FreshPatents.com Logo
stats FreshPatents Stats
9 views for this patent on FreshPatents.com
2014: 4 views
2013: 4 views
2012: 1 views
Updated: October 26 2014
newTOP 200 Companies filing patents this week


    Free Services  

  • MONITOR KEYWORDS
  • Enter keywords & we'll notify you when a new patent matches your request (weekly update).

  • ORGANIZER
  • Save & organize patents so you can view them later.

  • RSS rss
  • Create custom RSS feeds. Track keywords without receiving email.

  • ARCHIVE
  • View the last few months of your Keyword emails.

  • COMPANY DIRECTORY
  • Patents sorted by company.

Follow us on Twitter
twitter icon@FreshPatents

Predictors of patient response to treatment with egf receptor inhibitors

last patentdownload pdfdownload imgimage previewnext patent


20120270228 patent thumbnailZoom

Predictors of patient response to treatment with egf receptor inhibitors


The present invention provides methods and compositions to facilitate determining whether an EGFR-expressing cancer in an individual is an EGFR inhibitor-responsive cancer, as well as methods for determining the likelihood that a patient having an EGFR-expressing cancer will exhibit a beneficial response to an EGFR inhibitor therapy. The methods generally involve determining a normalized expression level of a gene product that correlates with EGFR inhibitor responsiveness.
Related Terms: Gene Product

Browse recent Genomic Health, Inc. patents - Redwood City, CA, US
Inventors: Joffre B. Baker, Drew Watson, Tara Maddala, Steven Shak, David J. Mauro, Shirin K. Ford
USPTO Applicaton #: #20120270228 - Class: 435 612 (USPTO) - 10/25/12 - Class 435 


view organizer monitor keywords


The Patent Description & Claims data below is from USPTO Patent Application 20120270228, Predictors of patient response to treatment with egf receptor inhibitors.

last patentpdficondownload pdfimage previewnext patent

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority benefit of U.S. Provisional Application Ser. No. 61/127,816 filed on May 14, 2008, the entire disclosure of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure provides genes and gene sets, the expression levels of which are useful for predicting response of cancer patients to an epidermal growth factor receptor (EGFR) inhibitor therapy.

INTRODUCTION

Epidermal growth factor receptor (EGFR), also known as ERBB and HER1, is a gene that encodes a receptor protein found on the surface of some cells and to which an epidermal growth factor binds, causing the cell to divide. EGFR is a member of the HER family of receptors, and the dimerization of EGFR and v-erb-b2 erythroblastic leukemia viral oncogene homolog 2 (ERBB2), also known as HER2, is an important stimulus for breast cancer growth in ERBB2-positive breast cancers. Expression levels of EGFR are amplified in a subset of breast cancers and the resulting overexpression of the receptor contributes to breast cancer etiology.

Because cancer is characterized by rapidly proliferating cells, most cancer drugs attack rapidly dividing cells indiscriminately and, as a result, often exhibited a high degree of toxicity. In most instances, little is known regarding the mechanisms of action of these cytotoxic drugs. Targeted EGFR inhibitors, e.g., have demonstrated activity against a number of cancer types. Anti-EGFR monoclonal antibodies have shown antitumor activity in advanced colorectal cancer, in squamous cell carcinomas of the head and neck, non-small-cell lung cancer (NSCLC) and renal cell carcinomas (Baselga J and Arteaga C L (2005) J Clin Oncol 2445-2459. Clinical trials are ongoing in early stage cancer and in other tumor types and are expected to show activity in cancer types where EGFR is expressed and where EGFR ligands promote tumor growth and progression.

Given the importance in selection of therapy, there has been a push to develop drugs in concert with companion diagnostic tests capable of identifying responsive patients. Such diagnostics would address the situations where some patients who might benefit from treatment do not receive the drug, and other patients who are unlikely to benefit are unnecessarily exposed to toxic side effects, incur unnecessary expense, and/or experience a delay in being treated with alternative drugs that might prove more effective.

SUMMARY

The present disclosure provides methods and compositions to facilitate determining whether an EGFR-expressing cancer in an individual is an EGFR inhibitor-responsive cancer, as well as methods for determining the likelihood that a patient having an EGFR-expressing cancer will exhibit a beneficial response to an EGFR inhibitor therapy. The methods generally involve determining a normalized expression level of a gene product that correlates with EGFR inhibitor responsiveness.

