Breast Cancer DOI 10.1007/s12282-015-0594-y
REVIEW ARTICLE
Gene expression-based prognostic and predictive tools in breast cancer Gyo¨ngyi Munka´csy • Marcell A. Sza´sz Otilia Menyha´rt
•
Received: 8 December 2014 / Accepted: 27 January 2015 Ó European Union 2015
Abstract Genomic assays measuring the expression of multiple genes have made their way into clinical practice and their utilization is now recommended by major international guidelines. A basic property of these tests is their capability to sub-divide patients into high- and low-risk cohorts thereby providing prognostic, and in certain settings, predictive decision support. Here, we summarize commercially available assays for breast cancer including RT-PCR and gene chip-based tests. Given the relative uncertainty in cancer treatment, multigene tests have the potential for a significant cost reduction as they can pinpoint those patients for whom chemotherapy proves to be unnecessary. However, concordance of risk assessment for an individual patient is still far from optimal. Additionally, emerging multigene approaches focus on predicting therapy response, which is a black spot of current tests. Promising techniques include the homologous recombination deficiency score, utilization of massive parallel sequencing to identify driver genes, employment of internet-based metaanalysis tools and investigation of miRNA expression signatures. Combination of multiple simultaneous analyses at diagnosis, including classical histopathological diagnostics, monogenic markers, genomic signatures and clinical
G. Munka´csy (&) MTA-SE Pediatrics and Nephrology Research Group, Semmelweis University, Bo´kay u. 53, Budapest 1083, Hungary e-mail: [email protected]
parameters will most likely bring maximal benefit for patients. As the main driving force behind such genomic tests is the power to achieve cost reduction due to avoiding unnecessary systemic treatment, the future is most likely to hold a further proliferation of such assays. Keywords Breast cancer Endocrine therapy Chemotherapy Survival Relapse Abbreviations BC Breast cancer CNA Copy number alterations ER Estrogen receptor FDA US Food and Drug Administration FFPE Formalin-fixed, paraffin-embedded FISH Fluorescence in situ hybridization GGI Genomic grade index HER2 Human epidermal growth factor receptor 2 HRD Homologous recombination deficiency IHC Immunohistochemistry miRNA Micro RNA NCCN National Comprehensive Cancer Network NGS Next (second) generation sequencing PgR Progesterone receptor ROR Risk of recurrence TCGA The cancer genome atlas
M. A. Sza´sz 2nd Department of Pathology, Semmelweis University, ¨ ll} U oi u´t 93, Budapest 1091, Hungary
Introduction
O. Menyha´rt MTA TTK Lendu¨let Cancer Biomarker Research Group, Magyar tudo´sok ko¨ru´tja 2, Budapest 1117, Hungary
Oncologists treating breast cancer (BC) are presented a plethora of clinical characteristics, mono- and multigenic tests all supporting the decision to be made. Most of the
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clinicopathological parameters including age, tumor size, lymphovascular invasion, grade and nodal status are correlated with prognosis. The few genotype-based monogenic markers specific for the patient itself include CYP2D6 for tamoxifen metabolism and UGT1A1 polymorphism for irinotecan detoxification. Currently, besides the above mentioned parameters, ER, PgR and HER2 status are routinely utilized to predict therapy response in early invasive breast cancer. These monogenic markers are determined using routine immunohistochemistry (IHC) and are employed as negative biomarkers—absent hormone receptor expression predicts lack of response to endocrine therapy and absence of HER2 overexpression/amplification predicts lack of response to anti-HER2 therapy. High sensitivity of these markers is supported by the most recent guidelines on ER testing suggesting positivity at as low as 1 % as cutoff to discriminate ER-positive and negative breast carcinomas [1]. However, the most important clinical question to be answered remains the one regarding adjuvant chemotherapy since uniform treatment for all tumors would result in substantial over- or undertreatment for the individual patient. ?A3B2 tlsb=-0.02w?>Recently, Ki67, a nuclear protein expressed in all phases of the cell cycle except G0 was added to the above monogenic markers as a surrogate marker for cell proliferation. High proportion of Ki67positive cells was associated with poor outcome [2]. By standardizing the method to assess mitotic activity, Ki67
could be made more reproducible than histological grade [3]. In current St. Gallen guidelines, an optimal cutoff of 14 % is established to discriminate between high and low risk of developing distant metastases [4]. However, substantial inter-observer variability in Ki67 scoring prevents its widespread clinical deployment to date [5]. In this field, multigene signatures emerged as more objective tools capable to predict prognosis and therapy response. Utilization of genomic data is now recommended by the ASCO, St. Gallen and NCCN guidelines. In this, although there are early-bird approaches utilizing alternative technologies, all commercially available tests measure expression for a defined set of genes. In this review, we summarize not only the well-known commercially available tests to predict therapy response and recurrence for BC patients, but also some promising multigene approaches. Advantages and disadvantages are discussed and we also touch upon the clinical effectiveness of these approaches.
Commercial multigene tests To date, only two assays have received FDA clearance, while seven tests are marketed in the European Union and the US. A brief technological summary of these is provided in Table 1. The first FDA approval for a breast cancer in vitro diagnostic multigene signature was granted to MammaPrint
Table 1 Summary of commercially available multigene tests utilized in breast cancer Test
FDA approved
Origin
# of markers
Technology
Advantages
Disadvantages
Ref
MammaPrint
Yes (2007)
Fresh/ FFPE
70
Gene chip
FDA approval, reduction of health-care cost confirmed [46]
Predictive power is limited with ER-negative patients
[7]
PAM50
Yes (2013)
FFPE
50 ? 5a
RT-PCR
FDA approval
High cost of equipment, need for trained personal to perform the analysis
[11]
IHC4
No
FFPE
IHC
Minimal cost of assay
Discrepancies in results among centers
[14]
Oncotype DX
No
FFPE
16 ? 5a
RT-PCR
Reduction of health-care cost confirmed [45], no need for fresh sample
Need to send each sample and clinical data to a central facility
[16]
GGI
No
Fresh/ FFPE
97
Gene chip
Better discrimination of breast cancer samples classified as histologic grade 2
Only informative in luminal, non-HER2-positive tumors [51]
[17]
EndoPredict
No
FFPE
8 ? 3a
RT-PCR
Reduced health-care cost [21], variations in tissue handling time have negligible effects on the results [52]
Missed to predict chemotherapy benefit [53]
[19]
BCI
No
FFPE
5?2
RT-PCR
Reduction of health-care cost confirmed [54], no need for fresh sample
Only for patients recurrencefree after an initial 5 years of adjuvant endocrine therapy
[22]
a
4
Number of housekeeping genes included in the assay
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in 2007 (Agendia AG, The Netherlands). The microarraybased MammaPrint is used to assess the prognosis in patients \62 years of age with lymph node-negative or positive (1–3 lymph nodes) and ER-positive or -negative BC having a tumor smaller than 5 cm [6]. MammaPrint includes 70 genes including 55 established genes and 15 genes with unknown function. The outcome dichotomizes patients into low- or high-risk cohorts, which was shown to be more accurate when predicting 5-year metastasis-free survival than conventional clinicopathological parameters [7]. The original in vitro diagnostic assay utilized fresh frozen material, but the test has also been developed for paraffin-embedded breast carcinomas [8]. A prospective trial (MINDACT) for MammaPrint is on the move with concluding results expected next year. MINDACT is performed using the complete gene chips from which the 70 probes were selected. Having the entire genomic profile for such a large cohort will open the possibility of additional targeted signatures after completing the trial. A second FDA approval has been received in 2013 for the PAM50 (ProsignaTM, Nanostring Technologies, USA). PAM50 uses a panel of 50 genes plus 5 housekeeping genes to compute a risk of recurrence score (ROR) which enables to identify the four intrinsic BC subtypes. Breast cancer subtypes were designated already in 2000 using microarray profiling [9]. These intrinsic subtypes are recommended by international guidelines since 2007 to improve clinical risk assessment [10]. PAM50 initially utilized RT-PCR from FFPE archival samples to designate these subtypes [11]. PAM50 also provides estimation for distant relapse-free survival and likelihood of recurrence at 10 years for ER-positive, tamoxifen-treated patients. PAM50 was validated in two retrospective trials including more than 2,400 patients and can also support identification those node-positive and ER-positive patients who do not necessarily need adjuvant chemotherapy. PAM50 is now performed using the nCounter diagnostic system utilizing hybridization of target sequences and detection of molecular ‘‘barcodes’’ and single-molecule imaging to count transcripts. Although local processing provides an advantage over tests performed at a central laboratory only, cost for the equipment itself is not negligible and there is additional need for trained personal to perform the analysis. One important point to consider is the clinician’s aim to define the subtypes of luminal A, luminal B, HER2-enriched and basal-like using various platforms including routine immunohistochemistry of the four most important markers (ER, PGR, HER2 and Ki67), or hybridization assays like cDNA microarray (as [9] ) and Nanostring (PAM50). However, clinical utility of the subtypes is hampered by the fact that these different approaches can designate the same patients into different classes and only the triple-negative/basal-like cancers are robustly
