An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing.

Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.

Coordinated single-cell tumor microenvironment dynamics reinforce pancreatic cancer subtype.

Bulk analyses of pancreatic ductal adenocarcinoma (PDAC) samples are complicated by the tumor microenvironment (TME), i.e. signals from fibroblasts, endocrine, exocrine, and immune cells. Despite this, we and others have established tumor and stroma subtypes with prognostic significance. However, understanding of underlying signals driving distinct immune and stromal landscapes is still incomplete. Here we integrate 92 single cell RNA-seq samples from seven independent studies to build a reproducible PDAC atlas with a focus on tumor-TME interdependence. Patients with activated stroma are synonymous with higher myofibroblastic and immunogenic fibroblasts, and furthermore show increased M2-like macrophages and regulatory T-cells. Contrastingly, patients with 'normal' stroma show M1-like recruitment, elevated effector and exhausted T-cells. To aid interoperability of future studies, we provide a pretrained cell type classifier and an atlas of subtype-based signaling factors that we also validate in mouse data. Ultimately, this work leverages the heterogeneity among single-cell studies to create a comprehensive view of the orchestra of signaling interactions governing PDAC.

FaStaNMF: a Fast and Stable Non-negative Matrix Factorization for Gene Expression.

Gene expression analysis of samples with mixed cell types only provides limited insight to the characteristics of specific tissues. In silico deconvolution can be applied to extract cell type specific expression, thus avoiding prohibitively expensive techniques such as cell sorting or single-cell sequencing. Non-negative matrix factorization (NMF) is a deconvolution method shown to be useful for gene expression data, in part due to its constraint of non-negativity. Unlike other methods, NMF provides the capability to deconvolve without prior knowledge of the components of the model. However, NMF is not guaranteed to provide a globally unique solution. In this work, we present FaStaNMF, a method that balances achieving global stability of the NMF results, which is essential for inter-experiment and inter-lab reproducibility, with accuracy and speed. Results: FaStaNMF was applied to four datasets with known ground truth, created based on publicly available data or by using our simulation infrastructure, RNAGinesis. We assessed FaStaNMF on three criteria - speed, accuracy, and stability, and it favorably compared to the standard approach of achieving reproduceable results with NMF. We expect that FaStaNMF can be applied successfully to a wide array of biological data, such as different tumor/immune and other disease microenvironments.

Geographic and Temporal Trends in COVID-Associated Acute Kidney Injury in the National COVID Cohort Collaborative.

BACKGROUND: AKI is associated with mortality in patients hospitalized with coronavirus disease 2019 (COVID-19); however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. METHODS: Electronic health record data were obtained from 53 health systems in the United States in the National COVID Cohort Collaborative. We selected hospitalized adults diagnosed with COVID-19 between March 6, 2020, and January 6, 2022. AKI was determined with serum creatinine and diagnosis codes. Time was divided into 16-week periods (P1-6) and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. RESULTS: Of a total cohort of 336,473, 129,176 (38%) patients had AKI. Fifty-six thousand three hundred and twenty-two (17%) lacked a diagnosis code but had AKI based on the change in serum creatinine. Similar to patients coded for AKI, these patients had higher mortality compared with those without AKI. The incidence of AKI was highest in P1 (47%; 23,097/48,947), lower in P2 (37%; 12,102/32,513), and relatively stable thereafter. Compared with the Midwest, the Northeast, South, and West had higher adjusted odds of AKI in P1. Subsequently, the South and West regions continued to have the highest relative AKI odds. In multivariable models, AKI defined by either serum creatinine or diagnostic code and the severity of AKI was associated with mortality. CONCLUSIONS: The incidence and distribution of COVID-19-associated AKI changed since the first wave of the pandemic in the United States. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_08_08_CJN0000000000000192.mp3.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

COVID-19-associated AKI in hospitalized US patients: incidence, temporal trends, geographical distribution, risk factors and mortality.

