As COVID-19 continues, an increasing number of patients develop long COVID symptoms varying in severity that last for weeks, months, or longer. Symptoms commonly include lingering loss of smell and taste, hearing loss, extreme fatigue, and "brain fog." Still, persistent cardiovascular and respiratory problems, muscle weakness, and neurologic issues have also been documented. A major problem is the lack of clear guidelines for diagnosing long COVID. Although some studies suggest that long COVID is due to prolonged inflammation after SARS-CoV-2 infection, the underlying mechanisms remain unclear. The broad range of COVID-19's bodily effects and responses after initial viral infection are also poorly understood. This workshop brought together multidisciplinary experts to showcase and discuss the latest research on long COVID and chronic inflammation that might be associated with the persistent sequelae following COVID-19 infection.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

Metabolism and inflammation have been viewed as two separate processes with distinct but critical functions for our survival: metabolism regulates the utilization of nutrients, and inflammation is responsible for defense and repair. Both respond to an organism's stressors to restore homeostasis. The interplay between metabolic status and immune response (immunometabolism) plays an important role in maintaining health or promoting disease development. Understanding these interactions is critical in developing tools for facilitating novel preventative and therapeutic approaches for diseases, including cancer. This trans-National Institutes of Health workshop brought together basic scientists, technology developers, and clinicians to discuss state-of-the-art, innovative approaches, challenges, and opportunities to understand and harness immunometabolism in modulating inflammation and its resolution.

The potential of artificial intelligence (AI) applied to clinical data from electronic health records (EHRs) to improve early detection for pancreatic and other cancers remains underexplored. The Kenner Family Research Fund, in collaboration with the Cancer Biomarker Research Group at the National Cancer Institute, organized the workshop entitled: "Early Detection of Pancreatic Cancer: Opportunities and Challenges in Utilizing Electronic Health Records (EHR)" in March 2021. The workshop included a select group of panelists with expertise in pancreatic cancer, EHR data mining, and AI-based modeling. This review article reflects the findings from the workshop and assesses the feasibility of AI-based data extraction and modeling applied to EHRs. It highlights the increasing role of data sharing networks and common data models in improving the secondary use of EHR data. Current efforts using EHR data for AI-based modeling to enhance early detection of pancreatic cancer show promise. Specific challenges (biology, limited data, standards, compatibility, legal, quality, AI chasm, incentives) are identified, with mitigation strategies summarized and next steps identified.

Pancreatic cancer continues to be one of the deadliest malignancies and is the third leading cause of cancer-related mortality in the United States. Based on several models, it is projected to become the second leading cause of cancer-related deaths by 2030. Although the overall survival rate for patients diagnosed with pancreatic cancer is less than 10%, survival rates are increasing in those whose cancers are detected at an early stage, when intervention is possible. There are, however, no reliable biomarkers or imaging technology that can detect early-stage pancreatic cancer or accurately identify precursors that are likely to progress to malignancy. The Alliance of Pancreatic Cancer Consortia, a virtual consortium of researchers, clinicians, and advocacies focused on early diagnosis of pancreatic cancer, was formed in 2016 to provide a platform and resources to discover and validate biomarkers and imaging methods for early detection. The focus of discussion at the most recent alliance meeting was on imaging methods and the use of artificial intelligence for early detection of pancreatic cancer.

Clinical translation of scientific discoveries from bench to bedside is typically a challenging process with sporadic progress along its trajectory. Analyzing R&D can provide key intelligence on advancing biomedical innovation in target domains of interest. In this study, we explore the feasibility of using a streamlined tech mining approach for identification of translational indicators and potential opportunities, using observable markers extracted from selected research literature. We apply this strategy to analyze a set of 23,982 PubMed records that involved gold nanostructures (GNSs) research. Nine indicators are generated to assess what different GNSs research activities had achieved and to predict where GNSs research will likely go. We believe such analysis can provide useful translation intelligence for researchers, funding agencies, and pharmaceutical and biotech companies.

