Method, system and equipment for evaluating treatment effect of PTT on pancreatic cancer

Abstract

The invention discloses a method, a system and equipment for evaluating the treatment effect of PTT on pancreatic cancer. According to the application disclosed by the invention, PTT is found to treat pancreatic cancer by regulating a caspase-1 / GSDMD pathway for the first time, and the expression level of GSDMD is found to be in positive correlation with the drug resistance of pancreatic cancer cells to 5-fluorouracil, irinotecan, paclitaxel and cis-platinum, which prompts that people can adopt a local treatment method of photothermal therapy instead of traditional chemotherapy for patients with high expression of GSDMD. Besides, the invention provides a method, a system and equipment for evaluating the treatment effect of PTT on pancreatic cancer and a method, a system and equipment for predicting the sensitivity of pancreatic cancer patients to chemotherapeutics, thereby assisting doctors in evaluating the treatment effect of pancreatic cancer patients and guiding clinicians to formulate individualized treatment schemes for patients.

Description

Technical Field

[0001] The present invention belongs to the field of bioinformatics, and in particular, relates to a method, system and device for evaluating the therapeutic effect of PTT on pancreatic cancer and a method, system and device for predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs. Background Art

[0002] Pancreatic cancer (PC) is one of the most lethal malignancies, with a 5-year survival rate of less than 10%. Despite advances in modern diagnosis, surgery, and drug therapy, the prognosis of PC remains very poor due to its unique immunosuppressive tumor microenvironment (TME) and natural resistance to conventional radiotherapy, chemotherapy, etc. In recent years, immunotherapy, including immune checkpoint inhibitors (ICIs), adoptive T cell therapy (ACT), and tumor vaccines, has made some progress in the treatment of PC. However, for PC patients with high microsatellite instability (MSI-H), the objective response rate (ORR) was only 18.2% in the single-arm KEYNOTE-158 trial, which was much lower than the response rates of MSI-H cholangiocarcinoma (40.9%), small intestine (42.1%), gastric (45.8%), and endometrial (57.1%) cancers. In addition, the use of cancer vaccines and ACTs is limited due to the difficulty in large-scale production and standardization.

[0003] Photothermal therapy (PTT) is a promising strategy for cancer treatment due to its advantages of short duration of action, few adverse reactions, good therapeutic effect, and minimal damage to normal tissues. Interventional PTT is a minimally invasive treatment that can effectively eradicate pancreatic tumors. Some studies have reported that PTT can control primary tumors and metastases, which may be due to the induction of immunogenic cell death (ICD) of tumor cells, the release of tumor antigens, and the subsequent enhancement of antitumor immune responses.

[0004] Therefore, accurately predicting the sensitivity of pancreatic cancer patients to chemotherapy drugs and evaluating the therapeutic effect of PTT on pancreatic cancer patients play a very important role in pancreatic cancer treatment selection, surgical design and efficacy evaluation. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, the purpose of the present invention is to provide a method, system and device for evaluating the therapeutic effect of PTT on pancreatic cancer and a method, system and device for predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a method for evaluating the therapeutic effect of PTT on pancreatic cancer.

[0007] Furthermore, the method is performed by a computer and comprises the following steps: Acquiring data: Acquiring expression data of caspase-1 substrates in samples of pancreatic cancer patients after PTT treatment, wherein the caspase-1 substrates include one or more of the following: cleaved GSDMD, IL-18, and IL-1β; Processing data: inputting the expression data of the caspase-1 substrate into the constructed PTT evaluation model, wherein the PTT evaluation model predicts the therapeutic effect of PTT on pancreatic cancer patients based on the expression data of the caspase-1 substrate; Output the result.

[0008] Furthermore, the expression data of the caspase-1 substrate includes mRNA expression level data or protein expression level data.

[0009] Furthermore, the mRNA expression level data include but are not limited to data obtained by RT-PCR, qRT-PCR, in situ hybridization and RNA sequencing.

[0010] Furthermore, the protein expression level data include but are not limited to data obtained by immunoblotting, immunohistochemistry, enzyme-linked immunosorbent assay and mass spectrometry.

[0011] Further, the steps of constructing the PTT evaluation model are as follows: The expression data of caspase-1 substrates are obtained, wherein the caspase-1 substrates include one or more of the following: cleaved GSDMD, IL-18, and IL-1β; the expression data of the caspase-1 substrates are from untreated pancreatic cancer patients and pancreatic cancer patients after PTT treatment; the expression data of the caspase-1 substrates are input into a machine learning algorithm to construct a PTT evaluation model.

