Immune t cell receptor marker for lung cancer diagnosis and application thereof and lung cancer diagnosis kit

Abstract

This invention discloses immune T-cell receptor biomarkers for lung cancer diagnosis and their applications in lung cancer diagnostic kits, belonging to the field of biomedical detection technology. The immune T-cell receptor biomarkers for lung cancer diagnosis include the amino acid sequences of the third complementarity-determining region (CDMR) of the T-cell receptor β chain as shown in SEQ ID NO: 1 to SEQ ID NO: 94. The immune T-cell receptor biomarkers provided by this invention exhibit high sensitivity and specificity for lung cancer diagnosis. Utilizing these biomarkers enables accurate diagnosis of lung cancer, particularly accurate differentiation between benign and malignant pulmonary nodules, providing more diagnostic options for lung cancer. Furthermore, the samples used for lung cancer diagnosis with these immune T-cell receptor biomarkers are peripheral blood samples, significantly reducing the pain and risks associated with puncture biopsy, resulting in high patient compliance. They also offer advantages such as convenient sample acquisition and long-term cryopreservation.

Description

Technical Field

[0001] This invention relates to the field of biomedical detection technology, and in particular to immune T-cell receptor markers for lung cancer diagnosis, their applications, and lung cancer diagnostic kits. Background Technology

[0002] Lung cancer is one of the most common malignant tumors. Currently, lung cancer screening and diagnostic techniques mainly include low-dose computed tomography (LDCT), liquid biopsy, sputum cytology, and tumor marker testing. LDCT is commonly used for screening high-risk groups for lung cancer; however, due to its high cost and numerous false-positive results (early lung cancer often presents as pulmonary nodules, and the false-positive rate among nodules detected by LDCT is high, leading to a large number of unnecessary invasive examinations or surgeries), it has significant limitations in clinical practice. Further diagnosis and treatment may be necessary, and even more invasive tests may be required, placing unnecessary burden and discomfort on patients. Liquid biopsy and sputum cytology are highly invasive, posing certain surgical risks and causing postoperative discomfort. Furthermore, sputum cytology cannot cover all areas of the lungs, limiting its diagnostic value for early lesions. Serum tumor markers (such as carcinoembryonic antigen and cytokeratin 19 fragments) have limited sensitivity and specificity and may be affected by other factors, leading to misdiagnosis or missed diagnosis, failing to meet the needs of accurate diagnosis. This poses a serious challenge to lung cancer diagnosis.

[0003] Therefore, there is an urgent need to develop biomarkers with high sensitivity and specificity to achieve accurate diagnosis of lung cancer, especially to accurately differentiate between benign and malignant pulmonary nodules, thus providing more options for the diagnosis of lung cancer. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide immune T-cell receptor biomarkers for lung cancer diagnosis and their applications in lung cancer diagnostic kits. The aim is to provide biomarkers with high sensitivity and specificity to achieve accurate diagnosis of lung cancer and accurate differentiation between benign and malignant pulmonary nodules, thus providing more options for lung cancer diagnosis.

[0005] The technical solution of the present invention is as follows: In a first aspect, the present invention provides an immune T-cell receptor biomarker for the diagnosis of lung cancer, wherein the immune T-cell receptor biomarker for the diagnosis of lung cancer comprises an amino acid sequence of the third complementarity-determining region of the T-cell receptor β chain as shown in SEQ ID NO: 1 to SEQ ID NO: 94.

[0006] A second aspect of the present invention provides an immune T-cell receptor biomarker for the diagnosis of lung cancer, wherein the immune T-cell receptor biomarker for the diagnosis of lung cancer comprises a nucleic acid sequence encoding an amino acid sequence of the third complementarity-determining region of the T-cell receptor β chain as shown in SEQ ID NO: 1 to SEQ ID NO: 94.

[0007] A third aspect of the invention provides the use of the immune T-cell receptor markers for lung cancer diagnosis described above in the preparation of products for diagnosing lung cancer.

[0008] Optionally, the product includes reagents, kits, or test strips.

[0009] In a fourth aspect, the present invention provides a lung cancer diagnostic kit, wherein the lung cancer diagnostic kit comprises an antibody capable of specifically binding to the immune T cell receptor markers for lung cancer diagnosis described above, a nucleic acid probe capable of recognizing the immune T cell receptor markers for lung cancer diagnosis described above, or primers capable of amplifying the immune T cell receptor markers for lung cancer diagnosis described above.

[0010] Optionally, the lung cancer diagnostic kit may also include standards and / or quality control products.

