This is a demo report used to showcase the functionality of the BC360 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. Breast cancer specific signatures are listed in pink. 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.
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.
This signature captures genes associated with apoptotic processes, specifically with genes involved in mitochondrial membrane integrity. It includes both pro- and anti-apoptotic genes.
This gene is a type of nuclear receptor that is activated by binding any of the androgenic hormones. AR is widely expressed in breast cancer and has been shown to characterize a distinct molecular subset of triple negative breast cancer (TNBC) and suggested as a potential target candidate in this form of breast cancer.
B7-H3 (CD276) gene expression. B7-H3 is a negative regulator of T cell activity that is expressed on both tumor and immune cells.
Basal-like tumors are typically characterized as having low expression of ER, PR, and HER2. Most clinically triple negative tumors are Basal-like subtype by molecular profiling. These tumors are poorly differentiated invasive high-grade ductal carcinomas that by have metastatic properties.
This signature categorizes p53 status by mutant-like vs wild-type-like in breast cancer and the signature is significantly associated with overall survival in breast cancer, identifying a group with high unmet need.
This signature outputs the PAM50 proliferation score by measuring key genes involved in breast tumor proliferation. In some cases, a highly proliferative breast tumor may correlate with an increase in disease progression or metastasis.
This signature captures breast cancer biology that is informative as to defects in the DNA damage repair-genes BRCA1 and BRCA2. Similar to the Homologous Recombination Deficiency signature this captures breakdown in DNA damage repair, however, these are specific to BRCA-related mutations and more heavily weighted to BRCA1 mutants.
This signature measures the abundance of CD8+ T cells in the tumor microenvironment.
Cyclin-dependent kinases 4 and 6 (CDK4/6) play a key role in the regulation of proliferation in normal breast tissue and breast tumors. CDK4/6 inhibitors have been indicated in hormone receptor (HR) positive metastatic breast cancer. Cyclin-dependent kinase 4 (CDK4) is an enzyme encoded by the CDK4 gene, mutations in this gene as well as in its related proteins have been shown to be associated with tumorigenesis.
Cyclin-dependent kinases 4 and 6 (CDK4/6) play a key role in the regulation of proliferation in normal breast tissue and breast tumors. CDK4/6 inhibitors have been indicated in hormone receptor (HR) positive metastatic breast cancer. CDK6, as well as CDK4, has been shown to phosphorylate and regulate the activity of the tumor suppressor protein Retinoblastoma and indicating a role in cancer development.
Epithelial cells use tight junction complexes to adhere to each other. Some breast cancers have greatly down-regulated expression of one or more of the genes coding for tight junction proteins. This phenomenon is common in claudin-low breast cancers, but it is not confined to that subtype. This signature scores samples for down-regulation in any of these tight junction genes.
This molecular subtype is characterized by low levels of luminal differentiation markers, high enrichment for epithelial-to-mesenchymal transition markers, immune response and cancer stem cell-like genes.
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.
This signature assigns a score of differentiation to the sample. Well-differentiated tumors that is phenotypically more similar to normal cells or tissue will grow and spread at a slow rated compared with poorly differentiated tumors, these present with abnormal cells that often grow rapidly.
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.2
Estrogen-binding systems associate with various proteins that direct cell cycle signaling, proliferation and survival. This signature captures ER-mediated signaling pathways to elucidate how ER modulates activity of key transcription factors through stabilizing DNA-protein complexes and recruiting co-activators. This signature also captures the impact to other signaling pathways induced by the binding of estrogens in the nuclear causing conformational changes in the receptors.
This gene encodes a member of the EGF receptor family of receptor tyrosine kinases. This protein has no ligand binding domain of its own and therefore cannot bind growth factors. However, it does bind tightly to other ligand-bound EGF receptor family members to form a heterodimer, stabilizing ligand binding and enhancing kinase-mediated activation of downstream signaling pathways. Amplification and overexpression are well established in breast cancer and the associated protein is a key pathological marker.
This gene encodes an estrogen receptor, a ligand-activated transcription factor composed of several domains important for hormone binding, DNA binding, and activation of transcription. The associated ER protein is a key pathological marker of breast cancer.
This transcription factor is involved in the regulation of gene expression in differentiated tissues. Sometimes associated with BRCA1 through cell cycle regulation. Also involved in ESR-1 mediated transcription and required for ESR1 binding to the NKX2-1 promoter in breast cancer.
The Genomic Risk of Recurrence score (Genomic Risk) is calculated by comparing the expression profiles of 46 genes in the sample with the four PAM50 centroids, to calculate four different correlation values. These correlation values are then combined with the PAM50 proliferation score to estimate the genomic risk of distant recurrence. The results are reported on a scale of 0 to 100, with 0 being lowest risk and 100 being highest risk. This score is distinct from the Risk of Recurrence (ROR) score, as it does not include the tumor size included in the score calculation – it is solely based on the genomic data.
HER2-Enriched tumors are typically characterized as clinically HER2 positive breast cancer as defined by traditional IHC/FISH criteria. Some studies have indicated that the HER2-Enriched molecular subtype may be a better predictor of response to HER2-targeted therapies when compared with IHC and FISH.
