Cancer Prevalence Across Vertebrates.

Cancer is pervasive across multicellular species, but what explains differences in cancer prevalence across species? Using 16,049 necropsy records for 292 species spanning three clades (amphibians, sauropsids and mammals) we found that neoplasia and malignancy prevalence increases with adult weight (contrary to Peto's Paradox) and somatic mutation rate, but decreases with gestation time. Evolution of cancer susceptibility appears to have undergone sudden shifts followed by stabilizing selection. Outliers for neoplasia prevalence include the common porpoise (<1.3%), the Rodrigues fruit bat (<1.6%) the black-footed penguin (<0.4%), ferrets (63%) and opossums (35%). Discovering why some species have particularly high or low levels of cancer may lead to a better understanding of cancer syndromes and novel strategies for the management and prevention of cancer.

Cancer Prevalence Across Vertebrates.

Cancer is pervasive across multicellular species. Are there any patterns that can explain differences in cancer prevalence across species? Using 16,049 necropsy records for 292 species spanning three clades (amphibians, sauropsids and mammals) we found that neoplasia and malignancy prevalence increases with adult weight and decreases with gestation time, contrary to Peto’s Paradox. Evolution of cancer susceptibility appears to have undergone sudden shifts followed by stabilizing selection. Outliers for neoplasia prevalence include the common porpoise (<1.3%), the Rodrigues fruit bat (<1.6%) the black-footed penguin (<0.4%), ferrets (63%) and opossums (35%). Discovering why some species have particularly high or low levels of cancer may lead to a better understanding of cancer syndromes and novel strategies for the management and prevention of cancer.

Veterinary informatics: forging the future between veterinary medicine, human medicine, and One Health initiatives-a joint paper by the Association for Veterinary Informatics (AVI) and the CTSA One Health Alliance (COHA).

OBJECTIVES: This manuscript reviews the current state of veterinary medical electronic health records and the ability to aggregate and analyze large datasets from multiple organizations and clinics. We also review analytical techniques as well as research efforts into veterinary informatics with a focus on applications relevant to human and animal medicine. Our goal is to provide references and context for these resources so that researchers can identify resources of interest and translational opportunities to advance the field. METHODS AND RESULTS: This review covers various methods of veterinary informatics including natural language processing and machine learning techniques in brief and various ongoing and future projects. After detailing techniques and sources of data, we describe some of the challenges and opportunities within veterinary informatics as well as providing reviews of common One Health techniques and specific applications that affect both humans and animals. DISCUSSION: Current limitations in the field of veterinary informatics include limited sources of training data for developing machine learning and artificial intelligence algorithms, siloed data between academic institutions, corporate institutions, and many small private practices, and inconsistent data formats that make many integration problems difficult. Despite those limitations, there have been significant advancements in the field in the last few years and continued development of a few, key, large data resources that are available for interested clinicians and researchers. These real-world use cases and applications show current and significant future potential as veterinary informatics grows in importance. Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives.

FasTag: Automatic text classification of unstructured medical narratives.

Unstructured clinical narratives are continuously being recorded as part of delivery of care in electronic health records, and dedicated tagging staff spend considerable effort manually assigning clinical codes for billing purposes. Despite these efforts, however, label availability and accuracy are both suboptimal. In this retrospective study, we aimed to automate the assignment of top-level International Classification of Diseases version 9 (ICD-9) codes to clinical records from human and veterinary data stores using minimal manual labor and feature curation. Automating top-level annotations could in turn enable rapid cohort identification, especially in a veterinary setting. To this end, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We investigated the accuracy of both separate-domain and combined-domain models and probed model portability. We established relevant baseline classification performances by training Decision Trees (DT) and Random Forests (RF). We also investigated whether transforming the data using MetaMap Lite, a clinical natural language processing tool, affected classification performance. We showed that the LSTM-RNNs accurately classify veterinary and human text narratives into top-level categories with an average weighted macro F1 score of 0.74 and 0.68 respectively. In the "neoplasia" category, the model trained on veterinary data had a high validation accuracy in veterinary data and moderate accuracy in human data, with F1 scores of 0.91 and 0.70 respectively. Our LSTM method scored slightly higher than that of the DT and RF models. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort identification for comparative oncology studies. Digitization of human and veterinary health information will continue to be a reality, particularly in the form of unstructured narratives. Our approach is a step forward for these two domains to learn from and inform one another.

