A method for constructing and analyzing cfDNA-TCR library based on liquid phase probe and application thereof

Abstract

The application provides a liquid-phase probe-based cfDNA-TCR library construction analysis method and application thereof, and belongs to the field of molecular immunology, and comprises the following steps: plasma cfDNA extraction, cfDNA UMI-UDI library construction, liquid-phase probe hybridization capture, enrichment of cfDNA library containing TCR genes, second-generation sequencing, off-machine data filtering and cleaning, merging of double-end sequencing data, alignment of sequencing data to a reference genome, removal of non-specific capture sequences, PCR deduplication according to UMI molecules, input of data into TRUST4 for contig assembly and annotation, and further immune repertoire analysis according to annotated CDR3 results. According to the method, TCR molecules in cfDNA can be more accurately detected by distinguishing PCR repeats and cloning repeats.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gene detection, and particularly relates to a cfDNA-TCR library construction analysis method based on a liquid phase probe and application thereof. BACKGROUND

[0002] TCR is a T cell antigen receptor, and is an important element for T cells to perform functions. In the process of individual development, T cells generate a large number of different TCRs through a random gene rearrangement mechanism to recognize a variety of antigens in nature. The TCR immune repertoire refers to the sum of the diversity of all TCRs in the body, and reflects the diversity of the T cell population in the body and the recognition ability of antigens. Research on the TCR immune repertoire helps to deeply understand the working principle of the immune system and the immune pathogenesis of diseases, and has made considerable progress in the field of tumors, such as predicting the efficacy of tumor immunotherapy by exploring the peripheral-tumor local infiltration, tumor immune microenvironment, the immune response level also shows the potential as a biomarker of disease occurrence or progression in part of the tumor, and has great potential in the transformation of TCR-T therapy.

[0003] The extensive application of TCR immune repertoire research promotes the development of personalized immunotherapy of tumors, but the current research mainly focuses on peripheral blood mononuclear cells (PBMC) and tumor tissues, and the research on circulating cell-free DNA (cfDNA) TCR is few, because about 79% of cfDNA released from previous white blood cells is regarded as noise, and a large amount of immune dynamic change information contained therein is ignored; and the mainstream method of TCR immune repertoire research is based on the principle of multiplex PCR, and the requirement of most multiplex reagents for template chain length limits its application on cfDNA samples. However, the TCR immune repertoire research on PBMC and tissue actually has limitations: first, the peripheral blood PBMC research will miss some local immune information that is not reflected in circulating blood cells, such as tissue resident T cells, but in fact, such T cells play a key role in anti-tumor immunity; when the tissue TCR is researched, the tumor reactive TCR information can be obtained more directly, but the tissue sample collection is more difficult, and the T cell information carrying tumor reactive TCR but losing activity may still be missed; the combined research application of PBMC and tissue is also very extensive, but due to the high antigen heterogeneity of tumors, the immune system needs to recruit more different T cell clones to recognize and target tumor antigens, thereby resulting in a large number and high diversity of PBMC-tissue shared TCR library, which will increase the difficulty of focusing on tumor antigen reactive TCR. At this time, the supplementary research from the perspective of cfDNA-TCR can reflect the circulating and tissue-derived T cells that are undergoing apoptosis and turnover, which can not only make up for the missing information, but also enable us to focus on some key immune dynamics with extremely high turnover rate.

[0004] Therefore, it is necessary to develop a method for library construction and data analysis of cfDNA-TCR. SUMMARY

[0005] To solve the above technical problems, the present application provides a method for constructing and analyzing cfDNA-TCR library based on liquid phase probe and application thereof.

[0006] The first object of the present application is to provide a liquid phase probe panel for hybridization capture of TCR alpha, beta, gamma and delta chains, wherein:

[0007] The liquid phase probe is designed according to the V and J gene sequences of human TCR genes in the international immunogenetics (IMGT) database; the IMGT data numbers of part of the genes are as follows:

[0008] AE000658 | TRAV1, AE000660 | TRAV25, X58768 | TRAV30, M95394 | TRAV38, AE000660 | TRAV8, X58804 | TRBV5, L36092 | TRBV6, X61444 | TRBV7, M22198 | TRDV1, X15207 | TRDV2, M23326 | TRDV3, M13431 | TRGV5, M13434 | TRGV8, X07205 | TRGV9, X07208 | TRGVA.

