Improving Cancer Data Interoperability: The Promise of the Minimal Common Oncology Data Elements (mCODE) Initiative

Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis: One Decade Later

In 2012, the National Academies of Sciences, Engineering, and Medicine convened a committee charged with addressing the quality of cancer care in the United States and providing recommendations to policymakers and the cancer care community on strategies to improve cancer care delivery from the time of diagnosis through end-of-life. The resulting committee report, titled Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis (2013), presented a conceptual framework that included six interconnected components of care with corresponding recommendations. Over the past decade, the delivery of high-quality of cancer care has become more challenging and increasingly demanding on the workforce. In this manuscript, we review the goals and recommendations made in 2013, describe progress to date, and offer insights into future dedicated efforts and/or new strategies needed to achieve high-quality cancer care.

MoCab: A framework for the deployment of machine learning models across health information systems

BACKGROUND AND OBJECTIVE

Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and diverse electronic health records (EHR) formats. We proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs, addressing the challenges highlighted by platforms such as EPOCH®, ePRISM®, KETOS, and others.

METHODS

The MoCab architecture is designed to streamline predictive modeling in healthcare through a structured framework incorporating several specialized parts. The Data Service Center manages patient data retrieval from FHIR servers. These data are then processed by the Knowledge Model Center, where they are formatted and fed into predictive models. The Model Retraining Center is crucial in continuously updating these models to maintain accuracy in dynamic clinical environments. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts. It uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop applications for displaying alerts, prediction results, and patient records.

RESULTS

The MoCab framework was demonstrated using three types of predictive models: a scoring model (qCSI), a machine learning model (NSTI), and a deep learning model (SPC), applied to synthetic data that mimic a major EHR system. The implementations showed how MoCab integrates predictive models with health data for clinical decision support, utilizing CDS Hooks and SMART on FHIR for seamless HIS integration. The demonstration confirmed the practical utility of MoCab in supporting clinical decision making, validated by its application in various healthcare settings.

CONCLUSIONS

We demonstrate MoCab's potential in promoting the interoperability of machine learning models and enhancing its utility across various EHRs. Despite facing challenges like FHIR adoption, MoCab addresses key challenges in adapting machine learning models within healthcare settings, paving the way for further enhancements and broader adoption.

Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysis

BACKGROUND

The rise of treatment resistance and variability across malignant profiles has made precision oncology an imperative in today's medical landscape. Prostate cancer is a prevalent form of cancer in males, characterized by significant diversity in both genomic and clinical characteristics. The tumor microenvironment consists of stroma, tumor cells, and various immune cells. The stromal components and tumor cells engage in mutual communication and facilitate the development of a low-oxygen and pro-cancer milieu by producing cytokines and activating pro-inflammatory signaling pathways.

METHODS

In order to discover new genes associated with tumor cells that interact and facilitate a hypoxic environment in prostate cancer, we conducted a cutting-edge bioinformatics investigation. This included analyzing high-throughput genomic datasets obtained from the cancer genome atlas (TCGA).

RESULTS

A combination of weighted gene co-expression network analysis and single-cell sequencing has identified nine dysregulated immune hub genes (AMACR, KCNN3, MME, EGFR, FLT1, GDF15, KDR, IGF1, and KRT7) that are believed to have significant involvement in the biological pathways involved with the advancement of prostate cancer enviriment. In the prostate cancer environment, we observed the overexpression of GDF15 and KRT7 genes, as well as the downregulation of other genes. Additionally, the cBioPortal platform was used to investigate the frequency of alterations in the genes and their effects on the survival of the patients. The Kaplan-Meier survival analysis indicated that the changes in the candidate genes were associated with a reduction in the overall survival of the patients.

CONCLUSIONS

In summary, the findings indicate that studying the genes and their genomic changes may be used to develop precise treatments for prostate cancer. This approach involves early detection and targeted therapy.

HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) in digital healthcare ecosystems for chronic disease management: Scoping review

BACKGROUND

The prevalence of chronic diseases has shifted the burden of disease from incidental acute inpatient admissions to long-term coordinated care across healthcare institutions and the patient's home. Digital healthcare ecosystems emerge to target increasing healthcare costs and invest in standard Application Programming Interfaces (API), such as HL7 Fast Healthcare Interoperability Resources (HL7 FHIR) for trusted data flows.

OBJECTIVES

This scoping review assessed the role and impact of HL7 FHIR and associated Implementation Guides (IGs) in digital healthcare ecosystems focusing on chronic disease management.

METHODS

To study trends and developments relevant to HL7 FHIR, a scoping review of the scientific and gray English literature from 2017 to 2023 was used.

RESULTS

The selection of 93 of 524 scientific papers reviewed in English indicates that the popularity of HL7 FHIR as a robust technical interface standard for the health sector has been steadily rising since its inception in 2010, reaching a peak in 2021. Digital Health applications use HL7 FHIR in cancer (45 %), cardiovascular disease (CVD) (more than 15 %), and diabetes (almost 15 %). The scoping review revealed that references to HL7 FHIR IGs are limited to ∼ 20 % of articles reviewed. HL7 FHIR R4 was most frequently referenced when the HL7 FHIR version was mentioned. In HL7 FHIR IGs registries and the internet, we found 35 HL7 FHIR IGs addressing chronic disease management, i.e., cancer (40 %), chronic disease management (25 %), and diabetes (20 %). HL7 FHIR IGs frequently complement the information in the article.

CONCLUSIONS

HL7 FHIR matures with each revision of the standard as HL7 FHIR IGs are developed with validated data sets, common shared HL7 FHIR resources, and supporting tools. Referencing HL7 FHIR IGs cataloged in official registries and in scientific publications is recommended to advance data quality and facilitate mutual learning in growing digital healthcare ecosystems that nurture interoperability in digital health innovation.

Differential expression of ion channel coding genes in the endometrium of women experiencing recurrent implantation failures

Our study probed the differences in ion channel gene expression in the endometrium of women with Recurrent Implantation Failure (RIF) compared to fertile women. We analyzed the relative expression of genes coding for T-type Ca2+, ENaC, CFTR, and KCNQ1 channels in endometrial samples from 20 RIF-affected and 10 control women, aged 22-35, via microarray analysis and quantitative real-time PCR. Additionally, we examined DNA methylation in the regulatory region of KCNQ1 using ChIP real-time PCR. The bioinformatics component of our research included Gene Ontology analysis, protein-protein interaction networks, and signaling pathway mapping to identify key biological processes and pathways implicated in RIF. This led to the discovery of significant alterations in the expression of ion channel genes in RIF women's endometrium, most notably an overexpression of CFTR and reduced expression of SCNN1A, SCNN1B, SCNN1G, CACNA1H, and KCNQ1. A higher DNA methylation level of KCNQ1's regulatory region was also observed in RIF patients. Gene-set enrichment analysis highlighted a significant presence of genes involved with ion transport and membrane potential regulation, particularly in sodium and calcium channel complexes, which are vital for cation movement across cell membranes. Genes were also enriched in broader ion channel and transmembrane transporter complexes, underscoring their potential extensive role in cellular ion homeostasis and signaling. These findings suggest a potential involvement of ion channels in the pathology of implantation failure, offering new insights into the mechanisms behind RIF and possible therapeutic targets.

Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review

The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.

The importance of studying the implementation of cancer data standards

Data standards promise better quality measurement, discovery, population health management, and interoperability. For oncology practices to make informed decisions about implementation, it is paramount that the cancer informatics community continue to study and publish diverse methods to structure data.

Gene Expression and DNA Methylation Profiling Suggest Potential Biomarkers for Azacitidine Resistance in Myelodysplastic Syndrome

Myelodysplastic syndrome/neoplasm (MDS) comprises a group of heterogeneous hematopoietic disorders that present with genetic mutations and/or cytogenetic changes and, in the advanced stage, exhibit wide-ranging gene hypermethylation. Patients with higher-risk MDS are typically treated with repeated cycles of hypomethylating agents, such as azacitidine. However, some patients fail to respond to this therapy, and fewer than 50% show hematologic improvement. In this context, we focused on the potential use of epigenetic data in clinical management to aid in diagnostic and therapeutic decision-making. First, we used the F-36P MDS cell line to establish an azacitidine-resistant F-36P cell line. We performed expression profiling of azacitidine-resistant and parental F-36P cells and used biological and bioinformatics approaches to analyze candidate azacitidine-resistance-related genes and pathways. Eighty candidate genes were identified and found to encode proteins previously linked to cancer, chronic myeloid leukemia, and transcriptional misregulation in cancer. Interestingly, 24 of the candidate genes had promoter methylation patterns that were inversely correlated with azacitidine resistance, suggesting that DNA methylation status may contribute to azacitidine resistance. In particular, the DNA methylation status and/or mRNA expression levels of the four genes (AMER1, HSPA2, NCX1, and TNFRSF10C) may contribute to the clinical effects of azacitidine in MDS. Our study provides information on azacitidine resistance diagnostic genes in MDS patients, which can be of great help in monitoring the effectiveness of treatment in progressing azacitidine treatment for newly diagnosed MDS patients.

Biomimetic biphasic microsphere preparation based on the thermodynamic incompatibility of glycosaminoglycan with gelatin methacrylate for hair regeneration

Hair follicle (HF) tissue engineering is promising for hair loss treatment especially for androgenetic alopecia. Physiologically, the initiation of HF morphogenesis relies on the interactions between hair germ mesenchymal and epithelial layers. To simulate this intricate process, in this study, a co-flowing microfluidic-assisted technology was developed to produce dual aqueous microdroplets capturing growth factors and double-layer cells for subsequent use in hair regeneration. Microspheres, called G/HAD, were generated using glycosaminoglycan-based photo-crosslinkable biological macromolecule (HAD) shells and gelatin methacrylate (GelMA) cores to enclose mesenchymal cells (MSCs) and mouse epidermal cells (EPCs). The findings indicated that the glycosaminoglycan-based HAD shells display thermodynamic incompatibility with GelMA cores, resulting in the aqueous phase separation of G/HAD cell spheres. These G/HAD microspheres exhibited favorable characteristics, including sustained growth factor release and wet adhesion properties. After transplantation into the dorsal skin of BALB/c nude mice, G/HAD cell microspheres efficiently induced the regeneration of HFs. This approach enables the mass production of approximately 250 dual-layer microspheres per minute. Thus, this dual-layer microsphere fabrication method holds great potential in improving current hair regeneration techniques and can also be combined with other tissue engineering techniques for various regenerative purposes.

Design and implementation of a prototype radiotherapy menu in a patient portal

PURPOSE

Radiotherapy patients often face undue anxiety due to misconceptions about radiation and their inability to visualize their upcoming treatments. Access to their personal treatment plans is one way in which pre-treatment anxiety may be reduced. But radiotherapy data are quite complex, requiring specialized software for display and necessitating personalized explanations for patients to understand them. Therefore, our goal was to design and implement a novel radiotherapy menu in a patient portal to improve patient access to and understanding of their radiotherapy treatment plans.

METHODS

A prototype radiotherapy menu was developed in our institution's patient portal following a participatory stakeholder co-design methodology. Customizable page templates were designed to render key radiotherapy data in the portal's patient-facing mobile phone app. DICOM-RT data were used to provide patients with relevant treatment parameters and generate pre-treatment 3D visualizations of planned treatment beams, while the mCODE data standard was used to provide post-treatment summaries of the delivered treatments. A focus group was conducted to gather initial patient feedback on the menu.

RESULTS

Pre-treatment: the radiotherapy menu provides patients with a personalized treatment plan overview, including a personalized explanation of their treatment, along with an interactive 3D rendering of their body, and treatment beams for visualization. Post-treatment: a summary of the delivered radiotherapy is provided, allowing patients to retain a concise personal record of their treatment that can easily be shared with future healthcare providers. Focus group feedback was overwhelmingly positive. Patients highlighted how the intuitive presentation of their complex radiotherapy data would better prepare them for their radiation treatments.

CONCLUSIONS

We successfully designed and implemented a prototype radiotherapy menu in our institution's patient portal that improves patient access to and understanding of their radiotherapy data. We used the mCODE data standard to generate post-treatment summaries in a way that is easily shareable and interoperable.

Navigating Precision Oncology: Insights from an Integrated Clinical Data and Biobank Repository Initiative across a Network Cancer Program

Advancing cancer treatment relies on the rapid translation of new scientific discoveries to patient care. To facilitate this, an oncology biobank and data repository program, also referred to as the "Moonshot" program, was launched in 2021 within the Integrated Network Cancer Program of the Allegheny Health Network. A clinical data program (CDP) and biospecimen repository were established, and patient data and blood and tissue samples have been collected prospectively. To date, the study has accrued 2920 patients, predominantly female (61%) and Caucasian (90%), with a mean age of 64 ± 13 years. The most common cancer sites were the endometrium/uterus (12%), lung/bronchus (12%), breast (11%), and colon/rectum (11%). Of patients diagnosed with cancer, 34% were diagnosed at stage I, 25% at stage II, 26% at stage III, and 15% at stage IV. The CDP is designed to support our initiative in advancing personalized cancer research by providing a comprehensive array of patient data, encompassing demographic characteristics, diagnostic details, and treatment responses. The "Moonshot" initiative aims to predict therapy responses and clinical outcomes through cancer-related biomarkers. The CDP facilitates this initiative by fostering data sharing, enabling comparative analyses, and informing the development of novel diagnostic and therapeutic methods.