VetTag: improving automated veterinary diagnosis coding via large-scale language modeling.

Unlike human medical records, most of the veterinary records are free text without standard diagnosis coding. The lack of systematic coding is a major barrier to the growing interest in leveraging veterinary records for public health and translational research. Recent machine learning effort is limited to predicting 42 top-level diagnosis categories from veterinary notes. Here we develop a large-scale algorithm to automatically predict all 4577 standard veterinary diagnosis codes from free text. We train our algorithm on a curated dataset of over 100 K expert labeled veterinary notes and over one million unlabeled notes. Our algorithm is based on the adapted Transformer architecture and we demonstrate that large-scale language modeling on the unlabeled notes via pretraining and as an auxiliary objective during supervised learning greatly improves performance. We systematically evaluate the performance of the model and several baselines in challenging settings where algorithms trained on one hospital are evaluated in a different hospital with substantial domain shift. In addition, we show that hierarchical training can address severe data imbalances for fine-grained diagnosis with a few training cases, and we provide interpretation for what is learned by the deep network. Our algorithm addresses an important challenge in veterinary medicine, and our model and experiments add insights into the power of unsupervised learning for clinical natural language processing.

A Bird's-Eye View of Regulatory, Animal Care, and Training Considerations Regarding Avian Flight Research.

A thorough understanding of how animals fly is a central goal of many scientific disciplines. Birds are a commonly used model organism for flight research. The success of this model requires studying healthy and naturally flying birds in a laboratory setting. This use of a nontraditional laboratory animal species presents unique challenges to animal care staff and researchers alike. Here we review regulatory, animal care, and training considerations associated with avian flight research.

The Functional Proximal Proteome of Oncogenic Ras Includes mTORC2.

Proximity-dependent biotin labeling (BioID) may identify new targets for cancers driven by difficult-to-drug oncogenes such as Ras. Therefore, BioID was used with wild-type (WT) and oncogenic mutant (MT) H-, K-, and N-Ras, identifying known interactors, including Raf and PI3K, as well as a common set of 130 novel proteins proximal to all Ras isoforms. A CRISPR screen of these proteins for Ras dependence identified mTOR, which was also found proximal to MT Ras in human tumors. Oncogenic Ras directly bound two mTOR complex 2 (mTORC2) components, mTOR and MAPKAP1, to promote mTORC2 kinase activity at the plasma membrane. mTORC2 enabled the Ras pro-proliferative cell cycle transcriptional program, and perturbing the Ras-mTORC2 interaction impaired Ras-dependent neoplasia in vivo. Combining proximity-dependent proteomics with CRISPR screening identified a new set of functional Ras-associated proteins, defined mTORC2 as a new direct Ras effector, and offers a strategy for finding new proteins that cooperate with dominant oncogenes.

Update on Cancer Treatment in Exotics.

Treatment options for animals with cancer are rapidly expanding, including in exotic animal medicine. Limited information is available about treatment effects in exotic pet species beyond individual case reports. Most cancer treatment protocols in exotic animals are extrapolated from those described in humans, dogs, and cats. This review provides an update on cancer treatment in exotic animal species. The Exotic Species Cancer Research Alliance accumulates clinical cases in a central location with standardized clinical information, with resources to help clinicians find and enter their cases for the collective good of exotic clinicians and their patients.

Clinical features, treatment, and outcomes of cutaneous and oral squamous cell carcinoma in avian species.

