A method and device for molecular typing of colorectal cancer based on immune characteristics

Abstract

The present invention relates to a method and device for molecular typing of colorectal cancer based on immune signatures, the method comprising the following steps: obtaining cohort data, gene expression profiles and clinical information data of colorectal cancer; obtaining immune-related characteristics, and obtaining prognosis-related immune signatures from the obtained cohort data, gene expression profiles and clinical information data of colorectal cancer based on the enrichment scores of the immune-related characteristics; performing subtype identification and verification according to the prognosis-related immune signatures; and classifying patient information data according to the obtained subtypes to determine the phenotype of colorectal cancer. Compared with the prior art, the molecular typing of colorectal cancer of the present invention has the advantages of high accuracy and high reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction, and in particular to a method and device for molecular typing of colorectal cancer based on immune characteristics. Background Art

[0002] Colorectal cancer (CRC) is one of the common malignant tumors. According to the latest data released by the World Health Organization's International Agency for Research on Cancer (IARC) in 2020, CRC is the third most common cancer in the world and the second leading cause of cancer death. Most patients with colorectal cancer are diagnosed with resectable tumors and undergo resection surgery plus adjuvant therapy when necessary. For patients with advanced colorectal cancer, targeted therapy combined with chemotherapy (containing oxaliplatin or irinotecan) is the main treatment strategy.

[0003] Considering the rapid progress and significant survival benefits for patients with various tumors, immunotherapy is now considered the fifth mainstay of anti-tumor treatment after surgery, chemotherapy, radiotherapy, and targeted therapy. Although multiple attempts have been made to use immunotherapy to address CRC, approximately 80-85% of CRC patients are referred to as "cold" tumors with microsatellite stable (MSS) or low microsatellite instability (MSI-L), known as MSS / MSI-L colorectal cancer, and therefore lack response to immune checkpoint inhibitors (ICIs). The effectiveness of immune intervention has been demonstrated in some tumor subsets, especially in typical immune-sensitive / "hot" tumors such as melanoma and non-small cell lung cancer, which have high levels of tumor mutation burden (TMB), CD8 lymphocytes, and programmed death ligand 1 (PD-L1) expression. Scientists have also tried various approaches, such as messenger RNA (mRNA) vaccines to reprogram the tumor microenvironment (TME) to increase immune-mediated responses, which have been tested to convert "cold" tumors into "hot" tumors. Possible reasons for CRC to develop immune tolerance to ICIs include: low tumor immunogenicity, suppression of the tumor immune microenvironment, lack of active T cell infiltration, etc. In general, the more mutations a tumor carries, the higher the immunogenicity detected in the TME. CRC can be divided into high mutation and non-high mutation types at the genomic level. The non-high mutation type contains fewer immune cells in the TME and immunotherapy effects. In addition, according to the consensus molecular subtype (CMS) system, only 14% of the colorectal cancer population is characterized by hypermutation, microsatellite instability (MSI), and a highly activated immune system, and is therefore sensitive to ICI. In terms of treatment strategies, accurate immune molecular typing is needed to screen CRC patients who may benefit from immunotherapy or need to reprogram their TME to increase immune-mediated responses. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method and device for molecular typing of colorectal cancer based on immune characteristics, so as to screen CRC patients who may benefit from immunotherapy or need to reprogram their TME to increase immune-mediated responses through accurate immune molecular typing.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for molecular typing of colorectal cancer based on immune characteristics, comprising the following steps:

[0007] Obtain colorectal cancer cohort data, gene expression profiles, and clinical information data;

[0008] Obtaining immune-related characteristics, and obtaining prognosis-related immune characteristics from the obtained colorectal cancer cohort data, gene expression profiles, and clinical information data based on the enrichment score of the immune-related characteristics;

[0009] subtype identification and validation based on the prognosis-associated immune signature;

[0010] Patient information data was classified according to the obtained subtypes to determine the colorectal cancer phenotype.

[0011] Furthermore, the process of acquiring the prognosis-related immune characteristics is specifically as follows: acquiring immune-related characteristics, performing feature matching from the acquired colorectal cancer cohort data, gene expression spectrum and clinical information data according to the enrichment score of the immune-related characteristics, and acquiring the prognosis-related immune characteristics of colorectal cancer.

[0012] Furthermore, the colorectal cancer cohort data, gene expression profile and clinical information data are all obtained from the Cancer Genome Atlas database.

