Whole Exome Sequencing of Cell-Free DNA for Early Lung Cancer: A Pilot Study to Differentiate Benign From Malignant CT-Detected Pulmonary Lesions

Introduction: Indeterminate pulmonary lesions (IPL) detected by CT pose a significant clinical challenge, frequently necessitating long-term surveillance or biopsy for diagnosis. In this pilot investigation, we performed whole exome sequencing (WES) of plasma cell free (cfDNA) and matched germline DNA in patients with CT-detected pulmonary lesions to determine the feasibility of somatic cfDNA mutations to differentiate benign from malignant pulmonary nodules.Methods: 33 patients with a CT-detected pulmonary lesions were retrospectively enrolled (n = 16 with a benign nodule, n = 17 with a malignant nodule). Following isolation and amplification of plasma cfDNA and matched peripheral blood mononuclear cells (PBMC) from patient blood samples, WES of cfDNA and PBMC DNA was performed. After genomic alignment and filtering, we looked for lung-cancer associated driver mutations and next identified high-confidence somatic variants in both groups.Results: Somatic cfDNA mutations were observed in both groups, with the cancer group demonstrating more variants than the benign group (1083 ± 476 versus 553 ± 519, p < 0.0046). By selecting variants present in >2 cancer patients and not the benign group, we accurately identified 82% (14/17) of cancer patients.Conclusions: This study suggests a potential role for cfDNA for the early identification of lung cancer in patients with CT-detected pulmonary lesions. Importantly, a substantial number of somatic variants in healthy patients with benign pulmonary nodules were also found. Such “benign” variants, while largely unexplored to date, have widespread relevance to all liquid biopsies if cfDNA is to be used accurately for cancer detection

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis

Mechanistic investigation of cell death and sphingolipid alteration induced by vitamin E forms in human colon cancer cells

Colon cancer is one of the leading causes of cancer death in the United States. It is important to develop effective chemoprevention and treatment agents with low toxicity. To that end, various molecules in the vitamin E family may be excellent candidates because of their beneficial properties and well-established safety. Vitamin E consists of eight structurally-related lipophilic antioxidants, &agr;-, β-, γ-, δ-tocopherol and &agr;-, β-, γ-, δ-tocotrienol. Most studies of vitamin E in cancer prevention have mainly focused on &agr;-tocopherol (&agr;T), the predominant form of vitamin E in tissues and multi-vitamin supplements. However, recent studies by us and others indicate that γ-tocopherol (γT), δ-tocopherol (δT) and γ-tocotrienol (γTE) have unique properties that are not shared by &agr;T but appear to be important to human disease prevention and therapy including cancer. Here we systemically investigated the effect and mechanism of different forms of vitamin E on the growth and death of human colon cancer HT29 and HCT116 cells. γT, δT and γTE inhibited the proliferation of HT29 and HCT116 cells in a time- and dose-dependent manner, with the potency of γTE \u3e δT \u3e γT. HCT116 cells appeared to be more sensitive to the treatment than HT29 cells. Studies using annexin V and propidium iodide staining revealed that γT (50 µM) and γTE (20 µM) induced externalization of phosphatidylserine and enhanced permeability of the plasma membrane, indicating apoptosis and necrosis. Interestingly, γT, δT and γTE enhanced expression of microtubule-associated protein light chain-3 II (LC3 II) in both HT29 and HCT116 cells, suggesting that these forms of vitamin E may induce autophagy. Consistently, electron microscopic results showed that γTE (20 µM) induced extensive formation of autophagic vacuoles in HCT116 cells. Because there was little induction of chromatin condensation and nuclear fragmentation, apoptosis seemed to be minimal. Although ER dilation was observed after γTE treatment, ER stress markers, Bip and CHOP, did not change following treatment. Thus, ER stress was not induced by γTE. Moreover, myriocin, a specific inhibitor of serine palmitoyltransferase, a key enzyme in de novo synthesis of sphingolipids, protected HT29 and HCT116 cells from γTE-induced cell death. Importantly, γTE caused marked accumulation of dihydrosphingosine and dihydroceramide but not ceramide or sphingosine as early as after 8-hour incubation. Sphingomyelins also accumulated following γTE treatment. These findings demonstrated that vitamin E may induce apoptosis, necrosis and more specifically, autophagy, in colon cancer cells by modulation of the sphingolipid synthesis pathway. These results provide mechanistic insights into potential chemopreventive activity of these vitamin E forms, and will therefore provide molecular bases for further investigation of in vivo chemopreventive efficacy of these vitamin E forms against colon cancer

