Computational optimisation of targeted DNA sequencing for cancer detection.
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ABSTRACT:
SUBMITTER: Martinez P
PROVIDER: S-EPMC6506443 | biostudies-literature | 2013 Dec
REPOSITORIES: biostudies-literature
Publications
Computational optimisation of targeted DNA sequencing for cancer detection.
Martinez Pierre P McGranahan Nicholas N Birkbak Nicolai Juul NJ Gerlinger Marco M Swanton Charles C
Scientific reports 20131203
Despite recent progress thanks to next-generation sequencing technologies, personalised cancer medicine is still hampered by intra-tumour heterogeneity and drug resistance. As most patients with advanced metastatic disease face poor survival, there is need to improve early diagnosis. Analysing circulating tumour DNA (ctDNA) might represent a non-invasive method to detect mutations in patients, facilitating early detection. In this article, we define reduced gene panels from publicly available da ...[more]
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Project description:BackgroundTargeted methylation sequencing of plasma cell-free DNA (cfDNA) has a potential to expand liquid biopsies to patients with tumors without detectable oncogenic alterations, which can be potentially useful in early diagnosis.Patients and methodsWe developed a comprehensive methylation sequencing assay targeting 9223 CpG sites consistently hypermethylated according to The Cancer Genome Atlas. Next, we carried out a clinical validation of our method using plasma cfDNA samples from 78 patients with advanced colorectal cancer, non-small-cell lung cancer (NSCLC), breast cancer or melanoma and compared results with patients' outcomes.ResultsMedian methylation scores in plasma cfDNA samples from patients on therapy were lower than from patients off therapy (4.74 versus 85.29; P = 0.001). Of 68 plasma samples from patients off therapy, methylation scores detected the presence of cancer in 57 (83.8%), and methylation-based signatures accurately classified the underlying cancer type in 45 (78.9%) of these. Methylation scores were most accurate in detecting colorectal cancer (96.3%), followed by breast cancer (91.7%), melanoma (81.8%) and NSCLC (61.1%), and most accurate in classifying the underlying cancer type in colorectal cancer (88.5%), followed by NSCLC (81.8%), breast cancer (72.7%) and melanoma (55.6%). Low methylation scores versus high were associated with longer survival (10.4 versus 4.4 months, P < 0.001) and longer time-to-treatment failure (2.8 versus 1.6 months, P = 0.016).ConclusionsComprehensive targeted methylation sequencing of 9223 CpG sites in plasma cfDNA from patients with common advanced cancers detects the presence of cancer and underlying cancer type with high accuracy. Methylation scores in plasma cfDNA correspond with treatment outcomes.
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Project description:Circulating tumor DNA (ctDNA) isolated from peripheral blood has recently been shown to be an alternative source to detect gene mutations in primary tumors; however, most previous studies have focused on advanced stage cancers, and few have evaluated ctDNA detection in early-stage lung cancer. In the present study, blood and tumor samples were collected prospectively from 58 early-stage non-small lung cancer (NSCLC) patients (stages IA, IB, and IIA) and a targeted sequencing approach was used to detect somatic driver mutations in matched tumor DNA (tDNA) and plasma ctDNA. We identified frequent driver mutations in plasma ctDNA and tDNA in EGFR, KRAS, PIK3CA, and TP53, and less frequent mutations in other genes, with an overall study concordance of 50.4% and sensitivity and specificity of 53.8% and 47.3%, respectively. Cell-free (cfDNA) concentrations were found to be significantly associated with some clinical features, including tumor stage and subtype. Importantly, the presence of cfDNA had a higher positive predictive value than that of currently used protein tumor biomarkers. This study demonstrates the feasibility of identifying plasma ctDNA mutations in the earliest stage lung cancer patients via targeted sequencing, demonstrating a potential utility of targeted sequencing of ctDNA in the clinical management of NSCLC.
