Patent Application
20210171617
The present invention relates to DNA methylation signatures in human DNA, particularly in the field of molecular diagnos.ics.
Cancer has become a major killer of humans. Early detection of cancer can significantly improve cure rates and reduce the horrific personal and financial cost to the patients their families and the health care system For example. Hepatocellular Carcinoma (HCC) is the fifth most common cancer world-wide (El-Serag, 2011). It is particularly prevalent in Asia, and its occurrence is highest in areas where hepatitis B is prevalent, indicating a possible causal relationship (Flores & Marrero, 2014). Follow up of high-risk populations such as chronic hepatitis patients and early diagnosis of transitions from chronic hepatitis to HCC wculd improve cure rates. The survival rate of hepatocellular carcinoma is currently extremely low because it is almost always diagnosed at the late stages. Liver cancer could be effectively treated with cure rates of >80% if diagnosed early. Advances in imaging have improved noninvasive detection of HCC (Tan, Low, & Thng, 2011; Valente et al., 2014). However, current diagnostic methods, which include imaging and immunoassays with single proteins such as alpha- fetoprotein often fail to diagnose HCC early (Flores & Marrero, 2014) These challenges are not limited to HCC but common to other cancers as well. For example, early detection of breast cancer and colorectal cancer could dramatically reduce morbidity and mortality and the cost to the public health system and insurance companies. Moreover, certain cancers such as pancreatic cancers are detected almost invariably late resulting in virtually certain mortality. Advances in imaging have improved early detection of Cancers, however high-resolution imaging such as MRI is expensive, requires highly trained personnel and is unavailable in many locations. It has not evolved yet to a method of screening of wide populations. To have an impact on reducing morbidity and mortality from cancer it is necessary to develop a noninvasive, robust but nevertheless low-cost method that could be used in wide geographic areas for routine screening of the population. The main challenge is that solid tumors hide in internal organs and evolve long before they exhibit clinical symptoms. It is however possible to obtain tumor material noninvasively.
It is widely established by now that tumor DNA is shed into the system and could be found in plasma (Warton & Samimi, 2015) and possibly other secreted body fluids such as urine and saliva, as well as feces. By measuring molecular characteristics of tumor DNA, it is possible to determine that the DNA found in body fluids originated in the tumor (Zhai et al., 2012). Although tumor cells develop mutations that could distinguish tumor DNA from normal cells DNA, the number of possible mutations is vast and common mutations don't occur in all tumors (Dominguez-Vigil, Moreno-Martinez, Wang, Roehrl, & Barrera-Saldana, 2018).
DNA methylation, a covalent modification of DNA, which is a primary mechanism of epigenetic regulation of genome function is ubiquitously altered in tumors (Aguirre-Ghiso, 2007; Baylin et al., 2001; Ehrlich, 2002; Issa et al., 1993). DNA methylation profiles of tumors are potentially robust tools for tumor classification, prognosis and prediction of response to chemotherapy (Stefansson et al., 2014). The major drawback for using tumor DNA methylation in early diagnosis is that it requires invasive procedures and anatomical visualization of the suspected tumor. Circulating tumor cells are a noninvasive source of tumor DNA and are used for measuring DNA methylation in tumor suppressor genes (Radpour et al., 2011). Hypomethylation of HCC DNA is detectable in patients' blood (Ramzy, Omran, Hamad, Shaker, & Abboud, 2011) and genome wide bisulfite sequencing was recently applied to detect hypomethylated DNA in plasma from HCC patients (Chan et al., 2013). However, this source is limited, particularly at early stages of cancer and the DNA methylation profiles are confounded by host DNA methylation profiles. Genome wide bisulfite sequencing is a relatively costly procedure and requires significant bioinformatics analysis which makes it unfeasible as a screening tool. The challenge is therefore to delineate a small number of CGs that could robustly differentiate tumor DNA from nontumor DNA and develop a high throughput low cost assay that will enable the screening of wide populations in broad and diverse geographic areas. More recently several groups have performed comparative analysis of genome wide DNA methylation maps of cancer and normal DNA and blood DNA (Zhai et al., 2012). However, the main challenge with these approaches is that they have not taken into account cell free DNA from other tissues that is found in blood at different levels that are unanticipated a priori. Contaminating DNA from another tissue that has a similar methylation profile as the cancer tissue could lead to false positives. In addition, past approaches have quantitatively compared DNA methylation in normal and cancer tissues. This quantitative difference is diluted when tumor DNA is mixed with different and unknown amounts of DNA from other untransformed tissues, which can cause false negatives. These deficiencies in current methods necessitate a different approach that is disclosed in the present inventive subject matter.
Further publications dealing with the use of systems and methods for detecting cancer are: Grigg G, Clark S. Sequencing 5-methylcytosine residues in genomic DNA. Bioessays. 1994 June; 16(6):431-6, 431; Zeschnigk M, Schmitz B, Dittrich B, Buiting K, Horsthemke B, Doerfler W. Imprinted segments in the human genome: different DNA methylation patterns in the Prader-Willi/Angelman syndrome region as determined by the genomic sequencing method. Hum Mol Genet. 1997 March; 6(3):387-95; Feil R, Charlton J, Bird A P, Walter J, Reik W. Methylation analysis on individual chromosomes: improved protocol for bisulphite genomic sequencing. Nucleic Acids Res. 1994 February 25; 22(4):695-6; Martin V, Ribieras S, Song-Wang X, Rio M C, Dante R. Genomic sequencing indicates a correlation between DNA hypomethylation in the 5′ region of the pS2 gene and its expression in human breast cancer cell lines. Gene. 1995 May 19; 157(1-2):261-4; WO 97 46705, WO 95 15373 and WO 45560.
