Globally, sepsis remains a major health issue, with Multiple Organ Dysfunction Syndrome (MODS) being a leading cause of mortality. MODS, a severe condition often seen in intensive care units, typically results from infections or trauma and involves complex pathophysiological processes requiring various clinical interventions. Although infections are the main triggers, the mechanisms driving MODS remain unclear. To investigate the transition of sepsis to MODS, we generated a single cell RNA sequencing dataset comprising 86,839 immune cells from pediatric sepsis patients at the clinical onset of MODS patients and age matched controls, identifying 22 distinct cell types. A cluster of S100 genes, located in the same genomic region, was highly expressed in neutrophils in MODS patients, demonstrating strong diagnostic potential across cohorts (AUC=0.94−0.99) and potential as therapeutic targets. We found that many B and T cells showed heightened inflammation and increased apoptotic activity during early MODS. Additionally, specific transcription regulators and surface proteins associated with inflammation and S100 regulations were uniquely expressed in MODS. Pseudotime analysis revealed distinct S100 gene expression patterns between controls and MODS. Cell−cell interaction analysis highlighted dendritic cells as key mediators, enhancing communication between plasma cells and Vδ T cells while activating inflammatory and immunosuppressive pathways. We also analyzed 116,803 immune cells from adult MODS patients, revealing stronger immune dysregulation compared to pediatric MODS, including altered S100 gene expression, and enhanced cell-cell interactions. These findings suggest that S100 genes may serve as a marker for MODS. Furthermore, insights gained from adult MODS could improve our understanding of rare pediatric MODS and contribute to the development of better therapeutics for all MODS patients. ### Competing Interest Statement The authors have declared no competing interest.

INTRODUCTION:Mild cognitive impairment (MCI) is a significant public health concern and a potential precursor to Alzheimer's disease (AD). This study leverages electronic health record (EHR) data to explore rural-urban differences in MCI incidence, risk factors, and healthcare navigation in West Michigan. METHODS:Analysis was conducted on 1,528,464 patients from Corewell Health West, using face-to-face encounters between 1/1/2015 and 7/31/2022. MCI cases were identified using International Classification of Diseases (ICD) codes, focusing on patients aged 45+ without prior MCI, dementia, or AD diagnoses. Incidence rates, cumulative incidences, primary care physicians (PCPs), and neuropsychology referral outcomes were examined across rural and urban areas. Risk factors were evaluated through univariate and multivariate Cox regression analyses. The geographic distribution of patient counts, hospital locations, and neurology department referrals were examined. RESULTS:Among 423,592 patients, a higher MCI incidence rate was observed in urban settings compared to rural settings (3.83 vs. 3.22 per 1,000 person-years). However, sensitivity analysis revealed higher incidence rates in rural areas when including patients who progressed directly to dementia. Urban patients demonstrated higher rates of referrals to and completion of neurological services. While the risk factors for MCI were largely similar across urban and rural populations, urban-specific factors for incident MCI are hearing loss, inflammatory bowel disease, obstructive sleep apnea, insomnia, being African American, and being underweight. Common risk factors include diabetes, intracranial injury, cerebrovascular disease, coronary artery disease, stroke, Parkinson's disease, epilepsy, chronic obstructive pulmonary disease, depression, and increased age. Lower risk was associated with being female, having a higher body mass index, and having a higher diastolic blood pressure. DISCUSSION:This study highlights rural-urban differences in MCI incidence and access to care, suggesting potential underdiagnosis in rural areas likely due to reduced access to specialists. Future research should explore socioeconomic, environmental, and lifestyle determinants of MCI to refine prevention and management strategies across geographic settings. Highlights:Leveraged EHRs to explore rural-urban differences in MCI in West Michigan.Revealed a significant underdiagnosis of MCI, especially in rural areas.Observed lower rates of neurological referrals and completions for rural patients.Identified risk factors specific to rural and urban populations.

