Patent Application
20120009177
Tocilizumab is the first humanized interleukin-6 receptor (IL-6R)-inhibiting monoclonal antibody that has been developed to treat rheumatoid arthritis. As with other treatments, the antibody exhibits a range of therapeutic efficacy in patients. Thus, there is a need to determine those patients that are more likely to respond positively to treatment with tocilizumab and/or patients that are likely to not respond to treatment. The present invention addresses this need.
The invention is based, in part, on the discovery of changes in gene expression that are associated with a positive therapeutic response to treatment with an agent that modulate IL-6-mediated signal transduction, such as an anti-IL-6 antibody that inhibits transduction or an IL-6R-inhibiting monoclonal antibody such as tocilizumab.
Thus, in one aspect, the invention provides a method of identifying a rheumatoid arthritis patient that is likely to respond to treatment with tocilizumab; or of identifying a patient that is likely not to respond to treatment with tocilizumab; wherein the method comprises identifying the levels of expression of a gene set forth in Table 1, Table 2, or Table 3. Such genes can be identified using a variety of techniques, including array probe sets and amplification techniques. The level of expression of the marker gene is then compared to the expression level shown in the data set used to establish a correlation.
In a further aspect, the invention provides, a kit for predicting the therapeutic response of a rheumatoid arthritis patient to a treatment regimen that comprises administration of an IL-6R antibody such as tocilizumab. In some embodiments, the kit also includes an electronic device or computer software to compare the marker gene expression level of a biomarker gene set forth in Table 1, Table 2, or Table 3 from the patient to a dataset. The endpoint for evaluating therapeutic response can be any symptom of rheumatoid arthritis, e.g., the endpoints evaluated in Example 1.
In some embodiments, the marker gene is any one of the genes set forth in Table 1. In some embodiments, the marker genes are at least two genes set forth in Table 1. Thus, in some embodiments any one of from 2 to 20, 30, 40, 50, 60, 70, 80, or all of the genes set forth in Table 1.
In some embodiments, the marker gene is any one of the genes set forth in Table 2. In some embodiments, the marker genes are at least two genes set forth in Table 2. Thus, in some embodiments any one of from 2 to 20, 30, 40, 50, 60, 70, 80, or all of the genes set forth in Table 2.
In some embodiments, the marker gene is any one of the genes set forth in Table 3. In some embodiments, the marker genes are at least two genes set forth in Table 3. Thus, in some embodiments any one of from 2 to 20, 30, 40, 50, 60, 70, 80, or all of the genes set forth in Table 3.
In some embodiments, the step of determining the level of expression of the biomarker gene comprises measure the level of RNA expressed by the marker gene. The amount of RNA expressed may be determined, e.g., using an amplification area reaction such as qPCR, or by using a probe array. For example, a nucleic acid array forming a probe set may be used to detect RNA expressed of the biomarker gene. RNA expression levels are typically determined by measuring the level of cDNA transcribed from the RNA isolated from the patient. RNA expression levels can be determined using known probesets to quantify expression level. As known in the art, such probes sets may comprises multiple probes that hybridize to the target sequence of interest. Alternatively, expression of a marker gene can be determined by measuring the level of expression of a protein encoded by the gene.
The levels of expression are compared to standard control data, e.g., the expression data set generated in Example 1 and 2. An increased level of expression of the marker gene or decreased level of expression of the biomarker gene may be determined by using statistical models for determining whether expression of the biomarker gene is indicative of therapeutic response of a patient to treatment with an IL-6R antibody such as tocilizumab. In some the invention provides an electronic device or computer software that employs the use of a statistical model to determine likelihood of therapeutic responses.
In some embodiments, the levels of expression of genes set forth in Table 5 are evaluated to identify rheumatoid arthritis patients that are likely to be responsive, or unresponsive, to treatment with an IL-6R antagonist such as tocilizumab. In typical embodiments, anywhere from 2 to 10, 20, 30, 40, 50, 60, 70, 80, or 90, or all of the genes in column C, column D, column E, column F, column G, column H, column I, or column J are analyzed to determined likelihood of a therapeutic response.
As used herein, a “positive therapeutic response” or “therapeutic benefit” refers to an improvement in, and/or delay in the onset of, any symptom of rheumatoid arthritis.
As used herein “negative therapeutic response” refers to a lack of improvement of one or more symptoms of rheumatoid arthritis.
An “interleukin-6 receptor (IL-6R) inhibiting antibody” refers to an antibody to IL-6 receptor where the antibody binds to IL-6 receptor and antagonizes (i.e., inhibits) IL-6 receptor activity. An example of such an antibody is tocilizumab, a humanized IL-6R monoclonal antibody (see, e.g., Sato et al., Cancer Res 1993; 53: 851-6; and U.S. Pat. No. 7,479,543) that is used for the treatment of rheumatoid arthritis.
In the current invention, a “gene set forth in Table 1” refers to the gene that corresponds to the probesets annotated in Table 1. Similarly, a “gene set forth in” Tables 2, 3, or 5 refers to the gene that corresponds to the probesets annotated in the respective Table. For Tables 1-3, the “Representative Public ID” is listed as the accession number Table 1. The “Representative Public ID” is the accession number of a representative sequence. For consensus-based probe sets, the representative sequence is only one of several sequences (sequence sub-clusters) used to build the consensus sequence in the probe set used in the Examples and it is not directly used to derive the probe sequences. The representative sequence is chosen during array design as a sequence that is best associated with the transcribed region being interrogated by the probe set. As understood in the art, there are naturally occurring polymorphisms for many gene sequences. Genes that are naturally occurring allelic variations for the purposes of this invention are those genes encoded by the same genetic locus. The proteins encoded by allelic variations of a gene set forth in Table 1, Table 2, or Table 3 typically have at least 95% amino acid sequence identity to one another, i.e., an allelic variant of a gene indicated in Table 1, Table 2, or Table 3 typically encodes a protein product that has at least 95% identity, often at least 96%, at least 97%, at least 98%, or at least 99%, or greater, identity to the amino acid sequence encoded by the nucleotide sequence denoted by the accession number shown in the Table for that gene. For example, an allelic variant of a gene encoding Eph receptor B2 (gene: EPHB2, representative accession number AF025304) typically has at least 95% identity, often at least 96%, at least 97%, at least 98%, or at least 99%, or greater, to the Eph receptor b2 protein encoded by the sequence available under accession number AF025304.