[0012] Furthermore, the PTT evaluation model obtains results through the following criteria: When the expression levels of any one or several of the cleavage types of the caspase-1 substrate GSDMD, IL-18, and IL-1β are higher than a threshold, a classification result is obtained that PTT is effective in treating pancreatic cancer patients; when the expression levels of any one or several of the cleavage types of the caspase-1 substrate GSDMD, IL-18, and IL-1β are lower than a threshold, a classification result is obtained that PTT is ineffective in treating pancreatic cancer patients.

[0013] In some embodiments of the present invention, the preset threshold is a representative value of normal samples of a pancreatic cancer population, including but not limited to a maximum value, a third quartile, and an average value. In some preferred embodiments of the present invention, the population sample includes more than 20 samples, such as 30, 50, 80, 100, 150, 200, 300, 500 or more.

[0014] Furthermore, the machine learning algorithm includes algorithm models developed using various development tools.

[0015] Furthermore, the development tools include but are not limited to TensorFlow, Scikit Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell.

[0016] Furthermore, the algorithm model includes but is not limited to linear regression model, logistic regression model, Lasso regression model, Ridge regression model, linear discriminant analysis model, nearest neighbor model, decision tree model, perceptron model, neural network model, support vector machine model, naive Bayes model, AdaBoost model, GBDT model, XGBoost model, LightGBM model, CatBoost model, and random forest model.

[0017] Furthermore, the patient includes humans and / or mammals.

[0018] Furthermore, the sample includes blood, tissue, and pancreatic juice.

[0019] A second aspect of the present invention provides a system for evaluating the therapeutic effect of PTT on pancreatic cancer.

[0020] Further, the system comprises: Data acquisition unit: used to obtain expression data of caspase-1 substrates in pancreatic cancer patient samples after PTT treatment, wherein the caspase-1 substrates include one or more of the following: cleaved GSDMD, IL-18, IL-1β; A data classification unit is used to classify and predict the data obtained by the data acquisition unit through the PTT evaluation model obtained by the construction method described in the first aspect of the present invention, and obtain a classification result of whether PTT is effective for the treatment of pancreatic cancer patients; Result output unit: used to output classification results.

[0021] A third aspect of the present invention provides a method for predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs.

[0022] Furthermore, the method is performed by a computer and comprises the following steps: Obtain data: Obtain the expression data of GSDMD in pancreatic cancer patient samples; Processing data: inputting the expression data of GSDMD into a constructed prediction model, wherein the prediction model predicts the sensitivity of pancreatic cancer patients to chemotherapeutic drugs based on the expression data of GSDMD, wherein the chemotherapeutic drug is selected from any one of 5-fluorouracil, irinotecan, paclitaxel and cisplatin; Output the prediction results.

[0023] Furthermore, the expression data of GSDMD includes mRNA expression level data or protein expression level data.

[0024] Furthermore, the mRNA expression level data include but are not limited to data obtained by RT-PCR, qRT-PCR, in situ hybridization and RNA sequencing.

[0025] Furthermore, the protein expression level data include but are not limited to data obtained by immunoblotting, immunohistochemistry, enzyme-linked immunosorbent assay and mass spectrometry.

[0026] Further, the steps of constructing the prediction model are as follows: Acquire the expression data of GSDMD; the expression data of GSDMD comes from a population sensitive to chemotherapy drugs and a population insensitive to chemotherapy drugs, and the chemotherapy drugs are selected from any one of 5-fluorouracil, irinotecan, paclitaxel and cisplatin; input the expression data of GSDMD into a machine learning algorithm to construct a prediction model.

[0027] Furthermore, the prediction model obtains prediction results through the following criteria: when the expression level of GSDMD is higher than a threshold, a classification result is obtained that the pancreatic cancer patient is insensitive to chemotherapy drugs; when the expression level of GSDMD is lower than a threshold, a classification result is obtained that the pancreatic cancer patient is sensitive to chemotherapy drugs.

[0028] A fourth aspect of the present invention provides a system for predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs.

[0029] Further, the system comprises: Data acquisition module: used to obtain the expression data of GSDMD in pancreatic cancer patient samples; Data classification module: used to classify and predict the data obtained in the data acquisition module through the prediction model obtained by the construction method described in the third aspect of the present invention, and obtain the classification result of whether the pancreatic cancer patient is sensitive to chemotherapy drugs; Output module: used to output classification results.

[0030] Furthermore, the machine learning algorithm includes algorithm models developed using various development tools.

