Elucidate Tumor-Immune Interactions from a Single Sample
In the immuno-oncology era, it’s becoming clear that understanding and improving response to cancer immunotherapies and developing new compounds relies on the examination of the interactions between tumor cells and immune cells in the TME. Historically, gaining molecular insights into both the tumor and the immune system has necessitated the analysis of data derived from multiple sources, packaged in various formats. When samples are precious and limited, researchers need a way to both simplify this process and to maximize the data generated from each individual sample. This is where ImmunoID NeXT™, built on the Personalis NeXT Platform®, is designed to address these challenges.

Broad Exploration of Modern Oncology Biomarkers
ImmunoID NeXT facilitates the identification of known and novel biomarkers that are predictive of resistance, response, and adverse event (AE) risk associated with modern oncology therapies. Click the appropriate biomarker category below to explore how ImmunoID NeXT can help you to evaluate relevant biomarkers of interest for each.
Despite the success of Immune Checkpoint Inhibitors (ICIs), the majority of initial non-responders tend to
progress at a natural rate, and a significant proportion of initial responders eventually relapse. Thus, understanding the underlying biological mechanisms of primary and acquired resistance to immunotherapy has become a major focus of the field. ImmunoID NeXT provides insight into the following known tumor escape mechanisms, while also aiding in the identification of previously-undefined ones:
Genomic alterations affecting HLA genes are a common mechanism which tumors use to evade the host’s immune response, and develop resistance to immunotherapy. At Personalis, we’ve been pioneers in utilizing exome data and proprietary in silico methods to accurately genotype HLA genes, and to reliably detect somatic mutations and loss of heterozygosity (LOH) events affecting these genes – alterations that can contribute directly to a tumor’s ability to evade the host’s immune system.
Elevated numbers of somatic copy number alterations (CNAs) are linked to tumor ICI resistance. It has been demonstrated that tumor aneuploidy can result in reduced expression of immune-related genes such as HLA, IFNγ pathway genes, chemokines, and cytolytic immune cell genes, conferring resistance in many cases (Keenan et al., 2019).
A tumor unresponsiveness to reduced expression of interferon – particularly IFNγ, which is secreted by effector T-cells – is a common mechanism of resistance to immunotherapy. Additionally, there is a strong association between cytotoxic activity and the expression of genes involved in T-cell recruitment to the tumor site including interleukins and other cytokines. ImmunoID NeXT can be used to elucidate the TME phenotype and facilitate the discovery of novel cytokine/ chemokine gene expression signatures of resistance to immunotherapies and combinations.
Mechanisms of Resistance | |
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Category | ImmunoID NeXT Solutions |
Human Leukocyte Antigen |
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Antigen Processing Machinery (Somatic mutations and gene expression) |
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Immune Checkpoint Modulation (Somatic mutations and gene expression) |
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Canonical Pathways (Somatic mutations and gene expression) |
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Tumor Aneuploidy |
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Cytokines/Chemokines (Somatic mutations and gene expression) |
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Tumor Surface Antigen Modulation |
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Understanding the reasons why some patients are resistant to oncology therapies is important, but, because of their potential side-effects, it is even more critical to ensure that they are only administered to patients who are expected to respond. Therefore, the identification and use of molecular markers that are predictive of response is essential in bringing the curative potential of immuno-oncology drugs to more patients.
Neoantigen Load & Tumor Mutational Burden (TMB)
Despite the initial excitement surrounding the predictive potential of TMB as a biomarker of response, some setbacks have called into question whether a simple count of non-synonymous somatic mutations is biologically informative enough to help guide treatment decisions in a pan-cancer fashion. At Personalis, we believe that determining which of these mutations will be expressed as neoantigens and presented for immunosurveillance may be more indicative of a tumor’s potential sensitivity to immunotherapy.
Predictive Biomarkers of Response | |
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Category | ImmunoID NeXT Solutions |
Neoantigen Load & Tumor Mutational Burden (TMB) |
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For certain types of immunotherapies, the incidence of adverse events (AEs) such as cytokine release syndrome (CRS) and other immune-related AEs (irAEs) has served to temper excitement related to otherwise promising clinical outcomes. This has prompted the exploration of biomarkers that can be used to predict AE risk both before and during treatment.
Predictive Biomarkers of AE Risk | |
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Category | ImmunoID NeXT Solutions |
Cytokine expression signatures |
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Germline genetic variations |
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Signature(s) identification |
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The use of single-analyte biomarkers (e.g., PD-L1 expression) has yielded modest results in the quest to accurately predict which patients are likely to respond (or not) to immunotherapies and their combinations with other treatment modalities. In the immunotherapy age, it’s clear that more effective patient stratification techniques will require the integration of multiple biomarkers that not only molecularly profile a given patient’s tumor, but that also reveal how the host’s immune system is reacting to – and interacting with – that tumor.
At Personalis, we believe that such an approach will deliver the benefits of the immuno-oncology revolution to a greater proportion of patients. With the comprehensive tumor- and immune-related biomarker information that’s generated by ImmunoID NeXT, we can help our biopharmaceutical partners identify composite biomarkers to improve upon the predictive power of single-analyte approaches and bring the potential of immunotherapy to more cancer patients. NEOPS™ (Neoantigen Presentation Score) is an example of how this approach is able to drive more accurate biomarkers. By accounting for tumor escape mechanisms and combining them into a composite neoantigen score, NEOPS provides a fuller representation of tumor antigen presentation to the immune system compared to simpler models. Further, NEOPS can also be clinically practical, with comprehensive tumor profiling achieved using very limited tumor tissue.
- Brochure, ImmunoID NeXT
- Brochure, Biomarker Discovery Solutions for Lymphoma and Multiple Myeloma
- Data sheet, RepertoireID
- Data sheet, ImmunogenomicsID
- Video, Biomarker Discovery Solutions for Lymphoma and Multiple Myeloma