The terms “identical” or “100% identity,” in the context of two or more nucleic acids or proteins refer to two or more sequences or subsequences that are the same sequences. Two sequences are “substantially identical” or a certain percent identity if two sequences have a specified percentage of amino acid residues or nucleotides that are the same (i.e., 70% identity, optionally 75%, 80%, 85%, 90%, or 95% identity, over a specified region, or, when not specified, over the entire sequence), when compared and aligned for maximum correspondence over a comparison window, or designated region as measured using known sequence comparison algorithms, e.g., BLAST using the default parameters, or by manual alignment and visual inspection.
A “gene product” or “gene expression product” in the context of this invention refers to an RNA or protein encoded by the gene.
The term “evaluating a biomarker” in a patient that has rheumatoid arthritis refers to determining the level of expression of a gene product encoded by a gene, or allelic variant of the gene, listed in Table 1, Table 2, Table 3, or Table 5. Typically, the RNA expression level is determined.
The invention is based, in part, on the identification of specific genes/transcripts whose gene expression level, prior to drug dosing or 8 weeks subsequent to dosing, are correlated with response to tocilizumab.
The invention therefore relates to measurement of expression level of a biomarker prior to the patient receiving the drug. In some embodiments, probes to detect such transcripts may be applied in the form of a diagnostic device to predict which rheumatoid arthritis patients will respond or not respond to an IL-6 receptor antagonist such as an IL-6 receptor antagonizing antibody, e.g., tocilizumab. Transcripts may also be measured to predict which RA patients will respond tocilizumab at a later time point. Further, the identification of proteins/metabolites and/or related transcripts and associated product that are linked by pathway or cell type or tissue expression to the transcripts identified herein in the Examples section can be used as alternative biomarkers for measurement of response to tocilizumab.
The expression levels of any gene expression product of one of the genes set forth in Table 1, Table 2, or Table 3 may be measured, however, typically expression of multiple genes is assessed. Gene expression levels may be measured using any number of methods known in the art. In typical embodiments, the method involves measuring the level of RNA. RNA expression can be quantified using any method, e.g., employing a quantitative amplification method such as qPCR. In other embodiments, the methods employ array-based assays. In still other embodiments, protein products may be detected. The gene expression patterns are determined using a whole blood or peripheral blood lymphocyte samples from the patient.
In some embodiments, gene products, typically RNA, encoded by a gene that is in the same pathway as a biomarker shown in Table 1, Table 2, or Table 3 may be quantified. In some embodiments, at least one of the biomarkers that is evaluated to identify a rheumatoid arthritis patient that is a candidate for treatment with tocilizumab is selected from the group consisting of JAM3, CD41, CD61, ephrin receptor B2. In some embodiments, at least one of the biomarkers selected for evaluation is JAM3, CD41, CD61, and a second biomarker evaluated is ephrin receptor B2. In some embodiments, a biomarker that is evaluated in a patient is a component of the inflammasome, caspase 1, caspase 5, IL-1 receptor, or CARD16. In some embodiments, at least one of the biomarkers that is evaluated is serine palmitoyltransferase long chain base subunit 2 or sphingosine-1-phosphate (S1P), ceramide or related sphingolipids.
In some embodiments, the methods of the invention comprise analyzing gene expression products of two or more biomarkers of Table 5 that have a value over “0” shown in one of columns C-J. Such biomarkers may be used in combination to predict likelihood of a rheumatoid arthritis patient's response to treatment in an IL-6R antagonist such as tocilizumab. Thus, for example, analysis of gene expression levels of at least two biomarkers, preferably three, four, five, or any number up to 100 of the biomarkers having a value above “0” in column C can be used in combination to predict response to treatment is tocilizumab. Similarly, at least two biomarkers, preferably three, four, five, or more, or all of the biomarkers from column D that have values above “0” can be analyzed for expression levels to identify rheumatoid arthritis patients likely to be responsive, or not responsive, to treatment with an IL-6R antagonist such as tocilizumab. In typical embodiments, those expression levels of those genes that have lower numbers, are evaluated. Thus, for example, a gene in column C that has a value of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, for example, is typically included in the analysis of gene expression. In some embodiments, the methods of the invention comprise analyzing expression level of two or more genes in column C; and analyzing expression levels of two or more genes in column D, or two or more genes in column E, etc.
In Table 5, the column “ID” refers to a probeset for the corresponding gene (Table 5B). One of skill understands that the probeset annotation in Table 5B and column L of Table 5A can be obtained through the database of the maker of the chip used for this analysis (Affymetrix).
The quantity of RNA encoded by a gene set forth in Table 1 can be readily determined according to any method known in the art for quantifying RNA. Various methods involving amplification reactions and/or reactions in which probes are linked to a solid support and used to quantify RNA may be used. Alternatively, the RNA may be linked to a solid support and quantified using a probe to the sequence of interest.
An “RNA nucleic acid sample” analyzed in the invention is obtained from peripheral blood lymphocytes. An “RNA nucleic acid sample” comprises RNA, but need not be purely RNA, e.g., DNA may also be present in the sample. Techniques for obtaining an RNA sample from peripheral blood lymphocytes are well known in the art.
