WO2016036705A1 - Panneaux de glycanes constituant des biomarqueurs de tissus de tumeur spécifiques - Google Patents

Panneaux de glycanes constituant des biomarqueurs de tissus de tumeur spécifiques Download PDF

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WO2016036705A1
WO2016036705A1 PCT/US2015/047889 US2015047889W WO2016036705A1 WO 2016036705 A1 WO2016036705 A1 WO 2016036705A1 US 2015047889 W US2015047889 W US 2015047889W WO 2016036705 A1 WO2016036705 A1 WO 2016036705A1
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glycan
tissue
glycans
cancer
tumor
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PCT/US2015/047889
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Richard R. Drake
Thomas W. POWERS
Benjamin A. NEELY
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Musc Foundation For Research Development
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • G01N33/6851—Methods of protein analysis involving laser desorption ionisation mass spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2400/00—Assays, e.g. immunoassays or enzyme assays, involving carbohydrates
    • G01N2400/10—Polysaccharides, i.e. having more than five saccharide radicals attached to each other by glycosidic linkages; Derivatives thereof, e.g. ethers, esters

Definitions

  • Tissues obtained from surgeries or diagnostic procedures are most commonly preserved by fixation in formalin and processed as paraffin-embedded tissue blocks.
  • the embedding process preserves the cellular morphology and allows tissues to be stored at room temperature, causing formalin- fixed paraffin-embedded (FFPE) fixation to be used by many tissue banks and biorepositories (Thompson et al, Proteomics Clin Appl, 2013, 7(3-4):241-51; Craven et al, Proteomics Clin Appl, 2013, 7(3-4):273-82).
  • FFPE tissues are particularly attractive because they are archived for years and are much more widely available than cryopreserved tissue.
  • FFPE tissues are a rich source of samples for biomarker discover and validation in retrospective studies. While the fixation method has many benefits, the formalin treatment results in the formation of methylene bridges between the amino acids of the proteins, complicating further analysis by mass spectrometry. There has been continued progress in improving extraction methods of trypsin digested peptides from FFPE tissues in recent years, in parallel with improved high resolution sequencing analysis of peptides by mass spectrometry (Magdeldin et al, Proteomics, 12: 1045-1058; Wisniewski et al, Proteomics Clin Appl, 2013, 7(3-4):225-33).
  • TMA tissue microarray
  • glycosylation of proteins are post-translational modifications most commonly involving either N-linked addition to asparagine residues or O-linked additions to serine or threonine residues.
  • Current approaches to evaluate glycosylation changes generally involve bulk extraction of glycans and glycoproteins from tumor tissues for analysis by mass spectrometry or antibody array platforms, however, this disrupts tissue architecture and distribution of the analytes.
  • Broad affinity carbohydrate binding lectins and a small number of glycan antigen antibodies can be used to target glycan structural classes in tissues, but not individual glycan species.
  • the present invention provides methods and compositions for the profiling of glycans in a biological sample.
  • the method includes the generation of panels of multiple glycans associated with a disease state.
  • the invention further relates to the use of glycan panels for the diagnosis and screening of disease states, particularly in the field of cancer biology.
  • the invention relates to a method of spatially profiling glycans on a tissue section.
  • the method comprises the steps of: (a) heating the tissue section in antigen retrieval buffer; (b) digesting in situ the proteins in the tissue section; (c) depositing a matrix solution onto the tissue section; (d) detecting released glycan ions by mass spectrometry; (e) deconvoluting the mass spectrometry data; (f) associating individual peaks with glycans based on mass accuracy; and (g) viewing identified glycans on imaging software to assess intensity distribution of ions.
  • the glycans are N-linked glycans.
  • the tissue section is a formalin fixed, paraffin embedded tissue section.
  • the proteins are digested enzymatically with PNGaseF.
  • the matrix solution is a-cyano-4-hydroxycinnamic acid.
  • the mass spectrometry is mass spectrometry is Fourier transform ion cyclotron resonance (FTICR) mass spectrometry.
  • the mass spectrometry is matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS).
  • the invention in another aspect, relates to a method of processing data collected from a tissue microarray section comprising a plurality of tissue microarray cores.
  • the method comprises the steps of: (a) heating the tissue microarray section in antigen retrieval buffer and washing in xylene; (b) digesting in situ the proteins in the tissue microarray section; (c) depositing a matrix solution onto the tissue microarray section; (d) detecting released glycan ions by mass spectrometry; (e) generating machine learning models using mass spectrometry data of a random selection of a first percent of tissue microarray cores; and (f) optimizing the machine learning models by cross-validation on the first percent of tissue microarray cores, and qualifying the performance using the mass spectrometry data of the second percent of tissue microarray cores to form panels of glycans with individual sensitivities and specificities.
  • the glycans are N-linked glycans.
  • the tissue microarray section is a formalin fixed, paraffin embedded tissue microarray section.
  • the proteins are digested enzymatically with PNGaseF.
  • the matrix solution is a-cyano-4- hydroxycinnamic acid.
  • the mass spectrometry is Fourier transform ion cyclotron resonance (FTICR) mass spectrometry.
  • the mass spectrometry is matrix-assisted laser desorption/ionization imaging mass spectroscopy (MALDI-IMS).
  • the machine learning model is a supervised machine learning model.
  • the supervised machine learning model is selected from the group consisting of: random forest, linear basis function kernel support vector machine, radial basis function kernel support vector machine, naive Bayes classifier, linear discriminant analysis, quadratic discriminant analysis, neural networks, artificial neural networks, genetic algorithm, k-nearest neighbors, and combinations thereof.
  • the machine learning model is optimized by forward sequential feature selection.
  • the tissue microarray section comprises tumor tissue cores and normal tissue cores.
  • the invention also relates to a glycan panel comprising glycans associated with a type of cancer tissue identified by the methods of the present invention.
  • the invention also relates to a glycan panel associated with pancreatic cancer tissue, comprising one or more of Hex6HexNAc2 glycan; Hex4dHexlHexNAc3 glycan; Hex3dHexlHexNAc4 glycan; Hex4HexNAc4 glycan; Hex7HexNAc2 glycan; Hex4dHexlHexNAc4 glycan; Hex3dHexlHexNAc5 glycan; Hex8HexNAc2 glycan; Hex5dHexlHexNAc4 glycan; Hex4dHexlHexNAc5 glycan; Hex5HexNAc4NeuAcl glycan; Hex5dHexlHexNAc5
  • Hex6HexNAc5 glycan Hex5dHexlHexNAc4NeuAcl glycan; Hex5dHex2HexNAc5 glycan; Hex6dHexlHexNAc5 glycan; Hex6dHex2HexNAc5 glycan; Hex7HexNAc6 glycan; Hex9HexNAc3NeuAcl glycan; Hex7dHexlHexNAc6 glycan;
  • Hex7dHexlHexNAc7 glycan Hex9dHexlHexNAc8 glycan.
  • the invention also relates to a kit for diagnosing or monitoring cancer in an individual wherein the glycan profile of a test sample from said individual is determined and comparing the measured profile with a profile of normal patient or profile of a patient with a family history of cancer, wherein said kit comprises an array of glycan molecules identified by the methods of the present invention.
  • the invention also relates to a method of diagnosing cancer in a tissue sample, comprising detecting in a tissue sample the presence of at least one glycan selected from a glycan panel.
  • the glycan panel comprises glycans associated with a type of cancer identified by the methods of the present invention.
  • the invention also relates to a method of identifying pancreatic cancer in a tissue sample, comprising detecting in a tissue sample the presence of one or more of Hex6HexNAc2 glycan; Hex4dHexlHexNAc3 glycan; Hex3dHexlHexNAc4 glycan; Hex4HexNAc4 glycan; Hex7HexNAc2 glycan;
  • Hex4dHexlHexNAc4 glycan Hex3dHexlHexNAc5 glycan; Hex8HexNAc2 glycan;
  • Hex5dHexlHexNAc4 glycan Hex4dHexlHexNAc5 glycan; Hex5HexNAc4NeuAcl glycan; Hex5dHexlHexNAc5 glycan; Hex6HexNAc5 glycan;
  • Hex5dHexlHexNAc4NeuAcl glycan Hex5dHex2HexNAc5 glycan;
  • Hex6dHexlHexNAc5 glycan Hex6dHex2HexNAc5 glycan; Hex7HexNAc6 glycan;
  • Hex9HexNAc3NeuAcl glycan Hex7dHexlHexNAc6 glycan; Hex7dHexlHexNAc7 glycan; and Hex9dHexlHex Ac8 glycan.
  • Figure 1 is a schematic of the methodology for imaging N-glycans from FFPE tissues. Prior to enzyme application, FFPE blocks are cut at 5um, incubated, deparaffinized and undergo antigen retrieval. PNGaseF is then applied and the slide is incubated before MALDI-IMS. The data is then linked with
  • histopathology either on the same tissue slice or a serial tissue slice.
  • Figure 2 is a series of images depicting MALDI-IMS of N-Glycans on mouse kidney tissue.
  • Two mouse kidneys were sliced at 5um prior to proceeding with the MALDI-IMS workflow.
  • One tissue was covered with a glass slide during PNGaseF application to serve as an undigested control tissue.
  • An average annotated spectra from the tissue that received PNGaseF application is provided (Figure 2A).
  • Tissue regions were assessed by H&E stain ( Figure 2B).
  • the labeled peaks correspond to native N-glycans that have been reported for the mouse kidney on the Consortium for Functional Glycomics mouse kidney database. Two of these ions were selected and their tissue localization was assessed.
  • Figure 3 is a series of images depicting MALDI-IMS of a human pancreas FFPE tissue block.
  • An FFPE block of pancreatic tissue from a human patient was cut at 5um prior to and selected for MALDI-IMS. Histopathology found four unique regions in the H&E of this tissue block.
  • the tissue block contained tumor tissue, non-tumor tissue, fibroconnective tissue representing desmoplasia surrounding the tumor tissue, and necrotic tissue ( Figures 3A and 3B).
  • MALDI-IMS was able to distinguish these four regions based off of specific ions after MALDI-IMS.
  • m/z 1663.64 (orange) was elevated corresponding to Hex5HexNAc4.
  • Image spectra were acquired at 200 um raster. ( Figure 3C). Representative individual glycan images for the pancreatic FFPE tissue slice.
  • Figure 4 is a series of images depicting MALDI-IMS of a human prostate FFPE tissue block.
  • An archived FFPE block of prostate tissue from a human patient was cut at Sum and prepared for MALDI-IMS glycan analysis, (Figure 4A).
  • Figure 5 is a series of images showing comparison of the fragmentation pattern of a glycan standard with the same ion on tissue.
  • Figure 5A A representative MALDI spectra for native N-linked glycans from pancreatic cancer FFPE tissue.
  • Figure 6 is a series of images depicting N-Glycan imaging of a liver TMA.
  • a liver TMA purchased by BioChain consisting of 2 tumor tissue cores and one normal tissue core from 16 patients was imaged.
  • the H&E Figure 6A
  • M/z 2393.95
  • Figure 6D m/z 1743.64
  • Figure 6B An overlay of these ions demonstrates that m/z 2393.95 is elevated in tumor tissue and m/z 1743.64 is elevated in normal tissue ( Figure 6B).
  • Table 1. 200 um raster).
  • Figure 7 shows a panel of mouse kidney N-glycans. Ions detected in the kidney with enzyme application were compared to the control tissue. Ions that were only observed in the tissue following PNGaseF application were compared to the glycans found in the mouse kidney database on the Consortium for Functional Glycomics. The panel provides the glycan species, the projected mass for the sodium adduct, and the observed mass for the sodium adduct.
  • Figure 8 is a chart showing permethylation of mouse kidney
  • N-glycans Mouse kidney N-glycans were extracted from the imaging slide after PNGaseF application and digestion. Glycans were dried down and underwent permethylation as described elsewhere herein. The permethylated m/z values were then compared to the permethylation data from the Consortium for Functional Glycomics mouse kidney database (www.functionalglycomics.org).
  • Figure 9 shows a panel of mouse kidney N-glycans linked to known glycan database.
  • Figure 10 is a series of images showing individual N-glycans from prostate cancer FFPE tissue.
  • Figure 11 is a series of images showing collision-induced dissociation (CID) of N-glycans from human pancreas tissue.
  • Figure 12 is a series of images showing CID of N-glycans from human pancreas tissue.
  • Figure 13 is a series of images from Ions corresponding to N-glycans.
  • the Ions identified in Table 1 were viewed in Fleximaging Software.
  • Figure 14 depicts the results of experiments demonstrating heterogeneous distribution of N-Glycans in pancreatic cancer tissue sections.
  • PDAC pancreatic ductal adenocarcinoma
  • Hex6dHexlHexNAc5 (14B, tumor), Hex5dHex3HexNAc5 (14C, intestine mucosa), Hex5HexNAc4NeuAcl (14D, fibroadipose connective tissue), Hex4dHexlHexNAc4 (14E, smooth muscle), and Hex6HexNAc2 (14F, non-tumor pancreas) are among these N-glycans.
  • Figure 15 depicts a panel of N-Glycan distribution in PDAC tissue section. Upwards of 90 ions corresponding to N-glycoforms were observed in an individual tissue section. In a representative panel of these glycoforms, many of these glycans exhibit a distribution pattern related to regions of complex histology.
  • Figure 16 depicts a workflow schematic for the identification of individual and panels of N-Glycan disease markers.
  • Six TMAs were imaged by MALDI-IMS to add an element of throughput to the disease marker discovery process. Many glycoforms were detected in all 6 of the TMAs and were selected for further analysis of individual discriminators and as panels of biomarkers.
  • 2/3rds of the data was used to optimize the variables for a linear discriminant analysis (LDA) model, while the remaining l/3 rd of the data was used to test the performance of the model.
  • LDA linear discriminant analysis
  • Figure 17 depicts the results of experiments demonstrating that combinations of individual discriminators reveal more robust differences in tumor and non-tumor samples.
  • tumor cores are outlined in green, while non-tumor cores are outlined in red.
  • Hex6dHexlHexNAc5 is presented in green
  • Hex6HexNAc2 is presented in red.
  • the overlay of the two glycans is able to distinguish tumor from non-tumor cores in both the whole tissue blocks and the tissue microarray.
  • Figure 18 depicts the results of experiments demonstrating that an LDA model of N-Glycan discriminates tumor from non-tumor tissue cores.
  • Supervised machine learning algorithms specifically the Linear Discriminant Analysis (LDA), were used to identify important features to distinguish tumor from non-tumor tissue sections.
  • LDA Linear Discriminant Analysis
  • the present invention relates to methods and compositions for cancer diagnosis, research and therapy, including but not limited to profiling glycans.
  • the present invention relates to glycan panels as diagnostic markers and clinical targets for cancer.
  • the cancer is pancreatic cancer.
  • embodiments of the present invention provide methods and compositions for the profiling of glycans for detecting and screening cancer.
