Marker combination for diagnosing basal-type pancreatic ductal adenocarcinoma (PDAC) and its application

Abstract

The present invention provides a biomarker combination for the diagnosis of basal pancreatic ductal adenocarcinoma (PDAC) and its applications. Specifically, the present invention provides the use of a tumor microorganism or its detection reagent for (a) classifying pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing basal pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of having basal pancreatic ductal adenocarcinoma (PDAC); or for preparing a reagent or kit for (a) classifying pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing basal pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of having basal pancreatic ductal adenocarcinoma (PDAC), wherein the tumor microorganism is selected from the group consisting of Acinetobacter, Pseudomonas, Sphingopyxis, or a combination thereof.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and particularly to a biomarker combination for diagnosing Basal-type pancreatic ductal adenocarcinoma (PDAC) and its applications. Background Art

[0002] In recent years, studies have identified a subtype of PDAC called the Basal type through transcriptome analysis. This tumor subtype has a more aggressive clinical phenotype and a worse prognosis. Scientists have attempted to uncover the genetic mechanisms of different PDAC subtypes, yet no significant differences in gene mutations have been found among different tumor subtypes. The reasons for the clinical heterogeneity of PDAC subtypes remain unclear to date.

[0003] For nearly two decades, pancreatic cancer has been regarded as an inflammation-induced cancer. Patients with pancreatitis have a 13-fold higher risk of developing pancreatic cancer than normal. Some studies have shown that the inflammatory stromal response has an important impact on the progression of PDAC, yet the detailed molecular mechanisms by which inflammation leads to PDAC progression have not been elucidated. Increasing evidence indicates that the human microbiome plays an important role in activating immune responses and inducing cancer-related inflammatory reactions. Past studies have mainly focused on the role of gut microbiota and have found that gut microbiota have certain effects on esophageal cancer, gastric cancer, and colon cancer.

[0004] So far, the composition and structure of pancreatic tumor microbiota have not been fully elucidated, and the role of microbiota in tumor development also requires further research and exploration.

[0005] Therefore, there is an urgent need in the art to focus on the microbial factors in PDAC subtype heterogeneity, reveal the unique tumor microbiota composition and structure characteristics in different PDAC subtypes, and develop new tumor microbiota for predicting the clinical phenotypes of PDAC patients. Summary of the Invention

[0006] The objective of the present invention is to focus on the microbial factors in PDAC subtype heterogeneity, reveal the unique tumor microbiota composition and structure characteristics in different PDAC subtypes, and develop new tumor microbiota for predicting the clinical phenotypes of PDAC patients.

[0007] In a first aspect of the present invention, there is provided a use of a tumor microorganism or its detection reagent for (a) classifying pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing Basal-type pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of having Basal-type pancreatic ductal adenocarcinoma (PDAC); or for preparing a reagent or kit for (a) classifying pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing Basal-type pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of having Basal-type pancreatic ductal adenocarcinoma (PDAC), wherein the tumor microorganism is selected from the group consisting of Acinetobacter, Pseudomonas, Sphingopyxis, or a combination thereof.

[0008] In another preferred example, the classification of pancreatic ductal adenocarcinoma (PDAC) includes Basal-type pancreatic ductal adenocarcinoma (PDAC) and UnBasal-type pancreatic ductal adenocarcinoma (PDAC).

[0009] In another preferred example, the reagent or kit is further used to distinguish between Basal-type pancreatic ductal adenocarcinoma (PDAC) and UnBasal-type pancreatic ductal adenocarcinoma (PDAC).

[0010] In another preferred example, the diagnosis includes early diagnosis, auxiliary diagnosis, or a combination thereof.

[0011] In another preferred example, the assessment or diagnosis includes the steps of:

[0012] (1) providing a sample from a subject to be tested, and detecting the level (such as abundance) of each tumor microorganism in the tumor microorganism in the sample;

[0013] (2) comparing the level (such as abundance) measured in step (1) with a reference data set or a reference value (such as the reference value of a healthy control);

[0014] Preferably, the reference data set includes the level (such as abundance) of each tumor microorganism in the tumor microorganism from patients with Basal-type pancreatic ductal adenocarcinoma (PDAC) and controls with UnBasal-type pancreatic ductal adenocarcinoma (PDAC).

[0015] In another preferred example, when compared with a reference value, an increase in the level (such as abundance) of each tumor microorganism in the tumor microorganism indicates that the subject to be tested has a risk of having Basal-type pancreatic ductal adenocarcinoma (PDAC) or has Basal-type pancreatic ductal adenocarcinoma (PDAC).

[0016] In another preferred example, comparing the level measured in step (1) with a reference data set further includes constructing a classification model for predicting whether a patient has basal pancreatic ductal adenocarcinoma. Preferably, the classification model is a random forest model.

