JP2007520995A - Biomarkers and methods for determining susceptibility to epidermal growth factor receptor modulators - Google Patents

Biomarkers and methods for determining susceptibility to epidermal growth factor receptor modulators Download PDF

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JP2007520995A
JP2007520995A JP2006500841A JP2006500841A JP2007520995A JP 2007520995 A JP2007520995 A JP 2007520995A JP 2006500841 A JP2006500841 A JP 2006500841A JP 2006500841 A JP2006500841 A JP 2006500841A JP 2007520995 A JP2007520995 A JP 2007520995A
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biomarker
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トーマス・ジャヌアリオ
ルーカス・シー・アムラー
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ブリストル−マイヤーズ スクイブ カンパニーBristol−Myers Squibb Company
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574Immunoassay; Biospecific binding assay; Materials therefor for cancer
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    • C12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/74Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving hormones or other non-cytokine intercellular protein regulatory factors such as growth factors, including receptors to hormones and growth factors
    • CCHEMISTRY; METALLURGY
    • C12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00Oligonucleotides characterized by their use
    • C12Q2600/106Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2500/00Screening for compounds of potential therapeutic value
    • G01N2500/04Screening involving studying the effect of compounds C directly on molecule A (e.g. C are potential ligands for a receptor A, or potential substrates for an enzyme A)
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00Detection or diagnosis of diseases
    • G01N2800/52Predicting or monitoring the response to treatment; Prognosis

Abstract

A method of identifying a mammal that is therapeutically responsive to a cancer therapy comprising administration of an EGFR modulator, comprising: (a) exposing the mammal to an EGFR modulator; (b) after exposure in step (a); Measuring the level of at least one biomarker in the mammal, wherein the level is compared to the level of the at least one biomarker measured in the mammal not exposed to the EGFR modulator An EGFR biomarker useful in a method characterized in that a difference in the level of the measured at least one biomarker indicates that the mammal will respond therapeutically to the cancer treatment.

Description

  The present invention relates generally to the field of pharmacogenomics, and more particularly to methods and procedures for determining patient susceptibility to enable the development of individualized genetic profiles, such individualized genetic profiles being It helps to treat diseases and disorders based on the patient's molecular response.

  Cancer is a histologically heterogeneous disease. Although conventional histological and clinical features are associated with prognosis, the same apparent prognostic type tumors differ greatly in response to treatment and the patient's subsequent survival.

  New prognostic and predictive markers that facilitate treatment personalization for each patient are needed to accurately predict the patient's response to treatments such as small molecules and biological molecular drugs in the clinic. This problem is solved by the identification of new parameters that can better predict the patient's sensitivity to treatment. Patient sample classification is an important aspect of cancer diagnosis and treatment. The association of patient response to therapy with molecular and genetic markers can open up new opportunities for the development of therapy in non-responding patients or, among other treatment options, because of greater certainty in effectiveness Can identify indications. In addition, pre-selecting patients who tend to respond well to drugs, drugs, or combination therapies reduces the number of patients required in clinical studies and reduces the time required to complete a clinical development program (M. Cockett et al., 2000, Current Opinion in Biotechnology, 11: 602-609).

  The ability to predict drug sensitivity in a patient is particularly interesting because drug responsiveness reflects not only the endogenous properties of the target cells but also the metabolic properties of the host. Efforts to predict drug susceptibility using genetic information have been mainly directed to individual genes with broad effects such as the multidrug resistance genes mdr1 and mrp1 (P. Sonneveld, 2000, J. Intern. Med. 247: 521-534).

  The development of microarray methods for large-scale characterization of gene mRNA expression patterns has led to systematic exploration of molecular markers and cancers to distinct subgroups that were not evident by traditional histopathological methods. (J. Khan et al., 1998, Cancer Res., 58: 5009-5013; AA Alizadeh et al., 2000, Nature, 403: 503-511; M. Bittner et al., 2000, Nature, 406: 536-540; J. Khan et al., 2001, Nature Medicine, 7 (6): 673-679; and TR Golub et al., 1999, Science, 286: 531-537; U. Alon et al., 1999, Proc. Natl. Acad Sci. USA, 96: 6745-6750). Such techniques and molecular tools made it possible to monitor the expression level of multiple transcripts within a molecular population at any given time (eg, Schena et al., 1995, Science, 270: 467-470 Lockhart et al., 1996, Nature Biotechnology, 14: 1675-1680; Blanchard et al., 1996, Nature Biotechnology, 14: 1649; see Ashby et al., US Pat. No. 5,569,588).

  Recent studies have shown that gene expression information generated by microarray analysis of human tumors can predict clinical outcome (LJ van't Veer et al., 2002, Nature, 415: 530-536; M. West et al. 2001, Proc. Natl. Acad. Sci. USA, 98: 11462-11467; T. Sorlie et al., 2001, Proc. Natl. Acad. Sci. USA, 98: 10869-10874; M. Shipp et al., 2002, Nature Medicine, 8 (1): 68-74). These findings bring hope that cancer treatment will be greatly improved by better predicting the response of individual tumors to treatment.

  There is a need for new and different methods and procedures for determining patient drug susceptibility to enable the development of individualized genetic profiles, and such individualized gene profiles are at the molecular level of patients. Necessary to treat diseases and disorders based on response.

  The present invention provides methods and procedures for determining a patient's sensitivity to one or more epidermal growth factor receptor (EGFR) modulators. The present invention is also a method of determining or predicting whether an individual in need of treatment for a disease state such as cancer will respond to the treatment before administering the treatment, wherein the treatment comprises one or more Also provided is a method comprising a EGFR modulator. The one or more EGFR modulators are, for example, compounds selected from one or more EGFR specific ligands, one or more small molecule EGFR inhibitors, or one or more EGFR binding monoclonal antibodies.

  In one aspect, the invention is a method of identifying a mammal that is therapeutically responsive to a cancer therapy comprising administration of an EGFR modulator, comprising: (a) at least selected from the biomarkers of Table 4 Measuring the level of one biomarker in the mammal; (b) exposing the mammal to an EGFR modulator; (c) after exposure in step (b), determining the level of the at least one biomarker in the mammal A difference in the level of the at least one biomarker measured in step (c) compared to the level of the at least one biomarker measured in step (a) Provide a therapeutic response to the cancer therapy.

  As used herein, therapeutic response refers to the alleviation or destruction of cancer. This means that the life expectancy of an individual suffering from cancer is increased or that one or more signs of cancer are reduced or improved. The term includes cancerous cell growth or tumor volume reduction. Whether a mammal responds therapeutically can be measured by a number of methods well known in the art, such as PET imaging.

  The at least one biomarker can also be selected from the biomarkers in Table 5. The mammal can be, for example, a human, rat, mouse, dog, rabbit, pig, sheep, cow, horse, cat, primate, or monkey.

  The method of the invention can be, for example, an in vitro method, wherein the at least one biomarker is measured in at least one mammalian biological sample from a mammal. Biological samples include, for example, fresh whole blood, peripheral blood mononuclear cells, frozen whole blood, fresh plasma, frozen plasma, urine, saliva, skin, hair follicles, or tumor tissue.

  In another aspect, the invention provides a method of identifying a mammal that is therapeutically responsive to a cancer treatment comprising administration of an EGFR modulator, comprising: (a) exposing the mammal to an EGFR modulator; (B) measuring the level of at least one biomarker selected from the biomarkers of Table 4 in the mammal after exposure in step (a), wherein the mammal has not been exposed to the EGFR modulator A difference in the level of the at least one biomarker measured in step (b) compared to the level of the at least one biomarker in the animal will cause the mammal to respond therapeutically to the cancer therapy. Provide a way to indicate that.