  • the profiled glycans are N-linked glycans.
  • the glycans are profiled directly on a tissue.
  • the method of profiling glycans is performed using MALDI-imaging mass spectrometry.
  • the present invention relates to the spatial identification of N-glycan species in relation to their histopathology expression and tissue distribution.
  • multiple profiled glycans are associated with a specific cancer. Accordingly, the invention provides a panel of multiple glycans for the detection and screening of cancer.
  • embodiments of the present invention provide glycan libraries or panels useful in the detection and screening of their associated underlying tissue features and morphologies.
  • the features and morphologies detected and screened include healthy tissue, cancerous tissue, and diseased tissue.
  • glycan types are increased relative to a control sample from a subject that does not have a disease or condition (e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.).
  • glycan types are decreased relative to a control sample from a subject that does not have a disease or condition (e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.).
  • a disease or condition e.g., a population average of samples, a control sample, a prior sample from the same patient, etc.
  • the invention in some instances provides a combination of markers for a disease or condition, wherein some of the glycan types are increased and other markers include decrease is some glycan types.
  • the present invention describes glycans, which are specifically expressed by certain cancer cells, tumors and other malignant tissues.
  • the present invention describes methods to detect cancer specific glycans as well as methods for the production of reagents binding to the glycans.
  • the invention is also directed to the use of the glycans and reagents binding to them for the diagnostics of cancer and malignancies.
  • the invention is directed to the use of the glycans and reagents binding to them for the treatment of cancer and
  • the present invention comprises efficient methods to differentiate between malignant and benign tumors by analyzing glycan structures.
  • an element means one element or more than one element.
  • abnormal when used in the context of organisms, tissues, cells or components thereof, refers to those organisms, tissues, cells or components thereof that differ in at least one observable or detectable characteristic (e.g., age, treatment, time of day, etc.) from those organisms, tissues, cells or components thereof that display the "normal” (expected) respective characteristic. Characteristics that are normal or expected for one cell or tissue type, might be abnormal for a different cell or tissue type.
  • biomarker and “marker” are used herein interchangeably.
  • They refer to a substance that is a distinctive indicator of a biological process, biological event and/or pathologic condition.
  • body sample or "biological sample” is used herein in its broadest sense.
  • a sample may be of any biological tissue or fluid from which biomarkers of the present invention may be assayed. Examples of such samples include but are not limited to blood, saliva, buccal smear, feces, lymph, urine, gynecological fluids, biopsies, amniotic fluid and smears. Samples that are liquid in nature are referred to herein as "bodily fluids.”
  • Body samples may be obtained from a patient by a variety of techniques including, for example, by scraping or swabbing an area or by using a needle to aspirate bodily fluids. Methods for collecting various body samples are well known in the art.
  • a sample will be a "clinical sample,” i.e., a sample derived from a patient.
  • samples include, but are not limited to, bodily fluids which may or may not contain cells, e.g., blood (e.g., whole blood, serum or plasma), urine, saliva, tissue or fine needle biopsy samples, and archival samples with known diagnosis, treatment and/or outcome history.
  • Biological or body samples may also include sections of tissues such as frozen sections taken for histological purposes.
  • the sample also encompasses any material derived by processing a biological or body sample. Derived materials include, but are not limited to, cells (or their progeny) isolated from the sample, proteins or nucleic acid molecules extracted from the sample. Processing of a biological or body sample may involve one or more of: filtration, distillation, extraction, concentration, inactivation of interfering components, addition of reagents, and the like.
  • carbohydrate is intended to include any of a class of aldehyde or ketone derivatives of polyhydric alcohols. Therefore, carbohydrates include starches, celluloses, gums and saccharides. Although, for illustration, the term “saccharide” or “glycan” is used elsewhere herein, this is not intended to be limiting. It is intended that the methods provided herein can be directed to any carbohydrate, and the use of a specific carbohydrate is not meant to be limiting to that carbohydrate only.
  • cell-surface glycoprotein refers to a glycoprotein, at least a portion of which is present on the exterior surface of a cell.
  • a cell-surface glycoprotein is a protein that is positioned on the cell-surface such that at least one of the glycan structures is present on the exterior surface of the cell.
  • control when used to characterize a subject, refers, by way of non-limiting examples, to a subject that is healthy, to a patient that otherwise has not been diagnosed with a disease.
  • control sample refers to one, or more than one, sample that has been obtained from a healthy subject or from a non-disease tissue such as normal colon.
  • control or reference standard describes a material comprising none, or a normal, low, or high level of one of more of the marker (or biomarker) expression products of one or more the markers (or biomarkers) of the invention, such that the control or reference standard may serve as a comparator against which a sample can be compared.
  • “Differentially increased levels” refers to biomarker levels which are at least 1%, 2%, 3%, 4%, 5%, 10% or more, for example, 5%, 10%, 20%, 30%, 40%, or 50%, 60%, 70%, 80%, 90% higher or more, and/or 0.5 fold, 1.1 fold, 1.2 fold, 1.4 fold, 1.6 fold, 1.8 fold higher or more, as compared with a control.
  • “Differentially decreased levels” refers to biomarker levels which are at least at least 1%, 2%, 3%, 4%, 5%, 10% or more, for example, 5%, 10%, 20%, 30%, 40%, or 50%, 60%, 70%, 80%, 90% lower or less, and/or 0.9 fold, 0.8 fold, 0.6 fold, 0.4 fold, 0.2 fold, 0.1 fold or less, as compared with a control.
  • a “disease” is a state of health of an animal wherein the animal cannot maintain homeostasis, and wherein if the disease is not ameliorated then the animal's health continues to deteriorate.
  • a “disorder” in an animal is a state of health in which the animal is able to maintain homeostasis, but in which the animal's state of health is less favorable than it would be in the absence of the disorder. Left untreated, a disorder does not necessarily cause a further decrease in the animal's state of health.
  • a disease or disorder is "alleviated” if the severity of a sign or symptom of the disease, or disorder, the frequency with which such a sign or symptom is experienced by a patient, or both, is reduced.
  • an effective amount and “pharmaceutically effective amount” refer to a sufficient amount of an agent to provide the desired biological result. That result can be reduction and/or alleviation of a sign, symptom, or cause of a disease or disorder, or any other desired alteration of a biological system. An appropriate effective amount in any individual case may be determined by one of ordinary skill in the art using routine experimentation.
  • endogenous refers to any material from or produced inside the organism, cell, tissue or system.
  • expression as used herein is defined as the transcription and/or translation of a particular nucleotide sequence driven by its promoter.
  • the “level” of one or more biomarkers means the absolute or relative amount or concentration of the biomarker in the sample.
  • level also refers to the absolute or relative amount of glycosylation of the biomarker in the sample.
  • glycocans are sugars (e.g., oligosaccharides and polysaccharides). Glycans can be monomers or polymers of sugar residues typically joined by glycosidic bonds also referred to herein as linkages. In some embodiments, the terms “glycan”, “oligosaccharide” and “polysaccharide” may be used to refer to the carbohydrate portion of a glycoconjugate (e.g., glycoprotein, glycolipid or proteoglycan).
  • a glycoconjugate e.g., glycoprotein, glycolipid or proteoglycan
  • a glycan may include natural sugar residues (e.g., glucose, N-acetylglucosamine, N-acetyl neuraminic acid, galactose, mannose, fucose, hexose, arabinose, ribose, xylose, etc.) and/or modified sugars (e.g., 2'-fluororibose, 2'-deoxyribose, phosphomannose, 6'-sulfo N-acetylglucosamine, etc.).
  • the term "glycan" includes homo and heteropolymers of sugar residues.
  • glycocan also encompasses a glycan component of a glycoconjugate (e.g., of a glycoprotein, glycolipid, proteoglycan, etc.).
  • glycoconjugate e.g., of a glycoprotein, glycolipid, proteoglycan, etc.
  • free glycans including glycans that have been cleaved or otherwise released from a glycoconjugate
  • glycan array refers to a tool used to identify agents that interact with any of a number of different glycans linked to the array substrate.
  • glycan arrays comprise a number of chemically synthesized glycans, referred to herein as "glycan probes".
  • glycan arrays comprise at least 2, at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 150, at least 350, at least 1000 or at least 1500 glycan probes.
  • glycan arrays may be customized to present a desired set of glycan probes.
  • glycan probes may be attached to the array substrate by a linker molecule.
  • glycan preparation refers to a set of glycans obtained according to a particular production method. In some embodiments, glycan preparation refers to a set of glycans obtained from a glycoprotein preparation.
  • glycoconjugate encompasses all molecules in which at least one sugar moiety is covalently linked to at least one other moiety.
  • the term specifically encompasses all biomolecules with covalently attached sugar moieties, including for example N-linked glycoproteins, O-linked glycoproteins, glycolipids, proteoglycans, etc.
  • glycoform is used herein to refer to a particular form of a glycoconjugate. That is, when the same backbone moiety (e.g., polypeptide, lipid, etc) that is part of a glycoconjugate has the potential to be linked to different glycans or sets of glycans, then each different version of the glycoconjugate (i.e., where the backbone is linked to a particular set of glycans) is referred to as a "glycoform.”
  • backbone moiety e.g., polypeptide, lipid, etc
  • glycosidase refers to an agent that cleaves a covalent bond between sequential sugars in a glycan or between the sugar and the backbone moiety (e.g. between sugar and peptide backbone of glycoprotein).
  • a glycosidase is an enzyme.
  • a glycosidase is a protein (e.g., a protein enzyme) comprising one or more polypeptide chains.
  • a glycosidase is a chemical cleavage agent.
  • glycosylation pattern refers to the set of glycan structures present on a particular sample.
  • a particular glycoconjugate e.g., glycoprotein
  • set of glycoconjugates e.g., set of glycoconjugates
  • glycoproteins will have a glycosylation pattern.
  • a glycosylation pattern can be characterized by, for example, the identities of glycans, amounts (absolute or relative) of individual glycans or glycans of particular types, degree of occupancy of glycosylation sites, etc., or combinations of such parameters.
  • the term "glycoprofile” refers to one or more properties of the glycans of a glycoprotein; for example, the glycoprofile can include, but is not limited to, one or more of the following: number or placement of glycans; number or placement of N-linked glycans; number or placement of O-linked glycans; sequence of one or more attached glycans; tertiary structure of one or more glycans, e.g., branching pattern, e.g., biantennary, triantennary, tetrantennary, and so on; number or placement of Lewis antigens; number or placement of fucosyl or sialyl groups; molecular weight or mass of the intact glycoprotein; molecular weight or mass of the glycoprotein following the application of one or more experimental constraints, e.g., digestion (enzymatic or chemical); molecular weight or mass of some or all of the glycans after being released from the glycoprotein, e
  • glycoprotein preparation refers to a set of individual glycoprotein molecules, each of which comprises a polypeptide having a particular amino acid sequence (which amino acid sequence includes at least one glycosylation site) and at least one glycan covalently attached to the at least one glycosylation site.
  • Individual molecules of a particular glycoprotein within a glycoprotein preparation typically have identical amino acid sequences but may differ in the occupancy of the at least one glycosylation sites and/or in the identity of the glycans linked to the at least one glycosylation sites. That is, a glycoprotein preparation may contain only a single glycoform of a particular glycoprotein, but more typically contains a plurality of glycoforms. Different preparations of the same glycoprotein may differ in the identity of glycoforms present (e.g., a glycoform that is present in one preparation may be absent from another) and/or in the relative amounts of different glycoforms.
  • lectin encompasses any amino acid and peptide bond-based compound having specific binding affinity to carbohydrates. Typically it relates to non-antibody polypeptides found in nature featuring specific carbohydrate binding.
  • lectin includes functional fragments and derivatives thereof, the latter terms being defined in analogy to the same terms used in the context of antibodies.
  • Measurement or “measurement,” or alternatively “detecting” or “detection,” means assessing the presence, absence, quantity or amount (which can be an effective amount) of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's clinical parameters.
  • N-glycan refers to a polymer of sugars that has been released from a glycoconjugate but was formerly linked to the
  • N-linked glycans are glycans that are linked to a glycoconjugate via a nitrogen linkage at asparagine residues within conserved protein structural motifs of N/X (any amino acid except proline)/S or T (serine or threonine).
  • a diverse assortment of N-linked glycans exists, but is typically based on the common core pentasaccharide (Man) 3 (GlcNAc)(GlcNAc).
  • “Naturally-occurring" as applied to an object refers to the fact that the object can be found in nature. For example, a polypeptide or polynucleotide sequence that is present in an organism (including viruses) that can be isolated from a source in nature and which has not been intentionally modified by man is a naturally occurring sequence.
  • nucleic acid is meant any nucleic acid, whether composed of deoxyribonucleosides or ribonucleosides, and whether composed of phosphodiester linkages or modified linkages such as phosphotriester, phosphoramidate, siloxane, carbonate, carboxymethylester, acetamidate, carbamate, thioether, bridged phosphoramidate, bridged methylene phosphonate, phosphorothioate,
  • nucleic acid also specifically includes nucleic acids composed of bases other than the five biologically occurring bases (adenine, guanine, thymine, cytosine and uracil).
  • nucleic acid typically refers to large polynucleotides.
  • the left-hand end of a single-stranded polynucleotide sequence is the 5'- end; the left-hand direction of a double-stranded polynucleotide sequence is referred to as the 5 '-direction.
  • the direction of 5 ' to 3 ' addition of nucleotides to nascent RNA transcripts is referred to as the transcription direction.
  • the DNA strand having the same sequence as an mRNA is referred to as the "coding strand”; sequences on the DNA strand that are located 5' to a reference point on the DNA are referred to as “upstream sequences”; sequences on the DNA strand which are 3' to a reference point on the DNA are referred to as "downstream sequences.”
  • O-glycan refers to a polymer of sugars that has been released from a glycoconjugate but was formerly linked to the
  • O-linked glycans are glycans that are linked to a glycoconjugate via an oxygen linkage.
  • O-linked glycans are typically attached to glycoproteins via N-acetyl-D-galactosamine (GalNAc) or via N-acetyl-D- glucosamine (GlcNAc) to the hydroxyl group of L-serine (Ser) or L-threonine (Thr).
  • GalNAc N-acetyl-D-galactosamine
  • GlcNAc N-acetyl-D- glucosamine
  • Some O-linked glycans also have modifications such as acetylation and sulfation.
  • O-linked glycans are attached to glycoproteins via fucose or mannose to the hydroxyl group of L-serine (Ser) or L-threonine (Thr).
  • Ser L-serine
  • Thr L-threonine
  • pre-cancerous or pre-neoplastic shall be taken to mean any cellular proliferative disorder that is undergoing malignant transformation.
  • Examples of such conditions include, in the context of colorectal cellular proliferative disorders, cellular proliferative disorders with a high degree of dysplasia and the following classes of adenomas: Level 1 : penetration of malignant glands through the muscularis mucosa into the submucosa, within the polyp head; Level 2: the same submucosal invasion, but present at the junction of the head to the stalk; Level 3: invasion of the stalk; and Level 4: invasion of the stalk's base at the connection to the colonic wall.