[0011] Beneficial Effects: The immune T-cell receptor biomarkers provided by this invention exhibit high sensitivity and specificity in lung cancer diagnosis. Utilizing these biomarkers enables precise diagnosis of lung cancer, particularly accurate differentiation between benign and malignant pulmonary nodules, providing more diagnostic options. Furthermore, the peripheral blood sample used for lung cancer diagnosis significantly reduces patient discomfort and risks associated with biopsies, resulting in high compliance. The biomarkers also offer advantages such as convenient sample acquisition and long-term cryopreservation. Attached Figure Description

[0012] Figure 1 The cumulative detection frequency distribution of 94 TCR CDR3 sequences in the invasive lung cancer group, adenocarcinoma in situ group, and benign pulmonary nodule group in the training set and validation set, respectively.

[0013] Figure 2 The cumulative detection frequency distribution of 94 TCR CDR3 sequences in the benign pulmonary nodule group, stage 0 group, stage I group, stage II-IV group and the group with unknown staging information is shown in the figure.

[0014] Figure 3 Receiver operating characteristic (ROC) curves were generated to distinguish between invasive lung cancer and benign pulmonary nodules using the cumulative frequencies of 94 TCR CDR3 sequences in the training and validation sets.

[0015] Figure 4To generate receiver operating characteristic (ROC) curves for distinguishing between malignant pulmonary nodules (invasive lung cancer + adenocarcinoma in situ) and benign pulmonary nodules using the cumulative frequencies of 94 TCR CDR3 sequences in the training and validation sets.

[0016] Figure 5 This is a summary graph summarizing the performance results of using the cumulative frequency of 94 TCR CDR3 sequences in distinguishing between invasive lung cancer and benign pulmonary nodules, as well as between malignant pulmonary nodules (invasive lung cancer + adenocarcinoma in situ) and benign pulmonary nodules.

[0017] Figure 6 The graph shows the detection sensitivity results based on the cumulative frequency and optimal threshold of 94 TCR CDR3 sequences in the adenocarcinoma in situ (stage 0), stage I, stage II-IV, and stage with unknown information groups. Detailed Implementation

[0018] This invention provides immune T-cell receptor markers for lung cancer diagnosis, their applications, and lung cancer diagnostic kits. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0020] T cell receptors (TCRs) are antigen recognition molecules expressed on the surface of T cells. They mediate specific cellular immune responses by recognizing pMHC (antigen peptide-MHC complex) molecules formed by antigen peptides and major histocompatibility complexes (MHC). During T cell development, TCR genes undergo rearrangements of gene segments in the variable region (V), diversification region (D), and linker region (J), with nucleotide insertions and deletions occurring in the linker region. Therefore, a highly diverse set of TCR sequences, known as a TCR immune repertoire, can be formed in the body. The complementarity-determining region 3 (CDR3) of the TCR directly participates in antigen recognition and is the core region that distinguishes different T cell clones and reflects their antigen specificity. With the development of multiplex PCR, high-throughput sequencing, and bioinformatics analysis technologies, quantitative analysis of TCR immune repertoires in peripheral blood, tumor tissue, or other biological samples containing T cells can be performed to obtain information on the composition, abundance, degree of clonal expansion, diversity, and dynamic changes of TCR clonal types. During tumorigenesis and development, mutated antigens, aberrantly expressed antigens, or tumor-associated antigens produced by tumor cells can induce the clonal expansion of specific T cells, thereby forming disease-related sequence characteristics in the patient's TCR immune repertoire. In other words, the TCR is a core molecule of the adaptive immune response, and its clonal proliferation directly reflects the presence of specific T cell responses against tumor antigens in vivo. As a biomarker, it has a solid immunological theoretical basis and is superior to biomarkers such as circulating DNA methylation, which lack biological significance. Therefore, TCR immune repertoires have been widely used in research on the tumor immune microenvironment, recognition of tumor-associated antigen-responsive T cells, prediction of immunotherapy efficacy, prognostic assessment, monitoring of postoperative recurrence and minimal residual disease, as well as early tumor screening and auxiliary diagnosis. Compared with simply detecting tumor-derived proteins or nucleic acids, TCR immune repertoires can reflect tumor-related biological changes from the perspective of host adaptive immune response and can be detected through peripheral blood. They offer potential advantages such as convenient sample acquisition, repeatable monitoring, and applicability to low tumor burden disease states, providing an important technical foundation for the development of tumor immunodiagnostic biomarkers.Based on this, embodiments of the present invention provide an immune T-cell receptor biomarker for lung cancer diagnosis (i.e., a TCR biomarker for lung cancer diagnosis), wherein the TCR biomarker for lung cancer diagnosis includes the TCR β-chain CDR3 amino acid sequence as shown in SEQ ID NO: 1 to SEQ ID NO: 94 (that is, the TCR biomarker for lung cancer diagnosis includes 94 TCR β-chain CDR3 amino acid sequences, each of which is shown in SEQ ID NO: 1 to SEQ ID NO: 94); or, the TCR biomarker for lung cancer diagnosis includes a nucleic acid sequence encoding the TCR β-chain CDR3 amino acid sequence as shown in SEQ ID NO: 1 to SEQ ID NO: 94 (that is, the TCR biomarker for lung cancer diagnosis includes a nucleic acid sequence encoding 94 TCR β-chain CDR3 amino acid sequences, each of which is shown in SEQ ID NO: 1 to SEQ ID NO: 94).

