Printer Friendly

Validation of a real-time PCR-based qualitative assay for the detection of methylated SEPT9 DNA in human plasma.

Worldwide, colorectal cancer (CRC) (5) will be diagnosed in an estimated 1 million people, with approximately 142 000 new cases in the US in 2013 (1, 2). Guidelines for CRC screening in the US have been in place for 30 years and are updated regularly (3). Despite substantial efforts to promote screening, CRC mortality rates remain high, with 600 000 deaths worldwide and nearly 50 000 in the US (4). Furthermore, nearly 1 in 3 adults in the US are not screened for the disease (5), whereas global screening rates are generally even lower (4).

The most commonly used screening methods recommended by the US Preventive Services Taskforce are colonoscopy and guiac-based fecal occult blood tests/fecal immunochemical tests (gFOBTs/FITs) (6). Although the reasons for low participation in screening programs are complex, they generally include aversion to bowel preparation for colonoscopy, fear of the procedure, cost, and pre- and postprocedure time requirements. FOBTs and FITs are considered effective (7), and in large prospective trials, screening with gFOBT resulted in significant reductions in CRC mortality (8, 9). However, both gFOBT and FIT continue to be underutilized (5).

Noninvasive approaches to screening include detection of genetic and epigenetic alterations in tumor-specific DNA that is shed into stool, serum, or plasma (4, 10, 11). A number of methylation biomarkers have been found in circulating cell-free DNA (ccfDNA) in blood and are promising avenues to address this clinical need (10, 12-17). Among those that have been rigorously studied, methylated SEPT9 (6) (septin 9) is the most well characterized (18, 19). The septin gene family is a conserved set of guanosine-5'-triphosphatebinding proteins that function in vesicle trafficking, apoptosis, and cytoskeletal remodeling (20). The SEPT9 gene has been associated with tumorigenesis in different cancers (21). The methylation status of a sequence in the gamma1 promoter (SEPT9_v2 transcript) has been shown to have excellent power to discriminate CRC tissue from healthy mucosa and noncolon cancers (15, 16, 22-24).

Clinical utility of the detection of methylated SEPT9 DNA in CRC has been evaluated in independent studies encompassing >5000 clinical samples (18, 19, 24-26). In retrospective case-control studies with subjects having CRC and healthy subjects as controls, sensitivities ranged from 69% in early research assays to 95% in the CE-marked test currently available in Europe (18,27). In a prospective trial (PRESEPT NCT00855348), cancer sensitivity was 50.9% at 91.5% specificity with an early version of the SEPT9 assay (26).

In support of a premarket approval submission to the FDA for Epi proColon[R], the test has been further optimized to increase clinical sensitivity and ease of use in the CLIA-certified high-complexity molecular diagnostics laboratory. In this report, we describe the analytical characteristics of the test for detecting methylated SEPT9 DNA in blood and its clinical validation by use of samples from subjects enrolled in the prospective PRESEPT clinical trial.

Materials and Methods


Simulated samples. We prepared samples by dilution of genomic DNA in either a matrix containing 10 mmol/L Tris-EDTAsolution with 5% (wt/vol) BSA or commercially available bulk plasma (Cliniqa). DNA for simulated samples was isolated from HeLa and Jurkat cell lines (BioChain). The HeLa cell line is methylated at the gamma1 promoter region of the SEPT9 gene, whereas the Jurkat cell line is not. We prepared 3.5-mL samples by spiking defined concentrations (1.5-50 pg/ mL) of HeLa cell DNA into 9 ng/mL Jurkat cell DNA in the respective matrices.

Plasma pools. For analytical studies, we prepared 14 plasma pools using samples collected from consenting subjects enrolled in institutional review board-approved protocols in their country of origin. Two pools were produced for each CRC stage (I-III), and 5 pools were produced by diluting plasma from CRC patients (1 part cancer plasma to 33 parts bulk plasma) into SEPT9-negative bulk plasma. The dilution was designed to simulate the very low concentration of methylated SEPT9 target DNA anticipated in some patient samples. We produced 3 plasma pools from age-matched healthy blood donors.

Prospectively collected samples. Prospectively collected plasma samples were from the PRESEPT clinical trial (NCT00855348) (26). Institutional review boards in the US and ethics committees in Germany approved the study, and all participants gave consent before enrollment. An independent Clinical Studies Steering Committee provided oversight.

