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E. Lugli
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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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P1.07-032 - 28-Color, 30 Parameter Flow Cytometry to Dissect the Complex Heterogeneity of Tumor Infiltrating T Cells in Lung Cancer (ID 10160)
09:30 - 09:30 | Author(s): E. Lugli
- Abstract
Background:
Defining the phenotypic, molecular and functional characteristics of tumor infiltrating leukocytes advances our understanding of how the immune system is defective in fighting cancer and may thus lead to the identification of new therapeutic targets to be exploited in the clinic. Considerable heterogeneity is found at the tumor site in terms of leukocyte populations and cellular subsets which may retain pro- or anti-cancer potential. Such heterogeneity can only be addressed by more powerful single cell technologies.
Method:
We used 30-parameter single cell flow cytometry to define the memory differentiation, activation, tissue-residency, exhaustion and transcription factor profile of millions of single T cells infiltrating human lung adenocarcinomas.
Result:
We revealed that PD-1[high] exhausted T cells were enriched at the tumor site compared to the peripheral blood or to the non-tumoral portion of the lung from the same patient, were mainly confined to the CD69+ tissue-resident memory compartment and expressed high levels of the transcription factor T-bet and the activation marker HLA-DR. Conversely, these PD-1[high] cells were nearly absent from the early-differentiated, circulating memory compartment identified by CCR7+ expression. Bona fide naïve T cells, as identified by the simultaneous expression of 5 markers, were virtually absent at the tumor site. The exhausted T cells also lacked markers of terminally-differentiated senescent T cells, which in turn are CD57+ T-bet[low]Eomes[high], thereby suggesting that exhaustion and senescence are divergent differentiation states.
Conclusion:
We anticipate that such high-content single cell profiling will identify patient-specific subpopulations capable to correlate with disease progression and clinical/metabolic parameters.
