Detection and characterization of lung cancer using cell-free DNA fragmentomes.
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ABSTRACT:
SUBMITTER: Mathios D
PROVIDER: S-EPMC8379179 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Lung cancer remains the leading cause of cancer death world-wide, largely due to its late diagnosis. Non-invasive approaches for assessment of cell-free DNA (cfDNA) provide an opportunity for detection and intervention that may have broader accessibility than current imaging approaches. Using a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation, we examined a prospective study of 365 individuals at risk for lung cancer (Lung Cancer Diagnostic Study, LUCAS), including 129 individuals ultimately diagnosed with lung cancer and 236 individuals determined to not have lung cancer. We externally validated the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 predominantly early stage lung cancer patients. Combining fragmentation features with clinical risk factors and CEA levels followed by CT imaging detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites could be used to distinguish individuals with small cell lung cancer (SCLC) from those with non-small cell lung cancer (NSCLC) with high accuracy (AUC=0.98). Among individuals with lung cancer, a higher cfDNA fragmentation score was associated with tumor size and invasion, and represented an independent prognostic indicator of survival. These studies provide a facile approach for non-invasive detection of lung cancer and clinical management of this disease.
| EGAS00001005340 | EGA
Project description:We investigated the feasibility of detecting aberrant DNA methylation of some novel and known genes in the serum of lung cancer patients.To determine the analytic sensitivity, we examined the tumor and the matched serum DNA for aberrant methylation of 15 gene promoters from 10 patients with primary lung tumors by using quantitative methylation-specific PCR. We then tested this 15-gene set to identify the more useful DNA methylation changes in the serum of a limited number of lung cancer patients and controls. In an independent set, we tested the six most promising genes (APC, CDH1, MGMT, DCC, RASSF1A, and AIM1) for further elucidation of the diagnostic application of this panel of markers.Promoter hypermethylation of at least one of the genes studied was detected in all 10 lung primary tumors. In majority of cases, aberrant methylation in serum DNA was accompanied by methylation in the matched tumor samples. In the independent set, using a single gene that had 100% specificity (DCC), 35.5% (95% CI: 25-47) of the 76 lung cancer patients were correctly identified. For patients without methylated DCC, addition of a logistic regression score that was based on the five remaining genes improved sensitivity from 35.5% to 75% (95% CI: 64-84) but decreased the specificity from 100% to 73% (95% CI: 54-88).This approach needs to be evaluated in a larger test set to determine the role of this gene set in early detection and surveillance of lung cancer.
| S-EPMC3131425 | biostudies-literature
Project description:BackgroundOvarian cancer (OC) is a highly lethal gynecologic cancer, and it is hard to diagnose at an early stage. Clinically, there are no ovarian cancer-specific markers for early detection. Here, we demonstrate the use of cell-free DNA (cfDNA) methylomes to detect ovarian cancer, especially the early-stage OC.Experimental designPlasma from 74 epithelial ovarian cancer patients, 86 healthy volunteers, and 20 patients with benign pelvic masses was collected. The cfDNA methylomes of these samples were generated by cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). The differentially methylated regions (DMRs) were identified by the contrasts between tumor and non-tumor groups, and the discrimination performance was evaluated with the iterative training and testing method.ResultsThe DMRs identified for cfDNA methylomes can well discriminate tumor groups and non-tumor groups (ROC values from 0.86 to 0.98). The late-stage top 300 DMRs are more late-stage-specific and failed to detect early-stage OC. However, the early-stage markers have the potential to discriminate all-stage OCs from non-tumor samples.ConclusionsThis study demonstrates that cfDNA methylomes generated with cfMeDIP-seq could be used to identify OC-specific biomarkers for OC, especially early OC detection. To detect early-stage OC, the biomarkers should be directly identified from early OC plasma samples rather than mix-stage ones. Further exploration of DMRs from a k larger early-stage OC cohort is warranted.
