Bringing the Genomic Revolution to Comparative Oncology: Human and Dog Cancers.

Dogs are humanity's oldest friend, the first species we domesticated 20,000-40,000 years ago. In this unequaled collaboration, dogs have inadvertently but serendipitously been molded into a potent human cancer model. Unlike many common model species, dogs are raised in the same environment as humans and present with spontaneous tumors with human-like comorbidities, immunocompetency, and heterogeneity. In breast, bladder, blood, and several pediatric cancers, in-depth profiling of dog and human tumors has established the benefits of the dog model. In addition to this clinical and molecular similarity, veterinary studies indicate that domestic dogs have relatively high tumor incidence rates. As a result, there are a plethora of data for analysis, the statistical power of which is bolstered by substantial breed-specific variability. As such, dog tumors provide a unique opportunity to interrogate the molecular factors underpinning cancer and facilitate the modeling of new therapeutic targets. This review discusses the emerging field of comparative oncology, how it complements human and rodent cancer studies, and where challenges remain, given the rapid proliferation of genomic resources. Increasingly, it appears that human's best friend is becoming an irreplaceable component of oncology research.

The model student: GPT-4 performance on graduate biomedical science exams.

The GPT-4 large language model (LLM) and ChatGPT chatbot have emerged as accessible and capable tools for generating English-language text in a variety of formats. GPT-4 has previously performed well when applied to questions from multiple standardized examinations. However, further evaluation of trustworthiness and accuracy of GPT-4 responses across various knowledge domains is essential before its use as a reference resource. Here, we assess GPT-4 performance on nine graduate-level examinations in the biomedical sciences (seven blinded), finding that GPT-4 scores exceed the student average in seven of nine cases and exceed all student scores for four exams. GPT-4 performed very well on fill-in-the-blank, short-answer, and essay questions, and correctly answered several questions on figures sourced from published manuscripts. Conversely, GPT-4 performed poorly on questions with figures containing simulated data and those requiring a hand-drawn answer. Two GPT-4 answer-sets were flagged as plagiarism based on answer similarity and some model responses included detailed hallucinations. In addition to assessing GPT-4 performance, we discuss patterns and limitations in GPT-4 capabilities with the goal of informing design of future academic examinations in the chatbot era.

Lipidomic changes in a novel sepsis outcome-based analysis reveals potent pro-inflammatory and pro-resolving signaling lipids.

The purpose of this study was to investigate changes in the lipidome of patients with sepsis to identify signaling lipids associated with poor outcomes that could be linked to future therapies. Adult patients with sepsis were enrolled within 24h of sepsis recognition. Patients meeting Sepsis-3 criteria were enrolled from the emergency department or intensive care unit and blood samples were obtained. Clinical data were collected and outcomes of rapid recovery, chronic critical illness (CCI), or early death were adjudicated by clinicians. Lipidomic analysis was performed on two platforms, the Sciex™ 5500 device to perform a lipidomic screen of 1450 lipid species and a targeted signaling lipid panel using liquid-chromatography tandem mass spectrometry. For the lipidomic screen, there were 274 patients with sepsis: 192 with rapid recovery, 47 with CCI, and 35 with early deaths. CCI and early death patients were grouped together for analysis. Fatty acid (FA) 12:0 was decreased in CCI/early death, whereas FA 17:0 and 20:1 were elevated in CCI/early death, compared to rapid recovery patients. For the signaling lipid panel analysis, there were 262 patients with sepsis: 189 with rapid recovery, 45 with CCI, and 28 with early death. Pro-inflammatory signaling lipids from ω-6 poly-unsaturated fatty acids (PUFAs), including 15-hydroxyeicosatetraenoic (HETE), 12-HETE, and 11-HETE (oxidation products of arachidonic acid [AA]) were elevated in CCI/early death patients compared to rapid recovery. The pro-resolving lipid mediator from ω-3 PUFAs, 14(S)-hydroxy docosahexaenoic acid (14S-HDHA), was also elevated in CCI/early death compared to rapid recovery. Signaling lipids of the AA pathway were elevated in poor-outcome patients with sepsis and may serve as targets for future therapies.

Many direct-to-consumer canine genetic tests can identify the breed of purebred dogs.