[0013] Furthermore, the prognosis-related immune signatures include Pan F TBRs signature, EMT1 signature, PPAGs signature, IFNG signature Ayers et al signature, T cell accumulation Peng et al signature, SW480 cancer cells Bindea et al signature, MDSC Peng et al signature, TMEscoreA plus signature, Effector CD8 T cell signature, TMEscore plus signature, TMEscore CIR signature, Th2 cells Bindea et al signature, TMEscoreB plus signature, TMEscoreB CIR signature, TGFb Family Member Li et al signature, Costimulation T Roonety et al signature and Treg Roonety et al signature.

[0014] Further, the obtained subtypes include a first subtype, a second subtype and a third subtype, wherein the first subtype has an immune "hot" and immunosuppressive phenotype, the second subtype has an immune "cold" phenotype, and the third subtype has an immune "hot" phenotype.

[0015] The present invention also provides a molecular typing device for colorectal cancer based on immune characteristics, comprising:

[0016] The data acquisition module is configured to: acquire cohort data, gene expression profiles and clinical information data of colorectal cancer;

[0017] The prognosis-related immune signature acquisition module is coordinated to: acquire immune-related signatures, and acquire prognosis-related immune signatures from acquired colorectal cancer cohort data, gene expression profiles, and clinical information data based on the enrichment scores of the immune-related signatures;

[0018] The subtype determination module is coordinated to: identify and verify the subtype according to the prognosis-related immune characteristics;

[0019] The prognosis prediction module is coordinated to classify the patient information data according to the obtained subtypes and determine the colorectal cancer phenotype.

[0020] Furthermore, the process of acquiring the prognosis-related immune characteristics is specifically as follows: acquiring immune-related characteristics, performing feature matching from the acquired colorectal cancer cohort data, gene expression spectrum and clinical information data according to the enrichment score of the immune-related characteristics, and acquiring the prognosis-related immune characteristics of colorectal cancer.

[0021] Furthermore, the colorectal cancer cohort data, gene expression profile and clinical information data are all obtained from the Cancer Genome Atlas database.

[0022] Furthermore, the prognosis-related immune signatures include Pan F TBRs signature, EMT1 signature, PPAGs signature, IFNG signature Ayers et al signature, T cell accumulation Peng et al signature, SW480 cancer cells Bindea et al signature, MDSC Peng et al signature, TMEscoreA plus signature, Effector CD8Tcell signature, TMEscore plus signature, TMEscore CIR signature, Th2 cells Bindea et al signature, TMEscoreB plus signature, TMEscoreB CIR signature, TGFbFamily Member Li et al signature, Costimulation T Roonety et al signature and Treg Roonety et al signature.

[0023] Further, the obtained subtypes include a first subtype, a second subtype and a third subtype, wherein the first subtype has an immune "hot" and immunosuppressive phenotype, the second subtype has an immune "cold" phenotype, and the third subtype has an immune "hot" phenotype.

[0024] Compared with the prior art, the present invention has the following advantages:

[0025] (1) High accuracy of colorectal cancer molecular typing: The colorectal cancer molecular typing prediction method of the present invention screens and detects prognosis-related immune features through a large amount of data in the existing database, and obtains molecular subtypes, which are used to determine the intestinal cancer phenotype of individual patients, thereby predicting prognosis; it can not only classify the colorectal cancer molecules of different patients, but also improve the accuracy of colorectal cancer molecular classification. Using this method to molecularly classify patients can facilitate doctors to classify patients into different immune and risk types, use different treatment methods to scientifically treat patients, improve patients' treatment recovery rate, and solve the problem of low accuracy of traditional colorectal cancer molecular typing.

[0026] (2) High reliability: The colorectal cancer molecular typing prediction method of the present invention is obtained from the Cancer Genome Atlas (TCGA) database, and the gene expression and clinical information data corresponding to TCGA-COAD (colon adenocarcinoma) are also obtained from the TCGA database, providing the source of research data, avoiding data falsification, and effectively ensuring the reliability of the model; at the same time, GEO cohort data is also used as a test set to verify the model, avoiding the appearance of generalizations due to data bias, and effectively improving the reliability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of a flow chart of a method for molecular typing of colorectal cancer based on immune characteristics provided in an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of a molecular classification of colorectal cancer provided in an embodiment of the present invention;

[0029] Figure 3 Schematic diagram of 17 prognosis-related immune features selected in the embodiment of the present invention;

[0030] Figure 4 Schematic diagram of the prognostic differences of the three immune subtypes identified in the examples of the present invention;

[0031] Figure 5 This is a diagram showing the prediction results of immunotherapy and chemotherapy responses of different subtypes identified in the examples of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0033] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0035] Example 1

[0036] like Figure 1 and 2 As shown, this embodiment provides a method for molecular typing of colorectal cancer based on immune characteristics, comprising the following steps:

[0037] S1: Obtain colorectal cancer cohort data, gene expression profiles, and clinical information data;

[0038] S2: Obtain a list of immune-related features, and obtain prognosis-related immune features from the obtained colorectal cancer cohort data, gene expression profiles, and clinical information data based on the enrichment scores of the immune-related features;

[0039] S3: Subtype identification and validation based on prognostic-related immune signatures;

[0040] S4: Classify the patient information data according to the obtained subtypes to determine the colorectal cancer phenotype.

