CN119724622A - A method, system and device for evaluating the therapeutic effect of PTT on pancreatic cancer - Google Patents
A method, system and device for evaluating the therapeutic effect of PTT on pancreatic cancer Download PDFInfo
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Abstract
本发明公开了一种评价PTT对胰腺癌治疗效果的方法、系统及设备。本发明首次发现PTT通过调控caspase‑1/GSDMD通路治疗胰腺癌，并发现GSDMD表达水平与胰腺癌细胞对5‑氟尿嘧啶、伊立替康、紫杉醇和顺铂的耐药呈正相关，这提示我们对于GSDMD高表达的病人，可以采取光热治疗这种局部治疗方法，而不是传统化疗。此外，本发明为本领域提供了一种评价PTT对胰腺癌治疗效果的方法、系统和设备及预测胰腺癌患者对化疗药物敏感性的方法、系统和设备，从而辅助医生对胰腺癌患者治疗效果的评价，并指导临床医师给患者制定个体化的治疗方案。
The present invention discloses a method, system and device for evaluating the therapeutic effect of PTT on pancreatic cancer. The present invention discovers for the first time that PTT treats pancreatic cancer by regulating the caspase‑1/GSDMD pathway, and finds that the expression level of GSDMD is positively correlated with the resistance of pancreatic cancer cells to 5‑fluorouracil, irinotecan, paclitaxel and cisplatin, which suggests that for patients with high expression of GSDMD, photothermal therapy, a local treatment method, can be used instead of traditional chemotherapy. In addition, 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 of pancreatic cancer patients and guiding clinicians to formulate individualized treatment plans for patients.
Description
Technical Field
The invention belongs to the field of bioinformatics, and particularly relates to 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 chemotherapeutic drugs.
Background
Pancreatic Cancer (PC) is one of the most fatal malignant tumors, with a survival rate of less than 10% in 5 years. Despite advances in modern diagnosis, surgery and drug therapy, the prognosis for 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 have made some progress in the treatment of PC. However, for PC patients with high microsatellite instability (MSI-H), the Objective Remission Rate (ORR) in the single arm KEYNOTE-158 trial was only 18.2%, well below that of MSI-H cholangiocarcinoma (40.9%), small intestine (42.1%), stomach (45.8%) and endometrial (57.1%). Furthermore, the use of cancer vaccines and ACT is limited due to the difficulty of mass production and standardization.
Photothermal therapy (PTT) has the advantages of short acting time, small adverse reaction, good treatment effect, small damage to normal tissues and the like, and is a promising cancer treatment strategy. Interventional PTT is a minimally invasive treatment that can effectively eradicate pancreatic tumors. Some studies report that PTT may control primary tumors and metastasis, possibly due to induction of Immunogenic Cell Death (ICD) of tumor cells, release of tumor antigens, and subsequent enhancement of anti-tumor immune responses.
Therefore, accurately predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs and evaluating the therapeutic effect of PTT on pancreatic cancer patients are important in pancreatic cancer treatment selection, surgical design and efficacy evaluation.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide 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 chemotherapeutic drugs.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
In a first aspect, the invention provides a method of evaluating the effect of PTT on pancreatic cancer treatment.
Further, the method is completed by a computer, and the method comprises the following steps:
Obtaining expression data of caspasae-1 substrates in a pancreatic cancer patient sample after PTT treatment, wherein the caspasae-1 substrates comprise one or more of cracking GSDMD, IL-18 and IL-1 beta;
inputting the expression data of the caspasae-1 substrate into a constructed PTT evaluation model, wherein the PTT evaluation model predicts the treatment effect of PTT on pancreatic cancer patients based on the expression data of the caspasae-1 substrate;
and outputting a result.
Further, the caspasae-1 substrate expression data includes mRNA expression level data or protein expression level data.
Further, the mRNA expression level data include, but are not limited to, data obtained by RT-PCR, qRT-PCR, in situ hybridization, and RNA sequencing.
Further, the protein expression level data includes, but is not limited to, data obtained by immunoblotting, immunohistochemistry, enzyme-linked immunoassay, and mass spectrometry.
