p53r248q mutation-based lung adenocarcinoma prognosis evaluation system

Abstract

This invention belongs to the field of tumor prognostic assessment technology and discloses a prognostic assessment system for lung adenocarcinoma based on the p53 R248Q mutation. The system includes a detection module, a data input module, a risk stratification module, and a result output module, which sequentially realize data interaction and flow. These four modules achieve unidirectional data flow and bidirectional interactive verification, forming a closed-loop prognostic assessment system. The functions of each module support each other and progress progressively. The core objective is to accurately screen patients with EGFR / p53 R248Q co-mutated lung adenocarcinoma and achieve individualized survival risk stratification based on the clinical cohort's overall survival (OS) quartile method. This system realizes the full-process implementation of molecular testing, patient screening, risk assessment, and clinical application.

Description

Technical Field

[0001] This invention belongs to the field of tumor prognostic assessment technology, specifically relating to a prognostic assessment system for lung adenocarcinoma based on p53 R248Q mutation. Background Technology

[0002] Lung adenocarcinoma is the most common pathological type of lung cancer, with persistently high incidence and mortality rates both globally and in my country. Furthermore, there are significant differences in prognosis among individual patients; even patients with similar pathological stages and driver gene status can have survival times that differ by several times, posing a significant challenge to individualized clinical diagnosis and prognostic management. As a classic tumor suppressor gene, mutations in the p53 gene are closely related to the occurrence, development, invasion, and metastasis of lung adenocarcinoma. Among these, p53 R248Q, a hotspot mutation type of the p53 gene, can lead to the loss of function of the DNA-binding domain of the p53 protein, preventing the normal regulation of cell cycle, DNA repair, and apoptosis processes. This, in turn, enhances the malignant proliferation capacity of tumor cells and increases the tumor mutational burden and the risk of lymphovascular invasion, making it one of the core molecular markers of poor prognosis in lung adenocarcinoma patients.

[0003] The current prognostic assessment system for lung adenocarcinoma still has many shortcomings and is difficult to meet the needs of precise clinical assessment. These shortcomings are mainly reflected in the following aspects: 1. Limitations of traditional staging and pathological grading: The commonly used TNM staging system assesses prognosis based solely on tumor size, lymph node metastasis, and distant metastasis. The IASLC lung adenocarcinoma grading system also only stratifies based on pathological histological characteristics. Neither of them incorporates molecular-level malignancy characteristics. Approximately 30% of lung adenocarcinoma patients with the same TNM stage show significant differences in prognosis due to the presence of mutations such as p53R248Q. At the same time, in the early-stage poorly differentiated lung adenocarcinoma population, only about 30% of patients experience postoperative recurrence. Relying solely on the degree of pathological differentiation cannot effectively identify truly high-risk patients, which can easily lead to biased prognostic assessments. 2. The application of molecular markers is singular and inefficient: Currently, clinical practice only uses next-generation sequencing technology to detect the presence of p53 gene mutations, without specific detection of p53 R248Q mutations, nor quantification of its protein expression level and the proportion of positive cells. This makes it impossible to distinguish between low and high mutation burden states, thus hindering accurate assessment of its prognostic impact. Furthermore, current assessments often use a single molecular marker, failing to integrate core mutations like p53 R248Q with clinical indicators, resulting in a limited assessment dimension. 3. Lack of multi-dimensional data integration: In clinical practice, a patient's molecular characteristics, baseline clinical indicators, pathological features, and treatment response are all key factors influencing the prognosis of lung adenocarcinoma. For example, p53 R248Q mutations often coexist with EGFR mutations and can reduce the efficacy of EGFR-TKI treatment. Current assessment systems do not systematically integrate this multi-source data, requiring physicians to subjectively judge and integrate various information, leading to highly subjective and unrepeatable assessment results, making it difficult to form standardized assessment conclusions. 4. Lack of precise prognostic stratification tools: Currently, there is a lack of specific prognostic assessment tools for p53 R248Q mutations. It is impossible to transform the molecular characteristics of this mutation into quantifiable and standardized assessment indicators, and it is also difficult to construct a multi-dimensional prognostic stratification system based on this mutation. As a result, clinicians are unable to accurately stratify the risk of lung adenocarcinoma patients with p53 R248Q mutations, which easily leads to overtreatment of low-risk patients and insufficient treatment and follow-up of high-risk patients.

[0004] In summary, there is an urgent clinical need for a prognostic assessment system for lung adenocarcinoma based on the p53 R248Q mutation. This system should specifically detect this mutation and quantify its expression level, while integrating multi-dimensional clinical and pathological data to achieve precise prognostic stratification for lung adenocarcinoma patients. This would provide a scientific basis for developing individualized follow-up strategies and treatment plans, thereby improving the diagnosis and treatment outcomes and quality of life for lung adenocarcinoma patients. 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 prognostic assessment system for lung adenocarcinoma based on the p53 R248Q mutation.

[0006] According to one aspect of the present invention, a prognostic assessment system for lung adenocarcinoma based on p53 R248Q mutation is proposed, comprising a detection module, a data input module, a risk stratification module, and a result output module that sequentially realize data interaction and transfer; The detection module includes an anti-p53 R248Q monoclonal antibody and IHC staining reagent, which are used to specifically detect p53 R248Q mutation in lung adenocarcinoma tissue, output a positive / negative result of p53 R248Q mutation, and automatically transmit the result to the data input module. The data input module is used to input clinical pathological treatment data, OS (overall survival) data and EGFR mutation detection data of lung adenocarcinoma patients, receive the judgment results transmitted by the detection module and complete co-mutation matching screening, and after verifying the data, transmit the standardized data package of target patients who meet the screening conditions to the risk stratification module. The risk stratification module has a built-in OS quartile stratification standard, which is used to receive the standardized data packets of the target patients, complete the risk level classification based on the OS data of the lung adenocarcinoma patients, and match the median OS prediction value (expected median survival time) corresponding to each risk level. The result output module is used to generate a visualized assessment report based on the stratification results of the risk stratification module; the assessment report includes the p53 R248Q mutation detection results, risk level, and median OS prediction value.

[0007] Specifically, the four modules of the prognostic assessment system of this invention realize one-way data flow + two-way interactive verification, forming a closed-loop prognostic assessment system. The functions of each module support each other and are progressive. The core positioning is to accurately screen patients with EGFR / p53 R248Q co-mutant lung adenocarcinoma and to achieve individualized survival risk stratification based on the clinical cohort OS quartile method. The detection module serves as the core detection end, utilizing a self-developed anti-p53 R248Q monoclonal antibody and a standardized IHC detection procedure to achieve specific qualitative detection of the p53R248Q mutation. The mutation results are automatically transmitted to the data input module, providing molecular markers for target patient screening. The data input module is the data screening and processing end, inputting clinical pathology treatment, OS, and EGFR mutation data. This data is matched with the p53 R248Q mutation results from the detection module to screen for co-mutant patients. After data verification, a standardized data package is output, providing qualified target patient data for risk stratification. The risk stratification module is the core analysis end, incorporating a clinically validated OS quartile stratification standard. It analyzes the actual OS data of target patients, classifies risk levels, and matches median OS prediction values, serving as a core decision-making step for prognostic assessment. The results output module is the results presentation end, integrating mutation detection results, risk levels, median OS prediction values, and other information to generate a visualized report containing clinical recommendations, translating technical results into clinical diagnosis and treatment decisions. Overall, this system is not a single detection tool or data analysis tool, but an integrated biomedical system that deeply integrates self-developed biological antibody products with clinical data stratification algorithms, realizing the full-process implementation of molecular detection, patient screening, risk assessment and clinical application.

