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Editor’s note iNature is China’s largest academic public account.
It is jointly created by a team of doctors from Tsinghua University, Harvard University, Chinese Academy of Sciences and other units.
The iNature talent public account is now launched, focusing on talent recruitment, academic progress, scientific research information, interested parties can Long press or scan the QR code below to follow us
.
Early detection of primary liver cancer (PLC) by iNature, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined HCC-ICC (cHCC-ICC), is vital to the survival of patients
.
This study aims to develop an accurate and affordable method for the early detection of PLC and the use of plasma cell-free DNA (cfDNA) fragment profiling to distinguish ICC from HCC
.
On December 25, 2021, Fudan University Zhou Jian, Fan Jia, Shi and Gene Shaoyang jointly published an online publication entitled "Ultra-Sensitive and Affordable Assay for Early Detection of Primary Liver Cancer Using Plasma cfDNA Fragmentomics" in Hepatology (IF=17) "’S research paper, the study used 192 PLC patients (159 HCC, 26 ICC, 7 cHCC-ICC) and 170 non-cancer controls (including 53 liver cirrhosis [LC] or hepatitis B virus [HBV ] Positive) plasma cfDNA samples were subjected to whole genome sequencing (WGS) and participated in the training cohort
.
The training queue was used to build an integrated stacking model for PLC detection
.
Model performance was evaluated in an independent test cohort (189 PLC patients [157 HCC, 26 ICC, 6 cHCC-ICC], 164 non-cancer controls [including 51 LC/HBV])
.
The model of this study showed excellent cancer detection performance in the test cohort (area under the curve [AUC]: 0.
995, 96.
8% sensitivity and 98.
8% specificity)
.
It shows excellent sensitivity in detecting early PLC (I: 95.
9%, II: 97.
9%), small tumors (<=3cm: 98.
2%) and HCC (96.
2%) or ICC (100%)
.
The AUC that distinguishes PLC from LC/HBV reaches 0.
985
.
Hopefully, the research model maintains consistent performance during downsampling, even with 1X coverage data (AUC: 0.
994, 93.
7% sensitivity, 98.
8% specificity)
.
A single model shows the potential to distinguish between ICC and HCC (AUC: 0.
776)
.
In summary, this research model surpassed previous reports at a lower cost by using only low-coverage WGS data, and showed excellent clinical potential in ultra-sensitive and affordable detection of PLC and its subtypes
.
Primary liver cancer (PLC) is the leading cause of cancer deaths worldwide, with more than 840,000 new cases and 780,000 related deaths detected each year
.
The prognosis of PLC is poor, with an estimated 5-year net survival rate of 19%, and its incidence is very high, and it is still increasing in many parts of the world
.
There are two main histological types of PLC, hepatocellular carcinoma (HCC, which accounts for approximately 75% of all liver cancers) and intrahepatic cholangiocarcinoma (ICC, which accounts for approximately 12-15% of all liver cancers)
.
The distinction between HCC and ICC is also important in clinical practice, because the treatment of these two diseases is very different
.
For disease management, early detection of PLC is essential for curative treatment and patient survival
.
In the past few decades, new imaging techniques and serological markers have been introduced to improve diagnostic capabilities
.
However, many techniques are not the best choice for PLC screening, and their applications are limited by different factors, such as lack of sufficient sensitivity or specificity and radiation exposure
.
For example, identifying PLC lesions, especially in the context of liver cirrhosis, is still challenging for radiological methods
.
Recently, research on cell-free DNA (cfDNA) provides a non-invasive method for the diagnosis of solid malignancies (such as PLC) in liquid biopsy
.
cfDNA represents the extracellular DNA fragments released into human body fluids (such as plasma) from apoptosis and necrosis, and therefore carries genetic and epigenetic information from cells and tissues of origin
.
Specific somatic mutations previously located in primary tumor tissues have been used as biomarkers to distinguish tumor and non-tumor cfDNA, but the use of somatic mutations is limited by their variability and low plasma concentrations
.
