WO2022008488A1 - Use of slamf1 as a biomarker in colorectal cancer - Google Patents

Abstract

Innate lymphoid cells (ILCs) are tissue-resident lymphocytes that differ from conventional T lymphocytes in having no antigen-specific receptors. ILCs include natural killer (NK) cells, ILC1, ILC2, ILC3 and lymphoid tissue-inducer cell (LTi) subsets. Tumor ILCs are frequently found in various cancers, but their roles in cancer immunity and immunotherapy remain much less clear than those of other lymphocytes, such as T cells and NK cells. The inventors report here the single-cell characterization of blood and gut ILC subsets in healthy conditions and in colorectal cancer (CRC). The healthy gut contains ILC1s, ILC3s, and ILC3/NKs, but no ILC2s. Additional tumor-specific ILC1-like and ILC2 subsets were identified in CRC patients. SLAMF1 (signaling lymphocytic activation molecule family member 1, CD150) was found to be selectively expressed on tumor-specific ILCs (TILCs). More importantly, the inventors show that higher levels of SLAMF1, including protein levels, RNA level as well as levels of SLAMF1⁺ ILCs were observed in CRC patients. The SLAMF1-high group of rectal cancer patients had a significantly higher survival rate than the SLAMF1-low group, suggesting that SLAMF1 is an anti-tumor biomarker in CRC.

Description

USE OF SLAMF1 AS A BIOMARKER IN COLORECTAL CANCER

FIELD OF THE INVENTION:

The present invention is in the field of medicine, in particular oncology.

BACKGROUND OF THE INVENTION:

T cell-based immunotherapy has been very successful clinically for the treatment of malignant tumors, but only in a small proportion of patients (1-6). Treatments targeting other immune components are required, to increase the proportion of patients benefiting from immunotherapy. Innate lymphoid cells (ILCs) are tissue-resident innate antigen-independent lymphocytes that regulate immunity to pathogens and commensal organisms for tissue homeostasis (7,8). ILCs form a heterogeneous population of cells that are currently classified into five major groups [natural killer (NK) cells, helper-like ILCls, ILC2s, ILC3s, and lymphoid tissue-inducer cells] on the basis of their cytokine production and transcription factor expression profiles (8). ILCs are involved in immune functions, including pathogen responses, inflammation, tissue development, remodeling, repair and homeostasis.

Given the large amounts and nature of the cytokines they produce, ILC subsets are likely to be involved in cancer immunity, but may also contribute to tumor-associated inflammation. NK cells are known to play a role in cancer, through their tumor-suppressive properties, and are efficient at controlling metastasis (9). The role of helper-ILCs in the context of tumorigenesis and cancer immunity is less clear and appears to depend on the tumor microenvironment. ILC 1 s produce large amounts of proinflammatory cytokines, such as IFN-g and TNF-a, which favor tumorigenesis (10). However, IFN-g can also limit tumor growth in certain tumor microenvironments (11,12). ILC2s have been shown to be mostly detrimental in various tumor settings. Indeed, large numbers of ILC2s are present in the peripheral blood of patients with gastric cancer (13) and acute promyelocytic leukemia (14). ILC2-derived IL-13 stimulates the immunosuppressive activity of myeloid-derived suppressor cells in acute promyelocytic leukemia (14), and in human bladder cancer and murine prostate tumors (15). However, ILC2- derived IL-5 may help to suppress primary and metastatic lung tumors in mouse models (16), and ILC2s activate tissue-specific tumor immunity in pancreatic cancer (17). ILC3s also have tumor suppressor properties, in the B 16 melanoma mouse model (18,19) and in non-small cell lung cancer (NSCLC) patients (20), for example. By contrast, ILC3 -derived IL-17 and IL-22 may contribute to gut cancer development (21,22). There is, therefore, a clear need to investigate the presence and role of ILC subsets in various cancer indications.

Colorectal cancer (CRC) is the third most prevalent cancer in both women and men, and the second most frequent cause of cancer-related deaths worldwide (23), despite remarkable improvements in therapeutic strategies. Dysregulated ILC responses have been linked to the development of intestinal cancers. ILC2s are present at low levels in many pathological conditions in humans (24,25). By contrast, CRC patients have large numbers of ILC Is in the intestines (24-27), and abnormally low levels of ILC3s (26,27), which normally densely populate the colon at steady state (25-27). Indeed, decreases in the ILC3/ILC1 ratio have been associated with the severity of CRC (27). The baseline ILC landscape, in terms of the composition, diversity, and functional status of these cells in the human gut, remains incompletely explored under tumor conditions.

SUMMARY OF THE INVENTION:

As defined by the claims, the present invention relates to the use of SLAMF1 as a biomarker in colorectal cancer.

DETAILED DESCRIPTION OF THE INVENTION:

Innate lymphoid cells (ILCs) are tissue-resident lymphocytes that differ from conventional T lymphocytes in having no antigen-specific receptors. ILCs include natural killer (NK) cells, ILC1, ILC2, ILC3 and lymphoid tissue-inducer cell (LTi) subsets. Tumor ILCs are frequently found in various cancers, but their roles in cancer immunity and immunotherapy remain much less clear than those of other lymphocytes, such as T cells and NK cells. The inventors report here the single-cell characterization of blood and gut ILC subsets in healthy conditions and in colorectal cancer (CRC). The healthy gut contains ILC Is, ILC3s, and ILC3/NKs, but no ILC2s. Additional tumor-specific ILC 1 -like and ILC2 subsets were identified in CRC patients. SLAMF1 (signaling lymphocytic activation molecule family member 1, CD 150) was found to be selectively expressed on tumor-specific ILCs (TILCs). More importantly, the inventors show that higher levels of SLAMF1, including protein levels, RNA level as well as levels of SLAMF1⁺ ILCs were observed in CRC patients. The SLAMFl-high group of rectal cancer patients had a significantly higher survival rate than the SLAMFl-low group, suggesting that SLAMF1 is an anti-tumor biomarker in CRC. Accordingly, the first object of the present invention relates to a method of predicting the survival time of a patient suffering from a colorectal cancer comprising determining the level of SLAMF1 in a sample obtained from the patient wherein said level correlates with the patient’s survival time.

In particular embodiment, the level of SLAMF1 is positively correlated with the patient’s survival time, meaning that the higher is the level of SLAMF1 in the sample, the higher is the probability that the patient will have a long survival time.

In some embodiments, the method of the present invention is performed in vitro or ex vivo

As used herein, the term “colorectal cancer” includes the well-accepted medical definition that defines colorectal cancer as a medical condition characterized by cancer of cells of the intestinal tract below the small intestine (i.e., the large intestine (colon), including the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum). Additionally, as used herein, the term “colorectal cancer” also further includes medical conditions, which are characterized by cancer of cells of the duodenum and small intestine (jejunum and ileum).

In some embodiments, the colorectal cancer is characterized by microsatellite instability.

As used herein the term “microsatellite instability” or “MSI” has its general meaning and is defined as the accumulation of insertion-deletion mutations at short repetitive DNA sequences (or ‘microsatellites’) is a characteristic feature of cancer cells with DNA mismatch repair (MMR) deficiency. Inactivation of any of several MMR genes, including MLH1, MSH2, MSH6 and PMS2, can result in MSI. Originally, MSI was shown to correlate with germline defects in MMR genes in patients with Lynch syndrome (LS), where >90% of colorectal cancer (CRC) patients exhibit MSI. It was later recognized that MSI also occurs in ~12% of sporadic CRCs occurring in patients that lack germline MMR mutations, and MSI in these patients is due to promoter methylation-induced silencing of the MLHl gene expression. Determination of MSI status in CRC involves routine methods well known in the art.

In some embodiments, the colorectal cancer is at Stage I, II, III, or IV as determined by the TNM classification, but however the present invention is accurately useful for predicting the survival time of patients when said cancer has been classified as Stage II or III by the TNM classification, i.e. non metastatic colorectal cancer. The method of the present invention is particularly suitable for predicting the duration of the overall survival (OS), progression-free survival (PFS) and/or the disease-free survival (DFS) of the cancer patient. Those of skill in the art will recognize that OS survival time is generally based on and expressed as the percentage of people who survive a certain type of cancer for a specific amount of time. Cancer statistics often use an overall five-year survival rate. In general, OS rates do not specify whether cancer survivors are still undergoing treatment at five years or if they've become cancer-free (achieved remission). DSF gives more specific information and is the number of people with a particular cancer who achieve remission. Also, progression-free survival (PFS) rates (the number of people who still have cancer, but their disease does not progress) includes people who may have had some success with treatment, but the cancer has not disappeared completely.