reproducible [12]. In case of discrepant designation by PAM50 and immunohistochemical analysis, patients should still be managed according to the IHC markers [13]. Quite unsurprisingly, there is a marketed test (IHC4) actually based on the four routine IHC markers of ER, PR, HER2 and Ki67 [14]. A combination of IHC4 score and clinical characteristics has been used in 101 patients suggesting that this could also support decision-making for BC chemotherapy [15]. An important advantage of IHC4 as compared to other tests is the minimal cost for the test reagents which warrants further development of this assay. At the same time, omission of a central testing facility could result in discrepancies among various centers as observed for Ki67 alone—this underpins the necessity for a comprehensive quality assurance before such a product can enter general market. Oncotype DX (Genomic Health, USA) measures expression of 21 genes including 16 cancer-related genes and five reference genes to compute a continuous recurrence score ranging between 0 and 100 [16]. Genes are grouped into virtual groups, but at the end each gene has its own weight. Most important genes in the assay include HER2, ER and Ki67 with highest score given to the two genes related to the HER2-pathway (HER2 and GRB7). Recurrence score is prognostic for estrogen receptor-positive breast cancer treated with tamoxifen regardless of nodal status (positivity up to 3 lymph nodes). In addition, predictive value of Oncotype has been shown for assessing benefit from chemotherapy in ER-positive breast cancers. Currently, a large prospective clinical trial (TAILORx) is en route to validate test performance. In this trial, more than 10,000 patients have been enrolled and the first results are expected in 2017. A major limitation for Oncotype is the need to send each sample (tissue block or 15 slides) and clinical data to a central facility. Histological grade is a strong prognostic factor both in estrogen receptor-positive and -negative BC. Genomic Grade Index (MapQuant DX, Ipsogen, France) is a gene chip-based test measuring expression of 97 genes to enroll histologic grade 2 BC into good and poor prognostic groups [17]. In addition to microarray, an FFPE-based RTPCR edition of the signature was established and validated. This PCR genomic grade assay includes only 6 genes from the original 97 genes [18]. Prognostic performance of the PCR-GGI was also confirmed using FFPE samples [18]. The most recent multigene signature already commercially available is the RT-PCR based EndoPredict (Sividon Diagnostics GmbH, Germany) which stratifies ER-positive, HER2-negative patients into a low-risk and a high-risk group [19]. The test was investigated using FFPE samples from two large randomized phase III trials of the Austrian Breast and Colorectal Cancer Study Groups ABCSG6 and ABCSG8 (n = 378 and n = 1,324 patients, respectively),
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and was validated to discriminate significant differences in 10-year distant recurrence rates [20]. The EndoPredict assay itself was established in seven different molecular pathology laboratories in three European countries providing a framework for a reliable decentralized assessment of gene expression in ER-positive BC [21]. Breast Cancer Index (BCI, bioTheranostics, USA) delivers a continuous assessment of individual risk of distant recurrence for early-stage estrogen-positive node-negative BC. The test includes ratio of two sovereign biomarkers, HOXB13:IL17BR (H:I) and the 5-gene molecular grade index, which quantify estrogen-mediated signaling and tumor grade [22]. The assay is RT-PCR based and utilizes routine FFPE samples. In an independent validation study, BCI classified 265 ER-positive node-negative patients administered tamoxifen alone or tamoxifen with chemotherapy into low-risk and high-risk cohorts. High-risk patients had a fivefold increase compared to the low-risk cohort in 10-year risk of distant recurrence [23].
Unanswered clinical questions Prediction of late recurrence For patients with ER-positive tumors endocrine therapy is given for 5 years routinely. An open question is to select which ER-positive patients will need extended adjuvant endocrine therapy beyond these 5 years. This question is particularly important as more than half of the distant metastases occur after 5 years. The BCI test report directly assesses risk of distant recurrence within 5 years and between five and 10 years. A recent study compared performance of BCI, Oncotype DX and IHC4 to predict late recurrence in 665 ER-positive, node-negative BC. Although each assay had significant prognostic ability for early distant recurrence, BCI was the only one also capable to predict late recurrence [24]. Future comparisons including other genomic tests will deliver a more comprehensive evaluation of late recurrence prediction.
results in poor prognosis but likely higher chemotherapy sensitivity) and a HER2 group (to benefit from administration of trastuzumab). Consequently, when six such genomic tests were compared, each had a significant correlation to pathologic complete response [25]. Although many direct prognostic markers have been identified to date, most of these did not make it into actual clinical decision algorithms. The first transcriptomic analyses focusing on predictive signatures utilized cell culture models to establish signature which were then examined in actual patients [26]. However, cell lines exhibit the same heterogeneity as patients, and when cell culture model of parallel evolution for chemotherapy resistance was utilized, the cell lines showed a highly heterogeneous profile [27]. Clinically more robust profiles associated with response to chemotherapy could only be identified by utilizing large patient cohorts. Recently, a new approach to develop a predictor was implemented which is likely to gain further foothold in the future. In this, a genomic predictor combining ER status, predicted chemoresistance, predicted chemosensitivity, and predicted endocrine sensitivity was established to identify patients with high probability of survival following taxane and anthracycline chemotherapy [28]. Taxane and anthracycline-based regimens are utilized in HER2-negative breast cancers. In the project, instead of dividing the patients into two cohorts, the genetic heterogeneity of BC was acknowledged and multiple signatures were established to answer the same question in a given patient. The assay was based on the Affymetrix HGU133 gene-chip data of 310 patients and was independently validated in 198 patients. A similarly imperative decision is the selection of endocrine therapy for ER-positive patients. Currently, up to 50 % of the ER-positive tumors relapse on or after discontinuation of hormonal therapy; therefore, new response biomarkers would have high clinical value in this cohort. Yet, most of gene expression-based monogenic markers identified previously have failed when assessed in independent patient cohorts [29].
Prediction of therapy response
Emerging multigene approaches
Adjuvant systemic therapy can deliver improved outcome for patients surviving BC. Secondary risk prediction tools are those prognostic factors which identify patients likely profiting from adjuvant therapy. Core components of all current multigene genomic tests include three main groups of markers: an ER-related group (high receptor expression result in good prognosis and in higher endocrine sensitivity), a proliferation-related group (high proliferation
Next-generation sequencing (NGS)
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The first NGS-based assay for solid tumors was developed in 2011 [30]. In this, an ultra-deep targeted sequencing of a 71.1-kb sequence encompassing mutational hotspots for 42 cancer genes is performed. In another approach, a PCR assay was constructed for genomic rearrangements in breast tumors to detect these in blood sample, with an
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excellent sensitivity of detecting one single copy of the tumor genome in the plasma [31]. Advantage of such a clinical assay is that it should not have any false positive rearrangements as there is no PCR amplification in nonrearranged, normal samples. NGS still faces a set of difficulties to be solved before large-scale clinical utility is achieved. There is an inherent challenge of contamination for tumor samples by other (normal) cells. Until now NGS was performed using frozen tissues, but operation with FFPE DNA will be more practical in a clinical environment. Though, sequencing accuracy also depends on the quality of DNA which is lower in FFPE samples and thereby discriminating artifacts and mutations will present a substantial challenge. Finally, NGS still requires a workintensive and expensive workflow. Internet-based tests Microarrays have the advantage of simultaneously possessing a large number of probes. This not only enables measurement of the entire transcriptome, but also the crossvalidation of different multigenic signatures. By utilizing public data repositories, one can construct large integrated databases enabling such confirmation studies [32]. Alternatively, the integration of different diagnostic tools into one software platform is possible as has been demonstrated by the online available www.recurrenceon line.com site [33]. Recurrence online utilizes Affymetrix microarrays to compute different prognostic signatures to predict the risk of relapse in ER-positive, node-negative and node-positive patients, to assess ER and HER2 status. Thus, different analysis can be performed in one step deriving primary, secondary and potentially also tertiary classification for a given patient. The initial version of recurrence online was validated using publicly available microarray data of more than 2000 patients. Recurrence online can also be used to compare new signatures to established ones. For example, in a preclinical study [34] a 95-gene classifier was developed to predict prognosis of node-negative and ER-positive breast cancer patients with high accuracy. This classifier combined with the 21-gene signature seems to result in the identification of more lowrisk patients who do not need chemotherapy than either classification alone. Patients in the high-risk group were found to be more chemosensitive so that they can benefit more from adjuvant chemotherapy [35]. An updated version of Recurrenceonline system is capable to discriminate good and bad prognosis cohorts within triple-negative cancers as well [36]. As such an approach is extendable for future gene sets, we can expect in future the emergence of single-step assays enabling computation of all available gene sets regardless of the initial classification of the tumor sample.
