CN113736870A - Application of tumor mutation load detection reagent based on circulating tumor DNA in preparation of T cell lymphoma prognosis prediction kit - Google Patents
Application of tumor mutation load detection reagent based on circulating tumor DNA in preparation of T cell lymphoma prognosis prediction kit Download PDFInfo
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Abstract
The invention provides an application of a tumor mutation load detection reagent based on circulating tumor DNA in preparation of a kit for predicting T cell lymphoma prognosis. The inventor of the present invention found for the first time that the tumor mutation load (panel-TMB13) calculated from the mutation of 13 gene exons in the plasma circulating tumor DNA of a patient with T-cell lymphoma was correlated with the prognosis of the patient with T-cell lymphoma. When a non-synonymous mutation of greater than 2 exons was found in panel-TMB13, there was a greater likelihood of a poor prognosis for TCL patients. The invention also finds that the risk stratification constructed by combining the panel-TMB13 with the international prognostic index is better than the existing risk stratification based on the international prognostic index, can carry out more accurate risk stratification on TCL patients, and has important guiding significance for prognosis judgment and clinical treatment scheme formulation of the TCL patients.
Description
Technical Field
The invention belongs to the field of biomedicine, and particularly relates to an application of a tumor mutation load detection reagent based on circulating tumor DNA in preparation of a kit for predicting T cell lymphoma prognosis.
Background
T-cell lymphoma (TCL) originates from lymphoblasts or mature T cells and is a highly invasive and heterogeneous disease. TCL accounts for 10-15% of non-hodgkin's lymphomas and can be further subdivided into many subtypes. Current treatment regimens, such as chemotherapy, radiation therapy, targeted therapy, and the like, can improve the complete remission rate of TCL patients; however, most of them still have poor prognosis due to relapse and progression, partly due to the heterogeneity associated with TCL. This heterogeneity is manifested at the clinical level and genetically. However, current risk stratification of the International Prognostic Index (IPI) based on clinical data, including age, stage, physical performance status, serum Lactate Dehydrogenase (LDH) levels and extranodal involvement, does not accurately predict prognosis for all TCL patients. In fact, the detection of mutated genes can accurately stratify risk in patients with acute leukemia, but the importance of genetics in risk stratification of patients with TCL is not clear.
Because peripheral blood samples are more readily available clinically than in situ tumor tissue; thus, blood samples can be used as a substitute for in situ tissue to monitor the prognosis of cancer patients. In cancer patients, apoptotic and necrotic cancer cells release circulating tumor DNA (ctDNA) into the peripheral blood, which carries tumor-specific molecular changes. There is increasing evidence that ctDNA sequencing is a promising approach to monitor cancer progression and prognosis and has been demonstrated in a variety of solid tumors. Recently, researchers have investigated the importance of ctDNA in predicting lymphoma prognosis. The results indicate that the analysis of ctDNA and in situ tumor tissue DNA shows good consistency in NK/T-cell lymphoma (NKTCL), and ctDNA has more advantages in monitoring residual lesions and predicting patient prognosis using whole-genome sequencing (WES). Furthermore, rhoa (ras homolog family member a) mutations in plasma ctDNA were significantly associated with progression in patients with peripheral T-cell lymphoma (PTCL). Notably, our previous studies showed that in T Lymphoblastic Lymphoma (T-Cell Lymphoma, T-LBL) patients, a higher frequency of ctDNA positively correlates with higher LDH and IPI scores. However, due to the cost of Whole Exon Sequencing (WES) and its timeliness and informatics challenges in the clinical setting; therefore, it is difficult to clinically popularize. Instead, it is now more common clinically to apply panels containing a smaller number of genes to accurately risk stratify cancer patients.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provides the application of a tumor mutation load detection reagent based on non-synonymous mutation of a gene combination exon in circulating tumor DNA in the preparation of a kit for predicting T cell lymphoma prognosis. The tumor mutation load (panel-TMB13) calculated based on the mutation of 13 gene exons in plasma circulating tumor DNA of a T cell lymphoma patient is firstly found by the inventor to be related to the prognosis of the T cell lymphoma patient and can be used as an index for predicting the prognosis evaluation of the T cell lymphoma patient.