[0009] Further, there are 113 alleles of TRAV of TCR alpha chain, 147 alleles of TRBV of TCR beta chain, 19 alleles of TRGV of TCR gamma chain, and 21 alleles of TRDV of TCR delta chain, and 100bp probes are designed for the 5' and 3' ends of these TCR V genes.

[0010] Further, there are 71 alleles of TRAJ of TCR alpha chain, 16 alleles of TRBJ of TCR beta chain, 6 alleles of TRGJ of TCR gamma chain, and 4 alleles of TRDJ of TCR delta chain, and 100bp probes are designed for the 5' and 3' ends of these TCR J genes, and since some J genes are short (<200bp), some constant region TCR C region genes will be combined for 5' end probe design.

[0011] Further, a total of 843 probes are finally designed, including 433 probes for V genes and 410 probes for JC combined genes, and some sequences are shown in the sequence table SEQ ID NO. 1-NO. 39.

[0012] The second object of the application is to provide a next-generation sequencing library construction method based on the above probes, comprising the following steps:

[0013] (1) UMI-UDI library construction is performed on cfDNA fragments;

[0014] (2) cfDNA-TCR gene liquid probe capture enrichment is performed on the cfDNA library;

[0015] (3) Next-generation sequencing.

[0016] Further, in step (1), UMI molecules are introduced in the library construction, which allows PCR deduplication of the results in the subsequent analysis steps, thereby distinguishing PCR amplification repeats and clonal amplification repeats. UDI molecules are also introduced, thereby reducing read mismatches.

[0017] Further, in step (2), the above-mentioned capture probe is modified with biotin, and after the capture probe is hybridized with the target nucleic acid sequence overnight for 24 hours, affinity capture is performed with streptavidin-modified magnetic beads. The streptavidin magnetic beads are further incubated with the system after the hybridization reaction, and after washing, hot elution and room temperature elution, the streptavidin magnetic beads and the captured target DNA fragments are finally separated by high-temperature heating in a PCR instrument, and the target DNA fragments are amplified after about 10-13 rounds of capture.

[0018] Further, in step (3), the sequencing library obtained by the enrichment is applied to the Illumina platform for second-generation sequencing, and the amount of sequencing data is 6G per sample.

[0019] A third object of the present application is to provide a process for immunorepertoire analysis of the above-mentioned capture-enriched cfDNA-TCR sequencing data, comprising the following steps:

[0020] (1) Filtering and cleaning the original sequencing data and merging the double ends.

[0021] (2) Aligning the data to the reference genome hg38, and distinguishing poorly mapped reads and mapped reads by the alignment score MAPQ value.

[0022] (3) Dividing the mapped reads into TCR gene-mapped data (TCR-mapped reads) and non-TCR gene-mapped data (non-TCR mapped reads) by alignment annotation information, and removing the non-TCR gene-mapped data.

[0023] (4) Merging the TCR gene-mapped data and the poorly mapped data, and inputting them to the subsequent step to continue UMI deduplication.

[0024] (5) Inputting the deduplicated data According to into the TRUST4 tool for contig assembly and TCR gene annotation.

[0025] Further, in step (1), the data is subjected to quality control by the Trimmomatic tool, and the Fastqc tool is used to analyze the quality of the data According to , low-quality sequences are cleaned (phred score > 20), adapter sequences are removed, UMI sequences are extracted by the Picard and Figbio tools, and the R1 R2 reads generated by double-end sequencing are merged by overlap by the flash tool.

[0026] Further, in step (2), the data is aligned to the reference genome by BWA tool, and the well-aligned data and the poorly-aligned data are distinguished by the alignment score (MAPQ) value.

[0027] Further, in step (3), considering the fragmentation characteristics of cfDNA, the data aligned to the hg38 TCR gene (14q11.2, 7q34, 7p14) in the well-aligned data will be retained as TCR gene alignment data, which is conducive to the subsequent contig assembly process. The proportion of non-TCR gene alignment data will be used as the non-specific capture rate of the technology.

