Patent Application
20220002811
The invention is in the field of prostate cancer detection using multi-gene models for classification of benign and malignant prostate with low false positive and false negative rates.
High prevalence and low risk for progression have complicated efforts to screen and manage prostate cancer (PC). Serum prostate-specific antigen (PSA) testing is the most commonly used tool to identify men suspected of harboring PC. Patients with elevated PSA levels are typically referred for biopsy testing for definitive diagnosis. With a false positive rate of >75% and positive predictive value of ˜25%, PSA results are most often inconclusive. As a result, in the United States over 975,000 prostate biopsies were performed unnecessarily, leading to complications such as infection and bleeding and thousands of hospitalizations (Aubry et al., 2013).
The use of changes in mRNA and protein levels or genetic mutations for detection of PC has been investigated. However, mutations are very rare in PC (Tokheim et al., 2016), and only a handful of biomarkers (PSA, PCA3, TMPRSS2-ERG gene fusions) are currently utilized in tissue and urine/blood based diagnostic tests in clinics. In addition, such tests exhibit low balanced accuracy, with false positive or false negative rates of greater than 36%. In contrast, cancer-specific DNA methylation alterations are highly prevalent in PC, making them attractive targets. Only one DNA methylation test (ConfirmMDx; MDxHealth, Irvine, Calif.) has been marketed for PC, intended for men suspected of PC who have negative biopsies. However, like many other tests, this test suffers from low sensitivity and specificity (Partin et al., 2014; Stewart et al., 2013).
Epigenetic modification of DNA by methylation of cytosine residues has become a focus of research, with growing evidence supporting its role in progression and risk stratification of PC (Fraser et al., 2017; Ruggero et al., 2018; Vanaja et al., 2009). Therapeutic strategies based on methylation inhibitors have been proposed (Ngollo et al., 2014; Perry et al., 2010), while others are based on harnessing DNA methylation aberrations as useful diagnostic biomarkers (Valdés-Mora and Clark, 2015). To date, a majority of research in PC epigenetics has been discovery research, often using extensive, microarray-based screening to identify potential loci of interest. For example, it was found that GSTP1, APC, RASSF1A, PTGS2, and ABCB1 were hypermethylated in >85% of cancers (Yegnasubramanian et al., 2004). In another study, AOX1, CCDC181, GAS6, HAPLN3, KLF8, and MOB3B were added as cancer-specific methylation sites (Haldrup et al., 2013). Others expanded the search to find gene sets associated with recurrence or risk of progression (Lin et al., 2013; Mahapatra et al., 2012; Vanaja et al., 2009). In general, these studies have relied on vast sets of genes tested on comparatively low numbers of samples. Few have validated their findings in independent cohorts.
According to one aspect of the invention there is provided a mastermix for methylation-specific PCR (MSP), comprising: reaction buffer, 1×; deoxyribonucleotide triphosphate (dNTP), 50-500 μM; MgCl2, 0-3.2 mM; DNA polymerase, 0.25 units (U); a concentration of BSA that stabilizes the DNA polymerase and neutralizes any potential inhibitors; and ROX reference dye, 24.5 nM.
In one embodiment, the mastermix is for use with genomic DNA, 50 ρg-1 μg.
In one embodiment, the mastermix is for use with bisulfite-converted genomic DNA, 50 ρg-1 μg.
In one embodiment, the mastermix is for a singleplex MSP, wherein: a gene forward primer concentration is 0.05-1 μM; a gene reverse primer concentration is 0.05-1 μM; and a gene probe or SYBR green dye concentration is 0.05-1 μM. In one embodiment, the gene forward primer concentration is 0.4 μM; the gene reverse primer concentration is 0.4 μM; and the gene probe or SYBR green dye concentration is 0.15 μM.
