PanCancer IO 360™ Panel

Facilitating rapid discovery and development of potentially predictive signatures with the most advanced view of immuno-oncology biology. 

The PanCancer IO 360 Gene Expression Panel is a unique 770 gene expression panel for research use only (RUO) that combines vital components involved in the complex interplay between the tumor, microenvironment and immune response in cancer allowing for a multifaceted characterization of disease biology and interrogation of mechanisms of immune evasion.

Developed specifically for translational research, this powerful new panel incorporates 48 potentially predictive Research Use Only (RUO) biological signatures including the 18-gene Tumor Inflammation Signature as recently described in JCI.

  • Tumor, micro-environment, and immune response biology signatures tableTranslational RUO panel for the research of possible predictive signatures for potential immunotherapy companion diagnostics
  • Allows for possible identification of responder/non-responder populations for immunotherapy research
  • Characterize disease biology
  • Interrogate mechanisms of immune evasion

Download Gene Lists:

Learn about our nCounter Analysis System and nSolver™ Analysis Software.


The 2018 Journal of Immunotherapy of Cancer (JITC) “Best Basic Science Paper” awarded to NanoString Scientists

Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

Nina Radosevic-Robin talking about the NanoString IO 360 Panel
Jan Davidson-Moncada talking about the NanoString IO 360 Panel
Pramod Thekkat talking about the NanoString IO 360 Panel

Tumor-Microenvironment-Immune Response

Tumor Immunogenicity Tumor Sensitivity to Immune Attack Inhibitory Immune Mechanisms Stromal Factors Inhibitory Metabolism Anti-Tumor Immune Activity Inhibitory Immune Signaling Immune Cell Population Abundance
Antigen Processing Machinery Apoptosis IDO1 Gene Expression Endothelial Cells Glycolysis Tumor Inflammation Signature (TIS) CTLA4 Gene Expression B Cells NK CD56dim Cells
Antigen Presenting Machinery Expression Loss Tumor Proliferation PD-L1 Gene Expression Stromal Tissue Abundance Hypoxia Cytotoxicity IL10 Gene Expression CD45+ Cells Natural Killer Cell Abundance
Immunoproteasome JAK-STAT Pathway Gene Expression Loss B7-H3 Gene Expression     Interferon Gamma Signaling Inflammatory Chemokines CD8 T Cell T Cells Abundance
MAGE Genes Expression   TGF-Beta Gene Expression     Interferon Signaling Response Myeloid-Derived Inflammatory Signaling Cytotoxic Cells TH1 Cell (TBX21/T-bet) Expression
Loss of Mismatch Repair Gene Expression         Lymphoid Compartment Activity PD-1 Gene Expression Dendritic Cells Treg (FOXP3 Expression)
Hypermutation         MHC Class II Antigen Presentation PD-L2 Gene Expression Exhausted CD8 Cell  
MSI Predictor         Myeloid Compartment Activity TIGIT Gene Expression Macrophage  
            ARG1 Gene Expression Mast Cells  
            NOS2 Gene Expression Neutrophils  


NanoString synthesizes biological knowledge and large gene expression datasets together to train signatures of biological processes. For a given process, we use literature searches and expert knowledge to derive lists of candidate genes. For the PanCancer IO 360 panel, we evaluated the co-expression of candidate genes in data from The Cancer Genome Atlas (TCGA), discarding genes whose co-expression patterns are incompatible with measuring their putative biological process. This approach safeguards the interpretability of our signatures: we only report signatures whose genes show evidence for measuring the desired biology. Finally, we further exploit co-expression patterns to obtain optimal weights for each signature gene.

PanCancer IO 360 Biological Signatures Supplement

ASCO Poster: Learn about the NanoString process of defining gene signatures in the PanCancer IO 360 Panel

13 Biological Pathways and Processes


Tumor Microenvironment Immune Response
Category/Gene Number Category/Gene Number Category/Gene Number
Release of Cancer Cell Antigens 74 Angiogenesis 40 Cancer Antigen Presentation 95
Cell Cycling and Proliferation 54 Extracellular Matrix Remodelling 43 T cell priming and Activation 151
Tumor Intrinsic Factors 156 Collagens 6 Immune Cells Localization to Tumors 293
Common Signaling Pathways 172 Metastasis 20 Recognition of Cancer Cells by T cells 103
    Killing of Cancers Cells 177
    Myeloid Cell Activity 262
    NK Cell Activity 28
    Immunometabolism 99


20 internal reference genes include overlapping genes from Hallmarks of Cancer PanCancer Collection for cross-panel comparisons.