[0041] The process of obtaining prognosis-related immune features is specifically as follows: obtaining a list of immune-related features, performing feature matching from the obtained colorectal cancer cohort data, gene expression profiles, and clinical information data based on the enrichment scores of the immune-related features, and obtaining the prognosis-related immune features of colorectal cancer from the list of immune-related features.

[0042] The cohort data, gene expression profiles, and clinical information data of colorectal cancer were obtained from the Cancer Genome Atlas database.

[0043] Different prognosis-related immune features can be obtained according to different data sources, thereby performing different subtype classifications; as an optional implementation, the cohort data, gene expression profiles, and clinical information data of colorectal cancer in this embodiment are all obtained from the Cancer Genome Atlas (TCGA) database;

[0044] like Figure 3As shown, in this example, 17 COAD-related prognosis-related immune features were obtained from 119 immune-related features, and the corresponding immune-related features included Pan F TBRs feature, EMT1 feature, PPAGs feature, IFNG signature Ayers et al feature, T cell accumulation Peng et al feature, SW480 cancer cells Bindea et al feature, MDSC Peng et al feature, TMEscoreA plus feature, Effector CD8T cell feature, TMEscore plus feature, TMEscore CIR feature, Th2 cells Bindea et al feature, TMEscoreB plus feature, TMEscoreB CIR feature, TGFbFamily Member Li et al feature, Costimulation T Roonety et al feature and Treg Roonety et al feature.

[0045] The obtained subtypes include subtype 1, subtype 2 and subtype 3. Subtype 1 has an immune "hot" and immunosuppressive phenotype, subtype 2 has an immune "cold" phenotype, and subtype 3 has an immune "hot" phenotype.

[0046] Equivalently, this embodiment is based on analyzing immune-related features and the prognosis of colorectal cancer patients, selecting prognosis-related immune features for analysis, and dividing COAD (colon adenocarcinoma) into three ISs based on 17 prognosis-related immune features, such as Figure 4 As shown. Patients with IS1 tumors have an immune “hot” and immunosuppressive phenotype, IS3 tumors have an immune “hot” phenotype, and IS2 tumors have an immune “cold” phenotype. Compared with other IS types, patients with IS1 tumors have the worst disease-free survival, while patients with IS3 tumors have the best prognosis. In addition, the predicted immunotherapy / chemotherapy response varies between ISs, as shown in the following table. Figure 5 shown.

[0047] The above is an introduction to a method embodiment. The following is a further explanation of the present invention through an apparatus embodiment.

[0048] This embodiment also provides a molecular typing device for colorectal cancer based on immune characteristics, comprising:

[0049] The data acquisition module is configured to: acquire cohort data, gene expression profiles and clinical information data of colorectal cancer;

[0050] The prognosis-related immune signature acquisition module is coordinated to: acquire a list of immune-related characteristics, and match and acquire the prognosis-related immune signature from the acquired colorectal cancer cohort data, gene expression profiles, and clinical information data based on the enrichment scores of the immune-related signatures;

[0051] The subtype determination module is coordinated to: identify and validate subtypes based on prognosis-related immune features;

[0052] The prognosis prediction module is coordinated to classify the patient information data according to the obtained subtypes and determine the colorectal cancer phenotype.

[0053] The process of obtaining prognosis-related immune features is specifically as follows: obtaining a list of immune-related features, performing feature matching from the obtained colorectal cancer cohort data, gene expression profiles, and clinical information data based on the enrichment scores of the immune-related features, and obtaining the prognosis-related immune features of colorectal cancer from the list of prognosis-related immune features.

[0054] The cohort data, gene expression profiles, and clinical information data of colorectal cancer were obtained from the Cancer Genome Atlas database.