Embodiments of the claimed subject matter show that cancer is associated with a set of “categorically” distinct DNA methylation signatures that are different from any normal tissue and blood cell DNA methylation profiles. These sites create a binary differentiation between cancer and other tissues, whereby these sites are only methylated in cancer and fully unmethylated in other cancers. Thus, it is possible using deep next generation sequencing to detect even a few molecules of cancer cells on the background of normal cell DNA profile of methylation. Embodiments of the inventive subject matter enable detection of cell free tumor DNA even on a high background of cell free DNA from other tissues and are thus particularly suitable for early detection of cancer using cell free (CF) DNA extracted from body fluids, for example saliva, plasma, urine, feces etc. Embodiments also allow for early detection of cancer in tissue smears such as pup smears as well as biopsies and needle biopsies. Previous analyses in the prior art only compared normal and cancer cells from the same tissue and blood and derived sites that are quantitatively different in their DNA methylation level (Xu et al., 2017). However, sites discovered by such prior art analyses can't detect CF tumor DNA when it is mixed with other tissue CF DNA (see
Embodiments demonstrate the utility detecting cancer of unknown samples using polygenic or categorical scores based on the DNA methylation measurement methods disclosed herein. The disclosed embodiments could be used by any person skilled in the art to detect cancer in body fluids, feces, urine and tissues of any cancer or diseased tissue using any method for methylation analysis that are available to those skilled in the art such as for example next generation bisulfite sequencing, Illumina Epic microarrays, capture sequencing, methylated DNA Immunoprecipitation (MeDIP) methylation specific PCR and any methylation measurements that becomes available.
Embodiments also disclose the potential for discovery of new “polygenic” categorical DNA methylation markers for other cancers and diseases using any method available to people skilled in the art for genome wide sequencing such as next generation bisulfite sequencing, MeDip sequencing, ion torrent sequencing, Epic microarrays etc. followed by binary-categorical differentiation (BCD) method of analysis for discovering specific and sensitive markers that will be used for noninvasive detection of disease Embodiments of the present inventive subject matter include:
In a first aspect, embodiments provide polygenic DNA methylation markers of cancer in cell free DNA in body fluids such as plasma for early detection of cancer, said polygenic DNA methylation markers set being derived using “binary-categorical differentiation (BCD) analysis” as disclosed herein on genome wide DNA methylation derived by mapping methods such as Illumina 450K or EPIC arrays, genome wide bisulfite sequencing, methylated DNA Immunoprecipitation (MeDIP) sequencing or hybridization with oligonucleotide arrays.
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below (or a short subset of this list such as the example listed below Table 1) for early detection of Liver cancer hepatocellular carcinoma (HCC) using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg02012576, cg03768777, cg24804544, cg05739190
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example shown below Table 2) for specifying the origin of the cancer as HCC and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for specificity:
cg14126493
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example listed below Table 3) for early detection of lung cancer using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg04223424, cg23141355
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example listed below Table 4) for specifying the origin of the cancer as lung cancer and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for spec:
cg05917732, cg25470077
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below (or a short subset of this list such as the example listed below Table 5) for early detection of prostate cancer as well as for specifying the origin of the cancer as prostate cancer and discriminating from other 16 common solid tumor cancers using plasma CF DNA or other body fluid CF DNA.
Subset for detect_spec:
cg14283569
[it is a subset of the 4 listed in the table above]
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example listed below Table 6) for early detection of breast cancer using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg13031251, cg09734791, cg09695735, cg03637878
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example listed below Table 7) for specifying the origin of the cancer as breast cancer and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for spec:
cg03113878, cg20180843
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example listed below Table 8) for early detection of colorectal cancer (CRC) as well as for specifying the origin of the cancer as colorectal cancer and discriminating from other 16 common solid tumor cancers using plasma CF DNA or other body fluid CF DNA.
Subset for detect-spec
cg09854653, cg01566242
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example listed below Table 9) for early detection of pancreatic cancer using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg25024074, cg15386964, cg16232979
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example listed below Table 10) for specifying the origin of the cancer as pancreatic cancer and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for spec:
cg01237565, cg08182975, cg20983577, cg25591377
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example listed below Table 11) for early detection of brain cancer (glioblastoma) as well as for specifying the origin of the cancer as brain cancer (glioblastoma) and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid CF DNA.