The Facilitators and Barriers of the Implementation of a Clinical Decision Support System for Breast Cancer Multidisciplinary Team Meetings-An Interview Study

BACKGROUND

AI-driven clinical decision support systems (CDSSs) hold promise for multidisciplinary team meetings (MDTMs). This study aimed to uncover the hurdles and aids in implementing CDSSs during breast cancer MDTMs.

METHODS

Twenty-four core team members from three hospitals engaged in semi-structured interviews, revealing a collective interest in experiencing CDSS workflows in clinical practice. All interviews were audio recorded, transcribed verbatim and analyzed anonymously. A standardized approach, 'the framework method', was used to create an analytical framework for data analysis, which was performed by two independent researchers.

RESULTS

Positive aspects included improved data visualization, time-saving features, automated trial matching, and enhanced documentation transparency. However, challenges emerged, primarily concerning data connectivity, guideline updates, the accuracy of AI-driven suggestions, and the risk of losing human involvement in decision making. Despite the complexities involved in CDSS development and integration, clinicians demonstrated enthusiasm to explore its potential benefits.

CONCLUSIONS

Acknowledging the multifaceted nature of this challenge, insights into the barriers and facilitators identified in this study offer a potential roadmap for smoother future implementations. Understanding these factors could pave the way for more effective utilization of CDSSs in breast cancer MDTMs, enhancing patient care through informed decision making.

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

Background and Aims

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action.

Methods

Gene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein-protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue.

Results

We identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest-scoring hub genes were screened to identify critical genes in the protein-protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para-cancer tissues.

Conclusion

Identifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms.

Enhancement and Implementation of a Health Information Technology Module to Improve the Discrete Capture of Cancer Staging in a Diverse Regional Health System

PURPOSE

Cancer staging is the foundation for all cancer management decisions. For real-time use, stage must be embedded in the electronic health record as a discrete data element. The objectives of this quality improvement (QI) initiative were to (1) identify barriers to utilization of an existing discrete cancer staging module, (2) identify health information technology (HIT) solutions to support discrete capture of cancer staging data, and (3) increase capture across the oncology enterprise in our diverse health system.

METHODS

Six sigma QI methodologies were used to define barriers and solutions to improve discrete cancer staging. Design thinking principles informed solution development to test prototypes. Two multidisciplinary teams of disease-specific clinicians within GI and genitourinary conducted phased testing pilots to determine health system solutions. Solutions were expanded to all oncology specialties across our health system.

RESULTS

Baseline average discrete staging capture across our health system was 31%. Poor workflow efficiency, limited accountability, and technical design gaps were key barriers to timely, complete staging. Implementation of more than 25 design enhancements to a HIT solution and passive user alerts led to a postimplementation capture rate of 58% across 55 outpatient clinics involving more than 400 clinicians.

CONCLUSION

We identified key barriers to discrete data capture and designed solutions through iterative use of QI methodologies and disease-specific pilots. After implementation, discrete capture of cancer staging nearly doubled across our diverse health system. This approach is scalable and transferable to other initiatives to develop and implement clinically relevant HIT solutions across a diverse health system.

Single cell and bulk transcriptome analysis identified oxidative stress response-related features of Hepatocellular Carcinoma

Background: Hepatocellular Carcinoma (HCC) is a common lethal digestive system tumor. The oxidative stress mechanism is crucial in the HCC genesis and progression. Methods: Our study analyzed single-cell and bulk sequencing data to compare the microenvironment of non-tumor liver tissues and HCC tissues. Through these analyses, we aimed to investigate the effect of oxidative stress on cells in the HCC microenvironment and identify critical oxidative stress response-related genes that impact the survival of HCC patients. Results: Our results showed increased oxidative stress in HCC tissue compared to non-tumor tissue. Immune cells in the HCC microenvironment exhibited higher oxidative detoxification capacity, and oxidative stress-induced cell death of dendritic cells was attenuated. HCC cells demonstrated enhanced communication with immune cells through the MIF pathway in a highly oxidative hepatoma microenvironment. Meanwhile, using machine learning and Cox regression screening, we identified PRDX1 as a predictor of early occurrence and prognosis in patients with HCC. The expression level of PRDX1 in HCC was related to dysregulated ribosome biogenesis and positively correlated with the expression of immunological checkpoints (PDCD1LG2, CTLA4, TIGIT, LAIR1). High PRDX1 expression in HCC patients correlated with better sensitivity to immunotherapy agents such as sorafenib, IGF-1R inhibitor, and JAK inhibitor. Conclusion: In conclusion, our study unveiled variations in oxidative stress levels between non-tumor liver and HCC tissues. And we identified oxidative stress gene markers associated with hepatocarcinogenesis development, offering novel insights into the oxidative stress response mechanism in HCC.

Tactical Considerations for Designing Real-World Studies: Fit-for-Purpose Designs That Bridge Research and Practice

Real-world evidence (RWE) is being used to provide information on diverse groups of patients who may be highly impacted by disease but are not typically studied in traditional randomized clinical trials (RCT) and to obtain insights from everyday care settings and real-world adherence to inform clinical practice. RWE is derived from so-called real-world data (RWD), ie, information generated by clinicians in the course of everyday patient care, and is sometimes coupled with systematic input from patients in the form of patient-reported outcomes or from wearable biosensors. Studies using RWD are conducted to evaluate how well medical interventions, services, and diagnostics perform under conditions of real-world use, and may include long-term follow-up. Here, we describe the main types of studies used to generate RWE and offer pointers for clinicians interested in study design and execution. Our tactical guidance addresses (1) opportunistic study designs, (2) considerations about representativeness of study participants, (3) expectations for transparency about data provenance, handling and quality assessments, and (4) considerations for strengthening studies using record linkage and/or randomization in pragmatic clinical trials. We also discuss likely sources of bias and suggest mitigation strategies. We see a future where clinical records - patient-generated data and other RWD - are brought together and harnessed by robust study design with efficient data capture and strong data curation. Traditional RCT will remain the mainstay of drug development, but RWE will play a growing role in clinical, regulatory, and payer decision-making. The most meaningful RWE will come from collaboration with astute clinicians with deep practice experience and questioning minds working closely with patients and researchers experienced in the development of RWE.