[0008] Specifically, the OS data in the data input module is actual clinical follow-up data for lung adenocarcinoma patients (the time from diagnosis to death or last follow-up). This data is manually compiled by medical staff based on the patients' medical records and follow-up records, and then entered into this module of the system. It is objective data actually collected clinically, not generated by the system. The median OS predicted value in the risk stratification module is a statistical predicted value for the corresponding risk group. One is raw clinical data, and the other is a cohort statistical result.

[0009] In some embodiments of the present invention, the anti-p53 R248Q monoclonal antibody binds specifically to p53 R248Q mutant lung adenocarcinoma cells / tissues. The amino acid sequence of the heavy chain variable region of the anti-p53 R248Q monoclonal antibody is shown in SEQ ID NO:1, and the amino acid sequence of the light chain variable region is shown in SEQ ID NO:2. Alternatively, the amino acid sequence of the heavy chain variable region is shown in SEQ ID NO:3, and the amino acid sequence of the light chain variable region is shown in SEQ ID NO:4; Alternatively, the amino acid sequence of the heavy chain variable region is shown in SEQ ID NO:5, and the amino acid sequence of the light chain variable region is shown in SEQ ID NO:6.

[0010] In some embodiments of the present invention, highly specific anti-p53 R248Q monoclonal antibodies are prepared by methods such as antigen design, animal immunization, and single B cell sorting. These antibodies can be used for IHC detection of R248Q mutations in lung adenocarcinoma tissues, and an OS quartile prognostic assessment system for EGFR / p53 R248Q co-mutant lung adenocarcinoma is constructed based on these antibodies.

[0011] In some embodiments of the present invention, the detection module further includes an optical microscope, cell counting software, p53 R248Q mutation positive control slides, and wild-type p53 negative control slides.

[0012] Specifically, the p53 R248Q mutation positive control section is a paraffin section of the p53 R248Q mutant cell line with a staining positivity rate ≥90%; the wild-type p53 negative control section is a paraffin section of the wild-type p53 cell line with a staining positivity rate ≤5%.

[0013] In some embodiments of the present invention, the specific detection and determination process of the p53 R248Q mutation of the detection module includes: preparing 3-4 μm sections from lung adenocarcinoma tissue specimens, staining with IHC staining reagent and anti-p53 R248Q monoclonal antibody, performing quality control with p53 R248Q mutation positive control sections and wild-type p53 negative control sections, selecting 4-5 hotspot areas under an optical microscope, and counting the proportion of positive cells using cell counting software. >80% of cell nuclei showing strong brownish-yellow staining are determined to be mutation positive, and the remaining staining patterns are determined to be mutation negative.

[0014] In some embodiments of the present invention, the clinical pathological treatment data includes the age, sex, clinical stage, tumor differentiation degree, vascular invasion, PS score, and PFS status of the lung adenocarcinoma patient after 6 months of EGFR-TKI treatment.

[0015] In some embodiments of the present invention, the comutation matching screening conditions of the data input module include: the p53 R248Q mutation determination result transmitted by the detection module is positive, and the entered EGFR mutation detection data is positive. If both conditions are met, the patient is determined to be an EGFR / p53 R248Q comutated lung adenocarcinoma patient; if only one is positive or both are negative, the patient is excluded and does not proceed to the subsequent risk stratification process.

[0016] In some embodiments of the present invention, verifying the data includes verifying the integrity and reasonableness of the data.

[0017] In some embodiments of the present invention, the data input module supports any one of manual entry, batch import from Excel spreadsheets, or automatic import from the HIS / LIS system interface, and has a built-in data verification function to provide real-time warnings for missing or abnormal data. After verification, it generates a standardized data package containing basic patient information, co-mutation status, and actual OS time.

[0018] In some embodiments of the present invention, the OS quartile stratification criteria are determined based on data from a clinical cohort of patients with EGFR / p53 R248Q co-mutated lung adenocarcinoma, wherein the OS P25 (25th percentile) of the clinical cohort is 7.0–9.5 months, and the OS P75 (75th percentile) is 28.0–32.0 months; the risk level classification and corresponding median OS prediction values ​​include: Patients with lung adenocarcinoma whose actual overall survival (OS) was >28.0–32.0 months were classified as low-risk, with a corresponding median OS predictor of ≥75.0 months. Patients with lung adenocarcinoma whose actual overall survival (OS) time meets the criteria of 7.0–9.5 months ≤ actual OS time ≤ 28.0–32.0 months are classified as the intermediate-risk group, with a corresponding median OS prediction of 28.0–32.0 months. Patients with lung adenocarcinoma whose actual overall survival (OS) was <7.0 to 9.5 months were classified as high-risk, with a corresponding median OS prediction of ≤10.0 months.

[0019] Specifically, the above-mentioned risk stratification was performed using the OS quartile method. First, the P25 and P75 of OS for all included cases were calculated. The group with actual OS time ≥ P75 was classified as low-risk group, also known as long-term survival group; the group with P25 ≤ actual OS time ≤ P75 was classified as medium-risk group, also known as intermediate survival group; and the group with actual OS time < P25 was classified as high-risk group, also known as short-term survival group.

[0020] In some embodiments of the present invention, the data input module also supports the supplementary entry of follow-up data, treatment response data, and OS update data; the risk stratification module can reclassify the risk level based on the supplementary OS update data; and the result output module can synchronously regenerate a visualized assessment report to achieve dynamic prognostic assessment of the target patient.

[0021] Specifically, based on the patient's subsequent follow-up results, the above data can be entered through the data input module. The system will automatically update and complete the risk stratification and generate a new visual assessment report, thereby realizing dynamic prognostic monitoring of the target patient.

[0022] In some embodiments of the present invention, the assessment report further includes at least one of the following: follow-up frequency recommendations, individualized treatment adjustment recommendations, survival curve reference graphs corresponding to each risk level, and clinical evidence-based medicine.

[0023] In some embodiments of the present invention, the visual evaluation report generated by the result output module supports exporting in PDF format, printing, emailing, or integration with an electronic medical record system.

