In an orthotopic breast cancer model, isogenic and cohoused mice responded variably to anti- PD-1 therapy. To compare responding and refractory tumors, Chen et al. used a bilateral tumor model; both tumors responded similarly to anti-PD-1 and one could be excised for analysis early in the treatment course. Responding tumors had fewer CD11b+Gr1+ myeloid cells and more CD8+ T cells, which increased expression of activation markers, cytokines, chemokine receptor CXCR3, and inflammation-associated gene signatures. These signatures correlated with transcriptional analysis of responding patient melanoma tumors and breast cancer survival.
Contributed by Alex Najibi
ABSTRACT: Immune checkpoint blockade (ICB) is efficacious in many diverse cancer types, but not all patients respond. It is important to understand the mechanisms driving resistance to these treatments and to identify predictive biomarkers of response to provide best treatment options for all patients. Here we introduce a resection and response-assessment approach for studying the tumor microenvironment before or shortly after treatment initiation to identify predictive biomarkers differentiating responders from nonresponders. Our approach builds on a bilateral tumor implantation technique in a murine metastatic breast cancer model (E0771) coupled with anti-PD-1 therapy. Using our model, we show that tumors from mice responding to ICB therapy had significantly higher CD8(+) T cells and fewer Gr1(+)CD11b(+) myeloid-derived suppressor cells (MDSCs) at early time points following therapy initiation. RNA sequencing on the intratumoral CD8(+) T cells identified the presence of T cell exhaustion pathways in nonresponding tumors and T cell activation in responding tumors. Strikingly, we showed that our derived response and resistance signatures significantly segregate patients by survival and associate with patient response to ICB. Furthermore, we identified decreased expression of CXCR3 in nonresponding mice and showed that tumors grown in Cxcr3 (-/-) mice had an elevated resistance rate to anti-PD-1 treatment. Our findings suggest that the resection and response tumor model can be used to identify response and resistance biomarkers to ICB therapy and guide the use of combination therapy to further boost the antitumor efficacy of ICB.
Author Info: (1) Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114. (2) Department of Data Sciences, Da
Author Info: (1) Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114. (2) Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215. (3) Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115. (4) Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115. (5) Center for Systems Biology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114. (6) Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114. (7) Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114. (8) Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02215. (9) Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215; msinger@ds.dfci.harvard.edu Arlene_Sharpe@hms.harvard.edu jain@steele.mgh.harvard.edu. Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02139. (10) Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115; msinger@ds.dfci.harvard.edu Arlene_Sharpe@hms.harvard.edu jain@steele.mgh.harvard.edu. Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02139. (11) Edwin L. Steele Laboratories, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114; msinger@ds.dfci.harvard.edu Arlene_Sharpe@hms.harvard.edu jain@steele.mgh.harvard.edu.
