Biomarker for predicting treatment response of ovarian cancer patient to VEGFR (vascular endothelial growth factor receptor) inhibitor and application of biomarker

Abstract

The invention provides a biomarker for predicting the treatment response of an ovarian cancer patient to a VEGFR (vascular endothelial growth factor receptor) inhibitor and application of the biomarker, and finds that the fat volume of the ovarian cancer patient can be used as the biomarker for predicting the curative effect of the VEGFR inhibitor to tumors. Based on two markers of visceral fat volume and subcutaneous fat volume, an SAS trajectory analysis model is constructed, and clinical benefit people of an ovarian cancer VEGFR inhibitor treatment scheme are screened out; further in-vitro co-culture and in-vivo animal experiments prove that the existence of fat cells can specifically enhance the anti-tumor effect of the VEGFR inhibitor, and clinical correlation is explained from a biological mechanism; the invention provides a new tool and theoretical basis for realizing individualized precise medication of ovarian cancer targeted therapy.

Description

Technical Field

[0001] This invention belongs to the field of disease diagnosis and treatment technology, specifically relating to a biomarker for predicting the response of ovarian cancer patients to VEGFR inhibitor treatment and its application. Background Technology

[0002] Ovarian cancer is the eighth most common malignant tumor among women worldwide and the leading cause of death from gynecological cancers. Early diagnosis of ovarian cancer is difficult, with approximately 75% of patients presenting at an advanced stage. The main reason for treatment failure in ovarian cancer is its widespread dissemination and metastasis to pelvic and abdominal organs, with the greater omentum being one of the most common sites of metastasis. The main cellular component of the greater omentum is adipocytes, which play a crucial role in maintaining the structure and endocrine function of the greater omentum. Studies have shown that adipocytes can release fatty acids that promote the rapid growth and metabolic remodeling of tumor cells and promote the metastasis of ovarian cancer cells to the greater omentum. More importantly, increasing evidence suggests that adipocytes can secrete large amounts of adipokines and have interactive connections with tumor angiogenesis signaling pathways, jointly promoting tumor cell growth and metastasis.

[0003] Vascular endothelial growth factor (VEGF) is a key driver of angiogenesis. Studies have shown that anti-angiogenic therapies targeting and blocking VEGF and its receptor (VEGFR) signaling pathways, including monoclonal antibodies and small molecule inhibitors, can effectively improve the prognosis of ovarian cancer patients. In a previous phase II clinical trial, our team observed that the combination of apatinib (a VEGFR-targeting tyrosine kinase inhibitor) and etoposide for platinum-resistant ovarian cancer achieved an objective response rate (ORR) of 54.3% and a median progression-free survival (PFS) of 8.1 months, both higher than existing international consensus treatment protocols. However, there is currently a lack of effective biomarkers to predict the clinical benefit of VEGFR inhibitor regimens in ovarian cancer, resulting in a limited number of patients benefiting from the combination therapy despite its efficacy. Summary of the Invention

[0004] To address the problems existing in current technologies, this invention provides a biomarker for predicting the response of ovarian cancer patients to VEGFR inhibitor therapy: individual fat volume, whose size is significantly correlated with prognosis. This invention elucidates the intrinsic link between adipocytes and VEGFR inhibitor therapy in ovarian cancer, providing a scientific basis and practical foundation for screening clinically beneficial populations for VEGFR inhibitor therapy regimens in ovarian cancer. One objective of this invention is to provide a biomarker for predicting the response of ovarian cancer patients to VEGFR inhibitor therapy and screening clinically beneficial populations for VEGFR inhibitor therapy regimens in ovarian cancer; another objective is to provide a model for predicting the response of ovarian cancer patients to VEGFR inhibitor therapy and screening clinically beneficial populations for VEGFR inhibitor therapy regimens in ovarian cancer.

[0005] To achieve the above objectives, the present invention employs the following technical solution: On one hand, the present invention provides the use of a biomarker for predicting the response of ovarian cancer patients to a VEGFR inhibitor treatment regimen, wherein the biomarker is an individual's fat volume.

[0006] Furthermore, the fat volume includes the volume of visceral adipose tissue and / or the volume of subcutaneous adipose tissue.

