WO2022018163A1 - Method for predicting survival time in patients suffering from cancer - Google Patents

Abstract

The inventors found that metabolic gene signatures of human pan-tumor associated myeloid cells correlate with patient survival and cancer cell mitotic index. Indeed by crossing i) metabolic genes expression from single cell RNA-seq data of myeloid cell subsets, and ii) their functional metabolism by SCENITH, a method to determine in parallel the phenotype and metabolic state of the immune, stromal and tumor cells, the inventors identified glycolytic and respiratory metabolic gene signatures which predict survival time in patients. They found that in different human tumors, the glycolytic signature was associated with significantly reduced patient survival, while a respiratory gene signature correlated with increased survival. Moreover they demonstrated that the presence of glycolytic myeloid cells in the tumor correlates with malignancy. Thus the present invention relates to new gene signatures that are suitable for predicting survival time in patients suffering from cancer and for diagnosing malignancy tumor.

Description

METHOD FOR PREDICTING SURVIVAL TIME IN PATIENTS SUFFERING

FROM CANCER

FIELD OF THE INVENTION:

The invention relates a method for predicting survival time in patients suffering from cancer, and in particular to a new metabolic gene signatures suitable for predicting survival time in patients suffering from cancer.

BACKGROUND OF THE INVENTION:

Cancer remains a serious public health problem in developed countries. Accordingly, to be most effective, cancer treatment requires not only early detection and treatment or removal of the malignancy, but a reliable assessment of the severity of the malignancy and a prediction of the likelihood of cancer recurrence. The success of immunotherapies is restricted to a variable and relatively small proportion of patients that require a functional and metabolically competent immune system to dramatically respond to treatment. Current paradigm for development of novel immunotherapeutic treatments seeks to identify a treatment that cures all cancer patients of a given indication. However, each immunotherapy needs a different component/target of the immune system to work and not all patients have the same type of immune response against the tumor.

The inventors have previously designed a method, called SCENITH, which rapidly and efficiently measures the protein synthesis level in a population of cells upon inhibition of the different energy producing pathways. Indeed the inventors combined puromycin incorporation with a novel anti-puromycin monoclonal antibody to develop a reliable method to perform EM profiling with single cell level resolution based on protein synthesis intensity as read-out. The energetic metabolism (EM) profile describes the main sources of energy and biochemical pathways from which cells depend on to produce ATP (dependency) and also their potential to exploit other alternatives (capacity). EM profile also determines the competence of cells to survive in different anatomic locations and upon exposure to intrinsic and extrinsic signaling cues. This information is central to understand the physiological function and cellular state of different cell types. Among others, cancer stem cells, tumoral cells, immune cells, neurons, have an EM profile that impact on their capacity to proliferate, differentiate, and perform their physiological functions. Moreover, EM profile is also key in tumors, as it allows to determine the susceptibility of transformed cells to inhibitors of particular metabolic pathways (1-4). The method previously developed by the inventor permits to acquire energetic metabolism profiles with single cell resolution in non-abundant cells ex-vivo and permits to decrease to a minimum manipulation time, incubations and cost of sample preparation. SCENITH, is a novel method to monitor energetic metabolism (EM) activity in individual cells, that can be applied to ex-vivo samples containing complex and heterogeneous cell populations. By using this functional metabolic profiling of immune cells, the inventors were able to exploit single cell RNA-seq data to identify genes whose pattern of expression highly correlates with different functional EM profile. Based on this EM gene list, the inventors analysed the RNA- seq data of sorted antigen presenting cells (APCs) isolated from 450 human tumors (i.e lung, head and neck, colon-rectal, bladder and hepatic cancers). They observed that APCs of patients fall into two clear clusters, one displaying a respiratory-APC gene expression profile, and other with glycolytic- APC gene expression profile.

SUMMARY OF THE INVENTION:

The present relates to new gene signatures that are suitable for predicting survival time in patients suffering from cancer. In particular, the present invention is defined by the claims.

DETAILED DESCRIPTION OF THE INVENTION:

The inventors found that metabolic gene signatures of human pan-tumor associated myeloid cells correlate with patient survival and cancer cell mitotic index. Indeed by crossing i) metabolic genes expression from single cell RNA-seq data of myeloid cell subsets, and ii) their functional metabolism by SCENITH, a method to determine in parallel the phenotype and metabolic state of the immune, stromal and tumor cells, they identified glycolytic and respiratory metabolic gene signatures which predict survival time in patients. They found that in different human tumors, the glycolytic signature was associated with significantly reduced patient survival, while a respiratory gene signature correlated with increased survival. Moreover they demonstrated that the presence of glycolytic myeloid cells in the tumor correlates with malignancy.

Method for yredictins survival time

Accordingly, in a first aspect the present invention relates to a method for predicting survival time in patients suffering from cancer comprising the steps consisting of i) determining the expression level of at least one gene in a sample obtained from the patient wherein said gene is selected from the group consisting of PYGL, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDH1, ii) comparing said level expression with a predetermined reference value and iii) providing a poor prognosis of the survival time when the level expression is significantly different than the reference value.

The inventors demonstrate that overexpression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 and low expression FABP5, IDH3A, PPA1 and ALDH1 correlates with a poor prognosis (“glycolytic gene signature”). In contrast, low expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 , and overexpression of FABP5, IDH3A, PPA1 and ALDH1 correlates with a good prognosis (“respiratory gene signature”).

In some embodiments, the method of the present invention comprising determining the expression levels of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 in the sample obtained from the patient.

In some embodiments, the method of the present invention comprising determining the expression levels of LABP5, IDH3A, PPA1 andALDHl in the sample obtained from the patient.

In some embodiments, the method of the present invention comprising determining the expression levels of PYGL, HK3, G6PD, PFKFB3, SLC2A3 , FABP5, IDH3A, PPA1 and ALDH1 in a sample obtained from the patient.

Thus, the present invention relates also to a method for predicting survival time in patients suffering from cancer comprising the steps consisting of i) determining the expression level PYGL, HK3, G6PD, PFKFB3, SLC2A3 , FABP5, IDH3A, PPA1 and ALDH 7in a sample obtained from the patient, ii) comparing said level expression with a predetermined reference value and iii) providing a poor prognosis of the survival time when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 gene is higher than the reference value and when the level expression of FABP5, IDH3A, PPA1 and ALDH1 gene is lower than the reference value.

The present invention relates also to a method for predicting survival time in patients suffering from cancer comprising the steps consisting of i) determining the expression level PYGL, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDH1 in a sample obtained from the patient, ii) comparing said level expression with a predetermined reference value and iii) providing a good prognosis of the survival time when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 gene is lower than the reference value and when the level expression of FABP5, IDH3A, PPA1 and ALDH1 gene is higher than the reference value.

As used herein, the term “survival time” denotes the expected duration of time until death of a patient suffering from 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 “subject” refers to any mammals, such as a rodent, a feline, a canine, and a primate. Particularly, in the present invention, the subject is a human afflicted with or susceptible to be afflicted with a cancer, preferably a glioblastoma, colorectal cancer or renal cell carcinoma.

As used herein, the term “cancer” refers to an abnormal cell having the capacity for autonomous growth, i.e., an abnormal state or condition characterized by rapidly proliferating cell growth with the potential to invade or spread to other parts of the body. The term is meant to include all types of cancerous growths or oncogenic processes, metastatic tissues or malignantly transformed cells, tissues, or organs, irrespective of histopathologic type or stage of invasiveness. The terms "cancer" or "neoplasms" include malignancies of the various organ systems, such as affecting lung, breast, thyroid, lymphoid, gastrointestinal, and genito-urinary tract, as well as adenocarcinomas which include malignancies such as most colon cancers, renal-cell carcinoma, prostate cancer and/or testicular tumors, glioblastoma non-small cell carcinoma of the lung, cancer of the small intestine and cancer of the esophagus. In the context of the invention, the cancer can be a solid cancer. The term “solid cancer” has its general meaning in the art and refers to solid cancer selected from the group consisting of, but not limited to, head and neck squamous cell carcinoma (HNSCC), adrenal cortical cancer, anal cancer, bile duct cancer (e.g. periphilar cancer, distal bile duct cancer, intrahepatic bile duct cancer), bladder cancer, bone cancer (e.g. osteoblastoma, osteochrondroma, hemangioma, chondromyxoid fibroma, osteosarcoma, chondrosarcoma, fibrosarcoma, malignant fibrous histiocytoma, giant cell tumor of the bone, chordoma, multiple myeloma), brain and central nervous system cancer (e.g. meningioma, astocytoma, oligodendrogliomas, ependymoma, gliomas, medulloblastoma, ganglioglioma, Schwannoma, germinoma, craniopharyngioma), breast cancer (e.g. ductal carcinoma in situ, infiltrating ductal carcinoma, infiltrating lobular carcinoma, lobular carcinoma in situ, gynecomastia), cervical cancer, colorectal cancer, endometrial cancer (e.g. endometrial adenocarcinoma, adenocanthoma, papillary serous adnocarcinoma, clear cell), esophagus cancer, gallbladder cancer (mucinous adenocarcinoma, small cell carcinoma), gastrointestinal carcinoid tumors (e.g. choriocarcinoma, chorioadenoma destruens), Kaposi's sarcoma, kidney cancer (e.g. renal cell cancer), laryngeal and hypopharyngeal cancer, liver cancer (e.g. hemangioma, hepatic adenoma, focal nodular hyperplasia, hepatocellular carcinoma), lung cancer (e.g. small cell lung cancer, non-small cell lung cancer), mesothelioma, plasmacytoma, nasal cavity and paranasal sinus cancer (e.g. esthesioneuroblastoma, midline granuloma), nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngeal cancer, ovarian cancer, pancreatic cancer, penile cancer, pituitary cancer, prostate cancer, retinoblastoma, rhabdomyosarcoma (e.g. embryonal rhabdomyosarcoma, alveolar rhabdomyosarcoma, pleomorphic rhabdomyosarcoma), salivary gland cancer, skin cancer (e.g. melanoma, nonmelanoma skin cancer), stomach cancer, testicular cancer (e.g. seminoma, nonseminoma germ cell cancer), thymus cancer, thyroid cancer (e.g. follicular carcinoma, anaplastic carcinoma, poorly differentiated carcinoma, medullary thyroid carcinoma), vaginal cancer, vulvar cancer, and uterine cancer (e.g. uterine leiomyosarcoma).

In some embodiment, the cancer is colorectal cancer, breast cancer, brain cancer, renal cell carcinoma or glioblastoma.

As used herein, the term "sample" refers to any substance of biological origin. Examples of samples includes, but are not limited to blood, tumor, saliva, urine, cerebrospinal fluids, or any of other biological fluids or tissues.