As of 2024, SARS-CoV-2 continues to propagate and drift as an endemic virus, impacting healthcare for years. The largest sequencing initiative for any species was initiated to combat the virus, tracking changes over time at a full virus base-pair resolution. The SARS-CoV-2 sequencing represents a unique opportunity to understand selective pressures and viral evolution but requires cross-disciplinary approaches from epidemiology to functional protein biology. Within this work, we integrate a two-year genotyping window with structural biology to explore the selective pressures of SARS-CoV-2 on protein insights. Although genotype and the Spike (Surface Glycoprotein) protein continue to drift, most SARS-CoV-2 proteins have had few amino acid alterations. Within Spike, the high drift rate of amino acids involved in antibody evasion also corresponds to changes within the ACE2 binding pocket that have undergone multiple changes that maintain functional binding. The genotyping suggests selective pressure for receptor specificity that could also confer changes in viral risk. Mapping of amino acid changes to the structures of the SARS-CoV-2 co-transcriptional complex (nsp7-nsp14), nsp3 (papain-like protease), and nsp5 (cysteine protease) proteins suggest they remain critical factors for drug development that will be sustainable, unlike those strategies targeting Spike.

The emerging large language models (LLMs) are actively evaluated in various fields including healthcare. Most studies have focused on established benchmarks and standard parameters; however, the variation and impact of prompt engineering and fine-tuning strategies have not been fully explored. This study benchmarks GPT-3.5 Turbo, GPT-4, and Llama-7B against BERT models and medical fellows' annotations in identifying patients with metastatic cancer from discharge summaries. Results revealed that clear, concise prompts incorporating reasoning steps significantly enhanced performance. GPT-4 exhibited superior performance among all models. Notably, one-shot learning and fine-tuning provided no incremental benefit. The model's accuracy sustained even when keywords for metastatic cancer were removed or when half of the input tokens were randomly discarded. These findings underscore GPT-4's potential to substitute specialized models, such as PubMedBERT, through strategic prompt engineering, and suggest opportunities to improve open-source models, which are better suited to use in clinical settings.

Cancer and dementia are common in aging populations. Mild cognitive impairment (MCI) is a stage between the cognitive changes of normal aging and dementia that can lead to a decline in quality of life. With the substantial improvement of survival in many cancers, maintaining a high quality of life has become a new goal in cancer care. Identifying those patients with a high risk of developing MCI may facilitate early intervention and further improve patient care. The objective of this study is to survey machine learning techniques and AutoML to model the early detection of MCI in patients with cancers using the features which are known risk factors in dementia and accessible in the electronic health records (EHR). We compared multiple machine learning methods and explored AutoML to predict 1-year risk of MCI for cancer patients. Among 27 models, XGBoost in AutoML gave the highest AUC (0.79), suggesting the superiority of using automated machine learning tools to search for the best model and parameters. The feature importance analysis revealed that cancer patients with brain malignancy, hypertension, or cardiovascular diseases are more likely to develop MCI. The overall poor performance indicates more efforts should be made to improve data quality and increase features and sample size.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThe research is supported by the MSU-Spectrum Health Alliance funds.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This study was approved by the Institutional Review Board (IRB) at Corewell Health (SH 2020-071).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced in the present work are contained in the manuscript

Idiopathic pulmonary fibrosis (IPF) is a pathological condition wherein lung injury precipitates the deposition of scar tissue, ultimately leading to a decline in pulmonary function. Existing research indicates a notable exacerbation in the clinical prognosis of IPF patients following infection with COVID-19. This investigation employed bulk RNA-sequencing methodologies to describe the transcriptomic profiles of small airway cell cultures derived from IPF and post-COVID fibrosis patients. Differential gene expression analysis unveiled heightened activation of pathways associated with microtubule assembly and interferon signaling in IPF cell cultures. Conversely, post-COVID fibrosis cell cultures exhibited distinctive characteristics, including the upregulation of pathways linked to extracellular matrix remodeling, immune system response, and TGF-β1 signaling. Notably, BMP signaling levels were elevated in cell cultures derived from IPF patients compared to non-IPF control and post-COVID fibrosis samples. These findings underscore the molecular distinctions between IPF and post-COVID fibrosis, particularly in the context of signaling pathways associated with each condition. A better understanding of the underlying molecular mechanisms holds the promise of identifying potential therapeutic targets for future interventions in these diseases.

