Is It Time to Expand Our Definition of "Clinical Utility"?

Currently, genetic tests that predict cancer risk or risk of recurrence in patients who have had their cancer treated with curative intent must have proven "clinical utility" to be recommended by the organizations responsible for publishing the standard-of-care guidelines for cancer care.Based on the current definition of clinical utility, most patients are denied testing for cancer-predisposing genes or pathogenic germline variants even though germline testing has been proven as highly accurate in identifying pathogenic germline variant carriers, there are measures recommended to prevent and diagnose early cancers associated with particular PGVs, and disparities in patient access to genetic tests are well described.Similarly, despite dozens of studies demonstrating that detected circulating tumor DNA (ctDNA) after curative intention therapy of different cancer types is a highly accurate biomarker that predicts recurrence, the major organizations that publish guidelines for cancer monitoring after curative intention therapy recommend against using ctDNA assays to detect minimal residual disease and thereby predict recurrence for all solid tumor malignancies.Here, the primary reasons that these genetic tests are considered to lack proven clinical utility and the primary evidence suggesting that a broader definition of clinical utility should be considered are discussed. By expanding the definition of clinical utility, many patients will benefit from the information gained from having these genetic tests.

A Systematic Review of Kidney Transplantation Decision Modelling Studies.

BACKGROUND: Genome-based precision medicine strategies promise to minimize premature graft loss after renal transplantation, through precision approaches to immune compatibility matching between kidney donors and recipients. The potential adoption of this technology calls for important changes to clinical management processes and allocation policy. Such potential policy change decisions may be supported by decision models from health economics, comparative effectiveness research and operations management. OBJECTIVE: We used a systematic approach to identify and extract information about models published in the kidney transplantation literature and provide an overview of the status of our collective model-based knowledge about the kidney transplant process. METHODS: Database searches were conducted in MEDLINE, Embase, Web of Science and other sources, for reviews and primary studies. We reviewed all English-language papers that presented a model that could be a tool to support decision making in kidney transplantation. Data were extracted on the clinical context and modelling methods used. RESULTS: A total of 144 studies were included, most of which focused on a single component of the transplantation process, such as immunosuppressive therapy or donor-recipient matching and organ allocation policies. Pre- and post-transplant processes have rarely been modelled together. CONCLUSION: A whole-disease modelling approach is preferred to inform precision medicine policy, given its potential upstream implementation in the treatment pathway. This requires consideration of pre- and post-transplant natural history, risk factors for allograft dysfunction and failure, and other post-transplant outcomes. Our call is for greater collaboration across disciplines and whole-disease modelling approaches to more accurately simulate complex policy decisions about the integration of precision medicine tools in kidney transplantation.

A knowledge graph to interpret clinical proteomics data.

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.

Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why Is It Different?

Innovations in health care are growing exponentially, resulting in improved quality of and access to care, as well as rising societal costs of care and variable reimbursement. In recent years, digital health technologies and artificial intelligence have become of increasing interest in cardiovascular medicine owing to their unique ability to empower patients and to use increasing quantities of data for moving toward personalised and precision medicine. Health technology assessment agencies evaluate the money spent on a health care intervention or technology to attain a given clinical impact and make recommendations for reimbursement considerations. However, there is a scarcity of economic evaluations of cardiovascular digital health technologies and artificial intelligence. The current health technology assessment framework is not equipped to address the unique, dynamic, and unpredictable value considerations of these technologies and highlight the need to better approach the digital health technologies and artificial intelligence health technology assessment process. In this review, we compare digital health technologies and artificial intelligence with traditional health care technologies, review existing health technology assessment frameworks, and discuss challenges and opportunities related to cardiovascular digital health technologies and artificial intelligence health technology assessment. Specifically, we argue that health technology assessments for digital health technologies and artificial intelligence applications must allow for a much shorter device life cycle, given the rapid and even potentially continuously iterative nature of this technology, and thus an evidence base that maybe less mature, compared with traditional health technologies and interventions.

Comparison of the Data of a Next-Generation Sequencing Panel from K-MASTER Project with That of Orthogonal Methods for Detecting Targetable Genetic Alterations.