In a preferred embodiment, the sample is tumor sample. As used herein, the term “tumor sample” means any tissue tumor sample derived from the subject. 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 subject. In some embodiments, the tumor sample may result from a biopsy performed in the primary tumor of the subject or performed in metastatic sample distant from the primary tumor of the subject. In some embodiments, the tumor sample is a sample of circulating tumor cells. As used herein, the term “circulating tumor cell” or “CTC” refers to a cancer cell derived from a cancerous tumor that has detached from the tumor and is circulating in the blood stream of the subject. Typically the CTCs are isolated from the blood sample using a filter and/or a marker based method. In some embodiments, the tumor sample is a sample of myeloid tumor cells.

As used herein, the term “ PYGL ” for “Glycogen phosphorylase, liver form”, also known as human liver glycogen phosphorylase, has its general meaning in the art and refers to gene encoding a homodimeric protein that catalyses the cleavage of alpha- 1,4-glucosidic bonds to release glucose- 1 -phosphate from liver glycogen stores. Its Entrez reference is 5836.

As used herein, the term “HK3” for “Hexokinase 3” has its general meaning in the art and refers to gene encoding hexokinases phosphorylate glucose to produce glucose-e- phosphate, the first step in most glucose metabolism pathways. Its Entrez reference is 3101.

As used herein, the term “G6RD” for “Glucose-6-phosphate dehydrogenase” has its general meaning in the art and refers to a housekeeping X-linked gene encoding cytosolic enzyme whose main function is to produce NADPH, a key electron donor in the defense against oxidizing agents and in reductive biosynthetic reactions. Its Entrez reference is 2539.

As used herein, the term “PFKFB3” for “6-Phosphofructo-2-Kinase/Fructose-2,6- Biphosphatase 3” has its general meaning in the art and refers to gene encoding for bifunctional protein that are involved in both the synthesis and degradation of fructose-2, 6-bisphosphate, a regulatory molecule that controls glycolysis in eukaryotes. This protein is required for cell cycle progression and prevention of apoptosis. It functions as a regulator of cyclin-dependent kinase 1, linking glucose metabolism to cell proliferation and survival in tumor cells. Its Entrez reference is 5209.

As used herein, the term “SLC2A3” for “Solute Carrier Family 2 Member 3”, also known as Glucose Transporter Type 3, Brain, has its general meaning in the art and refers to a gene encoding glucose transporter protein that can also mediate the uptake of various other monosaccharides across the cell membrane. This gene mediates the uptake of glucose, 2- deoxyglucose, galactose, mannose, xylose and fucose, and probably also dehydroascorbate. Its Entrez reference is 6515. As used herein, the term “ FABP5 ” for “Fatty Acid Binding Protein 5” has its general meaning in the art and refers to a gene encoding the fatty acid binding protein found in epidermal cells, and was first identified as being upregulated in psoriasis tissue. Fatty acid binding proteins are a family of small, highly conserved, cytoplasmic proteins that bind long- chain fatty acids and other hydrophobic ligands. Its Entrez reference is 2171.

As used herein, the term “IDH3A” for “Isocitrate Dehydrogenase (NAD(+)) 3 Catalytic Subunit Alpha” has its general meaning in the art and refers to a gene encoding for isocitrate dehydrogenases which catalyzes the oxidative decarboxylation of isocitrate to 2-oxoglutarate. These enzymes belong to two distinct subclasses, one of which utilizes NAD(+) as the electron acceptor and the other NADP(+). Its Entrez reference is 3419.

As used herein, the term “ RRAG for “Inorganic Pyrophosphatase 1” has its general meaning in the art and refers to a gene encoding for a member of the inorganic pyrophosphatase (PPase) family. PPases catalyze the hydrolysis of pyrophosphate to inorganic phosphate, which is important for the phosphate metabolism of cells. Its Entrez reference is 5464.

As used herein, the term “ ALDHF for “Aldehyde Dehydrogenase 1 Family Member Al”, also known as Retinal Dehydrogenase 1, has its general meaning in the art and refers to a gene encoding for a member of the aldehyde dehydrogenase family. Aldehyde dehydrogenase is the next enzyme after alcohol dehydrogenase in the major pathway of alcohol metabolism. Its Entrez reference is 216.

As used herein, the term "expression level" refers, e.g., to a determined level of expression of gene of interest. The expression level of expression indicates the amount of expression product in a sample. The expression product of a gene of interest can be the ribonucleic acid of interest itself, a nucleic acid transcribed or derived therefrom, or the a polypeptide or protein derived therefrom.

Measuring the expression level of the genes listed above can be done by measuring the gene expression level of these genes and can be performed by a variety of techniques well known in the art.

Typically, the expression level of a gene may be determined by determining the quantity of mRNA. 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 patient) 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) and nucleic acid sequence-based amplification (NASBA).

Nucleic acids having at least 10 nucleotides and exhibiting sequence complementarity or homology to the mRNA of interest herein find utility as hybridization probes or amplification primers. It is understood that such nucleic acids need not be identical, but are typically at least about 80% identical to the homologous region of comparable size, more preferably 85% identical and even more preferably 90-95% identical. In certain embodiments, it will be advantageous to use nucleic acids in combination with appropriate means, such as a detectable label, for detecting hybridization.

Typically, the nucleic acid probes include one or more labels, for example to permit detection of a target nucleic acid molecule using the disclosed probes. In various applications, such as in situ hybridization procedures, a nucleic acid probe includes a label (e.g., a detectable label). A “detectable label” is a molecule or material that can be used to produce a detectable signal that indicates the presence or concentration of the probe (particularly the bound or hybridized probe) in a sample. Thus, a labelled nucleic acid molecule provides an indicator of the presence or concentration of a target nucleic acid sequence (e.g., genomic target nucleic acid sequence) (to which the labelled uniquely specific nucleic acid molecule is bound or hybridized) in a sample. A label associated with one or more nucleic acid molecules (such as a probe generated by the disclosed methods) can be detected either directly or indirectly. A label can be detected by any known or yet to be discovered mechanism including absorption, emission and / or scattering of a photon (including radio frequency, microwave frequency, infrared frequency, visible frequency and ultra-violet frequency photons). Detectable labels include colored, fluorescent, phosphorescent and luminescent molecules and materials, catalysts (such as enzymes) that convert one substance into another substance to provide a detectable difference (such as by converting a colorless substance into a colored substance or vice versa, or by producing a precipitate or increasing sample turbidity), haptens that can be detected by antibody binding interactions, and paramagnetic and magnetic molecules or materials.

Particular examples of detectable labels include fluorescent molecules (or fluorochromes). Numerous fluorochromes are known to those of skill in the art, and can be selected, for example from Life Technologies (formerly Invitrogen), e.g., see, The Handbook — A Guide to Fluorescent Probes and Labeling Technologies). Examples of particular fluorophores that can be attached (for example, chemically conjugated) to a nucleic acid molecule (such as a uniquely specific binding region) are provided in U.S. Pat. No. 5,866, 366 to Nazarenko et al., such as 4-acetamido-4'-isothiocyanatostilbene-2,2' disulfonic acid, acridine and derivatives such as acridine and acridine isothiocyanate, 5-(2'-aminoethyl) aminonaphthalene-1 -sulfonic acid (EDANS), 4-amino -N- [3 vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate (Lucifer Yellow VS), N-(4-anilino-l- naphthyl)maleimide, antllranilamide, Brilliant Yellow, coumarin and derivatives such as coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4- trifluoromethylcouluarin (Coumarin 151); cyanosine; 4',6-diaminidino-2-phenylindole (DAPI); 5',5"dibromopyrogallol-sulfonephthalein (Bromopyrogallol Red); 7 -diethylamino -3 (4'-isothiocyanatophenyl)-4-methylcoumarin; diethylenetriamine pentaacetate; 4,4'- diisothiocyanatodihydro-stilbene-2,2'-disulfonic acid; 4,4'-diisothiocyanatostilbene-2,2'- disulforlic acid; 5-[dimethylamino] naphthalene- 1-sulfonyl chloride (DNS, dansyl chloride); 4-(4'-dimethylaminophenylazo)benzoic acid (DABCYL); 4-dimethylaminophenylazophenyl- 4'-isothiocyanate (DABITC); eosin and derivatives such as eosin and eosin isothiocyanate; erythrosin and derivatives such as erythrosin B and erythrosin isothiocyanate; ethidium; fluorescein and derivatives such as 5-carboxyfluorescein (FAM), 5-(4,6diclllorotriazin-2- yDaminofluorescein (DTAF), 2'7'dimethoxy-4'5'-dichloro-6-carboxyfluorescein (JOE), fluorescein, fluorescein isothiocyanate (FITC), and QFITC Q(RITC); 2',7'-difluorofluorescein (OREGON GREEN®); fluorescamine; IR144; IR1446; Malachite Green isothiocyanate; 4- methylumbelliferone; ortho cresolphthalein; nitrotyrosine; pararosaniline; Phenol Red; B- phycoerythrin; o-phthaldialdehyde; pyrene and derivatives such as pyrene, pyrene butyrate and succinimidyl 1 -pyrene butyrate; Reactive Red 4 (Cibacron Brilliant Red 3B-A); rhodamine and derivatives such as 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, rhodamine green, sulforhodamine B, sulforhodamine 101 and sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); N,N,N',N'-tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid and terbium chelate derivatives. Other suitable fluorophores include thiol-reactive europium chelates which emit at approximately 617 mn (Heyduk and Heyduk, Analyt. Biochem. 248:216-27, 1997; J. Biol. Chem. 274:3315-22, 1999), as well as GFP, LissamineTM, diethylaminocoumarin, fluorescein chlorotriazinyl, naphthofluorescein, 4,7-dichlororhodamine and xanthene (as described in U.S. Pat. No. 5,800,996 to Lee et al.) and derivatives thereof. Other fluorophores known to those skilled in the art can also be used, for example those available from Life Technologies (Invitrogen; Molecular Probes (Eugene, Oreg.)) and including the ALEXA FLUOR® series of dyes (for example, as described in U.S. Pat. Nos. 5,696,157, 6, 130, 101 and 6,716,979), the BODIPY series of dyes (dipyrrometheneboron difluoride dyes, for example as described in U.S. Pat. Nos. 4,774,339, 5,187,288, 5,248,782, 5,274,113, 5,338,854, 5,451,663 and 5,433,896), Cascade Blue (an amine reactive derivative of the sulfonated pyrene described in U.S. Pat. No. 5,132,432) and Marina Blue (U.S. Pat. No. 5,830,912).