In some embodiments, the target RNA is first reverse transcribed and the resulting cDNA is quantified. In some embodiments, RT-PCR or other quantitative amplification techniques are used to quantify the target RNA. Amplification of cDNA using PCR is well known (see U.S. Pat. Nos. 4,683,195 and 4,683,202; PCR PROTOCOLS: A GUIDE TO METHODS AND APPLICATIONS (Innis et al., eds, 1990)). Methods of quantitative amplification are disclosed in, e.g., U.S. Pat. Nos. 6,180,349; 6,033,854; and 5,972,602, as well as in, e.g., Gibson et al., Genome Research 6:995-1001 (1996); DeGraves, et al., Biotechniques 34(1):106-10, 112-5 (2003); Deiman B, et al., Mol Biotechnol. 20(2):163-79 (2002). Alternative method for determining the level of a mRNA of interest in a sample may involve other nucleic acid amplification methods such as ligase chain reaction (Barany (1991) Proc. Natl. Acad. Sci. USA 88:189-193), self-sustained sequence replication (Guatelli et al. (1990) Proc. Natl. Acad. Sci. USA 87:1874-1878), transcriptional amplification system (Kwoh et al. (1989) Proc. Natl. Acad. Sci. USA 86:1173-1177), Q-Beta Replicase (Lizardi et al. (1988) Bio/Technology 6:1197), rolling circle replication (U.S. Pat. No. 5,854,033) or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art.
In general, quantitative amplification is based on the monitoring of the signal (e.g., fluorescence of a probe) representing copies of the template in cycles of an amplification (e.g., PCR) reaction. One method for detection of amplification products is the 5′-3′ exonuclease “hydrolysis” PCR assay (also referred to as the TaqMan™ assay) (U.S. Pat. Nos. 5,210,015 and 5,487,972; Holland et al., PNAS USA 88: 7276-7280 (1991); Lee et al., Nucleic Acids Res. 21: 3761-3766 (1993)). This assay detects the accumulation of a specific PCR product by hybridization and cleavage of a doubly labeled fluorogenic probe (the “TaqMan™” probe) during the amplification reaction. The fluorogenic probe consists of an oligonucleotide labeled with both a fluorescent reporter dye and a quencher dye. During PCR, this probe is cleaved by the 5′-exonuclease activity of DNA polymerase if, and only if, it hybridizes to the segment being amplified. Cleavage of the probe generates an increase in the fluorescence intensity of the reporter dye.
Another method of detecting amplification products that relies on the use of energy transfer is the “beacon probe” method described by Tyagi and Kramer, Nature Biotech. 14:303-309 (1996), which is also the subject of U.S. Pat. Nos. 5,119,801 and 5,312,728. This method employs oligonucleotide hybridization probes that can form hairpin structures. On one end of the hybridization probe (either the 5′ or 3′ end), there is a donor fluorophore, and on the other end, an acceptor moiety. In the case of the Tyagi and Kramer method, this acceptor moiety is a quencher, that is, the acceptor absorbs energy released by the donor, but then does not itself fluoresce. Thus, when the beacon is in the open conformation, the fluorescence of the donor fluorophore is detectable, whereas when the beacon is in hairpin (closed) conformation, the fluorescence of the donor fluorophore is quenched. When employed in PCR, the molecular beacon probe, which hybridizes to one of the strands of the PCR product, is in “open conformation,” and fluorescence is detected, while those that remain unhybridized will not fluoresce (Tyagi and Kramer, Nature Biotechnol. 14: 303-306 (1996)). As a result, the amount of fluorescence will increase as the amount of PCR product increases, and thus may be used as a measure of the progress of the PCR. Those of skill in the art will recognize that other methods of quantitative amplification are also available.
Various other techniques for performing quantitative amplification of nucleic acids are also known. For example, some methodologies employ one or more probe oligonucleotides that are structured such that a change in fluorescence is generated when the oligonucleotide(s) is hybridized to a target nucleic acid. For example, one such method involves is a dual fluorophore approach that exploits fluorescence resonance energy transfer (FRET), e.g., LightCycler™ hybridization probes, where two oligo probes anneal to the amplicon. The oligonucleotides are designed to hybridize in a head-to-tail orientation with the fluorophores separated at a distance that is compatible with efficient energy transfer. Other examples of labeled oligonucleotides that are structured to emit a signal when bound to a nucleic acid or incorporated into an extension product include: Scorpions™ probes (e.g., Whitcombe et al., Nature Biotechnology 17:804-807, 1999, and U.S. Pat. No. 6,326,145), Sunrise™ (or Amplifluor™) probes (e.g., Nazarenko et al., Nuc. Acids Res. 25:2516-2521, 1997, and U.S. Pat. No. 6,117,635), and probes that form a secondary structure that results in reduced signal without a quencher and that emits increased signal when hybridized to a target (e.g., Lux Probes™).
In other embodiments, intercalating agents that produce a signal when intercalated in double stranded DNA may be used. Exemplary agents include SYBR GREEN™ and SYBR GOLD™. Since these agents are not template-specific, it is assumed that the signal is generated based on template-specific amplification. This can be confirmed by monitoring signal as a function of temperature because melting point of template sequences will generally be much higher than, for example, primer-dimers, etc.
In other embodiments, the mRNA is immobilized on a solid surface and contacted with a probe, e.g., in a dot blot or Northern format. In an alternative embodiment, the probe(s) are immobilized on a solid surface and the mRNA is contacted with the probe(s), for example, in a gene chip array. A skilled artisan can readily adapt known mRNA detection methods for use in detecting the level of mRNA encoding the biomarkers or other proteins of interest.
