Accuracy of a Cancer Registry Versus Clinical Care Team Chart Abstraction in Identifying Cancer Recurrence.

Objective: To evaluate the completeness and reliability of recurrence data from an institutional cancer registry for patients with head and neck cancer. Patients and Methods: Recurrence information was collected by radiation oncology and otolaryngology researchers. This was compared with the institutional cancer registry for continuous patients treated with radiation therapy for head and neck cancer at a tertiary cancer center. The sensitivity and specificity of institutional cancer registry data was calculated using manual review as the gold standard. False negative recurrences were compared to true positive recurrences to assess for differences in patient characteristics. Results: A total of 1338 patients who were treated from January 1, 2010, through December 31, 2017, were included in a cancer registry and underwent review. Of them, 375 (30%) had confirmed cancer recurrences, 45 (3%) had concern for recurrence without radiologic or pathologic confirmation, and 31 (2%) had persistent disease. Most confirmed recurrences were distant (37%) or distant plus locoregional (29%), whereas few were local (11%), regional (9%), or locoregional (14%) alone. The cancer registry accuracy was 89.4%, sensitivity 61%, and specificity 99%. Time to recurrence was associated with registry accuracy. True positives had recurrences at a median of 414 days vs 1007 days for false negatives. Conclusion: Currently, institutional cancer registry recurrence data lacks the required accuracy for implementation into studies without manual confirmation. Longer follow-up of cancer status will likely improve sensitivity. No identified differences in patients accounted for differences in sensitivity. New, ideally automated, data abstraction tools are needed to improve detection of cancer recurrences and minimize manual chart review.

Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms.

Anticipating and understanding cancers' need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors' dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Genomic markers of recurrence risk in atypical meningioma following gross total resection.

Background: Meningiomas are the most common primary central nervous system (CNS) tumor in adults and CNS World Health Organization grade 2 (atypical) meningiomas show an intermediate risk of recurrence/progression. Molecular parameters are needed to better inform management following gross total resection (GTR). Methods: We performed comprehensive genomic analysis of tumor tissue from 63 patients who underwent radiologically confirmed GTR of a primary grade 2 meningioma, including a CLIA-certified target next-generation sequencing panel (n = 61), chromosomal microarray (n = 63), genome-wide methylation profiling (n = 62), H3K27me3 immunohistochemistry (n = 62), and RNA-sequencing (n = 19). Genomic features were correlated with long-term clinical outcomes (median follow-up: 10 years) using Cox proportional hazards regression modeling and published molecular prognostic signatures were evaluated. Results: The presence of specific copy number variants (CNVs), including -1p, -10q, -7p, and -4p, was the strongest predictor of decreased recurrence-free survival (RFS) within our cohort (P < .05). NF2 mutations were frequent (51%) but did not show a significant association with RFS. DNA methylation-based classification assigned tumors to DKFZ Heidelberg benign (52%) or intermediate (47%) meningioma subclasses and was not associated with RFS. H3K27 trimethylation (H3K27me3) was unequivocally lost in 4 tumors, insufficient for RFS analysis. Application of published integrated histologic/molecular grading systems did not improve prediction of recurrence risk over the presence of -1p or -10q alone. Conclusions: CNVs are strong predictors of RFS in grade 2 meningiomas following GTR. Our study supports incorporation of CNV profiling into clinical evaluation to better guide postoperative patient management, which can be readily implemented using existing, clinically validated technologies.

A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer.