The disclosure provides methods for predicting the likelihood that a human patient with an EGFR -expressing cancer will exhibit a beneficial response to an EGFR inhibitor cancer therapy based on expression levels of one or more response indicator genes in a biological sample obtained from a tumor in the patient. Specifically, the method entails measuring an expression level of at least one response indicator gene, or its expression product. The response indicator gene is one or more selected from a group consisting of ATP5E, TITF1, CLTC, BRCA1, AREG, PTP4A3, EREG, VAV3, SATB2, CEACAM6, EGFR, CHN2, FGFR3, C13orf18, QPRT, AMACR1, CKMT2, ID1, SORBS1, SLC26A3, ErbB3, DUSP6, VDAC2, ANXA2P2, SERPINB1, NT5E, GPC3, DUSP4, PHLDA1, K-ras, DR5, VIL2, LAMC2, SFN, ANXA1, EPHA2, P14ARF, CA9, KRT17, p14ARF, Maspin, PLAUR, LAMAS, and GCNT3. The expression level is normalized, and the normalized expression level is used to determine or predict likelihood of beneficial response, wherein increased normalized expression levels of one or more of the following are measured: ATP5E, TITF1, CLTC, BRCA1, AREG, PTP4A3, EREG, VAV3, SATB2, CEACAM6, EGFR, CHN2, FGFR3, C13orf18, QPRT, AMACR1, CKMT2, ID1, SORBS1, SLC26A3, and ErbB3 are positively correlated with the likelihood that the patient will exhibit a beneficial response to the EGFR inhibitor cancer therapy; and increased normalized expression levels of DUSP6, VDAC2, ANXA2P2, SERPINB1, NT5E, GPC3, DUSP4, PHLDA1, K-ras, DR5, VIL2, LAMC2, SFN, ANXA1, EPHA2, P14ARF, CA9, KRT17, p14ARF, Maspin, PLAUR, LAMA3, and GCNT3 are negatively correlated with the likelihood that the patient will exhibit a beneficial response to the EGFR inhibitor cancer therapy. A report is generated based on the determined likelihood of response.

The disclosure provides methods for predicting a likelihood that a human patient with a KRAS-negative, EGFR-expressing cancer will exhibit a beneficial response to an EGFR inhibitor cancer therapy based on expression levels of one or more response indicator genes in a biological sample obtained from a tumor in the patient. Specifically, the method entails measuring an expression level of at least one response indicator gene, or its expression product, selected from a group consisting of EGF, ADAM17, PTP4A3, ADAM15, QPRT, SATB2, RASSF1, VAV3, CEACAM6, EREG, AREG, TITF1, SORBS1, C13orf18, CKMT2, BTC, ATP5E, B.Catenin, CCNE1, EGFR, Bclx, BRCA1, CDC25B, CHN2, ID1, SLC26A3, VDAC2, SERPINB1, PHLDA1, ANXA2P2, KRT17, EPHA2, DUSP4, CGA, CA9, Maspin, NEDD8, DUSP6, GPC3, NT5E, VIL2, and P14ARF. The expression level is normalized, and the normalized expression level is used to determine or predict likelihood of beneficial response, wherein increased normalized expression levels of one or more of the following are measured: EGF, ADAM17, PTP4A3, ADAM15, QPRT, SATB2, RASSF1, VAV3, CEACAM6, EREG, AREG, TITF1, SORBS1, C13orf18, CKMT2, BTC, ATP5E, B.Catenin, CCNE1, EGFR, Bclx, BRCA1, CDC25B, CHN2, ID1, and SLC26A3 are positively correlated with the likelihood that the patient will exhibit a beneficial response to the EGFR inhibitor cancer therapy; and increased normalized expression levels of VDAC2, SERPINB1, PHLDA1, ANXA2P2, KRT17, EPHA2, DUSP4, CGA, CA9, Maspin, NEDD8, DUSP6, GPC3, NT5E, VIL2, and P14ARF are negatively correlated with the likelihood that the patient will exhibit a beneficial response to the EGFR inhibitor cancer therapy.