Participants provided a 1-time blood sample drawn into four 10-mL K2-EDTA blood tubes (Becton Dickinson). Plasma was isolated, aliquoted into cryotubes, and stored at -80 [degrees]C. Colonoscopy procedures, including polypectomy, biopsy, histopathologic review, and staging of biopsy and surgical samples, were performed following routine standard-of-care practices by board-certified gastroenterologists and pathologists. Pathology results were reconfirmed by a second pathologist. Subjects with colorectal cancer were classified as such and grouped further on the basis of stage (I, II, III, or IV). Polyps 10 mm or greater or polyps having high-grade dysplasia or villous component were classified as advanced adenoma (AA), whereas polyps <10 mm with no high-grade dysplasia or villous component were classified as small polyps (SPs). Subjects with no lesions in screening colonoscopies were classified as no evidence of disease (NED).


We processed samples using Epi proColon test kits manufactured under good manufacturing practices and labeled as "investigational use only." Samples were bar coded, and technicians remained blinded to the identity of the samples. Processing batches included positive and negative controls, and the order of samples in each batch was individually randomized. The processing controls monitored execution of the procedure and ensured validity of the test result and model according to the requirements promulgated by the College of American Pathologists.

The Epi proColon test comprises the Epi proColon Plasma Quick kit, PCR kit, and Control kit (Fig. 1). By use of the Plasma Quick kit, 3.5 mL plasma was mixed with an equal volume of lysis buffer and incubated for 10 min, after which magnetic beads and absolute ethanol were added; the sample was incubated on a rotator for 45 min. Impurities were removed from the magnetic beads in a wash step. The purified DNA was then released from the beads in elution buffer and treated at 80 [degrees]C with a solution containing ammonium bisulfite for deamination of cytosine. The converted DNA (bisulfite-modified DNA [bisDNA]) was captured by use of magnetic beads, passed through a series of wash steps, and eluted in 60 [micro]L buffer. If not used immediately, bisDNA was stored at -20 [degrees]C until use within 3 days.

We assayed the bisDNA with the Sensitive PCR kit on a 7500 Fast Dx Real Time PCR device (Life Technologies). The assay was designed as a duplex real-time PCR for the methylated SEPT9 y promoter and ACTB (actin, beta) as an internal reference to assess the integrity of each sample. PCR was performed in triplicate with 15 [micro]L template DNA per well and run for 45 cycles.


We recorded PCR results from the 7500 Fast Dx software for ACTB and methylated SEPT9 for each of the triplicate reactions. The validity of each sample batch was determined on the basis of methylated SEPT9 and ACTB threshold count (Ct) values for the positive and negative controls. Participant samples were called SEPT9 positive if at least 1 methylated SEPT9 Ct value was reported by the instrument software (i.e., an amplification curve was detected within 45 cycles). Samples were called negative if no methylated SEPT9 Ct was reported in any of the 3 valid PCR replicates.


We performed limit of detection (LoD) studies on simulated samples and precision studies by replicate testing of aliquots of 14 plasma pools. Simulated samples or aliquots from plasma pools were randomized within each batch, and the identity of the samples was masked to the personnel conducting the experimental work.

We assessed the effects of interfering substances on assay performance using methylated SEPT9-positive and SEPT9-negative simulated plasma samples spiked with the following substances: albumin (40 g/L), bilirubin (0.2 g/L), cholesterol (5 g/L), D-(+)-glucose (10 g/L), hemoglobin (10 g/L), [K.sub.2]EDTA (20 g/L), red blood cells (0.4% vol/vol), triglycerides (12 g/L), human genomic DNA (100 ng/mL), and uric acid (0.235 g/L).

Analytical studies were performed at 4 sites: Epigenomics (Berlin) and 3 CLIA-certified high-complexity US laboratories. At each site, multiple technicians processed multiple batches by use of multiple real-time PCR instruments and Epi proColon kits from combinations of test kits from different manufacturing lots. The contributions of run-to-run, day-to-day, operator-to-operator, lotto-lot, instrument-to-instrument, and site-to-site variation were assessed.


We assessed clinical performance using archived prospectively collected samples from the PRESEPT trial (NCT 00855348). PRESEPT enrolled a total of 7941 participants, of whom 6857 fulfilled all study criteria. Though derived from the PRESEPT cohort, the samples used in this study were selected without knowledge of their SEPT9 test outcome in the PRESEPT study and were tested independently from the PRESEPT study. Available participant samples included 50 with invasive adenocarcinoma (CRC), 653 AA, 2369 SP, and 3785 NED. In this study, all available samples from participants with CRC and AA were tested. As PRESEPT included both US and non-US sites, a stratified random selection of the SP and NED groups was performed to enable an assessment of the impact of demographic parameters (sex, age, ethnicity, country of origin) of the US population. For the SP and NED classes, 450 individuals per class were selected to estimate test positivity in the non-CRC group with sufficient precision (95% CI <5%). Less-represented subgroups (nonwhites, subjects older than 70 years) were overrepresented compared with PRESEPT enrollment numbers, while still balanced for sex. On the basis of this approach, all CRC cases (50), all AA (650), 454 SP, and 469 NED samples were used for testing. Samples with invalid results were retested with additional aliquots from the same individual where available, or replaced with randomly selected samples with similar demographic characteristics.