Another object of the present invention is to provide a kit for predicting the prognosis of T-cell lymphoma.
The purpose of the invention is realized by the following technical scheme:
the application of a tumor mutation load detection reagent based on non-synonymous mutation of gene combination exons in circulating tumor DNA in the preparation of a kit for predicting T cell lymphoma prognosis;
the gene combination comprises the following genes: TET2(Gene ID:54790), ATM (Gene ID:472), NF1(Gene ID:4763), JAK1(Gene ID:3716), BRAF (Gene ID:673), STAT5B (Gene ID:6777), AP3B1(Gene ID:8546), BCL6(Gene ID:604), MDM2(Gene ID:4193), STAT6(Gene ID:6778), CDKN2B (Gene ID:1030), FBXO11(Gene ID:80204), EPCAM (Gene ID: 4072).
In the application, the prognosis of T cell lymphoma is predicted by detecting the mutation condition of the exon in gene combination in plasma circulating tumor DNA of clinical patients and calculating the tumor mutation load according to the mutation condition of the exon in gene combination.
The prediction specifically refers to:
when the panel-TMB13 is larger than 2 exon non-synonymous mutations, the probability of poor overall survival of the T cell lymphoma patient is high;
when the panel-TMB13 is less than or equal to 2 exon nonsynonymous mutations, the probability of good survival of the T cell lymphoma patient is high;
③ when the panel-TMB13 is larger than 2 exon non-synonymous mutations, the probability of poor survival of T cell lymphoma patients without progression is higher;
(iv) when the panel-TMB13 is less than or equal to 2 exon non-synonymous mutations, there is a greater likelihood that a T cell lymphoma patient will survive without progression.
The overall survival rate is equal to 0% in 3 years.
The total survival rate is more than 51% in 3 years.
The poor progression-free survival means that the 3-year progression-free survival rate is equal to 0%.
The good progression-free survival means that the 3-year progression-free survival rate is more than 42%.
Use of a detection reagent for a first test agent in combination with a detection reagent for a second test agent in the preparation of a T-cell lymphoma risk stratification kit;
the first detector is a tumor mutation load based on non-synonymous mutation of gene combination exons in circulating tumor DNA; the gene combination comprises the following genes: TET2, ATM, NF1, JAK1, BRAF, STAT5B, AP3B1, BCL6, MDM2, STAT6, CDKN2B, FBXO11, EPCAM;
the second detector is an International Prognosis Index (IPI);
in the application, the method comprises the following steps:
s1, detecting the mutation condition of the gene combination exon in the circulating tumor DNA of a clinical patient, and calculating the tumor mutation load panel-TMB13 according to the mutation condition of the gene combination exon;
s2, establishing a histogram model by inputting panel-TMB13, survival state and survival time in R language software, and obtaining a score 1 based on panel-TMB 13;
s3, obtaining a score of 2 according to the international prognostic index;
s4, carrying out risk stratification on the clinical patients according to the score sum of the score 1 and the score 2;
the danger stratification specifically means that:
when the sum of the scores is more than or equal to 0 and less than or equal to 49, the test result is a low risk group;
when the sum of the scores is more than 49 and less than 176, the Chinese medicinal composition is a middle risk group;
③ when the sum of the scores is more than or equal to 176, the score is a high risk group;
the low risk means that the overall survival rate in 2 years is more than 57 percent, and the survival rate without development in 2 years is more than 48 percent;
the risk is that the overall survival rate in 2 years is more than or equal to 23 percent and less than or equal to 57 percent, and the survival rate without progress in 2 years is more than 15 percent and less than or equal to 48 percent.
The high risk means that the overall survival rate in 2 years is less than 23 percent, and the survival rate without progress in 2 years is less than 15 percent.
Further, the sample for detecting the tumor mutation load is peripheral blood of a clinical patient.
