Patent Grant
8492328
The present invention relates generally to the field of pharmacogenomics, and more specifically, to methods and procedures used to monitor response or determine sensitivity in patients to allow the identification of individualized genetic profiles which will aid in treating diseases and disorders.
Incorporated herein by reference in its entirety is a Sequence Listing entitled, “11082USPCT ST25.txt”, comprising SEQ ID NO:1 through SEQ ID NO:286, which include amino acid sequences disclosed herein. The Sequence Listing has been submitted herewith in ASCII text format via EFS, and thus constitutes both the paper and computer readable form thereof. The Sequence Listing was first created using PatentIn on May 7, 2012, and is 52 KB in size.
Cancer is a disease with extensive histoclinical heterogeneity. Although conventional histological and clinical features have been correlated to prognosis, the same apparent prognostic type of tumors varies widely in its responsiveness to therapy and consequent survival of the patient.
New prognostic and predictive markers, which would facilitate an individualization of therapy for each patient, are needed to accurately predict patient response to treatments, such as small molecule or biological molecule drugs, in the clinic. The problem may be solved by the identification of new parameters that could better predict the patient's sensitivity to treatment. The classification of patient samples is a crucial aspect of cancer diagnosis and treatment. The association of a patient's response to a treatment with molecular and genetic markers can open up new opportunities for treatment development in non-responding patients, or distinguish a treatment's indication among other treatment choices because of higher confidence in the efficacy. Further, the pre-selection of patients who are likely to respond well to a medicine, drug, or combination therapy may reduce the number of patients needed in a clinical study or accelerate the time needed to complete a clinical development program (M. Cockett et al., Current Opinion in Biotechnology, 11:602-609 (2000)).
The ability to determine which patients are responding to anti-angiogenesis therapies (such as IGF1R modulators) or predict drug sensitivity in patients is particularly challenging because drug responses reflect not only properties intrinsic to the target cells, but also a host's metabolic properties. Efforts to use genetic information to predict or monitor drug response have primarily focused on individual genes that have broad effects, such as the multidrug resistance genes mdr1 and mrp1 (P. Sonneveld, J. Intern. Med., 247:521-534 (2000)).
The development of microarray technologies for large scale characterization of gene mRNA expression pattern has made it possible to systematically search for molecular markers and to categorize cancers into distinct subgroups not evident by traditional histopathological methods (J. Khan et al., Cancer Res., 58:5009-5013 (1998); A. A. Alizadeh et al., Nature, 403:503-511 (2000); M. Bittner et al., Nature, 406:536-540 (2000); J. Khan et al., Nature Medicine, 7(6):673-679 (2001); and T. R. Golub et al., Science, 286:531-537 (1999); U. Alon et al., P.N.A.S. USA, 96:6745-6750 (1999)). Such technologies and molecular tools have made it possible to monitor the expression level of a large number of transcripts within a cell population at any given time (see, e.g., Schena et al., Science, 270:467-470 (1995); Lockhart et al., Nature Biotechnology, 14:1675-1680 (1996); Blanchard et al., Nature Biotechnology, 14:1649 (1996); U.S. Pat. No. 5,569,588 to Ashby et al.).
Recent studies demonstrate that gene expression information generated by microarray analysis of human tumors can predict clinical outcome (L. J. van't Veer et al., Nature, 415:530-536 (2002); M. Shipp et al., Nature Medicine, 8(1):68-74 (2002); G. Glinsky et al., The Journal of Clin. Invest., 113(6):913-923 (2004)). These findings bring hope that cancer treatment will be vastly improved by better predicting and monitoring the response of individual tumors to therapy.
PCT Application No. PCT/US2006/034201 provides biomarkers useful for identifying a mammal that will respond therapeutically to a method of treating cancer comprising administering an IGF1R modulator.
Needed are new and alternative methods and procedures to determine drug sensitivity or monitor response in patients to allow the development of individualized diagnostics which are necessary to treat diseases and disorders based on patient response at a molecular level.
The invention provides methods and procedures for determining patient sensitivity or monitor response at the molecular level to one or more insulin growth factor 1 receptor (IGF1R) modulators. The invention also provides methods of determining or predicting whether an individual requiring therapy for a disease state such as cancer will or will not respond to treatment, prior to administration of the treatment, wherein the treatment comprises administration of one or more IGF1R modulators. The one or more IGF1R modulators are compounds that can be selected from, for example, one or more IGF1R specific ligands, one or more small molecule IGF1R inhibitors, or one or more IGF1R binding monoclonal antibodies.
In one aspect, the invention provides a method for predicting the likelihood a mammal will respond therapeutically to a method of treating cancer comprising administering an IGF1R modulator, wherein the method comprises: (a) measuring in the mammal the level of at least one biomarker selected from the biomarkers of Tables 2-8; (b) exposing a biological sample from said mammal to the IGF1R modulator; (c) following the exposing of step (b), measuring in said biological sample the level of the at least one biomarker, wherein an increase 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), indicates an increased likelihood that the mammal will respond therapeutically to said method of treating cancer when said at least one biomarker is a sensitivity biomarker, and indicates an increased likelihood that the mammal will not respond therapeutically to said method of treating cancer when said at least one biomarker is a resistance biomarker.
In another aspect, the invention provides a method for identifying a mammal that will respond therapeutically to a method of treating cancer comprising administering an IGF1R modulator, wherein the method comprises: (a) measuring in the mammal the level of at least one biomarker selected from the biomarkers of Tables 2-8; (b) exposing a biological sample from the mammal to the IGF1R modulator; (c) following the exposing in step (b), measuring in said biological sample the level of the at least one biomarker and using said measurement to determine whether the mammal is likely to respond therapeutically to said method of treating cancer.
A difference in the level of the biomarker that is sufficient to indicate whether the mammal will or will not respond therapeutically to the method of treating cancer can be readily determined by one of skill in the art using known techniques. The increase or decrease in the level of the biomarker can be correlated to determine whether the difference is sufficient to identify a mammal that will respond therapeutically. The difference in the level of the biomarker that is sufficient can, in one aspect, be predetermined prior to determining whether the mammal will respond therapeutically to the treatment. In one aspect, the difference in the level of the biomarker is a difference in the mRNA level (measured, for example, by RT-PCR or a microarray), such as at least a two-fold difference, at least a three-fold difference, or at least a four-fold difference in the level of expression. In another aspect, the difference in the level of the biomarker is determined by IHC. In another aspect, the difference in the level of the biomarker refers to a p-value of <0.05 in Anova analysis. In yet another aspect, the difference is determined in an ELISA assay.
As used herein, respond therapeutically refers to the alleviation or abrogation of the cancer. This means that the life expectancy of an individual affected with the cancer will be increased or that one or more of the symptoms of the cancer will be reduced or ameliorated. The term encompasses a reduction in cancerous cell growth or tumor volume. Whether a mammal responds therapeutically can be measured by many methods well known in the art, such as PET imaging.
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 step of measuring in the mammal the level of at least one biomarker comprises taking a biological sample from the mammal and then measuring the level of the biomarker(s) in the biological sample. The biological sample can comprise, for example, at least one of serum, whole fresh blood, peripheral blood mononuclear cells, frozen whole blood, fresh plasma, frozen plasma, urine, saliva, skin, hair follicle, bone marrow, or tumor tissue.
The level of the at least one biomarker can be, for example, the level of protein and/or mRNA transcript of the biomarker(s).
In another aspect, the invention provides a method for identifying a mammal that will respond therapeutically to a method of treating cancer comprising administering an IGF1R modulator, wherein the method comprises: (a) exposing a biological sample from the mammal to the IGF1R modulator; (b) following the exposing of step (a), measuring in said biological sample the level of at least one biomarker selected from the biomarkers of Tables 2-8, wherein a difference in the level of the at least one biomarker measured in step (b), compared to the level of the biomarker in a mammal that has not been exposed to said IGF1R modulator, indicates that the mammal will respond therapeutically to said method of treating cancer.
In yet another aspect, the invention provides a method for testing or predicting whether a mammal will respond therapeutically to a method of treating cancer comprising administering an IGF1R modulator, wherein the method comprises: (a) measuring in the mammal the level of at least one biomarker selected from the biomarkers of Tables 2-8; (b) exposing the mammal to the IGF1R modulator; (c) following the exposing of step (b), measuring in the mammal the level of the at least one biomarker, wherein 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) indicates that the mammal will respond therapeutically to said method of treating cancer.
In another aspect, the invention provides a method for determining whether a compound inhibits IGF1R activity in a mammal, comprising: (a) exposing the mammal to the compound; and (b) following the exposing of step (a), measuring in the mammal the level of at least one biomarker selected from the biomarkers of Tables 2-8, wherein a difference in the level of said biomarker measured in step (b), compared to the level of the biomarker in a mammal that has not been exposed to said compound, indicates that the compound inhibits IGF1R 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 IGF1R activity, comprising (a) exposing the mammal to the compound; and (b) following the exposing of step (a), measuring in the mammal the level of at least one biomarker selected from the biomarkers of Tables 2-8, wherein a difference in the level of said biomarker measured in step (b), compared to the level of the biomarker in a mammal that has not been exposed to said compound, indicates that the mammal has been exposed to a compound that inhibits IGF1R activity.
In another aspect, the invention provides a method for determining whether a mammal is responding to a compound that inhibits IGF1R activity, comprising (a) exposing the mammal to the compound; and (b) following the exposing of step (a), measuring in the mammal the level of at least one biomarker selected from the biomarkers of Tables 2-8, wherein 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 a mammal that has not been exposed to said compound, indicates that the mammal is responding to the compound that inhibits IGF1R activity.
As used herein, “responding” encompasses responding by way of a biological and cellular response, as well as a clinical response (such as improved symptoms, a therapeutic effect, or an adverse event), in a mammal
The invention also provides an isolated biomarker selected from the biomarkers of Tables 2-8. The biomarkers of the invention comprise sequences selected from the nucleotide and amino acid sequences provided in Tables 2-8 and the Sequence Listing, as well as fragments and variants thereof.
The invention also provides a biomarker set comprising two or more biomarkers selected from the biomarkers of Tables 2-8.