Polypharmacology guided drug repositioning approach for SARS-CoV2

Drug repurposing has emerged as an important strategy and it has a great potential in identifying therapeutic applications for COVID-19. An extensive virtual screening of 4193 FDA approved drugs has been carried out against 24 proteins of SARS-CoV2 (NSP1-10 and NSP12-16, envelope, membrane, nucleoprotein, spike, ORF3a, ORF6, ORF7a, ORF8, and ORF9b). The drugs were classified into top 10 and bottom 10 drugs based on the docking scores followed by the distribution of their therapeutic indications. As a result, the top 10 drugs were found to have therapeutic indications for cancer, pain, neurological disorders, and viral and bacterial diseases. As drug resistance is one of the major challenges in antiviral drug discovery, polypharmacology and network pharmacology approaches were employed in the study to identify drugs interacting with multiple targets and drugs such as dihydroergotamine, ergotamine, bisdequalinium chloride, midostaurin, temoporfin, tirilazad, and venetoclax were identified among the multi-targeting drugs. Further, a pathway analysis of the genes related to the multi-targeting drugs was carried which provides insight into the mechanism of drugs and identifying targetable genes and biological pathways involved in SARS-CoV2.

Analysis of Under-Diagnosed Malignancy during Fine Needle Aspiration Cytology of Lymphadenopathies

Fine needle aspiration cytology (FNAC) is a useful tool in the evaluation of lymphadenopathy. It is a safe and minimally invasive procedure that provides preoperative details for subsequent treatment. It can also diagnose the majority of malignant tumors. However, there are some instances where the diagnosis of tumors remains obscure. To address this, we re-analyzed the misinterpreted patients' samples using mRNA sequencing technology and then identified the characteristics of non-Hodgkin's lymphoma that tend to be under-diagnosed. To decipher the involved genes and pathways, we used bioinformatic and biological analysis approaches, identifying the response to oxygen species, inositol phosphate metabolic processes, and peroxisome and PPAR pathways as possibly being involved with this type of tumor. Notably, these analyses identified FOS, ENDOG, and PRKAR2B as hub genes. cBioPortal, a multidimensional cancer genomics database, also confirmed that these genes were associated with lymphoma patients. These results thus point to candidate genes that could be used as biomarkers to minimize the false-negative rate of FNAC diagnosis. We are currently pursuing the development of a gene chip to improve the diagnosis of lymphadenopathy patients with the ultimate goal of improving their prognosis.

Clinical Informatics Approaches to Facilitate Cancer Data Sharing

OBJECTIVES

Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data.

METHODS

We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models.

RESULTS

Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation.

CONCLUSIONS

Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.

Osteoarthritis related epigenetic variations in miRNA expression and DNA methylation

Osteoarthritis (OA) is chronic arthritis characterized by articular cartilage degradation. However, a comprehensive regulatory network for OA-related microRNAs and DNA methylation modifications has yet to be established. Thus, we aimed to identify epigenetic changes in microRNAs and DNA methylation and establish the regulatory network between miRNAs and DNA methylation. The mRNA, miRNA, and DNA methylation expression profiles of healthy or osteoarthritis articular cartilage samples were downloaded from Gene Expression Omnibus (GEO) database, including GSE169077, GSE175961, and GSE162484. The differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs), and differentially methylated genes (DMGs) were analyzed by the online tool GEO2R. DAVID and STRING databases were applied for functional enrichment analysis and protein-protein interaction (PPI) network. Potential therapeutic compounds for the treatment of OA were identified by Connectivity map (CMap) analysis. A total of 1424 up-regulated DEGs, 1558 down-regulated DEGs, 5 DEMs with high expression, 6 DEMs with low expression, 1436 hypermethylated genes, and 455 hypomethylated genes were selected. A total of 136 up-regulated and 65 downregulated genes were identified by overlapping DEGs and DEMs predicted target genes which were enriched in apoptosis and circadian rhythm. A total of 39 hypomethylated and 117 hypermethylated genes were obtained by overlapping DEGs and DMGs, which were associated with ECM receptor interactions and cellular metabolic processes, cell connectivity, and transcription. Moreover, The PPI network showed COL5A1, COL6A1, LAMA4, T3GAL6A, and TP53 were the most connective proteins. After overlapping of DEGs, DMGs and DEMs predicted targeted genes, 4 up-regulated genes and 11 down-regulated genes were enriched in the Axon guidance pathway. The top ten genes ranked by PPI network connectivity degree in the up-regulated and downregulated overlapping genes of DEGs and DMGs were further analyzed by the CMap database, and nine chemicals were predicted as potential drugs for the treatment of OA. In conclusion, TP53, COL5A1, COL6A1, LAMA4, and ST3GAL6 may play important roles in OA genesis and development.

Informatics tools to implement late cardiovascular risk prediction modeling for population management of high-risk childhood cancer survivors

BACKGROUND

Clinical informatics tools to integrate data from multiple sources have the potential to catalyze population health management of childhood cancer survivors at high risk for late heart failure through the implementation of previously validated risk calculators.

METHODS

The Oklahoma cohort (n = 365) harnessed data elements from Passport for Care (PFC), and the Duke cohort (n = 274) employed informatics methods to automatically extract chemotherapy exposures from electronic health record (EHR) data for survivors 18 years old and younger at diagnosis. The Childhood Cancer Survivor Study (CCSS) late cardiovascular risk calculator was implemented, and risk groups for heart failure were compared to the Children's Oncology Group (COG) and the International Guidelines Harmonization Group (IGHG) recommendations. Analysis within the Oklahoma cohort assessed disparities in guideline-adherent care.

RESULTS

The Oklahoma and Duke cohorts both observed good overall concordance between the CCSS and COG risk groups for late heart failure, with weighted kappa statistics of .70 and .75, respectively. Low-risk groups showed excellent concordance (kappa > .9). Moderate and high-risk groups showed moderate concordance (kappa .44-.60). In the Oklahoma cohort, adolescents at diagnosis were significantly less likely to receive guideline-adherent echocardiogram surveillance compared with survivors younger than 13 years old at diagnosis (odds ratio [OD] 0.22; 95% confidence interval [CI]: 0.10-0.49).

CONCLUSIONS

Clinical informatics tools represent a feasible approach to leverage discrete treatment-related data elements from PFC or the EHR to successfully implement previously validated late cardiovascular risk prediction models on a population health level. Concordance of CCSS, COG, and IGHG risk groups using real-world data informs current guidelines and identifies inequities in guideline-adherent care.

