Wednesday, May 18, 2016
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CST’s antibody multiplex IHC panel

Cell Signaling Technology

The field of cancer immunotherapy is focused on empowering the immune system to fight cancer. This approach has seen recent success in the clinic with targeting immune checkpoint control proteins, such as PD-1 (1,2). Despite this success, clinical biomarkers that predict response to therapeutic strategies involving PD-1 receptor blockade are still under investigation (3-5). While PD-L1 expression has been linked with an increased likelihood of response to anti-PD-1 therapy, research studies have shown that additional factors, such as tumor-immune infiltration and the ratio of effector to regulatory T cells within the tumor, could play a significant role in predicting treatment outcome (6-9).

CST is now offering new antibody detection packages for you to perform fluorescent multiplex IHC using tyramide signal amplification so that you can understand the expression of various targets within the same tissue section.

Benefits of the new mIHC panel

  • Save time with pre-validated panels
  • Save precious sample: use only one slide per triple target detection
  • Exactly same slide for triple IHC: excellent target comparison
  • Easy to use, protocol is optimized to streamline your workflow
  • Antibodies formulated for mIHC-P with standardized dilution of 1:250 for all kit reagents

Key antigen target of CST’s antibody multiplex IHC panel

  1. Programmed cell death 1 ligand 1 (PD-L1) is a member of the B7 family of cell surface ligands that regulate T cell activation and immune responses. The PD-L1 ligand binds the PD-1 transmembrane receptor and inhibits T cell activation. PD-L1 is expressed in several tumor types, including melanoma, ovary, colon, lung, breast, and renal cell carcinomas (10-12).
  2. CD3 (Cluster of Differentiation 3) is a multiunit protein complex that directly associates with the T cell receptor (TCR). CD3 is composed of four polypeptides (ζ, γ, ε and δ), each of which contains at least one immunoreceptor tyrosine-based activation motif (ITAM) (13). Engagement of TCR complex with foreign antigens induces tyrosine phosphorylation in the ITAM motifs and phosphorylated ITAMs function as docking sites for signaling molecules such as ZAP-70 and p85 subunit of PI-3 kinase (14,15).  
  3. CD8 (Cluster of Differentiation 8) is a disulphide-linked heterodimer consisting of α and β subunits. On T cells, CD8 is the coreceptor for the TCR, and these two distinct structures recognize the Antigen–Major Histocompatibility Complex (MHC). CD8 ensures specificity of the TCR–antigen interaction, prolongs the contact between the T cell and the antigen presenting cell, and the α chain recruits the tyrosine kinase Lck, which is essential for T cell activation (16).
  4. FoxP3 is a transcription factor that is crucial for the development of T cells with regulatory properties (Treg) (17). Mutations in FoxP3 are associated with immune dysregulation, polyendocrinopathy, enteropathy, and X-linked syndrome (IPEX) (18), while overexpression in mice causes severe immunodeficiency (19). Research studies have shown that FoxP3 functions as a tumor suppressor in several types of cancer (20-22).
Fig. 1 the PD-L1, CD3e, CD8a Multiplex IHC Antibody Panel enables researchers to assess tumor infiltrating lymphocytes while the PD-L1, FoxP3, CD8α Multiplex IHC Antibody Panel enables researchers understand the ratio of effector to regulatory T cell (Treg) infiltrates.
Fig. 2 Fluorescent mIHC analysis of paraffin-embedded human breast cancer using (A) PD-L1 (E1L3N®) XP® Rabbit mAb (green), FoxP3 (D2W8E™) Rabbit mAb (IHC Specific) (yellow), and CD8α (C8/144B) Mouse mAb (IHC Specific) (Red). (B) PD-L1 (E1L3N®) XP® Rabbit mAb (green), CD3ε (D7A6E™) XP® Rabbit mAb (yellow), and CD8α (C8/144B) Mouse mAb (IHC Specific) (Red). Blue pseudocolor = DAPI #8961 (fluorescent DNA dye). Image acquisition was performed with a multispectral camera.

Multiplex IHC Antibody Panel


Fluorescent Conjugation Reagent


1.  Topalian, S.L. et al. (2012) N Engl J Med 366, 2443-54.
2.  Piccinini, M. et al. (2014) Comput Methods Biomech Biomed Engin 17, 1403-17.
3.  Chakravarti, N. and Prieto, V.G. (2015) Transl Lung Cancer Res 4, 743-51.
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12.  Osman, N. et al. (1996) Eur J Immunol 26, 1063-8.
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14.  Hatada, M.H. et al. (1995) Nature 377, 32-8.
15.  Thompson, R.H. et al. (2006) Cancer Res 66, 3381-5.
16.  Pardoll, D.M. (2012) Nat Rev Cancer 12, 252-64.
17.  Ochs, H.D. et al. (2007) Immunol Res 38, 112-21.
18.  Bennett, C.L. et al. (2001) Nat Genet 27, 20-1.
19.  Kasprowicz, D.J. et al. (2003) J Immunol 171, 1216-23.
20.  Zuo, T. et al. (2007) Cell 129, 1275-86.
21.  Zuo, T. et al. (2007) J Clin Invest 117, 3765-73.
22.  Wang, L. et al. (2009) Cancer Cell 16, 336-46.

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