Subset for spec-detect
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example listed below Table 12) for early detection of stomach (gastric) cancer using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg05611779, cg09734791, cg15760257
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example shown below Table 13) for specifying the origin of the cancer as gastric cancer and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for spec:
cg05611779, cg19235339
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example shown below Table 14) for early detection of ovarian cancer using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg24339193, cg22694153, cg11252337, cg21210985
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example listed below Table 15) for specifying the origin of the cancer as ovarian cancer and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for spec:
cg07068768, cg19846609
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example shown below Table 16) for early detection of cervical cancer using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg00757182, cg01601746
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example listed below Table 17) for specifying the origin of the cancer as cervical cancer and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for spec:
cg07066594, cg09260640, cg12961842
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example listed below Table 18) for early detection of head and neck squamous cell carcinoma (HNSC) carcinoma using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg07900968, cg20334243, cg27420520
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example listed below Table 19) for specifying the origin of the cancer as head and neck squamous cell carcinoma (HNSC) and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for spec:
cg18006328, cg19287220
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or in a short subset of this list (such as the example listed below Table 20) for early detection of esophageal carcinoma using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg03280624, cg03735888, cg09734791, cg27420520
In one embodiment, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example listed below Table 21) for specifying the origin of the cancer as esophageal carcinoma and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for spec:
Cg09556952, cg12473285
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example listed below Table 22) for early detection of bladder cancer using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg04223424, cg10723962, cg25024074
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (as shown in the example listed below Table 23) for specifying the origin of the cancer as bladder cancer and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid DNA.
Subset for spec:
cg13544006
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as the example listed below Table 24) for early detection of renal (kidney) cancer for specifying the origin of the cancer as renal cancer and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid CF DNA.
Subset for detect spec:
cg08884571, cg00011225, cg00011225
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list (such as shown in the example listed below Table 25) for early detection of testicular cancer for specifying the origin of the cancer as testicular cancer and discriminating from other 10 common solid tumor cancers using plasma CF DNA or other body fluid CF DNA.
Subset for detect and spec:
cg14531093, cg25159927
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs in the list below or a short subset of this list such (as shown in the example listed below Table 26) for early detection of one of 13 most common solid tumors using plasma CF DNA or other body fluid CF DNA.
Subset for detect:
cg10723962, cg15759056, cg24427504, cg25024074
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs delineated by the BCD method on genome wide DNA methylation data as shown in Table 27 (or a short subset of this combination as shown below Table 27) for early detection of blood cancers such as AML, CLL, etc. using white blood cells, plasma CF DNA or other body fluid CF DNA.
Subset for detect-spec:
cg18658397, cg18780412, cg20439288, cg22828045, cg25375340
In other embodiments, the polygenic DNA methylation markers are a combination of CG IDs shown in the list below (or a short subset of this list shown in the example listed below Table 28) for early detection of Melanoma for specifying the origin of the cancer as melanoma and discriminating from other 16 common solid tumor cancers using plasma CF DNA or other body fluid CF DNA.
Subset for detect-spec:
cg15307891, cg18866529, cg27084903
In another aspect of the inventive subject matter, there is provided a kit and a process for detecting cancer, comprising means and reagents for detecting DNA methylation measurements of polygenic DNA methylation markers.
In one embodiment, a kit is provided for detecting hepatocellular carcinoma comprising means and reagents for DNA methylation measurements of the CG IDs of table 1 and 2.
In another embodiment, a kit is provided for detecting lung cancer comprising means and reagents for DNA methylation measurements of the CG IDs of table 3 and 4.
In another embodiment, a kit is provided for detecting prostate cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 5.
In another embodiment, a kit is provided for detecting breast cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 6 and 7.
In another embodiment, a kit is provided for detecting colorectal cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 8.
In another embodiment, a kit is provided for detecting pancreatic cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 9 and 10.
In yet another embodiment, a kit is provided for detecting brain cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 11.
In another embodiment, a kit is provided for detecting gastric cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 12 and 13.
In another embodiment, a kit is provided for detecting ovarian cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 14 and 15.
In another embodiment, a kit is provided for detecting cervical cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 16 and 17.
In another embodiment, a kit is provided for detecting head and neck squamous carcinoma (HNSC) comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 18 and 19.
In another embodiment, a kit is provided for detecting esophageal carcinoma comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 20 and 21.
In another embodiment, a kit is provided for detecting bladder cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 22 and 23.
In another embodiment, a kit is provided for detecting renal cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 24.
In another embodiment, a kit is provided for detecting testicular cancer comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 25
In other embodiments, a kit is provided for detecting one of 13 common cancers (bladder, brain, breast, cervical, colorectal, esophageal, HNSC, HCC (liver), lung, ovarian, pancreatic, prostate, stomach) comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 26.
In another embodiment, a kit is provided for detecting blood cancers such as AML and CLL comprising means and reagents for detecting DNA methylation measurements of the CG IDs detected by the BCD method that are specific for different subtypes of blood cancer Table 27
In another embodiment, a kit is provided for detecting melanoma comprising means and reagents for detecting DNA methylation measurements of the CG IDs of table 28.
In another embodiment, DNA pyrosequencing methylation assays are used for predicting HCC in body fluids such as plasma CF DNA by using CG IDs listed above, for example by using the below disclosed primers and standard conditions of pyrosequencing reactions:
The specificity of the tissue of origin of the cancer is determined by measuring the DNA methylation of the following CGID cg02012576 (HPX)
In another embodiment, a polygenic multiplexed amplicon bisulfite sequencing DNA methylation assay is used for predicting cancer in body fluids such as plasma CF DNA by using CG IDs listed above. For example, predicting prostate cancer using the below disclosed primers and standard conditions that involve bisulfite conversion, sequential amplification with target specific primers (PCR 1) followed by barcoding primers (PCR 2) and multiplexed sequencing in a single next generation MiSeq sequencer (Illumina), demultiplexing using Illumina software, data extraction and quantification of methylation using standard methods for methylation analysis such as Methylkit, followed by calculation of the weighted DNA methylation score and prediction of cancer from a small volume of body fluids such as plasma, saliva or urine.
