differences in the results based on genomics and transcriptomics between primary tumors and model systems. Finally, we introduce practical strategies to improve the accuracy of genomic analysis of primary tissues and model systems.

Expanded phenotype of primary ciliary dyskinesia related to DRC1 pathogenic variant with dysmorphisms and vascular anomalies

We present a case of a female diagnosed with primary ciliary dyskinesia (PCD) type 21 with non-previously reported extrapulmonary symptoms, including facial features and congenital vascular anomalies. Whole genome sequencing in our patient revealed a homozygous pathogenic variant in the DRC1 gene and no other notable structural nor punctual variants. This case demonstrates a unique clinical manifestation of PCD, which is possibly associated with the presence of a homozygous pathogenic DRC1 variant. Therefore, we suggest that analysis of DRC1 be considered with PCD type 21 when such features are present.

The effects of Tbx15 and Pax1 on facial and other physical morphology in mice

DNA variants in or close to the human TBX15 and PAX1 genes have been repeatedly associated with facial morphology in independent genome-wide association studies, while their functional roles in determining facial morphology remain to be understood. We generated Tbx15 knockout (Tbx15 -/-) and Pax1 knockout (Pax1 -/-) mice by applying the one-step CRISPR/Cas9 method. A total of 75 adult mice were used for subsequent phenotype analysis, including 38 Tbx15 mice (10 homozygous Tbx15 -/-, 18 heterozygous Tbx15 +/-, 10 wild-type Tbx15 +/+ WT littermates) and 37 Pax1 mice (12 homozygous Pax1 -/-, 15 heterozygous Pax1 +/-, 10 Pax1 +/+ WT littermates). Facial and other physical morphological phenotypes were obtained from three-dimensional (3D) images acquired with the HandySCAN BLACK scanner. Compared to WT littermates, the Tbx15 -/- mutant mice had significantly shorter faces (p = 1.08E-8, R2 = 0.61) and their ears were in a significantly lower position (p = 3.54E-8, R2 = 0.62) manifesting a "droopy ear" characteristic. Besides these face alternations, Tbx15 -/- mutant mice displayed significantly lower weight as well as shorter body and limb length. Pax1 -/- mutant mice showed significantly longer noses (p = 1.14E-5, R2 = 0.46) relative to WT littermates, but otherwise displayed less obvious morphological alterations than Tbx15 -/- mutant mice did. We provide the first direct functional evidence that two well-known and replicated human face genes, Tbx15 and Pax1, impact facial and other body morphology in mice. The general agreement between our findings in knock-out mice with those from previous GWASs suggests that the functional evidence we established here in mice may also be relevant in humans.

Mouse Genome Informatics (MGI): latest news from MGD and GXD

The Mouse Genome Informatics (MGI) database system combines multiple expertly curated community data resources into a shared knowledge management ecosystem united by common metadata annotation standards. MGI's mission is to facilitate the use of the mouse as an experimental model for understanding the genetic and genomic basis of human health and disease. MGI is the authoritative source for mouse gene, allele, and strain nomenclature and is the primary source of mouse phenotype annotations, functional annotations, developmental gene expression information, and annotations of mouse models with human diseases. MGI maintains mouse anatomy and phenotype ontologies and contributes to the development of the Gene Ontology and Disease Ontology and uses these ontologies as standard terminologies for annotation. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are MGI's two major knowledgebases. Here, we highlight some of the recent changes and enhancements to MGD and GXD that have been implemented in response to changing needs of the biomedical research community and to improve the efficiency of expert curation. MGI can be accessed freely at http://www.informatics.jax.org .

Identification of fifty-seven novel loci for abdominal wall hernia development and their biological and clinical implications: results from the UK Biobank

PURPOSE

Familial aggregation is known for both hernia development and recurrence. To date, only one genome-wide association study (GWAS) limited to inguinal hernia has been reported that identified four risk-associated loci. We aim to investigate polygenic architecture of abdominal wall hernia development and recurrence.

METHODS

A GWAS was performed in 367,394 subjects from the UK Biobank to investigate the polygenic architecture of abdominal wall hernia subtypes (inguinal, femoral, umbilical, ventral) and identify specific single nucleotide polymorphisms (SNPs) that are associated with their risk. Expression quantitative trait loci (eQTL) analysis was performed to identify genes whose expression levels are associated with these SNPs. A genetic risk score (GRS) was used to assess the cumulative effect of multiple independent risk-associated SNPs on hernia development and recurrence in independent subjects (n = 82,064).

