Patent Application
20170002426
This invention relates to the fields of system biology, pharmacology and drug discovery. More specifically, the invention provides an EGFR/NEDD9/TGF-β interactome that facilitates the identification of agents for the treatment of proliferative disorders, particularly metastatic cancer. Anti-cancer agents having efficacy when used alone and in combination identified using the methods described herein are also provided.
Several publications and patent documents are cited throughout the specification in order to describe the state of the art to which this invention pertains. Each of these citations is incorporated herein by reference as though set forth in full.
Cancer is a leading cause of death in the United States. Treatments for metastatic cancer are generally limited, and include radiation, chemotherapy with non-specific cytotoxic agents, and therapy with drugs targeted at specific proteins that have been identified as marking cancer cells, and actively contributing to the aggressiveness of cancer growth. Taking metastatic colorectal cancer as an example, among the relatively limited drugs available for treatment of this disease, the DNA damaging agent irenotecan (a pro-drug for camptothecin), and antibodies (cetuximab, panitumumab) and small molecules (erlotinib, gefitinib) targeting the receptor tyrosine kinase (RTK) EGFR, an upstream regulator of the Ras pathway, have shown some efficacy (1-3). It is likely that improvement of therapies directed against EGFR and its family members (e.g., ERBB2/HER2/NEU, ERBB3/HER3) will be beneficial for treatment of numerous metastatic cancers, including those of breast, lung, and pancreas, as these proteins are often abnormally abundant or active in these tumors (e.g. 4,5), and EGFR-family targeting agents such as erlotinib and cetuximab have recently been approved for use in combination therapies in these cancers (1).
While combination of DNA damaging agents such as irenotecan and EGFR-targeting antibodies in the clinic, in some cases, produces substantial therapeutic benefit, in other cases, patients fail to respond. It is extremely likely that the failed response arises from secondary mutations in cancer cells that confer resistance to DNA damage, or relieve dependence of cells on EGFR: for example, mutations in K-Ras are becoming appreciated as a resistance factor for EGFR-directed therapies (6). In other cases, the sources of resistance or sensitivity are obscure (7).
In accordance with the present invention, a method for identifying compounds, particularly siRNA molecules which modulate sensitivity to EGFR/MEK-1 targeting agents is provided.
An exemplary method entails providing an EGFR/NEDD9/TGF-β interactome, comprising genes which are involved in cellular proliferation and EGFR/MEK-1 signalling; synthesizing at least one compound (e.g., an siRNA molecule) which targets EGFR/NEDD9/TGF-β interactome genes; contacting a cancer cell with at least one EGFR-MEK-1 targeting agent in the presence and absence of at least one compound from above; and determining cell viability in the presence of said agent alone and in the presence and absence of said at least one compound, compounds which increase or decrease sensitivity modulating cell sensitivity to said agent. Compounds for use in the invention include, without limitation, siRNA, phosphatase inhibitors, kinase inhibitors, inhibitory antibodies, and cholesterol synthesis inhibitors. The method may further include examining the cells for the presence of at least one parameter selected from the group consisting of morphological alterations, altered migratory properties, altered levels of apoptosis, altered angiogenic properties, and altered chromosomal or DNA integrity.
In a preferred embodiment, the EGFR-MEK-1 targeting agent is selected from the group consisting of cetuximab, panitumumab, erlotinib, lapatinib, gefitinib, and U0126. Sequences for the siRNA molecules disclosed herein are provided in Table 3. Sensitizing siRNAs are provided in Table 2.
In yet another aspect of the invention, a pharmaceutical composition comprising an effective amount of at least one EGFR-MEK-1 targeting agent listed above and at least one sensitizing siRNA provided in Table 2, in a pharmaceutically acceptable carrier is provided. Methods of administering the same to patients in need thereof for the treatment of malignancy are also disclosed.
In one embodiment, the cancer cell to be screened is obtained from a cancer cell line. In a particularly preferred embodiment, the cancer cell is isolated from a patient.
Also provided herein is a plurality of biomarkers associated with chemoresistance. Exemplary markers are provided in Table 2.
In yet another aspect, a method for determining whether a patient will respond to EGFR/MEK-1 targeting therapy is provided. An exemplary method comprises assessing a cancer cell from said patient for expression levels of at least one of the biomarkers listed in Table 2.
The file of this application contains at least one drawing executed in color. Copies of this patent with color drawings will be provided by the Patent and Trademark Office upon request and payment of the necessary fee.
In accordance with the present invention, compositions and methods are provided to better improve the treatment of cancer. Using small interfering RNA (siRNA) technology to identify proteins that contribute to resistance to clinically important therapeutic agents, we have identified suitable candidates which inhibit the action of identified resistance proteins thereby enhancing therapy. Such proteins also provide novel biomarkers to better select individual patients for specific treatment regimens.
An effective strategy has been devised to enhance the potency of EGFR-targeting agents. First, systems biology studies in model organisms have begun to establish that synthetic lethal relationships commonly involve genes that are involved in redundant, parallel pathways, or are vertically linked in the same pathway (8). It is instructive that in a seminal genome-wide screen to identify sensitizers to the microtubule-targeting agent paclitaxel, many hits could be clustered into coherent groups of genes associated with the proteasome or mitotic spindle (9), which a priori had been linked to paclitaxel activity based on existing pathway knowledge. Further, in the design of combination therapies in the clinic, the selection strategy for drugs to combine frequently involves common principles: that is, identifying two drugs that 1) inhibit the same target, 2) inhibit functionally linked and/or semi-redundant targets, or 3) inhibit vertically linked targets (10). Together, these observations suggested that generation of a mid-throughput siRNA library (−500-800 genes) that is large enough to fully represent genes functionally linked to the EGFR-Ras-MAPK signaling axis, would greatly increase the useful “hit” rate for genes that chemosensitize EGFR-targeted therapies.
This strategy has produced numerous gains, outlined below in detail: 1) we have been able to conduct reiterative screens to test strategies of hits selection and validation; 2) we have been able to test drive our experimental system to identify systematic errors and biases and apply statistical and bioinformatics tools to compensate for these biases; 3) the EGFR-centered library allowed us to validate the siRNA screening as successful strategy to identify interesting chemosensitizing hits; 4) we have identified siRNA molecules that sensitize chemoresistance cancer cells to EGFR based therapies.
As used herein, the phrase “EGFR/MEK1 targeting agent refers to small molecules, antibodies, or RNA agents targeting EGFR, EGFR-related family members, or immediate effectors in the EGFR cascade including but not limited to Ras, Raf, and MEK1.
The phrase “EGFR/NEDD9/TGF-β interactome” refers to proteins linked by close physical or functional association with EGFR, or with the proteins NEDD9, TGF-beta, or their binding partners.
A “small nucleic acid inhibitor” refers to any sequence based nucleic acid molecule which, when introduced into a cell expressing the target nucleic acid, is capable of modulating expression of that target. While siRNA molecules are exemplified herein, antisense, miRNA, shRNA and the like may be utilized in the methods of the invention.
As used herein, the phrase “effective amount” of a compound or pharmaceutical composition refers to an amount sufficient to modulate tumor growth or metastasis in an animal, especially a human, including without limitation decreasing tumor growth or size or preventing formation of tumor growth in an animal lacking any tumor formation prior to administration, i.e., prophylactic administration.
Preferably, as used herein, the term “pharmaceutically acceptable” means approved by a regulatory agency of the Federal or a state government or listed in the U.S. Pharmacopeia or other generally recognized pharmacopeia for use in animals, and more particularly in humans. The term “carrier” refers, for example to a diluent, adjuvant, excipient, auxilliary agent or vehicle with which an active agent of the present invention is administered. Such pharmaceutical carriers can be sterile liquids, such as water and oils, including those of petroleum, animal, vegetable or synthetic origin, such as peanut oil, soybean oil, mineral oil, sesame oil and the like. Water or aqueous saline solutions and aqueous dextrose and glycerol solutions are preferably employed as carriers, particularly for injectable solutions. Suitable pharmaceutical carriers are described in “Remington's Pharmaceutical Sciences” by E. W. Martin.