[0007] Addressing the clinical bottleneck of lacking effective predictive biomarkers for VEGFR inhibitor targeted therapy in ovarian cancer, this invention overcomes the limitations of existing technologies that only focus on the biological function of adipocytes and fail to establish predictive associations for efficacy. It proposes an innovative approach that uses fat volume (visceral adipose tissue volume and subcutaneous adipose tissue volume) as the core biomarker for screening patients who will benefit from this type of drug, and constructs a predictive model based on CT image quantification and SAS trajectory analysis. By using standardized CT imaging measurements, the measurement site was clearly defined as the area from the upper edge of the third lumbar vertebra to the lower edge of the fourth lumbar vertebra. A threshold of -190 to -30 HU was set to determine adipose tissue. Furthermore, by using a narrower density range, the VAT (-150 to -50 HU) of visceral adipose tissue and the SAT (-190 to -30 HU) of subcutaneous adipose tissue were precisely defined. Combined with the double-blind measurement principle to reduce subjective bias, the fat volume was quantified accurately, objectively, and reproducibly. Then, through SAS trajectory analysis, the continuous variable of fat volume was divided into subgroups with different prognostic characteristics, such as "persistently high fat volume" and "persistently low fat volume". This made the prediction model no longer a single cutoff value, but a more refined stratified tool that integrates baseline status and dynamic response.

[0008] The technical solution of this invention fills the gap in screening the population that benefits from VEGFR inhibitors for ovarian cancer, providing the first biomarker that is both scientific and practical. On the other hand, the model's prediction results enable more accurate identification of the population that benefits, effectively improving the treatment efficacy rate, reducing the waste of medical resources and patient suffering caused by ineffective treatment, and providing key support for individualized treatment of ovarian cancer.

[0009] On the other hand, the present invention provides a model for predicting the response of ovarian cancer patients to VEGFR inhibitor treatment, wherein the input of the model is an individual's fat volume data.

[0010] Furthermore, the model is a SAS trajectory analysis model.

[0011] The SAS trajectory analysis model is a statistical method based on a finite mixture model. Its task is to automatically identify subgroups and their unique trajectories (e.g., linear increase, decrease, or stability) from multiple measurements. In this study, the model input data consisted of fat volume (VAT / SAT) measurements obtained from multiple CT scans before and after treatment. The model's task was to analyze the patterns of these volumes over time and categorize patients into different "trajectory groups," such as the "persistently high fat volume group" and the "persistently low fat volume group" identified in this study. Since fat volume is not a static indicator, it varies with treatment, metabolism, and disease status. The trajectory model does not rely on a single measurement "snapshot" but captures dynamic patterns throughout the treatment process. Distinguishing between "persistently high" and "persistently low" patterns provides a more stable and inherent pro-tumor microenvironment than baseline values, thus establishing a more fundamental correlation with long-term efficacy (PFS).

[0012] In another aspect, the present invention provides a system for screening patients who will clinically benefit from VEGFR inhibitor treatment regimens for ovarian cancer, the system including a data analysis module; the data analysis module is used to analyze changes in an individual's fat volume over time.

[0013] Furthermore, the data analysis module measures an individual's fat volume using CT or MRI images.

[0014] Furthermore, the data analysis module uses SAS software to analyze the trajectory of an individual's fat volume changes over time.

[0015] Furthermore, the data analysis module performs analysis according to the following steps: (1) Obtain CT image data of the individual to be tested; (2) Measure the fat volume of an individual based on CT images; (3) Input the fat volume data into the SAS analysis software; (4) Based on the output results, the clinically benefiting population is selected; among them, individuals classified by the model as the persistently high fat volume group are selected as the clinically benefiting population.

[0016] Furthermore, the measurement site is the abdominal region corresponding to the upper edge of the third lumbar vertebra to the lower edge of the fourth lumbar vertebra, and the fat volume includes the volume of visceral adipose tissue and the volume of subcutaneous adipose tissue.

