Follow us on Twitter
twitter icon@FreshPatents

Browse patents:
Next
Prev

Predictors of patient response to treatment with egf receptor inhibitors




Title: Predictors of patient response to treatment with egf receptor inhibitors.
Abstract: 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. ...


Browse recent Genomic Health, Inc. patents


USPTO Applicaton #: #20120270228
Inventors: Joffre B. Baker, Drew Watson, Tara Maddala, Steven Shak, David J. Mauro, Shirin K. Ford


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

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

- Top of Page


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

- Top of Page


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

- Top of Page


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.




← Previous       Next →
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.

###


Browse recent Genomic Health, Inc. patents

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.
- - -

Results in 0.26362 seconds


Other interesting Freshpatents.com categories:
Amazon , Microsoft , Boeing , IBM , 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
-g2-0.3653

66.232.115.224
Browse patents:
Next
Prev

stats Patent Info
Application #
US 20120270228 A1
Publish Date
10/25/2012
Document #
File Date
12/31/1969
USPTO Class
Other USPTO Classes
International Class
/
Drawings
0


Gene Product

Follow us on Twitter
twitter icon@FreshPatents

Genomic Health, Inc.


Browse recent Genomic Health, Inc. patents





Browse patents:
Next
Prev
20121025|20120270228|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 |Genomic-Health-Inc
';