[0021] This invention obtains the TCR biomarker by sequencing, comparing, differential enrichment screening, and independently validating peripheral blood TCR repositories from patients with invasive lung cancer, adenocarcinoma in situ, benign pulmonary nodules, and healthy individuals. The TCR biomarker exhibits a higher detection rate or higher abundance in invasive lung cancer and adenocarcinoma in situ samples, while it is rarely detected or undetectable in benign pulmonary nodules and large-scale healthy populations.

[0022] The TCR biomarkers provided by this invention exhibit high sensitivity and specificity for lung cancer diagnosis. Utilizing these biomarkers enables precise diagnosis of lung cancer, particularly accurate differentiation between benign and malignant pulmonary nodules, providing more diagnostic options. Specifically, the TCR biomarkers demonstrate a sensitivity of 0.82 and a specificity of 0.86 when distinguishing between lung cancer (i.e., malignant pulmonary nodules, including invasive lung cancer and adenocarcinoma in situ) and benign pulmonary nodules; and a sensitivity of 0.72 and a specificity of 0.86 when distinguishing between invasive lung cancer and benign pulmonary nodules. Furthermore, the TCR biomarkers used for lung cancer diagnosis employ peripheral blood samples, significantly reducing patient discomfort and risks associated with biopsies, resulting in high compliance. They also offer advantages such as convenient sample acquisition and long-term cryopreservation.

[0023] In the early pathological spectrum of lung adenocarcinoma, adenocarcinoma in situ (AIS) typically corresponds to stage 0 or non-invasive lesions. These lesions are small, have a low tumor burden, and often appear on imaging as pure ground-glass nodules or subsolid nodules. There may be radiographic overlap between AIS and inflammatory nodules, fibrotic foci, and atypical adenomatous hyperplasia. Diagnostic uncertainty can easily arise when relying solely on CT density, nodule size, the proportion of solid components, and follow-up rate for judgment. The clinical management of AIS presents a dilemma: on the one hand, AIS represents a crucial stage in the early, continuous evolution of lung adenocarcinoma, and accurate identification helps in intervention within the curable window; on the other hand, AIS progresses slowly and often presents with atypical imaging features, lacking reliable molecular or immunological markers, leading to long-term follow-up, repeated examinations, excessive surgery, or delayed treatment. Therefore, TCR markers that reflect the tumor immune response associated with AIS in peripheral blood have significant clinical value, distinguishing them from traditional tumor protein markers and imaging indicators.

[0024] The TCR biomarker provided by this invention exhibits higher accuracy, sensitivity, and specificity in distinguishing between lung cancer (invasive lung cancer + adenocarcinoma in situ) and benign pulmonary nodules (area under receiver operating characteristic curve reaches 0.867, sensitivity reaches 0.82, and specificity reaches 0.86). This demonstrates that the TCR biomarker provided by this invention has a strong ability to identify adenocarcinoma in situ, a clinically low-tumor-burden type that is easily confused with benign pulmonary nodules on imaging. It can provide a non-invasive tool for risk stratification, follow-up strategy selection, surgical timing selection, and accurate early diagnosis of lung cancer in patients with pulmonary nodules.