The methods of the present disclosure contemplate using a normalized expression level to determine or predict likelihood of beneficial response, based on normalized expression level(s) for single response indicator genes and/or multi-gene sets. Exemplary multi-gene sets are disclosed. The expression values are normalized relative to an expression level of one or more reference genes. The disclosure provides for measurement of normalized expression level(s) of at least one response indicator gene product. For all aspects of the present disclosure, the methods may further include determining the expression levels of at least two of said genes, or their expression products. It is further contemplated that the methods of the present disclosure may further include determining the expression levels of at least three of said genes, or their expression products. It is contemplated that the methods of the present disclosure may further include determining the expression levels of at least four of said genes, or their expression products. It is contemplated that the methods of the present disclosure may further include determining the expression levels of at least five of said genes, or their expression products. It is contemplated that the methods of the present disclosure may further include determining the expression levels of at least six of said genes, or their expression products. It is contemplated that the methods of the present disclosure may further include determining the expression levels of at least seven of said genes, or their expression products. It is contemplated that the methods of the present disclosure may further include determining the expression levels of at least eight of said genes, or their expression products. It is contemplated that the methods of the present disclosure may further include determining the expression levels of at least nine of said genes, or their expression products. The methods may involve determination of the expression levels of at least ten (10) or at least fifteen (15) of the genes listed above or their products.

A normalized expression level(s), generated as discussed above, is used to determine or predict likelihood of beneficial response, The normalized expression level(s) is indicative of the likelihood that the patient will exhibit a beneficial response to an EGFR inhibitor therapy, such as an EGFR-specific antibody or small molecule. A likelihood score (e.g., a score predicting likelihood of beneficial response to EGFR inhibitor treatment) can be calculated based on the normalized expression level(s). A score may be calculated using weighted values based on a normalized expression level of a response indicator gene and its contribution to response to EGFR inhibitor cancer therapy.

In addition, the disclosure provides arrays for carrying out the methods disclose herein, or for analyzing whether a mathematical combination of the normalized expression levels of any combination of the response indicator genes is more indicative of a likelihood that a patient will respond to treatment with an EGFR inhibitor. The arrays may include, for example, probes that hybridize to a nucleic acid sequence in a response indicator genes or an activating KRAS mutation.

Determining the expression level of one or more genes may be accomplished by, for example, a method of gene expression profiling. The method of gene expression profiling may be, for example, a PCR-based method. The expression level of said genes can be determined, for example, by RT-PCR (reverse transcriptase PCR), quantitative RT-PCR (qRT-PCR), or other PCR-based methods, immunohistochemistry, proteomics techniques, an array-based method, or any other methods known in the art or their combination. In one aspect the RNA transcripts are fragmented.

Detection of an RNA transcript of a response indicator gene may be accomplished by assaying for an exon-based sequence or an intron-based sequence, the expression of which correlates with the expression of a corresponding exon sequence.

In an exemplary embodiment, the assay for the measurement of response indicator genes, or their gene products, and/or activating KRAS mutations is provided in the form of a kit or kits.

The expression levels of the genes may be normalized relative to the expression levels of one or more reference genes, or their expression products.

The tumor sample may be, for example, a tissue sample containing cancer cells, or portion(s) of cancer cells, where the tissue can be fixed, paraffin-embedded or fresh or frozen tissue. For example, the tissue may be from a biopsy (fine needle, core or other types of biopsy) or obtained by fine needle aspiration, or by obtaining body fluid containing a cancer cell, e.g. urine, blood, etc.

For all aspects of the methods of the present disclosure, it is contemplated that for every increment of an increase in the level of one or more genes or their expression products, the patient is identified to show an incremental increase in clinical outcome.

The determination of expression levels may occur more than one time in the practice of the methods disclosed herein.

The methods may further include the step of creating a report based on the determined or predicted likelihood of beneficial response. In another aspect the present disclosure provides reports for a patient containing a summary of the expression levels of the one or more response indicator genes, or their expression products, in a tumor sample obtained from said patient. In one aspect the report is in electronic form.

In one embodiment, the EGFR inhibitor is an antibody specific for EGFR. In another, the EGFR inhibitor is a small molecule, for example an EGFR-selective tyrosine kinase inhibitor.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the sequences of probes and primers used to assay each gene identified in the studies.