The invention also provides kits for determining or predicting whether a patient would be susceptible or resistant to a treatment that comprises one or more IGF1R modulators. The patient may have a cancer or tumor such as, for example, a colon cancer or tumor.
In one aspect, the kit comprises a suitable container that comprises one or more specialized microarrays of the invention, one or more IGF1R modulators for use in testing cells from patient tissue specimens or patient samples, and instructions for use. The kit may further comprise reagents or materials for monitoring the expression of a biomarker set at the level of mRNA or protein.
In another aspect, the invention provides a kit comprising two or more biomarkers selected from the biomarkers of Tables 2-8.
In yet another aspect, the invention provides a kit comprising at least one of an antibody and a nucleic acid for detecting the presence of at least one of the biomarkers selected from the biomarkers of Tables 2-8. In one aspect, the kit further comprises instructions for determining whether or not a mammal will respond therapeutically to a method of treating cancer comprising administering a compound that inhibits IGF1R activity. In another aspect, the instructions comprise the steps of (a) measuring in the mammal the level of at least one biomarker selected from the biomarkers of Tables 2-8, (b) exposing the mammal to the compound, (c) following the exposing of step (b), measuring in the mammal the level of the at least one biomarker, wherein 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) indicates that the mammal will respond therapeutically to said method of treating cancer.
The invention also provides screening assays for determining if a patient will be susceptible or resistant to treatment with one or more IGF1R modulators.
The invention also provides a method of monitoring the treatment of a patient having a disease, wherein said disease is treated by a method comprising administering one or more IGF1R modulators.
The invention also provides individualized genetic profiles which are necessary to treat diseases and disorders based on patient response at a molecular level.
The invention also provides specialized microarrays, e.g., oligonucleotide microarrays or cDNA microarrays, comprising one or more biomarkers having expression profiles that correlate with either sensitivity or resistance to one or more IGF1R modulators.
The invention also provides antibodies, including polyclonal or monoclonal, directed against one or more biomarkers of the invention.
The invention will be better understood upon a reading of the detailed description of the invention when considered in connection with any accompanying figures.
Identification of biomarkers that provide rapid and accessible readouts of efficacy, drug exposure, or clinical response is increasingly important in the clinical development of drug candidates. Embodiments of the invention include measuring changes in the levels of secreted proteins, or plasma biomarkers, which represent one category of biomarker. In one aspect, plasma samples, which represent a readily accessible source of material, serve as surrogate tissue for biomarker analysis.
The invention provides biomarkers that respond to the modulation of a specific signal transduction pathway and also correlate with IGF1R modulator sensitivity or resistance. These biomarkers can be employed for predicting and monitoring response to one or more IGF1R modulators. In one aspect, the biomarkers of the invention are those provided in Tables 2-8 and the Sequence Listing, including both polynucleotide and polypeptide sequences. In another aspect, the biomarkers of the invention are nucleotide sequences that, due to the degeneracy of the genetic code, encodes for a polypeptide sequence provided in the sequence listing.
The biomarkers serve as useful molecular tools for predicting and monitoring response to IGF1R modulators that affect IGF1R activity or the IGF1R signal transduction pathway.
In addition to playing an important role in normal cell growth, maintenance and development, insulin-like growth factor receptor (IGF1R) and its ligands are also important in the establishment and maintenance of the malignant phenotype. Binding of IGF-1 and IGF-II ligands to the IGF1R initiates a cascade of events leading to activation of mitogenic signaling pathway (Ras/Raf/MAPK) and antiapoptotic/survival pathway (PI3K-Akt/mTor), resulting in proliferation, transformation and survival in tumor cells (D. LeRoith, et al., Cancer Lett., 195(2):127-37 (2003), R. Baserga, et al., Int. J. Cancer;107:873-7 (2003)). IGF1R overexpression and/or enhanced activity have been observed in diverse tumor types suggesting that the potential therapeutic use of agents targeting this pathway is broad. IGF1R provides a critical survival signal in multiple tumor types. The expression of this receptor is an indicator of poor prognosis, thus, it has emerged as an attractive and compelling target for cancer therapy to inhibit the progression of multiple tumor types in cancer patients. Various drug discovery approaches have been explored in recent years to modulate the function of IGF1R. Approaches aimed at the reduction of receptor number or enzymatic activity using a variety of strategies in preclinical models have been shown to reverse the malignant phenotype in tumor cells. These strategies include antisense (L. Long, et al., Cancer Res, 55(5):1006-9 (1995), D. Andrews et al., J. Clin. Oncol., 19(8):2189-200 (2001)), monoclonal antibody (C. Arteaga, et al., Cancer Res., 49(22):6237-41 (1989)), small molecule inhibitors (M. Wittman, et al., J. Med. Chem., September 8;48(18):5639-43 (2005), C. Garcia-Echeverria, et al., Cancer Cell, 5(3):231-9 (2004)), IGF-1 mimetic peptides (Z. Pietrzkowski, et al., Cancer Res., 53(5):1102-6 (1993)) as well as dominant negative mutants that lack enzyme activity (C. D'Ambrosio, et al., Cancer Res, 56(17): 4013-20 (1996)).
However, this targeted therapy may only be successful if the receptor is absolutely necessary for pathogenesis and tumor progression. IGF1R and its ligands have been shown to be important in the mesenchymal originated soft tissue sarcomas and neuroblastoma (G. Merlino, et al., Oncogene;18:5340-8 (1999), K. Scotlandi, et al., Cancer Res.;56:4570-4 (1996), S. Burrow, et al., J. Surg. Oncol.;69:21-7 (1998), D. Yee, et al., J. Clin. Invest., 86(6) : 1806-14 (1990)). These rare tumors have distinctive biological characteristics including aggressive local behavior and a predilection for metastasis. With multimodal treatments and very aggressive chemotherapeutic regimens, the survival rate is disappointingly low and, thus, there is a high unmet medical need for the treatment of patients with these tumor types. Inhibition of IGF1R by antibody or small molecule either alone or in combination with other chemotherapeutic agents have demonstrated antitumor activity in sarcomas (S. Benini, et al., Clinical Cancer Res., 7, 1790-97 (2001), K. Scotlandi, et al., Cancer Res., May 1;65(9):3868-76 (2005), M. Manara, et al., Clinical Cancer Res.; 13(4) 1322-30 (2007)).
One of the integral goals in the development of these targeted cancer therapies is to identify the targeted patients population who are most likely to benefit from the drug treatment. Utilizing biomarkers has successfully guided the development of Herceptin® and EGFR inhibitors (J. Baselga, et al., J Clin Oncol. 14, 737-744 (2005), T. Lynch, et al., N. Engl. J. Med., 350, 2129-2139 (2004)). More recently, gene expression profiling studies have demonstrated the advantages of molecular “signatures” or marker sets generated by microarray analysis in predicting chemotherapeutic response and guiding the targeted therapies (K. Iwao-Koizumi, et al., J. Clin. Oncol.;23:422-31 (2005), H. Dressman, et al., Clin. Cancer Res.;12:819-26 (2006), R. Rouzier, et al., Clin. Cancer Res.;11:5678-85 (2005), K. Hess, et al., J. Clin. Oncol.;24:4236-44 (2006), H. Dressman, et al., J. Clin. Oncol., February 10;25(5):517-25 (2007), A. Potti, et al., Nat. Med., November;12(11):1294-300 (2006)). These findings provide hope that cancer treatments of the future will be vastly improved by using molecular “signatures” to choose the most effective drug for patient targeting. One of the challenges is to determine the targeted patient population for the drug before clinical data is available.
To overcome this challenge, as described previously (F. Huang F, et al., Cancer Res., March 1;67(5):2226-38 (2007)), cultured cancer cell lines can be used as models to identify biomarkers that correlate with response to a therapy assuming these markers identified in vitro are ultimately applicable in clinical studies to select targeted patient population.
To this end, in this study, the gene and protein profiling by both microarray and LC/MS based “bottom-up” protein profiling (M. Lipton, et al., Proc. Natl. Acad. Sci. USA., August 20;99(17):11049-54 (2002), H. Prokisch et al., PLoS Biol., June;2(6):e160 (2004)) were conduced in parallel using a panel of 29 cell lines to identify genes or proteins whose basal expression levels are correlated with the in vitro sensitivity of cells to compound 1 or compound 2 (as defined below) and potentially could be used as predictive markers. To gain insights of acquired resistance of compound 1, gene expression profiles of a pair of sensitive and acquired resistant cell lines were compared to identify genes correlated with the acquired resistance. These genes were then compared to the genes over expressed in the resistant cell lines at basal level (correlated with the de novo resistance to the drug). Common mechanism of de novo resistance and acquired resistance to IGF1R inhibitor compound 1 was explored. Furthermore, genes/proteins modulated by drug treatment of compound 1 were identified and linked to the possible mechanisms of the drug action. These markers could be useful to monitor the biological effects of the drug and to select the optimal dose in the clinical studies. To explore the relation between these biomarkers and the drug target IGF1R, pathway analyses were performed and cross-talk between the IGF1R and other kinases was evident, this led us to propose the hypothesis for the potential synergistic activity between compound 1 and other agents targeting these kinases. Drug combination studies of compound 1 with other agents, such as EGFR inhibitors, were performed and synergy in tumor growth inhibition in vitro was observed with combined inhibition of multiple pathways. In this study, we identified the biomarkers of potentially predictive the targeted sub-population of patient with sarcomas that would be benefit from the treatment of IGF1R inhibitor.
IGF1R Modulators:
As used herein, the term “IGF1R modulator” is intended to mean a compound or drug that is a biological molecule or a small molecule that directly or indirectly modulates IGF1R activity or the IGF1R signal transduction pathway. Thus, compounds or drugs as used herein is intended to include both small molecules and biological molecules. Direct or indirect modulation includes activation or inhibition of IGF1R activity or the IGF1R signal transduction pathway. In one aspect, inhibition refers to inhibition of the binding of IGF1R to an IGF1R ligand such as, for example, VEGF. In another aspect, inhibition refers to inhibition of the kinase activity of IGF1R.
IGF1R modulators include, for example, IGF1R specific ligands, small molecule IGF1R inhibitors, and IGF1R monoclonal antibodies. In one aspect, the IGF1R modulator inhibits IGF1R activity and/or inhibits the IGF1R signal transduction pathway. In another aspect, the IGF1R modulator is an IGF1R monoclonal antibody that inhibits IGF1R activity and/or inhibits the IGF1R signal transduction pathway.