This study assesses the ability of state-of-the-art large language models (LLMs) including GPT-3.5, GPT-4, Falcon, and LLaMA 2 to identify patients with mild cognitive impairment (MCI) from discharge summaries and examines instances where the models' responses were misaligned with their reasoning. Utilizing the MIMIC-IV v2.2 database, we focused on a cohort aged 65 and older, verifying MCI diagnoses against ICD codes and expert evaluations. The data was partitioned into training, validation, and testing sets in a 7:2:1 ratio for model fine-tuning and evaluation, with an additional metastatic cancer dataset from MIMIC III used to further assess reasoning consistency. GPT-4 demonstrated superior interpretative capabilities, particularly in response to complex prompts, yet displayed notable response-reasoning inconsistencies. In contrast, open-source models like Falcon and LLaMA 2 achieved high accuracy but lacked explanatory reasoning, underscoring the necessity for further research to optimize both performance and interpretability. The study emphasizes the significance of prompt engineering and the need for further exploration into the unexpected reasoning-response misalignment observed in GPT-4. The results underscore the promise of incorporating LLMs into healthcare diagnostics, contingent upon methodological advancements to ensure accuracy and clinical coherence of AI-generated outputs, thereby improving the trustworthiness of LLMs for medical decision-making.

Throughout the last few years, understanding the physiological mechanisms to viral infections has highlighted individuals' unique outcomes. While SARS-CoV-2 and other pathogens can cause hospitalization and mortality, some individuals remain asymptomatic, and others will suffer from years of altered diverse physiological pathways (such as long COVID). Our team, through transcriptomics, has built a hypothesis that many of the individual-level responses are due to the interaction of a virus with a process known as nonsense-mediated decays (NMD) and how suppression of NMD interacts with an individual's genomic variants to drive diverse physiological response. This hypothesis has been built on nearly five hundred clinical transcriptomes of infants with RSV infection, newborns with genomic disorders, kids with multiple organ dysfunction syndrome, and adults with hospitalized COVID-19 paired with >16,000 publicly deposited blood transcriptomes of broad pathologies. Several virus interactions with human genomics have been observed within our work. First, a patient with EBV had an acute activation of viral-induced genetics through suppression of NMD, resulting in elevated RNASEH2B dominant-negative human protein resulting in Hemophagocytic lymphohistiocytosis (HLH), a mechanism of a rare transient disorders. Second, we have observed multiple individuals with viral infections that resulted in either the activation or suppression of interferon cascades correlated to common variant alleles altered by NMD changes, such as ISG15 and cytokines. Third, we show that several of these cytokine modulation genetics can elevate the risk of NMD-regulated Neutrophile Extracellular Trap (NET) production associated with long-term endocrine and hemodynamic modulation. Finally, we show that when a virus suppresses NMD (SARS-CoV-2, HIV, EBV), multiple individuals with latent infections can have an elevation of the dormant virus transcripts, including EBV and Torque teno virus. Thus, individuals can have multiple viruses activated that change physiological outcomes simultaneously. Overall, it is essential to physiology and medicine to refine each individual's precision and unique outcomes based on the complex interaction of a virus with NMD and genomic variants to modify cellular, systems, and inflammatory responses. This research was funded by the Gerber Foundation, National Institutes of Health (K01ES025435 and R01AI171984), Michigan Department of Health and Human Services, and Michigan State University. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Idiopathic pulmonary fibrosis (IPF) is a pathological condition of unknown etiology that results from injury to the lung and an ensuing fibrotic response that leads to the thickening of the alveolar walls and obliteration of the alveolar space. The pathogenesis is not clear, and there are currently no effective therapies for IPF. Small airway disease and mucus accumulation are prominent features in IPF lungs, similar to cystic fibrosis lung disease. The ATP12A gene encodes the α-subunit of the nongastric H+, K+-ATPase, which functions to acidify the airway surface fluid and impairs mucociliary transport function in patients with cystic fibrosis. It is hypothesized that the ATP12A protein may play a role in the pathogenesis of IPF. The authors' studies demonstrate that ATP12A protein is overexpressed in distal small airways from the lungs of patients with IPF compared with normal human lungs. In addition, overexpression of the ATP12A protein in mouse lungs worsened bleomycin induced experimental pulmonary fibrosis. This was prevented by a potassium competitive proton pump blocker, vonoprazan. These data support the concept that the ATP12A protein plays an important role in the pathogenesis of lung fibrosis. Inhibition of the ATP12A protein has potential as a novel therapeutic strategy in IPF treatment.