As used herein, the expression “short survival time” indicates that the patient will have a survival time that will be lower than the median (or mean) observed in the general population of patients suffering from said cancer. When the patient will have a short survival time, it is meant that the patient will have a “poor prognosis”. Inversely, the expression “long survival time” indicates that the patient will have a survival time that will be higher than the median (or mean) observed in the general population of patients suffering from said cancer. When the patient will have a long survival time, it is meant that the patient will have a “good prognosis”.

As used herein, the term “SLAMF1” refers to the signaling lymphocytic activation molecule 1. The term is also known as CDwl50, IPO-3, SLAM family member 1 and CD150. An exemplary amino acid sequence for SLAMF1 is represented by SEQ ID NO:l.

SEQ ID NO:l >sp|Q13291|SLAF1 HUMAN Signaling lymphocytic activation molecule OS=Homo sapiens OX=9606 GN=SLAMF1 PE=1 SV=1

MDPKGLLSLTFVLFLSLAFGASYGTGGRMMNCPKILRQLGSKVLLPLTYERINKSMNKSI

HIW TMAKSLENSVENKIVSLDPSEAGPPRYLGDRYKFYLENLTLGIRESRKEDEGWYLM

TLEKNVSVQRFCLQLRLYEQVSTPEIKVLNKTQENGTCTLILGCTVEKGDHVAYSWSEKA

GTHPLNPANSSHLLSLTLGPQHADNIYICTVSNPISNNSQTFSPWPGCRTDPSETKPWAV

YAGLLGGVIMILIMW ILQLRRRGKTNHYQTTVEKKSLTIYAQVQKPGPLQKKLDSFPAQ

DPCTTIYVAATEPVPESVQETNSITVYASVTLPES

As used herein, the term “sample” refers to any sample obtained from the subject for the purpose of performing the method of the present invention. In some embodiments, the sample is a bodily fluid (e.g. a blood sample), a population of cells, or a tissue. In some embodiments, the sample is a blood sample. As used herein, the term “blood sample” refers to a whole blood sample, serum sample and plasma sample. A blood sample may be obtained by methods known in the art including venipuncture or a finger stick. Serum and plasma samples may be obtained by centrifugation methods known in the art. The sample may be diluted with a suitable buffer before conducting the assay. In some embodiments, the sample is a PBMC sample. The term “PBMC” or “peripheral blood mononuclear cells” or “unfractionated PBMC”, as used herein, refers to whole PBMC, i.e. to a population of white blood cells having a round nucleus, which has not been enriched for a given sub-population. Cord blood mononuclear cells are further included in this definition. Typically, the PBMC sample according to the invention has not been subjected to a selection step to contain only adherent PBMC (which consist essentially of >90% monocytes) or non-adherent PBMC (which contain T cells, B cells, ILCs, NK T cells and DC precursors). A PBMC sample according to the invention therefore contains lymphocytes (B cells, T cells, ILCs cells, and NKT cells), monocytes, and precursors thereof. Typically, these cells can be extracted from whole blood using Ficoll, a hydrophilic polysaccharide that separates layers of blood, with the PBMC forming a cell ring under a layer of plasma. Additionally, PBMC can be extracted from whole blood using a hypotonic lysis buffer which will preferentially lyse red blood cells. Such procedures are known to the expert in the art.

In some embodiments, the sample is a tumor tissue sample. The term “tumor tissue sample” means any tissue tumor sample derived from the patient. Said tissue sample is obtained for the purpose of the in vitro evaluation. In some embodiments, the tumor sample may result from the tumor resected from the patient. In some embodiments, the tumor sample may result from a biopsy performed in the primary tumour of the patient or performed in metastatic sample distant from the primary tumor of the patient. For example an endoscopical biopsy performed in the bowel of the patient affected by a colorectal cancer. The tumor tissue sample can, of course, be subjected to a variety of well-known post-collection preparative and storage techniques (e.g., fixation, storage, freezing, etc.). The sample can be fresh, frozen, fixed (e.g., formalin fixed), or embedded (e.g., paraffin embedded).

In some embodiments, when the sample is a tumor tissue sample the level of SLAMFl is determined at the protein level by any well-known method in the art. Typically, such methods comprise contacting the tissue sample with at least one selective binding agent capable of selectively interacting with SLAMF1. The selective binding agent may be polyclonal antibody or monoclonal antibody, an antibody fragment, synthetic antibodies, or other protein-specific agents such as nucleic acid or peptide aptamers. For the detection of the antibody that makes the presence of the marker detectable by microscopy or an automated analysis system, the antibodies may be tagged directly with detectable labels such as enzymes, chromogens or fluorescent probes or indirectly detected with a secondary antibody conjugated with detectable labels. In some embodiments, when the sample is a tissue sample (e.g. tumor tissue sample), the level of the marker is determined by immunohistochemistry (IHC). Immunohistochemistry typically includes the following steps i) fixing said tissue sample with formalin, ii) embedding said tissue sample in paraffin, iii) cutting said tissue sample into sections for staining, iv) incubating said sections with the binding partner specific for the marker, v) rinsing said sections, vi) incubating said section with a biotinylated secondary antibody and vii) revealing the antigen-antibody complex with avidin-biotin-peroxidase complex. Accordingly, the tissue sample is firstly incubated the binding partners. After washing, the labeled antibodies that are bound to marker of interest are revealed by the appropriate technique, depending of the kind of label is borne by the labeled antibody, e.g. radioactive, fluorescent or enzyme label. Multiple labelling can be performed simultaneously. Alternatively, the method of the present invention may use a secondary antibody coupled to an amplification system (to intensify staining signal) and enzymatic molecules. Such coupled secondary antibodies are commercially available, e.g. from Dako, EnVision system. Counterstaining may be used, e.g. H&E, DAPI, Hoechst. Other staining methods may be accomplished using any suitable method or system as would be apparent to one of skill in the art, including automated, semi- automated or manual systems.

In some embodiments, when the sample is a tumor tissue sample, the level of SLAMF1 is determined at nucleic acid level. Typically, the level of SLAMF1 may be determined by determining the quantity of mRNA encoding for SLAMF1. Methods for determining the quantity of mRNA are well known in the art. For example the nucleic acid contained in the samples (e.g., cell or tissue prepared from the subject) is first extracted according to standard methods, for example using lytic enzymes or chemical solutions or extracted by nucleic-acid- binding resins following the manufacturer's instructions. The extracted mRNA is then detected by hybridization (e. g., Northern blot analysis, in situ hybridization) and/or amplification (e.g., RT-PCR). Other methods of Amplification include ligase chain reaction (LCR), transcription- mediated amplification (TMA), strand displacement amplification (SDA), nucleic acid sequence based amplification (NASBA), ISH procedures (for example, fluorescence in situ hybridization (FISH), chromogenic in situ hybridization (CISH) and silver in situ hybridization (SISH)) or comparative genomic hybridization (CGH). In some embodiments, the level of SLAMF1 mRNA is quantified by nCounter® analysis (Nanostring, USA). In some embodiments, the level of SLAMF1 mRNA is quantified by sequencing (RNA sequencing). In some embodiments, the level of SLAMF1 mRNA is quantified by nucleic acid array (e.g. microarrays).

In some embodiments, the method of the present invention comprises determining the level of SLAMF1+ ILC in the sample. In particular for said embodiments, the sample can be a tumor tissue sample or a blood sample.

As used herein, the term “innate lymphoid cell” or “ILC” has its general meaning in the art and the term includes natural killer (NK) cells and three other main subsets, ILC1, ILC2 and ILC3, referred as to helper-like ILCs. ILC can be classified into five subsets — NK cells, ILCls, ILC2s, ILC3s, and LTi cells — based on their development and function as described in Vivier E, Artis D, ColonnaM, et al. Innate Lymphoid Cells: 10 Years On. Cell. 2018; 174(5): 1054- 1066. doi:10.1016/j. cell.2018.07.017. The ILC nomenclature presented here is approved by the International Union of Immunological Societies (IUIS).

As used herein, the term “SLAMF1+ ILC” refers to an ILC that expresses SLAMF1 (i.e. SLAMF1 protein or nucleic acid encoding for SLAMF1 such as mRNA).

In some embodiments, the level of SLAMF1+ ILCs is expressed as measurement of the expression intensity of the marker (e.g. protein and/or mRNA) by ILCs (e.g. mean fluorescence intensity MFI) or as measurement of the amount of ILCs that express SLAMF1 (e.g. protein and/or mRNA) in a sample (e.g. frequencies (e.g. %) of SLAMF1+ ILCs and density of SLAMF1+ cells).