OBJECTIVE: To compare pedigree documentation and genetic test results to evaluate whether user-provided photographs influence the breed ancestry predictions of direct-to-consumer (DTC) genetic tests for dogs. ANIMALS: 12 registered purebred pet dogs representing 12 different breeds. METHODS: Each dog owner submitted 6 buccal swabs, 1 to each of 6 DTC genetic testing companies. Experimenters registered each sample per manufacturer instructions. For half of the dogs, the registration included a photograph of the DNA donor. For the other half of the dogs, photographs were swapped between dogs. DNA analysis and breed ancestry prediction were conducted by each company. The effect of condition (ie, matching vs shuffled photograph) was evaluated for each company's breed predictions. As a positive control, a convolutional neural network was also used to predict breed based solely on the photograph. RESULTS: Results from 5 of the 6 tests always included the dog's registered breed. One test and the convolutional neural network were unlikely to identify the registered breed and frequently returned results that were more similar to the photograph than the DNA. Additionally, differences in the predictions made across all tests underscored the challenge of identifying breed ancestry, even in purebred dogs. CLINICAL RELEVANCE: Veterinarians are likely to encounter patients who have conducted DTC genetic testing and may be asked to explain the results of genetic tests they did not order. This systematic comparison of commercially available tests provides context for interpreting results from consumer-grade DTC genetic testing kits.

Peptide-based capture-and-release purification of extracellular vesicles and statistical algorithm enabled quality assessment.

Circulating extracellular vesicles (EVs) have gained significant attention for discovering tumor biomarkers. However, isolating EVs with well-defined homogeneous populations from complex biological samples is challenging. Different isolation methods have been found to derive different EV populations carrying different molecular contents, which confounds current investigations and hinders subsequent clinical translation. Therefore, standardizing and building a rigorous assessment of isolated EV quality associated with downstream molecular analysis is essential. To address this need, we introduce a statistical algorithm (ExoQuality Index, EQI) by integrating multiple EV characterizations (size, particle concentration, zeta potential, total protein, and RNA), enabling direct EV quality assessment and comparisons between different isolation methods. We also introduced a novel capture-release isolation approach using a pH-responsive peptide conjugated with NanoPom magnetic beads (ExCy) for simple, fast, and homogeneous EV isolation from various biological fluids. Bioinformatic analysis of next-generation sequencing (NGS) data of EV total RNAs from pancreatic cancer patient plasma samples using our novel EV isolation approach and quality index strategy illuminates how this approach improves the identification of tumor associated molecular markers. Results showed higher human mRNA coverage compared to existing isolation approaches in terms of both pancreatic cancer pathways and EV cellular component pathways using gProfiler pathway analysis. This study provides a valuable resource for researchers, establishing a workflow to prepare and analyze EV samples carefully and contributing to the advancement of reliable and rigorous EV quality assessment and clinical translation.

Equitable machine learning counteracts ancestral bias in precision medicine, improving outcomes for all.

Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease [1-8]. Therapeutics and outcomes remain hidden because we lack insights that we could gain from analyzing ancestry-unbiased genomic data. To address this significant gap, we present PhyloFrame, the first-ever machine learning method for equitable genomic precision medicine. PhyloFrame corrects for ancestral bias by integrating big data tissue-specific functional interaction networks, global population variation data, and disease-relevant transcriptomic data. Application of PhyloFrame to breast, thyroid, and uterine cancers shows marked improvements in predictive power across all ancestries, less model overfitting, and a higher likelihood of identifying known cancer-related genes. The ability to provide accurate predictions for underrepresented groups, in particular, is substantially increased. These results demonstrate how AI can mitigate ancestral bias in training data and contribute to equitable representation in medical research.

DHCR7 Expression Predicts Poor Outcomes and Mortality From Sepsis.

This is a study of lipid metabolic gene expression patterns to discover precision medicine for sepsis. OBJECTIVES: Sepsis patients experience poor outcomes including chronic critical illness (CCI) or early death (within 14 d). We investigated lipid metabolic gene expression differences by outcome to discover therapeutic targets. DESIGN SETTING AND PARTICITPANTS: Secondary analysis of samples from prospectively enrolled sepsis patients (first 24 hr) and a zebrafish endotoxemia model for drug discovery. Patients were enrolled from the emergency department or ICU at an urban teaching hospital. Enrollment samples from sepsis patients were analyzed. Clinical data and cholesterol levels were recorded. Leukocytes were processed for RNA sequencing and reverse transcriptase polymerase chain reaction. A lipopolysaccharide zebrafish endotoxemia model was used for confirmation of human transcriptomic findings and drug discovery. MAIN OUTCOMES AND MEASURES: The derivation cohort included 96 patients and controls (12 early death, 13 CCI, 51 rapid recovery, and 20 controls) and the validation cohort had 52 patients (6 early death, 8 CCI, and 38 rapid recovery). RESULTS: The cholesterol metabolism gene 7-dehydrocholesterol reductase (DHCR7) was significantly up-regulated in both derivation and validation cohorts in poor outcome sepsis compared with rapid recovery patients and in 90-day nonsurvivors (validation only) and validated using RT-qPCR analysis. Our zebrafish sepsis model showed up-regulation of dhcr7 and several of the same lipid genes up-regulated in poor outcome human sepsis (dhcr24, sqlea, cyp51, msmo1, and ldlra) compared with controls. We then tested six lipid-based drugs in the zebrafish endotoxemia model. Of these, only the Dhcr7 inhibitor AY9944 completely rescued zebrafish from lipopolysaccharide death in a model with 100% lethality. CONCLUSIONS: DHCR7, an important cholesterol metabolism gene, was up-regulated in poor outcome sepsis patients warranting external validation. This pathway may serve as a potential therapeutic target to improve sepsis outcomes.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery.