  In yet another aspect, the present invention relates to a method for testing or predicting whether a mammal is therapeutically responsive to a cancer therapy comprising administration of an EGFR modulator comprising: (a) Measuring the level of at least one biomarker selected from biomarkers in the mammal; (b) exposing the mammal to an EGFR modulator; (c) after exposure in step (b), the at least one biomarker Measuring the level of the marker in the mammal, wherein the level of the at least one biomarker measured in step (c) compared to the level of the at least one biomarker measured in step (a) Provides a method wherein the difference indicates that the mammal will respond therapeutically to the treatment of the cancer.

  In another aspect, the invention provides a method for determining whether a compound inhibits EGFR activity in a mammal, comprising: (a) exposing the mammal to the compound; measuring the level of at least one biomarker selected from the biomarkers of Table 4 after exposure in a), wherein the biomarker in the mammal not exposed to the compound A method is provided wherein a difference in the level of the biomarker measured in step (b) compared to the level indicates that the compound inhibits EGFR activity in the mammal.

  In yet another aspect, the invention provides a method for determining whether a mammal has been exposed to a compound that inhibits EGFR activity, comprising: (a) exposing the mammal to the compound; and (b) Measuring the level of at least one biomarker selected from the biomarkers of Table 4 after exposure in step (a), wherein said biomarker in a mammal not exposed to said compound A method is provided wherein the difference in the level of the biomarker measured in step (b) compared to the level of the marker indicates that the mammal has been exposed to a compound that inhibits EGFR activity.

  In another aspect, the invention provides a method for determining whether a mammal responds to a compound that inhibits EGFR activity, comprising: (a) exposing the mammal to the compound, and then (b) Measuring the level of at least one biomarker selected from the biomarkers of Table 4 in the mammal after exposure in (a), wherein the biomarker in the mammal not exposed to the compound Providing a method wherein a difference in the level of the biomarker measured in step (b) compared to the level of is indicative of the mammal responding to a compound that inhibits EGFR activity.

  As used herein, “responding” includes responding with a biological and cellular response as well as a clinical response (such as improved signs, therapeutic effects, or adverse events) in a mammal.

  The present invention also provides an isolated biomarker selected from the biomarkers of Table 4. The biomarkers of the present invention include sequences selected from the nucleotide and amino acid sequences provided in Table 4 and the sequence listing, as well as fragments and variants thereof.

  The present invention also provides a biomarker set comprising two or more biomarkers selected from the biomarkers of Table 4.

  The invention also provides kits for determining or predicting whether a patient is sensitive or resistant to treatment comprising one or more EGFR modulators. The patient may have a cancer or tumor, such as, for example, a colon cancer or tumor.

  In one aspect, the kit of the invention is one or more used to test cells from a suitable container, patient tissue biopsy or patient sample containing one or more special microarrays of the invention. Includes the above EGFR modulators and instructions for use. The kit may further comprise reagents or materials for monitoring the expression of the biomarker set at the mRNA or protein level.

  In another aspect, the present invention provides a kit comprising two or more biomarkers selected from the biomarkers of Table 4.

  In yet another aspect, the present invention provides a kit comprising at least one of an antibody and a nucleic acid for detecting the presence of at least one biomarker selected from the biomarkers of Table 4. In one aspect, the kit further comprises instructions for determining whether the mammal is therapeutically responsive to a cancer treatment comprising administration of a compound that inhibits EGFR activity. In another aspect, the instructions comprise: (a) measuring the level of at least one biomarker selected from the biomarkers in Table 4 in the mammal; (b) exposing the mammal to the compound; c) measuring the level of the at least one biomarker in the mammal after exposure in step (b), wherein the level is compared to the level of the at least one biomarker measured in step (a) A difference in the level of the at least one biomarker measured in step (c) indicates that the mammal will respond therapeutically to the cancer therapy.

  The present invention also provides a screening assay for determining whether a patient is sensitive or resistant to treatment with one or more EGFR modulators.

  The present invention also provides a method of monitoring the treatment of a patient suffering from a disease that can be treated by one or more EGFR modulators.

  The present invention also provides the personalized genetic profiles necessary to treat diseases and disorders based on patient response at the molecular level.

  The present invention also includes special microarrays, such as oligonucleotide microarrays or cDNA microarrays, that contain one or more biomarkers that have an expression profile that correlates with either sensitivity or resistance to one or more EGFR modulators. Also provide.

  The invention also provides antibodies, including polyclonal or monoclonal antibodies directed against one or more biomarkers of the invention.

  The invention will be better understood when the detailed description of the invention is read in conjunction with the accompanying drawings.

  The present invention provides biomarkers that respond to modulation of specific signaling pathways and also correlate with EGFR modulator sensitivity or resistance. These biomarkers can be used to predict response to one or more EGFR modulators. In one aspect, the biomarkers of the present invention are the biomarkers provided in Table 4 and the sequence listing, and include both polynucleotide and polypeptide sequences.

  These biomarkers are determined by an in vitro assay using a microarray method that simultaneously monitors the expression pattern of thousands of distinct genes in untreated cells, and the modulation of the signal transduction pathway of the untreated cells, particularly the EGFR pathway. Responses were tested against untreated cells that were tested for sensitivity to the EGFR modulator. The biomarker has an expression level in the cell that depends on the activity of the EGFR signaling pathway and has a high correlation with the EGFR modulator sensitivity exhibited by the cell. Biomarkers are useful for predicting responses to EGFR modulators, preferably biological molecules, small molecules, etc. that affect EGFR kinase activity by direct or indirect suppression or antagonism of EGFR kinase function or activity. Work as a molecular tool.

EGFR Modulator As used herein, “EGFR modulator” means a compound or drug that is a biological molecule or small molecule that directly or indirectly modulates EGFR activity or EGFR signaling pathway. Thus, as used herein, a compound or drug includes both small molecules and biological molecules. Direct or indirect modulation includes activation or suppression of EGFR activity or EGFR signaling pathway. In one aspect, inhibition refers to inhibition of binding of EGFR to an EGFR ligand, such as EGF. In another aspect, inhibition refers to inhibition of EGFR kinase activity.

  EGFR modulators include, for example, EGFR specific ligands, small molecule EGFR inhibitors, and EGFR monoclonal antibodies. In one aspect, the EGFR modulator suppresses EGFR activity and / or suppresses the EGFR signaling pathway. In other aspects, the EGFR modulator is an EGFR antibody that suppresses EGFR activity and / or suppresses the EGFR signaling pathway.

  EGFR modulators include biological molecules or small molecules. Biological molecules include all lipid and monosaccharide polymers, amino acids, and nucleotides with molecular weights greater than 450. Thus, biological molecules include, for example, oligosaccharides and polysaccharides; oligopeptides, polypeptides, peptides, and proteins; and oligonucleotides and polynucleotides. Examples of oligonucleotides and polynucleotides include DNA and RNA.

  Biological molecules further include derivatives of any of the above molecules. For example, derivatives of biological molecules include lipid and glycosylated derivatives of oligopeptides, polypeptides, peptides, and proteins.

  Derivatives of biological molecules further include oligosaccharide and polysaccharide lipid derivatives, such as lipopolysaccharide. Most typically, the biological molecule is an antibody, or a functional equivalent of an antibody. A functional equivalent of an antibody has binding properties that are comparable to those of an antibody and inhibits the growth of cells that express EGFR. Such functional equivalents include, for example, chimeric antibodies, humanized antibodies, and single chain antibodies, and fragments thereof.

