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Katey S.S. Enfield
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MA24 - Genomic Evolution, KEAP 3 and More Non-Coding RNA (ID 928)
- Event: WCLC 2018
- Type: Mini Oral Abstract Session
- Track: Biology
- Presentations: 2
- Moderators:
- Coordinates: 9/26/2018, 10:30 - 12:00, Room 205 BD
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MA24.06 - Long Non-Coding Rna Expression Patterns Delineate Infiltrating Immune Cells in the Lung Tumour Microenvironment (ID 13978)
11:05 - 11:10 | Author(s): Katey S.S. Enfield
- Abstract
- Presentation
Background
The tumour microenvironment is characterized by complex interactions between different cell types, including immune cells that may exhibit pro- or anti-tumour effects. Sequencing and deconvolution techniques present opportunities to identify immune cell composition of bulk tumour data; similarly, these have renewed an interest in the non-coding transcriptome and its regulation of immune- and tumour-biology. Numerous long non-coding RNAs (lncRNAs; >200nt) have emerged as regulators of tumour initiation, progression, and metastasis. Additionally, several immune-related lncRNAs mediate fine-level regulation to balance pro- and anti-inflammatory phenotypes; yet, the landscape of lncRNA expression in human immune cells remains uncharacterized. Thus, delineating these multifaceted regulatory networks is critical to cancer immunology, particularly in immunogenic malignancies such as lung cancer.
a9ded1e5ce5d75814730bb4caaf49419 Method
RNA-sequencing data of purified immune-cell subsets (CD8+ T, CD4+ T, B, Monocytes, Neutrophils, and Natural Killer) obtained from flow-sorted healthy peripheral blood samples were probed for lncRNA expression. Sequencing reads were aligned to the hg38 reference genome and quantified to Ensembl v89, yielding 4919 expressed lncRNAs. These immune-associated lncRNAs were correlated with immune cell infiltrate in tumour and paired non-malignant lung adenocarcinoma samples (n=54, The Cancer Genome Atlas) as estimated by the proportion of consistent immune-associated methylation profiles, denoted by leukocytes unmethylation for purity (LUMP) scores.
4c3880bb027f159e801041b1021e88e8 Result
We observed that lncRNA expression patterns display a greater degree of cell-type specificity than protein-coding genes in immune cells. In fact, 676 lncRNAs had detectable expression in exclusively one cell type. We uncovered previously-uncharacterized lncRNAs that have expression patterns suggestive of immune-regulatory roles. Compared with lung tumour samples, 19 immune-associated lncRNAs were significantly negatively correlated with LUMP scores (r<-0.400, BH-p<0.0100), 17 of which were also strongly positively correlated with CD45 gene expression (r>0.400, BH-p<0.0100) suggesting expression from immune rather than tumour cells. For instance, the lncRNA USP30-AS1 is significantly downregulated in tumours (average fold-change=2.96, BH-p=6.88*10-13), suggesting its relevance to tumour biology; however, higher transcript expression is correlated with decreased LUMP score (r=-0.685, BH-p=1.02*10-4), illustrating its specificity to immune cells.
8eea62084ca7e541d918e823422bd82e Conclusion
Here, we present an atlas of cell-type specific lncRNAs in human immune cells. Our data suggest a functional relevance of lncRNAs to the biology of the tumour microenvironment, and the necessary consideration of tumour purity when examining non-coding RNA expression in order to avoid conclusions confounded by immune cells in bulk tumour data. Thus, we provide a resource for further elucidation of genomic links between immune and malignant cells, which may aid the development of future prognostic and therapeutic strategies.