In one embodiment, the mastermix is for a multiplex MSP, wherein, for each gene: a gene forward primer concentration is 0.05-1 μM; a gene reverse primer concentration is 0.05-1 μM; and a gene probe concentration is 0.05-1 μM; wherein the multiplex MSP comprises 2, 3, or 4 genes. In one embodiment, for each gene: the gene forward primer concentration is 0.4 μM; the gene reverse primer concentration is 0.4 μM; and the gene probe concentration is 0.15 μM.
In one embodiment, a gene probe for a first gene is replaced with SYBR green dye.
According to another aspect of the invention there is provided an MSP method, comprising adding the following to a mastermix as described herein: bisulfite-converted DNA (50 ρg-1 μg), a gene forward primer (0.05-1 μM), a gene reverse primer (0.05-1 μM), and a gene probe or SYBR green dye (0.05-1 μM); mixing; performing PCR cycles including: heating to about 95° C. for about 30 seconds; about seven cycles of about 95° C. for about 30 seconds, cool to about 68° C. with about −2° C. touchdown for about 30 seconds, and hold at about 68° C. for about 30 seconds; about 48 cycles of about 95° C. for about 30 seconds, about 68° C. for about 30 seconds, and about 68° C. for about 30 seconds; and one cycle of about 68° C. for about five minutes.
In one embodiment, the MSP method is for multiplex MSP, wherein, for each gene: a gene forward primer concentration is 0.05-1 μM; a gene reverse primer concentration is 0.05-1 μM; and a gene probe concentration is 0.05-1 μM; wherein the multiplex MSP comprises 2, 3, or 4 genes. In one embodiment, a gene probe for a first gene is replaced with SYBR green dye.
According to another aspect of the invention there is provided a method for detecting prostate cancer in a subject, comprising: bisulfite converting genomic DNA obtained from the subject; mixing the bisulfite-converted DNA with a mastermix for amplifying and/or sequencing the DNA; wherein amplifying includes a selected region of each of GAS6, GSTP1, and HAPLN3 genes; detecting hypermethylation of the selected regions of the GAS6, GSTP1, and HAPLN3 genes; using the detected hypermethylation to identify prostate cancer in the subject.
In one embodiment, the method comprises using probes comprising SEQ ID NOs. 23, 8, and 35, or functional equivalents thereof, for the GAS6, GSTP1, and HAPLN3 genes, respectively. In one embodiment, the method comprises subjecting the detected hypermethylation of the GAS6, GSTP1, and HAPLN3 genes to a classifier to identify prostate cancer in the subject. In one embodiment, the selected hypermethylated region of the GAS6 gene is between a forward primer of SEQ ID NO: 22 and a reverse primer of SEQ ID NO: 24, or functional equivalents thereof; the selected hypermethylated region of the GSTP1 gene is between a forward primer of SEQ ID NO: 7 and a reverse primer of SEQ ID NO: 9, or functional equivalents thereof; and the selected hypermethylated region of the HAPLN3 gene is between a forward primer of SEQ ID NO: 34 and a reverse primer of SEQ ID NO: 36, or functional equivalents thereof.
The method may comprise amplifying using methylation-specific PCR (MSP).
The method may comprise amplifying and sequencing comprises using next generation sequencing (NGS).
The method may comprise mixing the bisulfite-converted DNA with a mastermix as described herein.
According to another aspect of the invention there is provided a method for detecting prostate cancer in a subject, comprising: bisulfate converting DNA obtained from the subject;
mixing the bisulfite-converted DNA with a mastermix for amplifying and/or sequencing the DNA; wherein amplifying includes a selected region of each of GSTP1, CCDC181, HAPLN3, GSTM2, GAS6, RASSF1, and APC genes; detecting hypermethylation of the selected regions of the GSTP1, CCDC181, HAPLN3, GSTM2, GAS6, RASSF1, and APC genes; using the detected hypermethylation to identify prostate cancer in the subject.
