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J.C. English
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MA 05 - Immuno-Oncology: Novel Biomarker Candidates (ID 658)
- Event: WCLC 2017
- Type: Mini Oral
- Track: Immunology and Immunotherapy
- Presentations: 1
- Moderators:Yoichi Nakanishi, P. Mitchell
- Coordinates: 10/16/2017, 15:45 - 17:30, Room 303 + 304
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MA 05.12 - Oncogenic Drivers Induce Production of CCL5 to Recruit Regulatory T-Cells Early in Lung Cancer Progression (ID 10289)
15:45 - 17:30 | Author(s): J.C. English
- Abstract
- Presentation
Background:
Lung cancer development is driven by the expression of mutant oncogenes, with EGFR and KRAS the most frequent in lung adenocarcinoma. However, these mutations alone are not sufficient for tumorigenesis suggesting additional factors influence tumour development and progression, including the balance of anti-tumour immune effector cells and pro-tumorigenic immune suppressor cells. Tumour cells can evade immune surveillance by producing cytokines to recruit immune modulatory cells that promote an immune suppressive environment, such as regulatory T cells (Tregs). We hypothesized that oncogene signaling regulates the production of cytokines by tumour cells in order to recruit immune suppressive cells and promote lung tumour development.
Method:
We used CIBERSORT to quantify 22 immune cell types in over 300 human lung adenocarcinoma and 100 matched normal lung tissues, and validated findings with immunohistochemistry. Cells expressing doxycycline inducible EGFR[L858R] and KRAS[G12V]were analyzed for cytokine production using a multiplex assay (LUMINEX). EGFR (Afatinib) and MEK (Trametinib) inhibitors were used in lung cancer cell lines harbouring EGFR or KRAS mutations and cytokine production was quantified using ELISA. Conditioned media from EGFR[L858R] and KRAS[G12V] expressing cells were used in a trans-well assay to determine if secreted cytokines could induce Treg migration. Transgenic mouse models of lung adenocarcinoma and bronchoalveolar lavage (BAL) from patients with and without lung cancer were used to assess CCL5 and Tregs in vivo.
Result:
Treg cells were significantly enriched in lung tumours and not normal tissue. CCL5 production is increased rapidly upon oncogene induction and subsequent transformation of normal cells and is dependent on sustained ERK signaling for continued expression. Conditioned medium from EGFR[L858R] expressing cells increased Treg migration, which was mitigated by an anti-CCL5 antibody. Transgenic mice expressing EGFR[L858R ]or KRAS[G12D] in the lung epithelium recruited Tregs to the lung upon tumor induction. Assessment of CCL5 in BAL from patients with and without lung cancer is currently in progress.
Conclusion:
Oncogene driven ERK signaling may regulate expression of CCL5 from lung tumour cells, and oncogene induced CCL5 production stimulates Treg migration ex vivo. These data suggest CCL5-mediated Treg recruitment to lung tumours may occur in early stages of lung tumour development and that targeted inhibition of CCL5 or ERK signaling may represent therapeutic strategies to block recruitment of immunosuppressive Tregs by lung tumours.
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OA 15 - Diagnostic Radiology, Staging and Screening for Lung Cancer II (ID 684)
- Event: WCLC 2017
- Type: Oral
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:Y. Satoh, Jin Mo Goo
- Coordinates: 10/18/2017, 14:30 - 16:15, Room 303 + 304
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OA 15.01 - Lung Cancer Screening: Participant Selection by Risk Model – the Pan-Canadian Study (ID 8466)
14:30 - 16:15 | Author(s): J.C. English
- Abstract
- Presentation
Background:
Retrospective studies indicate that selecting individuals for low dose computed tomography (LDCT) lung cancer screening based on a highly predictive risk model is superior to applying National Lung Screening Trial (NLST)-like criteria, which use only categorized age, pack-year and smoking quit-time information. The Pan-Canadian Early Detection of Lung Cancer Study (PanCan Study) was designed to prospectively evaluate whether individuals at high risk for lung cancer could be identified for screening using a risk prediction model. This paper describes the study design and results.
