A cfDNA gene mutation scoring system to guide liver cancer treatment selection and the specific biomarkers used.

Abstract

This invention relates to the field of liver cancer biomarker technology. The invention provides a cfDNA gene mutation scoring system to guide liver cancer treatment selection, along with specific biomarkers used: the TP53 gene, AXIN1 gene, VCAN gene, and FBN3 gene. The biomarkers of this invention can classify early-stage liver cancer patients into low-micrometastasis risk subgroups and high-micrometastasis risk subgroups. By performing high-depth capture sequencing on peripheral blood cfDNA isolated preoperatively and detecting mutations in the four genes TP53, AXIN1, VCAN, and FBN3, early-stage liver cancer patients are differentiated into different risk subgroups. Different surgical margins are then selected for different subgroups during liver resection surgery. By using the biomarkers of this invention to classify the risk level of early-stage liver cancer, the selection of surgical margins can be further guided, improving the accuracy of surgical treatment selection for this type of liver cancer and achieving better survival prognosis.

Description

Technical Field

[0001] This invention relates to the field of liver cancer biomarker technology, and more particularly to a cfDNA gene mutation scoring system for guiding liver cancer treatment selection and the specific biomarkers used therein. Background Technology

[0002] With advancements in disease screening and diagnostic technologies, the detection rate of early-stage liver cancer has been gradually increasing worldwide. Early-stage liver cancer is currently the primary target for radical treatment; current radical treatment methods mainly include hepatectomy, radiofrequency / microwave ablation, and liver transplantation. Despite significant progress in liver cancer treatment techniques, the long-term survival rate of patients still needs further improvement.

[0003] Malignant tumors, including liver cancer, are primarily treated clinically according to disease staging. Accurate staging allows for more scientific and rational treatment decisions, effectively improving patient prognosis. In the absence of large vessel tumor thrombi and extrahepatic metastasis, based on tumor characteristics, the Barcelona Clinic of Liver Cancer (BCLC) staging defines a single tumor ≤2cm in diameter as very early-stage liver cancer (Stage 0); a single tumor >2cm in diameter, or multiple tumors ≤3 in number, each with a diameter ≤3cm, is defined as early-stage liver cancer (Stage A). The China National Health Commission's China staging system (CNLC) defines a single tumor ≤5cm in diameter (Ia), and a single tumor >5cm in diameter, or 2-3 tumors ≤3cm in diameter (Ib) as early-stage liver cancer. For early-stage liver cancer, liver resection and liver transplantation are the most radical treatment options. However, due to uncontrollable factors such as liver donor availability, liver transplantation is relatively limited, and liver resection is currently the most common choice for patients with early-stage liver cancer. However, even for early-stage liver cancer, the prognosis of liver resection varies considerably, with an overall postoperative recurrence rate between 43% and 70%, and a 5-year mortality rate due to tumor progression between 30% and 60%. The optimal treatment for early-stage liver cancer remains controversial.

[0004] In early-stage liver cancer, the choice of the appropriate resection margin (wider or narrower) is crucial. A wider margin may offer better radical resection, but could result in greater surgical trauma and poorer postoperative liver function recovery. Conversely, a narrower margin may ensure surgical safety and smoother recovery, but radical resection is less guaranteed, and the risk of local recurrence is higher. Current clinical staging systems for liver cancer are insufficient to guide and address these critical clinical issues.

[0005] Exploring biomarkers that can differentiate the invasiveness of early-stage liver cancer and using these biomarkers to classify the risk level of early-stage liver cancer can further guide the selection of surgical margins, improve the accuracy of surgical treatment selection for this type of liver cancer, and achieve better survival prognosis. Summary of the Invention

[0006] The purpose of this invention is to provide a cfDNA gene mutation scoring system and specific biomarkers for guiding the selection of liver cancer treatment. This system grades the risk level of early-stage liver cancer and further guides the selection of surgical margins, thereby improving the accuracy of surgical treatment selection for this type of liver cancer and achieving better survival prognosis.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solution: This invention provides a biomarker for detecting the risk of liver cancer, wherein the biomarker is the TP53 gene, AXIN1 gene, VCAN gene and FBN3 gene.

[0008] The present invention also provides the application of the aforementioned biomarker in the preparation of products for detecting the risk of early-stage liver cancer.

[0009] The present invention also provides the application of the aforementioned markers in the preparation of products for selecting resection margins during liver resection surgery.

[0010] The present invention also provides the application of the aforementioned biomarkers in the preparation of early liver cancer risk detection kits.

[0011] The present invention also provides a model for detecting the risk of liver cancer, wherein the model is: early liver cancer risk score = (1.257 × mutation frequency of TP53 gene) + (1.152 × mutation frequency of AXIN1 gene) + (1.194 × mutation frequency of VCAN gene) + (2.018 × mutation frequency of FBN3 gene).