FIG. 2 shows the sequences of amplicons expected from the use of the probes and primers shown in FIG. 1.

FIGS. 3A-10D provide sets of graphs showing probability curves. Each graph contains three lines—a center dark line and two lighter lines above and below the dark centerline. Each graph has a center dark line representing the model-predicted relationship between normalized gene expression on the x-axis and likelihood of beneficial response to an EGFR inhibitor on the y-axis (predicted probability curve). The lighter grey lines above and below the black line represent the 95% Confidence Interval for the predicted probability, i.e. at a particular expression value for the gene, there is a 95% probability that the upper and lower grey lines include the that the actual probability of response. Confidence Intervals for the probability curves were calculated using the normal approximation method. The name of the gene analyzed is indicated below each graph by the text preceding the period; for example, “AREG.2” refers to AREG. Each of FIGS. 3A-10D are described in more detail below.

FIGS. 3A-3D show probability curves that correspond to the data reported in Table 1A for analysis of ORR in all patients (K-ras negative and K-ras positive); Odds Ratio>1.

FIGS. 4A-4F show probability curves that correspond to the data reported in Table 1B for analysis of ORR in all patients (K-ras negative and K-ras positive); Odds Ratio<1.

FIGS. 5A-5E show probability curves that correspond to the data reported in Table 2A for analysis of DC in all patients (K-ras negative and K-ras positive); Odds Ratio>1.

FIGS. 6A-6F shows probability curves that correspond to the data reported in Table 2B for analysis of DC in all patients (K-ras negative and K-ras positive); Odds Ratio<1.

FIGS. 7A-7C show probability curves that correspond to the data reported in Table 6A for analysis of ORR in K-ras negative patients; Odds Ratio>1.

FIGS. 8A-8C show probability curves that correspond to the data reported in Table 6B for analysis of ORR in K-ras negative patients; Odds Ratio<1.

FIGS. 9A-9E show probability curves that correspond to the data reported in Table 7A for analysis of DC in K-ras negative patients; Odds Ratio>1.

FIGS. 10A-10D show probability curves that correspond to the data reported in Table 7B for analysis of DC in K-ras negative patients; Odds Ratio<1.

Each of Tables 1A, 1B, 2A, 2B, 6A, 6B, 7A and 7B provides an Odds Ratio for each response indicator and gene listed in the table. The Odds Ratio reported for a gene is related to the overall slope of the probability curve shown in the corresponding Figure and is a measure of the change in the model-predicted probability of response for every unit change in normalized gene expression. As shown, genes with stronger odds ratios (further from 1) in the tables are depicted with steeper slopes in the figures.

DETAILED DESCRIPTION

Definitions

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), and March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992), provide one skilled in the art with a general guide to many of the terms used in the present application.

One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials described. For purposes of the present invention, the following terms are defined below.

The terms “k-ras” and “KRAS” as used herein (including in tables and figures) are used interchangeably and refer to the KRAS gene identified as of the date of this filing in the NCBI Entrez Gene database as Accession No. NM—004985.3 (Entrez Gene database, NCBI), and/or its expression products.

As used herein, the term “KRAS status”, refers to whether a patient\'s cancer is negative for an activating KRAS mutation (KRAS-negative) or positive for an activating KRAS mutation (KRAS-positive).

As used herein, the term “activating KRAS mutation” refers to a mutation in a k-ras gene that results in constitutive activation of a protein encoded by k-ras, i.e. the k-ras protein activates molecules downstream in its signaling pathway in the absence of receptor bound ligand. As an example, the k-ras protein might activate downstream signaling in the absence of EGF, amphiregulin, or epiregulin binding to EGFR.

As used herein, the term “EGFR-expressing cancer” refers to a cancer tumor with cells that express a cell surface epidermal growth factor receptor (EGFR) polypeptide.