  A functional equivalent of an antibody also includes a polypeptide having an amino acid sequence that is substantially the same as the amino acid sequence of the variable or hypervariable region of the antibody. Amino acid sequences that are substantially the same as other sequences but differ from other sequences by one or more substitutions, additions, and / or deletions are considered equivalent sequences. Preferably, less than 50%, more preferably less than 25%, even more preferably less than 10% of the number of amino acid residues in a sequence are substituted from the protein, added to the protein, or deleted from the protein.

  The functional equivalent of an antibody is preferably a chimeric antibody or a humanized antibody. A chimeric antibody comprises the variable region of a non-human antibody and the constant region of a human antibody. A humanized antibody comprises the hypervariable region (CDR) of a non-human antibody. The variable regions other than the hypervariable region of the humanized antibody, such as the framework variable region, and the constant region are from human antibodies.

  Suitable variable and hypervariable regions of the non-human antibody may be from antibodies produced by any non-human mammal from which the monoclonal antibody was made. Suitable examples of mammals other than humans include, for example, rabbits, rats, mice, horses, goats, or primates.

  Functional equivalents further include fragments of antibodies that have binding characteristics that are the same as, or are comparable to, those of all antibodies. Appropriate fragments of antibodies include sufficient hypervariable (ie complementarity determining) regions that bind with specific and sufficient affinity to EGFR tyrosine kinase and inhibit the growth of cells expressing such receptors. Any fragment that contains any part.

Such a fragment may comprise, for example, one or both of a Fab fragment or an F (ab ′) 2 fragment. An antibody fragment preferably contains all six complementarity-determining regions of the whole antibody, although functional fragments containing fewer than all such regions, eg, three, four, or five CDRs. Is included.

  In one aspect, the fragment is a single chain antibody, or Fv fragment. A single chain antibody is a polypeptide that comprises at least the variable region of the heavy chain of an antibody linked to or without the variable region of the light chain of the antibody by or without an interconnecting linker. Thus, the Fv fragment contains the entire antibody binding site. These chains can be produced in bacteria or eukaryotic cells.

  Antibodies and functional equivalents may be members of any class of immunoglobulins, such as IgG, IgM, IgA, IgD, or IgE, and subclasses thereof. In one aspect, the antibody is a member of the IgG1 subclass. The functional equivalent may also be the equivalent of any combination of these classes and subclasses.

In one aspect, EGFR antibodies can be selected from chimeric antibodies, humanized antibodies, fully human antibodies, and single chain antibodies derived from mouse antibody 225 described in Mendelsohn et al., US Pat. No. 4,943,533. In one aspect, the 225-derived antibody has the following light chain and heavy chain hypervariable (CDR) regions (amino acid sequence is shown below the nucleotide sequence):
Heavy chain hypervariable region (VH):
CDR1
AACTATGGTGTACAC (SEQ ID NO: 179)
NYGVH (SEQ ID NO: 180)
CDR2
GTGATATGGAGTGGTGGAAACACAGACTATAATACACCTTTCACATCC (SEQ ID NO: 181)
VIWSGGNTDYNTPFTS (SEQ ID NO: 182)
CDR3
GCCCTCACCTACTATGATTACGAGTTTGCTTAC (SEQ ID NO: 183)
ALTYYDYEFAY (SEQ ID NO: 184)
Light chain hypervariable region (VL):
CDR1
AGGGCCAGTCAGAGTATTGGCACAAACATACAC (SEQ ID NO: 185)
RASQSIGTNIH (SEQ ID NO: 186)
CDR2
GCTTCTGAGTCTATCTCT (SEQ ID NO: 187)
ASESIS (SEQ ID NO: 188)
CDR3
CAACAAAATAATAACTGGCCAACCACG (SEQ ID NO: 189)
QQNNNWPTT (SEQ ID NO: 190)

  In other aspects, the EGFR antibody can be selected from the antibodies described in Jakobovits et al. US Pat. No. 6,235,883, Bendi et al. US Pat. No. 5,558,864, and Mateo de Acosta del Rio et al. US Pat. No. 5,891,996. .

  In addition to the biological molecules discussed above, EGFR modulators useful in the present invention may also be small molecules. Any molecule that is not a biological molecule is referred to herein as a small molecule. Some examples of small molecules include organic compounds, organometallic compounds, salts of organic compounds and organometallic compounds, sugars, amino acids, and nucleotides. Small molecules further include molecules that are considered biological molecules except that the molecular weight does not exceed 450. Thus, small molecules can be lipids, oligosaccharides, oligopeptides, and oligonucleotides and derivatives thereof having a molecular weight of 450 or less.

  It is emphasized that small molecules may have any molecular weight. Small molecules are only called small molecules because they typically have a molecular weight of less than 450. Small molecules include compounds found in nature as well as synthetic compounds. In one embodiment, the EGFR modulator is a small molecule that inhibits the growth of tumor cells that express EGFR. In other embodiments, the EGFR modulator is a small molecule that inhibits the growth of refractory tumor cells that express EGFR.

  A number of small molecules have been described as being useful for inhibiting EGFR. For example, US Pat. No. 5,656,655 to Spada et al. Discloses styryl substituted heteroaryl compounds that inhibit EGFR. The heteroaryl group is a monocyclic ring having 1 or 2 heteroatoms or a bicyclic ring having 1 to about 4 heteroatoms, and the compound is optionally substituted or polysubstituted.

  US Pat. No. 5,646,153 to Spada et al. Discloses bismono and / or bicyclic aryl heteroaryl, carbocyclic, and heterocarbocyclic compounds that inhibit EGFR.

  US Pat. No. 5,679,683 to Bridges et al. Discloses tricyclic pyrimidine compounds that inhibit EGFR. These compounds are fused heterocyclic pyrimidine derivatives described in the third column, line 35 to column 5, line 6.

  Barker US Pat. No. 5,616,582 discloses quinazoline derivatives having receptor tyrosine kinase inhibitory activity.

  Fry et al. (Science 265, 1093-1095 (1994)) disclose a compound having a structure that suppresses EGFR in FIG.

  Osherov et al. Disclose tyrphostins that inhibit EGFR / HER1 and HER2 in Tables I, II, III and IV, among others.

  US Pat. No. 5,196,446 to Levitzki et al. Discloses heteroarylethenediyl or heteroarylethendeiylaryl compounds that inhibit EGFR, especially in column 2, line 42 to column 3, line 40 is doing.

  Panek et al. (Journal of Pharmacology and Experimental Therapeutics 283, 1433-1444 (1997)) disclose compounds identified as PD166285 that inhibit the EGFR, PDGFR, and FGFR families of receptors. PD166285 has 6- (2,6-dichlorophenyl) -2- (4- (2-diethylaminoethoxy) phenylamino) -8-methyl-8H-pyrido (2,3- d) Identified as pyrimidin-7-one.

Biomarkers and biomarker sets The present invention relates to disease areas where signaling by the EGFR or EGFR pathway is important, such as in cancer or tumors, in immunological disorders, conditions or disorders, or in cell signaling and / or Includes individual biomarkers and biomarker sets that have value in both diagnosis and prognosis in disease states where the control of cell proliferation is abnormal or ectopic. The biomarker set includes a plurality of biomarkers, eg, a plurality of biomarkers that are highly correlated with sensitivity or resistance to one or more EGFR modulators, as shown in Table 4 below.

  The biomarker set of the present invention makes it possible to predict or reasonably predict the likely effects of one or more EGFR modulators in various biological systems or on cellular responses. The biomarker set can be used in an in vitro assay of EGFR modulator response by a test cell to predict in vitro results. In accordance with the present invention, the various biomarker sets described herein, or combinations of these biomarker sets with other biomarkers or markers, can be treated, for example, by a cancer patient with one or more EGFR modulators. Can be used to predict how to respond to an intervention.