[0028] Further, in step (4), the data with the same UMI molecule and insert sequence is de-duplicated.

[0029] Further, in step (5), the de-duplicated data is input into the TRUST4 tool as single-end data.

[0030] A fourth object of the present application is to provide an application of the cfDNA-TCR library construction and analysis process in human colorectal cancer samples, wherein:

[0031] 30 cases of CRC patients who have undergone neoadjuvant therapy are first subjected to radiotherapy at Day 8, and then subjected to immunotherapy at week 2-7, and tissue and whole blood samples are collected at Day 0 (-RT / -ICI), Day 8 (+RT / -ICI), and week 8 (+RT / +ICI), respectively, wherein the tissue is subjected to single-cell TCR sequencing, the cfDNA sample is detected by the cfDNA-TCR library construction and analysis process described above, and is jointly analyzed with the paired tissue sample.

[0032] Advantages:

[0033] (1) The liquid-phase probe has no strict requirement for the template chain, so it can be compatible with shorter cfDNA, and the liquid-phase probe hybridization capture technology supports the construction of the four chains of TCR in the same reaction, which reduces the loss of cfDNA samples and makes up for the information that may be missed by the TCR-seq kit based on the principle of multiple PCR, which only constructs the library for the beta chain.

[0034] (2) The UMI molecule introduced in the library construction process supports de-duplication in subsequent analysis, thereby distinguishing between clonal repeats and PCR amplification repeats, which is a great advantage compared to the previous multiple PCR method: the conventional bulk-TCR-seq method based on multiple PCR lacks UMI molecules, and the frequency of clonal amplification cannot be accurately estimated due to PCR amplification bias, thereby causing bias in the resulting dominant clone results (see Figure 8 ).

[0035] (3) In cfDNA-TCR, not only can a certain number of overlaps be generated with paired tissue single-cell TCR sequencing results (cfDNA-tissue shared CDR3), but also part of the paired alpha beta chains of tissue-TCR can be captured, verifying the accuracy of cfDNA-TCR results (Appendix Figure 6 ).

[0036] (3) When compared with the commonly used immune repertoire analysis process (fastp+MIXCR), it is found that the experimental analysis process of the present patent can enrich more highly amplified cfDNA-tissue shared CDR3 (Appendix Figure 7 ).

[0037] (4) cfDNA-TCR molecules can respond to immune dynamic changes, and show a significant upward trend in the diversity of cfDNA-tissue shared CDR3 after immunotherapy (Appendix Figure 9 ).

[0038] (5) After cell type annotation of cfDNA-TCR, the cell type annotation results of tissue-TCR have a very high rank correlation after immunotherapy, indicating that cfDNA-TCR reflects the immune situation inside the tissue well (Appendix Figure 10 ). BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is a schematic diagram of the library construction analysis process of the present patent.

[0040] Figure 2 It is the library fragment quality inspection situation generated by using the process of the present patent.

[0041] Figure 3 It is a schematic diagram of distinguishing poor alignment data and good alignment data by MAPQ value in the analysis process of the present patent.

[0042] Figure 4 It is the intersection situation of poor alignment data and IMGT database in the analysis process of the present patent.

[0043] Figure 5 It is the proportion of poor alignment data, TCR gene alignment data and non-TCR gene alignment data (non-specific capture) respectively.

[0044] Figure 6 It is the shared situation of cfDNA-TCR and tissue-TCR obtained by the process of the present patent.

[0045] Figure 7 It is the clone ordering distribution situation of cfDNA-tissue shared CDR3 obtained by the analysis process of the present patent and MIXCR process.

[0046] Figure 8 Changes in clone ordering before and after UMI deduplication.

[0047] Figure 9 Changes in CDR3 diversity sharing between cfDNA and tissue in CRC patients at different treatment stages.

[0048] Figure 10 Correlation of cell type rank between cfDNA-TCR and tissue TCR obtained by the process of the application. DETAILED DESCRIPTION

[0049] The application will be described in greater detail with reference to the accompanying drawings and specific embodiments. However, the following examples are only intended to explain the application, and the scope of protection of the application should include the entire content of the claims. Furthermore, through the description of the following examples, those skilled in the art can fully implement the entire content of the claims of the application.