[0017] In another preferred example, if the probability of the prediction being basal pancreatic ductal adenocarcinoma (PDAC) ≥ 0.5, the subject is determined to have a risk of having basal pancreatic ductal adenocarcinoma (PDAC) or to have basal pancreatic ductal adenocarcinoma (PDAC).

[0018] In another preferred example, the sample is selected from tumor tissue samples.

[0019] In another preferred example, the level (such as abundance) of each tumor microorganism in the tumor microorganisms is detected by one or more methods of the following group: sequencing, PCR, protein quantification detection.

[0020] In another preferred example, the PCR includes qPCR.

[0021] In another preferred example, the method for detecting the level of the tumor microorganism further includes one or more methods selected from the following group: quantitative PCR of characteristic genes, metagenomics analysis, 16s rRNA sequencing, mass spectrometry analysis, Western blot.

[0022] In another preferred example, before step (1), the method further includes a step of processing the sample.

[0023] The second aspect of the present invention provides a marker combination, which includes two or more selected from the following group: Acinetobacter, Pseudomonas, Sphingopyxis, or a combination thereof.

[0024] In another preferred example, the marker combination includes Acinetobacter, Pseudomonas, and Sphingopyxis.

[0025] In another preferred example, the marker combination is used for (a) typing pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing basal pancreatic ductal adenocarcinoma (PDAC) or evaluating the risk of having basal pancreatic ductal adenocarcinoma (PDAC).

[0026] In another preferred example, the marker combination is further used to distinguish basal pancreatic ductal adenocarcinoma (PDAC) from non-basal pancreatic ductal adenocarcinoma (PDAC).

[0027] In another preferred embodiment, the biomarker is derived from a tumor tissue sample, preferably from a PDAC tumor tissue sample.

[0028] In another preferred embodiment, the levels (such as abundances) of the individual biomarkers in the biomarker combination are detected by one or more methods of the following group: sequencing, PCR, protein quantification detection.

[0029] In another preferred embodiment, the PCR includes qPCR.

[0030] In another preferred embodiment, the method for detecting the biomarker group levels further includes one or more methods selected from the following group: quantitative PCR of characteristic genes, metagenomics analysis, 16S rRNA sequencing, mass spectrometry analysis, Western blot.

[0031] In another preferred embodiment, when compared with a reference value, an increase in the level (such as abundance) of each biomarker in the biomarker combination indicates that the subject to be tested has a risk of having basal-type pancreatic ductal adenocarcinoma (PDAC) or has basal-type pancreatic ductal adenocarcinoma (PDAC).

[0032] The third aspect of the present invention provides a reagent combination for (a) classifying pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing basal-type pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of having basal-type pancreatic ductal adenocarcinoma (PDAC), and the reagent combination includes reagents for detecting the individual biomarkers in the biomarker combination described in the second aspect of the present invention.

[0033] In another preferred embodiment, the reagent includes substances for detecting the levels of the individual biomarkers in the biomarker combination described in the second aspect of the present invention by one or more methods selected from the following group: sequencing, PCR, protein quantification detection.

[0034] In another preferred embodiment, the reagent further includes substances for detecting the levels of the individual biomarkers in the biomarker combination described in the second aspect of the present invention by one or more methods selected from the following group: quantitative PCR of characteristic genes, metagenomics analysis, 16S rRNA sequencing, mass spectrometry analysis, Western blot.

[0035] In another preferred embodiment, the reagent is used to detect the levels (such as abundances) of the individual biomarkers.

[0036] The fourth aspect of the present invention provides a kit, and the kit includes the biomarker combination described in the second aspect of the present invention and / or the reagent combination described in the third aspect of the present invention.

[0037] In another preferred example, each biomarker in the combination described in the second aspect of the present invention is used as a standard.

[0038] In another preferred example, the kit further includes an instruction manual, which records a reference data set of the levels of each biomarker in the combination described in the second aspect of the present invention from Basal type pancreatic ductal adenocarcinoma (PDAC) patients and / or UnBasal type pancreatic ductal adenocarcinoma (PDAC) controls.

[0039] The fifth aspect of the present invention provides a method for (a) classifying pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing Basal type pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of suffering from Basal type pancreatic ductal adenocarcinoma (PDAC), comprising the steps of:

[0040] (1) Providing a sample from a subject to be tested, and detecting the levels of each biomarker in the combination described in the second aspect of the present invention in the sample;

[0041] (2) Comparing the levels measured in step (1) with a reference data set or a reference value (such as the reference value of healthy controls).

