January 5, 2023 | We spoke with various companies in the Diagnostics World community to gain insights and predictions for the coming year. Many vendors are eager to predict and detect diseases earlier and faster to improve patient outcomes. Vangelis Vergetis of Intelligencia.AI believes more attention will be given to chronic, neurological, and immunological disorders. He said, “Clinical teams will begin focusing on the ability to predict the onset of diseases like Alzheimer’s and Parkinson’s disease so that we can treat the disease early and make existing medications more effective. They will also focus on the ability to predict the onset of episodic diseases like Multiple Sclerosis.”
This year, precision medicine dominated the conversation, and nearly everyone is excited to develop new diagnostic techniques using metagenomics, whole-genome sequencing, and gene expression profiling. “Multi-omics is the future of biological analysis. In the year ahead, we can expect continued advances in related technologies. Whole-genome sequencing will prove useful in diagnosing rare, complex, and common diseases,” said Madhuri Hegde of PerkinElmer Genomics.
Many we spoke with, including William Audeh of Agendia, believe that incorporating genomics in precision medicine will empower patients to make more informed decisions. Audeh said, “We’ll see genomic tests increasingly give early-stage breast cancer patients a larger role in decision-making regarding their treatment and future care… This year and beyond, we will see [a] growing recognition of the need for advanced RNA gene expression profiling that provides unprecedented levels of precise, actionable insights to inform treatment decisions for cancer patients based on their unique biology.”
As expected, artificial intelligence (AI) and machine learning were still championed as invaluable diagnostic tools. Industry leaders look forward to optimizing digital workflows, building on previous work, and focusing on data diversity. John Ellithorpe of DNAnexus believes combining AI and multi-omics will change how scientists approach diagnostic work moving forward. He said, “Clinical diagnostics will increasingly rely on high-quality predictive models based on multi-modal ‘omics and clinical data.” Rafael Rosengarten of Genialis reminds us that the goal of making AI advances in diagnostics is ultimately to serve the patient. He said, "Artificial intelligence and machine learning directly impact patient lives by incorporating predictive algorithms in diagnostic devices. Machine learning models are learning to interpret all patient data and direct therapeutic interventions and treatment decisions to optimize patient outcomes.”
Here are the top trends and predictions, including additional forecasts for newborn screening, deciphering immune responses through T-cell testing, even more advances in proteomics, and next-generation precision oncology. –the Editors
Metagenomics and the expanding role of whole genome sequencing in precision medicine: Discoveries enabled by this next-generation multi-omics approach leading to data convergence from different platforms for clinical care—combined with microbial and viral genome analysis—will ultimately help genetic counselors and clinicians reach diagnoses faster and initiate actions to improve patient health outcomes.
Petra Furu, General Manager of Reproductive Health, PerkinElmer
Newborn screening programs will become more accessible and robust: This year, we should expect newborn screening (NBS) programs worldwide to become more accessible, comprehensive, and accurate in diagnosing babies with rare diseases and inherited disorders. These programs will expand NBS panels to include conditions such as spinal muscular atrophy. In regions of Denmark and Spain, SMA screening is already underway. In Sub-Saharan Africa, there is a strong will to increase the number of newborns screened for sickle cell disease. Furthermore, technological improvements will be a driving force behind future advances in NBS globally. Whole genome sequencing studies worldwide reaffirm the utility of this approach to NBS, not only as a second-tier test but to effectively and expediently identify newborns more likely to develop a rare disorder.
High-precision T-Cell testing becomes a reality: Research aimed at understanding immune responses to SARS-CoV-2 has solidified the critical role T-cells play in responding to infections. As a result, vaccine developers are eager to incorporate T-cell testing into their clinical trials for future COVID-19 vaccines and to help prevent and defend against other infectious diseases. In 2023, high-precision routine T-cell testing will no longer be an aspiration but a reality. In the clinical space, T-cell testing has been a pillar in diagnosing latent tuberculosis infection using enzyme-linked immunosorbent spot and enzyme-linked immunosorbent assay. These capabilities and the broader availability of in vitro diagnostics test kits based on this approach will be essential for infectious disease diagnostics and vaccine development.
Maite Sabalza, Ph.D., Scientific Affairs Manager for PerkinElmer’s EUROIMMUN US
Tick-borne diseases on the rise: Tick-borne diseases (TBDs) have become a global public health challenge. In recent years, both tick-borne pathogens and TBD cases have increased globally (Infectious Diseases in Clinical Practice, DOI: 10.1097/IPC.0000000000001090). TBDs are the most common vector-borne diseases in the United States, with Lyme disease being the most reported. As more cases are reported, it is critical to detect TBDs accurately and rapidly to provide early treatment. Emerging TBDs include the Powassan virus (POWV), mainly transmitted by the same ticks as Lyme disease, I. scapularis. With the tick’s expanding territory and significant increases in Powassan cases in the last decade, there is an urgent need for methods to detect POWV and other non-Lyme TBDs.