Immune-related processes are important in underpinning the properties of clinical traits such as prognosis and drug response in cancer. The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery. Recent studies using a version of ANNs called autoencoders revealed their capability to store biologically meaningful information indicating that autoencoders can be utilized as knowledge discovery platforms aside from their initial assigned use for dimensionality reduction. Here, we devise an innovative weight engineering approach and ANN platform called artificial neural network encoder (ANNE) using an autoencoder and apply it to a breast cancer dataset to extract knowledge learned by the autoencoder model that explains clinical traits. Intriguingly, the extracted biological knowledge in the form of gene-gene associations from ANNE shows immune-related components such as chemokines, carbonic anhydrase, and iron metabolism that modulate immune-related processes and the tumor microenvironment play important roles in underpinning breast cancer clinical traits. Our work shows that biological "knowledge" learned by an ANN model is indeed encoded as weights throughout its neuronal connections, and it is possible to extract learned knowledge via a novel weight engineering approach to uncover important biological insights.

Uncovering Pharmacological Opportunities for Cancer Stem Cells-A Systems Biology View.

Cancer stem cells (CSCs) represent a small fraction of the total cancer cell population, yet they are thought to drive disease propagation, therapy resistance and relapse. Like healthy stem cells, CSCs possess the ability to self-renew and differentiate. These stemness phenotypes of CSCs rely on multiple molecular cues, including signaling pathways (for example, WNT, Notch and Hedgehog), cell surface molecules that interact with cellular niche components, and microenvironmental interactions with immune cells. Despite the importance of understanding CSC biology, our knowledge of how neighboring immune and tumor cell populations collectively shape CSC stemness is incomplete. Here, we provide a systems biology perspective on the crucial roles of cellular population identification and dissection of cell regulatory states. By reviewing state-of-the-art single-cell technologies, we show how innovative systems-based analysis enables a deeper understanding of the stemness of the tumor niche and the influence of intratumoral cancer cell and immune cell compositions. We also summarize strategies for refining CSC systems biology, and the potential role of this approach in the development of improved anticancer treatments. Because CSCs are amenable to cellular transitions, we envision how systems pharmacology can become a major engine for discovery of novel targets and drug candidates that can modulate state transitions for tumor cell reprogramming. Our aim is to provide deeper insights into cancer stemness from a systems perspective. We believe this approach has great potential to guide the development of more effective personalized cancer therapies that can prevent CSC-mediated relapse.

Manifold medicine: A schema that expands treatment dimensionality.

Drug discovery currently focuses on identifying new druggable targets and drug repurposing. Here, we illustrate a third domain of drug discovery: the dimensionality of treatment regimens. We formulate a new schema called 'Manifold Medicine', in which disease states are described by vectorial positions on several body-wide axes. Thus, pathological states are represented by multidimensional 'vectors' that traverse the body-wide axes. We then delineate the manifold nature of drug action to provide a strategy for designing manifold drug cocktails by design using state-of-the-art biomedical and technological innovations. Manifold Medicine offers a roadmap for translating knowledge gained from next-generation technologies into individualized clinical practice.

De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens.

Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of the notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies that were highly varied across patients, showing the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.

The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches.

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.

Characterization of a RAD51C-silenced high-grade serous ovarian cancer model during development of PARP inhibitor resistance.

Acquired PARP inhibitor (PARPi) resistance in BRCA1- or BRCA2-mutant ovarian cancer often results from secondary mutations that restore expression of functional protein. RAD51C is a less commonly studied ovarian cancer susceptibility gene whose promoter is sometimes methylated, leading to homologous recombination (HR) deficiency and PARPi sensitivity. For this study, the PARPi-sensitive patient-derived ovarian cancer xenograft PH039, which lacks HR gene mutations but harbors RAD51C promoter methylation, was selected for PARPi resistance by cyclical niraparib treatment in vivo. PH039 acquired PARPi resistance by the third treatment cycle and grew through subsequent treatment with either niraparib or rucaparib. Transcriptional profiling throughout the course of resistance development showed widespread pathway level changes along with a marked increase in RAD51C mRNA, which reflected loss of RAD51C promoter methylation. Analysis of ovarian cancer samples from the ARIEL2 Part 1 clinical trial of rucaparib monotherapy likewise indicated an association between loss of RAD51C methylation prior to on-study biopsy and limited response. Interestingly, the PARPi resistant PH039 model remained platinum sensitive. Collectively, these results not only indicate that PARPi treatment pressure can reverse RAD51C methylation and restore RAD51C expression, but also provide a model for studying the clinical observation that PARPi and platinum sensitivity are sometimes dissociated.

