Patent Application
20200095640
The present invention relates to detection and treatment of pancreatic cancer.
Pancreatic cancer is the 4th leading cause of cancer death in the world, even though it only accounts for 2-3% of all cancer cases. It is one of the most deadly cancers with a 5-year survival rate of less than 10%. The only chance for cure lies on early detection and complete resection of the tumor. Unfortunately only 10-20% of the cancers can be curatively resected at the time of diagnosis and despite surgery many patients experience recurrence. The mean survival time of patients, who do not undergo surgery, is 3 to 6 months after the time of diagnosis. One of the main reasons for the high mortality is difficulties in early detection due to lacking or non-specific symptoms during the early stage of the disease. Abdominal pain, weight loss, fatigue and jaundice are very similar to symptoms related to chronic pancreatitis which is an essential differential diagnosis and a known risk factors of pancreatic cancer.
Currently CA-19-9 (Carbohydrate antigen) is the best blood-based marker for pancreatic cancer. CA-19-9 levels can be elevated in pancreatic cancer but frequently only in advanced disease. Elevation of CA-19-9 is also seen in other types of cancer and inflammatory diseases like chronic pancreatitis and especially conditions of benign biliary obstruction. About 10% of the population lacks the ability to produce CA-19-9, which makes CA-19-9 insufficient as an early diagnostic marker. In the diagnosis of pancreatic cancer advanced imaging modalities, such as PET-CT/three-phase-CT scan, transabdominal ultrasound, endoscopic ultrasound, laparoscopic ultrasound and ERCP are necessary. The drawback is that several of these methods are invasive and entail a risk of complications. Therefore it will be a major advance for the patients if a blood based marker could be used for diagnosis.
Cancer cells releases cell-free DNA into the blood. The DNA fragments have a length of approximately 160 base pairs equivalent with nucleosomal DNA. The cell free DNA can be detected in plasma and serum. The DNA changes are potentially tumor specific and useable in the development of a blood based diagnostic marker for pancreatic cancer.
During the development of pancreatic cancer genetic and epigenetic changes arise. Epigenetic modifications occur at a genomic level, which will not change the sequence of the bases of the DNA. Epigenetic modifications change the DNA conformation, and as a consequence the expression of genes will change. The main epigenetic modifications includes among other DNA hypermethylation, which consists of the addition of a methyl (CH3) residue on cytosine preceding a guanosine, known as CpG dinucleotides. DNA hypermethylation often occurs in CpG-rich regions (CpG islands) of the promoter sequence of the genes. Hypermethylation in the promoter regions of tumor suppressor genes results in downregulation or silencing of the tumor suppressor function. DNA hypermethylation can be detected in cell-free DNA and the changes are potentially tumor specific and useable in the development of a blood based diagnostic marker for pancreatic cancer.
Cell free DNA hypermethylation as a blood based marker for pancreatic cancer has until now only been studied in few and small studies testing methylation status of only a single gene or small gene panel. Statistically significant difference in DNA hypermethylation between patients with pancreatic cancer and healthy controls has been found. However, it is hard to differentiate between malign and benign pancreatic disease based on changes in hypermethylation status. None of the previous examined genes have the potential to work as an individual diagnostic marker.
The present disclosure provides a broad gene panel, which is capable of determining pancreatic cancer with high sensitivity and specificity.
In one aspect, a method is provided of determining pancreatic cancer, a predisposition to pancreatic cancer, the prognosis of a pancreatic, and/or monitoring a pancreatic cancer in a human subject, said method comprising in cell-free DNA from a blood sample from said human subject determining the methylation status of at least two gene loci selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM.
In another aspect, a method is provided for assessing whether a human subject has pancreatic cancer and/or a predisposition to pancreatic cancer, assessing the prognosis of a pancreatic of a pancreatic cancer, and/or monitoring a pancreatic cancer, said method comprising
The present invention relates to methylation biomarkers detectable in plasma cell-free DNA for use in the diagnosis and treatment of pancreatic cancer. Generally, the methylation markers of the invention can be used in methods for identifying human subjects, which are predisposed to pancreatic cancer; i.e. subjects having an increased likelihood of developing pancreatic cancer. The methylation markers of the invention can also be used in methods for identifying subjects having pancreatic cancer, and in this case, the markers allow early diagnosis. Further, the markers of the invention provide prognostic information with respect to pancreatic cancer, and thus, the markers can be used to identify a subject having pancreatic cancer, and the cancer DNA can be tested for predictive prognostic information based on the methylation markers of the invention, as well as information on which curative, ameliorative or palliative treatment to provide for the pancreatic cancer. The methylation status of the methylation markers of the invention may also be used to monitor a treatment provided for the curing and/or ameliorating a pancreatic cancer. Additionally, the marker methylation status can be used to monitor relapse of pancreatic cancer for a human subject previously treated for pancreatic cancer.
Thus, aspects of the present invention relates to i) methods for identifying human subjects, which are predisposed to pancreatic cancer, and/or which have a pancreatic cancer, including early stages, such as asymptomatic stages of pancreatic cancer; ii) methods for providing prognostic information of a pancreatic cancer and/, iii) methods of monitoring a treatment of a pancreatic cancer, and/or monitoring relapse of a pancreatic cancer.
In order to facilitate the understanding of the invention a number of definitions are provided below.
Definitions
Amplification according to the present invention is the process wherein a plurality of exact copies of one or more gene loci or gene portions (template) is synthesised. In one preferred embodiment of the present invention, amplification of a template comprises the process wherein a template is copied by a nucleic acid polymerase or polymerase homologue, for example a DNA polymerase or an RNA polymerase. For example, templates may be amplified using reverse transcription, the polymerase chain reaction (PCR), ligase chain reaction (LCR), in vivo amplification of cloned DNA, isothermal amplification techniques, and other similar procedures capable of generating a complementing nucleic acid sequence.
Amplified copies of a targeted genetic region are sometimes referred to as an amplicon.
A double stranded nucleic acid contains two strands that are complementary in sequence and capable of hybridizing to one another. In general, a gene is defined in terms of its coding strand, but in the context of the present invention, an oligonucleotide primer, which hybridize to a gene as defined by the sequence of its coding strand, also comprise oligonucleotide primers, which hybridize to the complement thereof.
The term “dinucleotide” as used herein refers to two sequential nucleotides. The dinucleotide may be comprised in an oligonucleotide or a nucleic acid sequence. In particular, the dinucleotide CpG, which denotes a cytosine linked to a guanine by a phosphodiester bond, may be comprised in an oligonucleotide according to the present invention, and also comprised in a targeted gene locus sequence according to the present invention. A CpG dinucleotide is also herein referred to as a CpG site. CpG sites are targets for methylation of the cytosine residue.
The gene loci methylation markers of the present invention can be used to infer pancreatic cancer based on the detection of methylation positive cell-free DNA marker loci DNA in a sample comprising in a mixture of cell-free DNA molecules from a subject. For example, the methylation status of a specific gene locus can be detected as methylation positive in those cases where methylation positive DNA can be found in the sample regardless of the relative amount of methylation positive and methylation negative molecules that are present in the sample.
Cell-free DNA is DNA circulating freely in the blood stream.
Pancreatic cancer herein refers to pancreatic adenocarcinoma including any stages thereof, whether symptotic or asymptotic.
The term “predisposition” as used herein in respect of pancreatic cancer is meant to refer to a state of being more likely to develop pancreatic cancer in the future. The term is not meant to imply a genetic disposition as such. However, epigenetic regulation may contribute to predisposition to pancreatic cancer within the meaning of the present disclosure.