OBJECTIVE To determine the clinical features, treatment, and outcomes of treatment for oral and cutaneous squamous cell carcinoma (SCC) in avian species. DESIGN Retrospective case series with nested cohort study. ANIMALS 87 client-owned birds of various species with histologically confirmed SCC of the skin or oral cavity. PROCEDURES Clinicians entered case information through an online survey tool. Data were collected regarding patient signalment, concurrent conditions, treatments, adverse effects, and clinical outcomes. Relationships were examined between complete excision and partial or complete response. Survival analysis was performed to compare outcomes among groupings of therapeutic approaches. RESULTS Only 7 of 64 (11%) birds for which full outcome data were available had complete remission of SCC; 53 (83%) had progressive disease, were euthanized, or died of the disease. The unadjusted OR for partial or complete response following complete tumor excision (vs other treatment approaches) was 6.9 (95% confidence interval [CI], 1.8 to 25.8). Risk of death was 62% lower (hazard ratio, 0.38; 95% CI, 0.19 to 0.77) for birds that underwent complete excision versus conservative treatment. Median survival time from initial evaluation for birds receiving complete excision was 628 days (95% CI, 210 to 1,008 days), compared with 171 days (95% CI, 89 to 286 days) for birds receiving monitoring with or without conservative treatment. Birds receiving any other additional treatment had a median survival time of 357 days (95% CI, 143 to 562 days). CONCLUSIONS AND CLINICAL RELEVANCE For birds with SCC, complete excision was the only treatment approach significantly associated with complete or partial response and increased survival time.

DeepTag: inferring diagnoses from veterinary clinical notes.

Large scale veterinary clinical records can become a powerful resource for patient care and research. However, clinicians lack the time and resource to annotate patient records with standard medical diagnostic codes and most veterinary visits are captured in free-text notes. The lack of standard coding makes it challenging to use the clinical data to improve patient care. It is also a major impediment to cross-species translational research, which relies on the ability to accurately identify patient cohorts with specific diagnostic criteria in humans and animals. In order to reduce the coding burden for veterinary clinical practice and aid translational research, we have developed a deep learning algorithm, DeepTag, which automatically infers diagnostic codes from veterinary free-text notes. DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts. DeepTag extends multitask LSTM with an improved hierarchical objective that captures the semantic structures between diseases. To foster human-machine collaboration, DeepTag also learns to abstain in examples when it is uncertain and defers them to human experts, resulting in improved performance. DeepTag accurately infers disease codes from free-text even in challenging cross-hospital settings where the text comes from different clinical settings than the ones used for training. It enables automated disease annotation across a broad range of clinical diagnoses with minimal preprocessing. The technical framework in this work can be applied in other medical domains that currently lack medical coding resources.

Genetic Mapping and Biochemical Basis of Yellow Feather Pigmentation in Budgerigars.

Parrot feathers contain red, orange, and yellow polyene pigments called psittacofulvins. Budgerigars are parrots that have been extensively bred for plumage traits during the last century, but the underlying genes are unknown. Here we use genome-wide association mapping and gene-expression analysis to map the Mendelian blue locus, which abolishes yellow pigmentation in the budgerigar. We find that the blue trait maps to a single amino acid substitution (R644W) in an uncharacterized polyketide synthase (MuPKS). When we expressed MuPKS heterologously in yeast, yellow pigments accumulated. Mass spectrometry confirmed that these yellow pigments match those found in feathers. The R644W substitution abolished MuPKS activity. Furthermore, gene-expression data from feathers of different bird species suggest that parrots acquired their colors through regulatory changes that drive high expression of MuPKS in feather epithelia. Our data also help formulate biochemical models that may explain natural color variation in parrots. VIDEO ABSTRACT.

IQGAP1 is an oncogenic target in canine melanoma.

Canine oral mucosal melanoma is an aggressive malignant neoplasm and is characterized by local infiltration and a high metastatic potential. The disease progression is similar to that of human oral melanomas. Whereas human cutaneous melanoma is primarily driven by activating mutations in Braf (60%) or Nras (20%), human mucosal melanoma harbors these mutations much less frequently. This makes therapeutic targeting and research modeling of the oral form potentially different from that of the cutaneous form in humans. Similarly, research has found only rare Nras mutations and no activating Braf mutations in canine oral melanomas, but they are still reliant on MAPK signaling. IQGAP1 is a signaling scaffold that regulates oncogenic ERK1/2 MAPK signaling in human Ras- and Raf- driven cancers, including melanomas. To investigate whether IQGAP1 is a potential target in canine melanoma, we examined the expression and localization of IQGAP1 in primary canine melanomas and canine oral melanoma cell lines obtained from the University of California-Davis. Using CRISPR/Cas9 knockout of IQGAP1, we examined effects on downstream ERK1/2 pathway activity and assayed proliferation of cell lines when treated with a peptide that blocks the interaction between IQGAP1 and ERK1/2. We observed that canine IQGAP1 is expressed and localizes to a similar extent in both human and canine melanoma by qPCR, Western blot, and immunofluorescence. Deletion of IQGAP1 reduces MAPK pathway activation in cell lines, similar to effects seen in human BrafV600E cell lines. Additionally, we demonstrated reduced proliferation when these cells are treated with a blocking peptide in vitro.

