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Brilliant Violet 421™ anti-human CD56 (NCAM) antibody

RRID:AB_11218798

Antibody ID

AB_11218798

Target Antigen

CD56 (NCAM) See NCBI gene human, african green, baboon, cynomolgus, rhesus

Proper Citation

(BioLegend Cat# 318328, RRID:AB_11218798)

Clonality

monoclonal antibody

Comments

Applications: FC

Clone ID

Clone HCD56

Host Organism

mouse

Vendor

BioLegend Go To Vendor

Cat Num

318328

Publications that use this research resource

Selective FcγR Co-engagement on APCs Modulates the Activity of Therapeutic Antibodies Targeting T Cell Antigens.

  • Waight JD
  • Cancer Cell
  • 2018 Jun 11

Literature context:


Abstract:

The co-engagement of fragment crystallizable (Fc) gamma receptors (FcγRs) with the Fc region of recombinant immunoglobulin monoclonal antibodies (mAbs) and its contribution to therapeutic activity has been extensively studied. For example, Fc-FcγR interactions have been shown to be important for mAb-directed effector cell activities, as well as mAb-dependent forward signaling into target cells via receptor clustering. Here we identify a function of mAbs targeting T cell-expressed antigens that involves FcγR co-engagement on antigen-presenting cells (APCs). In the case of mAbs targeting CTLA-4 and TIGIT, the interaction with FcγR on APCs enhanced antigen-specific T cell responses and tumoricidal activity. This mechanism extended to an anti-CD45RB mAb, which led to FcγR-dependent regulatory T cell expansion in mice.

Funding information:
  • Medical Research Council - R01-GM083300(United Kingdom)

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.

  • Racle J
  • Elife
  • 2017 Nov 13

Literature context:


Abstract:

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).