Perceptions of Barriers and Facilitators to Becoming a Medical Professional Among Underrepresented Undergraduate and Postbaccalaureate Learners.

OBJECTIVE: To assess underrepresented undergraduate and postbaccalaureate learners' perceptions of (1) the medical field, (2) barriers that might prevent individuals from pursuing professional medical careers, and (3) resources that assist in overcoming these barriers. PARTICIPANTS AND METHODS: A qualitative study with focus groups was designed to achieve the objective. Participants were recruited from a community initiative to provide early exploration of the medical field to disadvantaged and minority individuals. Thirty-five individuals voluntarily participated in semistructured interviews. Audio from the interviews was analyzed using a qualitative descriptive approach and thematic analysis. This study was conducted from October 20, 2018, to April 6, 2019. RESULTS: Participants identified multiple characteristics related to the health care work environment and desirable attributes of health care personnel. The following barriers were identified: financial burden, lacking knowledge of the path to becoming a medical professional, inadequate social support, and lacking the metrics of a competitive candidate. Resources identified by participants to overcome barriers included professional networks and programmatic considerations. CONCLUSION: The study participants discussed negative and positive aspects of the health care environment, such as implicit and explicit biases and attributes that promote or sustain success. Participants expounded on financial, academic, social, and personal factors as barriers to success. In regard to resources that were believed to be helpful to mitigate barriers and promote success, participants commented on activities that simulate a professional medical environment, include networking with medical personnel, support well-being, and provide exposure to structured information on the process of obtaining professional medical training.

Machine Learning and Systems Biology Approaches to Characterize Dosage-Based Gene Dependencies in Cancer Cells.

Mapping of cancer survivability factors allows for the identification of novel biological insights for drug targeting. Using genomic editing techniques, gene dependencies can be extracted in a high-throughput and quantitative manner. Dependencies have been predicted using machine learning techniques on -omics data, but the biological consequences of dependency predictor pairs has not been explored. In this work we devised a framework to explore gene dependency using an ensemble of machine learning methods, and our learned models captured meaningful biological information beyond just gene dependency prediction. We show that dosage-based dependent predictors (DDPs) primarily belonged to transcriptional regulation ontologies. We also found that anti-sense RNAs and long- noncoding RNA transcripts display DDPs. Network analyses revealed that SOX10, HLA-J, and ZEB2 act as a triad of network hubs in the dependent-predictor network. Collectively, we demonstrate the powerful combination of machine learning and systems biology approach can illuminate new insights in understanding gene dependency and guide novel targeting avenues.

A mixed methods study to evaluate the impact of a student-run clinic on undergraduate medical education.

BACKGROUND: The purpose of this study was to evaluate the extent to which a longitudinal student-run clinic (SRC) is meeting its stated learning objectives, including providing critical community services and developing physicians who more fully appreciate the social factors affecting their patients' health. METHODS: This was a mixed methods program evaluation of an SRC at Mayo Clinic Alix School of Medicine (MCASOM). A survey was conducted of medical students who had participated in the clinic and seven interviews and three focus groups were conducted with SRC patients, students, faculty, staff, and board members. Transcripts were coded for systematic themes and sub-themes. Major themes were reported. Survey and interview data were integrated by comparing findings and discussing areas of convergence or divergence in order to more fully understand program success and potential areas for improvement. RESULTS: Greater than 85% of student survey respondents (N = 90) agreed or strongly agreed that the SRC met each of its objectives: to provide a vital community service, to explore social determinants of health (SDH), to understand barriers to healthcare access and to practice patience-centered examination. Qualitative data revealed that the SRC contextualized authentic patient care experiences early in students' medical school careers, but the depth of learning was variable between students. Furthermore, exposure to SDH through the program did not necessarily translate to student understanding of the impact of these social factors on patient's health nor did it clearly influence students' future practice goals. CONCLUSIONS: The MCASOM SRC experience met core learning objectives, but opportunities to improve long-term impact on students were identified. Participation in the SRC enabled students to engage in patient care early in training that is representative of future practices. SRCs are an avenue by which students can gain exposure to real-world applications of SDH and barriers to healthcare access, but additional focus on faculty development and intentional reflection may be needed to translate this exposure to actionable student understanding of social factors that impact patient care.