  • pre-neoplastic is used to describe a normal tissue that will form tumors.
  • predisposition refers to the property of being susceptible to a cellular proliferative disorder.
  • a subject having a predisposition to a cellular proliferative disorder has no cellular proliferative disorder, but is a subject having an increased likelihood of having a cellular proliferative disorder.
  • a “polynucleotide” means a single strand or parallel and anti-parallel strands of a nucleic acid.
  • a polynucleotide may be either a single-stranded or a double-stranded nucleic acid.
  • the following abbreviations for the commonly occurring nucleic acid bases are used. "A” refers to adenosine, “C” refers to cytidine, “G” refers to guanosine, “T” refers to thymidine, and “U” refers to uridine.
  • oligonucleotide typically refers to short polynucleotides, generally no greater than about 60 nucleotides. It will be understood that when a nucleotide sequence is represented by a DNA sequence (i.e., A, T, G, C), this also includes an RNA sequence (i.e., A, U, G, C) in which "U" replaces "T.”
  • the term "providing a prognosis” refers to providing a prediction of the probable course and outcome of colorectal cancer, including prediction of severity, duration, chances of recovery, etc.
  • the methods can also be used to devise a suitable therapeutic plan, e.g., by indicating whether or not the condition is still at an early stage or if the condition has advanced to a stage where aggressive therapy would be ineffective.
  • a “reference level” of a biomarker means a level of the biomarker, for example level of a type of glycan that is indicative of a particular disease state, phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or lack thereof.
  • a "positive" reference level of a biomarker means a level that is indicative of a particular disease state or phenotype.
  • a “negative” reference level of a biomarker means a level that is indicative of a lack of a particular disease state or phenotype.
  • saccharides include mono-, di-, tri- and polysaccharides (or glycans).
  • Glycans can be branched or branched.
  • Glycans can be found covalently linked to non-saccharide moieties, such as lipids or proteins (as a glycoconjugate). These covalent conjugates include glycoproteins, glycopeptides, peptidoglycans, proteoglycans, glycolipids and lipopolysaccharides. The use of any one of these terms also is not intended to be limiting as the description is provided for illustrative purposes. In addition to the glycans being found as part of a
  • the glycans can also be in free form (i.e., separate from and not associated with another moiety).
  • Standard control value refers to a predetermined glycan level.
  • the standard control value is suitable for the use of a method of the present invention, in order for comparing the amount of glycan of interest that is present in a sample.
  • An established sample serving as a standard control provides an average amount of glycan of interest that is typical for an average, healthy person of reasonably matched background, e.g., gender, age, ethnicity, and medical history.
  • a standard control value may vary depending on the biomarker of interest and the nature of the sample.
  • the term "subject” refers to a human or another mammal (e.g., primate, dog, cat, goat, horse, pig, mouse, rat, rabbit, and the like. In many embodiments of the present invention, the subject is a human being. In such embodiments, the subject is often referred to as an "individual” or a "patient.” The terms “individual” and “patient” do not denote a particular age.
  • ranges throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.
  • the present invention is based partly on the profiling of multiple glycans on a biological sample.
  • the invention is also based on the generation of glycan panels from the data obtained from profiling biological samples in various states.
  • the results presented herein demonstrate the application of MALDI-IMS glycan imaging to various formalin-fixed tissues.
  • formalin-fixed mouse kidney tissues were used to optimize antigen retrieval, PNGaseF digestion and glycan detection conditions for MALDI-IMS. This was followed by N-glycan analysis of clinical FFPE tissue blocks from prostate and pancreatic cancers, as well as a commercial tissue microarray of hepatocellular carcinoma (HCC).
  • HCC hepatocellular carcinoma
  • Glycan identity was confirmed by on-tissue collision-induced dissociation (CID) and off-tissue permethylation analysis.
  • CID on-tissue collision-induced dissociation
  • An optimized MALDI-IMS workflow is presented that allows routine simultaneous analysis of thirty or more glycans per FFPE tissue, including TMA formats. The approach is amenable to any FFPE tissue, and represents an additional molecular correlate assay for use with the TMA format.
  • the invention provides compositions and methods for identifying novel glycan biomarker panels for cancer detection and prognosis.
  • the glycans are N-glycans and the biological samples are tissue samples.
  • the tissue samples are formalin- fixed, paraffin-embedded tissue samples.
  • the biological samples are profiled using mass spectroscopy. In another embodiment, the biological samples are profiled using MALDI. In another embodiment, the spatial distribution of glycans on the biological samples is visualized using MALDI-IMS.
  • the profiled glycans are characterized using mass spectroscopy. In another embodiment, the profiled glycans are characterized using Fourier transform ion cyclotron resonance (FTICR). In another embodiment, profiled glycans are characterized and associated with spatial distribution and biological sample state, such as a cancer presence and progression.
  • FTICR Fourier transform ion cyclotron resonance
  • the invention provides a disease-specific glycan panel.
  • the glycan panel is specific to pancreatic cancer and can be effectively used to diagnose pancreatic cancer at an early stage, as well as to monitor the progression of pancreatic cancer.
  • the present invention relates to the analysis of glycans for assessing the presence of cancer.
  • the analysis is qualitative.
  • the invention provides profiles of glycans, including but not limited to type of glycan and expression of the type of glycan, which can be used for the profiling of glycans.
  • the present invention is directed in a preferred embodiment quantitative mass spectrometric profiling of human cancers according to the invention and analysis of alterations in cancer in comparison with normal corresponding normal tissues.
  • the analysis can be performed based on signals corresponding to glycan structures, these signals were translated to likely monosaccharide compositions and further analyzed to reveal structures and correlations between the signals.
  • the invention is especially directed to analysis of N-glycan and/or O-glycan derived from cancer proteins.
  • the glycans can be analyzed as neutral and/or acidic signals and glycan mixtures, multiple analysis methods are preferred to obtain maximal amount of data.
  • the present invention provides a method for high- throughput profiling of multiple glycans. Accordingly, the methods may be applied in multiple contexts to simultaneously profile glycans on samples in a global manner.
  • Glycans play multi-faceted roles in many biological processes and aberrant glycosylation is associated with many diseases. Glycans are post-translation modifications of proteins that are involved in cell growth, cytokinesis, differentiation, transcription regulation, signal transduction, ligand-receptor binding, and interactions of cells with other cells, extracellular matrix, and bacterial and viral infection, among other functions. Glycan misregulations and structural changes occur in most of the diseases that affect the human.
  • the methods of the present invention can include determining the glycoprofile of a glycoprotein.
  • the properties can be determined by analyzing the glycans of the intact glycoprotein, by releasing the glycans from the glycoprotein before analysis, or by digesting the intact glycoprotein and analyzing the glycans attached to one or more of the resulting glycopeptide fragments.
  • Properties of the glycans which can be determined include: the mass of part or all of the saccharide structure, the charges of the chemical units of the saccharide, identities of the chemical units of the saccharide, confirmations of the chemical units of the saccharide, total charge of the saccharide, total number of sulfates of the saccharide, total number of acetates, total number of phosphates, presence and number of carboxylates, presence and number of aldehydes or ketones, dye-binding of the saccharide, compositional ratios of substituents of the saccharide, compositional ratios of anionic to neutral sugars, presence of uronic acid, enzymatic sensitivity, linkages between chemical units of the saccharide, charge, branch points, number of branches, number of chemical units in each branch, core structure of a branched or unbranched saccharide, the hydrophobicity and/or charge/charge density of each branch, absence or presence of GlcNAc and/or fucose in the core of a
  • a property of a glycan can be identified by any means known in the art.
  • molecular weight can be determined by several methods including mass spectrometry.
  • mass spectrometry for determining the molecular weight of glycans is well known in the art.
  • Mass spectrometry has been used as a powerful tool to characterize polymers such as glycans because of its accuracy in reporting the masses of fragments generated (e.g., by enzymatic cleavage), and also because only minute sample concentrations are required.
  • any analytic method for analyzing the glycans so as to characterize them can be performed on any sample of glycans, such analytic methods include those described herein.
  • to "characterize" a glycan or other molecule means to obtain data that can be used to determine its identity, structure, composition or quantity.
  • the term can also include determining the glycosylation sites, the glycosylation site occupancy, the identity, structure, composition or quantity of the glycan and/or non-saccharide moiety of the glycoconjugate as well as the identity and quantity of the specific glycoform.
  • mass spectrometry nuclear magnetic resonance (NMR) (e.g., 2D-NMR), electrophoresis and chromatographic methods.
  • NMR nuclear magnetic resonance
  • electrophoresis electrophoresis
  • chromatographic methods examples include fast atom bombardment mass spectrometry (FAB-MS), liquid chromatography mass spectrometry (LC-MS), liquid
  • NMR methods can include, for example, correlation spectroscopy (COSY), two- dimensional nuclear magnetic resonance spectroscopy (TOCSY), Nuclear Overhauser effect spectroscopy (NOESY).
  • Electrophoresis can include, for example, capillary electrophoresis with laser induced fluorescence (CE-LIF), capillary gel
  • CGE capillary zone electrophoresis
  • COSY TOCSY
  • NOESY NOESY
  • Mass spectrometry imaging is a powerful tool that has been used to correlate various peptides, proteins, lipids and metabolites with their underlying histopathology in tissue sections. Taking advantage of the rapid advances in mass spectrometry, mass spectrometry imaging can push the limits of glycomics studies. Mass spectrometry imaging offers some advantages over the conventional methods that support its use as a complementary technique to lectin histochemistry. One significant advantage is that matrix-assisted laser desorption/ionization (MALDI) imaging combined with tandem mass spectrometry reveals detailed structural information about the glycans in a sample. A wide range of molecular weights can be detected by mass spectrometry imaging.
  • MALDI matrix-assisted laser desorption/ionization
  • the high mass resolution allows distinguishing two peaks with close molecular weights, which subsequently improves the detection specificity.
  • tens or even hundreds of glycans can be detected at femtomole levels in one single image, allowing detection of low concentrations of molecules. Therefore, MALDI imaging facilitates high-throughput analysis of tissue glycans.
  • MALDI imaging can also be used for performing quantitative assays.
  • MALDI imaging Another significant advantage of MALDI imaging is that it has the capability of detecting an unknown compound without any a prior knowledge of the analytes. Therefore, this technique is particularly suitable for biomarker discovery research.
  • MALDI is a soft ionization mass spectrometric technique that is suitable for use in the analysis of biomolecules, such as proteins, peptides, sugars, and the like, which tend to be fragile and fragment when ionized by conventional ionization methods.
  • MALDI comprises a two-step process.
  • desorption is triggered by an ultraviolet (UV) laser beam.
  • the matrix material absorbs the UV laser radiation, which leads to the ablation of an upper layer of the matrix material, thereby producing a hot plume.
  • the hot plume contains many species: neutral and ionized matrix molecules, protonated and deprotonated matrix molecules, matrix clusters, and nanodroplets.
  • the analyte molecules are ionized, e.g., protonated or deprotonated, in the hot plume.
  • the matrix material comprises a crystallized molecule capable of absorbing the UV laser radiation.
  • Common matrix materials include, but are not limited to, a-cyano-4-hydroxycinnamic acid, 2,5-dihydroxybenzoic acid, 2,5- dihydroxybenzoic acid/2-hydroxy-5-methoxybenzoic acid, 2,4,6- trihydroxyacetophenone, 6-aza-2-thiothymine, 3-hydroxypicolinic acid, 3- aminoquinoline, anthranilic acid, 5-chloro-2-mercaptobenzothiazole, 2,5- dihydroxyacetophenone, ferulic acid, and 2-(4-hydroxyphenylazo) benzoic acid.
  • a solution of the matrix material is made in highly purified water and an organic solvent, such as acetonitrile or ethanol. In some embodiments, a small amount of trifluoroacetic acid (TFA) also can be added to the solution.
  • TFA trifluoroacetic acid
  • the matrix solution can then be mixed with the analyte, e.g., a protein sample. This solution is then deposited onto a MALDI plate, wherein the solvents vaporize leaving only the recrystallized matrix comprising the analyte molecules embedded in the MALDI crystals.
  • analyte e.g., a protein sample.
  • the property of the glycan that is detected by this method can also be any structural property of a glycan or unit.
  • the property of the glycan can be the molecular mass or length of the glycan.
  • the property can be the compositional ratios of substituents or units, type of basic building block of a polysaccharide, hydrophobicity, enzymatic sensitivity, hydrophilicity, secondary structure and conformation (i.e., position of helices), spatial distribution of substituents, linkages between chemical units, number of branch points, core structure of a branched polysaccharide, ratio of one set of modifications to another set of modifications (i.e., relative amounts of sulfation, acetylation or phosphorylation at the position for each), and binding sites for proteins.
  • Methods of identifying other types of properties are easily identifiable to those of skill in the art and generally can depend on the type of property and the type of glycan; such methods include, but are not limited to capillary electrophoresis (CE), NMR, mass spectrometry (both MALDI and ESI), and high performance liquid chromatography (HPLC) with fluorescence detection.
  • hydrophobicity can be determined using reverse-phase high-pressure liquid chromatography (RP- HPLC).
  • Enzymatic sensitivity can be identified by exposing the glycan to an enzyme and determining a number of fragments present after such exposure. The chirality can be determined using circular dichroism. Protein binding sites can be determined by mass spectrometry, isothermal calorimetry and NMR.
  • Linkages can be determined using NMR and/or capillary electrophoresis.
  • Enzymatic modification (not degradation) can be determined in a similar manner as enzymatic degradation, i.e., by exposing a substrate to the enzyme and using MALDI-MS to determine if the substrate is modified.
  • a sulfotransferase can transfer a sulfate group to an oligosaccharide chain having a concomitant increase of 80 Da.
  • Conformation can be determined by modeling and nuclear magnetic resonance (NMR). The relative amounts of sulfation can be determined by compositional analysis or approximately determined by Raman spectroscopy.
  • the present invention provides a mass spectroscopy imaging technique that has been developed for profiling of glycans from a biological sample.
  • the glycans are N-linked glycans.
  • the biological sample is a tissue section from a formalin fixed, paraffin embedded (FFPE) tissue block.
  • FFPE tissues are sectioned on indium tin oxide coated glass slides for matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Deparaffinization and rehydration of the tissue sections are followed by antigen retrieval and denaturing of the proteins.
  • a releasing agent can be sprayed over the tissue sections to release glycans from the proteins, while preserving their spatial distribution.
  • Common enzymatic releasing agents include, but are not limited to, trypsin, Endoglycosidase H (Endo H), Endoglycosidase F (EndoF), N-Glycanase F (PNGaseF), PNGaseA, O-glycanase, and/or one or more proteases (e.g., trypsin, or LysC), or chemically (e.g., using anhydrous hydrazine (N) or reductive or non-reductive beta-elimination (O)).