In one embodiment, the method comprises using probes comprising SEQ ID NOs. 8, 11, 35, 17, 23, 5, and 32, or functional equivalents thereof, for the GSTP1, CCDC181, HAPLN3, GSTM2, GAS6, RASSF1, and APC genes, respectively.
In one embodiment, the method comprises subjecting the detected hypermethylation of the GSTP1, CCDC181, HAPLN3, GSTM2, GAS6, RASSF1, and APC genes to a classifier to identify prostate cancer in the subject.
In one embodiment, the selected hypermethylated region of the GSTP1 gene is between a forward primer of SEQ ID NO: 7 and a reverse primer of SEQ ID NO: 9, or functional equivalents thereof; the selected hypermethylated region of the CCDC181 gene is between a forward primer of SEQ ID NO: 10 and a reverse primer of SEQ ID NO: 12, or functional equivalents thereof; the selected hypermethylated region of the HAPLN3 gene is between a forward primer of SEQ ID NO: 34 and a reverse primer of SEQ ID NO: 36, or functional equivalents thereof; the selected hypermethylated region of the GSTM2 gene is between a forward primer of SEQ ID NO: 16 and a reverse primer of SEQ ID NO: 18, or functional equivalents thereof; the selected hypermethylated region of the GAS6 gene is between a forward primer of SEQ ID NO: 22 and a reverse primer of SEQ ID NO: 24, or functional equivalents thereof; the selected hypermethylated region of the RASSF1 gene is between a forward primer of SEQ ID NO: 4 and a reverse primer of SEQ ID NO: 6, or functional equivalents thereof; and the selected hypermethylated region of the APC gene is between a forward primer of SEQ ID NO: 31 and a reverse primer of SEQ ID NO: 33, or functional equivalents thereof.
The method may comprise amplifying using methylation-specific PCR (MSP).
The method may comprise amplifying and sequencing comprises using next generation sequencing (NGS).
The method may comprise mixing the bisulfite-converted DNA with a mastermix as described herein.
According to another aspect of the invention there is provided method for identifying a prostate cancer patient at risk of developing biochemical recurrence, and/or suitable for treatment with an additional and/or alternative therapy, comprising: bisulfite converting genomic DNA obtained from the subject; mixing the bisulfite-converted DNA with a mastermix for amplifying and/or sequencing the DNA; wherein amplifying includes a selected region in UCHL1 gene; detecting hypermethylation of the selected region of the UCHL1 gene; using the detected hypermethylation to identify risk of developing biochemical recurrence of prostate cancer, and/or suitability for treatment with an additional and/or alternative therapy.
One embodiment comprises using a probe comprising SEQ ID NO. 44, or a functional equivalent thereof, for the UCHL1 gene, respectively.
In one embodiment the selected hypermethylated region of the UCHL1 gene is between a forward primer of SEQ ID NO: 43 and a reverse primer of SEQ ID NO: 45, or a functional equivalent thereof.
In one embodiment amplifying comprises using methylation-specific PCR (MSP).
In one embodiment the method comprises using a mastermix as described herein.
In one embodiment amplifying and sequencing comprises using next generation sequencing (NGS).
In the aspects and embodiments described herein, the genomic DNA may be obtained from a biological sample selected from fresh/frozen prostate tissue, archival prostate tissue including formalin fixed and paraffin embedded (FFPE tissue), blood, and urine.
According to another aspect of the invention there is provided a kit for detecting prostate cancer comprising a mastermix as described herein, primers and probes for a selected methylation site in each of GAS6, GSTP1, and HAPLN3 genes, and instructions for detecting prostate cancer.
According to another aspect of the invention there is provided a kit for detecting prostate cancer comprising a mastermix as described herein, primers and probes for a selected methylation site in each of GSTP1, CCDC181, HAPLN3, GSTM2, GAS6, RASSF1, and APC genes, and instructions for detecting prostate cancer.
