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T. Karasaki
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MA15 - Immunotherapy Prediction (ID 400)
- Event: WCLC 2016
- Type: Mini Oral Session
- Track: Chemotherapy/Targeted Therapy/Immunotherapy
- Presentations: 1
- Moderators:O. Arrieta
- Coordinates: 12/07/2016, 14:20 - 15:50, Schubert 1
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MA15.01 - Immunogram for Cancer-Immunity Cycle towards Personalized Immunotherapy of Lung Cancer (ID 4519)
14:20 - 14:26 | Author(s): T. Karasaki
- Abstract
Background:
The interaction of immune cells and cancer cells shapes the immunosuppressive tumor microenvironment. For successful cancer immunotherapy, comprehensive knowledge of anti-tumor immunity as a dynamic spacio-temporal process is required for each individual patient. To this end, we developed an "immunogram for the cancer-immunity cycle" using next-generation sequencing.
Methods:
Whole-exome sequencing and RNA-Seq were performed in 20 non-small cell lung cancer patients (12 adenocarcinoma, 7 squamous cell carcinoma, and 1 large cell neuroendocrine carcinoma). Mutated neoantigens and cancer-germline antigens expressed in the tumor were assessed for predicted binding to patients’ HLA molecules. The expression of genes related to cancer-immunity was assessed and normalized; immunogram was drawn in a radar chart composed of 8 axes reflecting 7 steps of cancer-immunity cycle.
Results:
Distinctive patterns of immunogram were observed in lung cancer patients: T-cell-rich and T-cell-poor. Patients with T-cell-rich pattern had gene signatures of abundant T cells, Tregs and MDSCs, checkpoint molecules and immune-inhibitory molecules in the tumor, suggesting the presence of counter regulatory immunosuppressive microenvironment. Unleashing of counter regulations, i.e. checkpoint inhibitors, may be indicated for these patients (Figure A). Immunogram of T-cell-poor phenotype reflected lack of anti-tumor immunity, inadequate DC activation, and insufficient antigen presentation in the tumor (Figure B). When the immunograms were overlaid within each tumor histology, no typical pattern was elucidated. Both T-cell-rich and T-cell-poor phenotypes were present in each histology, suggesting that histology cannot necessarily reflect the cancer-immunity status of the tumor (Figure C,D). These results were consistent with previous studies showing that clinical responses of checkpoint blockade were not easily predicted by the histology. Figure 1
Conclusion:
Utilizing the immunogram, the landscape of the tumor microenvironment in each patient can be appreciated. Immunogram for the cancer-immunity cycle can be used as an integrated biomarker and thus may become a helpful resource toward optimal personalized immunotherapy.