HRD score Defects in genes supporting homologous recombination have been associated with predisposition to multiple cancers. Mutations in BRCA1, BRCA2 and other members of the homologous recombination pathway are linked to response to therapeutics that introduce or exploit doublestranded DNA breaks like platinum agents and PARP-inhibitors. Regions of loss of heterozygosity, the irreversible loss of one of the parental alleles were observed more frequently in tumors with defective BRCA genes. The number of such regions within a tumor sample was defined as the homologous recombination deficiency score (HRD, Myriad Genetics, USA). A high HRD score is strongly associated with homologous recombination deficiency regardless of etiology. In 639 ovarian tumor samples, patients having a high HRD score had a markedly better prognosis compared to patients having low HRD score [37]. The score performed using Affymetrix 500 K gene chip arrays could be utilized to identify patients with breast, ovarian, lung and pancreatic cancers and also a high likelihood of responding to DNA damaging agents. miRNA A further alternative approach is the analysis of the noncoding transcriptome including miRNAs. This is feasible both in frozen and FFPE material as miRNAs are generally smaller than 23 base pairs and are not being subjects of significant degradation and cross-linking by formalin. The largest miRNA study to date analyzed over 1,300 breast carcinomas and demonstrated an important modulatory role for miRNAs in the biology BC [38]. miRNAs can also be used to determine prognosis. For example, miR-210 was differentially regulated in BC and had a prognostic potential for overall survival and time to metastasis [39]. Genes with inverse expression correlated to miR-210 included top cancer genes like BRCA1, PARP1, E-cadherin and RB1. The TCGA project included an integrated analysis of mRNA and miRNA expression for 466 patients. In this, 30 mRNAs and seven miRNAs were correlated to overall survival and the highest prognostic value in early stage tumors was achieved by an integrated miRNA/mRNA signature [40]. This integrated signature was further validated in eight BC cohorts including a total of 2,399 patients.
The next 5 years Many of the assays intensively developed a few years ago have lost their significance including the 14-gene Celera
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Metastatic Score of Applera, the 30-gene PGx Rerporter of Nuvera Biosciences, the 55-gene Breast Bioclassifier of Arup Labs and many more. At the same time, there is a wide range of new, clinically not yet applicable tests having the potential to deliver future generation of assays for BC. Epigenetic studies including establishment of gene signatures of genome-wide reprogramming of the chromatin landscape could help to detect endocrine therapy resistance [41]. RT-PCR adaptation of chromosomal instability signatures could help to define tumor aneuploidy and to stratify outcome in grade 2 breast cancer [42]. In addition to the identification of new mutations, sequencing of RNA by next-generation sequencing technologies could also provide the basis for expressionmeasuring signatures. RNA-seq has the advantage of a higher dynamic range as compared to gene expression arrays. This can enable detecting transcripts at very low or high levels enabling establishment of more subtle gene sets for patient stratification. Another NGS approach is based on isolation of nuclei by flow-sorting and then the whole genome is amplified to achieve a low sequencing coverage (6 %) of the nuclear genome of a cell. Besides observing the origin of the primary tumor mass from a single clonal expansion [43], this approach could also make it possible to detect copy number variations of a single cell. Given the relative uncertainty in cancer treatment, multigene tests have the potential for a significant cost reduction as they can pinpoint those patients for whom chemotherapy proves to be unnecessary [44]. Multiple studies have been performed to gauge the cost effectiveness of multigene test. Testing with Oncotype DX resulted in 33 % fewer patients actually receiving chemotherapy as compared to that originally planned. Although the test had to be performed for all patients, there was still a cost reduction of about €561 for Oncotype as compared to standard of care (n = 379) [45]. Similar results were observed for EndoPredict with one-fourth of patients changing to endocrine therapy alone (n = 167) [21]. In case of MammaPrint, 32 % fewer patients had to be given chemotherapy (n = 427) [46]. Altogether, these results demonstrate that a substantial proportion of all therapy decisions will change after performing a multigene assay and this mainly leads to reduction of adjuvant chemotherapy in line with its direct and other indirect costs (e.g. treatment of side effects). Clinically, utilization of IHC is basically omnipresent and can be employed for both traditional and new markers [47]. One could consider the use routine IHC of ER, PGR, HER2 and Ki67 to accomplish the same results as a relatively expensive genomic test—and vice versa, e.g. using the genomic tool to predict receptor status. However, this seems not to function using current techniques due to multiple shortcomings. By using the four genes, in theory
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16 different ‘‘IHC-subtypes’’ could be established—no clinical utility has yet been demonstrated for such a setting. Moreover, finding a cutoff for Ki67 is challenging, as the majority of cutoff values are significant for predicting disease-free survival. On the other hand, the potential of Oncotype DX to predict HER2 status was evaluated in 784 patients—and the assay delivered an unacceptably high rate of false-negative results of 72 %. Merely 10 out of 36 positive patients were identified as compared to FISH [48].
Conclusions The majority of current assays utilize two platforms: RTPCR and Affymetrix gene chips. The FDA clearance of Affymetrix arrays opened an easy-to-implement technology for new assays, including many above described tests like the genomic predictor of response for taxaneanthracycline chemotherapy, the MapQuant DX assay, the recurrence online platform and the HRD score. A second deduction of research to date is the need to account for the fact that many gene alterations will be context dependent. Therefore the integration of multiple data-types like determination of mutation status by sequencing and measuring gene expression by gene chips will be increasingly useful for the identification of therapeutic targets within different subsets of breast cancer. Such a combinatorial study has been recently performed for TP53 mutant breast cancers [49]. Recently, a systematic review of the evidence on clinical effectiveness (analytical validity, clinical validity and clinical utility) and cost-effectiveness was conducted by Ward et al. [50] with 32 full-text papers and abstracts. The economic analysis suggested that treatment guided using IHC4 had the highest potential and OncotypeDX had also a robust evidence base. For MammaPrint there was a gap between available evidence and the estimates of cost-effectiveness produced. Evidence for the remaining tests investigated (PAM50, BCI) was limited [50]. Currently, several genomic signatures are used to predict prognosis in ER-positive patients. In contrast to previous expectations, instead of settling down the evolution of multigene assays is just about to run up its exponential phase as many more tests are coming to designate the patients into additional sub-cohorts. Acknowledgments This study was supported by the OTKA K108655 grant. A. M. S. was supported by the European Union and the State of Hungary, co-financed by the European Social Fund in the ´ MOP-4.2.4.A/2-11-1-2012-0001 ‘National Excelframework of TA lence Program’. Conflict of interest of interest.
The authors declare that they have no conflict
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References 1. Hammond ME, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, et al. American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol. 2010;28:2784–95. 2. Surowiak P, Suchocki S, Gyorffy B, Gansukh T, Wojnar A, Maciejczyk A, et al. Stromal myofibroblasts in breast cancer: relations between their occurrence, tumor grade and expression of some tumour markers. Folia Histochem Cytobiol. 2006;44:111–6. 3. Lende TH, Janssen EA, Gudlaugsson E, Voorhorst F, Smaaland R, van Diest P, et al. In patients younger than age 55 years with lymph node-negative breast cancer, proliferation by mitotic activity index is prognostically superior to adjuvant! J Clin Oncol. 2011;29:852–8. 4. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24:2206–23. 5. Polley MY, Leung SC, McShane LM, Gao D, Hugh JC, Mastropasqua MG, et al. An international Ki67 reproducibility study. J Natl Cancer Inst. 2013;105:1897–906. 6. Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, Floore A, et al. The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat. 2009;116:295–302. 7. van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999–2009. 8. Mittempergher L, de Ronde JJ, Nieuwland M, Kerkhoven RM, Simon I, Rutgers EJ, et al. Gene expression profiles from formalin fixed paraffin embedded breast cancer tissue are largely comparable to fresh frozen matched tissue. PLoS One. 2011;6:e17163. 9. Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003;100:8418–23. 10. Mihaly Z, Gyorffy B. Improving pathological assessment of breast cancer by employing array-based transcriptome analysis. Microarrays. 2013;2:14. 11. Dowsett M, Sestak I, Lopez-Knowles E, Sidhu K, Dunbier AK, Cowens JW, et al. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol. 2013;31: 2783–90. 12. Weigelt B, Mackay A, A’Hern R, Natrajan R, Tan DS, Dowsett M, et al. Breast cancer molecular profiling with single sample predictors: a retrospective analysis. Lancet Oncol. 2010;11: 339–49. 13. van de Vijver MJ. Molecular tests as prognostic factors in breast cancer. Virchows Arch. 2014;464:283–91. 14. Cuzick J, Dowsett M, Pineda S, Wale C, Salter J, Quinn E, et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the genomic health recurrence score in early breast cancer. J Clin Oncol. 2011;29:4273–8. 15. Barton S, Zabaglo L, A’Hern R, Turner N, Ferguson T, O’Neill S, et al. Assessment of the contribution of the IHC4 ? C score to decision making in clinical practice in early breast cancer. Br J Cancer. 2012;106:1760–5.
16. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, nodenegative breast cancer. N Engl J Med. 2004;351:2817–26. 17. Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006;98:262–72. 18. Toussaint J, Sieuwerts AM, Haibe-Kains B, Desmedt C, Rouas G, Harris AL, et al. Improvement of the clinical applicability of the Genomic Grade Index through a qRT-PCR test performed on frozen and formalin-fixed paraffin-embedded tissues. BMC Genom. 2009;10:424. 19. Filipits M, Rudas M, Jakesz R, Dubsky P, Fitzal F, Singer CF, et al. A new molecular predictor of distant recurrence in ERpositive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin Cancer Res. 2011;17:6012–20. 20. Dubsky P, Filipits M, Jakesz R, Rudas M, Singer CF, Greil R, et al. EndoPredict improves the prognostic classification derived from common clinical guidelines in ER-positive, HER2-negative early breast cancer. Ann Oncol. 2013;24:640–7. 21. Muller BM, Keil E, Lehmann A, Winzer KJ, Richter-Ehrenstein C, Prinzler J, et al. The EndoPredict gene-expression assay in clinical practice—performance and impact on clinical decisions. PLoS One. 2013;8:e68252. 22. Ma XJ, Salunga R, Dahiya S, Wang W, Carney E, Durbecq V, et al. A five-gene molecular grade index and HOXB13:iL17BR are complementary prognostic factors in early stage breast cancer. Clin Cancer Res. 2008;14:2601–8. 23. Jankowitz RC, Cooper K, Erlander MG, Ma XJ, Kesty NC, Li H, et al. Prognostic utility of the breast cancer index and comparison to adjuvant! Online in a clinical case series of early breast cancer. Breast Cancer Res. 2011;13:R98. 24. Sgroi DC, Sestak I, Cuzick J, Zhang Y, Schnabel CA, Schroeder B, et al. Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: a prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population. Lancet Oncol. 2013;14:1067–76. 25. Iwamoto T, Lee JS, Bianchini G, Hubbard RE, Young E, Matsuoka J, et al. First generation prognostic gene signatures for breast cancer predict both survival and chemotherapy sensitivity and identify overlapping patient populations. Breast Cancer Res Treat. 2011;130:155–64. 26. Gyorffy B, Serra V, Jurchott K, Abdul-Ghani R, Garber M, Stein U, et al. Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. Oncogene. 2005;24: 7542–51. 27. Tegze B, Szallasi Z, Haltrich I, Penzvalto Z, Toth Z, Liko I, et al. Parallel evolution under chemotherapy pressure in 29 breast cancer cell lines results in dissimilar mechanisms of resistance. PLoS One. 2012;7:e30804. 28. Hatzis C, Pusztai L, Valero V, Booser DJ, Esserman L, Lluch A, et al. A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. JAMA. 2011;305:1873–81. 29. Mihaly Z, Kormos M, Lanczky A, Dank M, Budczies J, Szasz MA, et al. A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer. Breast Cancer Res Treat. 2013;140:219–32. 30. Harismendy O, Schwab RB, Bao L, Olson J, Rozenzhak S, Kotsopoulos SK, et al. Detection of low prevalence somatic mutations in solid tumors with ultra-deep targeted sequencing. Genome Biol. 2011;12:R124.
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Breast Cancer 31. McBride DJ, Orpana AK, Sotiriou C, Joensuu H, Stephens PJ, Mudie LJ, et al. Use of cancer-specific genomic rearrangements to quantify disease burden in plasma from patients with solid tumors. Genes Chromosomes Cancer. 2010;49:1062–9. 32. Gyorffy B, Schafer R. Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients. Breast Cancer Res Treat. 2009;118:433–41. 33. Gyorffy B, Benke Z, Lanczky A, Balazs B, Szallasi Z, Timar J, et al. RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res Treat. 2012;132: 1025–34. 34. Naoi Y, Kishi K, Tanei T, Tsunashima R, Tominaga N, Baba Y, et al. Development of 95-gene classifier as a powerful predictor of recurrences in node-negative and ER-positive breast cancer patients. Breast Cancer Res Treat. 2011;128:633–41. 35. Naoi Y, Kishi K, Tsunashima R, Shimazu K, Shimomura A, Maruyama N, et al. Comparison of efficacy of 95-gene and 21-gene classifier (Oncotype DX) for prediction of recurrence in ER-positive and node-negative breast cancer patients. Breast Cancer Res Treat. 2013;140:299–306. 36. Gyorffy B, Karn T, Sztupinszki Z, Weltz B, Muller V, Pusztai L. Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer. Int J Cancer. 2014. 37. Abkevich V, Timms KM, Hennessy BT, Potter J, Carey MS, Meyer LA, et al. Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer. Br J Cancer. 2012;107:1776–82. 38. Dvinge H, Git A, Graf S, Salmon-Divon M, Curtis C, Sottoriva A, et al. The shaping and functional consequences of the microRNA landscape in breast cancer. Nature. 2013;497:378–82. 39. Volinia S, Galasso M, Sana ME, Wise TF, Palatini J, Huebner K, et al. Breast cancer signatures for invasiveness and prognosis defined by deep sequencing of microRNA. Proc Natl Acad Sci USA. 2012;109:3024–9. 40. Volinia S, Croce CM. Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci USA. 2013;110:7413–7. 41. Magnani L, Stoeck A, Zhang X, Lanczky A, Mirabella AC, Wang TL, et al. Genome-wide reprogramming of the chromatin landscape underlies endocrine therapy resistance in breast cancer. Proc Natl Acad Sci USA. 2013;110:E1490–9. 42. Szasz AM, Li Q, Eklund AC, Sztupinszki Z, Rowan A, Tokes AM, et al. The CIN4 chromosomal instability qPCR classifier defines tumor aneuploidy and stratifies outcome in grade 2 breast cancer. PLoS One. 2013;8:e56707.
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43. Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472:90–4. 44. Munkacsy G, Abdul-Ghani R, Mihaly Z, Tegze B, Tchernitsa O, Surowiak P, et al. PSMB7 is associated with anthracycline resistance and is a prognostic biomarker in breast cancer. Br J Cancer. 2010;102:361–8. 45. Blohmer JU, Rezai M, Kummel S, Kuhn T, Warm M, Friedrichs K, et al. Using the 21-gene assay to guide adjuvant chemotherapy decision-making in early-stage breast cancer: a cost-effectiveness evaluation in the German setting. J Med Econ. 2013;16:30–40. 46. Drukker CA, Bueno-de-Mesquita JM, Retel VP, van Harten WH, van Tinteren H, Wesseling J, et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer. 2013;133:929–36. 47. Maciejczyk A, Jagoda E, Wysocka T, Matkowski R, Gyorffy B, Lage H, et al. ABCC2 (MRP2, cMOAT) localized in the nuclear envelope of breast carcinoma cells correlates with poor clinical outcome. Pathol Oncol Res. 2012;18:331–42. 48. Ignatiadis M, Sotiriou C. Breast cancer: should we assess HER2 status by Oncotype DX? Nat Rev Clin Oncol. 2012;9:12–4. 49. Gyorffy B, Bottai G, Lehmann-Che J, Keri G, Orfi L, Iwamoto T, et al. TP53 mutation-correlated genes predict the risk of tumor relapse and identify MPS1 as a potential therapeutic kinase in TP53-mutated breast cancers. Mol Oncol. 2014;8:508. 50. Ward S, Scope A, Rafia R, Pandor A, Harnan S, Evans P, et al. Gene expression profiling and expanded immunohistochemistry tests to guide the use of adjuvant chemotherapy in breast cancer management: a systematic review and cost-effectiveness analysis. Health Technol Assess. 2013;17:1–302. 51. Desmedt C, Giobbie-Hurder A, Neven P, Paridaens R, Christiaens MR, Smeets A, et al. The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1–98 trial. BMC Med Genomics. 2009;2:40. 52. Poremba C, Uhlendorff J, Pfitzner BM, Hennig G, Bohmann K, Bojar H, et al. Preanalytical variables and performance of diagnostic RNA-based gene expression analysis in breast cancer. Virchows Arch. 2014;465:409–17. 53. Martin M, Brase JC, Calvo L, Krappmann K, Ruiz-Borrego M, Fisch K, et al. Clinical validation of the EndoPredict test in nodepositive, chemotherapy-treated ER ?/HER2- breast cancer patients: results from the GEICAM 9906 trial. Breast Cancer Res. 2014;16:R38. 54. Gustavsen G, Schroeder B, Kennedy P, Pothier KC, Erlander MG, Schnabel CA, et al. Health economic analysis of Breast Cancer Index in patients with ER?, LN-breast cancer. Am J Manag Care. 2014;20:e302–10.
