Gene expression-based prognostic and predictive markers for breast cancer: a primer for practicing pathologists.
Abstract: * Context.--Gene expression-based prognostic assays for breast cancer are now available as commercial reference laboratory tests covered by insurance.

Objective.--To provide practicing pathologists with information about the nature of these assays, differences among them, and their use by clinical oncologists in the management of patients diagnosed with breast cancer.

Data Sources.--Review of literature and unpublished data from the National Surgical Adjuvant Breast and Bowel

Project. This review focused on a general conceptual description of the technology behind these assays and differences among them to aid understanding by pathologists in practice.

Conclusions.--While these assays are clinically useful, they are still evolving. The future development of gene expression-based markers will need to be more clinical-context-specific to be clinically useful.
Article Type: Report
Subject: Breast cancer (Diagnosis)
Breast cancer (Genetic aspects)
Breast cancer (Prognosis)
Breast cancer (Care and treatment)
Pathology (Practice)
Gene expression (Research)
Biological markers (Usage)
Authors: Kim, Chungyeul
Taniyama, Yusuke
Paik, Soonmyung
Pub Date: 06/01/2009
Publication: Name: Archives of Pathology & Laboratory Medicine Publisher: College of American Pathologists Audience: Academic; Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2009 College of American Pathologists ISSN: 1543-2165
Issue: Date: June, 2009 Source Volume: 133 Source Issue: 6
Topic: Event Code: 200 Management dynamics; 310 Science & research
Accession Number: 230152013
Full Text: CLINICAL BACKGROUND

The identification of molecular markers with prognostic significance may help cancer patients avoid treatment that is unlikely to be successful. In breast cancer, for example, clinical studies have shown that adding adjuvant chemotherapy to tamoxifen in the treatment of node-negative, hormone receptor (HR)-positive breast cancer improves disease outcome. (1) However, treatment with tamoxifen alone is associated with a 15% likelihood of distant recurrence at 10 years in this population, suggesting that 85% of these patients would do well without the addition of cytotoxic chemotherapy and could avoid the adverse events inherent to such treatment.1 Nevertheless, the current National Comprehensive Cancer Network (NCCN) (2) and St. Gallen (3) clinical practice guidelines, using classical histopathology and immunohistochemical prognostic markers, categorize less than 10% of patients with nodenegative, HR-positive disease at low enough risk of recurrence to forgo adjuvant chemotherapy. These treatment guidelines assume that patients will derive the same degree of benefit from chemotherapy regardless of their baseline risk.

Clinical oncologists need 2 basic pieces of information to make treatment decisions for individual patients: (1) a patient's baseline risk after legacy treatment and (2) the expected degree of additional benefit she will receive from systemic therapy added to legacy treatment. Legacy treatment can consist of surgery alone, surgery plus antihormonal therapy, or surgery plus chemohormonal therapy, depending on the stage and hormone receptor status of the tumor. This varying clinical context dictates that most clinically useful prognostic and predictive markers are those developed with a specific clinical context in mind and tested and validated within that clinical context. Clinical tests can provide either prognostic or predictive information or both.

In the past there was no distinction made between "prognostic" and "predictive" markers when investigators conducted studies and such practice was a source of great confusion in the field. More recently, a distinction between the two has become the norm rather than the exception. However, it also has become clear that for most chemotherapy regimens in use today, general prognosticators end up as predictive markers of the degree of benefit from chemotherapy. For example, estrogen receptor status is not only prognostic and predictive of response to antihormone therapy, but also predictive of response to chemotherapy (the presence of the estrogen receptor is associated with less benefit from chemotherapy). Another example is uPA/PAI-1 (urokinase-type plasminogen activator/plasminogen activator inhibitor type 1). Harbeck and colleagues (4) analyzed samples from more than 8000 women with node-negative breast cancer. Intratumoral concentration of uPA and its inhibitor, PAI-1, were found to correlate directly with risk of disease recurrence. Despite the fact that these markers have no suspected direct role in chemotherapy response, patients categorized as high-risk, based on uPA/PAI-1 levels, derived significant benefit from chemotherapy, whereas low-risk patients gained little from the addition of chemotherapy.