Method of Determining Pancreatic Cancer
The present invention provides a number of methods for analysing a human subject with respect to pancreatic cancer. In particular, the invention provides methods for determining pancreatic cancer in a human subject, methods for determining a predisposition to pancreatic cancer for a human subject, methods for determining the prognosis of a pancreatic cancer and methods for categorizing or staging a pancreatic cancer of a human subject, methods for monitoring a pancreatic cancer, such as monitoring the treatment of a pancreatic cancer and/or relapse of a pancreatic cancer. The methylation biomarkers for pancreatic cancer are described in more detailed herein below. Generally, a group of at least two methylation biomarkers for pancreatic cancer are selected from a gene locus selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM.
Thus, in one aspect, a method is provided for determining pancreatic cancer, a predisposition to pancreatic cancer, the prognosis of a pancreatic cancer, and/or monitoring a pancreatic cancer in a subject, said method comprising in a sample from said subject determining the methylation status of at least one gene including regulatory sequences of said gene, wherein said gene locus is selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM.
In another aspect, a method is provided for categorizing or predicting the clinical outcome of a pancreatic cancer in a human subject, said method comprising in a sample from said subject determining the methylation status of at least two gene loci selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM.
In another aspect, a method is provided for evaluating the risk for a human subject of developing pancreatic cancer, or for monitoring relapse of a pancreatic cancer, said method comprising in a sample from said subject determining the methylation status of at least two gene loci selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM.
The invention also in one aspect relates to a method for assessing whether a human subject is likely to develop pancreatic cancer, said method comprising
The methods of the present invention, thus involve determining the methylation status of two or more gene loci as defined herein. Thus, methylation status is determined for multiple gene loci, for example methylation status for at least two gene loci are determined, such as at least three gene loci, such as at least four gene loci, or five or more gene loci. The plurality of gene loci is preferably selected from the gene loci selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM.
Generally, increased levels of methylation of the respective marker gene loci relative to methylation levels of a predetermined control sample of non-cancer DNA is indicative of the presence of a pancreatic cancer, higher likelihood of developing cancer, decreased overall survival, negative outcome, different stage cancer and/or higher risk of contracting cancer.
Thus, the methods of the invention preferably comprises the steps of comparing the methylation status of the respective gene loci determined for a subject with a predetermined methylation status for the corresponding gene of a reference sample comprising DNA from non-cancer subjects.
The predetermined status is preferably determined from DNA of non-cancer subjects. However, the control group could be patients with pancreatitis, both acute and chronic.
The methods disclosed herein could in principle be applied to any subject whether or not at risk of pancreatic cancer and whether or not suspected of having pancreatic cancer. However, in a preferred embodiment, the methods are applied to subjects, which are suspected of having such cancer or who are at risk of having such cancer based on other symptoms. For example, the methods disclosed herein could preferably be applied to subjects displaying nonspecific symptoms, such as fatigue, jaundice, nausea, loss of appetite and weight loss. The subject can also be an individual for whom an unspecific CT scan signal has been observed in the pancreas, which may or may not be a tumor.
In one embodiment, the methods disclosed herein are applied to a subject having pancreatitis, and the methods disclosed herein are particularly efficient for distinguishing pancreatic cancer from pancreatitis, which may have overlapping symptoms.
Method for Treatment of Pancreatic Cancer
Aspects of the invention also relates to methods for determining the prognosis of a pancreatic cancer in a human subject and/or inferring a suitable treatment, as well as for monitoring a pancreatic cancer, and in particular monitoring the treatment of a pancreatic cancer and/or monitoring relapse of a pancreatic cancer.
So in one aspect, a method is provided for treatment of pancreatic cancer in a human subject, the method comprises the steps of
The step of determining pancreatic cancer by a method of the present invention allows early detection of pancreatic cancer, and therefore allows treatment of the cancer to be initiated before developing into later stages and/or before forming metastases. This increases the possibilities of providing a curative treatment. In a preferred embodiment such treatment is surgical resection of the pancreatic cancer.
If however, surgical resection is not possible, preferred treatments include chemotherapy and/or radiotherapy. A possible treatment is also Irreversible electroporation (IRE), which involves short, repetitive, non-thermal high-energy pulses of electricity to destroy the cancer cells. In one embodiment, the selected human subject is subjected to a treatment selected from surgery, chemotherapy and/or radiotherapy, however, combination thereof can also be applied, such as surgical resection followed by chemotherapy and/or radiotherapy.
The methylation markers also allow monitoring relapse of pancreatic cancer, as well as offering a personalized treatment of pancreatic cancer by surveillance and quality of control of the treatment offered, thereby allowing terminating ineffective treatments and offering alternative treatments. Thus, in another aspect, the invention provides a method for personalized treatment of a pancreatic cancer of a human subject, said method comprising
Methylation Biomarkers for Pancreatic Cancer
As described herein above, the present invention provides a number of different methods for evaluating pancreatic cancer in a human subject based on methylation status of specific group of gene loci. The invention also provides specific oligonucleotide primers for use in determining methylation status of specific gene loci, which are methylation biomarkers for pancreatic cancer according to the present invention. These gene loci include BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM.
Methylation status is preferably determined in a GC-rich region upstream of the start codon of any gene locus identified herein. Such upstream regions comprise promoter elements and the methylation status is in a most preferred embodiment determined in a promoter region of a marker gene loci identified herein. It is preferably a promoter region located upstream of the open reading frame, i.e. upstream of the start codon (ATG: methionine) or the first exon of the gene encoded by any of the gene loci identified herein, i.e. ALX4, APC, BMP3, BNC1, BRCA1, CDKN2B, CHFR, ESR1, EYA2, GSTP1, HIC1, MGMT, MLH1, NEUROG1, MESTv1, NPTX2, p16, PENK, RARB, RASSF1A, SEPT9v2, SFRP1, SFRP2, SST, TAC1, TFPI2, VIM or WNT5a. The size of a promoter region may vary considerably between genes however a person of skill in the art is able to identify promoter elements on the basis of sequence elements using bioinformatics tools available to him. In the context of the loci identified herein, the promoter region is generally considered to comprise 1000 base pairs upstream of the start codon. Thus in a preferred embodiment, the methylation status is determined in a region within 1000 bp, such as within 900 bp, such as within 800 bp, such as within 700 bp, such as within 600 bp, such as within 500 bp, such as within 400 bp, such as within 300 bp, such as within 200 bp , such as within 100 bp upstream of the start codon of two or more gene loci selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM. However, methylation status is in a particular embodiment determined in a region within 1000 bp upstream of the start codon of two or more gene loci selected from any of the subgroups identified herein above, such as
Generally, in the methods of the invention, the methylation status is determined for at least two gene loci selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM.
In one embodiment, the methylation status is determined for at least two gene loci selected from the group consisting BMP3, RASSF1A, BNC1, MESTv2, TFPI2, APC, SFRP1 and SFRP2. For example the methylation status is determined at 3, 4, 5, 6, 7 or preferably 8 gene loci of the group consisting BMP3, RASSF1A, BNC1, MESTv2,TFPI2, APC, SFRP1 and SFRP2. Thus, in a preferred embodiment, the methylation status is determined in BMP3, RASSF1A, BNC1, MESTv2, TFPI2, APC, SFRP1 and SFRP2.
In one embodiment, methylation status is determined for at least BMP3, RASSF1A and BNC1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1 and MESTv1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1 and TFPI2. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2 and APC. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC and SFRP1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1 and SFRP2. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2 and EYA2. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2 and NPTX2. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2 and SEPT9v2. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2 and WNT5a. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a and CDKN2B. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B and ALX4. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4 and HIC1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1 and RARB. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB and SST. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST and ESR1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1 and TAC1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1 and BRCA1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1 and CHFR. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR and GSTP1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1 and MGMT. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT and MLH1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2,
NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1 and NEUROG1. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1 and p16. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16 and PENK. In another embodiment, methylation status is determined for at least BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16 PENK and VIM.