[0025] Specifically, lung cancer diagnosis and auxiliary differentiation of benign and malignant lung nodules and adenocarcinoma in situ can be performed by detecting the cumulative frequency of 94 TCR β chain CDR3 amino acid sequences (hereinafter referred to as TCR CDR3 sequences) in the sample. This method does not rely on complex machine learning models, but uses the cumulative frequency of TCR CDR3 sequences as a diagnostic indicator reflecting the degree of tumor-associated T cell clonal expansion, and outputs the detection results according to a pre-determined judgment threshold. The specific steps include: (1) Calculation of normalized frequencies of 94 TCR CDR3 sequences: TCR library sequencing was performed on peripheral blood samples from the subjects to obtain TCR CDR3 clone sequences in the samples and the sequencing reads, number of unique molecular identifiers or clone counts corresponding to each clone. For the i-th TCR CDR3 sequence, its normalized frequency in the peripheral blood sample of the subject can be calculated according to the following formula (1): (1); in, N i Indicates the relationship with the first isequencing reads, unique molecular identifier counts or clonotype counts matching the TCR CDR3 sequence; N total represents the sequencing reads, unique molecular identifier counts or clonotype counts of all valid productive TCR sequences (i.e., functional TCR sequences) in the peripheral blood sample of the test subject; represents the i normalized frequency of the TCR CDR3 sequence in the peripheral blood sample of the test subject.

[0026] For the 94 TCR CDR3 sequences, if they are not detected in the peripheral blood sample of the test subject, their is recorded as 0.

[0027] In an optional embodiment, the frequency may be expressed as a percentage, a normalized frequency per million valid sequences, or the frequency may be subjected to logarithmic transformation or other normalization processing.

[0028] (2) Calculation of the cumulative frequency of the 94 TCR CDR3 sequences: According to the following formula (2), the normalized frequencies of all 94 TCR CDR3 sequences detected in the peripheral blood sample of the test subject are accumulated to obtain the cumulative frequency of the 94 TCR CDR3 sequences in the peripheral blood sample of the test subject S : (2); wherein n represents the number of sequences contained in the marker (i.e., 94), represents the i normalized frequency of the TCR CDR3 sequence in the peripheral blood sample of the test subject.

[0029] The cumulative frequency S reflects the overall enrichment degree of lung cancer (invasive lung cancer and adenocarcinoma in situ) related T cell clones in the peripheral blood sample of the test subject. A higher cumulative frequency indicates a higher possibility that the test subject has an immune response related to lung cancer (invasive lung cancer and adenocarcinoma in situ).

[0030] (3) Diagnosis of the test subject: The above cumulative frequency S is compared with a predetermined determination threshold T.

[0031] When S≥T, it is determined that the test subject has a high risk related to lung cancer (invasive lung cancer or adenocarcinoma in situ), or the test subject is determined to be positive for the TCR marker detection; When S<T, it is determined that the test subject has a low risk related to lung cancer (invasive lung cancer or adenocarcinoma in situ), or the test subject is determined to be negative for the TCR marker detection.

[0032] The test results are used to assist in the differentiation of benign and malignant lung nodules, the identification of adenocarcinoma in situ, or the early risk assessment of lung cancer, and should not be used as the sole basis for diagnosis that is divorced from clinical, imaging and pathological information.

[0033] The method for determining the threshold T is as follows: Peripheral blood samples were obtained from patients with invasive lung cancer, adenocarcinoma in situ, and benign pulmonary nodules. The cumulative frequencies of 94 TCR CDR3 sequences in peripheral blood samples from patients with invasive lung cancer, adenocarcinoma in situ, and benign pulmonary nodules were calculated separately (see the calculation method for cumulative frequencies above). The samples were then integrated using the pathological or clinical diagnosis results as the true group, and receiver operating characteristic (ROC) curves were plotted.

[0034] For each possible cumulative frequency threshold t, calculate its sensitivity and specificity, and then calculate the Youden index according to the following formula: J(t) = Sensitivity(t) + Specificity(t) - 1.

[0035] J(t) represents the Youden index, and the cumulative frequency corresponding to the maximum value of the Youden index is determined as the optimal judgment threshold T.

[0036] This invention also provides an application of the TCR biomarker for lung cancer diagnosis described above in the preparation of products for diagnosing lung cancer.

[0037] In some embodiments, the product includes reagents, kits, or test strips.

[0038] This invention also provides a lung cancer diagnostic kit, wherein the lung cancer diagnostic kit includes an antibody that can specifically bind to the TCR biomarker (94 TCR CDR3 sequences) for lung cancer diagnosis as described above, a nucleic acid probe that can recognize the TCR biomarker (encoding nucleic acid sequence of 94 TCR CDR3 sequences) for lung cancer diagnosis as described above, or primers that can amplify the TCR biomarker (encoding nucleic acid sequence of 94 TCR CDR3 sequences) for lung cancer diagnosis as described above.