| S-EPMC9185905 | biostudies-literature
Project description:BACKGROUND:The present study sought to identify a panel of DNA markers for noninvasive diagnosis using cell-free DNA (cfDNA) from urine supernatant or cellular DNA from urine sediments of hematuria patients. A panel of 48 bladder cancer-specific genes was selected. A next-generation sequencing-based assay with a cfDNA barcode-enabled single-molecule test was employed. Mutation profiles of blood, urine, and tumor sample from 16 bladder cancer patients were compared. Next, urinary cellular DNA and cfDNA were prospectively collected from 125 patients (92 bladder cancer cases and 33 controls) and analyzed using the 48-gene panel. The individual gene markers and combinations of markers were validated according to the pathology results. The mean areas under the receiver operating characteristic (ROC) curves (AUCs) obtained with the various modeling approaches were calculated and compared. RESULTS:This pilot study of 16 bladder cancer patients demonstrated that gene mutations in urine supernatant and sediments had better concordance with cancer tissue as compared with plasma. Logistic analyses suggested two powerful combinations of genes for genetic diagnostic modeling: five genes for urine supernatant (TERT, FGFR3, TP53, PIK3CA, and KRAS) and seven genes for urine sediments (TERT, FGFR3, TP53, HRAS, PIK3CA, KRAS, and ERBB2). The accuracy of the five-gene panel and the seven-gene panel in the validation cohort yielded AUCs of 0.94 [95% confidence interval (CI) 0.91-0.97] and 0.91 (95% CI 0.86-0.96), respectively. With the addition of age and gender, the diagnostic power of the urine supernatant five-gene model and the urine sediment seven-gene model improved as the revised AUCs were 0.9656 (95% CI 0.9368-0.9944) and 0.9587 (95% CI 0.9291-0.9883). CONCLUSIONS:cfDNA from urine bears great diagnostic potential. A five-gene panel for urine supernatant and a seven-gene panel for urine sediments are promising options for identifying bladder cancer in hematuria patients.
| S-EPMC6960275 | biostudies-literature
Project description:Analyses of cell-free tumor DNA (ctDNA) have provided a non-invasive strategy for cancer diagnosis, the identification of molecular aberrations for treatment identification, and evaluation of tumor response. Sensitive and specific ctDNA sequencing strategies have allowed for implementation into clinical practice for the initial genotyping of patients and resistance monitoring. The specific need for EGFR mutation detection for the management of lung cancer patients has been an early imperative and has set the stage for non-invasive molecular profiling across other oncogenic drivers. Ongoing efforts are demonstrating the utility of ctDNA analyses in the initial genotyping of patients, the monitoring resistance clones, and the initial evaluation of response.
| S-EPMC7330324 | biostudies-literature
Project description:Plasma DNA from 558 malignancies, 263 benign and borderline tumors and 367 healthy control samples were collected and subjected to random short-gun whole genome sequencing.
2022-07-03 | E-MTAB-10934 | biostudies-arrayexpress
Project description:BackgroundEarly diagnosis benefits lung cancer patients with higher survival, but most patients are diagnosed after metastasis. Although cell-free DNA (cfDNA) analysis holds promise, its sensitivity for detecting early-stage lung cancer is unsatisfying. We leveraged cfDNA fragmentomics to develop a predictive model for invasive stage I lung adenocarcinoma (LUAD).Methods292 stage I LUAD patients from three medical centers were included together with 230 healthy controls whose plasma cfDNA samples were profiled by whole-genome sequencing (WGS). Multiple cfDNA fragmentomic motif features and machine learning models were compared in the training cohort to select the best model. Model performance was assessed in the internal and external validation cohorts and an additional dataset.FindingsA logistic regression model using the 6bp-breakpoint-motif feature was selected. It yielded 98·0% sensitivity and 94·7% specificity in the internal validation cohort [Area Under the Curve (AUC): 0·985], while 92·5% sensitivity and 90·0% specificity were achieved in the external validation cohort (AUC: 0·954). It is sensitive for early-stage (100% sensitivity for minimally invasive adenocarcinoma, MIA) and <1 cm (92·9%-97·7% sensitivity) tumors. The predictive power remained high when reducing sequencing depth to 0·5× (AUC: 0·977 and 0·931 for internal and external cohorts).InterpretationHere we have established a cfDNA breakpoint motif-based model for detecting early-stage LUAD, including MIA and very small-size tumors, shedding light on early cancer diagnosis in clinical practice.FundingNational Key R&D Program of China; National Natural Science Foundation of China; CAMS Initiative for Innovative Medicine; Special Research Fund for Central Universities, Peking Union Medical College; Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences; Beijing Hope Run Special Fund of Cancer Foundation of China.