According to another aspect of the invention there is provided a kit for detecting aggressive prostate cancer, prostate cancer patients at risk of developing biochemical recurrence, and/or prostate cancer patients suitable for treatment with an additional and/or alternative therapy, comprising a mastermix as described herein, primers and probes for a selected methylation site in USCHL1 gene, and instructions for use.
For a greater understanding of the invention, and to show more clearly how it may be carried into effect, embodiments will be described, by way of example, with reference to the accompanying drawings, wherein:
As described herein, 15 genes (Table 1) that are frequently methylated in PC were selected for quantitative DNA methylation analysis (one region was selected for each gene). Due to lack of thorough validation or limited sample sizes, clinical utility of these genes or regions in prostate cancer detection has not been fully demonstrated previously. Methylation-specific PCR (MSP) assays were employed to measure methylation levels in the selected regions in over 1250 cancer and ˜95 benign radical prostatectomy (RP) samples (from 699 RP cases) divided into independent training and validation cohorts. Using this data, seven of the gene regions were identified as being useful candidates for accurately identifying prostate cancer in the samples.
From that data a highly sensitive and specific three-gene classifier for PC was constructed and validated. In one embodiment the classifier is based on a statistical model that utilizes the changes in levels of DNA methylation in the selected regions of GAS6, GSTP1, and HAPLN3 genes to accurately identify malignant prostate tissue samples. The model can identify samples exhibiting prostate cancer using DNA methylation levels of these three genes with accuracy of about 99%. Thorough validation of the classifier in over one thousand samples from an independent patient population has confirmed the utility and clinical feasibility of the model.
Embodiments may employ methods other than MSP for DNA methylation analysis, such as, for example, next generation sequencing (NGS).
As part of a larger genomic profiling study, three patient cohorts were analyzed. They consisted of consecutive radical prostatectomies performed with curative intent for histologically verified clinically localized PC (Table 2). Cohorts were obtained from Kingston General Hospital (KGH; Kingston, Ontario) (2000-2012), McGill University/Montreal General Hospital (MGH; Montreal, Quebec) (1994-2013) and London Health Science Centre (LHSC; London, Ontario) (2003-2009). In total, 699 patients were included in the study.
A previously published protocol (Patel et al., 2016) was used to macro-dissect and extract DNA from index tumour foci from 699 RP cases and benign regions from RP cases, yielding over 1300 tissue samples (formalin fixed and paraffin embedded; FFPE). DNA was quantified on a Qubit 3.0 Fluorometer (Thermo Fisher Scientific) using the dsDNA HS (High Sensitivity) kit. A summary of final sample numbers for each DNA methylation assay is shown in Table 3.
Real time MSP assays were performed as previously described (Olkhov-Mitsel et al. 2014; Patel et al. 2016, 2017) targeting the selected methylation regions on the 15 genes (Table 4) in DNA samples collected from three RP cohorts. Briefly, individual DNA samples (50 ng) were bisulfite converted according to the manufacturer's protocol (EpiTect Bisulfite Kit, Qiagen). A mastermix was developed for this assay, embodiments of which are described below. In one embodiment used in this analysis, the mastermix included one of 15 primer pairs (400 nM; Thermo Fisher Scientific) and probe sets (150 nM; Thermo Fisher Scientific) (Table 4), nucleotides (250 μM; Invitrogen), MgCl2 (1.2 mM; NEB), BSA (0.5 mg/mL; NEB), ROX reference dye (24.5 nM; Invitrogen), EpiMark Taq polymerase (0.25 U; NEB) and 1× EpiMark reaction buffer (NEB) was prepared. Next, bisulfite-converted DNA (1 μL) was added to the mastermix and MSP reactions (10 uL) were carried out in a VIIA7 thermocycler (Applied Biosystems). The cycling conditions included denaturation at 95° C. for 30 s, 7 cycles of touchdown PCR with annealing temperatures decreasing by 2° C. per cycle and extension at 68° C. for 30 s, followed by 48 cycles of 30 s at 95° C., 30 s at 58° C., 30 s at 68° C., and a final extension step of 5 min at 68° C.