In addition to the fluorochromes described above, a fluorescent label can be a fluorescent nanoparticle, such as a semiconductor nanocrystal, e.g., a QUANTUM DOTTM (obtained, for example, from Life Technologies (QuantumDot Corp, Invitrogen Nanocrystal Technologies, Eugene, Oreg.); see also, U.S. Pat. Nos. 6,815,064; 6,682,596; and 6,649, 138). Semiconductor nanocrystals are microscopic particles having size-dependent optical and/or electrical properties. When semiconductor nanocrystals are illuminated with a primary energy source, a secondary emission of energy occurs of a frequency that corresponds to the handgap of the semiconductor material used in the semiconductor nanocrystal. This emission can he detected as colored light of a specific wavelength or fluorescence. Semiconductor nanocrystals with different spectral characteristics are described in e.g., U.S. Pat. No. 6,602,671. Semiconductor nanocrystals that can he coupled to a variety of biological molecules (including dNTPs and/or nucleic acids) or substrates by techniques described in, for example, Bruchez et al, Science 281 :20132016, 1998; Chan et ah, Science 281:2016-2018, 1998; and U.S. Pat. No. 6,274,323. Formation of semiconductor nanocrystals of various compositions are disclosed in, e.g., U.S. Pat. Nos. 6,927, 069; 6,914,256; 6,855,202; 6,709,929; 6,689,338; 6,500,622; 6,306,736; 6,225,198; 6,207,392; 6,114,038; 6,048,616; 5,990,479; 5,690,807; 5,571,018; 5,505,928; 5,262,357 and in U.S. Patent Publication No. 2003/0165951 as well as PCT Publication No. 99/26299 (published May 27, 1999). Separate populations of semiconductor nanocrystals can he produced that are identifiable based on their different spectral characteristics. For example, semiconductor nanocrystals can he produced that emit light of different colors hased on their composition, size or size and composition. For example, quantum dots that emit light at different wavelengths based on size (565 mn, 655 mn, 705 mn, or 800 mn emission wavelengths), which are suitable as fluorescent labels in the probes disclosed herein are available from Life Technologies (Carlshad, Calif.). Additional labels include, for example, radioisotopes (such as 3 H), metal chelates such as DOTA and DPTA chelates of radioactive or paramagnetic metal ions like Gd3+, and liposomes.

Detectable labels that can he used with nucleic acid molecules also include enzymes, for example horseradish peroxidase, alkaline phosphatase, acid phosphatase, glucose oxidase, beta-galactosidase, beta-glucuronidase, or beta-lactamase.

Alternatively, an enzyme can he used in a metallographic detection scheme. For example, silver in situ hyhridization (SISH) procedures involve metallographic detection schemes for identification and localization of a hybridized genomic target nucleic acid sequence. Metallographic detection methods include using an enzyme, such as alkaline phosphatase, in combination with a water-soluble metal ion and a redox-inactive substrate of the enzyme. The substrate is converted to a redox-active agent by the enzyme, and the redoxactive agent reduces the metal ion, causing it to form a detectable precipitate. (See, for example, U.S. Patent Application Publication No. 2005/0100976, PCT Publication No. 2005/ 003777 and U.S. Patent Application Publication No. 2004/ 0265922). Metallographic detection methods also include using an oxido-reductase enzyme (such as horseradish peroxidase) along with a water soluble metal ion, an oxidizing agent and a reducing agent, again to form a detectable precipitate. (See, for example, U.S. Pat. No. 6,670,113).

Probes made using the disclosed methods can be used for nucleic acid detection, such as 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 situ hybridization (ISH) involves contacting a sample containing target nucleic acid sequence (e.g., genomic target nucleic acid sequence) in the context of a metaphase or interphase chromosome preparation (such as a cell or tissue sample mounted on a slide) with a labelled probe specifically hybridizable or specific for the target nucleic acid sequence (e.g., genomic target nucleic acid sequence). The slides are optionally pre-treated, e.g., to remove paraffin or other materials that can interfere with uniform hybridization. The sample and the probe are both treated, for example by heating to denature the double stranded nucleic acids. The probe (formulated in a suitable hybridization buffer) and the sample are combined, under conditions and for sufficient time to permit hybridization to occur (typically to reach equilibrium). The chromosome preparation is washed to remove excess probe, and detection of specific labeling of the chromosome target is performed using standard techniques. For example, a biotinylated probe can be detected using fluorescein-labeled avidin or avi din-alkaline phosphatase. For fluorochrome detection, the fluorochrome can be detected directly, or the samples can be incubated, for example, with fluorescein isothiocyanate (FITC)- conjugated avidin. Amplification of the FITC signal can be effected, if necessary, by incubation with biotin-conjugated goat antiavidin antibodies, washing and a second incubation with FITC- conjugated avidin. For detection by enzyme activity, samples can be incubated, for example, with streptavidin, washed, incubated with biotin-conjugated alkaline phosphatase, washed again and pre-equilibrated (e.g., in alkaline phosphatase (AP) buffer). For a general description of in situ hybridization procedures, see, e.g., U.S. Pat. No. 4,888,278.

Numerous procedures for FISH, CISH, and SISH are known in the art. For example, procedures for performing FISH are described in U.S. Pat. Nos. 5,447,841; 5,472,842; and 5,427,932; and for example, in Pirlkel et ak, Proc. Natl. Acad. Sci. 83:2934-2938, 1986; Pinkel et ak, Proc. Natl. Acad. Sci. 85:9138-9142, 1988; and Lichter et ak, Proc. Natl. Acad. Sci. 85:9664-9668, 1988. CISH is described in, e.g., Tanner et ak, Am. .1. Pathol. 157:1467-1472, 2000 and U.S. Pat. No. 6,942,970. Additional detection methods are provided in U.S. Pat. No. 6,280,929.

Numerous reagents and detection schemes can be employed in conjunction with FISH, CISH, and SISH procedures to improve sensitivity, resolution, or other desirable properties. As discussed above probes labeled with fluorophores (including fluorescent dyes and QUANTUM DOTS®) can be directly optically detected when performing FISH. Alternatively, the probe can be labeled with a nonfluorescent molecule, such as a hapten (such as the following non limiting examples: biotin, digoxigenin, DNP, and various oxazoles, pyrrazoles, thiazoles, nitroaryls, benzofurazans, triterpenes, ureas, thioureas, rotenones, coumarin, courmarin-based compounds, Podophyllotoxin, Podophyllotoxin-based compounds, and combinations thereof), ligand or other indirectly detectable moiety. Probes labeled with such non-fluorescent molecules (and the target nucleic acid sequences to which they bind) can then be detected by contacting the sample (e.g., the cell or tissue sample to which the probe is bound) with a labeled detection reagent, such as an antibody (or receptor, or other specific binding partner) specific for the chosen hapten or ligand. The detection reagent can be labeled with a fluorophore (e.g., QUANTUM DOT®) or with another indirectly detectable moiety, or can be contacted with one or more additional specific binding agents (e.g., secondary or specific antibodies), which can be labeled with a fluorophore.

In other examples, the probe, or specific binding agent (such as an antibody, e.g., a primary antibody, receptor or other binding agent) is labeled with an enzyme that is capable of converting a fluorogenic or chromogenic composition into a detectable fluorescent, colored or otherwise detectable signal (e.g., as in deposition of detectable metal particles in SISH). As indicated above, the enzyme can be attached directly or indirectly via a linker to the relevant probe or detection reagent. Examples of suitable reagents (e.g., binding reagents) and chemistries (e.g., linker and attachment chemistries) are described in U.S. Patent Application Publication Nos. 2006/0246524; 2006/0246523, and 2007/ 01 17153.

It will be appreciated by those of skill in the art that by appropriately selecting labelled probe-specific binding agent pairs, multiplex detection schemes can he produced to facilitate detection of multiple target nucleic acid sequences (e.g., genomic target nucleic acid sequences) in a single assay (e.g., on a single cell or tissue sample or on more than one cell or tissue sample). For example, a first probe that corresponds to a first target sequence can he labelled with a first hapten, such as biotin, while a second probe that corresponds to a second target sequence can be labelled with a second hapten, such as DNP. Following exposure of the sample to the probes, the bound probes can he detected by contacting the sample with a first specific binding agent (in this case avidin labelled with a first fluorophore, for example, a first spectrally distinct QUANTUM DOT®, e.g., that emits at 585 mn) and a second specific binding agent (in this case an anti-DNP antibody, or antibody fragment, labelled with a second fluorophore (for example, a second spectrally distinct QUANTUM DOT®, e.g., that emits at 705 mn). Additional probes/binding agent pairs can he added to the multiplex detection scheme using other spectrally distinct fluorophores. Numerous variations of direct, and indirect (one step, two step or more) can he envisioned, all of which are suitable in the context of the disclosed probes and assays.

Probes typically comprise single-stranded nucleic acids of between 10 to 1000 nucleotides in length, for instance of between 10 and 800, more preferably of between 15 and 700, typically of between 20 and 500. Primers typically are shorter single- stranded nucleic acids, of between 10 to 25 nucleotides in length, designed to perfectly or almost perfectly match a nucleic acid of interest, to be amplified. The probes and primers are “specific” to the nucleic acids they hybridize to, i.e. they preferably hybridize under high stringency hybridization conditions (corresponding to the highest melting temperature Tm, e.g., 50 % formamide, 5x or 6x SCC. SCC is a 0.15 M NaCl, 0.015 M Na-citrate).

The nucleic acid primers or probes used in the above amplification and detection method may be assembled as a kit. Such a kit includes consensus primers and molecular probes. A preferred kit also includes the components necessary to determine if amplification has occurred. The kit may also include, for example, PCR buffers and enzymes; positive control sequences, reaction control primers; and instructions for amplifying and detecting the specific sequences.

In a particular embodiment, the methods of the invention comprise the steps of providing total RNAs extracted from cumulus cells and subjecting the RNAs to amplification and hybridization to specific probes, more particularly by means of a quantitative or semi- quantitative RT-PCR.

In another preferred embodiment, the expression level is determined by DNA chip analysis. Such DNA chip or nucleic acid microarray consists of different nucleic acid probes that are chemically attached to a substrate, which can be a microchip, a glass slide or a microsphere-sized bead. A microchip may be constituted of polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, or nitrocellulose. Probes comprise nucleic acids such as cDNAs or oligonucleotides that may be about 10 to about 60 base pairs. To determine the expression level, a sample from a test subject, optionally first subjected to a reverse transcription, is labelled and contacted with the microarray in hybridization conditions, leading to the formation of complexes between target nucleic acids that are complementary to probe sequences attached to the microarray surface. The labelled hybridized complexes are then detected and can be quantified or semi-quantified. Labelling may be achieved by various methods, e.g. by using radioactive or fluorescent labelling. Many variants of the microarray hybridization technology are available to the man skilled in the art (see e.g. the review by Hoheisel, Nature Reviews, Genetics, 2006, 7:200-210).

In another embodiment, the expression level is determined by metabolic imaging (see for example Yamashita T et ah, Hepatology 2014, 60:1674-1685 or Ueno A et ah, Journal of hepatology 2014, 61 :1080-1087).

Expression level of a gene may be expressed as absolute expression level or normalized expression level. Typically, expression levels are normalized by correcting the absolute expression level of a gene by comparing its expression to the expression of a gene that is not a relevant for determining the response of antipsychotic treatment, e.g., a housekeeping gene that is constitutively expressed. Suitable genes for normalization include housekeeping genes such as the actin gen Q ACTB, ribosomal 18S gene, GUSB, PGK1, TFRC, GAPDH, TBP and A IJ . This normalization allows the comparison of the expression level in one sample, e.g., a patient sample, to another sample, or between samples from different sources.

