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In collaboration with Science, we hosted a two-part webinar series on the theme of the changing landscape of diagnostic biomarkers in immuno-oncology. In the first part of this series, we heard from the following experts in the field:
Dr Sacha Gnjatic – Icahn School of Medicine at Mount Sinai, New York, NY
Professor David Rimm – Yale University School of Medicine, New Haven, CT
Dr Houssein Abdul Sater – NIH NCI Center for Cancer Research, Bethesda, MD
Conventional oncology biomarkers showed efficient patient stratification based on a single biomarker test, eg estrogen receptor or HER2. However, diagnostics for immuno-oncology have proven more complex. Professor David Rimm stated that these single biomarker tests in oncology 'paved the way for how we would do biomarkers for immunohistochemistry in clinical labs. As new targets came through if they weren't molecular they stayed in the IHC realm, traditionally read by pathologists and traditionally only a single biomarker. That's why we saw the introduction of PD-L1 as a single biomarker read in a similar way as HER2 or ER, even though perhaps it's inadequate to capture the range of events that affect immunotherapy response.'
Compared to standard oncology diagnostics, immuno-oncology no longer considers the tumor as the only target from which biomarkers can be derived, rather the entire immune cell network can be analyzed for appropriate biomarkers. Profiling the immune network in this way is vital to identify biomarkers that can accurately predict patient responsiveness to treatment. A single biomarker, such as PD-L1 expression, is insufficient to capture disease-relevant immune signatures and the interactions between key cell types. For example, some patients exhibiting high PD-L1 expression do not respond to the therapeutic blockade of this immune checkpoint, while conversely, some patients with low PD-L1 expression do respond.
Understanding the mechanisms that underlie different immune signatures is important for the development of innovative and integrated assays for accurate patient stratification. Previously identified immunotherapy biomarkers include immune cell infiltrate, tumor mutational burden (TMB), and microsatellite instability; these can be used to predict the efficacy of therapies in different patient groups.
One major focus of immuno-oncology research is the delivery of new biomarkers to guide immunotherapy. Professor Sacha Gnjatic discussed the possibility of building diagnostic tests based on many levels: the host level with the examination of the gut microbiome from stool samples, the tissue level with the examination of stromal markers and the periphery with the examination of circulating biomarkers from liquid biopsies, in the pursuit of accurate diagnostic immuno-oncological biomarker panels.
Large-scale patient studies, big-data analysis, and bioinformatics integrations are being employed in this field to accelerate biomarker discovery, with the use of multiplex assays and combination assays to improve diagnostic tests, using these novel biomarkers.
The adoption of multiple RNA and protein markers into a single specific biomarker signature for patient stratification is dependent upon the ability to generate, integrate, and monitor multiple antigens in parallel, on a sufficiently large scale for effective implementation within the clinic. This is not a simple task, requiring new technologies and complex protocols.
The Cancer Immune Monitoring and Analysis Centers and Cancer Immunologic Data Commons (CIMAC-CIDC) is an initiative that aims to improve clinical trials in immuno-oncology through the application of new technologies and data analytics. A suite of technologies including multiplex IHC, mass cytometry, RNA sequencing, whole-exome sequencing, and serum cytokine analysis is being used across four main centers. This network will serve as a resource to support immunotherapy research and early-phase clinical trials.
Mass cytometry is a high-throughput protein analysis technique that involves labeling specific proteins within a sample with heavy metal–conjugated "carrier-free" antibodies. This technology currently enables the simultaneous analysis of up to 50 markers from a single cell.
Challenges facing the adoption of multiple biomarkers as a signature, rather than single biomarkers, include harmonization, integration, reimbursement, and commercial viability. A lack of full concordance between different tests for the same target has been shown for the well-known biomarker PD-L1, where tests differ in their scoring criteria and interpretation. This variation may increase further when integrating multiple tests to different biomarkers, as differences in sampling, labs, equipment and conditions will all exacerbate the variations.
One factor that continues to hinder the adoption of multiplex testing according to Rimm is that 'when new assays come in, especially more complicated assays there has to be some need from the drug perspective. When we started to look at 20-30 different targets at once, we saw the adoption of NGS in pathology labs. Not all labs could do it, it's a very complicated test, but it is an example of where you could see a complicated test being adopted that solves a problem, that is the need to assess multiple genes and biomarkers at one time. I think that's now happening in the IO space for proteins. We will see whether these multiplex tests come to prominence. Once there's a need for it for the specificity in predicting response to therapy and that's what we're seeing now.
Identifying the best combination of biomarkers that can provide an accurate prediction of tumor response requires much further research. Gnjatic told us how ‘We are still trying to figure out the biology… All these biological insights of how the mechanisms of each biomarker Is actually working, will help to refine the test and make it more relevant in terms of why it is useful in predicting response to therapy’.
Many different markers and combinations of markers are under investigation. Professor Houssein Abdul Sater raised the possibility of implementing unsupervised diagnsotics, with data from ‘Segmentation of tissue and spatial analyses and having all these details and putting them into an AI-based model rather than a supervised annotated approach.’ While Sater also agreed with Rimm that the future immune-ocology diagnsotics would ‘Use high-plex assays to uncover smaller assays with predictive value,…finding the key set and the right biomarkers that display maximum sensitivity and specificity being reduced to a smaller set for clinical assay.’
The first webinar is still available to watch. To view the next webinar in the series, “The changing landscape of diagnostic biomarkers: Revealing the future of diagnostics, from singleplex to multiplex”, click here.
To push the boundaries of what we can achieve for future diagnostic tests, researchers need access to a diverse range of reliable research tools and expertise. At Abcam, we have a strong immuno-oncology focus, and we are frequently developing new research tools for emerging and established immuno-oncology targets. We strive to test and validate our products in relevant healthy and diseased tissues and assay situations to ensure the best performance.
We're happy to help you with the tools or support you need to accelerate your research, antibody discovery, and/or companion diagnostic development.