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Breast cancer prognostic markers: where are we now?

Breast cancer is a heterogeneous disease with diverse morphologies, molecular characteristics, clinical behavior, and response to therapeutics. Traditionally, prognostication in breast cancer relied on the clinicopathological parameters and individual molecular markers such as hormone receptor and human epidermal growth factor status (HER2), and Ki67. Despite their utility, these stratifies have significant limitations in predicting the recurrence risk. In the last decade, advances in gene expression profiling technologies have dramatically changed our understanding of the genomic and transcriptomic landscape of breast cancer. Most importantly, they have not only revealed the molecular heterogeneity of breast cancer but also contributed to development of gene signatures and associated improved ability to prognosticate behavior and responses to therapy. (1-9) Several ongoing studies are evaluating their prognostic/predictive utility and will influence the incorporation of these assays into routine clinical practice. Here we present a brief summary of traditional breast cancer prognostic factors and prognostic multigene classifiers used in current clinical decision making and their potential impact on future clinical management.

Tradutional prognostic factors

Histologically the majority of breast cancers (65% to 80%) belong to a single subtype, invasive ducal carcinoma of no special type. This significantly limits the use of type as a prognostic factor. Therefore, assessment of tumor behavior for any breast cancer has been based on three parameters: tumor size, lymph node status, and histological grade. Tumor size is a good prognostic marker for distant relapse in lymph node negative patients, although patients with small tumors (< 1 cm), left untreated, have approximately a 12% chance of recurrence. (11), (12) Nodal status, including the number of involved nodes, is predictive of both disease-free and overall survival. However, 30% of node-negative patients still may develop recurrences by 10 years.

Histological grade is a qualitative assessment of tumors based on the degree of differentiation (normal-like breast appearance). Low-grade tumors resemble normal breast tissues and have better prognosis. In contrast, high-grade tumors do not resemble normal tissues and have poor prognosis. However, grading is to some extent subjective. Comparative analyses have shown that agreement between different scorers is only modest (in the range of 59% to 65%, with greater degree of agreement for poorly differentiated tumors). Despite this limitation, grade remains a very strong prognostic factor and provides data comparable to molecular signatures.

Multiparametric tools have been developed to improve the predictive value of the above individual factors; these include TNM staging, Nottingham prognostic index (NPI) and Adjuvant! Online ( TNM staging comprises tumor size, nodal status, and presence or absence of metastases. The NP1 is computed on the basis of tumor size, lymph node stage, and histological grade. (13) Adjuvant! Online provides the assessment of probability of survival and benefit from specific therapies in patients with early breast cancer. (14) The model takes into account age, menopausal status, comorbidities, ER status, tumor grade tumor size, and number of involved axillary nodes. The prognostication ability of these tools depends heavily on anatomical markers such as size, grade, nodal involvement, and other biological characteristics discussed below.

Traditional molecular markers

Traditional molecular markers for early stage breast cancer include estrogen receptor (ER), progesterone receptor (PR) and HER2. (15) ER expression is strongly predictive of response to antiestrogenic therapy. ER positive tumors have a good 10-year prognosis. However, they have the potential to recur even after 10 to 20 years after initial diagnosis. This makes ER a weak prognostic factor. The evaluation of ER and PR has been part of standard of care for several decades using immunohistochemistry (IHC). One of the important shortcomings of IHC is its dependency on preanalytical variables such as time to fixation, method(s) used for tissue processing, and antigen retrieval. (16). (17) The reason for this is that these proteins are susceptible to digestion by tissue enzymes; improper fixation (inhibition of cellular enzymes) can thus lead to false negative results. More recently, ER and PR levels have also been assessed using quantitative reverse-transcri pulse polymerase chain reaction (qRT-PCR); there is a good agreement between the IHC and qRT-PCR assays. (18)

HER2 overexpression/amplification is a predictor of response to trastuzumab (monoclonal anti-HER2 antibody) therapy. (19) Fluorescence in situ hybridization (FISH) that measures HER2 gene amplification and IHC that can detect overexpress ion of HER2 are used to identify patients who are most likely to respond to therapy; both have their own advantages and limitations. (20) Although issues related to interlaboratory concordance still persist. there is a good agreement between these two methods. Accurate HER2 testing is essential since clinical benefit from trastuzumab seems to be restricted to patients with HER2-positive tumors. However, almost 50% of HER2-positive patients are unresponsive to trastuzumab. Newer agents to treat these patients are in advanced stages of development.

Proliferation rate of tumors has been shown to be a good predictor of aggressiveness of the tumor. Although this can be measured in multiple ways, IHC assay for Ki67 is often used for this purpose. Recent (St Gallen) guidelines emphasize the use of this assay in ER positive tumors. As per these guidelines, patients with tumors having high Ki67 expression (>14%) should be offered systemic chemotherapy. (21)

More recently, a combination of these four markers (ER. PR, HER2 and Ki67) has been shown to have a strong prognostic impact that is similar to that of gene expression assays described below. (22)

Other single gene/protein molecular markers were also assessed, but few of them met the criteria for the use of tumor markers in breast cancer. (15) Among these markers, the serine protease urokinase-type plasminogen activator (uPA) and its inhibitor (plasminogen activator inhibitor type-1;PAI-1) have reached the Level of Evidence I and been adjudged by the American Society for Clinical Oncology (ASCO) as suitable for clinical use in patients with newly diagnosed, node negative breast cancer using an ELISA assay. These assays, although quantitative, are limited to the use of fresh or frozen tissues. In the case of uPA/PAI-I, a minimum of 300 mg of fresh or frozen breast cancer tissue is required. Given the limited availability of frozen tissues in the clinical setting, this raises a question about its utility in routine practice.

