The immune system is essential to health, but its condition varies widely among people of the same age. Genetics, air pollution, smoking, diet and previous infections can influence how quickly the immune system ages, or its “biological age,” which has been linked to neurodegeneration, cancer and mortality risk.1,2,3 Researchers are now working to develop comprehensive “immunomes” that could measure immune health across cells, tissues, organs and molecular interactions, and eventually bring useful assessments into routine clinical care.
If the immune system is weakened, a person may become more vulnerable to infections and cancer. If it is misdirected, it can attack the body’s own tissues. Changes that occur with age can also contribute to chronic disease.
Despite its importance, clinicians do not yet have a routine way to assess how healthy a person’s immune system is. The immune system is a dynamic network that changes over time. It includes specialized organs that support immune-cell development and function, immune cells that circulate through the body or reside in tissues, and signaling molecules such as cytokines and chemokines that allow cells and tissues to communicate.4
Standard laboratory tests provide only a partial view. A doctor might measure a person’s white blood cell count to determine whether circulating immune cells are unusually abundant or scarce. Blood biomarkers such as C-reactive protein can indicate inflammation. But neither measurement is specific to a particular health condition, and neither shows whether the immune system as a whole is resilient, compromised or capable of responding effectively to infection or another challenge.
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Advances in immunology, molecular profiling, medical imaging and artificial intelligence are creating new possibilities for measuring immune status. Research initiatives such as the Human Immunome Project are working to provide the data needed for this effort. One of us, JST, is acting as a co-leader, and we have begun supplying the necessary data.
What remains unclear is how this information can best improve patient care. Which measurements capture clinically important aspects of immune health? How stable are those measurements over time? Most importantly, can assessing them improve clinical decisions and ultimately patient outcomes?
It will not be feasible to measure every aspect of the immune system in every person. Instead, researchers should identify a small number of features that can be assessed easily in clinical settings to reveal weakened immune functions or immune decline that is occurring faster than expected. Such assessments could support disease prevention, treatment selection and monitoring of interventions.
For example, they could help doctors recognize accelerated immunosenescence and identify people who need closer monitoring. In cancer care, measures of immune health could help identify patients most likely to benefit from treatments that depend on the immune system, as well as those at high risk of side effects.5,6
These measurements could also help explain why people of similar ages and with apparently similar health conditions respond very differently to the same infection. This variation became particularly clear during the COVID-19 pandemic. Antibody responses to the same vaccine also differ widely between individuals.2,7 If such differences can be measured, clinicians might be able to tailor preventive measures, treatments and monitoring to each person’s immune system.
Researchers, health systems and funders therefore need to identify the smallest and most scalable set of measurements that can capture meaningful differences in immune function and guide clinical decisions. The following roadmap describes how the immunome could move from the laboratory into the clinic.
Build the evidence base
The first step is to generate long-term datasets from large study populations and biobanks. A full picture of immune health will require repeated measurements of several immune-system components, combined with broader indicators of health and disease in the same individuals.
Analyzing genes and gene expression in single cells from large research populations and biobanks could show which immune-cell groups are present in different populations. It could also reveal the genes and signaling pathways active in specific cell populations, as well as combinations of markers that distinguish healthy, compromised and dysfunctional immune states.2,3,8
Blood-protein analysis can indicate whether a person is inflamed or whether the immune system is activated. Studying receptors on lymphocytes could reveal the diversity of immune cells available to recognize threats. These receptors determine which pathogen-associated antigens a cell can identify; in general, greater receptor diversity allows the immune system to detect a wider range of threats.
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Researchers must also examine how the immune system changes in response to challenges. Comparing people before and after vaccination provides one standardized way to do this. Evidence already shows that a person’s immune status before vaccination can predict the strength and characteristics of the later immune response.2,9 The next question is whether the response to a routine vaccination can reveal a person’s overall immune capacity.10 Researchers also need to determine whether immune health can be altered before interventions such as vaccination or cancer immunotherapy to improve the resulting response.2,5
Because both genetic and environmental factors influence immune status, datasets used to study the immunome must represent the diversity of human immunity.1 For example, the effectiveness of malaria vaccines can vary significantly among populations on different continents.7,11 Yet many existing immune-profiling datasets are concentrated in populations from high-income countries. When researchers study diverse populations, they will need to use consistent protocols so that results from different regions can be compared.
Interpreting these complex datasets will be a major challenge. Artificial-intelligence tools can identify patterns across thousands of immunoassays, but those patterns might reflect differences between populations, laboratories or technologies rather than underlying immune health. Models must therefore be tested over time and across independent populations to establish which signals are robust and clinically meaningful.
Scientists working on the Human Immunome Project are developing and applying AI approaches to immunome data collected from diverse populations. One goal is to create reference maps and foundational models that researchers can use for tasks such as identifying small, manageable sets of measurements suitable for clinical practice.
Find what works in practice
Once large-scale datasets are available, researchers will need to simplify them. Progress should not be judged by how many immune variables can be measured, but by whether clinical decisions can be improved using a small, manageable set of variables. Tests used in clinics should ideally be inexpensive and should not impose additional demands on patients.
Before AI tools can be adopted routinely, they must be tested in different hospitals and populations. Researchers will need to establish whether a tool’s output genuinely improves treatment decisions.
One possible clinical application is an AI model that extracts information about immune health from routine blood tests and clinical data such as age, gender, weight and medical history. In a study published last year,6 such an approach was used to predict health outcomes after immunotherapy in people with cancer.
Source: www.nature.com
