CN113899903A - Colorectal cancer biomarker and application thereof in diagnosis, prevention, treatment and prognosis - Google Patents

Colorectal cancer biomarker and application thereof in diagnosis, prevention, treatment and prognosis Download PDF

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CN113899903A
CN113899903A CN202010642872.3A CN202010642872A CN113899903A CN 113899903 A CN113899903 A CN 113899903A CN 202010642872 A CN202010642872 A CN 202010642872A CN 113899903 A CN113899903 A CN 113899903A
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slamf1
ilc
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ilcs
prdm1
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苏冰
齐晶晶
沈蕾
叶幼琼
艾德琳·克里尼耶
伯川德·艾斯卡利耶
艾米莉·纳尔尼·曼奇内利
埃里克·维维埃
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French National Institute Of Health And Medicine
Shanghai Institute of Immunology
Shanghai Jiaotong University School of Medicine
Aix Marseille Universite
Centre National de la Recherche Scientifique CNRS
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French National Institute Of Health And Medicine
Shanghai Institute of Immunology
Shanghai Jiaotong University School of Medicine
Aix Marseille Universite
Centre National de la Recherche Scientifique CNRS
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Abstract

The invention discloses colorectal cancer biomarkers and applications thereof in diagnosis, prevention, treatment and prognosis. The present invention also discloses different single cell characteristics of blood and subpopulations of intestinal ILCs in healthy conditions and colorectal cancer. A healthy intestinal tract consists of ILC1s, ILC3s, and ILC3s/NK, but no ILC2 s. Additional tumor-specific subsets of ILC1s and ILC2s were determined in CRC patients. The present invention also discloses that SLAMF1 was selectively expressed on tumor-specific ILCs and higher levels of SLAMF1+ ILCs were observed in the blood of CRC patients. Survival of patients with rectal cancer in the high SLAMF1 group was significantly higher than in the low SLAMF1 group, indicating SLAMF1 is an anti-tumor biomarker in CRC. The invention also discloses the use of unsupervised hierarchical clustering methods to study the supportive ILCs heterogeneity in blood, normal mucosal and intestinal tumors during homeostasis and CRC. The biomarker can be used for effectively diagnosing, preventing, treating, prognostically evaluating and predicting the survival rate of CRC.

Description

Colorectal cancer biomarker and application thereof in diagnosis, prevention, treatment and prognosis
Technical Field
The invention belongs to the technical field of medicines, and relates to a colorectal cancer biomarker and application thereof, and an unsupervised clustering analysis method of the biomarker.
Background
T cell-based immunotherapy has been used very successfully in the clinic for the treatment of malignancies, but is limited to a small fraction of patients (Baumeister et al, 2016; Chen and Mellman, 2017; Okazaki et al, 2013; Okazaki and Honjo, 2007; Schumacher and Schreiber, 2015; Sharma and Dallson, 2015). In general, when T cell immunotherapy is performed, it is necessary to perform treatment for other immune components at the same time to increase the cure rate of patients receiving the immunotherapy. Innate Lymphocytes (ILCs) are tissue-resident and antigen-independent lymphocytes that modulate the body's immunity to pathogens and commensals to maintain tissue homeostasis (Spits et al, 2013; viier et al, 2018). ILCs are capable of forming heterogeneous cell populations, and are currently classified into five major classes (natural killer (NK) cells, helper ILC1s, ILC2s, ILC3s, and lymphoid tissue inducing cells) based on the cytokines produced by and the transcription factors expressed by these cell populations (viier et al, 2018). ILCs are involved in immune functions of the body, including pathogen response, inflammation, tissue development, remodeling, repair, homeostasis, and the like.
Given the number and nature of cytokines produced by ILCs, subsets of ILCs may be involved in cancer immunity, but may also be associated with tumor-associated inflammation. NK cells are known to play a role in cancer through tumor suppressor properties and to be effective in controlling metastasis (Lopez-Soto et al, 2017). The role of helper ILCs in tumorigenesis and cancer immunity is not clear and may depend on the tumor microenvironment. ILC1s is capable of producing large amounts of proinflammatory cytokines, such as IFN- γ and TNF- α, and is therefore beneficial for tumorigenesis (Chiossone et al, 2018). However, IFN- α may also limit tumor growth in certain specific tumor microenvironments (Castro et al, 2018; Zaidi, 2019). ILC2s has been shown to be mostly harmful in various tumor environments, for example, in the peripheral blood of patients with gastric cancer (Bie et al, 2014) and acute promyelocytic leukemia (Trabanelli et al, 2017), with large numbers of ILCs present. IL-13 from ILC2s stimulates immunosuppressive activity of myeloid-derived suppressor cells in acute promyelocytic leukemia (Trabanelli et al, 2017) and human bladder cancer and murine prostate tumors (Chevalier et al, 2017). However, ILC2 s-derived IL-5 may contribute to the suppression of primary and metastatic lung tumors in a mouse model (sarancova et al, 2016), and ILC2s is capable of stimulating tissue-specific tumor immunity in pancreatic cancer (Moral et al, 2020). ILC3s has been reported to have cancer-inhibiting properties in patients with, for example, the B16 melanoma mouse model (Eisenring et al, 2010; Nussbaum et al, 2017) and non-small cell lung cancer (NSCLC) (Carrega et al, 2015). In contrast, ILC3 s-derived IL-17 and IL-22 may contribute to the development of intestinal cancers (Chanet al, 2014; Kirchberger et al, 2013). Therefore, there is a great need to investigate the presence and corresponding mechanism of action of subsets of ILCs in various cancer indications.
Despite the significant improvements of current therapeutic strategies for cancer, the incidence of colorectal cancer (CRC) is third in men and women, and is also the second highest mortality cancer worldwide (Bray et al, 2018). Dysregulation of ILCs responses has been associated with the development of intestinal cancer. ILC2s levels are also low (Fuchs et al, 2013; Simoni et al, 2017) and ILC3s (Ikedaet al, 2020; Simoni et al, 2017) levels are also abnormally low in many pathological conditions in humans, which are usually densely distributed in the colon in a steady state (Fuchs et al, 2013; Ikeda et al, 2020; Simoni et al, 2017), while in CRC patients the gut contains a large amount of ILC1s (Carrega et al, 2020; Fuchs et al, 2013; Ikeda et al, 2020; Simoni et al, 2017). Furthermore, the decrease in ILC3s/ILC1s ratio is related to the severity of CRC (Simoni et al, 2017). The ILCs subpopulation profile remains unclear in terms of composition, diversity and functional status of these cells in the human gut, both in steady state and tumor situations.
Disclosure of Invention
Innate Lymphocytes (ILCs) are tissue-resident lymphocytes that, unlike traditional T lymphocytes, do not have antigen-specific receptors. ILCs include Natural Killer (NK) cells, ILC1s, ILC2s, ILC3s, and subpopulations of lymphoid tissue inducing cells (LTi). Tumor ILCs are present in a variety of cancers, but their role in cancer immunity and immunotherapy is still far from being clear as other lymphocytes, such as T cells and NK cells.
The present invention uses unsupervised hierarchical clustering methods to study the heterogeneity of ILCs subpopulations in blood, normal mucosa and intestinal tumors under steady-state and CRC conditions. A healthy intestinal tract consists of ILC1s, ILC3s, and ILC3s/NK, but no ILC2 s. The present invention proposes for the first time that ILCs from patients with CRC comprise two additional subsets of tumor-specific ILCs TILCs: the tumor-specific ILC1 s-like subset (analogous to TILC1s) and ILC2s (TILC2s) subset. SLAMF1 (signal lymphocyte activating molecule family member 1, CD150) was selectively expressed on TILCs, with higher frequency of ILCs expressing SLAMF1 in the blood of CRC patients. The survival rate of patients with SLAMF 1-highly expressed CRC is significantly higher than that of patients with SLAMF 1-underexpressed CRC, which indicates that SLAMF1 is an anti-tumor biomarker of CRC.
The invention provides application of one or a combination of more of any one of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, tumor-specific ILC 1-like subgroup and ILC2 subgroup as a colorectal cancer biomarker, and the application takes one or a combination of more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, tumor-specific ILC 1-like subgroup and ILC2 subgroup as a target point to prepare a diagnostic reagent for the occurrence and/or metastasis of colorectal cancer, or prepare a medicine for treating colorectal cancer, or prepare a reagent for predicting the survival time of colorectal cancer, or prepare a reagent for evaluating the prognosis of colorectal cancer.
In the present invention, the tumor-specific ILC 1-like and ILC2 subpopulations may also be combined with the ILC3 subpopulation as biomarkers for diagnosis, treatment, survival prediction, and prognosis evaluation of colorectal cancer, etc.
The present invention also proposes a method of diagnosing colorectal cancer in a subject, or a method of preventing or treating colorectal cancer in a subject in need thereof, or a method of predicting or prognostically assessing the survival of a patient suffering from colorectal cancer, the method comprising: determining the level of any one or a combination of several of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, a tumor-specific ILC 1-like subpopulation, an ILC2 subpopulation in a sample obtained from said patient or subject, wherein by said level, a diagnosis is made as to whether said subject suffers from colorectal cancer, or a prediction or prognostic assessment is made of the survival time of said patient; or the like, or, alternatively,
the method comprises the following steps: and taking one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, a cytokine IL33, a tumor-specific ILC 1-like subgroup and an ILC2 subgroup as a target point, and preventing or treating the colorectal cancer of the patient.
In the application or the method, the immunogen of any one or a combination of more of SLAMF1, HPGD, TLE4, PRDM1, AQP3 and cytokine IL33 is used for preparing the antibody; and/or, preparing immune cells, proteins and/or small molecules by taking one or a combination of more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, tumor-specific ILC 1-like subgroup and ILC2 subgroup as targets.
In the use or method of the invention, the sample from which the biomarker is determined is a sample of tumour tissue or blood obtained from the patient, or a sample of gut tissue or blood from the subject.
In the use or method of the invention, the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33 is determined at the protein level; or the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3 and cytokine IL33 is determined at the nucleic acid level.
In the use or method of the invention, when the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33 is determined at the protein level, the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33 is determined by immunohistochemistry; or
When the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3 and cytokine IL33 is determined at the nucleic acid level, the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3 and cytokine IL33 is determined quantitatively by encoding mRNA of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3 and cytokine IL 33.
The use or method of the invention comprises determining the level of any one or more of SLAMF1+ ILC, HPGD + ILC, TLE4+ ILC, PRDM1+ ILC and AQP3+ ILC in said sample.
In a use or method of the invention, a panel of binding partners specific for the following cell surface markers is used to determine the level of SLAMF1+ cells in a sample obtained from the patient: CD45, CD127, CD117, CRTH2, CD5, TIGIT and SLAMF 1; or the like, or, alternatively,
the level of any one or more of SLAMF1+ ILC, HPGD + ILC, TLE4+ ILC, PRDM1+ ILC, AQP3+ ILC and IL33+ ILC is determined by a flow cytometry method; or the like, or, alternatively,
the level of any one or more of SLAMF1+ ILC, HPGD + ILC, TLE4+ ILC, PRDM1+ ILC, AQP3+ ILC, and IL33+ ILC is determined by single cell RNA sequencing.
In the use or method of the invention, the higher the level of SLAMF1, the higher the probability that the patient will have a long survival time.
In the application or method of the present invention, the diagnosis comprises the following steps: i) determining the level of SLAMF1 or the cytokine IL33 that activates ILC2 in a sample obtained from the subject; ii) comparing said level determined in step i) with a predetermined reference value; iii) the determined individual is a CRC patient when the level determined in step i) is above the predetermined reference value, or the determined individual is a healthy individual when the level determined in step i) is close to the predetermined reference value; or the like, or, alternatively,
the diagnosis comprises the following steps: i) determining the level of SLAMF1, HPGD, TLE4, PRDM1, AQP3 in an intestinal sample obtained from the subject; ii) comparing said level determined in step i) with a predetermined reference value; iii) when the characteristic upregulation of SLAMF1, HPGD, TLE4 and PRDM1 on the ILC cell surface of the gut, the downregulation of AQP3, as determined in step i), the individual is a CRC patient; or the like, or, alternatively,
the prediction or prognostic assessment comprises the steps of: i) determining the level of SLAMF1 or the cytokine IL33 that activates ILC2 in a sample obtained from the patient; ii) comparing said level determined in step i) with a predetermined reference value; iii) the patient has a good prognosis when said level determined in step i) is higher than said predetermined reference value, or a poor prognosis when said level determined in step i) is lower than said predetermined reference value; or the like, or, alternatively,
the prevention or treatment comprises the steps of: preventing or treating colorectal cancer in a subject in need thereof by targeting colorectal cancer tumor-specific biomarkers, signaling molecules, cell subsets, comprising: administering to the subject a pharmaceutical composition comprising a pharmaceutically acceptable carrier, an effective amount of a modulator of a tumor-specific biomarker of colorectal cancer, or a modulator of a signaling molecule, or a modulator of a subpopulation of cells, and optionally an additional therapeutic agent, thereby preventing or treating colorectal cancer; wherein the colorectal cancer tumor specific biomarker is selected from SLAMF1, HPGD, TLE4 and PRDM1, the signal molecule is AQP3, and the cell subset is specific ILC 1-like and ILC2 subset.
