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Katey SS Enfield
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MA04 - Models and Biomarkers (ID 122)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Biology
- Presentations: 1
- Now Available
- Moderators:Montse Sanchez-Cespedes, Kwok-kin Wong
- Coordinates: 9/08/2019, 13:30 - 15:00, Interlaken (1988)
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MA04.11 - Biological and Prognostic Implications of the Long Non-Coding Transcriptome in Tumour-Infiltrating Immune Cells (Now Available) (ID 2838)
13:30 - 15:00 | Author(s): Katey SS Enfield
- Abstract
- Presentation
Background
The lung tumour microenvironment is defined by complex infiltration patterns of immune cells which can contribute to both tumour progression and rejection. The advent of targeted immunotherapies has transformed cancer therapy, leading to durable regression even in late-stage lung tumours. Single-cell RNA sequencing and deconvolution of bulk tumour samples have provided insight into the transcriptomes of tumour-infiltrating immune populations and the regulatory networks that promote cytotoxicity and exhaustion transcriptional programs. Long non-coding RNAs (lncRNAs) have emerged as master regulators of gene expression in tumour cells, but their role in immune cells remains undercharacterized. We sought to delineate lncRNA expression profiles in healthy and lung tumour-infiltrating immune cells in order to better understand transcriptional reprogramming in tumour-infiltrating immune cells and to explore their potential as biomarkers of patient outcome and response to immunotherapy.
Method
RNAseq profiles of flow-purified adaptive and innate immune subsets were analysed for lncRNA expression, yielding 4919 expressed lncRNAs. Immune lncRNAs were then mapped to tumour and paired non-malignant lung adenocarcinoma samples (TCGA n=108, BCCA n=72) and associated with infiltrating immune populations by deconvolution and methylation-based purity scores. Associations with tumour immunogenicity were assessed by somatic mutational load and expression of tumour-associated antigens. Immune-specific expression of lncRNAs was confirmed in an external single cell RNAseq dataset of lung adenocarcinomas (n=5).
Result
We found that lncRNA expression patterns display markedly greater cell-type specificity than protein-coding genes in healthy samples, supporting their role in cell-intrinsic transcriptional regulation. 323 immune lncRNAs were differentially expressed in lung tumours compared to matched non-malignant tissue, with enriched expression of immune lncRNAs in tumours with high antigenic load. Many of these genes were positively correlated with CD45 expression and negatively correlated with tumour purity, suggestive of immune cell-restricted expression patterns. Furthermore, a substantial proportion of these genes showed decreased expression in microdissected tumour samples, suggesting that immune-derived lncRNAs may account for gene expression patterns observed in bulk tumour data. We validated these findings in a scRNAseq dataset and analysed co-expression patterns of immune lncRNAs with immune cell markers in order to identify specific immune cell phenotypes and assess the interaction of immune lncRNAs with cytotoxicity and exhaustion transcriptional networks. We identify immune lncRNAs which may regulate expression of important immune genes related to NK and CD8+ T cell cytotoxicity, as well as immune lncRNAs which predict patient outcome and response.
Conclusion
We present an atlas of lncRNAs expressed in innate and adaptive immune cells, emphasizing the multifaceted roles of lncRNAs in homeostasis and anti-tumour immunity. We highlight the potential of immune infiltrate to confound differential expression analysis of bulk tumour RNAseq data, with consideration needed for tumour purity and immune infiltration levels. Our data provide a resource that will facilitate further identification of functionally and clinically useful lncRNAs.
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MA15 - Usage of Computer and Molecular Analysis in Treatment Selection and Disease Prognostication (ID 141)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Pathology
- Presentations: 1
- Now Available
- Moderators:John Le Quesne, Noriko Motoi
- Coordinates: 9/09/2019, 15:45 - 17:15, Tokyo (1982)
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MA15.11 - Establishing a Cell Sociology Platform for the Assessment of Targetable Interactions to Predict Lung Cancer Outcome (Now Available) (ID 2652)
15:45 - 17:15 | Author(s): Katey SS Enfield
- Abstract
- Presentation
Background
The tumor microenvironment (TME) is a complex mixture of tumor epithelium, stroma and immune cells. The immune component of the TME is highly prognostic for tumor progression and patient outcome. Immune functionality, however, is often dictated by direct cell-to-cell contacts and cannot be resolved by simple metrics of cell density (for example, number of cells per mm2 or flow cytometry). For example, direct contact between CD8+ T cells and target cells is necessary for CD8+ T cell activity, and direct contact between PD1+ and PD-L1+ cells is necessary for the efficacy of immune checkpoint inhibitors. Current immunohistochemistry (IHC) techniques identify immune cell numbers and densities, but lack assessment of spatial relationships (or “cell sociology”). Here, we develop a platform to examine these direct interactions within the TME, and assess their relationship with patient outcome in two independent non-small cell lung cancer (NSCLC) cohorts.
