Biomarker, kit, model and system for prognosis analysis of recurrent ovarian cancer TIL cell therapy

Abstract

The invention discloses a biomarker, a kit, a model and a system for prognosis prediction of treatment of recurrent ovarian cancer by using TIL cells, and the biomarker is a TCR (T cell receptor) clone type MOI (Morisia Overlap Index) (Morisia Overlap Index, Morisia Overlap Index, Morisia Overlap Index, Morisia Overlap Index, Morisia Overlap Index, Morisia Overlap Index, Morisia Overlap Index, Morisia Overlap Index). According to the method, an information biological analysis technology and a statistical method are mainly utilized, and the TCR clone type Morisia overlap index is innovatively put forward to serve as an independent factor for predicting the TIL cell treatment outcome of the recurrent ovarian cancer patient, so that clinical doctors are helped to carry out TIL cell individualized curative effect evaluation, and the treatment accuracy and the individualized medical level are improved.

Description

Technical Field

[0001] This invention relates to the field of medical technology evaluation, and in particular to a biomarker, reagent kit, model, and system for prognostic analysis of TIL cell therapy for recurrent ovarian cancer. Background Technology

[0002] Ovarian cancer is currently the leading cause of death among gynecological malignancies. Of particular concern is the platinum-resistant recurrent ovarian cancer (PROC) patient population, whose clinical prognosis is extremely poor, with a median progression-free survival (PFS) of less than 4 months. Although PARP inhibitors have made significant progress in treating homologous recombination-deficient tumors, the efficacy of existing immunotherapies is limited; for example, the response rate of PD-1 inhibitors is less than 10%, thus leaving an unmet clinical need for PROC. While tumor-infiltrating lymphocytes (TIL) therapy has shown efficacy in other solid tumors, its application in ovarian cancer is severely limited due to rapid TIL depletion and poor survival in the immunosuppressive tumor microenvironment (TME). GC203 therapy modifies the surface of TILs with membrane-bound IL-7 (mbIL-7-GPI-TIL) using glycosylphosphatidylinositol (GPI) anchoring technology, forming a local, self-sustaining cytokine signaling network. Unlike systemic IL-7 administration or traditional TIL expansion protocols, the GPI-anchored structure of GC203 restricts IL-7 to the T cell membrane surface, achieving potent autocrine / paracrine activation while avoiding systemic toxicity. This design achieves two key breakthroughs: 1) sustained IL-7 signaling can reverse TIL depletion and induce a stem cell-like memory phenotype; 2) microenvironment-restricted activation avoids the cytokine "trap" effect of the ovarian cancer TME. Preclinical studies showed that GC203-engineered TILs had significantly higher tumor clearance efficiency than unmodified TILs in mouse models (P<0.001). We report the first first-in-human (FIH) phase I clinical trial (NCT05468307) evaluating GC203 for the treatment of recurrent ovarian cancer (ROC), with a median number of prior lines of therapy enrolled. This study pioneers a new paradigm for overcoming TME-driven resistance by combining membrane-bound cytokine engineering with adoptive cell therapy, a strategy with broad implications for immunotherapy of solid tumors. However, the efficacy of TIL cell therapy for ovarian cancer, especially recurrent ovarian cancer, remains limited, necessitating the search for biomarkers to predict the efficacy of TIL cell therapy.

[0003] Existing evaluation methods or systems have the following problems: Due to the leading role of TIL cell therapy, there are few existing protocols for predicting the efficacy of TIL cell therapy in ovarian cancer, and they suffer from limited clinical applicability, inability to capture key dynamic processes, insufficient specificity, and lack of quantitative prediction thresholds. For example, detecting the frequency of CD4+FOXP3+ T cells in nasopharyngeal carcinoma tumor tissue can be used to predict the prognosis of TIL cell therapy (Liu XF et al. CellRep Med. doi:10.1016 / j.xcrm.2025.102096), but it can only predict efficacy based on the frequency of CD4+FOXP3+ T cells in a single preoperative tumor tissue sample, and cannot dynamically monitor the frequency of T cells in vivo, resulting in insufficient specificity. Furthermore, there is currently insufficient evidence to support the use of CD4+FOXP3+ T cell frequency for prognosis prediction in ovarian cancer patients. Another method, single-cell sequencing, can be used to examine the infiltration of tumor-reactive TILs in tumor tissue and the persistence of tumor-reactive TILs in the blood after reinfusion, which can reflect the responsiveness of melanocytes to TIL cell therapy (Chiffelle J et al. Immunity.doi:10.1016 / j.immuni.2024.08.014). However, it has drawbacks such as high detection costs (single-cell sequencing) and the lack of a fixed quantitative prediction threshold. Furthermore, there is insufficient evidence to support its use for predicting the efficacy of TIL cell therapy in ovarian cancer.