Further, the construction steps of the PTT evaluation model are as follows:
Obtaining expression data of caspasae-1 substrates, wherein the caspasae-1 substrates comprise one or more of pyrolysis GSDMD, IL-18 and IL-1 beta, the expression data of caspasae-1 substrates come from untreated pancreatic cancer patients and PTT treated pancreatic cancer patients, and the expression data of caspasae-1 substrates are input into a machine learning algorithm to construct a PTT evaluation model.
Further, the PTT evaluation model obtained the results by the following criteria:
When the expression level of any one or more of caspasae-1 substrate cleavage type GSDMD, IL-18 and IL-1 beta is higher than a threshold value, a classification result of PTT effective on pancreatic cancer patients is obtained, and when the expression level of any one or more of caspasae-1 substrate cleavage type GSDMD, IL-18 and IL-1 beta is lower than the threshold value, a classification result of PTT ineffective on pancreatic cancer patients is obtained.
In some embodiments of the invention, the preset threshold is a representative value of a normal sample of a population of pancreatic cancer, including but not limited to a maximum, a third quartile, an average. In some preferred embodiments of the invention, the population sample comprises 20 or more samples, for example 30, 50, 80, 100, 150, 200, 300, 500 or more.
Further, the machine learning algorithm includes an algorithm model developed using various development tools.
Further, 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.
Further, 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, GBDT model, XGBoost model, lightGBM model, catBoost model, a random forest model.
Further, the patient includes a human and/or a mammal.
Further, the sample includes blood, tissue, pancreatic juice.
In a second aspect, the invention provides a system for evaluating the effect of PTT on pancreatic cancer treatment.
Further, the system includes:
The data acquisition unit is used for acquiring expression data of caspasae-1 substrates in a pancreatic cancer patient sample after PTT treatment, wherein the caspasae-1 substrates comprise one or more of pyrolysis GSDMD, IL-18 and IL-1 beta;
The data classification unit is used for carrying out classification prediction on the data obtained in the data acquisition unit through the PTT evaluation model obtained by the construction method of the first aspect of the invention to obtain a classification result of whether the PTT is effective for treating the pancreatic cancer patient;
and the result output unit is used for outputting the classification result.
In a third aspect, the invention provides a method of predicting susceptibility of a pancreatic cancer patient to a chemotherapeutic agent.
Further, the method is completed by a computer, and the method comprises the following steps:
Obtaining expression data of GSDMD in a pancreatic cancer patient sample;
Inputting the GSDMD expression data into a constructed prediction model, wherein the prediction model predicts the sensitivity of a pancreatic cancer patient to a chemotherapeutic drug based on GSDMD expression data, and the chemotherapeutic drug is selected from any one of 5-fluorouracil, irinotecan, paclitaxel and cisplatin;
and outputting a prediction result.
Further, the GSDMD expression data includes mRNA expression level data or protein expression level data.
Further, the mRNA expression level data include, but are not limited to, data obtained by RT-PCR, qRT-PCR, in situ hybridization, and RNA sequencing.
Further, the protein expression level data includes, but is not limited to, data obtained by immunoblotting, immunohistochemistry, enzyme-linked immunoassay, and mass spectrometry.
Further, the construction steps of the prediction model are as follows:
Obtaining GSDMD expression data, wherein the GSDMD expression data are from a group sensitive to a chemotherapeutic drug and a group insensitive to the chemotherapeutic drug, the chemotherapeutic drug is selected from any one of 5-fluorouracil, irinotecan, paclitaxel and cisplatin, and inputting the GSDMD expression data into a machine learning algorithm to construct a prediction model.
Further, the prediction model obtains a prediction result through the following standard that when the expression level of GSDMD is higher than a threshold value, a classification result that the pancreatic cancer patient is insensitive to the chemotherapeutic medicine is obtained, and when the expression level of GSDMD is lower than the threshold value, a classification result that the pancreatic cancer patient is sensitive to the chemotherapeutic medicine is obtained.
In a fourth aspect, the invention provides a system for predicting susceptibility of a pancreatic cancer patient to a chemotherapeutic agent.
