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H. Matsushita
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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): H. Matsushita
- 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.
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P1.02 - Poster Session with Presenters Present (ID 454)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Biology/Pathology
- Presentations: 1
- Moderators:
- Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)
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P1.02-035 - Concomitant Driver Mutation Determines Tumor Growth in EGFR Mutation-Positive Lung Adenocarcinoma (ID 5397)
14:30 - 14:30 | Author(s): H. Matsushita
- Abstract
Background:
In the practice of precision medicine, understanding tumor characteristics in the individual patient is crucial. The aim of this study was to analyze tumor aggressiveness from two perspectives: actual growth rate calculated from the tumor; and molecular profiles obtained by next-generation sequencing.
Methods:
Participants comprised patients who underwent preoperative CT two or more times. DNA and RNA of 10 lung adenocarcinoma tumor samples were extracted. Whole-exome and -transcriptome data were obtained, and somatic mutations were detected. Preoperative CT scans were retrospectively reviewed and volume doubling time (VDT) of each tumor was calculated using a modified Schwarz equation.
Results:
Median VDT was 104 days (range, 42-653 days). Median number of somatic missense mutations was 20 (range, 7-306). EGFR mutations were present in 6 patients. Patients were divided into two groups by VDT for further analyses: Slow group with VDT ≥104 days (n=5); and Rapid Group with VDT <104 days (n=5). All patients with EGFR mutation without concomitant KRAS mutation were in the Slow Group. In contrast, a patient with concomitant mutations of EGFR and KRAS showed a considerably rapid growing tumor with a VDT of 45 days. A patient with concomitant mutations in EGFR and PIK3CA had a relatively slow-growing tumor, although VDT was the shortest in the Slow Group (120 days). Figure 1
Conclusion:
EGFR mutation was associated with slow growth of the tumor, although the growth rate may be influenced by concomitant mutation of other driver genes. This may be one of the reasons that the clinical response of tyrosine kinase inhibitors are poor in some patients with EGFR mutation. Assessment of tumor aggressiveness by molecular profiling and by sequential CT are both important for the practice of precision medicine.