Inflammation is a normal process in our body; acute inflammation acts to suppress infections and support wound healing. Chronic inflammation likely leads to a wide range of diseases, including cancer. Tools to locate and monitor inflammation are critical for developing effective interventions to arrest inflammation and promote its resolution. To identify current clinical needs, challenges, and opportunities in advancing imaging‐based evaluations of inflammatory status in patients, the U.S. National Institutes of Health convened a workshop on imaging inflammation and its resolution in health and disease. Clinical speakers described their needs for image‐based capabilities that could help determine the extent of inflammatory conditions in patients to guide treatment planning and undertake necessary interventions. The imaging speakers showcased the state‐of‐the‐art in vivo imaging techniques for detecting inflammation in different disease areas. Many imaging capabilities developed for 1 organ or disease can be adapted for other diseases and organs, whereas some have promise for clinical utility within the next 5–10 yr. Several speakers demonstrated that multimodal imaging measurements integrated with serum‐based measures could improve in robustness for clinical utility. All speakers agreed that multiple inflammatory measures should be acquired longitudinally to comprehend the dynamics of unresolved inflammation that leads to disease development. They also agreed that the best strategies for accelerating clinical translation of imaging inflammation capabilities are through integration between new imaging techniques and biofluid‐based biomarkers of inflammation as well as already established imaging measurements.—Liu, C. H., Abrams, N. D., Carrick, D. M., Chander, P., Dwyer, J., Hamlet, M. R. J., Kindzelski, A. L., PrabhuDas, M., Tsai, S.‐Y. A., Vedamony, M. M., Wang, C., Tandon, P. Imaging inflammation and its resolution in health and disease: current status, clinical needs, challenges, and opportunities. FASEB J. 33, 13085–13097 (2019). www.fasebj.org

Abstract Pancreatic cancer is the fourth leading cause of cancer death in the United States and the 5-year relative survival for patients diagnosed with pancreatic cancer is less than 10%. Early intervention is the key to a better survival outcome. Currently, there are no biomarkers that can reliably detect pancreatic cancer at an early stage or identify precursors that are destined to progress to malignancy. The Alliance of Pancreatic Cancer Consortia for Biomarkers for Early Detection was formed under the auspices of a workshop organized by the National Cancer Institute in partnership with the Kenner Family Research Fund and the Pancreatic Cancer Action Network (December 5, 2016). The second, biannual workshop of the Alliance was convened December 12, 2018. The organizers of this workshop included Drs. Ralph Hruban, Kenneth Kinzler, Stephen Pandol, Brian Wolpin, Walter Park, Samir Hanash, and Anirban Maitra. Investigators from four NCI-supported consortia on pancreatic cancer detection, including the Pancreatic Cancer Detection Consortium (PCDC), the Early Detection Research Network (EDRN), the Consortium for the Study of Chronic Pancreatitis, Diabetes and Pancreatic Cancer (CPDPC), and the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), were invited to participate in the workshop. Other invited participants included members of the Kenner Family Research Fund, the Pancreatic Cancer Action Network, investigators from industry, and biostatisticians from NCI‘s Division of Cancer Prevention, Biometry Research Group, and from the EDRN. The keynote speakers included Dr. Ralph Hruban from Johns Hopkins University who spoke about the challenges in pancreatic cancer research, and Dr. James Abbruzzese from Duke Cancer Institute who provided an updated report from NCI’s Scientific Framework for Pancreatic Ductal Adenocarcinoma (PDAC), which met shortly before the workshop. The focus of the workshop included discussion of the molecular characterization of PDAC and its precursors and methodologies for early detection of pancreatic cancer, new imaging applications and artificial intelligence for mining big data, development and utilization of new methods for biomarker validation, and biospecimen science and resources. The members of the workshop agreed that the Alliance should immediately begin working on the development of a repository for images collected prior to the diagnosis of pancreatic cancer. These images could be shared with the Alliance members and would be useful for development of imaging biomarkers. Working with the NCI, the Alliance has begun establishing the common data elements (CDE) needed to develop a repository of pancreatic cancer images. The Alliance of Pancreatic Cancer Consortia provides a valuable forum for investigators and collaborators to discuss and address early detection of pancreatic cancer. Citation Format: Matthew R. Young, Natalie Abrams, Sharmistha Ghosh-Janjigian, Guillermo Marquez, Jo Ann Rinaudo, Sudhir Srivastava. The Alliance of Ppancreatic Cancer Consortia for Biomarkers for Early Detection [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr C62.

