Neoplasia: An International Journal for Oncology Research (Oct 2019)

VCAM-1 Density and Tumor Perfusion Predict T-cell Infiltration and Treatment Response in Preclinical Models

  • Johannes Riegler,
  • Herman Gill,
  • Annie Ogasawara,
  • Maj Hedehus,
  • Vincent Javinal,
  • Jason Oeh,
  • Gregory Z. Ferl,
  • Jan Marik,
  • Simon Williams,
  • Deepak Sampath,
  • Jill Schartner,
  • Richard A.D. Carano

Journal volume & issue
Vol. 21, no. 10
pp. 1036 – 1050

Abstract

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Cancer immunotherapies have demonstrated durable responses in a range of different cancers. However, only a subset of patients responds to these therapies. We set out to test if non-invasive imaging of tumor perfusion and vascular inflammation may be able to explain differences in T-cell infiltration in pre-clinical tumor models, relevant for treatment outcomes. Tumor perfusion and vascular cell adhesion molecule (VCAM-1) density were quantified using magnetic resonance imaging (MRI) and correlated with infiltration of adoptively transferred and endogenous T-cells. MRI biomarkers were evaluated for their ability to detect tumor rejection 3 days after T-cell transfer. Baseline levels of these markers were used to assess their ability to predict PD-L1 treatment response. We found correlations between MRI-derived VCAM-1 density and infiltration of endogenous or adoptively transferred T-cells in some preclinical tumor models. Blocking T-cell binding to endothelial cell adhesion molecules (VCAM-1/ICAM) prevented T-cell mediated tumor rejection. Tumor rejection could be detected 3 days after adoptive T-cell transfer prior to tumor volume changes by monitoring the extracellular extravascular volume fraction. Imaging tumor perfusion and VCAM-1 density before treatment initiation was able to predict the response of MC38 tumors to PD-L1 blockade. These results indicate that MRI based assessment of tumor perfusion and VCAM-1 density can inform about the permissibility of the tumor vasculature for T-cell infiltration which may explain some of the observed variance in treatment response for cancer immunotherapies.