[0017] This invention, based on establishing fat volume as a biomarker, further optimizes the entire technical process from image acquisition to model construction, solving the core defects of traditional methods such as inconsistent measurement and limited verification. Firstly, in the acquisition stage, the measurement site is clearly defined as the area from the upper edge of the third lumbar vertebra to the lower edge of the fourth lumbar vertebra to avoid numerical deviations caused by different measurement planes. Secondly, in the quantification stage, a strict threshold for fat tissue density (-190~-30 HU) is set, and the density ranges and anatomical definitions of VAT and SAT are distinguished to ensure the objectivity of fat volume measurement. Thirdly, in the modeling stage, the SAS Proc Traj program is used for trajectory analysis, and the optimal model is selected through BIC values ​​and grouped average probabilities, avoiding the subjective arbitrariness of traditional grouping.

[0018] Furthermore, given that existing biomarker research relies solely on statistical correlations of clinical data and lacks mechanistic explanations of why biomarkers can predict efficacy, this invention constructs a three-tiered chain of evidence: clinical validation, mechanism exploration, and in vivo validation. At the clinical level, data from 46 platinum-resistant ovarian cancer patients demonstrate that apatinib treatment significantly prolongs progression-free survival (PFS) in patients with high VAT / SAT, with even better efficacy in patients with hyperlipidemia, laying the foundation for clinical effectiveness. At the cellular level, co-culture experiments of 3T3L1 adipocytes and ovarian cancer cells (SKOV3, Hey) reveal that apatinib, in the presence of adipocytes, requires only a 10 μM subtoxic dose to significantly inhibit tumor migration and invasion, while tumor cells cultured alone do not respond to this concentration, confirming that adipocytes are a key mediator for apatinib's action. At the animal level, using high-fat diet (HFD) and normal diet (ND) mouse models, it is confirmed that apatinib significantly inhibits tumor formation only in HFD mice, further validating the association between fat content and drug efficacy in vivo. Three-layer validation ensures the reliability of biomarkers, far exceeding studies that rely solely on statistical analysis of clinical data.

[0019] In another aspect, the present invention provides an application of a biomarker in constructing a predictive model for the efficacy of VEGFR inhibitors in ovarian cancer, wherein the biomarker is an individual's fat volume.

[0020] In another aspect, the present invention provides an application of a biomarker in screening patients who will clinically benefit from VEGFR inhibitor treatment regimens for ovarian cancer, wherein the biomarker is an individual's fat volume.

[0021] The present invention has the following beneficial effects: 1. This study pioneered the use of fat volume as a biomarker to screen patients who would benefit from VEGFR inhibitors in ovarian cancer and constructed a predictive model. This fills a gap in biomarker technology, accurately identifies suitable patients, reduces blind treatment, and significantly improves patient prognosis, demonstrating significant clinical value. 2. A standardized model building process for CT threshold measurement and SAS trajectory analysis was established, which reduced subjective measurement bias, improved data reliability and repeatability, adapted to routine clinical workflows, required no additional equipment, and facilitated multi-center promotion and reuse. 3. By adopting a three-tiered validation protocol of clinical-cell-animal, the intrinsic mechanism between fat volume and drug efficacy was elucidated, the evidence chain was strengthened, the scientific validity and persuasiveness of the protocol were enhanced, and solid support was provided for clinical translation. Attached Figure Description

[0022] Figure 1 The study demonstrates that high VAT, SAT, and hyperlipidemia are associated with prolonged survival after apatinib treatment. Specifically, AB shows the SAS trajectory analysis of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) volumes in patients with refractory ovarian cancer (n = 46) during apatinib treatment; CD shows Kaplan-Meier curves for progression-free survival in patients with low and high VAT and SAT volumes; E presents representative axial CT images of patients with low and high VAT and SAT volumes, showing the first and last CT scans performed during apatinib treatment; and F shows Kaplan-Meier curves for progression-free survival in patients with and without hyperlipidemia.