RESULTS

Heritability (h²) was 0.12, 0.06, 0.16, and 0.07 for inguinal, femoral, umbilical, and ventral hernias, respectively. A high-level of genetic correlation (r_g) was found among these subtypes of hernia. We confirmed the aforementioned four loci and identified 57 novel loci (P < 5 × 10^-8), including 55, 3, 5, and 3 loci for inguinal, femoral, umbilical, and ventral hernias, respectively. Significantly different expression levels between risk/reference alleles of SNPs were found for 145 genes, including TGF-β2 and AIG1 for inguinal hernia risk and CALD1 for umbilical hernia risk. Finally, higher GRS deciles were significantly associated with increased risk for hernia development (P_trend = 3.33 × 10^-38) and recurrent hernia repair surgery (P_trend = 3.64 × 10^-14).

CONCLUSION

These novel results have potential biological and clinical implications for hernia management in high-risk patients.

RASAL2 Confers Collateral MEK/EGFR Dependency in Chemoresistant Triple-Negative Breast Cancer

PURPOSE

While chemotherapy remains the standard treatment for triple-negative breast cancer (TNBC), identifying and managing chemoresistant tumors has proven elusive. We sought to discover hallmarks and therapeutically actionable features of refractory TNBC through molecular analysis of primary chemoresistant TNBC specimens.

EXPERIMENTAL DESIGN

We performed transcriptional profiling of tumors from a phase II clinical trial of platinum chemotherapy for advanced TNBC (TBCRC-009), revealing a gene expression signature that identified de novo chemorefractory tumors. We then employed pharmacogenomic data mining, proteomic and other molecular studies to define the therapeutic vulnerabilities of these tumors.

RESULTS

We reveal the RAS-GTPase-activating protein (RAS-GAP) RASAL2 as an upregulated factor that mediates chemotherapy resistance but also an exquisite collateral sensitivity to combination MAP kinase kinase (MEK1/2) and EGFR inhibitors in TNBC. Mechanistically, RASAL2 GAP activity is required to confer kinase inhibitor sensitivity, as RASAL2-high TNBCs sustain basal RAS activity through suppression of negative feedback regulators SPRY1/2, together with EGFR upregulation. Consequently, RASAL2 expression results in failed feedback compensation upon co-inhibition of MEK1/2 and EGFR that induces synergistic apoptosis in vitro and in vivo. In patients with TNBC, high RASAL2 levels predict clinical chemotherapy response and long-term outcomes, and are associated via direct transcriptional regulation with activated oncogenic Yes-Associated Protein (YAP). Accordingly, chemorefractory patient-derived TNBC models exhibit YAP activation, high RASAL2 expression, and tumor regression in response to MEK/EGFR inhibitor combinations despite well-tolerated intermittent dosing.

CONCLUSIONS

These findings identify RASAL2 as a mediator of TNBC chemoresistance that rewires MAPK feedback and cross-talk to confer profound collateral sensitivity to combination MEK1/2 and EGFR inhibitors.

Development of Immunotherapy Combination Strategies in Cancer

Harnessing the immune system to treat cancer through inhibitors of CTLA4 and PD-L1 has revolutionized the landscape of cancer. Rational combination strategies aim to enhance the antitumor effects of immunotherapies, but require a deep understanding of the mechanistic underpinnings of the immune system and robust preclinical and clinical drug development strategies. We review the current approved immunotherapy combinations, before discussing promising combinatorial approaches in clinical trials and detailing innovative preclinical model systems being used to develop rational combinations. We also discuss the promise of high-order immunotherapy combinations, as well as novel biomarker and combinatorial trial strategies. SIGNIFICANCE: Although immune-checkpoint inhibitors are approved as dual checkpoint strategies, and in combination with cytotoxic chemotherapy and angiogenesis inhibitors for multiple cancers, patient benefit remains limited. Innovative approaches are required to guide the development of novel immunotherapy combinations, ranging from improvements in preclinical tumor model systems to biomarker-driven trial strategies.