In some embodiments, microarrays, e.g., are employed. DNA microarrays provide one method for the simultaneous measurement of the expression levels of large numbers of genes. Each array consists of a reproducible pattern of capture probes attached to a solid support. Labeled RNA or DNA is hybridized to complementary probes on the array and then detected by laser scanning. Hybridization intensities for each probe on the array are determined and converted to a quantitative value representing relative gene expression levels. See, U.S. Pat. Nos. 6,040,138, 5,800,992 and 6,020,135, 6,033,860, and 6,344,316. High-density oligonucleotide arrays are particularly useful for determining the gene expression profile for a large number of RNA's in a sample.
Techniques for the synthesis of these arrays using mechanical synthesis methods are described in, e.g., U.S. Pat. No. 5,384,261. Although a planar array surface is often employed the array may be fabricated on a surface of virtually any shape or even a multiplicity of surfaces. Arrays may be peptides or nucleic acids on beads, gels, polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate, see U.S. Pat. Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193 and 5,800,992. Arrays may be packaged in such a manner as to allow for diagnostics or other manipulation of an all-inclusive device.
Primer and probes for use in amplifying and detecting the target sequence of interest can be selected using well-known techniques.
In the context of this invention, “determining the levels of expression” of an RNA interest encompasses any method known in the art for quantifying an RNA of interest.
In some embodiments, e.g., where the expression level of a protein encoded by a biomarker gene set forth in Table 1 is measured. Often, such measurements may be performed using immunoassays. Although the protein expression level may be determined using a cellular sample, such as a peripheral blood lymphocyte sample, the protein expression is typically determined using a serum sample.
A general overview of the applicable technology can be found in Harlow & Lane, Antibodies: A Laboratory Manual (1988) and Harlow & Lane, Using Antibodies (1999). Methods of producing polyclonal and monoclonal antibodies that react specifically with an allelic variant are known to those of skill in the art (see, e.g., Coligan, Current Protocols in Immunology (1991); Harlow & Lane, supra; Goding, Monoclonal Antibodies: Principles and Practice (2d ed. 1986); and Kohler & Milstein, Nature 256:495-497 (1975)). Such techniques include antibody preparation by selection of antibodies from libraries of recombinant antibodies in phage or similar vectors, as well as preparation of polyclonal and monoclonal antibodies by immunizing rabbits or mice (see, e.g., Huse et al., Science 246:1275-1281 (1989); Ward et al., Nature 341:544-546 (1989)).
Polymorphic alleles can be detected by a variety of immunoassay methods. For a review of immunological and immunoassay procedures, see Basic and Clinical Immunology (Stites & Terr eds., 7th ed. 1991). Moreover, the immunoassays can be performed in any of several configurations, which are reviewed extensively in Enzyme Immunoassay (Maggio, ed., 1980); and Harlow & Lane, supra. For a review of the general immunoassays, see also Methods in Cell Biology: Antibodies in Cell Biology, volume 37 (Asai, ed. 1993); Basic and Clinical Immunology (Stites & Terr, eds., 7th ed. 1991).
Commonly used assays include noncompetitive assays, e.g., sandwich assays, and competitive assays. Typically, an assay such as an ELISA assay can be used. The amount of the polypeptide variant can be determined by performing quantitative analyses.
Other detection techniques, e.g., MALDI, may be used to directly detect the presence of proteins correlated with treatment outcomes.
In a further aspect, the invention provides diagnostic devices and kits for identifying gene expression products associated with improved responsiveness of a rheumatoid arthritis patient to a therapeutic agents that antagonizes IL-6 receptor signaling, such as an IL-6R antibody, e.g., tocilizumab.
In some embodiments, a diagnostic device comprises probes to detect at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 50, 60, 70, or 80, or all of, the gene expression products set forth in Table 1. In some embodiments, the present invention provides oligonucleotide probes attached to a solid support, such as an array slide or chip, e.g., as described in DNA Microarrays: A Molecular Cloning Manual, 2003, Eds. Bowtell and Sambrook, Cold Spring Harbor Laboratory Press. Construction of such devices are well known in the art, for example as described in US Patents and Patent Publications U.S. Pat. No. 5,837,832; PCT application WO95/11995; U.S. Pat. No. 5,807,522; U.S. Pat. Nos. 7,157,229, 7,083,975, 6,444,175, 6,375,903, 6,315,958, 6,295,153, and 5,143,854, 2007/0037274, 2007/0140906, 2004/0126757, 2004/0110212, 2004/0110211, 2003/0143550, 2003/0003032, and 2002/0041420. Nucleic acid arrays are also reviewed in the following references: Biotechnol Annu Rev 8:85-101 (2002); Sosnowski et al, Psychiatr Genet 12(4):181-92 (December 2002); Heller, Annu Rev Biomed Eng 4: 129-53 (2002); Kolchinsky et al, Hum. Mutat 19(4):343-60 (April 2002); and McGail et al, Adv Biochem Eng Biotechnol 77:21-42 (2002).
An array can be composed of a large number of unique, single-stranded polynucleotides, usually either synthetic antisense polynucleotides or fragments of cDNAs, fixed to a solid support. Typical polynucleotides are preferably about 6-60 nucleotides in length, more preferably about 15-30 nucleotides in length, and most preferably about 18-25 nucleotides in length. For certain types of arrays or other detection kits/systems, it may be preferable to use oligonucleotides that are only about 7-20 nucleotides in length. In other types of arrays, such as arrays used in conjunction with chemiluminescent detection technology, preferred probe lengths can be, for example, about 15-80 nucleotides in length, preferably about 50-70 nucleotides in length, more preferably about 55-65 nucleotides in length, and most preferably about 60 nucleotides in length.