Further, the non-synonymous mutation is at least one of the following mutation conditions or a combination of the following mutation conditions: missense mutations, splice mutations, frameshift mutations, truncation mutations, non-frameshift deletion mutations, non-frameshift insertion mutations, copy number deletions.
Further, the kit comprises any one or at least two of a reagent for extracting plasma circulating tumor DNA and a reagent for sequencing exons.
The reagent for extracting the plasma circulating tumor DNA is preferably a reagent in a QIAamp circulating nucleic acid extraction kit of Qiagen.
Compared with the prior art, the invention has the following advantages and effects:
1. the invention adopts a sequencing method for extracting plasma DNA and exon to detect the exon mutation conditions of 13 genes TET2, ATM, NF1, JAK1, BRAF, STAT5B, AP3B1, BCL6, MDM2, STAT6, CDKN2B, FBXO11 and EPCAM in circulating tumor DNA in peripheral blood of a TCL patient, and the tumor mutation load (panel-TMB13) calculated by mutation of 13 genes in the plasma circulating tumor DNA is firstly found to be related to the clinical prognosis of the TCL patient. The panel-TMB13 has important guiding significance for the prognosis of TCL patients and the establishment of clinical treatment protocols.
2. The invention can provide more clinical prognosis research data for the application of targeted therapy of TCL patients, and has wide application prospect in predicting prognosis evaluation of TCL patients and targeted drug application.
3. The invention uses the sequencing method of extracting plasma DNA and exon to detect the exon mutation conditions of 13 genes TET2, ATM, NF1, JAK1, BRAF, STAT5B, AP3B1, BCL6, MDM2, STAT6, CDKN2B, FBXO11 and EPCAM in the peripheral blood circulating tumor DNA of TCL patients, and the method is simple and easy to implement and stable. When the panel-TMB13 is detected to be more than 2 exon non-synonymous mutations, the possibility of poor prognosis of TCL patients is high, and the panel-TMB13 can be used as an index for predicting prognosis evaluation of TCL patients.
4. The invention discovers that the risk stratification constructed by combining the tumor mutation load (panel-TMB13) calculated by the mutation of the 13 gene exons in the plasma circulating tumor DNA with the international prognostic index is better than the existing risk stratification based on the international prognostic index for the first time, and can carry out more accurate risk stratification on TCL patients.
Drawings
FIG. 1 is a graph of an analysis of the effect of panel-TMB13 on overall survival and progression-free survival of naive TCL patients; in this figure, the overall survival analysis chart is shown in (A), and the progression-free survival analysis chart is shown in (B).
FIG. 2 is a graph of prediction and visualization of 1-year, 2-year, and 3-year overall survival and progression-free survival for naive TCL patients using a panel plot model constructed using the panel-TMB13 in conjunction with the international prognostic index.
FIG. 3 is a comparison of risk stratification constructed by combining panel-TMB13 with the international prognostic index with existing risk stratification based on the international prognostic index; wherein, panels (A), (B) are graphs analyzing the effect of the combination of panel-TMB13 and the international prognostic index on the overall survival and progression-free survival of patients with incipient TCL, respectively; panels (C) and (D) are graphs analyzing the effect of the international prognostic index on the overall survival and progression-free survival of patients with primary TCL, respectively.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
The reagent information used in the examples is specifically as follows:
plasma circulating tumor DNA extraction kit (purchased from Qiagen);
exon sequencer (Illumina HiSeq 4000 sequencer from south kyo and gene).
Example 1
(1) Peripheral blood was collected with the patient signed an informed consent. 79 primary TCL patients in lymphomatology department of Guangdong province's Hospital were collected as peripheral blood samples, and all samples were anticoagulated with heparin from the time of admission, which was approved by the ethical Committee of the unit. Meanwhile, clinical data such as survival time and survival state of TCL patients are collected.