According to the invention, the expression level of the genes listed above may also be measured by measuring the protein expression level encoding by said genes and can be performed by a variety of techniques well known in the art. Typically protein expression level may be measured for example by capillary electrophoresis-mass spectroscopy technique (CE-MS), flow cytometry, mass cytometry or ELISA performed on the sample.

In the present application, the “level of protein” or the “protein level expression” means the quantity or concentration of said protein. In particular embodiment, the protein is expressed at the cell surface for markers whose function is linked to their correct plasma membrane expression or total expression for markers whose function is not limited to membrane expression. In still another embodiment, the “level of protein” means the quantitative measurement of the proteins expression relative to a negative control.

Such methods comprise contacting a sample with a binding partner capable of selectively interacting with proteins present in the sample. The binding partner is generally an antibody that may be polyclonal or monoclonal, preferably monoclonal.

The presence of the protein can be detected using standard electrophoretic and immunodiagnostic techniques, including immunoassays such as competition, direct reaction, or sandwich type assays. Such assays include, but are not limited to, Western blots; agglutination tests; enzyme-labeled and mediated immunoassays, such as ELISAs; biotin/avidin type assays; radioimmunoassays; Immunoelectrophoresis; immunoprecipitation, capillary electrophoresis- mass spectroscopy technique (CE-MS). etc. The reactions generally include revealing labels such as fluorescent, chemioluminescent, radioactive, enzymatic labels or dye molecules, or other methods for detecting the formation of a complex between the antigen and the antibody or antibodies reacted therewith.

The aforementioned assays generally involve separation of unbound protein in a liquid phase from a solid phase support to which antigen-antibody complexes are bound. Solid supports which can be used in the practice of the invention include substrates such as nitrocellulose (e. g., in membrane or microtiter well form); polyvinylchloride (e. g., sheets or microtiter wells); polystyrene latex (e.g., beads or microtiter plates); polyvinylidine fluoride; diazotized paper; nylon membranes; activated beads, magnetically responsive beads, and the like.

More particularly, an ELISA method can be used, wherein the wells of a microtiter plate are coated with a set of antibodies against the proteins to be tested. A sample containing or suspected of containing the marker protein is then added to the coated wells. After a period of incubation sufficient to allow the formation of antibody-antigen complexes, the plate(s) can be washed to remove unbound moieties and a detectably labeled secondary binding molecule is added. The secondary binding molecule is allowed to react with any captured sample marker protein, the plate is washed and the presence of the secondary binding molecule is detected using methods well known in the art.

Particularly, a mass spectrometry-based quantification methods may be used. Mass spectrometry-based quantification methods may be performed using either labelled or unlabelled approaches [DeSouza and Siu, 2012] Mass spectrometry-based quantification methods may be performed using chemical labeling, metabolic labeling or proteolytic labeling. Mass spectrometry-based quantification methods may be performed using mass spectrometry label free quantification, a quantification based on extracted ion chromatogram (EIC) and then profile alignment to determine differential level of polypeptides.

Particularly, a mass spectrometry-based quantification method particularly useful can be the use of targeted mass spectrometry methods as selected reaction monitoring (SRM), multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), data independent acquisition (DIA) and sequential window acquisition of all theoretical mass spectra (SWATH) [Moving target Zeliadt N 2014 The Scientist;Liebler Zimmerman Biochemistry 2013 targeted quantitation pf proteins by mass spectrometry; Gallien Domon 2015 Detection and quantification of proteins in clinical samples using high resolution mass spectrometry. Methods v81 pi 5-23 ; Sajic, Liu, Aebersold, 2015 Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications. Proteomics Clin Appl v9 p 307-21]

Particularly, the mass spectrometry-based quantification method can be the mass cytometry also known as cytometry by time of flight (CYTOF) (Bandura DR, Analytical chemistry, 2009).

Particularly, the mass spectrometry-based quantification is used to do peptide and/or protein profiling can be use with matrix-assisted laser desorption/ionisation time of flight (MALDI-TOF), surface-enhanced laser desorption/ionization time of flight (SELDI-TOF; CLINPROT) and MALDI Biotyper apparatus [Solassol, Jacot, Lhermitte, Boulle, Maudelonde, Mange 2006 Clinical proteomics and mass spectrometry profiling for cancer detection. Journal: Expert Review of Proteomics V3, 13, p311-320 ; FDA K130831]

Methods of the invention may comprise a step consisting of comparing the proteins and fragments concentration in circulating cells with a control value. As used herein, "concentration of protein" refers to an amount or a concentration of a transcription product, for instance the proteins of the invention. Typically, a level of a protein can be expressed as nanograms per microgram of tissue or nanograms per milliliter of a culture medium, for example. Alternatively, relative units can be employed to describe a concentration. In a particular embodiment, "concentration of proteins" may refer to fragments of the proteins of the invention.

In some embodiments, the predetermined reference value is a threshold value or a cut off value. Typically, a "threshold value" or "cut-off 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 the score 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 score 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. S AS, 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 sample 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 expression levels of different genes for each sample contained in the collection provided at step a) so as to calculate the score as described above; 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 the score 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 score 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 patients 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 score has been assessed for 100 samples of 100 patients. The 100 samples are ranked according to the determined score. Sample 1 has the highest score and sample 100 has the lowest score. 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 score corresponding to the boundary between both subsets for which the p value is minimum is considered as the predetermined reference value. Such predetermined reference values of expression level may be determined for any gene defined above

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 calculated score 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 patient may be assessed by comparing values obtained by measuring the calculated score, where values higher than 5 reveal a poor prognosis and values less than 5 reveal a good prognosis. In some embodiments, a patient may be assessed by comparing values obtained by measuring the calculated score 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).

Typically, overexpression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 and low expression of FABP5, IDH3A, PPA1 and ALDH1 correlates with a poor prognosis.

Typically, overexpression of L A BP 5, IDH3A, PPA1 and ALDH1 and low expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 correlates with a good prognosis.

In some embodiments, a score which is a composite of the expression levels of the different genes (i.e PYGL, HK3, G6PD, PFKFB3, SLC2A3, PYGL, HK3, G6PD, PFKFB3 and SLC2A3) is determined and compared to a predetermined reference value wherein a difference between said score and said predetermined reference value is indicative whether the subject will have a long or short survival time. The score can be calculated in any appropriate manner, such as principal components analysis, support vector machines, or other techniques known to the person of ordinary skill in the art having the benefit of the present disclosure.

In some embodiments, the method of the invention comprises the use of a classification algorithm typically selected from unsupervised hierarchical clustering, Linear Discriminant Analysis (LDA), Topological Data Analysis (TDA), Neural Networks, Support Vector Machine (SVM) algorithm and Random Forests algorithm (RF). In some embodiments, the method of the invention comprises the step of determining the patient’s survival time using a classification algorithm.

As used herein, the term "classification algorithm" has its general meaning in the art and refers to classification and regression tree methods and multivariate classification well known in the art such as described in US 8,126,690; WO2008/156617. As used herein, the term “support vector machine (SVM)” is a universal learning machine useful for pattern recognition, whose decision surface is parameterized by a set of support vectors and a set of corresponding weights, refers to a method of not separately processing, but simultaneously processing a plurality of variables. Thus, the support vector machine is useful as a statistical tool for classification. The support vector machine non-linearly maps its n-dimensional input space into a high dimensional feature space, and presents an optimal interface (optimal parting plane) between features. The support vector machine comprises two phases: a training phase and a testing phase. In the training phase, support vectors are produced, while estimation is performed according to a specific rule in the testing phase. In general, SVMs provide a model for use in classifying each of n subjects to two or more disease categories based on one k-dimensional vector (called a k-tuple) of biomarker measurements per subject. An SVM first transforms the k-tuples using a kernel function into a space of equal or higher dimension. The kernel function projects the data into a space where the categories can be better separated using hyperplanes than would be possible in the original data space. To determine the hyperplanes with which to discriminate between categories, a set of support vectors, which lie closest to the boundary between the disease categories, may be chosen. A hyperplane is then selected by known SVM techniques such that the distance between the support vectors and the hyperplane is maximal within the bounds of a cost function that penalizes incorrect predictions. This hyperplane is the one which optimally separates the data in terms of prediction (Vapnik, 1998 Statistical Learning Theory. New York: Wiley). Any new observation is then classified as belonging to any one of the categories of interest, based where the observation lies in relation to the hyperplane. When more than two categories are considered, the process is carried out pairwise for all of the categories and those results combined to create a rule to discriminate between all the categories. As used herein, the term "Random Forests algorithm" or "RF" has its general meaning in the art and refers to classification algorithm such as described in US 8,126,690; WO2008/156617. Random Forest is a decision-tree-based classifier that is constructed using an algorithm originally developed by Leo Breiman (Breiman L, "Random forests," Machine Learning 2001, 45:5-32). The classifier uses a large number of individual decision trees and decides the class by choosing the mode of the classes as determined by the individual trees. The individual trees are constructed using the following algorithm: (1) Assume that the number of cases in the training set is N, and that the number of variables in the classifier is M; (2) Select the number of input variables that will be used to determine the decision at a node of the tree; this number, m should be much less than M; (3) Choose a training set by choosing N samples from the training set with replacement; (4) For each node of the tree randomly select m of the M variables on which to base the decision at that node; (5) Calculate the best split based on these m variables in the training set. In some embodiments, the score is generated by a computer program.

In some embodiments, the method of the present invention comprises a) quantifying the level of a plurality of genes in the sample; b) implementing a classification algorithm on data comprising the quantified plurality of genes so as to obtain an algorithm output; c) determining the survival time from the algorithm output of step b).

Thus, in some embodiment, the expression level of PYGC, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDH1 are implemented in a classification algorithm so as to obtain an algorithm output; wherein the survival time is determined from the algorithm output.

The algorithm of the present invention can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The algorithm can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. To provide for interaction with a user, embodiments of the invention can be implemented on a computer having a display device, e.g., in non-limiting examples, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. Accordingly, in some embodiments, the algorithm can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the invention, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet. The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

In some embodiment, the group of biomarkers as disclosed herein is useful for identifying patients with poor-prognosis, in particular patients with cancer that are likely to relapse and metastasize.

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

Method for diasnosins malignancy tumor Strikingly, when the inventor tested the glycolytic (overexpression of PYGC, HK3, G6PD, PFKFB3 and SLC2A3 genes and low expression FABP5, IDH3A, PPA1 and ALDH1) and respiratory (low expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 and overexpression of LABP5, IDH3A, PPA1 and ALDH1) signatures in RNA extracted from sorted myeloid cells from different tumor samples, they observed that the glycolytic signature correlated with a high mitotic index of tumor cells, and the respiratory with a lower index strongly suggesting that the presence of glycolytic myeloid cells in the tumor correlates with malignancy.

