A trispecific antibody targeting HER2 and T cells inhibits breast cancer growth via CD4 cells.

Effective antitumour immunity depends on the orchestration of potent T cell responses against malignancies1. Regression of human cancers has been induced by immune checkpoint inhibitors, T cell engagers or chimeric antigen receptor T cell therapies2-4. Although CD8 T cells function as key effectors of these responses, the role of CD4 T cells beyond their helper function has not been defined. Here we demonstrate that a trispecific antibody to HER2, CD3 and CD28 stimulates regression of breast cancers in a humanized mouse model through a mechanism involving CD4-dependent inhibition of tumour cell cycle progression. Although CD8 T cells directly mediated tumour lysis in vitro, CD4 T cells exerted antiproliferative effects by blocking cancer cell cycle progression at G1/S. Furthermore, when T cell subsets were adoptively transferred into a humanized breast cancer tumour mouse model, CD4 T cells alone inhibited HER2+ breast cancer growth in vivo. RNA microarray analysis revealed that CD4 T cells markedly decreased tumour cell cycle progression and proliferation, and also increased pro-inflammatory signalling pathways. Collectively, the trispecific antibody to HER2 induced T cell-dependent tumour regression through direct antitumour and indirect pro-inflammatory/immune effects driven by CD4 T cells.

An integrative approach of digital image analysis and transcriptome profiling to explore potential predictive biomarkers for TGFß blockade therapy.

Increasing evidence suggests that the presence and spatial localization and distribution pattern of tumor infiltrating lymphocytes (TILs) is associate with response to immunotherapies. Recent studies have identified TGFß activity and signaling as a determinant of T cell exclusion in the tumor microenvironment and poor response to PD-1/PD-L1 blockade. Here we coupled the artificial intelligence (AI)-powered digital image analysis and gene expression profiling as an integrative approach to quantify distribution of TILs and characterize the associated TGFß pathway activity. Analysis of T cell spatial distribution in the solid tumor biopsies revealed substantial differences in the distribution patterns. The digital image analysis approach achieves 74% concordance with the pathologist assessment for tumor-immune phenotypes. The transcriptomic profiling suggests that the TIL score was negatively correlated with TGFß pathway activation, together with elevated TGFß signaling activity observed in excluded and desert tumor phenotypes. The present results demonstrate that the automated digital pathology algorithm for quantitative analysis of CD8 immunohistochemistry image can successfully assign the tumor into one of three infiltration phenotypes: immune desert, immune excluded or immune inflamed. The association between "cold" tumor-immune phenotypes and TGFß signature further demonstrates their potential as predictive biomarkers to identify appropriate patients that may benefit from TGFß blockade.