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Upon completion of these articles, the reader will be able to:
1. Identify traditional prognostic factors for breast cancer.
2. Describe the use of molecular markers for classification of breast tumors.
3. Describe the use of multigene classifiers in the practice of breast cancer identification and prognosis.
The complexities of breast cancer in terms of disease heterogeneity associated with diverse morphologies, molecular characteristics, clinical behavior, and response to therapeutics are well documented. Prognostication in breast cancer has relied on the clinicopathological parameters such as age and tumor grade, and individual molecular markers such as hormone receptor and human epidermal growth factor status (HER2), and Ki67.
The limitations of these markers in predicting risk of recurrence has led to the use of mRNA- and DNA-based markers. Technological advances now permit large-scale analysis of the genetic makeup of the tumors and permit understanding of the genomic and transcriptomic landscape of breast cancer. These studies have contributed to the development of gene signatures to prognosticate behavior and responses to therapy.1-9 In addition, newer studies have focused on the presence of specific mutations in cancers and whether these can be targeted for prognostic or therapeutic purposes. Mutational analysis has become a common practice in clinical labs. Ongoing studies are evaluating their prognostic/predictive as well as therapeutic utility; these will determine their likelihood of routine use in patient care.
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. We further provide a brief glimpse into the landscape of mutational analysis that is likely to be part of large clinical laboratories.
Traditional prognostic factors
The American Joint Commission on Cancer stages breast cancer based on the assessment of three parameters: tumor size (T), lymph node status (N), and presence of distant metastases (M). The latter obviously is associated with poor outcomes. 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 percent chance of recurrence.10,11 Nodal status, including the number of involved nodes, is predictive of both disease-free and overall survival. However, 30 percent of node-negative patients still may develop recurrences by 10 years.
The World Health Organization classification of breast cancer recognizes at least 15 different types of cancers based on histological appearance of the tumors. However, the vast majority of tumors are classified as invasive ductal carcinoma of no special type; the other subtypes are rare, individually contributing to one percent to 10 percent of the tumors, limiting the value of tumor type as a prognostic factor.12 Histological (SBR) grade is a subjective assessment of tumors based on the degree of differentiation (normal-like breast appearance) and consists of a semi-quantitative scoring of tubule formation, nuclear pleomorphism, and mitotic activity. Low-grade tumors have better prognosis as compared to high-grade tumors. Studies analyzing the concordance between different observers have shown that agreement between different scorers is only modest (in the range of 59 percent to 65 percent). Despite this limitation, grade remains a very strong prognostic factor and provides data comparable to molecular signatures. It has been posited by many workers that grade itself is a molecular signature which if properly analyzed (perhaps with sophisticated image analysis methods) could provide information that is superior to the existing commercially molecular methods.
Two of the commonly used tools combine histological and clinical parameters to develop a score that is better than the individual parameters. These tools include Nottingham prognostic index (NPI) and Adjuvant! Online (http://www.adjuvantonline.com). The NPI is computed on the basis of tumor size, lymph node stage, and histological grade.13Adjuvant! Online provides the assessment of probability of survival and benefit from specific therapies in patients with early breast cancer.14 The model consolidates data regarding age, menopausal status, comorbidities, ER status, tumor grade, tumor size, and number of involved axillary nodes. The prognostic ability of these tools depends heavily on anatomical markers such as size, grade, nodal involvement, and other biological characteristics discussed below.
There has been renewed interest in the role of immune parameters in solid tumors such as breast cancer. A number of studies have now documented that the presence of mononuclear cell immune infiltrates in the stroma of breast cancer (so-called tumor infiltrating lymphocytes or TILs) is of prognostic value. Three large series have confirmed the value of TILs in triple negative breast cancers (tumors lacking ER, PR, and HER2).15-18 The role of TILs in HER2-positive tumors is somewhat controversial; they were predictive in one series but not associated with response to trastuzumab therapy in another19 (Perez et al, SABCS 2014).