Sample batches were randomly assigned to the 3 independent testing laboratories in the US. All clinical samples were identified by bar code, and technicians remained blinded to the clinical status of samples. Samples were processed by multiple technicians by use of Epi proColon kits from multiple manufacturing lots, and a positive and negative control were included with each batch. The test results were certified at the external sites and released after the completion of all laboratory processing, at which time samples were identified and statistical analyses of the data were performed at Epigenomics.


Estimates of proportions (positivity, sensitivity, specificity) are provided as standard frequency estimates together with respective 95% CIs (Wilson type without continuity correction). To account for the stratified random sampling of the SP and NED classes, we calculated additional specificity estimates by bootstrapping from the data according to weights calculated from the joint distribution of diagnostic and demographic groupings in this study and in the PRESEPT sample collection and US population census, respectively. We used logistic regression to estimate the LoD. Regression models including analyte concentration with and without additional covariables (testing site, operator, reagent lot) as parameters were also analyzed. Significance of these variables was assessed by likelihood ratio tests. The same methodology was applied for the analysis of covariates in the clinical data or to test the impacts of demographic or procedural parameters. The level of significance was set to 0.05 in all cases. All analyses were conducted by use of the R programming environment (28).


The test work flow is outlined in Fig. 1. Real-time PCR was performed on a 7500 Fast Dx platform with typical data outputs (see Supplemental Fig. 1, A and B, which accompanies the online version of this article at When run in a batch configuration, 30 participant results can be reported in 8 h.


The LoD for methylated SEPT9 was determined by use of 616 samples tested at 4 different laboratories (see online Supplemental Table 1). In initial testing, [greater than or equal to] 92% of samples having [greater than or equal to] 6 pg/mL methylated SEPT9 were positive. Therefore, additional lower-concentration samples (1.5 or 3 pg/mL) were tested to establish the [LoD.sub.95] estimate. Samples with spiked concentrations as low as 1.5 pg/mL tested positive in half of the replicates (14/28), and samples that corresponded to 1 diploid genome copy/mL (6 pg/mL) tested positive for 92% of the replicates (81/88). Only 1 sample generated a discordant result for 0 pg/mL methylated SEPT9 samples. The [LoD.sub.95] estimate was derived by logistic regression (Fig. 2), on the basis of aggregated data from samples containing spiked analyte (n = 529) analyzed at all 4 sites. The LoD for all samples was 7.8 pg/mL (95% CI 5.8-10.4 pg/mL) and 4.7 pg/mL (2.5-9.0 pg/mL) for samples spiked in plasma (Fig. 2). Operator, laboratory, and reagent lot were included in the logistic regression model, and the applied likelihood ratio test indicated no significant impact from these variables.


Reproducibility was determined by analysis of 232 aliquots of pooled plasma samples from individuals with CRC or from healthy blood donors. Samples were tested at multiple sites and by multiple operators (Table 1). In total, for 176 of 178 samples where CRC plasma or a 1:33 dilution thereof was tested, the test result was positive, resulting in a 98.9% (95% CI 96.0%-99.7%) positive agreement with clinical status. For the 3 pools derived from healthy blood donors, 43 of 54 samples tested negative, resulting in a 79.6% (67.1%-88.2%) negative agreement with clinical status. The total percentage agreement estimated from these data is 219 of 232, or 94.4% (90.7%-96.7%). There was no significant difference between testing sites (P = 0.796 likelihood ratio test), highlighting a high level of reproducibility across different testing sites, operators, Epi proColon kit lots, and PCR instruments.


The effects of interfering substances on performance of the assay were assessed by testing methylation-positive and -negative simulated samples spiked with potential interfering substances. Test substances were used in excess of potential levels of contamination. Performance was not affected by any of the substances tested.



The distribution of included samples by sex, age, ethnicity, and country of origin is outlined in Table 2. Compared with the original PRESEPT cohort, the stratified sampling approach resulted in proportionally more African Americans and more individuals who were >70 years old. From the original 1623 participants tested, we observed 1544 valid results. Sample validity was determined according to the instructions for use by the laboratories on the basis of the batch controls and the internal ACTB control for each sample. The 79 invalid samples included 6 individuals with CRC that had invalid outcomes as a result of a laboratory error.