In the use or method of the present invention, the modulator includes promoter, inhibitor, modifier, etc., for example, selected from small molecule chemical agent, antisense oligonucleotide, small interfering rna (sirna), short hairpin rna (shrna), antibody, and biologically active fragment or homologue of the antibody.
The invention also provides an anti-tumor biomarker SLAMF1 and application thereof in diagnosis and treatment of colorectal cancer (CRC) diseases.
The invention also proposes the use of SLAMF1 as a biomarker for diagnosing, treating, and predicting colorectal cancer (CRC) survival.
The invention also provides application of a detection reagent of the biomarker SLAMF1 in preparation of medicines for diagnosing, treating and predicting the survival rate of colorectal cancer (CRC).
In the present invention, high levels of SLAMF1 correlate with higher survival in patients with CRC and can be used as an anti-tumor biomarker for CRC. Wherein said high levels are obtained from TCGA data analysis.
The invention also provides application of the ILC1 s-like subgroup and the ILC2s subgroup serving as biomarkers in diagnosis and treatment of colorectal cancer (CRC).
The invention also proposes the use of a tumour specific ILC1 s-like subgroup and an ILC2s subgroup as biomarkers for predicting colorectal cancer (CRC) survival.
Wherein the "tumor-specific ILC1 s-like subpopulation" and the "tumor-specific ILC2s subpopulation" are a new population of cells.
The invention also provides the application of the tumor specific gene PTGDR2 and/or GATA3 as a biomarker in the diagnosis and treatment of colorectal cancer (CRC).
The invention also proposes the use of the tumor specific genes PTGDR2 and/or GATA3 as biomarkers for predicting colorectal cancer (CRC) survival.
The invention also provides the application of the tumor specific gene (or gene combination) AQP3 as a biomarker in the diagnosis and treatment of colorectal cancer (CRC).
The invention also proposes the use of a tumor specific gene (or combination of genes) AQP3 as a biomarker for predicting colorectal cancer (CRC) survival.
The invention also provides application of the ILC1s, ILC2s and ILC3s subgroup combination in blood as a biomarker for diagnosing and treating colorectal cancer (CRC).
The invention also proposes the use of a combination of ILC1s, ILC2s and ILC3s subpopulations in blood as a biomarker for predicting colorectal cancer (CRC) survival.
The invention also provides application of the gene combination of SLAMF1, HPGD, TLE4 and PRDM1 in blood as a biomarker in diagnosis and treatment of colorectal cancer (CRC).
The invention also provides application of the gene combination of SLAMF1, HPGD, TLE4 and PRDM1 in blood as a biomarker in predicting CRC survival rate.
In the present invention, the use of any one or a combination of several of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, a tumor-specific ILC 1-like subgroup, and an ILC2 subgroup, including for example SLAMF1 alone as a biomarker or target for diagnosing or treating colorectal cancer (CRC), for predicting colorectal cancer (CRC) survival, or for prognosis evaluation of colorectal cancer. Wherein, by detecting SLAMF1 on the cell surface of ILC in the intestinal tract, if SLAMF1 is characteristically up-regulated, healthy individuals and CRC patients can be distinguished.
In the present invention, the use of any one or a combination of several of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, a tumor-specific ILC 1-like subgroup, and an ILC2 subgroup, including for example cytokine IL33 alone as a biomarker or target for diagnosing or treating colorectal cancer (CRC), for predicting colorectal cancer (CRC) survival, or for prognosis evaluation of colorectal cancer. Wherein the cytokine IL33 is the cytokine IL33 activating ILC2, and the high expression of the cytokine IL33 is related to the longer survival time of CRC patients, which indicates that TILC2 may indicate that the prognosis of CRC patients is good.
In the present invention, the use of any one or combination of more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, tumor specific ILC 1-like subgroup, and ILC2 subgroup, including for example the use of a combination of SLAMF1, HPGD, TLE4, PRDM1, AQP3 as a biomarker or target in the diagnosis or treatment of colorectal cancer (CRC), in the prediction of colorectal cancer (CRC) survival, or in the prognostic assessment of colorectal cancer. The characteristic up-regulation of SLAMF1, HPGD, TLE4 and PRDM1, and down-regulation of AQP3 in intestinal TILCs, allows to distinguish healthy individuals from CRC patients.
In the present invention, the tumor-specific ILC 1-like subpopulation is TIGIT + TILC 1-like cells.
In one embodiment, the invention provides a method for predicting survival of a colorectal cancer patient, comprising determining a level of SLAMF1 from a sample obtained from the patient, wherein the level correlates with survival of the patient.
As used herein, the term "colorectal cancer" includes accepted medical definitions that define colorectal cancer as a medical condition characterized by cancer of cells of the lower intestinal tract (i.e., the large intestine (colon)), including the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum.
The methods of the invention are particularly useful for predicting Overall Survival (OS), progression-free survival (PFS) and/or disease-free survival (DFS) in cancer patients.
As used herein, the phrase "short survival time" means that the survival time of the patient will be less than the median (or mean) observed for the patient in the general population. When the survival time of the patient is short, it means that the patient will have a "poor prognosis". In contrast, the term "long-term survival time" means that the survival time of the patient will be higher than the median (or average) observed in normal cancer patients. When a patient has a long survival time, this means that the patient will have a "good prognosis".
The samples contemplated by the present invention include blood and intestinal mucosa and colorectal cancer tissue samples obtained from patients in any manner of storage. The levels of SLAMF1 detected include nucleic acid and protein levels of SLAMF1 detected using any means (a reagent that can selectively bind SLAMF 1).
The methods of the invention comprise determining the level of SMMLF1+ ILCs in a sample. In particular for the embodiments, the sample may be a tumor tissue sample or a blood sample. By current definition, ILCs are meant that the resident lymphocytes comprise Natural Killer (NK) cells, helper ILC1s, ILC2s, ILC3s, and lymphoid tissue inducing cells. As used herein, the term "SLAMF 1+ ILCs" refers to ILCs that express SLAMF1 (i.e., SLAMF1 protein or nucleic acid, e.g., mRNA, of SLAMF 1). SLAMF1+ ILCs levels are expressed by ILCs (e.g., mean fluorescence intensity MFI) as a measure of the expression intensity of a marker (e.g., protein and/or mRNA), or as an amount of ILCs (e.g., frequency (e.g., percentage) of SLAMF1+ ILCs and SLAMF1+ cells) that express SLAMF1 (e.g., protein and/or mRNA) in a sample. The quantification of SLAMF1+ ILCs generally relates to the presence or absence of specific cell surface markers. SLAMF1 levels were detected using qPCR/RT-PCR after Flow Cytometry (Flow Cytometry), mass Flow (CyTOF), and sorting of ILCs.
The cell surface marker combination used in the present invention comprises CD45, CD127, CD117, CRTH2, CD5, TIGIT and SLAMF 1. The present invention encompasses any form of antibody coupling agent to detect these surface proteins. These antibodies include monoclonal or polyclonal antibodies from different species. The coupling agent comprises lanthanide metal markers for mass flow and fluorescent conjugates for general flow.
The term "SLAMF 1" as used herein refers to signaling lymphocyte activating molecule 1, which is also known as CDw150, IPO-3, SLAM family member 1, and CD 150. An exemplary amino acid sequence of SEQ ID NO 1 is as follows:
SEQ ID NO:1>sp|Q13291|SLAF1_HUMAN Signaling lymphocytic activation molecule OS＝Homo sapiens OX＝9606GN＝SLAMF1 PE＝1SV＝1
MDPKGLLSLTFVLFLSLAFGASYGTGGRMMNCPKILRQLGSKVLLPLTYERINKSMNKSIHIVVTMAKSLENSVENKIVSLDPSEAGPPRYLGDRYKFYLENLTLGIRESRKEDEGWYLMTLEKNVSVQRFCLQLRLYEQVSTPEIKVLNKTQENGTCTLILGCTVEKGDHVAYSWSEKAGTHPLNPANSSHLLSLTLGPQHADNIYICTVSNPISNNSQTFSPWPGCRTDPSETKPWAVYAGLLGGVIMILIMVVILQLRRRGKTNHYQTTVEKKSLTIYAQVQKPGPLQKKLDSFPAQDPCTTIYVAATEPVPESVQETNSITVYASVTLPES。
the beneficial effects of the invention include: SLAMF1 was selectively expressed on tumor-specific ILCs (tilcs) and higher levels of SLAMF1+ ILCs were observed in the blood of CRC patients. Survival of patients with rectal cancer in the high SLAMF1 group was significantly higher than in the low SLAMF1 group, indicating SLAMF1 is an anti-tumor biomarker in CRC. The biomarker can be used for effectively diagnosing, preventing, treating, prognostically evaluating and predicting the survival rate of CRC.
Drawings
FIG. 1: analysis of scRNAseq showed that ILC1s, ILC3s, ILC3s/NKs were visible in normal mucosa, but not ILC2 s.
A.4 UMAP profiles of 16145 ILCs in normal mucosa. Wherein the color of the cells is defined according to a specific data set.
B. Colored UMAP according to donor source.
C. Unsupervised hierarchical clustering of 6 clusters per donor was performed based on the average of the expression levels of the variably expressed genes in the cells. Each sample is colored according to its relationship to a particular subset.
D. The heat map shows 542 genes that distinguish three groups of ILCs by Wilcoxon rank sum test (nmC0-3:256 gene, nmC 4: 97 gene, nmC 5: 189 gene). Cells are shown as columns and genes are shown as rows, arranged with adjusted p-values <0.05 pairs of genes. Gene expression intensities are shown in color, based on a z-score distribution, from-2.5 (purple) to 2.5 (yellow). The boxes refer to the transcriptome information for a particular subpopulation of ILCs.
E. Based on the mean expression level of the variably expressed genes, Principal Component Analysis (PCA) was performed on 3 clusters of ILCs per sample.
F. Distribution of genes in each ILCs subpopulation that accounted for 20% of the total information per PC of (E).
G. The top 10 expressed genes, the top 10 expressed genes encoding transcription factors, secreted proteins and cell membrane markers in the total gene list with differences in expression between groups of ILCs are shown in the list. The genetic symbols and annotations were retrieved from a public database. The genes are arranged in p-value size.
H. And performing modular scoring on 3 groups of ILCs gene expression groups on a single cell level according to the tonsil ILCs gene characteristics.
FIG. 2: scRNA-seq analysis showed the presence of tumor specific ILC1 s-like and ILC2s sub-populations in CRC tissues.
A.4 UMAP map of CRC tissue 15101 cases of tumor-specific ILCs (TILCs) in patients. The cells show different colors according to the different cell populations defined.
Different donors in the umap diagram are shown in different colors.
C. Unsupervised hierarchical clustering was performed on 4 clusters per donor according to the mean of variable gene expression levels.
D. Thermographic analysis was performed on 982 genes (463 out of TILCs C0, 100 out of TILCs C1, 314 out of TILCs C2, 105 out of TILCs C3) and Wilcoxon rank-sum test was used to differentiate the 4 TILCs subpopulations in tumor tissue. Cells are shown as columns and genes are shown as rows, arranged with adjusted p-values (< 0.05). Gene expression is shown in color, from-2.5 (purple) to 2.5 (yellow) based on a z-score distribution. Boxes identify the transcriptome of a particular subpopulation of TILCs.
E. Principal Component Analysis (PCA) was performed on 4 TILCs subpopulations of each sample based on the mean expression level of the variably expressed genes.