Further, the system includes:
The data acquisition module is used for acquiring expression data of GSDMD in a pancreatic cancer patient sample;
The data classification module is used for carrying out classification prediction on the data obtained in the data acquisition module through the prediction model obtained by the construction method of the third aspect of the invention to obtain a classification result of whether the pancreatic cancer patient is sensitive to the chemotherapeutic drug;
and the output module is used for outputting the classification result.
Further, the machine learning algorithm includes an algorithm model developed using various development tools.
Further, 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.
Further, 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, GBDT model, XGBoost model, lightGBM model, catBoost model, a random forest model.
A fifth aspect of the invention provides a computer device and a computer-readable storage medium.
Further, the apparatus comprises:
The processor is used for calling the program instructions, and realizing the method for evaluating the effect of PTT on pancreatic cancer treatment according to the first aspect of the invention or the method for predicting the sensitivity of a pancreatic cancer patient to chemotherapeutic drugs according to the third aspect of the invention when the program instructions are executed.
Further, the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method for evaluating the effect of PTT on pancreatic cancer treatment according to the first aspect of the present invention or the method for predicting susceptibility of pancreatic cancer patients to chemotherapeutic drugs according to the third aspect of the present invention.
The invention has the advantages and beneficial effects that:
The invention discovers that PTT treats pancreatic cancer by regulating and controlling a caspase-1/GSDMD pathway for the first time, and discovers that GSDMD expression level is positively correlated with drug resistance of PC cell lines to various chemotherapeutics, and based on the fact, 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 for the field, thereby assisting doctors in evaluating the treatment effect of pancreatic cancer patients and guiding clinicians to formulate individual treatment schemes for patients.
Drawings
FIG. 1 is a schematic flow chart of a method for evaluating the effect of PTT on pancreatic cancer treatment provided by the invention;
FIG. 2 is a schematic diagram of a system for evaluating the effect of PTT on pancreatic cancer treatment according to the present invention;
FIG. 3 is a flow chart of a method for predicting susceptibility of pancreatic cancer patients to chemotherapeutic agents provided by the present invention;
FIG. 4 is a schematic diagram of a system for predicting susceptibility of pancreatic cancer patients to chemotherapeutic agents according to the present invention;
FIG. 5 is a schematic diagram of a computer device according to the present invention;
FIG. 6 is a graph of experimental results of the initiation of coke-death of pancreatic cancer cell lines by PTT 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 treated with ICG (100. Mu.g mL -1), 1W cm -2 irradiated with 5min, white arrows indicate morphological characteristics of coke-death of cells, scale 100 μm), (b) coke-death index of SW-1990-LUC cells at different time points after PTT, (c and d) release of LDH from SW-1990-LUC and Pan02-LUC cells after PTT, (e) expression and cleavage of GSDMD and caspase-1 from SW-1990-LUC cells, (f and g) morphology and coke-death index (scale 100. Mu.m) of SW-1990-LUC cells after pretreatment of PTT with or without VX-765 (caspase-1 selective inhibitor), (h and i) release of different groups of SW-1990-LUC and IL-1. Beta. -18;
FIG. 7 is a graph showing the results of the correlation between GSDMD expression levels and resistance of PC cell lines to chemotherapeutic drugs, wherein GSDMD gene expression is positively correlated with resistance of PC cell lines to 5-fluorouracil, irinotecan, taxol, cisplatin, H & E staining of PDOs (parent-derived organoid, organoid) and immunohistochemical analysis of anti-GSDMD antibodies (scale 100 μm), and drug sensitivity of PDOs to 5-fluorouracil is indicated by IC 50.
Detailed Description
In order to enable those skilled in the art to better understand the present invention, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present invention with reference to the accompanying drawings.
In some of the flows described in the specification and claims of the present invention and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments according to the invention without any creative effort, are within the protection scope of the invention.
Fig. 1 is a schematic flow chart of a method for evaluating the effect of PTT on pancreatic cancer treatment, which specifically includes:
101, obtaining data, namely obtaining expression data of caspasae-1 substrates in a pancreatic cancer patient sample after PTT treatment, wherein the caspasae-1 substrates comprise one or more of cracking GSDMD, IL-18 and IL-1 beta;
in some embodiments of the invention, after the treatment of pancreatic cancer cells with PTT is found, the pancreatic cancer cells undergo scorching, the scorching mechanism is further explored, the caspase-1/GSDMD pathway is activated after PTT, the expression level of the cleaved GSDMD is obviously increased, in addition, the inflammatory cytokines IL-18 and IL-1 beta are activated, and the concentration of IL-18 and IL-1 beta in the supernatant of the PTT group cells is also obviously increased. This suggests that cleaved GSDMD, IL-18 and/or IL-1β may serve as good markers to evaluate the therapeutic effect of PTT on pancreatic cancer.