[0004] The aforementioned issues collectively hinder the precise application of TIL cell therapy efficacy prediction. There is an urgent need to develop a novel prediction system that integrates multi-dimensional biomarkers to quantify the efficacy of TIL cell therapy for ovarian cancer, in order to overcome the current technological bottlenecks. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems existing in the prior art. To this end, the present invention proposes a biomarker, reagent kit, model, and system for prognostic analysis of TIL cell therapy in recurrent ovarian cancer, which can quantify the dynamic characteristics of the tumor immune microenvironment and effectively predict the prognostic outcome of TIL cell therapy in recurrent ovarian cancer.

[0006] In a first aspect of the invention, a biomarker for prognostic analysis of TIL cell therapy for recurrent ovarian cancer is provided, the biomarker being the TCR clonal Morisita overlap index, which is an evaluation index used to quantify the receptor repertoire similarity between infused TILs and the patient's circulating T cells.

[0007] According to some embodiments of the present invention, the TCR clonal Morisita overlap index is the sum of the minimum frequencies of shared clones in the TCRβ chain CDR3 region of the infused TIL and the patient's circulating T cells.

[0008] According to some embodiments of the present invention, the formula for calculating the TCR clonal Morita overlap index is MOI = ∑min(input TIL clonal frequency, patient circulating T cell clonal frequency).

[0009] According to some embodiments of the present invention, the patient circulating T cells are circulating T cells in a peripheral blood sample from a patient on day 14 after receiving TIL infusion.

[0010] In a second aspect of the invention, a kit is provided for prognostic analysis of TIL cell therapy for recurrent ovarian cancer, the kit comprising reagents and components for detecting the Morisita clonal overlap index of TCRs.

[0011] According to some embodiments of the present invention, the following reagents and components are specifically included:

[0012] The sample collection assembly, including EDTA anticoagulant tubes, is used to collect peripheral blood from patients before TIL reinfusion and on day 14 after treatment.

[0013] Cell separation reagent, containing density gradient centrifugation solution, for separating PBMCs;

[0014] RNA extraction reagent, used to extract total RNA from PBMCs;

[0015] TCR sequencing primers, including primers targeting the TCRβ constant region TRBC; specifically, multiplex PCR primers covering the TRBV1-TRBV29 and TRBJ1-TRBJ2 genes;

[0016] Library building components for high-throughput sequencing on the Illumina platform.

[0017] In a third aspect of the invention, a model for prognostic analysis of TIL cell therapy for recurrent ovarian cancer is provided, characterized in that it comprises:

[0018] Sample collection module: used to collect peripheral blood from patients before TIL reinfusion and on day 14 after treatment;

[0019] Data acquisition module: Acquires the patient's TCR clonal Morisita overlap index;

[0020] Prognostic assessment module: Obtain prognostic assessment results based on the TCR clonal Morita overlap index.

[0021] According to some embodiments of the present invention, obtaining the prognostic assessment result based on the TCR clonal Morita overlap index includes:

[0022] Based on the peripheral blood of patients before TIL infusion and on the 14th day after treatment, calculate the TCR clonal type Morisita overlap index for each patient, and take the average value as X, which serves as the threshold for the high and low MOI groups;

[0023] When MOI ≥ X, it is predicted that there will be a clinical benefit after TIL cell therapy for recurrent ovarian cancer;

[0024] When MOI < X, it is predicted that there will be no clinical benefit after TIL cell therapy for recurrent ovarian cancer;

[0025] Among them, 0.3 ≤ X ≤ 0.4.

[0026] In the present invention, clinical benefit refers to achieving complete remission, partial remission or disease stabilization.

[0027] According to some embodiments of the present invention, when X = 0.03322, there is the best prediction effect. When MOI ≥ 0.03322, it is predicted that there will be a clinical benefit after TIL cell therapy for recurrent ovarian cancer, and the median progression-free survival is 9.1 months; when MOI < 0.03322, it is predicted that there will be no clinical benefit after TIL cell therapy for recurrent ovarian cancer, and the median progression-free survival is 2.7 months.