Methods for quantifying SLAMF1+ ILC levels in a sample are well known in the art and typically involve the presence or absence of specific cell surface markers. In some embodiments, determining the presence or absence of the cell surface markers involves use of a panel of binding partners specific for the cell surface markers of interest. Said binding partners include but are not limited to antibodies, aptamer, and peptides. The binding partners will allow for the screening of cellular populations expressing the marker. Various techniques can be utilized to screen for cellular populations expressing the cell surface markers of interest, and typically include magnetic separation using antibody-coated magnetic beads, “panning” with antibody attached to a solid matrix (i.e., plate), and flow cytometry (See, e.g., U.S. Pat. No. 5,985,660; and Morrison et al. Cell, 96:737-49 (1999)).

In some embodiments, the binding partners are antibodies that may be polyclonal or monoclonal, preferably monoclonal, specifically directed against one cell surface marker. Polyclonal antibodies of the invention or a fragment thereof can be raised according to known methods by administering the appropriate antigen or epitope to a host animal selected, e.g., from pigs, cows, horses, rabbits, goats, sheep, and mice, among others. Various adjuvants known in the art can be used to enhance antibody production. Although antibodies useful in practicing the invention can be polyclonal, monoclonal antibodies are preferred. Monoclonal antibodies of the invention or a fragment thereof can be prepared and isolated using any technique that provides for the production of antibody molecules by continuous cell lines in culture. Techniques for production and isolation include but are not limited to the hybridoma technique originally; the human B- cell hybridoma technique; and the EBV-hybridoma technique.

In some embodiments, the panel of binding partners that is specific for the following cell surface markers CD45, CD127, CD117, CRTH2, CD5, TIGIT and SLAMF1, can be used for determining the level of SLAMF1+ cells in the sample obtained from the patient.

Typically, the binding partners are conjugated with a label for use in separation. Labels include magnetic beads, which allow for direct separation, biotin, which can be removed with avidin or streptavidin bound to a support, fluorochromes, which can be used with a fluorescence activated cell sorter, or the like, to allow for ease of separation of the particular cell type. Fluorochromes that find use include phycobiliproteins, e.g. phycoerythrin and allophycocyanins, fluorescein and Texas red. Typically each antibody is labeled with a different fluorochrome, to permit independent sorting for each marker. Suitable fluorescent detection elements include, but are not limited to, fluorescein, rhodamine, tetramethylrhodamine, eosin, erythrosin, coumarin, methyl-coumarins, pyrene, Malacite green, stilbene, Lucifer Yellow, Cascade Blue™, Texas Red, IAEDANS, EDANS, BODIPY FL, LC Red 640, Cy 5, Cy 5.5, LC Red 705 and Oregon green. Suitable optical dyes are described in the 1996 Molecular Probes Handbook by Richard P. Haugland, hereby expressly incorporated by reference. Suitable fluorescent labels also include, but are not limited to, green fluorescent protein (GFP; Chalfie, et al., Science 263(5148):802-805 (Feb. 11, 1994); and EGFP; Clontech — Genbank Accession Number U55762), blue fluorescent protein (BFP; 1. Quantum Biotechnologies, Inc. 1801 de Maisonneuve Blvd. West, 8th Floor, Montreal (Quebec) Canada H3H 1J9; 2. Stauber, R. H. Biotechniques 24(3):462-471 (1998); 3. Heim, R. and Tsien, R. Y. Curr. Biol. 6:178-182 (1996)), enhanced yellow fluorescent protein (EYFP; 1. Clontech Laboratories, Inc., 1020 East Meadow Circle, Palo Alto, Calif. 94303), luciferase (Ichiki, et al, J. Immunol. 150(12):5408- 5417 (1993)), (b-galactosidase (Nolan, et al., Proc Natl Acad Sci USA 85(8):2603-2607 (April 1988)) and Renilla WO 92/15673; WO 95/07463; WO 98/14605; WO 98/26277; WO 99/49019; U.S. Pat. Nos. 5,292,658; 5,418,155; 5,683,888; 5,741,668; 5,777,079; 5,804,387; 5,874,304; 5,876,995; and 5,925,558). All of the above-cited references are expressly incorporated herein by reference. In some embodiments, detection elements for use in the present invention include: Alexa-Fluor dyes (an exemplary list including Alexa Fluor® 350, Alexa Fluor® 405, Alexa Fluor® 430, Alexa Fluor® 488, Alexa Fluor® 500, Alexa Fluor® 514, Alexa Fluor® 532, Alexa Fluor® 546, Alexa Fluor® 555, Alexa Fluor® 568, Alexa Fluor® 594, Alexa Fluor® 610, AlexaFluor® 633, Alexa Fluor® 647, Alexa Fluor® 660, Alexa Fluor® 680, Alexa Fluor® 700, and Alexa Fluor® 750), Cascade Blue, Cascade Yellow and R- phycoerythrin (PE) (Molecular Probes) (Eugene, Oreg.), FITC, Rhodamine, and Texas Red (Pierce, Rockford, Ill.), Cy5, Cy5.5, Cy7 (Amersham Life Science, Pittsburgh, Pa.). Tandem conjugate protocols for Cy5PE, Cy5.5PE, Cy7PE, Cy5.5APC, Cy7APC are known in the art. Fluorophores bound to antibody or other binding element can be activated by a laser and re emit light of a different wavelength. The amount of light detected from the fluorophores is related to the number of binding element targets associated with the cell passing through the beam. Any specific set of detection elements, e.g. fluorescently tagged antibodies, in any embodiment can depend on the types of cells to be studied and the presence of the activatable element within those cells. Several detection elements, e.g. fluorophore-conjugated antibodies, can be used simultaneously, so measurements made as one cell passes through the laser beam consist of scattered light intensities as well as light intensities from each of the fluorophores. Thus, the characterization of a single cell can consist of a set of measured light intensities that may be represented as a coordinate position in a multi-dimensional space. Considering only the light from the fluorophores, there is one coordinate axis corresponding to each of the detection elements, e.g. fluorescently tagged antibodies. The number of coordinate axes (the dimension of the space) is the number of fluorophores used. Modem flowcytometers can measure several colors associated with different fluorophores and thousands of cells per second. Thus, the data from one subject can be described by a collection of measurements related to the number of antigens for each of (typically) many thousands of individual cells. See Krutzik et al, High- content single-cell drug screening with phosphospecific flow cytometry. Nature Chemical Biology, Vol. 4 No. 2, Pgs. 132-42, February 2008. Such methods may optionally include the use of barcoding to increase throughput and reduce consumable consumption. See Krutzik, P. and Nolan, G., Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling. Nature Methods, Vol. 3 No. 5, Pgs. 361-68, May 2006.

Thus in some embodiments, the quantification of SLAMF1+ ILCs is carried out by a flow cytometric method. As used herein, the term "flow cytometric method" refers to a technique for counting cells of interest, by suspending them in a stream of fluid and passing them through an electronic detection apparatus. Flow cytometric methods allow simultaneous multiparametric analysis of the physical and/or chemical parameters of up to thousands of events per second, such as fluorescent parameters. Modern flow cytometric instruments usually have multiple lasers and fluorescence detectors. A common variation of flow cytometric techniques is to physically sort particles based on their properties, so as to purify or detect populations of interest, using "fluorescence-activated cell sorting". As used herein, "fluorescence-activated cell sorting" or “FACS” refers to a flow cytometric method for sorting a heterogeneous mixture of cells from a biological sample into two or more containers, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell and provides fast, objective and quantitative recording of fluorescent signals from individual cells as well as physical separation of cells of particular interest. Accordingly, FACS can be used with the methods described herein to isolate and detect the population of cells of the present invention. For example, fluorescence activated cell sorting (FACS) may be therefore used and typically involves a flow cytometer capable of simultaneous excitation and detection of multiple fluorophores, such as a BD Biosciences FACSCanto™ flow cytometer, used substantially according to the manufacturer's instructions. The cytometric systems may include a cytometric sample fluidic subsystem, as described below. In addition, the cytometric systems include a cytometer fluidically coupled to the cytometric sample fluidic subsystem. Systems of the present disclosure may include a number of additional components, such as data output devices, e.g., monitors, printers, and/or speakers, softwares (e.g. (Flowjo, Laluza.... ), data input devices, e.g., interface ports, a mouse, a keyboard, etc., fluid handling components, power sources, etc. More particularly, the blood sample is contacted with a panel of antibodies specific for the specific market of the population of cells of the interest. In some embodiments the binding partner is conjugated to a metallic chemical element such as lanthanides. Lanthanides offer several advantages over other labels in that they are stable isotopes, there are a large number of them available, up to 100 or more distinct labels, they are relatively stable, and they are highly detectable and easily resolved between detection channels when detected using mass spectrometry. Lanthanide labels also offer a wide dynamic range of detection. Lanthanides exhibit high sensitivity, are insensitive to light and time, and are therefore very flexible and robust and can be utilized in numerous different settings. Lanthanides are a series of fifteen metallic chemical elements with atomic numbers 57-71. They are also referred to as rare earth elements. Lanthanides may be detected using CyTOF technology. CyTOF is inductively coupled plasma time-of- flight mass spectrometry (ICP-MS). CyTOF instruments are capable of analysing up to 1000 cells per second for as many parameters as there are available stable isotope tags.