[0050] If not specifically indicated, the chemical reagents used in the examples are all conventional commercially available reagents, and the technical means used in the examples are conventional means well known to those skilled in the art. The sequencing device used in the examples of the application is Illumina NovaSeq 6000. In the sequencing step of the application, it is not limited to this sequencing device.

[0051] Example 1: Liquid probe design for detecting TCR in plasma cfDNA

[0052] The CDR3 region of TCR is the main region for TCR to bind to the antigen peptide-MHC complex (pMHC). Due to the randomness in the V(D)J recombination process, the length and amino acid sequence of this region are highly diverse, which ensures that the immune system can recognize a variety of antigens. Based on this, when conducting immune repertoire research, research on the CDR3 region is dominant. CDR3 is located at the junction of V(D)J genes. In order to capture this CDR3-containing fragment in cfDNA-TCR, 100bp liquid probes are designed at both ends of the CDR3 region, i.e. the 5' end of the V gene and the 3' end of the J gene. In addition, due to the random fragmentation characteristics of cfDNA, there will still be some fragments that do not contain the CDR3 region but contain the VJ partial gene, so probes are also designed at the 3' end of the V gene and the 5' end of the J gene. Some J genes are short (<200bp), and the 5' end of the probe will be designed by merging part of the constant region TCR C gene.

[0053] Probe design for TCRV gene

[0054] TCR α chain TRAV has 113 alleles in common, TCR β chain TRBV has 147 alleles in common, TCR γ chain TRGV has 19 alleles in common, TCR δ chain TRDV gene has 21 alleles in common, and 100bp probes are designed for the 5' and 3' ends of these TCR V genes.

[0055] Probe design of TCRJ gene

[0056] TCR α chain TRAJ has 71 alleles in common, TCR β chain TRBJ has 16 alleles in common, TCR γ chain TRGJ has 6 alleles in common, TCR δ chain TRDJ has 4 alleles in common, and 100bp probes are designed for the 5' and 3' ends of these TCR J genes. Some J genes are short (<200bp), and some constant region TCR C region genes will be combined for 5' end probe design.

[0057] Finally, 843 probes were designed, of which 433 were for V genes and 410 were for JC combined genes. Some sequences are shown in the sequence table SEQ ID NO. 1-NO. 39.

[0058] Example 2 Establishment of TCR immune library in plasma cfDNA of colorectal cancer patients

[0059] The samples detected in the embodiments of the application include 30 cases of colorectal cancer patients who have undergone neoadjuvant therapy. First, radiotherapy is performed at Day 8, and then immunotherapy is performed at week 2-7. Tissue and whole blood samples are taken at Day 0 (-RT / -ICI), Day 8 (+RT / -ICI), and week 8 (+RT / +ICI), respectively. The tissue is subjected to single-cell TCR sequencing, the cfDNA sample is subjected to cfDNA-TCR library construction analysis process for detection, and is jointly analyzed with the paired tissue sample, as shown in Figure 1 .

[0060] 1. Plasma cfDNA extraction:

[0061] 1.1 Separation of plasma: Extract the plasma into an EDTA anticoagulant tube, centrifuge at 1600g for 10 minutes at 4°C, and after centrifugation, separate into 3 layers. The supernatant (plasma) is transferred to a 1.5mL / 2mL centrifuge tube, and during the transfer process, avoid sucking the middle layer (white blood cells and platelets) and the lower layer (red blood cells); continue to centrifuge the upper layer plasma at 16000g for 10 minutes at 4°C, and similarly, suck the supernatant and remove the residual cells at the bottom, thereby obtaining the required plasma after separation, which is stored in a -80°C refrigerator to avoid repeated freezing and thawing.

[0062] 1.2 Plasma cfDNA extraction and quantification: The separated plasma was used to extract cfDNA according to the Magbead Free-Circulating DNA Maxi Kit (CWBIO, Jiangsu, China) extraction kit instructions, and then quantified using 1 x dsDNA HS Assay Kit (YEASEN, Shanghai, China) reagent on Qubit 4.0 Fluorometer, with a total amount of about 3-250 ng.