[0028] According to some embodiments of the present invention, based on the peripheral blood of a total of 14 patients before TIL infusion and on the 14th day after treatment, calculate the TCR clonal type Morisita overlap index for each patient, and take the average value of 0.03322 as the threshold for the high and low MOI groups;

[0029] When MOI ≥ 0.03322, it is the high MOI group, and it is predicted that there will be a clinical benefit after TIL cell therapy for recurrent ovarian cancer;

[0030] When MOI < 0.03322, it is the low MOI group, and it is predicted that there will be no clinical benefit after TIL cell therapy for recurrent ovarian cancer.

[0031] In the fourth aspect of the present invention, there is provided a system for prognostic analysis of TIL cell therapy for recurrent ovarian cancer, which is characterized by comprising:

[0032] A sample collection unit: used for collecting the peripheral blood of patients before TIL infusion and on the 14th day after treatment;

[0033] A data acquisition unit: acquires the TCR clonal type Morisita overlap index of the patient;

[0034] A prognostic evaluation unit: obtains a prognostic evaluation result based on the TCR clonal type Morisita overlap index.

[0035] In a fifth aspect of the invention, a predictive method for prognostic analysis of TIL cell therapy in recurrent ovarian cancer is provided, characterized by comprising the following steps:

[0036] S1. Sample collection and processing: Peripheral blood was collected from patients before TIL reinfusion and on day 14 after treatment, and PBMCs were separated.

[0037] S2 and TCRβ chain CDR3 region library construction: Library construction was performed using QIAseq Immune Repertoire T-cell ReceptorPanel;

[0038] S3. High-throughput sequencing and data analysis: Detecting the Morita overlap index of TCR clonal types in samples to predict the outcome of TIL cell therapy for recurrent ovarian cancer.

[0039] According to one technical solution of the present invention, at least the following beneficial effects are achieved: The present invention mainly utilizes information biology analysis technology and statistical methods to innovatively propose using the TCR clonal Morita overlap index as an independent factor for predicting the outcome of TIL cell therapy in patients with recurrent ovarian cancer, thereby helping clinicians to conduct individualized efficacy assessment of TIL cell therapy and improve the precision of treatment and the level of individualized medicine. Attached Figure Description

[0040] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0041] Figure 1 This is a dynamic change graph of the T-cell receptor (TCR) frequency in a patient's body over time after reinfusion in one embodiment of the present invention.

[0042] Figure 2 This is a dynamic change diagram of T cell receptor clonal types in a patient after reinfusion in one embodiment of the present invention; (a) in the figure is the Morisita overlap index (MOI) used to quantify the similarity of the T cell receptor repertoire between the infused tumor-infiltrating lymphocytes (TILs) and circulating T cells; (b) shows that the high MOI value on day 14 is associated with the persistence of clonal types; the heatmap shows the first 20 expanded clones.

[0043] Figure 3 In one embodiment of the present invention, the Morita overlap index (MOI) value on day 14 was significantly higher in respondents than in non-respondents (p = 0.038).

[0044] Figure 4 In one embodiment of the present invention, the Morisita overlap index (MOI) can predict clinical benefit. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0046] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0047] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0048] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “a,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process or method that includes a series of steps or units is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, or products. The terms “multiple” or “several” used in this application refer to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship. The terms “first,” “second,” “third,” etc., used in this application are merely to distinguish similar objects and do not represent a specific ordering of objects.

[0049] Unless otherwise stated, all reagents and consumables described in this invention are commercially available.

[0050] Example 1: Biomarkers for prognostic analysis of TIL cell therapy in recurrent ovarian cancer

[0051] This embodiment details a novel biomarker for predicting the prognosis of TIL cell therapy in recurrent ovarian cancer—the TCR clonal overlap index (MOI). This biomarker quantifies the clonal matching between infused TILs and the patient's own circulating T cells in peripheral blood on day 14 post-treatment, reflecting the survival, migration, and integration efficiency of infused TILs with the host immune repertoire. It characterizes TCR clonal diversity and is correlated with clinical response. The specific definitions are as follows:

[0052] Subjects for testing: Peripheral blood samples taken on day 14 after TIL reinfusion;

[0053] Calculation method: The minimum sum of shared clone frequencies is calculated by comparing the clone types of the input TIL with those of the patient's circulating T cells in the TCRβ chain CDR3 region (formula: MOI = ∑min(input TIL clone frequency, patient circulating T cell clone frequency)).