Implementing Innovation: Informatics-Based Technologies to Improve Care Delivery and Clinical Research

Improving technology has promised to improved health care delivery and the lives of patients. The realized benefits of technology, however, are delayed or less than anticipated. Three recent technology initiatives are reviewed: the Clinical Trials Rapid Activation Consortium (CTRAC), minimal Common Oncology Data Elements (mCODE), and electronic Patient-Reported Outcomes. Each initiative is at a different stage of maturity but promises to improve the delivery of cancer care. CTRAC is an ambitious initiative funded by the National Cancer Institute (NCI) to develop processes across multiple NCI-supported cancer centers to facilitate the development of centralized electronic health record (EHR) treatment plans. Facilitating interoperability of treatment regimens has the potential to improve sharing between centers and decrease the time to begin clinical trials. The mCODE initiative began in 2019 and is currently Standard for Trial Use version 2. This data standard provides an abstraction layer on top of EHR data and has been implemented across more than 60 organizations. Patient-reported outcomes have been shown to improve patient care in numerous studies. Best practices for how to leverage these in an oncology practice continue to evolve. These three examples show how innovative has diffused into practice and evolved cancer care delivery and highlight a movement toward patient-centered data and interoperability.

How to Improve Cancer Patients ENrollment in Clinical Trials From rEal-Life Databases Using the Observational Medical Outcomes Partnership Oncology Extension: Results of the PENELOPE Initiative in Urologic Cancers

PURPOSE

To compare the computability of Observational Medical Outcomes Partnership (OMOP)-based queries related to prescreening of patients using two versions of the OMOP common data model (CDM; v5.3 and v5.4) and to assess the performance of the Greater Paris University Hospital (APHP) prescreening tool.

MATERIALS AND METHODS

We identified the prescreening information items being relevant for prescreening of patients with cancer. We randomly selected 15 academic and industry-sponsored urology phase I-IV clinical trials (CTs) launched at APHP between 2016 and 2021. The computability of the related prescreening criteria (PC) was defined by their translation rate in OMOP-compliant queries and by their execution rate on the APHP clinical data warehouse (CDW) containing data of 205,977 patients with cancer. The overall performance of the prescreening tool was assessed by the rate of true- and false-positive cases of three randomly selected CTs.

RESULTS

We defined a list of 15 minimal information items being relevant for patients' prescreening. We identified 83 PC of the 534 eligibility criteria from the 15 CTs. We translated 33 and 62 PC in queries on the basis of OMOP CDM v5.3 and v5.4, respectively (translation rates of 40% and 75%, respectively). Of the 33 PC translated in the v5.3 of the OMOP CDM, 19 could be executed on the APHP CDW (execution rate of 58%). Of 83 PC, the computability rate on the APHP CDW reached 23%. On the basis of three CTs, we identified 17, 32, and 63 patients as being potentially eligible for inclusion in those CTs, resulting in positive predictive values of 53%, 41%, and 21%, respectively.

CONCLUSION

We showed that PC could be formalized according to the OMOP CDM and that the oncology extension increased their translation rate through better representation of cancer natural history.

Challenges to Using Big Data in Cancer

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.

Cellular senescence-related gene signature as a valuable predictor of prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a lethal tumor. Its prognosis prediction remains a challenge. Meanwhile, cellular senescence, one of the hallmarks of cancer, and its related prognostic genes signature can provide critical information for clinical decision-making.

METHOD

Using bulk RNA sequencing and microarray data of HCC samples, we established a senescence score model via multi-machine learning algorithms to predict the prognosis of HCC. Single-cell and pseudo-time trajectory analyses were used to explore the hub genes of the senescence score model in HCC sample differentiation.

RESULT

A machine learning model based on cellular senescence gene expression profiles was identified in predicting HCC prognosis. The feasibility and accuracy of the senescence score model were confirmed in external validation and comparison with other models. Moreover, we analyzed the immune response, immune checkpoints, and sensitivity to immunotherapy drugs of HCC patients in different prognostic risk groups. Pseudo-time analyses identified four hub genes in HCC progression, including CDCA8, CENPA, SPC25, and TTK, and indicated related cellular senescence.

CONCLUSIONS

This study identified a prognostic model of HCC by cellular senescence-related gene expression and insight into novel potential targeted therapies.

Precision Oncology Core Data Model to Support Clinical Genomics Decision Making

PURPOSE

Precision oncology mandates developing standardized common data models (CDMs) to facilitate analyses and enable clinical decision making. Expert-opinion-based precision oncology initiatives are epitomized in Molecular Tumor Boards (MTBs), which process large volumes of clinical-genomic data to match genotypes with molecularly guided therapies.

METHODS

We used the Johns Hopkins University MTB as a use case and developed a precision oncology core data model (Precision-DM) to capture key clinical-genomic data elements. We leveraged existing CDMs, building upon the Minimal Common Oncology Data Elements model (mCODE). Our model was defined as a set of profiles with multiple data elements, focusing on next-generation sequencing and variant annotations. Most elements were mapped to terminologies or code sets and the Fast Healthcare Interoperability Resources (FHIR). We subsequently compared our Precision-DM with existing CDMs, including the National Cancer Institute's Genomic Data Commons (NCI GDC), mCODE, OSIRIS, the clinical Genome Data Model (cGDM), and the genomic CDM (gCDM).

RESULTS

Precision-DM contained 16 profiles and 355 data elements. 39% of the elements derived values from selected terminologies or code sets, and 61% were mapped to FHIR. Although we used most elements contained in mCODE, we significantly expanded the profiles to include genomic annotations, resulting in a partial overlap of 50.7% between our core model and mCODE. Limited overlap was noted between Precision-DM and OSIRIS (33.2%), NCI GDC (21.4%), cGDM (9.3%), and gCDM (7.9%). Precision-DM covered most of the mCODE elements (87.7%), with less coverage for OSIRIS (35.8%), NCI GDC (11%), cGDM (26%) and gCDM (33.3%).

CONCLUSION

Precision-DM supports clinical-genomic data standardization to support the MTB use case and may allow for harmonized data pulls across health care systems, academic institutions, and community medical centers.

Re-envisioning the Paradigm for Oncology Electronic Health Record Documentation by Paying for What Matters for Patients, Quality, and Research

This Viewpoint discusses re-envisioning and incentivizing a unique approach to oncology electronic health record documentation.

Breast Cancer Screening, Diagnosis, and Surgery during the Pre- and Peri-pandemic: Experience of Patients in a Statewide Health Information Exchange

BACKGROUND

Measures taken to address the COVID-19 pandemic interrupted routine diagnosis and care for breast cancer. The aim of this study was to characterize the effects of the pandemic on breast cancer care in a statewide cohort.

PATIENTS AND METHODS

Using data from a large health information exchange, we retrospectively analyzed the timing of breast cancer screening, and identified a cohort of newly diagnosed patients with any stage of breast cancer to further access the information available about their surgical treatments. We compared data for four subgroups: pre-lockdown (preLD) 25 March to 16 June 2019; lockdown (LD) 23 March to 3 May 2020; reopening (RO) 4 May to 14 June 2020; and post-lockdown (postLD) 22 March to 13 June 2021.

