Parallel mutation screening and methylation quantification improves the molecular diagnostic yield for colorectal cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e13003-e13003
Author(s):  
Jun Min ◽  
Xiaoliang Lan ◽  
Zi Qin ◽  
Xiaojie Liu ◽  
Sufen Zhang ◽  
...  

e13003 Background: Recurrent somatic mutations and methylation patterns have previously been identified as biomarkers for non-invasive screening of colorectal cancer (CRC). However, most clinical screening assays have focused on a limited number of genomic loci, potentially reducing sensitivity and specificity. Here, we examined the potential utility of using an expanded set of mutation and methylation markers to improve screening accuracy of CRC. Methods: Pairs of colorectal tumor and tumor-adjacent tissues were resected from 41 CRC patients in Nanfang Hospital (NFH), China. The DNA was extracted, processed, and sequenced with Singlera Genomics’ OncoAim assay, which detects mutations across 59 genes implicated in cancer and quantifies the methylation level for key CRC genes. Matched pre- or post-surgery plasma samples were also collected and processed in parallel. Results: Thirty-eight tumor samples (93%) contained somatic mutations with greater than 5% allele frequency, while twenty-nine tumor samples (71%) contained aberrant methylation patterns. Among the tumor-adjacent samples, six contained somatic mutations and eight contained epigenetic abnormalities. Out of three tumor samples without any detectable somatic mutations, one showed an aberrant methylation pattern. Importantly, aberrant methylation patterns in tumor samples were also detected in matched plasma samples. Conclusions: Parallel mutation screening and methylation analysis not only improves the diagnostic yield, but also paves the way for non-invasive monitoring on patients with no common mutations.

2019 ◽  
Author(s):  
Qian Xiao ◽  
Wei Lu ◽  
Xiangxing Kong ◽  
Yang W. Shao ◽  
Yeting Hu ◽  
...  

AbstractObjectiveThe gut microbiota is closely associated with colorectal neoplasia. While most metagenomics studies utilized fecal samples, circulating microbial DNA in colorectal neoplasia patients remained unexplored. This study aimed to characterize microbial DNA in plasma samples and build a machine learning model for colorectal neoplasia early detection.DesignWe performed whole genome sequencing of plasma samples from 25 colorectal cancer (CRC) patients, 10 colorectal adenoma (CRA) patients and 22 healthy controls (HC). Microbial DNA was obtained by removing the host genome and relative abundance was measured by mapping reads into microbial genomes. Significant biomarker species were identified in the discovery cohort and built into a random forest model, which was tested in the validation cohort.ResultsIn the discovery cohort, there were 127 significant species between CRC patients and HC. Based on the random forest model, 28 species were selected from the discovery cohort (AUC=0.944) and yielded an AUC of 1 in the validation cohort. Interestingly, relative abundance of most biomarker species in CRA patients were between CRC patients and HC with a trend towards CRC patients. Furthermore, pathway enrichment analysis also showed similar pattern where CRA patients had intermediate relative abundance of significant pathways compared to CRC patients and HC. Finally, species network analysis revealed that CRC and HC displayed distinct patterns of species association.ConclusionsWe demonstrated characteristic alteration of circulating bacterial DNA in colorectal neoplasia patients. The predictive model accurately distinguished CRC and CRA from HC, suggesting the utility of circulating bacterial biomarkers as a non-invasive tool for colorectal neoplasia screening and early diagnosis.


2021 ◽  
Author(s):  
M. W. Wojewodzic ◽  
J. P. Lavender

AbstractAberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper, we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites.We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis.The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types.These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.


2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 11059-11059
Author(s):  
S. Pucciarelli ◽  
M. Enzo ◽  
M. Agostini ◽  
S. Pizzini ◽  
P. Del Bianco ◽  
...  