  • the proteins may, for example, first be unfolded prior to the use of the enzyme.
  • the unfolding of the protein can be accomplished with any of the denaturing agents provided above.
  • the denaturing of the sample in a) comprises: i) heating the sample for a sufficient period of time; ii) incubating the sample from i) with a proteolytic enzyme for a period of time; and iii) adding a sufficient amount of PNGaseF to the sample of ii) to release the glycans from the peptide fragments.
  • Common chemical releasing methods for cleaving glycans from glycoconjugates include hydrazinolysis or alkali borohydrate. After glycan release, samples can then be spray -coated with matrix and analyzed by MALDI-MS.
  • the denaturation of the glycoprotein(s) occurs by heating the biological sample and/or incubating the biological sample with a proteolytic enzyme for a sufficient period of time.
  • the glycans can be modified to improve ionization of the glycans, particularly when MALDI-MS is used for analysis. Such modifications include permethylation.
  • Another method to increase glycan ionization is to conjugate the glycan to a hydrophobic chemical (such as AA, AB labeling) for MS or liquid chromatographic detection.
  • spot methods can be employed to improve signal intensity.
  • a solid support such as a glass plate or slide, or similar support
  • a biological sample such as a tissue
  • mass spectra are acquired for each pixel on the tissue.
  • Practical m/z ranges comprising most of the important signals, as observed by the present invention, may be more limited than these.
  • Preferred practical ranges includes lower limit of about m/z 400, more preferably about m/z 500, and even more preferably about m/z 600, and most preferably m/z about 700 and upper limits of about m/z 4000, more preferably m/z about 3500 (especially for negative ion mode), even more preferably m/z about 3000 (especially for negative ion mode), and in particular at least about 2500 (negative or positive ion mode) and for positive ion mode to about m/z 2000 (for positive ion mode analysis).
  • the preferred range depends on the sizes of the sample glycans, samples with high branching or polysaccharide content or high sialylation levels are preferably analyzed in ranges containing higher upper limits as described for negative ion mode.
  • the limits are preferably combined to form ranges of maximum and minimum sizes or lowest lower limit with lowest higher limit, and the other limits analogously in order of increasing size.
  • the present invention provides a method for direct profiling of N-linked glycans in a biological sample wherein spatial distribution of at least one glycan is maintained, the method comprising: (a) obtaining a biological sample comprising at least one glycoprotein; (b) denaturing the at least one glycoprotein in the biological sample; (c) releasing at least one glycan from the at least one glycoprotein; (d) coating the biological sample with matrix; and (e) analyzing the at least one glycan using mass spectrometry.
  • the profiling of N-glycans on FFPE tissue comprises several steps, although not all steps have to be carried out in this order.
  • step (a) a tissue section is heated in antigen retrieval buffer and then washed in xylene to remove the paraffin.
  • step (b) digestion of the proteins on a tissue section is achieved by incubation with an enzyme.
  • step (c) a matrix is deposited on the tissue section.
  • step (d) released glycan ions are detected by mass spectrometry.
  • the mass spectrometry data is deconvoluted and individual peaks were associated with glycans based on mass accuracy.
  • step (f) identified glycans are viewed in imaging software to assess intensity distribution of ions.
  • the profiling of N-glycans on FFPE tissue comprises several steps, although not all steps have to be carried out in this order.
  • step (a) a thin FFPE tissue section is heated in antigen retrieval buffer and then washed in xylene to remove the paraffin.
  • step (b) enzymatic digestion of the proteins on the thin FFPE tissue section is achieved by incubation with PNGaseF.
  • step (c) an a-cyano-4-hydroxycinnamic acid matrix is deposited on the thin FFPE tissue section.
  • released glycan ions are detected by FTICR mass spectrometry.
  • step (e) the FTICR data is deconvoluted and individual peaks were associated with glycans based on mass accuracy.
  • step (f) identified glycans are viewed in imaging software to assess intensity distribution of ions.
  • Methods of the present disclosure can be applied to glycan mixtures obtained from a wide variety of sources including, but not limited to, therapeutic formulations and biological samples.
  • a biological sample may undergo one or more analysis and/or purification steps prior to or after being analyzed according to the present disclosure.
  • a biological sample is treated with one or more proteases and/or glycosidases (e.g., so that glycans are released); in some embodiments, glycans in a biological sample are labeled with one or more detectable markers or other agents that may facilitate analysis by, for example, mass spectrometry or NMR. Any of a variety of separation and/or isolation steps may be applied to a biological sample in accordance with the present disclosure.
  • the methods can be used to detect biomarkers indicative of, e.g., a disease state, prior to the appearance of symptoms and/or progression of the disease state to an unbeatable or less treatable condition, by detecting one or more specific glycans whose presence or level (whether absolute or relative) may be correlated with a particular disease state (including susceptibility to a particular disease) and/or the change in the concentration of such glycans over time.
  • biomarkers indicative of, e.g., a disease state prior to the appearance of symptoms and/or progression of the disease state to an unbeatable or less treatable condition
  • one or more specific glycans whose presence or level (whether absolute or relative) may be correlated with a particular disease state (including susceptibility to a particular disease) and/or the change in the concentration of such glycans over time.
  • the invention provides libraries of glycans (or referred to as glycan panels) that are useful for detecting and preventing cancer.
  • glycan libraries can include numerous different types of carbohydrates and oligosaccharides.
  • the major structural attributes and composition of the separate glycans within the libraries have been identified.
  • the libraries consist of separate, substantially pure pools of glycans, carbohydrates and/or oligosaccharides.
  • the glycans of the invention include straight chain and branched oligosaccharides as well as naturally occurring and synthetic glycans.
  • the glycan can be a glycoaminoacid, a glycopeptide, a glycolipid, a
  • glycosaminoglycan GAG
  • GAG glycosaminoglycan
  • a glycoprotein a whole cell, a cellular component, a glycoconjugate, a glycomimetic, a glycophospholipid anchor,
  • glycosylphosphatidylinositol (GPI)-linked glycoconjugates bacterial glycosylphosphatidylinositol (GPI)-linked glycoconjugates, bacterial
  • the glycans can also include N-glycans, ⁇ - glycans, glycolipids and glycoproteins.
  • the glycans of the invention include two or more sugar units.
  • Any type of sugar unit can be present in the glycans of the invention, including, for example, allose, altrose, arabinose, glucose, galactose, gulose, fucose, fructose, idose, lyxose, mannose, ribose, talose, xylose, or other sugar units.
  • Such sugar units can have a variety of modifications and substituents.
  • sugar units can have a variety of substituents in place of the hydroxy, carboxylate, and methylenehydroxy substituents.
  • lower alkyl moieties can replace any of the hydrogen atoms from the hydroxy, carboxylic acid and methylenehydroxy substituents of the sugar units in the glycans of the invention.
  • amino acetyl can replace any of the hydroxy or hydrogen atoms from the hydroxy, carboxylic acid and methylenehydroxy substituents of the sugar units in the glycans of the invention.
  • Libraries and panels of glycans can be embodiments of the implementations of the methods disclosed herein.
  • the libraries and panels of glycans can find uses directed to detecting, treating and/or preventing a variety of early stage diseases and/or cancers.
  • the presence of such glycans is indicative of the presence of cancer and can provide information on the prognosis of such a disease, for example, whether the disease is in remission or is becoming more aggressive.
  • Patients with familial history of cancer, and hence a heightened risk of developing the disease can be tested regularly to monitor their propensity for disease.
  • the methods of the present invention allow for direct imaging of glycans on tissues to determine disease-specific glycosylation changes. Therefore, in some embodiments, these methods provide a method of diagnosing a disease or condition in a subject comprising: (a) comparing the N-linked glycan profile from a subject to an N-linked glycan profile from a normal sample or diseased sample; (b) determining whether the subject has the disease or condition; and wherein the glycan profile is determined using the presently disclosed methods.
  • the present invention provides a panel for the analysis of a plurality of glycan structures.
  • the panel allows for the simultaneous analysis of multiple glycan structures correlating with carcinogenesis and/or metastasis.
  • a panel may include two or more glycan structures identified as correlating with cancerous tissue, metastatic cancer, localized cancer that is likely to metastasize, pre-cancerous tissue that is likely to become cancerous, chronic pancreatitis, and pre-cancerous tissue that is not likely to become cancerous.
  • panels may be analyzed alone or in combination in order to provide the best possible diagnosis and prognosis.
  • Any of the glycan structures described herein may be used in combination with each other or with other known or later identified cancer glycan structures.
  • the present invention provides an expression profile map comprising expression profiles of cancers of various stages or prognoses (e.g., likelihood of future metastasis). Such maps can be used for comparison with patient samples. Any suitable method may be utilized, including but not limited to, by computer comparison of digitized data. The comparison data is used to provide diagnoses and/or prognoses to patients. The diagnosis can be carried out in a person with or thought to have a disease or condition.
  • the diagnosis can also be carried out in a person thought to be at risk for a disease or condition.
  • a person at risk is one that has either a genetic predisposition to have the disease or condition or is one that has been exposed to a factor that could increase his/her risk of developing the disease or condition.
  • cancer can be any cancer.
  • cancer is meant any malignant growth or tumor caused by abnormal and uncontrolled cell division that may spread to other parts of the body through the lymphatic system or the blood stream.
  • the cancer can be a metastatic cancer or a non- metastatic (e.g., localized) cancer.
  • metastatic cancer refers to a cancer in which cells of the cancer have metastasized, e.g., the cancer is characterized by metastasis of a cancer cells.
  • the metastasis can be regional metastasis or distant metastasis, as described herein.
  • the present invention provides a use of a glycan profile prepared using the method disclosed herein to diagnose a disease or condition in a subject, comprising comparing the glycan profile from a subject to a glycan profile from a normal sample, or diseased sample, and determining whether the sample of the subject has the disease or condition.
  • cancers also include but are not limited to adrenal gland cancer, biliary tract cancer; bladder cancer, brain cancer; breast cancer; cervical cancer; choriocarcinoma; colon cancer; endometrial cancer; esophageal cancer; extrahepatic bile duct cancer; gastric cancer; head and neck cancer; intraepithelial neoplasms; kidney cancer; leukemia; lymphomas; liver cancer; lung cancer (e.g.
  • small cell and non-small cell melanoma; multiple myeloma; neuroblastomas; oral cancer; ovarian cancer; pancreas cancer; prostate cancer; rectal cancer; sarcomas; skin cancer; small intestine cancer; testicular cancer; thyroid cancer; uterine cancer; urethral cancer and renal cancer, as well as other carcinomas and sarcomas.
  • An extensive listing of cancer types includes but is not limited to acute lymphoblastic leukemia (adult), acute lymphoblastic leukemia (childhood), acute myeloid leukemia (adult), acute myeloid leukemia (childhood), adrenocortical carcinoma, adrenocortical carcinoma (childhood), AIDS-related cancers, AIDS- related lymphoma, anal cancer, astrocytoma (childhood cerebellar), astrocytoma (childhood cerebral), basal cell carcinoma, bile duct cancer (extrahepatic), bladder cancer, bladder cancer (childhood), bone cancer (osteosarcoma/malignant fibrous histiocytoma), brain stem glioma (childhood), brain tumor (adult), brain tumor— brain stem glioma (childhood), brain tumor— cerebellar astrocytoma (childhood), brain tumor— cerebral astrocytoma/malignant glioma (childhood), brain tumor— ependymoma (child
  • Childhood esophageal cancer, esophageal cancer (childhood), Ewing's family of tumors, extracranial germ cell tumor (childhood), extragonadal germ cell tumor, extrahepatic bile duct cancer, eye cancer (intraocular melanoma and retinoblastoma), gallbladder cancer, gastric (stomach) cancer, gastric (stomach) cancer (childhood), gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (gist), germ cell tumor (extracranial (childhood), extragonadal, ovarian), gestational trophoblastic tumor, glioma (adult), glioma (childhood: brain stem, cerebral astrocytoma, visual pathway and hypothalamic), hairy cell leukemia, head and neck cancer, hepatocellular (liver) cancer (adult primary and childhood primary), Hodgkin's lymphoma (adult and childhood), Hodgkin's lymphoma during pregnancy, hypopharyn
  • bone/osteosarcoma medulloblastoma (childhood), melanoma, melanoma— intraocular (eye), Merkel cell carcinoma, mesothelioma (adult) malignant, mesothelioma (childhood), metastatic squamous neck cancer with occult primary, multiple endocrine neoplasia syndrome (childhood), multiple myeloma/plasma cell neoplasm, mycosis fungoides, myelodysplastic syndromes,
  • myelodysplastic/myeloproliferative diseases myelogenous leukemia, chronic, myeloid leukemia (adult and childhood) acute, myeloma— multiple,
  • myeloproliferative disorders chronic, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, nasopharyngeal cancer (childhood), neuroblastoma, non-small cell lung cancer, oral cancer (childhood), oral cavity and lip cancer, oropharyngeal cancer, osteosarcoma/malignant fibrous histiocytoma of bone, ovarian cancer (childhood), ovarian epithelial cancer, ovarian germ cell tumor, ovarian low malignant potential tumor, pancreatic cancer, pancreatic cancer (childhood), pancreatic cancer— islet cell, paranasal sinus and nasal cavity cancer, parathyroid cancer, penile cancer, pheochromocytoma, pineoblastoma and supratentorial primitive neuroectodermal tumors (childhood), pituitary tumor, plasma cell neoplasm/multiple myeloma, pleuropulmonary blastoma, pregnancy and breast cancer, primary central nervous system lymphoma, prostate
  • Pancreatic cancer is a devastating cancer with uniformly poor prognosis and treatment options.
  • 5-year survival is approximately 15% and the median survival for locally advanced or metastatic disease is 10 months or less.
  • New approaches to identify more effective biomarkers for early disease, and identify potential therapeutic targets are needed.
  • the glycan panels identified by the present method fit in both of these areas.
  • Pancreatic cancer is the 4th leading cause of cancer-related death and one of the most highly aggressive and lethal of all solid malignancies.
  • available chemotherapy, radiation and combinatorial therapies are largely anecdotal, and less than 5% of patients survive up to five years post diagnosis.
  • Biomarkers are measurable indicators of a biological state or condition, and in the context of cancer, serum biomarkers present a non-invasive and relatively cost effective means to aid in detection, monitor tumor progression and response to therapy, and for other measurable outcomes of disease.
  • the most widely used biomarker in the clinic for pancreatic cancer is carbohydrate antigen 19.9 (CA19.9), a sialylated Lewis A antigen found on the surface of proteins. While CA19.9 is elevated in late stage disease, it is also elevated in benign and inflammatory diseases of the pancreas and in other malignancies of the
  • CA19.9 has a reported sensitivity of -55% and is often undetectable in many asymptomatic individuals.
  • Other tumor markers such as members of the carcinoembryonic antigen (CEA) and mucin (MUC) families have also been associated with pancreatic cancer.
