This is a demo report used to showcase the functionality of the IO360 Standard Report.
Signatures are organized here in alphabetical order. They are color-coded by biology, similar to the color-coding in this image. Tumor signatures are listed in orange, Immune signatures in blue, and Micronenvironment signatures in green. Below each signature name is the signature category with which it is associated.
Most scores can be interpreted on a log2 scale, with a unit increase in score corresponding to a doubling of its gene expression levels. The following scores occupy different scales:
APM loss, MMR loss, and JAK-STAT loss: these scores fall near zero in tumors without loss of expression events and increase sharply as any of their genes suffer expression loss.
Tumor Inflammation Signature. TIS measures the abundance of a peripherally suppressed adaptive immune response within the tumor.
Antigen presenting (or processing) machinery. This signature measures the abundance of genes in the MHC Class I antigen presentation pathway and some key genes involved in processing the antigens prior to presentation. Typically, antigens from the cell cytoplasm are presented on Class I and recognized by the TCR on cytolytic CD8+ T cells. MHC Class I is expressed by all nucleated cells in the body, but downregulation of Class I MHC pathways is an evasion strategy that can be employed by tumor cells. An effective anti-tumor immune response depends on cytolytic T cells encountering neoantigens presented on the tumor cell surface. Strong anti-tumor immune responses are typically accompanied by high expression of antigen presentation genes.
Antigen presenting (or processing) machinery expression loss. This signature measures the extent to which any of several key major histocompatibility complex (MHC) genes have atypically low expression conditional on total MHC Class I gene expression. Values below 2 are expected from tumors with intact antigen presenting machinery. Antigen presentation via MHC class I in tumor cells is a major mechanism for immune recognition of tumors. Mutation or loss of expression of key class I MHC genes has been observed to confer resistance to immunotherapy.
This signature captures genes associated with apoptotic processes, specifically with genes involved in mitochondrial membrane integrity. It includes both pro- and anti-apoptotic genes.
Arginase-1 gene expression. ARG1 is expressed by myeloid cells and catalyzes the conversion of arginine to ornithine and urea. This suppresses T cell responses by preventing proliferation, which is dependent upon the availability of arginine for protein synthesis.
This signature measures the abundance of B cells in the tumor microenvironment. B cells are one of the main components of the humoral immune response. B cells have many functions including developing into plasma cells that secrete antibodies and presenting antigens to T cells. Presence of B cells in the tumor microenvironment have been shown to be associated with tertiary lymphoid structures in some tumor types.
B7-H3 (CD276) gene expression. B7-H3 is a negative regulator of T cell activity that is expressed on both tumor and immune cells.
CD45 gene expression. CD45 is a protein tyrosine phosphatase, receptor type C (PTPRC) and is expressed on all immune cells.
This signature measures the abundance of CD8+ T cells in the tumor microenvironment.
Cytotoxic T-lymphocyte-associated protein 4 gene expression. CTLA4 is a checkpoint molecule that inhibits T cell priming by competitively binding CD80/86 to prevent co-stimulation of via CD28.
This signature measures the abundance of cytotoxic cells in the tumor microenvironment. Cytotoxic cells such as natural killer (NK) and CD8+ T cells use a number of molecules, including perforin, granzymes and killer cell lectin-like receptor (KLRG) family members to recognize, penetrate and kill infected cells. Cytotoxic activity is the mechanism by which the immune system most effectively kills tumor cells.
This signature measures the molecules used by natural killer (NK) and CD8+ T cells to mount a cytolytic attack on tumor cells. Cytotoxic cells such as NK and CD8+ T cells, use a number of molecules, including perforin, granzymes and granulysin to penetrate and kill infection cells and tumors. Cytotoxic activity is the mechanism by which the immune system most effectively kills tumor cells.
Dendritic cell abundance. This signature measures the abundance of dendritic cells in the tumor microenvironment.
This signature measures genes associated with vascular tissue and angiogenesis. Angiogenesis is important for nutrient trafficking to the tumor and proper oxygenation for tumor growth. Tumor angiogenesis forms leaky inefficient vessels that can reduce efficiency of lymphocyte trafficking to tumors.
This signature measures the abundance of exhausted CD8 cells in the tumor microenvironment. T cells in the tumor microenvironment often become less functional (exhausted) after exposure to factors from suppressive immune, stromal, and malignant cells. These exhausted T cells lose their ability to eliminate tumors. Exhausted T cells express markers and genes that are associated with reduced anti-tumor activity. This signature looks at the functional activity of T cells in the tumor microenvironment.
This signature measures genes participating in energy consumption. Up-regulated glycolysis and corresponding increased glucose consumption is nearly universal in tumors. Glycolysis may inhibit effective immune responses by depriving immune cells of glucose in the tumor microenvironment and changing other molecules that would ordinarily signal the immune system.
This signature measures downstream gene expression changes associated with loss of MMR activity in tumor types where MMR loss is more prevalent.