IGF1R modulators include biological molecules or small molecules.
Biological molecules include all lipids and polymers of monosaccharides, amino acids, and nucleotides having a molecular weight greater than 450. Thus, biological molecules include, for example, oligosaccharides and polysaccharides; oligopeptides, polypeptides, peptides, and proteins; and oligonucleotides and polynucleotides. Oligonucleotides and polynucleotides include, for example, DNA and RNA.
Biological molecules further include derivatives of any of the molecules described above. For example, derivatives of biological molecules include lipid and glycosylation derivatives of oligopeptides, polypeptides, peptides, and proteins.
Derivatives of biological molecules further include lipid derivatives of oligosaccharides and polysaccharides, e.g., lipopolysaccharides. Most typically, biological molecules are antibodies, or functional equivalents of antibodies. Functional equivalents of antibodies have binding characteristics comparable to those of antibodies, and inhibit the growth of cells that express IGF1R. Such functional equivalents include, for example, chimerized, humanized, and single chain antibodies as well as fragments thereof
Functional equivalents of antibodies also include polypeptides with amino acid sequences substantially the same as the amino acid sequence of the variable or hypervariable regions of the antibodies. An amino acid sequence that is substantially the same as another sequence, but that differs from the other sequence by means of one or more substitutions, additions, and/or deletions, is considered to be an equivalent sequence. Preferably, less than 50%, more preferably less than 25%, and still more preferably less than 10%, of the number of amino acid residues in a sequence are substituted for, added to, or deleted from the protein.
The functional equivalent of an antibody is preferably a chimerized or humanized antibody. A chimerized 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 (CDRs) of a non-human antibody. The variable region other than the hypervariable region, e.g., the framework variable region, and the constant region of a humanized antibody are those of a human antibody.
Suitable variable and hypervariable regions of non-human antibodies may be derived from antibodies produced by any non-human mammal in which monoclonal antibodies are 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 the whole antibody. Suitable fragments of the antibody include any fragment that comprises a sufficient portion of the hypervariable (i.e., complementarity determining) region to bind specifically, and with sufficient affinity, to IGF1R tyrosine kinase to inhibit growth of cells that express such receptors.
Such fragments may, for example, contain one or both Fab fragments or the F(ab′)2 fragment. Preferably, the antibody fragments contain all six complementarity determining regions of the whole antibody, although functional fragments containing fewer than all of such regions, such as three, four, or five CDRs, are also included.
In one aspect, the fragments are single chain antibodies, or Fv fragments. Single chain antibodies are polypeptides that comprise at least the variable region of the heavy chain of the antibody linked to the variable region of the light chain, with or without an interconnecting linker. Thus, Fv fragment comprises the entire antibody combining site. These chains may be produced in bacteria or in eukaryotic cells.
The antibodies and functional equivalents may be members of any class of immunoglobulins, such as IgG, IgM, IgA, IgD, or IgE, and the subclasses thereof.
In one aspect, the antibodies are members of the IgG1 subclass. The functional equivalents may also be equivalents of combinations of any of the above classes and subclasses.
In one aspect, the IGF1R antibody is provided in PCT publication nos. WO2005/016970, WO02/53596, WO2004/71529, WO2005/16967, WO2004/83248, WO03/106621, WO03/100008, WO03/59951, WO2004/87756, or WO2005/05635.
In another aspect, the IGF1R modulator is derived from fibronectin, such as an AdNectin (Adnexus Therapeutics) (See, PCT publication nos. WO00/34784, WO01/64942, WO02/32925).
In addition to the biological molecules discussed above, the IGF1R modulators useful in the invention may also be small molecules. Any molecule that is not a biological molecule is considered herein to be a small molecule. Some examples of small molecules include organic compounds, organometallic compounds, salts of organic and organometallic compounds, saccharides, amino acids, and nucleotides. Small molecules further include molecules that would otherwise be considered biological molecules, except their molecular weight is not greater than 450. Thus, small molecules may be lipids, oligosaccharides, oligopeptides, and oligonucleotides and their derivatives, having a molecular weight of 450 or less.
It is emphasized that small molecules can have any molecular weight. They are merely called small molecules because they typically have molecular weights less than 450. Small molecules include compounds that are found in nature as well as synthetic compounds. In one embodiment, the IGF1R modulator is a small molecule that inhibits the growth of tumor cells that express IGF1R. In another embodiment, the IGF1R modulator is a small molecule that inhibits the growth of refractory tumor cells that express IGF1R.
Numerous small molecules have been described as being useful to inhibit IGF1R.
In one aspect, the IGF1R modulator is selected from PCT publication nos. WO02/79192, WO2004/30620, WO2004/31401 WO2004/63151, and WO2005/21510, and from U.S. provisional application Nos. 60/819,171, 60/870,872, 60/883,601, and 60/912,446.
In another aspect, the IGF1R modulator is selected from (S)-4-(2-(3-chlorophenyl)-2-hydroxyethylamino)-3-(4-methyl-6-morpholino-1H-benzo[d]imidazol-2-yl)-pyridin-2(1-H)-one and (2S)-1-(4-((5-cyclopropyl-1H-pyrazol-3-yl)amino)pyrrolo[2,1-f][1,2,4]triazin-2-yl)-N-(6-fluoro-3-pyridinyl)-2-methyl-2-pyrrolidinecarboxamide.
In another aspect, the IGF1R modulator is selected from XL-228 (Exelixis), AEW-541 (Novartis), and OSI-906 (OSI).
Biomarkers and Biomarker Sets:
The invention includes individual biomarkers and biomarker sets having both diagnostic and prognostic value in disease areas in which signaling through IGF1R or the IGF1R pathway is of importance, e.g., in cancers or tumors, in immunological disorders, conditions or dysfunctions, or in disease states in which cell signaling and/or cellular proliferation controls are abnormal or aberrant. The biomarker sets comprise a plurality of biomarkers such as, for example, a plurality of the biomarkers provided in Tables 2-8 that highly correlate with resistance or sensitivity to one or more IGF1R modulators.
The biomarkers and biomarker sets of the invention enable one to predict or reasonably foretell the likely effect of one or more IGF1R modulators in different biological systems or for cellular responses. The biomarkers and biomarker sets can be used in in vitro assays of IGF1R modulator response by test cells to predict in vivo outcome. In accordance with the invention, the various biomarkers and biomarker sets described herein, or the combination of these biomarker sets with other biomarkers or markers, can be used, for example, to predict and monitor how patients with cancer might respond to therapeutic intervention with one or more IGF1R modulators.
A biomarker and biomarker set of cellular gene expression patterns correlating with sensitivity or resistance of cells following exposure of the cells to one or more IGF1R modulators provides a useful tool for screening one or more tumor samples before treatment with the IGF1R modulator. The screening allows a prediction of cells of a tumor sample exposed to one or more IGF1R modulators, based on the expression results of the biomarker and biomarker set, as to whether or not the tumor, and hence a patient harboring the tumor, will or will not respond to treatment with the IGF1R modulator.
The biomarker or biomarker set can also be used as described herein for monitoring the progress of disease treatment or therapy in those patients undergoing treatment for a disease involving an IGF1R modulator.
The biomarkers also serve as targets for the development of therapies for disease treatment. Such targets may be particularly applicable to treatment of cancer, such as, for example, hepatocellular carcinoma, colorectal cancer (CRC), NSCLC, and metastatic breast cancer.
Indeed, because these biomarkers are differentially expressed in sensitive and resistant cells, their expression patterns are correlated with relative intrinsic sensitivity of cells to treatment with IGF1R modulators. Accordingly, the biomarkers highly expressed in resistant cells may serve as targets for the development of new therapies for the tumors which are resistant to IGF1R modulators, particularly IGF1R inhibitors. The level of biomarker protein and/or mRNA can be determined using methods well known to those skilled in the art. For example, quantification of protein can be carried out using methods such as ELISA, 2-dimensional SDS PAGE, Western blot, immunopreciptation, immunohistochemistry, fluorescence activated cell sorting (FACS), or flow cytometry. Quantification of mRNA can be carried out using methods such as PCR, array hybridization, Northern blot, in-situ hybridization, dot-blot, Taqman, or RNAse protection assay.
Microassays:
The invention also includes specialized microarrays, e.g., oligonucleotide microarrays or cDNA microarrays, comprising one or more biomarkers, showing expression profiles that correlate with either sensitivity or resistance to one or more IGF1R modulators. Such microarrays can be employed in in vitro assays for assessing the expression level of the biomarkers in the test cells from tumor biopsies, and determining whether these test cells are likely to be resistant or sensitive to IGF1R modulators. For example, a specialized microarray can be prepared using all the biomarkers, or subsets thereof, as described herein and shown in Tables 2-8. Cells from a tissue or organ biopsy can be isolated and exposed to one or more of the IGF1R modulators. In one aspect, following application of nucleic acids isolated from both untreated and treated cells to one or more of the specialized microarrays, the pattern of gene expression of the tested cells can be determined and compared with that of the biomarker pattern from the control panel of cells used to create the biomarker set on the microarray. Based upon the gene expression pattern results from the cells that underwent testing, it can be determined if the cells show a resistant or a sensitive profile of gene expression. Whether or not the tested cells from a tissue or organ biopsy will respond to one or more of the IGF1R modulators and the course of treatment or therapy can then be determined or evaluated based on the information gleaned from the results of the specialized microarray analysis.
Antibodies:
The invention also includes antibodies, including polyclonal or monoclonal, directed against one or more of the polypeptide biomarkers. Such antibodies can be used in a variety of ways, for example, to purify, detect, and target the biomarkers of the invention, including both in vitro and in vivo diagnostic, detection, screening, and/or therapeutic methods.