[0054] Clinical threshold: When MOI ≥ 0.03322, it was defined as the high response group, which was associated with a significantly higher objective response rate (80% vs. 28.6% in the low response group) and longer survival (p = 0.038).

[0055] Example 2: Kit for prognostic analysis of TIL cell therapy in recurrent ovarian cancer

[0056] Based on the biomarkers of Example 1, this example developed a matching TIL treatment prognosis prediction kit, which includes the following core components:

[0057] Sample collection system (component): includes EDTA anticoagulant tubes (5-10ml) for standardized collection of peripheral blood on day 14 post-treatment;

[0058] Cell separation reagent: Ficoll-Paque PLUS density gradient centrifuge buffer, used for efficient separation of PBMCs;

[0059] RNA Extraction Kit: TRIzol Reagent and miRNeasy Mini Kit for extracting total RNA from PBMC samples;

[0060] TCR sequencing primers: multiplex PCR primers covering TRBV1-TRBV29 and TRBJ1-TRBJ2 genes;

[0061] Library construction tool (component): TruSeq DNAPCR-Free Library Prep Kit, which supports high-throughput sequencing on the Illumina platform.

[0062] Based on the above biomarkers and kits, the prognosis and efficacy analysis of TIL cell reinfusion for recurrent ovarian cancer were performed, as detailed in subsequent examples.

[0063] Example 3: A model and system for prognostic analysis of TIL cell therapy in recurrent ovarian cancer.

[0064] Based on the biomarkers of Example 1 and the detection kit of Example 2, this example develops a matching TIL treatment prognosis prediction model and system, which includes the following parts:

[0065] Sample collection module (unit): Used to collect peripheral blood from patients before TIL reinfusion and on day 14 after treatment;

[0066] Data acquisition module (unit): Acquires the patient's TCR clonal Morisita overlap index;

[0067] Prognostic assessment module (unit): Obtain prognostic assessment results based on the TCR clonal Morita overlap index.

[0068] Example 4: Predictive Method for Prognostic Analysis of TIL Cell Reinfusion in Recurrent Ovarian Cancer Based on MOI

[0069] I. Sample Collection and Processing

[0070] Venous blood was collected from patients before TIL reinfusion and on days 7, 14, and 21 after treatment using EDTA anticoagulant tubes (BD, 367841). PBMCs were separated using density gradient centrifugation with Ficoll-Paque plus (GE, 17144003). The specific procedure was as follows: whole blood was centrifuged at 300×g for 10 minutes at room temperature. The plasma layer was diluted to 1:1 PBS and slowly added to the Ficoll solution. Centrifugation was repeated at 400×g for 30 minutes at room temperature (acceleration / deceleration set to 0). The interfacial PBMCs were collected and washed twice with PBS. The separated PBMCs were resuspended in TRIzol (Ambion, 15596018) and stored at -80°C. RNA was extracted using the miRNeasy Mini Kit (Qiagen, 217084). The A260 / A280 ratio (1.8–2.0) was measured using Nanodrop, and RNA integrity (RIN ≥ 7) was assessed using Agilent Bioanalyzer.

[0071] II. Construction of the TCRβ Chain CDR3 Region Library

[0072] Library construction was performed using the QIAseq Immune Repertoire T-cell Receptor Panel (Qiagen, 333705-IMHS-001Z):

[0073] Reverse transcription: Using RNA as a template, reverse transcription was performed using a gene-specific primer pool (targeting the C region of TCRα, β, γ, and δ genes) (42℃ for 60 minutes → 95℃ for 5 minutes). Specifically, 3 μg of RNA (if less than 3 μg of extracted RNA is available, all total RNA is used) and TRBC-RT primers were used to synthesize first-strand cDNA using SuperScript III reverse transcriptase (Life Technologies, Thermo Fisher Scientific).