The steps to detect prostate cancer the first PCR is performed as follows:
For CGID cg02879662
To test that the cancer specifically originates in the prostate the first PCR is performed as follows:
To barcode the samples, use a second PCR reaction with the following primers:
In other embodiments, Receiver operating characteristics (ROC) assays are used for detecting cancer by defining a threshold value between cancer and normal using weighted DNA methylation measurements of CG IDs. Samples above/below threshold will be classified as cancer. For example, CGIDs listed above for detecting HCC:
In another embodiment, hierarchical Clustering analysis assays are used for predicting cancer by using measurements of methylation of CG IDs listed above.
In another aspect of the inventive subject matter, methods for identifying DNA methylation markers for detecting cancer and other disease comprise the step of performing statistical analysis with the “binary-categorical differentiation (BCD)” method previously disclosed regarding DNA methylation measurements obtained from clinical samples.
In another embodiment, the method includes performing statistical analysis and the “binary-categorical differentiation (BCD)” method on DNA methylation measurements obtained from samples, with DNA methylation measurements obtained by performing Illumina Beadchip 450K or EPIC array of DNA extracted from at least one sample.
In another embodiment, the DNA methylation measurements are obtained by performing DNA pyrosequencing of DNA extracted from a sample followed by mass spectrometry based (Epityper™), PCR based methylation assays and targeted amplification of a region spanning the target CG IDs disclosed herein from bisulfite converted DNA followed by barcoding in a second set of amplification and indexed-multiplexed sequencing on an Illumina next generation sequencer.
In other embodiments, the statistical analysis includes Receiver operating characteristics (ROC) assays.
In other embodiments, the statistical analysis includes hierarchical clustering analysis assays.
As used herein, the term “CG” refers to a di-nucleotide sequence in DNA containing cytosine and guanosine bases. These di-nucleotide sequences could become methylated in human and other animals' DNA. The CG ID reveals its position in the human genome as defined by the Illumina 450K manifest (The annotation of the CGs listed herein is publicly available at https://bioconductor.org/packages/release/data/annotation/html/IlluminaHumanMethylation405_db.html and installed as an R package IlluminaHumanMethylation450k.db IlluminaHumanMethlation450k.db: Illumina Human Methylation 450 k annotation data. R package version 2.0.9.).
As used herein, the term “beta-value” refers to an estimation of methylation level at a CG ID position derived by normalization and quantification of Illumina 450K arrays using the ratio of intensities between methylated and unmethylated probes using the formula beta value=methylated C intensity/(methylated C intensity+unmethylated C intensity) between 0 and 1 with 0 being fully unmethylated and 1 being fully methylated.
As used herein, the term “penalized regression” refers to a statistical method aimed at identifying the smallest number of predictors required to predict an outcome out of a larger list of biomarkers as implemented for example in the R statistical package “penalized” as described in Goeman, J. J., L1 penalized estimation in the Cox proportional hazards model. Biometrical Journal 52(1), 70-84.
As used herein, the term “clustering” refers to the grouping of a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).
As used herein, the term “Hierarchical clustering” refers to a statistical method that builds a hierarchy of “clusters” based on how similar (close) or dissimilar (distant) are the clusters from each other as described for example in Kaufman, L.; Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis (1 ed.). New York: John Wiley. ISBN 0-471-87876-6.
As used herein, the term “Receiver operating characteristics (ROC) assay” refers to a statistical method that creates a graphical plot that illustrates the performance of a predictor. The true positive rate of prediction is plotted against the false positive rate at various threshold settings for the predictor (i.e. different % of methylation) as described for example in Hanley, James A.; McNeil, Barbara J. (1982). “The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve”. Radiology 143 (1): 29-36.
As used herein, the term “multivariate or polygenic linear regression” refers to a statistical method that estimates the relationship between multiple “independent variables” or “predictors” such as percentage of methylation in CG IDs, and a “dependent variable” such as cancer. This method determines the “weight” or coefficient of each CG IDs in predicting the “outcome” (dependent variable such as cancer) when several “independent variables” such as CG IDs are included in the model.
All illustrations of the drawings are for the purpose of describing selected embodiments and are not intended to limit the scope of the claimed subject matter.
