CN112530581B - Immune molecule classification system for prostate cancer patients and application thereof - Google Patents
Immune molecule classification system for prostate cancer patients and application thereof Download PDFInfo
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Abstract
The invention provides an immune molecule classification system for a prostate cancer patient and application thereof, and relates to the technical field of biomedicine. The immune molecular classification system of the prostate cancer patients is used for classifying the prostate cancer patients into three subtypes, namely, a non-immune subtype, an immune activation subtype and an immune suppression subtype, and the clinical anti-PD-1/PD-L1 treatment is determined through classification. The invention overcomes the defects of the prior art, establishes three immunophenotypes by establishing a novel classification system based on immune characteristics, prompts the immune response to be a driving factor for the prognosis of the prostate cancer, and provides a new thought for the immunotherapy of the prostate cancer patients.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to an immune molecule classification system for a prostate cancer patient and application thereof.
Background
Prostate cancer is a malignant tumor with high incidence rate and fifth mortality rate in men, brings huge pressure to the world health system and also causes serious threat to the health of men. Low-medium risk prostate cancer patients may be treated by minimally invasive ablative therapy, radiation therapy, or radical prostatectomy.
However, about 26% -30% of prostate cancer patients will enter advanced stages of the tumor within 5 years after diagnosis. Although androgen deprivation therapy (Androgen Deprivation Therapy, ADT) is available to these advanced patients, they still face the conclusion of rapidly entering the Castration-resistant Stage (CRPC), which will lead to their death in 2 to 4 years. For CRPC patients receiving maximum dose androgen-blocking treatment, the Overall Survival (OS) for 5 years was 25.4%, whereas patients receiving surgery alone inhibited androgens had a survival rate of only 1.8%. Wallis et al report that the overall mortality rate (Adjusted Hazard Ratio, HR) for high risk prostate cancer is higher than for low and medium risk patients (1.88 vs.1.50, 1.47).
Currently, sipuleucel-T, abiraterone acetate, enzalutamide, carbazol, radium-223, and apatamide have been approved by the U.S. food and drug administration (Food and Drug Administration, FDA) for treatment of CRPC patients.
The tumor microenvironment (Tumor microvironment, TME) is a tumor environment consisting of a large number of blood vessels, immune cells, mesenchymal cells, cytokines and chemokines, playing a vital role in the development and progression of tumors. Numerous specialists at home and abroad have explored the role of TME in tumor progression or prognosis prediction. Previous studies by research teams have shown that M2 macrophages are a risk factor for prostate cancer patients. Zhao et al demonstrated a potential correlation between high expression levels of apoptosis 1ligand 2 (programmed cell death ligand 2, pd-L2) and poor prognosis of prostate cancer patients and post-operative radiation therapy. Rodrigues et al describe a positive correlation between defective mismatch repair characteristics and the overactivation of several immune checkpoints.
Sipuleucel-T is the first FDA approved immunotherapy regimen for prostate cancer patients, and recombinant fusion of prostatophosphoric acid phosphatase (prostatic acid phosphatase, PAP) activates antigen-presenting cells (APCs) and removes the cells from the tumor immunosuppressive environment.
Anti-apoptosis protein 1 (anti-programmed cell death protein, PD-1) and anti-apoptosis 1ligand1 (anti-programmed cell death 1ligand1, PD-L1) therapy is another immunotherapeutic option for prostate cancer patients, and has now been demonstrated to be beneficial in melanoma, gastric cancer, non-small cell lung cancer, breast cancer, and urothelial cancer patients. However, these immune checkpoint blockade (immune checkpoint blockade, ICB) treatments are effective in only a fraction of patients, and TME molecular characteristics are closely related to chemoradiotherapy and ICB responses. Thus, studying the sub-immunophenotype of prostate cancer is of great importance in guiding immunotherapy of prostate cancer.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an immune molecule classification system for a prostate cancer patient and application thereof, and establishes three immune phenotypes through establishing a novel classification system based on immune characteristics, so that the immune response is a driving factor for prognosis of the prostate cancer, and a novel thought is provided for immunotherapy of the prostate cancer patient.
