nCounter® ADC Development Panel
Helping Your Research
ADCs require the development and optimization of multiple steps in a complex process that combines chemistry and biology for the delivery and release of the drug. Although cell-based assays provide valuable endpoint readings, a more informative assay that characterizes the various stages of ADC development can greatly expand on insights gained during the process.
The novel nCounter® ADC Development Panel enables researchers to answer complex questions critical for the success of Antibody Drug Conjugates throughout discovery, pre-clinical and clinical development. The biological function can be assessed using quantitative molecular characterization spanning 6 stages in the lifecycle of the ADC. The comprehensive gene content covers:
- Mechanisms of resistance biology
- Immunogenic cell death
- Aspects of the immune response
- Traditional and emerging MOAs
- Current and developing targets for ADCs
The success of both traditional chemotherapy and immunotherapy as part of a combination treatment can be evaluated, and the panel can be customized with tumor-specific or ADC-specific targets of interest.
How it Works
Directly profile 770 genes that address essential biological questions relevant to each stage of ADC development
- Tumor Targeting & Antigen Expression
- ADC Internalization
- Payload Release
- Drug MOA
- Target Cell Death
- Mechanisms of Resistance
Address biological function with deep molecular characterization, expanding insights gained from traditional endpoint assays
Compatible with a variety of sample types, including treated cell lines (both in vivo and in vitro), tumor biopsies, xenografts, and mouse cells
Quantify the presence and relative abundance of 14 different immune cell types
Generate data in 24 hours with less than 30 minutes hands on time and simple data analysis
The ADC Development Process
The ADC Development Panel can be used throughout the ADC development process to characterize all the essential stages of ADC function.
The ADC Development Panel measures 6 distinct stages of ADC delivery and response in a single gene expression panel, gauging the success of both traditional chemotherapy and combination immunotherapy. Pathway coverage is outlined in the table below.
Mycoplasma is a common contaminant in cultured cells. Mycoplasma compete with cells for nutrients and can have a profound impact on global gene expression levels within the cells. The ADC Development Panel contains a probe to detect mycoplasma, allowing for quick and easy detection of culture contamination when using cell-based assays to understand ADC activity. The panel can also be customized by adding up to 55 genes of your choice with a Panel Plus spike-in for studying additional sources of potential contamination.
Customization with Panel Plus
Customize your research project by adding up to 55 user-defined genes of interest with nCounter® Panel Plus. Panel Plus capacity enables researchers to address content specific to the cancer type they are studying or specific ADC targets of interest.
The 18-gene Tumor Inflammation Signature (TIS) is included in the panel gene list and measures activity known to be associated with PD-1/PD-L1 inhibitors. Customers have the option to purchase a standalone TIS report with the ADC Development Panel.
- Includes four axes of biology that characterize a peripherally suppressed, adaptive immune response, including:
– Antigen presenting cells
– T cell/NK cell presence
– Interferon gamma biology
– T cell exhaustion
- Tissue-of-origin agnostic (Pan-Cancer)
- Potential surrogate for PD-L1 and mutational load in a research setting
Genes included in the ADC Development Panel provide unique cell profiling data to measure the relative abundance of 14 different immune cell types. These markers that identify specific immune cell types can efficiently define both the immunological activity of the samples as well as identify changes in immune cell populations in response to external stimuli from payload release. The table summarizes the genes included in each cell type signature, as qualified through biostatistical approaches and selected literature in the field of immunology.
In addition to the standard nSolver™ Analysis Software, genes included in the ADC Development Panel are organized and linked to various advanced analysis modules to allow for efficient analysis of relevant pathways.
Analysis Modules available for ADC Development:
- Quality Control
- Individual Pathway Analysis
- Cell Profiling
- Differential Expression
- Gene Set Analysis
- Built-in compatibility for Panel Plus and Protein analysis
ROSALIND is a cloud-based platform that enables scientists to analyze and interpret differential gene expression data without the need for bioinformatics or programming skills. ROSALIND makes analysis of nCounter data easy, with guided modules for:
Normalization / Quality Control / Individual Pathway Analysis Differential Expression / Gene Set Analysis
nCounter customers can access ROSALIND free of charge at rosalind.bio/nanostring.
Panel Selection Tool
Find the gene expression panel for your research with easy to use panel proFind Your Panel
Immunostimulatory cancer-associated fibroblast subpopulations can predict immunotherapy response in head and neck cancer.
Purpose: Cancer-associated fibroblasts (CAF) have been implicated as potential mediators of checkpoint immunotherapy response. However, the extensive heterogeneity of these cells has precluded rigorous understanding of their immunoregulatory role in the tumor microenvironment.
Spatial profiling reveals association between WNT pathway activation and T-cell exclusion in acquired resistance of synovial sarcoma to NY-ESO-1 transgenic T-cell therapy.
Background: Genetically engineered T-cell immunotherapies for adoptive cell transfer (ACT) have emerged as a promising form of cancer treatment, but many of these patients develop recurrent disease. Furthermore, delineating mechanisms of resistance may be challenging since the analysis of bulk tumor profiling can be complicated by spatial heterogeneity.
A Novel Artificial Intelligence–Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers.
To develop a novel artificial intelligence (AI)–powered method for the prediction of prostate cancer (PCa) early recurrence and identification of driver regions in PCa of all Gleason Grade Group (GGG). MATERIALS AND METHODS: Deep convolutional neural networks were used to develop the AI model.