Yves Dubaquie, Senior Vice President of Diagnostics, PerkinElmer
There will be innovation aimed at uncovering the causes of chronic disease: Long COVID has renewed the scientific community’s interest in diagnostics for chronic disease. An estimated one in five individuals with a confirmed SARS-CoV-2 infection develops post-COVID conditions such as fatigue, “brain fog,” difficulty breathing, or joint pain, which may last for weeks, months, or even longer. Recent research shows that individuals with long COVID were more likely to have autoimmunity markers in their blood than those who had recovered quickly or never contracted the virus (European Respiratory Journal, DOI: 10.1183/13993003.00970-2022). This should tell us that further development of new and existing diagnostic tools to understand immune response at the cellular level will be worthwhile in 2023 and beyond, not only for long COVID but for other conditions too. Current research around chronic fatigue syndrome could lead to improved diagnostics and treatment for more people affected by this lesser-understood condition.
William Audeh, MD, Chief Medical Officer, Agendia
Genomics-powered precision medicine: Patients deserve tests that comprehensively tell the most effective treatment approaches for their cancer, regardless of age, race, and other clinical factors. By understanding the underlying pathways driving the growth of one’s unique tumor through molecular subtyping and combining that with advanced RNA testing for the risk of distant metastasis, care providers can objectively and accurately define each tumor and treat it precisely. Gene expression profiling will be essential for the optimal application of new and innovative targeted therapies. Achieving this level of insight not only means many could avoid harmful overtreatment with chemotherapy, but this level of tumor analysis also helps us accurately identify aggressive, high-risk tumors that otherwise may be missed by conventional parameters. As further development takes place in gene expression profiling research, personalized medicine in cancer care will become the standard. It will fulfill a critical unmet need for precise, proactive treatment planning.
Empowering patients through shared clinical decision-making: Younger women who may choose to preserve their fertility and avoid the long-term impacts of chemotherapy will find that gene sequencing tests that provide precise insights into what treatments may be most effective in managing their cancer will allow many to avoid these unnecessary and harsh treatments. For example, recent research shows that premenopausal women with tumors that have a shallow risk of distant metastasis can likely safely forgo chemotherapy, with a distant metastasis-free interval of 97% after five years free from chemotherapy (Journal of Clinical Oncology, DOI: 10.1200/JCO.2021.39.15_suppl.500). Patients deserve the power of choice when evaluating their predicted chemotherapy benefit and its impact on their quality of life.
Naheed Kurji, Chair of AAIH and Co-Founder, President, and CEO of Cyclica
Radiologists will more accurately detect breast cancer with AI than without AI: In July of last year, scientists in Germany published a large-scale study demonstrating that radiologists working with AI were more accurate at detecting breast cancer than radiologists working without AI, and the AI was more accurate when working with a radiologist than when working independently. This is the first study to compare AI’s performance in breast cancer screening alone vs. with assistance from a human radiologist (The Lancet, DOI: 10.1016/S2589-7500(22)00070-X). This type of human/AI collaboration is expected to improve the accuracy of diagnosis, help detect breast cancer earlier, and improve survival rates.
Mark Kiel, MD, Ph.D., Founder and Chief Scientific Officer, Genomenon
Balance of sensitivity and specificity: Clinical end-consumers of data used for diagnostic purposes will require optimal output sensitivity to minimize false negative results. Their workflows will demand maximal specificity to preserve the efficiency of operations. Expanded use of AI in drug development programs will emphasize the value of an optimized sensitivity/specificity profile to maximize investment and de-risking potential. This balance will become a focus of AI approaches in the coming years.
AI predictions will personalize healthcare decisions: AI will be a more accurate, useful tool for healthcare decisions for two reasons: the shift to store patient records in digital formats and the widespread adoption of wearable technology. A patient’s medical and family history will become important data points for predictive health models that also draw on real-time health metrics reported by wearables or an app. This convergence of electronic records and consistent, accessible health data—heart rate, diet, exercise—will give rise to lightweight predictive models that can provide a real-time indication of risk. Location-based data from a wearable can also alert someone if they’re in an area with a dangerous disease outbreak and—based on family history—whether they might be particularly susceptible to that disease. In addition, we might see greater adoption of AI models in drug dose optimization, something we know can vary widely from patient to patient but remains pretty static at the time of prescription. By integrating metadata around family history, medical records of previous lab tests, and current baseline readings, we could model responsiveness to a drug and determine the dose that might be most appropriate for a particular patient.
Omid Farokhzad, CEO, Seer
There will be a significant acceleration in multi-omics-based precision medicine efforts: The past twenty years have seen incredible advances in our understanding of the genome and transcriptome, creating a new goal for the biotech industry: precision medicine. By decoding a patient’s genetic information, researchers hope to match them with the best treatment. Genetics-fueled precision medicine has yet to achieve this lofty goal. Still, in 2023, I expect we’ll get closer to making this a reality, thanks to a similar expansion in our knowledge of the proteome. We’ll get closer to understanding the full breadth and depth of the proteome, pushing the field of precision medicine forward. Understanding health, disease, and treatment is all about data, and proteomics is just one key part. Proteomics, transcriptomics, lipidomics, metabolomics, plus real-time heart and respiration metrics collected by wearable technologies, will combine for a wealth of personalized data that researchers can leverage to approach disease in ways that have never been possible before.
A wave of new personalized therapies: The ability to analyze individual cells and their interactions within tissue in a three-dimensional spatial context with high sensitivity and subcellular resolution will open the door to a wave of scientific findings that will redefine our understanding of disease and lead to new personalized therapies and improved patient outcomes.