Alternatively, the different molecular features present on cfDNA (including DNA methylation patterns and tumor-associated copy number aberrations) are related to the pathobiology of cancer and have been shown to be viable biomarkers in HCC
.
Several groups also used cfDNA fragment omics characteristics, such as cfDNA fragment size and end motif genome-wide analysis, to detect cancer at an early stage
.
Although considerable progress has been made in the discovery of biomarkers, the lack of sensitivity has limited the application of new cfDNA signatures, especially in the early detection of PLC
.
The use of DNA methylation analysis showed that the sensitivity of HCC detection was 83%, the specificity was 90%, and the detection performance for patients with stage I and II diseases was further reduced; other groups used the whole genome 5-hydroxymethylcytosine profile Analysis showed that the sensitivity to early HCC was 83% and the specificity was 76% (AUC = 0.
88)
.
Using the 4 nucleotide sequences at the 5'end of cfDNA, Jiang et al.
distinguished HCC from non-HCC controls, but the detection AUC was as low as 0.
86
.
Finally, the model that integrates multiple cfDNA molecular features provides some inspiration for the use of HCC detection performance, showing 95% detection sensitivity with 97% specificity
.
However, this model requires two different NGS protocols, which greatly increases the cost and time of this method
.
Early detection of PLC still requires the latest fast, economical and accurate models
.
This research uses low-coverage WGS data to construct a fragmentomics-based PLC detection machine learning model, which overcomes the above shortcomings by providing an ultra-sensitive and affordable method
.
Even using extremely shallow WGS data with a coverage rate as low as 1X, this research method always shows improved predictive performance for detecting PLC, showing higher specificity and sensitivity than previous reports
.
In addition, the method of this study illustrates for the first time the potential of using cfDNA analysis to distinguish HCC from ICC
.
Reference message: https://aasldpubs.
onlinelibrary.
wiley.
com/doi/10.
1002/hep.
32308
It is jointly created by a team of doctors from Tsinghua University, Harvard University, Chinese Academy of Sciences and other units.
The iNature talent public account is now launched, focusing on talent recruitment, academic progress, scientific research information, interested parties can Long press or scan the QR code below to follow us
.
Early detection of primary liver cancer (PLC) by iNature, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined HCC-ICC (cHCC-ICC), is vital to the survival of patients
.
This study aims to develop an accurate and affordable method for the early detection of PLC and the use of plasma cell-free DNA (cfDNA) fragment profiling to distinguish ICC from HCC
.
On December 25, 2021, Fudan University Zhou Jian, Fan Jia, Shi and Gene Shaoyang jointly published an online publication entitled "Ultra-Sensitive and Affordable Assay for Early Detection of Primary Liver Cancer Using Plasma cfDNA Fragmentomics" in Hepatology (IF=17) "’S research paper, the study used 192 PLC patients (159 HCC, 26 ICC, 7 cHCC-ICC) and 170 non-cancer controls (including 53 liver cirrhosis [LC] or hepatitis B virus [HBV ] Positive) plasma cfDNA samples were subjected to whole genome sequencing (WGS) and participated in the training cohort
.
The training queue was used to build an integrated stacking model for PLC detection
.
Model performance was evaluated in an independent test cohort (189 PLC patients [157 HCC, 26 ICC, 6 cHCC-ICC], 164 non-cancer controls [including 51 LC/HBV])
.
The model of this study showed excellent cancer detection performance in the test cohort (area under the curve [AUC]: 0.
995, 96.
8% sensitivity and 98.
8% specificity)
.
It shows excellent sensitivity in detecting early PLC (I: 95.
9%, II: 97.
9%), small tumors (<=3cm: 98.
2%) and HCC (96.
2%) or ICC (100%)
.
The AUC that distinguishes PLC from LC/HBV reaches 0.
985
.