[0039] In some embodiments, the lung cancer diagnostic kit further includes standards and / or quality control materials.

[0040] The present invention will be further described below through specific embodiments.

[0041] Example 1: Screening of TCR CDR3 sequences Inclusion criteria: Subjects with radiographic lung nodules and pathological diagnosis; peripheral blood collection time prior to surgery, radiotherapy, chemotherapy, immunotherapy, or systemic antitumor therapy; lung cancer, i.e., malignant lung nodules (invasive lung cancer, adenocarcinoma in situ), determined according to pathological diagnosis and TNM classification; benign lung nodules, including but not limited to inflammatory nodules, granulomas, hamartomas, tuberculosis-related nodules, etc.

[0042] Exclusion criteria for samples: acute infection, active autoimmune disease, immunosuppressive therapy or severe inflammatory events prior to blood collection; coexisting malignant tumors; insufficient sequencing depth or unqualified TCR library quality control; unclear pathological conclusions.

[0043] Healthy population filtering: In a large healthy cohort of 2,699 people, candidate TCR CDR3 sequences with a frequency (healthy_prev) greater than or equal to 0.1% in the healthy population were filtered out to reduce the risk of false positives caused by common TCRs, infection-related TCRs, or high-frequency background TCRs.

[0044] This study included 133 lung cancer patients (84 invasive lung cancer patients + 49 adenocarcinoma in situ patients) and 72 benign lung nodule patients, as well as 2699 healthy subjects.

[0045] Of the 133 lung cancer patients, 49 were stage 0 (i.e., adenocarcinoma in situ) (referred to as stage 0 group), 68 were stage I (referred to as stage I group), 7 were stage II (referred to as stage II group), 3 were stage III (referred to as stage III group), 3 were stage IV (referred to as stage IV group), and 3 had unknown staging information (referred to as stage unknown group).

[0046] In addition, in this embodiment, patients with lung cancer (i.e., malignant pulmonary nodules) and patients with benign pulmonary nodules are randomly sampled and grouped, with 70% selected as the training set and the remaining 30% as the validation set.

[0047] The training set included: 94 lung cancer patients (59 invasive lung cancer patients + 35 adenocarcinoma in situ patients) and 51 patients with benign pulmonary nodules; The validation set included 39 patients with lung cancer (25 with invasive lung cancer and 14 with adenocarcinoma in situ) and 21 patients with benign lung nodules.

[0048] The specific grouping information is shown in Table 1.

[0049] Table 1. Sample Grouping Information

[0050] Screening of TCR CDR3 sequences: Peripheral blood samples were collected from patients in the training set of lung cancer group (invasive lung cancer group + adenocarcinoma in situ group) and patients in the benign pulmonary nodule group. The TCR immune repertoire in the peripheral blood samples was detected to obtain the functional TCR CDR3 clones contained in each sample and their corresponding frequencies. Quality control was performed on the sequencing data, removing samples with substandard sequencing quality, non-productive TCR rearrangements, sequences with excessively low sequencing reads, and low-abundance noise clones that may be caused by sequencing or amplification errors. Candidate TCR CDR3 sequences with higher detection rates and cloning frequencies in the lung cancer group compared to the benign lung nodule group were screened.

[0051] Further filtering of the candidate sequences was performed using peripheral blood TCR immune repertoire data from 2699 healthy individuals, excluding common TCR sequences widely present in healthy individuals. TCR CDR3 sequences with a detection rate of less than 0.1% in healthy individuals, and exhibiting an enrichment trend or statistical difference in the lung cancer group compared to the benign pulmonary nodule group, were identified, resulting in 94 TCR CDR3 sequences, as shown in SEQ ID NO: 1 to SEQ ID NO: 94.

[0052] Finally, using the detection and calculation method of cumulative frequency S mentioned above (see formula (2)), the cumulative frequencies of 94 TCR CDR3 sequences in the invasive lung cancer group, adenocarcinoma in situ group, and benign lung nodule group in the training set and validation set were obtained. Then, the cumulative detection frequency distribution map of the 94 TCR CDR3 sequences in the invasive lung cancer group, adenocarcinoma in situ group, and benign lung nodule group in the training set and validation set was obtained by further using the Kruskal-Wallis test (see formula (2)). Figure 1 (As shown). Simultaneously, using the same method, the cumulative detection frequency distribution of 94 TCR CDR3 sequences was obtained in the benign pulmonary nodule group, stage 0 group, stage I group, stage II-IV group, and group with unknown staging information (as shown). Figure 2 As shown in the figure, these TCR CDR3 sequences are significantly enriched in curable early-stage lung cancer (stage 0 and stage I). Compared with traditional protein markers, their warning window may be earlier, and they can be used for the diagnosis of early-stage lung cancer.

