Statistical mining of triple-negative breast cancer-specific nanobodies among huge libraries from immunized alpacas

Abstract

Breast cancer can be classified into several types according to the expression patterns of human epidermal growth factor receptor 2 (Her2), oestrogen receptor (ER), and progesterone receptor (PgR) proteins. The prognosis of patients with tumors showing low Her2 expression and no ER and PgR expression--categorized as triple-negative breast cancer (TNBC)--is worst among these groups. Due to the lack of specific antibodies for TNBC, curative treatments for TNBC remain limited. Antibodies targeting TNBC have potential as diagnostic and therapeutic tools. Here, we generate a panel of nanobodies targeting TNBC cell lines by immunizing alpacas and subsequently panning the resulting phage libraries with TNBC cell lines. We show that several clones exclusively stain Her2-negative cells in tissues of breast cancer patients, and a few clones stain both Her2-positive and Her2-negative regions in these tissues. These clones can be applied to patient-specific therapies using drug-conjugated antibodies, radiolabelled antibodies, chimaera antigen receptor T cells, or drug delivery components, as well as to TNBC diagnosis.