In another embodiment, the methylation status is determined for at least two gene loci selected from the group consisting of MLH1, SEPT9v2, BNC1, ALX4, CDKN2B, NEUROG1, WNT5A and TFPI2. For example, the methylation status is determined at 3, 4, 5, 6, 7 or preferably 8 gene loci of the group consisting MLH1, SEPT9v2, BNC1, ALX4, CDKN2B, NEUROG1, WNT5A and TFPI2. Thus, in a preferred embodiment, the methylation status is determined in MLH1, SEPT9v2, BNC1, ALX4, CDKN2B, NEUROG1, WNT5A and TFPI2.
In another embodiment, the methylation status is determined for at least two gene loci selected from the group consisting of ALX4, BNC1, HIC1, Sept9v2, SST, TFPI2 and TAC1. For example, the methylation status is determined at 3, 4, 5, 6 or preferably 7 gene loci of the group consisting ALX4, BNC1, HIC1, Sept9v2, SST, TFPI2 and TAC1. Thus, in a preferred embodiment, the methylation status is determined in ALX4, BNC1, HIC1, Sept9v2, SST, TFPI2 and TAC1.
In another embodiment, the methylation status is determined for at least two gene loci selected from the group consisting of SEPT9v2, SST, ALX4, CDKN2B, HIC1, MLH1, NEUROG1 and BNC1. For example, the methylation status is determined at 3, 4, 5, 6, 7 or preferably 8 gene loci of the group consisting SEPT9v2, SST, ALX4, CDKN2B, HIC1, MLH1, NEUROG1 and BNC1. Thus, in a preferred embodiment, the methylation status is determined in SEPT9v2, SST, ALX4, CDKN2B, HIC1, MLH1, NEUROG1 and BNC1.
In one embodiment, the methylation status is determined for the gene loci BNC1 and TFPI2.
In another embodiment, the methylation status is determined for the gene loci ALX4, HIC1, Sept9v2 and SST. In another embodiment, the methylation status is determined for the gene loci ALX4 and SST. In another embodiment, the methylation status is determined for the gene loci ALX4, HIC1 and SST. In another embodiment, the methylation status is determined for the gene loci ALX4 and HIC1. In another embodiment, the methylation status is determined for the gene loci ALX4, Sept9v2 and SST. In another embodiment, the methylation status is determined for the gene loci HIC1, Sept9v2 and SST. In another embodiment, the methylation status is determined for the gene loci ALX4, HIC1, Sept9v2 and SST.
In a preferred embodiment, the methylation status is determined by a method comprising amplifying a gene locus of the invention. In a preferred embodiment, amplification is conducted using at least one primer selected from table 1 which identify specific primers and probes for each gene locus. For example for SST, using SEQ ID NO: 1 and 2 and/or 3 and 4. Thus, it is preferred that at least one primer is selected from the group consisting of SEQ ID Nos: 1-135
Sample
According to the present invention, the methylation status of one or more gene loci is determined in a blood sample from a human subject. The sample can also be a plasma sample, or a plasma sample can be prepared from the blood sample by centrifugation. The sample is preferably a cell-free DNA sample. The sample of the invention comprises biological material in the form of nucleic acid molecules. The nucleic acid molecules should be extracted from the sample prior to the analysis.
The nucleic acid to be analysed for the presence of methylated CpG may be extracted from the samples by a variety of techniques such as that described by Maniatis, et al (Molecular Cloning: A Laboratory Manual, Cold Spring Harbor, N.Y., pp 280, 281, 1982).
Any nucleic acid, in purified or nonpurified form, can be utilized as the starting nucleic acid or acids, provided it contains, or is suspected of containing, the specific nucleic acid sequence containing the methylation target site (e.g., CpG). The specific nucleic acid sequence which is to be amplified may be a part of a larger molecule or is present initially as a discrete molecule. The nucleic acid sequence to be amplified need not to be present in a pure form, it may for example be a fraction of a complex mixture of other DNA molecules, and/or RNA. In one example, the nucleic acid sequence is a fraction of a genomic nucleic acid preparation.
Extremely low amounts of nucleic acid may be used as target sequence according to the methods of the present invention. It is appreciated by the person skilled in the art that in practical terms no upper limit for the amount of nucleic acid to be analysed exists. The problem that the skilled person may encounter is that the amount of nucleic acid to be analysed is limited. Therefore, it is beneficial that the method of the present invention can be performed on small amounts of nucleic acids. The present methods allow the detection of very few nucleic acid copies. The amount of the nucleic acid to be analysed is in one embodiment at least 0.01 ng, such as 0.1 ng, such as 0.5 ng, for example 1 ng, such as at least 10 ng, for example at least 25 ng, such as at least 50 ng, for example at least 75 ng, such as at least 100 ng, for example at least 125 ng, such as at least 150 ng, for example at least 200 ng, such as at least 225 ng, for example at least 250 ng, such as at least 275 ng, for example at least 300 ng, 400 ng, for example at least 500 ng, such as at least 600 ng, for example at least 700 ng, such as at least 800, ng, for example at least 900 ng or such as at least 1000 ng.
Methylation Status
The methods of the present invention for determining pancreatic cancer in a human subject, methods for determining a predisposition to pancreatic cancer for a human subject, methods for determining the prognosis of a pancreatic cancer in a subject methods for categorizing or staging a pancreatic cancer of a human subject and methods for monitoring a pancreatic cancer, all include a step of providing or obtaining a sample from the human subject, and in that sample determining the methylation status of at least two genetic loci selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM., as well as subregions thereof, in particular promoter regions, such as the regions within 1000 bp upstream of the start codon, including the subregions delineated by the respective primer pairs identified in table 2, such as primer pairs 1 and 2, 3 and 4, 6 and 7, 8 and 9, 11 and 12 etc.
Methylation status of the target gene loci or genetic regions of the present invention may be determined by any suitable method available to the skilled person for detecting methylation status. In one embodiment, methylation status is determined by a quantitative method, which is capable of detecting levels of methylation positive alleles and/or methylation negative alleles in a population of target molecules present in a sample. For example, the quantitative method is preferably capable of detecting different levels of methylation positive alleles of a given target locus sequence, such as detecting whether 0%, less than 1%, more that 1%, such as approximately 10%, 25%, 50%, 75% or 100% of the alleles of a given marker locus are methylation positive. Some techniques in the art detect the presence of one or more methylation positive and/or methylation negative alleles of a given target sequence without providing quantitative data, and without providing information of the relative levels of methylation positive and methylation negative alleles.
Methylation status: the term “methylation status” as used herein, refers to the presence or absence of methylation in a specific nucleic acid region. In particular, the present invention relates to detection of methylated cytosine (5-methylcytosine). A nucleic acid sequence, e.g. a gene locus of the invention, may comprise one or more CpG methylation sites. The nucleic acid sequence of the gene locus may be methylated on all methylation sites (i.e. 100% methylated) or unmethylated on all methylation sites (i.e. 0% methylated). However, the nucleic acid sequence may also be methylated on a subset of its potential methylation sites (CpG-sites). In this latter case, the nucleic acid molecule is heterogeneously methylated. In a preferred embodiment, methylation status refers to the presence or absence of methylated nucleic acid of two or more of the loci described herein, preferably promoter regions of the loci described herein.
The methods for inferring pancreatic cancer of the present invention thus include determining methylation status of specific methylation markers by determining whether a specific methylation marker in a sample obtained or provided from a subject is methylation positive or methylation negative as well as detecting the relative level of methylated alleles of a given locus.