  • CCA carcinoembryonic antigen
  • MUC mucin
  • the present methods of profiling glycans and generating glycan panels are particularly useful because they can detect and monitor pancreatic cancer, for example, in the early stages of the disease, thereby increasing survival rates.
  • the generation of glycan biomarker panels comprises several steps, although not all steps have to be carried out in this order.
  • step (a) a tissue section is heated in antigen retrieval buffer and then washed in xylene to remove the paraffin.
  • step (b) digestion of proteins on a tissue microarray section is achieved by incubation with an enzyme.
  • step (c) a matrix is deposited on the tissue microarray section.
  • step (d) released glycan ions are detected by mass spectrometry. Data from a random selection of a first percent of the tissue cores is utilized for a model training set while the remaining second percent is used for external model validation.
  • step (e) data from the first percent is used to generate any of a variety of machine learning models.
  • step (f) the models are optimized by cross-validation on the first percent, and the performance is qualified using the second percent to generate panels of glycans with individual sensitivities and specificities.
  • the method is amenable to modification of the tissue by degradative enzymes to enhance and alter the types of analytes detected by the method.
  • the generation of glycan biomarker panels comprises several steps, although not all steps have to be carried out in this order.
  • step (b) a tissue section is heated in antigen retrieval buffer and then washed in xylene to remove the paraffin.
  • step (b) enzymatic digestion of proteins on a thin FFPE TMA section composed of normal and tumor cores is achieved by incubation with PNGaseF.
  • step (c) an a-cyano-4-hydroxycinnamic acid matrix is deposited on the thin FFPE TMA tissue section.
  • step (d) released glycan ions are detected by FTICR mass spectrometry.
  • step (e) Data from a random selection of a first percent of the tissue cores is utilized for a model training set while the remaining second percent is used for external model validation.
  • step (e) data from the first percent is used to generate any of a variety of machine learning models.
  • step (f) the models are optimized by cross-validation on the first percent, and the performance is validated using the second percent to generate panels of glycans with individual sensitivities and specificities.
  • the types of models used for machine learning is not necessarily limited.
  • the machine learning models may include random forest, support vector machines, discriminant analysis, neural networks, artificial neural networks, naive Bayes classifier, genetic algorithm, and k-nearest neighbors.
  • the invention provides a glycan panel for diagnosing cancer.
  • the glycan panel comprises one or more of a Hex6HexNAc2 glycan, a Hex4dHexlHexNAc3 glycan, a Hex3dHexlHexNAc4 glycan, a Hex4HexNAc4 glycan, a Hex7HexNAc2 glycan, a Hex4dHexlHexNAc4 glycan, a Hex3dHexlHexNAc5 glycan, a Hex8HexNAc2 glycan, a
  • Hex5dHexlHexNAc4 glycan a Hex4dHexlHexNAc5 glycan, a
  • Hex5HexNAc4NeuAcl glycan a Hex5dHexlHexNAc5 glycan, a Hex6HexNAc5 glycan, a Hex5dHexlHexNAc4NeuAcl glycan, a Hex5dHex2HexNAc5 glycan, a Hex6dHexlHexNAc5 glycan, a Hex6dHex2HexNAc5 glycan, a Hex7HexNAc6 glycan, a Hex9HexNAc3NeuAcl glycan, a Hex7dHexlHexNAc6 glycan, a
  • Hex7dHexlHexNAc7 glycan and a Hex9dHexlHexNAc8 glycan.
  • the invention should not be limited to these glycans. Rather, any glycan and combination of glycans identified from the methods of profiling glycans of the invention can be a component of the glycan panel of the invention.
  • pancreatic tissue cores 150 tumor and 140 non-tumor sections from 149 unique samples from 76 patients
  • tissue microarray (TMA) format were analyzed using the present methods of profiling glycans and generating glycan panels.
  • TMA tissue microarray
  • a training set of 52 tumor sections and 49 non-tumor sections was assessed for differential detection of glycan species, followed by qualification using a validation set of 23 tumor sections and 25 non-tumor sections.
  • the study conducted has provided a number of specific glycans that can be used in the diagnosis of pancreatic diseases such as cancer.
  • glycans include, for example, a Hex6HexNAc2 glycan, a Hex4dHexlHexNAc3 glycan, a Hex3dHexlHexNAc4 glycan, a Hex4HexNAc4 glycan, a Hex7HexNAc2 glycan, a Hex4dHexlHexNAc4 glycan, a Hex3dHexlHexNAc5 glycan, a
  • Hex8HexNAc2 glycan a Hex5dHexlHexNAc4 glycan, a Hex4dHexlHexNAc5 glycan, a Hex5HexNAc4NeuAcl glycan, a Hex5dHexlHexNAc5 glycan, a
  • Hex6HexNAc5 glycan a Hex5dHexlHexNAc4NeuAcl glycan, a
  • Hex5dHex2HexNAc5 glycan a Hex6dHexlHexNAc5 glycan, a
  • Hex6dHex2HexNAc5 glycan a Hex7HexNAc6 glycan, a Hex9HexNAc3NeuAc 1 glycan, a Hex7dHexlHexNAc6 glycan, a Hex7dHexlHexNAc7 glycan, and a
  • Hex9dHexlHexNAc8 glycan The composition of a glycan recited here is meant to refer to any glycan with the particular types and numbers of saccharides represented by the composition notation.
  • a "Hex5dHexlHexNAc4NeuAcl" glycan encompasses any glycan that contains 5 hexoses, 1 deoxyhexose, 4 N-acetyl hexosamines, and 1 N-acetyl neuraminic acids.
  • saccharides can be present in any order in the glycan and can be linked to each other with any of a number of types of linkages, including for example, al-2, al-3, al-6, a2-3, a2-6, ⁇ 1-2, or ⁇ 1-4 link.
  • linkages including for example, al-2, al-3, al-6, a2-3, a2-6, ⁇ 1-2, or ⁇ 1-4 link.
  • the glycans provided may exist in a modified form, for example, derivatives, enzymatically modified versions, a precursor form in a sample, or modified as part of an analytic method used for its detection.
  • Another aspect of the invention is a composition of glycans that can be used for treating or preventing ovarian cancer.
  • the compositions include glycans used to elicit protective immune response in patients with a high risk of developing cancer.
  • the compositions can also be used to enhance the immune response of patients that have cancer.
  • the compositions can also be used to prepare isolated antibody preparations useful for passive immunization of patients who have developed or may develop ovarian cancer.
  • the present invention describes glycans, which are specifically expressed by certain cancer cells, tumors and other malignant tissues.
  • the present invention describes methods to detect cancer specific glycans as well as methods for the production of reagents binding to the glycans.
  • the invention is also directed to the use of the glycans and reagents binding to them for the diagnostics of cancer and malignancies.
  • the invention is directed to the use of the glycans and reagents binding to them for the treatment of cancer and malignancies.
  • the present invention comprises efficient methods to differentiate between malignant and benign tumors by analyzing glycan structures.
  • the invention is further directed to methods of identifying glycoproteins attached to any of the glycans according to the invention, preferably from integral (cell bound/transmembrane) cancer tissue or cell released proteins and assigning the glycan structures with specific carrier proteins, preferably by specific purification of the protein, e.g. by affinity methods such as immunoprecipitation or by sequencing, preferably by mass spectrometric sequencing, glycopeptides including sequencing and recognizing peptides and thus proteins linked to the glycans.
  • affinity methods such as immunoprecipitation or by sequencing, preferably by mass spectrometric sequencing, glycopeptides including sequencing and recognizing peptides and thus proteins linked to the glycans.
  • the determined glycosylation marker of cancer can be used for identifying and isolating one or more glycoprotein biomarkers, i.e. glycoproteins that are specific for particular type of cancer.
  • the glycoprotein biomarker of the disease carries the glycosylation marker of cancer.
  • the isolation of the glycoprotein biomarkers of the cancer can be carried out using lectins or monoclonal antibodies.
  • the glycosylation of a protein may be indicative of a normal or a disease state. Therefore, methods are provided for diagnostic purposes based on the analysis of the glycosylation of a protein or set of proteins, such as the total glycome.
  • the methods provided herein can be used for the diagnosis of any disease or condition that is caused or results in changes in a particular protein glycosylation or pattern of glycosylation. These patterns can then be compared to "normal” and/or "diseased” patterns to develop a diagnosis, and treatment for a subject.
  • the methods provided can be used in the diagnosis of cancer, inflammatory disease, benign prostatic hyperplasia (BPH), etc.
  • the diagnosis can be carried out in a person with or thought to have a disease or condition.
  • the diagnosis can also be carried out in a person thought to be at risk for a disease or condition.
  • a person at risk is one that has either a genetic predisposition to have the disease or condition or is one that has been exposed to a factor that could increase his/her risk of developing the disease or condition.
  • the glycosylation marker is an organic biomolecule which is differentially present in a sample taken from an individual of one phenotypic status (e.g., having a disease) as compared with an individual of another phenotypic status (e.g., not having the disease).
  • a biomarker is differentially present between the two individuals if the mean or median expression level, including glycosylation level, of the biomarker in the different individuals is calculated to be statistically significant.
  • Biomarkers alone or in combination, provide measures of relative risk that an individual belongs to one phenotypic status or another. Therefore, they are useful as markers for diagnosis of disease, the severity of disease, therapeutic effectiveness of a drug, and drug toxicity.
  • the method of the invention is carried out by obtaining a set of measured values for a plurality of biomarkers from a biological sample derived from a test individual, obtaining a set of measured values for a plurality of biomarkers from a biological sample derived from a control individual, comparing the measured values for each biomarker between the test and control sample, and identifying biomarkers which are significantly different between the test value and the control value, also referred to as a reference value.
  • the process of comparing a measured value and a reference value can be carried out in any convenient manner appropriate to the type of measured value and reference value for the biomarker of the invention.
  • “measuring” can be performed using quantitative or qualitative measurement techniques, and the mode of comparing a measured value and a reference value can vary depending on the measurement technology employed.
  • the levels may be compared by visually comparing the intensity of the colored reaction product, or by comparing data from densitometric or spectrometric measurements of the colored reaction product (e.g., comparing numerical data or graphical data, such as bar charts, derived from the measuring device).
  • measured values used in the methods of the invention will most commonly be quantitative values (e.g., quantitative measurements of concentration).
  • measured values are qualitative.
  • the comparison can be made by inspecting the numerical data, or by inspecting representations of the data (e.g., inspecting graphical representations such as bar or line graphs).
  • a measured value is generally considered to be substantially equal to or greater than a reference value if it is at least about 95% of the value of the reference value.
  • a measured value is considered less than a reference value if the measured value is less than about 95% of the reference value.
  • a measured value is considered more than a reference value if the measured value is at least more than about 5% greater than the reference value.
  • the process of comparing may be manual (such as visual inspection by the practitioner of the method) or it may be automated.
  • an assay device such as a luminometer for measuring chemiluminescent signals
  • a separate device e.g., a digital computer
  • Automated devices for comparison may include stored reference values for the biomarker(s) being measured, or they may compare the measured value(s) with reference values that are derived from contemporaneously measured reference samples.
  • the above method for screening biomarkers can find biomarkers that are differentially glycosylated in cancer as well as at various dysplasic stages of the tissue which progresses to cancer.
  • the screened biomarker can be used for cancer screening, risk-assessment, prognosis, disease identification, the diagnosis of disease stages, and the selection of therapeutic targets.
  • the progression of cancer at various stages or phases can be diagnosed by determining the glycosylation stage of one or more biomarkers obtained from a sample.
  • a specific stage of cancer in the sample can be detected.
  • the glycosylation stage may be hyperglycosylation.
  • the glycosylation stage may be hypoglycosylation.
  • the present invention is directed to analysis of un-normally transformed tissues, when the transformation is benign and/or malignant cancer type transformation referred as cancer (or tumor).
  • benign transformation may be a step towards malignant transformation, and thus the benign cancers are also useful to be analyzed and differentiated from normal tissue, which may have also noncancerous or non-transformation related alterations such as swelling or trauma related to physical or e.g. infectious trauma, and it is useful and preferred to differentiate with benign and malignant cancers.
  • the tissue is human tissue or tissue part such as liquid tissue, cell and/or solid polycellular tumors, and in another embodiment preferably a solid human tissue.
  • the solid tissues are preferred for the analysis and/or targeting specific glycan marker structures from the tissues, including intracellularly and extracellularly, preferably cell surface associated, localized markers.
  • the invention is specifically directed to the recognition of cell surface localized and/or mostly cell surface localized marker structures from solid tumor tissues or parts thereof. It is realized that the contacts between cells and this glycomes mediating these are affected by presence of cells as solid tumor or as more individual cells.
  • the preferred individual cell type cancers or tumors include preferably blood derived tumors such as leukemias and lymphomas, while solid tumors are preferably includes solid tumors derived from solid tissues such as gastrointestinal tract tissues, other internal organs such as liver, kidneys, spleen, pancreas, lungs, gonads and associated organs including preferably ovary, testicle, and prostate.
  • the invention further reveals markers from individually or
  • the preferred cancer cells to be analyzed include metastatic cells released from tumors/cancer and blood cell derived cancers, such as leukemias and/or lymphomas. Metastasis from solid tissue tumors forms a separately preferred class of cancer samples with specific characteristics.
  • the cancer tissue materials to be analyzed according to the invention are in the invention also referred as tissue materials or simply as cells, because all tissues comprise cells, however the invention is preferably directed to unicellularly and/or multicellularly expressed cancer cells and/or solid tumors as separate preferred characteristics.
  • the invention further reveals normal tissue materials to be compared with the cancer materials.
  • the invention is specifically directed to methods according to the invention for revealing status of transformed tissue or suspected cancer sample when expression of specific structure of a signal correlated with it is compared to a expression level estimated to correspond to expression in normal tissue or compared with the expression level in an standard sample from the same tissue, preferably a tissue sample from healthy part of the same tissue from the same patient.
  • the invention is in a preferred embodiment directed to analysis of the marker structures and/or glycan profiles from both cancer tissue and corresponding normal tissue of the same patient because part of the glycosylations includes individual changes for example related to rare glycosylation related diseases such as congenital disorders in glycosylation (of glycoproteins/carbohydrates) and/or glycan storage diseases.
  • the invention is furthermore directed to method of verifying analyzing importance and/or change of a specific structure/structure group or glycan group in glycome in specific cancer and/or a subtype of a cancer optionally with a specific status (e.g. primary cancer, metastatic, benign transformation related to a cancer) by methods according to the present invention. Diagnostic
  • diagnostic tests that use the biomarkers of the invention exhibit a sensitivity and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and about 100%.
  • screening tools of the present invention exhibit a high sensitivity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and about 100%.
  • the sensitivity is from about 75% to about 99%, or from about 80% to about 90%, or from about 80% to about 85%.
  • the specificity is from about 75% to about 99%, or from about 80% to about 90%, or from about 80% to about 85%.
