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K. Nagayama
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P1.07 - Immunology and Immunotherapy (ID 693)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Immunology and Immunotherapy
- Presentations: 1
- Moderators:
- Coordinates: 10/16/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P1.07-017 - Assessment of Cancer Immunity Status in Each Patient Using Immunogram (ID 9115)
09:30 - 09:30 | Author(s): K. Nagayama
- Abstract
Background:
For successful cancer immunotherapy, comprehensive profiling of cancer-immune system interaction is required for each individual patient. To this end, we developed an immunogram reflecting the cancer immunity cycle and applied it to real patients with lung cancer.
Method:
Whole-exome sequencing and RNA-Seq were performed in 25 non-small cell lung cancer patients (13 adenocarcinoma, 11 squamous cell carcinoma, and 1 large cell neuroendocrine carcinoma). The number of somatic mutations and the expression of genes related to cancer-immunity were assessed and normalized using TCGA-LUAD and LUSC data (n=1035). Immunogram of each patient was drawn in a radar chart composed of 9 axes reflecting 7 steps of cancer-immunity cycle.
Result:
Various patterns of immunogram were observed in all 25 lung cancer patients, suggesting that each patient has their own pattern of immunosuppressive microenvironment (Figure 1). The hierarchical clustering using each scores of immunogram showed four clusters of patients characterized by T cell phenotype (inflamed vs non-inflamed) and tumor antigenicity (high vs low) (Figure 2). T cell-inflamed tumors (Clusters 3&4) had gene signatures of abundant T cells and interferon gamma (IFNG) response, as well as inhibitory cells and checkpoint molecules, suggesting the presence of counter regulatory immunosuppressive microenvironment. Unleashing of counter regulations by checkpoint inhibitors, for example, may be indicated for these patients. Each scores of immunogram had no correlation with histology. This result was consistent with previous studies of checkpoint blockade that clinical responses were not easily predicted solely by the histology. Patient age, gender and TNM stage also did not correlate with each immunogram scores. Figure 1
Conclusion:
The landscape of the tumor microenvironment in each patient can be appreciated by utilizing immunogram. Immunogram for the cancer-immunity cycle can be used for the assessment and visualization of cancer immunity status in each patient, and thus may become a helpful resource toward optimal personalized immunotherapy.