To understand the intriguing interaction between prognosis and prediction of chemotherapy response, one needs to look at the studies published by the "Seattle Project." (5)

In this project, a collection of mutant yeast strains that harbor specific mutations in genes involved in DNA repair or cell cycle control pathways were generated and then tested against US Food and Drug Administration-approved chemotherapeutic agents. Such tests provide synthetic lethality screening. For example, among all mutant yeast strains, only those with defects in double-strand DNA repair pathways will be sensitive to mitoxantrone, which binds topoisomerase II-a and causes DNA double-strand breaks. The investigators hoped to catalog specific targets for all chemotherapy agents. What they found, however, was highly disappointing--most of the chemotherapeutic agents tested, especially those used for treatment of breast cancer such as 5-fluorouracil and doxorubicin, did not show any selectivity for targets. Therefore, tumors with high proliferation rate are expected to be sensitive to such agents. This explains why most of the prognostic markers are also predictive markers for chemotherapy response.

Classic prognostic tools such as tumor grade have traditionally been regarded as important indicators of breast cancer risk. (6) Indeed, Adjuvant! Online, a SEER (Surveillance Epidemiology and End Results) data-based algorithm integrating clinical (age, nodal status) and histopathologic (estrogen receptor, size, grade) features of breast cancer, has been shown to accurately predict 10year mortality rate. (7,8) In a study we carried out (S. Paik, MD, and G. Tang, PhD, unpublished data, September 2008), Adjuvant! was also predictive of the degree of benefit from chemotherapy. Yet, while excellent in providing average risk assessment for cohorts of patients, assessment of these markers can be highly variable at the individual patient level. One study (9) that compared tumor grade assessments by 3 independent pathologists found that concordance was less than 50%, suggesting that the accuracy of risk estimates based on histologic grade may vary considerably. It is hoped that gene expression-based markers will provide more reproducible individualized risk assessments.

With this general background, let's examine the current state of the art of gene expression-based prognostic and predictive markers for breast cancer.

Assays for Gene Expression

There are many different ways to quantify the levels of expression of genes. On a broad scale, there are techniques that require the initial conversion of messenger RNA (mRNA) into complementary DNA (cDNA) by using reverse transcriptase in order to measure gene expression. Other methods do not require such conversion and directly manipulate mRNA to determine expression. All of the currently available clinical prognostic tests are based on the conversion of mRNA into cDNA, which can be achieved by using the polyadenylated (polyA) tail of mRNA as a template (oligo[dT] priming) or by using a gene-specific template (gene-specific priming). Oligo(dT) priming provides the ability to convert an entire mRNA species into a library of cDNAs. This library can then be used as a template for many different assays, for example, polymerase chain reaction (PCR) (of each gene target of interest) or microarray gene expression profiling, which allows the examination of expression levels of essentially all genes. Microarray gene expression profiling is achieved by hybridizing fluorescence-labeled cDNA to a library of oligonucleotides representing literally all known human genes that are bound to a solid matrix (Affymetrix GeneChip, Santa Clara, California, and Agilent array, Santa Clara, California) or beads (Illumina bead array, San Diego, California). (10) After hybridization, expression levels of each gene are quantified by using laser scanning microscopes (scanners). In the past, academic institutions printed their own microarrays on glass slides. However, because of decreasing cost and technical reproducibility issues, most have switched to mass-produced arrays from manufacturers such as Affymtetrix, Agilent, or Illumina. Each microarray manufacturer uses different methodologies to construct microarrays, and the resulting microarrays have different hybridization characteristics and dynamic ranges. One example of a clinical prognostic assay based on microarray technology is the MammaPrint assay (Agendia BV, Amsterdam, the Netherlands). (11,12) This assay is based on an Agilent microarray platform. Patients are assigned to either good or poor prognosis categories on the basis of a distance measure between the expression levels of 70 genes and the model expression levels of good or poor prognosis groups (centroids) developed from reference clinical samples used in the development of the assay. The MammaPrint is a validated US Food and Drug Administration-cleared assay that provides additional prognostic information beyond what clinical and pathologic features offer. However, it requires the use of high-quality RNA that can be obtained only from fresh tissue procured in RNARetain solution (Asuragen Inc, Austin, Texas) or snap-frozen tumor tissue and therefore cannot be applied to formalin-fixed, paraffin-embedded tumor blocks (FPETs).