Identification of New miRNA-mRNA Networks in the Development of Non-syndromic Cleft Lip With or Without Cleft Palate

Objective: To identify new microRNA (miRNA)-mRNA networks in non-syndromic cleft lip with or without cleft palate (NSCL/P).

Materials and Methods: Overlapping differentially expressed miRNAs (DEMs) were selected from cleft palate patients (GSE47939) and murine embryonic orofacial tissues (GSE20880). Next, the target genes of DEMs were predicted by Targetscan, miRDB, and FUNRICH, and further filtered through differentially expressed genes (DEGs) from NSCL/P patients and controls (GSE42589), MGI, MalaCards, and DECIPHER databases. The results were then confirmed by in vitro experiments. NSCL/P lip tissues were obtained to explore the expression of miRNAs and their target genes.

Results: Let-7c-5p and miR-193a-3p were identified as DEMs, and their overexpression inhibited cell proliferation and promoted cell apoptosis. PIGA and TGFB2 were confirmed as targets of let-7c-5p and miR-193a-3p, respectively, and were involved in craniofacial development in mice. Negative correlation between miRNA and mRNA expression was detected in the NSCL/P lip tissues. They were also associated with the occurrence of NSCL/P based on the MGI, MalaCards, and DECIPHER databases.

Conclusions: Let-7c-5p-PIGA and miR-193a-3p-TGFB2 networks may be involved in the development of NSCL/P.

Mouse Genome Database (MGD): Knowledgebase for mouse-human comparative biology

The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the community model organism knowledgebase for the laboratory mouse, a widely used animal model for comparative studies of the genetic and genomic basis for human health and disease. MGD is the authoritative source for biological reference data related to mouse genes, gene functions, phenotypes and mouse models of human disease. MGD is the primary source for official gene, allele, and mouse strain nomenclature based on the guidelines set by the International Committee on Standardized Nomenclature for Mice. MGD's biocuration scientists curate information from the biomedical literature and from large and small datasets contributed directly by investigators. In this report we describe significant enhancements to the content and interfaces at MGD, including (i) improvements in the Multi Genome Viewer for exploring the genomes of multiple mouse strains, (ii) inclusion of many more mouse strains and new mouse strain pages with extended query options and (iii) integration of extensive data about mouse strain variants. We also describe improvements to the efficiency of literature curation processes and the implementation of an information portal focused on mouse models and genes for the study of COVID-19.

53BP1 Repair Kinetics for Prediction of In Vivo Radiation Susceptibility in 15 Mouse Strains

We present a novel mathematical formalism to predict the kinetics of DNA damage repair after exposure to both low- and high-LET radiation (X rays; 350 MeV/n 40Ar; 600 MeV/n 56Fe). Our method is based on monitoring DNA damage repair protein 53BP1 that forms radiation-induced foci (RIF) at locations of DNA double-strand breaks (DSB) in the nucleus and comparing its expression in primary skin fibroblasts isolated from 15 mice strains. We previously reported strong evidence for clustering of nearby DSB into single repair units as opposed to the classic "contact-first" model where DSB are considered immobile. Here we apply this clustering model to evaluate the number of remaining RIF over time. We also show that the newly introduced kinetic metrics can be used as surrogate biomarkers for in vivo radiation toxicity, with potential applications in radiotherapy and human space exploration. In particular, we observed an association between the characteristic time constant of RIF repair measured in vitro and survival levels of immune cells collected from irradiated mice. Moreover, the speed of DNA damage repair correlated not only with radiation-induced cellular survival in vivo, but also with spontaneous cancer incidence data collected from the Mouse Tumor Biology database, suggesting a relationship between the efficiency of DSB repair after irradiation and cancer risk.

Serum miRNAs associated with tumor-promoting cytokines in non-small cell lung cancer

