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275 Flow cytometry and nanoString provide a comprehensive cell- and gene-based tumor profile following checkpoint inhibition in a murine bladder cancer model
  1. David Draper,
  2. Philip Lapinski and
  3. Scott Wise
  1. Labcorp Drug Development, Ann Arbor, MI, USA


Background Bladder cancer (BC) is the thirteenth leading cause of cancer-related deaths.1 Five checkpoint immunotherapies that target the PD-1/PD-L1 axis are FDA approved, and gene- and protein-based approaches are helping to identify new combination treatment strategies for therapeutic intervention.2 Using the murine MB49 model for BC, we demonstrate how non-targeted immune gene expression profiling can combine with flow cytometry to provide a gene and cell-specific signature for the tumor microenvironment and help identify potential targets for novel treatment approaches.

Methods Animals with established MB49 tumors were treated with anti-mPD-1 or isotype control antibodies. Tumors were collected 7 days after the last treatment. Flow cytometry examined anti-mPD-1 treatment-induced immunophenotypic modulation for eleven tumor-infiltrating immune subsets. The mouse PanCancer IO 360™ panel (NanoString) provided transcriptomic analysis of 770 immuno-oncology-related genes. The ROSALIND™ platform (OnRamp BioInformatics) was used to identify differentially regulated genes between treatment groups (±1.5 fold-change; p <0.05).

Results Anti-mPD-1 had moderate anti-tumor activity, with a 58% tumor growth inhibition at day 18 post-implant in treated compared to control animals. Flow cytometry revealed anti-mPD-1 triggered an increase in tumor-infiltrating CD8+ T cells (45%) compared to control animals. Furthermore, the CD8+ T cell phenotype was altered by anti-mPD-1 treatment. The percentage of CD8+ T cells that expressed ICOS and LAG-3 was increased in tumors from anti-mPD-1 treated animals (22% and 35% respectively). A reduction in PD-1 expression was also observed (33%). In myeloid cells, iNOS expression increased in tumor-associated macrophages from treated animals compared to controls. NanoString analysis revealed 62 genes were differentially regulated in tumors from anti-mPD-1 treated animals compared to controls. ROSALIND analysis classified 30 of the genes as regulators of interferon, cytotoxicity, antigen presentation, and cytokine/chemokine signaling. Also, among the genes upregulated by anti-mPD-1 were IDO, HAVCR2 (TIM-3), and CSFR1, which can promote tumor growth and are clinical targets actively being investigated for new immunotherapies.

Conclusions NanoString analysis complemented flow cytometry to provide a comprehensive profile of the MB49 tumor. Together, these data demonstrate that anti-mPD1 increases T cell recruitment into the tumor and upregulates the expression of genes known to enhance T cell recruitment and anti-tumor activity. iNOS protein upregulation indicates that anti-mPD-1 treatment may also exert effects by reprogramming M2 macrophages towards an M1 phenotype. Upregulation of IDO, HAVCR2, and CSFR1 genes may effectively counteract anti-mPD-1 treatment. Further investigation may elucidate clinical implications for inhibitors of these gene products as combination treatment partners with anti-mPD-1.


  1. Saginala K, Barsouk A, Aluru JS, Rawla P, Padala SA, Barsouk A. Epidemiology of bladder cancer. Med Sci 2020;1:15–26.

  2. Lopez-Beltran A, Cimadamore A, Blanca A, Massari F, Vau N, Scarpelli M, Cheng L, Montironi R. Immune checkpoint inhibitors for the treatment of bladder cancer. Cancers 2021;1:131–146.

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