This signature measures genes associated with reduced oxygenation in the tumor. Hypoxia can induce expression of many cancer promoting processes (e.g. invasion, motility, metabolic reprogramming) and can promote resistance to immune cell-mediated cytolysis and reduced cytolytic activity in natural killer (NK) and CD8+ T cells.
Indoleamine 2,3-dioxygenase 1 gene expression. IDO1 is expressed by tumor, immune, and stromal cells and is the rate-limiting enzyme of tryptophan catabolism. By catalyzing the degradation of tryptophan, which is necessary for cytolytic T cell proliferation and activity, IDO1 inhibits anti-tumor immune responses.
Interferon Signaling Response. This gene signature reflects activation of a broad set of interferon signaling pathways. Both type I and II interferons are implicated in tumor immune responses and regulate anti-tumor activity. Malignant and immune cells in the tumor microenvironment produce type 1 interferents, which have been shown to play a role in immunosurveillance. Interferon signaling is associated with improved patient outcome.
Interferon gamma signaling. This signature tracks the canonical response to type II interferon, including the most universal components of that response. IFNγ induces macrophage and natural killer (NK) cell activation, increases antigen presentation, and induces gene transcription patterns that can lead to immune cell recruitment to the tumor. IFNγ signaling expression is associated with response to anti-PD1/L1 therapy.
Interleukin-10 gene expression. IL10 is a pleiotropic cytokine expressed predominately by monocytes. It impacts multiple aspects of the tumor immune response, including antigen presentation, T cell activation, and cytokine production.
This signature measures key components of the immunoproteasome. The immunoproteasome is a specialized variant of the classical proteasome. It is assembled in immune cells and non-immune cells after exposure to proinflammatory cytokines or oxidative stress. The immunoproteasome induces different patterns of protein degradation, thus generating novel antigens that are presented in MHC class I. Immunoproteasome gene expression can be associated with increased tumor immunogenicity.
Inflammatory chemokines recruit both myeloid and lymphoid populations to the tumor microenvironment.
JAK-STAT loss = JAK-STAT pathway expression loss. This signature measures loss of genes associated with JAK-STAT signaling, which has been identified as a mechanism of acquired resistance to immune checkpoint blockade.
Lymphoid compartment activity. This signature measures a broad set of genes involved in the functioning of lymphoid cells, including genes quantifying T cell abundance, B cell abundance, natural killer (NK) cell abundance, cytotoxic activity, interferon gamma signaling, JAK-STAT signaling, and T-cell co-stimulatory and co-inhibitory molecules. This signature captures a broad look at the lymphoid immune status of a tumor.
This signature measures the abundance of macrophages in the tumor microenvironment. Macrophages can either augment tumor immunity (e.g. by presenting antigen) or suppress tumor immunity (e.g. by releasing immunosuppressive cytokines).
Melanoma-Associated Antigen Gene Expression. This signature measures several melanoma-associated antigens from the cancer testis (CT) antigen family. This family of CT antigens are expressed in a variety of cancers. They are important tumor-specific neoantigens that have been implicated in tumor biology and are often used as immunotherapy targets. This signature is useful to distinguish CT antigen expression before and after therapy.
This signature measures the abundance of mast cells in the tumor microenvironment.
Major histocompatibility complex class II antigen presentation. This signature measures the major human leukocyte antigens (HLA) involved in MHC Class II antigen presentation. Professional antigen presenting cells (dendritic cells, macrophages and B cells) use the class II MHC to present extracellular antigens to CD4+ T cells. Activation of CD4+ T cells induces expression of cytokines that can promote cytotoxic T cell activation and effective anti-tumor adaptive immune responses. Presence of MHC Class II molecules is associated with improved patient outcome.
Mismatch Repair Gene Expression Loss. This signature measures the expression levels of several key mismatch repair genes. Mismatch repair deficiency often results when one of these genes has significant expression loss. Mismatch repair deficient tumors have high mutation rates due to the loss of important DNA-repair mechanisms. MMR loss is associated with better immune recognition in certain types of tumors. High scores in this signature indicate MMR deficiency. Mismatch repair deficiency and microsatellite instability (MSI) predict response to immune checkpoint blockade.
This is a signature that integrates the output of the MMR loss signature and the hypermutation signature to predict MMR activity loss.
Myeloid compartment activity. This signature measures key marker and effector genes of myeloid lineage immune cells. Myeloid cells regulate both anti- and pro-tumor activities. This signature captures a broad look at the myeloid immune status of a tumor.
Myeloid-derived inflammatory signaling. This signature measures gene expression for myeloid lineage cells with pro- and anti-tumor functions. Myeloid cells produce a number of cytokines and chemokines that promote a state of inflammation. Depending on the context, these can promote tumorigenesis or anti-tumor immune responses.
This signature measures the abundance of neutrophils in the tumor microenvironment. In the context of the tumor, neutrophils tend to have immunosuppressive activity.
A subset of natural killer cells. This signature represents the subset of NK cells with the most cytolytic activity.
Natural Killer cell abundance. This signature measures the abundance of NK cells in the tumor microenvironment.
