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

Product Information

Development Process
Signatures/ Pathways
Contamination Detection
Development Process

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.



Signatures/ Pathways

Functional Annotations

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.


Contamination Detection

Detect Contamination

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.

Product Specifications

Tumor Inflammation Signature
Immune Cell Profiling Feature
Data Analysis
Product Specifications
Catalog Information

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.

Tumor Inflammation Signature

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
Immune Cell Profiling Feature

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.

Data Analysis

Data Analysis

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: 

  • Normalization
  • Quality Control
  • Individual Pathway Analysis
  • Cell Profiling
  • Differential Expression
  • Gene Set Analysis
  • Built-in compatibility for Panel Plus and Protein analysis

ROSALIND® Platform

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

Product Specifications

Product Specifications

Catalog Information

Catalog Information

Panel Selection Tool

Find the gene expression panel for your research with easy to use panel pro

Find Your Panel

Related Resources

View All Resources
Webinar “Linking” it All Together: Enhancing ADC Development with the NEW nCounter® ADC Development Panel
Product Bulletin nCounter ADC Development Panel – Product Bulletin


View All Publications

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.


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