Method:
2537 individuals were recruited through 8 centers across Canada based on a ≥2% of lung cancer risk estimated by the PanCan model, a precursor to the validated PLCOm2012 model. Individuals were screened at baseline and 1 and 4 years post-baseline.
Result:
At a median 5.5 years of follow-up, 164 individuals (6.5%) were diagnosed with 172 lung cancers. This was a significantly greater percentage of persons diagnosed with lung cancers than was observed in the NLST(4.0%)(p<0·001). Compared to 57% observed in the NLST, 77% of lung cancers in the PanCan Study were early stage (I or II) (p<0.001) and to 25% in a comparable population, age 50-75 during 2007-2009 in Ontario, Canada’s largest province, (p<0·001).
Conclusion:
Enrolling high-risk individuals into a LDCT screening study or program using a highly predictive risk model, is efficient in identifying individuals who will be diagnosed with lung cancer and is compatible with a strong stage shift – identifying a high proportion at early, potentially curable stage. Funding This study was funded by the Terry Fox Research Institute and Canadian Partnership Against Cancer. ClinicalTrials.gov number, NCT00751660
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P2.02 - Biology/Pathology (ID 616)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Biology/Pathology
- Presentations: 1
- Moderators:
- Coordinates: 10/17/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P2.02-038 - Imaging Platform for the Quantification of Cell-Cell Spatial Organization within the Tumour-Immune Microenvironment (ID 9605)
09:30 - 16:00 | Author(s): J.C. English
- Abstract
Background:
The contribution of the tumour-immune microenvironment to tumour progression and patient outcome has become increasingly evident. Newly developed genomic tools have enabled the study of immune cell composition from bulk tumour data. However, such tools (e.g. CIBERSORT) do not provide the key spatial information that is crucial to understand tumour-immune cell interactions. To this end, we have developed a multispectral imaging platform that improves upon traditional analysis methods of cell segmentation and cell density calculations by further quantifying nearest-neighbour interactions (cell-cell spatial relationships). We apply this technology to investigate tumour-immune cell spatial relationships and their clinical significance to discover novel biological insights.
Method:
Whole tissue sections from 20 lung adenocarcinomas were stained for CD3, CD8, and CD79a and counterstained with haematoxylin. Multispectral images were acquired for five fields of view and analyzed to quantify cell types. Regions of Interest (ROIs) were then identified for the characterization of intra-tumoural and dense inflammatory regions. Image files including ROIs were analyzed in order to quantify cell-cell spatial relationships. Non-random patterns of immune cell distributions were identified using the Monte Carlo re-sampling method (500 iterations). Immune cell counts, densities, spatial relationships, and significant immune cell distributions were associated with clinical features by two-group comparison (Kruskal-Wallis p<0.001).
Result:
Our analysis generated 234 image files for analysis, including ROIs. Each field of view contained an average of 16,400 cells. The densities of intra-tumoural CD3+CD8+ and CD3+ T cells were significantly lower in recurrent cases, agreeing with literature reports. Following Monte Carlo analysis, non-random cell-cell spatial proximities emerged that were not observed at a cell density level. For example, an increased proximity of CD3+ T cells and B cells was observed in never smokers, while a decreased proximity was observed in ever smokers.
Conclusion:
While immune cell densities are of clinical prognostic importance, their spatial organization within the tumour architecture is of functional importance (e.g. the inhibition of cytotoxic T cell activity by adjacent PD-L1 expressing cells). In addition to cell densities, our platform is capable of quantifying cell-cell spatial relationships, thereby providing further information for clinical associations and for the identification of novel prognostic interactions. This automated quantification could be used to complement visual diagnostics and improve prognostic interpretation of histology specimens.