[0012] This invention provides a cfDNA gene mutation scoring system to guide the selection of liver cancer treatment and the specific biomarkers used, namely the TP53, AXIN1, VCAN, and FBN3 genes. The biomarkers of this invention can classify early-stage liver cancer patients into low-micrometastasis risk subgroups and high-micrometastasis risk subgroups. By performing high-depth capture sequencing on peripheral blood cfDNA isolated preoperatively and detecting mutations in the four genes TP53, AXIN1, VCAN, and FBN3, early-stage liver cancer patients are differentiated into different risk subgroups, and different resection margins are selected for different subgroups during liver resection surgery. Because this score has a strong correlation with micrometastasis risk (MVI), it can effectively assess liver cancer risk and can be used to guide the precise selection of the following treatment methods: 1) selecting the resection margin or ablation range; 2) selecting the surgical procedure for liver resection (anatomical or non-anatomical); 3) selecting liver resection or minimally invasive ablation therapy; 4) selecting liver resection or liver transplantation; 5) determining whether neoadjuvant therapy is needed to improve efficacy. Attached Figure Description

[0013] Figure 1 This is a schematic diagram illustrating the clinical application of the present invention; Figure 2 The ROC curve for predicting MVI is used for scoring; Figure 3 A nomogram model for preoperative prediction of MVI; Figure 4 Results on the ability to predict the prognosis of patients with early-stage liver cancer. Detailed Implementation

[0014] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0015] Example 1: Construction process and predictive efficacy of cfDNA gene mutation scoring system

[0016] The applicant team collected peripheral blood samples from 286 patients with early-stage hepatocellular carcinoma (HCC), extracted cfDNA, and performed high-quality, high-depth NGS capture sequencing. Bioinformatics analysis of the sequencing data yielded 37 somatic mutation features and 524 hereditary mutation features. We analyzed the relationship between these features and MVI using a logistic regression model, ultimately identifying four mutation features highly associated with MVI. These four features are the four genes mentioned in the main text. Table 1 shows the logistic multivariate regression models established for these four features: Table 1

[0017] SE, standard error; CI, confidence interval; SNP, single nucleotide polymorphism.

[0018] By weighting each term using the β value obtained from the above Logistic multivariate regression model, the cfDNA gene mutation scoring system mentioned in the main text of this patent is obtained: Risk score = (1.257 × mutation frequency of TP53 gene) + (1.152 × mutation frequency of AXIN1 gene) + (1.194 × mutation frequency of VCAN gene) + (2.018 × mutation frequency of FBN3 gene).

[0019] Figure 2 The ROC curve for the MVI prediction based on this score was 0.758 for the modeling group and 0.723 for the validation group.

[0020] Because this score is highly correlated with MVI, it can effectively assess the risk of liver cancer and can be used to guide the precise selection of the following treatment methods: 1) selecting the resection margin or ablation range of the liver; 2) selecting the surgical procedure for liver resection (anatomical or non-anatomical liver resection); 3) selecting liver resection or minimally invasive ablation therapy; 4) selecting liver resection or liver transplantation; 5) determining whether neoadjuvant therapy is needed before surgery to improve efficacy.

[0021] Example 2: Method for detecting the risk of early-stage liver cancer

[0022] (1) Collect peripheral blood cfDNA isolated from patients before surgery for high-depth capture sequencing; (2) Obtain the mutation frequency of all mutation sites in the patient's TP53 gene, and take the maximum value as the mutation frequency of TP53, denoted as a (a is a value between 0 and 1, if a>1.5% is denoted as 1, ≤1.5% is denoted as 0). (3) Obtain the mutation frequency of all mutation sites in the patient's AXIN1 gene, and take the maximum value as the mutation frequency of AXIN1, denoted as b (b is a value between 0 and 1; if b > 0.5%, it is denoted as 1, and if ≤ 0.5%, it is denoted as 0). (4) Analyze whether the patient’s VCAN gene has a mutation “c.4323G>A, p.Gln1441Gln” at the 4323 base site and record it as c (if it exists, record it as 1, if it does not exist, record it as 0). (5) Analyze whether the patient’s FBN3 gene at the 5873 base site has the mutation “c.5873C>A, p.Pro1958His”, and record it as d (if it exists, record it as 1, if it does not exist, record it as 0). (6) Calculate using the following formula: Score = 1.257×a + 1.152×b + 1.194×c + 2.018×d; (7) If the score is less than or equal to 2 points, it is a low micro-transfer risk subgroup; if the score is greater than 2 points, it is a high micro-transfer risk subgroup.

[0023] In this invention, the probes used for high-depth capture sequencing of peripheral blood cfDNA are shown in Table 2.

[0024] Table 2 shows the design of the genomic DNA capture probes involved in this invention.

[0025] Note: The chromosome location in this table uses the human reference genome sequence hg19 / GRCh39.

[0026] For the low-risk subgroup of liver cancer cells, which are relatively quiescent and have weak invasive ability, the risk of tumor micrometastasis is low. The surgical efficacy of narrow-margin or wide-margin surgery is similar. Considering surgical safety and postoperative recovery, narrow-margin (<1cm) liver resection can be performed.