RNA-Seq Analysis of Differential Splice Junction Usage and Intron Retentions by DEXSeq

<div><p>Alternative splicing is an important biological process in the generation of multiple functional transcripts from the same genomic sequences. Differential analysis of splice junctions (SJs) and intron retentions (IRs) is helpful in the detection of alternative splicing events. In this study, we conducted differential analysis of SJs and IRs by use of DEXSeq, a Bioconductor package originally designed for differential exon usage analysis in RNA-seq data analysis. We set up an analysis pipeline including mapping of RNA-seq reads, the preparation of count tables of SJs and IRs as the input files, and the differential analysis in DEXSeq. We analyzed the public RNA-seq datasets generated from RNAi experiments on <i>Drosophila melanogaster</i> S2-DRSC cells to deplete RNA-binding proteins (GSE18508). The analysis confirmed previous findings on the alternative splicing of the <i>trol</i> and <i>Ant2</i> (<i>sesB</i>) genes in the CG8144 (<i>ps</i>)-depletion experiment and identified some new alternative splicing events in other RNAi experiments. We also identified IRs that were confirmed in our SJ analysis. The proposed method used in our study can output the genomic coordinates of differentially used SJs and thus enable sequence motif search. Sequence motif search and gene function annotation analysis helped us infer the underlying mechanism in alternative splicing events. To further evaluate this method, we also applied the method to public RNA-seq data from human breast cancer (GSE45419) and the plant <i>Arabidopsis</i> (SRP008262). In conclusion, our study showed that DEXSeq can be adapted to differential analysis of SJs and IRs, which will facilitate the identification of alternative splicing events and provide insights into the molecular mechanisms of transcription processes and disease development.</p></div

Number of significant signals and genes on each chromosome.

<p>For each chromosome, the left column shows the number of significant genes, and the right column shows the number of significant splicing junctions or intron retentions.</p><p>Number of significant signals and genes on each chromosome.</p

Significant sequence motifs detected at flanking regions of alternative splicing sites.

<p>N1, number of significant splice junctions; N2, number of splice junctions with motif (x); N3, number of splice junctions in whole genome; N4, number of splice junctions with motif in the whole genome.</p><p>Significant sequence motifs detected at flanking regions of alternative splicing sites.</p

Illustration of analysis steps in differential analysis of splice junctions and intron retentions.

<p>Illustration of analysis steps in differential analysis of splice junctions and intron retentions.</p

Motif analysis and multi-species comparison of splicing junctions and intron retentions.

<p>(A) Illustration of the donor upstream (DU), donor downstream (DD), acceptor upstream (AU), and acceptor downstream (AD) sequences used in the motif search. (B) Sequence motifs detected at flanking sequences of significant splicing junctions. Motif 1–4 corresponds to that in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136653#pone.0136653.t003" target="_blank">Table 3</a>. (C) Networks from genes with significant splice junctions (SJs) in CG8144 RNAi, IPA score 59. (D) Networks from genes with significant SJs in CG10279 RNAi, IPA score 54. The color denotes the significance of the SJ within the gene: the darker the red is, the more significant the SJ in the gene is. (E) The consistent trend of the expression level of an isoform and the differential usage of its unique SJs between HER2+ and benign tumor samples. (F) and (G) Intron retentions detected in AT1G25097 and AT1G54010 genes from <i>Arabidopsis</i> RNA-seq analysis. Red arrows indicate the intron retentions.</p

Visualization of splicing junctions and retained introns.

<p>In each panel, the first four lines denote samples from the untreated condition; the other lines denote samples from the RNAi-treated condition. The exons with differentially utilized splicing junctions are marked with red horizontal bars and the splicing junction positions are matched to the ones listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136653#pone.0136653.t002" target="_blank">Table 2</a>. (A) Gene <i>Ant2/sesB</i> from CG8144 RNAi experiment. (B) Gene <i>trol</i> from CG8144 RNAi experiment. (C) Gene CG9674 from CG6946 RNAi experiment. (D) Gene <i>gem</i> from CG6946 RNAi experiment. (E), (F) and (G) Retained introns detected at <i>wdb</i>, <i>pde8</i>, and <i>zfh1</i>genes from the CG10279 RNAi experiment. The retained introns are highlighted with red horizontal bars as well.</p