  • the present invention enables the screening of at-risk populations for the early detection of cancers, for example pancreatic cancer. Furthermore, in certain aspects, the present invention enables the differentiation of neoplastic (e.g. malignant) from benign (i.e. non-cancerous) cellular proliferative disorders.
  • neoplastic e.g. malignant
  • benign i.e. non-cancerous
  • biomarkers such as 2 or more biomarkers of the invention
  • practical considerations may dictate use of one or more biomarkers and smaller combinations thereof.
  • Any combination of markers for a specific cancer may be used which comprises 1, 2, 3, 4, 5, 6, 7 or more markers. Combinations of 1, 2, 3, 4, 5, 6, 7 or more markers can be readily envisioned given the specific disclosures of individual markers provided herein.
  • the prognostic methods can be used to identify patients with cancer or at risk of cancer. Such patients can be offered additional appropriate therapeutic or preventative options, including endoscopic polypectomy or resection, and when indicated, surgical procedures, chemotherapy, radiation, biological response modifiers, or other therapies. Such patients may also receive recommendations for further diagnostic or monitoring procedures, including but not limited to increased frequency of colonoscopy, virtual colonoscopy, video capsule endoscopy, PET-CT, molecular imaging, or other imaging techniques.
  • the subject diagnosed with cancer or at risk for having a proliferative disease can be treated against the disease.
  • the method comprises administering to the subject a therapeutically effective amount of a therapeutic agent, thereby treating a subject having or at risk for having a proliferative disease.
  • Anti-cancer drugs may be used in the various embodiments of the invention, including in pharmaceutical compositions and dosage forms and kits of the invention.
  • One type of anti-cancer drug includes cytotoxic agents (i.e., drugs that kill cancer cells in different ways). These include the alkylating agents, antimetabolites, antitumor antibiotics, and plant drugs.
  • Another type of anti-cancer drug includes hormones and hormone antagonists. Some tumors require the presence of hormones to grow. Many of these drugs block the effects of hormones at its tissue receptors or prevent the manufacture of hormones by the body.
  • Another type of anti-cancer drug includes biological response modifiers. These drugs increase the body's immune system to detect and destroy the cancer.
  • Non-limiting examples of anti-cancer drugs include but are not limited to: acivicin; aclarubicin; acodazole hydrochloride; acronine; adozelesin; aldesleukin; altretamine; ambomycin; ametantrone acetate; aminoglutethimide; amsacrine;
  • anastrozole anthramycin; asparaginase; asperlin; azacitidine; azetepa; azotomycin; batimastat; benzodepa; bicalutamide; bisantrene hydrochloride; bisnafide dimesylate; bizelesin; bleomycin sulfate; brequinar sodium; bropirimine; busulfan; cactinomycin; calusterone; caracemide; carbetimer; carboplatin; carmustine; carubicin
  • hydrochloride carzelesin; cedefingol; chlorambucil; cirolemycin; cisplatin;
  • dactinomycin dactinomycin
  • daunorubicin hydrochloride decitabine
  • dexormaplatin dezaguanine
  • dezaguanine mesylate diaziquone
  • docetaxel docetaxel
  • doxorubicin doxorubicin
  • duazomycin edatrexate; eflornithine hydrochloride; elsamitrucin; enloplatin;
  • masoprocol maytansine; mechlorethamine, mechlorethamine oxide hydrochloride rethamine hydrochloride; megestrol acetate; melengestrol acetate; melphalan;
  • menogaril mercaptopurine
  • methotrexate methotrexate sodium
  • metoprine metoprine
  • meturedepa mitindomide; mitocarcin; mitocromin; mitogillin; mitomalcin; mitomycin; mitosper; mitotane; mitoxantrone hydrochloride; mycophenolic acid; nocodazole; nogalamycin; ormaplatin; oxisuran; paclitaxel; pegaspargase;
  • peliomycin pentamustine; peplomycin sulfate; perfosfamide; pipobroman;
  • piposulfan piroxantrone hydrochloride; plicamycin; plomestane; porfimer sodium; porfiromycin; prednimustine; procarbazine hydrochloride; puromycin; puromycin hydrochloride; pyrazofurin; riboprine; rogletimide; safingol; safingol hydrochloride; semustine; pumprazene; sparfosate sodium; sparsomycin; spirogermanium
  • hydrochloride spiromustine; spiroplatin; streptonigrin; streptozocin; sulofenur;
  • talisomycin tecogalan sodium; tegafur; teloxantrone hydrochloride; temoporfin; teniposide; teroxirone; testolactone; thiamiprine; thioguanine; thiotepa; tiazofurin; tirapazamine; toremifene citrate; trestolone acetate; triciribine phosphate;
  • trimetrexate trimetrexate glucuronate; triptorelin; tubulozole hydrochloride; uracil mustard; uredepa; vapreotide; verteporfin; vinblastine sulfate; vincristine sulfate; vindesine; vindesine sulfate; vinepidine sulfate; vinglycinate sulfate; vinleurosine sulfate; vinorelbine tartrate; vinrosidine sulfate; vinzolidine sulfate; vorozole;
  • zeniplatin zinostatin; zorubicin hydrochloride, improsulfan, benzodepa, carboquone, triethylenemelamine, triethylenephosphoramide, triethylenethiophosphoramide, trimethylolomelamine, chlornaphazine, novembichin, phenesterine, trofosfamide, estermustine, chlorozotocin, gemzar, nimustine, ranimustine, dacarbazine, mannomustine, mitobronitol,aclacinomycins, actinomycin F(l), azaserine, bleomycin, carubicin, carzinophilin, chromomycin, daunorubicin, daunomycin, 6-diazo-5-oxo-l- norleucine, doxorubicin, olivomycin, plicamycin, porfiromycin, puromycin, tubercidin, zorubicin, denopterin, pteropterin, 6-mer
  • adecypenol adozelesin; aldesleukin; ALL-TK antagonists; altretamine; ambamustine; amidox; amifostine; aminolevulinic acid; amrubicin; amsacrine; anagrelide;
  • anastrozole andrographolide; angiogenesis inhibitors; antagonist D; antagonist G; antarelix; anti-dorsalizing morphogenetic protein- 1 ; antiandrogen, prostatic carcinoma; antiestrogen; antineoplaston; antisense oligonucleotides; aphidicolin glycinate; apoptosis gene modulators; apoptosis regulators; apurinic acid; ara-CDP-
  • DL-PTBA arginine deaminase; asulacrine; atamestane; atrimustine; axinastatin 1 ; axinastatin 2; axinastatin 3; azasetron; azatoxin; azatyrosine; baccatin III derivatives; balanol; batimastat; BCR/ABL antagonists; benzochlorins; benzoylstaurosporine; beta lactam derivatives; beta-alethine; betaclamycin B; betulinic acid; bFGF inhibitor; bicalutamide; bisantrene; bisaziridinylspermine; bisnafide; bistratene A; bizelesin; breflate; bropirimine; budotitane; buthionine sulfoximine; calcipotriol; calphostin C; camptothecin derivatives; canarypox IL-2; capecita
  • combretastatin analogue conagenin; crambescidin 816; crisnatol; cryptophycin 8; cryptophycin A derivatives; curacin A; cyclopentanthraquinones; cycloplatam;
  • cypemycin cytarabine ocfosfate; cytolytic factor; cytostatin; dacliximab; decitabine; dehydrodidemnin B; deslorelin; dexamethasone; dexifosfamide; dexrazoxane;
  • dexverapamil diaziquone; didemnin B; didox; diethylnorspermine; dihydro-5- azacytidine; dihydrotaxol, 9-; dioxamycin; diphenyl spiromustine; docetaxel;
  • docosanol dolasetron; doxifluridine; droloxifene; dronabinol; duocarmycin SA; ebselen; ecomustine; edelfosine; edrecolomab; eflornithine; elemene; emitefur;
  • etanidazole etoposide phosphate; exemestane; fadrozole; trasrabine; fenretinide; filgrastim; finasteride; flavopiridol; flezelastine; fluasterone; fludarabine; fluorodaunorunicin hydrochloride; forfenimex; formestane; fostriecin; fotemustine; gadolinium texaphyrin; gallium nitrate; galocitabine; ganirelix; gelatinase inhibitors; gemcitabine; glutathione inhibitors; hepsulfam; heregulin; hexamethylene
  • ilmofosine ilomastat
  • imidazoacridones imiquimod
  • immunostimulant peptides insulin-like growth factor-1 receptor inhibitor
  • interferon agonists interferons
  • isobengazole isohomohalicondrin B; itasetron; jasplakinolide; kahalalide F;
  • lamellarin-N triacetate lamellarin-N triacetate; lanreotide; leinamycin; lenograstim; lentinan sulfate;
  • leptolstatin letrozole
  • leukemia inhibiting factor leukocyte alpha interferon
  • leuprolide+estrogen+progesterone leuprorelin
  • levamisole liarozole
  • linear polyamine analogue lipophilic disaccharide peptide
  • lipophilic platinum compounds lipophilic platinum compounds
  • B mycobacterial cell wall extract; myriaporone; N-acetyldinaline; N-substituted benzamides; nafarelin; nagrestip; naloxone+pentazocine; napavin; naphterpin;
  • nartograstim nedaplatin
  • nemorubicin nemoronic acid
  • neutral endopeptidase nartograstim; nedaplatin; nemorubicin; nemoronic acid; neutral endopeptidase;
  • nilutamide nisamycin; nitric oxide modulators; nitroxide antioxidant; nitrullyn; 06- benzylguanine; octreotide; okicenone; oligonucleotides; onapristone; ondansetron; ondansetron; oracin; oral cytokine inducer; ormaplatin; osaterone; oxaliplatin;
  • palmitoylrhizoxin pamidronic acid; panaxytriol; panomifene; parabactin;
  • pazelliptine pazelliptine; pegaspargase; peldesine; pentosan polysulfate sodium; pentostatin; pentrozole; perflubron; perfosfamide; perillyl alcohol; phenazinomycin;
  • phenylacetate phosphatase inhibitors
  • picibanil pilocarpine hydrochloride
  • pirarubicin piritrexim; placetin A; placetin B; plasminogen activator inhibitor;
  • platinum complex platinum compounds; platinum-triamine complex; porfimer sodium; porfiromycin; prednisone; propyl bis-acridone; prostaglandin J2; proteasome inhibitors; protein A-based immune modulator; protein kinase C inhibitor; protein kinase C inhibitors, microalgal; protein tyrosine phosphatase inhibitors; purine nucleoside phosphorylase inhibitors; purpurins; pyrazoloacridine; pyridoxylated hemoglobin polyoxyethylene conjugate; raf antagonists; raltitrexed; ramosetron; ras farnesyl protein transferase inhibitors; ras inhibitors; ras-GAP inhibitor; retelliptine demethylated; rhenium Re 186 etidronate; rhizoxin; ribozymes; RII retinamide;
  • rogletimide rohitukine; romurtide; roquinimex; rubiginone Bl ; ruboxyl; safingol; saintopin; SarCNU; sarcophytol A; sargramostim; Sdi 1 mimetics; semustine; senescence derived inhibitor 1 ; sense oligonucleotides; signal transduction inhibitors; signal transduction modulators; single chain antigen binding protein; sizofiran;
  • sobuzoxane sodium borocaptate; sodium phenylacetate; solverol; somatomedin binding protein; sonermin; sparfosic acid; spicamycin D; spiromustine; splenopentin; spongistatin 1 ; squalamine; stem cell inhibitor; stem-cell division inhibitors;
  • stipiamide stromelysin inhibitors; sulfinosine; superactive vasoactive intestinal peptide antagonist; suradista; suramin; swainsonine; synthetic glycosaminoglycans; tallimustine; tamoxifen methiodide; tauromustine; tazarotene; tecogalan sodium; tegafur; tellurapyrylium; telomerase inhibitors; temoporfin; temozolomide;
  • thrombopoietin thrombopoietin mimetic
  • thymalfasin thrombopoietin mimetic
  • thymopoietin receptor agonist thymotrinan
  • thyroid stimulating hormone tin ethyl etiopurpurin; tirapazamine;
  • titanocene bichloride topsentin; toremifene; totipotent stem cell factor; translation inhibitors; tretinoin; triacetyluridine; triciribine; trimetrexate; triptorelin; tropisetron; turosteride; tyrosine kinase inhibitors; tyrphostins; UBC inhibitors; ubenimex;
  • urogenital sinus-derived growth inhibitory factor urokinase receptor antagonists
  • vapreotide variolin B
  • vector system erythrocyte gene therapy
  • velaresol veramine; verdins; verteporfin; vinorelbine; vinxaltine; vitaxin; vorozole; zanoterone; zeniplatin; zilascorb; and zinostatin stimalamer.
  • Preferred additional anti-cancer drugs are 5- fluorouracil and leucovorin.
  • Additional cancer therapeutics include monoclonal antibodies such as rituximab, trastuzumab and cetuximab.
  • the cancer specific glycans e.g., oligosaccharide sequences or analogs or derivatives thereof
  • the treatment may not necessarily cure cancer or tumor but it can reduce tumor burden or stabilize a cancer condition and lower the metastatic potential of cancers.
  • the oligosaccharides or analogs or derivatives thereof can be conjugated, for example, to proteins such as bovine serum albumin or keyhole limpet hemocyanin, lipids or lipopeptides, bacterial toxins such as cholera toxin or heat labile toxin, peptidoglycans, immunoreactive polysaccharides, or to other molecules activating immune reactions against a vaccine molecule.
  • a cancer or tumor vaccine may also comprise a pharmaceutically acceptable carrier and optionally an adjuvant. Suitable carriers or adjuvants are, e.g., lipids known to stimulate the immune response.
  • the saccharides or derivatives or analogs thereof, preferably conjugates of the saccharides, can be injected or administered mucosally, such as orally or nasally, to a cancer patient with tolerated adjuvant molecule or adjuvant molecules.
  • the cancer or tumor vaccine can be used as a medicine in a method of treatment against cancer or tumor.
  • the method is used for the treatment of a human patient.
  • the method of treatment is used for the treatment of cancer or tumor of a patient, who is under immunosuppressive medication or the patient is suffering from immunodeficiency.
  • a pharmaceutical composition comprising the cancer specific oligosaccharide sequences or analogs or derivatives thereof for the treatment of cancer or tumor.
  • the pharmaceutical composition is used for the treatment of a human patient.
  • the pharmaceutical composition is used for the treatment of a human patient.
  • composition is used for the treatment of cancer or tumor, when patient is under immunosuppressive medication or he/she is suffering from
  • the methods of treatment or the pharmaceutical compositions described above are especially preferred for the treatment of cancer or tumor diagnosed to express the cancer specific oligosaccharide sequences of the invention.
  • the methods of treatment or the pharmaceutical compositions can be used together with other methods of treatment or pharmaceutical compositions for the treatment of cancer or tumor.
  • the other methods or pharmaceutical compositions comprise cytostatics, anti-angiogenic pharmaceuticals, anti-cancer proteins, such as interferons or interleukins, or use of radioactivity.