RESULTS

During LD and RO, screening mammograms in the cohort decreased by 96.3% and 36.2%, respectively. The overall breast cancer diagnosis and surgery volumes decreased up to 38.7%, and the median time to surgery was prolonged from 1.5 months to 2.4 for LD and 1.8 months for RO. Interestingly, higher mean DCIS diagnosis (5.0 per week vs. 3.1 per week, p < 0.05) and surgery volume (14.8 vs. 10.5, p < 0.05) were found for postLD compared with preLD, while median time to surgery was shorter (1.2 months vs. 1.5 months, p < 0.0001). However, the postLD average weekly screening and diagnostic mammogram did not fully recover to preLD levels (2055.3 vs. 2326.2, p < 0.05; 574.2 vs. 624.1, p < 0.05).

CONCLUSIONS

Breast cancer diagnosis and treatment patterns were interrupted during the lockdown and still altered 1 year after. Screening in primary care should be expanded to mitigate possible longer-term effects of these interruptions.

Data standards in pediatric oncology: Past, present, and future

In this commentary, we highlight the central role that data standards play in facilitating data-driven efforts to advance research in pediatric oncology. We discuss the current state of data standards for pediatric oncology and propose five steps to achieve an improved future state with benefits for clinicians, researchers, and patients.

Introducing HL7 FHIR Genomics Operations: a developer-friendly approach to genomics-EHR integration

OBJECTIVE

Enabling clinicians to formulate individualized clinical management strategies from the sea of molecular data remains a fundamentally important but daunting task. Here, we describe efforts towards a new paradigm in genomics-electronic health record (HER) integration, using a standardized suite of FHIR Genomics Operations that encapsulates the complexity of molecular data so that precision medicine solution developers can focus on building applications.

MATERIALS AND METHODS

FHIR Genomics Operations essentially "wrap" a genomics data repository, presenting a uniform interface to applications. More importantly, operations encapsulate the complexity of data within a repository and normalize redundant data representations-particularly relevant in genomics, where a tremendous amount of raw data exists in often-complex non-FHIR formats.

RESULTS

Fifteen FHIR Genomics Operations have been developed, designed to support a wide range of clinical scenarios, such as variant discovery; clinical trial matching; hereditary condition and pharmacogenomic screening; and variant reanalysis. Operations are being matured through the HL7 balloting process, connectathons, pilots, and the HL7 FHIR Accelerator program.

DISCUSSION

Next-generation sequencing can identify thousands to millions of variants, whose clinical significance can change over time as our knowledge evolves. To manage such a large volume of dynamic and complex data, new models of genomics-EHR integration are needed. Qualitative observations to date suggest that freeing application developers from the need to understand the nuances of genomic data, and instead base applications on standardized APIs can not only accelerate integration but also dramatically expand the applications of Omic data in driving precision care at scale for all.

Comprehensive Prognostic Analysis of Immune Implication Value and Oxidative Stress Significance of NECAP2 in Low-Grade Glioma

Adaptin ear-binding coat-associated protein 2 (NECAP2) belongs to the family of proteins encoding adaptin-ear-binding coat-associated proteins. However, its immune effect on tumors and its microenvironment are still unclear. Here, we systematically evaluated the differences (variations) in NECAP2 expression for low-grade glioma (LGG) and pan-cancer in the LGG dataset of The Cancer Genome Atlas (TCGA) utilizing bioinformatics methods. We found for the first time that NECAP2 level was elevated in gliomas and that this upregulation increased as the tumor grade increased. In addition, Pearson correlations of NECAP2 with five immune pathways and significant gene mutations associated with NECAP2 were also analyzed. Univariate survival and multivariate Cox analyses were used to compare the clinical characteristics and survival of the patients. Glioma patients with NECAP2 overexpression have a remarkably higher risk of developing malignant behavior and a worse prognosis. The correlation between the expression levels of NECAP2 and the prognosis of glioma patients was identified. Kaplan-Meier curves showed that patients with upregulated NECAP2 expression exhibited an unfavorable prognosis. Western blotting showed that NECAP2 was overexpressed in glioma patients. IHC staining results illustrated an elevation in the NECAP2 protein expression level with the development of tumor malignancy. Additionally, qRT-PCR verified that oxidative stress in glioma tissues reduced the expression of stress-related genes and oxidative stress capacity compared to normal tissues, which may be associated with tumor evasion of immune surveillance and tumor progression. In vitro wound-healing and Transwell assay confirmed that NECAP2 promotes glioma cell migration and invasion. Our study also thoroughly examined the immune significance of NECAP2 in the TCGA-LGG samples, using CIBERSORT and ESTIMATE to explore the correlation between NECAP2 and cancer immune infiltration. The NECAP2 expression levels were correlated with the infiltration degree of immune cells such as neutrophils, CD4+ T cells, macrophages, CD8+ T cells, and B cells. Therefore, our results indicate that NECAP2 strongly correlates with the overall immune infiltration level of glioma and could independently serve as a prognostic biological marker for glioma patients.

A Prognostic Risk Model of a Novel Oxidative Stress-Related Signature Predicts Clinical Prognosis and Demonstrates Immune Relevancy in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is among the most prevalent malignant lung cancers with a poor prognosis due to high invasiveness and lethality despite multiple treatments. Since the lung is an important organ associated with oxidative stress, and it has been confirmed that oxidative stress represents a potential cancer-specific depletion, it is of important significance to investigate and evaluate the clinical value of oxidative stress mechanisms regulating tumor cell apoptosis. Furthermore, there are few studies on the impact of the microenvironment on reaction to immune-checkpoint inhibitors (ICIs) in patients with LUAD. Based on the TCGA-LUAD dataset, which is stratified into a training set as well as a validation set in a ratio of 2 : 1, this investigation constructs and validates a prognostic predictive power of a gene signature model of oxidative stress-related prognostic signatures. To ascertain the differences between the high-risk score group and the low-risk score group in tumor-infiltrating lymphocytes and patients' response to ICI therapy. This oxidative stress-related prognostic gene signature is composed of MAP3K19 and NTSR1 and is an independent prognosis-related factor in the LUAD group. The outcome of patients having a low risk score is better, and the difference was statistically significant, and individuals with a low risk score had a larger number of infiltrating immune cell distribution in the tumor microenvironment, which was closely related to clinical outcome. Our study suggests that the synergistic effect of oxidative stress-related prognostic gene markers-MAP3K19 and NTSR1 has clinical significance in the prognosis identification and immunotherapy of LUAD patients. Thus, the results may help to better intersect the oxidative stress-related mechanisms in clinical value in LUAD but requires prospective validation.