In a preferred embodiment, the methylation status is determined by methylation specific PCR, preferably on cell-free DNA, where the DNA is modified by an agent capable of converting unmethylated cytosine residues.
In another embodiment, the methylation status is determined by use of methylation-sensitive restriction enzymes. Many restriction enzymes are sensitive to the DNA methylation states. Cleavage can be blocked or impaired when a particular base in the recognition site is modified. For example, the MspJI family of restriction enzymes has been found to be dependent on methylation and hydroxymethylation for cleavage to occur. These enzymes excise ˜32 base pair fragments containing a centrally located 5-hmC or 5-mC modified residue that can be extracted and sequenced. Due to the known position of this epigenetic modification, bisulfite conversion is not required prior to downstream analysis. Methylation-sensitive enzymes are well-known in the art and include: AatII, AccII, Aor13HI, Aor51HI, BspT104I, BssHII, Cfr10I, ClaI CpoI, Eco52I, HaeII, HapII, HhaI, MluI, NaeI, NotI, NruI, NsbI, PmaCI, Psp1406I, PvuI, SacII, SalI, SmaI and SnaBI.
The digested nucleic acid sample is subsequently analysed by for example gel electrophoresis.
So, in one embodiment of the methods of the invention, methylation status is determined by a method comprising the steps of
Methylation status can be determined by modifying the DNA with an agent which targets either methylated or unmethylated sequences, amplifying the DNA, and analysing the amplification products.
For example, amplification product is analysed by detecting the presence or absence of amplification product, wherein the presence of amplification product indicates that the target nucleic acid has not been cleaved by the restriction enzymes, and wherein the absence of amplification product indicates that the target nucleic acid has been cleaved by the restriction enzymes.
Thus, generally, the in the methods of the invention methylation status is determined by a method comprising the steps of
For example, the method comprises the steps of
The amplification product can be analysed for nucleic acid substitutions resulting from conversion of modified cytosine residues, preferably wherein the presence of converted cytosine residues are indicative of unmethylated cytosine residues, and presence of unconverted cytosine residues is indicative of methylated cytosine residues. Typically, unmethylated cytosine is converted to thymidine after bisulphite treatment and amplification, while methylated cytosine is left unchanged after same treatment.
In a preferred embodiment, the nucleic acid material is modified using bisulfite and amplified using primers, which span at least one CpG dinucleotide in said gene loci. In this case, amplification primers specific for modified nucleic acid will only support amplification and generate an amplification product, for nucleic acids, which are unmethylated. In contrast, amplification primers specific for unmodified nucleic acid will only support amplification and generate an amplification product, for nucleic acids, which are methylated.
The amplification product, the amplicon, is in a preferred embodiment a genetic region of a gene of the invention, wherein said region is delineated by the primer pairs identified in table 2.
Modification of DNA
The method for determining methylation status in the present invention preferably comprise a step of modifying the nucleic acids comprised in the sample, or extracted from the sample, using an agent which specifically modifies unmethylated cytosine in the nucleic acid. As used herein the term “modifies” refers to the specific modification of either an unmethylated cytosine or a methylated cytosine, for example the specific conversion of an unmethylated cytosine to another nucleotide which will distinguish the modified unmethylated cytosine from a methylated cytosine. In one preferred embodiment, an agent modifies unmethylated cytosine to uracil. Such an agent may be any agent capable of said conversion, wherein unmethylated cytosine is modified, but not methylated cytosine. In one preferred embodiment the agent for modifying unmethylated cytosine is sodium bisulfite. Sodium bisulfite (NaHSO3) reacts readily with the 5,6-double bond of cytosine, but only poorly with methylated cytosine. The cytosine reacts with the bisulfite ion, forming a reaction intermediate in the form of a sulfonated cytosine which is prone to deamination, eventually resulting in a sulfonated uracil. Uracil can subsequently be formed under alkaline conditions which removes the sulfonate group.
During a nucleic acid amplification process, uracil will by the Taq polymerase be recognised as a thymidine. The product upon PCR amplification of a Sodium bisulfite modified nucleic acid contains cytosine at the position where a methylated cytosine (5-methylcytosine) occurred in the starting template DNA of the sample. Moreover, the product upon PCR amplification of a Sodium bisulfite modified nucleic acid contains thymidine at the position where an unmethylated cytosine (5-methylcytosine) occurred in the starting template DNA of the sample. Thus, an unmethylated cytosine is converted into a thymidine residue upon amplification of a bisulfite modified nucleic acid.
In a preferred embodiment of the present invention, the nucleic acids are modified using an agent which modifies unmethylated cytosine in the nucleic acid. In a specific embodiment, such an agent is a bisulfite, hydrogen sulfite, and/or disulfite reagent, for example sodium bisulfite.
However, in another embodiment, an agent is used, which specifically modifies methylated cytosine in the nucleic acid and does not modify unmethylated cytosine.
Amplifying Step
After modification of the nucleic acids of the sample, the specific genetic region selected for determination of methylation status is preferably amplified in order to generate and thereby obtain multiple copies (amplicons) of the respective genetic regions, which can allow its further analysis with respect to methylation status. The amplification is preferably performed using at least one oligonucleotide primer, which targets the specific genetic region comprising methylation markers for pancreatic cancer according to the present invention. Most preferably amplification is performed using two oligonucleotide primers, which delineates the analysed region. The skilled person may use his common general knowledge in designing suitable primers. However, in a preferred embodiment, at least one, and preferably two methylation-independent oligonucleotide primers are employed for amplification of the modified nucleic acid. The nature of methylation-independent primers is described on more detail herein below.
The amplifying step is a polymerisation reaction wherein an agent for polymerisation is involved, effecting an oligonucleotide primer extension. The agent for polymerization may be any compound or system which will function to accomplish the synthesis of primer extension products, including enzymes. Enzymes that are suitable for this purpose include, for example, E. coli DNA polymerase I, Klenow fragment of E. coli DNA polymerase I, T4 DNA polymerase, other available DNA polymerases, polymerase muteins, reverse transcriptase, and other enzymes, including heat-stable enzymes (i.e., those enzymes which perform primer extension after being subjected to temperatures sufficiently elevated to cause denaturation also known as Taq polymerases). Suitable enzymes will facilitate combination of the nucleotides in the proper manner to form the primer extension products which are complementary to each locus nucleic acid strand. Generally, the synthesis will be initiated at the 3′ end of each primer and proceed in the 5′ direction along the template strand, until synthesis terminates, producing molecules of different lengths. There may be agents for polymerization, however, which initiate synthesis at the 5′ end and proceed in the other direction, using the same process as described above.
The amplification product (amplicon) may be of any length, however usually, the amplification product comprise between 15 and 1000 nucleotides, such as between 15 and 500 nucleotides, such as between 50 and 120 nucleotides, preferably between 80 and 100 nucleotides. In a preferred embodiment, the amplicon is delineated by the primers identified in table 2 for each respective gene, cf. herein above.
The PCR reaction is characterised by three steps a) melting a nucleic acid template, b) annealing at least one methylation-independent oligonucleotide primer to said nucleic acid template, and c) elongating said at least one methylation-independent oligonucleotide primer. These three steps are repeated through multiple cycles, as is well known to those of skill in the art.
PCR is usually performed on a PCR machine, which is also known as a thermal cycler. Specifically, the thermal cycler may be coupled to a fluorometer, thus allowing the monitoring of the nucleic acid amplification in real time by use of intercalating fluorescent dyes, or other fluorescent probes. Applicable dyes according to the present invention include any DNA intercalating dye. Examples of methylation specific probes are listed in table 2 in respect of each gene locus provided herein, e.g. SEQ ID NO: 5 for detection of an amplicon of the SST gene, SEQ ID NO: 8 for detection of the APC gene etc.