Background: Acute kidney injury (AKI) is associated with mortality in patients hospitalized with COVID-19, however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. Methods: Electronic health record data were obtained from 53 health systems in the United States (US) in the National COVID Cohort Collaborative (N3C). We selected hospitalized adults diagnosed with COVID-19 between March 6th, 2020, and January 6th, 2022. AKI was determined with serum creatinine (SCr) and diagnosis codes. Time were divided into 16-weeks (P1-6) periods and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. Results: Out of a total cohort of 306,061, 126,478 (41.0 %) patients had AKI. Among these, 17.9% lacked a diagnosis code but had AKI based on the change in SCr. Similar to patients coded for AKI, these patients had higher mortality compared to those without AKI. The incidence of AKI was highest in P1 (49.3%), reduced in P2 (40.6%), and relatively stable thereafter. Compared to the Midwest, the Northeast, South, and West had higher adjusted AKI incidence in P1, subsequently, the South and West regions continued to have the highest relative incidence. In multivariable models, AKI defined by either SCr or diagnostic code, and the severity of AKI was associated with mortality. Conclusions: Uncoded cases of COVID-19-associated AKI are common and associated with mortality. The incidence and distribution of COVID-19-associated AKI have changed since the first wave of the pandemic in the US.

Spatial Characterization of Tumor-Infiltrating Lymphocytes and Breast Cancer Progression.

Tumor-infiltrating lymphocytes (TILs) have been established as a robust prognostic biomarker in breast cancer, with emerging utility in predicting treatment response in the adjuvant and neoadjuvant settings. In this study, the role of TILs in predicting overall survival and progression-free interval was evaluated in two independent cohorts of breast cancer from the Cancer Genome Atlas (TCGA BRCA) and the Carolina Breast Cancer Study (UNC CBCS). We utilized machine learning and computer vision algorithms to characterize TIL infiltrates in digital whole-slide images (WSIs) of breast cancer stained with hematoxylin and eosin (H&E). Multiple parameters were used to characterize the global abundance and spatial features of TIL infiltrates. Univariate and multivariate analyses show that large aggregates of peritumoral and intratumoral TILs (forests) were associated with longer survival, whereas the absence of intratumoral TILs (deserts) is associated with increased risk of recurrence. Patients with two or more high-risk spatial features were associated with significantly shorter progression-free interval (PFI). This study demonstrates the practical utility of Pathomics in evaluating the clinical significance of the abundance and spatial patterns of distribution of TIL infiltrates as important biomarkers in breast cancer.

Reciprocal regulation of pancreatic ductal adenocarcinoma growth and molecular subtype by HNF4α and SIX1/4.

OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a 5-year survival of less than 5%. Transcriptomic analysis has identified two clinically relevant molecular subtypes of PDAC: classical and basal-like. The classical subtype is characterised by a more favourable prognosis and better response to chemotherapy than the basal-like subtype. The classical subtype also expresses higher levels of lineage specifiers that regulate endodermal differentiation, including the nuclear receptor hepatocyte nuclear factor 4 α (HNF4α). The objective of this study is to evaluate the role of HNF4α, SIX4 and SIX1 in regulating the growth and molecular subtype of PDAC. DESIGN: We manipulate the expression of HNF4α, SIX4 and SIX1 in multiple in vitro and in vivo PDAC models. We determine the consequences of manipulating these genes on PDAC growth, differentiation and molecular subtype using functional assays, gene expression analysis and cross-species comparisons with human datasets. RESULTS: We show that HNF4α restrains tumour growth and drives tumour cells toward an epithelial identity. Gene expression analysis of murine models and human tumours shows that HNF4α activates expression of genes associated with the classical subtype. HNF4α also directly represses SIX4 and SIX1, two mesodermal/neuronal lineage specifiers expressed in the basal-like subtype. Finally, SIX4 and SIX1 drive proliferation and regulate differentiation in HNF4α-negative PDAC. CONCLUSION: Our data show that HNF4α regulates the growth and molecular subtype of PDAC by multiple mechanisms, including activation of the classical gene expression programme and repression of SIX4 and SIX1, which may represent novel dependencies of the basal-like subtype.