[0027] For the high-risk subgroup of patients with liver cancer cells, the cancer cells are highly invasive and may invade the surrounding normal liver tissue early on, forming micrometastases that are invisible to the naked eye. For this subgroup of patients, narrow-margin liver resection may result in residual tumor cells, leading to local recurrence and impacting patient survival. For this subgroup of patients, wide-margin (≥1cm) liver resection is recommended.

[0028] Example 3

[0029] Patient Zhang XX was admitted to the hospital due to "a mass in the right lobe of the liver discovered on ultrasound 9 days ago." Further enhanced CT scan of the liver showed a roundish abnormal signal mass in the right anterior lobe, measuring 5.3 × 4.5 cm. Significant enhancement was visible on the arterial phase scan, with marked decrease in the venous and delayed phases. Preoperative tests showed alpha-fetoprotein (AFP) 27621 ug / mL, total bilirubin 21.2 umol / L, albumin 40.5 g / L, alanine aminotransferase (ALT) 61 U / L, and prothrombin time 11.8 s. The patient had a history of hepatitis B for over 10 years without regular treatment. The hepatitis B panel showed "small three positives" (HBsAg, HBeAb, HBcAb positive), and HBV-DNA 583 IU / mL. The patient's physical condition and liver function were assessed as good (ECOGPS performance status score 0, Child-Pugh A liver function), with no distant organ metastasis. Hepatectomy was considered as the appropriate treatment. Preoperatively, 10 ml of peripheral blood was collected from the patient, and circulating cell-free DNA (cfDNA) was extracted and subjected to high-throughput capture sequencing (containing TP54, AXIN1, FNB3, and VCAN genes). The mutations were detected as shown in Table 3. Table 3

[0030] Based on calculations, those with a total score greater than 2 are assessed as belonging to the "high risk of micrometastasis" subgroup, and wide-margin hepatectomy is recommended.

[0031] Example 4: This cfDNA gene mutation score can be combined with common clinical high-risk factors for liver cancer to construct an early-stage liver cancer restaging system.

[0032] Common high-risk factors for early-stage liver cancer include tumor diameter, tumor number, alpha-fetoprotein (AFP) level, abnormal prothrombin level, microvascular invasion (MVI), tumor capsule integrity, tumor cell differentiation degree, surgical margin width, and whether R0 resection was performed. The applicant and their team selected five readily available preoperative risk factors: radiographic tumor diameter, radiographic tumor number, radiographic tumor capsule, AFP level, and abnormal prothrombin level. These, along with the cfDNA gene mutation score from this patent, were used to establish a preoperative MVI prediction nomogram model. Figure 3 : The sum of the scores for each item is the Nomogram score, and early-stage liver cancer is restaging according to the following rules: Early stage liver cancer, low-risk substage: <100 points Early-stage liver cancer, intermediate-risk substage: 100-133 points High-risk substage of early-stage liver cancer: >133 points Survival analysis of the 286 patients mentioned in Example 1 using this method revealed that restaging has a very good predictive ability for the prognosis of patients with early-stage liver cancer. See details below. Figure 4 .

[0033] In summary, the cfDNA mutation score mentioned in this invention can serve as a novel risk factor for liver cancer, possessing significant scientific research value and clinical application potential.

[0034] As can be seen from the above embodiments, the present invention provides a cfDNA gene mutation scoring system for guiding the selection of liver cancer treatment and specific biomarkers used, namely the TP53 gene, AXIN1 gene, VCAN gene, and FBN3 gene. The biomarkers of the present invention can classify early-stage liver cancer patients into low-micrometastasis risk subgroups and high-micrometastasis risk subgroups. By performing high-depth capture sequencing on peripheral blood cfDNA isolated preoperatively and detecting mutations in the four genes TP53, AXIN1, VCAN, and FBN3, early-stage liver cancer patients are distinguished into different risk subgroups, and different resection margins are selected for different subgroups during liver resection surgery. Because this score has a strong correlation with MVI (micrometastasis risk), it can effectively assess liver cancer risk and can be used to guide the precise selection of the following treatment methods: 1) selecting the resection margin or ablation range of the liver; 2) selecting the surgical procedure of liver resection (anatomical or non-anatomical liver resection); 3) selecting liver resection or minimally invasive ablation therapy; 4) selecting liver resection or liver transplantation; 5) determining whether neoadjuvant therapy is needed to improve efficacy.

[0035] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A biomarker for detecting liver cancer risk, characterized in that, The biomarkers are the TP53 gene, AXIN1 gene, VCAN gene, and FBN3 gene.

2. The use of the biomarker of claim 1 in the preparation of products for detecting the risk of early-stage liver cancer.

3. The use of the marker of claim 1 in the preparation of a product for selecting resection margins in liver resection surgery.

4. The use of the biomarker according to claim 1 in the preparation of an early liver cancer risk detection kit.

5. A prognostic model for detecting the risk of liver cancer, characterized in that, The prognostic model is as follows: Early liver cancer risk = (1.257 × mutation frequency of TP53 gene) + (1.152 × mutation frequency of AXIN1 gene) + (1.194 × mutation frequency of VCAN gene) + (2.018 × mutation frequency of FBN3 gene).

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