Suitable dyes include ethidium bromide, EvaGreen, LC Green, Syto9, SYBR Green, SensiMix HRM™ kit dye, however many dies are available for this same purpose.
Real-time PCR allows for easy performance of quantitative PCR (qPCR), which is usually aided by algorithms comprised in the software, which is usually supplied with the PCR machines.
The fluorometer can furthermore be equipped with software that will allow interpretation of the results. Such software for data analyses may also be supplied with the kit of the present invention.
Another variant of the PCR technique, multiplex PCR, enables the simultaneous amplification of many targets of interest in one reaction by using more than one pair of primers.
PCR according to the present invention comprise all known variants of the PCR technique known to people of skill within the art. Thus, the PCR technology comprise real-time PCR, qPCR, multiplex PCR.
Oligonucleotide Primers
The oligonucleotide primers of the present invention are capable of being employed in amplification reactions, wherein the primers are used in amplification of template DNA originating from a methylation-positive or methylation-negative strand. The preferred primers of the present invention comprise at least one CpG dinucleotide.
The design of oligonucleotide primers suitable for nucleic acid amplification techniques, such as PCR, is known to people skilled within the art. The design of such primers involves analysis of the primer's melting temperatures and ability to form duplexes, hairpins or other secondary structures. Both the sequence and the length of the oligonucleotide primers are relevant in this context. The oligonucleotide primers according to the present invention comprise between 10 and 200 consecutive nucleotides, such as at least 60, at least 65, at least 70, at least 75, at least 80, at least 85, at least 90, at least 95, at least 100, at least 110, at least 120, at least 130, at least 140, at least 150, at least 160, at least 180 or at least 200 nucleotides. In a specific embodiment, the oligonucleotide primers comprise between 15 and 60 consecutive nucleotides, such as 15, 16, 17, 18, 19, 20, preferably 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, such as 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, alternatively at least 41, at least 42, at least 44, at least 46, at least 48, at least 50, at least 52, at least 54, at least 56, at least 58, or at least 60 consecutive nucleotides.
The methods employed for determining the methylation status of a nucleic acid according to the present invention, preferably comprise amplification of a modified nucleic acid by use of a methylation independent oligonucleotide primer. In one embodiment, the oligonucleotide primers of the present invention are able to hybridize to a nucleic acid sequence comprising CpG islands. In one embodiment, the oligonucleotide primers comprise 2, alternatively 3, 4, 5, 6, 7, 8, 9 or 10 CpG dinucleotides. Primers specific for methylated alleles recognize the CpG dinucleotides present in the target nucleic sequence, whereas primers specific for unmethylated alleles are designed to recognize TpG dinucleotides due the PCR mediated conversion of unmethylated C residues to thymine.
In one embodiment, the oligonucleotide primer of the present invention is selected from the group of primers identified in table 2. Methylation status is preferably determined for a gene mentioned in table 2 using the respective forward primer and reverse primer set out in table 2.
In one embodiment, the oligonucleotide primers hybridize to a target nucleic acid sequence of a gene loci selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM, or the complement thereof.
In one embodiment, an oligonucleotide primer of the present invention specifically hybridizes to regions within 1000 bp upstream of the start codon of gene loci selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM, or the complement thereof.
In another embodiment, the oligonucleotide primer hybridizes to a target nucleic acid sequence of a gene locus selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv2, TFPI2, APC, SFRP1 and SFRP2, or the complement thereof, preferably to a target sequence within 1000 bp upstream of the start codon.
In another embodiment, the oligonucleotide primer hybridizes to a target nucleic acid sequence of a gene locus selected from the group consisting of MLH1, SEPT9v2, BNC1, ALX4, CDKN2B, NEUROG1, WNT5A and TFPI2, or the complement thereof, preferably to a target sequence within 1000 bp upstream of the start codon.
In another embodiment of the present invention the oligonucleotide primer hybridizes to a target nucleic acid sequence of a gene locus selected from the group consisting of ALX4, BNC1, HIC1, Sept9v2, SST, TFPI2 and TAC1, or the complement thereof, preferably to a target sequence within 1000 bp upstream of the start codon.
In another embodiment of the present invention the oligonucleotide primer hybridizes to a target nucleic acid sequence of a gene locus selected from the group consisting of SEPT9v2, SST, ALX4, CDKN2B, HIC1, MLH1, NEUROG1 and BNC1, or the or the complement thereof, preferably to a target sequence within 1000 bp upstream of the start codon.
In one embodiment, an oligonucleotide primer of the present invention specifically comprises or consists of 5-50, such as 5-30, such as 10-20 consecutive nucleotides of a subsequence (preferably promoter region subsequence) of a gene locus selected from the group consisting of BMP3, RASSF1A, BNC1, MESTv1, TFPI2, APC, SFRP1, SFRP2, EYA2, NPTX2, SEPT9v2, WNT5a, CDKN2B, ALX4, HIC1, RARB, SST, ESR1, TAC1, BRCA1, CHFR, GSTP1, MGMT, MLH1, NEUROG1, p16, PENK and VIM, or the complement thereof.
Analysis of Amplified CpG-Containing Nucleic Acids
According to the present invention the nucleic acid (target) sample is subjected to an agent that converts an unmethylated cytosine to another nucleotide which will distinguish the unmethylated from the methylated cytosine. In a preferred embodiment the agent modifies unmethylated cytosine to uracil. The modifying agent can be sodium bisulphite. During the amplification process uracil will be converted to thymidine.
Thus, after conversion of unmethylated cytosines to uracils in the nucleic acid (target) sample, the subsequent PCR amplification converts uracils to thymine. As a consequence of the sodium bisulfite and PCR-mediated specific conversion of unmethylated cytosines to thymines, G:C base pairs are converted to A:T base pairs at positions, where the cytosine was methylated.
The difference in nucleic acid sequence at previously methylated (methylation positive) or unmethylated (methylation negative) cytosines allows for the analysis of methylation status in a sample. This analysis can comprise identifying cytosine residues, which have been converted to thymidine after amplification, as unmethylated cytosine residues, and identifying cytosine residues, which has not been converted, as methylated cytosine residues.
By this method, analysis of the amplified nucleic acid after treatment with a modifying agent such as sodium bisulphite and subsequent PCR amplification can reveal the methylation status of the target nucleic acid sequence. Thus, in one embodiment, the method for determining methylation status of a nucleic acid according to the present invention further comprises a step of analyzing the amplified nucleic acids.
Specifically, the subsequent analysis can be selected from the group consisting of melting curve analysis, high resolution melting analysis, nucleic acid sequencing, primer extension, denaturing gradient gel electrophoresis, southern blotting, restriction enzyme digestion, methylation-sensitive single-strand conformation analysis (MS-SSCA) and denaturing high performance liquid chromatography (DHPLC).
In one embodiment, the methylation status of the amplified nucleic acid is determined by any method selected from the group consisting of Methylation-Specific PCR (MSP), Whole genome bisulfite sequencing (BS-Seq), HELP assays, ChIP-on-chip assays, Restriction landmark genomic scanning, Methylated DNA immunoprecipitation (MeDIP), Pyrosequencing of bisulfite treated DNA, Molecular break light assays, and Methyl Sensitive Southern Blotting.
In a preferred embodiment, the methylation status of the amplified nucleic acid (amplification product/amplicon) is determined by Methylation-Specific PCR (MSP), wherein the primers sequence determines whether an amplicon is generated, because one set of primers only support amplification of methylated alleles and another set of primers only support amplification of unmethylated alleles. In this case, analysis of the amplicon involves detection of the relative amount of each group of amplicons, methylation-specific amplicons vs. unmethylation specific amplicons.