Accordingly, the glycolytic signature could allow the diagnostic of malignancy tumor compared to benign tumor. It could allow to stratify patient with malignancy tumor and those with benign tumor.

Thus, the invention relates to PYGC, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDH1 as biomarkers for patient suffering from malignancy tumor.

In other words, the invention refers to a method for determining malignancy tumor in a patient in need thereof comprising the steps consisting of: i) determining the expression level of PYGC, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDH1 genes, ii) comparing said level expression with a predetermined reference value and iii) concluding that the patient have malignancy tumor when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 genes are higher than the reference value and when the level expression oiFABP5, IDH3A, PPA1 and ALDH1 genes are lower than the reference value.

As used herein, the term malignancy tumor has its general meaning in the art and refers to the tendency of a medical condition, i.e the tumor, to become progressively worse. A malignant tumor contrasts with a non-cancerous benign tumor in that a malignancy is not self- limited in its growth, is capable of invading into adjacent tissues, and may be capable of spreading to distant tissues. A benign tumor has none of those properties.

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

Thus, the invention refers to an in vitro method for determining malignancy tumor in a patient in need thereof comprising the steps consisting of: i) determining the expression level of PYGC, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDH1 genes in a sample obtained from the patient, ii) comparing said level expression with a predetermined reference value and iii) concluding that the patient have malignancy tumor when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 genes are higher than the reference value and when the level expression of FABP5, IDH3A, PPA1 and ALDH1 genes are lower than the reference value.

In some embodiments, the method of the invention comprises the use of a classification algorithm to determine the malignancy tumor.

In some embodiment, the expression level of PYGC, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDH1 are implemented in a classification algorithm so as to obtain an algorithm output wherein the malignancy tumor is determined from the algorithm output.

Method for treatins and monitoring cancer

Accordingly, subject identified with a poor prognosis according to the invention can be administered therapy, for example systematic therapy. Subject identified with a malignancy tumor according to the invention can also be administered therapy, for example systematic therapy.

Thus, the invention relates to a method for treating cancer in a patient in need thereof comprising administering a therapeutically effective amount of anti-cancer therapy when the patient is determined with poor prognosis according to the invention.

Thus, the invention relates to a method for treating cancer in a patient in need thereof comprising administering a therapeutically effective amount of anti-cancer therapy when the patient is determined with malignancy tumor according to the invention.

In some embodiment, the cancer is colorectal cancer, breast cancer, brain cancer, renal carcinoma or glioblastoma.

As used herein, the term "treatment" or "treat" refer to both prophylactic or preventive treatment as well as curative or disease modifying treatment, including treatment of subjects at risk of contracting the disease or suspected to have contracted the disease as well as subjects who are ill or have been diagnosed as suffering from a disease or medical condition, and includes suppression of clinical relapse. The treatment may be administered to a subject having a medical disorder or who ultimately may acquire the disorder, in order to prevent, cure, delay the onset of, reduce the severity of, or ameliorate one or more symptoms of a disorder or recurring disorder, or in order to prolong the survival of a subject beyond that expected in the absence of such treatment. By "therapeutic regimen" is meant the pattern of treatment of an illness, e.g., the pattern of dosing used during therapy. A therapeutic regimen may include an induction regimen and a maintenance regimen. The phrase "induction regimen" or "induction period" refers to a therapeutic regimen (or the portion of a therapeutic regimen) that is used for the initial treatment of a disease. The general goal of an induction regimen is to provide a high level of drug to a subject during the initial period of a treatment regimen. An induction regimen may employ (in part or in whole) a "loading regimen", which may include administering a greater dose of the drug than a physician would employ during a maintenance regimen, administering a drug more frequently than a physician would administer the drug during a maintenance regimen, or both. The phrase "maintenance regimen" or "maintenance period" refers to a therapeutic regimen (or the portion of a therapeutic regimen) that is used for the maintenance of a subject during treatment of an illness, e.g., to keep the subject in remission for long periods of time (months or years). A maintenance regimen may employ continuous therapy (e.g., administering a drug at a regular intervals, e.g., weekly, monthly, yearly, etc.) or intermittent therapy (e.g., interrupted treatment, intermittent treatment, treatment at relapse, or treatment upon achievement of a particular predetermined criteria [e.g., disease manifestation, etc.]).

A “therapeutically effective amount” is intended for a minimal amount of active agent which is necessary to impart therapeutic benefit to a subject. For example, a "therapeutically effective amount" to a subject is such an amount which induces, ameliorates or otherwise causes an improvement in the pathological symptoms, disease progression or physiological conditions associated with or resistance to succumbing to a disorder. It will be understood that the total daily usage of the compounds of the present invention will be decided by the attending physician within the scope of sound medical judgment.

As used herein, the term “anti-cancer therapy” has its general meaning in the art and refers to any compound, natural or synthetic, used for the treatment of cancer.

In a particular embodiment, the classical treatment refers to radiation therapy, antibody therapy or chemotherapy.

As used herein, the term "chemotherapeutic agent" refers to chemical compounds that are effective in inhibiting tumor growth. Examples of chemotherapeutic agents include multkinase inhibitors such as sorafenib and sunitinib, alkylating agents such as thiotepa and cyclosphosphamide; alkyl sulfonates such as busulfan, improsulfan and piposulfan; aziridines such as benzodopa, carboquone, meturedopa, and uredopa; ethylenimines and methylamelamines including altretamine, triethylenemelamine, trietylenephosphoramide, triethylenethiophosphaorarnide and trimethylolomelamine; acetogenins (especially bullatacin and bullatacinone); a carnptothecin (including the synthetic analogue topotecan); bryostatin; cally statin; CC-1065 (including its adozelesin, carzelesin and bizelesin synthetic analogues); cryptophycins (particularly cryptophycin 1 and cryptophycin 8); dolastatin; duocarmycin (including the synthetic analogues, KW-2189 and CBI-TMI); eleutherobin; pancrati statin; a sarcodictyin; spongistatin; nitrogen mustards such as chlorambucil, chlornaphazine, cholophosphamide, estrarnustine, ifosfamide, mechlorethamine, mechlorethamine oxide hydrochloride, melphalan, novembichin, phenesterine, prednimus tine, trofosfamide, uracil mustard; nitrosureas such as carmustine, chlorozotocin, fotemustine, lomustine, nimustine, ranimustine; antibiotics such as the enediyne antibiotics (e.g. calicheamicin, especially calicheamicin (11 and calicheamicin 211, see, e.g., Agnew Chem Inti. Ed. Engl. 33: 183-186 (1994); dynemicin, including dynemicin A; an esperamicin; as well as neocarzinostatin chromophore and related chromoprotein enediyne antiobiotic chromomophores), aclacinomysins, actinomycin, authramycin, azaserine, bleomycins, cactinomycin, carabicin, canninomycin, carzinophilin, chromomycins, dactinomycin, daunorubicin, detorubicin, 6- diazo-5-oxo-L-norleucine, doxorubicin (including morpholino- doxorubicin, cyanomorpholino-doxorubicin, 2-pyrrolino-doxorubicin and deoxydoxorubicin), epirubicin, esorubicin, idanrbicin, marcellomycin, mitomycins, mycophenolic acid, nogalarnycin, olivomycins, peplomycin, potfiromycin, puromycin, quelamycin, rodorubicin, streptomgrin, streptozocin, tubercidin, ubenimex, zinostatin, zorubicin; anti-metabolites such as methotrexate and 5-fluorouracil (5-FU); folic acid analogues such as denopterin, methotrexate, pteropterin, trimetrexate; purine analogs such as fludarabine, 6-mercaptopurine, thiamiprine, thioguanine; pyrimidine analogs such as ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, floxuridine, 5-FU; androgens such as calusterone, dromostanolone propionate, epitiostanol, mepitiostane, testolactone; anti- adrenals such as aminoglutethimide, mitotane, trilostane; folic acid replenisher such as frolinic acid; aceglatone; aldophospharnide glycoside; aminolevulinic acid; amsacrine; bestrabucil; bisantrene; edatraxate; defo famine; demecolcine; diaziquone; elfornithine; elliptinium acetate; an epothilone; etoglucid; gallium nitrate; hydroxyurea; lentinan; lonidamine; maytansinoids such as maytansine and ansamitocins; mitoguazone; mitoxantrone; mopidamol; nitracrine; pento statin; phenamet; pirarubicin; podophyllinic acid; 2-ethylhydrazide; procarbazine; PSK®; razoxane; rhizoxin; sizofiran; spirogennanium; tenuazonic acid; triaziquone; 2, 2', 2"- trichlorotriethylamine; trichothecenes (especially T-2 toxin, verracurin A, roridinA and anguidine); urethan; vindesine; dacarbazine; mannomustine; mitobromtol; mitolactol; pipobroman; gacytosine; arabinoside ("Ara-C"); cyclophosphamide; thiotepa; taxoids, e.g. paclitaxel (TAXOL®, Bristol-Myers Squibb Oncology, Princeton, N.].) and doxetaxel (TAXOTERE®, Rhone-Poulenc Rorer, Antony, France); chlorambucil; gemcitabine; 6- thioguanine; mercaptopurine; methotrexate; platinum analogs such as cisp latin and carbop latin; vinblastine; platinum; etoposide (VP- 16); ifosfamide; mitomycin C; mitoxantrone; vincristine; vinorelbine; navelbine; novantrone; teniposide; daunomycin; aminopterin; xeloda; ibandronate; CPT-1 1 ; topoisomerase inhibitor RFS 2000; difluoromethylornithine (DMFO); retinoic acid; capecitabine; and pharmaceutically acceptable salts, acids or derivatives of any of the above. Also included in this definition are antihormonal agents that act to regulate or inhibit honnone action on tumors such as anti-estrogens including for example tamoxifen, raloxifene, aromatase inhibiting 4(5)-imidazoles, 4-hydroxytamoxifen, trioxifene, keoxifene, LY117018, onapristone, and toremifene (Fareston); and anti-androgens such as flutamide, nilutamide, bicalutamide, leuprolide, and goserelin; and pharmaceutically acceptable salts, acids or derivatives of any of the above.