[0042] Preferably, the reference data set includes the levels of each biomarker in the combination described in the second aspect of the present invention from Basal type pancreatic ductal adenocarcinoma (PDAC) patients and UnBasal type pancreatic ductal adenocarcinoma (PDAC) controls.

[0043] The sixth aspect of the present invention provides a method for screening candidate compounds for treating Basal type pancreatic ductal adenocarcinoma (PDAC), characterized by comprising the steps of:

[0044] (1) In a test group, administering a test compound to a subject to be tested, and detecting the level V1 of each biomarker in the combination described in the second aspect of the present invention in the sample from the subject in the test group; in a control group, administering a blank control (including a solvent) to the subject to be tested, and detecting the level V2 of each biomarker in the combination described in the second aspect of the present invention in the sample from the subject in the control group;

[0045] (2) Comparing the levels V1 and V2 detected in the previous step to determine whether the test compound is a candidate compound for treating Basal type pancreatic ductal adenocarcinoma (PDAC).

[0046] In another preferred example, the subject to be tested is a patient with Basal type pancreatic ductal adenocarcinoma (PDAC).

[0047] In another preferred example, if the level V1 of each biomarker in the combination is significantly lower than the level V2, it indicates that the test compound is a candidate compound for treating Basal-type pancreatic ductal adenocarcinoma (PDAC).

[0048] In another preferred example, "significantly lower" means that the ratio of level V1 / level V2 ≤ 0.7, preferably ≤ 0.6.

[0049] The seventh aspect of the present invention provides the use of the biomarker combination described in the second aspect of the present invention and / or the reagent combination described in the third aspect of the present invention for screening candidate compounds for treating Basal-type pancreatic ductal adenocarcinoma (PDAC) and / or for evaluating the therapeutic effect of candidate compounds on Basal-type pancreatic ductal adenocarcinoma (PDAC).

[0050] The eighth aspect of the present invention provides a method for establishing a model for assessing the risk of Basal-type pancreatic ductal adenocarcinoma (PDAC) or for diagnosing Basal-type pancreatic ductal adenocarcinoma (PDAC). The method includes the step of identifying differentially expressed substances in tissue samples between Basal-type pancreatic ductal adenocarcinoma patients and UnBasal-type pancreatic ductal adenocarcinoma control subjects, wherein the differentially expressed substances include one or more biomarkers in the combination described in the second aspect of the present invention.

[0051] In another preferred example, the tissue sample includes a pancreatic tissue sample.

[0052] The ninth aspect of the present invention provides a system for (a) classifying pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing Basal-type pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of Basal-type pancreatic ductal adenocarcinoma (PDAC). The system includes:

[0053] (a) A feature input module, which is used to input the features of a tumor tissue sample of a certain subject;

[0054] Wherein the features of the tumor tissue sample include tumor microorganisms selected from the group consisting of Acinetobacter, Pseudomonas, Sphingopyxis, or a combination thereof.

[0055] (b) Discrimination processing module, which scores the characteristics of the input tumor tissue sample according to a predetermined judgment criterion to obtain a risk score; and compares the risk score with the risk threshold of Basal type pancreatic ductal adenocarcinoma (PDAC) to obtain an auxiliary screening result. Among them, when the risk score is higher than the risk threshold, it indicates that the subject has a higher risk of suffering from Basal type pancreatic ductal adenocarcinoma (PDAC) than the normal population; when the risk score is lower than the risk threshold, it also indicates that the subject has a higher risk of suffering from Basal type pancreatic ductal adenocarcinoma (PDAC) than the normal population; and

[0056] (c) Auxiliary screening result output module, which is used to output the auxiliary screening result.

[0057] In another preferred example, the subject is a human.

[0058] In another preferred example, the subject includes infants, adolescents or adults.

[0059] In another preferred example, the score includes (a) the score of a single feature; and / or (b) the sum of the scores of multiple features.

[0060] In another preferred example, the feature input module is selected from the following group: sample collector, feature signal input terminal, data preprocessing module, feature construction module, feature visualization module.

[0061] In another preferred example, the discrimination processing module includes a processor and a storage, wherein the storage stores the risk threshold data of Basal type pancreatic ductal adenocarcinoma (PDAC) based on the characteristics of the tumor tissue sample.

[0062] In another preferred example, the output module includes a reporting system.

[0063] It should be understood that within the scope of the present invention, the above technical features of the present invention and the technical features specifically described below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be repeated one by one here. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Shows the abundance distribution of Pseudomonas in different PDAC subtypes.

[0065] Figure 2 Shows the abundance distribution of Acinetobacter in different PDAC subtypes.

[0066] Figure 3 Shows the abundance distribution of Sphingopyxis in different PDAC subtypes.