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Project description:(1) Background: Firm consensus has yet to be established in relation to taxonomic classification and primer choice in targeted amplicon sequencing of the mycobiome. While the nuclear ribosomal internal transcribed spacer (ITS) region are recognized as the formal fungal taxonomic barcode, appraisal of different ITS sub-regions and the influence of DNA extraction methods have not been comprehensively undertaken using human respiratory specimens. (2) Methods: We performed ITS analysis of respiratory (sputum) samples by assessing (a) the effect of alternate DNA extraction techniques and (b) an evaluation of four different ITS primer pairs (ITS1F and ITS2; ITS1-30F and ITS1-217R; gITS7ngs and ITS4ng; and Fseq and Rseq) on the mycobiome profiles generated for mock fungal communities and their respective clinical (airway) specimens. (3) Results: Primer pairs varied in their resulting ITS mycobiome profiles, suggesting that particular pairs may be more relevant for analysis of respiratory samples compared to others. Assessment of DNA extraction methods highlighted lower final DNA concentrations achieved by mechanical disruption compared to enzymatic lysis. However, despite lower yields, DNA liberated by mechanical lysis more readily yielded ITS bands with highest success in combination with the Fseq and Rseq primers. (4) Conclusion: Choice of extraction method, primers used, and sequencing approach are all important considerations in sequencing the mycobiome and should be tailored to sample type. A standardization of approach to mycobiome studies using respiratory specimens will permit more reliable comparisons between studies and improve our understanding of the role of fungi in the human airway.
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Project description:Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.
| S-EPMC7072524 | biostudies-literature
Project description:In this longitudinal study, cell-free tumour DNA (a liquid biopsy) from plasma was explored as a prognostic biomarker for gastro-oesophageal cancer. Both tumour-informed and tumour-agnostic approaches for plasma variant filtering were evaluated in 47 participants. This was possible through sequencing of DNA from tissue biopsies from all participants and cell-free DNA from plasma sampled before and after surgery (n = 42), as well as DNA from white blood cells (n = 21) using a custom gene panel with and without unique molecular identifiers (UMIs). A subset of the plasma samples (n = 12) was also assayed with targeted droplet digital PCR (ddPCR). In 17/31 (55%) diagnostic plasma samples, tissue-verified cancer-associated variants could be detected by the gene panel. In the tumour-agnostic approach, 26 participants (59%) had cancer-associated variants, and UMIs were necessary to filter the true variants from the technical artefacts. Additionally, clonal haematopoietic variants could be excluded using the matched white blood cells or follow-up plasma samples. ddPCR detected its targets in 10/12 (83%) and provided an ultra-sensitive method for follow-up. Detectable cancer-associated variants in plasma correlated to a shorter overall survival and shorter time to progression, with a significant correlation for the tumour-informed approaches. In summary, liquid biopsy gene panel sequencing using a tumour-agnostic approach can be applied to all patients regardless of the presence of a tissue biopsy, although this requires UMIs and the exclusion of clonal haematopoietic variants. However, if sequencing data from tumour biopsies are available, a tumour-informed approach improves the value of cell-free tumour DNA as a negative prognostic biomarker in gastro-oesophageal cancer patients.
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Project description:BackgroundEpigenetic variants carried by circulating tumor DNA can be used as biomarkers for early detection of hepatocellular carcinoma (HCC) by noninvasive liquid biopsy. However, traditional methylation analysis method, bisulfite sequencing, with disadvantages of severe DNA damage, is limited in application of low-amount cfDNA analysis.ResultsThrough mild enzyme-mediated conversion, enzymatic methyl sequencing (EM-seq) is ideal for precise determination of cell-free DNA methylation and provides an opportunity for HCC early detection. EM-seq of methylation control DNA showed that enzymatic conversion of unmethylated C to U was more efficient than bisulfite conversion. Moreover, a relatively large proportion of incomplete converted EM-seq reads contains more than 3 unconverted CH site (CH = CC, CT or CA), which can be removed by filtering to improve accuracy of methylation detection by EM-seq. A cohort of 241 HCC, 76 liver disease, and 279 normal plasma samples were analyzed for methylation value on 1595 CpGs using EM-seq and targeted capture. Model training identified 283 CpGs with significant differences in methylation levels between HCC and non-HCC samples. A HCC screening model based on these markers can efficiently distinguish HCC sample from non-HCC samples, with area under the curve of 0.957 (sensitivity = 90%, specificity = 97%) in the test set, performing well in different stages as well as in serum α-fetoprotein/protein induced by vitamin K absence-II negative samples.ConclusionFiltering of reads with ≥ 3 CHs derived from incomplete conversion can significantly reduce the noise of EM-seq detection. Based on targeted EM-seq analysis of plasma cell-free DNA, our HCC screening model can efficiently distinguish HCC patients from non-HCC individuals with high sensitivity and specificity.