[0024] According to a preferred embodiment of the present invention, at least the following beneficial effects are achieved: 1. The p53 R248Q monoclonal antibody prepared by this invention can specifically recognize the p53 R248Q mutation site and bind strongly only to p53 R248Q mutant lung adenocarcinoma cells / tissues without cross-reaction. This solves the technical defect of existing commercial p53 antibodies that can only roughly predict TP53 mutations and cannot accurately identify specific mutation sites.

[0025] 2. The IHC detection technology based on the aforementioned antibodies has high sample compatibility. It can be used to detect conventional FFPE (formalin-fixed paraffin-embedded) tissues, small puncture specimens, and even cytological specimens. It does not require sufficient samples with a tumor cell ratio of ≥20%, solving the problems of difficult sample collection and NGS detection failure caused by unqualified samples in patients with advanced lung adenocarcinoma, and reducing the burden of repeated sampling for patients. The cost of a single indicator for IHC detection is only 1 / 10 to 1 / 20 of that of NGS. It does not require the purchase of sequencing equipment worth millions of yuan, nor does it require a professional molecular pathology laboratory and bioinformatics analysts. It can be carried out using the existing equipment in the pathology departments of hospitals at all levels, perfectly adapting to the budget and technical conditions of primary medical institutions such as county-level hospitals. From specimen slicing and IHC staining to result interpretation, the entire testing process can be completed within 4-6 hours. Pathologists can directly determine the results through a microscope without the need for complex bioinformatics analysis. This solves the problems of complex NGS testing processes, long cycles, and the need for testing to be sent to grassroots clinics. It enables rapid diagnosis and result feedback of p53 R248Q mutations, saving time for clinicians to develop timely treatment plans.

[0026] 3. Based on the p53 R248Q mutation detection results of the antibody of this invention, a risk stratification system for lung adenocarcinoma was constructed using the OS quartile method. This system can accurately classify patients with EGFR / p53 R248Q co-mutations into three risk levels: low, intermediate, and high. Significant differences in survival exist among the three groups, clearly distinguishing patient populations with different survival prognoses. This assessment system can match specific median OS prediction values ​​for patients at different risk levels and generate visualized reports including follow-up frequency recommendations and individualized treatment adjustment suggestions. It achieves an integrated service from mutation detection to prognostic assessment and treatment guidance, solving the clinical challenges of ambiguous prognostic judgment and lack of targeted treatment strategies for patients with EGFR / TP53 co-mutated lung adenocarcinoma. Furthermore, this assessment system supports dynamic updates, allowing for re-stratification of risk based on subsequent OS and treatment response data. This enables dynamic monitoring of patient prognosis and timely adjustment of treatment plans, helping to delay tumor progression and improve patient survival outcomes.

[0027] 4. Current methods for accurate detection and prognostic assessment of EGFR / TP53 co-mutations heavily rely on NGS technology. Limited by factors such as cost, technology, and sample availability, these methods are difficult to implement in primary healthcare institutions, resulting in a large number of patients in these areas being unable to access accurate diagnostic and treatment services. The antibody detection technology and prognostic assessment system of this invention can be implemented using existing pathology equipment in primary healthcare facilities. It offers rapid detection, low cost, and simple operation, breaking down the technical barriers to accurate diagnosis and treatment of lung cancer in primary healthcare settings. This promotes the widespread adoption of accurate detection and personalized prognostic assessment of EGFR / p53 R248Q co-mutated lung adenocarcinoma in county-level and other primary healthcare institutions, helping to improve the overall level of lung cancer diagnosis and treatment in my country and improve the survival prognosis of lung cancer patients, especially those in primary healthcare settings.

[0028] 5. EGFR / p53 R248Q co-mutation is an important cause of primary / secondary EGFR-TKI resistance and poor prognosis in patients with lung adenocarcinoma. The p53 R248Q monoclonal antibody prepared in this invention enables the precise identification and detection of this mutation site, which not only provides a tool for clinical prognostic assessment, but also provides a key molecular target and research foundation for further in-depth research on the EGFR-TKI resistance mechanism mediated by p53 R248Q mutation, and the development of targeted resistance reversal drugs or targeted therapy regimens. It has important scientific research and clinical value for promoting the further development of targeted therapy for lung cancer. Attached Figure Description

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein: Figure 1This image shows the results of IHC verification of paraffin-embedded lung adenocarcinoma samples using anti-p53 antibody in Example 2 of this invention; where A~D represent P151H+R248W+F270V mutant LUAD; E~H represent R248W mutant LUAD; I~L represent TP53 wild-type LUAD; M~P represent C105V mutant LUAD; Q~T represent R248Q mutant LUAD; U~X represent R248Q mutant LUAD; the magnification is 200×. Figure 2 This is a Kaplan-Meier survival curve from Example 3 of the present invention; Number at risk shows the number of patients in each group still under follow-up at different time points. Detailed Implementation

[0030] The following will describe the concept and technical effects of the present invention clearly and completely with reference to the embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention. Unless otherwise specified, the experimental methods used in the embodiments are conventional methods; the materials and reagents used, unless otherwise specified, are commercially available. Example

[0031] This embodiment prepared a monoclonal antibody against p53 R248Q, and the specific process is as follows: (1) According to the protein sequence information published by Uniprot: P04637. TP53_HUMAN, p53 protein is a transcription factor consisting of 11 exons, 10 introns and 393 amino acid residues. The main mutation types of p53 include missense mutations, truncation mutations, in-frame mutations and splicing mutations.

[0032] For the Human TP53 R248Q point mutation, two different amino acid regions were selected as polypeptide immunogenic sequences. These two polypeptide immunogenic sequences are linear B-cell epitope peptides containing the mutation site at position 248 and approximately 15-20 amino acids flanking it. The amino acid sequences of the two polypeptide immunogenics are shown in SEQ ID NO:7: SCMGGMNQRPILTII and SEQ ID NO:8: CMGGMNQRPILTI. Simultaneously, to enhance immunogenicity, a recombinant protein (three copies of the same mutant epitope tandemly linked by the flexible linker GGSSG, with a 6×His tag introduced at the N-terminus or C-terminus) was also designed as an immunogenicity. After codon optimization of its nucleotide sequence, the optimized nucleic acid sequence was artificially synthesized and constructed into the pET28a vector, which was then transformed into *E. coli* BL21(DE3). Protein expression was induced for 6 h at 25°C using 0.5 mM IPTG. A recombinant protein immunogen with a purity of up to 90% was obtained after purification by nickel column affinity chromatography. Based on a structure- and surface-dominant epitope scheme, the amino acid sequence of this recombinant protein immunogen is shown in SEQ ID NO:9: MSDKIIHLTDDSFDTDVLKADGAILVDFWAEWCGPGSGSGSCMGGMNQRPILTIITLEDGSGMGGMNQRPILTIITLEDGSGNSSCMGGMNQRPILTGSGSGPCKMIAPILDEIADEYQGKLTVAKLNIDQNPGTAPKYGIRGIPTLLLFKNGEVAATKVGALSKGQLKEFLDANLA. All the above peptides were conjugated using the protein carrier KLH.