In order to achieve the above object, the technical scheme of the present invention is realized by the following technical scheme:
an immune molecule classification system for a prostate cancer patient, the immune molecule classification system for a prostate cancer patient being such that the prostate cancer patient is classified into three subtypes, and the establishment of the three subtypes essentially comprises the steps of:
(1) Dividing the patient into non-immune subtype and immune subtype by reflecting the state of tumor immune microenvironment of the prostate cancer patient by highly infiltrated T cells, B cells, NK cells and macrophages;
(2) The immune subtypes are classified into immune-activating subtypes and immune-suppressing subtypes according to the characteristics of the matrix and the activation states of WNT/TGF- β, TGF- β1 and C-ECM signals.
Preferably, the immune activation of this subset is associated with a better relapse-free survival in prostate cancer patients.
The use of the immune molecular classification system of prostate cancer patients in the clinical selection of anti-PD-1/PD-L1 therapy.
Preferably, patients of the immune activation subtype screened by the immune molecular classification system are suitable for anti-PD-1/PD-L1 therapy, and patients of the immunosuppression group are more suitable for anti-TGF-beta plus anti-PD-1/PD-L1 therapy; for the non-immune subtype, the combined use of anti-CTLA-4 and anti-PD-1/PD-L1 therapies renders non-responders responsive to immunotherapy.
The invention provides an immune molecule classification system for prostate cancer patients and application thereof, which has the advantages compared with the prior art that:
the invention classifies patient samples into non-immune subtypes, immune activation subtypes and immune suppression subtypes by researching the relativity of immune microenvironment subtypes and prognosis of prostate cancer patients and treating response to immune checkpoint immunotherapy by using a non-negative matrix factorization algorithm on all the incorporated samples, wherein about 14.9% to 24.3% of patients belong to the immune activation subtype, which is closely related to clinical prognosis, and provides new insight for immune treatment strategies.
Description of the drawings:
FIG. 1 is a flow chart of the invention: the study included a total of 1,557 prostate cancer patients, with an immune subtype established in 495 patients based on the TCGA-PRAD cohort, and validated in GSE70770, GSE116918, GSE79021, MSKCC and AHMU-PC cohorts;
FIG. 2 is a graph of analysis of immune-related cluster factors identified by non-negative matrix factorization (non-negative matrix factorization, NMF) analysis in accordance with the present invention: (A) 11 cluster modules obtained from NMF analysis, the second module enriched most of patients with high immune enrichment scores; (B) Modulating immune and non-immune categories by a multidimensional scaling (multidimensional scaling, MDS) random forest method; (C) The heatmap shows patient distribution of different NMF modules, immune factor weights, clusters of exemplary genes, immune enrichment scores and final immune categories; (D) The results of the Gene Set enrichment analysis (Gene Set EnrichmentAnalysis, GSEA) show the signal pathways activated in the immune class;
FIG. 3 is a graph of the analysis of the immune subtypes in the TCGA-PRAD cohort of the present invention, comparing their differences in tumor infiltrating lymphocytes, copy number changes, gene mutations, neoantigens, tumor stem and PD-L1 expression levels: (A) Heat maps of consensus clustering of immune factors selected by example genes of NMF and refined by MDS random forest to define immune categories (200/495, 40.4%); two different immune-enriched subtypes were identified using the nearest template prediction method (Nearest Template Prediction, NTP) that captures the characteristics of the activated matrix: immunosuppression (126/495, 25.5%) and immune activation (74/495, 14.9%) categories; (B) In patients with a TCGA-PRAD cohort less than or equal to 60 years old, the three immune subtypes have different relapse-free survival; (C) Subclass mapping (SubMap) analysis showed that patients with immune-activated subtypes were more likely to respond to anti-PD-1 treatment (Bonferroni corrected p=0.0079); (D) amplification and deletion of chromosome arm gene copy number; (E) chromosome focus gene copy number amplification and deletion; (F) differences in tumor mutational burden; (G) Genes that were differentially mutated in three immune subgroups (some patients of the non-immune class without gene mutation were not shown); (H) a new antigen differential; (I) differences in tumor infiltrating lymphocytes; (J) PD-L1 expression differences; (K) The difference in tumor stem cell characteristics expressed in mRNAsi, and the comparison between the two groups was performed by T-test;