Typically, the binding partners are added to a suspension of cells, and incubated for a period of time sufficient to bind the available cell surface antigens. The incubation will usually be at least about 5 minutes and usually less than about 30 minutes. It is desirable to have a sufficient concentration of binding partners in the reaction mixture, such that the efficiency of the separation is not limited by lack of binding partners. The appropriate concentration is determined by titration. The medium in which the cells are separated will be any medium that maintains the viability of the cells.

In some embodiments, the level of SLAMF1+ ILC may also be quantified by single cell analysis. In some embodiments, the level of SLAMF1+ ILCs may be quantified by single cell RNA sequencing. The method typically involves the steps of i) isolation of single cells; ii) lysis of the singles cells and extraction of the RNA molecules, iii) reverse transcription (RT) of said RNA molecules, iv) amplification of the cDNAs obtained at step C), v) cDNA pooling and purification, vi) preparation of a cDNA library, and, vii) sequencing said cDNA library.Typically the methods involves separation of individual cells into separate wells (e.g. by any cell sorting method such as FACS). More recent methods encapsulate individual cells in droplets in a microfluidic device, where the reverse transcription reaction takes place. Each droplet carries a DNA "barcode" that uniquely labels the cDNAs derived from a single cell. Once reverse transcription is complete, the cDNAs from many cells can be mixed together for sequencing; transcripts from a particular cell are identified by the unique barcode. Several scRNA-seq protocols have been published: Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, XuN, et al. (May 2009). "mRNA-Seq whole-transcriptome analysis of a single cell". Nature Methods. 6 (5): 377-82; Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, Linnarsson S (July 2011). "Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq". Genome Research. 21 (7): 1160-7; Ramskold D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. (August 2012). "Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells". Nature Biotechnology. 30 (8): 777-82; Hashimshony T, Wagner F, Sher N, Yanai I (September 2012). "CEL-Seq: single-cell RNA- Seq by multiplexed linear amplification". Cell Reports. 2 (3): 666-73; Singh M, Al-Eryani G, Carswell S, Ferguson JM, Blackburn J, Barton K, et al. (July 2019). "High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes". Nature Communications; Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T, Ueda HR (April 2013). "Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity". Genome Biology. 14 (4): R31; Kouno T, Moody J, Kwon AT, Shibayama Y, Kato S, Huang Y, et al. (January 2019). "Cl CAGE detects transcription start sites and enhancer activity at single-cell resolution". Nature Communications. 10 (1): 360.

In some embodiments, the higher is the level of SLAMFl (e.g. SLAMF1+ ILCs) in the sample, the higher is the probability that the patient will have a long survival time.

In some embodiments, the method of the present invention comprises the steps of i) determining the level of SLAMFl (e.g. SLAMF1+ ILCs) in the sample obtained from the patient, ii) comparing the level determined at step i) with a predetermined reference value and iii) concluding that the patient has a good prognosis when the level determined at step i) is higher than the predetermined reference value or concluding that the patient has a poor prognosis when the level determined at step i) is lower than the predetermined reference value.

As used herein, the term “predetermined reference value” refers to a threshold value or a cut off value that discriminates the patients having a good prognosis from those having a poor prognosis. Typically, the predetermined reference value can be determined experimentally, empirically, or theoretically. A threshold value can also be arbitrarily selected based upon the existing experimental and/or clinical conditions, as would be recognized by a person of ordinary skilled in the art. For example, retrospective measurement of level of SLAMFl in properly banked historical subject samples may be used in establishing the predetermined reference value. The threshold value has to be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit/risk balance (clinical consequences of false positive and false negative). Typically, the optimal sensitivity and specificity (and so the threshold value) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data. For example, after determining the level of SLAMF1 in a group of reference, one can use algorithmic analysis for the statistic treatment of the measured expression levels of the gene(s) in samples to be tested, and thus obtain a classification standard having significance for sample classification. The full name of ROC curve is receiver operator characteristic curve, which is also known as receiver operation characteristic curve. It is mainly used for clinical biochemical diagnostic tests. ROC curve is a comprehensive indicator that reflects the continuous variables of true positive rate (sensitivity) and false positive rate (1-specificity). It reveals the relationship between sensitivity and specificity with the image composition method. A series of different cut-off values (thresholds or critical values, boundary values between normal and abnormal results of diagnostic test) are set as continuous variables to calculate a series of sensitivity and specificity values. Then sensitivity is used as the vertical coordinate and specificity is used as the horizontal coordinate to draw a curve. The higher the area under the curve (AUC), the higher the accuracy of diagnosis. On the ROC curve, the point closest to the far upper left of the coordinate diagram is a critical point having both high sensitivity and high specificity values. The AUC value of the ROC curve is between 1.0 and 0.5. When AUC>0.5, the diagnostic result gets better and better as AUC approaches 1. When AUC is between 0.5 and 0.7, the accuracy is low. When AUC is between 0.7 and 0.9, the accuracy is moderate. When AUC is higher than 0.9, the accuracy is quite high. This algorithmic method is preferably done with a computer. Existing software or systems in the art may be used for the drawing of the ROC curve, such as: MedCalc 9.2.0.1 medical statistical software, SPSS 9.0, ROCPOWER.SAS, DESIGNROC.FOR, MULTIREADER POWER. S AS, CREATE-ROC.SAS, GB STAT VIO.O (Dynamic Microsystems, Inc. Silver Spring, Md., USA), etc.

In some embodiments, the predetermined reference value is determined by carrying out a method comprising the steps of a) providing a collection of samples; b) providing, for each ample provided at step a), information relating to the actual clinical outcome for the corresponding subject (i.e. the duration of the survival); c) providing a serial of arbitrary quantification values; d) determining the level of SLAMFl for each sample contained in the collection provided at step a); e) classifying said samples in two groups for one specific arbitrary quantification value provided at step c), respectively: (i) a first group comprising samples that exhibit a quantification value for level that is lower than the said arbitrary quantification value contained in the said serial of quantification values; (ii) a second group comprising samples that exhibit a quantification value for said level that is higher than the said arbitrary quantification value contained in the said serial of quantification values; whereby two groups of samples are obtained for the said specific quantification value, wherein the samples of each group are separately enumerated; f) calculating the statistical significance between (i) the quantification value obtained at step e) and (ii) the actual clinical outcome of the subjects from which samples contained in the first and second groups defined at step f) derive; g) reiterating steps f) and g) until every arbitrary quantification value provided at step d) is tested; h) setting the said predetermined reference value as consisting of the arbitrary quantification value for which the highest statistical significance (most significant) has been calculated at step g)·

For example the level of SLAMF1 has been assessed for 100 samples of 100 subjects. The 100 samples are ranked according to the level of SLAMF1. Sample 1 has the highest level and sample 100 has the lowest level. A first grouping provides two subsets: on one side sample Nr 1 and on the other side the 99 other samples. The next grouping provides on one side samples 1 and 2 and on the other side the 98 remaining samples etc., until the last grouping: on one side samples 1 to 99 and on the other side sample Nr 100. According to the information relating to the actual clinical outcome for the corresponding subject, Kaplan Meier curves are prepared for each of the 99 groups of two subsets. Also for each of the 99 groups, the p value between both subsets was calculated. The predetermined reference value is then selected such as the discrimination based on the criterion of the minimum p value is the strongest. In other terms, the level of SLAMF1 corresponding to the boundary between both subsets for which the p value is minimum is considered as the predetermined reference value.

It should be noted that the predetermined reference value is not necessarily the median value of level of SLAMF1. Thus in some embodiments, the predetermined reference value thus allows discrimination between a poor and a good prognosis for a patient. Practically, high statistical significance values (e.g. low P values) are generally obtained for a range of successive arbitrary quantification values, and not only for a single arbitrary quantification value. Thus, in one alternative embodiment of the invention, instead of using a definite predetermined reference value, a range of values is provided. Therefore, a minimal statistical significance value (minimal threshold of significance, e.g. maximal threshold P value) is arbitrarily set and a range of a plurality of arbitrary quantification values for which the statistical significance value calculated at step g) is higher (more significant, e.g. lower P value) are retained, so that a range of quantification values is provided. This range of quantification values includes a "cut-off value as described above. For example, according to this specific embodiment of a "cut-off value, the outcome can be determined by comparing the level of SLAMF1 (e.g. level of SLAMF1+ ILC) with the range of values which are identified. In some embodiments, a cut-off value thus consists of a range of quantification values, e.g. centered on the quantification value for which the highest statistical significance value is found (e.g. generally the minimum p value which is found). For example, on a hypothetical scale of 1 to 10, if the ideal cut-off value (the value with the highest statistical significance) is 5, a suitable (exemplary) range may be from 4-6. For example, a subject may be assessed by comparing values obtained by measuring the level of SLAMF1, where values higher than 5 reveal a poor prognosis and values less than 5 reveal a good prognosis. In some embodiments, a subject may be assessed by comparing values obtained by measuring the level of SLAMF1 and comparing the values on a scale, where values above the range of 4-6 indicate a poor prognosis and values below the range of 4-6 indicate a good prognosis, with values falling within the range of 4-6 indicating an intermediate occurrence (or prognosis).