[0023] Figure 2This study demonstrates that apatinib inhibits the migration and invasion of ovarian cancer cells in the presence of adipocytes. In Figure A, the workflow for the co-culture experiment of adipocytes and ovarian cancer cells is shown. Undifferentiated 3T3-L1 adipocytes were seeded and cultured in the lower chamber of a Transwell plate for 14 days, then pretreated with apatinib for 24 hours. Ovarian cancer cells were then seeded in the upper chamber and cultured for another 24 hours with or without adipocytes and with or without apatinib. Figures BC show Transwell migration and invasion experiments of SKOV3 and Hey cells alone after apatinib treatment, as well as cells co-cultured with adipocytes. The magnification was 10x, and three random regions were selected for statistical analysis. The bar graph represents the mean ± standard error. Figure D shows the Transwell invasion experiment using SKOV3 cells alone and cells co-cultured with adipocytes under anlotinib treatment. The magnification was 10x, and three random regions were selected for statistical analysis. The bar graph represents the mean ± standard error. P <0.05,** P <0.01, P The value indicates a significant difference compared to the untreated control group.

[0024] Figure 3 This study demonstrates that apatinib specifically inhibits ovarian tumor formation in mice on a high-fat diet. In the diagram, A represents a schematic of the animal experiment; B represents the body weight of mice during the experiment (n = 7 in the normal diet group and n = 8 in the high-fat diet group), ND represents the normal diet group, and HFD represents the high-fat diet group; C shows representative images of ovarian cancer burden and progression; D represents the quantification of tumor burden using bioluminescence, where p / s / cm² / sr is the number of photons per second per square centimeter per s, and the bars represent the mean ± standard error for each mouse; EF shows representative images of hematoxylin-eosin stained sections of tumor tissue from the mesenteric and splenic regions, with the distribution of tumor nodules circled in black dashed lines, and the scale bar is 2.5 mm; G shows the statistical analysis of tumor nodules in each group, with the bars representing the mean ± standard error for each mouse. Detailed Implementation

[0025] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1: Construction and clinical validation of the VEGFR inhibitor efficacy prediction model for ovarian cancer provided by the present invention.

[0026] This embodiment first collects CT images (according to RECIST 1.1v criteria) of patients with platinum-resistant or refractory ovarian cancer during treatment with the targeted VEGFR inhibitor apatinib for efficacy evaluation. Continuous changes in visceral and subcutaneous adipose tissue volume are measured, and a SAS trajectory analysis model is constructed. Then, Kaplan-Meie analysis is used to analyze the correlation between changes in fat volume and patient treatment efficacy, verifying the impact of fat volume changes on the efficacy of targeted VEGFR therapy in ovarian cancer patients. The specific protocol is as follows: (1) Clinical sample collection After obtaining informed consent from patients and approval from the ethics committee of the affiliated institution, CT images were collected from patients with platinum-resistant or refractory ovarian cancer to assess the efficacy of treatment with the targeted VEGFR inhibitor apatinib (according to RECIST v1.1 criteria). Relevant clinical data were also collected, including but not limited to age, sex, location, clinical stage, TNM stage, pathological differentiation, and histological type.

[0027] (2) Measurement of fat volume All CT images were measured and analyzed by two radiologists unaware of the patient's clinical information. Measurements were taken of the area of ​​the patient's abdominal adipose tissue (VAT) and subcutaneous adipose tissue (SAT) corresponding to the distance from the upper edge of the third lumbar vertebra to the lower edge of the fourth lumbar vertebra. The volume was calculated using the vertebral height, with a threshold set to -190 to -30 Hounsfield units (HU). The VAT region was defined as intra-abdominal fat bound by the parietal peritoneum or transversalis fascia (excluding the spine and paraspinal muscles), with a density between -150 and -50 HU; the SAT region was defined as fat on the surface of the abdominal and back muscles, with a density between -190 and -30 HU.

[0028] (3) Construction of SAS trajectory analysis model The trajectory model was completed using the Proc Traj. program in SAS software. Since fat volume is a continuous variable, we first determined the number of trajectory groups based on its characteristics, and then further determined the polynomial order of each trajectory group. The best trajectory model was selected using the BIC and the average probability after grouping, and the trajectory fit was evaluated. Continuous variables were expressed as mean ± standard deviation (±s). One-way ANOVA was used for comparisons among multiple groups. For homogeneous variances, the Least Significant Difference (LSD) test was used; for unequal variances, Dunnett's T3 test was used for pairwise comparisons of means. Categorical variables were expressed as n (%), and inter-group comparisons were performed using the t-test.