Kits:
The invention also includes kits for determining or predicting whether a patient would be susceptible or resistant to a treatment that comprises one or more IGF1R modulators. The patient may have a cancer or tumor such as, for example, a breast cancer or tumor. Such kits would be useful in a clinical setting for use in testing a patient's biopsied tumor or cancer samples, for example, to determine or predict if the patient's tumor or cancer will be resistant or sensitive to a given treatment or therapy with an IGF1R modulator. The kit comprises a suitable container that comprises: one or more microarrays, e.g., oligonucleotide microarrays or cDNA microarrays, that comprise those biomarkers that correlate with resistance and sensitivity to IGF1R modulators, particularly IGF1R inhibitors; one or more IGF1R modulators for use in testing cells from patient tissue specimens or patient samples; and instructions for use. In addition, kits contemplated by the invention can further include, for example, reagents or materials for monitoring the expression of biomarkers of the invention at the level of mRNA or protein, using other techniques and systems practiced in the art such as, for example, RT-PCR assays, which employ primers designed on the basis of one or more of the biomarkers described herein, immunoassays, such as enzyme linked immunosorbent assays (ELISAs), immunoblotting, e.g., Western blots, or in situ hybridization, and the like, as further described herein.
Application of Biomarkers and Biomarker Sets:
The biomarkers and biomarker sets may be used in different applications. Biomarker sets can be built from any combination of biomarkers listed in Tables 2-8 to make predictions about the likely effect of any IGF1R modulator in different biological systems. The various biomarkers and biomarkers sets described herein can be used, for example, as diagnostic or prognostic indicators in disease management, to predict how patients with cancer might respond to therapeutic intervention with compounds that modulate the IGF1R, and to predict how patients might respond to therapeutic intervention that modulates signaling through the entire IGF1R regulatory pathway.
While the data described herein were generated in cell lines that are routinely used to screen and identify compounds that have potential utility for cancer therapy, the biomarkers have both diagnostic and prognostic value in other diseases areas in which signaling through IGF1R or the IGF1R pathway is of importance, e.g., in immunology, or in cancers or tumors in which cell signaling and/or proliferation controls have gone awry.
In accordance with the invention, cells from a patient tissue sample, e.g., a tumor or cancer biopsy, can be assayed to determine the expression pattern of one or more biomarkers prior to treatment with one or more IGF1R modulators. Success or failure of a treatment can be determined based on the biomarker expression pattern of the cells from the test tissue (test cells), e.g., tumor or cancer biopsy, as being relatively similar or different from the expression pattern of a control set of the one or more biomarkers. Thus, if the test cells show a biomarker expression profile which corresponds to that of the biomarkers in the control panel of cells which are sensitive to the IGF1R modulator, it is highly likely or predicted that the individual's cancer or tumor will respond favorably to treatment with the IGF1R modulator. By contrast, if the test cells show a biomarker expression pattern corresponding to that of the biomarkers of the control panel of cells which are resistant to the IGF1R modulator, it is highly likely or predicted that the individual's cancer or tumor will not respond to treatment with the IGF1R modulator.
The invention also provides a method of monitoring the treatment of a patient having a disease treatable by one or more IGF1R modulators. The isolated test cells from the patient's tissue sample, e.g., a tumor biopsy or blood sample, can be assayed to determine the expression pattern of one or more biomarkers before and after exposure to an IGF1R modulator wherein, preferably, the IGF1R modulator is an IGF1R inhibitor. The resulting biomarker expression profile of the test cells before and after treatment is compared with that of one or more biomarkers as described and shown herein to be highly expressed in the control panel of cells that are either resistant or sensitive to an IGF1R modulator. Thus, if a patient's response is sensitive to treatment by an IGF1R modulator, based on correlation of the expression profile of the one or biomarkers, the patient's treatment prognosis can be qualified as favorable and treatment can continue. Also, if, after treatment with an IGF1R modulator, the test cells don't show a change in the biomarker expression profile corresponding to the control panel of cells that are sensitive to the IGF1R modulator, it can serve as an indicator that the current treatment should be modified, changed, or even discontinued. This monitoring process can indicate success or failure of a patient's treatment with an IGF1R modulator and such monitoring processes can be repeated as necessary or desired.
The biomarkers of the invention can be used to predict an outcome prior to having any knowledge about a biological system. Essentially, a biomarker can be considered to be a statistical tool. Biomarkers are useful primarily in predicting the phenotype that is used to classify the biological system. In an embodiment of the invention, the goal of the prediction is to classify cancer cells as having an active or inactive IGF1R pathway. Cancer cells with an inactive IGF1R pathway can be considered resistant to treatment with an IGF1R modulator.
Methods and Samples:
In the following examples, the compound (S)-4-(2-(3-chlorophenyl)-2-hydroxyethylamino)-3-(4-methyl-6-morpholino-1H-benzo[d]imidazol-2-yl)-pyridin-2(1-H)-one was used:
This compound is referred to herein as “compound 1.”
In the following examples, the compound (2S)-1-(4-((5-cyclopropy1-1H-pyrazol-3-yl)amino)pyrrolo[2,1-f][1,2,4]triazin-2-yl)-N-(6-fluoro-3-pyridinyl)-2-methyl-2-pyrrolidinecarboxamide was used:
This compound is referred to herein as “compound 2.”
Methods and Materials
Cell Lines:
All pediatric sarcoma and neuroblastoma cell lines were obtained from Dr. Lee Helman at NIH. All cell lines were grown in RPMI medium supplemented with Glutamax (Gibco/Invitrogen #61870-036), 10% inactivated fetal bovine serum (Gibco/Invitrogen #16140-071), 10 mM Hepes, penicillin and streptomycin. For the baseline profiling study, cells were harvested at 70-80% confluence; and for drug treatment study, two rhabdomyosarcoma (RMS) cell lines Rh36 and Rh41 were treated with 0.35 μM compound 1 for 6, 36 and 72 hours before harvest. To develop the compound 1-resistant RD1, the sensitive RD-1 cells (IC50=0.238 μM to compound 1) were first exposed to the dug at the IC50 concentration and passed as the cultures reached 70-80% confluence. The concentration of compound 1 was increased gradually every other culture passage and the IC50 value for the compound in these cells was measured periodically during this treatment time until the resistance level reached a plateau. The resulted RD1-Resist cells has IC50=1.999 μM to compound 1, more than 8 fold of the parental RD1.
In vitro Cellular Proliferation Assays:
Proliferation was evaluated by incorporation of [3H]-thymidine into DNA after exposure to IGF1R inhibitor compound 1 or compound 2 to determine the sensitivity of cell lines to these compounds. Cells were plated at an optimized density for each cell line per well in 96-well microtiter Falcon plates, incubated overnight, and then exposed to a serial dilution of drug. After 72 hours incubation with drug at 37° C., cells were pulsed with 4 μCi/ml [6-3H] thymidine (Amersham Pharmacia Biotech, UK) for 3 hours, trypsinized, harvested onto UniFilter-96, GF/B plates (PerkinElmer, Boston, Mass.) and scintillation was measured on a TopCount NXT (Packard, Conn.). Results were expressed as an IC50, which is the drug concentration required to inhibit cell proliferation by 50% to that of untreated control cells. The mean IC50 and standard deviation (SD) from multiple tests for each cell line were calculated.
Drug Combination Study:
A Dilution of Ratios Drug Combination method was used in cellular proliferation assays to determine whether there was synergy, additivity or antagonism when two compounds were added simultaneously to a variety of human tumor cells in vitro (R. Tallarida, R. J., Drug Synergism and Dose-Effect Data Analysis. 1st edition ed. Chapman & Hall/CRC (2000)). Drug stock solutions for two compounds, are combined in ratios of 10:1, 5:1, 3:1, 1:1, 1:3, 1:5. These ratios, as well as the individual compound stock solutions, are diluted in a serial manner, using 70% DMSO. These serial dilutions are then mixed with RPMI growth medium, and added to cells to test the IC50 values of single agent as well as two compounds in the cellular proliferation assays. Combination Indexes with 95% confidence intervals were used to determine if the combination results represented synergistic, additive, or antagonistic effect.
Gene Expression Profiling:
RNA was isolated from the cultured cells using the RNeasy™ kits from Qiagen (Valencia, Calif.). 10 μg of total RNA from each cell line was used to prepare biotinylated probe according to the Affymetrix GeneChip® Expression Analysis Technical Manual, 2001. Targets were hybridized to Affymetrix high density oligonucleotide array human HG-U133A 2.0 GeneChip® (Affymetrix, Santa Clara, Calif.). The arrays were then washed and stained using the GeneChip® Fluidics station and quantitated with GeneChip® Operating Software (GCOS) V1.0 according to the manufacture's instructions.
Protein Extraction and Tryptic Digestion:
Total protein content for each cell lysate was determined by the bicinchoninic acid assay (Pierce, Rockford, Ill.). An aliquot from each lysate containing 200 μg total protein was withdrawn for further processing.
The normalized samples were chloroform-methanol precipitated using a protein extraction kit (Calbiochem, San Diego, Calif.). Pellets were solublized in a solution containing 8M urea, 200 mM ammonium bicarbonate, and 40 mM DTT. Samples were diluted 4-fold, trypsin was added at 1:50 enyzme:substrate ratio and incubated overnight at 37° C.
Solid Phase Extraction:
Solid phase extraction was performed using an Empore C18 SPE plate (3M, St Paul, Minn.) on a Quadr3 liquid handling workstation (Tomtec, Hamden, Conn.). Sample sequence on the plate was randomized to minimize systemic bias during processing. After sample loading, the SPE plate was washed with 450 μl water in 0.1% trifluoroacetic acid twice and eluted with 300 μl 95% acetonitrile in 0.1% trifluoroacetic acid twice.
Randomization, Duplication, and Lyophilization:
Samples were then split across two separate 96-well plates (VWR, West Chester, Pa.) in a separate randomized order. This process generated two technical replicates for each sample. Following lyophilization on a SpeedVac (Thermo Savant, Holbrook, N.Y.), samples were stored at −80° C. before analysis.