The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention.

FIGURES:

Figure 1 SLAMF1 is a biomarker in CRC

A. Flow cytometry analysis of SLAMF1 expression on total ILCs from normal mucosa (i n=l ) and CRC tissues (n= 7). The data show frequencies of SLAMF1⁺ cells among total ILCs.

B. Flow cytometry analysis of SLAMF1 expression on total blood ILCs from healthy donors («=14) and CRC patients («=5). Data show frequencies of SLAMF1⁺ cells among total ILCs.

Kaplan-Meier curves for overall survival stratified by SLAMF1 expression level in colon (C) and rectum (D) cancer patients from TCGA. The optimal cut-off for patient stratification was obtained with a Cox proportional hazards model and the p-v alue was calculated in a log-rank test. In (C), SLAMF1- high group («=393); SLAMFl-low group («=60). In (D), SLAM!· /-high group (n=l 19); SLAMFl-low group (n=46).

In (A), (B), statistical significance was calculated by Mann-Whitney tests of unpaired nonparametric / tests.

EXAMPLE:

Methods:

Collection of clinical samples

Clinical samples were obtained from colorectal cancer (CRC) patients and healthy individuals who gave informed consent, after approval had been obtained from the local medical ethics committee of Ruijin Hospital and Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. Four CRC patients were recruited for scRNAseq at diagnosis (not shown). Healthy individuals for scRNAseq were recruited from patients undergoing routine colonoscopy who were generally in good health, with no other relevant medical history, such as inflammatory bowel disease (IBD) or CRC (not shown).

Isolation of human lymphocytes

Fresh intestine tissues were prepared immediately after surgery. Adipose tissue and visible blood vessels were removed from the tissue manually. Specimens were weighed and washed with PBS and then cut into small pieces. Normal tissue was incubated with 10 mL freshly prepared intraepithelial lymphocyte solution (5 mM EDTA, 15 mM HEPES, 10% FBS, 1 mM DTT in PBS), for 1 hour at 37°C, with shaking at 200 rpm. CRC tissue was washed with 10 mL freshly prepared 65 mM DTT in PBS for 15 min at 37°C, with shaking. After incubation, the tissue pieces were rinsed twice with PBS and subjected to enzymatic digestion for 1 hour at 37°C, with shaking [0.38 pg/mL collagenase VIII, 0.1 mg/mL DNase I, 100 U/ml penicillin (Thermo Fisher Scientific 15140-122), 100 mg/mL streptomycin (Thermo Fisher Scientific 15140-122), 10% FBS in RPMI 1640 medium]. The digested tissues were then shaken vigorously by hand for 5 min and mechanically dissociated with a 21 -gauge syringe. The resulting cell suspension was filtered through a cell strainer with 100 pm pores into a new 50 mL conical tube, and PBS was added to a final volume of 30 mL. Cells were then centrifuged at 1,800 rpm for 5 min. The supernatants were discarded and the cell pellets were resuspended in RMPI 1640 medium supplemented with 10% FBS. Cells were centrifuged on a Ficoll gradient and then washed with PBS before use.

Peripheral blood mononuclear cells (PBMCs) were obtained from human blood samples centrifuged on a Ficoll gradient. Briefly, blood was mixed with an equal volume of 2% FBS in PBS and gently layered on the Ficoll gradient. Cells were centrifuged at 1000 x g for 25 min, without braking. The cells in the middle layer were then washed once with PBS and resuspended in 2% FBS in PBS for use.

Sorting of ILCs

Freshly prepared human cells were resuspended in PBS and incubated with a live/dead cell marker for 10 min at 4°C. Cells were washed and suspended in 2% FBS, 2 mM EDTA in PBS (FACS buffer), supplemented with 10% mouse serum and 40% Brilliant Strain Buffer. Cells were first stained with 1:50 human Fc Block for 10 min at 4°C and then incubated with antibodies directed against CD45, CD127, CD117, CRTH2, and against lineage markers (TCRY5, TCR ab, CD3, CD 19, CD 14, CD16, CD94, CD123, CD34, CD303 and FceRI) for 30 min at room temperature. Human cells were washed with FACS buffer, centrifuged and resuspended in FACS buffer. Live ILCs in RPMI 1640 supplemented with 20% FBS were sorted in a BD FACS Aria III cell sorter (BD Biosciences).

Flow cytometry for ILCs

For cell surface staining of ILCs, freshly prepared human cells were stained with live/dead cell markers and Fc Block as for ILCs sorting. Cells were stained with surface antibodies against CD45, CD 127, CD117, CRTH2, CD5, TIGIT and SLAMF1 and antibodies against same lineage markers for ILCs sorting for 30 min at room temperature. For each of the staining, paired samples from same patients were used. PBMC from more healthy donors was stained at same time. Cells were kept at 4°C and analyzed on a BD Symphony (BD Biosciences). Flow data was analyzed with FlowJo software (FlowJo LLC). Statistical analysis was performed by Mann- Whitney tests of unpaired nonparametric t tests or Kruskal- Wallis tests with Dunn’s multiple comparison tests. / values were adjusted with the Benjamini-Hochberg method for multiple comparison tests. * p- value < 0.05, **/>-value < 0.01, *** >-value < 0.001, **** >-value < 0.0001.

Single-cell RNA sequencing

Purified ILCs were resuspended in PBS supplemented with 0.04% BSA, and kept on ice. Cells were counted and cell density was adjusted to that recommended for lOx Genomics Chromium single-cell 3’ v3 processing and library preparation. Sequencing was performed on an Illumina platform (NovaSeq 6000), by GENERY BIO (Shanghai, China), at a sequencing depth of about 90,000 reads per single cell.

Raw 10X read alignment, quality control and normalization Raw sequencing reads were subjected to quality control with FastQC software vO.11.9 (https://www.bioinformatics.babraham.ac.uk/proiects/fastqc/). Sequencing data in a bcl file were converted to FASTQ format with Illumina bcl2fastq2 Conversion Software v2.20 (https://support.illumina.com/downloads/bcl2fastq-conversion-software-v2-20.html). We then used Cell Ranger Single Cell Software Suite v. 2.2 to process, align, and summarize unique molecular identifier (UMI) counts, according to the standard pipeline and default parameters described at https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines /latest/. Briefly, we used the standard Cell Ranger Count pipeline to align FASTQ reads with the GRch38 genome. We then filtered sequencing reads on the basis of base-calling quality scores, and assigned cell barcodes and UMIs to each read. The Cell Ranger aggr pipeline was used to normalize all scRNAseq data with default parameters, to obtain a uniform sequencing depth. The combined feature-barcode matrix was used for downstream analysis.

During quality control with Seurat analysis, raw UMI count matrices were filtered to remove genes expressed in fewer than three cells, cells with fewer than 200 genes, cells with more than 4000 genes, and cells with high percentages of mitochondrial genes (more than 8%). The resulting matrix was then normalized by a global-scaling method, converted with a scaling factor (10,000 by default) and log-transformed with the “LogNormalize” function in Seurat for downstream analysis.

Filtering contaminated cells

We used the R package SingleR (51) and default parameters to assign individual cells to cell types, with the Human Primary Cell Atlas Data as the reference dataset. Each single cell was annotated with a cell type in “label. main” of the dataset. As the dataset did not include the human ILC dataset, and, given the similarities between ILCs and T cells or NK cells, we retained cells labeled as both NK and T cells. Small clusters of fewer than 200 cells were removed, to eliminate doublets. Donor-specific clusters with a strong specific NK cell signature were considered to be true NK cell contaminants and were removed from downstream analysis.

Reduction of the number of dimensions and clustering

The top 2000 variable genes were selected with the “FindVariableGenes” function of Seurat (52) and used for principal component analysis (PCA). For ILCs in normal mucosa, we retained the first 40 PCs. For normal blood, CRC blood and tumor tissue, we retained the top 20 PCs. Clusters were identified with the “FindClusters” function, with the algorithm based on the optimization of nearest-neighbor modularity implemented in Seurat and visualized with the uniform manifold approximation and projection (UMAP) algorithm. For comparisons of different tissues, the “merge” function was used to pool the individual Seurat objects. For donor and tissue data visualization, the “group. by” parameters were set as intended information when plotting with “DimPlot” function in Seurat.