11059 Background: Since the pathologic stage is the most powerful prognostic factor for colorectal cancer (CRC), there is a strong need of non-invasive methods for early detection. Cell-free circulating DNA (cfDNA) released from cancer cells varies in size. It has been suggested that cfDNA (ALU repeats of 115 bp, representative of total DNA; ALU repeats of 247 DNA, representative of tumor DNA) may be associated with presence of tumor. Aim of this study was then to investigate whether the cfDNA may have a role as marker of CRC detection and progression. Methods: cfDNA was extracted from plasma samples from 136 patients with primary CRC at different stages [median age 64 yrs; male/female 78/58; stages I-II, 61; stages III-IV, 75], and from 24 patients with adenomas [median age 67 yrs; male/female 17/7)] and from 55 clean-colon healthy subjects [median age 56 yrs; male/female 13/43). cfDNA was assessed by quantitative real-time PCR (qPCR) of ALU repeats with 2 sets of primers (115 and 247 bp) amplifying different lengths of DNA. The levels of cfDNA (ALU-115, ALU-247) of CRC patients (stages I-II and stages III-IV) were compared with those of healthy subjects and patients with adenoma. Results: The median concentrations of total cfDNA (ALU115) in the plasma samples from patients with stages III-IV and stages I-II CRC, adenoma and normal controls were 52,4, 11.9; 1.9, and 1.7 ng/ml, respectively (p<.0001). The corresponding figures for tumor-related cfDNA (ALU247) were 48.8, 4.7, 2.2, and 0.7 ng/ml, respectively. (p<.0001). With a cut-off of 4.86 ng/ml, total DNA (ALU115) showed a sensitivity of 78.52 (95% CI 70.6–85.1) and a specificity of 86.08 (95% CI 76.4–92.8) in distinguishing patients with CRC from non-CRC [AUC: 0.860 (95% CI 0.81–0,90), p-value=.0001]. With a cut-off of 3.04, cfDNA tumor-related (ALU247) showed a sensitivity of 77.94 (95% CI 70.0–84.6) and a specificity of 82.28 (95% CI 72.1–90.0) in distinguishing patients with CRC from non-CRC [AUC: 0.864 (95% CI 0.81–0,91), p-value=.0001]. Conclusions: Both ALU115 and ALU 247 fragments of circulating cfDNA seem promising non-invasive molecular markers of detection and progression of CRC. The findings of the current study require to be confirmed on larger cohorts of patients with CRC and colonic adenoma. No significant financial relationships to disclose.


2019 ◽  
Vol 20 (17) ◽  
pp. 4119 ◽  
Author(s):  
Dana Dvorská ◽  
Dušan Braný ◽  
Bálint Nagy ◽  
Marián Grendár ◽  
Robert Poka ◽  
...  

Ovarian cancer is a highly heterogeneous disease and its formation is affected by many epidemiological factors. It has typical lack of early signs and symptoms, and almost 70% of ovarian cancers are diagnosed in advanced stages. Robust, early and non-invasive ovarian cancer diagnosis will certainly be beneficial. Herein we analysed the regulatory sequence methylation profiles of the RASSF1, PTEN, CDH1 and PAX1 tumour suppressor genes by pyrosequencing in healthy, benign and malignant ovarian tissues, and corresponding plasma samples. We recorded statistically significant higher methylation levels (p < 0.05) in the CDH1 and PAX1 genes in malignant tissues than in controls (39.06 ± 18.78 versus 24.22 ± 6.93; 13.55 ± 10.65 versus 5.73 ± 2.19). Higher values in the CDH1 gene were also found in plasma samples (22.25 ± 14.13 versus 46.42 ± 20.91). A similar methylation pattern with positive correlation between plasma and benign lesions was noted in the CDH1 gene (r = 0.886, p = 0.019) and malignant lesions in the PAX1 gene (r = 0.771, p < 0.001). The random forest algorithm combining methylation indices of all four genes and age determined 0.932 AUC (area under the receiver operating characteristic (ROC) curve) prediction power in the model classifying malignant lesions and controls. Our study results indicate the effects of methylation changes in ovarian cancer development and suggest that the CDH1 gene is a potential candidate for non-invasive diagnosis of ovarian cancer.


Oncotarget ◽  
2017 ◽  
Vol 8 (8) ◽  
pp. 12820-12830 ◽  
Author(s):  
Danielle Fernandes Durso ◽  
Maria Giulia Bacalini ◽  
Ítalo Faria do Valle ◽  
Chiara Pirazzini ◽  
Massimiliano Bonafé ◽  
...  

2020 ◽  
Author(s):  
Sangeetha Muthamilselvan ◽  
Abirami Raghavendran ◽  
Ashok Palaniappan

ABSTRACTBackgroundAberrant methylation of DNA acts epigenetically to skew the gene transcription rate up or down. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer. Methods: The methylation β - matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with sample stages, and analysed for stage-salient genes using multiple approaches involving stage-differentiated linear modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against control samples (adjusted p-value <0.001 and |log fold-change of M-value| >2). These results were filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by Kaplan–Meier survival analysis to explore the relationship between methylation and prognosis for the consensus stage-salient biomarkers.ResultsWe found significant genome-wide changes in methylation patterns in cancer samples relative to controls agnostic of stage. Our stage-differentiated analysis yielded the following stage-salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were hypermethylated, indicating down-regulation and signifying a CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1was significantly associated with patient survival (p-value < 0.01) and could be used as a biomarker panel for early-stage CRC prognosis.ConclusionOur workflow for stage-differentiated consensus analysis has yielded stage-salient diagnostic biomarkers as well as an early-stage prognostic biomarker panel. In addition, our studies have affirmed a novel CIMP-like signature in colorectal cancer, urging clinical validation.


2021 ◽  
Author(s):  
Marcin W. Wojewodzic ◽  
Jan P. Lavender

Abstract Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper, we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis. The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types. These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.


2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Antonio Francavilla ◽  
Sonia Tarallo ◽  
Barbara Pardini ◽  
Alessio Naccarati

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