PURPOSE: K-MASTER project is a Korean national precision medicine platform that screened actionable mutations by analyzing next-generation sequencing (NGS) of solid tumor patients. We compared gene analyses between NGS panel from the K-MASTER project and orthogonal methods. MATERIALS AND METHODS: Colorectal, breast, non-small cell lung, and gastric cancer patients were included. We compared NGS results from K-MASTER projects with those of non-NGS orthogonal methods (KRAS, NRAS, and BRAF mutations in colorectal cancer [CRC]; epidermal growth factor receptor [EGFR], anaplastic lymphoma kinase [ALK] fusion, and reactive oxygen species 1 [ROS1] fusion in non-small cell lung cancer [NSCLC], and Erb-B2 receptor tyrosine kinase 2 (ERBB2) positivity in breast and gastric cancers). RESULTS: In the CRC cohort (n=225), the sensitivity and specificity of NGS were 87.4% and 79.3% (KRAS); 88.9% and 98.9% (NRAS); and 77.8% and 100.0% (BRAF), respectively. In the NSCLC cohort (n=109), the sensitivity and specificity of NGS for EGFR were 86.2% and 97.5%, respectively. The concordance rate for ALK fusion was 100%, but ROS1 fusion was positive in only one of three cases that were positive in orthogonal tests. In the breast cancer cohort (n=260), ERBB2 amplification was detected in 45 by NGS. Compared with orthogonal methods that integrated immunohistochemistry and in situ hybridization, sensitivity and specificity were 53.7% and 99.4%, respectively. In the gastric cancer cohort (n=64), ERBB2 amplification was detected in six by NGS. Compared with orthogonal methods, sensitivity and specificity were 62.5% and 98.2%, respectively. CONCLUSION: The results of the K-MASTER NGS panel and orthogonal methods showed a different degree of agreement for each genetic alteration, but generally showed a high agreement rate.

Revisiting the Implications of Positive Germline Testing Results Using Multi-gene Panels in Breast Cancer Patients.

BACKGROUND/AIM: The use of multi-gene panels for germline testing in breast cancer enables the estimation of cancer risk and guides risk-reducing management options. The aim of this study was to present data that demonstrate the different levels of actionability for multi-gene panels used in genetic testing of breast cancer patients and their family members. MATERIALS AND METHODS: We performed an analysis in our clinical database to identify breast cancer patients undergoing genetic testing. We reviewed positive results in respect of risk estimation and management, cascade family testing, secondary findings and information for treatment decision-making. RESULTS: A total of 415 positive test reports were identified with 57.1%, 18.1%, 10.8% and 13.5% of individuals having pathogenic/likely pathogenic variants in high, moderate, low and with insufficient evidence for breast cancer risk genes, respectively. Six point seven percent of individuals were double heterozygotes. CONCLUSION: Germline findings in 92% of individuals are linked to evidence-based treatment information and risk estimates for predisposition to breast and/or other cancer types. The use of germline findings for treatment decision making expands the indication of genetic testing to include individuals that could benefit from targeted treatments.

Germline Sequencing Improves Tumor-Only Sequencing Interpretation in a Precision Genomic Study of Patients With Pediatric Solid Tumor.

PURPOSE: Molecular tumor profiling is becoming a routine part of clinical cancer care, typically involving tumor-only panel testing without matched germline. We hypothesized that integrated germline sequencing could improve clinical interpretation and enhance the identification of germline variants with significant hereditary risks. MATERIALS AND METHODS: Tumors from pediatric patients with high-risk, extracranial solid malignancies were sequenced with a targeted panel of cancer-associated genes. Later, germline DNA was analyzed for a subset of these genes. We performed a post hoc analysis to identify how an integrated analysis of tumor and germline data would improve clinical interpretation. RESULTS: One hundred sixty participants with both tumor-only and germline sequencing reports were eligible for this analysis. Germline sequencing identified 38 pathogenic or likely pathogenic variants among 35 (22%) patients. Twenty-five (66%) of these were included in the tumor sequencing report. The remaining germline pathogenic or likely pathogenic variants were single-nucleotide variants filtered out of tumor-only analysis because of population frequency or copy-number variation masked by additional copy-number changes in the tumor. In tumor-only sequencing, 308 of 434 (71%) single-nucleotide variants reported were present in the germline, including 31% with suggested clinical utility. Finally, we provide further evidence that the variant allele fraction from tumor-only sequencing is insufficient to differentiate somatic from germline events. CONCLUSION: A paired approach to analyzing tumor and germline sequencing data would be expected to improve the efficiency and accuracy of distinguishing somatic mutations and germline variants, thereby facilitating the process of variant curation and therapeutic interpretation for somatic reports, as well as the identification of variants associated with germline cancer predisposition.

Treatment optimization of locally advanced and metastatic pancreatic cancer (Review).