F. 20% of the total contribution of genes to each PC in (E) was shown and their distribution in each subpopulation was differentiated by different colors.
G. The list shows the top 10 expressed genes ranked top by p-value size, the top 10 expressed genes encoding transcription factors, secreted proteins and cell membrane markers in the total gene list with significant differences between TILCs groups. The genetic symbols and annotations are retrieved from a public database.
H. At the single cell level, 4 TILCs subpopulations were scored modularly according to tonsil ILCs gene characteristics.
FIG. 3: tumor tissue specific ILCs subset characteristics.
A.41,603 UMAP plots of ILCs from normal blood, CRC blood and CRC tissue. Cells are labeled with different colors according to different defined subpopulations.
B. Unsupervised hierarchical clustering was performed on normal blood, CRC blood and CRC tissue ILCs for each donor based on the average expression level of the variably expressed genes.
C. The heat map shows 899 (44 in normal blood, 70 in CRC blood, 775 in CRC tissue) genes used to distinguish three tissues using the Wilcoxon rank sum test. Cells are shown as columns and genes are shown as rows, arranged with adjusted p-values (< 0.05). Gene expression is shown in color, scored from-2.5 (purple) to 2.5 (yellow) according to a z-score distribution. The box identifies the transcriptome characteristics of a particular subpopulation of ILCs.
D. Unsupervised hierarchical clustering was performed at the subset level for normal blood ilcs (nbilcs), CRC blood ilcs (cbilcs), and CRC tissue ilcs (tilcs) for each donor based on the average expression level of the variably expressed genes.
Venn represents the gene intersection of 4 ILC1s subsets from normal blood, CRC blood, normal mucosa and CRC tissue.
Venn represents the gene intersection of 3 ILC2s subsets in normal blood, CRC blood and CRC tissue.
FIG. 4: ILCs specific gene signature in CRC.
A. UMAP map of normal mucosa and CRC tissue with 31,246 ILCs. The cells show different colors according to the defined cell population.
B. Unsupervised hierarchical clustering of ILCs from normal mucosa and CRC tissue was performed for each donor based on the average expression level of the variably expressed genes.
C. The heatmap shows 331 genes (266 in CRC tumors, 51 in normal mucosa) that differentiate the two organs using Wilcoxon rank-sum test. Cells are shown as columns and genes are shown as rows, arranged with adjusted p-values (< 0.05). Gene expression is shown in color, from-2.5 (purple) to 2.5 (yellow) based on a z-score distribution. Within the box is transcriptome information for a particular subpopulation of ILCs.
D. UMAP plots of 26,502 ILCs in normal blood and CRC blood. Cells were differently colored according to the defined dataset.
E. Unsupervised hierarchical clustering was performed on normal blood and CRC blood ILCs from each donor based on the average expression level of the variably expressed genes.
F. The heat map shows 254 genes (233 CRC blood, 23 normal blood) that differentiate two organs using the Wilcoxon rank sum test. Cells are shown as columns and genes are shown as rows, arranged with adjusted p-values (< 0.05). Gene expression is expressed in color, from-2.5 (purple) to 2.5 (yellow) based on a z-score distribution. The box identifies the transcriptome information for a particular subpopulation of ILCs.
Venn indicates the gene intersection between normal blood and CRC blood, normal mucosa and CRC tissue.
Featureplot showed the relative expression level of SLAMF1 for each ILCs in normal blood, CRC blood, normal mucosa, and tumor tissue.
FIG. 5: SLAMF1 is a biomarker of CRC.
A. Representative flow cytometry analysis plots showed surface TIGIT expression of the TILC1 s-like subpopulation.
B. Flow cytometry analyzed subsets of ILCs in normal mucosa (8-16 cases) and CRC tissue (7-16 cases). The data show the proportion of the indicated subpopulations of ILCs in total ILCs.
Overall survival Kaplan-Meier curves for different IL-33 expression groupings in CRC patients in tcga. The optimal cut-off points for patient stratification were obtained using the proportional risk Cox model and p-values were calculated by log rank test. IL-33-high group (n 192); IL-33-low group (n ═ 91).
D. Healthy donors (n-18) and CRC patients (n-16) subpopulations of blood ILCs were analyzed by flow cytometry.
Facs representative picture shows the expression of SLAMF1 on the surface of ILCs.
F. SLAMF1 was analyzed by flow cytometry for expression of ILCs in normal mucosa (n-7) and CRC tissue (n-7). The data show the proportion of SLAMF1+ ILCs to total ILCs.
G. Flow cytometry analyzed the expression of SLAMF1 in total blood ILCs from healthy donors (n-14) and CRC patients (n-5). Data show the proportion of SLAMF1+ cells in total ILCs.
Tcga analysis of Kaplan-Meier curves grouped by SLAMF1 expression levels in CRC patients. The optimal cut-off points for patient cohorts were obtained using the proportional risk Cox model and p-values were calculated by the log rank test. SLAMF1-high group (n-73); SLAMF1-low group (n ═ 20).
(B) And (D) carrying out Kruskal-Wallis test by adopting Dunn multiple comparison test, and adjusting the p value by adopting a benjamin-hochberg method. (F) And (G) Mann-Whitney test using nonparametric t-test.
*p-value<0.05；**p-value<0.01；***p-value<0.001；****p-value<0.0001.
FIG. 6: experimental design of this study
A. Single cell transcriptome analysis workflow was performed on ILCs isolated from blood of healthy donors or colorectal cancer (CRC) patients, adjacent normal mucosa, and CRC tissue.
B. Gating strategy for human ILCs defined as lineage marker negative (Lin-) and CD127 positive (CD127 +). SSC, side scatter; and (3) FSC: forward angle scattering.
C. Percentage of ILCs in CD45+ lymphocytes in the tissue (left). Statistics on the proportion of ILCs and number of samples (right). Data shown are mean ± standard deviation, p-values are Mann-Whitney test and nonparametric t-test. ns, not significant. P value < 0.0001.
D. The graphs summarize the scrseq data for this study. Including the total number of cells in normal blood (n-4), CRC blood (n-3), adjacent normal mucosa (n-4), and CRC tissue (n-4), the number of genes detectable in each tissue, the number of genes detected in each cell in each tissue, the number of cells captured in each sample, the total genes detected in each sample, and the average genes detected in each cell in each sample. Number.
FIG. 7: based on the distribution of known characteristics or genes to a subpopulation of normal mucosal ILCs.
Featurepolot relatively expressed by IL7R, GATA3, NCR3, EOMES, TBX21, PTGDR2, KIT, RORC, NCR1, NCR2, KLRF1 in nmilcs, separated by a dotted color line, as shown in fig. 7A.
B. At the single cell level, 6 nmILCs cell populations were scored using the list of ILC2s signatures of human jejunum (ileum) ILC1s, ILC3s, NK cells and spleen, respectively (Yudanin et al, 2019).
C. The FeaturePlot, expressed relatively to CD5 in each nmILCs cell, is separated by a dotted color line, as shown.
FIG. 8: cell markers that help define subsets of ILCs in tumor tissue.
FeaturePlot showed relative expression levels of IL7R, GATA3, NCR3, EOMES, TBX21, PTGDR2, KIT, RORC, NCR1, NCR2 and KLRF1 in TILCs, with dashed color lines to distinguish the various subsets of ILCs, as shown.
FIG. 9: ILC3s subset characteristic of normal mucosa and CRC tissue.
UMAP map of 15701 normal mucosal ILC3 s. The cells are stained according to the cell population to which they belong.
Different colors in umap indicate different donors.
C. Unsupervised hierarchical clustering was performed on 4 nmILC3s clusters per donor based on the average expression levels of the different expressed genes.
D. 316 genes (0: 74 genes, 1: 85 genes, 2: 48 genes, 3: 109 genes) that differentiated the nmILC3s cell population were screened using the Wilcoxon rank sum test and shown on a heat map. Cells are shown as columns and genes are shown as rows, arranged with adjusted p-values (< 0.05). Gene expression is expressed in color, from-2.5 (purple) to 2.5 (yellow) based on a z-score distribution. The boxes identify the transcriptome signature genes of specific subsets of ILCs.
E. The list shows the top 10 expressed genes ranked top by p-value size, the top 10 expressed genes encoding transcription factors, secreted proteins and cell membrane markers among the total gene list with significant differences between groups of ILCs. The genetic symbols and annotations are retrieved from a public database.
Umap displayed 9260 ILC3s cells in tumor tissue. Different colors indicate that the cells belong to different cell populations.
Umap shows individuals from different sources and are represented in different colors.
H. Unsupervised hierarchical clustering of ILC3s of CRC tissue of each donor was performed based on the average expression level of the variably expressed genes.
I. The 420 genes (0, 98 genes; 1, 106 genes; 2, 49 genes; 3, 167 genes) of ILC3s were shown to differentiate tumor tissue using the Wilcoxon rank-sum test using a heat map. Cells are shown as columns and genes are shown as rows, arranged with adjusted p-values (< 0.05). Gene expression is expressed in color, from-2.5 (purple) to 2.5 (yellow) based on a z-score distribution. The box identifies the transcriptome information for a particular subpopulation of ILCs.
J. The list shows the top 10 expressed genes ranked by p-value size, the top 10 expressed genes encoding transcription factors, secreted proteins and cell membrane markers in the total gene list with significant differences between groups of ILCs. The genetic symbols and annotations are retrieved from a public database.
FIG. 10: scrseq defined ILC1s, ILC2s and ILC3s in the blood of healthy donors.
UMAP profile of A.19,603 healthy blood ILCs. The cells are stained according to the cell population to which they belong.
Different colors in umap indicate different donors.
C. Unsupervised hierarchical clustering was performed on 4 nbILCs per donor based on the average expression levels of the different expressed genes.
D. The 356 gene (nbC0, 90; nbc1, 191; nbC2, 75 genes) cells of ILCs differentiated from normal blood tissue using the Wilcoxon rank sum test are shown in heatmap as columns and genes as rows, aligned by adjusted p-value (< 0.05). Gene expression is expressed in color, from-2.5 (purple) to 2.5 (yellow) based on a z-score distribution. The box identifies the transcriptome information for a particular subpopulation of ILCs.
E. Principal component analysis was performed on 3 ILCs clusters per sample based on the average expression level of the variably expressed genes.
F. Distribution of genes in each ILCs subpopulation that accounted for 20% of the total information per PC of (E).
G. The list shows the top 10 expressed genes ranked by p-value size, the top 10 expressed genes encoding transcription factors, secreted proteins and cell membrane markers in the total gene list with significant differences between groups of ILCs. The genetic symbols and annotations are retrieved from a public database.
H. At the single cell level, 3 subsets of ILCs were block scored according to the genetic profile of tonsil ILCs.
Featureplot showed relative expression levels of IL7R, GATA3, NCR3, EOMES, TBX21, PTGDR2, KIT, RORC, NCR1, NCR2, KLRF1 in nbILCs, separated by a dotted color line, as shown.
FIG. 11: scrseq defined ILC1s, ILC2s and ILC3s in the blood of CRC patients.
UMAP map of blood ILCs from A.6,899 CRC patients. The cells are stained according to the cell population to which they belong.
Different colors in umap indicate different donors.
C. Unsupervised hierarchical clustering was performed on 3 cbILCs per donor based on the average expression levels of the different expressed genes.
D. The 359 gene (cbc0, 143; cbC1, 81; cbC2, 135 gene) cells of ILCs differentiated from normal blood tissue using the Wilcoxon rank sum test are shown in a heat map, with the genes shown in rows, arranged by adjusted p-value (< 0.05). Gene expression is expressed in color, from-2.5 (purple) to 2.5 (yellow) based on a z-score distribution. The box identifies the transcriptome information for a particular subpopulation of ILCs.
E. Principal component analysis was performed on 3 ILCs clusters per sample based on the average expression level of the variably expressed genes.
F. Distribution of genes in each ILCs subpopulation that accounted for 20% of the total information per PC of (E).
G. The list shows the top 10 expressed genes ranked by p-value size, the top 10 expressed genes encoding transcription factors, secreted proteins and cell membrane markers in the total gene list with significant differences between groups of ILCs. The genetic symbols and annotations are retrieved from a public database.
H. At the single cell level, 3 subsets of ILCs were block scored according to the genetic profile of tonsil ILCs.
Featureplot showed the relative expression levels of IL7R, GATA3, NCR3, EOMES, TBX21, PTGDR2, KIT, RORC, NCR1, NCR2, KLRF1 in cbILCs, separated by a dotted color line, as shown.