| S-EPMC9251329 | biostudies-literature
Project description:BackgroundLung cancer (LC) is a leading cause of cancer-deaths globally. Its lethality is due in large part to the paucity of accurate screening markers. Precision Medicine includes the use of omics technology and novel analytic approaches for biomarker development. We combined Artificial Intelligence (AI) and DNA methylation analysis of circulating cell-free tumor DNA (ctDNA), to identify putative biomarkers for and to elucidate the pathogenesis of LC.MethodsIllumina Infinium MethylationEPIC BeadChip array analysis was used to measure cytosine (CpG) methylation changes across the genome in LC. Six different AI platforms including support vector machine (SVM) and Deep Learning (DL) were used to identify CpG biomarkers and for LC detection. Training set and validation sets were generated, and 10-fold cross validation performed. Gene enrichment analysis using g:profiler and GREAT enrichment was used to elucidate the LC pathogenesis.ResultsUsing a stringent GWAS significance threshold, p-value <5x10-8, we identified 4389 CpGs (cytosine methylation loci) in coding genes and 1812 CpGs in non-protein coding DNA regions that were differentially methylated in LC. SVM and three other AI platforms achieved an AUC=1.00; 95% CI (0.90-1.00) for LC detection. DL achieved an AUC=1.00; 95% CI (0.95-1.00) and 100% sensitivity and specificity. High diagnostic accuracies were achieved with only intragenic or only intergenic CpG loci. Gene enrichment analysis found dysregulation of molecular pathways involved in the development of small cell and non-small cell LC.ConclusionUsing AI and DNA methylation analysis of ctDNA, high LC detection rates were achieved. Further, many of the genes that were epigenetically altered are known to be involved in the biology of neoplasms in general and lung cancer in particular.
| S-EPMC9114890 | biostudies-literature
Project description:The detection of rare mutants using next generation sequencing has considerable potential for diagnostic applications. Detecting circulating tumor DNA is the foremost application of this approach. The major obstacle to its use is the high read error rate of next-generation sequencers. Rather than increasing the accuracy of final sequences, we detected rare mutations using a semiconductor sequencer and a set of anomaly detection criteria based on a statistical model of the read error rate at each error position. Statistical models were deduced from sequence data from normal samples. We detected epidermal growth factor receptor (EGFR) mutations in the plasma DNA of lung cancer patients. Single-pass deep sequencing (>100,000 reads) was able to detect one activating mutant allele in 10,000 normal alleles. We confirmed the method using 22 prospective and 155 retrospective samples, mostly consisting of DNA purified from plasma. A temporal analysis suggested potential applications for disease management and for therapeutic decision making to select epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI).
| S-EPMC3836767 | biostudies-literature
Project description:PurposeTumor genotyping using cell-free plasma DNA (cfDNA) has the potential to allow noninvasive assessment of tumor biology, yet many existing assays are cumbersome and vulnerable to false-positive results. We sought to determine whether droplet digital PCR (ddPCR) of cfDNA would allow highly specific and quantitative assessment of tumor genotype.Experimental designddPCR assays for EGFR, KRAS, and BRAF mutations were developed using plasma collected from patients with advanced lung cancer or melanoma of a known tumor genotype. Sensitivity and specificity were determined using cancers with nonoverlapping genotypes as positive and negative controls. Serial assessment of response and resistance was studied in patients with EGFR-mutant lung cancer on a prospective trial of erlotinib.ResultsWe identified a reference range for EGFR L858R and exon 19 deletions in specimens from KRAS-mutant lung cancer, allowing identification of candidate thresholds with high sensitivity and 100% specificity. Received operative characteristic curve analysis of four assays demonstrated an area under the curve in the range of 0.80 to 0.94. Sensitivity improved in specimens with optimal cfDNA concentrations. Serial plasma genotyping of EGFR-mutant lung cancer on erlotinib demonstrated pretreatment detection of EGFR mutations, complete plasma response in most cases, and increasing levels of EGFR T790M emerging before objective progression.ConclusionsNoninvasive genotyping of cfDNA using ddPCR demonstrates assay qualities that could allow effective translation into a clinical diagnostic. Serial quantification of plasma genotype allows noninvasive assessment of response and resistance, including detection of resistance mutations up to 16 weeks before radiographic progression.
| S-EPMC3959249 | biostudies-literature