Cancer and dementia are common in aging populations. Mild cognitive impairment (MCI) is a stage between the cognitive changes of normal aging and dementia that can lead to a decline in quality of life. With the substantial improvement of survival in many cancers, maintaining a high quality of life has become a new goal in cancer care. Identifying those patients with a high risk of developing MCI may facilitate early intervention and further improve patient care. The objective of this study is to survey machine learning techniques and AutoML to model the early detection of MCI in patients with cancers using the features which are known risk factors in dementia and accessible in the electronic health records (EHR). We compared multiple machine learning methods and explored AutoML to predict 1-year risk of MCI for cancer patients. Among 27 models, XGBoost in AutoML gave the highest AUC (0.79), suggesting the superiority of using automated machine learning tools to search for the best model and parameters. The feature importance analysis revealed that cancer patients with brain malignancy, hypertension, or cardiovascular diseases are more likely to develop MCI. The overall poor performance indicates more efforts should be made to improve data quality and increase features and sample size. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The research is supported by the MSU-Spectrum Health Alliance funds. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Institutional Review Board (IRB) at Corewell Health (SH 2020-071). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present work are contained in the manuscript

IPF is a condition in which an injury to the lung leads to the accumulation of scar tissue. This fibrotic tissue reduces lung compliance and impairs gas exchange. Studies have shown that infection with COVID-19 significantly worsens the clinical outcomes of IPF patients. The exact etiology of IPF is unknown, but recent evidence suggests that the distal small airways, (those having a diameter less than 2 mm in adults), play a role in the early pathogenesis of IPF. TGF-β1 is a main driver of fibrosis in a variety of tissues; the binding of TGF-β1 to its receptor triggers a signaling cascade that results in inflammatory signaling, accumulation of collagen and other components of the extracellular matrix, and immune system activation. This study aimed to investigate possible mechanisms that contribute to worsening lung fibrosis in IPF patients after being diagnosed with COVID-19, with a particular focus on the role of TGF-β1. Small airway cell cultures derived from IPF and post-COVID-19 IPF patient transplant tissues were submitted for RNA-sequencing and differential gene expression analysis. The genetic signatures for each disease state were determined by comparing the differentially expressed genes present in the cells cultured under control conditions to cells cultured with TGF-β1. The genes shared between the culture conditions laid the framework for determining the genetic signatures of each disease. Our data found that genes associated with pulmonary fibrosis appeared to be more highly expressed in the post-COVID fibrosis samples, under both control and TGF-β1-treated conditions. A similar trend was noted for genes involved in the TGF-β1 signaling pathway; the post-COVID fibrosis cell cultures seemed to be more responsive to treatment with TGF-β1. Gene expression analysis, RT-PCR, and immunohistochemistry confirmed increased levels of BMP signaling in the IPF small airway cell cultures. These findings suggest that TGF-β1 signaling in IPF small airway cells could be inhibited by BMP signaling, leading to the differences in genetic signatures between IPF and post-COVID fibrosis.