The velocity of ILCs RNA in crc patient blood is projected on the UMAP graph by gaussian smoothing on a regular grid.
FIG. 12: expression patterns of AQP3, HPGD, TLE4 and PRDM1 across different tissues.
FeaturePlot showed the relative expression levels of AQP3, HPGD, TLE4, PRDM1 in normal blood, CRC patient blood, normal mucosa and tumor tissue.
Detailed Description
The invention is further illustrated below with reference to examples and figures. These examples are to be construed as merely illustrative, and not a limitation of the scope of the present invention. Variations and advantages that may occur to those skilled in the art may be incorporated into the invention without departing from the spirit and scope of the inventive concept, which is set forth in the following claims and their equivalents.
Example 1a healthy intestinal tract contains ILC1s, IL3Cs and ILCs/NK, but no ILC2 s.
1. Materials and methods
(1.1) clinical specimen Collection
Healthy blood samples of scRNAseq were obtained from individuals who received a conventional colonoscopy, who were generally healthy with no other history of the disease, such as Inflammatory Bowel Disease (IBD) or CRC.
(1.2) isolation of human lymphocytes
Fresh intestinal tissue was prepared immediately after surgery. Adipose tissue and visible blood vessels were removed. Samples were weighed and washed with PBS and then cut into small pieces. Normal tissues were incubated in 10mL of freshly prepared endothelial lymphocyte solution (PBS containing 5mM EDTA, 15mM HEPES, 10% FBS, 1mM DTT) at 37 ℃ for 1 hour with shaking at 200 rpm. CRC tissues were washed with 10mL freshly prepared PBS containing 65mM DTT for 15 minutes at 37 ℃ with shaking. After the incubation was completed, the tissue mass was washed twice with PBS and subjected to enzymatic digestion at 37 ℃ for 1 hour. The digestive juice mainly comprises: RPMI 1640 with 0.38. mu.g/mL collagenase VIII, 0.1mg/mL DNase I, 100U/mL penicillin, 100mg/mL streptomycin, and 10% FBS. The digested tissue was then shaken vigorously by hand for 5 minutes and then mechanically separated using a 21 gauge syringe. The resulting cell suspension was filtered using a cell strainer of 100 μm pore size into a new 50mL conical tube. The volume was made up to 30mL using PBS. Then, the cells were centrifuged at 1800rpm for 5 minutes. The supernatant was removed and suspended with RMPI 1640 containing 10% FBS. Density gradient centrifugation was performed using Ficoll, and resuspension was performed using PBS after centrifugation was completed.
Peripheral Blood Mononuclear Cells (PBMCs) were obtained from human blood samples centrifuged on a Ficoll density gradient. Briefly, blood was mixed in PBS in equal amounts with 2% FBS and gently layered on a Ficoll density gradient. Centrifugation was carried out at 1000x g for 25 minutes with a brake of 0. The cells in the middle layer were aspirated and then resuspended in 2% FBS in PBS for use.
(1.3) removal of contaminating cells
Cell type annotation was performed on each Cell using the Cell type information in "label.main" using the R package SingleR (Aran et al, 2019) default parameters, with reference to Human Primary Cell Atlas Data as the reference Data set. Since the dataset does not include a human ILCs dataset, and given the similarities between ILCs and cells or NK cells, the present invention retains cells labeled NK and T cell types. Small clusters of less than 200 cells were removed to eliminate the adhesion bodies. Certain individual-specific cell populations with strong specific NK cell characteristics are considered to be truly NK cell contaminating and removed from downstream analysis.
(1.4) sorting ILCs cells
Freshly prepared cells were resuspended using PBS and incubated with dead cell dye at 4 ℃ for 10 min. Cells were resuspended using FAC buffer (PBS containing 2% FBS,2mM EDTA) containing 10% mouse serum, 40% brilliant Stain buffer. Blocking with Fc blocking antibody for 10min and adding antibodies containing anti-human CD45, CD127, CD117, CRTH2, and lineage (TCR γ δ, TCR α β, CD3, CD19, CD14, CD16, CD94, CD123, CD34, CD303, and Fc ∈ RI) were incubated at room temperature for 30 min. After the incubation was finished, the cells were washed with FACS buffer and centrifuged. The collected cells were resuspended using FACS and the viable ILCs were sorted using a BD FACSAria III instrument.
2. Results of the experiment
The present invention dissects the role of ILCs in CRC by studying paired CRC tissue and adjacent mucosal tissue (used as control) samples and comparing blood from patients with that of age-matched healthy donors (fig. 6A). Lin-CD127+ ILCs were found to be higher in normal mucosal and CRC tissues than in blood, consistent with known ILCs tissue retention characteristics (Gasteiger et al, 2015) (FIGS. 6B-C). The ILCs content in CRC tissue was significantly lower than normal mucosa, but the ILCs content ratios were similar in the blood samples of the normal and CRC groups (fig. 6B-C).
Example 2 single cell transcriptome sequencing (scRNAseq) was performed on 58,000 ILCs in blood samples, healthy blood, normal mucosa and CRC tissue samples from CRC patients (FIG. 6D).
1. Materials and methods
(1.1) Single-cell RNA sequencing
Purified ILCs were resuspended in PBS containing 0.04% BSA and kept on ice. The cells were counted and cell density adjusted to the concentration suggested by the v3 kit from 10X Genomics and used for library construction. Library sequencing was performed using the Illumina platform from crystal energy corporation (NovaSeq 6000) to a sequencing depth of approximately 90,000 read lengths per cell.
(1.2) alignment, quality control and normalization of original sequences
Using FastQC software v0.11.9
(https:// www.bioinformatics.babraham.ac.uk/projects/fastqc /) quality control was performed on the original sequencing read length. Sequencing data in bcl files were converted to FASTQ format (https:// support. illumina. com/downloads/bcl2FASTQ-conversion-software-v2-20.html) using bcl2FASTQ2 conversion software v 2.20. The sequencing data were processed, aligned and summarized for Unique Molecular Identifiers (UMI) using standard protocols and default parameters of the Cell range Single Cell Software Suite v.2.2 Software. Briefly, the present invention aligns the FASTQ sequence to the GRch38 genome using the Cell range Count standard protocol. Low quality sequencing reads were filtered out according to base call scores, and a cellular barcode and a unique molecular identifier were assigned to each read. The single Cell sequencing data were normalized using the default parameters of the Cell range aggregate standard procedure to obtain data at the same sequencing depth. The matrix containing the gene signature and the cell signature is used for subsequent analysis.
During quality control of the securat analysis, the raw UMI count matrix was filtered to remove genes of less than three cells, cells with genes less than 200, cells with over 4000 genes, and cells with a higher proportion of mitochondrial genes (over 8%). The generated matrix is then normalized by a global scaling method, transformed using a scaling factor (10,000 by default), and lognormal' function is used in the securit for log data transformation for downstream analysis.
2. Results of the experiment
The heterogeneity of ILCs in the normal mucosa of CRC patients was assessed along with 16,145 Lin-CD127+ ILCs from colon tumor carcinoma juxter-intestinal tissue (FIG. 6D). In Unified Manifold Approximation and Projection (UMAP) analysis, the two-dimensional projection of cells shows a separation into six different cell populations: normal mucosal cell populations (nmC)0 to nmC5 (fig. 1A). nmC4 and nmC5 contained cells from all donors, indicating that these two populations of ILCs had no donor-specific transcriptome profile (FIGS. 1B-C). In contrast, most cells from nmC0 to nmC3 were single donor-specific (fig. 1B-C).
Example 3 hierarchical clustering
1. Materials and methods
(1.1) unsupervised hierarchical clustering
The gene expression values of individual cells in each cell population were calculated. Only the genes previously selected as variable characteristics were used. The invention uses the heatmap. plus package to draw unsupervised clustering maps. The Euclidean distance is calculated for the genes in all cell populations. For normal mucosa, only 0-4 clusters of cell populations were analyzed.
(1.2) dimensionality reduction and clustering
The first 2000 genes were screened using the "findVariablegenes" function of Seurat (Stuart et al, 2019) and used for Principal Component Analysis (PCA). For ILCs in normal mucosa, the present invention retained the first 40 major components. The present invention retained the first 20 PCs for normal blood, CRC blood and tumor tissue. Clustering is recognized by adopting a 'FindClusters' function, the algorithm is based on the nearest neighbor modularization optimization realized in Seurat, and visualization is carried out by adopting a Uniform Manifold Approximation and Projection (UMAP) algorithm. For comparisons of different individuals and different organizations, a single Seurat object is merged using the "merge" function. For visualization of donor and tissue data, when plotted using the "DimPlot" function in securat, the information of the tissue and donor is used to be presented in "group.
(1.3) differential expression analysis
The present invention uses the "findall markers" function in sourat to identify differential expression of genes between each cluster in a sample. Using the nonparametric Wilcoxon rank sum test, and based on Bonferroni correction, p-values for all genes in the dataset for comparison, and adjusted p-values were obtained. The present invention calculates log-fold change in expression value (logFC) using the following parameters and obtains p-values for all variable genes of each cluster: min.pct ═ 0.05, min.diff.pct ═ 0.1, and logfc.threshold ═ 0.25. Logarithmic transformation and scaled expression values of the genes were used to generate heatmaps.
(1.4) principal component analysis
And performing principal component analysis according to the average expression value of the variable genes of each cluster. The top 20 gene weights for the contributions to PC1 and PC2 or PC1 and PC3 are plotted. For normal mucosal ILCs, principal component analysis was performed to exclude subpopulations of ILCs from non-major individual sources in 0-4 clusters. The first 20 genes contributing the most to PC1 and PC2 or PC1 and PC3 are shown graphically.
(1.5) Scoring samples Using ILCs Gene signatures
ILCs and NK genes from tonsils are characterized by Bjorklund et al (Bjorklund et al, 2016). ILC1s and ILC3s gene characteristics of the jejunum (ileum) and ILC2s characteristics of the spleen were derived from study findings of Yudanin and co-workers (Yudanin et al, 2019). Each ILCs was block scored using "AddModuleCore" by Seurat. Briefly, the mean gene expression value in each cell was calculated and the expression values of the control gene in all cells were subtracted. The control gene was randomly selected from all genes. Differentially expressed genes of the paracancerous tissue ILC3s were used as a gene list when scoring ILC3s in tumor tissue. Violin plots were used to present the gene module scores for each subpopulation.
2. Results of the experiment
Using hierarchical clustering (fig. 1C) and gene signature heatmaps (fig. 1D), Principal Component Analysis (PCA) (fig. 1E-F), top-expressing 10 gene analyses (fig. 1G), and modular score analysis (fig. 1H), the present invention compares gene signatures nmC0 through nmC5 to the previously described transcriptome signatures of a subpopulation of human ILCs (bjorkllundeltal, 2016).
Of the top 10 highly expressed genes, nmC0 to nmC3 have a common transcriptome signature with ILC3 s: REL, KIT, CXCL8, IL4l1 and IL1R 1. Among them, REL can encode NF-. kappa.B family protooncogene signal (Victor et al, 2017) through the IL22 promoter site in ILC3s (FIGS. 1F-G). nmC4 is characterized in that: NKG7 encoding cytolytic granular membrane protein (Medley et al, 1996), encoding CD94, KLRD1 expressed as a driver gene in T and NK cells, together with GNLY, GZMK, XCL2 and CCL4 etc. constitute the highest expressed genes, which are an integral feature common to tonsil NK cells and ILC3 s. Thus, nmC4 was identified as ILC3s/NK subgroup. nmC5 is similar to ILC1 s: the expression levels of T cell markers (CD3D, CD3G, and CD3E) were higher, as previously described (Bjorklund et al, 2016; Ercolano et al, 2020; Robinette et al, 2015), transcription factors that control the development of ILCs (IKZF3, BCL11B, PRDM1, and ID3), and NK/ILC1s cell function cytokines (GZMM, IFNG, IL32, CCL4, and CCL5) (FIGS. 1F-G). These cell annotations were further determined from known selective expression markers for ILCs, such as IL7R, GATA3, NCR3, EOMES, TBX21, KIT, RORC, NCR1, NCR2, and KLRF1 (fig. 7A). The present inventors found a difference between nmC5 and the previously reported healthy gut ILC1s (Yudanin et al, 2019), probably because the gating strategy used by the present invention did not exclude CD5+ cells (fig. 7B-C). Thus, normal intestinal mucosa as defined by the scrseq profile of Lin-CD127+ contained ILC1s, ILC3s, and ILCs/NK, but no ILC2s, consistent with the absence of PTGDR2 gene expression (fig. 7A).