A person skilled in the art will recognize that, based on the known sequence information, detection reagents can be developed and used to assay any gene expression product set forth in Table 1, Table 2, or Table 3 and that such detection reagents can be incorporated into a kit. The term “kit” as used herein in the context of biomarker detection reagents, are intended to refer to such things as combinations of multiple biomarker detection reagents, or one or more biomarker detection reagents in combination with one or more other types of elements or components (e.g., other types of biochemical reagents, containers, packages such as packaging intended for commercial sale, substrates to which biomarker detection reagents are attached, electronic hardware components, etc.). Accordingly, the present invention further provides biomarker detection kits and systems, including but not limited to, packaged probe and primer sets (e.g., TaqMan probe/primer sets), arrays/microarrays of nucleic acid molecules where the arrays/microarrays comprise probes to detect the level of biomarker transcript, and beads that contain one or more probes, primers, or other detection reagents for detecting one or more biomarkers of the present invention. The kits can optionally include various electronic hardware components; for example, arrays (“DNA chips”) and microfluidic systems (“lab-on-a-chip” systems) provided by various manufacturers typically comprise hardware components. Other kits (e.g., probe/primer sets) may not include electronic hardware components, but may be comprised of, for example, one or more biomarker detection reagents (along with, optionally, other biochemical reagents) packaged in one or more containers.
In some embodiments, a biomarker detection kit typically contains one or more detection reagents and other components (e.g. a buffer, enzymes such as DNA polymerases) necessary to carry out an assay or reaction, such as amplification for detecting the level of biomarker transcript. A kit may further contain means for determining the amount of a target nucleic acid, and means for comparing the amount with a standard, and can comprise instructions for using the kit to detect the biomarker nucleic acid molecule of interest. In one embodiment of the present invention, kits are provided which contain the necessary reagents to carry out one or more assays to detect one or more biomarkers disclosed herein. In one embodiment of the present invention, biomarker detection kits/systems are in the form of nucleic acid arrays, or compartmentalized kits, including microfluidic/lab-on-a-chip systems.
Biomarker detection kits/systems may contain, for example, one or more probes, or pairs or sets of probes, that hybridize to a nucleic acid molecule encoded by a gene set forth in Table 1, Table 2, or Table 3. In some embodiments, the presence of more than one biomarker can be simultaneously evaluated in an assay. For example, in some embodiments probes or probe sets to different biomarkers are immobilized as arrays or on beads. For example, the same substrate can comprise biomarkers probes for detecting at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, or or more of the biomarkers set forth in Table 1, Table 2, or Table 3.
Using such arrays or other kits/systems, the present invention provides methods of identifying the biomarkers described herein in a test sample. Such methods typically involve incubating a test sample of nucleic acids obtained from peripheral blood lymphocytes from a patient with an array comprising one or more probes that selectively hybridizes to a nucleic acid encoded by a gene set forth in Table 1, Table 2, or Table 3. Conditions for incubating a biomarker detection reagent (or a kit/system that employs one or more such biomarker detection reagents) with a test sample vary. Incubation conditions depend on such factors as the format employed in the assay, the detection methods employed, and the type and nature of the detection reagents used in the assay. One skilled in the art will recognize that any one of the commonly available hybridization, amplification and array assay formats can readily be adapted to detect a biomarker set forth in Table 1, Table 2, or Table 3.
A biomarker detection kit of the present invention may include components that are used to prepare nucleic acids from a test sample for the subsequent amplification and/or detection of a biomarker nucleic acid molecule.
Correlating Gene Expression Levels with Therapeutic Response
The present invention provides methods of determining the levels of a gene expression product to evaluate the likelihood that a rheumatoid arthritis patient will respond to treatment with an IL-6R antibody, such as tocilizumab. Either female or male rheumatoid arthritis patients can be analyzed for gene expression levels.
The presence of certain markers, e.g., base line expression markers in Table 1 that are associated with an improvement in therapeutic outcomes, are indicative of patients who are expected to exhibit a positive therapeutic response to treatment with an IL-6R antibody, such as tocilizumab. Typically, the likelihood of the positive therapeutic response is increased with increasing amounts of the gene expression marker.
Similarly, a patient may have a gene expression marker, e.g., baseline expression of a biomarker set forth in Table 1, that is associated with a negative therapeutic outcome.
Accordingly, such a patient is not likely to response to IL-6R antibody, e.g., tocilizumab. Typically, the likelihood of the negative therapeutic response is increased with increased amount of the biomarker.
In Tables 1, 2, and 3, the “co-efficient” column represents the effect of the gene expression value on the response measured by change in DAS28 score, adjusted for baseline DAS (data in Table 3 are also adjusted for baseline platelet number). The sign of the coefficient represent the direction of the effect. For example, a coefficient of −1.6 means that higher expression is associated with better response. Every 2-fold increase in gene expression value corresponds to a further reduction on DAS score by 1.6 unit. Likewise, a positive coefficient indicates that higher expression value is associated with poorer response (higher DAS28 score). Table 1 show biomarkers in which the baseline expression (i.e., level prior to undergoing treatment with an IL-6R antibody such as tocilizumab) of a biomarker is predictive for a therapeutic response. Thus, for example, the level of a gene expression product encoded by a gene set forth in Table 1 can be determined in a peripheral blood sample obtained from a rheumatoid arthritis patient. A biomarker positive/negative groups is defined using a threshold in gene expression level. The exact thresholds for each marker can be determined using algorithms well known in the art and will depend on the particular platform and assay used and the desired performance parameters, e.g., sensitivity, specificity, of the assay.
For example, a patient is determined to be likely to exhibit a therapeutic response, or not to exhibit a therapeutic response to the IL-6 antagonizing agent, e.g., tocilizumab, if the level of expression of a biomarker in Table 1 is either above (predicted to exhibit a positive therapeutic response) or below (predicted to the not exhibit a positive therapeutic response) a threshold.