Liquid Chromatography—Mass Spectrometry:
Samples of tryptic peptides were separated on an Zorbax 300SB-C18 column (0.5×150 mm, 3.5 nm) from Agilent (Santa Clara, Calif.) equipped with a 0.5 μm pre-column filter (Opti-solve). The mobile phases were delivered at a total flow rate of 12 μl/min by an Agilent 1100 Capillary HPLC system. Mobile phase A was water in 0.2% isopropyl alcohol, 0.1% acetic acid and 0.001% trifluoroacetic acid; Mobile B was 95% acetonitrile in 0.2% isopropyl alcohol, 0.1% acetic acid and 0.001% trifluoroacetic acid. The following gradient was used to separate the peptides:
Samples were re-dissolved in 40 μl reconstitution solution in 0.2% isopropyl alcohol, 5% acetic acid and 0.001% trifluoroacetic acid. Six microliters of sample was injected for each run using an Agilent 1100 micro well plate sampler chilled at 4° C. To achieve optimum mass accuracy, a peptide standard Glu-Fibrinopeptide B (Sigma, St. Louis, Mo.) was introduced through a Valco-type mixing tee into the flowing system immediately after the HPLC column outlet at 1 μl/min. The HPLC eluent was detected on a Qtof Ultima QqTOF hybrid mass spectrometer (Waters, Manchester, UK) operated in the electrospray positive ionization mode. Mass spectra were acquired for the mass range of 300 to 1800 Da. Each acquisition was 80 minutes long, with 1 second scan time and 0.1 second inter scan delay. Accuracy of the mass measurement was typically within 20 parts per million. A collection of proprietary algorithms (Extractor, Cluster and Time Adjustment) written in-house were applied to extract and quantify peptide peak information, adjust peaks for retention time shifts that may occur during HPLC separation and match peaks across runs. This preprocessing resulted the raw peptide expression measures for each sample.
Peptide Identification:
The peptide ions generated from statistical analyses were sequenced by tandem mass spectrometry (MS/MS). Samples were rerun onto the same LC-MS system in data dependant mode in which the MS survey scan would switch to MSMS product scan when targeted peptide ions were found at the same retention time and mass. MS/MS spectra were generated and submitted to SEQUEST search (J. Eng, et al., J. Am. Soc. Mass. Spectrom., 5: 976-989 (1994)) to yield protein identifications.
Statistical Analysis
Analysis of Baseline Gene and Protein Expression of 29 Cell Lines:
The gene expression raw data were normalized by the Robust Multichip Average (RMA, R. Irizarry, et al., Biostatistics;4(2):249-64 (2003)) method and log2 transformed, while the protein profiling data was quantile normalized and log2 transformed. To identify genes or proteins whose expression level significantly correlation with the drug sensitivity for the compounds, two separate statistic analyses were performed. First, a two-sample t-test between the resistant and sensitive cell lines (based on a threshold IC50 cutoff of 0.35 μM) was performed. Second, Pearson correlation between the normalized expression level of each gene/protein and the log2(IC50) values of the 29 cell lines was calculated to identify genes/proteins correlated with the drug sensitivity (IC50).
Analysis of Gene and Protein Expression Data of Cells with Drug Treatment:
Pre-filter was applied to both gene and protein expression data. Lowly expressed probe sets with normalized and log2 transformed expression values less than 5 cross all samples were removed resulting probe sets of 10,479 for gene expression data. For peptide, it must be found in at least 15 LCMS experimental runs and for subsequent statistical analysis in at least 20 cell lines. This filter reduced the number of overall peptides to 9022 for further analysis. A two-way ANOVA mixed model was applied to each probe set in the gene expression profiling data, as well as each peptide in the protein expression profiling data, separately. The model that was applied to each dataset was nearly identical except for a single term in the model applied to the protein expression data which appropriately accounted for the existence of the technical replicates. The analysis was run using SAS version 9.1 (SAS Institute Inc., Cary, N.C., USA). The general form of the model was as follows: (Intensity˜Treatment+Time+Treatment×Time+Error) where Intensity represents the normalized, log2 transformed intensity; Treatment is a term that captures candidates that display significant differential expression upon treatment; Time is a term that captures candidates that display significant differential expression over time; and the Treatment×Time term captures candidates that display significant differential expression upon treatment with compound over time as compared with control. The multiple testing with False Discovery Rate (FDR; Y. Benjamini, et al., J. Roy. Stat. Soc. B.; 57:289-300 (1995)) was apply to each dataset separately, the total of 2056 probe sets with FDR p value<0.05 in either the Treatment effect or Treatment×Time interaction and the fold change between treatment group vs. DMSO control group is greater than 1.2 fold or less than −1.2 fold were selected.
Globaltest Pathway Analysis:
RMA normalized baseline expression data for 28 sarcoma cell lines was first filtered based on following criteria: 1) Maximal expression level across all samples must be greater than 5; 2) Coefficient of Variation (CV) must by greater than 0.03. These resulted a total of 17276 probe sets for further analysis. Globaltest was carried out with Bioconductor (www.bioconductor.org) package (Version 4.4.0) using this filtered dataset and compound 1 resistant/sensitive classification for each cell line against a collection of 183 KEGG pathways. The p value indicating the association between expression values and resistant/sensitive classifications, as well as its multiplicity-adjusted version, FWER (Holm's method), were reported for each pathway. Gene plots were generated for interesting pathways according to user's guide (J. Geoman, et al., Testing association of a pathway with a clinical variable. Package globaltest. Version 4.4.0. (October 2006)) and used to assess the influence of each gene in a specific pathway on the drug sensitivity classification. For Rh41 drug treatment study, expression datasets were first filtered to remove probe sets whose maximal expression level less than 5, which resulted 9269 probe sets. Globaltest was carried out similar as above, with treatment (DMSO control vs. compound 1) as grouping factor. It gives a bar and a reference line for each gene tested. The reference line for each bar gives the expected height under the null hypothesis that the gene is not associated with the sensitivity classification. Marks indicate with how many standard deviations (under the null hypothesis) the bar exceeds the reference line. Bars are colored based on sensitivity classification. The test statistic for a pathway is the average of the bars for all the genes tested.
Ingenuity Pathway Analysis:
497 baseline markers correlated with sensitivity to compound 1 were imported into Ingenuity pathway analysis. Network nodes were colored by the fold change between sensitive and resistant cells. For the drug modulated markers, treatment-induced probe sets in the sensitive cell line Rh41 (FDR adjusted treatment effect less than 0.05, or FDR adjusted treatment-time-interaction less than 0.05) were used for pathway analysis. Genes on canonical pathways were colored based on the fold change between cells with compound 1 treatment and DMSO control.
Results
The Sensitivity Classification of the 29 Pediatric Sarcoma Cell Lines to IGF1R Inhibitors:
The sensitivity to IGF1R inhibitors compound 1 and compound 2 for each of the 29 pediatric sarcoma and neuroblastoma cell lines was determined by cellular proliferation assays and expressed in term of drug concentration required for 50% cell proliferation inhibition (IC50). The results are summarized in Table 1, and a wide range of activity in this panel of cancer cell lines was observed for both compound 1 and compound 2 compounds.
Comparing the IC50 data of these two compounds, compound 2 is more potent than compound 1 in most of the cell lines tested. To classify the cell lines as sensitive or resistant to the compounds, the IC50 value for each cell line was log-transformed, and the mean of log10(IC50) across all cell lines was calculated. The sensitivity/resistance phenotype of the cell lines to compound 1 or compound 2 was classified as follows: the cell lines with log10(IC50) below the mean log10(IC50) of all cell lines were defined as sensitive to the compound, while those with log10(IC50) above the mean log10(IC50) were considered to be resistant to the compound. As shown in Table 1, 16 cell lines were classified as sensitive and 12 cell lines classified as resistant (To184-T was not tested) for compound 1; whereas 15 and 14 cell lines were classified as either sensitive or resistant to compound 2, respectively. Although the sensitivity/resistance demarcation is arbitrary, apparently, the cut off is around 0.35 μM for both compounds. In general, both compounds have a similar sensitivity/resistance profile in this panel of cell lines with the exception of three cell lines that having IC50 values around the borderline of the sensitive/resistant demarcation: CTR was defined as sensitive to compound 2 (IC50=0.2526 μM) but resistant to compound 1 (IC50=0.37 μM), whereas JD and SK-NAS were defined as resistant to compound 2 but resistant to compound 1.
Relation Between the Drug Sensitivity and Cell Subtypes:
The correlation between the sensitive/resistant classification and different subtypes of cell lines was further explored. Interestingly, the sensitivity of these cell lines to IGF1R inhibitor compounds was found to be closely related to specific subtypes. As shown in Table 1, most of Ewing's, RMS and neuroblastoma cells are sensitive to the compounds, whereas all fibrosarcoma, leiomyosarcoma, liposarcoma and malignant pleural mesothelioma cells are resistant. The distribution of cell sub-types is significantly different in sensitive and resistant classes with p-value=0.011 for compound 2 and p-value=0.004 for compound 1, respectively in the chi-square test. Although the sample size for each subtype is not big enough to be conclusive, the results may suggest that certain subtypes of sarcoma are more responsive than others, and these responsive tumor types may represent patient subpopulations to be targeted in clinical studies for IGF1R inhibitors.
Identification of Genes/Proteins with Expression Significantly Correlated with the Sensitivity to IGF1R Inhibitors:
The expression level of the drug target IGF1R was evaluated and apparently did not significantly correlate to the sensitivity of compound 2 and/or compound 1 in the 29 cancer cell lines, so IGF1R level is not useful to predict response to compound 2 and/or compound 1 in cancer cells or in patients. Other predictive biomarkers are needed for selecting the potential targeted patient population.
To identify genes or proteins whose basal expression patterns were strongly correlated with the sensitivity to compounds 1 and 2, gene expression profiling and proteomics were performed in parallel using the 29 pediatric sarcoma and neuroblastoma cancer cell lines (RDES and Rh4 were not included in protein profiling). Two statistical methods were used in analyzing both expression datasets: first, two sample t-test was performed to identify genes differentially expressed between sensitive and resistant cell line groups (p<0.001, 2-fold); second, to avoid the bias of arbitrary cut off for the sensitive/resistant demarcation, the Pearson correlations between the log2(IC50) value and the expression level of each gene or protein in all cell lines were calculated to identify genes/proteins significantly correlated with the drug sensitivity (p<0.001). The overlap between these two analyses led to selection of genes/proteins that are significantly correlated with the drug sensitivity/resistance classification for compound 1 or compound 2. For gene expression profiling, there are a total of 497 probe sets with 386 unique genes significantly correlated with the sensitivity to compound 1 (Table 2), and 368 probe sets with 282 unique genes significantly correlated with the sensitivity to compound 2 (Table 3); and. There are 98 or 124 genes highly expressed in the cell lines sensitive to compound 2 or to compound 1, conversely, 184 or 262 genes are highly expressed in the cell lines resistant to compound 2 or compound 1, respectively, with 227 common markers for these two compounds.