Example 4 tumor ILC1 s-like and ILC2s subpopulations were present in CRC patients.
The present invention investigates the composition and diversity of 15, 101ILCs (hereinafter intestinal TILCs) from patients with CRC tumors. UMAP analysis identified four different cell populations: TILCs C0 to C3 (FIG. 2A). In contrast to what was observed for normal mucosa, no massive batch effect was observed, with each cell population present in all samples (fig. 2B-C). According to the manner of application to the general mucosal cell population (fig. 1), TILCs C0 was assigned (associations) to ILC3s, consistent with its over-expression of KIT, CXCL8, NFIL3 and IL4l1, e.g. nmC 0-3. TILCs C1 is similar to ILC1s and, similar to nmC5, shows differential expression of genes encoding effectors secreted by T cell molecule 8(CD3D, CD3G) (CCL4, IFNG) and ILCs-associated transcription factors (IKZF3, PRDM1, and BCL 11B). There are two other subgroups not present in normal mucosa, tics C2 and tics C3. TILCs C2 cells corresponded to another ILC1s subset (hereinafter TILC1 s-like subset) characterized by enriched gene expression of gene-coding repressive and costimulatory markers (TIGIT, CTLA4, TNFRSF18, and TNFRSF 4). TILCs C3 cells (defined as ILC2s, hereafter TILC2s) highly expressed genes required for ILC2s development encoding transcription factors (GATA3, RORA and ZBTB16) and ILC2 s-reactive cytokine receptor genes (IL1RL1 and IL17RB) (FIG. 2D-H). Selective expression of ILCs markers, such as IL7R, GATA3, NCR3, EOMES, TBX21, KIT, RORC, NCR1, NCR2, and KLRF1, are known to provide support for these assignments (assignments) (FIG. 8). PTGDR2 and higher levels of GATA3 expression were found in particular in ILC2s (fig. 8). Thus, as with normal mucosal ILCs, intestinal TILCs form a heterogeneous subpopulation comprising four distinct subpopulations: TILCs C0 (like ILC3s), TILCs C1 (like ILC1s), TILCs C2 (a novel subgroup like ILC1s), and TILCs C3 (like ILC2 s).
Example 5 batch effect correction of ILC3s cell populations in normal mucosal and tumor tissues.
The ILC3s cell population in tumor tissue and the ILC3s and ILC3s/NK population in normal mucosa were further clustered for downstream clustering. Batch effects among different patients were corrected according to the detected anchor points using the "IntegrateData" function of the Seurat standard workflow.
The heterogeneity of ILC3s in tumor tissue appears to be less than that in normal mucosa. Therefore, the present invention focuses on nmC0-3, nmC4 and TILCs C0, and after applying batch effect correction, compares ILC3s heterogeneity between normal mucosal ILCs and intestinal TILCs using the same analytical procedure described above (FIG. 9). Four distinct subsets were found in ILC3s in both tissues (fig. 9A-I), including the possibly immature SELL expression subset, and the subset rich in HLA-encoded records also present in the human tonsils (fig. 9E and J). Each subpopulation of normal mucosal ILC3s had a corresponding cell population in tumor tissue (fig. 9K). Given the overlapping heterogeneity of normal mucosal and intestinal TILCs at ILC3s, the present invention may conclude that CRC does not affect the heterogeneity of ILC3s subpopulations. Thus, intestinal TILCs are distinguished from nmILCs by the appearance of a subpopulation of TILC2s and a second, similar subpopulation of TILC1 s.
Example 6 blood ILCs heterogeneity was stable in CRC.
The invention searches for potential biomarkers of the disease by studying the difference between blood ILCs of healthy individuals and CRC patients. UMAP analysis of 19,603 ILCs from healthy donors according to the present invention revealed three distinct cell populations (hereinafter nbC0, nbC1, and nbC2) (FIGS. 10A-C). Based on the upregulation of CD3D, CD3E, CD3G, NK/ILC1s cellular effector proteins (CCL5, GZMK, GZMM, and GZMA) and ILCs transcription factors (BCL11B, PRDM1, and IKZF3), nbC0 was considered to correspond to ILC1s (fig. 10D-H). nbC1 was identified as ILC3s, and is characterized by ILC3s transcription factor (MAFF, RUNX3) and costimulatory markers (TNFRSF4, TNFRSF 18). nbC2 shows upregulation of ILC2s characteristic (GATA3, RORA) gene and gene encoding regulatory receptor (KLRB1, KLRG1) (FIG. 10D-H). These assignments were supported by the selective expression of markers for known ILCs, such as IL7R, GATA3, NCR3, EOMES, TBX21, PTGDR2, KIT, RORC, NCR1, and KLRF1 (fig. 10I).
Example 7 scRNAseq defined ILC1s, ILC2s and ILC3s in the blood of CRC patients.
1. Materials and methods
(1.1) evaluation of RNA Rate
And analyzing the dynamic change of gene expression in the single cell sequencing data according to the condition that spliced RNA and unspliced RNA in the single cell sequencing data bam file are transferred into the single cell sequencing data bam file so as to evaluate the RNA speed. RNA velocity values for each gene and embedded RNA velocity parameters were calculated using R package veloycoyo.R (La Manno et al, 2018) (https:// github.com/velocy-team/velocy.R). RNA velocities are projected on the UMAP graph by gaussian smoothing on a regular grid.
2. Results of the experiment
This example further identified three subpopulations (hereinafter cbC0, cbC1, and cbC2) based on UMAP profiles of 6,899 blood ILCs from CRC donors (fig. 11A-C). The driver, ten major and modular fractional features highlighted the similarity of cbILCs to nbILCs (FIGS. 11D-H). cbC0 has an ILC3s spectrum, like nbC1, enriched for MAFF, RUNX3, TNFRSF18, NCR1, and KIT. cbC1 was identified as ILC2s because it, like nbC2, expressed high levels of GATA3, RORA, KLRB1, KLRG1 and PTGDR 2. cbILC2s, then, like nbC0, was also enriched in the ILC1 s-tagged gene: CD3D, CD3G, CD3E, CCL5, GZMK, GZMM, GZMA, BCL11B, PRDM1, IKZF3, and TBX21 (fig. 11D-H). Selective expression of IL7R, GATA3, NCR3, EOMES, TBX21, PTGDR2, KIT, RORC, NCR1, and KLRF1 also supported these assignments (FIG. 11I). However, although the cbILCs subpopulation was similar to the nbILCs subpopulation, RNA velocity analysis predicted that ILC1s might be converted to ILC3s only under conditions of CRC tumor blood (fig. 11J). In summary, blood ILCs from healthy donors and CRC patients form a heterogeneous subpopulation containing subpopulations of ILC1s, ILC2s, and ILC3 s.
Example 8 identification of a novel subset of CRC-specific TILC1s
Tumor tissue ILCs contain two subpopulations that are not present in normal mucosal ILCs, with transcriptome characteristics similar to ILC2s and ILC1s (fig. 1A and H, fig. 2A and H). The present inventors investigated the correlation of these two tumor tissue-specific cell populations with healthy blood and subpopulations of ILCs from the blood of CRC patients by grouping 41,603 ILCs into a single global analysis. This analysis revealed organ-specific imprinting between ILCs, an overlap between the two blood samples (healthy blood and blood from CRC tumor patients as mentioned above) (fig. 3A), while TILCs were clustered individually. The nbILCs and cbILCs share a high degree of similarity in gene characteristics, which is significantly different from TILCs (FIGS. 3B-C). The present invention further analyzes the relationship between defined subsets of ILCs from CRC tissue, normal blood and CRC blood samples. Although this subpopulation has the core ILC1s transcriptional characteristics, a subpopulation like TILC1s appears to be separated from other cell populations (especially TILC1s) (fig. 3D). Similarly, another specific subset of TILCs, TILC2s, aggregated with other TILCs and ILC2s in the blood. In blood, each nbILCs aggregated with a corresponding subpopulation of cbILCs (fig. 3D). The present invention evaluates whether tumor-specific ILCs share more genes with corresponding cells in normal blood or CRC blood by creating venn maps (fig. 3E and F) comparing their entire transcriptome signatures. The TILC1 s-like subset shares more genes with cbILC1s (total 57 genes) than nbILC1s (total 34 genes) (fig. 3E). The number of genes shared by TILC2s with cbILC2s and nbILC2s was similar, 39 and 34, respectively (FIG. 3F).
Example 9 specific labelling of ILCs in CRC
The invention searches the tumor specific tissue characteristics of the ILCs by gathering 31,246 ILCs in the normal mucosa and the tumor tissue. There were several subsets of ILCs in common between these two tissues, but UMAP highlighted the transition between the two tissues, indicating that there was a difference in transcription levels (fig. 4A). Unsupervised hierarchical clustering also showed that the tissue origin signature was stronger than the ILCs subpopulation signature (fig. 4B-C). UMAP analysis of 26,502 ILCs showed a similar pattern of separation between nbILCs and cbILCs (FIG. 4D); according to unsupervised hierarchical cluster analysis, two subpopulations of blood sample sources were classified according to health status, revealing transcriptome differences between them (fig. 4E-F). As a result, it was found that one gene (AQP3) was up-regulated in normal blood and normal mucosa and four genes (SLAMF1, HPGD, TLE4 and PRDM1) were up-regulated in CRC blood and intestinal TILCs, compared to the control group (fig. 4G). The profile of these five genes of interest confirmed the characteristic up-regulation of SLAMF1, HPGD, TLE4 and PRDM1, and the down-regulation of AQP3 in intestinal TILCs (fig. 4H and 11). SLAMF1 (signal lymphocyte activating molecule family member 1 or CD150), which encodes a protein involved in activating T cells, B cells and NK cells (Gordiienko et al, 2019), is a major surface protein gene that is upregulated in tumors. This gene was expressed in cbILC2s, TILC2s, and TILC1 s-like subpopulations, but only 12 were weakly expressed in healthy controls (fig. 4H), indicating that expression of SLAMF1 on the cell surface of ILCs can distinguish between healthy individuals and CRC patients.
Example 10 SLAMF1 is a biomarker for CRC
1. Materials and methods
(1.1) detection of ILCs by flow cytometry
Freshly prepared cells were stained with dead and live dye and after blocking with Fc antibody, surface antibody staining was performed as with sorting ILCs cells. Staining was performed using antibody cocktail (anti-lineage antibody cocktail plus CD45, CD127, CD117, CRTH2, CD5, TIGIT and SLAMF1) for 30min at room temperature. Blood-derived PBMCs from CRC patients, paracancerous and tumor samples were used for concurrent staining, and PBMCs from healthy donors were used for controls. The stained cells were placed at 4 ℃ and flow-tested on a BD Symphony instrument. Flow data were analyzed using FlowJo software. Statistical analysis the "mann-whitney rank sum test" using the nonparametric unpaired t test, or the "Kruskal-Walis" test using the multiple comparisons of doun. The p-values of the multiple comparisons were corrected using the Benjamini-Hochberg method. P-value <0.05, p-value <0.01, p-value <0.001, p-value < 0.0001.
(1.2) TCGA assay
Primary tumor and clinically annotated RNAseq data were downloaded using the tcgaiolinks software package. The kaplan-meier curve was plotted using an R-package surfminer. To split the expression levels into two groups, the expression amount giving the lowest p-value was the cut-off value. The best cut-off point for patient stratification was obtained by the Cox proportional hazards model and the p-value shown in the graph was calculated by the log rank test.
2. Results of the experiment
The present invention demonstrates that expansion of the ILC1s subpopulation in tumor tissue of CRC patients is at the expense of a reduction in ILC3s subpopulation relative to normal paracancerous tissue by flow cytometry (fig. 5A-B).
The present invention also observed the presence of a novel TIGIT + TILC1 s-like cell and TILC2s subpopulation in tumors, but not in normal tissues (fig. 5A-B), consistent with the scrseq data (fig. 1). Patient data from a cancer genomic map (TCGA) showed that high expression of IL33, a cytokine that activates ILC2s, in tumors was associated with longer survival of CRC patients, suggesting that TILC2s may be predictive of a good prognosis in CRC patients (fig. 5C). In contrast to the findings of intestinal ILCs, each subpopulation of ILCs accounted for similar frequencies of total ILCs in the blood of CRC patients and healthy donors (fig. 5D).