In the study, 30 of 44 CRCs (all stages) were detected (Table 3), resulting in a clinical sensitivity of 68% (95% CI 53%-80%). Clinical sensitivity for stages I-III CRC was 64% (47%-77%), 22% (18%-24%) for advanced adenomas, and 20% (15%--23%) for SPs (Table 3). Observed clinical specificity was 78.8% (76.7-80.8%) on the basis of the participants in the study (Table 3), and specificity estimates were 79.1% (77.0-81.4%) and 80.0% (77.5-82.4%) when adjusted to the US census population and the PRESEPT cohort, respectively. By use of these estimates together with the prevalence for CRC observed in the PRESEPT cohort, the negative and positive predictive value (NPV, PPV) were calculated as 99.7% and 2.5%, respectively. The positivity data grouped by age and ethnicity are presented in Table 4. Two trends are apparent from this analysis: increased positivity in African Americans and a trend to increased positivity with age.


The Epi proColon test for colorectal cancer screening detects methylated SEPT9 DNA in ccfDNA in plasma. The utility of ccfDNA detection has been demonstrated in other clinical applications and is a promising noninvasive prognostic, diagnostic, and screening tool (29-31).

In this study, the test was optimized for use in a clinical laboratory, including improvements to the magnetic particle-based DNA extraction and the use of ammonium bisulfite in the conversion reaction. This change allowed a shortened bisulfite treatment (45 min compared with overnight treatment in the previous protocol). The assay was developed as a duplex PCR of methylated SEPT9 and an ACTB sequence measuring total DNA as an internal control. The PCR assay was run in triplicate to analyze the maximal volume of bisulfite DNA to achieve the highest analytical sensitivity. The test protocol can be completed in an 8-h work shift or paused at specific points to accommodate laboratory work flow.

The test had an LoD of 7.8 pg or approximately 1.2 genome equivalents of methylated SEPT9 per milliliter, with minimal operator-to-operator, run-to-run, instrument-to-instrument, or lot-to-lot variability. It had an overall 94.4% agreement in repeatability studies and was not affected by interfering substances commonly occurring in blood-based samples. Collectively, these data provide strong evidence that the analytical characteristics of the test are suitable for use in a blood-based screening assay.

Clinical performance of the test was determined by use of archived samples from the prospective PRESEPT clinical trial. Sensitivity for the detection of CRC (all stages) was 68%, with sensitivities for early stage (0 to II) and later stage (III and IV) of 59% and 87%, respectively, and clinical specificity of 79% (Table 3). Compared with previous results (26), the observed clinical sensitivity compares favorably with the original (51%) and the post hoc triplicate observation (63.9%). However, the adenoma detection rates were low in both studies, essentially equivalent to the positivity in the NED cohort (14% and 21%, respectively). Although the detection of polyps having high-grade dysplasia and adenomas with higher likelihood of progression (e.g., >1 cm) would be of great value, the importance of finding and managing diminutive polyps remains an area of controversy (32). Specificity estimates on the basis of the weightings for the PRESEPT cohort and the US population were 80.0% and 79.1%, respectively, lower than previously reported (25, 26). Additional logistic regression analysis of our data suggests that the difference in specificity can be correlated, in part, with subject age and ethnicity. This is intriguing, as it has been previously reported that DNA methylation patterns for some genes depend on both subject age and ethnicity (33).

As a blood-based test, SEPT9 has the potential to improve CRC screening participation and possibly complement existing gFOBTs/FITs that have already been demonstrated to reduce cancer mortality (8, 9). In a recent direct comparison by use of paired samples, the sensitivity of Epi proColon (72%) was statistically noninferior to that of FIT (68%) (34). These results are consistent with large population-based screening studies that have demonstrated a clinical sensitivity for FIT of 65.8% for cancer detection (35). Further, the 99.7% NPV estimate for methylated SEPT9 is essentially identical to that of FIT (35), so a negative test result provides the same information on absence of CRC. For patients with a positive SEPT9 test, the proportion with CRC is increased compared with the untested population (PPV 2.5%). Although this is lower than the PPV for FIT (8.4%) (35), this difference needs to be weighed against the potential value that a simple blood-based test can offer toward increasing CRC screening participation, as an alternative method for patients struggling with current screening options. Regardless, because of the low prevalence of CRC, the PPV for any CRC screening test is relatively low. For example, the PPV for a hypothetical test with a sensitivity and specificity of 95%, at a prevalence of 0.7%, would be 11.7%. Increasing screening participation will increase the number of colonoscopy referrals. Although the number of unnecessary colonoscopies differs based on the choice of first-line screening methods, colonoscopy remains the current standard of care and carries a very low risk of adverse events (0.5%) (36).