(2) Plasma circulating tumor DNA extraction
2.1 adding the blood sample into a 50mL centrifuge tube, centrifuging for 5-10 minutes at 3000g, and sucking 10mL plasma at the upper layer into a new 50mL centrifuge tube;
2.2 Add 1mL of QIAGEN protease K to the tube;
2.3 adding 8mL buffer ACL, and fully and uniformly mixing for 30 seconds;
2.4 incubation at 60 ℃ for 30 min;
2.5 adding 18mL buffer ACB into the lysate in the tube, and fully and uniformly mixing for 15-30 seconds;
2.6 incubate lysate-buffer ACB mixture in tube on ice for 5 min;
2.7 insert the QIAamp Mini column into the VacConnector on the QIAvac 24Plus, insert the 20mL tube extender into the open QIAamp Mini column;
2.8 add the lysate-buffer ACB mixture from step 2.6 to the tube expander of the QIAamp Mini column and turn on the vacuum pump; when all the lysate has passed completely through the column, the vacuum pump is turned off and the pressure is released to 0 mbar; carefully remove and discard the tube extender;
2.9 Add 600. mu.L of buffer ACW1 to a QIAamp Mini column; opening the cover of the column and turning on the vacuum pump; after all buffer ACW1 had passed through the QIAamp Mini column, the vacuum pump was turned off and the pressure was released to 0 mbar;
2.10 mu.L of buffer ACW2 was applied to a QIAamp Mini column, the lid of the column was opened and the vacuum pump was turned on; after all buffer ACW2 had passed through the QIAamp Mini column, the vacuum pump was turned off and the pressure was released to 0 mbar;
2.11 mu.L of ethanol (96-100 wt%) was applied to the QIAamp Mini column, the lid of the column was opened and the vacuum pump was turned on; after all the ethanol had passed through the spin column, the vacuum pump was turned off and the pressure was released to 0 mbar;
2.12 close the lid of the QIAamp Mini column; remove it from the vacuum manifold and discard the VacConnector; put the QIAamp Mini column into a clean 2mL collection tube and centrifuge at full speed (20,000g or 14,000rpm) for 3 minutes;
2.13 put QIAamp Mini Column into a new 2mL collection tube; the lid was opened and the assembly was incubated at 56 ℃ for 10min to allow the membrane to dry completely;
2.14 put the QIAamp Mini column into a clean 1.5mL elution tube and discard the 2mL collection tube in step 2.13; 20-150. mu.L of buffer AVE was added to the center of the QIAamp Mini membrane; cover and incubate for 3 minutes at room temperature;
2.15 spin at full speed (20,000g or 14,000rpm) in a microcentrifuge for 1 minute to elute the DNA.
(3) Exon sequencing and identification of gene mutations
Plasma DNA was sent to Nanjing and Gene company for exon sequencing and analysis on an Illumina HiSeq 4000 sequencer to determine the exon mutations of 13 genes TET2, ATM, NF1, JAK1, BRAF, STAT5B, AP3B1, BCL6, MDM2, STAT6, CDKN2B, FBXO11 and EPCAM.
The exon mutations of 13 genes TET2, ATM, NF1, JAK1, BRAF, STAT5B, AP3B1, BCL6, MDM2, STAT6, CDKN2B, FBXO11 and EPCAM are shown in Table 1, and the clinical prognosis data of TCL patients are shown in Table 2. Tumor mutation load was calculated from exon mutation of 13 genes.
The panel-TMB13 was analyzed in conjunction with TCL patient clinical prognosis data and survival curves were plotted in the R language software (version 4.0.2, https:// www.r-project. org /) using the "survivval" R package by entering the panel-TMB13, survival status and survival time. It was found that when the panel-TMB13 is greater than 2 exon non-synonymous mutations, there is a greater likelihood of overall poor survival in TCL patients; when the panel-TMB13 was less than or equal to 2 exon non-synonymous mutations, there was a greater likelihood of overall survival in TCL patients (FIG. 1 (A)). When the panel-TMB13 is greater than 2 exon non-synonymous mutations, there is a greater likelihood of poor progression-free survival in TCL patients; when the panel-TMB13 was less than or equal to 2 exon non-synonymous mutations, there was a greater likelihood that TCL patients survived without progression (FIG. 1 (B)).