As used herein, the term “radiation therapy” has its general meaning in the art and refers the treatment of cancer with ionizing radiation. Ionizing radiation deposits energy that injures or destroys cells in the area being treated (the target tissue) by damaging their genetic material, making it impossible for these cells to continue to grow. One type of radiation therapy commonly used involves photons, e.g. X-rays. Depending on the amount of energy they possess, the rays can be used to destroy cancer cells on the surface of or deeper in the body. The higher the energy of the x-ray beam, the deeper the x-rays can go into the target tissue. Linear accelerators and betatrons produce x-rays of increasingly greater energy. The use of machines to focus radiation (such as x-rays) on a cancer site is called external beam radiation therapy. Gamma rays are another form of photons used in radiation therapy. Gamma rays are produced spontaneously as certain elements (such as radium, uranium, and cobalt 60) release radiation as they decompose, or decay. In some embodiments, the radiation therapy is external radiation therapy. Examples of external radiation therapy include, but are not limited to, conventional external beam radiation therapy; three-dimensional conformal radiation therapy (3D-CRT), which delivers shaped beams to closely fit the shape of a tumor from different directions; intensity modulated radiation therapy (IMRT), e.g., helical tomotherapy, which shapes the radiation beams to closely fit the shape of a tumor and also alters the radiation dose according to the shape of the tumor; conformal proton beam radiation therapy; image-guided radiation therapy (IGRT), which combines scanning and radiation technologies to provide real time images of a tumor to guide the radiation treatment; intraoperative radiation therapy (IORT), which delivers radiation directly to a tumor during surgery; stereotactic radiosurgery, which delivers a large, precise radiation dose to a small tumor area in a single session; hyperfractionated radiation therapy, e.g., continuous hyperfractionated accelerated radiation therapy (CHART), in which more than one treatment (fraction) of radiation therapy are given to a subject per day; and hypofractionated radiation therapy, in which larger doses of radiation therapy per fraction is given but fewer fractions.

As used herein, the term "immune checkpoint inhibitor" refers to molecules that totally or partially reduce, inhibit, interfere with or modulate one or more immune checkpoint proteins.

As used herein, the term "immune checkpoint protein" has its general meaning in the art and refers to a molecule that is expressed by T cells in that either turn up a signal (stimulatory checkpoint molecules) or turn down a signal (inhibitory checkpoint molecules).

Examples of stimulatory checkpoint include CD27 CD28 CD40, CD 122, CD 137, 0X40, GITR, and ICOS. Examples of inhibitory checkpoint molecules include A2AR, B7-H3, B7-H4, BTLA, CTLA-4, CD277, IDO, KIR, PD-1, PD-L1, LAG-3, TIM-3 and VISTA.

The compounds used in connection with the treatment methods of the present invention are administered and dosed in accordance with good medical practice, taking into account the clinical condition of the individual subject, the site and method of administration, scheduling of administration, patient age, sex, body weight and other factors known to medical practitioners. The pharmaceutically “effective amount” for purposes herein is thus determined by such considerations as are known in the art. The amount must be effective to achieve improvement including, but not limited to, improved survival rate or more rapid recovery, or improvement or elimination of symptoms and other indicators as are selected as appropriate measures by those skilled in the art.

The invention relates to a therapeutic composition comprising a chemotherapeutic compound according to the invention for use in the treatment of cancer in a patient with a bad prognosis as described above.

Any therapeutic agent of the invention may be combined with pharmaceutically acceptable excipients, and optionally sustained-release matrices, such as biodegradable polymers, to form therapeutic compositions.

"Pharmaceutically" or "pharmaceutically acceptable" refers to molecular entities and compositions that do not produce an adverse, allergic or other untoward reaction when administered to a mammal, especially a human, as appropriate. A pharmaceutically acceptable carrier or excipient refers to a non-toxic solid, semi-solid or liquid filler, diluent, encapsulating material or formulation auxiliary of any type. The form of the pharmaceutical compositions, the route of administration, the dosage and the regimen naturally depend upon the condition to be treated, the severity of the illness, the age, weight, and sex of the patient, etc. The pharmaceutical compositions of the invention can be formulated for a topical, oral, intranasal, parenteral, intraocular, intravenous, intramuscular or subcutaneous administration and the like. Particularly, the pharmaceutical compositions contain vehicles which are pharmaceutically acceptable for a formulation capable of being injected. These may be in particular isotonic, sterile, saline solutions (monosodium or disodium phosphate, sodium, potassium, calcium or magnesium chloride and the like or mixtures of such salts), or dry, especially freeze-dried compositions which upon addition, depending on the case, of sterilized water or physiological saline, permit the constitution of injectable solutions. The doses used for the administration can be adapted as a function of various parameters, and in particular as a function of the mode of administration used, of the relevant pathology, or alternatively of the desired duration of treatment. In addition, other pharmaceutically acceptable forms include, e.g. tablets or other solids for oral administration; time release capsules; and any other form currently can be used.

In some embodiments, the method of the present invention can be used to identify patients in need of frequent follow-up by a physician or clinician to monitor cancer progression.

This invention also provides a method for selecting a therapeutic regimen or determining if a certain therapeutic regimen is more appropriate for a patient identified as having a poor prognosis as identified by the methods as disclosed herein.

For example, an aggressive anti-cancer therapeutic regime can be perused in which a patient having a poor prognosis, where the patient is administered a therapeutically effective amount of an anti-cancer agent to treat the cancer. In some embodiments, a patient can be monitored for cancer using the methods and biomarkers as disclosed herein, and if on a first (i.e. initial) testing the patient is identified as having a poor prognosis, the patient can be administered an anti-cancer therapy, and on a second (i.e. follow-up testing), the patient is identified as having a good prognosis, the patient can be administered an anti-cancer therapy at a maintenance dose. The method of the present invention is particularly suited to determining which patients will be responsive or experience a positive treatment outcome to a treatment.

Thus, in another aspect, the invention relates to an in vitro method for monitoring cancer comprising the steps of i) determining the level of PYGL, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 andALDHl in a sample obtained from the patient at a first specific time of the cancer, ii) determining the level PYGL, HK3, G6PD, PFKFB3, SLC2A3 , FABP5, IDH3A, PPA1 andALDHl in a sample obtained from the subject at a second specific time of the cancer, iii) comparing the level determined at step i) with the level determined at step ii) and iv) concluding that the cancer has evolved in worse manner when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 gene is higher than the reference value and when the level expression of FABP5, IDH3A, PPA1 andALDHl gene is lower than the reference value.

Thus, in another aspect, the invention relates to an in vitro method for monitoring anti cancer therapy comprising the steps of i) determining the level oiPYGL, HK3, G6PD, PFKFB3, SLC2A3 , FABP5, 1DH3A, PPA1 andALDHl in a sample obtained from the patient before the therapy, ii) determining the level PYGL, HK3, G6PD, PFKFB3, SLC2A3 , FABP5, IDH3A, PPA1 and ALDH1 in a sample obtained from the subject after the therapy, iii) comparing the level determined at step i) with the level determined at step ii) and iv) concluding that the therapy is not efficient when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 gene is higher than the reference value and when the level expression of L A BP 5, IDH3A, PPA1 andALDHl gene is lower than the reference value.

In other words, the invention relates to an in vitro method for monitoring anti-cancer therapy comprising the steps of i) determining the level of PYGL, HK3, G6PD, PFKFB3, SLC2A3 , FABP5, IDH3A, PPA1 andALDHl in a sample obtained from the patient before the therapy, ii) determining the level PYGL, HK3, G6PD, PFKFB3, SLC2A3 , FABP5, IDH3A, PPA1 and ALDH1 in a sample obtained from the subject after the therapy, iii) comparing the level determined at step i) with the level determined at step ii) and iv) concluding that the therapy is efficient when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 gene is lower than the reference value and when the level expression of FABP5, IDH3A, PPA1 andALDHl gene is higher than the reference value.

According to the invention, an efficient therapy (i.e positive effect of the therapy) indicates that the therapy reduce or prevent the severity of cancer (i.e proliferation or growth of the tumor), or ameliorate one or more symptoms of cancer, or in order to prolong the survival of a subject beyond that expected in the absence of such treatment.

Kit of the invention

The present invention includes a kit for performing the method of the present invention comprising means for determining the level of PYGL, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and/or ALDH1 expression in a sample.

Thus, a further object of the invention is a kit suitable for predicting survival time in patients suffering from cancer comprising:

At least a means for determining the expression level of PYGL, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and/or ALDH1 expression in a sample obtained from a subject, Instructions for use.

As used herein, the term “means for determining” denotes all physical means which are able to bind to the different markers. For example, means for determining the markers may be an antibody against a marker coupling with a signalling system. Typically the kit may include primers, probes, an antibody, or a set of antibodies. In a particular embodiment, the antibody or set of antibodies are labelled. The kit may also contain other suitably packaged reagents and materials needed for the particular detection protocol, including solid-phase matrices, if applicable, and standards. 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. SCENITH based metabolic gene signatures predict patient survival and correlated with malignancy. Three cohorts of tumor patients (A. colorectal cancer CRC, B. renal carcinoma RC and C. glioblastoma GBM) were stratified by their expression of SCENITH metabolic gene signatures Glycolytic (1, top 30%)), all (2) and Respiratory (3, top 30%). D. These Glycolytic and Respiratory signatures were tested in RNA extracted from sorted myeloid cells from different tumor samples (n=520) to observe their correlation with mitotic index.

RNA-seg and SCENITH.

Table 2: Expression of glycolytic and respiratory gene signatures in all cells extractec from the tumor. Table summarizing the results obtained by SCENITH and scRNA-seq in tumor and juxta-tumoral myeloid cells. Clusters of myeloid cells were identified in the renal carcinoma and juxta-tumoral tissue by single cell RNA-seq.

EXAMPLE:

Material & Methods Mice experiments

Wild type C57BL/6 mice were purchased from Jackson Laboratories and maintained in the animal facility of CIML under specific pathogen-free conditions. This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals the French Ministry of Agriculture and of the European Union. Animals were housed in the CIML animal facilities accredited by the French Ministry of Agriculture to perform experiments on alive mice. All animal experiments were approved by Direction Departementale des Services Veterinaires des Bouches du Rhone (Approval number A13-543). All efforts were made to minimize animal suffering.

To obtain blood, six to eight week male mice where euthanized by CO2 and blood was collected by cardiac puncture in Heparin tubes. To obtain splenocytes, six to eight weeks old wild type C57BL/6J male mice were euthanized by cervical dislocation and splenectomized. Mouse splenocytes were cultured in DMEM containing 5% of Fetal Calf Serum (FCS) and 50 mM of 2-Mercaptoethanol (Mouse cells culture media, MCCM) at 37 °C 5% of CO2. Single cells suspentions from the spleens were generated and cultured in MCCM.

For in vitro studies, FLT3L BM-derived dendritic cells (FLT3L-bmDCs) were differentiated in vitro from the bone marrow of 6-8 week/old from the same male mice. Bone marrow were kept from femur and tibia and plate at 1,5.10⁶ cells/mL with 4mL/well in 6-well plates in RPMI (GIBCO), 10% of Fetal Calf Serum (FCS) and 50 mM of 2-Mercaptoethanol (Mouse cells culture media, MCCM) during 6 days at 37 °C 5% of CO2 culture. Differeciation has been done by adding directly FLT3L in culture at day 0.

Human experiments

The renal carcinoma patient enrolled in this study provided written and informed consent to tissue collection under a University of California, San Francisco (UCSF) institutional review board (IRB)-approved protocol (UCSF Committee on Human Research (CHR) no. 13- 12246). The meningioma and brain meastasis patients enroled in this study provided written and informed consent in accordance with institutional, national guidelines and the Declaration of Helsinki. This protocol was approved by institutional review board (AP-HM CRB-TBM tumor bank: authorization number AC-2018-31053, B-0033-00097). The identity, age (between 30 and 60) and sex of the adult cancer patients and healthy donors was kept confidential following the ethics comitee guidelines.