Cell free DNA originating in tumors is known to be found in body fluids such as plasma, urine and in feces. It is also established that DNA methylation profiles of CF tumor DNA are similar to tumor DNA (Dominguez-Vigil et al., 2018). A vast body of data has established that tumor DNA is differentially methylated compared to normal tissues (Luczak & Jagodzinski, 2006). Therefore, many groups have tried to delineate by logistic regressions CGID positions in DNA (CG IDs in the Illumina 450K manifest) that are differentially methylated between cancerous and its normal tissue of origin for example, liver cancer versus adjacent liver tissue. However, since these methods measure quantitative differences between cancer and untransformed tissue rather than categorical qualitative differences, these quantitative differences between tumor and normal tissue would be diluted and erased by CF DNA from normal tissue, leading to false negatives and reduced sensitivity. In addition, other tissues that were not included in the analysis might have a DNA methylation profile similar to tumor DNA and since most studies only compare the tumor DNA to its untransformed counterparts and not to other tissues, this could lead to false positives. Varying and unpredictable quantities of DNA from different tissues have been detected in CF DNA (Breitbach et al., 2014) and thus the measured DNA methylation reflects a composite of unknown and unpredictable mixture of tissue DNA from different sources and tumor DNA. Thousands of tumor samples have been subjected to genome wide DNA methylation analysis using Illumina 450K arrays and are found in the public domain (TCGA). Examining the profiles of methylation of many normal tissues as well as cancer tissues, the inventors noticed that there is a significant group of CGs in the genome that are completely unmethylated in all normal tissues but methylated in DNA from tumors. A subset of these sites is unmethylated across numerous individuals whose DNA methylation was profiled in the public domain. The inventors also noticed that in many cancers these robustly unmethylated sites become methylated in cancer. Thus, creating a qualitative “categorical difference” between tumor DNA and all other DNA that might be found in blood. Using deep next generation sequencing even few methylated molecules could be easily identified on a background of completely unmethylated copies.
We used publicly available data bases of normalized beta values of methylation for ˜450,000 CG across the human genome from a large number of individuals deposited either in the Gene expression Omnibus (GEO) https://www.ncbi.nlm.gov/geo/ or The Cancer Genome Atlas TCGA https://cancergenome.nih.gov/ public data bases. We used the following databases to derive the list of robustly unmethylated CG IDs in many normal tissues and blood DNA: GSE50192, GSE50192, GSE40279.
DNA from white blood cells is one of the main sources of CF DNA in plasma. The inventors first generated a list of 47981 CGIDS that are unmethylated in all individuals in 17 different somatic human tissues using Illumina 450K data in GSE50192 and the logical COUNTIF and IF functions in Excel:
NmCGIDx=COUNTIF (betaCGIDxn1:ni,“>0.1”)
umCGIDx=IF(NmCGIDx=0, TRUE, FALSE)
NmCGIDx=number of normal subjects that have the CGIDx methylated.
umCGIDx=unmethylated CGIDx in all subjects
betaCGIDx=the methylation values for a given CGIDx
x=any CGID on the Illumina 450 k array
n1=the first subject in the array,
ni=the last subject in the array.
The inventors then generated a list of 68260 unmethylated CGIDs (UMCGIDs) in blood DNA from 312 individuals using the same criteria. The inventors then overlapped the list of 47981 and 68260 CG IDS and obtained a list of 33477 CG IDs that are unmethylated in both blood and somatic tissues across all individuals (
To identify DNA methylation positions that are categorically different between cancer and normal tissues the inventors examined whether any of these 28754 CG IDs are methylated in different cancers. The inventors noticed following examination of tumor DNA methylation data that methylation of a subset of these 28754 CG IDs is common in tumor DNA from individual patients. However, not all individuals have the same position methylated. Thus, a combination of CG IDs is required to detect cancer with high specificity. The inventors therefore discovered a polygenic combination of CG IDs for detection of cancers.
The inventors used 10 to 50 DNA methylation profiles from the public domain from either TCGA or GEO as a “discovery set” to discover a polygenic set of CGIDs whose methylation state is “categorically” different between tumor and normal tissues that could detect cancer with highest sensitivity and specificity. These CGIDs were then tested on hundreds of TCGA and GEO tumor DNA methylation array data as a “validation set” to validate the sensitivity and specificity of the polygenic DNA methylation markers for detecting cancer as disclosed in Embodiment 2.
The following publicly available data bases of normalized beta values of methylation for ˜450,000 CGs (CG IDs) across the human genome were used to derive the list of cancer specific DNA methylation markers:
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The following are the steps of the Binary Categorical Differentiation method (BCD) invented by the inventors to discover polygenic DNA methylation markers for early prediction of different cancers.
We filtered the 28,754 CGIDs that are robustly unmethylated in normal tissues.
For the discovery cohort we delineated within the list of 28,754 CG IDs that are robustly unmethylated in normal tissues, CGIDs that are categorically methylated in a particular cancer and are not methylated in unaffected tissue and normal tissues using the COUNTIF and IF functions in Microsoft excel.
NmcCGIDx=COUNTIF (betaCGIDxCancer n1:ni,“>0.2”)
NmnCGIDx=COUNTIF (betaCGIDxNormal n1:ni,“>0.1”)
DM CGIDx were sorted from highest to lowest number
Up to 20 top TRUE DM CGIDx positions were selected
NmcCGIDx=Number of cancer patients with methylated CGIDx
Nmn=number of normal adjacent or similar tissue samples with methylated CGIDx
betaCGIDx=level of methylation of CGIDx
n=patient from 1 to i
DM=differentially methylated CGIDx
The inventors noticed that testicular and kidney cancer exhibit pervasive lack of methylation at CG IDs that are highly methylated in all tissues. We therefore used a modification of the BCD method to discover categorically differentially methylated CG ID positions for testicular and kidney (renal) cancer which we term “BCDhypo”; categorically unmethylated in cancer and methylated in normal tissue. The following steps were used for discovery of Differentially hypomethylated CGID positions in testicular and kidney cancer.