The US National Institutes of Health convened a workshop on disease-promoting chronic inflammation to identify the challenges and needs in the development of clinically feasible strategies for monitoring a person's inflammation status before, during and after disease occurrence.

Clinical translation of technological discoveries from bench to bedside has been a slow and incremental process. Capturing early events in technology development can provide key insights into the nature of biomedical innovation and its bottlenecks. The sheer volume of the available information, however, presents a significant barrier to a systematic assessment of current capabilities. Nanomedicine, which deals with medical applications of nanotechnology, perfectly exemplifies the challenges facing translational research. In this study, we have explored the feasibility of using a streamlined tech mining approach for identification of translational innovation pathways using observable markers found in research literature. The framework contains three sections: 1) extraction of feature terms from titles and abstracts; 2) tagging research articles with translational stages and application markers; and 3) analysis of topical changes, translational phases, and innovation pathways. We applied this strategy to analyze a set of 23,982 PubMed records that involved gold nanostructures (GNSs), which have been extensively studied in a wide range of biomedical applications. The studies were classified based on their intended clinical application, research field, disease, and translational stage. Our results have identified a significant increase in GNSs studies in the areas of cancer, therapeutic applications, and animal testing. Additionally, the tags along with feature terms were used to build innovation pathway maps for three types of biomedical applications: treatment, in vitro detection, or imaging. The framework described in this paper can be useful for academic researchers, funding agencies, as well as pharmaceutical and medical device companies to facilitate assessment of translational readiness and future research planning

The epithelial-mesenchymal transition (EMT) is thought to be essential for cancer metastasis. While chromatin remodeling is involved in EMT, which processes contribute to this remodeling remain poorly investigated. Recently, we showed that silencing or removal of the histone variant H2A.X induced mesenchymal-like characteristics, including activation of the EMT transcription factors, Slug and Zeb1 in human colon cancer cells.Here, we provide the evidence that H2A.X loss in human non-tumorigenic breast cell line MCF10A results in a robust EMT activation, as substantiated by a genome-wide expression analysis. Cells deficient for H2A.X exhibit enhanced migration and invasion, along with an activation of a set of mesenchymal genes and a concomitant repression of epithelial genes. In the breast model, the EMT-related transcription factor Twist1 cooperates with Slug to regulate EMT upon H2A.X Loss. Of interest, H2A.X expression level tightly correlates with Twist1, and to a lesser extent with Slug in the panel of human breast cancer cell lines of the NCI-60 datasets. These new findings indicate that H2A.X is involved in the EMT processes in cells of different origins but pairing with transcription factors for EMT may be tissue specific.

The epithelial–mesenchymal transition (EMT), considered essential for metastatic cancer, has been a focus of much research, but important questions remain. Here, we show that silencing or removing H2A.X, a histone H2A variant involved in cellular DNA repair and robust growth, induces mesenchymal-like characteristics including activation of EMT transcription factors, Slug and ZEB1, in HCT116 human colon cancer cells. Ectopic H2A.X re-expression partially reverses these changes, as does silencing Slug and ZEB1. In an experimental metastasis model, the HCT116 parental and H2A.X-null cells exhibit a similar metastatic behaviour, but the cells with re-expressed H2A.X are substantially more metastatic. We surmise that H2A.X re-expression leads to partial EMT reversal and increases robustness in the HCT116 cells, permitting them to both form tumours and to metastasize. In a human adenocarcinoma panel, H2A.X levels correlate inversely with Slug and ZEB1 levels. Together, these results point to H2A.X as a regulator of EMT.