In some embodiments, the patient may be a human or a non-human, and may include, for example, an animal strain or species that is used as a "model system" for research purposes. Also, the patient may include an adult or adolescent (e.g., a child). Furthermore, a patient may refer to any living organism, preferably a mammal (e.g., human or non-human), that may benefit from the PTT described herein. Examples of mammals include, but are not limited to, any member of the mammalian class including humans, non-human primates (e.g., chimpanzees) and other apes and monkeys, livestock such as cows, horses, sheep, goats, pigs, domestic animals such as rabbits, dogs and cats, laboratory animals including rodents such as rats, mice and guinea pigs, and the like. Examples of non-mammals include, but are not limited to, birds, fish, and the like.
In the context of the present invention, the term "sample" is used to refer to a composition obtained or derived from a patient/subject, comprising cells and/or other molecular entities to be characterized and/or identified according to, 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 the 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, lymph, synovial fluid, follicular fluid, semen, pancreatic juice, amniotic fluid, emulsions, whole blood, blood derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tissue culture fluid, tissue extracts, homogenized tissue, cell extracts, and combinations thereof.
In a specific embodiment of the invention, the samples include the human pancreatic adenocarcinoma cell line SW1990-LUC and the mouse pancreatic adenocarcinoma cell line Pan02-LUC, the human pancreatic adenocarcinoma cell line SW1990-LUC being purchased from the Xuan-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 on Dulbecco's modified Eagle's Medium (DMEM; 11965092, gibco).
In one embodiment of the present invention, indocyanine green (ICG, dandong beneficial wound pharmaceutical company, china) was selected as the photosensitizer for PTT, which has good biosafety and has been approved for clinical use by the FDA. 808 A laser light source (MDL-H-808 nm-2W-13040029, vincristine industry photoelectric technology Co., ltd.) PTT was used by China semiconductor Co., ltd, china, and the output power density was set to 1W cm -2. The ICG group cells and PTT group cells were incubated with ICG (100. Mu.g mL -1) at 37℃for 6 h. Washed three times with PBS, normal medium was added, and then irradiated for 5min with or without near infrared laser (λ=808 nm, 1W cm -2).
In some embodiments, the expression data for caspasae-1 substrates can be detected by applying methods well known in the art. For example, caspasae-1 substrate expression level data 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.
In other embodiments, the expression level data for caspasae-1 substrates may also be obtained by measuring expression levels at the protein level, including quantitative proteomics techniques based on mass spectrometry, immunoassays, western blots, spectrophotometry, enzymatic assays, ultraviolet light assays, kinetic assays, electrochemical assays, colorimetric assays, turbidimetry assays, atomic absorption assays, flow cytometry, mass spectrometry, or any combination thereof.
In a specific example of the present invention, the following manner was used to obtain the expression levels of cleaved GSDMD, IL-18, IL-1β. The expression level of GSDMD was detected by Western blotting, the cells were washed with cold PBS, lysed in IP buffer (20 mM pH7.5 Tris, 150mM NaCl, 1% Triton X-100), and the protease inhibitor Cocktail (Beyotime Biotechnology, china) was added. Total cellular proteins were extracted and protein concentrations were determined using the BCA method (P0012, beyotime Biotechnology, china). Equal amounts of protein were separated by SDS-PAGE, transferred to PVDF membrane (Millipore, billerica, mass., USA) and probed with anti-DFNA 5/GSDME antibody (1:1000,ab215191,Abcam,Cambridge, CB, 0AX, UK), anti-caspase-3 antibody (1:1000,9662,Cell Signaling Technology, danvers, MA, USA), anti-GSDMD antibody (1:1000, YT7991, immunoway Biotechnology, plano, TX, USA), anti-caspase-1 antibody (1:1000,2225,Cell Signaling Technology), anti-p-IRF 3 antibody (1:1000, 4947, CST) and anti-beta-Actin antibody (1:1000, HX1827, china Huaxing organism). The ratio of the enzyme-labeled secondary antibody to the recognized mouse and rabbit IgG is 1:5000. Immunoblots used ECL reaction systems and gel imaging systems (Tanon, china). The release of mature IL-1 beta and IL-18 from cell culture supernatants was detected using an IL-1 beta ELISA kit (EH 001) and an IL-18 ELISA kit (EH 047). According to the manufacturer's instructions. Absorbance measurements were performed using a microplate reader (Bio-Rad Laboratories, USA) at a wavelength of 450 nm and a calibration wavelength of 655 nm.