[0067] Figure 4 Shows the clustering results of PDAC patients based on microbial markers.

[0068] Figure 5 Shows the microbial genera significantly enriched in Basal-type PDAC.

[0069] Figure 6 Shows that the candidate microbial markers can indicate the prognosis and survival of patients.

[0070] Figure 7 Shows the predictive efficacy of the constructed classifier model in evaluating Basal-type PDAC. Detailed implementation mode

[0071] Through extensive and in-depth research, the inventors of the present invention have first discovered that Acinetobacter, Pseudomonas, and / or Sphingopyxis can be used for (a) typing pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing Basal-type pancreatic ductal adenocarcinoma (PDAC) or evaluating the risk of suffering from Basal-type pancreatic ductal adenocarcinoma (PDAC). Moreover, the inventors of the present invention have also first discovered a new marker combination: Acinetobacter, Pseudomonas, and Sphingopyxis. The marker combination of the present invention can be used for (a) typing pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing Basal-type pancreatic ductal adenocarcinoma (PDAC) or evaluating the risk of suffering from Basal-type pancreatic ductal adenocarcinoma (PDAC), and has the advantages of high sensitivity and high specificity, and has important application value. On this basis, the inventors have completed the present invention.

[0072] Terms

[0073] The terms used in the present invention have the meanings commonly understood by those of ordinary skill in the relevant art. However, for a better understanding of the present invention, the explanations of some definitions and related terms are as follows:

[0074] According to the present invention, the term "marker combination" refers to a biomarker or a combination of two or more biomarkers.

[0075] According to the present invention, the level of the marker substance is determined by the presence content or abundance of the microorganism. According to the present invention, the term "individual" refers to an animal, especially a mammal, such as a primate, preferably a human.

[0076] According to the present invention, terms such as "a", "an", and "the" not only refer to a single individual, but also include the general category that can be used to illustrate a specific embodiment.

[0077] As used herein, when referring to a specifically recited numerical value, the term "about" means that the value can vary by no more than 1% from the recited value. For example, as used herein, the expression "about 100" includes all values between 99 and 101 (e.g., 99.1, 99.2, 99.3, 99.4, etc.).

[0078] As used herein, the term "comprising" or "including" can be open-ended, semi-closed, and closed. In other words, the term also includes "consisting essentially of...", or "consisting of...".

[0079] It should be noted that the interpretation of the terms provided herein is only for better understanding of the present invention by those skilled in the art, and is not a limitation to the present invention.

[0080] According to the present invention, the reference set refers to the training set.

[0081] According to the present invention, as is known in the prior art, the training set and the validation set have the same meaning. In one embodiment of the present invention, the training set refers to the set of marker levels in the biological samples of patients with Basal-type pancreatic ductal adenocarcinoma (PDAC) and UnBasal-type pancreatic ductal adenocarcinoma (PDAC) controls. In one embodiment of the present invention, the validation set is a data set used to test the performance of the training set. In one embodiment of the present invention, the level of the marker can be represented as an absolute value or a relative value according to the measurement method. For example, when using mass spectrometry to measure the level of the marker, the intensity of the peak can represent the level of the marker, which is a relative value level; when using PCR to measure the level of the marker, the copy number of the gene or the copy number of the gene fragment can represent the level of the marker.

[0082] In one embodiment of the present invention, the reference value refers to the reference value or normal value of the UnBasal-type pancreatic ductal adenocarcinoma (PDAC) control. Those skilled in the art are aware that, when the number of samples is large enough, the range of the normal value (absolute value) of each biomarker can be obtained through testing and calculation methods. Therefore, when using other methods than mass spectrometry to detect the level of the biomarker, the absolute values of these biomarker levels can be directly compared with the normal value to evaluate the risk of having Basal-type pancreatic ductal adenocarcinoma (PDAC), as well as to diagnose or early diagnose Basal-type pancreatic ductal adenocarcinoma (PDAC). Optionally, statistical methods can also be used.

[0083] Pancreatic ductal adenocarcinoma (PDAC)

[0084] Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer and ranks third in cancer mortality worldwide. The prognosis of most PDAC patients is extremely poor, with a five-year survival rate of only 9%. Surgical resection remains the main treatment for PDAC. However, the postoperative recurrence rate is relatively high, and most patients eventually die of tumor metastasis. Currently, there is no effective method for screening high-risk patients with pancreatic cancer. Traditional treatment options such as surgical resection, radiotherapy, chemotherapy, and combined therapies have little effect on pancreatic cancer. Therefore, it is particularly important to find scientific and effective diagnostic and treatment options.