The PPV differential, although less of a safety concern, does raise the question of the health economic benefit of the SEPT9 test. Previous analysis of SEPT9 on the basis of the PRESEPT sensitivity of 64% and specificity of 88% indicated that the test was cost-effective compared with not screening, and that the test would be beneficial if it improved participation among the currently unscreened (26, 37). A new health economic analysis on the basis of current test performance is in preparation.

Ultimately, test use will be determined by guideline recommendations. Current guidelines support the use of different screening methods or combinations of methods (38). This approach is similar to updated screening guidelines for cervical cancer in women of average risk (39). It is conceivable that the new CRC screening guidelines could incorporate Epi proColon as either a single option for the noncompliant patient or combine its use with FIT in a manner that is similar to human papilloma virus (HPV) co-testing (HPV and cytology). This would combine the comparable sensitivities of both tests and the potential benefit of increased adherence from a blood test with the superior specificity of FIT (34, 35) in an algorithm that derives benefit from the strengths of each test.

In summary, the Epi proColon test, with an analytical detection limit of <2 genome equivalents per milliliter of plasma, minimal operator-to-operator variability, and a sensitivity for cancer detection of 68%, provides a simple, robust, and sensitive blood-based screening test for colorectal cancer. On the basis of its ease of use, the test can be integrated into standard molecular diagnostics laboratory work flows, where it offers an additional screening choice. Given that choice drives improvement in CRC screening participation (40), this novel test has the potential to reach the otherwise noncompliant patient.

Previously published online at DOI: 10.1373/clinchem.2013.221044

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, oranalysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article.

Authors' Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest:

Employment or Leadership: N.T. Potter, Molecular Pathology Laboratory-Network, Inc.; P. Hurban, EA | A Quintiles Company; M.N. White, Molecular Pathology Laboratory Network, Inc.; K.D. Whitlock, Molecular Pathology Laboratory Network, Inc.; C.E. Lofton-Day, Epigenomics AG; R. Tetzner, Epigenomics AG; T. Koenig, Epigenomics AG; N.B. Quigley, Molecular Pathology Laboratory Network, Inc., Epigenomics AG; G. Weiss, Epigenomics AG. Consultant or Advisory Role: N.T. Potter, Epigenomics AG (member of Medical Advisory Board).

Stock Ownership: C.E. Lofton-Day, Epigenomics AG; T. Koenig, Epigenomics AG.

Honoraria: N.T. Potter, Epigenomics AG (presentation of data at scientific meetings).

Research Funding: N.T. Potter, Molecular Pathology Laboratory Network, Inc.; M.N. White, Molecular Pathology Laboratory Network, Inc.; K.D. Whitlock, Epigenomics AG; N.B. Quigley, Molecular Pathology Laboratory Network, Inc.

Expert Testimony: None declared.

Patents: C.E. Lofton-Day, US Patent 7,749,702.

Role of Sponsor: The funding organizations played a direct role in the design of study, review and interpretation of data, and preparation and approval of manuscript.

Acknowledgments: Christine Kuepfer provided assistance in the preparation of this manuscript. Study sponsor, design and management, and laboratory testing: Epigenomics AG, Berlin, Germany; laboratory testing: Molecular Pathology Laboratory Network, Maryville, TN; EA | A Quintiles Company, Durham, NC; and Quest Diagnostics Clinical Labs, Valencia, CA.


(1.) WHO Cancer Fact Sheet 2012. (Accessed June 2014).

(2.) Siegel R, Naishadham MA, Jemal A. Cancer statistics, 2013. CA Cancer J Clin 2013;63:11-30.

(3.) Levin B, Lieberman DA, McFarland B, Smith RA, Brooks D, Andrews KS, et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. CA Cancer J Clin 2008;58:130-60.

(4.) Kuipers EJ, Rosch T, Bretthauer M. Colorectal cancer screening: optimizing current strategies and new directions. Nat Rev Clin Oncol 2013;10: 130-42.

(5.) Joseph DA, King JB, Miller JW, Richardson LC. Prevalence of colorectal cancer screening among adults: behavioral risk factor surveillance system, United States, 2010. MMWR Morb Mortal Wkly Rep 2012;61 Suppl:51-6.