  • Example 1 MALDI Imaging Mass Spectrometry Profiling of N-Glycans in
  • results presented herein are based on the application of a Matrix Assisted Laser Desorption Ionization Imaging Mass Spectrometry (MALDI-IMS) method to spatially profile the location and distribution of multiple N-linked glycan species in clinically derived formalin-fixed paraffin-embedded (FFPE) tissues.
  • MALDI-IMS Matrix Assisted Laser Desorption Ionization Imaging Mass Spectrometry
  • Formalin- fixed tissues from normal mouse kidney, human pancreatic and prostate cancers and a human hepatocellular carcinoma tissue microarray were processed by antigen retrieval followed by on-tissue digestion with peptide N-glycosidase F (PNGaseF).
  • PNGaseF peptide N-glycosidase F
  • the released N-glycans were detected by MALDI-IMS analysis, and the structural composition of a subset of glycans was verified directly by on-tissue collision-induced fragmentation. Other structural assignments were confirmed by off- tissue permethylation analysis combined with multiple database comparisons.
  • the glycan standard NA2 was obtained from ProZyme (Hayward, CA). Trifluoroacetic acid, sodium hydroxide, dimethyl sulfoxide, iodomethane and a- cyano-4-hydroxycinnamic acid (CHCA) were obtained from Sigma-Aldrich (St. Louis, MO). HPLC grade methanol, ethanol, acetonitrile, xylene and water were obtained from Fisher Scientific (Pittsburgh, PA). ITO slides were purchased from Bruker Daltonics (Billerica, MA). Citraconic anhydride for antigen retrieval was from Thermo Scientific (Bellefonte, PA). Recombinant Peptide N-Glycosidase F from Flavobacterium meningosepticum was expressed and purified as previously described (Powers et al, Anal Chem, 2013 85: 9799-9806).
  • mice were excised from euthanized C57BL/6 mice and immediately placed in 10% formalin prior to processing for routine histology and paraffin embedding. Mice were housed in an Institutional Animal Care and Use Committee-approved small animal facility at MUSC, and kidneys were harvested as part of approved projects.
  • a liver TMA was purchased from BioChain consisting of 16 cases of liver cancer in duplicates, and one adjacent normal tissue for each case. Tissues were from 14 male and two female patients with an average age of 47.5 with a range of 33 to 68 years old, with additional information provided in Table 2.
  • a de- identified prostate tumor FFPE block stored for 10 years representing a Gleason grade 6 (3+3)/stage T2c adenocarcinoma from a 62 year old Caucasian male, was obtained from the Hollings Cancer Center Biorepository at the Medical University of South Carolina. A pathologist confirmed the presence of approximately 10% prostate cancer gland content in the sample.
  • a de-identified large-cell undifferentiated pancreatic carcinoma FFPE tissue section with low CA19-9 staining was obtained from the Van Andel Institute Biospecimen Repository. For each section analyzed, histological analysis and staining with hematoxylin and eosin (H & E) were performed.
  • Tissue and TMA blocks were sectioned at 5 ⁇ and mounted on MALDI-IMS ITO slides.
  • the slides were heated at 60°C for lHr. After cooling, tissue sections were deparaffinized by washing twice in xylene (3 minutes each).
  • Tissue sections were then rehydrated by submerging the slide twice in 100% ethanol (1 minute each), once in 95% ethanol (one minute), once in 70% ethanol (one minute), and twice in water (3 minutes each). Following the wash, the slide was transferred to a coplin jar containing the citraconic anhydride buffer for antigen retrieval and the jar was placed in a vegetable steamer for 25 minutes.
  • Citraconic anhydride (Thermo) buffer was prepared by adding 25 ⁇ ., citraconic anhydride in 50mL water, and adjusted to pH 3 with HCI. After allowing the buffer to cool, the buffer was exchanged with water five times by pouring out 1/2 of the buffer and replacing with water, prior to replacing completely with water on the last time. The slide was then desiccated prior to enzymatic digestion. Tris buffer pH 9-10 was also effective, but the citraconic anhydride buffer for all experiments in this study.
  • PNGaseF sprayed mouse kidney tissue slides were incubated for 2 hr at 37°C; 50 ⁇ , water was applied on top of the tissue and incubated for 20 minutes to extract the released native N-glycans. The water was removed from the tissue, then concentrated under vacuum by centrifugation. Permethylation was performed as described (Powers et al., Anal Chem, 2013 85: 9799-9806), and glycans analyzed by MALDI. Masses detected in the permethylation experiments were searched against the permethylated glycan database provided by the Consortium for Functional Glycomics (www. functionalglycomics . org) .
  • Glycan standards were spotted on a stainless steel MALDI plate using CHCA matrix and desiccated to yield a homogenous layer.
  • Tissues were prepared as previously described for MALDI imaging of FFPE tissues. 10 spectra of 1000 laser shots with a laser frequency of 1000 Hz were averaged for each spectra provided. The collision energy varied between 60-70V.
  • Mass spectra from TMA tissue Regions of Interest (ROIs) representing each tissue core were exported directly from Flexlmaging and analyzed using an in- house workflow.
  • the peak lists were first deconvoluted followed by calculating the mean peak intensity of points in each ROI, resulting in a monoisotopic peak list corresponding to signal intensity in each region. Comparison of tumor versus non- tumor was accomplished with a Wilcoxon rank sum test followed by Benjamini- Hochberg correction.
  • Mouse kidney tissues were fixed in formalin and used as an initial model system to develop MALDI-IMS glycan imaging workflows for FFPE tissues. These tissues were chosen due to the availability of reference glycan structures and spectra (Consortium for Functional Glycomics; www.functionalglycomics.org), and previous MALDI-IMS glycan imaging data for fresh frozen mouse kidney tissue analysis (Powers et al, Anal Chem, 2013 85: 9799-9806).
  • a summary workflow schematic is provided ( Figure 1). Tissues were cut at 5 microns, deparaffinized and rehydrated in sequential xylene/ethanol/water rinses, followed by antigen retrieval in citraconic anhydride pH 3.
  • the rehydrated tissues were sprayed with PNGaseF, incubated for glycan release, and then analyzed by MALDI-IMS. As shown in Figure 2, there were multiple ions detectable only in the tissue incubated with PNGaseF that were not present in the control tissue with no PNGaseF application. Different glycans were distributed across the cortex or medulla regions. For example, a
  • An overlay of the MALD-IMS images for these two ions from the PNGaseF treated sections ( Figure 2E) and the control tissue ( Figure 2F) demonstrated that these two ions were released by PNGaseF.
  • a summary glycan image panel of 28 glycan ions detected in these kidneys, sodium adducts and observed/expected m/z values is provided in Figure 7.
  • N-glycans were extracted from the tissue following on-tissue PNGaseF digestion, permethylated and analyzed by MALDI.
  • a representative spectra from this analysis is provided in Figure 8. These permethylated values were also compared with MALDI reference spectra for mouse kidney glycans from the Consortium for Functional Glycomics.
  • the imaged glycan ions were correlated to the reference spectra glycans, illustrated in Figure 9, and could be matched to all 28 glycan species highlighted in the reference spectra.
  • the glycan structures identified by imaging of the FPPE tissue blocks were assigned based on the comparison to permethylated species, glycan reference databases and previous studies (Powers et al, Anal Chem, 2013 85: 9799-9806).
  • An on-tissue approach to further verify N-glycan structures was done using collision- induced dissociation (CID) directly on the human pancreatic tissue. Released native glycans from pancreatic cancer FFPE tissues were used as a source for on-tissue CID analysis, and a representative MALDI spectra of these glycans is shown in Figure 5A.
  • the same glycan ion was abundant in pancreatic tissue after PNGaseF release of N-glycans ( Figure 3) and was selected for CID.
  • N-glycan analysis on FFPE tissues enables the analysis of multiple FFPE tissue cores in a TMA format.
  • Initial experiments were performed using a commercially available hepatocellular carcinoma (HCC) TMA (BioChain) consisting of samples from 16 individual patients, with two tumor tissue cores and one non-tumor tissue core per patient (Figure 7). Additional patient data are provided in Table 2.
  • a list of mono isotopic ions that were identified in the Liver TMA were cross- referenced against a library of known glycan m/z values.
  • N-linked glycans can be directly profiled from FFPE tissue blocks and TMAs while maintaining intact architecture.
  • the basic methodology which mirrors that of MALDI-IMS analysis of peptides in FFPE tissues and TMAs (Groseclose et al, Proteomics, 2008, 8: 3715-3724; Quaas et al, Histopathology, 2013, 63 : 455-462; Casadonte et al., Nat Protoc, 2011 , 6: 1695-1709), requires deparaffinization and antigen retrieval prior to PNGaseF application.
  • polymer peaks seem to vary in terms of intensity compared to N- glycan ions depending on what tissue is being used. It is possible that this variation is a function of different formalin formulations, variations is tissue processing (Le. amount of time in formalin), storage time or variations in the tissue itself (Thompson et al, Proteomics Clin Appl, 2013, 7(3-4):241-51; Craven et al, Proteomics Clin Appl, 2013, 7(3-4):273-82). These considerations can be further monitored and evaluated as more glycans from FFPE tissues are analyzed.
  • N-glycan MALDI-IMS of the BCC TMA ( Figure 7) is provided as an example, but an initial glycan profiling data has already been obtained from TMAs representing prostate, lung, breast, colon, and pancreas cancers.
  • the data analysis identified a total of 176 ions in the tissue, with the majority of significantly different ions being increased in HCC relative to non-tumor tissue, including 21 known or previously identified glycans. It is unclear how this trend of increased glycan levels relates specifically to tumor related biochemical changes, though the role of glycosylation in tumor development is well documented. Future work will also focus on determining the identity of the remaining ions to distinguish other glycan species from the aforementioned polymer peak contaminants. Evaluation of methods to stabilize larger branched chain sialic acid containing glycans is also ongoing. Overall, the ability to effectively profile N- glycans on FFPE tissue blocks and TMAs provides new opportunities to evaluate glycan profiles associated with disease status.
  • Example 2 A Novel Glycomic Approach to Identify Pancreatic Cancer Disease Markers in FFPE Tissue Blocks and Microarrays
  • Pancreatic cancer represents one of the most deadly cancers, both in terms cancer related deaths per year and 5-year survival rates.
  • the dismal 5-year survival rate following detection which is around 6%, can be attributed to poor understanding of disease etiology, rapid disease progression, late diagnosis, limited effective treatment options, and resistance to therapeutic intervention (Ma J et al, Future Oncol Lond Engl, 2013 Jul; 9(7):917— 9).
  • Patient outcome is significantly improved when the cancer is detected at an early stage and before it has spread to regional lymph nodes or metastasized to distant sites.
  • pancreatic cancer pancreatic cancer
  • Wilfgang CL et al AJR Am J Roentgenol, 201 1 Dec; 197(6): 1343-50
  • SEER Stat Fact Sheets Pancreatic Cancer [Internet].
  • National Cancer Institute Surveillance, Epidemiology, and End Results Program, [cited 2015 Apr 14]. Available from: http://seer.cancer.gov/statfacts/html/pancreas.html; Al Haddad AHI et al, Expert Opin Investig Drugs, 2014 Nov; 23(1 1): 1499-515).
  • glycoproteins and glycan antigens are ideal targets for initial discovery phase disease marker studies, as they [1] are prevalent in biological fluids, [2] play a direct role in cancer progression and metastasis, and [3] represent a majority of the protein-based FDA approved biomarkers. Both individual glycan antigens and alterations in the glycosylation biosynthetic machinery have been implicated in pancreatic cancer.
  • the carbohydrate antigen 19-9 (SLeA, CA19-9) is the most well defined disease marker for pancreatic cancer and the target of the only FDA approved blood test for the management of pancreatic cancer (Koprowski H et al, Somatic Cell Genet, 1979 Nov; 5(6):957-71 ; Fong ZV et al., Cancer J Sudbury Mass, 2012 Dec; 18(6):530-8). Functionally, CA19-9 has been implicated in disease metastasis through the interaction of the antigen with endothelium E-selectin (Ugorski M et al, Acta Biochim Pol, 2002; 49(2):303-l 1).
  • CA19-9 is not used as a clinical disease marker for pancreatic cancer, as elevated levels are detected in benign diseases and other sites of cancer (Marrelli D et al, Am J Surg, 2009 Sep; 198(3):333-9; Parra JL et al, Dig Dis Sci, 2005 Apr; 50(4):694-5; Nie S et al, J Proteome Res, 2014 Apr 4; 13(4): 1873- 84).
  • pancreatic cancer Between 5-15% of the population do not express fucosyltransferase 3 and are therefore incapable of expressing CA19-9, even with advanced pancreatic cancer (Fong ZV et al, Cancer J Sudbury Mass, 2012 Dec; 18(6):530-8; Nie S et al, J Proteome Res, 2014 Apr 4; 13(4): 1873-84). While elevated CA19-9 is the most pronounced glycosylation-related change in pancreatic cancer, global alterations in glycosylation have been detected. At a fundamental level, the malignant processes resulting in the formation of pancreatic cancer involve several common mutations in oncogenes and tumor suppressors. These mutations ultimately affect the glycan biosynthesis pathway in pancreatic cancer.
  • pancreatic cancer results in a hypoxic microenvironment and initiates anaerobic glucose metabolism through glycolysis (Upadhyay M et al, Pharmacol Ther, 2013 Mar; 137(3):318-30; Guillaumond F et al, Arch Biochem Biophys, 2014 Mar;
  • glycosyltransferases In addition to enzymatic regulation of the hexosamine biosynthetic pathway, glycosyltransferases have been reported to be alternatively expressed in pancreatic cancer (Perez-Garay M et al., Int J Biochem Cell Biol, 2013 Aug; 45(8): 1748-57; Taniuchi K et al., Oncogene, 2011 Dec 8; 30(49):4843-54; Mas E et al, Glycobiology, 1998 Jun; 8(6):605-13; Bassaganas S et al, Cytokine, 2015 Apr 28; 75(1): 197-206).
  • the results presented herein demonstrate how MALDI-IMS of N- linked glycans can be adapted to the identification of disease markers for pancreatic cancer.
  • the study reports MALDI-IMS of N-glycans in TMAs and whole tissue blocks, followed by subsequent glycan characterization by mass accuracy and derivatization.
  • analysis of tissue blocks can be further interrogated to look at glycosylation patterns in histological regions other than the tumor and non-tumor regions.
  • N-glycan distribution was correlated with CA19-9 staining on serial slides, to determine any correlations between CA19-9 and released N-glycans.
  • Trifluoroacetic acid, a-cyano-4-hydroxycinnamic acid (CHCA), sodium hydroxide (NaOH), and 1-hydroxybenzotriazole hydrate (HOBt) were obtained from Sigma-Aldrich (St. Louis, MO).