[0033] (2) New Zealand white rabbits (healthy, 8-week-old females, specific pathogen-free, SPF grade) were immunized with two polypeptide immunogens and one recombinant protein immunogen from step (1). The mass ratio of the specific polypeptide immunogen to the recombinant protein immunogen was 1:1. During immunization, 100 μg of immunogen (total mass of polypeptide and recombinant protein immunogen) was thoroughly emulsified with 2 μg of GM-CSF2 (immune chaperone) and 100 μg of Freund's adjuvant, and then administered via subcutaneous injection at multiple sites over a period of 3 months. After immunization, blood was collected from the marginal ear vein of the rabbit using the p53 R248Q immunogen for antibody titer detection (using indirect ELISA). The results showed an antibody titer greater than 6 × 10⁻⁶. 4The results were verified as qualified. New Zealand white rabbits were then immunized with the above-mentioned immunogen p53 R248Q. Seven days later, 10 mL of blood was collected via the ear artery, and total PBMCs were isolated using a commercial rabbit peripheral lymphocyte isolation kit. T lymphocytes and monocytes were removed by secondary screening using the surface markers CD4, CD8, and CD28 of T lymphocytes. Activated B lymphocytes were then selected using FITC-labeled goat anti-rabbit IgG. The immunogen p53 was then labeled using a commercial biotinylation kit according to the instructions, and conjugated with avidin-fluorescein PE reagent. The mixture was incubated at 37°C for 1 hour (with several agitations during incubation) to obtain the antigen-biotin-avidin screening reagent. The reagent was used according to the cell quantity (1×10⁻⁶). 6 Add 5 μL of the sample to the B lymphocytes and then perform positive screening by flow cytometry to obtain antigen-specific B lymphocytes.

[0034] (3) The sorted antigen-specific B lymphocytes were plated and cultured, and pre-treated mouse myeloma trophoblast cells (5×10⁶ cells / well) were added at a density of 1-2 cells / well. 4 Cells were cultured in 96-well plates with IL-2 (50 U / mL), IL-4 (50 ng / mL), and IL-6 (100 ng / mL) cytokines added simultaneously, and co-cultured at 37°C and 5% CO2 for 10 days. Cell supernatants were collected and screened using ELISA plates coated with p53 R248Q immunogen and reverse screening antigens (R248W (amino acid sequence as shown in SEQ ID NO:10: SCMGGMNWRPILTII), R248L (amino acid sequence as shown in SEQ ID NO:11: SCMGGMNLRPILTII), and WT (amino acid sequence as shown in SEQ ID NO:12: SCMGGMNRRPILTII)). Serum from unimmunized New Zealand white rabbits was used as a negative control. Only antigen-specific B lymphocytes that could bind to the R248Q immunogen but not the reverse screening antigen were retained. A positive result was defined as a result greater than 2.1 times the negative mean. The presence of target antibodies in the cell culture supernatant was measured.

[0035] All cells from positive wells were collected and lysed. Total RNA was extracted and reverse transcribed into cDNA. The heavy chain variable region (VH) gene and light chain variable region (VL) gene of the target antibody were amplified by PCR. The PCR product was cloned into a T vector, and at least 10 single clones were selected for sequencing. The correct VH and VL genes were confirmed by sequence alignment. The amino acid sequence of the heavy chain variable region encoded by the VH gene is shown in SEQ ID NO:1: MGTGLFWLLLVAVLKGVQCQSVEESGGRLVTPGTPLTLTCTVSGFSLSGKAMSWVRQAPGKGLEWIGAIDGGSGSTWSANWAKGRFTISKTSTTVHLKITSPTTEDTATYFCAGGYNIWGPGTLVTVSL. The amino acid sequence of the light chain variable region encoded by the VL gene is shown in SEQ ID NO:1. NO:2: MDTRAPTQLLGLLLLWLPGATFAQVLTQTPSPVSAAVGGTVTINCQASQSVYNNKNLAWYQQKPGQPPKLLIYEASKLASGVPPRFSGSGSGTQFTLTISGVQCDDAATYYCQGEFMCGSVDCILFGGGTEVVVKGDPVAPTVLIFPPAADQVATGTVTIVCVANKYFPDVTVTWEVDGTTQTTGIENSKTPQNSADCTYNLSSTLTLTSTQYNSHKEYTCKVTQGTTSVVQSFNRGDC (The clone number corresponding to this gene pair is 2G7).Alternatively, the amino acid sequence of the heavy chain variable region encoded by the VH gene is shown in SEQ ID NO:3: MGTGLYWLLLVAVLKGVQCQSVEESGGRLVTPGTPLTLTCTVSGFSLSGKAMSWVRQAPGKGLEWIGAIDGGSGSTWSANWAKGRFTISKTSTTVHLKITSPTTEDTATYFCAGGYNIWGPGTLVTVSL, and the amino acid sequence of the light chain variable region encoded by the VL gene is shown in SEQ ID NO:3. NO:4: MDTRAPTQLLGLLLLWLPGATFAQVLTQTPSPVSAAVGGTVTINCQASQSVYNNKNLAWYQQKPGQPPKLLIYEASKLASGVPPRFSGSGSGTQFTLTISGVQCDDAATYYCQGEFMCGSVDCILFGGGTEVVVKGDPVAPTVLIFPPAADQVATGTVTIVCVANKYFPDVTVTWEVDGTTQTTGIENSKTPQNSADCTYNLSSTLTLTSTQYNSHKEYTCKVTQGTTSVVQSFNRGDC (The clone number corresponding to this gene pair is 5D12).Alternatively, the amino acid sequence of the heavy chain variable region encoded by the VH gene is shown in SEQ ID NO:5: METGLHWLLLVAVLKGVQCQSVEESGGRLVTPGTPLTLTCTVSGFSLSGKAMSWVRQAPGKGLEWIGAIDGGSGSTWSANWAKGRFTISKTSTTVHLKITSPTTEDTATYFCAGGYNIWGPGTLVTVSL, and the amino acid sequence of the light chain variable region encoded by the VL gene is shown in SEQ ID NO:5. IDNO:6: MDTRAPTQLLGLLLLWLPGATFAQVLTQTPSPVSAAVGGTVTINCQASQSVYNNKNLAWYQQKPGQPPKLLIYEASKLASGVPPRFSGSGSGTQFTLTISGVQCDDAATYYCQGEFMCGSVDCILFGGGTEVVVKGDPVAPTVLIFPPAADQVATGTVTIVCVANKYFPDVTVTWEVDGTTQTTGIENSKTPQNSADCTYNLSSTLTLTSTQYNSHKEYTCKVTQGTTSVVQSFNRGDC (The clone number corresponding to this gene pair is 8G3). The VH and VL gene clones, verified by sequencing, were expressed in a mammalian expression vector using the pTT5 plasmid. Using polyethyleneimine (PEI) transfection, the high-concentration, high-purity antibody heavy and light chain recombinant plasmid obtained after expansion culture was transfected into HEK293F cells according to the PEI transfection reagent instructions. The antibody VH and VL genes were premixed at a molar ratio of 1:2. The plasmid was diluted using HEK293F cell basal medium, and an equal volume of PEI was diluted with the same medium (plasmid:PEI = 1:3, w / w). The mixture was transfected into HEK293F cells cultured in the logarithmic growth phase. Forty-eight hours after transfection, the antibody titer secreted in the supernatant was detected using an indirect ELISA method to identify cell lines with relatively high expression levels.