(6.) US Preventive ServicesTask Force Recommendation (2008): http://www.uspreventiveservicestaskforce. org/uspstf/uspscolo.htm (Accessed June 2014).

(7.) Moiel D, Thompson J. Early detection of colon cancer: the Kaiser Permanente northwest 30-year history: how do we measure success? Is it the test, the number of tests, the stage, or the percentage of screen-detected patients? Perm J 2011;4:30-8.

(8.) Mandel JS, Bond JH, Church TR, Snover DC, Bradley GM, Schuman LM, Ederer F. Reducing mortality from colorectal cancer by screening for fecal occult blood. N Engl J Med 1993;328:1366-71.

(9.) Mandel JS, Church TR, Bond JH, Ederer F, Geisser MS, Mongin SJ, Snover DC, Schuman LM. The effect of fecal occult-blood screening on the incidence of colorectal cancer. N Engl J Med 2000; 343:1603-7.

(10.) Creeden J, Junker F, Vogel-Ziebolz S, Rex D. Serum tests for colorectal cancer screening. Mol Diagn Ther 2011;15:129-41.

(11.) Ahlquist DA, Zou H, Domanico M, Mahoney DW, Yab TC, et al. Next-generation stool DNA test accurately detects colorectal cancer and large adenomas. Gastroenterology 2012;142:248-56.

(12.) Schwarzenbach H, Hoon DS, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer 2011;11:426-37.

(13.) Oh T, Kim N, Moon Y, Kim MS, Hoehn BD, Park CH, et al. Genome-wide identification and validation of a novel methylation biomarker, SDC2, for blood-based detection of colorectal cancer. J Mol Diagn 2013;15:498-507.

(14.) Lange CP, Campan M, Hinoue T, Schmitz RF, van der Meulen-de Jong AE, et al. Genome-scale discovery of DNA-methylation biomarkers for blood-based detection of colorectal cancer. PLoS One 2012;7:e50266.

(15.) Lofton-Day C, Model F, Devos T, Tetzner R, Distler J, Schuster M, et al. DNA methylation biomarkers for blood-based colorectal cancer screening. Clin Chem 2008;54:414-23.

(16.) Wasserkort R, Kalmar A, Valcz G, Spisak S, Krispin M, Toth K, et al. Aberrant septin 9 DNA methylation in colorectal cancer is restricted to a single CpG island. BMC Cancer 2013;13:398.

(17.) LaPointe LC, Pedersen SK, Dunne R, Brown GS, Pimlott L, Gaur S, et al. Discovery and validation of molecular biomarkers for colorectal adenomas and cancer with application to blood testing. Plos One 2012;7:e29059.

(18.) Payne SR. From discovery to the clinic: the novel DNA methylation biomarker (m) SEPT9 for the detection of colorectal cancer in blood. Epigenomics 2010;2:575-85.

(19.) deVos T, Tetzner R, Model F, Weiss G, Schuster M, Distler J, et al. Circulating methylated SEPT9 DNA in plasma is a biomarker for colorectal cancer. Clin Chem 2009;55:1337-46.

(20.) Hall PA, Russell SE. The pathobiology of the septin gene family. J Pathol 2004;204:489-505.

(21.) Connolly D, Abdesselam I, Verdier-Pinard P, Montagna C. Septin roles in tumorigenesis. Biol Chem 2011;392:725-38.

(22.) McDade SS, Hall PA, Russell SE. Translational control of SEPT9 isoforms is perturbed in disease. Hum Mol Genet 2007;16:742-52.

(23.) Ahmed D, Danielsen SA, Aagesen TH, Bretthauer M, Thiis-Evensen E, et al. A tissue-based comparative effectiveness analysis of biomarkers for early detection of colorectal tumors. Clin Transl Gastroenterol 2012;3:e27.

(24.) Grutzmann R, Molnar B, Pilarsky C, Habermann JK, Schlag PM, Saeger HD, et al. Sensitive detection of colorectal cancer in peripheral blood by Septin 9 DNA methylation assay. PLoS One 2008; 3:1-8.

(25.) Warren JD, Xiong W, Bunker AM, Vaughn CP, Furtado LV, Roberts WL, et al. Septin 9 methylated DNA is a sensitive and specific blood test for colorectal cancer. BMC Med. 2011;9:133.

(26.) Church TR, Wandell M, Lofton-Day C, Mongin, SJ, Burger M, Payne SR, et al. Prospective evaluation of methylated SEPT9 in plasma for detection of asymptomatic colorectal cancer. Gut 2014;63: 317-25.