[0031] Furthermore, the development tools include but are not limited to TensorFlow, Scikit Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell.

[0032] Furthermore, the algorithm model includes but is not limited to linear regression model, logistic regression model, Lasso regression model, Ridge regression model, linear discriminant analysis model, nearest neighbor model, decision tree model, perceptron model, neural network model, support vector machine model, naive Bayes model, AdaBoost model, GBDT model, XGBoost model, LightGBM model, CatBoost model, and random forest model.

[0033] A fifth aspect of the present invention provides a computer device and a computer-readable storage medium.

[0034] Furthermore, the device comprises: A memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the method for evaluating the therapeutic effect of PTT on pancreatic cancer described in the first aspect of the present invention or the method for predicting the sensitivity of pancreatic cancer patients to chemotherapy drugs described in the third aspect of the present invention is implemented.

[0035] Furthermore, the computer-readable storage medium stores a computer program, which, when executed by the processor, implements the method for evaluating the therapeutic effect of PTT on pancreatic cancer described in the first aspect of the present invention or the method for predicting the sensitivity of pancreatic cancer patients to chemotherapy drugs described in the third aspect of the present invention.

[0036] Advantages and beneficial effects of the present invention: The present invention discovers for the first time that PTT treats pancreatic cancer by regulating the caspase-1 / GSDMD pathway, and finds that the GSDMD expression level is positively correlated with the resistance of PC cell lines to multiple chemotherapeutic drugs. Based on this, the present invention provides a method, system and device for evaluating the therapeutic effect of PTT on pancreatic cancer and a method, system and device for predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs, thereby assisting doctors in evaluating the therapeutic effect on pancreatic cancer patients and guiding clinical physicians to formulate individualized treatment plans for patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a method for evaluating the therapeutic effect of PTT on pancreatic cancer provided by the present invention; Figure 2 A schematic diagram of the system structure for evaluating the therapeutic effect of PTT on pancreatic cancer provided by the present invention; Figure 3 A schematic flow chart of a method for predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs provided by the present invention; Figure 4 A schematic diagram of the system structure for predicting the sensitivity of pancreatic cancer patients to chemotherapy drugs provided by the present invention; Figure 5 A schematic diagram of the structure of a computer device provided by the present invention; Figure 6 Figure 2 shows the experimental results of PTT inducing pyroptosis of pancreatic cancer cell lines through the caspase-1 / GSDMD pathway; (a) Real-time morphological tracking of SW-1990-LUC cells after PTT treatment under bright field microscopy (cells were incubated with ICG (100 μg mL -1 ) treatment, 1 W cm -2 Irradiated for 5 min, the white arrows indicate the morphological characteristics of cell pyroptosis, scale bar 100 μm); (b): Pyroptosis index of SW-1990-LUC cells at different time points after PTT; (c and d): LDH release in SW-1990-LUC and Pan02-luc cells after PTT; (e): Expression and cleavage of GSDMD and caspase-1 in SW-1990-LUC cells; (f and g): Morphology and pyroptosis index of SW-1990-LUC cells after PTT with or without VX-765 (caspase-1 selective inhibitor) (scale bar 100 μm); (h and i): Release of IL-18 and IL-1β in SW-1990-LUC cells in different groups; Figure 7The results of the correlation between GSDMD expression level and PC cell line resistance to chemotherapeutic drugs are shown in the figure; (a) Drug resistance analysis: GSDMD gene expression is positively correlated with PC cell line resistance to 5-fluorouracil, irinotecan, paclitaxel, and cisplatin; (bc): H&E staining and anti-GSDMD antibody immunohistochemical analysis of PDOs (patient-derived organoids) (scale 100 μm); (d): Drug sensitivity of PDOs to 5-fluorouracil, measured by IC 50 As an indicator. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0039] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0041] Figure 1 The present invention provides a flow chart of a method for evaluating the therapeutic effect of PTT on pancreatic cancer. Specifically, the method comprises: 101: Obtaining data: Obtaining expression data of caspase-1 substrates in samples of pancreatic cancer patients after PTT treatment, wherein the caspase-1 substrates include one or more of the following: cleaved GSDMD, IL-18, and IL-1β; In some embodiments of the present invention, through extensive and in-depth research, it was found that after PTT treatment of pancreatic cancer cells, pancreatic cancer cells underwent pyroptosis. Further exploration of the pyroptosis mechanism revealed that the caspase-1 / GSDMD pathway was activated after PTT, and the expression level of cleaved GSDMD was significantly increased; in addition, inflammatory cytokines IL-18 and IL-1β were activated, and the concentrations of IL-18 and IL-1β in the cell supernatant of the PTT group were also significantly increased. This suggests that cleaved GSDMD, IL-18 and / or IL-1β can be used as good markers to evaluate the therapeutic effect of PTT on pancreatic cancer.