In one embodiment of the invention, we demonstrate that PTT is capable of inducing the occurrence of focal death in pancreatic cancer cells. We used near infrared light (808 nm) to illuminate the ICG treated SW-1990-LUC and Pan02-LUC cells to see if PTT induced apoptosis. First we studied the conditions of PTT and the temperature rise curves corresponding to different concentrations of ICG. The results show that when the ICG solution concentration is 100 μg mL -1 and the irradiation power density is 1W cm -2, the temperature can be maintained at 42-48 ℃. Thereafter, different sets of SW-1990-LUC and Pan02-LUC cell morphologies were observed. After 5min of irradiation, the PTT group observes typical morphological characteristics of cell death such as cell swelling, large bubbles on plasma membrane and the like, which suggests that PTT causes cell death. After PTT we also recorded the real-time morphological changes of SW-1990-LUC cells (FIG. 6 a). The ability of PTT to dynamically induce apoptosis in vitro was evaluated using the coke-death index. As shown in FIG. 6b, the percentage of apoptotic cells of the PTT treated SW-1990-LUC cells increased significantly over time. As previously mentioned, cell death (pyro-death) by perforation of the cell membrane is an effective combination of apoptosis and necrosis, occurring at a much faster rate than other programmed cell death. The release of Lactate Dehydrogenase (LDH) was detected using SW-1990-LUC and Pan02-LUC cell supernatants. As shown in fig. 6c, LDH release was much higher in PTT group SW-1990-LUC cells than in the blank group (25.25% vs. 6.55%; n=3; p < 0.0001) and ICG group (25.25% vs. 8.75%; n= 3;P =0.0001). Similar results were also obtained in Pan02-luc cells (fig. 6 d), with PTT group LDH release significantly higher than in the blank group (22.81% vs. 4.70%; n=3; p < 0.0001) and ICG group (22.81% vs. 8.24%; n=3; p < 0.0001). Together, these results demonstrate that PTT induces coke death in pancreatic cancer cell lines.
In another embodiment of the invention, we demonstrate that PTT-triggered focal death is dependent on the caspase-1/GSDMD pathway. According to current studies of the focal death pathway, the most common pathways for focal death are the GSDMD-dependent classical inflammatory body pathway and the GSDME-dependent non-classical pathway. Thus, we studied the expression and cleavage patterns of GSDMD and GSDME in post-PTT pancreatic cancer cell lines. Both GSDMA, GSDMB, GSDMC, GSDMD and GSDME were detected by RT-qPCR in SW-1990-LUC cells, with GSDMD expressed in significantly higher amounts than GSDMA (p=0.0002), GSDMB (P < 0.0001), GSDMC (P < 0.0001) and GSDME (P < 0.0001). Next, as shown in FIG. 6e, full lengths of GSDMD and GSDME (GSDMD-fl and GSDME-fl) were detected in both SW-1990-LUC cells. PTT group GSDMD-fl expression levels were reduced compared to the blank and ICG groups, while the lytic GSDMD (GSDMD-n) expression levels were significantly increased. No difference in GSDME-fl expression was observed between the three groups, and no cleavage was detected GSDME (GSDME-n). These data indicate that GSDMD was cleaved instead of GSDME in post-PTT pancreatic cancer cells.