6f8b794f3246b0c1e1780bb4d4d5dc53Only 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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MA24.07 - A Novel cis-Acting lncRNA Controls HMGA1 Expression in Lung Adenocarcinoma (ID 13979)
11:10 - 11:15 | Author(s): Katey S.S. Enfield
- Abstract
- Presentation
Background
High mobility group A1 (HMGA1) chromatin remodeling protein is enriched in several aggressive cancer types, including NSCLC, where mRNA and protein expression are markedly increased. Additionally, high HMGA1 expression has been associated with poor overall survival and chemotherapy resistance. While HMGA1 is deregulated in lung cancer, the mechanisms that mediate its expression are only beginning to emerge. Long non-coding RNAs (lncRNAs), are a class of transcripts have been implicated in the onset of cancer-associated phenotypes in tumourigenesis and metastasis. Recently, an emerging class of lncRNAs - cis-acting - has been shown to regulate the expression of neighbouring protein-coding genes, including oncogenes and tumour suppressor genes. Thus, lncRNAs may represent novel actionable therapeutic intervention points in known cancer driving pathways. Here we investigate the role of a cis-acting lncRNA, RP11.513I15.6, its deregulation in NSCLC, and its relationship with HMGA1.
a9ded1e5ce5d75814730bb4caaf49419 Method
LncRNA transcriptomes were deduced from RNA-sequences of 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). Sequencing data obtained from The Cancer Genome Atlas were analyzed to validate these results. SiRNA-mediated knockdown of lncRNA candidates identified in these analyses were performed in a non-malignant lung epithelial cell line (BEAS-2B). Quantitative real-time PCR quantified the effects of lncRNA knockdown on the expression of neighbouring cancer-associated protein-coding genes.
4c3880bb027f159e801041b1021e88e8 Result
Our analyses identified RP11.513I15.6, an undescribed lncRNA neighbouring HMGA1, to be significantly downregulated in adenocarcinoma (>2-fold downregulation in 81.5% of cases). This observation was confirmed in our validation cohort. HMGA1 expression was found to be anticorrelated with RP11.513I15.6, as tumours with downregulated RP11.513I15.6 displayed significant overexpression of HMGA1. This suggested that this lncRNA may be a key negative regulator of HMGA1. In vitro experiments demonstrated siRNA-mediated inhibition of RP11.513I15.6 in immortalized lung epithelial cells resulted in a significant increase in the expression of HMGA1 mRNA and protein. Taken together, 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 cancer phenotypes.
8eea62084ca7e541d918e823422bd82e Conclusion
We have discovered a novel, 18-fold downregulated transcript that is anti-correlated with expression of HMGA1, a well established oncogene. In vitro studies support the hypothesis that this transcript, RP11.513I15.6, is a cis-acting lncRNA as siRNA-mediated inhibition led to upregulation of neighbouring HMGA1. Characterizing this oncogene regulatory mechanism will not only further our understanding of cancer biology, but could uncover a novel therapeutic intervention point.
6f8b794f3246b0c1e1780bb4d4d5dc53Only 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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OA08 - Mesothelioma: Immunotherapy and microRNA for Diagnosis and Treatment (ID 907)
- Event: WCLC 2018
- Type: Oral Abstract Session
- Track: Mesothelioma
- Presentations: 1
- Moderators:
- Coordinates: 9/24/2018, 15:15 - 16:45, Room 201 BD
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OA08.07 - In Silico Discovery of Unannotated miRNAs in Malignant Pleural Mesothelioma Reveals Novel Tissue-of-Origin Markers (ID 14155)
16:20 - 16:30 | Author(s): Katey S.S. Enfield
- Abstract
- Presentation
Background
Malignant pleural mesothelioma (MPM) is an aggressive disease. One of the major clinical challenges associated with MPM is the lack of biomarkers capable of distinguishing primary MPM from cancers that have metastasized to the pleura. The current gold standard consists of a panel of positive and negative protein markers to confirm tissue-of-origin; however, many cases remain undistinguishable from other thoracic cancers. Recent studies have suggested that the human genome encodes more microRNAs (miRNAs) than currently annotated. These undescribed sequences have been shown to display enhanced tissue and lineage specificity. Therefore, we hypothesize that MPM tumors express a specific set of previously unannotated miRNA sequences with tissue-specific expression capable of distinguishing MPM from other thoracic diseases.
a9ded1e5ce5d75814730bb4caaf49419 Method
Novel miRNA candidates were detected from small RNA-sequencing data generated by The Cancer Genome Atlas (TCGA) (n=87 MPM) using the miRDeep2 algorithm, a well-established novel-miRNA prediction algorithm. The possible biological roles of these miRNA candidates were investigated by performing a genome-wide 3’UTR target prediction analysis. Additionally, their tissue-specificity was assessed using expression profiles of 1,093 lung tumors from four independent cohorts of adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Finally, we developed a miRNA-based classifier model using the weighted voting class prediction method to distinguish MPM from other thoracic cancers.