[0042] In some embodiments, the patient may be human or non-human, and may include, for example, animal strains or species used as a "model system" for research purposes. Likewise, patients may include adults or adolescents (e.g., children). In addition, patients may refer to any living organism that can benefit from the PTT described herein, preferably mammals (e.g., humans or non-humans). Examples of mammals include, but are not limited to, any member of the class of mammals: humans, non-human primates (e.g., chimpanzees), and other apes and monkeys; livestock, such as cattle, horses, sheep, goats, pigs; domestic animals, such as rabbits, dogs, and cats; experimental animals include rodents, such as rats, mice, and guinea pigs, etc. Examples of non-mammals include, but are not limited to, birds, fish, etc.

[0043] In the context of the present invention, the term "sample" as used herein refers to a composition obtained from or derived from a patient / subject, which contains cells and / or other molecular entities to be characterized and / or identified based on, for example, physical, biochemical, chemical and / or physiological characteristics. For example, a sample refers to any sample derived from a patient / subject that is expected or known to contain cells and / or molecular entities to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell cultures, cell supernatants, cell lysates, platelets, serum, plasma, vitreous humor, lymphatic fluid, synovial fluid, follicular fluid, semen, pancreatic juice, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tissue culture fluid, tissue extracts, homogenized tissues, cell extracts, and combinations thereof.

[0044] In a specific embodiment of the present invention, the sample includes human pancreatic adenocarcinoma cell line SW1990-LUC and mouse pancreatic cancer cell line Pan02-LUC, wherein the human pancreatic adenocarcinoma cell line SW1990-LUC was purchased from Xuanyi Biotechnology Service Center (Shanghai, China) and cultured in RPMI-1640 medium (8122269, Gibco). The mouse pancreatic cancer cell line Pan02-LUC was purchased from IMMOCELL (Xiamen, Fujian, China) and cultured in Dulbecco's modified Eagle medium (DMEM; 11965092, Gibco).

[0045] In one embodiment of the present invention, indocyanine green (ICG, Dandong Yichuang Pharmaceutical Co., Ltd., China) was selected as the photosensitizer for PTT, which has good biosafety and has been approved by the FDA for clinical use. The 808 nm laser source (MDL-H-808nm-2W-13040029, Changchun New Industrial Optoelectronics Technology Co., Ltd.) was used for PTT, and the output power density was set to 1 W cm -2 The cells in the ICG group and the PTT group were treated with ICG (100 μg mL -1 ) were incubated at 37°C for 6 h. The cells were washed three times with PBS, added with normal culture medium, and then irradiated with or without near-infrared laser for 5 min (λ = 808 nm, 1 W cm -2 ).

[0046] In some embodiments, the expression data of the caspase-1 substrate can be detected by applying methods well known in the art. For example, the expression level data of the caspase-1 substrate can be obtained by measuring the amount of RNA, mRNA or any other RNA species using methods well known in the art to obtain expression at the nucleic acid level, including digital PCR and real-time (RT) quantitative or semi-quantitative PCR, fluorescence activated cell sorting (FACS) and in situ hybridization.

[0047] In other embodiments, expression level data of caspase-1 substrates can also be obtained by measuring expression levels at the protein level, including mass spectrometry-based quantitative proteomics techniques, immunoassays, Western blotting, spectrophotometry, enzyme assays, ultraviolet assays, kinetic assays, electrochemical assays, colorimetric assays, turbidimetric assays, atomic absorption assays, flow cytometry, mass spectrometry flow cytometry, or any combination thereof.