[0055] Prognosis-related immune signatures include Pan F TBRs signature, EMT1 signature, PPAGs signature, IFNG signatureAyers et al signature, T cell accumulationPeng et al signature, SW480 cancer cells Bindea et al signature, MDSC Peng et al signature, TMEscoreA plus signature, Effector CD8 T cell signature, TMEscore plus signature, TMEscore CIR signature, Th2 cells Bindea et al signature, TMEscoreB plus signature, TMEscoreB CIR signature, TGFbFamily Member Li et al signature, Costimulation T Roonety et al signature, and Treg Roonety et al signature.

[0056] The obtained subtypes include subtype 1, subtype 2 and subtype 3. Subtype 1 has an immune "hot" and immunosuppressive phenotype, subtype 2 has an immune "cold" phenotype, and subtype 3 has an immune "hot" phenotype.

[0057] It should be noted that the specific content and beneficial effects of the device of the present application can be found in the above-mentioned method embodiment, which will not be repeated here.

[0058] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for molecular typing of colorectal cancer based on immune characteristics, characterized in that: The following steps are involved: Obtain colorectal cancer cohort data, gene expression profiles, and clinical information data; Obtain immune-related characteristics, and obtain prognosis-related immune features from the obtained colorectal cancer cohort data, gene expression profiles, and clinical information data based on the enrichment scores of immune-related characteristics; subtype identification and validation based on the prognosis-associated immune signature; Classify patient information data according to the obtained subtypes to determine the colorectal cancer phenotype; The colorectal cancer cohort data, gene expression profiles, and clinical information data were obtained from the Cancer Genome Atlas database; The prognosis-related immune signatures include Pan F TBRs signature, EMT1 signature, PPAGs signature, IFNG signature Ayers et al signature, T cell accumulation Peng et al signature, SW480 cancer cells Bindea et al signature, MDSC Peng et al signature, TMEscoreA plus signature, Effector CD8 T cell signature, TMEscore plus signature, TMEscore CIR signature, Th2 cells Bindea et al signature, TMEscoreB plus signature, TMEscoreB CIR signature, TGFb Family Member Li et al signature, Costimulation T Roonety et al signature, and Treg Roonety et al signature; The obtained subtypes include a first subtype, a second subtype and a third subtype, wherein the first subtype has an immune "hot" and immunosuppressive phenotype, the second subtype has an immune "cold" phenotype, and the third subtype has an immune "hot" phenotype.

2. A method for molecular typing of colorectal cancer based on immune characteristics according to claim 1, characterized in that: The process of acquiring the prognosis-related immune features is specifically as follows: acquiring immune-related features, performing feature matching from the acquired colorectal cancer cohort data, gene expression profiles, and clinical information data according to the enrichment scores of the immune-related features, and acquiring the prognosis-related immune features of colorectal cancer.

3. A molecular typing device for colorectal cancer based on immune characteristics, characterized in that: include: The data acquisition module is configured to: acquire cohort data, gene expression profiles and clinical information data of colorectal cancer; The prognosis-related immune signature acquisition module is coordinated to: acquire immune-related signatures, and acquire prognosis-related immune signatures from acquired colorectal cancer cohort data, gene expression profiles, and clinical information data based on the enrichment scores of the immune-related signatures; The subtype determination module is coordinated to: identify and verify the subtype according to the prognosis-related immune characteristics; The prognosis prediction module is coordinated to: classify the patient information data according to the obtained subtypes to determine the colorectal cancer phenotype; The colorectal cancer cohort data, gene expression profiles, and clinical information data were obtained from the Cancer Genome Atlas database; The prognosis-related immune signatures include Pan F TBRs signature, EMT1 signature, PPAGs signature, IFNG signature Ayers et al signature, T cell accumulation Peng et al signature, SW480 cancer cells Bindea et al signature, MDSC Peng et al signature, TMEscoreA plus signature, Effector CD8 T cell signature, TMEscore plus signature, TMEscore CIR signature, Th2 cells Bindea et al signature, TMEscoreB plus signature, TMEscoreB CIR signature, TGFb Family Member Li et al signature, Costimulation T Roonety et al signature, and Treg Roonety et al signature; The obtained subtypes include a first subtype, a second subtype and a third subtype, wherein the first subtype has an immune "hot" and immunosuppressive phenotype, the second subtype has an immune "cold" phenotype, and the third subtype has an immune "hot" phenotype.

4. The intestinal cancer molecular typing device based on immune characteristics according to claim 3, characterized in that: The process of acquiring the prognosis-related immune features is specifically as follows: acquiring immune-related features, performing feature matching from the acquired colorectal cancer cohort data, gene expression profiles, and clinical information data according to the enrichment scores of the immune-related features, and acquiring the prognosis-related immune features of colorectal cancer.

Images