AML offers a multifaceted immunotherapy testing platform that is designed to classify the tumor-immune microenvironment. AML combines NGS sequencing (DNA sequencing and RNA-seq) with IHC to screen for immune-related biomarkers and to support cancer immunotherapy drug development.
iScore (Determining the relationship between DNA mutations and the immune response)
AML conducts high-throughput DNA sequencing data to identify correlations between mutation rate and immunogenicity in tumors. AML uses a proprietary score (iScore) that combines the somatic mutation rate and average mutant allele frequency. The iScore more significantly characterizes TIL (Tumor-infiltrating lymphocytes) levels than somatic mutation rates alone , which suggests that mutant allele frequency may be an important factor in the estimation of tumor antigenicity and TIL levels.
The quantification of TIL levels is clinically relevant to both tumor characterization and treatment selection: TILs are associated with better outcome, and a response to immune checkpoint inhibitors may be associated with TIL levels.
The iScore serves as an NGS-based tool to examine the relationship between mutations, TIL levels, and response to immunotherapy. The iScore is capable of detecting a robust immune response, and it may be useful in the identification of patients/cancers that are amenable to immunotherapy treatment.
AML’s proprietary immune panel is designed to identify a gene expression signature that is associated with a response/non-response to immunotherapy. AML’s immune panel relies on high-throughput RNA sequencing (RNA-Seq) to characterize the expression of more than 350 genes that have a role in the tumor-immune microenvironment.
AML’s immune panel profiles the expression of genes that have a function in:
- Immune cell signaling (adaptive and innate)
- Immune cell activation
- Immune cell suppression
- Immune cell migration
- Checkpoint inhibition
- Inflammatory signaling
- Tumor cell signaling
- Immune cell exhaustion
- Tertiary lymphoid structures (Lymphoid aggregates)
The immune panel expression data correlates with histologic immune-related observations (internal data; currently unpublished), and the panel reveals gene expression changes that influence pathways ranging from immune cell activation to T cell exhaustion. The immune panel demonstrates that tumors can be classified on the basis of immune-related gene expression.
Neoantigen prediction is available with DNA exome sequencing assays. The neoantigen analysis combines exome sequencing data (over 20,000 genes) with in silico neoantigen prediction. Neoantigen candidates are prioritized with the following criteria:
- Identification of mutated peptides and novel tumor-specific open reading frames (neoORFs)
- in silico pipeline analysis: HLA prediction and MHC-peptide binding analysis
Immunotherapy-related IHC Testing
- Additional IHC assays target specific immune signaling pathways (list available upon request)
Figure Legend. Dual Immunostaining reveals PD-L1 expressing NSCLC cells surrounded by immune cell aggregates. CD8+ immune cells aggregate surround the tumor cells, however, PD-L1 expression may contribute to immune escape. Our immune signature data (summarized in Table-1) indicate that, in addition to PD-L1, other immune signaling pathways may allow the tumor cells to evade the immune system. CD8 and PD-L1 Immunostaining (20X).