[0048] In a specific embodiment of the present invention, the expression levels of cleaved GSDMD, IL-18, and IL-1β were obtained in the following manner. The expression level of GSDMD was detected by Western blotting, and the cells were washed with cold PBS, lysed in IP buffer (20mM pH 7.5 Tris, 150mM NaCl, 1% Triton X-100), and a protease inhibitor cocktail (Beyotime Biotechnology, China) was added. Total cell protein was extracted, and the protein concentration was determined by the BCA method (P0012, Beyotime Biotechnology, China). Equal amounts of protein were separated by SDS-PAGE, transferred to PVDF membranes (Millipore, Billerica, MA, USA), and probed with anti-DFNA5 / GSDME antibodies (1:1000, ab215191, Abcam, Cambridge, CB2 0AX, UK), anti-caspase-3 antibodies (1:1000, 9662, Cell Signaling Technology, Danvers, MA, USA), anti-GSDMD antibodies (1:1000, YT7991, Immunoway Biotechnology, Plano, TX, USA), anti-caspase-1 antibodies (1:1000, 2225, Cell Signaling Technology), anti-p-IRF3 antibodies (1:1000, 4947, CST), and anti-ß-Actin antibodies (1:1000, HX1827, Huaxing Biotechnology, China). Enzyme-labeled secondary antibodies recognizing mouse and rabbit IgG were used at a ratio of 1:5000. Immunoblotting was performed using an ECL reaction system and a gel imaging system (Tanon, China). The release of mature IL-1β and IL-18 in the cell culture supernatant was detected using an IL-1β enzyme-linked immunosorbent assay kit (EH001) and an IL-18 enzyme-linked immunosorbent assay kit (EH047). The manufacturer's instructions were followed. The absorbance was measured using a microplate reader (Bio-Rad Laboratories, USA) at a wavelength of 450 nm and a calibration wavelength of 655 nm.

[0049] In one embodiment of the present invention, we demonstrated that PTT can induce pyroptosis in pancreatic cancer cells. We used near-infrared light (808 nm) to irradiate SW-1990-LUC and Pan02-luc cells treated with ICG to observe whether PTT can induce cell pyroptosis. First, we explored the conditions of PTT and the temperature rise curves corresponding to different concentrations of ICG. The results showed that when the concentration of ICG solution was 100 μg mL-1 , the irradiation power density is 1 W cm -2 The temperature can be maintained at 42℃~48℃. Afterwards, the morphology of SW-1990-LUC and Pan02-luc cells in different groups was observed. After 5 minutes of irradiation, typical morphological characteristics of cell pyroptosis such as cell swelling and large bubbles on the plasma membrane were observed in the PTT group, indicating that PTT caused cell pyroptosis. After PTT, we also recorded the real-time morphological changes of SW-1990-LUC cells ( Figure 6 a). The pyroptosis index was used to evaluate the dynamic pyroptosis-inducing ability of PTT in vitro. Figure 6 As shown in b, the percentage of pyroptotic cells in PTT-treated SW-1990-LUC cells increased significantly over time. As mentioned above, cell death by perforating holes in the cell membrane (pyroptosis) is an effective combination of apoptosis and necrosis, and occurs much faster than other programmed cell death. The release of lactate dehydrogenase (LDH) was detected using the supernatant of SW-1990-LUC and Pan02-luc cells. Figure 6 As shown in c, the amount of LDH released from SW-1990-LUC cells in the PTT group was much higher than that in the blank group (25.25% vs. 6.55%; n=3; P<0.0001) and the ICG group (25.25% vs. 8.75%; n=3; P = 0.0001). Similar results were obtained in Pan02-luc cells ( Figure 6 d). The amount of LDH released in the PTT group was significantly higher than that in the blank group (22.81% vs. 4.70%; n=3; P<0.0001) and the ICG group (22.81% vs. 8.24%; n=3; P<0.0001). These results together prove that PTT induces pyroptosis in pancreatic cancer cell lines.

[0050] In another embodiment of the present invention, we demonstrated that PTT-triggered pyroptosis depends on the caspase-1 / GSDMD pathway. According to current research on pyroptosis pathways, the most common pathways of pyroptosis are the GSDMD-dependent classical inflammasome pathway and the GSDME-dependent non-classical pathway. Therefore, we studied the expression and cleavage of GSDMD and GSDME in pancreatic cancer cell lines after PTT. RT-qPCR detected GSDMA, GSDMB, GSDMC, GSDMD and GSDME in SW-1990-LUC cells, among which the expression level of GSDMD was significantly higher than that of GSDMA (P=0.0002), GSDMB (P<0.0001), GSDMC (P<0.0001) and GSDME (P<0.0001). Secondly, as Figure 6As shown in Figure e, the full length of GSDMD and GSDME (GSDMD-fl and GSDME-fl) were detected in SW-1990-LUC cells. The expression level of GSDMD-fl in the PTT group was lower than that in the blank group and ICG group, while the expression level of cleaved GSDMD (GSDMD-n) was significantly increased. There was no difference in the expression of GSDME-fl among the three groups, and no cleaved GSDME (GSDME-n) was detected. These data indicate that GSDMD is cleaved but not GSDME in pancreatic cancer cells after PTT.