A pharmaceutical composition of the present invention can be administered by any suitable route, for example, by injection, by oral, pulmonary, nasal or other forms of administration. In general, pharmaceutical compositions contemplated to be within the scope of the invention, comprise, inter alia, pharmaceutically acceptable diluents, preservatives, solubilizers, emulsifiers, adjuvants and/or carriers. Such compositions can include diluents of various buffer content (e.g., Tris HCl, acetate, phosphate), pH and ionic strength; additives such as detergents and solubilizing agents (e.g., Tween 80, Polysorbate 80), anti oxidants (e.g., ascorbic acid, sodium metabisulfite), preservatives (e.g., Thimersol, benzyl alcohol) and bulking substances (e.g., lactose, mannitol); incorporation of the material into particulate preparations of polymeric compounds such as polylactic acid, polyglycolic acid, etc., or into liposomes. Such compositions may influence the physical state, stability, rate of in vivo release, and rate of in vivo clearance of components of a pharmaceutical composition of the present invention. See, e.g., Remington's Pharmaceutical Sciences, 18th Ed. (1990, Mack Publishing Co., Easton, Pa. 18042) pages 1435 1712 which are herein incorporated by reference. A pharmaceutical composition of the present invention can be prepared, for example, in liquid form, or can be in dried powder, such as lyophilized form. Particular methods of administering such compositions are described infra.
In yet another embodiment, a pharmaceutical composition of the present invention can be delivered in a controlled release system, such as using an intravenous infusion, an implantable osmotic pump, a transdermal patch, liposomes, or other modes of administration. In a particular embodiment, a pump may be used [see Langer, supra; Sefton, CRC Crit. Ref. Biomed. Eng. 14:201 (1987); Buchwald et al., Surgery 88:507 (1980); Saudek et al., N. Engl. J. Med. 321:574 (1989)]. In another embodiment, polymeric materials can be used [see Medical Applications of Controlled Release, Langer and Wise (eds.), CRC Press: Boca Raton, Fla. (1974); Controlled Drug Bioavailability, Drug Product Design and Performance, Smolen and Ball (eds.), Wiley: New York (1984); Ranger and Peppas, J. Macromol. Sci. Rev. Macromol. Chem. 23:61 (1983); see also Levy et al., Science 228:190 (1985); During et al., Ann. Neurol. 25:351 (1989); Howard et al., J. Neurosurg. 71:105 (1989)]. In yet another embodiment, a controlled release system can be placed in proximity of the target tissues of the animal, thus requiring only a fraction of the systemic dose [see, e.g., Goodson, in Medical Applications of Controlled Release, supra, vol. 2, pp. 115 138 (1984)]. In particular, a controlled release device can be introduced into an animal in proximity of the site of inappropriate immune activation or a tumor. Other controlled release systems are discussed in the review by Langer [Science 249:1527 1533 (1990)].
As used herein the term “biomarker” refers to a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
As used herein, the terms “modulate”, “modulating” or “modulation” refer to changing the rate at which a particular process occurs, inhibiting a particular process, reversing a particular process, and/or preventing the initiation of a particular process. Accordingly, if the particular process is tumor growth or metastasis, the term “modulation” includes, without limitation, decreasing the rate at which tumor growth and/or metastasis occurs; inhibiting tumor growth and/or metastasis; reversing tumor growth and/or metastasis (including tumor shrinkage and/or eradication) and/or preventing tumor growth and/or metastasis.
As used herein, the terms “tumor”, “tumor growth” or “tumor tissue” can be used interchangeably, and refer to an abnormal growth of tissue resulting from uncontrolled progressive multiplication of cells and serving no physiological function. A solid tumor can be malignant, e.g. tending to metastasize and being life threatening, or benign. Examples of solid tumors that can be treated or prevented according to a method of the present invention include sarcomas and carcinomas such as, but not limited to: fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon carcinoma, colorectal cancer, gastic cancer, pancreatic cancer, breast cancer, ovarian cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, liver metastases, bile duct carcinoma, choriocarcinoma, seminoma, embryonal carcinoma, thyroid carcinoma such as anaplastic thyroid cancer, Wilms' tumor, cervical cancer, testicular tumor, lung carcinoma such as small cell lung carcinoma and non-small cell lung carcinoma, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, melanoma, neuroblastoma, and retinoblastoma.
Moreover, tumors comprising dysproliferative changes (such as metaplasias and dysplasias) can be treated or prevented with a pharmaceutical composition or method of the present invention in epithelial tissues such as those in the cervix, esophagus, and lung. Thus, the present invention provides for treatment of conditions known or suspected of preceding progression to neoplasia or cancer, in particular, where non-neoplastic cell growth consisting of hyperplasia, metaplasia, or most particularly, dysplasia has occurred (for review of such abnormal growth conditions, see Robbins and Angell, 1976, Basic Pathology, 2d Ed., W.B. Saunders Co., Philadelphia, pp. 68 to 79). Hyperplasia is a form of controlled cell proliferation involving an increase in cell number in a tissue or organ, without significant alteration in structure or function. For example, endometrial hyperplasia often precedes endometrial cancer. Metaplasia is a form of controlled cell growth in which one type of adult or fully differentiated cell substitutes for another type of adult cell. Metaplasia can occur in epithelial or connective tissue cells. Atypical metaplasia involves a somewhat disorderly metaplastic epithelium. Dysplasia is frequently a forerunner of cancer, and is found mainly in the epithelia; it is the most disorderly form of non-neoplastic cell growth, involving a loss in individual cell uniformity and in the architectural orientation of cells. Dysplastic cells often have abnormally large, deeply stained nuclei, and exhibit pleomorphism. Dysplasia characteristically occurs where there exists chronic irritation or inflammation, and is often found in the cervix, respiratory passages, oral cavity, and gall bladder. For a review of such disorders, see Fishman et al., 1985, Medicine, 2d Ed., J. B. Lippincott Co., Philadelphia.
Other examples of tumors that are benign and can be treated or prevented in accordance with a method of the present invention include arteriovenous (AV) malformations, particularly in intracranial sites and myoleomas.
Methods for Modulating Tumor Growth or Metastasis As explained above, the present invention is directed towards methods for modulating tumor growth and metastasis comprising, inter alia, the administration of a EGFR/Mek-1 targeting agent and at least one sensitizing siRNA molecule. The agents of the invention can be administered separately (e.g, formulated and administered separately), or in combination as a pharmaceutical composition of the present invention.
The present invention provides for both prophylactic and therapeutic methods of treating a subject at risk of (or susceptible to) a disorder or having a disorder associated with aberrant or unwanted target gene expression or activity. In one embodiment, the subject is administered a lipid/therapeutic agent complex, for example, a liposome comprising an siRNA for suppressing the expression of an the undesired gene product. It is understood that “treatment” or “treating” as used herein, is defined as the application or administration of a therapeutic agent (e.g., an RNAi agent or vector or transgene encoding same, a polypeptide, e.g., an antibody or fragment thereof, or small molecule) to a patient, or application or administration of a therapeutic agent to an isolated tissue or cell line from a patient, who has a disease or disorder, a symptom of disease or disorder or a predisposition toward a disease or disorder, with the purpose to cure, heal, alleviate, relieve, alter, remedy, ameliorate, improve or affect the disease or disorder, the symptoms of the disease or disorder, or the predisposition toward disease.
In another aspect, the invention provides a method for preventing in a subject, a disease or condition associated with an aberrant or unwanted target gene expression or activity, by administering to the subject a lipid/therapeutic agent complex of the invention (e.g., an siRNA agent or vector or transgene encoding same, a polypeptide, e.g., an antibody or fragment thereof, or small molecule). Subjects at risk for a disease which is caused, or contributed to, by aberrant or unwanted target gene expression or activity can be identified by, for example, any or a combination of diagnostic or prognostic assays as described herein. Administration of a prophylactic agent can occur prior to the manifestation of symptoms characteristic of the target gene aberrancy, such that a disease or disorder is prevented or, alternatively, delayed in its progression. Depending on the type of target gene aberrancy, for example, a target gene, target gene agonist or target gene antagonist agent can be used for treating the subject. The appropriate agent can be determined based on screening assays described herein.