Measurement of the level of expression of a gene set forth in Table 2 also provides the ability to measure the likelihood of a patient to respond to treatment with an IL6-R antagonist, e.g., an IL-6R antibody such as tocilizumab, at later time points. For example, measurement of the expression of a gene set forth in Table 2 is made at base line and, e.g., at 8 weeks following treatment. The change in gene expression between the two measurements is used to calculate likelihood of response at a later time point, such as 16 or 24 weeks. Here again, a threshold of change in response may be applied.
Alternatively, a measurement can be made after initiation of treatment, e.g., at week 8, and an observed normalization' of a level of gene expression against a predetermined value may be used to make the response predication.
Gene expression can also be evaluated for genes listed in Table 5. Each of columns A-J of Table 5 represent genes that were analyzed for the clinical response noted in the column head.
The top 100 genes for ACR are listed in the table with the rank>0. If the value is 0, the gene is not selected for ACR. For each column at least two, typically most, or all of the genes indicated with a value>0 can be analyzed. The gene expression values are used as a linear combination of expression signals from multiple genes in order to predict the classification of clinical response as outlined in the Examples section of ‘class index's’ in the description relating to Table 5. The cutoffs for these linear combinations of gene expression levels are determined by classification algorithms known in the art, such as support vector machines (SVM) (see, e.g., Vapnik, The Nature of Statistical Learning, Springer, N.Y., 1995; Cristianini & Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK, 2000.)
The methods of the invention typically involve recording the level of a gene expression product associated with a beneficial therapeutic outcome, or a negative therapeutic outcome, in a rheumatoid arthritis patient treated with an IL-6R antibody such as tocilizumab. This information may be stored in a computer readable form. Such a computer system typically comprises major subsystems such as a central processor, a system memory (typically RAM), an input/output (I/O) controller, an external device such as a display screen via a display adapter, serial ports, a keyboard, a fixed disk drive via a storage interface and a floppy disk drive operative to receive a floppy disc, and a CD-ROM (or DVD-ROM) device operative to receive a CD-ROM. Many other devices can be connected, such as a network interface connected via a serial port.
The computer system also be linked to a network, comprising a plurality of computing devices linked via a data link, such as an Ethernet cable (coax or 10BaseT), telephone line, ISDN line, wireless network, optical fiber, or other suitable signal transmission medium, whereby at least one network device (e.g., computer, disk array, etc.) comprises a pattern of magnetic domains (e.g., magnetic disk) and/or charge domains (e.g., an array of DRAM cells) composing a bit pattern encoding data acquired from an assay of the invention.
The computer system can comprise code for interpreting the results of an expression analysis evaluating the baseline level of one or more gene expression products encoded by a gene noted in Table 1. Thus in an exemplary embodiment, the expression analysis results are provided to a computer where a central processor executes a computer program for determining the propensity for a therapeutic response to treatment with an IL-6 receptor antibody.
The invention also provides the use of a computer system, such as that described above, which comprises: (1) a computer; (2) a stored bit pattern encoding the expression results obtained by the methods of the invention, which may be stored in the computer; (3) and, optionally, (4) a program for determining the likelihood for a positive therapeutic response.
The invention further provides methods of generating a report based on the detection of gene expression products in a patient that has rheumatoid arthritis. Such a report is based on the detection of gene expression products encoded by the genes set forth in Table 1 that are associated with either a positive or negative therapeutic outcome.
A patient that has an increased likelihood of having a positive therapeutic response to treatment with IL-6R antibody has at least one gene expression product in Table 1 that is associated with a positive therapeutic response. Typically such a patient has an expression pattern where at least two products encoded by a gene set forth in Table 1 are determined. In some embodiments, the patient may be evaluated for expression levels of products encoded by 3, 4, 5, 6, 7, 8, 9, or 10 or more of the genes set forth in Table 1.
Analysis of Gene Expression Data for Association with Response to Change in DAS28 Score.
RNA samples collected from patients with active RA dosed with 8 mg/Kg tocilizumab as a monotherapy in the AMBITION study (Jones, et al., Ann Rheum Dis 2 69:88-96, 2010) were collected at baseline and at week 8 post dose. Two hundred and nine samples (113 baseline samples and 96 “week 8” samples) underwent gene expression profiling through use of an Affymetrix GeneChip® Human Genome U133 Plus 2.0 Array.
After a number of quality control steps on the gene expression data, 2 samples were highlighted as having lower quality, and 207 samples were subjected to further analysis.
The Affymetrix RMA algorithm was used in generating the normalized gene expression data for further analysis. Only probesets with high expression levels (max>4) and those with larger dynamic range (max-min>2) were included. The max and min were taken over all samples. Linear regression was performed for the following analyses. In all analyses, change in Disease Activity Score 28 (DAS28) at week 16 (cDAS28) was used as response endpoint. Week 16 was chosen because it was the earliest time point for escape therapy in the most tocilizumab clinical trials). Baseline DAS was used as a covariate in all analysis since it has significant effect on cDAS.
1. Baseline gene expression versus cDAS28. 111 subjects were included in the analysis.
2. Linear Regression with LASSO Variable Selection using baseline expression data. This is a multivariate analysis method that include all probesets in the model, with L1 penalty on the coefficients of the probesets added to the objective function. (Tibshirani, R. (1996). J. Royal. Statist. Soc B., Vol. 58(1): 267-288)). A subset of the probesets was selected by the model. The number of probesets selected by the model depends on the level of penalty. The optimal level of penalty, which subsequently determined optimal number of probesets selected to achieve the best prediction, was determined using 10-fold cross validation.