Natural Language Processing in Surgery: A Systematic Review and Meta-analysis.

OBJECTIVE: The aim of this study was to systematically assess the application and potential benefits of natural language processing (NLP) in surgical outcomes research. SUMMARY BACKGROUND DATA: Widespread implementation of electronic health records (EHRs) has generated a massive patient data source. Traditional methods of data capture, such as billing codes and/or manual review of free-text narratives in EHRs, are highly labor-intensive, costly, subjective, and potentially prone to bias. METHODS: A literature search of PubMed, MEDLINE, Web of Science, and Embase identified all articles published starting in 2000 that used NLP models to assess perioperative surgical outcomes. Evaluation metrics of NLP systems were assessed by means of pooled analysis and meta-analysis. Qualitative synthesis was carried out to assess the results and risk of bias on outcomes. RESULTS: The present study included 29 articles, with over half (n = 15) published after 2018. The most common outcome identified using NLP was postoperative complications (n = 14). Compared to traditional non-NLP models, NLP models identified postoperative complications with higher sensitivity [0.92 (0.87-0.95) vs 0.58 (0.33-0.79), P < 0.001]. The specificities were comparable at 0.99 (0.96-1.00) and 0.98 (0.95-0.99), respectively. Using summary of likelihood ratio matrices, traditional non-NLP models have clinical utility for confirming documentation of outcomes/diagnoses, whereas NLP models may be reliably utilized for both confirming and ruling out documentation of outcomes/diagnoses. CONCLUSIONS: NLP usage to extract a range of surgical outcomes, particularly postoperative complications, is accelerating across disciplines and areas of clinical outcomes research. NLP and traditional non-NLP approaches demonstrate similar performance measures, but NLP is superior in ruling out documentation of surgical outcomes.

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer.

Quantitative assessment of spatial relations between tumor and tumor-infiltrating lymphocytes (TIL) is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and evaluated convolutional neural network analysis pipelines to generate combined maps of cancer regions and TILs in routine diagnostic breast cancer whole slide tissue images. The combined maps provide insight about the structural patterns and spatial distribution of lymphocytic infiltrates and facilitate improved quantification of TILs. Both tumor and TIL analyses were evaluated by using three convolutional neural network networks (34-layer ResNet, 16-layer VGG, and Inception v4); the results compared favorably with those obtained by using the best published methods. We have produced open-source tools and a public data set consisting of tumor/TIL maps for 1090 invasive breast cancer images from The Cancer Genome Atlas. The maps can be downloaded for further downstream analyses.

Therapeutic strategies to induce ERα in luminal breast cancer to enhance tamoxifen efficacy.

Breast cancer is the most prevalent malignancy and second leading cause of death in women worldwide, with hormone receptor-positive luminal breast cancers being the most widespread subtype. While these tumors are generally amenable to endocrine therapy, cellular heterogeneity and acquired ability of tumor cells to undergo cell state switching makes these populations difficult to be fully targeted and eradicated through conventional methods. We have leveraged a quality-by-design (QbD) approach that integrates biological responses with predictive mathematical modeling to identify key combinations of commercially available drugs to induce estrogen receptor expression for therapeutic targeting. This technology utilizes a high level of automation through a custom-built platform to reduce bias as well as design-of-experiments methodology to minimize the experimental iterations required. Utilizing this approach, we identified a combination of clinical compounds, each at concentrations well below their efficacious dose, able to induce the expression of estrogen receptor alpha (ESR1) in hormone-positive breast cancer cells. Induction of ESR1 in luminal cells leads to chemosensitization. These findings provide proof of concept for the utility of the QbD strategy and identify a unique drug cocktail able to sensitize breast cancer cells to tamoxifen.

Development of a Cx46 Targeting Strategy for Cancer Stem Cells.