  • HPLC grade methanol, ethanol, acetonitrile, xylene and water were obtained from Fisher Scientific (Pittsburgh, PA).
  • l-(3-Dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC) was obtained from Oakwood Chemical (West Columbia, SC).
  • Tissue Tack microscope slides were purchased from Polysciences, Inc (Warrington, PA).
  • Citraconic anhydride for antigen retrieval was from Thermo Scientific (Bellefonte, PA).
  • PNGaseF Glycosidase F
  • Flavobacterium meningosepticum was expressed and purified as previously described, and is available commercially as PNGase F PrimeTM from Bulldog Bio (Portsmouth, NH) (Powers TW et al, Anal Chem, 2013 Oct 15; 85(20):9799-806).
  • Cotton tips for HILIC enrichment of N-glycans were produced using 100% cotton swabs from Assured (Rio Collins, NM).
  • Tissues and TMA blocks were sectioned at 5 ⁇ and mounted on slides (25 x 75mm) compatible with the Bruker slide adaptor. After mounting, sections were dewaxed and antigen retrieval proceeded as described (Powers TW et al., PloS One, 2014; 9(9):el06255).
  • PNGaseF (20 ⁇ g/slide) was applied to slide-mounted tissue blocks and TMAs using the ImagePrep spray station (Bruker Daltonics) as previously described (Powers TW et al, PloS One, 2014; 9(9):el06255). N-glycan release occurred during a 2hr incubation at 37°C in a humidified chamber, followed by desiccation and matrix application.
  • a-Cyano-4-hydroxycinnamic acid matrix (CHCA), consisting of 0.021g CHCA in 3mL 50% acetonitrile/50% water and 12 ⁇ , 25%TFA, was applied using the ImagePrep sprayer.
  • CHCA The TM-Sprayer (HTX Imaging) was used to coat slides containing whole tissue blocks with CHCA.
  • CHCA was prepared at a concentration of 5mg/mL in 50% ACN/50% H 2 0 at 0.1 % TFA.
  • CHCA was applied at 70°C at 0.0017mg/mm 2 .
  • N-glycans were extracted from slides as described previously and dried by vacuum centrifugation (Powers TW et al, Anal Chem, 2013 Oct 15; 85(20):9799- 806).
  • the ethyl esterification protocol, including the modification and enrichment, was adapted from Reiding et al. (Reiding KR et al, Anal Chem, 2014 May 28;
  • TMAs Serial sections of five of the TMAs were stained with CA19-9 and SLeX. Individual cores were manually scored for the presence or absence of stain and annotated for the presence of tumor or non-tumor tissue. Sensitivity, specificity, positive predictive value and negative predictive value were calculated for both stains individually across the five TMAs.
  • Imaging data was loaded into Flexlmaging 4.1 (Bruker Daltonics) for visual analysis of tissue blocks and TMAs. TMAs were searched against an N-glycan library to identify N-glycans present across all six TMAs. For computational peak picking of the TMAs, Regions of Interest (ROIs) representing each tissue core were exported from data analysis using the hierarchical clustering option and further processed using an in-house workflow.
  • ROIs Regions of Interest
  • N-glycans present in all six
  • TMAs were manually tabulated along with the accurate mass of the glycans.
  • m/z values were reported from the imaging peak picking tool as well as individual spectra from a tissue imaging experiment.
  • To generate the m/z values from individual spectra one spectra localized to the tumor tissue and one spectra localized to the non-tumor tissue were loaded into FTMS processing (Bruker Daltonics) and recalibrated. PPM errors were calculated for both the picked peaks and the individual spectra.
  • Supervised machine learning was used to train classifiers of pancreatic cancer using a 2/3 training set (52 tumor and 49 non-tumor) equally distributed across arrays.
  • the following approaches were evaluated: random forest, support vector machine (linear and radial basis function kernel), Naive Bayes classifier, discriminant analysis (linear and quadratic), and artificial neural networks.
  • Classifiers were trained using interarray normalized data for the known 24 glycans as well as smaller groups of glycans determined using forward sequential feature selection with 10-fold cross- validation. Final models were qualified using an independent 1/3 test set (23 tumor and 25 non-tumor). The best performing classifier was linear discriminant analysis (LDA) model.
  • LDA linear discriminant analysis
  • the LDA had an error rate of 0.2083 and a sensitivity of 0.7826 and a specificity of 0.8000.
  • LDA comprising two masses, 2320.75 (Hex6dHex2HexNAc5 + INa) and 2742.93 (Hex7dHexlHexNAc7 + INa) m/z, had an error rate of 0.1458, a sensitivity of 0.8696, and a specificity of 0.8400.
  • Plasma from patients with pancreatitis and PDAC were pooled together for glycomic analysis.
  • ⁇ of plasma from each pool was diluted in 90 ⁇ , H 2 0 prior to digestion with ⁇ g PNGaseF overnight at 37°C.
  • N-glycans were extracted from the sample following the addition of 400 ⁇ . MeOH, ⁇ . CHCI 3 , and 300 ⁇ , H 2 0 and centrifugation at 14,000 x g for 2 minutes.
  • the aqueous phase was collected, dried by vacuum centrifugation, and ethyl esterified as described (Reiding KR et al, Anal Chem, 2014 May 28; 86(12):5784-5793).
  • N-Glycan Variation in Complex Histopathology Regions FFPE tissue blocks containing both pancreatic cancer and matched non-cancer tissues, as well as other complex histopathology regions, were selected for N-glycome analysis by MALDI-IMS. N-glycans in these tissue sections displayed patterns of regionalized distribution that correlated with histopathology analysis by H&E staining. In the example provided, the regions of the tissue section are outlined in different colors, with each color corresponding to a different histological region, as annotated by a pathologist ( Figure 14A).
  • pancreas tumor/ precancerous lesions green
  • intestine mucosa yellow
  • fibroadipose connective tissue blue
  • smooth muscle oval
  • non-tumor pancreas tissue red
  • N-glycans were identified that were either highly elevated or nearly exclusively detected in each region.
  • N-glycans were identified in the tissue section that correspond to the glycan library, most having a distribution that correlates with a certain histology-derived region of the pancreas.
  • the most abundant N-glycans are annotated in the overall average spectra from the imaging experiment ( Figure 14G).
  • a selected panel of N-glycans demonstrates the differential distribution of N-glycans across the tissue section ( Figure 15).
  • N-Glycan Discriminators for Pancreatic Cancer As N-glycan distribution varies between tumor and non-tumor regions of the pancreas in whole tissue blocks, the study determined if individual glycans were consistently altered between tumor and non-tumor tissue sections across different patients. To this end, a higher-throughput study based on the analysis of six TMAs was performed to allow a larger number of samples to be surveyed in a reduced amount of time. Each TMA consisted of patient matched tumor and non- tumor tissue cores.
  • Figure 16 provides a detailed workflow schematic of the data processing steps utilized to generate individual or panels of N-glycans that can distinguish tumor from non-tumor tissue cores.
  • Hex6dHexlHexNAc5 was primarily localized to the tumor tissue, but was frequently detected in regions not characterized as tumor tissue by a pathologist (Figure 17A).
  • Table 3 Individual Glycan Discriminators for Pancreatic Cancer.
  • N-glycans improved the ability of the platform to distinguish tumor from non-tumor tissues, both visually in the MALDI-IMS data and empirically in the statistical data.
  • the visual image overlay of Hex6dHexlHexNAc5 (green) and Hex6HexNAc2 (red) in both the tissue blocks (Figure 17C) and TMA (Figure 17G) yields more pronounced differences between the tumor from non-tumor tissue regions, as outlined by the pathologist (Figure 17D).
  • a supervised machine learning algorithm was applied across the glycans identified in all six TMAs, focusing on the ability of the multiple N-glycans to distinguish tumor from non-tumor tissue cores (Table 4).
  • 2/3 rd of the data was subjected to a sequential feature selection process to reduce the number of variables used while minimizing the misclassification error of the LDA model, and the remaining l/3 rd was used as a validation set.
  • sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated based off of the markers ability to distinguish tumor from non-tumor tissue sections.
  • MALDI-IMS Analysis of N-Glycans can be Utilized as a Disease Marker Identification Platform
  • Hex6dHexlHexNAc5 and Hex6HexNAc2 is able to visually differentiate tumor from non-tumor tissues in whole tissue blocks and TMAs (Figure 17).
  • the 24 peaks corresponding to N-glycans were subjected to supervised machine learning algorithms and sequential feature selection to identify N-glycan panels that are capable of distinguishing tumor from non-tumor tissue cores.
  • the LDA model highlights the importance of Hex7dHexlHexNAc7 and
  • Hex6dHex2HexNAc5 in distinguishing tumor from non-tumor sections ( Figure 18). This model outperforms CA19-9 staining metrics on serial TMA sections (Table 4). Both comparisons mentioned above, Hex6dHexlHexNAc5 vs. Hex6HexNAc2 and Hex7dHexlHexNAc7 vs. Hex6dHex2HexNAc5, contrast N-glycans with a large Log 2 FC and a p-value ⁇ 0.05 with N-glycans with a negative Log 2 FC and p-value > 0.05 (Table 3).
  • Hex6dHexlHexNAc5 and Hex6HexNAc2 are able to differentiate both low and high grade IPMN lesions from the non-tumor pancreas tissue.
  • Hex6dHexlHexNAc5 ( Figure 17A, top tissue) has a higher relative intensity in the tumor regions than the pre-cancerous lesions, demonstrating a signature gradient that coincides with cancer stage.
  • not all of these lesions ultimately become cancerous so additional tests are required to determine if these glycans can distinguish lesions that will become cancerous from lesions that will remain dormant.
  • one of the most significant limitations of cancer biomarkers is their poor ability to differentiate tumor from benign diseases.
  • the present study demonstrates that a novel disease marker discovery platform was developed that utilizes MALDI-IMS of N-linked glycans in a high- throughput TMA study. Expression levels of individual and panels of N-glycans were shown to be capable of differentiating tumor from non-tumor tissue, as well as other complex pathologies. Furthermore, the developed platform described herein can be applied to any disease for initial screening purposes.

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Abstract

La présente invention porte sur des procédés et sur des compositions pour le profilage de glycanes dans un échantillon biologique. Dans un mode de réalisation, le procédé met en œuvre la génération de panneaux de glycanes multiples associés à un état pathologique. L'invention porte également sur l'utilisation de panneaux de glycanes pour le diagnostic et le dépistage d'états pathologiques, en particulier dans le domaine de la biologie du cancer.
PCT/US2015/047889 2014-09-03 2015-09-01 Panneaux de glycanes constituant des biomarqueurs de tissus de tumeur spécifiques WO2016036705A1 (fr)

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CN114002030A (zh) * 2021-10-15 2022-02-01 北京龙迈达斯科技开发有限公司 一种复合型组织芯片及其制备方法
CN114660290A (zh) * 2022-03-25 2022-06-24 中国医学科学院北京协和医院 预测甲状腺癌术后复发的糖链标志物及其应用
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WO2023102443A3 (fr) * 2021-11-30 2023-08-03 Venn Biosciences Corporation Diagnostic du cancer du pancréas à l'aide d'une quantification ciblée d'une glycosylation de protéine spécifique à un site

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CN106950379B (zh) * 2017-03-02 2019-01-22 江苏先思达生物科技有限公司 一种肺癌监测试剂盒及其使用方法
CN106950379A (zh) * 2017-03-02 2017-07-14 先思达（南京）生物科技有限公司 一种肺癌监测试剂盒及其使用方法
CN106950380A (zh) * 2017-03-02 2017-07-14 先思达（南京）生物科技有限公司 一种胃癌监测试剂盒及其使用方法
CN106950380B (zh) * 2017-03-02 2019-01-11 江苏先思达生物科技有限公司 一种胃癌监测试剂盒及其使用方法
JP2020516892A (ja) * 2017-04-14 2020-06-11 ジュノー セラピューティクス インコーポレイテッド 細胞表面グリコシル化を評価するための方法
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WO2018191723A1 (fr) * 2017-04-14 2018-10-18 Juno Therapeutics, Inc. Procédés d'évaluation de la glycosylation de surface cellulaire
US10837970B2 (en) 2017-09-01 2020-11-17 Venn Biosciences Corporation Identification and use of glycopeptides as biomarkers for diagnosis and treatment monitoring
US11624750B2 (en) 2017-09-01 2023-04-11 Venn Biosciences Corporation Identification and use of glycopeptides as biomarkers for diagnosis and treatment monitoring
CN109870515A (zh) * 2017-12-01 2019-06-11 中国科学院大连化学物理研究所 一种基于中药色谱-质谱高维图像数据库的中药识别方法
CN109870515B (zh) * 2017-12-01 2021-12-24 中国科学院大连化学物理研究所 一种基于中药色谱-质谱高维图像数据库的中药识别方法
WO2019150653A1 (fr) * 2018-02-05 2019-08-08 株式会社島津製作所 Dispositif de traitement d'affichage, système de spectrométrie de masse d'imagerie et procédé de traitement d'affichage
JPWO2019150653A1 (ja) * 2018-02-05 2020-11-19 株式会社島津製作所 表示処理装置、イメージング質量分析システムおよび表示処理方法
CN112654642A (zh) * 2018-06-01 2021-04-13 Musc研究发展基金会 蛋白质和细胞的聚糖分析
JP2021526654A (ja) * 2018-06-01 2021-10-07 エムユーエスシー ファウンデーション フォー リサーチ ディベロップメント タンパク質と細胞のグリカン分析
EP3802624A4 (fr) * 2018-06-01 2022-03-23 Musc Foundation for Research Development Analyse de glycanes de protéines et de cellules
CN113383236A (zh) * 2018-11-23 2021-09-10 新加坡科技研究局 多属性鉴定未知生物样品的方法
WO2020106218A1 (fr) * 2018-11-23 2020-05-28 Agency For Science, Technology And Research Procédé d'identification d'un échantillon biologique inconnu à partir de multiples attributs
CN109682974A (zh) * 2018-12-29 2019-04-26 江苏先思达生物科技有限公司 一种胰腺癌检测试剂及其在胰腺癌检测中的应用
WO2023034383A3 (fr) * 2021-09-01 2023-04-13 The Regents Of The University Of California Diagnostics cliniques utilisant des glycanes
CN114002030A (zh) * 2021-10-15 2022-02-01 北京龙迈达斯科技开发有限公司 一种复合型组织芯片及其制备方法
WO2023102443A3 (fr) * 2021-11-30 2023-08-03 Venn Biosciences Corporation Diagnostic du cancer du pancréas à l'aide d'une quantification ciblée d'une glycosylation de protéine spécifique à un site
CN114660290A (zh) * 2022-03-25 2022-06-24 中国医学科学院北京协和医院 预测甲状腺癌术后复发的糖链标志物及其应用
CN114660290B (zh) * 2022-03-25 2022-11-15 中国医学科学院北京协和医院 预测甲状腺癌术后复发的糖链标志物及其应用

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