Method
Tissue sections of primary tumors from lung adenocarcinoma (LUAD) patients with known clinical outcome were stained using 2 multiplex IHC panels: CD3/CD8/CD79a (Panel 1) and PD1/PDL1/CD8 (Panel 2). Hyperspectral image analysis determined the phenotype of all cells. Using the same IHC panels, these observations were assessed in a secondary NSCLC dataset (n=674). Deconvolution of these images was used to identify cell types, and cellular ‘neighborhoods’ were assessed using a Voronoi approach. This cohort was also profiled by for gene expression to validate immune subset fractions. We further identified other tumor features, including the presence of tertiary lymphoid organs (TLOs; transient immune structures necessary for antibody production from B cells).
Result
High density of intra-tumoral CD8+ T cells was associated with non-recurrence of tumors. However, we find that a non-random cell sociology pattern of CD8+ T cells directly surrounded by tumor cells was more significantly associated with non-recurrence compared to density alone. Monte Carlo re‐sampling analysis determined that these cell sociology patterns were non-random.
Conclusion
Hyperspectral cell sociology expands our understanding of the complex interplay between tumor cells and immune infiltrate. This technology improves our understanding of the tumour microenvironment and allows us to directly quantify interactions that dictate immune responses to cancers. Consequently, the implementation of this platform could improve predictions of responses to immunotherapy and lead to a deeper understanding of anti-tumor immunity.
Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.
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P2.03 - Biology (ID 162)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Biology
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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P2.03-47 - Deregulation of a Novel Cis-Acting lncRNA in Non-Small Cell Lung Cancer May Control HMGA1 Expression (ID 2727)
10:15 - 18:15 | Author(s): Katey SS Enfield
- Abstract
Background
Since the discovery of long non-coding RNAs (lncRNAs), they have been increasingly implicated in cancer-associated phenotypes. Recently, some lncRNAs have been shown to regulate the expression of neighbouring protein-coding genes, including oncogenes and tumour suppressor genes. High mobility group A1 (HMGA1) is aberrantly expressed in several aggressive cancer types, including non-small cell lung cancer (NSCLC), where high HMGA1 expression has been associated with poor overall survival and chemotherapy resistance. While HMGA1 is known to be deregulated in lung cancer, the mechanisms that mediate its expression remain unknown. These lncRNAs, known as cis-acting, may represent undiscovered therapeutic action points in cancer driving pathways.
Here we investigate the deregulation of a putative cis-acting lncRNA in NSCLC, and it’s relationship with the oncogene HMGA1.
Method
LncRNA expression was generated from RNA-sequencing data from 36 microdissected tumour and matched non-malignant tissues. Normalized sequence read counts were used to identify transcripts with significantly deregulated expression (Wilcoxon Signed-Rank Test, BH-p<0.05). Validation was performed in sequencing data obtained from The Cancer Genome Atlas (TCGA). SiRNA-mediated knockdown of lncRNA candidates were performed in a non-malignant epithelial lung cell line (BEAS-2B). Quantitative real-time PCR was used to observe the effects of lncRNA knockdown on the expression of neighbouring protein-coding genes.
Result
Our analyses identified a lncRNA neighbour to HMGA1, RP11.513I15.6, to be significantly downregulated in 2 cohorts of LUAD samples. Conversely, we found HMGA1 expression to be significantly overexpressed in LUAD tumours, and was found to be anticorrelated with RP11.513I15.6. Additionally while RP11.513I15.6 decreased with tumour stage, HMGA1 expression increased with stage. In vitro experiments demonstrated siRNA-mediated inhibition of RP11.513I15.6 in immortalized lung epithelial cells resulted in a significant increase in HMGA1 expression.
Conclusion
Our results suggest that RP11.513I15.6 is a novel cis-acting lncRNA that negatively regulates HMGA1, and may contribute mechanistically to the maintenance of lung cancer phenotypes. Further characterization of this oncogene regulatory mechanism may uncover a novel therapeutic intervention point for tumours driven by HMGA1.