Development of an Electronic Health Record Registry to Facilitate Collection of Commission on Cancer Metrics for Patients Undergoing Surgery for Breast Cancer

PURPOSE

Accurate and efficient data collection is a challenge for quality improvement initiatives and clinical research. We describe the development of a custom electronic health record (EHR)–based registry to automatically extract structured Commission on Cancer axillary surgery-specific metrics from a custom synoptic note template included in the operative reports for patients with breast cancer undergoing surgery.

METHODS

The smart functionality of our enterprise-based EHR system was leveraged to create a custom smart phrase to capture axillary surgery-specific variables. A multidisciplinary team developed structured data elements correlating to each axillary surgery-specific variable. These data elements were then included in a note template for the operative report. Each variable could be aggregated and converted into a single flat database through the EHR's reporting workbench and serve as a live, prospective registry for all users within the EHR.

RESULTS

The final axillary surgery-specific note template in a synoptic format allowed for efficient and easy entry and automatic collection of breast cancer–specific metrics. From initial adoption in February 2021-December 2021, there were 1,254 patients who underwent breast surgery with axillary surgery. The operative notes allowed for automatic capture of metrics from 60.5% (n = 759) of patients. Data capture improved from 37.6% in the initial adoption period of 6 months to 86.2% in the last 5 months.

CONCLUSION

We were able to demonstrate successful implementation of provider-driven structured data entry into EHR systems that permits automatic data capture. The end result is a custom synoptic note template and a real-time, prospective registry of breast cancer–specific Commission on Cancer metrics that are robust enough to use for quality improvement initiatives and clinical research.

Precision Oncology in Canada: Converting Vision to Reality with Lessons from International Programs

Canada's healthcare system, like others worldwide, is immersed in a process of evolution, attempting to adapt conventional frameworks of health technology assessment (HTA) and funding models to a new landscape of precision medicine in oncology. In particular, the need for real-world evidence in Canada is not matched by the necessary infrastructure and technologies required to integrate genomic and clinical data. Since healthcare systems in many developed nations face similar challenges, we adopted a solutions-based approach and conducted a search of worldwide programs in personalized medicine, with an emphasis on precision oncology. This search strategy included review articles published between 1 January 2016 and 1 March 2021 and hand-searches of their reference lists for relevant publications back to 1 December 2005. Thirty-nine initiatives across 37 countries in Europe, Australasia, Africa, and the Americas had the potential to lead to real-world data (RWD) on the clinical utility of oncology biomarkers. We highlight four initiatives with helpful lessons for Canada: Genomic Medicine France 2025, UNICANCER, the German Medical Informatics Initiative, and CANCER-ID. Among the 35 other programs evaluated, the main themes included the need for collaboration and systems to support data harmonization across multiple jurisdictions. In order to generate RWD in precision oncology that will prove acceptable to HTA bodies, Canada must take a national approach to biomarker strategy and unite all stakeholders at the highest level to overcome jurisdictional and technological barriers.

Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing

PURPOSE

The advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements.

METHODS

Published literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards.

RESULTS

A total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists.

CONCLUSION

We conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.

Bioinformatics analysis identifies diagnostic biomarkers and their correlation with immune infiltration in diabetic nephropathy

Background

Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD). Currently, microalbuminuria is mainly used as a diagnostic indicator of DN, but there are still limitations and lack of immune-related diagnostic markers. In this study, we aimed to explore diagnostic biomarkers associated with immune infiltration of DN.

Methods

Immune-related differentially expressed genes (DEGs) were derived from those at the intersection of the ImmPort database and DEGs identified from 3 datasets, which were based on the Gene Expression Omnibus (GEO). Functional enrichment analyses were performed; a protein-protein interaction (PPI) network was constructed; and hub genes were identified by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). After screening the key genes using least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), a prediction model for DN was constructed. The predictive performance of the model was quantified by receiver-operating characteristic curve, decision curve analysis, and nomogram. Next, infiltration of 22 types of immune cells in DN kidney tissue was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Expression of diagnostic markers was analyzed in DN and control patient groups to determine the genes with the maximum diagnostic potential. Finally, we explored the correlation between diagnostic markers and immune cells.

Results

Overall, 191 immune-related DEGs were identified, that primarily positively regulated with cell adhesion, T cell activation, leukocyte proliferation and migration, urogenital system development, lymphocyte differentiation and proliferation, and mononuclear cell proliferation. Gene sets were related to the PI3K-Akt, MAPK, Rap1, and WNT signaling pathways. Finally, CCL19, CD1C, and IL33 were identified as diagnostic markers of DN and recognized in the 3 datasets [area under the curve (AUC) =0.921]. Immune cell infiltration analysis demonstrated that CCL19 was positively correlated with macrophages M1 (R=0.47, P<0.001) and macrophages M2 (R=0.75, P<0.001). CD1C was positively correlated with macrophages M1 (R=0.47, P<0.05), macrophages M2 (R=0.75, P<0.01), and monocytes (R=0.42, P<0.01). IL33 was positively correlated with macrophages M1 (R=0.45, P<0.05), macrophages M2 (R=0.74, P<0.01), and monocytes (R=0.41, P<0.01).

Conclusions

Our results provide evidence that CCL19, CD1C, and IL33, which are associated with immune infiltration, are the potential diagnostic biomarkers for DN candidates.

Supporting Structured Data Capture for Patients With Cancer: An Initiative of the University of Wisconsin Carbone Cancer Center Survivorship Program to Improve Capture of Malignant Diagnosis and Cancer Staging Data

PURPOSE

Structured data elements within electronic health records are health-related information that can be entered, stored, and extracted in an organized manner at later time points. Tracking outcomes for cancer survivors is also enabled by structured data. We sought to increase structured data capture within oncology practices at multiple sites sharing the same electronic health records.

METHODS

Applying engineering approaches and the Plan-Do-Study-Act cycle, we launched dual quality improvement initiatives to ensure that a malignant diagnosis and stage were captured as structured data. Intervention: Close Visit Validation (CVV) requires providers to satisfy certain criteria before closing ambulatory encounters. CVV may be used to track open clinical encounters and chart delinquencies to encourage optimal clinical workflows. We added two cancer-specific required criteria at the time of closing encounters in oncology clinics: (1) the presence of at least one malignant diagnosis on the Problem List and (2) staging all the malignant diagnoses on the Problem List when appropriate.

RESULTS

Six months before the CVV implementation, the percentage of encounters with a malignant diagnosis on the Problem List at the time of the encounter was 65%, whereas the percentage of encounters with a staged diagnosis was 32%. Three months after cancer-specific CVV implementation, the percentages were 85% and 75%, respectively. Rates had increased to 90% and 88% more than 2 years after implementation.