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignant tumor types, being the sixth leading cause of mortality worldwide and the fourth in Europe. Globally, it has a mortality/incidence ratio of 98%, and the 5­year survival rate in Europe is only 3%. Although risk factors, such as obesity, diabetes mellitus, smoking, alcohol consumption and genetic factors, have been identified, the causes of PDAC remain elusive. Additionally, the only curative treatment for PDAC is surgery with negative margins. However, upon diagnosis, ~30% of the patients already present with locally advanced disease. In these cases, a multidisciplinary approach is required to improve disease­related symptoms and prolong patient survival. In the present article, a comprehensive review of PDAC epidemiology, physiology and treatment is provided. Moreover, guidelines on patient treatment are suggested. Among the different available therapeutic options for the treatment of advanced PDAC, results are modest, most likely due to the complexity of the disease, and so the prognostic remains poor. Molecular approaches based on multi­omics research are promising and will contribute to groundbreaking personalized medicine. Thus, economic investment that promotes research of pancreatic cancer will be critical to the development of more efficient diagnostic and treatment strategies.

Precision-Enhancing Risk Stratification Tools for Selecting Optimal Treatment Durations in Tuberculosis Clinical Trials.

Rationale: No evidence-based tools exist to enhance precision in the selection of patient-specific optimal treatment durations to study in tuberculosis clinical trials. Objectives: To develop risk stratification tools that assign patients with tuberculosis into risk groups of unfavorable outcome and inform selection of optimal treatment duration for each patient strata to study in clinical trials. Methods: Publicly available data from four phase 3 trials, each evaluating treatment duration shortening from 6 to 4 months, were used to develop parametric time-to-event models that describe unfavorable outcomes. Regimen, baseline, and on-treatment characteristics were evaluated as predictors of outcomes. Exact regression coefficients of predictors were used to assign risk groups and predict optimal treatment durations. Measurements and Main Results: The parametric model had an area under the receiver operating characteristic curve of 0.72. A six-item risk score (HIV status, smear grade, sex, cavitary disease status, body mass index, and Month 2 culture status) successfully grouped participants into low (1,060/3,791; 28%), moderate (1,740/3,791; 46%), and high (991/3,791; 26%) risk, requiring treatment durations of 4, 6, and greater than 6 months, respectively, to reach a target cure rate of 93% when receiving standard-dose rifamycin-containing regimens. With current one-duration-fits-all approaches, high-risk groups have a 3.7-fold (95% confidence interval, 2.7-5.1) and 2.4-fold (1.9-2.9) higher hazard risk of unfavorable outcomes compared with low- and moderate-risk groups, respectively. Four-month regimens were noninferior to the standard 6-month regimen in the low-risk group. Conclusions: Our model discrimination was modest but consistent with current models of unfavorable outcomes. Our results showed that stratified medicine approaches are feasible and may achieve high cure rates in all patients with tuberculosis. An interactive risk stratification tool is provided to facilitate decision-making in the regimen development pathway.

Multi-site tumor sampling highlights molecular intra-tumor heterogeneity in malignant pleural mesothelioma.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a heterogeneous cancer. Better knowledge of molecular and cellular intra-tumor heterogeneity throughout the thoracic cavity is required to develop efficient therapies. This study focuses on molecular intra-tumor heterogeneity using the largest series to date in MPM and is the first to report on the multi-omics profiling of a substantial series of multi-site tumor samples. METHODS: Intra-tumor heterogeneity was investigated in 16 patients from whom biopsies were taken at distinct anatomical sites. The paired biopsies collected from apex, side wall, costo-diaphragmatic, or highest metabolic sites as well as 5 derived cell lines were screened using targeted sequencing. Whole exome sequencing, RNA sequencing, and DNA methylation were performed on a subset of the cohort for deep characterization. Molecular classification, recently defined histo-molecular gradients, and cell populations of the tumor microenvironment were assessed. RESULTS: Sequencing analysis identified heterogeneous variants notably in NF2, a key tumor suppressor gene of mesothelial carcinogenesis. Subclonal tumor populations were shared among paired biopsies, suggesting a polyclonal dissemination of the tumor. Transcriptome analysis highlighted dysregulation of cell adhesion and extracellular matrix pathways, linked to changes in histo-molecular gradient proportions between anatomic sites. Methylome analysis revealed the contribution of epigenetic mechanisms in two patients. Finally, significant changes in the expression of immune mediators and genes related to immunological synapse, as well as differential infiltration of immune populations in the tumor environment, were observed and led to a switch from a hot to a cold immune profile in three patients. CONCLUSIONS: This comprehensive analysis reveals patient-dependent spatial intra-tumor heterogeneity at the genetic, transcriptomic, and epigenetic levels and in the immune landscape of the tumor microenvironment. Results support the need for multi-sampling for the implementation of molecular-based precision medicine.

Basic principles of biobanking: from biological samples to precision medicine for patients.