In another embodiment, the methylation status of the amplified containing nucleic acid is determined by a method selected from the group consisting bisulfite sequencing, COBRA, melting curve analysis, or DNA methylation arrays.
In one embodiment, the analysis of the amplified nucleic acid region is melting curve analysis. In another embodiment, the analysis of the amplified nucleic acid is high resolution melting analysis (HRM).
Methods
Study Design
This study was conducted as a cross sectional observational study of the cell-free DNA hypermethylation profile in plasma of patients with suspected or biopsy-verified pancreatic cancer admitted to the Department of Gastrointestinal Surgery, Aalborg University Hospital between February 2008 and February 2011. An additional benign control groups were patients with chronic pancreatitis treated at the hospital or at the outpatient clinic at Aalborg University Hospital from August 2013 until August 2014 and patients admitted with acute pancreatitis at the Department of Gastrointestinal Surgery, Aalborg University Hospital or the Department of General Surgery, Hospital of Vendsyssel from November 2013 until May 2015.
The study was approved by the Ethical Committee of Northern Jutland, Denmark (N-2013037) and registered in ClinicalTrails.gov: NCT02079363. All participants gave written informed consent.
Participants
Patients with suspected or biopsy-verified upper gastrointestinal cancer were included prospectively and consecutively and had blood drawn on admission before diagnostic work-up and before any kind of treatment. Patients were divided into the following groups (
Blood Sampling and Analytical Method
All blood samples were obtained by skilled technicians using venipuncture according to procedure recommended by the European Concerted Action on Thrombosis. Routine analyzes (C-reactive protein (CRP), leucocytes, alanine aminotransferase (ALT), alkaline phosphatase (ALP), amylase, bilirubin) were performed immediately afterwards. EDTA plasma for methylation analysis was centrifuged 20 min. (4000 rpm) at 4° C. and stored within two hours after sampling in a biobank at −80° C. until methylation analysis.
All methylation analyzes were performed by a single skilled laboratory scientist.
Extraction and deamination: Plasma nucleic acids were extracted using the EasyMag platform (Biomerieux) according to manufacturer's instruction. Four-hundred-fifty-1000 μl EDTA plasma was used for the extraction and purified nucleic acids were eluted in 35 μl elution buffer (Biomerieux). Five μl was used for quantitation of extracted DNA, the rest was deaminated as described previously by our group. In brief, 30 μl DNA extract was mixed with 60 μl deamination solution, deaminated for 10 min at 90° C., followed by purification using EasyMag and eluted in 25 μl 10 mM KOH.
PCR: In order to expand the amount of relevant deaminated DNA a first round PCR was conducted using a mix of outer primers (Table 2) for all promoter regions investigated. Subsequently, a second round of PCR was carried out using each of the inner primers and probes (Table 2) in individual reactions.
ALX4
0.0034
0.57
APC
9.67 × 10
−6
0.65
BMP3
2.64 × 10
−6
0.64
BNC1
5.02 × 10
−7
0.65
ESR1
0.0095
0.58
HIC1
0.0097
0.55
MESTv2
0.0004
0.62
NPTX2
4.34 × 10
−5
0.64
RARB
0.0348
0.57
RASSF1A
1.4 × 10
−6
0.65
SFRP1
0.0001
0.62
SFRP2
0.0197
0.57
SEPT9v2
0.0029
0.56
SST
8.69 × 10
−5
0.64
TFPI2
7.96 × 10
−5
0.60
TAC1
3.63 × 10
−5
0.64
VIM
*
WNT5a
0.0234
0.54
PENK
age65
1.14 × 10
−6
0.67
First round PCR amplification: In order to expand the amount of relevant deaminated DNA a first round PCR was conducted using a mix of outer primers (table 2) for all promoter regions investigated. The reaction buffer for each sample consisted of 25 μl containing; PCR stock, 13 μM MgCl2, 0.6 mM dNTP, 250 nM of each outer primer (table 2) 1.5 U Taq polymerase (Bioline), and 0.3 U UNG (Invitrogen). The first round reaction mix was distributed to individual 200 μI PCR tubes and incubated for 5 min at 37° C. (UNG activity), followed by 95° C. for 5 min. and cooled to room temperature. Twenty-five ul purified deamination product was added to each tube containing first round reaction mix. PCR was performed for 20 rounds: 92° C. for 15 sec., 55° C. for 30 sec., and 72° C. for30 sec.
Second round PCR: Ten μl mix containing 0.4 μM inner primers and probes (table 2) were distributed in 30 individual wells in a 96 well PCR plate. Ten μl first round PCR product were added to 710 μl reaction mix containing; PCR stock, 250 μM dNTP, 10 μM MgCl2, and 15 U Taq polymerase (Bioline). Twenty μl of the reaction mix were added to each of the 30 wells containing primers and probes. Real time PCR was carried out for 45 rounds of 94° C. for 15 sec., 55° C. for 30 sec. (anneal and detection), and 72° C. for 30 sec.
Gene panel: Twenty-eight genes (Table 3), which all have the potential to participate in development of pancreatic cancer, were selected for methylation analysis. Primer and probe design: “Beacon Designer” was used to design potential primers and probes for the selected genes. Primers were designed to be rich on CpG's and to be located up-stream of exon one which was interpreted as the promoter regions of the genes. The aim was to design PCR products with a length less than 140-150 base pairs, since the cell-free DNA fragments most likely have a length of 160 base pairs consistent with nucleosomal DNA size (table 2).
Outcome
The primary outcome of the prediction model was pancreatic adenocarcinoma.
Statistical Methods
Each gene in the gene panel was analyzed as a binary variable.
Validation of dichotomous data: We calculated differences between the threshold cycle (Ct) values of the hemimethylated reference gene MEST transcript variant 1 and Ct values of each gene for which Ct>0. To assess the amount of information lost in the dichotomization, histograms of the differences for the cancer group and control group 1 combined with control group 3 were produced. No clear indication of difference in the two distributions was observed. This was interpreted as an indication that no significant amount of information was lost by dichotomizing the genes as hypermethylated or non-methylated disregarding the observed Ct value.
The methylation frequency of each gene and the (exact) 95% confidence interval (CI) were calculated for each group. The mean number of hypermethylated genes in each group and the 95% CI was calculated. The means were compared as numerical data with nonparametric Wilcoxon rank sum test. P-values less than 0.05 were considered statistically significant.
Prediction Model Development
All data were analyzed using STATA 14.0 software.
Results
Ninety-five patients with pancreatic adenocarcinoma were included in the study (
The methylation frequency of each gene is presented in table 1. The mean number of methylated genes of the whole gene panel (28 genes) was 8.41 (95% CI 7.62-9.20) for the cancer group compared to 4.34 (95% CI 3.85-4.83) for patients with chronic pancreatitis (control group 1), 4.89 (95% CI 4.07-5.71) for patients screened for but not having pancreatic cancer (control group 3) and 5.34 (95% CI 4.76-5.91) for patients with acute pancreatitis (control group 2). The difference was highly statistically significant between the cancer group and the three benign control groups (Table 4).
