Analytical Performance of an Immunoprofiling Assay Based on RNA Models

Schillebeeckx, Ian; Armstrong, Jon R.; Forys, Jason T.; Hiken, Jeffrey F.; Earls, Jon; Flanagan, Kevin C.; Cui, Tiange; Glasscock, Jarret; Messina, David N.; Duncavage, Eric J.

doi:10.1016/j.jmoldx.2020.01.009

Cited by 9 publications

(15 citation statements)

References 30 publications

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“…An individual ImmunoPrism report, including RNA expression characterization and immune cell quantification, was generated for each sample processed. Immune cell type characterization was defined by RNA models for the ImmunoPrism analysis, 20 derived from immune cell populations sorted using canonical flow cytometry markers defined by the Human Immunology Project. 21 Samples were grouped according to clinical outcomes for overall survival (OS), and recurrence-free survival (RFS) and OS and RFS at 3 years was considered a clinical meaningful cut-off point as this has been cited as the median OS for patients with advanced DDLPS.…”

Section: Methodsmentioning

confidence: 99%

“…The ImmunoPrism platform used RNA sequencing data to evaluate the cellular immune profile. 20 Tumor-infiltrating immune cells selected for analysis included CD4 + T cells, CD8 + T cells, CD19 + B cells, CD14 + monocytes, CD56 + NK cells, M1 macrophages, M2 macrophages, and Tregs. The overall mean percent of immune cells per DDLPS sample was 36.9% (±21.8) of the total cell population, and the most prevalent infiltrating immune cells were CD14 + monocytes, with a mean of 7.6% (±7.6) of the total cell population.…”

Section: Quantification Of Tumor-infiltrating Immune Cells In Lps Using Immunoprismmentioning

confidence: 99%

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CD4+ T cell and M2 macrophage infiltration predict dedifferentiated liposarcoma patient outcomes

Schroeder

LaFranzo

LaFleur

et al. 2021

J Immunother Cancer

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BackgroundDedifferentiated liposarcoma (DDLPS) is one of the most common soft tissue sarcoma subtypes and is devastating in the advanced/metastatic stage. Despite the observation of clinical responses to PD-1 inhibitors, little is known about the immune microenvironment in relation to patient prognosis.MethodsWe performed a retrospective study of 61 patients with DDLPS. We completed deep sequencing of the T-cell receptor (TCR) β-chain and RNA sequencing for predictive modeling, evaluating both immune markers and tumor escape genes. Hierarchical clustering and recursive partitioning were employed to elucidate relationships of cellular infiltrates within the tumor microenvironment, while an immune score for single markers was created as a predictive tool.ResultsAlthough many DDLPS samples had low TCR clonality, high TCR clonality combined with low T-cell fraction predicted lower 3-year overall survival (p=0.05). Higher levels of CD14+ monocytes (p=0.02) inversely correlated with 3-year recurrence-free survival (RFS), while CD4+ T-cell infiltration (p=0.05) was associated with a higher RFS. Genes associated with longer RFS included PD-1 (p=0.003), ICOS (p=0.006), BTLA (p=0.033), and CTLA4 (p=0.02). In a composite immune score, CD4+ T cells had the strongest positive predictive value, while CD14+ monocytes and M2 macrophages had the strongest negative predictive values.ConclusionsImmune cell infiltration predicts clinical outcome in DDLPS, with CD4+ cells associated with better outcomes; CD14+ cells and M2 macrophages are associated with worse outcomes. Future checkpoint inhibitor studies in DDLPS should incorporate immunosequencing and gene expression profiling techniques that can generate immune landscape profiles.

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Section: Methodsmentioning

confidence: 99%

Section: Quantification Of Tumor-infiltrating Immune Cells In Lps Using Immunoprismmentioning

confidence: 99%

CD4+ T cell and M2 macrophage infiltration predict dedifferentiated liposarcoma patient outcomes

Schroeder

LaFranzo

LaFleur

et al. 2021

J Immunother Cancer

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“…All stages of the assay were performed in a CAP-accredited, CLIA-licensed clinical laboratory. Schillebeeckx et al (16) describes the machine learning approach used to generate "health expression models" for accurately identifying percentages of immune cell populations in tumor tissue. When developing the immune Health Expression Models used in the ImmunoPrism assay, all models were built using purified immune cell populations, and validated using flow cytometry.…”