Batch effect correction for ILC3 subsets in normal and tumor mucosa

ILC3 clusters in tumor tissue and both ILC3 and ILC3/NK clusters in normal mucosa were sub sampled for downstream clustering. The batch effect was corrected with the “IntegrateData” function of the standard workflow of Seurat, based on previously identified anchors (Butler et al, 2018).

Unsupervised hierarchical clustering

Mean gene expression was analyzed for single cells in each cluster. Only genes previously shown to display variable expression were used. We used the Heatmap.plus package to plot the unsupervised clustering map. The Euclidean distance was calculated for genes in all clusters. For normal mucosa, only the major donor-derived ILCs for cluster 0-4 were used for analysis.

Principal component analysis

Principal component analysis (PCA) was performed on the mean level of expression of variable genes in clusters. The top 20 genes contributing to PCI and PC2 or PCI and PC3 were plotted. For the analysis of ILCs in normal mucosa, we removed ILCs from donors other than the major source of cluster 0-4. PCA gene loadings for the PCs corresponded to the 20 genes making the largest contribution to the total amount of information represented by PCI and PC2 or PCI and PC3.

Differential expression analysis

We used the “FindAllMarkers” function in Seurat to identify genes differentially expressed between samples, for each cluster. The non-parametric Wilcoxon rank-sum test was used to obtain p-values for comparisons, and the adjusted / values, based on Bonferroni correction, for all genes in the dataset. We used the following parameters for the calculation of log fold-change (logFC) in expression values and to obtain -values for all the variable genes for each cluster: min. pet = 0.05, min.diffpct = 0.1, logfe. threshold = 0.25. The log-transformed and scaled expression values of the genes were used to generate a heatmap.

Gene annotations

Genes encoding transcription factors (TFs) were retrieved from four TF-related public datasets: JASPAR(53) (http://jaspar.genereg.net/), DBD (54) (http://www.transcriptionfactor.org/), AnimalTFDB (55) (http://bioinfo.life.hust.edu.cn/AnimalTFDB/), and TF2DNA (56) (http://www.fiserlab.org/tf2dna_db/). Genes encoding cell membrane and secreted proteins were obtained from The Human Protein Atlas (57)

(https://www.proteinatlas.org/humanproteome/tissue/secretome). RNA velocity estimation

We analyzed expression dynamics, using scRNA-seq data, by estimating the RNA velocities of single cells by distinguishing between unspliced and spliced transcripts on the basis of the previously aligned bam files of scRNA-seq data. The RNA velocity values for each gene in each cell and the embedding of RNA velocity vectors in a low-dimension space were calculated with the R package velocyto.R (58) (https :// github . com/velocvto-team/velocvto .R) . RNA velocities were then visualized on the UMAP projection by Gaussian smoothing on a regular grid.

Scoring samples for ILC signatures

ILC and NK cell signatures from tonsil tissue were defined by Bjorklund et al, (29). ILC1 and ILC3 signatures from jejunum, and ILC2 signatures from spleen were obtained from Yudanin et al, (34). Module scores were calculated with “AddModule Score” in Seurat, for each ILC. Briefly, the mean level of gene expression in a single cell was calculated, and the aggregate expression of control feature sets was then subtracted from it. The control features were selected at random from all features. For the module scores of ILC3 subsets from normal mucosa, the differentially expressed gene signatures for each ILC3 subset were used, at single-cell level, on each ILC3 subset in CRC tissues. Violin plots were used to visualize the module scores of each cluster. TCGA analysis

RNAseq data from primary tumors and clinical annotations were downloaded using the package TCGAbiolinks in September 2019. Kaplan-Meier curves were plotted using the R package survminer. In order to split the expression levels in two groups, the cut-off which gave the lowest p-value was used. Optimal cut-off for patient stratification was obtained with a Cox proportional hazards model and p-value indicated in the plot was calculated with a log-Rank test.

Results

Healthy gut contains ILCls, ILC3s, and ILC/NKs, but no ILC2s

We dissected the role of ILCs in CRC by studying paired CRC tissue and adjacent mucosal tissue (used as a control) samples, and comparing blood from patients with blood from age- matched healthy donors (data not shown). Lin CD127⁺ ILCs were more abundant in both normal mucosa and CRC tissue than in blood, consistent with the known tissue residence properties of ILCs (28) (data not shown). The percentage of ILCs was lower in CRC tissues than in normal mucosa, but similar in normal and CRC blood samples (data not shown).

We performed scRNAseq on -58,000 total ILCs from blood samples from CRC patients, healthy blood, normal mucosa and CRC tissue samples (data not shown). The heterogeneity of ILCs in normal mucosa was assessed with a total of 16,145 Lin CD127⁺ ILCs from colon tissues adjacent to the colon tumor in CRC patients (data not shown). The projection of cells onto two dimensions in a uniform manifold approximation and projection (UMAP) analysis revealed segregation into six distinct clusters: normal mucosa cluster (nmC)0 to nmC5 (data not shown). Two clusters, nmC4 and nmC5, contained cells from all donors, suggesting that there was no donor-specific transcriptomic profile for these two ILC populations (data not shown). By contrast, most of the cells from nmCO to nmC3 were single donor-specific (data not shown).

Using hierarchical clustering (data not shown) and gene signature heatmaps (data not shown), principal component analysis (PC A), (data not shown), top 10 expressed gene analysis (data not shown), and module score analysis (data not shown), we then compared the gene signatures of nmCO to nmC5 with previously described transcriptomic signatures of human ILC subsets (29). nmCO to nmC3 had a common transcriptomic signature characteristic of ILC3s, with REL, encoding a proto-oncogene member of the NF-kB family signaling via the IL22 prom data not shown Fig. 1F-G). nmC4 was characterized by NKG7, encoding a cytolytic granule membrane protein (31), and KLRD1, encoding CD94, expressed in T and NIC cells, as driver genes, with ONLY , GZMK , XCL2, and CCL4 , among the top expressed genes and with a whole signature common to NK cells and ILC3s from tonsils. nmC4 was, thus, identified as an ILC3/NK subset. nmC5 resembled ILCls, with higher levels of expression of T-cell markers ( CD3D , CD3G , and CD3E ), as previously described (29,32,33), transcription factors controlling ILC development ( IKZF3 , BCL11B , PRDM1 , and ID3 ), and NK/ILCl cell functional cytokines ( GZMM , IFNG, IL32 , CCL4 , and CCL5) (data not shown). These assignments were supported by the selective expression of known ILC markers, such as IL7R , GATA3, NCR3, ROMES, TBX21, KIT RORC, NCR1, NCR2 and KLRF1 (data not shown). We found differences between nmC5 and previously reported healthy gut ILCls (34), probably because the gating strategies used here did not exclude CD5⁺ cells (data not shown C). Thus, the normal gut mucosa defined by scRNAseq profiling of Lin CD127⁺ contains ILCls, ILC3s, and ILCs/NKs but no ILC2s, consistent with the lack of PTGDR2 gene expression (data not shown).

Tumor ILCl-like and ILC2 subsets are present in CRC patients

We then investigated the composition and diversity of 15,101 ILCs from the tumors of CRC patients (hereafter referred to as gut TILCs). UMAP analysis identified four distinct clusters: TILC CO to C3 (data not shown). Contrary to what was observed for normal mucosa, no overwhelming batch effect was observed, each cluster being present in all samples (data not shown). Based on the strategy applied to normal mucosa clusters (data not shown), TILC CO was assigned to ILC3s, consistent with its overexpression of KIT, CXCL8 , NFIL3 , and IL411, like nmCO-3. TILC Cl resembled ILCls and, like nmC5, displayed differential expression of genes encoding T-cell molecules {CD 3D, CD3G), secreted effectors ( CCL4 , IFNG), and ILC- related transcription factors ( IKZF3 , PRDM1 and BCL11B ). The other two subsets present, TILC C2 and TILC C3, were absent from normal mucosa. TILC C2 cells corresponded to an additional ILCl subset, hereafter called the TILC 1 -like subset, characterized by an enrichment in the expression of genes encoding inhibitory and costimulatory markers ( TIGIT CTLA4 , TNFRSF18, and TNFRSF4). TILC C3 cells, identified as ILC2s and hereafter referred to as TILC2, had high levels of expression for genes encoding transcription factors required for ILC2 development ( GATA3 , RORA , and ZBTB16) and ILC2-responsive cytokine receptor genes {IL1RL1 and IL17RB ) (data not shown). These assignments were supported by the selective expression of known ILC markers, such as IL7R, GATA3 , NCR3, EOMES, TBX21, KIT, RORC, NCR1, NCR2 and KLRFl (data not shown). In particular, PTGDR2 and higher levels of GAT A3 expression were found in the ILC2s (data not shown). Thus, like the normal mucosa nmILCs, gut TILCs formed heterogeneous populations encompassing four different subsets: TILC CO (resembling ILC3s), TILC Cl (resembling ILCls), TILC C2 (a novel population of ILCl -like), and TILC C3 (resembling ILC2s).