[0053] Example 2: Auxiliary Differentiation of Invasive Lung Cancer and Benign Pulmonary Nodules Based on 94 TCR CDR3 Sequences This embodiment provides a method for auxiliary differentiation between invasive lung cancer and benign pulmonary nodules using the cumulative frequency of 94 TCR CDR3 sequences in a test sample. This method does not rely on complex machine learning models, but instead uses the cumulative frequency of TCR CDR3 sequences as a diagnostic indicator reflecting the degree of tumor-associated T cell clonal expansion, and outputs the detection results according to a pre-determined judgment threshold. Specifically, it includes the following steps: (1) Calculation of normalized frequencies of 94 TCR CDR3 sequences: TCR library sequencing was performed on peripheral blood samples from each group in the training and validation sets obtained in Example 1 to obtain the TCR CDR3 clonal sequences in the samples and the sequencing reads corresponding to each clonal type. For the first i Calculate the normalized frequencies in the sample using TCR CDR3 sequences. Fi (See above) Fi The calculation method is shown in formula (1).

[0054] (2) Calculation of the cumulative frequency of 94 TCR CDR3 sequences: The normalized frequency of each sequence in the peripheral blood samples of each group in the training set and validation set that matches the 94 TCR CDR3 sequences. Fi The cumulative frequency S of the 94 TCR CDR3 sequences in each sample is obtained by summing them up (the calculation method is described in the above text, see formula (2)).

[0055] (3) Determination of the judgment threshold and diagnosis: The cumulative frequency of 94 TCR CDR3 sequences in peripheral blood samples of patients in the invasive lung cancer group and benign pulmonary nodule group in the training set was calculated, and the pathological diagnosis or clinical diagnosis results of the samples were used as the true grouping to integrate and draw the receiver operating characteristic curve.

[0056] For each possible cumulative frequency threshold t, calculate the corresponding sensitivity (t) and specificity (t), and calculate the Youden index (J) according to the following formula: J(t) = Sensitivity(t) + Specificity(t) - 1.

[0057] The cumulative frequency corresponding to the maximum Youden index is selected as the optimal decision threshold T1, where T1 = 2.8 × 10⁻⁶. -7 The optimal decision threshold is determined in the training set and is used in the validation set below. It will not be readjusted based on the validation set samples.

[0058] When the cumulative frequency of 94 TCR CDR3 sequences in the sample is greater than or equal to T1, the sample is considered to be positive for TCR marker detection, indicating a high risk of invasive lung cancer (or malignant pulmonary nodules); when the cumulative frequency of 94 TCR CDR3 sequences is less than T1, the sample is considered to be negative for TCR marker detection, indicating a high probability of benign pulmonary nodules.

[0059] The cumulative frequency S of 94 TCR CDR3 sequences in peripheral blood samples from patients with invasive lung cancer and benign pulmonary nodules in the training set was calculated. Using the cumulative frequency S of 94 TCR CDR3 sequences in each sample as a continuous detection indicator, and combined with the optimal decision threshold T1, the sample was judged as positive or negative. Receiver operating characteristic curve analysis was performed on the invasive lung cancer group and the benign pulmonary nodule group. The results are as follows: Figure 3 As shown, the area under the receiver operating characteristic curve (AUC) is 0.907.

[0060] The receiver operating characteristic (ROC) curve is a curve used to analyze the relationship between sensitivity and specificity. It is plotted with sensitivity on the vertical axis (Y-axis) and 1-specificity on the horizontal axis (X-axis). Diagnostic performance is usually assessed using AUC. When the AUC value is greater than 0.5, the closer the AUC is to 1, the stronger the diagnostic ability; conversely, if the AUC is less than 0.5, it indicates a weaker diagnostic ability.