[0036] (4) The selected cell line was cultured to a volume of 100 mL, and the cell growth state was adjusted to the logarithmic growth phase. Plasmids were transfected, and feeding was performed every other day. The cell supernatant was harvested on day 6 post-transfection. The supernatant was filtered through a 0.22 μm filter and purified by affinity chromatography using a Protein G column. The antibodies obtained from the binding adsorption were eluted with citrate buffer at pH 6.0, and the eluent was collected. The pH was rapidly neutralized to between 7.2 and 7.4 using Tris-HCl solution at pH 8.8. After purification, the antibody purity and polymer content were evaluated by SDS-PAGE and SEC-HPLC. Antibodies with a purity >95% and a polymer content <5% were selected, concentrated by ultrafiltration and centrifugation to a concentration above 1 mg / mL, aliquoted, and stored at -80℃. Example

[0037] This embodiment describes the immunohistochemical (IHC) identification of the anti-p53 R248Q monoclonal antibody prepared in Example 1. The specific process is as follows: Seventeen surgical specimens of lung adenocarcinoma (LUAD) fixed in formaldehyde and embedded in paraffin (FFPE) were selected. Clinical information is shown in Table 1. All specimens underwent NGS testing to confirm the TP53 gene mutation status (including R248Q mutation) and the tumor cell content was >20%.

[0038] Table 1 Case gender Age (years) Specimen source Clinical staging 1 male 58 upper lobe of the right lung T2N0M0 (Phase II) 2 female 85 left lower lobe T2N2M0 (Phase III) 3 female 55 upper lobe of the right lung T1N0M0 (Phase I) 4 male 58 upper lobe of the right lung T1N0M0 (Phase I) 5 male 59 upper lobe of the right lung T1N0M0 (Phase I) 6 male 70 upper lobe of the right lung T1cN0M0 (Phase I) 7 male 67 left upper lobe T1N1M0 (Phase I) 8 female 73 left lower lobe T1N0M0 (Phase I) 9 female 58 left upper lobe T1N2M0 (Phase III) 10 male 58 right lower lobe T2N0M0 (Phase II) 11 female 61 left upper lobe T1N0M0 (Phase I) 12 female 47 Right middle lobe T1N0M0 (Phase I) 13 male 59 upper lobe of the right lung T1N0M0 (Phase I) 14 male 69 Right middle lobe T1N0M1b (Stage IV) 15 male 67 4R group lymph nodes T3N1M1 (Stage IV) 16 male 58 left upper lobe T1N0M0 (Phase I) 17 female 56 left lower lobe T1N1M0 (Phase I) FFPE specimen sections were approximately 4 μm thick and stained using the EnVision two-step method. The control used was a commercially available p53 antibody (clone DO-7), purchased from Dako. All antibodies produced a brownish-yellow stain. Staining was performed using a fully automated immunohistochemical staining system. The interpretation criteria for p53 antibody (DO-7) were as follows: >80% of cell nuclei showed strong staining, indicating abnormal expression (overexpression) / mutant staining pattern (usually missense mutations or in-frame deletions in the TP53 gene); all negative staining indicated abnormal expression (complete deletion) / mutant staining pattern (usually nonsense, frameshift, splicing, or deletion / insertion mutations in the TP53 gene); the simultaneous presence of scattered or patchy nuclei with varying proportions and different staining intensities (negative, weakly positive, moderately positive, strongly positive) indicated normal expression / wild-type staining pattern. The interpretation criteria for preparing anti-p53 R248Q monoclonal antibodies were as follows: >80% of cell nuclei showed strong staining, indicating a positive result, i.e., the presence of an R248Q mutation in the TP53 gene; other staining patterns were considered negative, i.e., the absence of an R248Q mutation. The IHC test results of the above specimens are shown in Table 2 and... Figure 1 As shown.

[0039] Table 2 Case Exons Mutation type Abundance p53 R248Q antibody Control p53 antibody (DO-7) Control antibody staining pattern 1 exon5exon7exon8 p.P151Hp.R248Wp.F270V 2.55%21.15%1.10% All negative Strong, 90% mutant 2 exon7 p.R248W 49.65% Weak, 5% Strong, 70%; Medium, 30% Unable to assess 3 WT WT / All negative In China, 5% wild type 4 exon4 p.C105V 28.50% All negative Strong, 73%; Medium, 25%; Weak, 2% Unable to assess 5 exon10 p.G360A 48.64% All negative Weak, 30% wild type 6 exon5 p.R181P 35.39% Medium, 1%; Weak, 5% Strong, 95%; Medium, 5% mutant 7 exon8 p.R282W 45.41% Weak, 2% Strong, 80%; Medium, 15%; Weak, 5% mutant 8 exon8 p.V274fs32 26.51% All negative All negative mutant 9 exon5 p.K161E 16.43% Medium, 30%; Weak, 20% Strong, 95%; Medium, 5% mutant 10 exon6 p.Y220C 29.92% Medium, 5%; Weak, 10% Strong, 90%; Medium, 7%; Weak, 3% mutant 11 exon5 p.A159V 12.48% All negative Strong, 25%; Medium, 70%; Weak, 5% Unable to assess 12 exon8 p.D281E 1.00% Medium, 10%; Weak, 30% Strong, 98%; Medium, 2% mutant 13 exon5 p.C176F 10.58% Weak, 2% Strong, 93%; Medium, 6%; Weak, 1% mutant 14 exon4exon5 p.A69Tp.C176F 1.14%21.10% Weak, 2% Strong, 90%; Medium, 10% mutant 15 exon7 p.R248Q 40.66% Strong, >90% Strong, 100% mutant 16 exon7 p.R248Q 33.62% Strong, >90% Strong, 100% mutant 17 exon7 p.R248Q 14.82% Strong, >90% Strong, 100% mutant Table 2 and Figure 1 The results showed that the anti-p53 R238Q monoclonal antibody prepared in Example 1 included three clones: 2G7, 5D12, and 8G3, and their staining results were basically consistent. Specifically, the three patients with R248Q mutations (cases 15, 16, and 17) all showed strong positivity in >90% of their cell nuclei, and IHC was positive, consistent with the results of the control p53 antibody (DO-7) and NGS; the six patients (cases 1, 3, 4, 5, 8, and 11) showed all-negative staining (corresponding to...). Figure 1 (The results are as follows: 3 cases (i.e., cases 7, 13, and 14) showed 2% weak positive nuclei, 1 case (i.e., case 2) showed 5% weak positive nuclei, and the remaining 4 cases (i.e., cases 6, 9, 10, and 12) showed varying degrees of positive nuclei, but the intensity was moderate to weak staining, with no strong staining. The above IHC results indicated negative results and the absence of R248Q mutation, which is consistent with the NGS results.)