[0051] GSDMD has been identified as a specific substrate of caspase-1. Western blotting was used to detect the expression and cleavage of caspase-1. Figure 6 As shown in Figure 5, cleaved caspase-1 was only detected in the PTT group, indicating that caspase-1 was cleaved in pancreatic cancer cells after PTT. To further analyze the role of caspase-1 in PTT-triggered pyroptosis, we applied the caspase-1 selective inhibitor VX-765 before PTT. Figure 6 As shown in f and g, after pretreatment with VX-765, the pyroptosis index at 60 min after PTT was significantly reduced (79.77% vs. 7.77%, P=0.0008). These results confirmed that caspase-1 is involved in PTT-induced pyroptosis of pancreatic cancer.

[0052] In the GSDMD-dependent classical inflammasome pathway, cleaved caspase-1 not only activates GSDMD, but also activates the inflammatory cytokines IL-18 and IL-1β, after which mature IL-18 and IL-1β are cleared from the cells. Therefore, the content of mature IL-18 and IL-1β in the cell supernatant is also an indicator of the caspase-1 / GSDMD pathway. In this study, the concentrations of IL-18 and IL-1β in the cell supernatant of the PTT group were significantly higher than those in the blank group and ICG group ( Figure 6 h, i). The above results indicate that PTT-triggered pyroptosis depends on the caspase-1 / GSDMD pathway.

[0053] 102: Processing data: inputting the expression data of the caspase-1 substrate into the constructed PTT evaluation model, wherein the PTT evaluation model predicts the therapeutic effect of PTT on pancreatic cancer patients based on the expression data of the caspase-1 substrate; In some embodiments of the present invention, the method for constructing the PTT evaluation model is known to those skilled in the art, and the step of associating the expression level of the caspase-1 substrate with a certain possibility or risk can be implemented and realized in different ways.

[0054] In the context of the present invention, the term "machine learning" refers to the use of computers to simulate or implement human learning activities, and technicians usually use different development tools to build machine learning algorithm models. The development tools include but are not limited to TensorFlow, Scikit Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, ApacheMahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, Spell. The algorithm model includes but is not limited to a linear regression model, a logistic regression model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a neighbor model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, a naive Bayes model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model, a CatBoost model, or a random forest model.

[0055] In one embodiment, after constructing the PTT evaluation model, the ROC curve analysis can be used to evaluate the effectiveness of the model.

[0056] ROC curve is a graph of the true positive rate (sensitivity) of a test against the false positive rate (100%-specificity) of the test. It is useful for describing the performance of a specific feature when distinguishing between two populations. Typically, feature data is selected across the entire population in ascending order based on the value of a single feature. Then, for each value of the feature, the true positive and false positive rates of the data are calculated. The true positive rate is determined by counting the number of cases with a value higher than the feature and divided by the total number of cases. The false positive rate is determined by counting the number of controls with a value higher than the feature and divided by the total number of controls. Although this definition refers to the case where the feature is increased compared to the control, the definition is also applied to the case where the feature is lower compared to the control (in this case, the sample with a value lower than the feature will be counted). ROC curves can be generated with respect to individual features, and can be generated with respect to other individual outputs, for example, a combination of two or more features can be combined with mathematical methods (e.g., addition, subtraction, multiplication, etc.) to provide a separate sum value, and the separate sum value can be plotted in the ROC curve. Additionally, any combination of multiple features where the combination is derived from separate output values ​​can be plotted in a ROC curve.

[0057] 103: Output the prediction result.

[0058] Figure 2 This is a schematic diagram of the system structure for evaluating the therapeutic effect of PTT on pancreatic cancer provided by the present invention.

[0059] The system is programmed or otherwise configured to include a data acquisition unit 201, a data classification unit 202, and a result output unit 203: Data acquisition unit: used to obtain expression data of caspase-1 substrates in pancreatic cancer patient samples after PTT treatment, wherein the caspase-1 substrates include one or more of the following: cleaved GSDMD, IL-18, IL-1β; A data classification unit is used to classify and predict the data obtained by the data acquisition unit through the PTT evaluation model obtained by the construction method described in the first aspect of the present invention, and obtain a classification result of whether PTT is effective for the treatment of pancreatic cancer patients; Result output unit: used to output classification results.

[0060] The system may be the user's electronic device or a computer system located remotely from the electronic device.