Molecular classification intrinsic suhtypes

Gene expression profiling studies have classified breast cancer into five molecular subtypes; luminal A, luminal B, HER2, basal-like, and normal-like (intrinsic classification). (5) Luminal A ([ER.sup.+]/[Ki67.sup.1ow]) cancers are shown to have the best prognosis; HER2 and basal cancers (also sometimes referred to as triple negative tumors) have the worst prognosis, and the prognosis for luminal B ([ER.sup.+]/[Ki67.sup.high]) cancers is in between. This intrinsic classification is more focused on tumor subtypes than on prognosis, but a prognostic assay has been recently developed (see PAM50 below). More recent studies using gene expression and copy number analyses have identified at least 10 subtypes of breast cancer. (9) The incorporation of gene expression profiling (GEP) studies into a clinical assay is challenging, since analysis of a large number of markers is often required. In addition, these tumor subgroups do not have morphological correlates. The microarray platforms used for signature discovery are unstable for clinical use; therefore a qRT-PCR platform is often used.

Prognostic multigene classifiers in routine clinical practice

Among the prognostic multigene classifiers, the top three assays in current clinical decision making are the .21-gene assay (Oncotype Dx), 70-gene signature of van' t Veer, and the clinically updated version of intrinsic subtypes, PAM50 assay. (2), (23), (24) The Oncotype Dx assay assesses 21 genes (16 test genes and 5 controls) and provides a continuous score (Recurrence Score) that predicts the risk of recurrence. This test is in routine clinical use for women of all ages with newly diagnosed ER-positive stage I or II breast cancer. The 70-gene signature (Mammaprint) assay, originally based on analysis of 70 genes from fresh frozen tissue, has now been adapted to the analysis of paraffin tissues with a smaller number of genes being assessed. It is in use to predict distant disease-free survival and overall survival for lymph node-negative patients. The utility of both of these assays is being assessed in prospective clinical trials, TAILORx and MINDACT respectively. The PAM50 assay is based on the analysis of 50 genes that determine intrinsic classification and outcomes. A clinically updated version is available based on qRT-PCR and Nanostring[R] technology and measures a risk of relapse (ROR) prognostic of relapse-free survival for patients who have node-negative tumors and have not received adjuvant systemic therapy.

These assays involve extracting messenger RNAs from archival paraffin sections followed by a quantitative analysis. The ability to normalize the data with reference (control) genes is a major advantage; this results in significant improvement in the stability of the assays. They categorize patients into groups primarily based on prognosis, although they may also have utility in predicting likelihood of response to chemo or endocrine therapies.. Thus patients who are going to get maximal benefit from hormonal therapy (and minimal from chemotherapy) can be spared taking toxic chemotherapies for little or no gain. The assays can be integrated with pathological and clinical information to increase their performance.

The future of breast cancer prognostication

Prognostication in breast cancer is a multifaceted process and can involve the assessment of anatomical extent of the disease, and levels of expression of proteins (by IHC), DNA (FISH) and RNA (gene signatures). Although gene expression profiling studies have contributed significantly to the understanding of the multifaceted nature of breast cancer, their incorporation into clinical decision making is a slow process and limited in various aspects. Existing GEPs are dependent on proliferation and do not take into account racial and genetic differences. The genes within the signatures often do not have biological relevance and cannot be used for targeted therapeutics. They also have not shown significant superiority over classical/traditional prognostic tools. There is a need for a comprehensive approach that will combine the different modalities and provide individualized prognostic information. This is particularly true for ER-negative tumors where there is a dire need for prognostic indicators. The ability to perform massively parallel (next generation) sequencing will undoubtedly affect our understanding of breast cancer and methods by which we assess prognostic and predictive markers: These will need to be clinically validated in the appropriate settings.




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By Yesim Gokmen-Polar, PhD, and Sunil Badve, MD, FRCPath

Yesim Gokmen-Polar, PhD, is Assistant Scientist of Medicine at Indiana University School of Medicine. Her research expertise focuses on the identification of molecular targets that play an important role in cancer resistance and recurrence, and development of novel treatment strategies that will eventually help design effective clinical trials. Sunil Badve is Professor in the Departments of Pathology and Laboratory Medicine and Internal Medicine and is the Director of Translational Genomics Core at the Indiana University Simon Cancer Center. Dr. Badve's main research and clinical expertise is in breast cancer and thymic neoplasms.
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Title Annotation:Clinical issues
Author:Gokmen-Polar, Yesim; Badve, Sunil
Publication:Medical Laboratory Observer
Article Type:Report
Geographic Code:1USA
Date:Sep 1, 2012
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