Team-Based Ultrasound Objective Structured Practice Examination (OSPE) in the Anatomy Course.

The clinical use of ultrasound has dramatically increased, necessitating early ultrasound education and the development of new tools in ultrasound training and assessment. The goal of this study was to devise a novel low-resource examination that tested the anatomical knowledge and technical skill of early undergraduate medical students in a gross anatomy course. The team-based ultrasound objective structured practice examination (OSPE) was created as a method for assessing practical ultrasound competencies, anatomical knowledge, and non-technical skills such as teamwork and professionalism. The examination utilized a rotation of students through four team roles as they scanned different areas of the body. This station-based examination required four models and four instructors, and tested ultrasound skills in the heart, abdominal vessels, abdominal organs, and neck regions. A Likert scale survey assessed student attitudes toward the examination. Survey data from participants (n = 46) were examined along with OSPE examination grades (n = 52). Mean and standard deviations were calculated for examination items and survey responses. Student grades were high in both technical (96.5%). and professional (96.5%) competencies with structure identification scoring the lowest (93.8%). There were no statistical differences between performances in each of the body regions being scanned. The survey showed that students deemed the examination to be fair and effective. In addition, students agreed that the examination motivated them to practice ultrasound. The team-based OSPE was found to be an efficient and student-favored method for evaluating integrated ultrasound competencies, anatomical knowledge, team-work, and professional attributes.

Single-Cell Transcriptomic Analysis Reveals BCMA CAR-T Cell Dynamics in a Patient with Refractory Primary Plasma Cell Leukemia.

Chimeric antigen receptor T cell (CAR-T) therapy has revolutionized the clinical treatment of hematological malignancies due to the prominent anti-tumor effects. B cell maturation antigen (BCMA) CAR-T cells have demonstrated promising effects in patients with relapsed/refractory multiple myeloma. However, the dynamics of CAR-T cell proliferation and cytotoxicity in clinical patients remains unexplored. Here, we longitudinally profiled the transcriptomes of 55,488 T cells including CAR-T products, CAR-T cells, and endogenous T cells at the peak and remission phases in a plasma cell leukemia (PCL) patient treated with BCMA CAR-T cells by single-cell transcriptomic analysis. Our results showed distinct CAR-T and endogenous T cell subsets indicating stage-specific expression in proliferation, cytotoxicity, and intercellular signaling pathways. Furthermore, we found that CAR-T cells at peak phase gradually convert to a highly cytotoxic state from a highly proliferative state along a development trajectory. Moreover, re-analysis of a single cell study from CD8+ CD19 CAR-T confirmed our findings. These commonalities suggest conserved mechanisms for CAR-T treatment across hematological malignancies. Taken together, our current study provides insight into CAR-T cell dynamics during CAR-T therapy and proves that both BCMA CAR-T and CD19 CAR-T have similar transcriptional characteristics, especially at the CAR-T peak phase.

Collateral Lethal Effects of Complementary Oncolytic Viruses.