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Project description:MOTIVATION:Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensive and minimally invasive techniques. Current sequencing technologies make the 'liquid biopsy' possible, which involves sampling a patient's blood or urine and sequencing the circulating cell free DNA (cfDNA). A certain percentage of this DNA originates from the tumor, known as circulating tumor DNA (ctDNA). The ratio of ctDNA may be extremely low in the sample, and the ctDNA may originate from multiple tumors or clones. These factors present unique challenges for applying existing tools and workflows to the analysis of ctDNA, especially in the detection of structural variations which rely on sufficient read coverage to be detectable. RESULTS:Here we introduce SViCT?, a structural variation (SV) detection tool designed to handle the challenges associated with cfDNA analysis. SViCT?can detect breakpoints and sequences of various structural variations including deletions, insertions, inversions, duplications and translocations. SViCT?extracts discordant read pairs, one-end anchors and soft-clipped/split reads, assembles them into contigs, and re-maps contig intervals to a reference genome using an efficient k-mer indexing approach. The intervals are then joined using a combination of graph and greedy algorithms to identify specific structural variant signatures. We assessed the performance of SViCT?and compared it to state-of-the-art tools using simulated cfDNA datasets with properties matching those of real cfDNA samples. The positive predictive value and sensitivity of our tool was superior to all the tested tools and reasonable performance was maintained down to the lowest dilution of 0.01% tumor DNA in simulated datasets. Additionally, SViCT?was able to detect all known SVs in two real cfDNA reference datasets (at 0.6-5% ctDNA) and predict a novel structural variant in a prostate cancer cohort. AVAILABILITY:SViCT?is available at https://github.com/vpc-ccg/svict. Contact:faraz.hach@ubc.ca.
| S-EPMC6468241 | biostudies-literature
Project description:PurposePrecision medicine in pancreatic ductal adenocarcinoma (PDAC) could be substantially supported by tools that allow to establish and monitor the molecular setup of the tumor. In particular, noninvasive approaches are desirable, but not validated. Characterization of circulating tumor DNA (ctDNA) may help to achieve this goal.Experimental designBlood samples from patients with metastatic PDAC prior to and during palliative treatment were collected. ctDNA and corresponding tumor tissue were analyzed by targeted next generation sequencing and droplet digital PCR for the 7 most frequently mutated genes in PDAC (TP53, SMAD4, CDKN2A, KRAS, APC, ATM, and FBXW7). Findings were correlated with clinical and imaging data.ResultsA total of 20 patients (therapy naïve n = 11; pretreated n = 9) were included. All therapy naïve patients (n = 11/11) presented with detectable ctDNA at baseline. In pretreated patients, 3/7 (prior to 2nd line treatment) and 2/2 (prior to 3rd line chemotherapy) had detectable ctDNA. The combined mutational allele frequency (CMAF) of KRAS and TP53 was chosen to reflect the amount of ctDNA. The median CMAF level significantly decreased during treatment (P = 0.0027) and increased at progression (P = 0.0104). CA19-9 analyses did not show significant differences. In treatment naïve patients, the CMAF levels during therapy significantly correlated with progression-free survival (Spearman, r = -0.8609, P = 0.0013).ConclusionsMonitoring of ctDNA and its changes during treatment may enable to adapt therapeutic strategies to the specific molecular changes present at a certain time during treatment of mPDAC.
| S-EPMC5788622 | biostudies-literature