An assay targeting Alu repeat elements was used as the reference control and distilled water was used as a negative control. CpG methylated Jurkat DNA (New England Biolabs) was used as a positive control sample, and assay efficiency of each MSP assay was determined by generating standard curves as described previously (Bustin, et al. 2009) (Table 4).
The relative threshold method, Crt (Applied Biosystems Relative Quantification (“RQ”) application on ThermoFisher Cloud) was used to determine cycle quantification (Cq) values for each amplification curve. Crt parameter optimization (Early access version, ThermoFisher Scientific Cloud) was conducted to enhance reliable detection of amplification. Sample reactions with inconclusive amplification curves, contamination, or poor reaction efficiency were excluded from further analysis. Reactions with confirmed negative amplification were assigned two Cq values higher than the maximum observed Cq value in the respective cohort. Number of samples included in downstream analysis are listed in Table 3. Normalized methylation levels or abundance ratios were calculated using delta-delta Ct method (Pfaffl, 2001) as described below:
Where,
Exploratory analyses were performed using the training cohort dataset, and differential methylation levels of the 15 selected DNA regions were assessed as fold changes using a Mann-Whitney test. p values were adjusted for false discovery using the family-wise Bonferroni method. All DNA methylation changes with significant enrichment in cancer samples compared to benign were considered for downstream analysis. After testing several supervised learning algorithms including liner regression, linear and quadratic discriminant analysis, and support vector machines, logistic regression was identified as the most suitable for the analyses as it consistently produced better classifiers. Univariate and multivariate logistic regression analysis assessing all possible combinations of DNA methylation changes were performed and the resulting models were ranked according to their balanced accuracy. Receiver operating characteristic (ROC) curve analysis, areas under these curves (AUC), and confusion matrices were generated for best-performing models using model thresholds determined from the “closest topleft” method (R Core Team, 2017; Robin et al., 2011). The best model was selected using the training cohort dataset, and was then applied to the validation cohort dataset. Statistical analysis was performed in R (v3.4.1) using “pROC”, “caret”, “ggrepel” and “ggplot2” packages (Kamil Slowikowski, 2017; R Core Team, 2017; Robin et al., 2011; Wickham, 2009).
The three radical prostatectomy cohorts from which over 1300 DNA samples extracted (see Tables 2 and 3) were selected originally to study prognostic biomarkers. However, as a by-product of that work, diagnostic biomarkers were identified using the following approach. Cases from two cohorts with 41 benign samples and 890 cancer samples from 480 patients were merged into a training dataset. An independent cohort from a 3rd hospital (LHSC) contained 55 benign samples and 377 cancer samples from 219 patients, and was used for validation.
Real-time MSP assays were used to profile methylation changes in small (˜100 bp) regions covering 15 CpG islands which are frequently hypermethylated in PC. In the training dataset, 14 out of 15 of these regions were significantly hypermethylated (adjusted P value <0.01) with normalized methylation levels or abundance ratios >2) in 890 cancer samples compared to 41 benign samples (
GSTP1 was highly methylated (i.e., hypermethylated) in cancer, but not in benign samples. As a cancer classifier, GSTP1 alone demonstrated an AUC of 95% and balanced accuracy of 88%. TCGA PC data show similar results (The Cancer Genome Atlas Research Network, 2015). Two other loci, GAS6 and APC, demonstrated strong diagnostic capabilities with comparable balanced accuracies to GSTP1, but with AUCs of <90%. It was found that regardless of the model threshold chosen, each single gene had false positive and/or false negative rates of 10% or higher. Therefore, to improve accuracy multigene logistic modelling was performed.