Model development: In the following analyzes we chose to combine control group 1 and 3. The combined group has symptoms resembling those of pancreatic cancer, which makes a biomarker to distinguish these from pancreatic cancer of utmost relevance. For the rest of the analysis patients with acute pancreatitis were left out, since a clinical picture of acute inflammation is rarely seen in pancreatic cancer. There was a highly significant difference (p<0.001) between the cancer group and control group 1+3 in hypermethylation frequency of ten genes (APC, BMP3, BNC1, MESTv2, NPTX2, RASSF1A, SFRP1, SST, TFPI2, and TAC1) (Table 3) and significant difference (p<0.05) in seven genes (ALX4, ESR1, HIC1, RARB, SFRP2, SEPT9v2, and WNT5A) (Table 1). VIM and PENK could not be evaluated by logistic regression as none of the patients in the control group had hypermethylation of these two genes (Table 3 and table 4). There was no significant difference in gender, why this variable was excluded from the following analysis. Smoking however, was a preventive factor for cancer when comparing patients with pancreatic cancer and patients with chronic pancreatitis. Smoking was therefore excluded from the model, since it is a known risk factor for cancer. By dividing the patients into groups according to age; >65 year, <=65 year, a statistically significant difference was found. Consequently, age>65 was included in the multivariable logistic regression analysis.
All genes with an individual p-value below 0.20 (20 genes out of 28 examined genes) and age>65, were included in multivariable logistic regression model. Backward stepwise selection was performed (
Forty patients had stage I or II tumors. Model 13 had an apparent AUC of 0.86 (95% CI 0.79-0.92) for Stage I/II tumors (probability cut point of 0.50; sensitivity 72.50% and specificity 83.06%) (
Discussion
We examined cell-free DNA promoter hypermethylation of 28 genes in plasma of patients with pancreatic cancer (n=95) and compared it to three different control groups. The gene-panel was composed of genes previously tested in relation to pancreatic cancer (BNC1, NPTX2, PENK, CDKN2A, RASSF1A, SFRP1 (SARP2), APC, BRCA1, CDKN2B, ESR1, MGMT, MLH1, RARB) and genes which have not earlier been examined in plasma from patients with pancreatic cancer (ALX4, BMP3, CHFR, EYA2, GSTP1, HIC1, SFRP2, MESTv2, NEUROG1, SEPT9v2, SST, TFPI2, TAC1, VIM, WNT5a). This is the first study to examine cell-free DNA hypermethylation in a wide selection of genes by methylation specific PCR in a large group of patients with either benign or malignant pancreatic disease. Statistically significant difference in the hypermethylation status in 19 out of the 28 genes was found when comparing pancreatic cancer patients and a control group containing patients screened for, but not having pancreatic cancer as well as in patients with chronic pancreatitis. Cell-free DNA hypermethylation of BMP3, MESTv2, SST, TFPI2, TAC1, ALX4, HIC1, SFRP2, SEPT9v2 and WNT5A is not previously described in the literature in relation to pancreatic cancer. Yi et al. described BNC1 hypermethylation to have a sensitivity of 79% and a specificity of 89% when comparing pancreatic cancer and healthy individuals. Park et al. examined hypermethylation of a small gene-panel (NPTX2, RASSF1A, SFRP1, UCHL1, PENK and p16 (CDKN2A)) by methylation specific PCR. The gene-panel could differentiate pancreatic cancer from healthy controls, however it was not able to discriminate benign and malignant pancreatic disease.
The example shows that patients with pancreatic cancer have a higher level of hypermethylated genes in plasma derived cell-free DNA compared to relevant control groups. In consistence with previous studies, our gene panel did not demonstrate a single gene which had the potential of being used as a diagnostic marker for pancreatic cancer. This suggests that a larger gene panel is needed to achieve sufficient accuracy. We developed a prediction model (age>65, BMP3, RASSF1A, BNC1, MESTv2, TFPI2, APC, SFRP1, and SFRP2) which was able to differentiate between pancreatic cancer and a large relevant control group consisting of patients with chronic pancreatitis or patients referred to the hospital with symptoms of pancreatic cancer. The AUC was high and the predictive value of our model is superior to the predictive value of CA-19-9, in particular keeping in mind that CA-19-9 is highly dependent on the Lewis blood group status of the patients. Only Lea+b− or Lea−b+ individuals are able to express CA-19-9, and not Lea+b− individuals which represent 5-10% of the caucasian population. CA-19-9 could in a recent study differentiate patients with stage I-II pancreatic cancer from patients with chronic pancreatitis with an AUC of 0.77 (sensitivity of 53% and a specificity of 91.9%) and pancreatic cancer patients from patients with benign biliary obstruction with an AUC of only 0.65.6 Our study included patients with stage I-IV pancreatic cancer. It is most important to diagnose patients with stage I and II disease as early detection at this stage of disease have the potential to improve outcome of surgery. We tested our model on stage I and II disease and found an AUC of 0.86. This shows that the performance of the prediction model is independent of cancer stage. Alteration in DNA hypermethylation is detectable in plasma even in the early stage disease and thereby potential usable as early diagnostic marker.
In order to further distinguish DNA hypermethylation related to malign and benign pancreatic disease, patients with acute pancreatitis were included. The aim was achieve more basic knowledge about hypermethylated DNA during the cause of an acute pancreatic inflammatory reaction, which not earlier have been described in literature. Our study shows that DNA hypermethylation takes place during acute pancreatitis, however the changes are more pronounced in patient with pancreatic cancer.
The data provided herein has several strengths. We tested cell-free DNA hypermethylation of a broad gene panel in plasma from a large group of patients with pancreatic cancer all included prospectively and consecutively, before the diagnosis and before any treatment. We include a large relevant control group of patients with either benign pancreatic disease or symptoms mimicking pancreatic cancer. We used an optimized method of methylations specific PCR, with an improved sensitivity compared to previous methods. However, the study also has some limitations. In the end of the analyzes we discovered that the use of UNG (Invitrogen) had a tendency to lower the sensitivity compared to the use of COD UNG (ArcticZymes). All our samples are analyzed using UNG (Invitrogen), since it was not possible to repeat all analyzes with COD UNG (ArcticZymes) due to lack of sample material. Furthermore, patients are not matched according to age, which one should be aware of because epigenetic changes can be a part of aging. To address this problem we incorporated age in our prediction model.
Conclusion
This example shows statistically significant differences in hypermethylation of several genes between malignant and benign pancreatic disease. Pancreatic cancer patients have a higher number of hypermethylated genes compared to patients with benign pancreatic disease. Based on the data provided in this example, alterations in cell-free DNA hypermethylation can be applied as blood based biomarkers for pancreatic cancer.
Methods
Study Design
This study was conducted as a cross sectional observational study of plasma derived cell-free DNA hypermethylation from patients with pancreatic cancer, who were admitted to the Department of Gastrointestinal Surgery, Aalborg University Hospital from February 2008 until February 2011.(21) All participants gave written informed consent. The study was registered in ClinicalTrails.gov: NCT02079363 and approved by the Research Ethics Committee for the North Denmark Region (N-2013037).
Participants
Patients with suspected or biopsy-verified upper gastrointestinal cancer were included prospectively and consecutively. Blood samples were drawn on admission before the diagnostic work-up and before any kind of treatment. Only patients with pancreatic adenocarcinoma were included in this study (
Diagnosis and Stage Classification.
CT or PET scan of thorax and abdomen were performed in the diagnostic work up of all patients. Histopathological analysis of biopsy specimens obtained by either endoscopic or laparoscopic ultrasound confirmed the cancer diagnosis. Patients were staged according to AJCC TNM classification 7th. T and N categories were determined by histopathological analysis for patients who underwent intended curative surgery. If surgery was not performed, the final clinical decision determined the T and N categories. Cancer stage and treatment of all patients were discussed at a multidisciplinary team conference, where a final decision was made.
Blood Sampling and Analytical Method
Blood samples were obtained by skilled technicians. EDTA plasma for methylation analysis was centrifuged 20 min. (4000 rpm) at 4° C. and stored within two hours after sampling in a biobank at −80° C. until further analysis.
All methylation analyzes were performed by a single skilled laboratory scientist. Extraction and deamination of cell-free DNA was performed as described by our group. A first round PCR amplification was conducted in order to expand the amount of relevant deaminated DNA. A mix of outer primers for all the investigated promoter regions was used (table 2). Afterwards, a second round of PCR was carried out using each of the inner primers and probes in individual reactions (table 2).