Nitric Oxide Synthase 2 gene expression. NOS2 is induced by Interferon gamma signaling (IFNγ). It regulates expression of nitric oxide, which at low levels can promote tumor growth but at high levels may be cytostatic or cytotoxic to tumor cells.
Program cell death receptor 1 gene expression. Program cell death receptor 1 (PD-1, PDCD1, CD279) is expressed predominantly on lymphocytes. It is upregulated upon activation and becomes a negative regulator of activation by preventing proliferation and cytokine secretion. PD-1 expression has been shown to be associated with tumor-specific T cells.
Program cell death ligand 1 gene expression. Program cell death ligand 1 (PD-L1, CD274) is a ligand for PD-1 and negative regulator of T cell activity that is expressed on both tumor and immune cells.
Program cell death ligand 2 gene expression. Program cell death ligand 2 (PD-L2, PDCDLG2, CD273) is a ligand for PD-1 and negative regulator of T cell activity that is expressed on antigen-presenting cells.
Tumor Proliferation. This signature measures genes involved in tumor proliferation. A highly proliferative tumor can overcome an immune response if replication exceeds immune mediated detection and elimination.
Stromal Tissue Abundance. This signature measures stromal components in the tumor microenvironment. The tumor stroma is the collection of non-cancerous and nonimmune tissue components surrounding the tumor. Stroma can act as a physical barrier that excludes immune cells from the tumor, preventing effective anti-tumor immunity even when tumor-associated antigens have induced immune cell priming and activation. These cells can also secrete important signals to the tumor, affecting tumor biology and response to the immune system.
This signature measures the abundance of T cells in the tumor microenvironment.
Transforming Growth Factor Beta gene expression. TGFβ (TGFB1) is a pleotropic cytokine which inhibits anti-tumor immune activity and promotes tumor growth and survival.
T-box transcription factor TBX21 (T-bet) expressing cell abundance. T-bet is the canonical transcription factor that defines TH1 T cells and is used to measure TH1 cell abundance. TH1 T cells promote anti-tumor immune activity (particularly supporting CD8+ T cell function) by producing Interferon gamma (IFNγ).
T cell immunoreceptor and Ig and ITIMS gene expression. T cell immunoreceptor and Ig and ITIMS domains (TIGIT) is an immune checkpoint molecule that suppresses anti-tumor immune activity in CD8+ T cells and NK cells.
Regulatory T cell abundance. Treg is measured by gene expression of Forkhead box P3 (FOXP3). FOXP3 is the canonical transcription factor that defines the regulatory T cell (Treg) population and is used to measure Treg abundance. Regulatory T cells suppress other T cell activities through a variety of mechanisms.
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Normalization in the 360 reports differs from normalization in nSolver. The goal is to adjust for cartridge differences using a panel standard containing all 360 gene targets that is run on each cartridge within the experiment such that comparisons can be made between the scores across batches. Normalization takes place in two steps. The first step differs depending on whether the genes are in the TIS signature, or not, and is described below. Zero counts on the raw scale are converted to ones prior to normalization.
Genes are normalized using a ratio of the expression value to the geometric mean of all housekeeping genes on the panel.
Genes in the TIS signature are normalized using a ratio of the expression value to the geometric mean of the housekeeper genes used only for the TIS signature.
Genes are additionally normalized using a ratio of the housekeeper-normalized data and a panel standard run on the same cartridge as the observed data. In the absence of a panel standard column, values from panel standard run on the same codeset lot as the observed data may be substituted.
The housekeeper-normalized and panel standard-normalized data is Log(2) transformed. A constant of 8 is added to TIS so that it is on the same scale as investigational use only (IUO) TIS, making scores comparable across research use only (RUO) and IUO assays. Other non-TIS signatures are also adjusted with constants to express values in a similar range.
Differential expression is fit on a per gene or per signature basis using a linear model for analyses without a blocking factor. The statistical model uses the expression value or signature score as the dependent variable and fits a grouping variable as a fixed effect to test for differences in the levels of that grouping variable.
Expression(gene or signature)= μ+Group+ε
P-values are adjusted within each analysis, gene or signature, and on the grouping variable level difference t-test using the Benjamini and Yekutieli False Discovery Rate (FDR) adjustment to account for correlations amongst the tests. All models are fit using the limma package in R.
If a grouping variable is present, the survival analysis used to create the forest plot incorporates a proportional hazards model with the survival outcome as a dependent variable, the observed normalized gene expression or signature score data as a continuous covariate, and the grouping variable included as a strata variable in the model which results in the model being a frailty model.
Survival(time,event)= μ+Expression_(gene or signature)+Group+ ε
The analysis method is performed on a by gene or by signature basis, as appropriate, and uses the regression routines implemented in the R package survival. All p-values are adjusted for the number of tests within each type of analysis (gene or signature) using the Benjamini and Yekutieli False Discovery Rate (FDR) method to account for correlations amongst the tests.
There are no Kaplan-Meier curves available for frailty models and thus are not present when the analysis is fit as a frailty model with a random effect.
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Therneau TM (2015). A Package for Survival Analysis in S. version 2.38, https://CRAN.R-project.org/package=survival.
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