Abstract The vast majority of deaths from cancer are due to its progression from primary tumor to metastatic disease. Understanding how some cells can migrate from the primary tumor to seed new tumors throughout the body is a longstanding challenge in developing novel cancer treatments. Current models propose that these migratory cells acquire an invasive phenotype through a process known as the epithelial to mesenchymal transition (EMT), characterized by the loss of cell polarity and cell-to-cell adhesion, and the acquisition of migratory and invasive properties. Such changes allow the cells to invade the extracellular matrix and migrate throughout the body, aided by the formation of lamellipodia, filopodia and invadopodia. In a converse transition, the MET, mesenchymal cells may revert back to the epithelial phenotype and establish tumors at distant sites.We have preliminary observations showing that when one member of the H2A family of histones, H2AX, is silenced or inactivated in HCT116 cells, these cells exhibit mesenchymal-like characteristics including increased invasiveness. Furthermore, genome-wide expression analysis implicates the critical EMT transcription factors, SLUG and ZEB1, as mediators of H2AX loss-induced EMT. To make these studies more rigorous, we generated a novel HCT116 cell line with H2AX destroyed. Our findings show that ectopic expression of H2AX in H2AX-null cells reverses the invasiveness induced by H2AX inactivation. Moreover, in 233 human colon cancer samples, a strong correlation is found between the levels of H2AX and SLUG/ZEB1 expression. These observations lead us to hypothesize that H2AX is a key regulator of EMT and plays a critical role in the downstream events leading to increased metastasis. Note: This abstract was not presented at the meeting. Citation Format: SOSSOU U. WEYEMI, Christophe E. Redon, Rohini Choudhuri, Daisuke Maeda, Manjula Kasoji, Natalie Abrams, William M. Bonner. Histone H2AX is a novel regulator of epithelial to mesenchymal transition. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4774. doi:10.1158/1538-7445.AM2015-4774

The pmel-1 T cell receptor transgenic mouse has been extensively employed as an ideal model system to study the mechanisms of tumor immunology, CD8+ T cell differentiation, autoimmunity and adoptive immunotherapy. The 'zygosity' of the transgene affects the transgene expression levels and may compromise optimal breeding scheme design. However, the integration sites for the pmel-1 mouse have remained uncharacterized. This is also true for many other commonly used transgenic mice created before the modern era of rapid and inexpensive next-generation sequencing. Here, we show that whole genome sequencing can be used to determine the exact pmel-1 genomic integration site, even with relatively 'shallow' (8X) coverage. The results were used to develop a validated polymerase chain reaction-based genotyping assay. For the first time, we provide a quick and convenient polymerase chain reaction method to determine the dosage of pmel-1 transgene for this freely and publically available mouse resource. We also demonstrate that next-generation sequencing provides a feasible approach for mapping foreign DNA integration sites, even when information of the original vector sequences is only partially known.

BACKGROUND:The genera Aspergillus and Penicillium include some of the most beneficial as well as the most harmful fungal species such as the penicillin-producer Penicillium chrysogenum and the human pathogen Aspergillus fumigatus, respectively. Their mitochondrial genomic sequences may hold vital clues into the mechanisms of their evolution, population genetics, and biology, yet only a handful of these genomes have been fully sequenced and annotated.RESULTS:Here we report the complete sequence and annotation of the mitochondrial genomes of six Aspergillus and three Penicillium species: A. fumigatus, A. clavatus, A. oryzae, A. flavus, Neosartorya fischeri (A. fischerianus), A. terreus, P. chrysogenum, P. marneffei, and Talaromyces stipitatus (P. stipitatum). The accompanying comparative analysis of these and related publicly available mitochondrial genomes reveals wide variation in size (25-36 Kb) among these closely related fungi. The sources of genome expansion include group I introns and accessory genes encoding putative homing endonucleases, DNA and RNA polymerases (presumed to be of plasmid origin) and hypothetical proteins. The two smallest sequenced genomes (A. terreus and P. chrysogenum) do not contain introns in protein-coding genes, whereas the largest genome (T. stipitatus), contains a total of eleven introns. All of the sequenced genomes have a group I intron in the large ribosomal subunit RNA gene, suggesting that this intron is fixed in these species. Subsequent analysis of several A. fumigatus strains showed low intraspecies variation. This study also includes a phylogenetic analysis based on 14 concatenated core mitochondrial proteins. The phylogenetic tree has a different topology from published multilocus trees, highlighting the challenges still facing the Aspergillus systematics.CONCLUSIONS:The study expands the genomic resources available to fungal biologists by providing mitochondrial genomes with consistent annotations for future genetic, evolutionary and population studies. Despite the conservation of the core genes, the mitochondrial genomes of Aspergillus and Penicillium species examined here exhibit significant amount of interspecies variation. Most of this variation can be attributed to accessory genes and mobile introns, presumably acquired by horizontal gene transfer of mitochondrial plasmids and intron homing.