[0061] Figure 3 A schematic flow chart of a method for predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs provided by the present invention.

[0062] 301: Obtain data, obtain the expression data of GSDMD in pancreatic cancer patient samples; In one embodiment of the present invention, we analyzed the correlation between the expression level of GSDMD and the response to five pancreatic cancer treatment drugs, namely 5-fluorouracil, irinotecan, paclitaxel, gemcitabine and cisplatin, used clinically. Figure 7 As shown in a, GSDMD gene expression was positively correlated with the resistance of pancreatic cancer cell lines to 5-fluorouracil, irinotecan, paclitaxel, and cisplatin, but was not associated with the drug response to gemcitabine. These results suggest that overexpression of GSDMD in pancreatic cancer may indicate increased resistance to chemotherapy.

[0063] In another embodiment of the present invention, we established patient-derived organoids for validation. The process of establishing patient-derived organoids is as follows: Immediately after dissection, the tumor tissue was immersed in transfer medium at 4°C. After thorough washing with a wash buffer (KS100121, Daxiang Biotech), the washed tissue was minced and combined with a dissociation agent (KS100123, Daxiang Biotech). After digestion, the cells were collected, suspended and filtered. The resulting filtrate was centrifuged to collect the cells, and then the cells were resuspended in pancreatic cancer organoid culture medium (OC100138, Daxiang Biotech) and 3D matrix (DatrixGel™, Daxiang Biotech). The cell suspension was inoculated into a 24-well plate for organoid culture. All organoid models were routinely tested for mycoplasma. Tumor tissue and PDO were used for H&E staining and WES staining for validation. PDOs were established using tumor tissues with differential expression of GSDMD, named PDO A and PDO B, respectively. We then used patient-derived organoids to detect 5-fluorouracil drug sensitivity and found that PDO A IC 50 higher, GSDMD expression was higher ( Figure 7 d). These results suggest that GSDMD is a biomarker for predicting sensitivity to chemotherapeutic drugs in pancreatic cancer patients.

[0064] 302: Processing data: inputting the expression data of GSDMD into a constructed prediction model, wherein the prediction model predicts the sensitivity of pancreatic cancer patients to chemotherapy drugs based on the expression data of GSDMD, wherein the chemotherapy drugs are selected from any one of 5-fluorouracil, irinotecan, paclitaxel and cisplatin; In some embodiments of the present invention, the method of constructing a prediction model is known to those skilled in the art, and the step of associating the GSDMD expression level with a certain possibility or risk can be implemented and realized in different ways.

[0065] In one embodiment, after the prediction model is constructed, the diagnostic performance of the prediction model can be evaluated using ROC curve analysis.

[0066] 303: Output prediction results.

[0067] Figure 4 A schematic diagram of the system structure for predicting the sensitivity of pancreatic cancer patients to chemotherapy drugs provided by the present invention.

[0068] The system is programmed or otherwise configured to include a data acquisition module 401, a data classification module 402, and an output module 403: Data acquisition module 401: used to obtain the expression data of GSDMD in pancreatic cancer patient samples; Data classification module 402: used to classify and predict the data obtained in the data acquisition module using the prediction model obtained by the construction method described in the third aspect of the present invention, and obtain a classification result of whether the pancreatic cancer patient is sensitive to chemotherapy drugs; Output module 403: used to output classification results Figure 5 A schematic diagram of the structure of a computer device provided by the present invention.

[0069] The computer device 500 includes a processor 501 and a memory 502 coupled to the processor 501. The memory 502 stores program instructions. When the program instructions are executed by the processor 501, the processor 501 executes the above-mentioned method for evaluating the therapeutic effect of PTT on pancreatic cancer or the above-mentioned method for predicting the sensitivity of pancreatic cancer patients to chemotherapy drugs.

[0070] The processor 501 may also be referred to as a CPU (Central Processing Unit). The processor 501 may be an integrated circuit chip having the ability to process signals. The processor 501 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0071] The computer device 500 may be a mobile electronic device.

[0072] It should be understood that the systems, devices and methods described in the present invention can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0073] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0074] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0075] The above are only implementation methods of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for evaluating the therapeutic effect of PTT on pancreatic cancer, characterized in that: The method is performed by a computer and comprises the following steps: Acquiring data: Acquiring expression data of caspase-1 substrates in samples of pancreatic cancer patients after PTT treatment, wherein the caspase-1 substrates include one or more of the following: cleaved GSDMD, IL-18, and IL-1β; Processing data: inputting the expression data of the caspase-1 substrate into the constructed PTT evaluation model, wherein the PTT evaluation model predicts the therapeutic effect of PTT on pancreatic cancer patients based on the expression data of the caspase-1 substrate; Output the result.