[0063] 2. Library construction of cfDNA:

[0064] According to the VAHTS Universal DNA Library Prep Kit (Vazyme Biotech, ND607) kit instructions, the cfDNA was constructed into a library, and the VAHTS Dual UMI UDI Adapters Set 1 - Set4 for Illumina kit was used to introduce adapters with UMI and UDI molecules. The specific steps are as follows:

[0065] 2.1 First, the cfDNA sample was end-repaired, and the repaired sample was connected to the adapter by two-step method, first connected to the adapter sequence with UMI molecule, and according to the initial input amount of cfDNA, different UMI adapter concentrations were input.

[0066] 2.2 The first round of 0.6X purification was performed using VAHTS DNA Clean Beads, which should be balanced to room temperature (30 min) before use, and cleaned with freshly prepared and balanced to room temperature 80% ethanol.

[0067] 2.3 Library amplification, the second step of two-step adapter construction was performed at this step, introducing amplification adapters with different indexes, the PCR reaction program was: 95°C pre-denaturation for 3 min; 98°C denaturation for 20 s, 65°C annealing for 20 s, 72°C extension for 30 s, the cycle was adjusted according to the initial input amount of cfDNA, about 7-13X cycle number; 72°C final extension for 5 min; 4°C storage.

[0068] 2.4 According to the recommended magnetic bead concentration in the instructions, cfDNA double-round sorting was performed, with concentrations of 0.73x / 0.25x. Finally, the library was quantified using 1 x dsDNA HS Assay Kit (YEASEN, Shanghai, China) reagent on Qubit 4.0 Fluorometer, with a total amount of about 500-1800 ng.

[0069] 3. cfDNA-TCR library hybridization capture:

[0070] Following the instructions of the TargetSeq One® Hyb & Wash Kit v2.0 condition (iGeneTech, Beijing, Shanghai), cfDNA was hybridized and captured to enrich a cfDNA library containing the TCR gene. The specific steps are as follows:

[0071] 3.1 For the prepared libraries, perform library mixing. Combine up to six libraries with similar total amounts in a 1.5 mL centrifuge tube and vacuum concentrate and dry them. Control the drying time according to the volume, approximately 20-40 minutes. When the mixture is just dry, add the hybridization reaction solution, adding the probe after the RNase block, as the RNase block protects the RNA probe from degradation. Then, incubate the probe and library overnight at 50°C for 24 hours.

[0072] 3.2 Biotin probes capturing cfDNA library fragments were screened using Invitrogen™ 65601 Dynabeads MyOne streptavidin T1 magnetic beads. After washing with 80% ethanol, the magnetic bead-probe-library complex was placed together in a PCR instrument for capture and amplification. Since the probe binds to the pre-library at a maximum of 100 bp (i.e., the probe length), high temperature causes the probe-pre-library binding site to unwind, forming a single-stranded DNA pre-library, which is then used as a template for enrichment. Furthermore, only the captured pre-library fragments contain amplification primer binding sites; theoretically, only the captured pre-library fragments will be amplified. The amplification cycle number after capture was 10⁻¹³X.

[0073] 3.3 1.1X purification was performed using VAHTS DNA Clean Beads.

[0074] 4. Library concentration determination, fragment quality control, and next-generation sequencing

[0075] After capture, the cfDNA-TCR library was quantified using a 1×dsDNA HS Assay Kit (YEASEN, Shanghai, China) on a Qubit 4.0 Fluorometer, yielding approximately 25–200 ng. Fragment quality control was then performed using an Agilent 4200 TapeStation instrument, as shown in the attached image. Figure 2 This is the quality control result of a library fragment from a sample sample. The sequencing platform was Illumina NovaSeq 6000, and 6G of data was obtained for each sample.

[0076] 5. Immunorepertoire analysis

[0077] 5.1 Filtering and merging of raw data

[0078] Low quality data filtering, adapter sequence removal and UMI sequence information extraction were performed using Trimmomatic (v0.39) and Fastqc (v0.12.1), Picard (v3.0) and Figbio (v2.2.1) respectively. Finally, paired-end data were merged using Flash (v1.2.11).