Tumor tissue ILC3s seemed to be less heterogeneous than those in the normal mucosa. We therefore focused on nmC0-3, nmC4 and TILC CO, comparing ILC3 heterogeneity between normal mucosa ILCs and gut TILCs, with the same analysis pipeline as described above after applying a batch effect correction (data not shown). Four different populations were found in ILC3s from both types of tissue (data not shown I), including a potentially immature SELL- expressing population, and a population enriched in HLA-encoding transcripts also present in human tonsils (data not shown). Each subset of normal mucosa ILC3 had a counterpart in tumor tissue (data not shown). Given the overlap in ILC3s heterogeneity between normal mucosa and gut TILCs, we can conclude that CRC did not affect the subset heterogeneity of ILC3s. Thus, gut TILCs differed from nmILCs in the appearance of a TILC2 subset and a second TILC 1 -like subset.

Blood ILC heterogeneity is stable in CRC

We searched for potential biomarkers of the disease, by investigating differences in blood ILCs between healthy individuals and CRC patients. A UMAP analysis of 19,603 ILCs from healthy donors revealed three distinct clusters, hereafter referred to as nbCO, nbCl, and nbC2 (data not shown). nbCO was considered to correspond to ILCls, based on the upregulation of CD3D , CD3E , CD3G, the NK/ILC1 cell effector proteins ( CCL5 , GZMK , GZMM , and GZMA ), and ILC transcription factors ( BCL11B , PRDM1 , and IKZl'3) (data not shown. nbCl was identified as ILC3s, and was characterized by ILC3 transcription factors (MAFF, RUNX3 ) and costimulation markers ( TNFRSF4 , TNFRSF18). nbC2 displayed an upregulation of genes from the ILC2 signature ( GATA3 , RORA ), and genes encoding regulatory receptors ( KLRB1 , KLRG1 ) (data not shown). These assignments were supported by the selective expression of known ILC markers, such as IL7R , GATA3 , M7«, EOMES, TBX21 , PTGDR2, KIT , AOAC, NCR1, and KLRF1 (data not shown).

A UMAP plot of 6,899 blood ILCs from CRC donors also identified three subsets, hereafter referred to as cbCO, cbCl and cbC2 (data not shown). Driver genes, top ten genes and module score signatures highlighted the similarity of cbILCs to nbILCs (data not shown). cbCO, like nbCl, had an ILC3 profile with enrichment for MA l·, RUNX3 , TNFRSF18 , NCR1 and KIT. cbCl were identified as ILC2s, with high levels of GATA3, RORA , KLRB1 , KLRG1 , and PTGDR2 expression, like nbC2. cbILC2, like nbCO, displayed an enrichment in the genes of the ILC1 signature: CD3D, CD3G, CD3E , CCL5 , GZMK , GZMM , GZMA, BCL11B , PRDM1, IKZF3 and TBX21 (data not shown). These assignments were also supported by the selective expression of IL7R , GAT A3, NCR3 , EOMES, TBX21, PTGDR2, KIT, RORC, NCR1, and KLRF1 (data not shown). However, despite the similarity of cbILC subsets to nbILCs subsets, velocity analysis predicted a possible conversion of ILCls into ILC3s only in the context of CRC, in tumor blood (data not shown). In summary, the blood ILCs of both healthy donors and CRC patients formed heterogeneous populations containing ILCl, ILC2 and ILC3 subsets.

Identification of a novel population of CRC-specific TILCls

Tumor tissue ILCs contained two additional populations not present in the ILCs of the normal mucosa, with transcriptomic signatures resembling those ofILC2s and ILCls (data not shown). We investigated the relatedness of these two tumor tissue-specific clusters and the ILC subsets from healthy blood and blood from CRC patients, by grouping the 41,603 ILCs into a single global analysis. This analysis revealed organ-specific imprinting in ILCs, with an overlap between the two types of blood samples, and TILCs clustering separately (data not shown). There was a high degree of similarity between nbILCs and cbILCs in gene signature, it was remarkably different from that of TILCs (data not shown C). We further investigated the relationship between defined ILC subsets from CRC tissue, normal blood and CRC blood samples. The TILCl-like subset appeared to segregate away from the other clusters, including TILCl in particular, despite having a core ILCl-transcriptomic signature in common with this subset (data not shown). Likewise, another TILC-specific subset, TILC2, clustered away from the other TILCs and the ILC2 in the blood. In the blood, each nbILC clustered with the corresponding cbILC subset (data not shown D). We investigated whether the tumor-specific ILCs shared more genes to their normal blood or CRC blood counterparts, by creating Venn diagrams comparing their whole transcriptomic signatures (data not shown). The TILCl-like subset shared more genes with cbILC 1 (57 genes in common) than with nbILC 1 (34 genes in common) (data not shown), whereas TILC2 shared comparable numbers of genes with cbILC2 and nbILC2, with which this subset had 39 and 34 genes, respectively, in common (data not shown).

ILC specific signature in CRC

We searched for tumor-specific tissue features of ILCs, by clustering the 31,246 ILC cells from normal mucosa and tumor tissues. These two tissues had some ILC populations in common, but UMAP highlighted a shift between the two tissues, suggesting differences at the transcriptomic level (data not shown). Unsupervised hierarchical clustering also showed the tissue-of-origin signature to be stronger than the ILC subset identity signature (data not shown). The clustering of nbILCs and cbILCs revealed a similar pattern of separation for the 26,502 ILCs in the UMAP analysis (data not shown), and in unsupervised hierarchical clustering, which segregated blood samples according to health status, revealing differences in transcription between the two subsets (data not shown). One gene was found to be upregulated in normal blood and mucosa (. AQP3 ). Four genes were identified as upregulated in both CRC blood and gut TILCs relative to their healthy counterparts ( SLAMF1 , HPGD , TLE4, and PRDM1 ) (data not shown). Feature plots of these five genes of interest confirmed the specific upregulation of SLAMF1 , HPGD , TLE4, and PRDM1 in gut TILCs, and the downregulation of AQP3 (data not shown). SLAMF1 (signaling lymphocytic activation molecule family member 1 or CD150), which encodes a protein involved in the activation of T cells, B cells, and NK cells (35), was the principal surface protein gene upregulated in tumors. This gene was expressed in cbILC2, TILC2 and TILCl-like subsets, but only weakly in their healthy counterparts (data not shown), suggesting that SLAMFl expression at the ILC cell surface can differentiate healthy individuals from CRC patients.

SLAMFl is a biomarker of CRC We confirmed, by flow cytometry, the expansion of the ILC1 subset at the expense of the ILC3 subset in tumor tissues from CRC patients relative to adjacent normal mucosa (data not shown). We also observed the presence of a novel population of TIGIT⁺ TILCl-like cells and TILC2 in tumors, but not in normal tissue (data not shown B), consistent with scRNAseq data (data not shown). Higher levels of expression of the ILC2-activating cytokine IL33 in tumors were correlated with longer survival in CRC patients from The Cancer Genome Atlas (TCGA) dataset, suggesting that TILC2 might be indicative of a good prognosis in CRC patients (data not shown). By contrast to the findings for gut ILCs, the frequency of each ILC subset among total ILCs in blood was similar in CRC patients and healthy donors (data not shown). Larger numbers of ILCs expressing SLAMF1 at their surface was found in tumors than in the adjacent tissues, from which SLAMF1 was almost absent (Fig. 1A). By contrast, SLAMF1 was expressed by blood ILCs from healthy donors, but high frequencies of SLAMF1 -expressing ILCs were also found in the blood of CRC patients (Fig. IB). We then investigated the potential role of SLAMF1 in CRC disease development and progression further, by studying the clinical outcome of cancer patients. Survival was much higher in patients with SLAMFl-high colon (Fig. 1C) and rectal cancer (Fig. ID) than in those with SLAMFl-low tumors, strongly suggesting that SLAMF1 is an anti-tumor biomarker in CRC.

Discussion:

Over the last decade, helper-like ILCs have emerged as key elements in protection against pathogens, tissue remodeling and homeostasis (8). The contribution of helper-like ILCs to cancer remains poorly understood, as they may promote tumor-associated inflammation or, conversely, may display anti-tumor properties, depending on the tumor microenvironment.