Drosophila);
Drosophila);
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laevis)
The same two statistical methods were applied to protein profiling data to identify proteins that correlated with the sensitivity of the cell lines to IGF1R inhibitors. This yielded 251 unique peptide ions for compound 1, and 228 unique peptide ions for compound 2, 153 of which overlap between the two compounds. These unique peptide ions were then sequenced by tandem mass spectrometry to obtain the identity of the corresponding protein markers. As shown in Table 4, 76 peptides were identified to represent 46 proteins that were differentially expressed between the sensitive and resistant cell line groups for compound 1; and 70 peptides representing 45 proteins (Table 5) for compound 2, with 39 proteins common for both compounds.
In general, a higher number of genes than proteins was identified using the same statistic analyses due to the complicity of the protein profiling technology. Cross comparing the gene and protein profiling results, the overlaps between identified genes and proteins are significantly large as indicated in Tables 4 and 5. 71.7% (33 out 46) proteins for compound 1 and 73.3% (33 out 45) proteins for compound 2 also identified in gene expression profiling, respectively, suggesting good concordance between the results detected by the two technologies. For some of the markers identified by the protein profiling, for example, ATP2B4, it was also detected as significantly differentially expressed between the sensitive and resistant cell lines (−2.3 fold and p values are 0.0043 and 0.0016 for t-test and correlation test, respectively) by gene profiling, yet did not meet the stringent statistical cutoff of p values of 0.001.
Genes/Proteins Modulated by IGF1R Inhibitor Compound 1 Treatment:
There is differential sensitivity of compound 1 in a pair of human RMS cell lines, Rh36 (resistant, IC50=1.6 μM) and Rh41 (sensitive, IC50=0.069 μM). In order to understand the mechanism of the differential sensitivity, both cell lines were evaluated using gene expression profiling and proteomics analyses to search at genomics scale for genes or proteins that are differentially modulated by IGF1R inhibitor compound 1 in a time course study with the drug treatment for 6, 30 and 72 hours. Cell lysates were subjected in parallel to microarray and LC/MS based “bottom-up” protein profiling analyses. A two-way ANOVA mixed model was utilized to identify drug treatment effect as well as treatment and time interaction on the expression of genes and protein. Overall, there were some mRNA transcripts or peptides with expression change upon the drug treatment in Rh36 cell line at 6 hr, and these changes mainly reflected the initial stress response to the stimuli the cell encountered. However, the drug had little to no effect at later time points, 36 and 72 hrs in Rh36 cells. This makes sense for the given concentration of compound 1 cell treated, Rh36 is highly resistant, so the drug had little effect on cell growth compared to the sensitive cell Rh41, which had dramatic changes in gene/protein expression. The genes and protein that were modulated by compound 1 in the sensitive RH41 cell line are listed in Table 6 and Table 7, respectively.
Homo sapiens
Drosophila)
laevis, cdk-
purpuratus)
musculus]
Drosophila)
Drosophila)-like 1
Drosophila);
Drosophila);
Remarkably, these genes are mainly involved in apoptosis (AIFM1, ACIN1, BIRC2, BIRC5, BAD, BAG3, BAX, CARD10, CIAPIN1, DAP, DAXX, DAPK1, PDCD11 and PDCD4), cell growth and proliferation (Ki67, BOP1, GAS1 and ING3), cell cycles (cyclin family members of B, D, E, G, H, I, K, L; CDK 2 and 7; centromere protein A, B, M, N; CDKN3 and CDKN1B) and multiple tyrosine kinases such as ERBB3, PDGFR, FGFR1, 2 and 4, MET, EPH as well as the down-stream IGF1R signaling pathways: MAPK, PI3K and AKT. Interestingly, contrary to what was reported with IGF1R antibody (R. Baserga, Expert Opin. Ther. Targets; 9:753-68 (2005)), inhibition of kinase activity by compound 1 did not result in the receptor down-regulation, but rather induced the expression of IGF1R, insulin receptor substrate 2 (IRS2) and insulin-like growth factor binding protein 3 (IGFBP3). This could be one of the mechanisms by which cells attempt to compensate the reduced activity of the IGF1R caused by the inhibitor, and the feedback loop then restores the inhibited pathway activity.
Genes Related to Acquired Resistance to IGF1R Inhibitor Compound 1:
Acquired resistance to anti-cancer agents is a major clinical problem. To study the mechanism of acquired resistance to IGF1R inhibitor, we derived resistance cells by culturing a compound 1 sensitive RMS cell line RD-1(IC50=0.238 μM) in the presence of a gradually increased concentration of compound 1 in every other passage to reach the resistance level of plateau. The resulted resistant RD1 cells had IC50=1.999 μM to compound 1, which is 8 fold higher than the IC50 of the parent RD1. In order to identify markers that may contribute to acquired resistance to the IGF1R inhibitor, global gene expression profiles for both parental RD1 and resistant RD1 were analyzed using Affymetrix gene chip. A statistical analysis was conducted using GeneChip® Expression Analysis software MAS 5.0 to identify the genes differentially expressed between the sensitive and acquired resistant cells (Table 8).
Homo sapiens, clone IMAGE: 4214654,
Drosophila)
These genes may reflect the mechanism of acquired resistance to IGF1R inhibitor. Interestingly, several insulin-like growth factor binding proteins were expressed at higher level in the acquired resistant cells compared to the sensitive parent RD-1, such as IGFBP2 (10.6 fold), IGFBP3 (6.4 fold), IGFBPS (4.8 fold), IGFBP7 (15 fold), whereas, IGF2 had conversely expression pattern. SRC family members FYN and LYN also had increased expression levels in the acquired resistant cells. Comparison of the genes correlated with the intrinsic (de novo) resistance (Table 3) to the ones related to acquired resistance (Table 8), there are common genes, such as CAST, CD44, PLAUR, SP100, STK10, TFPI2 and THBS1 with higher expression in both de novo and acquired resistant cells, suggesting there may be some common mechanisms for both types of drug resistance. Thrombospondin 1 (THBS1) was reported to prevent camptothecin- and doxorubicin-induced apoptosis in human thyroid carcinoma cells, this shed new light on a possible role for THBS 1 in drug resistance (G. Rath, et al., Biochim. Biophys. Acta., October;1763(10):1125-34 (2006)). THBS1 was up regulated by IGF stimulation to promote cell survival (data not shown) and over expressed in the resistant cells, and thus may involved in both de novo and acquired resistance to IGF1R inhibitors.
Significant Differences in Multiple Pathways Between the Sensitive and Resistant Cell Lines at Basal Level:
Global pathway analysis on the basal gene expression of 28 cell lines using globaltest indicated that 75 out 183 tested pathways were significantly associated with the sensitive/resistant classification to compound 1 (FEW adjusted p value<0.05). Several interesting pathways including apoptosis pathway, and signaling pathways of MAPK, TGFβ, Jak-STAT, insulin, VEGF and natural killer cell mediated cytotoxicity were among those significant pathways that were different between the sensitive and resistant cell lines, while mTOR signaling pathway is different (p value=0.0005) yet slightly higher than the the significance cutoff (FWE adjusted p value=0.0559). Pathway analysis of expression data for the compound 1 treated Rh41 cell compared to that for the untreated control cells, above mentioned pathways are also different between the cells treated with or without the drug with p value all less than 0.01 but not reach the statistical significant cutoff based on the FWE adjusted p values.
Ingenuity pathway analysis on the 497 probe sets that significantly correlated with the sensitivity of compound 1 in the 28-cell line panel were performed, over expression of multiple kinases (e.g., EGFR, MET, TGFβR2) in the compound 1 resistant cell lines was observed in the most significant network. This may explain why these cell lines are still proliferating and surviving even in presence of the drug, since they use alternative growth signal pathways instead of IGF1R. In this specific situation, targeting multiple pathways may be necessary to sufficiently inhibit the growth of these cells and synergistic effects with the combination of inhibitors targeting these kinases could be possible.
Synergistic Activity Between Inhibitors of IGF1R and Other Kinases:
IGF signaling through IGF1R has also been shown to protect cancer cells from the cytotoxic effects of chemotherapy and radiation, and this may be an important factor in tumor cell drug resistance (J. Gooch, et al., Breast Cancer Res. Treat., 56(1): 1-10 (1999), B. Turner, et al., Cancer Res, 57(15):3079-83 (1997)). Recent evidence suggests that resistance to Herceptin® in some forms of breast cancer may be due to activation of IGF1R signaling in those cancers (Y. Lu, et al., J. Natl. Cancer Inst., 93(24):1852-7 (2001)). Tumor cells rely on alternative receptors for activation of critical signaling pathways, therefore, strategies designed to block signaling from multiple receptors may be advantageous. Due to the wide expression of IGF1R and the potential cross-talk between the IGF1R pathway and the other signaling pathways implicated in oncogenesis (EGFR, Her2 and mTOR), IGF1R inhibitors may have potential to be combined with other therapies in a wide range of tumors to increase the overall survival of patients. Through ingenuity pathway analysis, the overexpression of multiple kniases (EGFR, MET, TGFR) in the compound 1 resistant cell lines was observed to be within the top network. Given the important roles of IGF1R, EGFR and Her2 in cell cycle progression, we carried out combination studies to investigate whether inhibition of multiple targets might result in enhanced inhibition of tumor growth. Table 9 summarizes the data for in vitro combination studies in multiple types of tumor cell lines and indicated that the drug combinations resulted in synergistic effects between IGF1R inhibitor compound 1 and various inhibitors of EGFR, including both Her 1 and/or Her2 inhibitors in the forms of either antibody (cetuximab) or small molecules (gefitnib, erlotinib and lapatinib).
These results confirmed our hypothesis based on the pathway analysis. In vivo synergistic effects were also observed in selected xenograft models (data not shown). In addition, combination between multiple cytotoxic agents and compound 1 also resulted in additive activity.