For the discovery cohort we delineated hypomethylated CGIDs in testis or kidney that are fully methylated in normal tissues using the COUNTIF and IF functions in excel.
NucCGIDx=COUNTIF (betaCGIDxCancer ni:ni,“<0.2”)
NunCGIDx=COUNTIF (betaCGIDxNormal ni:ni,“<0.9”)
DHM CGID positions were sorted from highest to lowest number
20 top TRUE DHM sites were selected and subjected to penalized regression analysis
NucCGIDx=Number of cancer patients with unmethylated CGID X
NunCGIDx=number of normal tissue samples with unmethylated CGID X
n=patient from 1 to i
DHM=differentially hypomethylated CGID
The inventors then performed on the top 20 DM (or DHM) CGIDx a penalized regression using the package penalized in R to delineate the minimal combination of CGIDx that predicts cancer at highest sensitivity and specificity. The polygenic combination of CGIDx was further tested in a multivariate linear regression equation to determine the regression coefficient between methylation levels of these CGIDs in the polygenic combination and cancer. The model was used to calculate a methylation score for each patient for a typical cancer.
Ms=α+Σ
i=1
nβiCGi
Ms=methylation score, α=intercept, βi=coefficient for CG IDi, CGi=methylation level per CG in combination. 1 to i=number of CGs in combination.
The inventors used normalized Illumina 450K DNA methylation data from GSE61258 (normal liver) and 66 randomly selected samples from the TCGA HCC collection of HCC DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDx that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect HCC with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted HCC DNA methylation score detected HCC in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from GSE76269 for 227 HCC patients for the CGIDs in Table 1. Using this method, 95% of the HCC samples were detected as HCC (
The inventors used normalized Illumina 450K DNA methylation data for 10 people from GSE61258 (normal lung) and 10 randomly selected samples from the TCGA lung cancer collection of lung cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect lung cancer with high sensitivity and specificity (Samples included both adenocarcinoma and squamous cell carcinoma) in the training cohort (
The inventors then demonstrated that the weighted lung cancer DNA methylation score and threshold developed in embodiment 3 (detect) detects lung cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from GSE66836, GSE63704, GSE76269 and 919 lung cancer patients from TCGA. Using this method 96% of the lung cancer samples were detected as lung cancer (
The inventors used normalized Illumina 450K DNA methylation data for 5 people from GSE52955 (normal prostate) and 10 randomly selected samples from the TCGA prostate cancer collection of prostate cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect prostate cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted prostate cancer DNA methylation score and threshold developed in embodiment 3 (detect) detects prostate cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from GSE73549, GSE2955, and 430 prostate cancer patients from TCGA. Using this method 99% of the prostate cancer samples were detected as prostate cancer (
The inventors used normalized Illumina 450K DNA methylation data for 17 people from GSE60185(normal breast) and 10 randomly selected samples from the TCGA breast cancer collection of breast cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect breast cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted breast cancer DNA methylation score and threshold developed in embodiment 9 (detect) detects breast cancer in a “validation cohort” that included 891 breast cancer patients using normalized Illumina 450K DNA methylation beta values from GSE60185, GSE75067, and from TCGA. Using this method 91% of the breast cancer samples were detected as breast cancer (
The inventors used normalized Illumina 450K DNA methylation data for 25 people from GSE(32146) (normal) and 50 randomly selected samples from the TCGA colorectal cancer collection of colorectal cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect colorectal cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted colorectal cancer DNA methylation score and threshold developed in embodiment 11 (detect) detects colorectal cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from GSE69550 and 459 colorectal cancer patients from TCGA. Using this method 98% of the colorectal cancer samples were detected as colorectal cancer (
The inventors used normalized Illumina 450K DNA methylation data for 12 people from GSE53051 (normal) and 20 randomly selected samples from the TCGA collection of pancreatic cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect pancreatic cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted pancreatic cancer DNA methylation score and threshold developed in embodiment 13 (detect) detects pancreatic cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 891 pancreatic cancer patients from TCGA. Using this method 86% of the pancreatic cancer samples were detected as pancreatic cancer (
The inventors used normalized Illumina 450K DNA methylation data for 10 people from GSE65820 (normal) and 10 randomly selected samples from the TCGA collection of brain cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a set of binary categorical differentially methylated CGIDs that detect brain cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted brain cancer DNA methylation score and threshold developed in embodiment 15 (detect) detects brain cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 689 brain cancer patients from TCGA, 40 patients from GSE58298 and 136 patients from GSE36278. Using this method 91-97% of the brain cancer samples were detected as brain cancer (
The inventors used normalized Illumina 450K DNA methylation data for 18 people from GSE99553 (normal) and 10 randomly selected samples from the TCGA collection of stomach cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect stomach cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted stomach cancer DNA methylation score and threshold developed in embodiment 17 (detect) detects stomach cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 397 stomach cancer patients from TCGA. Using this method, 88% of the stomach cancer samples were detected as stomach cancer (