Gap-junction-mediated cell-cell communication enables tumor cells to synchronize complex processes. We previously found that glioblastoma cancer stem cells (CSCs) express higher levels of the gap junction protein Cx46 compared to non-stem tumor cells (non-CSCs) and that this was necessary and sufficient for CSC maintenance. To understand the mechanism underlying this requirement, we use point mutants to disrupt specific functions of Cx46 and find that Cx46-mediated gap-junction coupling is critical for CSCs. To develop a Cx46 targeting strategy, we screen a clinically relevant small molecule library and identify clofazimine as an inhibitor of Cx46-specific cell-cell communication. Clofazimine attenuates proliferation, self-renewal, and tumor growth and synergizes with temozolomide to induce apoptosis. Although clofazimine does not cross the blood-brain barrier, the combination of clofazimine derivatives optimized for brain penetrance with standard-of-care therapies may target glioblastoma CSCs. Furthermore, these results demonstrate the importance of targeting cell-cell communication as an anti-cancer therapy.

Cx26 drives self-renewal in triple-negative breast cancer via interaction with NANOG and focal adhesion kinase.

Tumors adapt their phenotypes during growth and in response to therapies through dynamic changes in cellular processes. Connexin proteins enable such dynamic changes during development, and their dysregulation leads to disease states. The gap junction communication channels formed by connexins have been reported to exhibit tumor-suppressive functions, including in triple-negative breast cancer (TNBC). However, we find that connexin 26 (Cx26) is elevated in self-renewing cancer stem cells (CSCs) and is necessary and sufficient for their maintenance. Cx26 promotes CSC self-renewal by forming a signaling complex with the pluripotency transcription factor NANOG and focal adhesion kinase (FAK), resulting in NANOG stabilization and FAK activation. This FAK/NANOG-containing complex is not formed in mammary epithelial or luminal breast cancer cells. These findings challenge the paradigm that connexins are tumor suppressors in TNBC and reveal a unique function for Cx26 in regulating the core self-renewal signaling that controls CSC maintenance.

Heterogeneity of Reporting Outcomes in the Spine Surgery Literature.

STUDY DESIGN: Review of spine surgery literature between 2005 and 2014 to assess the reporting of patient outcomes by determining the variability of use of patient outcomes metrics in the following categories: pain and disability, patient satisfaction, readmission, and depression. OBJECTIVE: Expose the heterogeneity of outcomes reporting and discuss current initiatives to create more homogenous outcomes databases. SUMMARY OF BACKGROUND DATA: There has been a recent focus on the reporting of quality metrics associated with spine surgery outcomes. However, little consensus exists on the optimal metrics that should be used to measure spine surgery outcomes. MATERIALS AND METHODS: A PubMed search of all spine surgery manuscripts from January 2005 through December 2014 was performed. Linear regression analyses were performed on individual metrics as well as outcomes categories as a fraction of total papers reviewing surgical outcomes. RESULTS: Outcomes reporting has increased significantly between January 1, 2005 and December 31, 2014 [175/2871 (6.1%) vs. 764/5603 (13.6%), respectively; P<0.001; R=98.1%]. For the category of pain and disability reporting, Visual Analog Score demonstrated a statistically significant decrease in use from 2005 through 2014 [56/76 (73.7%) vs. 300/520 (57.7%), respectively; P<0.001], whereas Oswestry Disability Index increased significantly in use [19/76 (25.0%) vs. 182/520 (35.0%), respectively; P<0.001]. For quality of life, EuroQOL-5 Dimensions increased significantly in use between 2005 and 2014 [4/23 (17.4%) vs. 30/87 (34.5%), respectively; P<0.01]. In contrast, use of 36 Item Short Form Survey significantly decreased [19/23 (82.6%) vs. 57/87 (65.5%), respectively; P<0.01]. For depression, only the Zung Depression Scale underwent a significant increase in usage between 2005 and 2014 [0/0 (0%) vs. 7/13 (53.8%), respectively; P<0.01]. CONCLUSIONS: Although spine surgery outcome reporting has increased significantly over the past 10 years, there remains considerable heterogeneity in regards to individual outcomes metrics utilized. This heterogeneity makes it difficult to compare outcomes across studies and to accurately extrapolate outcomes to clinical practice.