Tumor-promoting cytokines are a cause of tumor progression; therefore, identifying key regulatory microRNAs (miRNAs) for controlling their production is important. The aim of this study is to identify promising miRNAs associated with tumor-promoting cytokines in non-small cell lung cancer (NSCLC). We identified circulating miRNAs from 16 published miRNA profiles. The selected miRNAs were validated in the serum of 32 NSCLC patients and compared with 33 patients with other lung diseases and 23 healthy persons using quantitative real-time PCR. The cytokine concentration was investigated using the enzyme-linked immunoassay in the same sample set, with clinical validation of the miRNAs. The correlation between miRNA expression and cytokine concentration was evaluated by Spearman’s rank correlation. For consistent direction, one up-regulated miRNA (miR-145) was found in four studies, and seven miRNAs were reported in three studies. One miRNA (miR-20a) and four miRNAs (miR-25-3p, miR-223, let-7f, and miR-20b) were reported in six and five studies. However, their expression was inconsistent. In the clinical validation, serum miR-145 was significantly down-regulated, whereas serum miR-20a was significantly up-regulated in NSCLC, compared with controls. Regarding serum cytokine, all cytokines [vascular endothelial growth factor (VEGF), interleukin-6 (IL-6), and transforming growth factor β (TGF-β)], except tumor necrosis factor-α (TNF-α), had a higher level in NSCLC patients than controls. In addition, we found a moderate correlation between the TGF-β concentration and miR-20a (r = −0.537, p = 0.002) and miR-223 (r = 0.428, p = 0.015) and a weak correlation between the VEGF concentration with miR-20a (r = 0.376, p = 0.037) and miR-223 (r = −0.355, p = 0.046). MiR-145 and miR-20a are potential biomarkers for NSCLC. In addition, the regulation of tumor-promoting cytokine, through miR-20a and miR-223, might be a new therapeutic approach for lung cancer.

Quantitative Proteomics Links the LRRC59 Interactome to mRNA Translation on the ER Membrane

Protein synthesis on the endoplasmic reticulum (ER) requires the dynamic coordination of numerous cellular components. Together, resident ER membrane proteins, cytoplasmic translation factors, and both integral membrane and cytosolic RNA-binding proteins operate in concert with membrane-associated ribosomes to facilitate ER-localized translation. Little is known, however, regarding the spatial organization of ER-localized translation. This question is of growing significance as it is now known that ER-bound ribosomes contribute to secretory, integral membrane, and cytosolic protein synthesis alike. To explore this question, we utilized quantitative proximity proteomics to identify neighboring protein networks for the candidate ribosome interactors SEC61β (subunit of the protein translocase), RPN1 (oligosaccharyltransferase subunit), SEC62 (translocation integral membrane protein), and LRRC59 (ribosome binding integral membrane protein). Biotin labeling time course studies of the four BioID reporters revealed distinct labeling patterns that intensified but only modestly diversified as a function of labeling time, suggesting that the ER membrane is organized into discrete protein interaction domains. Whereas SEC61β and RPN1 reporters identified translocon-associated networks, SEC62 and LRRC59 reporters revealed divergent protein interactomes. Notably, the SEC62 interactome is enriched in redox-linked proteins and ER luminal chaperones, with the latter likely representing proximity to an ER luminal chaperone reflux pathway. In contrast, the LRRC59 interactome is highly enriched in SRP pathway components, translation factors, and ER-localized RNA-binding proteins, uncovering a functional link between LRRC59 and mRNA translation regulation. Importantly, analysis of the LRRC59 interactome by native immunoprecipitation identified similar protein and functional enrichments. Moreover, [³⁵S]-methionine incorporation assays revealed that siRNA silencing of LRRC59 expression reduced steady state translation levels on the ER by ca. 50%, and also impacted steady state translation levels in the cytosol compartment. Collectively, these data reveal a functional domain organization for the ER and identify a key role for LRRC59 in the organization and regulation of local translation.

Induction of PIK3CA alterations during neoadjuvant letrozole may improve outcome in postmenopausal breast cancer patients

PURPOSE

Estrogen receptor positive (ER+) breast cancer constitutes almost 85% of all breast cancer patients and are a genetically highly heterogenic group. Data on the association of somatic alterations to outcome and prognosis are however sparse. In this neoadjuvant endocrine phase II trial including postmenopausal breast cancer patients with ER+, HER2 normal breast cancer, we investigated the rate of pathogenic mutations before and after treatment as well as the association with treatment response and survival.

METHODS

Pretreatment and posttreatment tumour samples from 109 patients treated with neoadjuvant letrozole were collected and analysed with Next Generation Sequencing utilizing a panel of 12 genes (ALK, BRAF, EGFR, ERBB2, ERBB3, ESR1, KIT, KRAS, NRAS, PDGFRA, PIK3CA, and RAF1). Residual disease was assessed by a modified Miller Payne scale and the Residual Cancer Burden index. Survival data were collected prospectively.