[0079] 5.2 Mapping sequencing data to reference genome

[0080] 5.2.1 Mapping data to hg38 reference genome using BWA (v0.7.18) will get the mapping score MAPQ value, which reflects the quality of data mapping. The median value of MAPQ is selected as the threshold, and the data with MAPQ value greater than the threshold is considered as good mapping data, and the data with MAPQ value less than the threshold is considered as poor mapping data, as shown in FIG. 1. Figure 3 Due to the highly random gene rearrangement process of TCR molecules, the high variable region (such as CDR3 region) has poor mapping quality to the reference genome, so the poor mapping data is considered as TCR candidate data.

[0081] 5.2.2 The reliability of poor mapping data as TCR candidate data is verified by intersecting the TCR sequences in IMGT database with the sequences of poor mapping data. It is found that the intersection can reach about 80%, as shown in FIG. 2, which proves that most of these poor mapping data belong to TCR genes. Figure 4

[0082] 5.2.3 In addition, due to the random fragmentation characteristics of cfDNA, there may still be some short fragments with TCR more conservative region genes in good mapping data, which have high mapping quality to the reference genome. Therefore, we get the mapping annotation information by Bedtools (v2.31.1) to distinguish the TCR gene mapping data and non-TCR gene mapping data.

[0083] 5.3 Removing non-specific capture data

[0084] Removing non-TCR gene mapping data, and taking the proportion of non-TCR gene mapping data as the non-specific capture rate of the technology, which is about 15%, as shown in FIG. 3. Figure 5

[0085] 5.4 UMI deduplication of sequencing data

[0086] ​​Using Awk (4.0.2) and Figbio (v2.2.1), the number of UMI molecules and insert sequences that are completely identical was analyzed. According to Perform deduplication.

[0087] 5.5 Input the data into TRUST4 (v1.0.10.1) for contig assembly and TCR gene annotation.

[0088] 6. Example Result Analysis

[0089] Library construction and result analysis were performed on 90 cfDNA samples from CRC using the above-mentioned methods. cfDNA-TCR results were obtained and then combined with paired tissue single-cell TCR-seq results for further analysis.

[0090] 6.1 Intersection analysis results of paired cfDNA-TCR and tissue-TCR: as attached. Figure 6 It was found that the results of cfDNA-TCR and tissue-TCR share a certain number of CDR3s (only a portion is shown in the figure). Moreover, since the cfDNA-TCR process captures all four strands of TCR simultaneously, it was found that some α and β strands paired with tissue-TCR were captured in the bulk cfDNA-TCR results, which proves the accuracy of the cfDNA-TCR results.

[0091] 6.2 Further Validation of the Analysis Process: The data generated by the above library construction process was simultaneously input into our supporting analysis process and the widely used MIXCR process. The analysis results from both were then compared with those from the supporting analysis process. To the group Figure 7 Overlap analysis was performed on CDR3 to obtain cfDNA-tissue shared CDR3s. The distribution of these CDR3s in both sets of results was then observed, as shown in the attached figure. Figure 8 We found significant differences in the distribution: In the cfDNA-TCR results generated by our analysis workflow, the cfDNA-tissue shared CDR3 distribution peak was located earlier in the ~Top50 clones, indicating that they had higher clone counts and clone frequencies. In contrast, in the cfDNA-TCR results generated by the MIXCR workflow, the clones with the cfDNA-tissue shared CDR3 distribution peak were ranked later.

[0092] 6.3 Major changes in clone sorting before and after deduplication: such as Figure 9 It can be observed that in the original clone sorting, the clone count was the sum of PCR replicates and clone replicates. After removing PCR replicates, the clone sorting changed significantly, indicating that the deduplication process can indeed obtain more accurate clone frequency statistics.