[0061] (4) Diagnostic performance evaluation: The cumulative frequency S of 94 TCR CDR3 sequences in peripheral blood samples from patients with invasive lung cancer and benign pulmonary nodules in the validation set was calculated. The cumulative frequency S of TCR CDR3 sequences in each sample in the validation set was used as a continuous detection index. Combined with the optimal decision threshold T1, the sample was judged as positive or negative. Receiver operating characteristic curve analysis was performed on the invasive lung cancer group and the benign pulmonary nodule group. The diagnostic performance of the above method was evaluated by AUC, sensitivity, and specificity. The results are as follows: Figure 3 As shown, the cumulative frequency of the 94 TCR CDR3 sequences in distinguishing between invasive lung cancer (malignant nodules) and benign lung nodules had an AUC of 0.802, a sensitivity of 0.72, and a specificity of 0.86, indicating that the 94 TCR CDR3 sequences can be used for the auxiliary differentiation between invasive lung cancer and benign lung nodules.

[0062] Example 3: Auxiliary Differentiation of Benign and Malignant Lung Nodules and Auxiliary Differentiation of Adenocarcinoma in Situ Based on 94 TCR CDR3 Sequences (1) Accumulated frequency acquisition: Peripheral blood samples from patients in the lung cancer group (i.e., malignant pulmonary nodules (invasive lung cancer + adenocarcinoma in situ)) and patients in the benign pulmonary nodule group were obtained in Example 1. Peripheral blood samples from patients in the benign pulmonary nodule group were used as controls. The cumulative frequency of 94 TCR CDR3 sequences in each sample was calculated according to the method in Example 2.

[0063] (2) Determination of the judgment threshold and diagnosis: The optimal judgment threshold T2 for differentiating lung cancer (invasive lung cancer + adenocarcinoma in situ) from benign pulmonary nodules is the same as T1 in Example 2, i.e., T2 = 2.8 × 10 -7 .

[0064] When the cumulative frequency of 94 TCR CDR3 sequences in the sample is greater than or equal to T2, the sample is considered to be positive for TCR marker detection, indicating a high risk of malignant pulmonary nodules (invasive lung cancer + adenocarcinoma in situ); when the cumulative frequency of 94 TCR CDR3 sequences is less than T2, the sample is considered to be negative for TCR marker detection, indicating a high probability of benign pulmonary nodules.

[0065] The cumulative frequency (S) of 94 TCR CDR3 sequences in peripheral blood samples from patients with in situ adenocarcinoma, invasive lung cancer, and benign pulmonary nodules in the training set was calculated. Using the cumulative frequency (S) of the 94 TCR CDR3 sequences as a continuous detection indicator, and combined with the optimal decision threshold (T2), the sample was judged as positive or negative. Receiver operating characteristic (ROC) curve analysis was performed on the malignant and benign pulmonary nodule groups. Results are as follows: Figure 4 As shown, the AUC is 0.874.

[0066] (3) Evaluation of diagnostic performance: The cumulative frequency S of 94 TCR CDR3 sequences in peripheral blood samples of patients in the malignant pulmonary nodule group and patients in the benign pulmonary nodule group in the validation set was calculated. The cumulative frequency S of TCR CDR3 sequences in each sample in the validation set was used as a continuous detection index. Combined with the optimal judgment threshold T2, the sample was judged to be positive or negative. Receiver operating characteristic curve analysis was performed on the malignant pulmonary nodule group and the benign pulmonary nodule group. The diagnostic performance of the above method was evaluated by AUC, sensitivity and specificity.

[0067] The results are as follows Figure 4 As shown, when invasive lung cancer patients and adenocarcinoma in situ were designated as the lung cancer group (i.e., the malignant pulmonary nodule group), and benign pulmonary nodule patients were designated as the control group, the AUC reached 0.867, the sensitivity reached 0.82, and the specificity reached 0.86 when distinguishing between malignant and benign pulmonary nodules based on the cumulative frequency of 94 TCR CDR3 sequences.

[0068] Compared with the results of differentiating invasive lung cancer and benign pulmonary nodules in Example 2, the inclusion of peripheral blood samples from patients with adenocarcinoma in situ in this example showed that the 94 TCR CDR3 sequences exhibited higher discriminative ability and improved sensitivity. This indicates that the combination of 94 TCR CDR3 sequences provided by the present invention can reflect tumor-related immune responses in adenocarcinoma in situ, a stage where the lesion volume is small, the tumor burden is low, and imaging differentiation is difficult.

[0069] In Examples 2 and 3, the area under the receiver operating characteristic (ROC) curve was used to evaluate the discriminative power of the cumulative frequencies of 94 TCR CDR3 sequences against different clinical groups. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were calculated at the optimal decision threshold and summarized. Figure 5 middle.