GSDMD have been identified as specific substrates for caspasae-1. Western blotting detects caspase-1 expression and cleavage. As shown in FIG. 6e, cleaved caspase-1 was detected only 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 apoptosis, we applied a caspase-1 selective inhibitor VX-765 prior to PTT. As shown in fig. 6f, g, after VX-765 pretreatment, the coke death index of 60min after PTT was significantly reduced (79.77% vs. 7.77%, p=0.0008). These results confirm that caspase-1 is involved in PTT-induced pancreatic cancer pyro-death.
In the GSDMD-dependent classical inflammatory body pathway, cleaved caspase-1 activates not only GSDMD, but also 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 cell supernatants is also an indicator of the caspase-1/GSDMD pathway. In this study, the concentration of IL-18 and IL-1β in the cell supernatants of the PTT group was significantly higher than in the blank and ICG groups (FIG. 6h, i). The above results indicate that PTT-triggered apoptosis is dependent on the caspase-1/GSDMD pathway.
102, Processing data, namely inputting the expression data of the caspasae-1 substrate into a constructed PTT evaluation model, wherein the PTT evaluation model predicts the treatment effect of PTT on pancreatic cancer patients based on the expression data of the caspasae-1 substrate;
In some embodiments of the invention, methods of constructing a PTT evaluation model are known to those skilled in the art, and the step of correlating the level of expression of caspasae-1 substrates with a certain likelihood or risk may be implemented and realized in different ways.
In the context of the present invention, the term "machine learning" refers to the use of a computer to simulate or implement human learning activities, and the skilled person typically builds an algorithmic model of machine learning using different development tools. 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. algorithm models including, but not limited to, linear regression models, logistic regression models, lasso regression models, ridge regression models, linear discriminant analysis models, neighbor models, decision tree models, perceptron models, neural network models, support vector machine models, naive bayes models, adaBoost models, GBDT models, XGBoost models, lightGBM models, catBoost models, or random forest models.
In one embodiment, after the PTT evaluation model is built, ROC curve analysis can be used to evaluate the performance of the model.
ROC curves are graphs of true positive rate (sensitivity) of the test versus false positive rate (100% -specificity) of the test. Useful for characterizing the performance of a particular feature when distinguishing between two populations. Typically, feature data is selected across the entire population in ascending order based on the values of individual features. Then, for each value of the feature, the true and false positive rates of the data are calculated. The true positive rate is determined by counting the number of cases above the value of the feature and dividing by the total number of cases. False positive rates were determined by counting the number of controls above the value of the feature and dividing by the total number of controls. Although the definition refers to the case where the characteristic is increased in the case compared to the control, the definition also applies to the case where the characteristic is lower in the case compared to the control (in this case, a sample below the value of the characteristic will be counted). The ROC curve may be generated with respect to individual features and may be generated with respect to other individual outputs, for example, a combination of two or more features may be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide an individual sum value, and the individual sum value may be plotted in the ROC curve. In addition, any combination of features, the combination of which results from separate output values, may be plotted in the ROC curve.
And 103, outputting a prediction result.
Fig. 2 is a schematic diagram of the system for evaluating the effect of PTT on pancreatic cancer treatment provided by the invention.
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:
The data acquisition unit is used for acquiring expression data of caspasae-1 substrates in a pancreatic cancer patient sample after PTT treatment, wherein the caspasae-1 substrates comprise one or more of pyrolysis GSDMD, IL-18 and IL-1 beta;
The data classification unit is used for carrying out classification prediction on the data obtained in the data acquisition unit through the PTT evaluation model obtained by the construction method of the first aspect of the invention to obtain a classification result of whether the PTT is effective for treating the pancreatic cancer patient;
and the result output unit is used for outputting the classification result.
The system may be the user's electronic device or a computer system remotely located relative to the electronic device.
FIG. 3 is a flow chart of a method for predicting susceptibility of pancreatic cancer patients to chemotherapeutic agents according to the present invention.
301, Acquiring data and expression data of GSDMD in a pancreatic cancer patient sample;
In one embodiment of the invention, we analyzed the correlation of GSDMD expression levels with the response of clinically used 5-fluorouracil, irinotecan, paclitaxel, gemcitabine, and cisplatin 5 pancreatic cancer therapeutic drugs. As shown in FIG. 7a, GSDMD gene expression correlated positively with the resistance of pancreatic cancer cell lines to 5-fluorouracil, irinotecan, paclitaxel, cisplatin, but not with the drug response to gemcitabine. These results indicate that over-expression of GSDMD in pancreatic cancer may indicate increased resistance to chemotherapy.