Increased expression level of SRC family members FYN (3 fold) and LYN (11 fold) in the compound 1 acquired resistant RD-1 cells may suggest SRC family members act as an alternative signal pathway to play an important role in growth and survival of the acquired resistant cells, and targeting IGF1R in this case is not sufficient enough. Targeting multiple pathways could be one of ways to prevent the acquired resistance. The results in Table 9 also demonstrated that synergistic effect observed between IGF1R inhibitor compound 1 and dasatinib, a novel, oral, multi-targeted kinase inhibitor that targets important oncogenic pathways, including SRC family kinases, BCR-ABL, PDGFR, c-KIT and EPHA2 (L. Lombardo, et al., J. Med. Chem.;47:6658-61 (2004), A. Todd, et al., PNAS; 102;11011-11016 (2005)).
Discussion
The development of trastuzumab and EGFR tyrosine kinase inhibitors have demonstrated that careful measurement of biomarkers is necessary when only a small percentage of patients have receptor-driven tumors. Identification of molecular markers predictive of response to IGF1R inhibitors could assist in clinical development by selecting patients most likely to derive clinical benefit. In the present study, by utilizing both microarray gene expression profiling and LC/MS based “bottom-up” protein profiling technologies and a panel of 29 sarcoma and neuroblastoma cell lines, we identified genes and proteins that differentially expressed between the sensitive and resistant cell lines to IGF1R inhibitors compound 2 and compound 1. Realizing the sensitivity/resistance demarcation in the panel of the cell lines is arbitrarily defined and its relevance to the peak range of the drug concentrations in plasma of patients treated with the clinical achievable dose is unclear, we applied Pearson correlation analysis of expression levels with IC50 values to identify genes/protein correlated with the sensitivity of the drugs in combination with two-sample t-test.
There is a correlation between sensitive/resistant classification to IGF1R inhibitors and different sub-types of cell lines. Notably, most of neuroblastoma, Ewing's and RMS cell lines are sensitive to the inhibitors, making these cell types as the preferred targeting population for IGF1R inhibitors. However, it may create potential possibility of the markers identified are the ones reflecting the specific cell types rather than reflecting the sensitivity of cells to the IGF1R inhibitors. This is not the case for two reasons: first, the sensitive cell lines are consisted of three cell types rather than a single one, and vise visa for the resistant cell llines; second, comparing the drug sensitivity markers identified in this study with the signatures for specific subtypes of sarcomas further excluded the possibility.
A number of gene expression profiling studies of soft tissue tumors have identified the signatures for specific subtypes of sarcomas (C. Baer, et al., Int. J. Cancer,110(5):687-94 (2004), K. Baird, et al., Cancer Res., 65: (20)9226-35 (2005), T. Nielsen, et al., Lancet, 359(9314):1301-7 (2002)). For example, the top discriminators for Ewing's sarcoma include FVT1, DCC, DKK2, PAX3 and JAK1; for fibrosarcoma are PMP22, PTPRZ1, FN1; for RMS are MYL4, FGFR4, TNN11, ACTC, FLNC, and CDH15; for liposarcoma are PPARG, FABP4, FALCS, SH3KBP1, HOXAS and AIM1, for leiomyosarcoma MYLK, CCN1, PBX1 and SLMAP. The comparison results indicated that none of the subtype specific markers are among the intrinsic sensitivity markers we identified.
Since IGF1R plays a role in cell survival and in resistance to the anti-HER2 monoclonal antibody trastuzumab, the expression patterns of the components of IGFs/IGF1R system were evaluated in this study to see if they have any correlation with the intrinsic sensitivity/resistance to IGF1R inhibitors in the panel of cell lines. The results showed that IGF1R expression level was not significantly correlated to the sensitivity of compound 2 and/or compound 1 in the 29 sarcoma cell lines even through some of the sensitive cell lines had higher IGF1R expression, this does not exclude the possibility that the sensitive cell lines have elevated activity of IGF1R or IGF1R is the major player for the growth of these cell lines. On the other hand, one member of the IGF-binding proteins (IGFBPs), IGFBP6, was significantly higher (>10 fold) in the group of resistant cell lines at basal level. Intriguingly, in the compound 1 acquired resistant RD-1 cells, IRS2 was 2 fold and IGFBP2, IGFBP 3, IGFBP 5 and IGFBP 7 were 7 to 15 fold elevated compared to the parent sensitive cells. IRS2 is the substrate for IGF1R and can enhance the IGF1R activity associated with a metastatic phenotype which correlated with increased migration and motility (J. Jackson, et al., Oncogene, 20(50): 7318-25 (2001)). IGFBPs influenced IGF signaling by modulate the bioavailability and bioactivity of the IGFs. Several IGFBPs are implicated in drug resistance. IGFBP3 and IGFBP5 were reported to be significantly higher from ovarian cancer non-response patients to aromatase inhibitor letrozole compared with responders (G. Walker, et al., Clin. Cancer Res., 13(5):1438-44 (2007)). IGFBP-2 mRNA and protein level were found to be overexpressed in resistant cell lines to antiestrogen Faslodex/Fulvestrant, tamoxifen or RU 58,668 (A. Juncker-Jensen, et al., Growth Horm. IGF. Res., 16(4):224-39 (2006)). IGFBPs' actions can be modulated by IGFBP proteases, such as cathepsins that cleave IGFBPs. Higher basal expression level (4 to 12 fold) of lysosomal cysteine protease cathepsin B, L and Z and lysosomal aspartyl protease cathepsin D were observed in the resistant cell lines. In cancer patients, elevated cathepsin B activity correlates to poor therapy outcome. Several studies have shown that levels of cathepsin B and/or cathepsin L are correlated with drug-resistance (e.g., adriamycin) (M. Osmak, et al., Anticancer Res., 21(1A):481-3 (2001), P. Scaddan, et al., Invasion Metastasis., 13(6):301-13 (1993)). Cathepsin L acts as a cell survival molecule responsible for initiation of resistance to chemotherapy, inhibition of cathepsin L with siRNA facilitates induction of senescence and reversal of drug resistance (A. Juncker-Jensen, et al., Growth Horm. IGF Res., 16(4):224-39 (2006)). The association between drug-resistance and cathepsin D was also studied in nine cervical and laryngeal carcinoma cell lines resistant to different cytostatics, and all drug resistant cell lines had increased concentration of cathepsin D (M. Osmak, et al., Anticancer Res., July-August;19(4B):3193-7 (1999)).
Another group of genes involved in the intrinsic (de novo) drug resistance to
IGF1R inhibitors was metallothionein family members (1F, G, H, M, X, 2A), with over-expression (4 to 9 fold) in resistant cell lines. Metallothionein family members are cysteine-rich proteins that involved in many pathophysiological processes such as metal ion homeostasis and detoxification, protection against oxidative damage, cell proliferation and apoptosis, chemo-resistance (platinum agents, tamoxifen) and radiotherapy resistance (M. Ebadi, et al., Gen Pharmacol., 25(7):1297-310 (1994)). The basal level overexpression of these family members was also observed in a group of colon cancer lines that are resistant to compound 1 compared to the sensitive cell lines, suggesting the role of metallothionein family members in IGF1R inhibitor drug resistance.
Inhibition of oncogenic protein kinases by small molecule inhibitors has proven to be a valuable strategy for the directed and target-specific treatment of an ever-increasing number of cancer types. Unfortunately, initially successful therapy is often hampered by relatively rapid onset of resistance to the drug and subsequent relapse, particularly in patients with advanced disease. In addition to deriving mutations, loss of target dependence due to the activation of parallel signaling pathways has been also reported as cause for acquired drug resistance. Therefore, novel therapeutic approaches are based on concepts to prevent or circumvent drug resistance, e.g., with target-specific novel drugs interfering with signaling and apoptotic pathways. Revelation of mechanistic details of drug resistance also provides the basis for the development of therapies with novel or conventional antitumor drugs in combination with specific inhibitors to re-establish chemosensitivity. From the present study, it is noteworthy that in the cell lines with de novo resistant to IGF1R inhibitors, other tyrosine kinases such as Met and EGFR were overexpressed. Furthermore, SRC family members FYN and LYN also increased expression levels in the compound 1 acquired resistant cells. These observations may suggest that loss of IGF1R dependence due to overexpression of other kinases and activation of different signal pathways plays an important role as one of the possible mechanisms in the de novo and acquired resistances to IGF1R inhibitors. It is possible that in the IGF 1R inhibitor resistant cell lines, MET and EGFR pathways are presumably more activated and are major factors for the growth signaling, so targeting only IGF1R is not sufficient enough to inhibit growth of these cells.
The same hypothesis could be applied to the IGF1R inhibitor acquired resistant cells because SRC family members FYN and LYN elevated their expression level after the IGF1R inhibitor treatment. Based on these observations, we hypothesize that there should be a potential sensitize activity between IGF1R inhibitors and inhibitors for Met, EGFR or SRC pathways. The combination studies in several tumor cell lines (Table 9) actually demonstrated the synergy effects between IGF1R inhibitor compound 1 and multiple HER1/HER2 inhibitors or dasatinib, a multi-targeted kinase inhibitor that targets important oncogenic pathways including SRC family kinases. The same synergy effects were also observed for IGF1R inhibitor compound 2 (data not show). The results further support the observation that co-inhibition of IGF1R and EGFR synergistically sensitizes cancer cells to induce apoptosis (A. Camirand, et al., Breast Cancer Res., 7(4):R570-9 (2005), J. Steinbach, et al., Biochem. Biophys. Res. Commun., 321(3):524-30 (2004)).