  A biomarker set of cellular gene expression patterns that correlate with cellular sensitivity or resistance after the cells have been exposed to one or more EGFR modulators is one or more tumors prior to treatment with the EGFR modulator. Provides a useful tool for screening samples. This screening is based on the expression results of the biomarker set, and the tumor sample exposed to one or more EGFR modulators as to whether the tumor, and hence the patient suffering from the tumor, responds to treatment with the EGFR modulator Allows prediction of cells.

  A biomarker or biomarker set can also be used to monitor disease treatment or the progress of therapy in a patient undergoing treatment for a disease involving an EGFR modulator, as described herein.

  Biomarkers can be targeted for the development of therapies for the treatment of diseases. Such targets can be particularly applied to the treatment of breast diseases such as breast cancer and breast tumors. Indeed, because these biomarkers are differentially expressed in sensitive and resistant cells, their expression pattern correlates with the relative endogenous sensitivity of the cells to treatment with the EGFR modulator. Thus, biomarkers that are highly expressed in resistant cells can be targets for the development of new therapies for tumors resistant to EGFR modulators, especially EGFR inhibitors.

Microarrays The present invention also includes special microarrays, such as oligonucleotide microarrays or cDNAs, that contain one or more biomarkers that exhibit an expression pattern that correlates with either sensitivity or resistance to one or more EGFR modulators. Also includes microarrays. Such microarrays are used in in vitro assays to assess biomarker expression levels in test cells from tumor biopsies and to determine whether these test cells are likely to be resistant or sensitive to EGFR modulators. be able to. For example, special microarrays can be prepared using all or a subset of the biomarkers described herein and shown in Table 4. Cells from tissue or organ biopsies can be isolated and exposed to one or more EGFR modulators. Nucleic acids isolated from both untreated and treated cells were applied to one or more special microarrays, and then gene expression patterns of test cells were determined and used to create biomarker sets on the microarrays It can be compared to that of the biomarker pattern from the cell control panel. Based on the results of gene expression patterns from the cells subjected to the test, it can be determined whether the cells exhibit a resistance or sensitivity profile of gene expression. The test cell from the tissue or organ biopsy then determines whether or not to respond to one or more EGFR modulators and the treatment or therapy strategy based on information gathered from the results of special microarray analysis. can do.

Antibodies The present invention also encompasses antibodies, including polyclonal or monoclonal antibodies directed against one or more polypeptide biomarkers. Such antibodies can be used in a variety of ways to purify, detect, and target the biomarkers of the invention, including, for example, detection, screening, and / or therapy in both in vitro and in vivo diagnostics. Can be used.

Kits The present invention also encompasses kits for determining or predicting whether a patient is sensitive or resistant to a treatment comprising one or more EGFR modulators. The patient may have a cancer or tumor, such as breast cancer or breast tumor. Such kits are for use in testing a patient's biopsy tumor or cancer sample, for example, if the patient's tumor or cancer is resistant or sensitive to a given treatment or therapy using an EGFR modulator. It may be useful in clinical settings to determine or predict whether there is. The kit includes a suitable container containing the following: one or more microarrays, eg, oligonucleotide microarrays or cDNA microarrays, containing biomarkers correlated with resistance and sensitivity to EGFR modulators, particularly EGFR inhibitors One or more EGFR modulators used to examine cells from a patient tissue biopsy or patient sample; and instructions for use. In addition, kits encompassed by the present invention may include other techniques and systems practiced in the art, such as an RT-PCR assay (1 or 1 described herein), as further described herein. Using primers designed based on further biomarkers), immunoassays such as enzyme-linked antibody immunosorbent assay (ELISA), immunoblotting, eg Western blotting, or in situ hybridization, eg, the present invention It may further comprise reagents or materials for monitoring the expression of the biomarkers at the mRNA or protein level.

Applications of biomarkers and biomarker sets Biomarkers and biomarker sets can be used for various applications. Biomarker sets can be constructed from any combination of the biomarkers listed in Table 4 to allow predictions about the likely effects of any EGFR modulator in various biological systems. The various biomarkers and biomarker sets described herein are how cancer patients respond to therapeutic intervention with compounds that modulate EGFR, for example, as a diagnostic or prognostic indicator in disease management Can be used to predict and how patients respond to therapeutic interventions that modulate signal transduction through all EGFR regulatory pathways.

  Although the data described herein were obtained in cell lines commonly used to screen and identify compounds with potential utility in cancer therapy, the biomarkers of the present invention can be signaled by the EGFR or EGFR pathway. It has value in both diagnosis and prognosis in other disease areas where transmission is important, for example, in immunology or in cancers or tumors where cellular signaling and / or growth control is compromised.

  In the examples described below, sensitivity and resistance classification in 22 colon cell lines was similar for the two EGFR modulators tested. The biomarkers of the present invention are therefore expected to have both diagnostic and prognostic value for EGFR or other compounds that modulate the EGFR signaling pathway.

  One of ordinary skill in the relevant art will appreciate that the EGFR signaling pathway can be used and functions in cell types other than colon tissue cell lines. Therefore, the biomarkers described herein interact with or suppress EGFR activity in cells from other tissues or organs associated with disease states or in cancers or tumors derived from other tissue types. Expected to be useful in predicting drug sensitivity or resistance to compounds. Non-limiting examples of such cells, tissues and organs include breast, colon, lung, prostate, testis, ovary, cervix, esophagus, pancreas, spleen, liver, kidney, stomach, lymphocyte cells and brain Thus providing broad and advantageous applicability of the biomarkers described herein. Cells for analysis can be obtained by conventional methods known in the art, such as tissue biopsy, aspiration, sloughed cells, colon cells, clinical or medical tissue or cell sampling. Can be obtained by law.

  In accordance with the present invention, cells from a patient tissue sample, such as a tumor or cancer biopsy, are assayed to determine the expression pattern of one or more biomarkers prior to treatment with one or more EGFR modulators. be able to. The success or failure of the treatment is based on the expression pattern of a biomarker from a test tissue (test cell), eg, a tumor or cancer biopsy, compared to the expression pattern of a control set of one or more biomarkers. It can be decided whether it is different or not. Therefore, if a test cell exhibits a biomarker expression profile that corresponds to that of a biomarker in a control panel of cells sensitive to the EGFR modulator, the patient's cancer or tumor is treated with the EGFR modulator. The probability that it will respond well to is very high or expected. In contrast, if the test cell exhibits a biomarker expression pattern that corresponds to that of a control panel biomarker of cells resistant to EGFR modulators, the patient's cancer or tumor is treated with the EGFR modulator. The probability of not responding to is very high or predicted.

  The present invention also provides a method of monitoring the treatment of a patient suffering from a disease that can be treated by one or more EGFR modulators. The expression pattern of one or more biomarkers can be determined before and after exposure of a patient tissue sample, eg, a tumor biopsy or isolated test cells from a tumor sample, to an EGFR modulator, preferably The EGFR modulator is an EGFR inhibitor. The resulting biomarker expression profile of the test cells before and after treatment as described herein is described herein to be highly expressed in a control panel of cells that are either resistant or sensitive to EGFR modulators. Compare to that of one or more of the indicated biomarkers. Therefore, if a patient's response is sensitive to treatment with an EGFR modulator based on the correlation of the expression profile of one or more biomarkers, the patient's treatment prognosis has been identified as good and the treatment Can continue. Also, if the test cells do not show changes in the biomarker expression profile corresponding to the control panel of cells sensitive to the EGFR modulator after treatment with the EGFR modulator, this modifies and modifies the current treatment. Or even an indication that it should be stopped. This monitoring process can indicate the success or failure of the patient's treatment with the EGFR modulator, and such monitoring process can be repeated as needed or desired.