[0040] Figure 1 The staining results are as follows: A~D, P151H+R248W+F270V mutant LUAD, with staining results of negative, negative, negative, and approximately 90% strong positive, respectively; E~H, R248W mutant LUAD, with staining results of approximately 5% weakly positive, approximately 5% weakly positive, approximately 3% weakly positive, and approximately 60% moderately to strongly positive, respectively; I~L, TP53 wild-type LUAD, with staining results of negative, negative, negative, and approximately 20% weakly to moderately positive, respectively; M~P, C105 V-mutant LUAD, staining results were negative, negative, negative, and approximately 70% moderately to strongly positive; Q~T, R248Q mutant LUAD, staining results were approximately 70% moderately to strongly positive, approximately 50% moderately to strongly positive, approximately 50% moderately to strongly positive, and approximately 100% strongly positive; U~X, R248Q mutant LUAD, staining results were approximately 100% moderately to strongly positive, approximately 100% moderately to strongly positive, approximately 100% moderately to strongly positive, and approximately 100% strongly positive. Example

[0041] This embodiment constructs a prognostic assessment system for lung adenocarcinoma based on the p53 R248Q mutation, which includes a detection module, a data input module, a risk stratification module, and a result output module. The effectiveness of the integration of each module and the overall clinical value of the system in patient risk stratification and survival prediction are verified, providing a standardized technical solution for personalized clinical diagnosis and treatment.

[0042] Study participants included lung adenocarcinoma patients carrying both EGFR and TP53 mutations (including p.R248Q and p.R248W subtypes), with only patients positive for the p53 R248Q mutation included in the prognostic assessment system. Case data were derived from two original clinicopathological and genetic testing datasets. The mutation status was confirmed by IHC detection and NGS validation using the p53 R248Q monoclonal antibody prepared in Example 1, making this the target population for the assessment system.

[0043] Inclusion criteria: a. Pathologically confirmed lung adenocarcinoma, and verified by antibody IHC+NGS in Example 1 as EGFR mutation combined with TP53 mutation (including subtypes such as p.R248Q and p.R248W); b. Complete clinical pathology and follow-up data, meeting the data input requirements of the assessment system, with no missing core fields such as OS and survival status (1 dead / 0 alive); c. Received standard EGFR-TKI targeted therapy, with complete and traceable TNM staging data before receiving EGFR targeted therapy, meeting the clinical baseline conditions for stratification in the assessment system.

[0044] Exclusion criteria: a. Single EGFR mutation or TP53 mutation without co-mutation; TP53 mutation not being the target subtype such as p.R248Q or p.R248W; b. Combined with other driver gene mutations such as ALK, ROS1, or BRAF, interfering with survival prognosis assessment; c. Combined with other malignant tumors or serious underlying diseases such as heart, liver, or kidney, affecting survival assessment; d. Not receiving standard EGFR-TKI treatment, interrupted follow-up, or having an empty TNM stage field before receiving EGFR targeted therapy, making it impossible to obtain complete OS data and treatment-related baseline information; e. Tumor cell content in tumor tissue specimens <20%, not meeting the IHC test sample requirements of the assessment system's detection module.

[0045] Experimental reagents, equipment, and analytical tools: a. Detection module-specific reagents and equipment: p53 R248Q monoclonal antibody from Example 1, IHC staining reagent, p53 R248Q mutation positive control slides (staining positivity rate ≤90%), wild-type p53 negative control slides (staining positivity rate ≤5%); fully automated immunohistochemical staining instrument, optical microscope (200x), cell counting software; EnVision two-step staining reagent kit. b. Data input module-specific tools: computer terminal, data entry system (supports manual, batch import from Excel spreadsheets, and automatic import via HIS / LIS system interface), data verification plugin (built-in integrity, rationality, and logical consistency verification functions, with a focus on verifying the integrity of EGFR / TP53 co-mutant subtypes, TNM staging, and OS data). c. Dedicated analysis tools for the risk stratification module: R 4.5.0 software (including core R packages such as survival (for survival data modeling (Kaplan-Meier curve fitting, Log-rank test)), survminer (for survival curve visualization (drawing survival plots with confidence intervals)), dplyr (for data filtering, grouping, and descriptive statistics), and ggplot2 (for adjusting survival plot format (colors, labels, etc.))), Excel; built-in analysis database of 191 cases of EGFR / TP53 co-mutated (including p.R248Q, p.R248W, and other subtypes) lung adenocarcinoma patients with clinical cohort OS quartile stratification criteria. d. Dedicated tools for the results output module: Visualized report generation system (supports PDF export, printing, email sending, and integration with electronic medical record systems).

[0046] This embodiment revolves around the sequential data flow logic of the assessment system's detection module → data input module → risk stratification module → result output module, fully realizing the system's construction, implementation, and verification. The operation process and technical requirements of each module are as follows: 1. Detection module: Provides the detection results of the core molecular marker of p53 R248Q mutation for the evaluation system. It is a prerequisite for the subsequent screening and stratification of comutated patients. The operation procedure is as follows: (1) Collect FFPE tissue specimens from lung adenocarcinoma patients and make 4μm thick slices to ensure that the tumor cell content is >20%; (2) Use the EnVision two-step method and complete the immunohistochemical staining with a fully automated immunohistochemical staining instrument. Use positive control slices and negative control slices for full-process quality control; (3) Select 5 hot spots under a 200x optical microscope and count the proportion of positive cells using cell counting software. >80% of cell nuclei are strongly brownish-yellow stained and are judged as p53R248Q mutation positive, and the remaining staining patterns are judged as negative; (4) Automatically transmit the mutation positive / negative judgment results to the data input module of the evaluation system for subsequent matching and screening of patients with EGFR / TP53 comutation (including p.R248Q, p.R248W and other subtypes).

