US 12,391,992 B2
Kit for early screening of liver cell cancer and preparation method and use thereof
Yuchen Jiao, Beijing (CN); Chunfeng Qu, Beijing (CN); Yuting Wang, Beijing (CN); Pei Wang, Beijing (CN); Kun Chen, Beijing (CN); Qianqian Song, Beijing (CN); Hui Liu, Beijing (CN); Sizhen Wang, Beijing (CN); and Hai Yan, Beijing (CN)
Assigned to GENETRON HEALTH (BEIJING) CO, LTD., Beijing (CN); and CANCER HOSPITAL, CHINESE ACADEMY OF MEDICAL SCIENCES, Beijing (CN)
Appl. No. 17/438,050
Filed by GENETRON HEALTH (BEIJING) CO, LTD., Beijing (CN); and CANCER HOSPITAL, CHINESE ACADEMY OF MEDICAL SCIENCES, Beijing (CN)
PCT Filed Sep. 17, 2019, PCT No. PCT/CN2019/106064
§ 371(c)(1), (2) Date Sep. 10, 2021,
PCT Pub. No. WO2020/181752, PCT Pub. Date Sep. 17, 2020.
Claims priority of application No. 201910179499.X (CN), filed on Mar. 11, 2019.
Prior Publication US 2022/0145399 A1, May 12, 2022
Int. Cl. C12Q 1/68 (2018.01); C12Q 1/6883 (2018.01); C12Q 1/6886 (2018.01); G01N 33/574 (2006.01); G16B 5/20 (2019.01); G16B 40/00 (2019.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01)
CPC C12Q 1/6886 (2013.01) [G01N 33/57438 (2013.01); G16B 5/20 (2019.02); G16B 40/00 (2019.02); G16H 50/30 (2018.01); C12Q 2600/156 (2013.01)] 7 Claims
 
1. A method for early screening for hepatocellular carcinoma in alpha-feto protein (AFP) negative subjects, comprising
obtaining characteristic scores of gene mutation characteristics, content of protein markers, cell-free (cfDNA) physical characteristics and clinical characteristics of the subjects;
wherein, the gene mutation characteristics comprise the following 13 gene mutation characteristics: a tumor protein p53 (TP53) gene non-R249S mutation, a telomerase reverse transcriptase (TERT) gene mutation, an axis inhibition protein 1 (AXIN1) gene mutation, a catenin beta 1 (CTNNB1) gene mutation, a TP53 R249S hot spot region mutation, a copy number variation (CNV) dimensionality reduction characteristic 1, a CNV dimensionality reduction characteristic 2, a CNV dimensionality reduction characteristic 3, a CNV dimensionality reduction characteristic 4, a CNV dimensionality reduction characteristic 5 and a CNV dimensionality reduction characteristics 6, hepatitis B virus (HBV) and TERT integrated variation, HBV and non-TERT integrated variation;
the protein markers comprise AFP and des-gamma-carboxy prothrombin (DCP);
the cfDNA physical characteristics comprise the following 5 cfDNA physical characteristics: interval percentage of cfDNA fragment length less than 90 bp, interval percentage of cfDNA fragment 90-140 bp, interval percentage of cfDNA fragment 141-200 bp, interval percentage of cfDNA fragment greater than 200 bp and the concentration of cfDNA;
the clinical characteristics comprise at least blood sample information including sex and age;
inputting the characteristic scores of the gene mutation characteristics, the content of the protein markers, the cfDNA physical characteristics and the clinical characteristics into a liver cancer prediction model, calculating a hepatocellular carcinoma screening score; and
comparing the screening score with a threshold value to determine whether the subject is a liver cancer patient;
wherein the hepatocellular carcinoma screening score and the threshold value are obtained through the liver cancer prediction model;
and wherein the method for constructing the prediction model of liver cancer includes:
constructing a training set, wherein the training set consists of a plurality of liver cancer patients and a plurality of patients at high risk of liver cancer;
taking gene mutation characteristics, the content of the protein markers, cfDNA physical characteristics and clinical characteristics of a training set as characteristics, converting detection results into characteristic scores, constructing a liver cancer prediction model by using a penalty logistic regression algorithm, and calculating a hepatocellular carcinoma screening score; and
obtaining a receiver operating characteristic (ROC) curve of sensitivity and specificity of the penalty logistic regression model according to the hepatocellular carcinoma screening score and the sample grouping information, and determining a cut-off value according to the ROC curve, wherein the cut-off value serves as a threshold value for distinguishing liver cancer patients from patients at high risk of liver cancer.