Genomic analysis of mycosis fungoides and Sézary syndrome identifies recurrent alterations in TNFR2.

Mycosis fungoides and Sézary syndrome comprise the majority of cutaneous T cell lymphomas (CTCLs), disorders notable for their clinical heterogeneity that can present in skin or peripheral blood. Effective treatment options for CTCL are limited, and the genetic basis of these T cell lymphomas remains incompletely characterized. Here we report recurrent point mutations and genomic gains of TNFRSF1B, encoding the tumor necrosis factor receptor TNFR2, in 18% of patients with mycosis fungoides and Sézary syndrome. Expression of the recurrent TNFR2 Thr377Ile mutant in T cells leads to enhanced non-canonical NF-κB signaling that is sensitive to the proteasome inhibitor bortezomib. Using an integrative genomic approach, we additionally discovered a recurrent CTLA4-CD28 fusion, as well as mutations in downstream signaling mediators of these receptors.

Burkholderia pseudomallei isolates in 2 pet iguanas, California, USA.

Burkholderia pseudomallei, the causative agent of melioidosis, was isolated from abscesses of 2 pet green iguanas in California, USA. The international trade in iguanas may contribute to importation of this pathogen into countries where it is not endemic and put persons exposed to these animals at risk for infection.

Enhancer-targeted genome editing selectively blocks innate resistance to oncokinase inhibition.

Thousands of putative enhancers are characterized in the human genome, yet few have been shown to have a functional role in cancer progression. Inhibiting oncokinases, such as EGFR, ALK, ERBB2, and BRAF, is a mainstay of current cancer therapy but is hindered by innate drug resistance mediated by up-regulation of the HGF receptor, MET. The mechanisms mediating such genomic responses to targeted therapy are unknown. Here, we identify lineage-specific enhancers at the MET locus for multiple common tumor types, including a melanoma lineage-specific enhancer 63 kb downstream from the MET TSS. This enhancer displays inducible chromatin looping with the MET promoter to up-regulate MET expression upon BRAF inhibition. Epigenomic analysis demonstrated that the melanocyte-specific transcription factor, MITF, mediates this enhancer function. Targeted genomic deletion (<7 bp) of the MITF motif within the MET enhancer suppressed inducible chromatin looping and innate drug resistance, while maintaining MITF-dependent, inhibitor-induced melanoma cell differentiation. Epigenomic analysis can thus guide functional disruption of regulatory DNA to decouple pro- and anti-oncogenic functions of a dominant transcription factor and block innate resistance to oncokinase therapy.

IQGAP1 scaffold-kinase interaction blockade selectively targets RAS-MAP kinase-driven tumors.

Upregulation of the ERK1 and ERK2 (ERK1/2) MAP kinase (MAPK) cascade occurs in >30% of cancers, often through mutational activation of receptor tyrosine kinases or other upstream genes, including KRAS and BRAF. Efforts to target endogenous MAPKs are challenged by the fact that these kinases are required for viability in mammals. Additionally, the effectiveness of new inhibitors of mutant BRAF has been diminished by acquired tumor resistance through selection for BRAF-independent mechanisms of ERK1/2 induction. Furthermore, recently identified ERK1/2-inducing mutations in MEK1 and MEK2 (MEK1/2) MAPK genes in melanoma confer resistance to emerging therapeutic MEK inhibitors, underscoring the challenges facing direct kinase inhibition in cancer. MAPK scaffolds, such as IQ motif-containing GTPase activating protein 1 (IQGAP1), assemble pathway kinases to affect signal transmission, and disrupting scaffold function therefore offers an orthogonal approach to MAPK cascade inhibition. Consistent with this, we found a requirement for IQGAP1 in RAS-driven tumorigenesis in mouse and human tissue. In addition, the ERK1/2-binding IQGAP1 WW domain peptide disrupted IQGAP1-ERK1/2 interactions, inhibited RAS- and RAF-driven tumorigenesis, bypassed acquired resistance to the BRAF inhibitor vemurafenib (PLX-4032) and acted as a systemically deliverable therapeutic to significantly increase the lifespan of tumor-bearing mice. Scaffold-kinase interaction blockade acts by a mechanism distinct from direct kinase inhibition and may be a strategy to target overactive oncogenic kinase cascades in cancer.