Virus-infected cells release type 1 interferons, which induce an antiviral state in neighboring cells. Naturally occurring viruses are therefore equipped with stealth replication strategies to limit virus sensing and/or with combat strategies to prevent or reverse the antiviral state. Here we show that oncolytic viruses with simple RNA genomes whose spread was suppressed in tumor cells pretreated with interferon were able to replicate efficiently when the cells were coinfected with a poxvirus known to encode a diversity of innate immune combat proteins. In vivo the poxvirus was shown to reverse the intratumoral antiviral state, rescuing RNA virus replication in an otherwise restrictive syngeneic mouse tumor model leading to antitumor efficacy. Pairing of complementary oncolytic viruses is a promising strategy to enhance the antitumor activity of this novel class of anticancer drugs.

Lipid Droplets: Formation to Breakdown.

One of the most exciting areas of cell biology during the last decade has been the study of lipid droplets. Lipid droplets allow cells to store non-polar molecules such as neutral lipids in specific compartments where they are sequestered from the aqueous environment of the cell yet can be accessed through regulated mechanisms. These structures are highly conserved, appearing in organisms throughout the phylogenetic tree. Until somewhat recently, lipid droplets were widely regarded as inert, however progress in the field has continued to demonstrate their vast roles in a number of cellular processes in both mitotic and post-mitotic cells. No doubt the increase in the attention given to lipid droplet research is due to their central role in current pressing human diseases such as obesity, type-2 diabetes, and atherosclerosis. This review provides a mechanistic timeline from neutral lipid synthesis through lipid droplet formation and size augmentation to droplet breakdown.

The protein and neutral lipid composition of lipid droplets isolated from the fission yeast, Schizosaccharomyces pombe.

Lipid droplets consist of a core of neutral lipids surrounded by a phospholipid monolayer with bound proteins. Much of the information on lipid droplet function comes from proteomic and lipodomic studies that identify the components of droplets isolated from organisms throughout the phylogenetic tree. Here, we add to that important inventory by reporting lipid droplet factors from the fission yeast, Schizosaccharomyces pombe. Unique to this study was the fact that cells were cultured in three different environments: 1) late log growth phase in glucose-based media, 2) stationary phase in glucosebased media, and 3) late log growth phase in media containing oleic acid. We confirmed colocalization of major factors with lipid droplets using live-cell fluorescent microscopy. We also analyzed droplets from each of the three conditions for sterol ester (SE) and triacylglycerol (TAG) content, along with their respective fatty acid compositions. We identified a previously undiscovered lipid droplet protein, Vip1p, which affects droplet size distribution. The results provide further insight into the workings of these ubiquitous organelles.

Online, Absolute Quantitation of Propranolol from Spatially Distinct 20- and 40-µm Dissections of Brain, Liver, and Kidney Thin Tissue Sections by Laser Microdissection-Liquid Vortex Capture-Mass Spectrometry.

Spatial resolved quantitation of chemical species in thin tissue sections by mass spectrometric methods has been constrained by the need for matrix-matched standards or other arduous calibration protocols and procedures to mitigate matrix effects (e.g., spatially varying ionization suppression). Reported here is the use of laser "cut and drop" sampling with a laser microdissection-liquid vortex capture electrospray ionization tandem mass spectrometry (LMD-LVC/ESI-MS/MS) system for online and absolute quantitation of propranolol in mouse brain, kidney, and liver thin tissue sections of mice administered with the drug at a 7.5 mg/kg dose, intravenously. In this procedure either 20 µm × 20 µm or 40 µm × 40 µm tissue microdissections were cut and dropped into the flowing solvent of the capture probe. During transport to the ESI source drug related material was completely extracted from the tissue into the solvent, which contained a known concentration of propranolol-d7 as an internal standard. This allowed absolute quantitation to be achieved with an external calibration curve generated from standards containing the same fixed concentration of propranolol-d7 and varied concentrations of propranolol. Average propranolol concentrations determined with the laser "cut and drop" sampling method closely agreed with concentration values obtained from 2.3 mm diameter tissue punches from serial sections that were extracted and quantified by HPLC/ESI-MS/MS measurements. In addition, the relative abundance of hydroxypropranolol glucuronide metabolites were recorded and found to be consistent with previous findings.