The term "biobanking" is often misapplied to any collection of human biological materials (biospecimens) regardless of requirements related to ethical and legal issues or the standardization of different processes involved in tissue collection. A proper definition of biobanks is large collections of biospecimens linked to relevant personal and health information (health records, family history, lifestyle, genetic information) that are held predominantly for use in health and medical research. In addition, the International Organization for Standardization, in illustrating the requirements for biobanking (ISO 20387:2018), stresses the concept of biobanks being legal entities driving the process of acquisition and storage together with some or all of the activities related to collection, preparation, preservation, testing, analysing and distributing defined biological material as well as related information and data. In this review article, we aim to discuss the basic principles of biobanking, spanning from definitions to classification systems, standardization processes and documents, sustainability and ethical and legal requirements. We also deal with emerging specimens that are currently being generated and shaping the so-called next-generation biobanking, and we provide pragmatic examples of cancer-associated biobanking by discussing the process behind the construction of a biobank and the infrastructures supporting the implementation of biobanking in scientific research.

Original Article: MicroRNA Dysregulation in the Gastric Carcinogenesis Cascade: Can We Anticipate Its Role in Individualized Care?

BACKGROUND: Gastric carcinogenesis progresses from normal mucosa, atrophic/metaplastic gastritis, and dysplasia to adenocarcinoma. MicroRNAs (miRNAs) regulate DNA expression and have been implicated; however, their role is not fully established. AIMS: The aim of this study was to characterize plasma and tissue expression of several miRNAs in gastric carcinogenesis stages. METHODS: Single-center cross-sectional study in 64 patients: 19 controls (normal mucosa); 15 with extensive atrophic/metaplastic gastritis; and 30 with early gastric neoplasia (EGN). Seven miRNAs (miR-21, miR-146a, miR-181b, miR-370, miR-375, miR 181b, and miR-490) were quantified by real time-qPCR in peripheral blood and endoscopic biopsy samples. RESULTS: We found a significant upregulation of miR-181b, miR-490, and miR-21 in the EGN mucosa (overexpression 2-14-times higher than controls). We observed a significant underexpression of miR-146a and miR-370 in atrophic/metaplastic gastritis (86 and 66% decrease, p = 0.008 and p = 0.001) and in EGN (89 and 62% reduction, p = 0.034 and p = 0.032) compared with controls. There were no differences between lesions and nonneoplastic mucosa and no dysregulation of plasma miRNAs. CONCLUSION: We found significant dysregulation of 5 miRNAs in gastric carcinogenesis, suggesting a tumor suppressor role for miR-146a and miR-370 and oncogenic potential for miR-21, miR-181, and miR-490. These changes happen diffusely in the gastric mucosa, suggesting a high-risk field defect, which may influence these patients' surveillance.

An Evidence-Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine.

Clinical annotations are one of the most popular resources available on the Pharmacogenomics Knowledgebase (PharmGKB). Each clinical annotation summarizes the association between variant-drug pairs, shows relevant findings from the curated literature, and is assigned a level of evidence (LOE) to indicate the strength of support for that association. Evidence from the pharmacogenomic literature is curated into PharmGKB as variant annotations, which can be used to create new clinical annotations or added to existing clinical annotations. This means that the same clinical annotation can be worked on by multiple curators over time. As more evidence is curated into PharmGKB, the task of maintaining consistency when assessing all the available evidence and assigning an LOE becomes increasingly difficult. To remedy this, a scoring system has been developed to automate LOE assignment to clinical annotations. Variant annotations are scored according to certain attributes, including study size, reported P value, and whether the variant annotation supports or fails to find an association. Clinical guidelines or US Food and Drug Administration (FDA)-approved drug labels which give variant-specific prescribing guidance are also scored. The scores of all annotations attached to a clinical annotation are summed together to give a total score for the clinical annotation, which is used to calculate an LOE. Overall, the system increases transparency, consistency, and reproducibility in LOE assignment to clinical annotations. In combination with increased standardization of how clinical annotations are written, use of this scoring system helps to ensure that PharmGKB clinical annotations continue to be a robust source of pharmacogenomic information.

A Simple Practical Guide to Genomic Diagnostics in a Pediatric Setting.

With limited access to trained clinical geneticists and/or genetic counselors in the majority of healthcare systems globally, and the expanding use of genetic testing in all specialties of medicine, many healthcare providers do not receive the relevant support to order the most appropriate genetic test for their patients. Therefore, it is essential to educate all healthcare providers about the basic concepts of genetic testing and how to properly utilize this testing for each patient. Here, we review the various genetic testing strategies and their utilization based on different clinical scenarios, and test characteristics, such as the types of genetic variation identified by each test, turnaround time, and diagnostic yield for different clinical indications. Additional considerations such as test cost, insurance reimbursement, and interpretation of variants of uncertain significance are also discussed. The goal of this review is to aid healthcare providers in utilizing the most appropriate, fastest, and most cost-effective genetic test for their patients, thereby increasing the likelihood of a timely diagnosis and reducing the financial burden on the healthcare system by eliminating unnecessary and redundant testing.