RESULTS

Among the 109 patients, 52 had at least one pathogenic mutation in the pretreatment sample and 60 in the posttreatment sample. The most frequently mutated gene was PIK3CA, followed by EGFR and KRAS. Twelve different pathogenic PIK3CA mutations were identified, primarily in exon 20 and exon 9. An altered PIK3CA mutation profile from the pre- to the posttreatment specimen was significantly associated to improved pathological outcome. Overall and Disease-Free Survival benefits in PIK3CA mutated patients was observed.

CONCLUSION

Considerable heterogeneity was identified both among patients and between pre- and posttreatment samples. PIK3CA has the potential to be a predictive biomarker. To further assess the implications of a treatment related altered PIK3CA mutation profile, more data are needed.

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Patient-derived xenografts (PDX) are tumor-in-mouse models for cancer. PDX collections, such as the NCI PDXNet, are powerful resources for preclinical therapeutic testing. However, variations in experimental and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three prevalidated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers using independently selected standard operating procedures. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. In addition, we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-sequencing and RNA-sequencing analyses and for evaluating growth. SIGNIFICANCE: The PDXNet Consortium shows that PDX drug responses and sequencing results are reproducible across diverse experimental protocols, establishing the potential for multisite preclinical studies to translate into clinical trials.

Mouse Genome Database (MGD) 2019

The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the community model organism genetic and genome resource for the laboratory mouse. MGD is the authoritative source for biological reference data sets related to mouse genes, gene functions, phenotypes, and mouse models of human disease. MGD is the primary outlet for official gene, allele and mouse strain nomenclature based on the guidelines set by the International Committee on Standardized Nomenclature for Mice. In this report we describe significant enhancements to MGD, including two new graphical user interfaces: (i) the Multi Genome Viewer for exploring the genomes of multiple mouse strains and (ii) the Phenotype-Gene Expression matrix which was developed in collaboration with the Gene Expression Database (GXD) and allows researchers to compare gene expression and phenotype annotations for mouse genes. Other recent improvements include enhanced efficiency of our literature curation processes and the incorporation of Transcriptional Start Site (TSS) annotations from RIKEN's FANTOM 5 initiative.

Pre-clinical assessment of chimeric antigen receptor t cell therapy targeting CD19+ B cell malignancy

Background

Autologous chimeric antigen receptor (CAR) T cell therapy is a promising therapeutic strategy for treating hematologic malignancies. A spectrum of serious complications caused by CAR-T cells has caught great attention. We developed a novel CAR against CD19 namely UWC19, consisting anti-CD19 single-chain variable fragment (scFv) hinged with 4-1BB and CD3z signaling domains. In this study, preclinical assessments of UWC19 were conducted to evaluate the safety and efficacy in vitro and in vivo.

Methods

To evaluate the binding activity of UWC19 cells to CD19, we measured the saturation degree of CAR with human CD19 molecules using flow cytometry in vitro. The antitumor efficacy of UWC19 cells was determined by in vitro cytotoxicity assay against CD19 positive cells and in vivo using a xenograft mouse model. Cross tissue reactivity of UWC19 cells was examined by co-culturing with cell lines from difference human tissues. Tumorigenicity was determined by subcutaneously injecting UWC19 in immunodeficient mice. Persistence was analyzed using quantitative PCR.

Results

We showed that UWC19 CAR T cells exerted highly specific binding affinity and cytotoxicity against CD19+ cells in vitro. In vivo, UWC19 CAR T cells are able to fully control disease progression in a Raji-xenografted immunodeficient mouse model. UWC19 exerted no obvious effects on the mean body mass and graft versus host disease were observed in surviving mice. We showed that UWC19 cells specifically recognized and eliminated CD19 positive cells, whereas CD19 negative cells were much less affected. No tumorigenicity of UWC19 in immunodeficient mice was observed.

Conclusions

UWC19 treatment effectively eliminated CD19 positive tumor cells with favorable toxicity profile. The findings suggest encouraging clinical prospects for its use in patients with CD19 positive B cell malignancies. Our study presented an alternative evaluation strategy for CAR-T cell products.