[0085] Despite the generally high lethality of PDAC, different patients still show clinical heterogeneity. Among patients who have undergone surgical resection of the tumor, some progress to the advanced stage within just a few months, while some patients can maintain a stable quality of life for several years. Many genomics studies have shown that the genetic mutations in pancreatic cancer at different stages of progression are homogeneous, mainly frequent mutations in genes such as KRAS, CDKN2A, TP53, and SMAD4. The reasons for the clinical heterogeneity of pancreatic cancer remain unknown. The phenotypic heterogeneity of pancreatic cancer is also reflected in mouse models. Even in genetically engineered mouse models with exactly the same genotype, there are still differences in the speed and degree of tumor progression. We speculate that some factors in the tumor microenvironment, such as the tumor microbiome, may be the cause of this heterogeneity.

[0086] In recent years, studies have identified a subtype of PDAC called the Basal type through transcriptome analysis. This tumor subtype has a more aggressive clinical phenotype and a worse prognosis. The molecular typing characteristics of pancreatic cancer reveal the molecular basis of the clinical phenotypic heterogeneity of the tumor, which will help us further explore the underlying functional mechanisms.

[0087] Classification of Pancreatic Ductal Adenocarcinoma (PDAC)

[0088] In the present invention, pancreatic ductal adenocarcinoma (PDAC) can be further classified into 'Classical', 'Hybrid', and 'Basal'.

[0089] Through gene function enrichment analysis, it was found that Basal tumors exhibit extremely high DNA replication activity, epithelial-mesenchymal transition activity, and activation of cancer-related signaling pathways, all of which indicate active cancer progression and a highly aggressive phenotype in the Basal subtype. At the same time, we also found that Basal tumors show a significantly activated immune response and inflammatory response process of pathogen recognition. These results suggest that excessive immune activation and pathogen-induced inflammatory responses may play an important role in the cancer progression specific to the Basal subtype.

[0090] Existing research suggests that Basal-type tumors are associated with squamous transformation of pancreatic duct epithelial cells, possibly induced by the accumulation of lesions in the tumor microenvironment. This tumor subtype has a more aggressive tumor progression rate. In contrast, the Classical type is considered to be the classic development of pancreatic cancer, with a more stable tumor progression compared to the Basal type. The Hybrid type may be an intermediate state between the Classical type and the Basal type, with both Basal-type and Classical-type tumor cells present in the tumor tissue.

[0091] Acinetobacter

[0092] Acinetobacter is a common opportunistic pathogen that can easily cause infections when the body's resistance is reduced, leading to respiratory infections, sepsis, meningitis, skin infections, etc. This bacterium is widely distributed in the environment and also exists in the skin, pharynx, gastrointestinal tract, etc. of ordinary people, and is likely to cause infections when the body's immunity is low.

[0093] Pseudomonas

[0094] Pseudomonas is widely distributed in water, air, and food, can cause human infectious diseases, and is relatively common in hospital infections. It is worth noting that this bacterium has also been confirmed to exist in human pancreatic organs, but its role in the development of pancreatic cancer is not clear.

[0095] Sphingopyxis

[0096] Sphingopyxis is a Gram-negative bacterium, and its presence has been detected in human fecal samples, but the abundance is not prominent. Currently, no clear pathogenicity has been found in this bacterium. Some studies have found that the antigenic substance in the outer membrane of this bacterium, glycosphingolipid, can effectively stimulate the activation of the body's immunity, indicating that the outer membrane of this bacterium has a typical pathogen-associated molecular pattern.

[0097] The present invention deeply explores the microbial composition characteristics in pancreatic cancer tumors. It is found that there are significant differences in the composition structures of the detected microorganisms among the three PDAC tumor subtypes. Three indicators are used to evaluate species diversity, including the Shannon index, Bray-Curtis dissimilarity, and species richness, and then the microbial characteristic differences between different PDAC subtypes are compared. The results show that the microorganisms in the Basal subtype exhibit higher species richness and a lower Shannon index. In addition, the Bray-Curtis dissimilarity among the Basal subtype samples is significantly increased. The results of the present invention indicate that, compared with other PDAC subtypes, the Basal subtype has a relatively unique microbial composition structure, which may be related to the special cancer progression in the Basal subtype.

[0098] Furthermore, this study discovered a group of microorganisms with significantly increased abundance in Basal tumors, among which the three genera of bacteria, Acinetobacter, Pseudomonas, and Sphingopyxis, were the most prominent. The abundance indicators of these three genera of bacteria have potential predictive value for the prognosis of PDAC. In addition, through microbial gene function analysis, the microorganisms in the Basal subtype exhibited higher metabolic activity, cell motility, replication efficiency, antibiotic resistance, and cell membrane synthesis activity, and these functions reflected the higher pathogenicity and inflammation-inducing potential of the microorganisms. These results revealed the role of microorganisms in the Basal subtype in cancer development, and treatment strategies targeting these microorganisms or blocking the pro-inflammatory processes of microorganisms would effectively intervene in the progression of PDAC.