(27.) Toth K, Sipos F, KalmarA, Patai AV, Wichmann B, Stoehr R, et al. Detection of methylated SEPT9 in plasma is a reliable screening method for both left-and right-sided colon cancers. PLoS One. 2012;7: e46000.

(28.) R Core Team (2012). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

(29.) Suzuki N, Kamataki A, Yamaki J, Homma Y. Characterization of circulating DNA in healthy human plasma. Clin Chim Acta 2008;387:55-8.

(30.) Ehrich M, Deciu C, Zwiefelhofer T, Tynan JA, Cagasan L, Tim R, et al. Noninvasive detection of fetal trisomy 21 by sequencing of DNA in maternal blood: a study in a clinical setting. Am J Obstet Gynecol 2011;204:205.e1-11.

(31.) Perkins G, Yap TA, Pope L, Cassidy AM, Dukes JP, Riisnaes R, et al. Multi-purpose utility of circulating plasma DNA testing in patients with advanced cancers. PLoS One 2012;7(11):e47020.

(32.) Coe SG, Wallace MB. Management of small and diminutive colorectal polyps: a review of the lit erature. Minerva Gastroenterol Dietol 2011;57: 167-76.

(33.) Kwabi-Addo B, Wang S, Chung W, Jelinek J, Patierno SR, Wang BD, et al. Identification of differentially methylated genes in normal prostate tissues from African American and Caucasian men. Clin Cancer Res 2010;16:3539-47.

(34.) Johnson DA, Barclay RL, Mergener K, Weiss G, Konig T, Beck J, Potter NT. Plasma Septin9 versus fecal immunochemical testing for colorectal cancer screening: a prospective multicenter study. PLoS One 2014;9:e98238.

(35.) Morikawa T, Kato J, Yamaji Y, Wada R, Mitsushima T, Shiratori Y. A comparison of the immunochemical fecal occult blood test and total colonoscopy in the asymptomatic population. Gastroenterology 2005;129:422-8.

(36.) Young PE, Womeldorph CM. Colonoscopy for colorectal cancer screening. J Cancer 2013;4: 217-26.

(37.) Ladabaum U, Allen J, Wandell M, Ramsey SD. Colorectal cancer screening with blood-based biomarkers: cost-effectiveness of methylated Septin 9 DNA vs. current strategies. Cancer Epidemiol Biomarkers Prev 2013;22:1567-76.

(38.) Calonge N, Petitti DB, DeWitt TG, Dietrich AJ, Gregory KD, Harris R, et al. Screening for colorectal cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 2008;149:627-37.

(39.) Saslow D, Solomon D, Lawson HW, Killackey M, Kulasingam SL, Cain J, et al.; ACS-ASCCP-ASCP Cervical Cancer Guideline Committee. American Cancer Society, American Society for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology screening guidelines for the prevention and early detection of cervical cancer. CA Cancer J Clin 2012;62:147-72.

(40.) Inadomi JM, Vijan S, Janz NK, Fagerlin A, Thomas JP, Lin YV, et al. Adherence to colorectal cancer screening: a randomized clinical trial of competing strategies. Arch Intern Med 2012;172:575-82.

Nicholas T. Potter, [1] * Patrick Hurban, [2] Mary N. White, [1] Kara D. Whitlock, [1] Catherine E. Lofton-Day, [3] Reimo Tetzner, [4] Thomas Koenig, [4] Neil B. Quigley, [1] and Gunter Weiss [4]

[1] Molecular Pathology Laboratory Network, Maryville TN; [2] EA | A Quintiles Company, Durham, NC; [3] Medical Sciences, Amgen, Thousand Oaks, CA; [4] Epigenomics AG, Berlin, Germany.

[5] Nonstandard abbreviations: CRC, colorectal cancer; gFOBT/FIT, guiac-based fecal occult blood test/fecal immunochemical test; ccfDNA, circulating cell-free DNA; AA, advanced adenoma; SP, small polyp; bisDNA, bisulfite-modified DNA; Ct, threshold count; NED, no evidence of disease; LoD, limit of detection; NPV, negative predictive value; PPV, positive predictive value; HPV, human papilloma virus.

[6] SEPT9, septin 9; ACTB, actin, beta.