In another embodiment of the invention we have established that patient-derived organoids are validated. The patient-derived organoids were established by immersing tumor tissue in a transfer medium immediately after dissection at a temperature of 4 ℃. After thorough washing with wash buffer (KS 100121, daxiang Biotech), the washed tissue was minced and bound to a dissociating agent (KS 100123, daxiang Biotech). After digestion, cells were collected, suspended and filtered. The resulting filtrate was centrifuged to collect the cells, which were then resuspended in pancreatic cancer organoids medium (OC 100138, daxiang Biotech) and 3D matrix (DatrixGelTM, daxiang Biotech). The cell suspension was inoculated into 24-well plates for organoid culture. All organoid models routinely detect mycoplasma. H & E staining and WES staining validation was performed with tumor tissue and PDO. PDO was established using GSDMD differentially expressed tumor tissues, designated PDO a and PDO B, respectively. Thereafter, we examined 5-fluorouracil drug sensitivity using patient-derived organoids and found higher PDO a IC 50 and higher GSDMD expression (fig. 7 d). These results indicate GSDMD is a biomarker for predicting the sensitivity of pancreatic cancer patients to chemotherapeutic drugs.
302, Processing data, namely inputting the GSDMD expression data into a constructed prediction model, wherein the prediction model predicts the sensitivity of a pancreatic cancer patient to a chemotherapeutic drug based on GSDMD expression data, and the chemotherapeutic drug is selected from any one of 5-fluorouracil, irinotecan, paclitaxel and cisplatin;
In some embodiments of the invention, the method of constructing the predictive model is known to those skilled in the art and the step of correlating GSDMD expression levels with a certain likelihood or risk may be implemented and realized in different ways.
In one embodiment, after the predictive model is constructed, ROC curve analysis may be used to evaluate the diagnostic efficacy of the predictive model.
And 303, outputting a prediction result.
FIG. 4 is a schematic diagram of the system for predicting susceptibility of pancreatic cancer patients to chemotherapeutic drugs according to the present invention.
The system is programmed or otherwise configured to include a data acquisition module 401, a data classification module 402, and an output module 403:
the data acquisition module 401 is used for acquiring expression data of GSDMD in a pancreatic cancer patient sample;
The data classification module 402 is configured to classify and predict the data obtained in the data obtaining module by using the prediction model obtained by the construction method according to the third aspect of the present invention, so as to obtain a classification result of whether the pancreatic cancer patient is sensitive to the chemotherapeutic drug;
an output module 403 for outputting the classification result
Fig. 5 is a schematic structural diagram of a computer device according to the present invention.
The computer device 500 includes a processor 501 and a memory 502 coupled to the processor 501, where the memory 502 stores program instructions that, when executed by the processor 501, cause the processor 501 to perform the above-described method of evaluating the effect of PTT on pancreatic cancer therapy or the above-described method of predicting susceptibility of a pancreatic cancer patient to a chemotherapeutic agent.
The processor 501 may also be referred to as a CPU (Central Processing Unit ). The processor 501 may be an integrated circuit chip having signal processing capabilities. 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 device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The computer device 500 may be a mobile electronic device.
It should be understood that the systems, devices and methods described herein may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over 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.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules.
The foregoing is only the embodiments of the present application, and therefore, the patent scope of the application is not limited thereto, and all equivalent structures or equivalent processes using the descriptions of the present application and the accompanying drawings, or direct or indirect application in other related technical fields, are included in the scope of the application.
Claims (10)
1. A method of evaluating the effect of PTT on pancreatic cancer treatment, the method being performed by a computer, the method comprising the steps of:
Obtaining expression data of caspasae-1 substrates in a pancreatic cancer patient sample after PTT treatment, wherein the caspasae-1 substrates comprise one or more of cracking GSDMD, IL-18 and IL-1 beta;
inputting the expression data of the caspasae-1 substrate into a constructed PTT evaluation model, wherein the PTT evaluation model predicts the treatment effect of PTT on pancreatic cancer patients based on the expression data of the caspasae-1 substrate;
and outputting a result.