FIG. 4 is a schematic diagram showing successful validation of immune subtypes in an AHMU-PC cohort according to the present invention: (A) The heat map shows immune signatures from different enrichments in immune-activated, immunosuppressive and non-immune groups; (B) Kaplan-Meier plots show the recurrence-free survival results for the three immune subtypes; (C) SubMap analysis showed that immune-activated subtype patients were more likely to respond to anti-PD-1/PD-L1 treatment (Bonferroni corrected p= 0.0399); CD163 (D) and α -SMA (E) immunohistochemical staining and quantification of prostate cancer patients with different immune status (non-immune, immunosuppressive and immune activation categories) in the AHMU-PC cohort;
FIG. 5 is a graph showing the results of non-recurrent survival of patients with three immune subtypes of prostate cancer according to the present invention: (a-C) heat maps of consensus clusters of immune factors selected by exemplary genes of NMF and refined by MDS random forests to define immune categories; two different immune response subtypes were determined using the NTP method that captures the characteristics of the activated matrix: immunosuppression and immune activation subtypes; (D) Different relapse-free survival of the three immune subtypes of GSE70770 cohort; (E) Different relapse-free survival of the three immune subtypes of GSE116918 cohort; (F) Different relapse-free survival of the three immune subtypes in the MSKCC cohort.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described in the following in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
determination of immune subclass:
1. patient collection:
(1) 1557 cases of prostate cancer patients were enrolled in the current study, the information including available gene expression profiles, clinical pathology and relapse-free survival records, and the study protocol flow is shown in figure 1;
(2) taking 495 cases of prostate cancer genome map (TCGA-PRAD) as a training queue, taking three public queues of Stoneal Sloan-Kettering Cancer Center (MSKCC), GSE70770, GSE116918 and GSE79021 as verification queues, and taking 993 cases of prostate cancer patients into study;
(3) a relapse-free survival record of 69 cases of Formalin-fixed paraffin-embedded (FFPE) patients in urology surgery (AHMU-PC cohort) at a first affiliated hospital of university of medical science of the Anhui was collected;
(4) based on an Illumina NovaSeq platform, a paired-end 150bp sequencing strategy is adopted to analyze a gene expression profile; at the same time, the first affiliated hospital ethics committee (PJ 2019-09-11) of the university of Anhui medical science recognizes ethics of the AHMU-PC queue.
Details of all the inclusion studies are given in table 1 below:
table 1: clinical pathology parameters of six patients in the prostate cancer dataset studied
2. Bioinformatics analysis:
(1) in the training queue, patients are divided into subgroups by an unsupervised NMF method using gene expression profiles, and a gene expression matrix V is decomposed into a gene factor matrix W and a sample factor matrix H by an NMF algorithm (shown in figure 1);
(2) generating the above-described immune scores using a single sample gene set enrichment analysis (ssGSEA) to select immune-related NMF factors, classifying immune and non-immune subtypes into gene expression of the first 150 sample genes of immune-related NMF factors using the gene pattern module "nfcon ssentus";
(3) the immune subtypes are further classified into immunosuppressive subtypes and immune-activating subtypes based on recent template prediction of activating matrix (gene pattern module "NTP");
(4) based on immune suppressor genes or various immune markers (GSEA) or immune suppressor genes (SSE 2), carrying out artificial immune characteristic analysis on tumor cells, and comparing copy number variation (CNA) among different immune groups and variation of tumor infiltrating lymphocytes (til);
(5) to verify the immunophenotype obtained from the training cohort, 150 Differentially Expressed Genes (DEG) were used in the immune and non-immune subtypes, the subfractions in the external validation cohort were split in half with the gene pattern module "NMFConsus", and then the immune activation and inhibition subpopulations were shown by activating the matrix signal.
3. IHC staining verifies the immunophenotype in the AHMU-PC cohort:
(1) CD163 was selected as a cellular marker for macrophages, and α -SMA was selected to reflect the activation of stromal cells and to differentiate between immunocompetent and immunosuppressive subtypes;
(2) alpha-SMA is ubiquitously expressed in stromal cells using positive staining area scores (0, negative; 1, 1% -10;2, 11% -50%;3, 51% -80%;4, >80 positive area percent) multiplied by immunostaining intensity scores (0, no staining; 1, weak; 2, light; 3, strong intensity) in semi-quantitative results;
(3) for CD163 staining, positively stained cells were counted directly using ImageJ software (NIH, bescenda, usa).