[0047] 2. Data Input Module: Provides complete data of the selected target patients for the evaluation system, realizes data connection between modules, and meets the requirements of EGFR / TP53 co-mutation subtypes and treatment-related baseline data. The operation process is as follows: (1) Enter the patient's core gene mutation data, clinical pathology treatment data, and OS data through any one of the three methods: manual entry, Excel batch import, and HIS / LIS interface automatic import. Core gene mutation data: EGFR mutation detection results, p53 R248Q mutation detection results, TP53 mutation detection results and specific subtypes (including p.R248Q, p.R248W, etc.). Clinical pathology treatment data: age, gender, clinical stage, tumor differentiation degree, vascular invasion, PS score, PFS status at 6 months of EGFR-TKI treatment, and TNM stage before receiving EGFR targeted therapy. OS data: The time from diagnosis to death or last follow-up of the patient, calculated in months (divided by 30.42), and retained to one decimal place. (2) The built-in verification function is used to verify the completeness and rationality of the entered data, with a focus on verifying the comutation subtype, TNM stage, and OS core fields. Real-time warnings are given for missing or abnormal data. If the verification fails, the data is returned for supplementation. If the verification passes, the next step is initiated. (3) The p53R248Q mutation determination result transmitted by the detection module is received and matched with the entered EGFR mutation and TP53 mutation subtype data. If the EGFR mutation is positive, the p53R248Q mutation is positive, and the TP53 mutation is a subtype such as p.R248Q or p.R248W, the patient is identified as a target patient. Non-comutation and non-target mutation subtypes (i.e., other TP53 subtypes) are excluded and do not proceed to the subsequent stratification process. (4) For the target patients who are successfully matched, a standardized data package containing the patient's basic information, EGFR / p53 R248Q comutation, TNM stage before receiving EGFR targeted therapy, and actual OS time is generated and automatically transmitted to the risk stratification module of the assessment system.

[0048] 3. Risk Stratification Module: This is the core analysis module of the evaluation system. It achieves accurate risk level classification based on the clinical cohort of patients with EGFR / TP53 co-mutation. The operation process is as follows: (1) Call the clinical cohort data of 191 patients with EGFR / TP53 co-mutation lung adenocarcinoma who are positive for p53 R248Q mutation. The OS 25th percentile (P25) of this cohort is determined to be 8.97 months and the OS 75th percentile (P75) is 30.4 months. (2) Compare the actual OS time of the target patients transmitted by the data input module with the quartile threshold and execute the stratification logic: Low risk group: actual OS time of patients > 30.4 months (P75); Medium risk group: 8.97 months (P25) ≤ actual OS time of patients ≤ 30.4 months (P75); High risk group: actual OS time of patients < 8.97 months (P25). (3) Automatically match the corresponding median OS prediction value for each risk level to complete the stratification analysis. (4) The analysis results of patient risk level + corresponding median OS prediction value + comutation subtype information are automatically transmitted to the result output module of the assessment system.

[0049] 4. Results Output Module: Realizes the clinical application of the assessment system results, and provides direct evidence for clinical diagnosis and treatment by combining EGFR / TP53 co-mutation subtypes. The operation process is as follows: (1) Based on the results transmitted by the risk stratification module, a standardized visual assessment report is automatically generated. The report includes p53 R248Q mutation detection results, specific EGFR / TP53 co-mutation subtypes, patient risk level, median OS prediction value, TNM stage before receiving EGFR targeted therapy, survival curve reference graphs for each risk level, clinical evidence-based medicine evidence, and matching targeted follow-up frequency suggestions and individualized treatment adjustment suggestions for different risk levels + co-mutation subtypes. (2) The report is generated in PDF format, supports direct printing, email sending and push through the hospital's electronic medical record system, and also supports the regeneration of dynamic reports after subsequent follow-up data and treatment response data updates.

[0050] 5. Evaluation of the overall effectiveness of the system: In order to verify the smoothness of the connection between the modules of the system and the reliability of the stratification results for patients with EGFR / TP53 co-mutation, the following verification steps were performed after the system was implemented: (1) Descriptive statistics: The frequency (percentage) statistics of the group sample size, number of events (deaths), median OS, mortality rate of 191 target patients were used to analyze the comparability of baseline data such as p53 R248Q mutation status, co-mutation subtype, and TNM stage of each group; (2) Survival analysis: The Kaplan-Meier method was used to draw survival curves for patients of each risk level, and the median OS, survival rate and 95% confidence interval (95% CI) of each group were marked; (3) Statistical verification: The Log-rank test was used to compare the survival differences of the three groups of patients, with a significance level of α=0.05, to determine the statistical significance of the stratification results; (4) Dynamic verification: The OS data, treatment response data and disease progression data of patients were simulated and supplemented to verify the supplementary input function of the system data input module, the re-stratification function of the risk stratification module and the report update function of the result output module.

[0051] Experimental results: 1. Evaluation of the system's module integration results: Seamless data interaction and flow between modules enable accurate identification and screening of EGFR / TP53 co-mutant target patients with p53 R248Q mutation positivity. Mutation results from the detection module are automatically transmitted to the data input module, which performs dual verification and screening of co-mutant subtype and TNM staging, and transmits standardized data packets to the risk stratification module. The analysis results from the risk stratification module are pushed to the results output module in real time. There is no manual data transfer throughout the process, no data loss or deviation, and the integration between modules is smooth and the response is efficient, meeting the technical requirements of actual clinical applications.

[0052] 2. Data Input Module Screening Results: Through EGFR / TP53 co-mutation subtype matching and core data verification in the data input module, 191 patients with EGFR / TP53 co-mutated lung adenocarcinoma who were positive for p53 R248Q mutation were finally screened from the original dataset. Among them, there were 76 patients in the low-risk group, 64 patients in the intermediate-risk group, and 51 patients in the high-risk group. The baseline data of co-mutation subtype, TNM stage before EGFR targeted therapy, age, and gender of patients in each group were balanced and there were no statistical differences, so they were comparable.

[0053] 3. Risk stratification module stratification and median OS matching results: Low-risk group: actual OS > 30.4 months, matched median OS predictive value 82.0 months (95% CI); Intermediate-risk group: 8.97 months ≤ actual OS ≤ 30.4 months, matched median OS predictive value 30.4 months (95% CI); High-risk group: actual OS ≤ 8.97 months, matched median OS predictive value 8.97 months (95% CI). Survival time decreased significantly with increasing risk level in all three groups, validating the effectiveness of OS quartile-based stratification.

[0054] 4. Statistical results of survival characteristics of patients at different risk levels: According to descriptive statistics, there were significant differences in survival characteristics of patients with EGFR / TP53 co-mutation at different risk levels. The low-risk group had the best survival prognosis, while the high-risk group had the worst. The specific results are shown in Table 3.

[0055] Table 3 Risk grouping Sample size (N) number of events Median survival time (95% CI) Incidence rate (per 1000 person-years) Low risk 76 27 82 327.01 Medium risk 64 22 30.4 1010.72 High risk 51 24 8.97 1506.28 5. Statistical validation results for evaluating system effectiveness: Survival curves were plotted using the Kaplan-Meier method, and the Log-rank test results are shown (see...). Figure 2 The study showed that among 191 patients with EGFR / TP53 co-mutation positive p53 R248Q mutation, the survival differences among the low, intermediate, and high-risk groups were statistically significant (Log-rank P < 0.001). The survival curve of the low-risk group was consistently at the top, with a significantly higher survival rate than the intermediate and high-risk groups. The survival curve of the high-risk group was at the bottom, with a significantly lower survival rate than the low and intermediate-risk groups. The survival rate of the intermediate-risk group was between the two groups. There was no significant overlap between the survival curves of the groups, demonstrating that the risk stratification results of the assessment system can effectively distinguish EGFR / TP53 co-mutated lung adenocarcinoma patients with different survival prognoses.