In yet another aspect, the invention pertains to methods of modulating target gene expression, protein expression or activity for therapeutic purposes. Accordingly, in an exemplary embodiment, the modulatory method of the invention involves contacting a cell capable of expressing target gene with a lipid/therapeutic agent complex (e.g., an siRNA agent or vector or transgene encoding same) that is specific for the target gene or protein (e.g., is specific for the mRNA encoded by said gene or specifying the amino acid sequence of said protein) such that expression or one or more of the activities of target protein is modulated. These modulatory methods can be performed in vitro (e.g., by culturing the cell with the agent), in vivo (e.g., by administering the agent to a subject), or ex vivo. As such, the present invention provides methods of treating an individual afflicted with a disease or disorder characterized by aberrant or unwanted expression or activity of a target gene polypeptide or nucleic acid molecule. Inhibition of target gene activity is desirable in situations in which target gene is abnormally unregulated and/or in which decreased target gene activity is likely to have a beneficial effect.
When employing the methods or compositions of the present invention, other agents used in the modulation of tumor growth or metastasis in a clinical setting, such as antiemetics, can also be administered as desired.
Materials and methods are provided herein to facilitate the practice of the present invention.
The A431 cervical adenocarcinoma (K-Ras wt, p53 mutant (Kwok et al. (1994) Cancer Res. 54:2834), HCT116 and LoVo (K-Ras mutant, p53 wt) colorectal carcinoma and the PANC-1 (K-Ras mutant, p53 mutant) and MIA PaCa-2 (K-Ras mutant, p53 and p16 mutant) pancreatic adenocarcinoma (Brunner et al., (2005) Cancer Res. 65:8433) cell lines were obtained from the ATCC (USA). The DLD-1 (K-Ras mutant, p53 mutant) and DKS-8 (with the activated K-Ras allele disrupted [derived from DLD-1], p53 mutant; Sarthy et al. (2007) Mol. Cancer Ther. 6:269) were a kind gift of Dr. Robert J. Coffey (Vanderbilt University, TN). SCC61 cells (K-Ras wt, p53 mutant), derived from squamous cell carcinomas of the head and neck, were kindly provided by Dr. Tanguy Y. Seiwert (University of Chicago). All cell lines were maintained in DMEM supplemented with 10% v/v fetal bovine serum (FBS) and L-glutamine without antibiotics. Cetuximab, panitumumab and erlotinib were purchased from the Fox Chase Cancer Center pharmacy; CPT11 and C1368 from Sigma-Aldrich (USA); Stattic and Ro-318220 from EMD Chemicals (Gibbstown, N.J., USA). PHA-680632 was obtained from Nerviano Medical Sciences (Nerviano, Italy), as a kind gift of Dr. Jurgen Moll. Enzastaurin was generously provided by the Elli Lilly Company (Indianapolis, Ind.). All antibodies used in Western blot experiments were purchased from Cell Signaling (Danvers, Mass., USA), except anti-p53 mouse monoclonal antibody was from Calbiochem (EMD Biosciences, USA).
Four sources of information were used, including 1) published EGFR pathway maps, 2) human protein-protein interaction (PPI) data, gleaned from various databases, 3) human orthologs of PPIs and genetic interactions modeled from Drosophila, and 4) microarray data obtained at brief intervals after treatment of cells with stimulators or inhibitors of EGFR/ErbB2. Following initial assembly of a larger gene list, genes were parsed into high confidence (“core”, denoted as “1” after the corresponding letter code) versus lower confidence sets (denoted as “2”), based on the confidence criteria outlined for each section below. For each category of information, all “core” components were included in the final library, as were genes noted as lower confidence but included in at least two categories of search criteria (e.g., second order protein-protein interaction and microarray linkage). Finally, for the assembled set of EGFR interactors, multiple paralogous genes were identified in humans using the KEGG Sequence Similarity DataBase (SSDB) resource, see the world wide web at .genome.jp/kegg/ssdb/. 77 paralogs of the best-characterized and biologically significant genes were added to the set. All data storage, handling and analysis were done primarily in Cytoscape (on the world wide web at cytoscape.org).
1) Pathway map sources. Protein names for were extracted from the following pathway maps focused on EGFR: STKE (on the world wide web at stke.sciencemag.org/cgi/cm/stkecm %3BCMP_14987); Biocarta (on the world wide web at biocarta.com/pathfiles/PathwayProteinList.asp?showPFID=102); the Systems Biology model repository (on the world wide web at systems biology.org/001/005.html); NetPath (on the world wide web at netpath.org/pathways?path_id=NetPath_4); and from Protein Lounge (on the world wide web at proteinlounge.com/pop_pathwaysl.asp?id=EGF+Pathway). Protein names were individually inspected and, where necessary, converted to the corresponding official (NCBI/EMBL) symbols. Proteins mentioned on at least two EGFR-centered pathways were designated as “pathway core”; we note, significant divergence was seen among different interpretations of the “EGFR pathway” by the 5 sources.
2) PPIs. EGFR/ERBB1 and ErbB2 were used as seeds for PPI searches. Curated information regarding human PPIs of these seeds was collected from the following databases: Biomolecular Object Network Databank (BOND) (on the world wide web at bond.unleashedinformatics.com/); General Repository for Interaction Datasets (on the web at thebiogrid.org/); EMBL_EBI IntAct on the web at ebi.ac.uk/intact/site/index.jsf); The Human Protein Reference Database (on the web at hprd.orgf); Kyoto Encyclopedia of Genes and Genomes (KEGG) (on the web at genome.jp/about_genomenet/service.html); and Prolinks Database 2.0 (on the web at mysql5.mbi.ucla.edu/cgi-bin/functionator/pronav). Data for first rank (direct) interactors were collected both by export from the corresponding database and subsequent import into Cytoscape, and by directly querying those databases using the BioNetBuilder plugin (on the web at err.bio.nyu.edu/cytoscape/bionetbuilder/), and then cross-comparing retrieved results. Data for the second order interactions were obtained by using EGFR and ERBB2 first rank interactors as seeds for an additional round of query, and only through the BioNetBuilder plugin. Finally, an orthogonal set of second rank interactors was obtained by analysis of protein complexes with more than 2 subunits, which included EGFR. Information for complexes was obtained from BOND and IntAct, and manually compared to the lists in the corresponding publications. We also used the SHC1 and SHC3 adaptors, which bridge between EGFR and downstream signaling effectors, and the CAS (EFS, BCAR1, and NEDD9) scaffolding proteins, which connect EGFR to the SRC and TGF-β core signaling cascades (O'Neill et al., (2000) Trends Cell Biol. 10:111; Defilippi et al. (2006) Trends Cell Biol. 16:257), as seeds for first order PPI searches. Second order PPIs EGFR and ErbB2 were ranked higher (i.e., P1) if they were also first order interactors of SHC or CAS proteins.
3) To extract a set of EGFR-centered interactions potentially conserved between humans and D. melanogaster, information assembled by the Michigan Proteomics Consortium in the Drosophila Interactions Database (DIP) (on the web at proteome.wayne.edu/PIMdb.htm) was used. Briefly, this database integrates genetic and or protein interaction data from a variety of non-mammalian species (yeast, worms, flies). Of 105 EGFR interactors (almost exclusively from Drosophila genetic interactions), 65 had 1-2 conserved human orthologs (117 genes).
4) Microarray data were obtained from The Gene Expression Omnibus (GEO, release date Dec. 15, 2006). In the selected dataset (GSE6521; raw data available at on the web at ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6521), MCF-7 human breast cancer cells were incubated with the growth hormone heregulin (HRG), or AG1478 (an EGFR kinase inhibitor), or both growth hormone and AG1478. Controls were set as growth hormone/inhibitor non-treated cells. A total of 348 genes with a >1.5 fold change (+ or −) upon AG1478 treatment was identified. In this group, the core set includes 89 genes which showed a >2-fold change in expression level upon AG1478 treatment, or which were inducible by HRG (>1.5 fold) and repressible by AG1478 (>1.5 fold).