4. Discovery of immune-related factors and identification of immune subclasses for prostate cancer
(1) The gene expression profile of 495 prostate cancer patients from the training TCGA-PRAD cohort was first used for virtual microdissection by NMF algorithm; the second factor of 11 expression patterns (NMF clusters) is immunologically significant and has a relatively higher immune enrichment score than other expressions (as in fig. 2A), i.e., this NMF module is called the "immune module";
(2) the first 150 weighted genes were selected as exemplary genes representing the second immune factor. Carrying out gene ontology enrichment analysis to obtain 150 exemplary genes which are most enriched in T cell activation, leukocyte migration and lymphocyte differentiation pathways, wherein the first 5 exemplary genes are positively correlated with B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells;
(3) all 495 patients were subjected to consensus clustering based on 150 signature genes, and the patients were further classified into immune and non-immune subclasses using a multidimensional scale (multidimensional scaling, MDS) random forest (see fig. 2B-C);
(4) comparing the differences in activated signaling pathways of immune and non-immune subtypes by GSEA analysis, wherein immune cell-related pathways, immune response pathways, pro-inflammatory pathways and tumor promoting pathways are significantly activated in immune subtypes [ all false discovery rate (false discovery rate, FDR) <0.05; as in fig. 2D ]; also, patients belonging to the immune subtype showed higher immune signal enrichment scores than the non-immune subtype, including T-cell, B-cell, NK-cell and macrophage related markers, as well as tertiary lymphoid structure scores (tertiary lymphoid structure, TLS), cytolytic activity scores (cytolytic activity score, CYT) and IFN-markers (all P <0.05, as shown in figure 3A on the top).
Taken together, the results from the graphs in FIGS. 2 and 3A demonstrate that the identified immune-related factors and exemplary genes are capable of determining immune subclasses of prostate cancer.
Example 2:
subtype establishment of immune subtype:
1. since the activated interstitial response is inversely related to immune activation, 63.0% (126/200) of immunocompromised patients have features of high interstitial rich score (stromal enrichment scores, ses) (see figure 3A bottom panel);
2. transforming growth factor-beta is thought to be a central mediator of immunosuppression in the immune microenvironment, whereas high levels of extracellular matrix cytokines (extracellular matrix cytokines, C-ECM) induced by cancer-associated fibroblast activation are capable of recruiting immunosuppressive cells; consistent with expectations, wnt/transforming growth factor- β, transforming growth factor- β1 and C-ECM signals were found to be more enriched in the matrix-activated subtype, also known as the immunosuppressive subtype, than in the non-immune subtype (both P <0.05, fig. 3A), with the remaining 37.0% of patients (74/200) belonging to the immune-activated subtype;
3. the increased expression of IL-11, TGFB1 and TGFB2 in the immunosuppressive subtype compared to the immunocompetent subtype, a result consistent with current studies; and a recent study showed that PAK4 is enriched in unresponsive tumor biopsies, PAK4 being significantly more expressed in immunosuppressive subtypes than in immunocompetent subtypes;
4. tumor-infiltrating Treg (timor-infiltrating Treg, TITR) characteristics (P < 0.01) and Treg cell characteristics (p=0.017) are mainly concentrated in the immunosuppressive subtype (as shown in the lower panel of fig. 3A and table 2 below), while Th17 cell infiltration characteristics are mainly concentrated in the immune activating subtype (p=0.034, supplementary fig. 3).
From fig. 3A, two different immunophenotypes, namely, immunosuppressive subclasses and immune activating subclasses, can be defined according to tumor microenvironment activity.
Table 2: distribution of three immune subtypes in six patients in the prostate cancer dataset studied
Example 3:
relationship between immune activation and good relapse-free survival and anti-PD-1 immunotherapy verifies that:
1. since the clinical pathological features are important indicators for evaluating the malignancy of prostate cancer. In patients under 60 years of TCGA-PRAD cohort, the immune-activated subtype had better relapse-free survival, while the immune-suppressed subtype had lower relapse-free survival, the relapse-free prognosis of the non-immune subtype was intermediate (p=0.033, as in fig. 3B);
2. testing the immune molecule classification system for potential ability in selecting candidate patients receiving anti-PD-1/PD-L1 immunotherapy; the results indicate that patients of the immune activation subtype responded better to anti-PD-1 immunotherapy (Bonferroni corrected p=0.0079, fig. 3C).
Taken together, with reference to fig. 3B-C, patients in the immune activation category showed the best recurrence-free survival results and could benefit more from anti-PD-1/PD-L1 immunotherapy.