2. The method according to claim 1, characterized in that The steps for constructing the PTT evaluation model are as follows: The expression data of caspase-1 substrates are obtained, wherein the caspase-1 substrates include one or more of the following: cleaved GSDMD, IL-18, and IL-1β; the expression data of the caspase-1 substrates are from untreated pancreatic cancer patients and pancreatic cancer patients after PTT treatment; the expression data of the caspase-1 substrates are input into a machine learning algorithm to construct a PTT evaluation model.

3. The method according to claim 1, characterized in that The PTT evaluation model obtains results through the following criteria: When the expression levels of any one or several of the cleavage types of the caspase-1 substrate GSDMD, IL-18, and IL-1β are higher than a threshold, a classification result is obtained that PTT is effective in treating pancreatic cancer patients; when the expression levels of any one or several of the cleavage types of the caspase-1 substrate GSDMD, IL-18, and IL-1β are lower than a threshold, a classification result is obtained that PTT is ineffective in treating pancreatic cancer patients.

4. A system for evaluating the therapeutic effect of PTT on pancreatic cancer, characterized in that: The system comprises: Data acquisition unit: used to obtain expression data of caspase-1 substrates in pancreatic cancer patient samples after PTT treatment, wherein the caspase-1 substrates include one or more of the following: cleaved GSDMD, IL-18, IL-1β; A data classification unit: used to classify and predict the data obtained in the data acquisition unit through the PTT evaluation model obtained by the construction method according to claim 2, and obtain a classification result of whether PTT is effective for the treatment of pancreatic cancer patients; Result output unit: used to output classification results.

5. A method for predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs, characterized in that: The method is performed by a computer and comprises the following steps: Obtain data: Obtain the expression data of GSDMD in pancreatic cancer patient samples; Processing data: inputting the expression data of GSDMD into a constructed prediction model, wherein the prediction model predicts the sensitivity of pancreatic cancer patients to chemotherapeutic drugs based on the expression data of GSDMD, wherein the chemotherapeutic drug is selected from any one of 5-fluorouracil, irinotecan, paclitaxel and cisplatin; Output the prediction results.

6. The method according to claim 5, characterized in that The steps of constructing the prediction model are as follows: obtaining the expression data of GSDMD; the expression data of GSDMD comes from a population sensitive to chemotherapy drugs and a population insensitive to chemotherapy drugs, and the chemotherapy drugs are selected from any one of 5-fluorouracil, irinotecan, paclitaxel and cisplatin; the expression data of GSDMD is input into a machine learning algorithm to construct a prediction model.

7. The method according to claim 5, characterized in that The prediction model obtains prediction results through the following criteria: when the expression level of GSDMD is higher than a threshold, a classification result is obtained that the pancreatic cancer patient is insensitive to chemotherapy drugs; when the expression level of GSDMD is lower than a threshold, a classification result is obtained that the pancreatic cancer patient is sensitive to chemotherapy drugs.

8. A system for predicting the sensitivity of pancreatic cancer patients to chemotherapy drugs, characterized in that: The system comprises: Data acquisition module: used to obtain the expression data of GSDMD in pancreatic cancer patient samples; Data classification module: used to classify and predict the data obtained in the data acquisition module through the prediction model obtained by the construction method of claim 6, and obtain the classification result of whether the pancreatic cancer patient is sensitive to chemotherapy drugs; Output module: used to output classification results.

9. The method according to claim 2 or claim 6, characterized in that: The machine learning algorithm includes an algorithm model developed using various development tools; Preferably, the development tools include but are not limited to TensorFlow, Scikit Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, Spell; Preferably, the algorithm model includes but is not limited to a linear regression model, a logistic regression model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a nearest neighbor model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, a naive Bayes model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model, a CatBoost model, and a random forest model.

10. A computer device and a computer readable storage medium, characterized in that: The device comprises: A memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the method for evaluating the therapeutic effect of PTT on pancreatic cancer as described in any one of claims 1 to 3 or the method for predicting the sensitivity of pancreatic cancer patients to chemotherapy drugs as described in any one of claims 5 to 7 is implemented; The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for evaluating the therapeutic effect of PTT on pancreatic cancer as described in any one of claims 1-3 or the method for predicting the sensitivity of pancreatic cancer patients to chemotherapy drugs as described in any one of claims 5-7 is implemented.

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