The panel-TMB13 was analyzed in combination with TCL patient clinical prognosis data, and a histogram model was created in R language software (version 4.0.2, https:// www.r-project. org /) by inputting the panel-TMB13, survival status and survival time; clinical patients were also scored according to the international prognostic index. Among them, the scoring criteria of the international prognostic index are shown in table 3.
The panel-TMB13 and the International prognostic index jointly constructed nomogram model can individually and visually predict 1, 2 and 3 year overall survival and progression-free survival in patients with incipient TCL (FIG. 2). Notably, based on the scores in the histogram model, TCL patients were classified into low, medium and high risk groups, with less than or equal to 49 being classified as a low risk group, greater than 49 and less than 176 being classified as a medium risk group, and greater than or equal to 176 being classified as a high risk group (fig. 3). Overall survival in the medium-risk group was worse than that in the low-risk group (P ═ 0.009), and in the high-risk group was worse than that in the medium-risk group (P ═ 0.002) (fig. 3 (a)); meanwhile, the intermediate-risk group had poor progression-free survival than the low-risk group (P0.025), and the high-risk group had poor progression-free survival than the intermediate-risk group (P <0.001) (fig. 3 (B)). Although the international prognostic index 2-3 was worse than the overall survival of patients in the international prognostic index 0-1 group (P ═ 0.047), the international prognostic index 4-5 was not significantly different from the overall survival of patients in the international prognostic index 2-3 group (P ═ 0.069) (fig. 3 (C)). Meanwhile, the international prognostic index 2-3 was not significantly different from the progression-free survival of patients with the international prognostic index 0-1 group (P ═ 0.093), but the international prognostic index 4-5 was worse than the progression-free survival of patients with the international prognostic index 2-3 group (P ═ 0.022) (fig. 3 (D)). In general, the risk stratification constructed by combining the panel-TMB13 and the international prognostic index is better than the existing risk stratification based on the international prognostic index.
To determine whether gender, age, TCL subtype or treatment regimen affected the predictive effect of panel-TMB13 on overall survival OS and progression free survival FPS, we performed one-and multi-factor COX regression analyses. The results show that panel-TMB13 is an independent predictor of OS (HR 7.04, 95% CI: 2.64 to 18.79, P <0.001) and PFS (HR 4.15, 95% CI: 1.77 to 9.73, P0.001), and that the predictive effect of panel-TMB13 on OS and PFS is independent of gender, age, TCL subtype or treatment regimen.
The experimental results show that the panel-TMB13 has important significance in the evaluation of the prediction of the clinical prognosis of TCL, and provides research data for the clinical application of targeted drugs and the prediction of the prognosis.
TABLE 1 exon mutation profiles of TET2, ATM, NF1, JAK1, BRAF, STAT5B, AP3B1, BCL6, MDM2, STAT6, CDKN2B, FBXO11, and EPCAM
TABLE 2 clinical data of TCL patients
TABLE 3 International prognostic index Scoring criteria
| Index (I) | 0 point (min) | 1 minute (1) |
| Age (age) | Less than or equal to 60 years old | >60 years old |
| |
0 or 1 | 2，3，4 |
| Ann Arbor staging | I or II | III or IV |
| Lactate Dehydrogenase (LDH) | Is normal | Higher than normal |
| The number of affected sites of extranodal lesions | <2 parts of the body | More than or equal to 2 parts |
Although the gene mutation determined as described above cannot directly lead to future diagnosis and health status, it can be used as an intermediate result as one of reference information for the clinical treatment planning of patients.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. The application of the tumor mutation load detection reagent based on non-synonymous mutation of gene combination exons in circulating tumor DNA in the preparation of a kit for predicting T cell lymphoma prognosis is characterized in that:
the gene combination comprises the following genes: TET2, ATM, NF1, JAK1, BRAF, STAT5B, AP3B1, BCL6, MDM2, STAT6, CDKN2B, FBXO11, EPCAM.