Example 4:
correlation detection of immune subtype with tumor gene copy number change, tumor infiltrating lymphocytosis and tumor stem cell reduction:
1. in the TCGA-PRAD cohort, the immunoenriched subtypes exhibited higher gene amplification load at both arm and focus levels (PARM-amp=0.033 and Pfocal-amp=0.015) rather than gene deletion load (pamm-del=0.54, pfocal-del=0.14) (as in fig. 3D-E); furthermore, the Copy Number Alterations (CNA) of immune checkpoints PD-1, PD-L1, LGALS9 and CD48 are positively correlated with infiltration of immune cells; there was no difference between immunized and non-immunized subtypes in terms of TMB and neoantigen (ptmb=0.661, as in fig. 3f, pneoags=0.271, as in fig. 3H).
2. Based on MutSigCV algorithm analysis, there were different mutation cases between the three immunophenotypes (as in fig. 3G); the mutation rate of immune subtype (14.00%) is significantly higher than that of non-immune subtype (9.83%), especially that of immunosuppressive subtype (19.05%) is significantly higher than that of non-immune subtype (19.05%, P < 0.001); the SPOP mutations of the suppressed subtypes are significantly less than those of the immunocompetent and non-immunocompetent subtypes (5.56% vs.13.51% and 13.22%, P < 0.001).
3. Mutations in the BRCA2, SCN10A, C14orf115, OR4C6, KAL1, CHRNA6, KIAA1012, OR10R2 genes were immune subtype specific (all P < 0.05).
4. Regarding TILs, TILs density was significantly higher in the immunized group than in the non-immunized group (p=0.001, as in fig. 3I); expression of PD-L1 also increased, infiltration of cd8+ T cells was greater in immune subtypes than in non-immune subtypes (P <0.001, fig. 3J), consistent with previous studies; the stem cell characteristics of tumors, represented by mrnas, were reduced in the immunized group compared to the non-immunized group (P <0.001, figure 3K).
From fig. 3, it can be seen that the immune-enriched subtype was associated with significantly higher CNAs and higher TIL abundance, but not TMB and neoantigen.
Example 5:
reproduction of three immunophenotypes in the AHMU-PC cohort:
1. to confirm the accuracy of NMF algorithm and immunophenotype based on activating matrix features, 150 up-regulated genes were identified between immune and non-immune subtypes as classifiers to distinguish immune and non-immune subtypes; the genes of which differential expression rows are arranged at the top 5 positions are positively correlated with infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells (all P < 0.05), which indicates that the genes can effectively reflect infiltration of immune cells in tumor microenvironment.
2. In an AHMU-PC queue, collecting paraffin section tissues of 69 patients with clinical pathological characteristics, carrying out long-term follow-up, and carrying out RNA sequencing to obtain a gene expression profile; with the aid of the NMF consensus pattern, 47.8% (33/69) of patients had a higher immune concentration score (IES) and were classified as immune-enriched subtypes, 52.2% of patients belonging to the non-immune subtype; furthermore, the immune subtypes are subsequently classified into immune-activating subtypes (14/69, 20.3%) and immune-suppressing subtypes (19/69, 27.5%) (see table 2 above); similar to the above results, the enrichment scores for immune subtype patients T cells, B cells, macrophages, TLS, cytotoxic T cells (CyT) and Interferon (IFN) were all higher than for non-immune subtype patients (all P < 0.05); the immunosuppressive subtype SESs, TITR, MDSC and C-ECM signature scores were higher (both P <0.05, fig. 4A).
3. A Kaplan-Meier analysis was used to determine the recurrence-free survival differences between the three immunophenotypes; relapse-free survival results for immunosuppressive subtypes were worse than for immunocompetent and non-immunocompetent subtypes (p=0.0083, fig. 4B); in addition, patients of the immune activation subtype are mostly shown to be abundant in early pathological stages, with Gleason scores (92.3% to 44.4%,48.57%, kruskal-Wallis test, p=0.0127) and pathological T-stage (92.9% to 65.0%,75.0%, kruskal-Wallis test, p=0.368); patients in the immune-activated subset in the AHMU-PC cohort benefited more from anti-PD-1/PD-L1 immunotherapy than the non-immune-activated subset (Bonferroni corrected p= 0.0399, fig. 4C).