[0056] 6. Evaluation of the system's dynamic verification results: After simulating the supplementary entry of OS update data, treatment response data, and disease progression data from subsequent patient follow-ups, the evaluation system's data input module can quickly supplement and verify co-mutation subtype association data, the risk stratification module can complete re-stratification within 5 minutes, and the result output module synchronously generates an updated visual evaluation report. Each module responds efficiently, and the stratification results are accurate, proving that the system can achieve dynamic prognostic monitoring and evaluation of patients with EGFR / TP53 co-mutations.

[0057] 7. Results Output Module: This module automatically generates a standardized PDF assessment report based on stratification results, p53 R248Q mutation status, and co-mutation subtype. The report includes all core test, typing, and analysis results, and supports printing, email delivery, and integration with electronic medical record systems. The follow-up frequency and treatment adjustment recommendations in the report are tailored to the clinical characteristics of EGFR / TP53 co-mutant patients, specifically: Low-risk group: Routine follow-up every 6 months, maintaining the original EGFR-TKI treatment regimen, and developing a personalized follow-up plan based on TNM staging; Intermediate-risk group: Intensive follow-up every 3 months, closely monitoring disease progression, optimizing EGFR-TKI dosage based on co-mutation subtype, and increasing the frequency of imaging follow-up when necessary; High-risk group: Close follow-up every 1-2 months, recommending changes to the treatment regimen (such as EGFR-TKI combined with chemotherapy, anti-angiogenic therapy) based on TNM staging and co-mutation subtype, and timely assessment of treatment response.

[0058] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention. Furthermore, the embodiments of the present invention and the features thereof can be combined with each other unless otherwise specified.

Claims

1. A prognostic assessment system for lung adenocarcinoma based on p53 R248Q mutation, characterized in that, The lung adenocarcinoma prognostic assessment system includes a detection module, a data input module, a risk stratification module, and a result output module that sequentially realize data interaction and transfer. The detection module includes an anti-p53 R248Q monoclonal antibody and IHC staining reagent, which are used to specifically detect p53 R248Q mutation in lung adenocarcinoma tissue, output a positive / negative result of p53 R248Q mutation, and automatically transmit the result to the data input module. The data input module is used to input clinical pathological treatment data, OS data and EGFR mutation detection data of lung adenocarcinoma patients, receive the judgment results transmitted by the detection module and complete co-mutation matching screening, and after verifying the data, transmit the standardized data package of target patients who meet the screening conditions to the risk stratification module. The risk stratification module has a built-in OS quartile stratification standard, which is used to receive the standardized data packets of the target patients, complete the risk level classification based on the OS data of the lung adenocarcinoma patients, and match the median OS prediction value corresponding to each risk level. The result output module is used to generate a visualized assessment report based on the stratification results of the risk stratification module; the assessment report includes the p53 R248Q mutation detection results, risk level, and median OS prediction value.

2. The lung adenocarcinoma prognostic assessment system according to claim 1, characterized in that, The anti-p53 R248Q monoclonal antibody binds specifically to p53 R248Q mutant lung adenocarcinoma cells / tissues. The amino acid sequence of the heavy chain variable region of the anti-p53 R248Q monoclonal antibody is shown in SEQ ID NO:1, and the amino acid sequence of the light chain variable region is shown in SEQ ID NO:

2. Alternatively, the amino acid sequence of the heavy chain variable region is shown in SEQ ID NO:3, and the amino acid sequence of the light chain variable region is shown in SEQ ID NO:4; Alternatively, the amino acid sequence of the heavy chain variable region is shown in SEQ ID NO:5, and the amino acid sequence of the light chain variable region is shown in SEQ ID NO:

6.

3. The lung adenocarcinoma prognostic assessment system according to claim 1, characterized in that, The detection module also includes an optical microscope, cell counting software, p53 R248Q mutation positive control slides, and wild-type p53 negative control slides.

4. The lung adenocarcinoma prognostic assessment system according to claim 1, characterized in that, The clinical pathological treatment data included the age, sex, clinical stage, tumor differentiation degree, vascular invasion, PS score, and PFS status of lung adenocarcinoma patients after 6 months of EGFR-TKI treatment.

5. The lung adenocarcinoma prognostic assessment system according to claim 1, characterized in that, The comutation matching screening criteria of the data input module include: the p53 R248Q mutation detection result transmitted by the detection module is positive, and the entered EGFR mutation detection data is positive. If both conditions are met, the patient is identified as an EGFR / p52 R248Q comutated lung adenocarcinoma patient; if only one is positive or both are negative, the patient is excluded and does not proceed to the subsequent risk stratification process.

6. The lung adenocarcinoma prognostic assessment system according to claim 1, characterized in that, Verifying the data includes verifying its completeness and reasonableness.

7. The lung adenocarcinoma prognostic assessment system according to claim 6, characterized in that, The data input module supports manual entry, batch import from Excel spreadsheets, and automatic import from the HIS / LIS system interface. It also has a built-in data verification function that provides real-time alerts for missing or abnormal data. After successful verification, it generates a standardized data package containing basic patient information, co-mutation status, and actual OS time.

8. The lung adenocarcinoma prognostic assessment system according to claim 7, characterized in that, The OS quartile stratification criteria were determined based on data from a clinical cohort of patients with EGFR / p52 R248Q co-mutated lung adenocarcinoma, where the OS P25 was 7.0–9.5 months and the OS P75 was 28.0–32.0 months. The risk stratification and corresponding median OS predictions included: Patients with lung adenocarcinoma whose actual overall survival (OS) was >28.0–32.0 months were classified as low-risk, with a corresponding median OS predictor of ≥75.0 months. Patients with lung adenocarcinoma whose actual overall survival (OS) time meets the criteria of 7.0–9.5 months ≤ actual OS time ≤ 28.0–32.0 months are classified as the intermediate-risk group, with a corresponding median OS prediction of 28.0–32.0 months. Patients with lung adenocarcinoma whose actual overall survival (OS) was <7.0 to 9.5 months were classified as high-risk, with a corresponding median OS prediction of ≤10.0 months.

9. The lung adenocarcinoma prognostic assessment system according to claim 1, characterized in that, The data input module also supports the supplementary entry of follow-up data, treatment response data, and OS update data; the risk stratification module can reclassify the risk level based on the supplementary OS update data; and the result output module can synchronously regenerate a visualized assessment report to achieve dynamic prognostic assessment of the target patient.

10. The lung adenocarcinoma prognostic assessment system according to claim 1, characterized in that, The assessment report also includes at least one of the following: recommendations for follow-up frequency, recommendations for individualized treatment adjustments, reference survival curves for each risk level, and clinical evidence-based medicine.

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