4c3880bb027f159e801041b1021e88e8 Result
Our initial analysis revealed 424 miRNA candidates, which were subsequently filtered by RNA structure, abundance of sequencing reads, and genomic location, resulting in 154 previously unannotated miRNA sequences. Interestingly, the novel miRNAs were predicted to target protein-coding genes involved in MPM biology, including the Ataxia Telangiectasia Mutated (ATM) gene, a tumour-supressor gene frequently mutated in MPM. Likewise BRCA1 Associated Protein 1 (BAP1), involved in the DNA damage response pathway, was also a predicted target. Principal component analyses revealed that novel-miRNA expression was able to distinguish MPM from LUAD and LUSC. Furthermore, our miRNA-based classifier model revealed 10 novel miRNAs capable of successfully identifying 86 out of the 87 MPM cases (98.80%) and 100% of LUAD cases (true positive rate = 98.85%, false positive rate = 1.150%).
8eea62084ca7e541d918e823422bd82e Conclusion
Here, we provide evidence for the presence of 154 previously unannotated miRNA species relevant to MPM. These miRNAs not only significantly expand the miRNA repertoire but also unveil specific roles in MPM biology. Most importantly, the strikingly high sensitivity and specificity of the novel miRNA-based classifier in distinguishing MPM from LUAD illustrates the potential of using these novel miRNAs to supplement current clinical markers to define MPM.
6f8b794f3246b0c1e1780bb4d4d5dc53Only 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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P3.09 - Pathology (Not CME Accredited Session) (ID 975)
- Event: WCLC 2018
- Type: Poster Viewing in the Exhibit Hall
- Track:
- Presentations: 1
- Moderators:
- Coordinates: 9/26/2018, 12:00 - 13:30, Exhibit Hall
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P3.09-11 - Genomic Organization at Large Scales (GOALS) within Nuclei and Cell Sociology for Predicting Lung Cancer Outcomes (ID 14160)
12:00 - 13:30 | Author(s): Katey S.S. Enfield
- Abstract
Background
Accurate prediction of the biological aggressiveness of lung cancers in patients from limited material could have utility with respect to patient treatment planning. We examined the hypothesis that the quantification of large scale DNA organization or GOALS within the nucleus combined with tumour microenvironment as quantified by cell sociology (which cells types are adjacent which cell types) could predict patient outcomes.
a9ded1e5ce5d75814730bb4caaf49419 Method
Tissue (TMA cores) from patients with poor outcomes (lung cancer mortality within 24 months) and good outcomes (5+ year survivors) was stained using a stochiometric DNA stain (Feulgen-Thionin). High resolution brightfield imaging of 29 cores was performed using multiple wavelengths and spectral unmixing used to retrieve the DNA concentration at every pixel. In house software was used to automatically segment all the nuclei within each core, calculate 100+ features for each nucleus and classify each nucleus as epithelial, stromal or immune in origin. Further each nucleus was scored as coming from a patient with poor or good outcome.
For each TMA core the percentage of these cell categories was tabulated as well as cell-cell association frequencies (for example the frequently an epithelial cell predicted to have come from a patient with good outcome was found next to a stromal cell predicted to come from a patient with poor outcome). Cell percentages and cell-cell interaction frequencies were used to predict patient outcome.
4c3880bb027f159e801041b1021e88e8 Result
8eea62084ca7e541d918e823422bd82e Conclusion
Many of the individually calculated features had a statistically significant association with patient outcome. Four+ features could predict outcome with an 79% accuracy, 15+ different pairs of features could predict patient outcome with greater than 85% accuracy. Epithelial- stromal cell interactions and stromal cell – immune cell interactions were particularly predictive of outcome suggesting that microenvironment cell - tumour cell interactions predict future biological activity.
This pilot study suggests that GOALS and cell sociology could predict patient outcomes.
6f8b794f3246b0c1e1780bb4d4d5dc53
