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.1
- Translational 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:
The 2018 Journal of Immunotherapy of Cancer (JITC) “Best Basic Science Paper” awarded to NanoString Scientists
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.
13 Biological Pathways and Processes
|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|
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.
The tumor inflammation gene expression signature highlights the complex biology of the host immune microenvironment.
|18-gene Tumor Inflammation Signature|
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).
- 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
|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|
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.