[0029] (4) Model prediction results Between November 2016 and July 2021, 46 ovarian cancer patients who participated in the AEROC trial were enrolled in this study. The cutoff date for clinical data analysis was June 10, 2022. The median follow-up was 14.53 months (range 4.07–29.17 months). Figure 1 As shown in A and B, the SAS trajectory analysis model of VAT volume and SAT volume divided the samples into a persistently high fat volume group and a persistently low fat volume group. In the VAT group, 63.3% of patients had high fat volume and 36.7% had low fat volume; in the SAT group, 48.2% of patients had high fat volume and 51.8% had low fat volume.

[0030] Subsequently, we used Kaplan-Meier curve analysis to compare the prognosis of patients with high and low visceral adipose tissue volume after apatinib treatment. Based on consecutive CT scans, we also found that patients with persistently high visceral adipose tissue volume had significantly longer progression-free survival (PFS) compared to patients with low visceral adipose tissue volume. p = 0.022), patients with persistently high subcutaneous adipose tissue volume had significantly prolonged progression-free survival (PFS) compared to patients with low subcutaneous adipose tissue volume. p = 0.056)( Figure 1 C, D). We also investigated the association between lipid metabolism and patient response to apatinib. Interestingly, we found that during treatment, patients with hyperlipidemia had longer progression-free survival (PFS) compared to those without hyperlipidemia. p = 0.012)( Figure 1 D).

[0031] The above results demonstrate for the first time in 46 patients with platinum-resistant ovarian cancer that the dynamic trajectory of visceral adipose tissue volume during treatment is a significant predictor of apatinib efficacy. p =0.022), its predictive value is better than subcutaneous adipose tissue volume ( p =0.056). This not only provides direct evidence for the idea of ​​fat volume as a biomarker, but also suggests that visceral fat, an active endocrine organ, may be a key microenvironment affecting treatment efficacy. Furthermore, the finding that patients with hyperlipidemia have better prognoses further links the imaging phenotype of fat volume with the functional state of lipid metabolism, laying a solid foundation for subsequent research and mechanistic exploration, and indicating that visceral fat and related metabolic pathways are an important direction for optimizing targeted therapy for ovarian cancer. Example 2: Apatinib inhibits the migration and invasion of ovarian cancer cells in the presence of adipocytes.

[0032] To further verify the accuracy and universality of the prediction model constructed in this invention, in vitro cell experiments were conducted in this embodiment. First, a co-culture experimental model of 3T3L1 adipocytes and ovarian cancer cells (SKOV3 and Hey) was established, as follows: Figure 2 As shown in Figure A, 3T3L1 cells were then induced to differentiate into mature adipocytes and pretreated with apatinib for 24 hours. Ovarian cancer cells were then placed in the upper chamber, and adipocytes in the lower chamber for co-culture. The migration and invasion abilities of ovarian cancer cells that crossed the chamber membrane were then calculated. The specific experimental methods are as follows: (1) Differentiation of 3T3L1 adipocytes 3T3L1 preadipocytes were cultured in DMEM medium containing 10% fetal bovine serum at 37°C with 5% CO2. Once the cells reached complete contact inhibition, the medium was replaced with an induction differentiation medium of DMEM containing 10% fetal bovine serum, supplemented with 1.5 μM dexamethasone, 1.5 μg / ml insulin, 1 μM rosiglitazone, and 500 μM IBMX. After four days of induction differentiation, the medium was replaced with a maintenance medium of DMEM containing 10% fetal bovine serum, supplemented with 10 μg / ml insulin. Adipocyte formation and lipid droplet appearance were observed after approximately 8-14 days of maintenance. Oil Red O staining confirmed the completion of adipocyte differentiation.

[0033] (2) Establishment of a co-culture system of 3T3L1 adipocytes and ovarian cancer cells Transwell cell culture plates with 0.4 μm pore size (6-well or 24-well) were used. During cell seeding, pre-differentiated mature adipocytes were placed in the lower layer, while tumor cells were seeded in the upper chamber.