REVIEW ARTICLE
Gene expression-based prognostic and predictive tools in breast cancer Gyo¨ngyi Munka´csy • Marcell A. Sza´sz Otilia Menyha´rt
•
Received: 8 December 2014 / Accepted: 27 January 2015 Ó European Union 2015
Abstract Genomic assays measuring the expression of multiple genes have made their way into clinical practice and their utilization is now recommended by major international guidelines. A basic property of these tests is their capability to sub-divide patients into high- and low-risk cohorts thereby providing prognostic, and in certain settings, predictive decision support. Here, we summarize commercially available assays for breast cancer including RT-PCR and gene chip-based tests. Given the relative uncertainty in cancer treatment, multigene tests have the potential for a significant cost reduction as they can pinpoint those patients for whom chemotherapy proves to be unnecessary. However, concordance of risk assessment for an individual patient is still far from optimal. Additionally, emerging multigene approaches focus on predicting therapy response, which is a black spot of current tests. Promising techniques include the homologous recombination deficiency score, utilization of massive parallel sequencing to identify driver genes, employment of internet-based metaanalysis tools and investigation of miRNA expression signatures. Combination of multiple simultaneous analyses at diagnosis, including classical histopathological diagnostics, monogenic markers, genomic signatures and clinical
G. Munka´csy (&) MTA-SE Pediatrics and Nephrology Research Group, Semmelweis University, Bo´kay u. 53, Budapest 1083, Hungary e-mail: [email protected]
parameters will most likely bring maximal benefit for patients. As the main driving force behind such genomic tests is the power to achieve cost reduction due to avoiding unnecessary systemic treatment, the future is most likely to hold a further proliferation of such assays. Keywords Breast cancer Endocrine therapy Chemotherapy Survival Relapse Abbreviations BC Breast cancer CNA Copy number alterations ER Estrogen receptor FDA US Food and Drug Administration FFPE Formalin-fixed, paraffin-embedded FISH Fluorescence in situ hybridization GGI Genomic grade index HER2 Human epidermal growth factor receptor 2 HRD Homologous recombination deficiency IHC Immunohistochemistry miRNA Micro RNA NCCN National Comprehensive Cancer Network NGS Next (second) generation sequencing PgR Progesterone receptor ROR Risk of recurrence TCGA The cancer genome atlas
M. A. Sza´sz 2nd Department of Pathology, Semmelweis University, ¨ ll} U oi u´t 93, Budapest 1091, Hungary
Introduction
O. Menyha´rt MTA TTK Lendu¨let Cancer Biomarker Research Group, Magyar tudo´sok ko¨ru´tja 2, Budapest 1117, Hungary
Oncologists treating breast cancer (BC) are presented a plethora of clinical characteristics, mono- and multigenic tests all supporting the decision to be made. Most of the
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clinicopathological parameters including age, tumor size, lymphovascular invasion, grade and nodal status are correlated with prognosis. The few genotype-based monogenic markers specific for the patient itself include CYP2D6 for tamoxifen metabolism and UGT1A1 polymorphism for irinotecan detoxification. Currently, besides the above mentioned parameters, ER, PgR and HER2 status are routinely utilized to predict therapy response in early invasive breast cancer. These monogenic markers are determined using routine immunohistochemistry (IHC) and are employed as negative biomarkers—absent hormone receptor expression predicts lack of response to endocrine therapy and absence of HER2 overexpression/amplification predicts lack of response to anti-HER2 therapy. High sensitivity of these markers is supported by the most recent guidelines on ER testing suggesting positivity at as low as 1 % as cutoff to discriminate ER-positive and negative breast carcinomas [1]. However, the most important clinical question to be answered remains the one regarding adjuvant chemotherapy since uniform treatment for all tumors would result in substantial over- or undertreatment for the individual patient. ?A3B2 tlsb=-0.02w?>Recently, Ki67, a nuclear protein expressed in all phases of the cell cycle except G0 was added to the above monogenic markers as a surrogate marker for cell proliferation. High proportion of Ki67positive cells was associated with poor outcome [2]. By standardizing the method to assess mitotic activity, Ki67
could be made more reproducible than histological grade [3]. In current St. Gallen guidelines, an optimal cutoff of 14 % is established to discriminate between high and low risk of developing distant metastases [4]. However, substantial inter-observer variability in Ki67 scoring prevents its widespread clinical deployment to date [5]. In this field, multigene signatures emerged as more objective tools capable to predict prognosis and therapy response. Utilization of genomic data is now recommended by the ASCO, St. Gallen and NCCN guidelines. In this, although there are early-bird approaches utilizing alternative technologies, all commercially available tests measure expression for a defined set of genes. In this review, we summarize not only the well-known commercially available tests to predict therapy response and recurrence for BC patients, but also some promising multigene approaches. Advantages and disadvantages are discussed and we also touch upon the clinical effectiveness of these approaches.
Commercial multigene tests To date, only two assays have received FDA clearance, while seven tests are marketed in the European Union and the US. A brief technological summary of these is provided in Table 1. The first FDA approval for a breast cancer in vitro diagnostic multigene signature was granted to MammaPrint
Table 1 Summary of commercially available multigene tests utilized in breast cancer Test
FDA approved
Origin
# of markers
Technology
Advantages
Disadvantages
Ref
MammaPrint
Yes (2007)
Fresh/ FFPE
70
Gene chip
FDA approval, reduction of health-care cost confirmed [46]
Predictive power is limited with ER-negative patients
[7]
PAM50
Yes (2013)
FFPE
50 ? 5a
RT-PCR
FDA approval
High cost of equipment, need for trained personal to perform the analysis
[11]
IHC4
No
FFPE
IHC
Minimal cost of assay
Discrepancies in results among centers
[14]
Oncotype DX
No
FFPE
16 ? 5a
RT-PCR
Reduction of health-care cost confirmed [45], no need for fresh sample
Need to send each sample and clinical data to a central facility
[16]
GGI
No
Fresh/ FFPE
97
Gene chip
Better discrimination of breast cancer samples classified as histologic grade 2
Only informative in luminal, non-HER2-positive tumors [51]
[17]
EndoPredict
No
FFPE
8 ? 3a
RT-PCR
Reduced health-care cost [21], variations in tissue handling time have negligible effects on the results [52]
Missed to predict chemotherapy benefit [53]
[19]
BCI
No
FFPE
5?2
RT-PCR
Reduction of health-care cost confirmed [54], no need for fresh sample
Only for patients recurrencefree after an initial 5 years of adjuvant endocrine therapy
[22]
a
4
Number of housekeeping genes included in the assay
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in 2007 (Agendia AG, The Netherlands). The microarraybased MammaPrint is used to assess the prognosis in patients \62 years of age with lymph node-negative or positive (1–3 lymph nodes) and ER-positive or -negative BC having a tumor smaller than 5 cm [6]. MammaPrint includes 70 genes including 55 established genes and 15 genes with unknown function. The outcome dichotomizes patients into low- or high-risk cohorts, which was shown to be more accurate when predicting 5-year metastasis-free survival than conventional clinicopathological parameters [7]. The original in vitro diagnostic assay utilized fresh frozen material, but the test has also been developed for paraffin-embedded breast carcinomas [8]. A prospective trial (MINDACT) for MammaPrint is on the move with concluding results expected next year. MINDACT is performed using the complete gene chips from which the 70 probes were selected. Having the entire genomic profile for such a large cohort will open the possibility of additional targeted signatures after completing the trial. A second FDA approval has been received in 2013 for the PAM50 (ProsignaTM, Nanostring Technologies, USA). PAM50 uses a panel of 50 genes plus 5 housekeeping genes to compute a risk of recurrence score (ROR) which enables to identify the four intrinsic BC subtypes. Breast cancer subtypes were designated already in 2000 using microarray profiling [9]. These intrinsic subtypes are recommended by international guidelines since 2007 to improve clinical risk assessment [10]. PAM50 initially utilized RT-PCR from FFPE archival samples to designate these subtypes [11]. PAM50 also provides estimation for distant relapse-free survival and likelihood of recurrence at 10 years for ER-positive, tamoxifen-treated patients. PAM50 was validated in two retrospective trials including more than 2,400 patients and can also support identification those node-positive and ER-positive patients who do not necessarily need adjuvant chemotherapy. PAM50 is now performed using the nCounter diagnostic system utilizing hybridization of target sequences and detection of molecular ‘‘barcodes’’ and single-molecule imaging to count transcripts. Although local processing provides an advantage over tests performed at a central laboratory only, cost for the equipment itself is not negligible and there is additional need for trained personal to perform the analysis. One important point to consider is the clinician’s aim to define the subtypes of luminal A, luminal B, HER2-enriched and basal-like using various platforms including routine immunohistochemistry of the four most important markers (ER, PGR, HER2 and Ki67), or hybridization assays like cDNA microarray (as [9] ) and Nanostring (PAM50). However, clinical utility of the subtypes is hampered by the fact that these different approaches can designate the same patients into different classes and only the triple-negative/basal-like cancers are robustly