Mononuclear cell enriched from blood of healthy donors was submitted to Ficoll-paque plus (PBL Biomedical Laboratories). PBMCs and Whole blood were cultured in the absense (non stimulated) or in the presence of LPS for 4hs. Immune cell stimulations were performed in the absence (Control) or presence of 0,1 pg/ml of extrapure Lipopolysacharide (Invivogen), 10 pg/ml Poly I:C (Invivogen) or PMA (5 ng/ml; Sigma) and ionomycin (500 ng/ml) over night for T cell stimulations and 4 hours for Dendritic cells. T cells from different human donors (PI, P2, P3) were isolated using the RosetteSep™ negative isolation method and activated (using BD Human T cell activator beads coated with anti-CD3 and anti-CD28) or not.

Cell lines

Mouse Embryonic Fibroblast (MEF) cells derived from C57BL/6 background male and female gender mixed were used. For experiments, MEFs were cultured in DMEM culture media supplemented with 10% FCS at 37 °C 5% of CO2 culture.

METHOD DETAILS

ATP measurement

2.10⁴ MEFs were seeded in lOOul of 5% FCS DMEM culture media ON in opaque 96 well plates. Cells were incubated with the inhibitors. After, lOOul of Cell titer-Glo luminiscence ATP reconstituted buffer and substrate (Promega) was added to each well and Luminiscence was measured after 10 minutes following manufacturer instructions. A standard curve with ATP was performed using the same kit and following manufacturer instructions.

Metabolic flux analysis (Seahorse®)

OCR and ECAR were measured with the XF24 Extracellular Flux Analyzer (Seahorse Bioscience). 4.10⁵ cells with aCD3/aCD28 beads or not, were placed in triplicates in XF medium (nonbuffered Dulbecco’s modified Eagle’s medium containing 2.5 mM glucose, 2 mM L-glutamine, and 1 mM sodium pyruvate) and monitored 25 min under basal conditions and in response to lOmM Glucose, 1 mM oligomycin, 100 mM 2-Deoxy-Glucose. Glycolytic capacity was measured by the difference between ECAR level after add oligomycin and before add glucose. OCR, ECAR and SRC parameters was analyzed and extract from Agilent Seahorse Wave Desktop software. Glycolytic capacity was obtained by the difference between ECAR level after add Oligomycin and before add Glucose.

SCENITH

Fifty microliters of blood were seeded in 96 well plates for studying blood cells metabolism. Alternatively cells were plated at 1.10⁶ cells/ml, 0,5 ml/well in 48-well plates. Experimental duplicates/triplicates were performed in all conditions. After differentiation, activation or harvesting of human of cells, wells were treated during 30-45 minutes with Control, 2-Deoxy-D-Glucose (DG, final concentration lOOmM), Oligomycin (Oligo, final concentration ImM), or a sequential combination of the drugs at the final concentrations before mentioned. As negative control, the translation initiation inhibitor Harringtonine was added 15 minutes before addition of Puromycin (Harringtonine, 2 pg/ml). Puromycin (Puro, final concentration 10 pg/ml) is added during the last 15-45 minutes of the metabolic inhibitors treatment. After puro treatment, cells were washed in cold PBS and stained with a combination of Fc receptors blockade and fluorescent cell viability marker, then primary conjugated antibodies against different surface markers during 25 minutes at 4°C in PBS IX 5% FCS, 2mM EDTA (FACS wash buffer). After washing, cells were fixed and permeabilized using FOXP3 fixation and permeabilization buffer (Thermofisher eBioscience™) following manufacturer instructions. Intracellular staining of Puromycin using our in house produced fluorescently labeled anti-Puro monoclonal antibody with Alexa Fluor 647 to obtain up to 10 times better signal to noise ratio than commercially available monoclonal antibodies was performed by incubating cells during 1 hour at 4°C diluted in permeabilization buffer. For SCENITH troubleshooting see Table 2 in additional resources.

Processing of human and mouse solid tumors SCENITH 0,2-0, 4 grams of solid tumor tissue was partially dissociated using chirurgical scissors or tissue chopper (Mcllwain Tissue Chopper® Standard plate) to generate “tumor explant suspention”. Tissue explants suspention, containing tissue cubes of approximately 400pm of cross section, were put in suspention in complete RPMI media and incubated directly with control or metabolic inhibitors, and with Puromycin following the SCENITH protocol. Next, tumor explants were dissociated using Tissue Liberase and DNAsel with the help of a Gentle Macs (Miltenyi biotec) following manufacturers instructions. Cell suspentions were washed, counted and 2-5.10⁶ total cells were seed in triplicates before proceeding with lived dead and FC block staining. Next, cells were stained for surface makers, fixed and permeabilized with FOXP3 fixation and permeabilization kit and stained for nuclear and cytoplasmic markers as mentioned above.

Human single cell RNA-sequencing

Live CD3-CD19/20-CD56- cells were sorted from renal carcinoma tumor and juxta tumoral tissue using a BD FACSAria Fusion. After sorting, cells were pelleted and resuspended at 1.10³ cell s/ pi in 0.04%BSA/PBA and loaded onto the Chromium Controller (10X Genomics). Samples were processed for single-cell encapsulation and cDNA library generation using the Chromium Single Cell 3’ v2 Reagent Kits (10X Genomics). The library was subsequently sequenced on an Illumina HiSeq 4000 (Illumina). The human blood myeloid cells single cell RNA-seq data analyzed was from Vilani et al 2017.

Single cell data processing

Sequencing data was processed using 10X Genomics Cell Ranger VI.2 pipeline. The Cell Ranger subroutine mkfastq converted raw, Illumina bcl files to fastqs which were then passed to Cell Ranger’s count, which aligned all reads using the aligner STAR (Dobin et al., 2013)ref against GRCh38 genomes for human cells. After filtering reads with redundant unique molecular identifiers (UMI), count generated a final gene-cellular barcode matrix. Both mkfastq and count were run with default parameters.

Cellular Identification and Clustering of scRNA-seq data

For each sample, the gene - barcode matrix was passed to the R (v. 3.6.0) software package Seurat (Satija et al., 2015) (http://satijalab.org/seurat) (v3.1.1) for all downstream analyses. We then filtered on cells that expressed a minimum of 200 genes and required that all genes be expressed in at least 3 cells. We also removed cells that contained > 5% reads associated with cell cycle genes (Kowalczyk et al., 2015; Macosko et al, 2015). Count data was then log2 transformed and scaled using each cell’s proportion of cell cycle genes as a nuisance factor (implemented in Seurat’s ScaleData function) to correct for any remaining cell cycle effect in downstream clustering and differential expression analyses. For each sample, principal component (PC) analysis was performed on a set of highly variable genes defined by Seurat’s FindVariableGenes function. Genes associated with the resulting PCs (chosen by visual inspection of scree plots) were then used for graph-based cluster identification and subsequent dimensionality reduction using t-distributed stochastic neighbor embedding (tSNE). Cluster-based marker identification and differential expression were performed using Seurat’s FindAllMarkers for all between-cluster comparisons.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analysis

Statistical analysis was performed with GraphPad Prism software. When several conditions were to compare, we performed a one-way ANOVA, followed by Tukey range test to assess the significance among pairs of conditions. When only two conditions were to test, we performed Student’s t-test or Welch t-test, according the validity of homoscedasticity hypothesis (* P<0.05, ** P<0.01, *** P<0.005).

Quantification and meaning of SCENITH derived parameters

To quantify the different energetic metabolism parameters that constitute the metabolic profile of a cell, such as pathways dependency, we used simple algorithms that quantifiy the relative impact of inhibiting a given pathway compared to a complete inhibition of ATP synthesis (data not shown). While SCENITH allow the use of any combination of metabolic or signalling inhibitors, herein we focused on inhibitors of glycolysis and of mitochondrial respiration to derive metabolic parameters. The percentual of glucose dependence (Glue. Dep.) quantifies how much the translation levels are dependent on glucose oxidation. Glue. Dep. is calculated as the difference between PS levels in 2-Deoxy-D-Glucose (DG) treated cells compared to control (Co), divided by the difference in PS upon complete inhibition of ATP synthesis (DG, first and then Oligomycin A, combined; treatment DGO) compared to control cells (data not shown). In a similar fashion, percentual mitochondrial dependence (Mitoc. Dep) quantifies how much translation is dependent on oxydative phosphorylation. Mitoc. Dep. is defined as the difference in PS levels in Oligomycin A (“O”, mitochondrial inhibitor) treated cells compared to control relative to the decreased in PS levels upon full inhibition of ATP synthesis inhibition (treatment DGO) also compared to control cells (data not shown). Two additional derived parameters, “Glycolytic capacity” (Glyc. Cap.) and “Fatty acids and amino acids oxidation capacity” (FAAO Cap.) were also calculated. Glycolytic capacity is defined as the maximum capacity to sustain protein synthesis levels when mitochondrial OXPHOS is inhibited (data not shown). Converserly, FAAO Capacity is defined as the capacity to use fatty acids and aminoacids as sources for ATP production in the mitochondria when glucose oxidation is inhibited (Glycolysis and Glucose derived Acetyl-CoA by OXPHOS) (data not shown). While the total level of translation correlates with the global metabolic activity of the cells, the dependency parameters underline essential cellular pathways that cannot be compensated, while “capacity”; as the inverse of dependency, shows the maximun compensatory capacity of a subpopulation of cells to exploit alternative pathway/s when a particular one is inhibited (data not shown).

For standard deviation calculation of SCENITH, we followed the propagation of error that is required when the means of means are used into a formula:

For error calculation:

Co- GeoMFI of anti-Puromycin-Fluorochrome upon Control treatment DG- GeoMFI of anti-Puromycin-Fluorochrome upon 2-Deoxy-D-Glucose treatment 0- GeoMFI of anti-Puromycin-Fluorochrome upon Oiigomycin A treatment DGO- GeoMFI of anti-Puromycin-Fluorochrome upon DG+O treatment

100( Co - DG )

Glucose Dependence (Glue. Dep.) —

(Co - DGO )

Statistical analysis

Statistical analysis was performed with GraphPad Prism software. When several conditions were to compare, we performed a one-way ANOVA, followed by Tukey range test to assess the significance among pairs of conditions. When only two conditions were to test, we performed Student’s t-test or Welch t-test, according the validity of homoscedasticity hypothesis (* P<0.05, ** P<0.01, *** P<0.005).

RESULTS

Profiling the metabolic state of human tumor-associated myeloid cells.

