DNA methylation markers for noninvasive detection of cancer and uses thereof

A methylation and cancer technology, applied in recombinant DNA technology, DNA/RNA fragments, chemical instruments and methods, etc., can solve problems such as false positives, false negatives, and cell-free DNA.

Pending Publication Date: 2021-01-15
HKG EPITHERAPEUTICS LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, a major challenge with these methods is that they do not take into account cell-free DNA from other tissues found in blood at varying levels that could not be predicted a priori
Contamination of DNA from another tissue with a similar methylation profile to cancerous tissue can lead to false positives
Additionally, past methods have quantitatively compared DNA methylation in normal and cancerous tissues
This quantitative difference is diluted when tumor DNA is mixed with different and unknown amounts of DNA from other non-transformed tissues, which can lead to false negatives

Method used

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  • DNA methylation markers for noninvasive detection of cancer and uses thereof
  • DNA methylation markers for noninvasive detection of cancer and uses thereof
  • DNA methylation markers for noninvasive detection of cancer and uses thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0307] Example 1. "Classically unmethylated CGIDs found in normal tissue and blood DNA across hundreds of individuals"

[0308]Cell-free DNA derived from tumors is known to be found in body fluids such as plasma, urine and feces. The DNA methylation profile of CF tumor DNA was also determined to be similar to that of tumor DNA (Dominguez-Vigil et al., 2018). A large body of data has established that tumor DNA is differentially methylated compared to normal tissue (Luczak and Jagodzinski, 2006). Consequently, many groups have attempted to use logistic regression to delineate the CGID locations in DNA (CG IDs in the Illumina 450K inventory) that are discriminatively between cancerous tissue and its normal tissue of origin (e.g., liver cancer versus adjacent liver tissue). Basicization. However, since these methods measure quantitative differences between cancer and non-transformed tissue rather than qualitative differences in classification, these quantitative differences betw...

Embodiment 2

[0323] Example 2: "Binary Classification of Differentiation (BCD)" method for the detection of cancer in cell-free DNA.

[0324] The following publicly available database of normalized beta values ​​for methylation of approximately 450,000 CG(ID)s across the human genome was used to derive a list of cancer-specific DNA methylation markers:

[0325] Table 29 Liver cancer

[0326]

[0327]

[0328] Table 30 Lung Cancer

[0329]

[0330]

[0331] Table 31 Prostate cancer

[0332] disease state source group N Detection / specificity control GSE5295 Find 5 detection PRAD TCGA Find 10 detection PRAD TCGA Find 10 specificity Non-PRAD TCGA Find 80 specificity PRAD GSE7354 verify 77 detection PRAD GSE5295 verify 25 detection PRAD TCGA verify 553 specificity bladder TCGA verify 439 specificity brain TCGA verify 689 specificity breast TCGA verif...

Embodiment 3

[0398] Example 3. Discovery of multigene DNA methylation markers in liver cancer (HCC).

[0399] 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. First, the inventors screened the candidate list in the "training cohort" dataset of 28754 CGIDx found in Example 1 to be robustly unmethylated sites across normal tissue and blood samples. The inventors then used the BCD approach described in Example 2 to discover a multi-gene group of binary taxonomically discriminative methylated CGIDs that detected HCC with high sensitivity and specificity in the training cohort ( Figure 5 B, Table 1) (assay). Cancer-specific weighted DNA methylation scores and thresholds were generated for CGID as described in Example 2. The inventors then generated a "training cohort" from 80 randomly selected DNA methylation samples from TCGA represen...

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Abstract

A "binary-categorical differentiation" method for finding a combination of a small number of exquisite DNA methylation positions in the human genome (CG IDs) for detecting cancer in DNA in biologicalmaterial derived from a patient and distinguish it from other tissue cell free DNA and blood cells DNA. Another method for detecting tissue of origin of tumor DNA uses a combination of unique DNA methylation positions in the human genome (CG IDs). Combinations of CG IDs derived from tumor DNA are disclosed for accurately detecting with cancers by measuring the DNA methylation of a combination of specific CG IDs and deriving a "methylation score". Kits for predicting cancer using CG IDs using multiplexed next generation sequencing methylation assays, pyrosequencing assays and methylation specific PCR from a small volume of plasma. Various methods using biological material help lead to prediction of cancer in persons with no other clinical evidence for cancer.

Description

technical field [0001] The present invention relates to DNA methylation signatures in human DNA, particularly in the field of molecular diagnostics. Background technique [0002] Cancer has become the main killer of human beings. Early detection of cancer can dramatically improve cure rates and reduce the dire personal and financial costs to patients, their families and the healthcare system. For example, hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide (El-Serag, 2011). It is particularly prevalent in Asia, and its incidence is highest in areas where hepatitis B is endemic, suggesting a possible causal relationship (Flores and Marrero, 2014). Follow-up of high-risk groups (such as patients with chronic hepatitis) and early diagnosis of transition from chronic hepatitis to HCC will improve the cure rate. Survival rates for hepatocellular carcinoma are currently extremely low because it is almost always diagnosed at an advanced stage. If diagnosed ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/6886C12Q1/6883C12N15/11
CPCC12Q2600/154C12Q1/6886A61K45/06A61K2039/505C07K16/18C07K2317/24C07K2317/34C07K2317/76C07K2317/92G01N33/57496
Inventor 戴维·车世瑞李慧黄志发
Owner HKG EPITHERAPEUTICS LTD
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