Electronic Health Records and Genomics: Perspectives from the Association for Molecular Pathology Electronic Health Record (EHR) Interoperability for Clinical Genomics Data Working Group

Challenges in IBD Research 2024: Precision Medicine

Precision medicine is part of 5 focus areas of the Challenges in IBD Research 2024 research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. Building on Challenges in IBD Research 2019, the current Challenges aims to provide a comprehensive overview of current gaps in inflammatory bowel diseases (IBDs) research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient-centric research prioritization. In particular, the precision medicine section is focused on the main research gaps in elucidating how to bring the best care to the individual patient in IBD. Research gaps were identified in biomarker discovery and validation for predicting disease progression and choosing the most appropriate treatment for each patient. Other gaps were identified in making the best use of existing patient biosamples and clinical data, developing new technologies to analyze large datasets, and overcoming regulatory and payer hurdles to enable clinical use of biomarkers. To address these gaps, the Workgroup suggests focusing on thoroughly validating existing candidate biomarkers, using best-in-class data generation and analysis tools, and establishing cross-disciplinary teams to tackle regulatory hurdles as early as possible. Altogether, the precision medicine group recognizes the importance of bringing basic scientific biomarker discovery and translating it into the clinic to help improve the lives of IBD patients.

Realizing the Dream of Precision Oncology: A Solution for All Patients

MICRO-ABSTRACT

As molecularly informed oncology care has increasingly become standard practice for patients with cancer, society must prioritize equitable access to genetic testing that guides subsequent care. Despite the availability of genomic testing laboratories, published guidelines, US Food and Drug Administration-approved targeted therapies, financial assistance programs, and clinical decision tools, precision medicine remains out of reach for many patients. While there has been modest improvement in testing rates in recent years, molecular testing and targeted therapy for cancer patients continue to vary by practice setting and patient insurance status, and racial and socioeconomic disparities persist. National standards and centralized solutions are needed to promote the equitable distribution of patient benefit from precision medicine technology. Although various online resources are currently available, no single all-encompassing precision oncology tool currently exists. A one-stop shop to address all aspects of precision oncology-tissue selection and test ordering, interpretation of results, prescribing targeted therapies, and enrolling patients in clinical trials-would disrupt cancer care. Recent advances in artificial intelligence, digital pathology, and data science provide an opportunity for stakeholders to partner together to leverage these technologies to develop this unified, freely accessible, national solution. Whether locoregionally, nationally, or internationally, only collaborative efforts can fully realize the potential of technological advancements in molecular pathology and oncology therapeutics for all cancer patients.

Is early-onset cancer an emerging global epidemic? Current evidence and future implications

Over the past several decades, the incidence of early-onset cancers, often defined as cancers diagnosed in adults <50 years of age, in the breast, colorectum, endometrium, oesophagus, extrahepatic bile duct, gallbladder, head and neck, kidney, liver, bone marrow, pancreas, prostate, stomach and thyroid has increased in multiple countries. Increased use of screening programmes has contributed to this phenomenon to a certain extent, although a genuine increase in the incidence of early-onset forms of several cancer types also seems to have emerged. Evidence suggests an aetiological role of risk factor exposures in early life and young adulthood. Since the mid-20th century, substantial multigenerational changes in the exposome have occurred (including changes in diet, lifestyle, obesity, environment and the microbiome, all of which might interact with genomic and/or genetic susceptibilities). However, the effects of individual exposures remain largely unknown. To study early-life exposures and their implications for multiple cancer types will require prospective cohort studies with dedicated biobanking and data collection technologies. Raising awareness among both the public and health-care professionals will also be critical. In this Review, we describe changes in the incidence of early-onset cancers globally and suggest measures that are likely to reduce the burden of cancers and other chronic non-communicable diseases.

Analyzing Precision Medicine Utilization with Real-World Data: A Scoping Review

Precision medicine (PM), specifically genetic-based testing, is currently used in over 140,000 individual tests to inform the clinical management of disease. Though several databases (e.g., the NIH Genetic Testing Registry) demonstrate the availability of these sequencing-based tests, we do not currently understand the extent to which these tests are used. There exists a need to synthesize the body of real-world data (RWD) describing the use of sequencing-based tests to inform their appropriate use. To accomplish this, we performed a scoping review to examine what RWD sources have been used in studies of PM utilization between January 2015 and August 2021 to characterize the use of genome sequencing (GS), exome sequencing (ES), tumor sequencing (TS), next-generation sequencing-based panels (NGS), gene expression profiling (GEP), and pharmacogenomics (PGx) panels. We abstracted variables describing the use of these types of tests and performed a descriptive statistical analysis. We identified 440 articles in our search and included 72 articles in our study. Publications based on registry databases were the most common, followed by studies based on private insurer administrative claims. Slightly more than one-third (38%) used integrated datasets. Two thirds (67%) of the studies focused on the use of tests for oncological clinical applications. We summarize the RWD sources used in peer-reviewed literature on the use of PM. Our findings will help improve future study design by encouraging the use of centralized databases and registries to track the implementation and use of PM.

Artificial intelligence-assisted serial analysis of clinical cancer genomics data identifies changing treatment recommendations and therapeutic targets

PURPOSE

Given the pace of predictive biomarker and targeted therapy development, it is unknown if repeat annotation of the same next-generation sequencing data can identify additional clinically actionable targets that could be therapeutically leveraged. In this study, we sought to determine the predictive yield of serial re-analysis of clinical tumor sequencing data.

EXPERIMENTAL DESIGN

Using artificial intelligence (AI)-assisted variant annotation, we retrospectively re-analyzed sequencing data from 2,219 cancer patients from a single academic medical center at 3-month intervals totaling 9 months in 2020. The yield of serial re-analysis was assessed by the proportion of patients with improved strength of therapeutic recommendations.

RESULTS

1,775 patients (80%) had {greater than or equal to}1 potentially clinically actionable mutation at baseline, including 243 (11%) patients who had an alteration targeted by an FDA-approved drug for their cancer type. By month nine, the latter increased to 458 (21%) patients mainly due to a single pan-cancer agent directed against tumors with high tumor mutation burden. Within this timeframe, 67 new therapies became available and 45 were no longer available. Variant pathogenicity classifications also changed leading to changes in treatment recommendations for 124 patients (6%).

CONCLUSIONS

Serial re-annotation of tumor sequencing data improved the strength of treatment recommendations (based on level of evidence) in a mixed cancer cohort and showed substantial changes in available therapies and variant classifications. These results suggest a role for repeat analysis of tumor sequencing data in clinical practice, which can be streamlined with AI support.