As used herein, the term “epidermal growth factor receptor” (“EGFR”) refers to a gene that encodes a membrane polypeptide that binds, and is thereby activated by, epidermal growth factor (EGF). EGFR is also known in the literature as ERBB, ERBB 1 and HER1. An exemplary EGFR is the human epidermal growth factor receptor (see Ullrich et al. (1984) Nature 309:418-425; Genbank accession number NP—005219.2). Binding of an EGF ligand activates the EGFR (e.g. resulting in activation of intracellular mitogenic signaling, autophosphorylation of EGFR). One of skill in the art will appreciate that other ligands, in addition to EGF, can bind to and activate the EGFR. Examples of such ligands include, but are not limited to, amphiregulin, epiregulin, TGF-α, betacellulin, and heparin-binding EGF (HB-EGF) (Strawn and Shawver (1998) Exp. Opin. Invest. Drugs 7(4)553-573, and “The Protein Kinase Facts Book: Protein Tyrosine Kinases” (1995) Hardie, et al. (eds.), Academic Press, NY, N.Y.). See also, Oda et al. ((2005) Molec. Systems Biol. 1:2005.0010; and Moulder et al. ((2001) Cancer Res. 61:8887.

As used herein, an “EGFR gene” refers to a nucleic acid that encodes an EGFR gene product, e.g., an EGFR mRNA, an EGFR polypeptide, and the like.

As used herein, “EGFR inhibitor” refers to any agent capable of directly or indirectly inhibiting activation of an EGFR. EGFR inhibitors include agents that bind to an EGFR and inhibit its activation. EGFR inhibitors include antibodies that bind to an EGFR and inhibit activation of the EGFR; as well as small molecule tyrosine kinase inhibitors that inhibit activation of an EGFR. Antibodies to EGFR include IgG; IgM; IgA; antibody fragments that retain EGFR binding capability, e.g., Fv, Fab, F(ab)2, single-chain antibodies, and the like; chimeric antibodies; etc. Small molecule tyrosine kinase inhibitors of EGFR include EGFR-selective tyrosine kinase inhibitors. Small molecule tyrosine kinase inhibitors of EGFR can have a molecular weight in a range of from about 50 Da to about 10,000 Da.

The term “tumor,” as used herein, refers to any neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.

The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized in part by unregulated cell growth. Examples of cancer include, but are not limited to, colorectal cancer, breast cancer, ovarian cancer, lung cancer, prostate cancer, hepatocellular cancer, gastric cancer, pancreatic cancer, cervical cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, brain cancer, non-small cell lung cancer, squamous cell cancer of the head and neck, endometrial cancer, multiple myeloma, rectal cancer, and esophageal cancer. In an exemplary embodiment, the cancer is colorectal cancer.



Download full PDF for full patent description/claims.

Advertise on FreshPatents.com - Rates & Info


You can also Monitor Keywords and Search for tracking patents relating to this Predictors of patient response to treatment with egf receptor inhibitors patent application.
###
monitor keywords



Keyword Monitor How KEYWORD MONITOR works... a FREE service from FreshPatents
1. Sign up (takes 30 seconds). 2. Fill in the keywords to be monitored.
3. Each week you receive an email with patent applications related to your keywords.  
Start now! - Receive info on patent apps like Predictors of patient response to treatment with egf receptor inhibitors or other areas of interest.
###


Previous Patent Application:
Oligonucleotide sequences that identify species of animal
Next Patent Application:
Device for detection of analytes and uses thereof
Industry Class:
Chemistry: molecular biology and microbiology
Thank you for viewing the Predictors of patient response to treatment with egf receptor inhibitors patent info.
- - - Apple patents, Boeing patents, Google patents, IBM patents, Jabil patents, Coca Cola patents, Motorola patents

Results in 1.10238 seconds


Other interesting Freshpatents.com categories:
Amazon , Microsoft , IBM , Boeing Facebook

###

Data source: patent applications published in the public domain by the United States Patent and Trademark Office (USPTO). Information published here is for research/educational purposes only. FreshPatents is not affiliated with the USPTO, assignee companies, inventors, law firms or other assignees. Patent applications, documents and images may contain trademarks of the respective companies/authors. FreshPatents is not responsible for the accuracy, validity or otherwise contents of these public document patent application filings. When possible a complete PDF is provided, however, in some cases the presented document/images is an abstract or sampling of the full patent application for display purposes. FreshPatents.com Terms/Support
-g2-0.2982
     SHARE
  
           


stats Patent Info
Application #
US 20120270228 A1
Publish Date
10/25/2012
Document #
File Date
11/01/2014
USPTO Class
Other USPTO Classes
International Class
/
Drawings
0


Gene Product


Follow us on Twitter
twitter icon@FreshPatents