[0093] 6.4 Diversity changes of CRC samples at different treatment stages: As shown in FIG. 6B, the diversity of CDR3 shared by cfDNA and tissue has a significant increase after immunotherapy, indicating that the part of cfDNA-TCR and tissue-TCR shared can show the dynamic changes of the patient's immune after treatment. ​

[0094] 6.5 Rank correlation of cfDNA-TCR and tissue-TCR at different cell types at different CRC treatment stages: It can be found that both pCR patients who have complete remission after treatment and nonpCR patients who have incomplete remission after treatment show very high rank correlation of cell types at +RT / +ICI stage after immunotherapy, indicating that after immune stimulation, a large number of T cell turnover produces cfDNA-TCR, thus leading to close contact between cfDNA-TCR and tissue-TCR.

[0095] The above description is merely that of a particular implementation of the application, and variations and modifications will be apparent to those skilled in the art from this disclosure. It is intended that the application encompass such variations and modifications as fall within the scope of the appended claims. Accordingly, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement which is calculated to achieve the same or similar results will be encompassed by the claims. The claims are intended to cover at least the principal of the application and generic equivalents thereof with reference to the pictures, figures, and descriptions accompanying them (which forms a portion of this disclosure) and deciding its true scope. It is recognized that various modifications are possible within the scope of the concept, and the present application is to be limited only by the scope of the following claims.​

Claims

1. A method for constructing a liquid-phase probe-based cfDNA-TCR library analysis, characterized in that, Comprising the following steps: (1) Extract cfDNA molecules from plasma; (2) Construct cfDNA library with UMI and UDI markers; (3) Enrich cfDNA library containing TCR genes using liquid probe hybridization capture technology; (4) Perform second-generation sequencing on the enriched library; (5) Data processing and analysis, including: a) Raw data filtering and cleaning and double-end merging; b) Aligning sequencing data to the reference genome hg38 to distinguish between poorly aligned data and well-aligned data; c) Identifying TCR gene alignment data in well-aligned data; d) Removing non-TCR gene alignment data, i.e. non-specific capture data; e) UMI molecule deduplication; f) Contig assembly and TCR gene annotation by TRUST4 tool.

2. The method of claim 1, wherein, The UMI molecules in step (2) are random oligonucleotide sequences used to distinguish PCR amplification repeats from cloning amplification repeats; the UDI molecules are double-end unique adapters used to reduce double-end sequencing data mismatches.

3. The method of claim 1, wherein, In step (3), the liquid probes are designed to cover the V / J gene regions of TCR α, β, γ, and δ chains, with a probe length of 100 bp, targeting the 5' and 3' ends of TRAV, TRBV, TRGV, and TRDV genes in the IMGT database, as well as the TRAJ, TRBJ, TRGJ, and TRDJ gene combined constant region designed probes, totaling 843.

4. The method of claim 1, wherein, In step (5) b), the alignment quality is divided by MAPQ value, with the median value of MAPQ as the threshold. Poorly aligned data below the threshold are considered TCR candidate data and verified by the IMGT database with an intersection rate of over 80%.

5. The method of claim 1, wherein, In step (5) c), considering the random fragmentation characteristics of cfDNA, data aligned to the hg38 TCR gene in well-aligned data will be retained as TCR gene alignment data for subsequent contig assembly.

6. The method of claim 1, wherein, In step (5) d), the proportion of non-TCR genes is used to calculate the non-specific capture rate, with an average of 15%.

7. The method of claim 1, wherein, In step (5) e), UMI deduplication is based on UMI molecules and insertion sequence consistency, removing identical reads to eliminate PCR repeats and make clonal frequency calculation more accurate.

8. The method of claim 1, wherein, In step (5) f), contig assembly is supported by self-selected TRUST4 and MIXCR immune repertoire tools, with CDR3 annotation results output.

9. The method of claim 1, wherein, Applied to colorectal cancer patient treatment monitoring, including 30 patients receiving radiotherapy combined with immunotherapy, paired whole blood and tissue samples at 3 time points before and after treatment, through shared CDR3 analysis of cfDNA-TCR and tissue single-cell TCR sequencing results, immune dynamic changes are tracked.

10. Use based on any of the methods of claims 1 to 9, characterized in that Used for tumor immunotherapy monitoring or immune repertoire diversity analysis, the shared clonal proportion and diversity changes of cfDNA-TCR and tissue TCR are used as biomarkers to monitor immune dynamic changes.

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