[0099] Subsequently, to further study the interaction between tumor microorganisms and host cell functions, through the correlation analysis of tumor cell gene expression and microbial abundance, it was found that the abundance of certain bacteria, including Acinetobacter, Pseudomonas, and Sphingopyxis mentioned above, was significantly positively correlated with functions such as host antigen recognition, lipopolysaccharide response, and complement system activation, and these results reflected the activation of the host's response to exogenous pathogens. In addition, the results of the present invention also showed that some cancer-related functions, such as the Kras signaling pathway, epithelial-mesenchymal transition process, MAPK signaling pathway, etc., were also significantly correlated with the above-mentioned bacterial abundance. These data revealed the impact of tumor microorganisms on host cell functions at the gene function level, and also proved the important role of certain bacteria in tumorigenesis.

[0100] Why is there a difference in the microbial composition among different PDAC subtypes, and what factors lead to the diverse tumor microbial structures in the population? This study attempted to explore whether host genetic factors play a certain role in this. Based on the genotype information of the patients, the present invention evaluated the genetic similarity among individual patients. The results of the present invention showed that the closer the genetic variation among individuals, the more similar their tumor microbial composition, which supported our conjecture that host genetic factors play a key role in shaping the microbial community. The present invention deeply explored the association between host genetics and microorganisms, and identified multiple genetic loci significantly associated with microbial abundance through QTL analysis. Mutations in these loci may cause host immune-related functional deficiencies, including IFN-γ signal activation, presentation of exogenous antigens, etc. These findings demonstrated the association between host gene mutations and microorganisms, and at the same time proposed a possible theoretical hypothesis - certain immune function deficiencies in the host may lead to microbial structural imbalance, thereby increasing the colonization risk of specific pathogens in the pancreas and inflammation induction, ultimately resulting in severe progression of pancreatic cancer.

[0101] Kit

[0102] In the present invention, the kit of the present invention comprises the biomarker combination described in the second aspect of the present invention and / or the reagent combination described in the third aspect of the present invention.

[0103] In another preferred embodiment, each biomarker in the combination described in the second aspect of the present invention is used as a standard.

[0104] The main advantages of the present invention include:

[0105] (1) The present invention discovers for the first time that Acinetobacter, Pseudomonas, and / or Sphingopyxis can be used for (a) classifying pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing Basal-type pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of developing Basal-type pancreatic ductal adenocarcinoma (PDAC).

[0106] (2) The present invention discovers for the first time that Acinetobacter, Pseudomonas, and / or Sphingopyxis can also be used to distinguish Basal-type pancreatic ductal adenocarcinoma (PDAC) from UnBasal-type pancreatic ductal adenocarcinoma (PDAC).

[0107] (3) The present invention focuses on the microbial factors in PDAC subtype heterogeneity for the first time, reveals the unique tumor microbial composition characteristics in different PDAC subtypes, discovers several types of microorganisms closely related to cancer progression, and functionally demonstrates the inflammation-inducing potential of these microorganisms.

[0108] (4) The research results of the present invention show for the first time the application value of tumor microorganisms in predicting the clinical phenotypes of PDAC patients, and also suggest that microorganisms can be effective targets for pancreatic cancer intervention and treatment.

[0109] (5) The present invention provides a new method for classifying PDAC based on microbial composition, providing a new means for prognostic diagnosis of patients.

[0110] (6) The present invention provides a new treatment plan for intervening or treating PDAC by targeting microorganisms or blocking the pro-inflammatory process of microorganisms.

[0111] (7) The present invention reveals the role of microorganisms in the development of PDAC tumors, providing a new understanding of the tumorigenesis mechanism.

[0112] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. The experimental methods without specific conditions noted in the following embodiments are generally carried out under conventional conditions or according to the conditions recommended by the manufacturer. Unless otherwise specified, percentages and parts are calculated by weight.

[0113] Unless otherwise specified, the reagents and materials used in the embodiments of the present invention are all commercially available products.

[0114] General method

[0115] 1.1 Clinical sample collection and metagenomic sequencing:

[0116] All tumor samples were sourced from Shanghai Changhai Hospital. The enrolled cases needed to meet the following requirements: 1. Diagnosed as pancreatic ductal adenocarcinoma (PDAC) through imaging or tissue pathology; 2. The patient had not received antibiotic treatment in the last month; 3. The patient had a primary, resectable tumor. A total of 62 patients were included in the study cohort, and relevant clinical information was recorded in detail.