[0034] (3) Cell experiments Apatinib was administered in either a co-culture system or a tumor cell-only culture system (control) to investigate its effects on the migration and invasion of ovarian cancer cells using Transwell assays. The experimental groups were divided into six groups: ① ovarian cancer cells; ② ovarian cancer cells + 10 μM apatinib; ③ ovarian cancer cells + 30 μM apatinib; ④ ovarian cancer cells + 60 μM apatinib; ⑤ ovarian cancer cells + adipocytes; ⑥ ovarian cancer cells + adipocytes + 10 μM apatinib; ⑦ ovarian cancer cells + adipocytes + 30 μM apatinib; ⑧ ovarian cancer cells + adipocytes + 60 μM apatinib. The specific experimental procedures are as follows: 1) Transwell assay to analyze the migration ability of ovarian cancer cells: 100 μL of 8×10⁻⁶ mcg of ovarian cancer cells was used. 5 ~3×10 6Tumor cells per mL were seeded into the upper chamber of an 8 μm Transwell chamber, with mature 3T3L1 adipocytes at the bottom of the lower chamber. 600 μL of complete cell culture medium containing 5%–10% FBS was added, along with the corresponding concentration of apatinib from the above groupings. The cells were incubated at 37°C and 5% CO2 for 24–32 h. Cells were fixed with paraformaldehyde, stained with crystal violet, and counted in three fields of view under a 20x optical microscope. The average value was taken.

[0035] 2) Transwell assay to analyze the invasive ability of ovarian cancer cells: Matrigel was pre-coated on the upper chamber membrane, and tumor cells were added after hydration for 5-6 hours. The results were observed using the same method as above to detect the invasive ability of the cells.

[0036] (4) Validation of other VEGFR-targeting anti-angiogenic drugs Using the small molecule VEGFR-targeting anti-angiogenic drug anlotinib, the Transwell invasion assay described above was conducted to investigate whether apatinib's effect on adipocytes to inhibit the migration and invasion of ovarian cancer cells is specific.

[0037] (5) Results Analysis like Figure 2 As shown in BC, when SKOV3 or Hey cells were cultured alone, no detectable inhibitory effect of apatinib on migration and invasion was observed; in contrast, in the presence of adipocytes, apatinib at concentrations as low as 10 μM significantly inhibited the migration and invasion of both ovarian cancer cell lines. Cell viability assays using the MTS assay after 72 hours further demonstrated that 10 μM apatinib did not cause significant cytotoxicity to either ovarian cancer cells or adipocytes. Furthermore, we found that subtoxic doses of anlotinib also inhibited the invasion of SKOV3 cells in the presence of adipocytes, while having no significant effect when cancer cells were cultured alone. Figure 2 (D) This result indicates that VEGFR inhibitors (such as apatinib and anlotinib) can effectively inhibit the migration and invasion of ovarian cancer cells only in the presence of adipocytes. This demonstrates, from a mechanism of action perspective, that the efficacy of VEGFR inhibitors can be predicted by an individual's fat volume, supporting the accuracy of the predictive model constructed in this invention. Example 3: Apatinib specifically inhibits the formation of ovarian tumors in mice fed a high-fat diet.

[0038] To investigate whether increased obesity affects the efficacy of apatinib, this embodiment further tested its antitumor effect using mice fed a high-fat diet. The experimental protocol is as follows: Figure 3 As shown in Figure A, the specific experimental procedure is as follows: (1) Experimental methods Animal experiments were approved by the Animal Welfare Committee of Sun Yat-sen University Cancer Center. Four-week-old female BALB / c mice were fed either a normal diet (ND group, containing 10 calories / percentage of normal feed, provided by Research Dietetics, New Brunswick, USA) or a high-fat diet (HFD group, containing 60 calories / percentage of fat, also provided by Research Dietetics, New Brunswick, USA) starting on day 0. Energy intake and body weight were measured every 10 days. On day 40, 2 × 10⁻⁶ calories / percentage of fat were administered via intraperitoneal injection (IP). 6 SKOV3 cells stably expressing the fluorescein reporter gene were transplanted into mice to simulate advanced ovarian cancer. Intraperitoneal injection of ovarian cancer cells led to tumor spread to the peritoneum. On day 50, all mice in the ND group (n=7 per group) and the HFD group (n=8 per group) were randomly assigned to the control and treatment groups. The control group received saline, while the treatment group received apatinib orally at 50 mL / kg daily until day 80. On days 50 and 75, tumor burden was monitored by injecting fluorescein into the animals under isoflurane anesthesia and performing fluorescein imaging using the IVIS Spectrum system (PerkinElmer). All mice were sacrificed on day 80, and the mesentery, spleen, and parasplenic tissues were fixed in formalin, embedded in paraffin, and then stained with hematoxylin and eosin and immunohistochemically.