Traditional molecular markers
Traditional molecular markers for early-stage breast cancer include estrogen receptor (ER), progesterone receptor (PR), and HER2.20 In spite of initial controversies, it is now well established that only tumors exhibiting ER expression are likely to respond to anti-estrogenic therapy. It is also clear that although ER-positive tumors have a good 10-year prognosis, they are associated with a potential to recur 10 to 20 years after initial diagnosis. The standard method for evaluation of ER and PR has been immunohistochemistry (IHC), in part because these methods can be applied all over the world. It must be recognized, however, that pre-analytical variables such as time to fixation, method(s) used for tissue processing, and antigen retrieval significantly impact the analysis (see ASCO-CAP guidelines for details).21,22 More recently, quantitative immunofluorescence and reverse-transcriptase polymerase chain reaction (qRT-PCR)-based methods have been used to assess ER and PR levels. Although there is a good agreement between the IHC and qRT-PCR assays, there is limited if any data to suggest that these should be used to make treatment decisions.23
The overexpression (by IHC) or amplification (by FISH) of HER2 is an indicator of likelihood of response to anti-HER2 therapies.24 These can take the form of monoclonal antibody (trastuzumab) therapy or a small molecular inhibitor (lapatinib). There is a good agreement between IHC and FISH methods, each having its advantages and limitations.25 One of the major limitations of FISH is the need for a dark room with a special microscope; newer methods such as chromogenic ISH (CISH) and dual ISH (DISH) are being utilized to enable assessment by standard light microscope.
As recognized in the SBR grading system, the proliferation rate of tumors is a good predictor of aggressiveness of the tumor; this is particularly true for ER+ tumors. The expression of Ki67 by IHC is often used to assess proliferation. Although there are methodological and inter-observer consistency issues and controversies regarding cutoff values, tumors having high Ki67 expression should be offered systemic chemotherapy.26More recently, a combination of ER, PR, HER2, and Ki67 markers (IHC4) has been shown to have a strong prognostic impact.27
Although hundreds of other markers have been evaluated in small and large series of breast cancers, these have not reached the standard required for routine clinical assessment. This is in part due to the inability to document their superiority to standard markers in routinely processed (FFPE) tissues.
Molecular classification: intrinsic subtypes
Pioneering studies by Perou et al established the molecular classification of breast cancer, which recognizes five molecular subtypes; luminal A, luminal B, HER2-enriched, basal-like, and normal-like (intrinsic classification).5 The luminal tumors express ER and are subclassified into luminal A and luminal B largely based on proliferation. HER2-enriched tumors are not always clinically HER2-positive, and these should not be considered as candidates for HER2-directed therapies. Basal cancers (sometimes referred to as triple negative tumors) have the worst prognosis and tend to be aggressive tumors. The prognostic or therapeutic role of intrinsic classification (in contrast to PAM50, see below) is poorly defined. However, the terms have become part of the breast cancer lexicon, and are often loosely used to define broad categories of tumors. More recent studies using gene expression and copy number analyses have identified at least 10 subtypes of breast cancer.9 One of the major problems with gene expression classification is that it lacks morphological or traditional IHC marker correlates. More recently, concerns have been raised regarding the stability of the classifiers and the concordance of the different molecular classifiers with one another (discussed below).
Prognostic multigene classifiers in clinical practice
These types of assays are mostly based on quantitative analysis of messenger RNAs extracted from archival paraffin sections. The protocol includes reference (control) genes that enable normalization of the data and result in significant improvement in the stability. The assays typically 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 from taking toxic chemotherapies for little or no gain. The assays can be integrated with pathological and clinical information to increase their performance.
A number of multigene prognostic classifiers have now been described. These include the 21-gene assay (Oncotype Dx), Mammaprint and Blueprint gene tests28 (Agendia), the Breast Cancer Index (BCI),29 and the PAM50-based Prosigna assay.2,30,31
The Oncotype Dx assay assesses 21 genes (16 test genes and five controls) and provides a continuous score (Recurrence Score) that predicts the risk of recurrence. This test is recommended in a number of national guidelines (ASCO, NCCN) for guiding treatment of women with newly diagnosed stage I or II, ER-positive breast cancer. This assay also routinely provides quantitative mRNA levels for ER, PR, and HER2.
The Mammaprint assay is an FDA-approved assay that was originally based on analysis of frozen tissue. 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 80-gene BluePrint assay identifies the molecular subtype and predicts tumor response to targeted therapies before and after surgery.
The ProSigna (PAM50) assay uses the Nanostring technology to analyze the expression of 46 to 50 genes that determine intrinsic classification and outcomes. The risk of relapse (ROR) prognostic of relapse-free survival is for patients who have node-negative tumors and have not received adjuvant systemic therapy. Of note, the ProSigna assay is FDA-approved for providing ROR score but not for providing intrinsic classification.
The BCI is also FDA-approved for prognostication in ER+ tumors. The Endopredict assay is available in Europe for similar purposes.32
Of note, all of these assays have utility in prognostication of ER+ tumors. There are few, if any assays for ER-negative tumors except for the HRD assay from Myriad.33,34Similarly, very few assays are available for prediction of therapy. This is an area of active research, and many groups, including ours, are working to generate commercially viable tools.