PATHBIO: an international training program for precision mouse phenotyping

Design and production of genetically engineered mouse strains by individual research laboratories, research teams, large-scale consortia, and the biopharmaceutical industry have magnified the need for qualified personnel to identify, annotate, and validate (phenotype) these potentially new mouse models of human disease. The PATHBIO project has been recently established and funded by the European Union's ERASMUS+ Knowledge Alliance program to address the current shortfall in formally trained personnel. A series of teaching workshops will be given by experts on anatomy, histology, embryology, imaging, and comparative pathology to increase the availability of individuals with formal training to contribute to this important niche of Europe's biomedical research enterprise. These didactic and hands-on workshops are organized into three modules: (1) embryology, anatomy, histology, and the anatomical basis of imaging, (2) image-based phenotyping, and (3) pathology. The workshops are open to all levels of participants from recent graduates to Ph.D., M.D., and veterinary scientists. Participation is available on a competitive basis at no cost for attending. The first series of Workshop Modules was held in 2019 and these will continue for the next 2 years.

Advances in molecular classification and precision oncology in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) arises from hepatocytes through the sequential accumulation of multiple genomic and epigenomic alterations resulting from Darwinian selection. Genes from various signalling pathways such as telomere maintenance, Wnt/β-catenin, P53/cell cycle regulation, oxidative stress, epigenetic modifiers, AKT/mTOR and MAP kinase are frequently mutated in HCC. Several subclasses of HCC have been identified based on transcriptomic dysregulation and genetic alterations that are closely related to risk factors, pathological features and prognosis. Undoubtedly, integration of data obtained from both preclinical models and human studies can help to accelerate the identification of robust predictive biomarkers of response to targeted biotherapy and immunotherapy. The aim of this review is to describe the main advances in HCC in terms of molecular biology and to discuss how this knowledge could be used in clinical practice in the future.

Computational resources associating diseases with genotypes, phenotypes and exposures

The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.

Impact of mouse contamination in genomic profiling of patient-derived models and best practice for robust analysis

BACKGROUND

Patient-derived xenograft and cell line models are popular models for clinical cancer research. However, the inevitable inclusion of a mouse genome in a patient-derived model is a remaining concern in the analysis. Although multiple tools and filtering strategies have been developed to account for this, research has yet to demonstrate the exact impact of the mouse genome and the optimal use of these tools and filtering strategies in an analysis pipeline.

RESULTS

We construct a benchmark dataset of 5 liver tissues from 3 mouse strains using human whole-exome sequencing kit. Next-generation sequencing reads from mouse tissues are mappable to 49% of the human genome and 409 cancer genes. In total, 1,207,556 mouse-specific alleles are aligned to the human genome reference, including 467,232 (38.7%) alleles with high sensitivity to contamination, which are pervasive causes of false cancer mutations in public databases and are signatures for predicting global contamination. Next, we assess the performance of 8 filtering methods in terms of mouse read filtration and reduction of mouse-specific alleles. All filtering tools generally perform well, although differences in algorithm strictness and efficiency of mouse allele removal are observed. Therefore, we develop a best practice pipeline that contains the estimation of contamination level, mouse read filtration, and variant filtration.

CONCLUSIONS

The inclusion of mouse cells in patient-derived models hinders genomic analysis and should be addressed carefully. Our suggested guidelines improve the robustness and maximize the utility of genomic analysis of these models.

The design, analysis and application of mouse clinical trials in oncology drug development

Background

Mouse clinical trials (MCTs) are becoming wildly used in pre-clinical oncology drug development, but a statistical framework is yet to be developed. In this study, we establish such as framework and provide general guidelines on the design, analysis and application of MCTs.

Methods

We systematically analyzed tumor growth data from a large collection of PDX, CDX and syngeneic mouse tumor models to evaluate multiple efficacy end points, and to introduce statistical methods for modeling MCTs.

Results

We established empirical quantitative relationships between mouse number and measurement accuracy for categorical and continuous efficacy endpoints, and showed that more mice are needed to achieve given accuracy for syngeneic models than for PDXs and CDXs. There is considerable disagreement between methods on calling drug responses as objective response. We then introduced linear mixed models (LMMs) to describe MCTs as clustered longitudinal studies, which explicitly model growth and drug response heterogeneities across mouse models and among mice within a mouse model. Case studies were used to demonstrate the advantages of LMMs in discovering biomarkers and exploring drug’s mechanisms of action. We introduced additive frailty models to perform survival analysis on MCTs, which more accurately estimate hazard ratios by modeling the clustered mouse population. We performed computational simulations for LMMs and frailty models to generate statistical power curves, and showed that power is close for designs with similar total number of mice. Finally, we showed that MCTs can explain discrepant results in clinical trials.