  The biomarkers of the present invention can be used to predict results before gaining any knowledge about a biological system. In essence, biomarkers can be considered statistical tools. Biomarkers are primarily useful for predicting phenotypes used to classify biological systems. In one embodiment of the invention, the aim of prediction is to classify cancer cells as having an active or inactive EGFR pathway. Cancer cells with an inactive EGFR pathway can be considered resistant to treatment with an EGFR modulator. The inactive EGFR pathway is referred to herein as “tolerance” or “based on IC50 values for each colon cell line as insignificant expression of EGFR or against the compound exemplified herein (EGFR inhibitor compound BMS-461453). Defined by classification as “sensitivity”.

  Several biomarkers described herein, such as mucin 2, are known to be regulated by EGFR (J Biol Chem. 2002 Aug 30; 277 (35): 32258-67). Another biomarker, betacellulin, is known to be an EGFR ligand (Biochem Biophys Res Commun. 2002 Jun 28; 294 (5): 1040-6). A functional relationship of the top biomarker to EGFR is expected because biomarkers that contribute to high biomarker accuracy often play a functional role in the pathway to be modulated. For example, when the Her2 gene is overexpressed, Perception therapy (ie, an antibody that binds to the Her2 receptor and interferes with function by internalization) is indicated. It is unlikely that the therapy will have a therapeutic effect if the target enzyme is not expressed.

  However, not all complete functions of biomarkers are currently known, but some of the biomarkers appear to be directly or indirectly involved in the EGFR signaling pathway. In addition, some biomarkers may function in metabolic or other resistance pathways specific to the EGFR modulator tested. Nevertheless, knowledge about the function of the biomarker is not a requirement for determining the accuracy of the biomarker according to the practice of the present invention.

Biomarker discovery A protein, enzyme, or molecule (eg, receptor) whose expression pattern in a subset of cell lines is directly or indirectly involved in cell proliferation, cellular responses to external stimuli (eg, ligand binding), or signaling Body), for example, an in vitro marker of cellular response to treatment or therapy using a compound or a series of compound combinations known to inhibit or activate receptor tyrosine kinase function, such as An approach has been found in which biomarkers that can be used have been identified. Preference is given to antagonists or inhibitors of the function of certain proteins, such as receptor tyrosine kinases.

  Two exemplary strategies have been developed to discover biomarkers that are useful in predicting the sensitivity or resistance of cancer cells to treatment with one or more EGFR modulators. FIG. 1 illustrates the identification and prioritization strategy of EGFR biomarkers. In one strategy, EGFR mRNA expression levels were used to identify six colon cancer cell lines that were deduced from the mRNA expression levels and that were not significantly present in EGFR protein and therefore did not have significant activity in the EGFR pathway. (FIG. 2A). In subsequent analyses, biomarkers were identified that had no significant mRNA expression levels in these six cell lines and no presumed presence of EGFR protein. In addition, these biomarkers were required to have significant mRNA expression levels in at least 6 other cell lines.

  In the second strategy, an EGFR-specific tyrosine kinase inhibitor compound was used to determine compound sensitivity in a panel of 22 colon cancer cell lines after exposure of the cells to the compound. Some of these cell lines were determined to be resistant to treatment with the inhibitor compound, while others were determined to be sensitive to the inhibitor (FIG. 2B). A subset of the cell lines examined provided biomarker expression patterns or profiles that correlate with cellular responses to EGFR inhibitor compounds as well as the absence of significant EGFR expression and could thus be utilized as biomarkers.

  By combining the use of EGFR co-regulation studies in tumor cells as a model for in vivo effects with experimental studies in cultured cells, the present invention aims to identify biomarkers that predict compound sensitivity and resistance. Note also advantageously the cell-intrinsic properties exposed to the cell culture. The discovery and identification of biomarkers in tumor cells or cell lines assayed in vitro can be used to predict responses to one or more EGFR modulators in vivo, and thus the same biomarker can be It can be extended to clinical settings used to predict patient response to treatments that include more EGFR modulators and one or more EGFR modulators.

  As described in the examples below, the expression levels of more than 44,792 probe sets are measured in a panel of 31 untreated colon cancer cell lines that have determined EGFR expression status and drug sensitivity to EGFR inhibitor compounds. An oligonucleotide microarray was used. This analysis was performed to determine whether the gene expression signature of untreated cells was sufficient to predict disease susceptibility to suppression of EGFR by small molecules or biological molecular compounds. Data analysis identified biomarkers whose expression levels were found to be very inversely correlated with EGFR status and correlated with drug sensitivity. Furthermore, treatment of cells with small molecule EGFR inhibitors also resulted in gene expression signatures that predicted sensitivity to the compound.

  Means for performing gene expression and biomarker identification analysis encompassed by the present invention are described in further detail and without limitation.

IC 50 determination and phenotyping based on the sensitivity of 22 colon cancer cell lines to EGFR inhibitor compounds :
Twenty-two colon cancer cell lines were treated with small molecule EGFR inhibitor (BMS-461453) to determine individual IC 50 values. The IC 50 of each cell line was evaluated by MTS assay. Mean IC 50 values along with standard deviations were calculated from 2-5 individual determinations for individual cell lines. As shown in FIG. 2B, a 4-fold variation in IC 50 values was observed for small molecule EGFR inhibitors in 22 colon cancer cell lines. The unit of IC 50 is μM.

All cell lines with an IC 50 at least 1.75 times lower than the most resistant cell lines were considered sensitive to treatment with small molecule EGFR inhibitors. FIG. 2B shows the resistance / sensitivity classification of 22 colon cancer cell lines to small molecule EGFR inhibitors. Five cell lines were classified as sensitive and 17 cell lines were classified as resistant.

Description of Strategy for Identifying Biomarkers Biomarkers were found based on two criteria: (i) Correlation of mRNA expression levels to EGFR expression in cell lines where EGFR expression is not significant; and ( ii) Correlation between IC 50 values and gene expression levels for small molecule EGFR inhibitor BMS-461453.

For each of these two biomarker selection strategies, we identified two independent "" by identifying genes whose expression correlates with either EGFR status or IC 50 values using statistical filters with different stringency levels. The “discovery” probe set list was established. These statistical methods are described below and resulted in four discovery probe set lists: EGFR-A and EGFR-B (correlated with the absence of significant EGFR expression) and IC-50-A, IC- 50-B (correlation with IC 50 expression), A list contains probe sets selected by more stringent conditions. Subsequently, two biomarker probe set lists were established, ie, probe sets that appeared in both EGFR-A and IC-50-B (biomarker probe set list A, Table 2) and EGFR-B and IC- 50-A and the probe set which appeared in both (biomarker probe set list B, Table 3).

Identification of genes significantly correlated with EGFR status classification RT-PCR expression data for EGFR was obtained from 31 colon cancer cell lines and compared to other cell lines as described in Example 1 below. Six cell lines with significantly lower expression levels were identified (FIG. 2A). Expression profile data for 44,792 probe sets shown in the HG-U133 array set for all 31 untreated colon cancer cell lines was obtained, and 6 cell lines with no significant EGFR mRNA expression as described above Analyzed for identification of correlated probe sets. For the discovery probe set list EGFR-A, all probe sets that were determined to be absent by Affymetrix Mas 5.0 software in 6 out of 6 colon cancer cell lines with significantly lower expression of EGFR were identified. . Second, these probe sets needed to be determined to be present in at least 6 cell lines of 25 cell lines classified as having significant mRNA expression of EGFR. This analytical strategy resulted in the identification of 280 probe sets that could be analyzed compared to the discovery probe set list IC-50-B.

