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Non-invasive cancer early screening system based on cfDNA omics characteristics

A cancer and omics technology, applied in the fields of genomics, biochemical equipment and methods, combinatorial chemistry, etc., can solve the problems of easily missed areas of cancer gene copy number variation, not involving specific applications, and lack of comprehensive evaluation of patient cfDNA genomes, etc. Achieving the effect of avoiding omission, large heterogeneity, lower sequencing cost, and low cost

Active Publication Date: 2021-07-23
人科(北京)生物技术有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This patented technology allows for better understanding about how genetic material from different parts of DNA (called chromosomes) are used during tumorigenesis. It also helps identify which part(s) of these materials they belong to at diagnosis time rather than just their presence). By analyzing this data, we aimed towards developing methods that could help predict treatment outcomes based upon patient's response to therapy.

Problems solved by technology

Technologies described earlier have been developed into various methods called liquid biological probings (LM), including immunology techniques like Western Blotting analysis and flowcytography imaging. These advancements provide technical benefits over current diagnostic tools. However they still require significant effort and time before being able to accurately determine whether any given sample contains certain gene sequences associated with disease states.

Method used

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  • Non-invasive cancer early screening system based on cfDNA omics characteristics
  • Non-invasive cancer early screening system based on cfDNA omics characteristics
  • Non-invasive cancer early screening system based on cfDNA omics characteristics

Examples

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Effect test

Embodiment 1

[0067] A noninvasive early screening system for cancer based on cfDNA omics characteristics, including a cfDNA omics characteristic model and a machine learning training model, characterized in that the noninvasive screening method for cancer comprises the following steps:

[0068] S101. Establishing a cfDNA omics feature model;

[0069] S102, blood collection;

[0070] S103, extract cfDNA;

[0071] S104. Building and sequencing the extracted cfDNA;

[0072] S105. Extracting cfDNA omics features and using them for comparison.

[0073] Preferably, the establishment of the cfDNA omics feature model described in step S101 includes the following steps:

[0074] S201. Blood collection;

[0075] S202, extract cfDNA;

[0076] S203. Building and sequencing the extracted cfDNA;

[0077] S204, cfDNA omics feature extraction;

[0078] S205. Machine learning training model.

[0079] Preferably, the blood collection in step S102 and step S201 uses Streck blood collection tubes for ...

Embodiment 2

[0107] This embodiment is carried out on the basis of the above-mentioned embodiment 1, and the similarities with the above-mentioned embodiment 1 will not be repeated.

[0108] This embodiment introduces a method for cfDNA extraction, the specific steps are as follows:

[0109] S601, put the Streck tube in a centrifuge at 4°C, 2000rpm, centrifuge for 10min, and separate the plasma;

[0110] S602. Add 500ul proteinase K and 4ML ACL buffer to the 50ML centrifuge tube containing plasma, mix thoroughly with vortex for 30 seconds, and incubate in a 60°C water bath for 30 minutes;

[0111] S603. Use a vacuum pump to filter the incubated collection tube for 10 minutes, add 600ul of ACW1, 750ul of ACW2, and 750ul of 100% ethanol in order to wash away impurities;

[0112] S604, centrifuge at 12000rpm, centrifuge for 3min;

[0113] S605. Place the collection tube in a metal bath at 50°C for 10 minutes, volatilize ethanol, add 110ul AVE into the collection tube, and incubate at room t...

Embodiment 3

[0116] This embodiment is carried out on the basis of the above-mentioned embodiment 1, and the similarities with the above-mentioned embodiment 1 will not be repeated.

[0117] This embodiment introduces a method for feature extraction of cfDNA three-omics, including the following steps:

[0118] A.fragment pattern: First compare the cfDNA sequence file to the reference genome hg19, discard low-quality sequences and filter out repeated sequences from the resulting BAM file; then compare the low-coverage regions of the hg19 reference genome and the Duke black box The region is excluded; next, the hg19 autosome is divided into 504 contiguous and non-intersecting fragments, each fragment is 5mb in length; the number of cfDNA with a length greater than 150bp and the number of cfDNA with a length less than 150bp are counted in each fragment region ; Then use the LOESS regression method to correct the GC content of these cfDNA numbers, and then use the mean normalization method to ...

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Abstract

The invention relates to a non-invasive cancer early screening system based on cfDNA omics characteristics, the system comprises a cfDNA omics characteristic model and a machine learning training model, and comprises the following steps: establishing the cfDNA omics characteristic model; extracting cfDNA through blood collection; carrying out library building and sequencing on the extracted cfDNA; and extracting the omics characteristics of the cfDNA for comparison. According to the method, the characteristics of the cfDNA in the gastric cancer patients are comprehensively described by combining the length distribution characteristics of the cfDNA, the copy number variation density distribution characteristics and the peripheral openness characteristics of the cfDNA promoter in a cfDNA low-depth whole genome sequencing mode, and the early gastric cancer patients are accurately identified.

Description

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Claims

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Application Information

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Owner 人科(北京)生物技术有限公司
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