The number of ILCs expressing the SLAMF1 surface molecule was greater in tumors than in paraneoplastic tissues, and in fact, there was little expression of SLAMF1 in paraneoplastic tissues (FIGS. 5E and F). In contrast, healthy donor blood ILCs expressed SLAMF1, and a high proportion of ILCs expressing SLAMF1 were found in the blood of CRC patients (FIG. 5G). The present invention further explores the potential role of SLAMF1 in the development and progression of CRC disease by studying the clinical outcome of cancer patients. The survival rate of the colorectal cancer patients with high expression of SLAMF1 was much higher than that of the patients with low expression of SLAMF1 (fig. 5H), strongly suggesting that SLAMF1 is an anti-tumor biomarker of CRC.
Discussion of the related Art
In the past decade, helper ILCs have become key to preventing pathogens, tissue remodeling, and maintaining homeostasis (viier et al, 2018). The contribution of adjuvant ILCs to cancer is poorly understood because they may promote tumor-associated inflammation or otherwise exhibit anti-tumor properties, depending on the tumor microenvironment.
The invention studies the heterogeneity of helper ILCs in the human gut by constructing single-cell transcriptional profiles of Lin-CD127+ cells from patients with healthy status and CRC. This unbiased characterization of helper ILCs differs from the intestinal ILCs transcription factor analysis provided by another recent study; in this transcription factor analysis, the cells were subjected to transcriptome profiling after flow cytometric sorting assays by CD103, CD300LF and CD196 cell surface markers (Cella et al, 2019). It is further disclosed herein that the healthy intestinal tract contains ILC1s, ILC3s, one ILC3s/NK population, but no ILC2 s. Interestingly, ILC2s had almost no healthy human tissue at all other than lung and adipose tissue (Trabanelli et al, 2018) (in contrast to the reported mouse experiments). The present invention detects tumor-infiltrating TILC2s in CRC patients. Similarly to what is disclosed in the prior art, TILC2s (Chevalier et al, 2017) was also observed in urine of patients with breast (Salimi et al, 2018), stomach (Salimi et al, 2018), pancreatic (Moral et al, 2020) and bladder cancer. Data have been reported showing that in one model, ILC2s infiltrates tumors through the IL-33 dependent pathway (Chevalier et al, 2017; Moral et al, 2020; Saranchova et al, 2016) and mediates tumor immune surveillance by promoting a cytolytic CD8+ T cell response. IL-33 is overexpressed in colorectal tumors (Cui et al, 2015), and high levels of IL-33 are often observed in low grade adenocarcinomas and early colorectal tumors (Mertz et al, 2016). The survival of colon cancer patients with high IL-33 expression was higher than that of IL-33 low expression patients (FIG. 5C), indicating that TILC2s may be predictive of a good prognosis for CRC. Therefore, it is highly desirable to further study the anti-tumor immune effects of TILC2s in CRC and other cancer indications.
An additional subpopulation of ILC1s, named TILC1 s-like TIGIT + (TILCs-like TIGIT +), was found in the present invention, which was present only in tumors but not in the blood of CRC patients. The transcriptional profile of TILC1 s-like TIGIT + was more similar to the ILC1s gene signature than that of any other ILCs, but they were isolated from TILC1s, indicating that they are clearly distinct from the "conventional" intestinal TILC1 s. ILC1 s-like cells, termed "intermediate ILC1s" (inTILC1s), were also described in a mouse model of Methylcholine (MCA) induced tumors, and experimental RM-1 and B16F10 lung metastases (Gao et al, 2017). In humans, similar CD56-CD16+ ILC1 s-like cells are found in peritoneal and pleural effusions in solid tumor and cancer patients (Levi et al, 2015), and the cytotoxic function of these cells is altered in the donor peripheral blood of acute myeloid leukemia (saliome et al, 2019). Intratumoral inTILC1s may result from NK cell differentiation driven by TGF- β signaling, a phenomenon known as ILCs plasticity (cortex et al, 2016; Gao et al, 2017). TGF- β signalling has been shown to convert ILC3s to ILC1s in humanized mouse experiments, and a transient ILC3s-ILC1s subgroup has been identified in the human intestine (Cella et al, 2019). No such phenomenon was observed in the present intestinal ILCs dataset, and none of the tested algorithms were able to determine the correlation between TILC1s TIGIT + and another subset of intestinal ILCs reflecting possible differentiation (data not shown). Therefore, the mechanism by which TILC1s TIGIT + appears in CRC tumors remains to be determined. The present inventors observed that ILC1s in the blood of CRC patients is plastic to ILC3s, but not in healthy donors, indicating that there may be soluble signals driving the plasticity of ILC1ss-ILC3ss, such as sustained IL-23 levels (Koh et al, 2019). The biological relevance of this ILC1s-ILCs plasticity in the blood of CRC patients is unclear.
Both InTILC1s and ILC1s produce large amounts of TNF- α, which studies have found to be ineffective in controlling carcinogens and may even promote tumor metastasis in mouse models (Gao et al, 2017). In humans, CD56+ CD16-ILC1 s-like cells express the angiogenic factor VEGF, which may also be beneficial for tumor growth (Levi et al, 2015). In CRC patients, the proportion of ILC1s in tumor tissue is higher than in normal mucosal tissue, and as the tumor progresses, ILC1s increases at the expense of ILC3s (Ikeda et al, 2020) these results suggest that high levels of ILC1s may be predictive of poor cancer prognosis. The problem of the specific biological function of the TILC1 s-like subpopulation in CRC tumors relative to classical TILC1s remains to be further solved, since TILC1 s-like cells have high levels of PD1 and TIGIT and are likely to be abruptly released by anti-PD 1 and anti-TIGIT immunotherapy.
The present invention describes the characteristics of three subgroups of normal mucosa (ILC3s, ILC3s/NK and ILC1s) in all donors. Donor-specific effects were observed in ILC3s subgroup, indicating that ILC3s imprinting may be present in the microbiota. Interestingly, CRC tumors had much lower levels of ILC3s and showed this apparent loss of donor specificity. CRC is often associated with tumor immune dysregulation, which involves large changes in microbiota composition (Feng et al, 2015; Liang et al, 2017; Nakatsu et al, 2015; Yazici et al, 2017; Yu et al, 2017). ILC3s is the primary regulator cell of gut barrier integrity and immune homeostasis. Therefore, promoting the re-colonization and diversification of ILC3s in CRC patients may be of therapeutic benefit. Increasing microbial diversity may also increase LC3 heterogeneity.
The present invention also defines a sub-population of ILC3s/NK cells of healthy intestinal mucosa, which are not present in CRC patient tumor tissue. These cells have the same transcriptional function as ILC3s and NK cells. They differ from ILC3s mainly in the expression of NKG7, KLRD1(CD94), GNLY, GZMK, XCL2 and CCL 4. The biological role of this ILC3s/NK subgroup and its association with "classical" ILC3s remains to be studied.
SLAMF1 is the only cell surface marker with higher transcription levels in TILCs and blood ILCs of CRC patients. The frequency of surface-expressed SLAMF 1ILCs in tumors and blood was also higher in CDC patients compared to healthy donors. SLAMF1 is a single-chain type I transmembrane receptor with two Immunoreceptor Tyrosine Switch Motifs (ITSM) in the cytoplasmic tail (gordiiienko et al, 2019). SLAMF1 is an autoligand, also a microbial receptor for measles virus, and is involved as a bacterial receptor in the elimination of gram-negative bacteria (gordiiienko et al, 2019). SLAMF1 is expressed in almost all hematopoietic cells except NK cells, especially those with an activated phenotype, and is upregulated upon cell activation. In blood, SLAMF1 was in a steady state as indicated by the majority of the surface of ILCs, but this expression was not observed in ILCs of normal intestinal mucosa. In contrast, SLAMF1 was expressed in TILCs in CRC patients, indicating that TILCs in the tumor bed are more active than in the mucosa of the paraneoplastic tissue. However, the biological effect of SLAMF1 on surface expression of helper ILCs on these cells remains to be investigated. High levels of SLAMF1 were associated with higher survival in patients with CRC. Thus, the results of the present invention indicate that SLAMF1 is an anti-tumor biomarker for CRC.
ILCs can control various aspects of immunotherapy and have become tissue-specific regulatory targets for cancer immunity. Because ILCs and T cells coexist in human cancers and share common stimulatory and inhibitory pathways, immunotherapeutic strategies against anti-cancer ILCs may be equally important as strategies against T cells. The present study results indicate that there is a subpopulation of TILCs in CRC, so ILCs are part of the tumor microenvironment. They can be presumed to modulate tumor bed immunity or have a direct effect on tumor cells. Whether ILCs have more tumor-specific subsets among cancers of different tissue origins and different activation states remains to be further investigated for their promotion or inhibition.
Gene annotation
Transcription factor-related genes according to 4 transcription factor-related databases: JASPAR (Khan et al, 2018)
(http://jaspar.genereg.net/),DBD(Wilson et al.,2008)
(http://www.transcriptionfactor.org/),AnimalTFDB(Hu et al.,
(http://bioinfo.life.hust.edu.cn/AnimalTFDB/),and TF2DNA(Pujato et
(http://www.fiserlab.org/tf2dna_db/). Cell membrane surface and secreted protein genes according to The human protein profile (Uhlen et al, 2015) ((b))https://www.proteinatlas.org/humanproteome/ tissue/secretome) The annotations are made.
Reference documents:
Aran,D.,Looney,A.P.,Liu,L.,Wu,E.,Fong,V.,Hsu,A.,Chak,S.,Naikawadi,R.P.,Wolters,P.J.,Abate,A.R.,et al.(2019).Reference-based analysis oflung single-cell sequencing reveals atransitional profibrotic macrophage.Nat Immunol 20,163-172.
Bumeister,S.H.,Freeman,G.J.,Dranoff,G.,and Sharpe,A.H.(2016).Coinhibitory Pathways in Immunotherapy for Cancer.Annu Rev Immunol 34,539-573.
Bie,Q.,Zhang,P.,Su,Z.,Zheng,D.,Ying,X.,Wu,Y.,Yang,H.,Chen,D.,Wang,S.,and Xu,H.(2014).Polarization ofILC2s in peripheral bloodmight contribute to immunosuppressive microenvironment in patients with gastric cancer.J Immunol Res 2014,923135.
Bjorklund,A.K.,Forkel,M.,Picelli,S.,Konya,V.,Theorell,J.,Friberg,D.,Sandberg,R.,and Mjosberg,J.(2016).The heterogeneity ofhuman CD127(+)innate lymphoid cells revealedby single-cell RNA sequencing.Nat Immunol 17,451-460.
Bray,F.,Ferlay,J.,Soerjomataram,I.,Siegel,R.L.,Torre,L.A.,and Jemal,A.(2018).
Global cancer statistics 2018:GLOBOCAN estimates ofincidence andmortality worldwide for 36cancers in 185countries.CA Cancer J Clin 68,394-424.
Carrega,P.,Loiacono,F.,Di Carlo,E.,Scaramuccia,A.,Mora,M.,Conte,R.,Benelli,R.,Spaggiari,G.M.,Cantoni,C.,Campana,S.,et al.(2015).NCR(+)ILC3s concentrate in human lung cancer and associate with intratumoral lymphoid structures.Nat Commun 6,8280.
Carrega,P.,Orecchia,P.,Quatrini,L.,Tumino,N.,Vene,R.,Benelli,R.,Poggi,A.,Scabini,S.,Mingari,M.C.,Moretta,L.,and Vacca,P.(2020).Characterisation ofinnate lymphoid cell subsets infiltrating colorectal carcinoma.Gut.
Castro,F.,Cardoso,A.P.,Goncalves,R.M.,Serre,K.,and Oliveira,M.J.(2018).Interferon-Gamma at the Crossroads ofTumor Immune Surveillance or Evasion.Front Immunol 9,847.
Cella,M.,Gamini,R.,Secca,C.,Collins,P.L.,Zhao,S.,Peng,V.,Robinette,M.L.,Schettini,J.,Zaitsev,K.,Gordon,W.,et al.(2019).Subsets ofILC3s-ILC1s-like cells generate a diversity spectrum ofinnate lymphoid cells in human mucosal tissues.Nat Immunol 20,980-991.