The inventors used normalized Illumina 450K DNA methylation data for 5 people from GSE65820 (normal) and 10 randomly selected samples from the TCGA collection of ovarian cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect ovarian cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted ovarian cancer DNA methylation score and threshold developed in embodiment 19 (detect) detects ovarian cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 114 ovarian cancer patients from TCGA. Using this method 86% of the ovarian cancer samples were detected as ovarian cancer (
The inventors used normalized Illumina 450K DNA methylation data for 20 people from GSE46306 (normal) and 10 randomly selected samples from the TCGA collection of cervix cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect cervical cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted cervical cancer DNA methylation score and threshold developed in embodiment 21 (detect) detects cervical cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 313 cervical cancer patients from TCGA. Using this method 91% of the cervical cancer samples were detected as cervical cancer (
The inventors used normalized Illumina 450K DNA methylation data for 10 people from GSE(52068) (normal) and 10 randomly selected samples from the TCGA cancer collection of HNSC DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect HNSC with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted HNSC DNA methylation score and threshold developed in embodiment 23 (detect) detects HNSC in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from GSE52068 and. Using this method 88%-96% of the HNSC samples were detected (
The inventors used normalized Illumina 450K DNA methylation data for 10 people from GSE(52068) (normal) and 10 randomly selected samples from the TCGA cancer collection of esophageal cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect esophageal cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted esophageal cancer DNA methylation score and threshold developed in embodiment 25 (detect) detects esophageal cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from GSE52068 and. Using this method 88%-96% of the esophageal cancer samples were detected (
The inventors used normalized Illumina 450K DNA methylation data for 5 people from GSE52955 (normal) and 10 randomly selected samples from the TCGA collection of bladder cancer DNA methylation data as a “training” cohort. The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detects bladder cancer with high sensitivity and specificity in the training cohort (
The inventors then demonstrated that the weighted bladder cancer DNA methylation score and threshold developed in embodiment 27 (detect) detects bladder cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 439 bladder cancer patients from TCGA. Using this method 96% of the bladder cancer samples were detected as bladder cancer (
The inventors used normalized Illumina 450K DNA methylation data for kidney (renal) cancer from 10 people from GSE52955 (normal) and 10 randomly selected samples per cancer from 13 cancers in the TCGA dataset as a “training” cohort as well as normal tissues and blood (GSE40279, GSE 52955). The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that are robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD hypo method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect kidney cancer with high sensitivity and specificity in the training cohort and are specific for kidney cancer against other cancers “Detect-Spec” (
The inventors demonstrated that the weighted kidney cancer DNA methylation score and threshold developed in embodiment 27 (“Detect-Spec”) detects kidney cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 871 kidney cancer patients from TCGA and differentiate kidney cancer from other cancers. Using this method 90% of the kidney cancer samples were detected as kidney cancer (
The inventors used normalized Illumina 450K DNA methylation data for testicular cancer from 10 people from GSE46306 (normal) and 10 randomly selected samples per cancer from 13 cancers in the TCGA dataset as a “training” cohort as well as normal tissues and blood (GSE40279, GSE 61496). The inventors first shortlisted in the “training cohort” dataset 28754 CGIDs that were discovered in embodiment 1 as sites that robustly unmethylated across normal tissues and blood samples. The inventors then used the BCD hypo method described in embodiment 2 to discover a polygenic set of binary categorical differentially methylated CGIDs that detect testicular cancer with high sensitivity and specificity in the training cohort and are specific for testicular cancer against other cancers “Detect-Spec” (
The inventors then demonstrated that the weighted testicular cancer DNA methylation score and threshold developed in embodiment 31 (“Detect-Spec”) detects testicular cancer in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 156 testicular cancer patients from TCGA and differentiate testicular cancer from other cancer. Using this method 96% of the testicular cancer samples were detected as testicular cancer (
The inventors used normalized Illumina 450K DNA methylation data for 10 randomly selected samples per cancer from 13 cancers (bladder cancer, brain cancer, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, HNSC, liver cancer, lung cancer, ovarian cancer, pancreatic cancer, prostate cancer, stomach cancer) in the TCGA dataset as a “training” cohort as well as normal tissues and blood from TCGA an GEO. The inventors then performed a penalized regression on the combined list of CGIDs for detection of 10 different cancer listed in tables x-y and shortlisted CGIDs that detect any of the 10 common cancers at high sensitivity and specificity (
The inventors then demonstrated that the weighted cancer DNA methylation score and threshold developed in embodiment 33 (“Detect”) detects 13 common cancers (bladder cancer, brain cancer, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, HNSC, liver cancer, lung cancer, ovarian cancer, pancreatic cancer, prostate cancer, stomach cancer) in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 3644 cancer patients from TCGA from other normal tissues. Using this method 90-95% of cancer samples were detected (
The inventors used normalized Illumina 450K DNA methylation data for 10 randomly selected melanoma samples and 220 samples from other cancers (bladder cancer, brain cancer, breast cancer, cervical cancer, colorectal cancer, esophageal cancer, HNSC, liver cancer, lung cancer, ovarian cancer, pancreatic cancer, prostate cancer, stomach cancer) and normal blood in the TCGA and GEO datasets as a “training” cohort. The inventors then performed a penalized regression on the combined list of CGIDs for detection of melanoma and shortlisted CGIDs that detect melanoma at high sensitivity and specificity (