The gene panel consisted of 28 genes (table 1), all having the potential to participate in the development of pancreatic cancer.
Outcome
The primary outcome of the prognostic prediction models were cancer stage according to AJCC staging. Models to differentiate (stage I, II, III vs IV), (stage I, II vs III, IV), (stage I, IIa vs IIb) and (stage I, II vs III) was developed.
Statistical Analysis Methods
Each gene in the gene panel was analyzed as binary variables. Validation of dichotomous data was described in our previous study.
Patients were divided into groups according to AJCC staging. The mean of number of hypermethylated genes and the (exact) 95% confidence interval (CI) was calculated for each group. The means were compared as numerical data with nonparametric Wilcoxon rank sum test. A p-value less than 0.05 was considered statistically significant.
Prognostic Prediction Model Development:
All data were analyzed using STATA 14.0 software.
Results
Ninety-five patients with pancreatic adenocarcinoma were included in the study (
The methylation frequencies of each gene in each cancer stage are listed in
Stage I, II, III vs IV
In the following analyzes patients with stage I, II or III (n=53) disease are pooled and compared to stage IV (n=42) disease to develop a model to differentiate between patients having pancreatic cancer with distant metastasis and pancreatic cancer patients without distant metastasis. There was significant difference between stage I, II and III and stage IV in hypermethylation frequency of seven genes (ALX4, BNC1, HIC1, Sept9v2, SST, TFPI2 and TAC1) (
0.0018
0.64
0.0333
0.59
0.0002
0.69
0.0006
0.68
*
*
0.0192
0.0031
0.0189
0.0009
0.0444
0.0124
0.0030
0.0807
*
Stage I, II vs III, IV
Patients with stage I or II (n=40) disease are combined and compared to stage III or IV (n=55) disease to develop a model to differentiate between patients with pancreatic cancer which are potentially resectable (stage I or II) and patients with unresectable pancreatic cancer (stage III or IV). There was statistical significant difference (p<0.05) between stage I or II and stage III or IV in hypermethylation frequency of four genes (ALX4, BNC1, Sept9v2, SST) (
Stage I, IIa vs IIb
It was not possible to differentiate pancreatic cancer patients with lymph node metastasis (stage IIb) from patients without lymph node metastasis (stage I or IIa) based on the hypermethylation profile.
Stage I, II vs III
The hypermethylation profile was not able to differentiate unresectable primary tumor (T4) from potentially resectable primary tumors (T1-T3).
Discussion
We examined cell-free DNA promoter hypermethylation of 28 genes in plasma of patients with pancreatic cancer and show that alteration in cell-free DNA hypermethylation is detectable in all cancer stages, which is consistent with previous studies. In our study patients with stage IV disease stands out by having significantly higher number of hypermethylated genes in cell-free DNA than stage I, II and III disease. This may be caused by distant metastasis resulting in larger amount of cell-free DNA and an accumulation of epigenetic changes during cancer development. The association between more advanced pancreatic cancer stage and higher number of hypermethylated genes in cell-free DNA, has not previously been described. Two small studies on cell-free DNA hypermethylation in pancreatic cancer were not able to show this association. Sato et al. demonstrated the presence of aberrant methylations in pancreatic tissue from early precursor lesions (pancreatic intraductal neoplasia (PanIN-1)), and found an increase in methylation prevalence from PanIN-1 to PanIN-3, suggesting DNA hypermethylations progressively increase during neoplastic progression. Our study is not able to demonstrate that detectable epigenetic alteration in cell-free DNA also accumulate from stage Ito stage III disease, which probably is due to insufficient power.
We found that cell-free DNA hypermethylation of seven genes (ALX4, BNC1, HIC1, SEPT9v2, SST, TFPI2, and TAC1) was significantly associated with pancreatic cancer with distant metastases. This has not previously been described. Zhao et al. found HIC1 hypermethylation to be significantly higher in pancreatic cancer tissue stage III and IV compared to stage I and II. Cell-free DNA hypermethylation of TFPI2 has also been associated with stage four colorectal cancer. In contrast to pancreatic cancer, hypermethylation of ALX4 and SEPT9v2 has been found at same frequency in all stages of colorectal cancer tissue. In addition, SEPT9v2 hypermethylation was detectable in cell-free DNA of colorectal cancer patients in all stages, suggesting SEPT9v2 not to be associated with distant metastases in colorectal cancer. A recent study on colorectal cancer described a panel of ten genes (including BNC1) which was more frequently hypermethylated in tissue from adenomas and early stage colorectal cancer compared to tissue from metastatic colorectal cancer. This suggests that hypermethylations not only accumulate, but also changes throughout cancer progression. In this study we developed two gene panels; a panel to distinguish stage I, II, III from IV and a panel to distinguish stage I, II from III and IV. The panels overlap with five genes out of eight, indicating that specific hypermethylations are of importance at different cancer stages. We previously analyzed the same 28 genes as a diagnostic marker for pancreatic cancer, and found an alternative gene panel to differentiate between patients with benign and malign pancreatic disease. Only BNC1 was recurring in the diagnostic and prognostic gene panels. This again supports the hypothesis that alteration in DNA hypermethylation are changing during the cause of pancreatic cancer.
We developed two prognostic markers, which are able to differentiate patients with pancreatic cancer according to staging. In particular, the gene panel to distinguish patients with distant metastasis (stage IV) from patients without distant metastasis (stage I, II and III), has a very high performance. To our knowledge, this is the first time prognostic prediction models based on alterations in cell-free DNA hypermethylation have been described regarding pancreatic cancer. The gene panels have the potential to be used as supplements to existing diagnostic tools in stage classification of patients with pancreatic cancer. The gene panels are blood based markers which are minimal invasive and thereby of great benefit to the patients. In addition, the analytical method is not a time consuming procedure, but can be performed in only two hours. Prognostic markers based on cell-free DNA hypermethylation are not depending on blood group status like CA-19-9,(9) which is a significant advantage. However, extern validation is needed before the prognostic markers can be applied in a clinical setting.
Some of the groups contained limited numbers of patients. This could result in lacking power, and hence, lack of difference in methylation profile between stage I, IIa vs IIb and stage I, II vs III. We discovered in the end of the analysis that the use of UNG (Invitrogen) had a tendency to lower the sensitivity compared to the use of COD UNG (ArcticZymes). All the samples in our study were analyzed using UNG (Invitrogen). Due to lack of sample material it was impossible to repeat the analysis with COD UNG (ArcticZymes). Still, plasma derived cell-free DNA hypermethylation of a broad gene panel was tested in a large group of patients all having pancreatic adenocarcinoma. Patients were included prospectively and consecutively. Blood samples were obtained before the diagnosis and any treatment. All patients had a systematic and comprehensive diagnostic work up to ensure the correct diagnosis and stage classification. Methylation specific PCR with an optimized method of bisulfite treatment was performed, which has an improved sensitivity compared to previous methods.
Conclusion
Our study shows detectable alterations in DNA hypermethylation of plasma derived cell-free DNA even in early stage pancreatic cancer. The hypermethylations accumulate and change during the neoplastic development and with aggravating cancer stage. With high performance, panels of genes are able to differentiate pancreatic cancer patients according to cancer stage. Based on our study, alterations in cell-free DNA hypermethylation have the potential of being blood based prognostic markers for pancreatic cancer and a supplement to existing clinical tools in stage classification.
| Number | Date | Country | Kind |
|---|---|---|---|
| EP16161073.8 | Mar 2016 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2017/056393 | 3/17/2017 | WO | 00 |