Immunotherapies are a game changer in oncology yet only a fraction of patients show complete immune-mediated rejection of the tumor. The variations observed in patients responses to treatment have created a strong need for understanding the functional state of tumor-associated immune cells (immunoprofiling) (5). We thus tested whether SCENITH, the method we previously designed which rapidly and efficiently measures the protein synthesis level in a population of cells upon inhibition of the different energy producing pathways, could be used for paralleled phenotypic and metabolic profiling of human tumor samples and what this would reveal about the heterogeity of immune cell subsets comparing tumors of diverse orgins, notably comparing a tumor with tumor-free adjacent tissue. We thus performed SCENITH using PMBCs from healthy donors, using two cancers from the same tissue (explanted meningioma, brain metastasis (originated from a breast cancer)), and comparing renal carcinoma tumors and renal juxtatumoral tissue. In the case of renal carcinoma and juxta- tumoral tissue, both SCENITH and single cell RNA seq analysis were performed in parallel on the same sample. While we focused on the tissue settings, in our results heatmap, we included the EM profile of when the same immune cells where identified in the blood from healthy donors.

We observed 8 different myeloid populations in meningioma and 6 different subset in renal carcinoma (data not shown), that were all profiled by SCENITH. Upon clustering of the different cell subsets based on EM profiling, two groups emerged, a “Glycolytic cluster” and a “Respiratory cluster” (data not shown). Monol and Neutrophils displayed glycolytic metabolism profiles in all blood samples and tumors tested (data not shown). In contrast, Mono2, DC1 and DC2 showed relatively high glycolytic capacity when isolated from kidney tumor and juxtatumoral tissues, while these subsets showed high respiratory metabolism profile in the two brain tumors. Conversely, tumor-associated macrophages (TAM), showed high mitochondrial dependence, while juxta-tumoral macrophages displayed high glycolytic capacity (data not shown), suggesting that tumor microenvironment modifies TAM EM. The decrease of glycolytic capacity in TAM as compared to juxta-tumoral macrophages was previously associated with increased immunosuppression in the tumor environment, tumor progression from human and mouse (40-55% vs. 80-100%, respectively), an important difference was observed in the glucose dependency of mouse versus human pDCs (15% vs 60%). LPS treatment induced a clear change in metabolic profile towards lower mitochondrial dependence of DC1 and DC2, where only DC2 increased the global level of protein synthesis. Considering that the comparison is between human blood and mouse bone marrow, our results indicate that the metabolic profile of human blood DCs and in-vitro derived mouse DCs is in relatively good agreement. These results show that SCENITH allows to identify a cluster of different cell populations with similar EM profile and we show that the metabolic profile in DCs varies according to their state of activation and maturation.

Linking scRNA-seq and functional energetic metabolism profile in tumor-associated myeloid cells.

As these results were not previously observed and the SCENITH method is new, we also sought to extend and validate the findings, by processing in parallel the same sample using single-cell RNA-seq. We therefore aimed to compare in each population, the functional EM profile obtained by SCENITH and metabolic gene expression profile obtained by mRNA sequencing. To do this, we first identified specific glyolytic and respiratory gene signature that correlate in with functional metabolism in different myeloid cells in the blood (Table 1). Then we aimed to test the expression (mRNA levels) of these glycolytic and respiratory metabolic gene signatures in the different myeloid populations of the tumor. To do so, sorted myeloid cells (CD45+Lin-HLA-DR+) from the renal carcinoma and its juxtatumoral tissue and performed single cell RNA-seq using the 10X Genomics Chromium platform paired with deep sequencing (data not shown). Analysis of 12,801 cells for the tumor and 2,080 for the juxta tumoral tissue yielded 6 and 5 high quality population clusters respectively. To rigourously identify the myeloid populations we checked the expression of characteristic signatures of these populations33 to establish cellular identities in the tSNE representations. This process allowed us to identify both Monol, Mono2, and DC clusters (data not shown). We focused on 5 monocytes and macrophages (clusters expressing MAFB and/or CSF1R) that we monitored by flow cytometry and were present both in tumor and juxta-tumoral tissue (data not shown By checking the expression of classical markers (i.e FCGR3A/CD16 and CD14, we confirmed that clusters 0 and 1 represent CD14+CD16- classical monocytes. Cluster 2 represents CD 14- CD16+ non classical monocytes (Mono2), while co-expression of CD 14 and CD 16 for the clusters 3 and 4, suggest macrophage-like phenotype. In order to confirmed cell type identities in unsupervised manner we then performed differential expression (DE) analysis for each of the myeloid cluster versus all the other cluster identified in the tumor bed and generated heatmaps for the top 5 most differentially expressed genes in the tumor (data not shown) and the juxta tumoral tissue (data not shown). In addition to highlighting key genes that contributed to the unbiased segregation of these populations in both tissue, we confirmed the high expression of macrophage specific genes by cluster 3 and 4, such as APOE, C1QC and RGS1. We next overlayed on the t-SNE plots our two EM gene signatures. In strong agreement with our SCENITH data by FACS, monocytes clusters (0, 1 ,2) presented an enrichement in glycolytic signature both in tumor and juxta tumoral tissue. However, and still in agreement with SCENITH functional data, macrophages (cluster 3) showed high expression of the respiratory signature in the tumor while this was not detectable in juxta tumoral tissue. (Table 2). As observed for the monocytes dendritic cells presented an enrichment in glycolytic signature both in tumor and juxta tumoral tissue (data not shown).

We then performed differential expression (DE) analysis for each of the myeloid cluster versus all the other cluster identified in the tumor bed and generated heatmaps for the top 5 most differentially expressed genes (data not shown). In addition to highlighting key genes that contributed to the unbiased segregation of these populations, we confirmed the high expression of macrophage specific genes by cluster 3 and 4, such as APOE, C1QC and RGS1. We used the same gene list to generate a heatmap corresponding to the myeloid cells present in juxta tumoral tissue (data not shown) and confirmed the existence of equivalent cell populations at this site in a unbiaised way. We next overlayed on the t-SNE plots our two EM gene signatures.

Altogether, those results indicate that tumor micro environment specifically modify macrophages metabolism profile functionally and at the transcriptional level. Therefore, single cell RNA sequencing analysis confirmed results obtained by performing SCENITH on all the different myeloid cell subsets identified (data not shown). Moreover, performing SCENITH allowed us to identify a functional gene signature identified on myeloid cells sorted from PBMC (data not shown) that can be extended to myeloid cell sorted from tissue and tumors. Therefore, combinig SCENITH profiling and single cell RNA sequencing can be used to profile energetic metabolism of a variety of cell type and tissues. Metabolic gene signatures of human pan-tumor associated myeloid cells correlate with patient survival and cancer cell mitotic index.

By crossing i) metabolic genes expression from single cell RNA-seq data of myeloid cell subsets, and ii) their functional metabolism by SCENITH; glycolytic and respiratory metabolic gene signatures were identified. To test whether the expression of these signatures predict survival, we stratified patients according to the level of expression of metabolic signatures in total RNA extracted from tumor samples. We found that in different human tumors, the glycolytic signature was associated with significantly reduced patient survival (Fig. 1 A), while a respiratory gene signature correlated with increased survival.

Strikingly, when we tested these signatures in RNA extracted from sorted myeloid cells from different tumor samples (n=520) we observed that the glycolytic signature correlated with a high mitotic index of tumor cells, and the respiratory with lower index (Fig. IB). This result strongly suggests that the presence of glycolytic myeloid cells in the tumor correlates with malignancy.

REFERENCES:

Throughout this application, various references describe the state of the art to which this invention pertains. The disclosures of these references are hereby incorporated by reference into the present disclosure.

1. Wallace et al., Mitochondrial Energetics and Therapeutics. Annu Rev Pathol. 5:297-348 (2010).

2. Connolly et al, Single-cell Imaging of Bioenergetic Responses to Neuronal Excitotoxicity and Oxygen and Glucose Deprivation. JNeurosci. 34(31): 10192-205 (2014).

3. Ganeshan and Chawla, Metabolic Regulation of Immune Responses. Annu Rev Immunol. 32:609-34 (2014).

4. Maclver et al., Metabolic Regulation of T Lymphocytes. Annu Rev Immunol. 31:259-83 (2013).

5. Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 313,1960-1964 (2006).

Claims

1. A method for predicting survival time in patients suffering from cancer comprising the steps consisting of i) determining the expression level of at least one gene in a sample obtained from the patient wherein said gene is selected from the group consisting of PYGL, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDH1 , ii) comparing said level expression with a predetermined reference value and iii) providing a poor prognosis of the survival time when the level expression is significantly different than the reference value.

2. The method for predicting survival time according to claim 1, comprising the steps consisting of i) determining the expression level PYGL, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDHPm a sample obtained from the patient, ii) comparing said level expression with a predetermined reference value and iii) providing a poor prognosis of the survival time when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 gene is higher than the reference value and when the level expression of FABP5, IDH3A, PPA1 and A L1)H I gene is lower than the reference value.

3. The method for predicting survival time according to claims 1 or 2, wherein the cancer is colorectal cancer, breast cancer, brain cancer, renal carcinoma or glioblastoma.

4. The method for predicting survival time according to claims 1 to 3, wherein a classification algorithm is used to determine the patient’s survival time.

5. A method for treating cancer in a patient in need thereof comprising administering a therapeutically effective amount of anti-cancer therapy when the patient is determined with poor prognosis according to the claims 1 to 4.

6. A method for determining malignancy tumor in a patient in need thereof comprising the steps consisting of: i) determining the expression level of PYGC, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and ALDH1 genes, ii) comparing said level expression with a predetermined reference value and iii) concluding that the patient have malignancy tumor when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 genes are higher than the reference value and when the level expression of FABP5, IDH3A, PPA1 and ALDH1 genes are lower than the reference value.

7. The method for determining malignancy tumor according to claim 6, wherein a classification algorithm is used to determine the malignancy tumor.

8. A method for treating cancer in a patient in need thereof comprising administering a therapeutically effective amount of anti-cancer therapy when the patient is determined with malignancy tumor according to claim 6 or 7.

9. An in vitro method for monitoring anti-cancer therapy comprising the steps of i) determining the level of PYGL, HK3, G6PD, PFKFB3, SLC2A3 , FABP5, IDH3A, PPA1 and ALDH1 in a sample obtained from the patient before the therapy, ii) determining the level PYGL, HK3, G6PD, PFKFB3, SLC2A3 , FABP5, IDH3A, PPA1 and ALDH1 in a sample obtained from the subject after the therapy, iii) comparing the level determined at step i) with the level determined at step ii), and iv) concluding that the therapy is not efficient when the level expression of PYGL, HK3, G6PD, PFKFB3 and SLC2A3 gene is higher than the reference value and when the level expression of FABP5, IDH3A, PPA1 and ALDH1 gene is lower than the reference value.

10. A kit suitable for predicting survival time in patients suffering from cancer comprising:

At least a means for determining the expression level of PYGL, HK3, G6PD, PFKFB3, SLC2A3, FABP5, IDH3A, PPA1 and/or ALDH1 expression in a sample obtained from a subject,

Instructions for use.