[0070] based on Figures 3 to 5 The results showed that the 94 TCR CDR3 sequences achieved an AUC of 0.802, a sensitivity of 0.72, and a specificity of 0.86 in differentiating between invasive lung cancer and benign pulmonary nodules; and an AUC of 0.867, a sensitivity of 0.82, and a specificity of 0.86 in differentiating between malignant pulmonary nodules (invasive lung cancer + adenocarcinoma in situ) and benign pulmonary nodules. These results indicate that the 94 TCR CDR3 sequences can not only identify invasive lung cancer but also enhance the identification of adenocarcinoma in situ, an early non-invasive type of lung cancer, and can effectively differentiate between benign and malignant pulmonary nodules.

[0071] Furthermore, adenocarcinoma in situ typically presents as small lesions with low cellular burden and weak serum tumor marker signals, and its imaging findings overlap with those of benign ground-glass nodules. This invention utilizes peripheral blood TCR CDR3 sequences to reflect the host's adaptive immune response, enabling the capture of tumor-associated immune signals at the adenocarcinoma in situ stage. When adenocarcinoma in situ was included in the lung cancer group (i.e., the malignant pulmonary nodule group), both the AUC and sensitivity were higher than those used in the task of differentiating between invasive lung cancer and benign pulmonary nodules alone, demonstrating its superior performance in identifying clinical types of adenocarcinoma in situ.

[0072] Meanwhile, based on the cumulative frequency and optimal decision threshold of 94 TCR CDR3 sequences, the detection sensitivity results in the in situ adenocarcinoma group, stage I group, stage II-IV group, and group with unknown stage information are as follows: Figure 6 As shown, they all exhibit high sensitivity.

[0073] In summary, this invention provides immune T-cell receptor biomarkers for lung cancer diagnosis, their applications, and lung cancer diagnostic kits. The immune T-cell receptor biomarkers provided by this invention exhibit high sensitivity and specificity in lung cancer diagnosis, enabling precise diagnosis of lung cancer, particularly accurate differentiation between benign and malignant pulmonary nodules, thus providing more diagnostic options. The samples used for lung cancer diagnosis with these immune T-cell receptor biomarkers are peripheral blood samples, significantly reducing the pain and risks associated with biopsy, resulting in high patient compliance. Furthermore, these biomarkers offer advantages such as convenient sample acquisition and long-term cryopreservation. Moreover, compared to differentiating between invasive lung cancer and benign pulmonary nodules, the immune T-cell receptor biomarkers provided by this invention demonstrate higher accuracy, sensitivity, and specificity (area under the receiver operating characteristic curve reaches 0.867, sensitivity reaches 0.82, and specificity reaches 0.86) when differentiating between lung cancer (invasive lung cancer + adenocarcinoma in situ) and benign pulmonary nodules. This demonstrates that the immune T-cell receptor markers provided by this invention have a strong ability to identify adenocarcinoma in situ, a clinically low-tumor-burden type that is easily confused with benign pulmonary nodules on imaging. It can provide a non-invasive tool for risk stratification, follow-up strategy selection, surgical timing selection, and early accurate diagnosis of lung cancer in patients with pulmonary nodules.

[0074] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. An immune T-cell receptor marker for lung cancer diagnosis, characterized in that, The immune T-cell receptor biomarkers used for lung cancer diagnosis include the amino acid sequences of the third complementarity-determining region of the T-cell receptor β chain as shown in SEQ ID NO: 1 to SEQ ID NO:

94.

2. An immune T-cell receptor marker for lung cancer diagnosis, characterized in that, The immune T-cell receptor biomarkers for lung cancer diagnosis include nucleic acid sequences encoding the amino acid sequence of the third complementarity-determining region of the T-cell receptor β chain as shown in SEQ ID NO: 1 to SEQ ID NO:

94.

3. The use of the immune T-cell receptor marker for lung cancer diagnosis as described in claim 1 or 2 in the preparation of products for diagnosing lung cancer.

4. The application according to claim 3, characterized in that, The products include reagents, kits, or test strips.

5. A lung cancer diagnostic kit, characterized in that, The lung cancer diagnostic kit includes an antibody capable of specifically binding to the immune T-cell receptor marker for lung cancer diagnosis as described in claim 1, a nucleic acid probe capable of recognizing the immune T-cell receptor marker for lung cancer diagnosis as described in claim 2, or primers capable of amplifying the immune T-cell receptor marker for lung cancer diagnosis as described in claim 2.

6. The lung cancer diagnostic kit according to claim 5, characterized in that, The lung cancer diagnostic kit also includes standards and / or quality control products.

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