reproducible [12]. In case of discrepant designation by PAM50 and immunohistochemical analysis, patients should still be managed according to the IHC markers [13]. Quite unsurprisingly, there is a marketed test (IHC4) actually based on the four routine IHC markers of ER, PR, HER2 and Ki67 [14]. A combination of IHC4 score and clinical characteristics has been used in 101 patients suggesting that this could also support decision-making for BC chemotherapy [15]. An important advantage of IHC4 as compared to other tests is the minimal cost for the test reagents which warrants further development of this assay. At the same time, omission of a central testing facility could result in discrepancies among various centers as observed for Ki67 alone—this underpins the necessity for a comprehensive quality assurance before such a product can enter general market. Oncotype DX (Genomic Health, USA) measures expression of 21 genes including 16 cancer-related genes and five reference genes to compute a continuous recurrence score ranging between 0 and 100 [16]. Genes are grouped into virtual groups, but at the end each gene has its own weight. Most important genes in the assay include HER2, ER and Ki67 with highest score given to the two genes related to the HER2-pathway (HER2 and GRB7). Recurrence score is prognostic for estrogen receptor-positive breast cancer treated with tamoxifen regardless of nodal status (positivity up to 3 lymph nodes). In addition, predictive value of Oncotype has been shown for assessing benefit from chemotherapy in ER-positive breast cancers. Currently, a large prospective clinical trial (TAILORx) is en route to validate test performance. In this trial, more than 10,000 patients have been enrolled and the first results are expected in 2017. A major limitation for Oncotype is the need to send each sample (tissue block or 15 slides) and clinical data to a central facility. Histological grade is a strong prognostic factor both in estrogen receptor-positive and -negative BC. Genomic Grade Index (MapQuant DX, Ipsogen, France) is a gene chip-based test measuring expression of 97 genes to enroll histologic grade 2 BC into good and poor prognostic groups [17]. In addition to microarray, an FFPE-based RTPCR edition of the signature was established and validated. This PCR genomic grade assay includes only 6 genes from the original 97 genes [18]. Prognostic performance of the PCR-GGI was also confirmed using FFPE samples [18]. The most recent multigene signature already commercially available is the RT-PCR based EndoPredict (Sividon Diagnostics GmbH, Germany) which stratifies ER-positive, HER2-negative patients into a low-risk and a high-risk group [19]. The test was investigated using FFPE samples from two large randomized phase III trials of the Austrian Breast and Colorectal Cancer Study Groups ABCSG6 and ABCSG8 (n = 378 and n = 1,324 patients, respectively),
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and was validated to discriminate significant differences in 10-year distant recurrence rates [20]. The EndoPredict assay itself was established in seven different molecular pathology laboratories in three European countries providing a framework for a reliable decentralized assessment of gene expression in ER-positive BC [21]. Breast Cancer Index (BCI, bioTheranostics, USA) delivers a continuous assessment of individual risk of distant recurrence for early-stage estrogen-positive node-negative BC. The test includes ratio of two sovereign biomarkers, HOXB13:IL17BR (H:I) and the 5-gene molecular grade index, which quantify estrogen-mediated signaling and tumor grade [22]. The assay is RT-PCR based and utilizes routine FFPE samples. In an independent validation study, BCI classified 265 ER-positive node-negative patients administered tamoxifen alone or tamoxifen with chemotherapy into low-risk and high-risk cohorts. High-risk patients had a fivefold increase compared to the low-risk cohort in 10-year risk of distant recurrence [23].
Unanswered clinical questions Prediction of late recurrence For patients with ER-positive tumors endocrine therapy is given for 5 years routinely. An open question is to select which ER-positive patients will need extended adjuvant endocrine therapy beyond these 5 years. This question is particularly important as more than half of the distant metastases occur after 5 years. The BCI test report directly assesses risk of distant recurrence within 5 years and between five and 10 years. A recent study compared performance of BCI, Oncotype DX and IHC4 to predict late recurrence in 665 ER-positive, node-negative BC. Although each assay had significant prognostic ability for early distant recurrence, BCI was the only one also capable to predict late recurrence [24]. Future comparisons including other genomic tests will deliver a more comprehensive evaluation of late recurrence prediction.
results in poor prognosis but likely higher chemotherapy sensitivity) and a HER2 group (to benefit from administration of trastuzumab). Consequently, when six such genomic tests were compared, each had a significant correlation to pathologic complete response [25]. Although many direct prognostic markers have been identified to date, most of these did not make it into actual clinical decision algorithms. The first transcriptomic analyses focusing on predictive signatures utilized cell culture models to establish signature which were then examined in actual patients [26]. However, cell lines exhibit the same heterogeneity as patients, and when cell culture model of parallel evolution for chemotherapy resistance was utilized, the cell lines showed a highly heterogeneous profile [27]. Clinically more robust profiles associated with response to chemotherapy could only be identified by utilizing large patient cohorts. Recently, a new approach to develop a predictor was implemented which is likely to gain further foothold in the future. In this, a genomic predictor combining ER status, predicted chemoresistance, predicted chemosensitivity, and predicted endocrine sensitivity was established to identify patients with high probability of survival following taxane and anthracycline chemotherapy [28]. Taxane and anthracycline-based regimens are utilized in HER2-negative breast cancers. In the project, instead of dividing the patients into two cohorts, the genetic heterogeneity of BC was acknowledged and multiple signatures were established to answer the same question in a given patient. The assay was based on the Affymetrix HGU133 gene-chip data of 310 patients and was independently validated in 198 patients. A similarly imperative decision is the selection of endocrine therapy for ER-positive patients. Currently, up to 50 % of the ER-positive tumors relapse on or after discontinuation of hormonal therapy; therefore, new response biomarkers would have high clinical value in this cohort. Yet, most of gene expression-based monogenic markers identified previously have failed when assessed in independent patient cohorts [29].
Prediction of therapy response
Emerging multigene approaches
Adjuvant systemic therapy can deliver improved outcome for patients surviving BC. Secondary risk prediction tools are those prognostic factors which identify patients likely profiting from adjuvant therapy. Core components of all current multigene genomic tests include three main groups of markers: an ER-related group (high receptor expression result in good prognosis and in higher endocrine sensitivity), a proliferation-related group (high proliferation
Next-generation sequencing (NGS)
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The first NGS-based assay for solid tumors was developed in 2011 [30]. In this, an ultra-deep targeted sequencing of a 71.1-kb sequence encompassing mutational hotspots for 42 cancer genes is performed. In another approach, a PCR assay was constructed for genomic rearrangements in breast tumors to detect these in blood sample, with an
Breast Cancer
excellent sensitivity of detecting one single copy of the tumor genome in the plasma [31]. Advantage of such a clinical assay is that it should not have any false positive rearrangements as there is no PCR amplification in nonrearranged, normal samples. NGS still faces a set of difficulties to be solved before large-scale clinical utility is achieved. There is an inherent challenge of contamination for tumor samples by other (normal) cells. Until now NGS was performed using frozen tissues, but operation with FFPE DNA will be more practical in a clinical environment. Though, sequencing accuracy also depends on the quality of DNA which is lower in FFPE samples and thereby discriminating artifacts and mutations will present a substantial challenge. Finally, NGS still requires a workintensive and expensive workflow. Internet-based tests Microarrays have the advantage of simultaneously possessing a large number of probes. This not only enables measurement of the entire transcriptome, but also the crossvalidation of different multigenic signatures. By utilizing public data repositories, one can construct large integrated databases enabling such confirmation studies [32]. Alternatively, the integration of different diagnostic tools into one software platform is possible as has been demonstrated by the online available www.recurrenceon line.com site [33]. Recurrence online utilizes Affymetrix microarrays to compute different prognostic signatures to predict the risk of relapse in ER-positive, node-negative and node-positive patients, to assess ER and HER2 status. Thus, different analysis can be performed in one step deriving primary, secondary and potentially also tertiary classification for a given patient. The initial version of recurrence online was validated using publicly available microarray data of more than 2000 patients. Recurrence online can also be used to compare new signatures to established ones. For example, in a preclinical study [34] a 95-gene classifier was developed to predict prognosis of node-negative and ER-positive breast cancer patients with high accuracy. This classifier combined with the 21-gene signature seems to result in the identification of more lowrisk patients who do not need chemotherapy than either classification alone. Patients in the high-risk group were found to be more chemosensitive so that they can benefit more from adjuvant chemotherapy [35]. An updated version of Recurrenceonline system is capable to discriminate good and bad prognosis cohorts within triple-negative cancers as well [36]. As such an approach is extendable for future gene sets, we can expect in future the emergence of single-step assays enabling computation of all available gene sets regardless of the initial classification of the tumor sample.