The multivariate approach chosen relied on the simplest binary classifier model, logistic regression. Using the training dataset, all possible combinations of all 15 methylation regions were tested to identify a multigene model with higher sensitivity and specificity. A three-gene model based on GAS6/GSTP1/HAPLN3 was selected as the best binary (i.e., cancer/benign) classifier with an AUC of 97% for the ROC curve (Table 5,
Having optimized an accurate binary classifier, the same threshold was used to validate the GAS6/GSTP1/HAPLN3 model in an independent cohort. As shown in Table 7 and
The embodiments described herein provide and validate differentially and consistently hypermethylated genomic loci in PC, along with inexpensive assays that are expected to be compatible with routine workflow in clinical laboratories. The superior performance of the three-gene classifier (GAS6/GSTP1/HAPLN3) demonstrated in tissue samples as described herein provides compelling evidence suggesting the classifier's use in other non-invasive assays, such as urine or blood tests.
For example, to demonstrate use with urine, urine was collected from a patient with early stage prostate cancer after attentive digital rectal examination. DNA was isolated from 5 mL of the urine using a Urine DNA Isolation Kit—Slurry Format (Norgen Biotek Corp., Thorold, ON, Canada). The DNA (25 ng) was bisulfite converted using EpiTect® Bisulfite Conversion Kit (Qiagen, Toronto, ON, Canada). Bisulfite converted DNA was used in quantitative methylation specific PCR (MSP). As shown in
In addition, a urine sample was subject to next generation sequencing (NGS). DNA was isolated from 5 mL of urine collected from a patient with early stage prostate cancer after attentive digital rectal examination. The DNA (50 ng) was bisulfite converted using the MethylCode™ Bisulfite Conversion Kit (Thermo Fisher Scientific Inc.). An AmpliSeg™ (Illumina, Inc.) multiplex library construction protocol was performed, followed by automated templating with the Ion 520 & Ion 530 ExT Kit-Chef™ (Thermo Fisher Scientific Inc.) and Ion S5 Sequencing. Analysis was performed using the Methylation Analysis plugin available for the Torrent™ Suite Software (Thermo Fisher Scientific Inc.).
Association of DNA Methylation Alterations with Biochemical Recurrence in Prostate Cancer
While cancer specific survival (CSS) and overall survival (OS) are most often used to describe prognosis in cancer, prostate cancer is diagnosed early and progresses very slowly, with few men dying, and most deaths occurring after ˜20 years of diagnosis. Thus, cancer recurrence after surgery or radiotherapy has been adopted as a more practical surrogate of mortality-related indices. Recurrence, usually detected by rising PSA levels and therefore referred to as biochemical recurrence (BCR), has been associated with metastatic disease progression and prostate cancer-specific mortality. After prostatectomy, the presence of BCR typically pre-dates the appearance of metastasis by about eight years, and prostate cancer-specific mortality by about 15 years. As a result, BCR is widely used to assess treatment success and manage secondary therapy decisions. Unfortunately, defining BCR after treatment of localized prostate cancer is challenging because the post-treatment PSA level which is indicative of prostate cancer recurrence varies with the type of therapy. Many BCR definitions have been proposed in the literature for patients who have undergone radical prostatectomy (e.g., Stephenson et al., 2006; Cookson et al., 2007; Amling et al., 2001), each of them associated with varying probability of prostate cancer progression. After consulting with lead urologists in Canada, the American Urological Association Prostate Guideline Update Panel's recommended BCR definition was used for this study, and patients with two consecutive PSA values of >0.2 ng/mL after prostatectomy were identified as recurrent.
The number of cases with BCR in the Queen's, McGill, and LHSC cohorts were 51 (22.9%), 52 (20.2%), and 19 (8.7%), respectively. In contrast to analyses involving prostate cancer grade group as an endpoint, to study BCR in prostate cancer, samples from McGill and LHSC cohorts were combined to form a training dataset (which included 71 BCR patients) as the proportion of cases with BCR was higher in the Queen's cohort. The Queen's cohort (n=399 samples from n=223 cases) was used for validation, since it had the highest fraction of recurrent cases. Cases form McGill and LHSC cohorts were combined to form a training cohort (n=879 samples from n=475 cases).