  The discovery probe set list EGFR-B was determined to be absent by Affymetrix Mas 5.0 software in 5 out of 6 colon cancer cell lines with significantly lower expression of EGFR and had significant mRNA expression of EGFR. It was then generated by selecting all probe sets determined to be present in at least 6 of the 25 cell lines classified. The discovery probe set list EGFR-B contains 1,852 probe sets (U133A: 876; U133B: 976).

Identification of genes significantly correlated with drug resistance / susceptibility classification Expression profile data of 44,792 probe sets shown in the HG-U133 array set for 22 untreated colon cancer cell lines were obtained and performed as follows Pretreatment as described in the examples. These data were analyzed using Student's T test to identify genes whose expression patterns were strongly correlated with drug resistance / sensitivity classification. Table 1 provides a resistance / sensitivity phenotyping of 22 colon cancer cell lines based on IC 50 results for the EGFR antagonist BMS-461453. Mean IC 50 values with standard deviation (SD) for each cell line indicated were calculated from 2-5 individual determinations. The average IC 50 of 22 colon cancer cell lines was calculated for BMS-461453 and used to normalize the IC 50 data for each cell line. All cell lines with an IC 50 at least 1.75 times lower than the most resistant cell line were considered sensitive to treatment with BMS-461453. Cell lines marked with an asterisk are defined as sensitive to drug treatment.

Table 1: Resistance / Sensitivity Phenotype Classification of 22 Colon Cancer Cell Lines

An “ideal expression pattern” corresponds to genes that are uniformly high in one class (eg, sensitivity) and uniformly low in the other class (eg, resistance). First, a Student T test was performed to obtain a T value for each probe set. Once the T value was obtained, its corresponding confidence value (P) was found in the standard significance table. The confidence value is a measure of the probability that an average expression difference is observed only by chance between two groups and is obtained using the following formula:
T (gc) = (X 1 −X 2 ) / (var 1 / n 1 + var 2 / n 2 ) 1/2
In the above formula,
T (gc) represents the T value between gene expression g and sensitivity / resistance classification c;
X 1 represents the average gene expression level of samples in class 1;
X 2 represents the average gene expression level of samples in class 2;
var 1 represents the variance of the gene expression of the sample in class 1;
var 2 represents the variance of the gene expression of the sample in class 2;
n 1 represents the number of samples in class 1;
n 2 represents the number of samples in class 2; and the corresponding confidence value (P) for the T value is obtained from the standard significance table.

  In order to generate the discovery probe set list IC-50-B, a confidence value of 0.05 or less was used as a cutoff for probe sets to be included in the list. The discovery probe set list IC-50-B includes 5,050 probe sets (U133A: 2,498; U133B: 2,552).

The discovery probe set list IC-50-A was generated using the Pearson correlation coefficient (a dimensionless index in the range of -1.0 to 1.0). This value was calculated by treating IC 50 data as a continuous variable and correlating gene expression levels with IC 50 values for 22 colon cancer cell lines using a linear regression model. A total of 902 probe sets (U133A: 467; U133B: 435) with a correlation coefficient of less than -0.5 were selected (p <0.02).

  Eventually present in EGFR-A and IC-50-B (biomarker probe set list A) (Table 2) or present in EGFR-B and IC-50-A (biomarker probe set list B) Table 3 By identifying the probe sets, two separate biomarker probe set lists, biomarker probe sets A and B, were obtained.

Biomarker probe set A (Table 2) comprises a total of 74 probe sets (U133A: 43; U133B: 31) and provides a polynucleotide identified using strategy A as an EGFR antagonist sensitive biomarker. In Strategy A, the polynucleotides had to meet stringent criteria for simultaneous control of EGFR status and less stringent conditions for IC 50 values. That is, the polynucleotide is said to be absent by Affymetrix software in 6 of the 6 cell lines with the lowest expression of EGFR and is sensitive to a cell line with a P value equal to or less than 0.05. And tolerant cell lines needed to be expressed differently.

Table 2: Biomarker probe set A

Biomarker probe set B (Table 3) includes 95 probe sets (U133A: 47; U133B: 48). Biomarker probe set B includes polynucleotides identified using strategy B as EGFR antagonist sensitive biomarkers. In strategy B, the polynucleotides had to meet stringent criteria for correlation to IC 50 values and less stringent conditions for simultaneous control of EGFR status. That is, the polynucleotide must have a Pearson correlation of -0.5 or less for IC 50 and is absent by Affymetrix software in 5 of the 6 cell lines with the lowest expression of EGFR. Needed to be called.

Table 3: Biomarker probe set B

  The two biomarker probe sets A and B were then combined (a total of 161 different probe sets) and the overlapping polynucleotides were deleted to 125 unique polynucleotides, which are shown in Table 4 below. The polynucleotides in Table 4 are the biomarkers of the present invention.

Table 4: Biomarkers

Biological efficacy of biomarker candidates: Modulation of expression by treatment with EGFR ligand or by treatment with EGFR inhibitor Bioactivity to predict the activity of the EGFR pathway and hence the sensitivity of cancer cells to EGFR suppression by therapy In order to confirm the significance of marker candidates, genes controlled by the EGFR pathway were identified. Proof of the property for the above EGFR biomarker candidates will confer additional reliability from functionally linking these genes to the EGFR pathway. Colon or lung cancer cell lines were treated with epidermal growth factor in the absence of serum or in the presence of serum with EGFR modulator BMS-461453 or EGFR modulator cetuximab (C225, also known as chimeric monoclonal EGFR antibody). To identify genes induced by epidermal growth factor, serum-deprived cells were treated with 20 ng / ml EGF for 0.5, 6, and 18 hours. Control cells were treated with medium only. Perform expression profiling, data GeneChip R Expression Analysis software MAS 5.0 (Affymetrix, Santa Clara, California) were analyzed using.

Genes repressed by EGFR antagonists were identified by treatment with 0.5 μM BMS-461453 or 1 μg / ml or 5 μg / ml C225 for 6 and 24 hours in the presence of 10% serum. Cells exposed to 0.05% DMSO were used as experimental controls. Perform expression profiling, the data were analyzed using the GeneChip R Expression Analysis software MAS 5.0.

  Inhibitor-treated or EGFR-treated cell lines were compared on a one-to-one basis with untreated controls. Polynucleotides from the biomarker list that expression increased 2-fold with EGFR exposure or decreased 2-fold with EGFR inhibitor treatment compared to untreated controls were considered modulated by EGFR. These biomarkers are shown in Table 4. Examples of biomarkers include EphA1, B-cell translocation gene 2, prostaglandin-endoperoxide synthase 2 and serine (or cysteine) proteinase inhibitor (clade B), which are highly expressed in sensitive cells, Up-regulated by treatment with EGFR. On the other hand, spondin 1, talin 2 and nuclear receptor subfamily 3 have their expression levels correlated with the sensitivity or resistance of colon cancer cell lines and are consistently down-regulated upon treatment with EGFR inhibitors BMS-461453 and C225 Gene. These biomarkers appear to be directly or indirectly involved in the EGFR signaling pathway based on their expression modulation by EGF or EGFR inhibitor treatment.

Identification of Top Biomarkers In an attempt to further prioritize biomarkers for use in predicting the response of cancer cells to treatment with one or more EGFR modulators, the following filter criteria were identified in Table 4 A total of 14 biomarkers (Table 5) were identified as top biomarkers using the markers.
(1) Results from a very significant correlation of gene expression with IC 50 : p value <0.01 or Pearson value <−0.6 in the Student T test
(2) Results from modulation of expression by the above EGFR ligand and / or EGFR inhibitor treatment; and (3) Biomarkers supported by literature that reveal the direct relationship between EGFR pathways and biomarkers.