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

DHCR7 Expression Predicts Poor Outcomes and Mortality from Sepsis.

Objective: Sepsis patients experience poor outcomes including chronic critical illness (CCI) or early death (within 14 days). We investigated lipid metabolic gene expression differences by outcome to discover therapeutic targets. Design: Secondary analysis of samples from prospectively enrolled sepsis patients and a zebrafish sepsis model for drug discovery. Setting: Emergency department or ICU at an urban teaching hospital. Patients: Sepsis patients presenting within 24 hours. Methods: Enrollment samples from sepsis patients were analyzed. Clinical data and cholesterol levels were recorded. Leukocytes were processed for RNA sequencing (RNA-seq) and reverse transcriptase polymerase chain reaction (RT-qPCR). A lipopolysaccharide (LPS) zebrafish sepsis model was used for confirmation of human transcriptomic findings and drug discovery. Measurements and Main Results: There were 96 samples in the derivation (76 sepsis, 20 controls) and 52 in the validation cohort (sepsis only). The cholesterol metabolism gene 7-Dehydrocholesterol Reductase ( DHCR7) was significantly upregulated in both derivation and validation cohorts in poor outcome sepsis compared to rapid recovery patients and in 90-day non-survivors (validation only) and validated using RT-qPCR analysis. Our zebrafish sepsis model showed upregulation of dhcr7 and several of the same lipid genes upregulated in poor outcome human sepsis (dhcr24, sqlea, cyp51, msmo1 , ldlra) compared to controls. We then tested six lipid-based drugs in the zebrafish sepsis model. Of these, only the Dhcr7 inhibitor AY9944 completely rescued zebrafish from LPS death in a model with 100% lethality. Conclusions: DHCR7, an important cholesterol metabolism gene, was upregulated in poor outcome sepsis patients warranting external validation. This pathway may serve as a potential therapeutic target to improve sepsis outcomes.

EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION.

ABSTRACT: Objective: The aim of this study was to characterize early urinary gene expression differences between patients with sepsis and patients with sterile inflammation and summarize in terms of a reproducible sepsis probability score. Design: This was a prospective observational cohort study. Setting: The study was conducted in a quaternary care academic hospital. Patients: One hundred eighty-six sepsis patients and 78 systemic inflammatory response syndrome (SIRS) patients enrolled between January 2015 and February 2018. Interventions: Whole-genome transcriptomic analysis of RNA was extracted from urine obtained from sepsis patients within 12 hours of sepsis onset and from patients with surgery-acquired SIRS within 4 hours after major inpatient surgery. Measurements and Main Results: We identified 422 of 23,956 genes (1.7%) that were differentially expressed between sepsis and SIRS patients. Differentially expressed probes were provided to a collection of machine learning feature selection models to identify focused probe sets that differentiate between sepsis and SIRS. These probe sets were combined to find an optimal probe set (UrSepsisModel) and calculate a urinary sepsis score (UrSepsisScore), which is the geometric mean of downregulated genes subtracted from the geometric mean of upregulated genes. This approach summarizes the expression values of all decisive genes as a single sepsis score. The UrSepsisModel and UrSepsisScore achieved area under the receiver operating characteristic curves 0.91 (95% confidence interval, 0.86-0.96) and 0.80 (95% confidence interval, 0.70-0.88) on the validation cohort, respectively. Functional analyses of probes associated with sepsis demonstrated metabolic dysregulation manifest as reduced oxidative phosphorylation, decreased amino acid metabolism, and decreased oxidation of lipids and fatty acids. Conclusions: Whole-genome transcriptomic profiling of urinary cells revealed focused probe panels that can function as an early diagnostic tool for differentiating sepsis from sterile SIRS. Functional analysis of differentially expressed genes demonstrated a distinct metabolic dysregulation signature in sepsis.