Chan,I.H.,Jain,R.,Tessmer,M.S.,Gorman,D.,Mangadu,R.,Sathe,M.,Vives,F.,Moon,C.,Penaflor,E.,Turner,S.,et al.(2014).Interleukin-23 is sufficient to induce rapid de novo gut tumorigenesis,independent ofcarcinogens,through activation ofinnate lymphoid cells.Mucosal Immunol 7,842-856.
Chen,D.S.,and Mellman,I.(2017).Elements ofcancer immunity and the cancer-immune set point.Nature 541,321-330.
Chevalier,M.F.,Trabanelli,S.,Racle,J.,Salome,B.,Cesson,V.,Gharbi,D.,Bohner,P.,Domingos-Pereira,S.,Dartiguenave,F.,Fritschi,A.S.,et al.(2017).ILC2s-modulated T cell-to-MDSC balance is associated with bladder cancer recurrence.J Clin Invest 127,2916-2929.
Chiossone,L.,Dumas,P.Y.,Vienne,M.,and Vivier,E.(2018).Natural killer cells and other innate lymphoid cells in cancer.Nat Rev Immunol 18,671-688.
Cortez,V.S.,Cervantes-Barragan,L.,Robinette,M.L.,Bando,J.K.,Wang,Y.,Geiger,T.L.,Gilfillan,S.,Fuchs,A.,Vivier,E.,Sun,J.C.,et al.(2016).Transforming Growth Factor-beta Signaling Guides the Differentiation ofInnate Lymphoid Cells in Salivary Glands.Immunity44,1127-1139.
Cui,G.,Qi,H.,Gundersen,M.D.,Yang,H.,Christiansen,I.,Sorbye,S.W.,Goll,R.,and Florholmen,J.(2015).Dynamics ofthe IL-33/ST2 network in the progression of human colorectal adenoma to sporadic colorectal cancer.Cancer Immunol Immunother 64,181-190.Eisenring,M.,vom Berg,J.,Kristiansen,G.,Saller,E.,and Becher,B.(2010).IL-12 initiates tumor rejection via lymphoid tissue-inducer cells bearing the natural cytotoxicity receptor NKp46.Nat Immunol 11,1030-1038.
Ercolano,G.,Wyss,T.,Salome,B.,Romero,P.,Trabanelli,S.,and Jandus,C.(2020).
Distinct and shared gene expression for human innate versus adaptive helper lymphoid cells.J Leukoc Biol.
Feng,Q.,Liang,S.,Jia,H.,Stadlmayr,A.,Tang,L.,Lan,Z.,Zhang,D.,Xia,H.,Xu,X.,Jie,Z.,et al.(2015).Gut microbiome development along the colorectal adenoma-carcinoma sequence.Nat Commun 6,6528.
Fuchs,A.,Vermi,W.,Lee,J.S.,Lonardi,S.,Gilfillan,S.,Newberry,R.D.,Cella,M.,and Colonna,M.(2013).Intraepithelial type 1 innate lymphoid cells are a unique subset ofIL-12-and IL-15-responsive IFN-gamma-producing cells.Immunity 38,769-781.
Gao,Y.,Souza-Fonseca-Guimaraes,F.,Bald,T.,Ng,S.S.,Young,A.,Ngiow,S.F.,Rautela,J.,Straube,J.,Waddell,N.,Blake,S.J.,et al.(2017).Tumor immunoevasion by the conversion ofeffector NK cells into type 1 innate lymphoid cells.Nat Immunol 18,1004-1015.
Gasteiger,G.,Fan,X.,Dikiy,S.,Lee,S.Y.,and Rudensky,A.Y.(2015).Tissue residency of innate lymphoid cells in lymphoid and nonlymphoid organs.Science 350,981-985.
Gordiienko,I.,Shlapatska,L.,Kovalevska,L.,and Sidorenko,S.P.(2019).SLAMF1/CD150in hematologic malignancies:Silent marker or active playerClin Immunol 204,14-22.
Hu,H.,Miao,Y.R.,Jia,L.H.,Yu,Q.Y.,Zhang,Q.,and Guo,A.Y.(2019).AnimalTFDB 3.0:a comprehensive resource for annotation and prediction ofanimal transcription factors.
Nucleic Acids Res 47,D33-D38.
Ikeda,A.,Ogino,T.,Kayama,H.,Okuzaki,D.,Nishimura,J.,Fujino,S.,Miyoshi,N.,Takahashi,H.,Uemura,M.,Matsuda,C.,et al.(2020).Human NKp44+group 3 innate lymphoid cells associate with tumor-associated tertiary lymphoid structures in colorectal cancer.Cancer Immunol Res.
Khan,A.,Fornes,O.,Stigliani,A.,Gheorghe,M.,Castro-Mondragon,J.A.,van der Lee,R.,Bessy,A.,Cheneby,J.,Kulkarni,S.R.,Tan,G.,et al.(2018).JASPAR 2018:update of the open-access database oftranscription factor binding profiles and its web framework.Nucleic Acids Res 46,D260-D266.
Kirchberger,S.,Royston,D.J.,Boulard,O.,Thornton,E.,Franchini,F.,Szabady,R.L.,Harrison,O.,and Powrie,F.(2013).Innate lymphoid cells sustain colon cancer through production ofinterleukin-22 in a mouse model.J Exp Med 210,917-931.
Koh,J.,Kim,H.Y.,Lee,Y.,Park,I.K.,Kang,C.H.,Kim,Y.T.,Kim,J.E.,Choi,M.,Lee,W.W.,Jeon,Y.K.,and Chung,D.H.(2019).IL23-Producing Human Lung Cancer Cells Promote Tumor Growth via Conversion ofInnate Lymphoid Cell 1(ILC1s)into ILC3s.Clin Cancer Res 25,4026-4037.
La Manno,G.,Soldatov,R.,Zeisel,A.,Braun,E.,Hochgerner,H.,Petukhov,V.,Lidschreiber,K.,Kastriti,M.E.,Lonnerberg,P.,Furlan,A.,et al.(2018).RNA velocity of single cells.Nature 560,494-498.
Levi,I.,Amsalem,H.,Nissan,A.,Darash-Yahana,M.,Peretz,T.,Mandelboim,O.,and Rachmilewitz,J.(2015).Characterization oftumor infiltrating natural killer cell subset.
Oncotarget 6,13835-13843.
Liang,Q.,Chiu,J.,Chen,Y.,Huang,Y.,Higashimori,A.,Fang,J.,Brim,H.,Ashktorab,H.,Ng,S.C.,Ng,S.S.M.,et al.(2017).Fecal Bacteria Act as Novel Biomarkers for Noninvasive Diagnosis ofColorectal Cancer.Clin Cancer Res 23,2061-2070.
Lopez-Soto,A.,Gonzalez,S.,Smyth,M.J.,and Galluzzi,L.(2017).Control ofMetastasis by NK Cells.Cancer Cell 32,135-154.
Medley,Q.G.,Kedersha,N.,O'Brien,S.,Tian,Q.,Schlossman,S.F.,Streuli,M.,and Anderson,P.(1996).Characterization ofGMP-17,a granule membrane protein that moves to the plasma membrane ofnatural killer cells following target cell recognition.Proc Natl Acad Sci U S A 93,685-689.
Mertz,K.D.,Mager,L.F.,Wasmer,M.H.,Thiesler,T.,Koelzer,V.H.,Ruzzante,G.,Joller,S.,Murdoch,J.R.,Brummendorf,T.,Genitsch,V.,et al.(2016).The IL-33/ST2 pathway contributes to intestinal tumorigenesis in humans and mice.Oncoimmunology 5,e1062966.
Moral,J.A.,Leung,J.,Rojas,L.A.,Ruan,J.,Zhao,J.,Sethna,Z.,Ramnarain,A.,Gasmi,B.,Gururajan,M.,Redmond,D.,et al.(2020).ILC2s amplify PD-1 blockade by activating tissuespecific cancer immunity.Nature 579,130-135.
Nakatsu,G.,Li,X.,Zhou,H.,Sheng,J.,Wong,S.H.,Wu,W.K.,Ng,S.C.,Tsoi,H.,Dong,Y.,Zhang,N.,et al.(2015).Gut mucosal microbiome across stages ofcolorectal carcinogenesis.Nat Commun 6,8727.
Nussbaum,K.,Burkhard,S.H.,Ohs,I.,Mair,F.,Klose,C.S.N.,Arnold,S.J.,Diefenbach,A.,Tugues,S.,and Becher,B.(2017).Tissue microenvironment dictates the fate and tumorsuppressive function oftype 3 ILCs.J Exp Med 214,2331-2347.
Okazaki,T.,Chikuma,S.,Iwai,Y.,Fagarasan,S.,and Honjo,T.(2013).A rheostat for immune responses:the unique properties ofPD-1 and their advantages for clinical application.Nat Immunol 14,1212-1218.
Okazaki,T.,and Honjo,T.(2007).PD-1 and PD-1 ligands:from discovery to clinical application.Int Immunol 19,813-824.
Pujato,M.,Kieken,F.,Skiles,A.A.,Tapinos,N.,and Fiser,A.(2014).Prediction of DNA binding motifs from 3D models oftranscription factors；identifying TLX3 regulated genes.
Nucleic Acids Res 42,13500-13512.
Robinette,M.L.,Fuchs,A.,Cortez,V.S.,Lee,J.S.,Wang,Y.,Durum,S.K.,Gilfillan,S.,Colonna,M.,and Immunological Genome,C.(2015).Transcriptional programs define molecular characteristics ofinnate lymphoid cell classes and subsets.Nat Immunol 16,306-317.
Salimi,M.,Wang,R.,Yao,X.,Li,X.,Wang,X.,Hu,Y.,Chang,X.,Fan,P.,Dong,T.,and Ogg,G.(2018).Activated innate lymphoid cell populations accumulate in human tumour tissues.BMC Cancer 18,341.
Salome,B.,Gomez-Cadena,A.,Loyon,R.,Suffiotti,M.,Salvestrini,V.,Wyss,T.,Vanoni,G.,Ruan,D.F.,Rossi,M.,Tozzo,A.,et al.(2019).CD56 as a marker ofan ILC1s-like population with NK cell properties that is functionally impaired in AML.Blood Adv 3,3674-3687.
Saranchova,I.,Han,J.,Huang,H.,Fenninger,F.,Choi,K.B.,Munro,L.,Pfeifer,C.,Welch,I.,Wyatt,A.W.,Fazli,L.,et al.(2016).Discovery ofa Metastatic Immune Escape Mechanism Initiated by the Loss ofExpression ofthe Tumour Biomarker Interleukin-33.SciRep 6,30555.
Schumacher,T.N.,and Schreiber,R.D.(2015).Neoantigens in cancer immunotherapy.Science 348,69-74.
Sharma,P.,and Allison,J.P.(2015).Immune checkpoint targeting in cancer therapy:toward combination strategies with curative potential.Cell 161,205-214.
Simoni,Y.,Fehlings,M.,Kloverpris,H.N.,McGovern,N.,Koo,S.L.,Loh,C.Y.,Lim,S.,Kurioka,A.,Fergusson,J.R.,Tang,C.L.,et al.(2017).Human Innate Lymphoid Cell Subsets Possess Tissue-Type Based Heterogeneity in Phenotype and Frequency.Immunity 46,148-161.
Spits,H.,Artis,D.,Colonna,M.,Diefenbach,A.,Di Santo,J.P.,Eberl,G.,Koyasu,S.,Locksley,R.M.,McKenzie,A.N.,Mebius,R.E.,et al.(2013).Innate lymphoid cells--a proposal for uniform nomenclature.Nat Rev Immunol 13,145-149.
Stuart,T.,Butler,A.,Hoffman,P.,Hafemeister,C.,Papalexi,E.,Mauck,W.M.,3rd,Hao,Y.,Stoeckius,M.,Smibert,P.,and Satija,R.(2019).Comprehensive Integration ofSingle-Cell Data.Cell 177,1888-1902 e1821.
Trabanelli,S.,Chevalier,M.F.,Martinez-Usatorre,A.,Gomez-Cadena,A.,Salome,B.,Lecciso,M.,Salvestrini,V.,Verdeil,G.,Racle,J.,Papayannidis,C.,et al.(2017).Tumour-derived PGD2 and NKp30-B7H6 engagement drives an immunosuppressive ILC2s-MDSC axis.Nat Commun 8,593.