The IMPAKT 2012 Working Group evaluated the validity of six different prognostic genomic tests, including Oncotype DX, MammaPrint, PAM50, Genomic Grade Index (Ipsogen), Breast Cancer Index (Biotheranostics), and EndoPredict (Sividon Diagnostics).35
The Working Group proposed the following recommendations: 1) models should be developed that integrate clinicopathologic factors along with genomic tests; 2) demonstration of clinical utility should be made in the context of a prospective randomized trial; and 3) registries should be created for patients who are subjected to genomic testing in daily practice. More recent data seem to suggest a poor concordance between these assays in predicting outcomes (Bartlett et al, SABCS Dec 2014).
NGS technologies in breast cancer clinical practice
Next-generation sequencing (NGS) technologies permit detailed analysis of mutations, rearrangements, amplifications and deletions (DNA-sequencing), or coding and non-coding RNA (RNA-sequencing).36 Most of the clinically used assays are based on DNA-sequencing. A variety of commercial panels are available through Illumina, Life Technologies, Qiagen, Foundation One, and a number of other vendors. In addition, academic institutions are designing their own panels based on the genes of interest.
NGS technologies permit analysis of all the exomes or even entire genes. This has led to expansion of the number of genes that can be thoroughly analyzed at one time and thereby better identification of the individuals at risk. The NGS panels have been used in two specific scenarios:
1) Risk assessment. Risk assessment has been traditionally performed by analyzing BRCA1and BRCA2 genes for mutations at “hotspots” (sites of common mutations) by Sanger sequencing or by PCR. Recently, other NGS panels have been entered as clinical diagnostics including BRCAplus and BreastNext. BRCAplus and BreastNext simultaneously analyze five and 17 genes associated with increased risk for breast cancer, includingBRCA1 and BRCA2, respectively.
2) Identifying patients likely to respond to novel (non-“standard of care”) therapies. Patients with metastatic disease have tumors that are resistant to standard first-, second-, and third-line therapies. Identification of “actionable” mutations in the tumors has the potential for prescribing these patients agents that are typically not used in treating breast cancer. These agents could be drugs that have been shown to have utility in other cancers or novel drugs that target the specific genes/pathways. One of the problems with these assays is that the term “actionable mutation” is defined very broadly and patients often do not respond to drugs targeting these mutations. Another reason is that metastatic lesions do not represent the same genomic profile as in tumors; they often acquire novel mutations and genome alterations that may lead to alterations in signaling pathways. In some academic centers, early stage breast tumors are also being sequenced using NGS-based panels to identify genomic alterations for clinical trials based on putative predictive biomarkers.36
A number of new clinical trials are being designed using the “basket” or “umbrella” designs. Basket trials are often designed on the basis of a common molecular alteration(s). Most basket trials are histology-independent and aberration-specific clinical trials. An example of a basket trial is Vemurafenib basket trial (VE-BASKET), where the same agent was used in patients with BRAF mutated tumors arising at eight different body sites. Umbrella trials are built on a centrally performed molecular portrait in a single organ such as the breast. The molecular and/or mutational phenotype is matched with appropriate drugs. The NCI “MATCH” (Molecular Analysis for Therapeutic Choice; ECOG trial -EAY 131) trial is one example of this type of trial.
The future of breast cancer prognostication
Prognostication in breast cancer has been a multifaceted process involving the assessment of anatomical extent of the disease, and levels of expression of proteins (by IHC), DNA (FISH), and RNA (gene signatures). Existing gene signatures are limited to ER+ breast cancer and are dependent on proliferation. More importantly, apart from providing objective assessment, they also have not shown significant superiority over classical/traditional prognostic tools. The ability to perform massively parallel (next generation) sequencing is significantly affecting our understanding of breast cancer and methods by which we assess prognostic and predictive markers. A number of NGS assays are now available; these are being clinically validated in the appropriate settings and in single and multi-institutional trials. The costs of these assays is still high, and they analyze only a fraction of the genome; this is expected to change with the passage of time. In addition, a number of novel assays that seek to identify circulating tumor-derived DNA or RNA are also being actively investigated.37,38
It should be apparent from the above discussion that prognostication in breast cancer is no longer limited to assessment of histological grade and a couple of biomarkers by immunohistochemistry or other in situ methods. It has become a highly complex process involving technologies that simultaneously assess the alterations of millions of segments of DNA and RNA. The rate of this progression is fairly rapid, and the clinical community—oncologists and pathologists—and patients themselves are struggling to keep up with these advances.
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