Table 5: 14 top biomarkers

*: Gene betacellulin showed a reverse control (counter regulation) for EGFR expression determined in EGFR-A list had a p-value of 0.04 in Student's T-test for correlation between the IC 50. Nonetheless, betacellulin is one of the published ligands of EGFR and was selected as a top biomarker with strong literature support.

Polynucleotides that correlate with specific properties of a biological system in which a biomarker is useful can be used to make predictions for that biological system and other biological systems. To demonstrate the predictive utility of biomarkers that correlate with EGFR modulator sensitivity and resistance, the ability of these polynucleotides to predict the response of 22 colon cancer cell lines to small molecule EGFR modulators was tested.

The present invention encompasses a single biomarker comprising, for example, 14 top biomarkers tested in a voting scheme. For this purpose, average expression values were calculated for all 14 biomarkers. The colon cancer cell lines that showed expression levels above average were then voted for sensitivity, and colon cancer cell lines with expression levels below average were voted for resistance. After this procedure, this vote was compared to the actual susceptibility / resistance state according to the definition based on IC 50 (see above) and the error rate was calculated. The error rates for the 14 top biomarkers are shown in Table 6.

Table 6: Error rate of top 14 biomarkers

  Biomarkers talin, Cystic fibrosis conductance regulator (CFTR), and mucin 3 were the best single biomarkers with an error rate of less than 12%.

Example
Example 1: Method
IC 50 determination-in vitro cytotoxicity assay The small molecule EGFR inhibitor erlotinib HCl (BMS-461453) is cytotoxic in vitro against a panel of 22 human colon cancer cell lines available from the American Type Culture Collection. Was tested. Cytotoxicity is assessed by MTS (3- (4,5-dimethylthiazol-2-yl) -5- (3-carboxymethoxyphenyl) -2- (4-sulfenyl) -2H-tetrazolium, internal salt) assay (TL Riss et al., 1992, Mol. Biol. Cell, 3 (Suppl.): 184a).

To perform the assay, colon cells were plated at 4,000 cells / well in 96 well microtiter plates and serially diluted drugs were added 24 hours later. The EGFR inhibitor concentration range was 5 μg / ml to 0.0016 μg / ml (approximately 10 μM to 0.0032 μM). Cells were incubated at 37 ° C. for 72 hours, at which time the tetrazolium dye MTS (final concentration 333 μg / ml) was added along with the electron conjugate agent phenazine methosulfate (final concentration 25 μM). Dehydrogenase in living cells reduces MTS to a form that absorbs light at 492 nm, which can be quantified spectrophotometrically. The greater the absorbance, the greater the number of living cells. These results were expressed as IC 50 , which is the concentration of drug required to inhibit cell growth (ie, absorbance at 450 nm) to 50% of that of untreated control cells. The average IC 50 and standard deviation (SD) from multiple tests was calculated for each cell line.

Cell lines with a resistance / sensitivity classification IC 50 of less than 6 μM were defined as sensitive to EGFR inhibitors, while cell lines with an IC 50 of greater than 6 μM were considered resistant. The resistance / sensitivity classification is shown in Table 1 above, with 5 cell lines classified as sensitive and 17 cell lines classified as resistant.

Gene expression profiling supplemented colon cells to include standard cell culture conditions: 10% bovine resistant serum, 100 IU / ml penicillin, 100 mg / ml streptomycin, 2 mM L-glutamine and 10 mM Hepes (all from GibcoBRL, Rockville, MD) Was grown using RPMI 1640. RNA was isolated from 50-70% confluent cells or drug-treated cells using the RNeasy kit commercially available from Qiagen (Valencia, CA). RNA quality was checked by measuring the 28s: 18s ribosomal RNA ratio using an Agilent 2100 bioanalyzer (Agilent, Technologies, Rockville, MD). The total RNA concentration was determined spectrophotometrically. Biotinylated probes were prepared according to Affymetrix Genechip Expression Analysis Technical Manual, 2001 using 10 μg of total RNA from each cell line. The target was hybridized with an Affymetrix high density oligonucleotide array human HG-U133 set chip (Affymetrix, Santa Clara, Calif.). The array was then washed and stained using a GeneChip Fluidics station according to the manufacturer's instructions. The HG-U133 set consisting of two GeneChip R arrays contained nearly 45,000 probe sets and represented more than 39,000 transcripts from about 33,000 well-documented human genes.

And visually inspected for artifact pretreated scanning the shadow image file microarray data for selecting biomarkers was analyzed by GeneChip R Expression Analysis software MAS 5.0 (Affymetrix, Santa Clara, California). Similarly to measure the relative abundance of the transcript "signal" (see Affymetrix Genechip R Expression Analysis Technical Manual, 2001), transcripts with (see Affymetrix manual) "Detection Call (Detection Call)" is It was determined whether it was detected in one sample. Scale up the average intensity trimmed for each chip to 1,500 (see Affymetrix manual) to account for subtle differences in comprehensive chip strength and compare overall expression levels for each cell line I was able to get it. Affymetrix control sequences were excluded prior to analysis.

Induction studies of colon and breast cancer cell lines using EGFR inhibitors or EGFR ligands and selection of genes modulated by the induction Drug induction of 5 colon cell lines and 1 lung cell line indicated by asterisks in Table 1 Used for research. Three of these colon cell lines express EGFR and are sensitive to the EGFR inhibitor BMS-461453. The SW480 cell line expresses EGFR but is not sensitive to EGFR inhibitors, and COLO320_DM does not express EGFR and is resistant to EGFR inhibitors. Lung cancer cell line H292 expresses EGFR, but its sensitivity state is unknown. Cells were seeded on a 10 cm 2 culture plate using the above medium and cultured for 24 hours.

  For EGF induction studies, the colon cell line CACO2 and the lung cancer H292 cell line were washed with 2 × PBS and the medium was changed to serum-free RPMI. The next day, cells were treated with 20 ng / ml EGF and finally lysed at 0, 6 and 18 hours after treatment for RNA isolation. Gene expression was profiled as follows.

EGFR suppression studies were performed on colon cell lines GEO, CCD33-CO, SW480 and COLO320DM. Expression profiling performed as described above, the data were analyzed using the GeneChip R Expression Analysis software MAS 5.0. The expression data of cells treated with EGFR inhibitor were compared one-to-one with the expression data of untreated cell lines. A change was considered significant when a 2-fold difference in expression was shown between the treated and untreated controls. All four cell lines were analyzed to compare gene expression with and without EGFR inhibitor treatment.

Example 2: RT-PCR expression profiling RNA was quantified using SYBR Green real-time PCR. The SYBR Green real-time PCR assay is one of the most accurate methods for assaying the concentration of nucleic acid templates.

RNA can be prepared using standard methods, preferably using the RNeasy kit commercially available from Qiagen (Valencia, Calif.). A cDNA template for real-time PCR can be generated using the Superscript R First Strand Synthesis system for RT-PCR. The SYBR Green real-time PCR reaction is prepared as follows: the reaction mixture is 20 ng first strand cDNA; 50 nM forward primer; 50 nM reverse primer; 0.75 × SYBR Green I (Sigma); 1 × SYBR Green PCR buffer (50 mM Tris-HCl, pH 8.3, 75 mM KCl); 10% DMSO; 3 mM MgCl 2 ; 300 μM of each dATP, dGTP, dTTP, dCTP; 1 U Platinum R Taq DNA Polymerase High Fidelity (Cat # 11304- 029; Life Technologies; Rockville, Maryland). Real-time PCR is performed using Applied Biosystems 5700 Sequence Detection System. Conditions are 95 ° C. for 10 minutes (Platinum R Taq DNA polymerase denaturation and activation), 40 cycles of PCR (95 ° C. for 15 minutes, 60 ° C. for 1 minute). The homogeneous melting of the PCR product is analyzed using an analysis algorithm built into the 5700 Sequence Detection System.