Using log-rank statistics, we selected cut-offs that maximize association of DNA methylation alterations with BCR-free survival, and dichotomized DNA methylation changes at each of the 15 loci (Table 4). DNA hypermethylation at UCHL1 locus was found to be an independent risk factor (hazard ratio >2.25) for BCR in the training and validation cohorts (Table 8). Kaplan-Meier curve analysis further verified these findings and showed that patients with hypermethylation at UCHL1 locus experience BCR at a significantly faster pace in both training and validation cohorts (
To obtain the data, patients with two consecutive post-surgery PSA levels above 0.2 were considered biochemical recurrence events. As multiple samples were collected from each prostatectomy patient, the highest normalized methylation levels were used to tabulate patient-based dataset for each of 15 DNA methylation alterations. The patient-based datasets, from training and validation cohorts, were used in the downstream analyses. Using the MaxStat™ package (MaxStat Software, Jever-OT Cleverns, Germany), an outcome-oriented method was used to select a cut-off point for each DNA methylation alteration in the training cohort. The cut-off corresponded to the most significant association with BCR-free survival. The cut-offs selected from the training cohort were validated in an independent cohort. Kaplan-Meier estimates of BCR-free survival were graphically plotted for significant DNA methylation alterations. Cox's proportional hazards models were used for univariate and multivariate analysis, and multiple pathological variables and molecular markers were adjusted.
Of available molecular techniques, the methylation-specific PCR (MSP) is effective for assessing DNA methylation levels. However, one of the major limitations of this technique is that conventional MSP assays can only analyze one amplicon at a time. For feasibility and efficient amplification of DNA templates by traditional PCR, ready-to-use solutions containing all the required reagents at optimal concentrations are commercially available. However, none is suitable for DNA methylation analysis. Due to the requirement of chemical pre-treatment step (bisulfite treatment) in methylation analysis, the DNA sample is left fragmented and in poor quality. A majority of the commercially-available solutions perform sub-optimal on the bisulfite-treated DNA samples. Thus, as described herein, a highly sensitive and robust mastermix was developed specifically for DNA methylation analysis in DNA samples extracted from various samples including archived patient samples (e.g., FFPE; fixed in formalin and embedded in paraffin).
Mastermix (MMx) embodiments were formulated specifically to work with bisulfite-treated DNA samples. Over 15 different MSP assays were tested using the mastermix in singleplex format, all of which showed robust amplification from bisulfite treated DNA from archival tissue samples (FFPE). A representative amplification plot from a singleplex MSP assay is shown in
Referring to Table 9 it is noted deoxyribonucleotide triphosphate (dNTP) is provided at 250 μM, although a range of concentration such as 50-500 μM may be used. The reaction buffer may contain MgCl2 in sufficient amounts such that additional MgCl2 is not required, hence 0 mM is specified as the low end of the range. However, in most cases the amount of MgCl2 in the reaction buffer is not sufficient and it is added up to 3.2 mM. The concentration of BSA specified is generally considered suitable for stabilizing the DNA polymerase and neutralizing (any) potential inhibitors. Other suitable concentrations may also be used. Since BSA does not participate in the reaction, a concentration close to that specified is expected to be appropriate.
The performance of the APC MSP assay in multiplex reactions remained robust over several rounds of serial dilutions (
An exemplary protocol for using the mastermix is as follows:
All cited publications are incorporated herein by reference in their entirety.
While the invention has been described with respect to illustrative embodiments thereof, it will be understood that various changes may be made to the embodiments without departing from the scope of the invention. Accordingly, the described embodiments are to be considered merely exemplary and the invention is not to be limited thereby.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/CA2019/051403 | 10/1/2019 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62739602 | Oct 2018 | US |