Tumor Inflammation Signature1

The Tumor Inflammation Signature includes 18 functional genes known to be associated with response to PD-1/PD-L1 inhibitors pathway blockade.

Includes 4 Areas of Immune Biology: IFN-ү-responsive genes related to antigen presentation, chemokine expression, cytotoxic activity, and adaptive immune resistance genes.

tumor inflammation gene expression signature

The tumor inflammation gene expression signature highlights the complex biology of the host immune microenvironment.

18-gene Tumor Inflammation Signature



View publication and video.

1. Ayers, Mark, et al. "IFN-y-related mRNA profile predicts clinical response to PD-1 blockade." The Journal of Clinical Investigation 127.8 (2017).

Data analysis report for the human* nCounter PanCancer IO360™ Gene Expression Panel

  • Unique 360 view of gene expression for the tumor, microenvironment and immune response
  • Interactive reports prepared by NanoString expert scientists
  • 48 signatures including TIS, 14 signatures measuring immune cell populations and 34 novel signatures measuring important tumor and immune activities
  • Tumor Inflammation Score (TIS) provided for each sample to determine “hot” and “cold” tumors
  • Analysis includes sample signature score in relation to immune response
  • All data undergoes QC and normalization

Report only available for analysis of human panel data - report not available for mouse.

Feature Specifications
Number of Targets 770 (Human and Mouse), Including internal reference genes
Sample Input – Standard (No amplification required) 50 – 300 ng
Sample Input – Low Input As little as 10 ng with nCounter RNA Low Input Kit and Panel specific primer pools (sold separately)
IO 360 Panel Standard Synthetic oligonucleotide pool corresponding to all panel gene targets used for normalization
Sample Type(s) FFPE-derived RNA, total RNA, and cell lysates
Customizable Add up to 30 unique genes with Panel-Plus
Time to Results Approximately 24 hours
Data Analysis nSolver Analysis software, IO 360 Data Analysis Service


Development of Gene Expression Signatures Characterizing the Tumor-Immune Interaction

Warren S. et al. Development of Gene Expression-Based Biomarkers on the nCounter® Platform for Immuno-Oncology Applications. 2020. In: Thurin M., Cesano A., Marincola F. (eds) Biomarkers for Immunotherapy of Cancer. Methods in Molecular Biology, vol 2055. Humana, New York, NY.

Damotte D et al. The tumor inflammation signature (TIS) is associated with anti-PD-1 treatment benefit in the CERTIM pan-cancer cohort. Journal of Translational Medicine. 2019;17:357.

Bondar G et al. Comparing NGS and NanoString platforms in peripheral blood mononuclear cell transcriptome profiling for advanced heart failure biomarker development. Journal of Biological Methods. 2020;7(1):e123.

Luminari S et al. A Gene Expression–based Model to Predict Metabolic Response After Two Courses of ABVD in Hodgkin Lymphoma Patients. Clinical Cancer Research. 2020;26(2): 373-383.

House IG et al. Macrophage-Derived CXCL9 and CXCL10 Are Required for Antitumor Immune Responses Following Immune Checkpoint Blockade. Clinical Cancer Research. 2020;26(2): 487-504.

Sorenson L et al. Targeted transcriptional profiling of the tumor microenvironment reveals lymphocyte exclusion and vascular dysfunction in metastatic osteosarcoma. Journal of OncoImmunology. 2019;8(9): e1629779.

Voorwerk L et al. Immune induction strategies in metastatic triple-negative breast cancer to enhance the sensitivity to PD-1 blockade: the TONIC trial. Nature Medicine. 2019;25(6):920-928.

Danaher P et al. A gene expression assay for simultaneous measurement of microsatellite instability and anti-tumor immune activity. Journal of Immunotherapy of Cancer. 2019;7(1):15.

Li X et al. Immune profiling of pre- and posttreatment breast cancer tissues from the SWOG S0800 neoadjuvant trial. Journal of Immunotherapy of Cancer. 2019;7(88).

Vujanovic L et al. CD56dim CD16− Natural Killer Cell Profiling in Melanoma Patients Receiving a Cancer Vaccine and Interferon-α. Frontiers in Immunology. 2019;10(14).

Renner K et al. Restricting Glycolysis Preserves T Cell Effector Functions and Augments Checkpoint Therapy. Cell Reports. 2019;29(1):135-150.

For Research Use Only. Not for use in diagnostic procedures.