2. The method according to claim 1, characterized in that the PTT evaluation model is constructed as follows:
Obtaining expression data of caspasae-1 substrates, wherein the caspasae-1 substrates comprise one or more of pyrolysis GSDMD, IL-18 and IL-1 beta, the expression data of caspasae-1 substrates come from untreated pancreatic cancer patients and PTT treated pancreatic cancer patients, and the expression data of caspasae-1 substrates are input into a machine learning algorithm to construct a PTT evaluation model.
3. The method of claim 1, wherein the PTT evaluation model obtains results by:
When the expression level of any one or more of caspasae-1 substrate cleavage type GSDMD, IL-18 and IL-1 beta is higher than a threshold value, a classification result of PTT effective on pancreatic cancer patients is obtained, and when the expression level of any one or more of caspasae-1 substrate cleavage type GSDMD, IL-18 and IL-1 beta is lower than the threshold value, a classification result of PTT ineffective on pancreatic cancer patients is obtained.
4. A system for evaluating the effect of PTT on pancreatic cancer treatment, the system comprising:
The data acquisition unit is used for acquiring expression data of caspasae-1 substrates in a pancreatic cancer patient sample after PTT treatment, wherein the caspasae-1 substrates comprise one or more of pyrolysis GSDMD, IL-18 and IL-1 beta;
The data classification unit is used for carrying out classification prediction on the data obtained in the data acquisition unit through the PTT evaluation model obtained by the construction method of claim 2 to obtain a classification result of whether the PTT is effective for treating the pancreatic cancer patient;
and the result output unit is used for outputting the classification result.
5. A method of predicting susceptibility to a chemotherapeutic agent in a patient with pancreatic cancer, the method being performed by a computer, the method comprising the steps of:
Obtaining expression data of GSDMD in a pancreatic cancer patient sample;
Inputting the GSDMD expression data into a constructed prediction model, wherein the prediction model predicts the sensitivity of a pancreatic cancer patient to a chemotherapeutic drug based on GSDMD expression data, and the chemotherapeutic drug is selected from any one of 5-fluorouracil, irinotecan, paclitaxel and cisplatin;
and outputting a prediction result.
6. The method according to claim 5, wherein the predictive model is constructed by obtaining GSDMD expression data, wherein the GSDMD expression data is from a population sensitive to a chemotherapeutic agent selected from any one of 5-fluorouracil, irinotecan, paclitaxel, and cisplatin, and wherein the chemotherapeutic agent is not sensitive to a chemotherapeutic agent, and wherein the GSDMD expression data is input to a machine learning algorithm to construct the predictive model.
7. The method of claim 5, wherein the predictive model obtains the predictive result by obtaining a classification of pancreatic cancer patients insensitive to the chemotherapeutic agent when the expression level of GSDMD is above a threshold and obtaining a classification of pancreatic cancer patients sensitive to the chemotherapeutic agent when the expression level of GSDMD is below a threshold.
8. A system for predicting susceptibility to a chemotherapeutic agent in a pancreatic cancer patient, the system comprising:
The data acquisition module is used for acquiring expression data of GSDMD in a pancreatic cancer patient sample;
The data classification module is used for carrying out classification prediction on the data obtained in the data acquisition module through the prediction model obtained by the construction method of claim 6 to obtain a classification result of whether the pancreatic cancer patient is sensitive to the chemotherapeutic drug;
and the output module is used for outputting the classification result.
9. The method of claim 2 or claim 6, wherein the machine learning algorithm comprises an algorithm model developed with 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 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, GBDT model, XGBoost model, lightGBM model, catBoost model, a random forest model.
10. A computer device and a computer-readable storage medium, the device comprising:
A memory for storing program instructions, and a processor for invoking the program instructions, which when executed, implements the method of assessing the effectiveness of a PTT treatment for pancreatic cancer of any one of claims 1-3 or the method of predicting susceptibility of a pancreatic cancer patient to a chemotherapeutic agent of any one of claims 5-7;
The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of assessing the effect of PTT on pancreatic cancer treatment of any of claims 1-3 or the method of predicting susceptibility of a pancreatic cancer patient to a chemotherapeutic agent of any of claims 5-7.
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