[0074] 5' Ligation: An oligonucleotide sequence containing the sample index and UMI was ligated to the end of the cDNA (25°C for 15 minutes → 65°C for 10 minutes). This protocol introduces a unique molecular identifier (UMI) during cDNA synthesis to control experimental bottlenecks (such as amplification bias) and eliminate PCR and sequencing errors. A 5' RACE adapter sequence was added to the 5' end of the cDNA. Subsequently, the synthesized cDNA was purified using a PCR purification kit (QIAquick, Qiagen).

[0075] Single-primer extension capture: 12 rounds of PCR amplification were performed using gene-specific primers targeting the TCR V region (95℃ 3 min → 95℃ 30 s → 60℃ 30 s → 72℃ 30 s × 12 cycles → 72℃ 5 min). Specifically, using fully purified cDNA, 15 cycles of PCR (first round PCR, PCR-1) were performed in the presence of 1 unit of DNA polymerase (Platinum Taq DNA Polymerase High Fidelity; Life Technologies, Thermo Fisher Scientific) using primers with adapter sequences and a nested TRBC primer. The reaction conditions were: 94℃ 30 s, 56℃ 30 s, 68℃ 90 s, and a final extension at 68℃ for 10 min. The amplified TRB product was purified using AgenecourtAMPure XP magnetic beads (catalog number A63882, Beckman Coulter, Inc.).

[0076] Library amplification and purification: Ten rounds of PCR were performed using sample index primers and universal primers. After purification using QIAseq magnetic beads (1.8× ratio), fragment size (300-600 bp) was detected using an Agilent 2100 Bioanalyzer. Specifically, for the preparation and... The final library, compatible with the sequencing platform, underwent a "second round PCR" and was supplemented with... Sequencing index. The final PCR products were purified again using AgenecourtAMPure XP magnetic beads (catalog number A63882, Beckman Coulter, Inc.). The purified final PCR products were then analyzed using an Agilent 2100 Bioanalyzer system to determine the required dilution molar concentration for sequencing sample pool preparation. Sequencing was performed using Illumina. The system was tested using the PE150 (150bp dual-ended) mode (Illumina).

[0077] III. High-throughput sequencing and data analysis

[0078] The constructed libraries were sequenced at 150 bp paired ends using an Illumina NovaSeq 6000 system (≥500,000 reads / sample). Raw data were processed using MIXCR v4.0.0 (adapter removal, alignment to the human TCR gene database, clonotype clustering), and clonotypes were tracked using the TrackClonotypes command in VDJtools2. The MOI value was calculated as: MOI = ∑min(input TIL clonal frequency, patient circulating T cell clonal frequency).

[0079] Specifically, UMIs attached to each raw sequencing read are used for PCR and sequencing error correction, and PCR repetitive sequences are removed. Database alignment of V, D, J, and C gene fragments was performed, followed by extraction of the complementarity-determining region 3 (CDR3) and assembly of clonotypes for all clones. The final nucleotide and amino acid sequences of the TCRβ CDR3 region were determined, and sequences containing out-of-frame mutations and stop codons were removed from the identified TCRβ library. The abundance of each TCRβ clonotype was further defined by summing the number of TCRβ clones sharing the same CDR3 nucleotide sequence. A custom script written in R was used to process the TCRβ clonotype data for subsequent analysis.

[0080] IV. Clinical Application Standards

[0081] MOI ≥ 0.03322 was used as the threshold for the high response group, and MOI < 0.03322 was used as the low response group.

[0082] V. Effect Verification

[0083] In the cohort of 14 patients, the TCR frequency peaked on day 7 post-treatment (mean 35.2% ± 8.7%). On day 14, the MOI value in the high-response group (n=5) was significantly higher than that in the low-response group (n=9) (0.52 ± 0.11 vs. 0.28 ± 0.09, p = 0.038). The ORR was 80% in the high-MOI group (4 / 5) and 28.6% in the low-MOI group (2 / 7). Survival analysis showed that the median OS was not reached in the high-MOI group, while it was 12 months in the low-MOI group (HR = 0.31, p = 0.017).

[0084] Figure 1 The study showed dynamic changes in the frequency of T-cell receptors (TCRs) in the patient's peripheral blood after reinfusion. The frequency peaked on day 7 post-infusion and then gradually declined, suggesting that T cells may migrate to the tumor site.

[0085] Figure 2 The dynamic changes in T-cell receptor clonal types in the patient after reinfusion are shown. In the figure, (a) uses the Morisita overlap index (MOI) to quantify the T-cell receptor repertoire similarity between infused tumor-infiltrating lymphocytes (TILs) and circulating T cells; (b) shows that a high MOI value on day 14 is associated with persistent clonal types. The heatmap shows the first 20 expanded clones.