3. Change in gene expression at week 8 versus cDAS28. Ninety four subjects were included in the analysis.
4. Linear Regression with LASSO Variable Selection using change in gene expression
5. Baseline gene expression versus cDAS28, adjusting for baseline platelets
Analysis (1) identified a number of probesets that represented activated platelet expressed genes e.g. ITGA2B (CD41), ITGB3 (CD61), JAM3 were present at the top of the list of data ordered by p-value (see, Table 1). There is a correlation of expression of these genes with cDAS28.
This observation prompted a regression analysis of baseline platelet count against change in DAS28. The analysis demonstrated a modest but statistically significant link to baseline platelet count. A far stronger effect size is noted through the correlation of ITGA2B, ITGB3, JAM3 to cDAS28, suggesting that markers of platelet activation are a better predictors of response than platelet count alone.
From analysis (1), it was determined that baseline expression levels of EPHB2 (Ephrin receptor B2) has a correlation to cDAS28. EPHB2 transduces signals that regulate cell attachment and migration and is expressed at higher levels in synovial fibroblasts and exudate lymphocytes in RA, than in those from OA. It's ligand, EphrinB1, is expressed at levels higher in RA peripheral blood lymphocytes (PBL) than healthy controls.
Recombinant EphrinB1 stimulates normal PBL's to exhibit enhanced migration and TNF production, and RA synovial cells to produce IL-6. These results indicate that it is also a useful biomarker for predicting response to tocilizumab.
We reasoned that the high correlation of platelet expressed genes with cDAS observed in analysis (1) could be ‘masking’ the identification of other important response signals. Baseline correction of platelet number in the regression model was therefore performed. From this analysis, ordered by p-value 3 out of 4 components of the NALP1 inflammasome were identified. Inflammasomes are multi-protein cytoplasmic complexes that mediate activation of pro-inflammatory caspases. The NALP1 inflammasome activates caspase 1 and caspase 5. Caspase 1 cleaves pro-IL-1β to IL-1β, and also activates IL-18 and potentially IL-33. We also identified the association of baseline expression of CARD16, a negative regulator of Caspase 1, and the baseline expression of IL-1 receptor, with cDAS. Serum levels of IL1B/IL-18/IL-33 and gene expression signature of transcripts identified above also may be used as biomarkers to predict response to tocilizumab.
From analysis (3), a number of transcripts have been identified that may be used to predict response through change in gene expression 8 weeks from tocilizumab administration. (Table 2). These include caspase 1, a link to the IL-1β/IL-18/IL-33 pathway (and see (4) above), serine palmitoyltransferase, long chain base subunit 2, a link to de novo sphingolipid synthesis of molecules such ceramide and sphingosine-1-phosphate (SIP), and platelet expressed genes such as CD41, CD61, and JAM3.
Lasso variable selection multivariate methodology (analyses 2 and 4) allows identification of transcripts that each contribute a different ‘component’ to the prediction of response. An optimal number of probesets (n=12 and n=13 respectively) were determined by 10 fold cross validation. This analysis identified a number of genes that may be used as predictive biomarkers.
The list of probesets/genes identified by these analyses are shown in Table 1. Table 1 cDASvs.bExp contains probesets/genes whose baseline expression is predictive of tocilizumab treatment response. This list consists of 95 probesets, 12 of which were unmapped, the remaining probesets mapped to 72 unique gene symbols. Among the probesets, 88 were identified by univariate linear regression (analysis 1) and 12 were identified using the multivariate LASSO analysis (analysis 2), with 5 probesets identified by both analyses.
Table 2 cDASvs.cEXP contains probeset/gene expression change from baseline to week 8 that is predictive of tocilizumab treatment response. This list consists of 104 probesets, 6 of which were unmapped, the remaining mapped to 92 unique genes symbols. Among the probesets, 97 were identified by univariate linear regression analysis (analysis 3) and 13 were identified using the multivariate LASSO analysis (analysis 4), with 6 probesets identified by both analyses.
Table 3 (cDASvs.bEXP.AdjustforPlatelet) contains probeset/genes whose baseline expression, combined with baseline platelet count, is predictive of tocilizumab treatment response. This list consists of 81 probesets, 10 of which were unmapped, the remaining mapped to 61 unique genes symbols. All of the probesets were identified by univariate linear regression analysis (analysis 5).
All of the biomarkers may be used univariately or in combination in a multivariate model.
An analysis to identify groups of probesets with predictive value of extreme response to tocilizumab, namely ACR response and EULAR response, was also undertaken.
Two hundred nine CEL files (Affymetrix expression data files) were generated for patients treated with tocilizumab. Two CEL files were excluded from the dataset for technical reasons. One hundred eleven of the remaining 207 CEL files are for the samples at the baseline. This example is focused on the dataset N111.
We considered the four classes of American College of Rheumatology (ACR) response are shown in Table 4.
We also considered 3 classes of European League Against Rheumatism (EULAR) response at week 16 (1 for no response, 2 for moderate and 3 for good response). Change in DAS28 at beginning and DAS28 at week 16 (“dDAS28” or “cDAS28”), as well as DAS28 at week 16 was also evaluated. There is one missing data point in DAS28, we therefore have a dataset N110 for DAS28 at week 16 and cDAS28.
For DAS28 at week 16, we define C1 as the class with DAS28 value x>=4 (non response), C2 as the class with x in the range of 2.6 to 4, and C3 as the class with x<2.6 (good response). For ΔDAS28, we define C1 as the class with ΔDAS28 value y<=2.5 (poor response), C2 as the class with y in the range of 2.5 to 3.6, and C3 as the class with y>3.6 (good response).
In all the above class assignments, C1 represents the group with poor response and C4 (ACR) or C3 (other indicators) for good response. C2 (or C2 and C3 for ACR) is the class of moderate response.