The morbidity of Hepatocellular carcinoma (HCC) is highest in individuals with chronic liver diseases (CLD). However, the effects of cell composition on the progression of CLDs to HCC remain elusive. To gain a better understanding of the spatial distribution of cells and their interactions, we created spatial transcriptome data from two HCC and their normal adjacent FFPE tissues using the 10x visium platform. We processed the data using cellRanger and mapped it to the Human Hg38 reference genome with GRCh38.p3 annotation. All data generated by cellRanger is provided here

Background:Some studies conducted before the Delta and Omicron variant-dominant periods have indicated that influenza vaccination provided protection against COVID-19 infection or hospitalization, but these results were limited by small study cohorts and a lack of comprehensive data on patient characteristics. No studies have examined this question during the Delta and Omicron periods (08/01/2021 to 2/22/2022).Methods:We conducted a retrospective cohort study of influenza-vaccinated and unvaccinated patients in the Corewell Health East(CHE, formerly known as Beaumont Health), Corewell Health West(CHW, formerly known as Spectrum Health) and Michigan Medicine (MM) healthcare system during the Delta-dominant and Omicron-dominant periods. We used a test-negative, case-control analysis to assess the effectiveness of the influenza vaccine against hospitalized SARS-CoV-2 outcome in adults, while controlling for individual characteristics as well as pandameic severity and waning immunity of COVID-19 vaccine.Results:The influenza vaccination has shown to provided some protection against SARS-CoV-2 hospitalized outcome across three main healthcare systems. CHE site (odds ratio [OR]=0.73, vaccine effectiveness [VE]=27%, 95% confidence interval [CI]: [18-35], p<0.001), CHW site (OR=0.85, VE=15%, 95% CI: [6-24], p<0.001), MM (OR=0.50, VE=50%, 95% CI: [40-58], p <0.001) and overall (OR=0.75, VE=25%, 95% CI: [20-30], p <0.001).Conclusion:The influenza vaccine provides a small degree of protection against SARS-CoV-2 infection across our study sites.

Motivation: Mapping internal, locally used lab test codes to standardized logical observation identifiers names and codes (LOINC) terminology has become an essential step in harmonizing electronic health record (EHR) data across different institutions. However, most existing LOINC code mappers are based on text-mining technology and do not provide robust multi-language support.Materials and methods: We introduce a simple, yet effective tool called big data-guided LOINC code mapper (BGLM), which leverages the large amount of patient data stored in EHR systems to perform LOINC coding mapping. Distinguishing from existing methods, BGLM conducts mapping based on distributional similarity.Results: We validated the performance of BGLM with real-world datasets and showed that high mapping precision could be achieved under proper false discovery rate control. In addition, we showed that the mapping results of BGLM could be used to boost the performance of Regenstrief LOINC Mapping Assistant (RELMA), one of the most widely used LOINC code mappers.Conclusions: BGLM paves a new way for LOINC code mapping and therefore could be applied to EHR systems without the restriction of languages. BGLM is freely available at https://github.com/Bin-Chen-Lab/BGLM.

Distant metastasis is the major cause of cancer-related deaths; however, early diagnosis of cancer metastasis remains a significant challenge. The recent advances in pre-trained natural language processing models coupled with the accumulation of publicly available Electronic Health Records (EHR) data provide an unprecedented opportunity to computationally tackle the challenge. Here, we fine-tuned multiple state-of-the-art BERT-based models using discharge summaries from the open MIMIC-III dataset and derived MetBERT, a novel model tailored to predict cancer metastasis from clinical notes. MetBERT achieved high performance (AUC=0.94) on our in-house validation dataset, suggesting its high generalizability. In addition, MetBERT enabled determining the date of cancer metastasis using the rich information in clinical notes and therefore could be potentially deployed as a tool for early diagnosis. Finally, we interpreted MetBERT at different scales and revealed a possible association between radiation therapy and metastasis risk in multiple cancer types.