[0039] (2) Results Analysis like Figure 3 As shown in Figure B, throughout the treatment period, the body weight of mice in the high-fat diet group was consistently approximately 20% higher than that of mice in the normal diet group. On day 50, signs of SKOV3 cell engraftment were observed in all groups, specifically, SKOV3 cell-derived bioluminescence was observed in all groups. Figure 3 C). Notably, on day 75, tumor progression was significantly faster in the high-fat diet group than in the normal diet group, while apatinib had no significant effect on tumor progression in the normal diet group. However, apatinib significantly reduced the ovarian cancer burden in high-fat diet mice (HFD apatinib). Figure 3 D). At day 80, during autopsy examination of the mice, we observed that the tumor infiltration rate in the high-fat diet control group (HFD control) was higher than that in the normal diet group. (See below for further information.) Figure 3 As shown in EF, hematoxylin and eosin (H&E) staining revealed an increase in the size or number of tumor nodules in the mesentery and spleen regions of mice on a high-fat diet compared to mice on a normal diet. Figure 3 The statistical results of G showed that, consistent with the results of bioluminescence imaging, apatinib treatment significantly reduced the number of tumor nodules in these regions in mice on a high-fat diet, while having only a slight effect on mice on a normal diet.

[0040] In summary, this embodiment used mice on a normal diet to simulate individuals with low fat volume and mice on a high-fat diet to simulate individuals with high fat volume. The experimental results confirmed that apatinib has no significant anti-tumor effect on individuals with low fat volume, but can significantly inhibit tumor growth in individuals with high fat volume. This strongly demonstrates that the efficacy of apatinib is highly dependent on the presence of adipocytes, revealing the effectiveness and scientific validity of individual fat volume as a predictive indicator of the efficacy of VEGFR inhibitors in ovarian cancer.

[0041] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. The use of a biomarker for predicting the response of ovarian cancer patients to a VEGFR inhibitor treatment regimen, characterized in that, The biomarker is the individual's fat volume.

2. The use as described in claim 1, characterized in that, The fat volume includes the volume of visceral adipose tissue and / or the volume of subcutaneous adipose tissue.

3. A model for predicting the response of ovarian cancer patients to VEGFR inhibitor therapy, characterized in that, The model takes an individual's fat volume data as input.

4. The model as described in claim 3, characterized in that, The model is the SAS trajectory analysis model.

5. A system for screening patients who will clinically benefit from VEGFR inhibitor therapy for ovarian cancer, characterized in that, The system includes a data analysis module; the data analysis module is used to analyze changes in an individual's fat volume over time.

6. The system as described in claim 5, characterized in that, The data analysis module measures an individual's fat volume using CT or MRI images.

7. The system as described in claim 6, characterized in that, The data analysis module uses SAS software to analyze the trajectory of an individual's fat volume changes over time.

8. The system as described in claim 7, characterized in that, The data analysis module performs the analysis according to the following steps: (1) Obtain CT image data of the individual to be tested; (2) Measure the fat volume of an individual based on CT images; (3) Input the fat volume data into the SAS analysis software; (4) Based on the output results, the clinically benefiting population is selected; among them, individuals classified by the model as the persistently high fat volume group are selected as the clinically benefiting population.

9. The application of a biomarker in constructing a predictive model for the efficacy of VEGFR inhibitors in ovarian cancer, characterized in that, The biomarker is the individual's fat volume.

10. The application of a biomarker in screening patients who will clinically benefit from VEGFR inhibitor therapy for ovarian cancer, characterized in that, The biomarker is the individual's fat volume.

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