High Throughput siRNA Screening Methods.
The custom siRNA library targeting 638 human genes was designed and synthesized with two siRNA duplexes for each gene target (Qiagen, Valencia, Calif.). Transfection conditions were established for the A431 cervical adenocarcinoma (K-Ras wt, p53 mutant) cell line (data not shown) using PLK1 & GL2 siRNA controls to achieve Z′ values of 0.5 or greater. IC values using erlotinib, panitumumab, and camptothecin (CPT11) were established (data not shown). Details of establishment of Z′ factor for transfections, and statistical consideration for selection of preliminary positive candidates graphically outlined in
For screening, A431 cells were transfected with siRNA followed by exposure to vehicle (0.02% DMSO), or drug used at inhibitory concentrations of 30% KA. Viability was determined for each target gene and normalized to the averaged GL2 viability on each plate. Sensitization index (SI) was calculated for each individual well on a 96-well plate as SI=(Vdrug/GL2drug)/(VDMSO/GL2DMSO), where V was viability in wells transfected with targeting duplexes and GL2 was the averaged viability of 4 wells with non-targeting negative control siRNA on the same plate. All calculations were automated using cellHTS package within open source Bioconductor Package (on the web at bioconductor.org) (Gentleman et al. (2004) Genome Biol. 5:R80). The effect of drug treatment on viability was measured based on the normalized viabilities in the drug treated and vehicle wells using Limma (Smyth, G. K. (2004) Statistical Applications in genetics and molecular biology 3, Article 3). Limma borrows strength across genes based on an empirical Bayes approach and identifies statistically significant changes in viability by combining information from a set of gene-specific tests. Hits were identified based on statistical significance as well as biological significance. Statistical significance was determined by p-value controlled for the false discovery rate (FDR) using the Benjamini-Hochberg step-up method (Benjamini et al. (1995) J. Royal Stat Soc B 57:289) to account for multiple testing. Hits showing an FDR of less than 20% were considered statistically significant. Biological significance was arbitrarily defined as an increase or decrease in SI greater than 15%. Hits identified as statistically and biologically significant were further validated.
Primary sensitizing hits obtained with erlotinib and/or cetuximab were further tested with erlotinib and DMSO in the A431 cell line by using 4 siRNA duplexes individually (the two originally used in the screen, plus two new non-overlapping RNA oligoribonucleotides), to confirm the sensitization phenotype at 10 nM and 50 nM concentrations. Hits were considered as validated by this method if at least 2 out of 4 siRNA reproduced the sensitization phenotype with SI≦0.85, FDR≦20% for each individual siRNA sequence in at least two independent experiments. For a number of hits, we additionally confirmed sensitizing siRNAs reduced mRNA levels for the targeted genes, using qRT-PCR; and used Western analysis to confirm protein knockdown (data not shown).
Cell line specificity of hit activity. Of the confirmed set of 61 siRNA targets identified for erlotinib in A431 cells, 45 were further tested for sensitization to erlotinib, cetuximab and camptothecin in A431 versus refractory adenocarcinoma cell lines for which optimal transfection conditions and drug sensitivity had been established. In this analysis, for each target, the two most active siRNA duplexes identified during the validation stage were pooled in a 96-well format, cells were reverse-transfected and drug-treated under conditions similar to those described above for the initial A431 screen. SI and statistical significance were calculated as in the validation experiments. All experiments were performed at least three times independently.
In subsequent data analysis, two approaches were used. For the relative ranking approach, for each experiment, SI values for each siRNA pool were ranked from the strongest (assigned a value of 0) to the weakest (assigned as 1). For all experiments performed with a given cell:drug combination (e.g., A431:erlotinib, or HCT116:CPT11) averages were determined based on at least three experimental runs. The averaged data were imported and clustered in MultiExperiment Viewer (MeV_4_3) software (Saeed et al. (2003) Biotechniques 34:374), and dendrograms were created using HCL Support Trees (using Euclidian Distance as a metric, and bootstrapping with 100 iterations). For the absolute threshold approach, specific SI thresholds were applied for each data point, considering only data with an FDR≦20% in each independent experiment. Data were visualized in MultiExperiment Viewer using color assignments to indicate SI cutoffs obtained in at least two independent experiments, as described in figure legends. The resulting output of both analytic strategies was processed in standard graphic software to improve visualization of data.
For evaluation of expression of validated target genes, each of the cell lines was grown to 70% confluency in full DMEM media, then total RNA was extracted using RNeasy Minikit (Qiagen, Valencia, Calif.). To confirm mRNA depletion by siRNA, 48 hrs after transfection of A431 cells grown in 96-well plates, total RNA was extracted at using a Cell-to-Ct kit (Applied Biosystems, Foster City, Calif.). Subsequent quantitative RT-PCR reactions were performed using TaqMan probes and primers designed by the manufacturer, using an ABI PRISM 7700 detection system (Applied Biosystems, Foster City, Calif.). The results were analyzed using the comparative Ct method to establish relative expression curves.
To assess whether gene expression levels correlated with the ability of gene-targeted siRNAs to inhibit intrinsic cell growth, we used a Pearson correlation of the mean values of gene expression relative to that obtained in A431 cells measured by RT-PCR, against the mean growth observed in DMSO-treated cells in all experiments. To test significance, we performed 100 permutations of the cell line labels in the RT-PCR measurements and generated Pearson correlation values. Significance was defined as a false discovery rate (FDR) of 5%, setting Pearson correlation greater than 0.745 or less than −0.71 for positive correlated or negative-correlated, respectively. Positive correlation indicates that higher expression (lower number of RT-PCR cycles) is correlated with greater growth inhibition, while negative correlation indicates higher expression is correlated with lower inhibition.
Network Analysis with Hits.
For all genes in the library, the String search engine (Jensen et al., (2009) Nucleic Acids Res. 37:D412) was used in subsequent analysis to augment information on PPIs in human cells, PPIs between homologous genes in model organisms, database/pathway links, and textmining (coappearance of gene names in PubMed). Data regarding experimentally proven interactions in human and model organisms were merged. Topological properties of the library network were assessed using NetworkAnalyzer plugin for Cytoscape (Assenov et al., (2008) Bioinformatics 24: 282), based on STRING-expanded defined interactions among genes in the library (based only on experimental data). In this analysis, for each node, degree (reflecting the number of edges linked to it), stress (reflecting how frequently it lies in the shortest paths connecting other nodes), and neighborhood connectivity (the average number of neighbors for each direct interactor of the node) were separately assessed. The topological coefficient was calculated to provide an estimate for the trend of the nodes in the network to have shared neighbors. To provide additional context in some analyses (
Apoptosis was measured using the Annexin V assay (Guava Technologies, Hayward, Calif.). Annexin V-positive A431 cells were counted using Guava flow cytometry 72 hours post transfection, 48 hours after treatment. Statistical significance versus control GL2 siRNA was determined by logistic regression models to identify genes increasing apoptosis with erlotinib relative to vehicle.
To measure the effect of siRNAs on the activity of EGFR effectors, cells were transfected with siRNA, and the culture media replaced with glutamine-supplemented serum-free DMEM at 24 hrs post-transfection. After overnight incubation, cells were treated with DMSO, erlotinib or PHA-680632 for 2 hrs, then either left untreated, or stimulated with EGF at 15 ng/ml for 15 minutes. Cell extracts were prepared using M-PER™ mammalian protein extraction buffer (Thermo Scientific, Rockford, Ill.) supplemented with the Halt™ phosphatase inhibitor cocktail (Thermo Scientific, Rockford, Ill.) and the Complete Mini™ protease inhibitor cocktail (Roche Diagnostics Gmbh, Manheim, Germany). Extracts were centrifuged at 15,000 g for 10 min at 4° C. Western signal detection was performed using antibodies to indicated proteins with LiCor technology (Lincoln, Nebr., USA) or standard X-ray film.