Trabanelli,S.,Gomez-Cadena,A.,Salome,B.,Michaud,K.,Mavilio,D.,Landis,B.N.,Jandus,P.,and Jandus,C.(2018).Human innate lymphoid cells(ILCs):Toward a uniform immunephenotyping.Cytometry B Clin Cytom 94,392-399.
Uhlen,M.,Fagerberg,L.,Hallstrom,B.M.,Lindskog,C.,Oksvold,P.,Mardinoglu,A.,Sivertsson,A.,Kampf,C.,Sjostedt,E.,Asplund,A.,et al.(2015).Proteomics.Tissue-based map ofthe human proteome.Science 347,1260419.
Victor,A.R.,Nalin,A.P.,Dong,W.,McClory,S.,Wei,M.,Mao,C.,Kladney,R.D.,Youssef,Y.,Chan,W.K.,Briercheck,E.L.,et al.(2017).IL-18 Drives ILC3s Proliferation and Promotes IL-22 Production viaNF-kappaB.J Immunol 199,2333-2342.
Vivier,E.,Artis,D.,Colonna,M.,Diefenbach,A.,Di Santo,J.P.,Eberl,G.,Koyasu,S.,Locksley,R.M.,McKenzie,A.N.J.,Mebius,R.E.,et al.(2018).Innate Lymphoid Cells:10Years On.Cell 174,1054-1066.
Wilson,D.,Charoensawan,V.,Kummerfeld,S.K.,and Teichmann,S.A.(2008).DBD--taxonomically broad transcription factor predictions:new content and functionality.Nucleic Acids Res 36,D88-92.
Yazici,C.,Wolf,P.G.,Kim,H.,Cross,T.L.,Vermillion,K.,Carroll,T.,Augustus,G.J.,Mutlu,E.,Tussing-Humphreys,L.,Braunschweig,C.,et al.(2017).Race-dependent association ofsulfidogenic bacteria with colorectal cancer.Gut 66,1983-1994.
Yu,J.,Feng,Q.,Wong,S.H.,Zhang,D.,Liang,Q.Y.,Qin,Y.,Tang,L.,Zhao,H.,Stenvang,J.,Li,Y.,et al.(2017).Metagenomic analysis offaecal microbiome as a tool towards targeted noninvasive biomarkers for colorectal cancer.Gut 66,70-78.
Yudanin,N.A.,Schmitz,F.,Flamar,A.L.,Thome,J.J.C.,Tait Wojno,E.,Moeller,J.B.,Schirmer,M.,Latorre,I.J.,Xavier,R.J.,Farber,D.L.,et al.(2019).Spatial and Temporal Mapping ofHuman Innate Lymphoid Cells Reveals Elements ofTissue Specificity.
Immunity 50,505-519 e504.
Zaidi,M.R.(2019).The Interferon-Gamma Paradox in Cancer.J Interferon Cytokine Res 39,30-38.
SEQUENCE LISTING
<110> Shanghai City institute of immunology, Shanghai university of traffic medical school, French national institute of health and medicine, French national center for science, Exxose-mosaic university
<120> colorectal cancer biomarkers and their use in diagnosis, prevention, treatment and prognosis
<160> 1
<170> PatentIn version 3.3
<210> 1
<211> 335
<212> PRT
<213> Artificial sequence
<400> 1
Met Asp Pro Lys Gly Leu Leu Ser Leu Thr Phe Val Leu Phe Leu Ser
1 5 10 15
Leu Ala Phe Gly Ala Ser Tyr Gly Thr Gly Gly Arg Met Met Asn Cys
20 25 30
Pro Lys Ile Leu Arg Gln Leu Gly Ser Lys Val Leu Leu Pro Leu Thr
35 40 45
Tyr Glu Arg Ile Asn Lys Ser Met Asn Lys Ser Ile His Ile Val Val
50 55 60
Thr Met Ala Lys Ser Leu Glu Asn Ser Val Glu Asn Lys Ile Val Ser
65 70 75 80
Leu Asp Pro Ser Glu Ala Gly Pro Pro Arg Tyr Leu Gly Asp Arg Tyr
85 90 95
Lys Phe Tyr Leu Glu Asn Leu Thr Leu Gly Ile Arg Glu Ser Arg Lys
100 105 110
Glu Asp Glu Gly Trp Tyr Leu Met Thr Leu Glu Lys Asn Val Ser Val
115 120 125
Gln Arg Phe Cys Leu Gln Leu Arg Leu Tyr Glu Gln Val Ser Thr Pro
130 135 140
Glu Ile Lys Val Leu Asn Lys Thr Gln Glu Asn Gly Thr Cys Thr Leu
145 150 155 160
Ile Leu Gly Cys Thr Val Glu Lys Gly Asp His Val Ala Tyr Ser Trp
165 170 175
Ser Glu Lys Ala Gly Thr His Pro Leu Asn Pro Ala Asn Ser Ser His
180 185 190
Leu Leu Ser Leu Thr Leu Gly Pro Gln His Ala Asp Asn Ile Tyr Ile
195 200 205
Cys Thr Val Ser Asn Pro Ile Ser Asn Asn Ser Gln Thr Phe Ser Pro
210 215 220
Trp Pro Gly Cys Arg Thr Asp Pro Ser Glu Thr Lys Pro Trp Ala Val
225 230 235 240
Tyr Ala Gly Leu Leu Gly Gly Val Ile Met Ile Leu Ile Met Val Val
245 250 255
Ile Leu Gln Leu Arg Arg Arg Gly Lys Thr Asn His Tyr Gln Thr Thr
260 265 270
Val Glu Lys Lys Ser Leu Thr Ile Tyr Ala Gln Val Gln Lys Pro Gly
275 280 285
Pro Leu Gln Lys Lys Leu Asp Ser Phe Pro Ala Gln Asp Pro Cys Thr
290 295 300
Thr Ile Tyr Val Ala Ala Thr Glu Pro Val Pro Glu Ser Val Gln Glu
305 310 315 320
Thr Asn Ser Ile Thr Val Tyr Ala Ser Val Thr Leu Pro Glu Ser
325 330 335

Claims (11)

  1. Use of one or a combination of several of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, tumour-specific ILC 1-like subgroup, and ILC2 subgroup as a biomarker for colorectal cancer, characterized in that it uses one or a combination of several of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, tumour-specific ILC 1-like subgroup, and ILC2 subgroup as a target for the preparation of a diagnostic agent for the occurrence and/or metastasis of colorectal cancer, or for the preparation of a medicament for the treatment of colorectal cancer, or for the preparation of a reagent for predicting the survival time of colorectal cancer, or for the preparation of a reagent for the prognosis of colorectal cancer.
  2. 2. The use as claimed in claim 1, wherein the antibodies are prepared by immunizing an animal with an immunogen selected from any one or a combination of SLAMF1, HPGD, TLE4, PRDM1, AQP3, and cytokine IL 33; and/or, preparing immune cells, proteins and/or small molecules by taking one or a combination of more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, tumor-specific ILC 1-like subgroup and ILC2 subgroup as targets.
  3. 3. A method of diagnosing colorectal cancer in a subject, or a method of preventing or treating colorectal cancer in a subject in need thereof, or a method of predicting survival or prognostic assessment of a patient suffering from colorectal cancer, comprising: determining the level of any one or a combination of several of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33, a tumor-specific ILC 1-like subpopulation, an ILC2 subpopulation in a sample obtained from said patient or subject, wherein by said level, a diagnosis is made as to whether said subject suffers from colorectal cancer, or a prediction or prognostic assessment is made of the survival time of said patient; or the like, or, alternatively,
    the method comprises the following steps: and taking one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, a cytokine IL33, a tumor-specific ILC 1-like subgroup and an ILC2 subgroup as a target point, and preventing or treating the colorectal cancer of the patient.
  4. 4. The use according to claim 1 or 2 or the method according to claim 3, wherein the sample for determining the biomarker is a sample of tumor tissue or blood obtained from the patient, or a sample of intestinal tissue or blood of the subject.
  5. 5. The use or method as claimed in claim 4 wherein the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33 is determined at the protein level; or the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3 and cytokine IL33 is determined at the nucleic acid level.
  6. 6. The use or method of claim 5, wherein when the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33 is determined at the protein level, the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3, cytokine IL33 is determined by immunohistochemistry;
    when the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3 and cytokine IL33 is determined at the nucleic acid level, the level of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3 and cytokine IL33 is determined quantitatively by encoding mRNA of any one or more of SLAMF1, HPGD, TLE4, PRDM1, AQP3 and cytokine IL 33.
  7. 7. The use or method of claim 4, which comprises determining the level of any one or more of SLAMF1+ ILC, HPGD + ILC, TLE4+ ILC, PRDM1+ ILC and AQP3+ ILC in the sample.
  8. 8. The use or method of claim 7, wherein a panel of binding partners specific for the following cell surface markers is used to determine the level of SLAMF1+ cells in a sample obtained from the patient: CD45, CD127, CD117, CRTH2, CD5, TIGIT and SLAMF 1; or the like, or, alternatively,
    the level of any one or more of SLAMF1+ ILC, HPGD + ILC, TLE4+ ILC, PRDM1+ ILC, AQP3+ ILC and IL33+ ILC is determined by a flow cytometry method; or the like, or, alternatively,
    the level of any one or more of SLAMF1+ ILC, HPGD + ILC, TLE4+ ILC, PRDM1+ ILC, AQP3+ ILC, and IL33+ ILC is determined by single cell RNA sequencing.
  9. 9. The use or method of claim 1, wherein the higher the level of SLAMF1, the higher the probability that the patient will have a long survival time.
  10. 10. The use or method according to claim 1, wherein said diagnosing comprises the steps of: i) determining the level of SLAMF1 or the cytokine IL33 that activates ILC2 in a sample obtained from the subject; ii) comparing said level determined in step i) with a predetermined reference value; iii) the determined individual is a CRC patient when the level determined in step i) is above the predetermined reference value, or the determined individual is a healthy individual when the level determined in step i) is close to the predetermined reference value; or the like, or, alternatively,
    the diagnosis comprises the following steps: i) determining the level of SLAMF1, HPGD, TLE4, PRDM1, AQP3 in an intestinal sample obtained from the subject; ii) comparing said level determined in step i) with a predetermined reference value; iii) when the characteristic upregulation of SLAMF1, HPGD, TLE4 and PRDM1 on the ILC cell surface of the gut, the downregulation of AQP3, as determined in step i), the individual is a CRC patient; or the like, or, alternatively,
    the prediction or prognostic assessment comprises the steps of: i) determining the level of SLAMF1 or the cytokine IL33 that activates ILC2 in a sample obtained from the patient; ii) comparing said level determined in step i) with a predetermined reference value; iii) the patient has a good prognosis when said level determined in step i) is higher than said predetermined reference value, or a poor prognosis when said level determined in step i) is lower than said predetermined reference value; or the like, or, alternatively,
    the prevention or treatment comprises the steps of: preventing or treating colorectal cancer in a subject in need thereof by targeting colorectal cancer tumor-specific biomarkers, signaling molecules, cell subsets, comprising: administering to the subject a pharmaceutical composition comprising a pharmaceutically acceptable carrier, an effective amount of a modulator of a tumor-specific biomarker of colorectal cancer, or a modulator of a signaling molecule, or a modulator of a subpopulation of cells, and optionally an additional therapeutic agent, thereby preventing or treating colorectal cancer; wherein the colorectal cancer tumor specific biomarker is selected from SLAMF1, HPGD, TLE4 and PRDM1, the signal molecule is AQP3, and the cell subset is specific ILC 1-like and ILC2 subset.
  11. 11. The use or method of claim 10, wherein the modulator is selected from the group consisting of a small molecule chemical agent, an antisense oligonucleotide, a small interfering rna (sirna), a short hairpin rna (shrna), an antibody, and a biologically active fragment or homologue of said antibody.
CN202010642872.3A 2020-07-06 2020-07-06 Colorectal cancer biomarker and application thereof in diagnosis, prevention, treatment and prognosis Pending CN113899903A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114174495A (en) * 2019-07-24 2022-03-11 英研生物(英国)有限公司 Tumor infiltrating lymphocyte therapy and uses thereof

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114174495A (en) * 2019-07-24 2022-03-11 英研生物(英国)有限公司 Tumor infiltrating lymphocyte therapy and uses thereof

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