[0117] The PDAC tumor tissue samples excised surgically were immediately transferred to liquid nitrogen for preservation and subjected to subsequent nucleic acid extraction within one month of freezing. The bacterial cell wall was lysed using QIAGEN Pathogen Lysis Tube, and DNA was extracted using a kit. The nucleic acid concentration and quality were detected to ensure the qualified quality of the samples, and then library construction and second-generation sequencing were carried out. The off-machine data was subjected to quality control processing, and approximately 80 GB of high-quality DNA sequencing data was obtained for each sample on average.

[0118] 1.2 Microbial taxonomic identification and abundance calculation:

[0119] The high-quality DNA sequences after quality control processing were first aligned to the human reference genome to remove the interference of host sequences in the sequencing data. Subsequently, sequence alignment was performed based on the microbial reference genome database in the NCBI database. The Kraken2 software was used to assign each sequencing read to known species classifications, and the Bayesian probability estimation provided by the Bracken software was combined to evaluate the theoretical number of sequences aligned to each species classification, and the abundance results of each microorganism were calculated accordingly. Based on the abundance map of the tumor microbiome, further statistical optimization analysis was carried out to calculate microbial community diversity indicators and evaluate the differences in microbial composition between different tumor subtypes.

[0120] Example 1 Screening for microbiota markers related to Basal-type pancreatic ductal adenocarcinoma

[0121] The 62 pancreatic ductal adenocarcinoma (PDAC) tumor samples collected in this invention include 17 cases of Basal-type PDAC, 23 cases of Hybrid-type PDAC, and 22 cases of Classical-type PDAC. The Kruskal-Wallis test was used to screen for microbial genera with significantly different abundances among different PDAC molecular subtypes. We found three genera of bacteria, Acinetobacter, Pseudomonas, and Sphingopyxis, whose abundances were significantly enriched in Basal-type PDAC tumors, as Figures 1-3 shown. The boxplot shows the distribution of Acinetobacter, Pseudomonas, and Sphingopyxis in different tumor subtypes of PDAC. The y-axis represents the normalized abundance value. The results of the Kurskal-Wallis test show that the p-value is extremely significant, indicating that there are significant differences in the abundance of this bacterium among groups. This figure shows that the abundance of this bacterium is significantly increased in the Basal subtype.

[0122] Subsequently, we used the abundances of the above three genera of bacteria to perform hierarchical clustering analysis on all tumor samples. The results showed that these three genera of bacteria could well distinguish Basal-type PDAC samples ( Figure 4 ). The clustering heatmap shows the distribution of these three genera of bacteria among different subtypes of PDAC. The columns represent each sample (the column annotation information is the grouping information), the rows represent each genus of bacteria, the abundances are normalized by row, and the values are uniformly converted to the range of -2 to 2. The redder the color, the higher the abundance value. This figure shows that these three genera of bacteria are mostly enriched in the Basal subtype.

[0123] Furthermore, this invention used linear discriminant analysis (LDA) to screen for the microbiota markers of Basal-type PDAC. By default, a logarithmic LDA score greater than 2 is considered to have a significant difference. The analysis results indicate that these three genera of bacteria are significantly enriched in the Basal subtype ( Figure 5 ).

[0124] We grouped the patients according to the abundance levels of the above three genera of bacteria, and then carried out survival analysis to compare the survival situations of the patients. As Figure 6 shown, the results indicate that the high abundances of these three genera of bacteria predict a shorter survival period for PDAC patients, suggesting that they can be used as predictive indicators for the prognosis of PDAC.

[0125] The Kaplan-Meier survival curve shows that the high abundances of three genera of bacteria can significantly predict the low survival period of patients. We used the Cox proportional hazards regression model to calculate the hazard ratio (HR) values of the three genera of bacteria. The results showed that the HR values of the three genera of bacteria were all greater than 1, indicating that they are all risk factors in the development of pancreatic cancer.

[0126] Example 2 Construction of a PDAC prediction model based on tumor microorganisms

[0127] 62 PDAC samples were randomly sampled, and 70% of the samples were taken as the training set (n = 44, including 12 Basal type cases and 30 unBasal type cases), and the remaining 30% of the samples were taken as the validation set (n = 18, including 5 Basal type cases and 13 unBasal type cases).

[0128] The abundance values of the above three types of microorganisms (normalized by SizeFactor) were used as input features, and a classification model was constructed using the random forest algorithm. The constructed model was used to predict the validation set, and the prediction results were plotted as an AUC curve as the basis for evaluating the model performance. The discrimination performance of the classifier constructed by these 3 types of microorganisms for 18 validation set samples was: AUC = 96.9% (the combination of these 3 types of microorganisms), 95% confidence interval CI = 89.6 - 100%. ( Figure 7 )

[0129] Table 1 shows the importance index of each feature in the prediction model and the discrimination performance of each biomarker alone. It can be seen that each of these three biomarkers has good prediction performance, and the combination of the three can exert the best prediction performance. In addition, it can be inferred from Table 1 and Figure 7 that the pairwise combinations of the three types of microorganisms of the present invention also have good prediction performance.