For phosphoproteomic analysis, we used the Proteome Profiler™ array (R&D Systems, Minneapolis, Minn., USA) according to the manufacturer's protocol. In brief, A431 cells were grown for 24 hours in DMEM supplemented with L-glutamine and 1% FBS to 70% confluency. Cells then were either serum starved overnight or remained in the same media. Serum starved and cells incubated in 1% serum were either left untreated or incubated with IC30 concentrations of inhibitors for 3 hours. For
The coefficient of interaction (CI) between pharmacological inhibitors was established by the Chou-Talalay method (Chou et al., (1984) Adv Enzyme Regul 22:27). The software package CalcuSyn (BioSoft, UK) was used to automate calculations. Briefly, for each drug tested, an IC50 curve was established in each cell line, and used to select combination doses of drugs for subsequent synergy tests. 3500 cells were plated per well in 96-well plates. After 24 hours, cells were treated with serial dilutions of individual inhibitors, or combinations of two inhibitors maintained at a constant molar ratio. After 72 hours incubation, cell viability was measured using either CellTiter Blue (Promega, USA) or a WST1 assay (Roche Applied Science, Indianapolis, Ind.). The CI values for each dose and corresponding cytotoxicity were expressed as the fraction affected (Fa) and were calculated using CalcuSyn computer software and presented as Fa-CI plots. CI values <1 indicate synergy, and <0.5 strong synergy, between the two agents in producing cytotoxic effect.
Soft agar assays were done essentially as described (10). Cells were seeded at 2000 cells per well and grown for 2-3 weeks. Colonies were stained with thiazolyl blue tetrazolium bromide, and scored using a Nikon SMZ1500 microscope coupled with Cool Snap charge coupled device camera (Roper Scientific, Inc., Tucson, Ariz.) with Image Pro-Plus software (Media Cybernetics, Silver Spring, Md.). Survival curves were based on at least two concentration points, with values determined in at least two separate experiments, with each assay done in duplicate. Drug interactions were calculated as above using CalcuSyn software (Biosoft, Ferguson, Mo.) to establish the combination index (CI). For motility assays, movement of A431 cells grown in 1% FCS into a scratched area of the monolayer was monitored with a phase contrast 10× objective using an inverted microscope (Nikon TE2000). Images were obtained every 20 min for 18 hours. Areas of migration were estimated using MetaMorph software. For both studies, analysis of variance was used to determine the treatment effect for each comparison. The logarithm of normalized ratios (to vehicle control) was used in the analysis. Multiple hypothesis testing was accounted for by the false discovery rate method of Benjamini & Hochberg (supra).
Male CB.17/scid mice aged 6-8 weeks were obtained from the Fox Chase Cancer Center breeding colony. All experiments were carried out according to protocols approved by the institutional animal use committee. Mice were injected with 3×106 A431 cells subcutaneously into the flanks. Palpable tumors appeared in all animals in 10-14 days, and were measured 3 times a week in two dimension and volume calculated by modified ellipsoidal formula as Length×Width2×0.52. Mice were randomized and treatments commenced when tumor volume exceeded 65 mm3. Erlotinib at doses 10-20 mg/kg was given by oral gavage as in 10% DMSO/saline. Enzastaurin was suspended in 5% dextrose in water and dosed at 75 mg/kg by gavage twice daily. PHA-680632 was freshly dissolved in acidified 5% dextrose in water and administered intraperitoneally twice daily at 15 mg/kg dose. The generalized estimating equations approach (with an autoregressive correlation structure) was used to model tumor growth. A linear time-effect was included in the model for the logarithm of tumor volume and interacted with the treatments in each comparison.
The following examples are provided to illustrate certain embodiments of the invention. They are not intended to limit the invention in any way.
To generate an EGFR interactome library, we extensively mined public access databases containing information about protein-protein interactions and mRNA expression profiles generated in humans. These databases included among others NetPath, BioGrid, DIP, BIND, KEGG, HPRD, CellCircuits, and NCBI GEO, as well as five different “expert systems” focused on pathway analysis (NetPath, Protein Lounge, Molecular Systems Biology, Biocarta, Science's STKE). This approach identified a set of genes for which the encoded proteins either directly bound EGFR, EGFR-family members such as ERBB2 and ERBB3, and their immediate downstream effectors, or were purified in complexes including these proteins; a set of genes transcriptionally upregulated by EGFR family stimulation and downregulated by EGFR pathway inhibition; and a set of genes otherwise involved in EGFR signaling based on published literature. We also incorporated data generated from genetic interactions reported in Drosophila, C. elegans, and other organisms for strongly conserved evolutionary orthologs of genes in this pathway (11, 12). Using resources in the systems analysis programs Cytoscape and Osprey, we then identified a core high value set of genes that fell into at least two of these linkage categories, and based on other weighting functions.
Drug resistance and propensity to metastasis commonly both characterize the most aggressive tumors. A growing number of proteins that function in pro-metastatic signaling pathways related to cellular invasiveness are also known to function in promoting apoptosis resistance (via modulation of integrin- and cadherin-dependent signaling cascades). To enrich our candidate siRNA set, we then performed a similar analysis for genes physically and functionally linked to TGFβ, Src and NEDD9. It is becoming well established that cancer metastasis requires parallel inputs from EGFR superfamily members, and the Src and TGFβ signaling pathways (13, 14). NEDD9 (also known as HEF1) has long been studied in the Golemis laboratory (15, 16), and elevated expression of NEDD9 has recently been shown to be a critical driver of melanoma metastasis, and linked to metastasis in lung cancers (17, 18); moreover, NEDD9 physically interacts with multiple components of the EGFR/Ras, TGFβ, and Src signaling pathways (19, 20).
From these combined data mining efforts, we selected a set of 638 target genes (see Table 1). These genes were ordered as a custom library from Qiagen. Two independent siRNAs for each gene were pooled and arrayed in wells. For the screening experiment, aliquots of the library were transferred to the inner 60 wells of 11 assay plates with a layout intended to seed in negative control siRNA (siControl targeting insect luciferase transcript, Dharmacon), positive control cytotoxic siRNA (targeting Polo-like kinasel), and cytotoxic drug (0.7 mM campthotecin). The siRNA was mixed with the transfection reagent (Dharmafect 1, Dharmacon) diluted 1:100 in Hank's balanced saline in a total volume of 22 ml to produce 25 nM final concentration of each siRNA oligo. The mixture was incubated for 45 minutes. A reverse transfection protocol was used (9) where cells resuspended in DMEM/1% FBS/Glutamine were added at 4000 per well to the assay plates using a Thermo Multidrop bulk reagent dispenser. After overnight incubation, 10 ml per well of the drug was added. For each screening experiment, the effective inhibitory concentration (IC) of the screen drug was pre-determined by rigorous IC50 testing, and screens were consistently performed aiming for IC30-40 range. Viability was measured in the Perkin Elmer Envision plate reader using fluorescent metabolite of Alamar blue (CellTiterBlue™, Promega).
Our initial screens were performed in A431 cells, for two compelling reasons. First, we wanted to maximize our chances of obtaining any hits that would sensitize to EGFR-targeting antibodies. A431 cells are well known to over-express the EGFR receptor, and to be exquisitely sensitive to inhibition of EGFR-dependent signaling. Second, as outlined below, we have had the opportunity to add value to our screening strategy based on addition of a high throughput microscope-based detection system: based on their regular growth properties, A431 cells have proven excellent for this purpose.
As a second conservative approach, initial EGFR-pathway sensitization screens were performed with panitumumab, and also with the small molecule erlotinib. This was done because of literature suggesting that a significant component of the anti-tumor effect of EGFR-targeting antibodies arises from cell-mediated immunity, which would not apply in cultured cell lines. This restriction should not apply with erlotinib. Because of the relatively low cost of screening, this project also serves as an opportunity to probe the overall “resistance structure” of the EGFR signaling pathway, by examining the overlapping pattern of “hits” arising from drugs targeting different points along the pathway. Hence, we have now screened A431 cells for siRNA sensitizers to erlotinib, panitumumab, and U0126 (targets MEK1), in each case normalizing against the base line inhibitory profile of each siRNA in cells treated with a DMSO (vehicle) control. We have also screened the A431 cell line with CPT11 (an active camptothecin analog of irenotecan). We anticipated that comparison of the CPT11 hit pattern with the EGFR-Ras-MEK1 inhibitor pattern would segment siRNAs into 3 classes: those that sensitize to CPT11 and EGFR-Ras-MEK1 inhibitors, targeting general apoptosis-resistance genes; and those specific either to CPT11 or the EGFR-Ras-MEK1 pathway. Knowledge of each these classes has specific value for specific therapeutic/biomarker applications.