Because the mutations in tyrosine receptor kinases have been linked to the efficacy of the inhibitors targeting these kinases, it is reasonable to ask whether mutations or/and single nucleotide polymorphisms (SNPs) in IGF1R would correlate with the response to the IGF1R inhibitors, compound 2 and compound 1. Sequencing of the IGF1R in Rh41 and RD-1 (sensitive lines), Rh36 (primary resistant line) and resistant RD-1 (acquired resistant line) has shown no mutations or/and SNPs exist that might cause resistance to the IGF1R inhibitors. Interestingly, no specific mutations in IGF receptors or ligands have been identified in human cancers, but there is clear evidence of epigenetic alterations such as the loss of imprinting (LOI) of IGF-II in a variety of human tumors (S. Ranier, et al., Hum. Mol. Genet., 3(2):386 (1994), S. Zhan, et al., Clin. Invest., 94(1): 445-8 (1994)). Furthermore, no SNPs that cause amino acid changes in the IGF1R protein have been reported in the NCBI dbSNP database, the Japanese SNP database or the Incyte Foundation database. In addition, no human inherited disorders are attributed to mutations in IGF1R. In order to further evaluate potential IGF1R mutations in human cancers, we sequenced the human IGF1R gene from a panel of genomic DNA derived from 24 tumor-derived cell lines and from 48 human primary lung tumors and no germline variations or somatic mutations were identified in these tumor/cell line samples during tumorigenesis, although amplification of the IGF1R locus has been reported in a small number of breast cancer and melanoma specimens (A. Almeida, et al., Genes Chromosomes Cancer, 11(1): 63-5 (1994)).
From protein profiles of the baseline expression in 29 cell lines, a strikingly large number of the proteins identified are involved in cell adhesion and cell motility and extracellular signaling regulated processes. Most actin binding proteins found were present in lower levels in the IGF1R sensitive cell lines. The overall goal of this study is to understand the difference in the proteomic signatures of resistant and sensitive cell lines. The pattern emerging from these data is a profile of sensitive cell lines with lower levels of cytoskeleton binding proteins, and overall higher levels of DNA and RNA binding proteins found in the nucleus. The open question is how these features confer the sensitivity to IGF1R inhibitors on the sarcoma cell lines studied here.
There is differential sensitivity of compound 2 and compound 1 in a pair of human RMS cells that are sensitive (Rh41) or resistant (Rh36) to the drugs in a cellular proliferation assay (Table 1). These two cell lines have different chromosomal translocations: Rh41 cells harbor a PAX3-FKHR translocation, t(2;13)(q35;q14), whereas Rh36 cells harbor a EWS-FLI-1 translocation, t(11;22)(q24;q12). Gene expression revealed that Rh41 cells have a significant higher level of IGF1R but limited expression of IR, the results were confirmed by RT-PCR measurements (IGF1R/IR ratio=445) and by FACS analysis (data not shown). No mutations in the IGF1R were apparent in both cell lines. In general, the level of IGF1R expression has not been correlated with increased sensitivity in multiple cell lines, thus the sensitivity might be due to differential signaling pathways in these cells. In order to better understand the mechanism of the differential sensitivity, the two cell lines were evaluated by drug treatment and global gene/protein profiling to monitor the differential changes in gene/protein expression. Overall, in Rh36 cells the drug had little, to no effect especially at later time points, 36 and 72 hrs. This makes sense considering the drug concentration of compound 1 used in the study, Rh36 is highly resistant. However, in the sensitive cell Rh41, 30% of genes have significant expression changes upon the drug treatment. These genes are mainly involved in apoptosis, cell growth and proliferation, cell cycles and multiple tyrosine kinases pathways as well as the down-stream IGF1R signaling pathways: MAPK, PI3K and AKT. The expression level of survivin was significantly reduced especially at 36 and 72 hours and programmed cell death 4 (PDCD4) was up-regulated by compound 1 treatment in the sensitive Rh41 cells, but not in resistant Rh36 cells. PDCD4 is a tumor suppressor protein and its expression is strongly induced during apoptosis in a number of cell types (0. Afonja, et al., Oncogene, 23(49):8135-45 (2004)). Thus, up-regulation of PDCD4 expression may suggest apoptosis induced by compound 1 in sensitive cell lines only. This is in agreement with Western blot analysis showed both Rh41 and Rh36 cell lines have high expression of pBad, but only Rh41 cells showed significant reduction in pBad activity by 6 hrs of drug treatment. The reduction in pBad activity was accompanied by an increase in apoptosis when cells were exposed to the drugs for 24-48 hrs, followed by a G1 arrest. Remarkable but not complete decrease of Ki-67 in compound 1 treated cells may suggest the inhibitor with the cytostatic rather than cytotoxic effect. The inhibitor may induce apoptosis and lower the survival threshold of cancer cells thereby augmenting a second apoptotic stimulus by another agent in a combination therapy.
There are 41% of 183 total tested KEGG pathways significantly different between the sensitive and resistant cell lines to IGF1R inhibitor. This provides a general clue on what are the main differences conferring the cell's sensitivity to IGF1R inhibitor. For example, apoptosis pathway is one of the differences, many genes promoting apoptosis have higher expression levels in the resistant cell lines. Detailed dissection of the pathway is needed to get insights for whether resistant cell lines have higher threshold to be apoptotic due to presence of other growth signaling pathways (e.g., EGFR, MET) in addition to IGF1R pathway, whereas in the sensitive cell lines, IGF1R is probably the dominant growth and survival driver.
In conclusion, molecular characterization of a panel of soft tissue sarcoma cell lines lead to identified signatures or candidate markers correlating with the intrinsic sensitivity in vitro to IGF1R inhibitors, compound 1 and compound 2. Whether these signatures or markers have the utility in predicting the response to IGF1R inhibitors in the patients with sarcoma needs to be tested in clinic. The possible mechanisms for both intrinsic and acquired drug resistances were explored and could be due to the alternative activation of other parallel signaling pathways besides IGF1R. Based on these possible mechanisms, the combination strategies to target multiple pathways was proposed and tested, synergistic activity of inhibitors for IGF1-R and EGFR, or SRC was observed. This provides some clues on the strategies for developing IGF1R inhibitor and possible combination therapies in clinical trials to achieve synergy between inhibitors for IGF1R and other kinases.
Antibodies against the biomarkers can be prepared by a variety of methods. For example, cells expressing an biomarker polypeptide can be administered to an animal to induce the production of sera containing polyclonal antibodies directed to the expressed polypeptides. In one aspect, the biomarker protein is prepared and isolated or otherwise purified to render it substantially free of natural contaminants, using techniques commonly practiced in the art. Such a preparation is then introduced into an animal in order to produce polyclonal antisera of greater specific activity for 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, however, it is preferable to culture cells in Earle's modified Eagle's medium supplemented to contain 10% fetal bovine serum (inactivated at about 56° C.), and supplemented to contain about 10 g/l nonessential amino acids, about 1,00 U/ml penicillin, and about 100 μg/ml streptomycin.
The splenocytes of immunized (and boosted) mice can be extracted and fused with a suitable myeloma cell line. Any suitable myeloma cell line can be employed in accordance with the invention, however, it is preferable to employ the parent myeloma cell line (SP2/0), available from the ATCC. 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 through such a selection are then assayed to identify those cell clones that secrete antibodies capable of binding to the polypeptide immunogen, or a portion thereof.
Alternatively, additional antibodies capable of binding to the biomarker polypeptide can be produced in a two-step procedure using anti-idiotypic antibodies. Such a method makes use of the fact that antibodies are themselves antigens and, therefore, it is possible to obtain an antibody that binds to a second antibody. In accordance with this method, protein specific antibodies can be used to immunize an animal, preferably a mouse. The splenocytes of such an immunized animal are then used to produce hybridoma cells, and the hybridoma cells are screened to identify clones that produce an antibody whose ability to bind to the protein-specific antibody can be blocked by the polypeptide. Such antibodies comprise anti-idiotypic antibodies to the protein-specific antibody and can be used to immunize an animal to induce the formation of further protein-specific antibodies.
The following immunofluorescence protocol may be used, for example, to verify IGF1R biomarker protein expression on cells or, for example, to check for the presence of one or more antibodies that bind IGF1R biomarkers expressed on the surface of cells. Briefly, Lab-Tek II chamber slides are coated overnight at 4° C. with 10 micrograms/milliliter (μg/ml) of bovine collagen Type II in DPBS containing calcium and magnesium (DPBS++). The slides are then washed twice with cold DPBS++ and seeded with 8000 CHO-CCRS or CHO pC4 transfected cells in a total volume of 125 μl and incubated at 37° C. in the presence of 95% oxygen/5% carbon dioxide.
The culture medium is gently removed by aspiration and the adherent cells are washed twice with DPBS++ at ambient temperature. The slides are blocked with DPBS++ containing 0.2% BSA (blocker) at 0-4° C. for one hour. The blocking solution is gently removed by aspiration, and 125 μl of antibody containing solution (an antibody containing solution may be, for example, a hybridoma culture supernatant which is usually used undiluted, or serum/plasma which is usually diluted, e.g., a dilution of about 1/100 dilution). The slides are incubated for 1 hour at 0-4° C. Antibody solutions are then gently removed by aspiration and the cells are washed five times with 400 μl of ice cold blocking solution. Next, 125 μl of 1 μg/ml rhodamine labeled secondary antibody (e.g., anti-human IgG) in blocker solution is added to the cells. Again, cells are incubated for 1 hour at 0-4° C.
The secondary antibody solution is then gently removed by aspiration and the cells are washed three times with 400 μl of ice cold blocking solution, and five times with cold DPBS++. The cells are then fixed with 125 μl of 3.7% formaldehyde in DPBS++ for 15 minutes at ambient temperature. Thereafter, the cells are washed five times with 400 μl of DPBS++ at ambient temperature. Finally, the cells are mounted in 50% aqueous glycerol and viewed in a fluorescence microscope using rhodamine filters.
This application claims benefit to International Application No. PCT/US2008/063621, filed May 15, 2008, under 35 U.S.C. §365(a); which claims priority to provisional application U.S. Serial No. 60/938,570, filed May 17, 2007; under 35 U.S.C. §119(e). The entire teachings of the referenced applications are incorporated herein by reference.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US2008/063621 | 5/15/2008 | WO | 00 | 11/17/2009 |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2008/144345 | 11/27/2008 | WO | A |
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| 5569588 | Ashby et al. | Oct 1996 | A |
| 7534792 | Wittman et al. | May 2009 | B2 |
| 20050112630 | Shaughnessy et al. | May 2005 | A1 |
| 20060140960 | Wang et al. | Jun 2006 | A1 |
| 20080089837 | Laing et al. | Apr 2008 | A1 |
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| Number | Date | Country | |
|---|---|---|---|
| 20100184125 A1 | Jul 2010 | US |
| Number | Date | Country | |
|---|---|---|---|
| 60938570 | May 2007 | US |
