Gene expression profiles to predict breast cancer outcomes

a gene expression and breast cancer technology, applied in the field of gene expression profiles to predict breast cancer outcomes, can solve the problems of poor ten-year outcome of approximately 20% of women diagnosed with early-stage breast cancer, disease recurrence, metastasis or death, etc., and achieve the effect of accurately predicting the intrinsic subtype of a subject and evaluating the prognosis and treatmen

Inactive Publication Date: 2016-06-02
THE UNIV OF NORTH CAROLINA AT CHAPEL HILL +4
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0006]Methods for classifying and for evaluating prognosis and treatment of a subject with breast cancer are provided. The methods include prediction of breast cancer subtype using a supervised algorithm trained to stratify subjects on the basis of breast cancer intrinsic subtype. The prediction model is based on the gene expression profile of the intrinsic genes listed in Table 1. In some embodiments, the algorithm is a nearest centroid algorithm, similar to the Prediction Analysis of Microarray (PAM) algorithm. The algorithm can be trained based on data obtained from the gene expression profiles deposited as accession number GSE10886 in the National Center for Biotechnology Information Gene Expression Omnibus. This prediction model, herein referred to as the PAM50 classification model, can be used to accurately predict the intrinsic subtype of a subject diagnosed with or suspected of having breast cancer.

Problems solved by technology

Despite these advances, approximately 20% of women diagnosed with early-stage breast cancer have a poor ten-year outcome and will suffer disease recurrence, metastasis or death within this time period.

Method used

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  • Gene expression profiles to predict breast cancer outcomes
  • Gene expression profiles to predict breast cancer outcomes
  • Gene expression profiles to predict breast cancer outcomes

Examples

Experimental program
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example 1

Methods

Samples and Clinical Data:

[0081]Patient cohorts for trailing and test sets consisted of samples with data already in the public domain (Loi et al. (2007) J. Clin. Oncol. 25:1239-1246; va de Vijver et al. (2002) N Engl J Med 247:1999-2009; Wang et al (2005) Lancet 365:671-679; Ishvina et al. (2006) Cancer Res 66:10292-10301; and Hess et al (2006) J Clin Oncol 24:4236-4244, each of which is incorporated by reference in its entirety) and fresh frozen and formalin-fixed paraffin-embedded (FFPE) tissues collected under institutional review board-approved protocols at the respective institutions.

[0082]A training set of 189 breast tumor samples and 29 normal samples was procured as fresh frozen and FFPE tissues under approved IRB protocols at the University of North Carolina at Chapel Hill, The University of Utah, Thomas Jefferson University, and Washington University. The training set, which was gene expression profiled by microarray and qRT-PCR, had a median follow-up of 49 months...

example 2

Introduction and Background Data

[0132]This technology also covers the use of the PAM50-based intrinsic subtype classifier as a predictive and prognostic signature in the neoadjuvant endocrine therapy setting. Postmenopausal patients with Stage 2 and 3 ER and / or PgR positive breast cancer can be treated with an endocrine agent, typically an aromatase inhibitor or tamoxifen, before surgery to improve clinical outcomes, i.e., to promote the use of breast conserving surgery or to improve operability in the setting of a tumor that has invaded into the tissues surrounding the breast. A predictive test to increase the confidence that an individual patient will respond to neoadjuvant endocrine therapy is a significant advance.

Summary

[0133]The PAM50 based intrinsic subtype and proliferation-weighted risk score, when applied to samples from ER+breast cancers harvested after initiating treatment with an endocrine agent, can be used to predict response to neoadjuvant endocrine therapy and deter...

example 3

[0140]A risk of relapse analysis was performed on the samples described in Example 1, except the normal-like class was removed from the model. The normal-like class was represented using true “normals” from reduction mammoplasty or grossly uninvolved tissue. Thus, this class has been removed from the all outcome analyses and this classification is considered as a quality-control measure. Methods not described below are identical to the methods described in Example 1.

Methods

Prognostic and Predictive Models Using Clinical and Molecular Subtype Data:

[0141]Univariate and multivariate analyses were used to determine the significance of the intrinsic substypes (LumA, LumB, HER2-enriched, and basal-like) in untreated patients and in patients receiving neoadjuvant chemotherapy. For prognosis, subtypes were compared with standard clinical variables (T, N, ER status, and histological grade), with time to relapse (i.e., any event) as the end point. Subtypes were compared with grade and molecul...

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Abstract

Methods for classifying and for evaluating the prognosis of a subject having breast cancer are provided. The methods include prediction of breast cancer subtype using a supervised algorithm trained to stratify subjects on the basis of breast cancer intrinsic subtype. The prediction model is based on the gene expression profile of the intrinsic genes listed in Table 1. This prediction model can be used to accurately predict the intrinsic subtype of a subject diagnosed with or suspected of having breast cancer. Further provided are compositions and methods for predicting outcome or response to therapy of a subject diagnosed with or suspected of having breast cancer. These methods are useful for guiding or determining treatment options for a subject afflicted with breast cancer. Methods of the invention further include means for evaluating gene expression profiles, including microarrays and quantitative polymerase chain reaction assays, as well as kits comprising reagents for practicing the methods of the invention.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application is a divisional of U.S. patent application Ser. No. 12 / 995,450, filed Nov. 30, 2010. U.S. Ser. No. 12 / 995,450 is a National Stage application, filed under 35 U.S.C. §371 of International Application No. PCT / US2009 / 045820, filed Jun. 1, 2009, which claims priority under 35 U.S.C. §119 to U.S. Provisional Application Ser. No. 61 / 057,508, filed May 30, 2008. The contents of the aforementioned applications are incorporated herein by reference in their entireties.SEQUENCE LISTING[0002]The instant application contains a Sequence Listing which has been submitted in ASCII format via EFS-Web and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Sep. 28, 2015, is named NATE-702D01US_ST25.txt and is 20,204 bytes in size.FIELD OF THE INVENTION[0003]The present invention relates to methods for classifying breast cancer specimens into subtypes and for evaluating prognosis and response to therapy for patie...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): C12Q1/68G16B25/10G16B40/20
CPCC12Q1/6886C12Q2600/106C12Q2600/158C12Q2600/112G16B25/00G16B40/00G16B40/20G16B25/10
Inventor PEROU, CHARLES M.PARKER, JOEL S.MARRON, JAMES S.NOBEL, ANDREWBERNARD, PHILIP S.ELLIS, MATTHEWMARDIS, ELAINENIELSEN, TORSTEN O.CHEANG, MAGGIE
Owner THE UNIV OF NORTH CAROLINA AT CHAPEL HILL
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