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MINI 12 - Biomarkers and Lung Nodule Management (ID 109)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Screening and Early Detection
- Presentations: 1
MINI12.14 - Exhaled microRNAs as Potential Biomarkers of Lung Cancer Case versus Control Status (ID 2948)
16:45 - 18:15 | Author(s): J. Lin
There is a need for non-invasive airway-based biomarkers in lung carcinogenesis for both risk assessment of the ex-smoker, and earlier diagnosis. Exhaled breath condensate (EBC) contains airway molecules, presumably in part from bronchial and alveolar epithelial cellular origins. Our previous study showed microRNAs could qualitatively be detected in EBC. Here both qualitative and quantitative multivariate analysis were applied to look for microRNA candidates in EBC from a new sample of lung cancer patients and controls.
MicroRNA expression profiling using RNA-specific RT-qPCR was performed in EBC from 41 patients and 41 contols with clinical and microRNA expression data. The panel of microRNAs was assembled based on literature-derived reports of blood or lung microRNAs which segregate with case-control status, combined with our own lung tissue-based discovery effort using microRNA-seq on lung tumor-non-tumor pairs. The assembled panel for this effort included n=19 tumor-non-tumor differentiating microRNAs (miR-9, 18a, 20a, 31, 130b, 142, 146, 182, 183, 196a, 200a, 200c, 205, 210, 212, 221, 224, 330 and 708) chosen from the literature and our own lung tissue-based discovery data. Small nuclear RNA U1 was a housekeeping gene in the study based on its universality. Qualitative and quantitative (miRNA qPCR data normalized to internal reference U1 small ncRNA) analyses were considered. Multivariate analyses considered clinical information, including age, smoking status, underlying lung disease (COPD or not).
By univariate analyses, between cases (all histologies) and controls, qualitative/binary data showed miR-221 (p=0.030; OR=3.11) and miR-708 (p=0.016; OR=3.04) were significantly different. The case-adenocarcinoma subgroup (n=13) also differed from the controls in miR 708 frequency (p=0.034, OR=4.71). Examples of multivariate analyses (qualitative/binary data, case – all histologies) are shown in the Table: ontrols.
Similar multivariate models were obtained for miR 221 and miR708 in the cancer-adenocarcinoma subgroup. No clear case-control discriminant exhaled microRNAs were found in the analogous quantitative data (delta CT) analyses, by univariate or multivariate analyses.
miRNA Odds Ratio lower bound of CI upper bound of CI p-value miR.221 3.339 0.994 12.482 0.059 age 1.084 1.026 1.158 0.008 smoking 1vs0 1.467 0.304 8.372 0.642 smoking 2vs0 2.211 0.411 14.436 0.371 Underlying lung dz (COPD vs no COPD) 3.400 1.184 10.349 0.026 miR.708 5.041 1.651 17.603 0.007 age 1.093 1.031 1.172 0.006 smoking former vs never 1.378 0.273 8.145 0.704 smoking current vs never 2.144 0.386 14.269 0.397 Underlying lung dz (COPD vs no COPD) 4.437 1.448 15.047 0.012
From the qualitative analysis, two possible miRNA biomarkers of case status (miR-221 and miR-708) were obtained. Previous work had suggested miR 221 as a discriminant microRNA in lung cancer case versus control setting. Quantitative data was not informative. We are working on expanding and refining the miR panel, and larger sample size to partition covariates such age, underlying lung disease, and other factors. Our goal is to test this non-invasive biomarker approach to lung cancer risk assessment.
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P1.04 - Poster Session/ Biology, Pathology, and Molecular Testing (ID 233)
- Event: WCLC 2015
- Type: Poster
- Track: Biology, Pathology, and Molecular Testing
- Presentations: 1
- Coordinates: 9/07/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
P1.04-097 - Genome-Wide Methylome Alterations in Lung Cancer (ID 3117)
09:30 - 17:00 | Author(s): J. Lin
DNA cytosine methylation profiles are important features of malignancy. This study was designed to identify 5-methyl cytosines on a genome-wide scale in non-small cell lung cancers (NSCLC) relative to paired non-tumor lung which, analyzed alone or coupled to transcriptome data, could suggest methylome-deregulated loci.
Twenty-four NSCLC tumor (T) – non-tumor (NT) pairs were interrogated for 1.2 million CCGG-bounded fragments across all genomic compartments, using a methylation-sensitive restriction enzyme based HELP-microarray assay. Expression microarrays were also employed, from specimens from the same lung resections.
We found: (i) Good correlation (r =0.52, p=0.0006) between HELP and the reference quantitative methylation assay MassArray ®; (ii) Wide distribution of differential methylation (DM) among 32,037 promoters (PR, 26% of array-represented loci), 248,721 gene bodies (GB, 39 %), and 171,996 intergenic (IG, 48%) loci; (iii) In PR CpG island (CGI) hypermethylation exceeded CGI hypomethylation; (iv) DM hypermethylation in adenocarcinoma specifically was observed in many unexpected PR [e.g., RASL12; SPTAN1, mir-26a,] and GB [e.g., AKAP13, ANK family, PRKCE, ROS1] regions; (v) Overlay of DMxDE (differential expression) for adenocarcinoma yielded loci with canonical DM:DE patterns (e.g. PR hyper/hypo-methylation:mRNA down/up-regulated n=80; GB hyper/hypo-methylated:mRNA up/down-regulated GB n=3,136). (vi) Examples in adenocarcinoma hypermethylated PR loci with reduced expression included: HBEGF, DPT, AGER, SPARCL1, PTPRM; GB hypermethylated loci with upregulated expression included FERMT1, SLC7A5, FAP, TFAP2a genes. (vii) IPA analyses showed adenocarcinoma-specific promoter DMxDE overlay identifying familiar lung cancer nodes [tP53, Akt] and less familiar nodes [HBEGF, NQO1, GRK5, VWF, HPGD, CDH5, CTNNAL1, PTPN13, DACH1, SMAD6, LAMA3, AR].Figure 1
Methylome sampling, alone and combined with transcriptome data, yields new loci, as well as previously recognized ones, distributed throughout the genome that are deregulated in NSCLC.