HRD score Defects in genes supporting homologous recombination have been associated with predisposition to multiple cancers. Mutations in BRCA1, BRCA2 and other members of the homologous recombination pathway are linked to response to therapeutics that introduce or exploit doublestranded DNA breaks like platinum agents and PARP-inhibitors. Regions of loss of heterozygosity, the irreversible loss of one of the parental alleles were observed more frequently in tumors with defective BRCA genes. The number of such regions within a tumor sample was defined as the homologous recombination deficiency score (HRD, Myriad Genetics, USA). A high HRD score is strongly associated with homologous recombination deficiency regardless of etiology. In 639 ovarian tumor samples, patients having a high HRD score had a markedly better prognosis compared to patients having low HRD score [37]. The score performed using Affymetrix 500 K gene chip arrays could be utilized to identify patients with breast, ovarian, lung and pancreatic cancers and also a high likelihood of responding to DNA damaging agents. miRNA A further alternative approach is the analysis of the noncoding transcriptome including miRNAs. This is feasible both in frozen and FFPE material as miRNAs are generally smaller than 23 base pairs and are not being subjects of significant degradation and cross-linking by formalin. The largest miRNA study to date analyzed over 1,300 breast carcinomas and demonstrated an important modulatory role for miRNAs in the biology BC [38]. miRNAs can also be used to determine prognosis. For example, miR-210 was differentially regulated in BC and had a prognostic potential for overall survival and time to metastasis [39]. Genes with inverse expression correlated to miR-210 included top cancer genes like BRCA1, PARP1, E-cadherin and RB1. The TCGA project included an integrated analysis of mRNA and miRNA expression for 466 patients. In this, 30 mRNAs and seven miRNAs were correlated to overall survival and the highest prognostic value in early stage tumors was achieved by an integrated miRNA/mRNA signature [40]. This integrated signature was further validated in eight BC cohorts including a total of 2,399 patients.
The next 5 years Many of the assays intensively developed a few years ago have lost their significance including the 14-gene Celera
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Metastatic Score of Applera, the 30-gene PGx Rerporter of Nuvera Biosciences, the 55-gene Breast Bioclassifier of Arup Labs and many more. At the same time, there is a wide range of new, clinically not yet applicable tests having the potential to deliver future generation of assays for BC. Epigenetic studies including establishment of gene signatures of genome-wide reprogramming of the chromatin landscape could help to detect endocrine therapy resistance [41]. RT-PCR adaptation of chromosomal instability signatures could help to define tumor aneuploidy and to stratify outcome in grade 2 breast cancer [42]. In addition to the identification of new mutations, sequencing of RNA by next-generation sequencing technologies could also provide the basis for expressionmeasuring signatures. RNA-seq has the advantage of a higher dynamic range as compared to gene expression arrays. This can enable detecting transcripts at very low or high levels enabling establishment of more subtle gene sets for patient stratification. Another NGS approach is based on isolation of nuclei by flow-sorting and then the whole genome is amplified to achieve a low sequencing coverage (6 %) of the nuclear genome of a cell. Besides observing the origin of the primary tumor mass from a single clonal expansion [43], this approach could also make it possible to detect copy number variations of a single cell. Given the relative uncertainty in cancer treatment, multigene tests have the potential for a significant cost reduction as they can pinpoint those patients for whom chemotherapy proves to be unnecessary [44]. Multiple studies have been performed to gauge the cost effectiveness of multigene test. Testing with Oncotype DX resulted in 33 % fewer patients actually receiving chemotherapy as compared to that originally planned. Although the test had to be performed for all patients, there was still a cost reduction of about €561 for Oncotype as compared to standard of care (n = 379) [45]. Similar results were observed for EndoPredict with one-fourth of patients changing to endocrine therapy alone (n = 167) [21]. In case of MammaPrint, 32 % fewer patients had to be given chemotherapy (n = 427) [46]. Altogether, these results demonstrate that a substantial proportion of all therapy decisions will change after performing a multigene assay and this mainly leads to reduction of adjuvant chemotherapy in line with its direct and other indirect costs (e.g. treatment of side effects). Clinically, utilization of IHC is basically omnipresent and can be employed for both traditional and new markers [47]. One could consider the use routine IHC of ER, PGR, HER2 and Ki67 to accomplish the same results as a relatively expensive genomic test—and vice versa, e.g. using the genomic tool to predict receptor status. However, this seems not to function using current techniques due to multiple shortcomings. By using the four genes, in theory
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16 different ‘‘IHC-subtypes’’ could be established—no clinical utility has yet been demonstrated for such a setting. Moreover, finding a cutoff for Ki67 is challenging, as the majority of cutoff values are significant for predicting disease-free survival. On the other hand, the potential of Oncotype DX to predict HER2 status was evaluated in 784 patients—and the assay delivered an unacceptably high rate of false-negative results of 72 %. Merely 10 out of 36 positive patients were identified as compared to FISH [48].
Conclusions The majority of current assays utilize two platforms: RTPCR and Affymetrix gene chips. The FDA clearance of Affymetrix arrays opened an easy-to-implement technology for new assays, including many above described tests like the genomic predictor of response for taxaneanthracycline chemotherapy, the MapQuant DX assay, the recurrence online platform and the HRD score. A second deduction of research to date is the need to account for the fact that many gene alterations will be context dependent. Therefore the integration of multiple data-types like determination of mutation status by sequencing and measuring gene expression by gene chips will be increasingly useful for the identification of therapeutic targets within different subsets of breast cancer. Such a combinatorial study has been recently performed for TP53 mutant breast cancers [49]. Recently, a systematic review of the evidence on clinical effectiveness (analytical validity, clinical validity and clinical utility) and cost-effectiveness was conducted by Ward et al. [50] with 32 full-text papers and abstracts. The economic analysis suggested that treatment guided using IHC4 had the highest potential and OncotypeDX had also a robust evidence base. For MammaPrint there was a gap between available evidence and the estimates of cost-effectiveness produced. Evidence for the remaining tests investigated (PAM50, BCI) was limited [50]. Currently, several genomic signatures are used to predict prognosis in ER-positive patients. In contrast to previous expectations, instead of settling down the evolution of multigene assays is just about to run up its exponential phase as many more tests are coming to designate the patients into additional sub-cohorts. Acknowledgments This study was supported by the OTKA K108655 grant. A. M. S. was supported by the European Union and the State of Hungary, co-financed by the European Social Fund in the ´ MOP-4.2.4.A/2-11-1-2012-0001 ‘National Excelframework of TA lence Program’. Conflict of interest of interest.
The authors declare that they have no conflict
Breast Cancer
References 1. Hammond ME, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, et al. American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol. 2010;28:2784–95. 2. Surowiak P, Suchocki S, Gyorffy B, Gansukh T, Wojnar A, Maciejczyk A, et al. Stromal myofibroblasts in breast cancer: relations between their occurrence, tumor grade and expression of some tumour markers. Folia Histochem Cytobiol. 2006;44:111–6. 3. Lende TH, Janssen EA, Gudlaugsson E, Voorhorst F, Smaaland R, van Diest P, et al. In patients younger than age 55 years with lymph node-negative breast cancer, proliferation by mitotic activity index is prognostically superior to adjuvant! J Clin Oncol. 2011;29:852–8. 4. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24:2206–23. 5. Polley MY, Leung SC, McShane LM, Gao D, Hugh JC, Mastropasqua MG, et al. An international Ki67 reproducibility study. J Natl Cancer Inst. 2013;105:1897–906. 6. Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, Floore A, et al. The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat. 2009;116:295–302. 7. van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999–2009. 8. Mittempergher L, de Ronde JJ, Nieuwland M, Kerkhoven RM, Simon I, Rutgers EJ, et al. Gene expression profiles from formalin fixed paraffin embedded breast cancer tissue are largely comparable to fresh frozen matched tissue. PLoS One. 2011;6:e17163. 9. Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003;100:8418–23. 10. Mihaly Z, Gyorffy B. Improving pathological assessment of breast cancer by employing array-based transcriptome analysis. Microarrays. 2013;2:14. 11. Dowsett M, Sestak I, Lopez-Knowles E, Sidhu K, Dunbier AK, Cowens JW, et al. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol. 2013;31: 2783–90. 12. Weigelt B, Mackay A, A’Hern R, Natrajan R, Tan DS, Dowsett M, et al. Breast cancer molecular profiling with single sample predictors: a retrospective analysis. Lancet Oncol. 2010;11: 339–49. 13. van de Vijver MJ. Molecular tests as prognostic factors in breast cancer. Virchows Arch. 2014;464:283–91. 14. Cuzick J, Dowsett M, Pineda S, Wale C, Salter J, Quinn E, et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the genomic health recurrence score in early breast cancer. J Clin Oncol. 2011;29:4273–8. 15. Barton S, Zabaglo L, A’Hern R, Turner N, Ferguson T, O’Neill S, et al. Assessment of the contribution of the IHC4 ? C score to decision making in clinical practice in early breast cancer. Br J Cancer. 2012;106:1760–5.
16. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, nodenegative breast cancer. N Engl J Med. 2004;351:2817–26. 17. Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006;98:262–72. 18. Toussaint J, Sieuwerts AM, Haibe-Kains B, Desmedt C, Rouas G, Harris AL, et al. Improvement of the clinical applicability of the Genomic Grade Index through a qRT-PCR test performed on frozen and formalin-fixed paraffin-embedded tissues. BMC Genom. 2009;10:424. 19. Filipits M, Rudas M, Jakesz R, Dubsky P, Fitzal F, Singer CF, et al. A new molecular predictor of distant recurrence in ERpositive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin Cancer Res. 2011;17:6012–20. 20. Dubsky P, Filipits M, Jakesz R, Rudas M, Singer CF, Greil R, et al. EndoPredict improves the prognostic classification derived from common clinical guidelines in ER-positive, HER2-negative early breast cancer. Ann Oncol. 2013;24:640–7. 21. Muller BM, Keil E, Lehmann A, Winzer KJ, Richter-Ehrenstein C, Prinzler J, et al. The EndoPredict gene-expression assay in clinical practice—performance and impact on clinical decisions. PLoS One. 2013;8:e68252. 22. Ma XJ, Salunga R, Dahiya S, Wang W, Carney E, Durbecq V, et al. A five-gene molecular grade index and HOXB13:iL17BR are complementary prognostic factors in early stage breast cancer. Clin Cancer Res. 2008;14:2601–8. 23. Jankowitz RC, Cooper K, Erlander MG, Ma XJ, Kesty NC, Li H, et al. Prognostic utility of the breast cancer index and comparison to adjuvant! Online in a clinical case series of early breast cancer. Breast Cancer Res. 2011;13:R98. 24. Sgroi DC, Sestak I, Cuzick J, Zhang Y, Schnabel CA, Schroeder B, et al. Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: a prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population. Lancet Oncol. 2013;14:1067–76. 25. Iwamoto T, Lee JS, Bianchini G, Hubbard RE, Young E, Matsuoka J, et al. First generation prognostic gene signatures for breast cancer predict both survival and chemotherapy sensitivity and identify overlapping patient populations. Breast Cancer Res Treat. 2011;130:155–64. 26. Gyorffy B, Serra V, Jurchott K, Abdul-Ghani R, Garber M, Stein U, et al. Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. Oncogene. 2005;24: 7542–51. 27. Tegze B, Szallasi Z, Haltrich I, Penzvalto Z, Toth Z, Liko I, et al. Parallel evolution under chemotherapy pressure in 29 breast cancer cell lines results in dissimilar mechanisms of resistance. PLoS One. 2012;7:e30804. 28. Hatzis C, Pusztai L, Valero V, Booser DJ, Esserman L, Lluch A, et al. A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. JAMA. 2011;305:1873–81. 29. Mihaly Z, Kormos M, Lanczky A, Dank M, Budczies J, Szasz MA, et al. A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer. Breast Cancer Res Treat. 2013;140:219–32. 30. Harismendy O, Schwab RB, Bao L, Olson J, Rozenzhak S, Kotsopoulos SK, et al. Detection of low prevalence somatic mutations in solid tumors with ultra-deep targeted sequencing. Genome Biol. 2011;12:R124.
123
Breast Cancer 31. McBride DJ, Orpana AK, Sotiriou C, Joensuu H, Stephens PJ, Mudie LJ, et al. Use of cancer-specific genomic rearrangements to quantify disease burden in plasma from patients with solid tumors. Genes Chromosomes Cancer. 2010;49:1062–9. 32. Gyorffy B, Schafer R. Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients. Breast Cancer Res Treat. 2009;118:433–41. 33. Gyorffy B, Benke Z, Lanczky A, Balazs B, Szallasi Z, Timar J, et al. RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res Treat. 2012;132: 1025–34. 34. Naoi Y, Kishi K, Tanei T, Tsunashima R, Tominaga N, Baba Y, et al. Development of 95-gene classifier as a powerful predictor of recurrences in node-negative and ER-positive breast cancer patients. Breast Cancer Res Treat. 2011;128:633–41. 35. Naoi Y, Kishi K, Tsunashima R, Shimazu K, Shimomura A, Maruyama N, et al. Comparison of efficacy of 95-gene and 21-gene classifier (Oncotype DX) for prediction of recurrence in ER-positive and node-negative breast cancer patients. Breast Cancer Res Treat. 2013;140:299–306. 36. Gyorffy B, Karn T, Sztupinszki Z, Weltz B, Muller V, Pusztai L. Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer. Int J Cancer. 2014. 37. Abkevich V, Timms KM, Hennessy BT, Potter J, Carey MS, Meyer LA, et al. Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer. Br J Cancer. 2012;107:1776–82. 38. Dvinge H, Git A, Graf S, Salmon-Divon M, Curtis C, Sottoriva A, et al. The shaping and functional consequences of the microRNA landscape in breast cancer. Nature. 2013;497:378–82. 39. Volinia S, Galasso M, Sana ME, Wise TF, Palatini J, Huebner K, et al. Breast cancer signatures for invasiveness and prognosis defined by deep sequencing of microRNA. Proc Natl Acad Sci USA. 2012;109:3024–9. 40. Volinia S, Croce CM. Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci USA. 2013;110:7413–7. 41. Magnani L, Stoeck A, Zhang X, Lanczky A, Mirabella AC, Wang TL, et al. Genome-wide reprogramming of the chromatin landscape underlies endocrine therapy resistance in breast cancer. Proc Natl Acad Sci USA. 2013;110:E1490–9. 42. Szasz AM, Li Q, Eklund AC, Sztupinszki Z, Rowan A, Tokes AM, et al. The CIN4 chromosomal instability qPCR classifier defines tumor aneuploidy and stratifies outcome in grade 2 breast cancer. PLoS One. 2013;8:e56707.
123
43. Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472:90–4. 44. Munkacsy G, Abdul-Ghani R, Mihaly Z, Tegze B, Tchernitsa O, Surowiak P, et al. PSMB7 is associated with anthracycline resistance and is a prognostic biomarker in breast cancer. Br J Cancer. 2010;102:361–8. 45. Blohmer JU, Rezai M, Kummel S, Kuhn T, Warm M, Friedrichs K, et al. Using the 21-gene assay to guide adjuvant chemotherapy decision-making in early-stage breast cancer: a cost-effectiveness evaluation in the German setting. J Med Econ. 2013;16:30–40. 46. Drukker CA, Bueno-de-Mesquita JM, Retel VP, van Harten WH, van Tinteren H, Wesseling J, et al. A prospective evaluation of a breast cancer prognosis signature in the observational RASTER study. Int J Cancer. 2013;133:929–36. 47. Maciejczyk A, Jagoda E, Wysocka T, Matkowski R, Gyorffy B, Lage H, et al. ABCC2 (MRP2, cMOAT) localized in the nuclear envelope of breast carcinoma cells correlates with poor clinical outcome. Pathol Oncol Res. 2012;18:331–42. 48. Ignatiadis M, Sotiriou C. Breast cancer: should we assess HER2 status by Oncotype DX? Nat Rev Clin Oncol. 2012;9:12–4. 49. Gyorffy B, Bottai G, Lehmann-Che J, Keri G, Orfi L, Iwamoto T, et al. TP53 mutation-correlated genes predict the risk of tumor relapse and identify MPS1 as a potential therapeutic kinase in TP53-mutated breast cancers. Mol Oncol. 2014;8:508. 50. Ward S, Scope A, Rafia R, Pandor A, Harnan S, Evans P, et al. Gene expression profiling and expanded immunohistochemistry tests to guide the use of adjuvant chemotherapy in breast cancer management: a systematic review and cost-effectiveness analysis. Health Technol Assess. 2013;17:1–302. 51. Desmedt C, Giobbie-Hurder A, Neven P, Paridaens R, Christiaens MR, Smeets A, et al. The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1–98 trial. BMC Med Genomics. 2009;2:40. 52. Poremba C, Uhlendorff J, Pfitzner BM, Hennig G, Bohmann K, Bojar H, et al. Preanalytical variables and performance of diagnostic RNA-based gene expression analysis in breast cancer. Virchows Arch. 2014;465:409–17. 53. Martin M, Brase JC, Calvo L, Krappmann K, Ruiz-Borrego M, Fisch K, et al. Clinical validation of the EndoPredict test in nodepositive, chemotherapy-treated ER ?/HER2- breast cancer patients: results from the GEICAM 9906 trial. Breast Cancer Res. 2014;16:R38. 54. Gustavsen G, Schroeder B, Kennedy P, Pothier KC, Erlander MG, Schnabel CA, et al. Health economic analysis of Breast Cancer Index in patients with ER?, LN-breast cancer. Am J Manag Care. 2014;20:e302–10.