Section: Cofactor Immunoprism ® Assaymentioning

confidence: 99%

“…Populations were confirmed by flow cytometry using the following markers: M1 macrophages are CD80+/CCR7+/CD206 low/CD209 low and M2 macrophages are CD206+/CD209+/CD80 low/CCR7 low. LaFranzo et al describes in detail the Cofactor Genomics predictive immune modeling workflow (16,20), which is summarized as follows:…”

Section: Cofactor Immunoprism ® Assaymentioning

confidence: 99%

Immune Modeling Analysis Reveals Immunologic Signatures Associated With Improved Outcomes in High Grade Serous Ovarian Cancer

et al. 2021

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Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy worldwide, as patients are typically diagnosed at a late stage and eventually develop chemoresistant disease following front-line platinum-taxane based therapy. Only modest results have been achieved with PD-1 based immunotherapy in ovarian cancer patients, despite the fact that immunological responses are observed in EOC patients. Therefore, the goal of this present study was to identify novel immune response genes and cell subsets significantly associated with improved high grade serous ovarian cancer (HGSOC) patient prognosis. A transcriptomic-based immune modeling analysis was employed to determine levels of 8 immune cell subsets, 10 immune escape genes, and 22 co-inhibitory/co-stimulatory molecules in 26 HGSOC tumors. Multidimensional immune profiling analysis revealed CTLA-4, LAG-3, and Tregs as predictive for improved progression-free survival (PFS). Furthermore, the co-stimulatory receptor ICOS was also found to be significantly increased in patients with a longer PFS and positively correlated with levels of CTLA-4, PD-1, and infiltration of immune cell subsets. Both ICOS and LAG-3 were found to be significantly associated with improved overall survival in The Cancer Genome Atlas (TCGA) ovarian cancer cohort. Finally, PVRL2 was identified as the most highly expressed transcript in our analysis, with immunohistochemistry results confirming its overexpression in HGSOC samples compared to normal/benign. Results were corroborated by parallel analyses of TCGA data. Overall, this multidimensional immune modeling analysis uncovers important prognostic immune factors that improve our understanding of the unique immune microenvironment of ovarian cancer.

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“…RNA data from each sample is compared to a database of gene expression models of immune cells, called immune Health Expression Models, to quantify immune cells as a percentage of total cells present in the sample. Briefly, these models were built using machine-learning methods to identify unique multigenic expression patterns from whole-transcriptome data generated from purified immune cell populations (isolated using canonical cell-surface markers) 17,18 . The multidimensional Health Expression Models underlying the technology enables the assay to quantify each immune cell as a percent of the total cells present in the heterogenous mixture.…”

Section: Introductionmentioning

confidence: 99%

Predictive Immune Modeling of Solid Tumors

LaFranzo

Flanagan

Quintanilha

2020

JoVE

Self Cite

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Immunotherapies show promise in the treatment of oncology patients, but complex heterogeneity of the tumor microenvironment makes predicting treatment response challenging. The ability to resolve the relative populations of immune cells present in and around the tumor tissue has been shown to be clinically-relevant to understanding response, but is limited by traditional techniques such as flow cytometry and immunohistochemistry (IHC), due the large amount of tissue required, lack of accurate cell type markers, and many technical and logistical hurdles. One assay (e.g., the ImmunoPrism Immune Profiling Assay) overcomes these challenges by accommodating both small amounts of RNA and highly degraded RNA, common features of RNA extracted from clinically archived solid tumor tissue. The assay is accessed via a reagent kit and cloud-based informatics that provides an end-to-end quantitative, high-throughput immuno-profiling solution for Illumina sequencing platforms. Researchers start with as few as two sections of formalin-fixed paraffin-embedded (FFPE) tissue or 20-40 ng of total RNA (depending on sample quality), and the protocol generates an immune profile report quantifying eight immune cell types and ten immune escape genes, capturing a complete view of the tumor microenvironment. No additional bioinformatic analysis is required to make use of the resulting data. With the appropriate sample cohorts, the protocol may also be used to identify statistically significant biomarkers within a patient population of interest.

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Analytical Performance of an Immunoprofiling Assay Based on RNA Models

Cited by 9 publications

References 30 publications

CD4+ T cell and M2 macrophage infiltration predict dedifferentiated liposarcoma patient outcomes

CD4+ T cell and M2 macrophage infiltration predict dedifferentiated liposarcoma patient outcomes

Immune Modeling Analysis Reveals Immunologic Signatures Associated With Improved Outcomes in High Grade Serous Ovarian Cancer

Predictive Immune Modeling of Solid Tumors

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