CONCLUSION

Oncologist performance improved after the implementation of cancer-specific CVV criteria, with persistently high percentages of relevant malignant diagnoses and cancer stage structured data capture 2 years after the intervention.

Reaching beyond maximum grade: progress and future directions for modernising the assessment and reporting of adverse events in haematological malignancies

Remarkable improvements in outcomes for many haematological malignancies have been driven primarily by a proliferation of novel therapeutics over the past two decades. Targeted agents, immune and cellular therapies, and combination regimens have adverse event profiles distinct from conventional finite cytotoxic chemotherapies. In 2018, a Commission comprising patient advocates, clinicians, clinical investigators, regulators, biostatisticians, and pharmacists representing a broad range of academic and clinical cancer expertise examined issues of adverse event evaluation in the context of both newer and existing therapies for haematological cancers. The Commission proposed immediate actions and long-term solutions in the current processes in adverse event assessment, patient-reported outcomes in haematological malignancies, toxicities in cellular therapies, long-term toxicity and survivorship in haematological malignancies, issues in regulatory approval from an international perspective, and toxicity reporting in haematological malignancies and the real-world setting. In this follow-up report, the Commission describes progress that has been made in these areas since the initial report.

A systematic review of federated learning applications for biomedical data

OBJECTIVES

Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology.

METHODS

We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool.

RESULTS

13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data.

CONCLUSION

Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning- and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision-assisted image analysis in particular having several U.S. Food and Drug Administration-approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI's "black box" mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities. Midfuture projections of AI maturation involve increasing a model's complexity by using multimodal data elements to better approximate an organic system. Far-future positing includes living databases that accumulate all aspects of a person's health into discrete data elements; this will fuel highly convoluted modeling that can tailor treatment selection, dose determination, surveillance modality and schedule, and more. The field of AI has had a historical dichotomy between its proponents and detractors. The successful development of recent applications, and continued investment in prospective validation that defines their impact on multilevel outcomes, has established a momentum of accelerated progress.

Epigenetic Biomarkers Screening of Non-Coding RNA and DNA Methylation Based on Peripheral Blood Monocytes in Smokers

This study aims to use bioinformatics methods to determine the epigenetic changes in microRNA expression and DNA methylation caused by cigarette smoking. The data of mRNA, miRNA expression, and methylation microarray were obtained from the GEO database to filter differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs), and methylated CpG probes (DMPs) through the limma package. The R clusterProfile package was used for functional annotation and enrichment analysis. The protein-protein interaction (PPI) network was constructed by the String database and visualized in Cytoscape software. Starbase database was employed to predict lncRNA and CirRNA based on the sequence of miRNA, and to establish a regulatory network of ceRNA. By overlapping DEG and DEM, 107 down-miRNA-targeted up-regulated genes and 65 up-miRNA-target down-regulated genes were obtained, which were mainly enriched in autophagy signaling pathways and protein ubiquitination pathways, respectively. In addition, 324 genes with low methylation and high expression and 204 genes with high methylation and low expression were respectively related to the degeneration of the nervous system and the function of the cardiovascular system. Interestingly, 43 genes were up-regulated under the dual regulation of reduced miRNA and hypomethylation, while 14 genes were down-regulated under the dual regulation of increased miRNA and hypermethylation. Ten chemicals have been identified as putative therapeutic agents for pathological conditions caused by smoking. In addition, among these genes, HSPA4, GRB2, PRKCA, and BCL2L1 could play a fundamental role in related diseases caused by smoking and may be used as the biomarkers for precise diagnosis and targets for future therapies of smoking-related diseases.

Real-World Analysis of Off-Label Use of Molecularly Targeted Therapy in a Large Academic Medical Center Cohort

PURPOSE

The primary objective of this study is to quantify the use of off-label molecularly targeted therapy and describe the clinical situations in which off-label targeted therapy are used. A key secondary objective is to report the outcomes of patients treated with off-label use of targeted therapy.

PATIENTS AND METHODS

We searched the electronic health record between 2000 and 2020 at our center to characterize the volume, clinical settings, and outcomes associated with off-label use of targeted therapies in different types of solid tumors.

RESULTS

Among 46,712 patients who received targeted therapies, we identified 119 instances of off-label use of targeted therapy. Colon cancer was the most common cancer type to receive off-label targeted therapy in 18 patients (15.1%), followed by 13 with non-small-cell lung cancer (10.9%), eight with cholangiocarcinoma (6.7%), and seven with glioblastoma (5.9%). The most frequent molecular rationale for off-label therapy came from a comprehensive next-generation sequencing test (53.7%). The most frequently mutated gene that provided the rationale for targeted therapy was BRAF (20.1%), with BRAFV600E being the most common molecular alteration overall (15.1%). The median duration of off-label targeted therapy was 3.58 months, and the overall survival of treated patients was 7.59 months. There were 37 patients (31.1%) treated for longer than 6 months, 23 patients (19.3%) who survived ≥ 2 years, and 13 patients who were still on therapy as of June 2020.

CONCLUSION

In this large cohort study of patients with solid tumors, off-label use of targeted therapy was uncommon. With that said, a notable proportion of patients had treatment durations ≥ 6 months and survivals of ≥ 2 years.

Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality

PURPOSE

The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) requires eligible clinicians to report clinical quality measures (CQMs) in the Merit-Based Incentive Payment System (MIPS) to maximize reimbursement. To determine whether structured data in electronic health records (EHRs) were adequate to report MIPS CQMs, EHR data aggregated by ASCO's CancerLinQ platform were analyzed.

MATERIALS AND METHODS

Using the CancerLinQ health technology platform, 19 Oncology MIPS (oMIPS) CQMs were evaluated to determine the presence of data elements (DEs) necessary to satisfy each CQM and the DE percent population with patient data (fill rates). At the time of this analysis, the CancerLinQ network comprised 63 active practices, representing eight different EHR vendors and containing records for more than 1.63 million unique patients with one or more malignant neoplasms (1.73 million cancer cases).

RESULTS

Fill rates for the 63 oMIPS-associated DEs varied widely among the practices. The average site had at least one filled DE for 52% of the DEs. Only 35% of the DEs were populated for at least one patient record in 95% of the practices. However, the average DE fill rate of all practices was 23%. No data were found at any practice for 22% of the DEs. Since any oMIPS CQM with an unpopulated DE component resulted in an inability to compute the measure, only two (10.5%) of the 19 oMIPS CQMs were computable for more than 1% of the patients.

CONCLUSION

Although EHR systems had relatively high DE fill rates for some DEs, underfilling and inconsistency of DEs in EHRs render automated oncology MIPS CQM calculations impractical.