This signature is used to functionally assess Homologous Recombination Repair status, with potential to predict sensitivity to DNA-damage repair inhibitors such as PARP inhibitors. This captures cell cycle regulation, DNA damage, DNA replication, and DNA recombination and repair pathways. Additionally, this signature is also used to predict overall survival in breast cancer.
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.
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.
Inflammatory chemokines recruit both myeloid and lymphoid populations to the tumor microenvironment.
Luminal A tumors are typically characterized by high expression of estrogen receptor (ER), progesterone receptor (PR), and genes associated with ER activation1. These tumors are low-grade, tend to grow slowly, exhibit low expression of genes associated with cell cycle activation and have the best prognosis.
Luminal B tumors are typically characterized by high expression of estrogen receptor (ER), progesterone receptor (PR), and genes associated with ER activation1. These tumors tend to grow slightly faster than Luminal A tumors, exhibit high expression of genes associated with cell cycle activation and proliferation, and have a slightly worse prognosis than Luminal A tumors.
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).
This signature measures a cluster of epithelial-to-mesenchymal transition (EMT) genes that are up-regulated in tumors with stem-cell-like expression profiles. Higher signature scores indicate more stem-like tumors.
This signature measures the abundance of mast cells in the tumor microenvironment.
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.
This 50-gene signature measures a gene expression profile that allows for the classification of breast cancer into four biologically distinct subtypes (Luminal A, Luminal B, HER2-Enriched, Basal-like).
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.
This gene encodes a member of the steroid receptor superfamily. The encoded protein mediates the physiological effects of progesterone, which plays a central role in reproductive events and the associated protein is a key pathological marker of breast cancer.
Phosphatase and tensin homolog gene expression. PTEN is a tumor suppressor gene that functions through the regulation of the Akt/PKB signaling pathway. Mutations or loss of PTEN expression are common across a range of cancer types, including breast cancer.
Retinoblastoma protein gene expression. RB is involved in cell cycle regulation and tumor progression. RB gene loss is occurs predominantly in triple negative breast cancer, as a result of homozygous deletion.
SRY (sex determining region Y)-box 2 transcription factor gene expression. SOX2 regulates a number of critical processes in breast cancer including cell proliferation and metastasis. SOX2 expression has been shown to be associated with the prognosis of metastatic tumors and disease recurrence.
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.
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 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.
Tumor Inflammation Signature. TIS measures the abundance of a peripherally suppressed adaptive immune response within the tumor.
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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Perou, Charles M., et al. Molecular portraits of human breast tumours. Nature 406.6797 (2000): 747.
Ayers, Mark, et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. The Journal of Clinical Investigation 127.8 (2017).
Haddad, Robert I., et al. Genomic determinants of response to pembrolizumab in head and neck squamous cell carcinoma (HNSCC). The Journal of Clinical Investigation (2017): 6009-6009.
Prat, Aleix, et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast cancer research 12.5 (2010): R68.
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Troester, Melissa A., et al. Gene expression patterns associated with p53 status in breast cancer. BMC cancer 6.1 (2006): 276.
Severson, Tesa M., et al. The BRCA1 ness signature is associated significantly with response to PARP inhibitor treatment versus control in the I-SPY 2 randomized neoadjuvant setting. Breast Cancer Research 19.1 (2017): 99.
Peng, Guang, et al. Genome-wide transcriptome profiling of homologous recombination DNA repair. Nature communications 5 (2014): 3361.
Normalization in BC 360 differs from normalization in nSolver. The goal is to adjust for cartridge differences using either a panel standard or reference sample, such that comparisons can be made between the scores across batches. Panel standard is a DNA oligo blend containing all BC 360 probe target sequences, that is run on each cartridge within the experiment for normalization of non-PAM50 genes; while reference sample is an RNA oligo blend containing PAM50 probe target sequences for PAM50 genes. Normalization takes place in two steps. The first step differs depending on whether the genes are in the PAM50 or TIS signatures, 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 in the PAM50 signature are normalized using a ratio of the expression value to the geometric mean of the housekeeper genes used only for the PAM50 signature.
Genes not in the PAM50 signature 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. If a cartridge is missing a panel standard run, the average of all panel standards present is substituted for that cartridge.
Genes in the PAM50 signature are additionally normalized using a ratio of the housekeeper-normalized data and a reference sample run on the same codeset lot from a NanoString archive is used.
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.
PAM50 Subtype calls are the result of a three step algorithm. The first step involves a scaling using two sets of scaling factors to bring the housekeeper and reference sample expression values into the scale necessary for the next step. This second step calculates the correlation between the observed scaled expression for the PAM50 genes and a centroid for each of the four subtypes resulting in a set of four correlation values for each sample. The remaining step is to identify the subtype correlation with the greatest value and set that subtype as the subtype call for that sample.
Genomic Risk scores are the result of a multiple step algorithm. The first step involves a scaling using two sets of scaling factors to bring the housekeeper and reference sample expression values into the scale necessary for the next step. This second step calculates the correlation between the observed scaled expression for the PAM50 genes and a centroid for each of the four subtypes that is different than that for calling subtypes and results in a set of four correlation values for each sample. The next step is to calculate a proliferation score for each sample, followed by taking a weighted sum of the proliferation score and the four subtype correlations. This last score is then scaled to be between 0 and 100. No tumor size information is utilized, only the genomic information portion of ROR.
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