Drivers of genomic loss of heterozygosity in leiomyosarcoma are distinct from carcinomas.

Leiomyosarcoma (LMS) is a rare, aggressive, mesenchymal tumor. Subsets of LMS have been identified to harbor genomic alterations associated with homologous recombination deficiency (HRD); particularly alterations in BRCA2. Whereas genomic loss of heterozygosity (gLOH) has been used as a surrogate marker of HRD in other solid tumors, the prognostic or clinical value of gLOH in LMS (gLOH-LMS) remains poorly defined. We explore the genomic drivers associated with gLOH-LMS and their clinical import. Although the distribution of gLOH-LMS scores are similar to that of carcinomas, outside of BRCA2, there was no overlap with previously published gLOH-associated genes from studies in carcinomas. We note that early stage tumors with elevated gLOH demonstrated a longer disease-free interval following resection in LMS patients. Taken together, and despite similarities to carcinomas in gLOH distribution and clinical import, gLOH-LMS are driven by different genomic signals. Additional studies will be required to isolate and confirm the unique differences in biological factors driving these differences.

Modeling molecular development of breast cancer in canine mammary tumors.

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.

PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.

Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely available for tumor specimens, the datasets upon which computational learning methods can be trained vary in coverage from sample to sample and from data type to data type. Methods that can 'connect the dots' to leverage more of the information provided by these studies could offer major advantages for maximizing predictive potential. We introduce a multi-view machinelearning strategy called PLATYPUS that builds 'views' from multiple data sources that are all used as features for predicting patient outcomes. We show that a learning strategy that finds agreement across the views on unlabeled data increases the performance of the learning methods over any single view. We illustrate the power of the approach by deriving signatures for drug sensitivity in a large cancer cell line database. Code and additional information are available from the PLATYPUS website https://sysbiowiki.soe.ucsc.edu/platypus.

Integrative Molecular Characterization of Malignant Pleural Mesothelioma.

Malignant pleural mesothelioma (MPM) is a highly lethal cancer of the lining of the chest cavity. To expand our understanding of MPM, we conducted a comprehensive integrated genomic study, including the most detailed analysis of BAP1 alterations to date. We identified histology-independent molecular prognostic subsets, and defined a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity. We also report strong expression of the immune-checkpoint gene VISTA in epithelioid MPM, strikingly higher than in other solid cancers, with implications for the immune response to MPM and for its immunotherapy. Our findings highlight new avenues for further investigation of MPM biology and novel therapeutic options. SIGNIFICANCE: Through a comprehensive integrated genomic study of 74 MPMs, we provide a deeper understanding of histology-independent determinants of aggressive behavior, define a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity, and discovered strong expression of the immune-checkpoint gene VISTA in epithelioid MPM.See related commentary by Aggarwal and Albelda, p. 1508.This article is highlighted in the In This Issue feature, p. 1494.

Genomic Evidence of Widespread Admixture from Polar Bears into Brown Bears during the Last Ice Age.

Recent genomic analyses have provided substantial evidence for past periods of gene flow from polar bears (Ursus maritimus) into Alaskan brown bears (Ursus arctos), with some analyses suggesting a link between climate change and genomic introgression. However, because it has mainly been possible to sample bears from the present day, the timing, frequency, and evolutionary significance of this admixture remains unknown. Here, we analyze genomic DNA from three additional and geographically distinct brown bear populations, including two that lived temporally close to the peak of the last ice age. We find evidence of admixture in all three populations, suggesting that admixture between these species has been common in their recent evolutionary history. In addition, analyses of ten fossil bears from the now-extinct Irish population indicate that admixture peaked during the last ice age, whereas brown bear and polar bear ranges overlapped. Following this peak, the proportion of polar bear ancestry in Irish brown bears declined rapidly until their extinction. Our results support a model in which ice age climate change created geographically widespread conditions conducive to admixture between polar bears and brown bears, as is again occurring today. We postulate that this model will be informative for many admixing species pairs impacted by climate change. Our results highlight the power of paleogenomics to reveal patterns of evolutionary change that are otherwise masked in contemporary data.

Prophetic Granger Causality to infer gene regulatory networks.

We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.

TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal.

Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex "omics" data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111-4. ©2017 AACR.

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines.

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

Revealing cancer subtypes with higher-order correlations applied to imaging and omics data.

BACKGROUND: Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings. METHODS: Here, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes. RESULTS: In an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification. CONCLUSIONS: Subtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.