A growing consensus among clinicians is that a major source of resistance to EGFR-targeted therapies is the presence of an activating K-Ras mutation (present in up to 70% of resistant tumors) or an activating B-Raf mutation (present in up to 10% of resistant tumors). The colon cancer cell lines HCT116 harbors an activating K-Ras mutation, conferring relative resistance to the EGFR antagonists in vitro. The HCT116 colorectal carcinoma cell line offers advantage of comparison between p53null and p53-positive (intact apoptotic checkpoint) isogenic variants (9) which will be important to determine the p53-dependancy of the synthetic lethal phenotype and the mechanism of apoptosis induction. Conditions for these cell lines have been established for siRNA-based screening with high siRNA transfection efficiency (>75% depletion of two housekeeping genes GADPH, PLK1); and consistency in Alamar Blue viability yielding robust Z′-scores with control drugs (see Results below). Additional cell lines with activating mutations in B-Raf will also be worked up for hit validation.
We are using these additional cell lines in two ways. For HCT116, we will repeat interactome library screens essentially as performed in the A431 cell line, using DMSO (vehicle), erlotinib, and CPT11. We will determine whether the overall landscape of hits is similar, or distinct. We anticipate that CPT11 may yield a similar profile in the two lines, while erlotinib may yield a different, extremely restricted, hit map. Hits arising from this screen that also were detected in the A431 line would obviously be of particular interest. Second, for selected, validated hits in the A431 cell line that are of specific interest based on the validation steps described below, we will selectively analyze these hits in HCT116 positive and negative for p53, and in LoVo cells. A summary of our complete set of screens is shown in
Positive candidates from the first 5 screens were selected based on standard approaches described in detail in (9) and Swanton et al. (9, 21). Specifically, the Alamar blue fluorescence intensity in the assay wells (R) read after 2 hours of incubation was normalized to the mean siControl (C) on each plate (R/C). The effect of drug treatment on viability was measured based on the normalized viabilities in the drug treated and vehicle wells using Limma (22), Limma borrows strength across genes based on an empirical Bayes approach and identifies statistically significant changes in viability by combining information from a set of gene-specific tests. We utilized the Limma implementation in the Open Source R/Bioconductor Package (available on the world wide web at bioconductor.org) (23), Hits were identified based on statistical significance as well as biological significance. Statistical significance was measured by p-values controlled for the false discovery rate (FDR) using the Benjamini-Hochberg step-up method (24) to account for multiple testing. Hits showing an FDR of less than the desired cut-off were considered statistically significant. The choice of the cut-off itself was flexible. Biological significance was measured by a change of at least 20% in viability relative to vehicle treated cells. Hits identified from each of the above filters were combined and a list of common hits showing greater statistical and biological significance (lower FDR and at least 20% change in viability) were identified.
An initial question working with a custom library is whether a custom panel of siRNAs pre-selected to be relevant to EGFR signaling might contain many siRNAs that strongly inhibit cell growth even in the absence of drug treatment.
An important fundamental question in relationship of the likelihood of identifying drug sensitizing siRNAs that are useful in predicting a useful clinical strategy is, do the sensitizing siRNAs selectively emerge from the part of the library that intrinsically inhibits cell growth? Or in other words, does sensitization simply reflect the idea that an unhealthy cell is more likely to succumb to drug treatment, or are there specific siRNA-drug interactions? We compared the distribution of siRNAs selected as hits for erlotinib (
In addition to possessing high throughput robotics supporting a platereader, we also employed a Molecular Devices ImageXpress automated microscope. The ImageXpress captures 1-2 fields, representing 100-200 cells, for each individual well of a 96 well microtiter plate (see
Table 2 summarizes the set of hits obtained to date based on screening the library with erlotinib, panitumumab, U0126, and CPT11, so far extracted solely from plate reader data. In each case the SI (sensitization index) value reflects reduction of Alamar blue signal in relation to the same siRNA used in combination with DMSO/vehicle. Taking as initial threshold for hit selection reduction of signal 20% below vehicle, 145 hits (representing a hit rate of 23% of the total library) were obtained with erlotinib, 19 (3%) with panitumumab, 55 (9%) with CPT11, and 7 (1%) with U0126.
Color-coding indicates a subset of hits that are identified with more than one drug treatment (Table 1; also see Venn Diagram,
We will also perform additional validation experiments described below. However, preliminary consideration of the hit profile obtained to date is suggestive in a number of ways that indicate the clinical relevance of this data. First, erlotinib and panitumumab yielded a large number of overlapping hits. This seems highly unlikely to be a random occurrence. Second, we have used the Cytoscape resource to map the profile of interactions among the screening hits (
We have also identified a set of siRNAs that induce resistance to erlotinib, panitumumab, and CPT11. These include siRNAs that have little or no effect on the viability of cells treated solely with vehicle. However, these siRNAs reduce the ability of panitumumab and/or other agents to decrease cell viability. It appears such genes may target suicide pathways specifically triggered by drug treatment, and as such, these genes may also be valuable for exploitation to improve therapy. Further study of these genes will also inform our understanding of drug resistance mechanisms.
There is an emerging consensus that a significant degree of the resistance to EGFR-targeting agents is due to activating mutations in K-Ras or B-Raf. Because of the central role of Ras in modulating apoptosis, these mutations may similarly confer resistance to agents such as irenotecan. Following screening and hit validation in the A431 cell line we will perform two classes of screen in colorectal cancer cell lines with mutated K-Ras (HCT116, LoVo) or B-Raf (e.g, WiDr, TCO. First, we will use the same regimen of comparing DMSO, erlotinib, and CPT11 screens as described above for A431 cells, but instead use HCT116 cells to identify a validated sensitization network. Second, we will take all validated hits from the A431 and HCT116 screens, and test them for sensitization in the LoVo and WiDr cell lines.
A relatively small subset of A431-predicted sensitizing siRNAs will be active in conjunction with erlotinib in the resistant cell lines. Among these, siRNAs identified as broadly sensitizing (to erlotinib as well as CPT11), are likely to maintain activity. A higher percentage of the siRNAs identified as sensitizing solely to CPT11 would maintain activity. It also appears that siRNAs targeting genes “upstream” of KRas would be less likely to function in K-Ras mutated lines than siRNAs targeting genes “downstream”, or in independent signaling pathways.
Sensitizing siRNAs will be identified for erlotinib and/or CPT11, but they will be very different from those identified with A431 cells. This outcome would initially suggest that some mRNAs for sensitizers well expressed in A431 cells are not present in HCT116 and other model cell lines, and vice versa. This could be immediately tested with qRT-PCR; if so, sensitization strategies will be tailored based on cell lineage. Regardless of outcome, the results of these experiments will facilitate identification of the factors controlling resistance as a factor of drug resistance network function.
The sensitization network construction shown in
Complementation groups define sets of genes whose protein products work in a single pathway or multi-protein complex, providing a single chain of input into a biological endpoint. In synthetic lethal analysis, targeting two members of the same complementation group will not enhance the endpoint phenotype, but targeting two members of different complementation groups, which provide parallel input into the biological endpoint, will enhance the final phenotype. The network mapping analysis described above has the potential to identify important “sensitization complementation groups”, which we define as small clusters of proteins known to physically interact with each other, or act in proximity on a sub-pathway: the BCAR1-BCAR3-CXCL 12-ALK cluster would define one such group. After segmentation of the hit network into proposed complementation groups, we would determine the consequences for sensitization of simultaneously targeting two siRNAs within the same group (e.g., BCAR1 and BCAR3) versus different groups (e.g., BCAR1 and BCL3). We will thus identify synergistic interactions that can greatly sensitize cells to the effect of treatment with EGFR-targeting agents, CPT11, or potentially both.