We investigated the heterogeneity of helper-like ILCs in the human gut by building a single cell transcriptomic landscape of Lin CD127⁺ cells at steady state and in CRC patients. This unbiased helper-like ILC characterization differed from the analysis of gut ILC transcriptomes provided by another recent study, in which these cells were subjected to sorting by flow cytometry on the basis of the CD103, CD300LF and CD196 cell surface markers before transcriptomic profiling (36). We show here that the healthy gut contains ILCls, ILC3s, a population of ILC3/NKs, but no ILC2s. Interestingly, ILC2s are almost entirely absent from healthy human tissues, by contrast to what has been reported for mice, with the exception of the lungs and adipose tissue (37). In our study, we detected tumor infiltrating TILC2 in CRC patients. TILC2 were also observed in breast (38), gastric (38), and pancreatic (17) tumors and in urine from bladder cancer patients (15). Several data support a model in which ILC2s infiltrate tumors via an IL-33 -dependent pathway (15-17) and mediate tumor immune surveillance by promoting cytolytic CD8⁺ T-cell responses. IL-33 is overexpressed in colorectal tumors (39) and high levels of IL-33 are frequently observed in low-grade adenocarcinomas and early colorectal tumors (40). Survival rate is higher in the IL-33 -high group of colon cancer patients than in IL-33 -low patients (data not shown), suggesting that TILC2 might be indicative of a good prognosis in CRC. There is, therefore, a clear need to investigate further the role of TILC2s in anti-tumor immunity in CRC and other cancer indications.

We identified an additional helper-like ILC1 subset, named TILCl-like TIGIT⁺, present in tumors from CRC patients, but absent from the blood. TILCl-like TIGIT⁺ had a transcriptional profile more closely resembling the ILC1 gene signature than that of any other ILCs, but they segregated away from TILC1, suggesting that they differed markedly from ‘conventional’ gut TILCl. ILCl-like cells known as ‘intermediate ILCls’ (intILCIs) have also been described in a mouse model of methylcholanthrene (MCA)-induced tumors, and experimental RM-1 and B16F10 lung metastases (41). In humans, CD56⁺CD16 ILCl-like cells have been found in solid tumors and in peritoneal and pleural fluids from cancer patients (42), and the cytotoxic functions of these cells are altered in the peripheral blood of donors with acute myeloid leukemia (43). Intratumoral intlLCl may emerge from NK cell differentiation driven by TGF- b signaling, a phenomenon known as ILC plasticity (41,44). The conversion of ILC3s into ILCls upon TGF-b signaling has been demonstrated in humanized mice and a transitional ILC3-ILC1 population has been identified in the human intestine (36). We observed no such phenomenon in our gut ILC dataset and none of the algorithms tested was able to establish a relatedness between TILCl-like TIGIT⁺ and another gut ILC subset reflecting possible differentiation (data not shown). The mechanisms by which TILCl-like TIGIT⁺ emerge in CRC tumors thus remain to be determined. We observed a plasticity of ILC1 towards ILC3 in the blood of CRC patients, but not in healthy donors, suggesting the presence of soluble signals driving ILC1-ILC3 plasticity, such as sustained IL-23 levels (45). The biological relevance of such ILC1-ILC3 plasticity in the blood of CRC patients is not clear. intILCIs and ILCls produced large amounts of TNF-a and were found to be ineffective at controlling carcinogenesis, potentially even promoting metastasis in mouse models (41). In human, CD56⁺CD16 ILCl-like cells express the pro-angiogenic factor VEGF, which may also favor tumor growth (42). In CRC patients, the frequency of ILCls has been shown to be higher in tumor tissues than in the normal mucosa (26,27), and to increase, at the expense of ILC3s, with tumor progression (27). These results suggest that high ILC1 levels may be predictive of a poor prognosis in cancer. The issue of the specific biological function of the TILCl-like subset relative to classical TILC1 in CRC tumors also needs to be addressed, because TILCl-like cells have high levels of PD1 and TIGIT, and may be further unleashed by anti-PDl and anti-TIGIT immunotherapies.

We characterized the levels of three subsets: ILC3, ILC3/NK and ILC1, in the normal mucosa of all donors. A donor-specific effect was observed in the ILC3 subset, suggesting possible ILC3 -imprinting by the microbiota. Interestingly, CRC tumors had much lower levels of ILC3s and displayed a loss of this apparent donor specificity. CRC is frequently associated with tumor dysbiosis, involving massive changes to the composition of the microbiota (46-50). ILC3s are major regulators of intestinal barrier integrity and immune homeostasis. It might therefore be beneficial to promote both ILC3 recolonization and diversification in CRC patients. ILC3 heterogeneity could potentially be boosted by increasing microbial diversity.

We also defined a population of ILC3/NK cells in healthy gut mucosa that was not present in tumors from CRC patients. These cells had transcriptomic features in common with both ILC3 and NK cells. They differ from ILC3s mostly in terms of their NKG7, KLRDl (CD94), GNLY, GZMK, XCL2 and CCL4 expression. The biological role of this ILC3/NK subset and its relatedness to ‘classical’ ILC3 remain to be addressed.

SLAMF1 was the only cell surface marker for which transcript levels were higher in TILCs and blood ILCs from CRC patients. ILCs expressing SLAMF1 on their surface were also present at higher frequency in tumors and blood from CRC patients than in healthy donors. SLAMF1 is a single-chain type I transmembrane receptor bearing two immunoreceptor tyrosine-based switch motifs (ITSM) in its cytoplasmic tail (35). SLAMF1 is a self-ligand but also a microbial receptor for morbilliviruses and a bacterial sensor involved in the elimination of Gram-negative bacteria (35). SLAMF1 is expressed by almost all hematopoietic cells except NIC cells, particularly those with an activated phenotype, and is upregulated upon cell activation. A large proportion of ILCs in the bloodstream expressed SLAMF1 on their surface at steady state, but no such expression was observed on ILCs from normal gut mucosa. By contrast, SLAMF1 was expressed on TILCs from CRC patients, suggesting that TILCs may be more activated in the tumor bed than in the normal adjacent mucosa. Nevertheless, the effect of SLAMF1 engagement at the cell surface of helper-like ILCs on the biology of these cells remains to be investigated. High levels of SLAMF1 were correlated with better survival of CRC patients. Our results therefore suggest that SLAMF1 is an anti-tumor biomarker in CRC.

ILCs have emerged as tissue-specific modulators of cancer immunity that can control various aspects of immunotherapy. As ILCs and T cells co-exist in human cancers and have stimulatory and inhibitory pathways in common, immunotherapy strategies targeting anti-cancer ILCs may be as important as strategies targeting T cells. Our results suggest that ILCs are part of the tumor microenvironment, as subsets of TILCs are present in CRC. It is tempting to speculate that they may regulate immunity at the tumor bed or have a direct effect on tumor cells. Further studies are required to determine whether it is possible to define more tumor-specific subsets differing in terms of activation status, with either pro- or anti-tumor immunity effects, in cancers arising in different tissues.

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Claims

CLAIMS:

1. A method of predicting the survival time of a patient suffering from a colorectal cancer comprising determining the level of SLAMF1 in a sample obtained from the patient wherein said level correlates with the patient’s survival time.

2. The method of claim 1 wherein the sample is a tumor tissue sample obtained from the patient.

3. The method of claim 2 wherein the level of SLAMF1 is determined at the protein level.

4. The method of claim 3 wherein the level of SLAMF1 is determined by immunohi stochemi stry .

5. The method of claim 2 wherein the level of SLAMF1 is determined at the nucleic acid level.

6. The method of claim 5 wherein the level of SLAMF1 is determined by quantifying the mRNA encoding for SLAMF 1.

7. The method of claim 1 wherein the sample is a blood sample obtained from the patient.

8. The method of claim 2 or 7 wherein the method comprises determining the level of SLAMF 1+ innate lymphoid cells (ILCs) in the sample.

9. The method of claim 8 wherein a panel of binding partners that are specific for the following cell surface markers CD45, CD127, CD117, CRTH2, CD5, TIGIT and SLAMF 1, are be used for determining the level of SLAMF 1+ cells in the sample obtained from the patient.

10. The method of claim 8 wherein the level of SLAMF 1+ ILCs is determined by a flow cytometric method.

11. The method of claim 8 wherein the level of SLAMF1+ ILCs is determined by single cell RNA sequencing.

12. The method of claim 1 wherein the higher is the level of SLAMFl in the sample, the higher is the probability that the patient will have a long survival time.

13. The method of claim 1 that comprises the steps of i) determining the level of SLAMF1 in the sample obtained from the patient, ii) comparing the level determined at step i) with a predetermined reference value and iii) concluding that the patient has a good prognosis when the level determined at step i) is higher than the predetermined reference value or concluding that the patient has a poor prognosis when the level determined at step i) is lower than the predetermined reference value.

14. The method of claim 13 wherein the level of SLAMF1+ cells is determined.