The physiology of critical care patients is more complex than normally appreciated. Patients arrive at the intensive care unit (ICU) or the pediatric ICU (PICU) with a variety of infections, trauma, organ damage, and dysfunctional immune systems. This population is the prime target for testing and applying new precision medicine tools to decipher the unique biology occurring within each patient. This is particularly important as COVID‐19 has made such an impact on the United States healthcare system. Thus, there is a need to develop strategies to find multiple levels of information while minimizing the number of tests performed, shifting the balance of testing to more proactive than reactive. With the collection of ~2mL of blood (about half a teaspoon), our collaboration between Spectrum Health and Michigan State University has shown the ability to use PAXgene tubes and RNAseq to simultaneously map human gene/transcript signatures, score panels of corresponding risk genes, deconvolute immune cells, detect markers of organ/cell damage, detect RNA from bacteria/viruses/plants/fungi, profile the immune repertoire, address how patients are unique from other samples, and address common/rare genetic mutations. These tools have been applied to three cohorts of patients (and age matched controls) for critical care medicine physiology understanding for nearly all ages: 1) Infants with Respiratory syncytial virus (RSV); 2) Kids with multiple organ dysfunction syndrome; and 3) Adults with hospitalized or lethal COVID‐19. Our findings from these tools shows the complexity of immune system activation, secondary infections, and under appreciated interactions of the environment with genetics. This is highlighted by our discovery of Viral Induced Genetics in a patient with Epstein Barr virus, a process where viruses suppress nonsense mediated decay (NMD) to survive, resulting in activation of dominant negative genetics that give rise to immune cell disorder overlapping COVID‐19 pathology. The promise of blood‐based transcriptomics to reveal cellular and cell free signatures opens a door for building more detailed physiological mechanisms from precision medicine.

The immune response to COVID-19 infection is variable. How COVID-19 influences clinical outcomes in hospitalized patients needs to be understood through readily obtainable biological materials, such as blood. We hypothesized that a high-density analysis of host (and pathogen) blood RNA in hospitalized patients with SARS-CoV-2 would provide mechanistic insights into the heterogeneity of response amongst COVID-19 patients when combined with advanced multidimensional bioinformatics for RNA. We enrolled 36 hospitalized COVID-19 patients (11 died) and 15 controls, collecting 74 blood PAXgene RNA tubes at multiple timepoints, one early and in 23 patients after treatment with various therapies. Total RNAseq was performed at high-density, with >160 million paired-end, 150 base pair reads per sample, representing the most sequenced bases per sample for any publicly deposited blood PAXgene tube study. There are 770 genes significantly altered in the blood of COVID-19 patients associated with antiviral defense, mitotic cell cycle, type I interferon signaling, and severe viral infections. Immune genes activated include those associated with neutrophil mechanisms, secretory granules, and neutrophil extracellular traps (NETs), along with decreased gene expression in lymphocytes and clonal expansion of the acquired immune response. Therapies such as convalescent serum and dexamethasone reduced many of the blood expression signatures of COVID-19. Severely ill or deceased patients are marked by various secondary infections, unique gene patterns, dysregulated innate response, and peripheral organ damage not otherwise found in the cohort. High-density transcriptomic data offers shared gene expression signatures, providing unique insights into the immune system and individualized signatures of patients that could be used to understand the patient's clinical condition. Whole blood transcriptomics provides patient-level insights for immune activation, immune repertoire, and secondary infections that can further guide precision treatment.

Next-generation sequencing (NGS) capabilities can affect therapeutic decisions in patients with complex, advanced, or refractory cancer. We report the feasibility of a tumor sequencing advisory board at a regional cancer center. Specimens were analyzed for approximately 2800 mutations in 50 genes. Outcomes of interest included tumor sequencing advisory board function and processes, timely discussion of results, and proportion of reports having potentially actionable mutations. NGS results were successfully generated for 15 patients, with median time from tissue processing to reporting of 11.6 days (range, 5 to 21 days), and presented at a biweekly multidisciplinary tumor sequencing advisory board. Attendance averaged 19 participants (range, 12 to 24) at 20 days after patient enrollment (range, 10 to 30 days). Twenty-seven (range, 1 to 4 per patient) potentially actionable mutations were detected in 11 of 15 patients: TP53 (n = 6), KRAS (n = 4), MET (n = 3), APC (n = 3), CDKN2A (n = 2), PTEN (n = 2), PIK3CA, FLT3, NRAS, VHL, BRAF, SMAD4, and ATM. The Hotspot Panel is now offered as a clinically available test at our institution. NGS results can be obtained by in-house high-throughput sequencing and reviewed in a multidisciplinary tumor sequencing advisory board in a clinically relevant manner. The essential components of a center for personalized cancer care can support clinical decisions outside the university.