4. To verify the accuracy of the classification system, immunohistochemical (IHC) staining was performed on AHMU-PC cohort samples (tissue sections from 69 patients above); CD163 is a marker for macrophages, used to distinguish between immune and non-immune subpopulations, and the matrix marker α -SMA is used to distinguish between immune activating and immunosuppressive subpopulations; comparing the IHC result with the result of the NMF-based immune molecule classifier to obtain a consistent result; both immunosuppressive (P < 0.0001) and immune-activated (p=0.0018) subtypes cd163+ cells were higher than the non-immune subtype (as in fig. 4D); the immunosuppressive subtype α -SMA immunohistochemical staining H integrates higher than the immune activated subtype (p=0.0033) and the non-immune subtype (p=0.0261) (see fig. 4E).
As can be seen from fig. 4, three immunophenotypes in the AHMU-PC cohort were validated, confirming the agreement of RNA sequence-based immunophenotypes with the true IHC staining results; patients in the immunosuppressed group had the worst recurrence-free survival, while patients in the immunocompetent subtype may benefit from anti-PD-1/PD-L1 treatment.
Example 6:
verification of three immunophenotypes in the external queue:
1. the recruitment of 993 prostate cancer patients with available gene expression profiles and matching clinical pathology (table 1). Of the immune signal analysis results of the 4 external validation queues, IES and immune signal features were significantly enriched in immune subtypes (P all < 0.05), as were ssGSEA results of T cells, B cells, macrophages, TLS, cytotoxic T cells and interferon signals (P all < 0.05). Meanwhile, the SES was classified into activation and inhibition subtypes by the matrix activation signal generated by nearest neighbor template prediction (Nearest Template Prediction, NTP), and the SES of the immunosuppressive subtype was higher than that of the immune activation subtype (P < 0.05). Furthermore, in these three external cohorts, the abundance of Treg cells, TITR, MDSC, wnt/TGF- β and C-ECM features of the immunosuppressive subtype was higher than that of the immune activating subtype (all P < 0.05).
2. In the GSE70770 cohort, 51.7% (105/203) of the patients were of the non-immune subtype, 42 (20.7%) were of the immune activation subtype and 56 (27.6%) were of the immune suppression subtype (fig. 5A, table 2). Of 248 patients in the GSE116918 cohort, 54.5% (140/248) were of the non-immune subtype, 75 (30.2%) were of the immunosuppressive subtype, and the other 33 (13.3%) were of the immune activating subtype (see fig. 5B, table 2). In the MSKCC cohort, 73 patients (52.1%) were classified as non-immune subtypes, while 34 (24.3%) and 33 (23.6%) patients were classified as active and inhibitory subtypes, respectively (see fig. 5C, table 2); similar results were also found for another 402 patients extracted from GSE79021 cohorts: 21.6% (87/402) of the patients were of the immunocompetent subtype, 18.7% (75/402) of the patients were of the immunosuppressive subtype, and the remaining 240 patients were of the non-immunocompetent type.
3. Consistent with the above results, patients belonging to the immune-activated subtype had the highest recurrence-free survival, while patients belonging to the non-immune subtype had the worst recurrence-free survival, while patients belonging to the immunosuppressive subtype had a recurrence-free survival intermediate between those (fig. 5D-F).
As can be seen from fig. 5, the immune molecule based prostate cancer classification system has stability and importance.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. An immune molecule classification system for a prostate cancer patient, wherein the immune molecule classification system for a prostate cancer patient classifies the prostate cancer patient into three subtypes, and the establishment of the three subtypes mainly comprises the following steps:
(1) Dividing the patient into non-immune subtype and immune subtype by reflecting the state of tumor immune microenvironment of the prostate cancer patient by highly infiltrated T cells, B cells, NK cells and macrophages;
(2) The immune subtypes are classified into immune-activating subtypes and immune-suppressing subtypes according to the characteristics of the matrix and the activation states of WNT/TGF- β, TGF- β1 and C-ECM signals.
2. An immune molecule classification system for prostate cancer patients according to claim 1, wherein: the immune activation subtype is associated with a better relapse-free survival in prostate cancer patients.
3. An immune molecule classification system for prostate cancer patients according to claim 1, wherein: the doctor can be guided to select the application of the anti-PD-1/PD-L1 treatment in clinic.
4. An immune molecule classification system for prostate cancer patients according to claim 3, wherein: patients of the immune activation subtype screened by the immune molecular classification system are suitable for anti-PD-1/PD-L1 treatment, and patients of the immune inhibition subtype are more suitable for anti-TGF-beta plus anti-PD-1/PD-L1 treatment; for non-immune subtypes, anti-CTLA-4 and anti-PD-1/PD-L1 therapies can be used in combination to cause non-responders to respond to immunotherapy.
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