The set of genes included in the EGFR interactome includes many plasma membrane associated receptors and kinases that had already drawn clinical interest, and for which small molecule and/or antibody inhibitory agents already exist. Some of these inhibitory agents have already passed through Phase I/II trials, and are being effectively used in the clinic. First, we will test whether combination of the siRNA-matched drug with erlotinib (and panitumumab, or CPT11, as appropriate) produces a notable synergistic effect using Alamar blue assay to detect reduced viability in A431 and HCT116 cells. We will determine whether the erlotinib-drug combination has a similar mode of action (see Example 6) as the erlotinib-siRNA combination. We will use the A431 and/or HCT116 cell lines to establish xenografts in nude mice, and erlotinib-drug combination in vivo synergy. This analysis provides the means to rapidly identify valuable synergies that would be immediately translatable to the clinic.
We will first assess if siRNAs directly affect the expression, activation, or localization of the EGFR receptor itself. We will use antibodies to EGFR and phospho(active) EGFR in Western analysis of cell lysates treated with each siRNA, to look for siRNA-dependent loss of signal. We will use antibody to EGFR and the early endosomal marker EEA 1 in immunofluorescence experiments to determine whether siRNAs induce increased internalization of EGFR from the cell surface. As part of microscope-based analysis, we will also determine whether siRNAs specifically alter the morphology (attachment; cytoskeletal integrity) of drug-treated cells. Reciprocally, we will also determine whether EGFR signaling regulates the genes targeted by the sensitizing siRNAs. We will use qRT-PCR to determine whether treatment of quiescent cells with EGFR stimulates expression of the sensitizing genes, and whether treatment of actively growing cells with panitumumab or erlotinib influences expression of these genes. We will also use FACS and/or Guava analysis to measure whether specific siRNAs confer cell cycle arrest and/or apoptosis.
A complete database of images of calcein- and Hoechst-stained cells from experiments performed in exact parallel with the Alamar blue values (
The congruence of specific endpoint phenotypes (cytokinetic block; reduction in cell spreading; etc) with the overall Alamar blue hit list can be established. We will also validate the hits by a similar strategy to that used above for Alamar blue hits, i.e., regenerating the phenotypes with independent siRNAs, and confirming that degree of mRNA depletion of target correlates with degree of induction of the phenotype. This analysis is extremely likely to result in the addition of new siRNAs to the hit list, i.e., siRNAs that induce phenotypes that are ultimately likely to result in cell death, but which have sufficiently delayed action that moribund cells did not score as positive using Alamar blue-based cutoff values.
Next, we will repeat the network construction analysis described above (Examples 3 and 4). This will be done in two ways: by analyzing the expanded network (Alamar blue hits+microscope hits), and by analyzing networks associated with specific phenotypic endpoints. This can extend definition of sensitization clusters/complementation groups, and may bring more druggable (or already drugged) targets into the groups. It will also illuminate the mechanism by which sensitization is induced. As a hypothetical example, one cluster of closely interacting proteins includes some with strong Alamar blue sensitization to erlotinib, and several members that induce loss of cell area only in cells treated with erlotinib. This might imply the entire group is functionally antagonizing the integrin-dependent cell adhesion machinery, and that antibodies generally antagonizing this process might be of interest for exploring experimental synergies with erlotinib.
Besides colorectal cancer, lung cancers, head and neck cancers, and a number of other types of cancer respond to EGFR-targeting agents and/or irenotecan. However, because of differences in cell lineage, these cells will not express an identical complement of proteins as colorectal tumors, implying that their cell signaling/cell survival networks will be non-equivalent. Hence, a subset of the siRNAs that sensitize colorectal tumor cells to EGFR-targeting agents in colorectal cells may not be active in other tumor types, while additional sensitizing siRNAs may be detected in screens of these tumors. We will focus on lung cancer cell lines as a first counter-model to compare with A431 and HCT116 data, and essentially parallel the three Validation Steps outlined above. For the collection of validated hits from A431 and HCT116 screening, we will first use qRT-PCR to determine if the mRNAs are expressed in two erlotinib-sensitive and two K-Ras-mutated, erlotinib resistant lung cancer cell lines. We will then determine what percent of the expressed siRNAs are sensitizing in the lung cancer cell lines. If a significant percentage of A431/HCT116 hits maintain function in lung cancer cell lines, such hits are relevant for improved chemotherapy, particularly if they include siRNAs depleting proteins with existing clinical agents (as discussed in Example 5). If only a very limited number of hits are found to be functional, we will instead repeat the primary screens outlined above, to define the sensitization network of lung cancer tumors. We note this last experiment is of interest in its own right as an inquiry into the basic properties of alternative network construction.
Although preclinical and clinical analyses are empirically establishing useful synergies between erlotinib or EGFR-targeting antibodies and drugs targeting the mTOR pathway, such as rapamycin or temsirolimus (Costa, 2007; Jimeno, 2007; Buck, 2006, IGF Morgillo, 2006), and other high-value targets, it is currently largely unknown as to which EGFR-dependent signaling pathways contribute to the sensitization. Using the EGFR interactome library, we can rapidly establish this point, using a strategy similar to that outlined for primary screening above to look for a network of sensitizers to selected drugs in the A431 and HCT116 colorectal cancer cell lines. This screen should identify a number of the hits already identified as common sensitizers to erlotinib, panitumumab, and CPT11, but may also identify a sub-network (sensitization complementation group) of hits that is specific to inhibition by the new test drug.
The table below provides 16 genes and combinations of agents which should have efficacy for the treatment of the indicated cancer types. Anti-EGFr drugs (cetuximab, an anti-EGFR antibody; erlotinib, lapatinib or any tyrosine kinase inhibitor specific for the EGFR kinase) can be combined with either existing or potentially available inhibitors of the 16 targets presented. In general, cetuximab is used in colorectal, lung, head and neck (HNSCC). Erlotinib is used in lung, pancreas. Potentially, cetuximab or any other anti-EGFR antibody can be approved for lung cancer (NSCLC), and cancer of the pancreas. Lapatinib is preferred for combination treatment of breast, and ovarian cancers. The gene target activity may be inhibited using small interfering molecules, agents already known to inhibit their function, (e.g., commercially available and clinically safe phosphatase and kinase inhibitors or the small siRNAs interfering molecules described herein.
One of the most important uses of the information generated from this study will be as a guide to select patients likely to respond to EGFR-centered treatments. Patients with tumors expressing high levels of the mRNAs and proteins targeted by sensitization hits would prove to be resistant to therapy, while patients with low levels of these mRNAs and proteins might be excellent candidates for treatment with the synergistic combination described herein. Additionally, it will be more informative and better exploit the sensitization network that we have identified if we screen tumors for the expression of sets of sensitizing hits, rather than “cherrypicking” a small number of individual hits. Already, hits of clinical relevance have been identified. We will use these genes to generate a screening chip; alternatively, we can generate a Taqman primer set for qRT-PCR. We will then employ a set of at least 10 pre-treatment colorectal tumors from patients who responded to EGFR-targeted therapy, and a matching set of non-responder tumors, and we will systematically compare the expression of the sensitization panel.
As described above, we have developed a protein network centered on the highly validated target EGFR, and used siRNA screening to comparatively probe this network for proteins that regulate the effectiveness of both EGFR-targeted and chemotherapeutic agents. This approach identified sub-networks of proteins influencing resistance, with hits enriched among first order protein interactors of the network seeds. Extrapolation from the network structure led to the identification of synergy between EGFR antagonists and drugs targeting PRKC, STAT3, and AURKA, suggesting a direct path to clinical exploitation of study results. Such a focused approach has significant potential to enhance the future coherent design of combination therapies.
A robust network paradigm has critical implications for targeted cancer therapies, predicting that in cells treated with therapies inhibiting an oncogenic node, rescue signaling can be provided by modifying signaling output from any of a number of distinct proteins that are components of a web of interactions centered around the target of inhibition. This concept is reinforced by studies in model organisms demonstrating that quantitatively significant signal-modulating relationships commonly involve proteins that have closely linked functions. In this example, we describe additional regulators of resistance to EGFR-targeted therapies, which can be used to clinical advantage to overcome therapeutic resistance.