Inhibition of Farnesyltransferase Potentiates NOTCH-Targeted Therapy against Glioblastoma Stem Cells.

Accumulating evidence suggests that cancer cells with stem cell-like phenotypes drive disease progression and therapeutic resistance in glioblastoma (GBM). NOTCH regulates self-renewal and resistance to chemoradiotherapy in GBM stem cells. However, NOTCH-targeted γ-secretase inhibitors (GSIs) exhibited limited efficacy in GBM patients. We found that farnesyltransferase inhibitors (FTIs) significantly improved sensitivity to GSIs. This combination showed significant antineoplastic and radiosensitizing activities in GBM stem cells, whereas non-stem GBM cells were resistant. These combinatorial effects were mediated, at least partially, through inhibition of AKT and cell-cycle progression. Using subcutaneous and orthotopic GBM models, we showed that the combination of FTIs and GSIs, but not either agent alone, significantly reduced tumor growth. With concurrent radiation, this combination induced a durable response in a subset of orthotopic tumors. These findings collectively suggest that the combination of FTIs and GSIs is a promising therapeutic strategy for GBM through selectively targeting the cancer stem cell subpopulation.

New Advances and Challenges of Targeting Cancer Stem Cells.

The second International Cancer Stem Cell Conference in Cleveland, Ohio, on September 20-23, 2016, convened 330 attendees from academic, industrial, and clinical organizations. It featured a debate on the concepts and challenges of the cancer stem cells (CSC) as well as CSC-centered scientific sessions on clinical trials, genetics and epigenetics, tumor microenvironment, immune suppression, metastasis, therapeutic resistance, and emerging novel concepts. The conference hosted 35 renowned speakers, 100 posters, 20 short talks, and a preconference workshop. The reported advances of CSC research and therapies fostered new collaborations across national and international borders, and inspired the next generation's young scientists. Cancer Res; 77(19); 5222-7. ©2017 AACR.

Isolation, Characterization, and Expansion of Cancer Stem Cells.

The ability to isolate, characterize, and expand distinct tumor cell populations from primary tissue or xenografts is vital to identifying molecular mechanisms specific to cancer stem cells. Once cells have been extracted from tissue, there are multiple methods by which they can be sorted and cultured. We will describe the approaches that can be taken from cancer stem cell isolation through expansion, including Magnetic-activated Cell Sorting (MACS), Fluorescence-activated Cell Sorting (FACS), the use of reporter systems, and various cell culture methods.

Microelectrode array stimulation combined with intrinsic optical imaging: A novel tool for functional brain mapping.

BACKGROUND: Functional brain mapping via cortical microstimulation is a widely used clinical and experimental tool. However, data are traditionally collected point by point, making the technique very time consuming. Moreover, even in skilled hands, consistent penetration depths are difficult to achieve. Finally, the effects of microstimulation are assessed behaviorally, with no attempt to capture the activity of the local cortical circuits being stimulated. NEW METHOD: We propose a novel method for functional brain mapping, which combines the use of a microelectrode array with intrinsic optical imaging. The precise spacing of electrodes allows for fast, accurate mapping of the area of interest in a regular grid. At the same time, the optical window allows for visualization of local neural connections when stimulation is combined with intrinsic optical imaging. RESULTS: We demonstrate the efficacy of our technique using the primate motor cortex as a sample application, using a combination of microstimulation, imaging and electrophysiological recordings during wakefulness and under anesthesia. Comparison with current method: We find the data collected with our method is consistent with previous data published by others. We believe that our approach enables data to be collected faster and in a more consistent fashion and makes possible a number of studies that would be difficult to carry out with the traditional approach. CONCLUSIONS: Our technique allows for simultaneous modulation and imaging of cortical sensorimotor networks in wakeful subjects over multiple sessions which is highly desirable for both the study of cortical organization and the design of brain machine interfaces.