[0086] Figure 3The results showed that the Morisita overlap index (MOI) value on day 14 was significantly higher in responders than in non-responders (p = 0.038), indicating enhanced integration of the infused tumor-infiltrating lymphocytes (TILs) with the endogenous T cell repertoire.

[0087] Figure 4 The results showed that the Morisita overlap index (MOI) could predict clinical benefit. Patients with higher MOI on day 14 (n=5) showed better progression-free survival (median 9.1 months (high response group) vs 2.7 months (low response group), P=0.21).

[0088] The above description is merely a preferred embodiment of the present invention and does not limit the implementation and protection scope of the present invention. Those skilled in the art should realize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A biomarker for recurrent ovarian cancer TIL cell therapy prognosis analysis, characterized in that, The biomarker is a TCR clonotype Morisita overlap index, which is an evaluation index for quantifying the similarity of receptor repertoire between input TILs and patient circulating T cells.

2. The biomarker of claim 1, wherein, The TCR clonotype Morisita overlap index is the minimum frequency sum of shared clones of TCR beta chain CDR3 regions of input TILs and patient circulating T cells.

3. The biomarker of claim 1 or 2, characterized in that, The calculation formula of the TCR clonotype Morisita overlap index is MOI = ∑min(input TIL clonal frequency, patient circulating T cell clonal frequency).

4. The biomarker of any one of claims 1 to 3, wherein, The patient circulating T cells are circulating T cells in the peripheral blood sample of the patient on the 14th day after input TILs.

5. A kit for recurrent ovarian cancer TIL cell therapy prognosis analysis, characterized in that, The kit comprises reagents and components for detecting the TCR clonotype Morisita overlap index.

6. The kit of claim 5, wherein Specifically comprising the following reagents and components: Sample collection components, including EDTA anticoagulant tubes, for collecting peripheral blood before and on the 14th day after treatment of patient TIL reinfusion; Cell separation reagents, including density gradient centrifugation solution, for separating PBMCs; RNA extraction reagents for extracting total RNA in PBMCs; TCR sequencing primers, including primers for TCR beta constant region TRBC; Library construction components for high-throughput sequencing on the Illumina platform.

7. A model for recurrent ovarian cancer TIL cell therapy prognosis analysis, characterized in that, Including: Sample collection module: for collecting peripheral blood before and on the 14th day after treatment of patient TIL reinfusion; Data acquisition module: acquiring the TCR clonotype Morisita overlap index of the patient; Prognosis evaluation module: obtaining prognosis evaluation results according to the TCR clonotype Morisita overlap index.

8. The model of claim 7, wherein, The prognosis evaluation results obtained according to the TCR clonotype Morisita overlap index include: Calculating the TCR clonotype Morisita overlap index of each patient respectively according to the peripheral blood before and on the 14th day after treatment of patient TIL reinfusion, and taking the average value X as the threshold value of high and low MOI groups; When MOI ≥ X, it is predicted that there is clinical benefit after TIL cell treatment for recurrent ovarian cancer; When MOI < X, it is predicted that there is no clinical benefit after TIL cell treatment for recurrent ovarian cancer; Wherein, 0.3 ≤ X ≤ 0.

4.

9. A system for recurrent ovarian cancer TIL cell therapy prognosis analysis, characterized in that, Including: Sample collection unit: for collecting peripheral blood before and on the 14th day after treatment of patient TIL reinfusion; Data acquisition unit: acquiring the TCR clonotype Morisita overlap index of the patient; Prognosis evaluation unit: obtaining prognosis evaluation results according to the TCR clonotype Morisita overlap index.

10. A predictive method for recurrent ovarian cancer TIL cell therapy prognosis analysis, characterized in that, Including the following steps: S1, sample collection and processing: collecting peripheral blood before and on the 14th day after treatment of patient TIL reinfusion, and separating to obtain PBMCs; S2, TCR beta chain CDR3 region library construction: using QIAseq Immune Repertoire T-cell Receptor Panel for library construction; S3, high-throughput sequencing and data analysis: detecting the Morisita overlap index of TCR clonotype in the sample, and predicting the treatment outcome of recurrent ovarian cancer TIL cells.

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