Conclusions

Methods proposed in this study can make the design and analysis of MCTs more rational, flexible and powerful, make MCTs a better tool in oncology research and drug development.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5907-7) contains supplementary material, which is available to authorized users.

miR-147b-mediated TCA cycle dysfunction and pseudohypoxia initiate drug tolerance to EGFR inhibitors in lung adenocarcinoma

Drug-tolerance is an acute defense response prior to a fully drug-resistant state and tumor relapse, however there are few therapeutic agents targeting drug-tolerance in the clinic. Here we show that miR-147b initiates a reversible tolerant-state to the EGFR inhibitor osimertinib in non-small cell lung cancer. With miRNA-seq analysis we find that miR-147b is the most upregulated microRNA in osimertinib-tolerant and EGFR mutated lung cancer cells. Whole transcriptome analysis of single-cell derived clones reveals a link between osimertinib-tolerance and pseudohypoxia responses irrespective of oxygen levels. Further metabolomics and genetic studies demonstrate that osimertinib-tolerance is driven by miR-147b repression of VHL and succinate dehydrogenase linked to the tricarboxylic acid cycle and pseudohypoxia pathways. Finally, pretreatment with a miR-147b inhibitor delays osimertinib-associated drug tolerance in patient-derived three-dimensional (3D) structures. This link between miR-147b and tricarboxylic acid cycle may provide promising targets for preventing tumor relapse.

Factors that influence response classifications in chemotherapy treated patient-derived xenografts (PDX)

In this study, we investigated the impact of initial tumor volume, rate of tumor growth, cohort size, study duration, and data analysis method on chemotherapy treatment response classifications in patient-derived xenografts (PDXs). The analyses were conducted on cisplatin treatment response data for 70 PDX models representing ten cancer types with up to 28-day study duration and cohort sizes of 3-10 tumor-bearing mice. The results demonstrated that a 21-day dosing study using a cohort size of eight was necessary to reliably detect responsive models (i.e., tumor volume ratio of treated animals to control between 0.1 and 0.42)-independent of analysis method. A cohort of three tumor-bearing animals led to a reliable classification of models that were both highly responsive and highly nonresponsive to cisplatin (i.e., tumor volume ratio of treated animals to control animals less than 0.10). In our set of PDXs, we found that tumor growth rate in the control group impacted treatment response classification more than initial tumor volume. We repeated the study design factors using docetaxel treated PDXs with consistent results. Our results highlight the importance of defining endpoints for PDX dosing studies when deciding the size of cohorts to use in dosing studies and illustrate that response classifications for a study do not differ significantly across the commonly used analysis methods that are based on tumor volume changes in treatment versus control groups.

Model organism data evolving in support of translational medicine

Model organism databases (MODs) have been collecting and integrating biomedical research data for 30 years and were designed to meet specific needs of each model organism research community. The contributions of model organism research to understanding biological systems would be hard to overstate. Modern molecular biology methods and cost reductions in nucleotide sequencing have opened avenues for direct application of model organism research to elucidating mechanisms of human diseases. Thus, the mandate for model organism research and databases has now grown to include facilitating use of these data in translational applications. Challenges in meeting this opportunity include the distribution of research data across many databases and websites, a lack of data format standards for some data types, and sustainability of scale and cost for genomic database resources like MODs. The issues of widely distributed data and application of data standards are some of the challenges addressed by FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles. The Alliance of Genome Resources is now moving to address these challenges by bringing together expertly curated research data from fly, mouse, rat, worm, yeast, zebrafish, and the Gene Ontology consortium. Centralized multi-species data access, integration, and format standardization will lower the data utilization barrier in comparative genomics and translational applications and will provide a framework in which sustainable scale and cost can be addressed. This article presents a brief historical perspective on how the Alliance model organisms are complementary and how they have already contributed to understanding the etiology of human diseases. In addition, we discuss four challenges for using data from MODs in translational applications and how the Alliance is working to address them, in part by applying FAIR data principles. Ultimately, combined data from these animal models are more powerful than the sum of the parts.