2. Use according to claim 1, characterized in that:
in the application, the prognosis of T cell lymphoma is predicted by detecting the mutation condition of the exon in gene combination in plasma circulating tumor DNA of a clinical patient and calculating the tumor mutation load according to the mutation condition of the exon in gene combination;
the prediction specifically refers to:
when the panel-TMB13 is larger than 2 exon non-synonymous mutations, the probability of poor overall survival of the T cell lymphoma patient is high;
when the panel-TMB13 is less than or equal to 2 exon nonsynonymous mutations, the probability of good survival of the T cell lymphoma patient is high;
③ when the panel-TMB13 is larger than 2 exon non-synonymous mutations, the probability of poor survival of T cell lymphoma patients without progression is higher;
(iv) when the panel-TMB13 is less than or equal to 2 exon non-synonymous mutations, there is a greater likelihood that a T cell lymphoma patient will survive without progression.
3. Use according to claim 2, characterized in that:
the overall survival rate is equal to 0% in 3 years;
the total survival rate is more than 51% in 3 years;
the poor progression-free survival means that the 3-year progression-free survival rate is equal to 0 percent;
the good progression-free survival means that the 3-year progression-free survival rate is more than 42%.
4. Use of a detection reagent for a first test substance in combination with a detection reagent for a second test substance for the manufacture of a T-cell lymphoma risk stratification kit, characterized in that:
the first detector is a tumor mutation load based on non-synonymous mutation of gene combination exons in circulating tumor DNA; the gene combination comprises the following genes: TET2, ATM, NF1, JAK1, BRAF, STAT5B, AP3B1, BCL6, MDM2, STAT6, CDKN2B, FBXO11, EPCAM;
the second test object is an international prognostic index.
5. The use according to claim 4, wherein:
in the application, the method comprises the following steps:
s1, detecting the mutation condition of the gene combination exon in the circulating tumor DNA of a clinical patient, and calculating the tumor mutation load panel-TMB13 according to the mutation condition of the gene combination exon;
s2, establishing a histogram model by inputting panel-TMB13, survival state and survival time in R language software, and obtaining a score 1 based on panel-TMB 13;
s3, obtaining a score of 2 according to the international prognostic index;
s4, carrying out risk stratification on the clinical patients according to the score sum of the score 1 and the score 2;
the danger stratification specifically means that:
when the sum of the scores is more than or equal to 0 and less than or equal to 49, the test result is a low risk group;
when the sum of the scores is more than 49 and less than 176, the Chinese medicinal composition is a middle risk group;
and thirdly, when the sum of the scores is more than or equal to 176, the group is a high risk group.
6. Use according to claim 5, characterized in that:
the low risk means that the overall survival rate in 2 years is more than 57 percent, and the survival rate without development in 2 years is more than 48 percent;
the risk is that the overall survival rate in 2 years is more than or equal to 23 percent and less than or equal to 57 percent, and the survival rate without progress in 2 years is more than 15 percent and less than or equal to 48 percent;
the high risk means that the overall survival rate in 2 years is less than 23 percent, and the survival rate without progress in 2 years is less than 15 percent.
7. Use according to any one of claims 1 to 6, characterized in that:
the sample for detecting the tumor mutation load is peripheral blood of a clinical patient.
8. Use according to any one of claims 1 to 6, characterized in that:
the non-synonymous mutation is at least one of the following mutation conditions or a combination of the following mutation conditions: missense mutations, splice mutations, frameshift mutations, truncation mutations, non-frameshift deletion mutations, non-frameshift insertion mutations, copy number deletions.
9. Use according to any one of claims 1 to 6, characterized in that:
the kit comprises any one or at least two of a reagent for extracting plasma circulating tumor DNA and a reagent for sequencing exons.
10. Use according to claim 9, characterized in that:
the reagent for extracting the plasma circulating tumor DNA is a reagent in a QIAamp circulating nucleic acid extraction kit of Qiagen.
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