  CDNA quantification used for template quantification normalization is performed using SYBR Green real-time PCR. EGFR expression is normalized to GAPDH expression as described below.

The sequence of the GAPDH oligonucleotide used for SYBR Green real-time PCR is as follows:
GAPDH-F: 5′-AGCCGAGCCACATCGCT-3 ′ (SEQ ID NO: 191)
GAPDH-R: 5′-GTGACCAGGCGCCCAATAC-3 ′ (SEQ ID NO: 192)

The sequence of the EGFR oligonucleotide used for SYBR Green real-time PCR is as follows:
EGFR-F: 5'- GCGTCTCTTGCCGGAATGT-3 '(SEQ ID NO: 193)
EGFR-R: 5′-AGCCGAGGCAGGGAATGCGTG-3 ′ (SEQ ID NO: 194)

The Sequence Detection System generates a Ct (threshold cycle) value that is used to calculate the concentration of each input cDNA template. The cDNA level of each target gene was normalized to the GAPDH cDNA level to compensate for variations in the total cDNA amount in the input sample. This is done by generating a GAPDH Ct value for each cell line. A modified δδCt equation (Applied Biosystems Prism R 5700 Sequence Detection System User Manual) used to calculate the normalized GAPDH by comparing the Ct values for the target gene and GAPDH with the cDNA for each specific cDNA. insert. The δδCt equation is as follows:
Relative amount of nucleic acid template = 2 δδCt = 2 (δCta−δCtb)
In the above formula, δCta = Ct target−CtGAPDH and δCtb = Ct reference−CtGAPDH.

Example 3: Production of antibodies against biomarkers Antibodies against biomarkers can be prepared by various methods. For example, cells expressing a biomarker polypeptide can be administered to an animal to induce the animal to produce serum containing polyclonal antibodies directed against the expressed polypeptide. In one aspect, biomarker proteins are prepared and isolated or purified using techniques commonly practiced in the art to be free of natural contaminants. Such preparations are then introduced into animals in order to produce polyclonal antisera with high specific activity against the expressed and isolated polypeptide.

  In one aspect, the antibodies of the invention are monoclonal antibodies (or protein binding fragments thereof). Cells expressing the biomarker polypeptide can be cultured in any suitable tissue culture medium, but supplemented to contain 10% fetal calf serum (inactivated at about 56 ° C.) and about 10 g / l The cells are preferably cultured in Earle's modified Eagle's medium supplemented with nonessential amino acids, about 1,0 U / ml penicillin, and about 100 μg / ml streptomycin.

  Spleen cells from immunized (and boosted) mice can be extracted and fused with an appropriate myeloma cell line. Any suitable myeloma cell line can be used in accordance with the present invention, but the parent myeloma cell line (SP2 / 0) available from ATCC is preferably used. After fusion, the resulting hybridoma cells are selectively maintained in HAT medium and then cloned by limiting dilution as described by Wands et al. (1981, Gastroenterology, 80: 225-232). The hybridoma cells obtained by such selection are then assayed to identify cell clones that secrete antibodies capable of binding to the polypeptide immunogen or portion thereof.

  Alternatively, additional antibodies can be produced that can bind to the biomarker polypeptide in a two-step procedure using anti-idiotype antibodies. Such a method uses the fact that the antibody itself is an antigen and therefore an antibody that binds to a second antibody can be obtained. According to this method, an animal, preferably a mouse, can be immunized using a protein-specific antibody. The spleen cells of such immunized animals are then used to produce hybridoma cells and the hybridoma cells are screened to identify clones that produce antibodies whose ability to bind to protein-specific antibodies can be blocked by the polypeptides. . Such antibodies include anti-idiotypic antibodies against protein specific antibodies and can be used to immunize animals to induce the production of additional protein specific antibodies.

Example 4: Immunofluorescence assay One or more of the following immunofluorescence protocols is used to confirm, for example, EGFR biomarker protein expression on a cell or to bind to an EGFR biomarker expressed on the surface of a cell, for example. Can be used to check for the presence of other antibodies. Briefly, Lab-Tek II chamber slides are coated overnight at 4 ° C. with 10 μg / ml bovine collagen type II in DPBS with calcium and magnesium (DPBS ++). The slide is then washed twice with cold DPBS ++ and seeded with a total volume of 125 μl of 8000 CHO-CCR5 or CHO pC4 transfected cells and incubated at 37 ° C. in the presence of 95% oxygen / 5% carbon dioxide.

  The medium is gently removed by aspiration and the adsorbed cells are washed twice with DPBS ++ at ambient temperature. Slides are blocked with DPBS ++ containing 0.2% BSA (blocking agent) at 0-4 ° C. for 1 hour. The blocking solution is gently removed by aspiration, and 125 μl of antibody-containing solution (antibody-containing solution, eg, hybridoma culture supernatant (usually used undiluted), or serum / plasma (usually diluted, eg about 1 / 100 dilution) may be added. Incubate slides at 0-4 ° C for 1 hour. The antibody solution is then gently removed by aspiration and the cells are washed 5 times with 400 μl ice-cold blocking solution. Next, a rhodamine labeled secondary antibody (eg, anti-human IgG) (125 μl, 1 μg / ml) in blocking solution is added to the cells. Again, the cells are incubated at 0-4 ° C. for 1 hour.

  The secondary antibody is then gently removed by aspiration and the cells are washed 3 times with 400 μl ice-cold blocking solution and then 5 times with cold DPBS ++. The cells are then fixed with 3.7% formaldehyde (125 μl) in DPBS ++ for 15 minutes at ambient temperature. The cells are then washed 5 times with 400 μl DPBS ++ at ambient temperature. Finally, the cells are placed in 50% aqueous glycerin and observed with a fluorescence microscope using a rhodamine filter.

EGFR biomarker identification and prioritization strategy is shown.

Figure 5 shows EGFR RT-PCR results in 31 colon cancer cell lines to identify cell lines that do not have significant mRNA expression of EGFR.

Figure 2 shows the IC 50 profile of 21 colon cancer cell lines using EGFR inhibitor compounds and the determination of sensitive and resistant cell lines.

Claims (4)

  1. A method of identifying a mammal that is therapeutically responsive to a cancer treatment comprising administration of an EGFR modulator comprising:
    (A) measuring the level of at least one biomarker selected from the biomarkers of Table 4 in the mammal;
    (B) exposing the mammal to an EGFR modulator;
    (C) measuring the level of the at least one biomarker in the mammal after exposure in step (b), wherein the level is compared with the level of the at least one biomarker measured in step (a) Wherein the difference in the level of the at least one biomarker measured in step (c) indicates that the mammal will respond therapeutically to the therapy for the cancer.
  2.   The method of claim 1, wherein the at least one biomarker is selected from the biomarkers of Table 5.
  3.   2. The method of claim 1, wherein the method is an in vitro method and the at least one biomarker is measured in at least one mammalian biological sample from the mammal.
  4. A method of identifying a mammal that is therapeutically responsive to a cancer treatment comprising administration of an EGFR modulator comprising:
    (A) exposing the mammal to an EGFR modulator;
    (B) measuring the level of at least one biomarker selected from the biomarkers of Table 4 in the mammal after exposure in step (a), wherein the mammal has not been exposed to the EGFR modulator The difference in the level of the at least one biomarker measured in step (b) compared to the level of the at least one biomarker measured in the animal is such that the mammal is therapeutically responsive to the cancer therapy. A method characterized by indicating what will happen.
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