[0130] Table 1

[0131] Microbial biomarker Importance Discriminant AUC Acinetobacter 7.302 0.923 Pseudomonas 7.038 0.954 Sphingopyxis 2.699 0.938

[0132] The ROC curves plotted with the abundances of 3 genera of bacteria (and the integrated model) as feature factors to predict the tumor classification of samples. The legend in the lower right corner indicates the AUC value of each type of feature. This figure shows that these 3 genera of bacteria and their integrated model have excellent performance in predicting tumor classification in 18 validation sets.

[0133] The specific prediction results of the classification model (the combination of these 3 types of microorganisms) for 18 validation samples are shown in Table 2. Samples with a probability ≥ 0.5 are predicted to be Basal type PDAC tumors.

[0134] Table 2

[0135]

[0136] The results show that the microbial markers we screened and the established prediction model have high accuracy and specificity, can accurately predict Basal-type PDAC patients, and have good prospects for market development.

[0137] All documents mentioned in this invention are cited herein as references, as if each document was cited individually as a reference. In addition, it should be understood that after reading the above teachings of this invention, those skilled in the art can make various changes or modifications to this invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

Claims

1. Use of a tumor microorganism abundance detection reagent, characterized in that, For preparing a reagent combination or kit for (a) classifying pancreatic ductal adenocarcinoma (PDAC), wherein the classification of pancreatic ductal adenocarcinoma (PDAC) includes Basal-type pancreatic ductal adenocarcinoma (PDAC) and UnBasal-type pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing Basal-type pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of suffering from Basal-type pancreatic ductal adenocarcinoma (PDAC), wherein the tumor microorganisms are: Acinetobacter, Pseudomonas, and Sphingopyxis.

2. A method for constructing a classification model for non-diagnostic and non-therapeutic purposes, the classification model being used to predict the typing of pancreatic ductal adenocarcinoma (PDAC), characterized in that, Comprising the steps of: (1) Providing a sample from a subject to be tested, and detecting the abundances of Acinetobacter, Pseudomonas, and Sphingopyxis markers in the sample; (2) Comparing the abundances measured in step (1) with a reference data set or a reference value, wherein the reference value is the reference value of healthy controls; the reference data set includes the abundances of Acinetobacter, Pseudomonas, and Sphingopyxis markers from patients with Basal-type pancreatic ductal adenocarcinoma (PDAC) and control subjects with UnBasal-type pancreatic ductal adenocarcinoma (PDAC).

3. The method according to claim 2, wherein The classification model is a random forest model.

4. A method for establishing a model for assessing the risk of Basal-type pancreatic ductal adenocarcinoma (PDAC) or predicting the subtype of Basal-type pancreatic ductal adenocarcinoma (PDAC) for non-diagnostic and non-therapeutic purposes, characterized in that, The method includes the step of identifying the abundances of differentially expressed substances in tissue samples between patients with Basal-type pancreatic ductal adenocarcinoma and control subjects with UnBasal-type pancreatic ductal adenocarcinoma, wherein the differentially expressed substances include Acinetobacter, Pseudomonas, and Sphingopyxis.

5. A system for (a) classifying pancreatic ductal adenocarcinoma (PDAC); and / or (b) diagnosing basal-type pancreatic ductal adenocarcinoma (PDAC) or assessing the risk of developing basal-type pancreatic ductal adenocarcinoma (PDAC), characterized in that, The system includes: (a) A feature input module for inputting the features of a tumor tissue sample of a subject; wherein the features of the tumor tissue sample include the abundances of the following tumor microorganisms: Acinetobacter, Pseudomonas, and Sphingopyxis; (b) A discrimination processing module that scores the input features of the tumor tissue sample according to a predetermined judgment criterion to obtain a risk score; and compares the risk score with the risk threshold of Basal-type pancreatic ductal adenocarcinoma (PDAC) to obtain an auxiliary screening result. When the risk score is higher than the risk threshold, it indicates that the subject has a higher risk of suffering from Basal-type pancreatic ductal adenocarcinoma (PDAC) than the normal population; when the risk score is lower than the risk threshold, it indicates that the subject has a lower risk of suffering from Basal-type pancreatic ductal adenocarcinoma (PDAC) than the normal population; and (c) Auxiliary screening result output module, and the output module is used to output the auxiliary screening results described above.

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