Hopefully, the research model maintains consistent performance during downsampling, even with 1X coverage data (AUC: 0.
994, 93.
7% sensitivity, 98.
8% specificity)
.
A single model shows the potential to distinguish between ICC and HCC (AUC: 0.
776)
.
In summary, this research model surpassed previous reports at a lower cost by using only low-coverage WGS data, and showed excellent clinical potential in ultra-sensitive and affordable detection of PLC and its subtypes
.
Primary liver cancer (PLC) is the leading cause of cancer deaths worldwide, with more than 840,000 new cases and 780,000 related deaths detected each year
.
The prognosis of PLC is poor, with an estimated 5-year net survival rate of 19%, and its incidence is very high, and it is still increasing in many parts of the world
.
There are two main histological types of PLC, hepatocellular carcinoma (HCC, which accounts for approximately 75% of all liver cancers) and intrahepatic cholangiocarcinoma (ICC, which accounts for approximately 12-15% of all liver cancers)
.
The distinction between HCC and ICC is also important in clinical practice, because the treatment of these two diseases is very different
.
For disease management, early detection of PLC is essential for curative treatment and patient survival
.
In the past few decades, new imaging techniques and serological markers have been introduced to improve diagnostic capabilities
.
However, many techniques are not the best choice for PLC screening, and their applications are limited by different factors, such as lack of sufficient sensitivity or specificity and radiation exposure
.
For example, identifying PLC lesions, especially in the context of liver cirrhosis, is still challenging for radiological methods
.
Recently, research on cell-free DNA (cfDNA) provides a non-invasive method for the diagnosis of solid malignancies (such as PLC) in liquid biopsy
.
cfDNA represents the extracellular DNA fragments released into human body fluids (such as plasma) from apoptosis and necrosis, and therefore carries genetic and epigenetic information from cells and tissues of origin
.
Specific somatic mutations previously located in primary tumor tissues have been used as biomarkers to distinguish tumor and non-tumor cfDNA, but the use of somatic mutations is limited by their variability and low plasma concentrations
.
Alternatively, the different molecular features present on cfDNA (including DNA methylation patterns and tumor-associated copy number aberrations) are related to the pathobiology of cancer and have been shown to be viable biomarkers in HCC
.
Several groups also used cfDNA fragment omics characteristics, such as cfDNA fragment size and end motif genome-wide analysis, to detect cancer at an early stage
.
Although considerable progress has been made in the discovery of biomarkers, the lack of sensitivity has limited the application of new cfDNA signatures, especially in the early detection of PLC
.
The use of DNA methylation analysis showed that the sensitivity of HCC detection was 83%, the specificity was 90%, and the detection performance for patients with stage I and II diseases was further reduced; other groups used the whole genome 5-hydroxymethylcytosine profile Analysis showed that the sensitivity to early HCC was 83% and the specificity was 76% (AUC = 0.
88)
.
Using the 4 nucleotide sequences at the 5'end of cfDNA, Jiang et al.
distinguished HCC from non-HCC controls, but the detection AUC was as low as 0.
86
.
Finally, the model that integrates multiple cfDNA molecular features provides some inspiration for the use of HCC detection performance, showing 95% detection sensitivity with 97% specificity
.
However, this model requires two different NGS protocols, which greatly increases the cost and time of this method
.
Early detection of PLC still requires the latest fast, economical and accurate models
.
This research uses low-coverage WGS data to construct a fragmentomics-based PLC detection machine learning model, which overcomes the above shortcomings by providing an ultra-sensitive and affordable method
.
Even using extremely shallow WGS data with a coverage rate as low as 1X, this research method always shows improved predictive performance for detecting PLC, showing higher specificity and sensitivity than previous reports
.
In addition, the method of this study illustrates for the first time the potential of using cfDNA analysis to distinguish HCC from ICC
.
Reference message: https://aasldpubs.
onlinelibrary.
wiley.
com/doi/10.
1002/hep.
32308