Control of somatic tissue differentiation by the long non-coding RNA TINCR.

Several of the thousands of human long non-coding RNAs (lncRNAs) have been functionally characterized; however, potential roles for lncRNAs in somatic tissue differentiation remain poorly understood. Here we show that a 3.7-kilobase lncRNA, terminal differentiation-induced ncRNA (TINCR), controls human epidermal differentiation by a post-transcriptional mechanism. TINCR is required for high messenger RNA abundance of key differentiation genes, many of which are mutated in human skin diseases, including FLG, LOR, ALOXE3, ALOX12B, ABCA12, CASP14 and ELOVL3. TINCR-deficient epidermis lacked terminal differentiation ultrastructure, including keratohyalin granules and intact lamellar bodies. Genome-scale RNA interactome analysis revealed that TINCR interacts with a range of differentiation mRNAs. TINCR-mRNA interaction occurs through a 25-nucleotide 'TINCR box' motif that is strongly enriched in interacting mRNAs and required for TINCR binding. A high-throughput screen to analyse TINCR binding capacity to approximately 9,400 human recombinant proteins revealed direct binding of TINCR RNA to the staufen1 (STAU1) protein. STAU1-deficient tissue recapitulated the impaired differentiation seen with TINCR depletion. Loss of UPF1 and UPF2, both of which are required for STAU1-mediated RNA decay, however, did not have differentiation effects. Instead, the TINCR-STAU1 complex seems to mediate stabilization of differentiation mRNAs, such as KRT80. These data identify TINCR as a key lncRNA required for somatic tissue differentiation, which occurs through lncRNA binding to differentiation mRNAs to ensure their expression.

Calculation of body surface area via computed tomography-guided modeling in domestic rabbits (Oryctolagus cuniculus).

OBJECTIVE: To optimize the use of CT-guided modeling for the calculation of body surface area (BSA) in domestic rabbits (Oryctolagus cuniculus). Animals-12 domestic rabbits. PROCEDURES: Adult rabbits (body weight, 1 to > 4 kg) that were client-owned animals undergoing CT for disease diagnosis or deceased laboratory animals donated from other research projects were scanned with a CT scanner. Images were transferred to a radiation therapy planning software program. Image slices were captured as contiguous slices at 100 kVp and 100 mA and processed to 0.1-cm-thick sections. The length of each contoured slice was summed to calculate a final BSA measurement. Nonlinear regression analysis was then used to derive an equation for the calculation of BSA in rabbits. RESULTS: The constant calculated by use of this method was 9.9 (range, 9.59 to 10). The R(2) for the goodness of fit was 0.9332. The equation that best described BSA as a function of body weight for domestic rabbits with this method was as follows: BSA = (9.9 × [body weight {in grams}](2/3))/10,000. CONCLUSIONS AND CLINICAL RELEVANCE: The BSA calculated via the CT-guided method yielded results similar to those obtained with equations for other similarly sized mammals and verified the use of such equations for rabbits. Additionally, this technique can be used for species that lack equations for the accurate calculation of BSA.

Suppression of progenitor differentiation requires the long noncoding RNA ANCR.

Long noncoding RNAs (lncRNAs) regulate diverse processes, yet a potential role for lncRNAs in maintaining the undifferentiated state in somatic tissue progenitor cells remains uncharacterized. We used transcriptome sequencing and tiling arrays to compare lncRNA expression in epidermal progenitor populations versus differentiating cells. We identified ANCR (anti-differentiation ncRNA) as an 855-base-pair lncRNA down-regulated during differentiation. Depleting ANCR in progenitor-containing populations, without any other stimuli, led to rapid differentiation gene induction. In epidermis, ANCR loss abolished the normal exclusion of differentiation from the progenitor-containing compartment. The ANCR lncRNA is thus required to enforce the undifferentiated cell state within epidermis.