The inventors then demonstrated that the weighted melanoma DNA methylation score and threshold developed in embodiment 35 (“Detect-spec”) detects melanoma in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 475 melanoma patients from TCGA from other cancer and normal tissues. Using this method 98% of melanoma samples were detected (
The inventors used normalized Illumina 450K DNA methylation data for 10 randomly selected AML samples and 10 normal blood samples in the GEO datasets as a “training” cohort. The inventors then performed a penalized regression on the combined list of CGIDs for detection of AML and shortlisted CGIDs that detect melanoma at high sensitivity and specificity (
The inventors then demonstrated that the weighted melanoma DNA methylation score and threshold developed in embodiment 37 (“Detect-spec”) detects AML in a “validation cohort” that included normalized Illumina 450K DNA methylation beta values from 79 AML patients from GEO and 140 patients from TGCA and normal blood. Using this method 100% of AIL samples were detected (
Blood was collected in 9-ml tubes containing K3-EDTA and processed within 1 h. Fresh blood samples were centrifuged at 1000 g for 10 min at 4° C. The supernatant was carefully transferred to a Falcon tube without disturbing the cellular layer and centrifuged again for 10 min for complete removal of any residual cells and frozen at −80 C. Plasma samples are thawed, and DNA is extracted by several available methods and commercially available kits for plasma DNA extraction such as Qiagen kit for plasma DNA or EZ DNA direct extraction method. DNA is purified using commercially available methods such as on AMPure XP magnetic beads and purified DNA is treated with sodium bisulfite using for example the EZ DNA bisulfite treatment kit. A library of targeted sequences is generated by two step PCR reactions (
The amplified fragments were fractionated on an agarose gel
To barcode the samples, we use a second PCR reaction with the following primers:
CGgTgACTggAgTTCAgACgTg3′
(bold bases are the index; 200 variations of this index are used. The second set of primers introduces the index for each patient as well as the reverse and forward sequencing primers. A multiplex PCR1 reaction for the three markers of prostate cancer HIF3A 232 bp, TPM4 213 bp, and CTTN 199 bp is shown on the right panel using varying primer concentrations as indicated in
The inventors demonstrate that embodiment 35 could be used for high throughput prediction of prostate cancer and other cancers using plasma samples from hundreds of patients at the same time. Indexed amplification of highly predictive CG IDs and a streamlined method for calculating a methylation score that is indicative of cancer could be used for early detection of prostate cancer and any other cancer.
Plasma DNA was extracted from plasma prepared from 40 healthy individuals and was subjected to targeted amplification with cancer specific primers for the following cancers: liver, prostate, lung (
PCR2 products are combined quantified and purified and subjected to next generation sequencing on a Miseq Illumina sequencer. Sequence is demultiplexed using Illumina software for indexed sequencing, FASTQ files are generated for each patient. A Perl text editing script https://www.activestate.com/activeperl/downloads is use to count Ts and Cs in the FASTQ files per patient per CG IDS and quantify the fraction of methylated Cs in a CG ID in a patient by dividing the number of C/C+T. (see scheme in
The applications of the inventive subject matter are in the field of molecular diagnostics and early prediction of cancer in general. Any person skilled in the art could use the present inventive subject matter to derive similar noninvasive biomarkers for early prediction of other cancers and other diseases that are accompanied by cell death and shedding of cell free DNA into the system such as neurological diseases, diabetes, heart disease such as cirrhosis and damage to heart tissue in cardiovascular disease. The present inventive subject matter provides a path to finding exquisite methylation markers of specific cell types and tissues using the BCD and BCDhypo method. Also disclosed are methods and biomarkers for early prediction of a wide range of cancers that could be used by anyone skilled in the art to detect cancer early and dramatically enhance survival rates and cure from cancer. The methods disclosed by the present invention could be used by anyone skilled in the art for routine yearly screening of healthy populations, to identify people who are starting to develop cancer and to treat them immediately and prevent the dire personal social and economic consequences of cancer mortality and morbidity, as well as for monitoring “high risk” people and monitoring response to therapy in patients undergoing treatment to detect recurrence or metastasis. Adoption of the present invention described here for routine health care management by health providers and health check-up facilities will have a vast impact on reducing the burden of cancer as well as health care costs.
The fact that the inventive subject matter includes a number of different dependent claims does not mean that one cannot use a combination of these claims for predicting cancer. The embodiments disclosed herein for measuring and statistically analyzing and predicting cancer should not be considered limiting. Various other modifications will be apparent to those skilled in the art to measure DNA methylation in cancer patients such as Illumina EPIC arrays, capture array sequencing, next generation sequencing, methylation specific PCR, epityper, restriction enzyme-based analyses and other methods found in the public domain. Similarly, there are numerous statistical methods in the public domain in addition to those listed here to use the inventive subject matter for prediction of cancer in patient samples.
Although the inventive subject matter has been explained in relation to its embodiments including one or more preferred embodiments, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the claimed subject matter.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IB2019/055855 | 7/9/2019 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62695429 | Jul 2018 | US |