For each indicator (ACR, EULAR, ΔDAS28, and DAS28 at week 16), we used Dn3 expression signals (see Liu, et al., J. Theortical Biol 243:273-278, 2006; and pending U.S. application Ser. No. 12/578,417) and two different ways of grouping. One grouping is the poor response class versus others (good and moderate response classes). The other grouping is to use only the extreme classes (poor response class versus the good response classes). The sample sizes for the first grouping method are given before, N111 or N110. The sample sizes for the grouping of extreme classes are N62 (ACR), N45 (EULAR), N70 (DAS28 at week 16) and N80 (ΔDAS28).
Dn3 signals (with improvements on MASS using differences of perfect match and mismatch intensities) are typically robust for classification results. For completeness, we also included the probe sets selected with Pn3 signals (using only perfect match intensities and similar to RMA in certain sense).
For each grouping method, we calculated the absolute values of t-statistics and selected the top 100 probe sets with highest absolute values of t-statistics. Their union for 4 different indicators, 2 different signals and 2 different grouping methods (total 8 groups) contains 628 probesets and are listed in Table 5. (For “union of the four different indicators, the 4 different indicators (or 4 different types of responses) are ACR, EULAR, DAS and cDAS. The union is the combination of all probe sets without counting the replicated ones. For example, if set 1 is {1, 3, 5, 7, 9}, set 2 is {1, 2, 3, 4}, Set 3 is {3, 5}, set 4 is {9, 10, 11}, then the union of these 4 sets is {1, 2, 3, 4, 5, 7, 9, 10, 11}).
In Table 5, the first column “N1:54630” lists the 1-based indices in the list of 54630 probe sets targeting human genes on the HG-U133 Plus 2.0 microarray. The second column “ID” lists the Affymetrix probe set IDs.
The next 8 columns provide the ranks of 8 groups of probesets and the information whether a probe set is selected in a particular group. The column names are indicator name, sample size, and signals (Dn3). The value 0 means the probe set is not selected in a particular group. The values 1 through 100 give the ranks of the selected probe sets, where 1 is the top (most significant) one.
The column “AverageScore” provides a score for the summary of the previous 8 columns. The value 0 has no contribution to the score (i.e., the score is 0). For all other values (1 through 100), we calculated (101−value) (so the difference is in the range 1 through 100, but in the reverse order, the largest difference, 100, corresponds to the most significant rank 1). We calculated the average score for the 8 columns and list all average scores in the column. In general, the higher the score, the more significant a probeset for all groups.
The columns “Gene Symbol” and “Gene Title” provide annotations from Affymetrix web site for the selected probe sets.
For Table 5, each group of genes identified in columns C-J of table 5 may be used to form one or more linear combinations of expression signals from multiple genes in order to predict the clinical response as outlined in the description of ‘class index's’ in lines 0080-0084. The cutoffs for these linear combinations of gene expression levels will be determined by classification algorithms such as support vector machines (SVM, The Nature of Statistical Learning, Springer, N.Y., 1995; Cristianini and Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK, 2000). For Table 5, each indications shows a number; expression of at least two genes that have a number greater than 0 can be used (within the same column).
Examples 3 and 4 below provide example of how two and three gene transcripts are used to predict patient response to treatment with an IL-6R antagonist, such as an IL-6R antibody, e.g., tocilizumab. As understood in the art, a multivariate model can be employed that involves additional genes identified herein, e.g., probe sets corresponding to those set forth in Table 1, Table 2, or Table 3.
Gene transcripts in patient baseline blood samples are measured using Affymetrix human genome U133 plus v2 array. The raw data file are normalized against the data from a set of reference samples from which the algorithm was derived. Expression at the gene transcript level (RMA type of data) will be extracted, in this example, for at the three probesets 12345_at, 12346_at and 12347_at (denoted as e1, e2 and e3) and used in a linear model to give predictions of the week 24 change from baseline DAS28 score (cDAS) if the patient undergoes tocilizumab (TCZ) treatment at 8 mg/kg in combination with methotrexate (MTX).
For TCZ treatment: cDAS=a0*DAS_baseline+a1*e1+a2*e2+a3*e3
The predicted mean change in DAS for the patients will be from 1 to −7, depending on the baseline DAS and gene expression values of e1, e2 and e3. If the patient were to undergo treatment with MTX alone, the predicted mean change in DAS given by:
For MTX treatment: cDAS=b0*DAS_baseline
The predicted mean change is DAS will be from 0 to −3, depending on the patient baseline DAS alone
The treatment choice for each patient is then made based on the difference of these predictions. For example, if patient A has a predicted change in DAS of −4.5 on tocilizumab, and −2 on MTX, the doctor may recommend TCZ treatment. Patient B has the predicted change in DAS of −3 on TCZ and −2.5 on MTX, the doctor may recommend treatment with MTX, as the small additional therapeutic benefit may be not worth the additional cost and any potential risk.
Expression levels of two genes in patient baseline blood samples are measured using quantitative PCR (qPCR). The relative expression levels are represented by ACT. Biomarker groups are defined as following:
Positive: a1*ΔCT1+a2*ΔCT2>=2.1
Negative: a1*ΔCT1+a2*ΔCT2<2.1
Biomarker positive patients are likely to have better response rate compared with biomarker negative patients under tocilizumab treatment, (ACR50 response rate of 55% vs. 38%), while both group have similar response rate when treated with methotrexate, with ACR50 response rate of 35%.
It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.
indicates data missing or illegible when filed
This application claims benefit of U.S. provisional application No. 61/352,285, filed Jun. 7, 2010, which is herein incorporated by reference for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 61352285 | Jun 2010 | US |