As described in Example 1, the A431 cervical adenocarcinoma cell line is highly dependent on EGFR pathway signaling. This cell line was reiteratively screened with the targeted siRNA library used in combination with DMSO (vehicle), or with EGFR-targeting small molecule and antibody inhibitors, or the non-specific cytotoxic agent camptothecin (CPT11) applied at IC25-IC35 concentrations. Primary hits were defined as siRNAs reducing negative control-normalized viability by at least 15% in the presence of a drug compared to DMSO (defined as the Sensitization Index (SI<0.85), with a false discovery rate (FDR)<20%. The distribution of primary hits was independent of the tendency of a siRNA to affect cell viability in the absence of drug treatment (
In analyzing the erlotinib-sensitizing hits in comparison to the overall properties of the 638-gene library, there was a highly significant enrichment for genes that were first order PPIs of the seeds, and were also nominated by pathway maps (
In additional experiments, we comparatively profiled the efficacy of the hit panel as sensitizers of erlotinib, cetuximab, and CPT11 across a set of cell lines, including A431, the colorectal adenocarcinoma cell lines HCT116, DLD-1, DKS-8, and LoVo, the head and neck squamous cell carcinoma cell line SCC61, and the pancreatic adenocarcinoma cell lines PANC-1 and MIA PaCa-2 (
No gene target sensitized to erlotinib in all tested cell lines. Considering only statistically significant thresholds (
Considering instead sensitization rank (
As the in vivo effects of inhibiting a selected target will reflect the cumulative sum of intrinsic effect on viability and sensitizing activity, we also established the baseline intrinsic activity of the validated siRNAs in reducing cell growth in DMSO-treated cells (
These findings supported the idea that a cogently designed network focused around a core cancer target such as EGFR would provide a rich source of genes that modulate resistance to EGFR pathway-targeted agents. In general, a greater effect was seen on the core viability of cell lines containing wt versus mutant Ras, although the stronger hits were typically active in both; in contrast, it was impossible to establish a meaningful correlation between sensitization profile and Ras mutational status, suggesting that sensitizing activity occurred downstream or independently from core Ras signaling outputs. We investigated the relative interactions of the stronger hits within the overall topology of the EGFR signaling network (
We directly tested the ability of a number of hits to directly modulate both basal and EGF-stimulated activation of the core pathway effectors MAPK1 and AKT (
A major goal of this work was to gain insights that could be rapidly translated to the clinic. Although the clinical use of RNAi is a topic of intense current research, small molecules and monoclonal antibodies remain the most broadly applicable therapy platforms. Further, given that most drugs target catalytic enzymes, whereas siRNAs typically reduce protein levels by no more than 80-90%, we hypothesized that combining small molecule inhibitors of siRNA-predicted catalytic hits with erlotinib might enhance sensitization phenotypes over those detected in initial screens. For some sensitizing hits, targeted small molecules exist, including Stattic (a small molecule inhibitor of STAT3 activation and dimerization, enzastaurin and Ro-318220 (both targeting the PRKC family, with members well-represented among the hits.
Stattic synergized with erlotinib in inhibiting the growth of both A431 and HCT116 cells (
The proteins of the consistently sensitizing BCAR1-SH3D2C-NEDD9 cluster have been linked previously to cell survival control in the context of integrin-mediated signaling cascades, suggesting this cluster is of particular interest for therapeutic exploitation. However, these proteins are not catalytic, and have not been targeted by existing small molecule agents. Given the results suggesting the enrichment of sensitizing genes among proteins closely linked to core hits, we hypothesized that small molecules targeting kinases closely linked to this cluster by physical interactions might similarly provide a rich source of synergizing agents for combination with erlotinib.
The small molecule Aurora-A inhibitors PHA-680632 (Soncini et al. (2006) Clin. Cancer Res. 12:4080) synergized strongly with erlotinib in both A431 and HCT116 cells (
We explored the signaling changes underlying the synergy. Treatment of cells with PHA-680632 alone did not inhibit EGFR expression, autophosphorylation, and activation, and had little effect on ERK1/2 or AKT phosphorylation in response to transient EGF stimulation (
To explore signaling consequences of co-inhibition of Aurora-A and EGFR in greater depth, we next undertook a more comprehensive phospho-proteomic analysis of 46 signaling proteins linked to cell proliferation and survival responses following cell treatment with erlotinib, PHA-680632, or both. Analysis of two independently performed screens (
As described in Example 11, another potential use of this data set is for the nomination of new biomarkers for selecting patient responsiveness. However, extensive analysis of the expression of siRNA targets in cell lines used for functional analysis (
A major goal of systems-level bioinformatics analyses is to nominate critical nodes to target in combination to enhance therapy in the clinic, with clear successes beginning to emerge from this information-driven strategy (Pritchard et al., (2009) Mol Cancer Ther. 8:2183). Separately, screening of siRNA libraries has emerged as a powerful approach to identify genes that can kill cancer cells, or sensitize them to cytotoxic agents. To date, such screening has typically employed either full genome screens, or screens of small libraries targeting limited groups of proteins, such as the kinome/phosphatome. Interestingly, a genome-wide screen to identify sensitizers to the microtubule-targeting agent paclitaxel identified a number of hits that clustered into coherent groups of genes associated with the proteasome or mitotic spindle (Whitehurst et al., (2007) Nature 446:815), which a priori had been linked to paclitaxel activity based on existing pathway knowledge.
In the current study, we employed bioinformatics design and direct screening, and found that many proteins influencing cellular resistance to EGFR-targeting agents clustered in connection-dense, highly interactive portions of the EGFR signaling network, thus supporting our core hypothesis that these characteristics would enrich for synthetic lethal interactions. These sensitizing protein clusters were useful for predicting the efficacy of combining protein-targeted drugs with EGFR-pathway signaling inhibitors, suggesting the potential of this approach for speeding the translation of results to the clinic. We believe this targeted approach has several advantages in comparison to a full genome screen. Beyond the obvious factors of convenience, speed, and cost, all hits arising from a targeted screen already have at least some defined functional relationships to the signaling pathway being probed, accelerating validation and mechanism testing. Further, the limited size of the library being probed allowed the use of more relaxed statistical criteria in nominating hits for validation than would be necessary in a full genome screen, and allowed us to repeat the primary screen multiple times: given the intrinsic noise in siRNA screening, these are important advantages. Finally, our observation that the single greatest source of enrichment for hits (
We have defined the network structure for EGFR-pathway specific and general drug resistance in several cell lines. Accordingly, the present invention provides a unique resource: a deeply probed, heavily annotated library with a linked live database that provides a “Rosetta Stone” for drug resistance studies. This work can be rapidly translated into improved therapy for cancer patients, as the information is used to design new Phase I trials. Indeed, new combinatorial approaches for the eradication of cancer cells is discloses as are efficacious agents for effecting the same.
)
indicates data missing or illegible when filed
While certain preferred embodiments of the present invention have been described and specifically exemplified above, it is not intended that the invention be limited to such embodiments. Various modifications may be made to the invention without departing from the scope and spirit thereof as set forth in the following claims.
This application is a continuation application of U.S. patent application Ser. No. 13/942,032, filed Jul. 15, 2013, now abandoned, which is a continuation application of U.S. patent application Ser. No. 12/777,112, filed May 10, 2010, now abandoned, which is a continuation-in-part of PCT/US08/83067, filed Nov. 10, 2008, which in turn claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application 60/986,964 filed Nov. 9, 2007, the entire contents of each being incorporated by reference herein as though set forth in full. Incorporated herein by reference in its entirety is the Sequence Listing being concurrently submitted via EFS-Web as a text file named SeqList.txt, created Jun. 20, 2016, and having a size of 230,641 bytes.
| Number | Date | Country | |
|---|---|---|---|
| 60986964 | Nov 2007 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13942032 | Jul 2013 | US |
| Child | 15171663 | US | |
| Parent | 12777112 | May 2010 | US |
| Child | 13942032 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US08/83067 | Nov 2008 | US |
| Child | 12777112 | US |