* Address correspondence to this author at: 250 East Broadway, Maryville, TN 37804. Fax 865-380-9191; e-mail

Received January 15, 2014; accepted May 15, 2014.
Table 1. Reproducibility study using pooled plasma specimens. (a)

number   Sample type          Site 1   Site 2   Site 3

1        CRC stage I          4 (4)    4 (4)    4 (4)
2        CRC stage I          4 (4)    4 (4)    4 (4)
3        CRC stage II         4 (4)    4 (4)    4 (4)
4        CRC stage II         4 (4)    4 (4)    4 (4)
5        CRC stage III        4 (4)    4 (4)    4 (4)
6        CRC stage III        4 (4)    4 (4)    4 (4)
7        Healthy donor        1 (4)    1 (4)    1 (4)
8        Healthy donor        1 (4)    1 (4)    1 (4)
9        Healthy donor        1 (4)    0 (4)    2 (4)
10       Diluted CRC plasma   4 (4)    4 (4)    4 (4)
11       Diluted CRC plasma   4 (4)    4 (4)    4 (4)
12       Diluted CRC plasma   4 (4)    4 (4)    4 (4)
13       Diluted CRC plasma   4 (4)    3 (4)    4 (4)
14       Diluted CRC plasma   4 (4)    3 (4)    4 (4)

Pool                         Positive test
number   Site 4    Total      results, %

1        6 (6)    18 (18)         100
2        6 (6)    18 (18)         100
3        6 (6)    18 (18)         100
4        6 (6)    18 (18)         100
5        6 (6)    18 (18)         100
6        6 (6)    18 (18)         100
7        2 (6)     5 (18)          28
8        0 (6)     3 (18)          17
9        0 (6)     3 (18)          17
10       2 (2)    13 (14)          93
11       2 (2)    14 (14)         100
12       2 (2)    14 (14)         100
13       2 (2)    13 (14)          93
14       2 (2)    13 (14)          93

(a) Data are number of positive test results
(number of replicates tested) at each testing site.

Table 2. Demographic distribution of the subset of participants
tested from the PRESEPT prospective sample collection. (a)

  Variable             Final       CRC        AA

n                      1544        44        621
  Female             725 (47)    14(32)    267 (43)
  Male                819(53)    30 (68)   354 (57)
  50-59              615 (40)     4 (9)    218(35)
  60-69              576 (37)    24 (55)   294 (47)
  >69                353 (23)    16(36)    109 (18)
  White              1132 (73)   39 (89)   527 (85)
  African American   261 (17)     3(7)      56 (9)
  Other              151 (10)     2 (5)     38 (6)
  US                 1244 (81)   26 (59)   480 (77)
  Germany            300 (19)    18(41)    141 (23)

  Variable              SP        NED

n                      435        444
  Female             221 (51)   223 (50)
  Male               214(49)    221 (50)
  50-59              195 (45)   198 (45)
  60-69              131 (30)   127 (29)
  >69                109 (25)   119(27)
  White              288 (66)   278 (63)
  African American   92 (21)    110(25)
  Other               55(13)    56 (13)
  US                 365 (84)   373 (84)
  Germany            70 (16)    71 (16)

(a) Data are n (%).

Table 3. Summary of Epi proColon test performance
with specimens collected prospectively from
a screening population (PRESEPT). (a)

Diagnosis        Valid       Positive     Negative     Positivity
               specimens,   specimens,   specimens,     rate, %
                   n            n            n          (95% CI)

CRC                44           30           14        68 (53-80)
Stage I-III        39           25           14        64 (48-77)
Stage IV           5            5            0        100 (57-100)
Non-CRC           1500         318          1182       21 (19-23)
AA                621          134          487        22 (18-24)
SP                435           87          348        20 (16-24)
NED               444           97          347        22 (18-26)

(a) A detailed breakdown by stage is provided
in online Supplemental Table 2.

Table 4. Positivity of individuals with no evidence
of CRC stratified by ethnicity and age.

  Age (years)        Positive   Negative    Positive
  and ethnicity                             detected
                                           fraction, %

n                      318        1182
  African American      33         94         26.0
  White                 56        363         13.4
  Other                 11         54         16.9
  African American      25         64         28.1
  White                 97        314         23.6
  Other                 8          44         15.4
  African American      12         30         28.6
  White                 68        195         25.9
  Other                 8          24         25.0
COPYRIGHT 2014 American Association for Clinical Chemistry, Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2014 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Molecular Diagnostics and Genetics; polymerase chain reaction
Author:Potter, Nicholas T.; Hurban, Patrick; White, Mary N.; Whitlock, Kara D.; Lofton-Day, Catherine E.; T
Publication:Clinical Chemistry
Article Type:Report
Date:Sep 1, 2014
Previous Article:T2 magnetic resonance: a diagnostic platform for studying integrated hemostasis in whole blood--proof of concept.
Next Article:Microsatellite instability detection by next generation sequencing.

Terms of use | Copyright © 2017 Farlex, Inc. | Feedback | For webmasters