Overcoming FPET Barriers

What makes RNA extracted from FPETs a poor starting material for microarray analysis?

Masuda et al (13) analyzed the RNA extracted from FPETs for chemical modifications. RNA extracted from freshly made FPETs that were fixed and processed in ideal conditions (fixed in 10% buffered formalin at 4[degrees]C) were fairly well preserved. Although the extracted RNAs showed no sign of degradation as compared with RNA from fresh samples, they were poor substrates for cDNA synthesis and subsequent PCR amplification, so that only PCR amplification of short targets was possible. This seems to be due to chemical modification of nucleic acids in FPETs, that is, the addition of monomethylol (-CH2OH) groups to all 4 bases to varying degrees, and dimerization of adenines through methylene bridging. In addition to chemical modification, RNAs in FPETs continue to be degraded or fragmented over time during storage for reasons that are unclear. Cronin et al (14) systematically examined the quality of RNA extracted from breast cancer FPET specimens taken at different times. RNAs from FPETs archived for approximately 1 year were less fragmented than RNA archived for approximately 6 or 17 years.

For this reason, gene-specific priming with short PCR amplification target sequences is recommended for FPETs. (14) In this scenario, a gene-specific cDNA of short length is synthesized by using reverse transcription with a gene-specific primer, and then a new set of primers targeting that cDNA is used for PCR amplification. This process is called reverse transcription PCR (RT-PCR).

Reverse transcription PCR can be performed with endpoint product quantification or real-time product quantification. For clinical assays, usually real-time product quantification is used (QRT-PCR). This QRT-PCR can be performed because DNA polymerase used for a PCR reaction also has exonuclease activity which degrades DNA already bound to the template. Thus, if an oligonucleotide probe (which binds to the middle of the PCR-amplified DNA region and is designed in such a way that the fluorescence signal is released only upon degradation by exonuclease) is mixed into a PCR reaction tube, then the release of the fluorescence signal should be directly proportional to the quantity of PCR product. Real-time PCR devices measure fluorescence signals at defined points of each thermal cycle, which allows quantification of PCR products in real time. In the gene expression field, QRTPCR is regarded as the gold standard to which all other assays are compared. When combined with gene-specific priming, QRT-PCR can provide highly accurate relative expression levels with degraded RNA from FPETs as a starting material. An additional problem with FPETs is that the absolute signal of RT-PCR from the same amount of starting RNA decreases significantly if blocks have been stored for a long time, resulting in an approximate 100fold reduction in signal if the block is 10 years old compared to a freshly made block. (14) However, careful normalization, based on genes with minimal variation of expression level among different tumor samples, can largely compensate for these differences in absolute signal. (14) The OncotypeDX assay (Genomic Health Inc, Redwood City, California), a prognostic test for node-negative, estrogen receptor-positive breast cancer offered by a commercial reference laboratory, is based on QRT-PCR measurement of 16 cancer genes that are normalized to the measurement of 5 reference genes. (9,15)

Development and Validation of OncotypeDX Assay

Statistical experts agree that the steps taken to develop the OncotypeDX was exemplary and recommend that others take a similar approach in developing new clinical assays. (16)

To develop a context-specific prognostic assay that addresses which women diagnosed with axillary node-negative and estrogen receptor-positive breast cancer require more than 5 years of tamoxifen therapy, National Surgical Adjuvant Breast and Bowel Project (NSABP) investigators have collaborated with scientists at Genomic Health, Inc, the developers of methods for high-throughput QRT-PCR with RNA extracted from FPETs. First, individual QRTPCR assays optimized for fragmented RNA substrates that can be isolated from FPETs were developed for 250 candidate genes identified through literature and database searches. Many genes identified as prognostic genes through gene expression profiling studies, including the 70 genes from the MammaPrint assay, were included in this set of 250 genes. Three cohorts, including women in the tamoxifen-treated arm of NSABP trial B-20, were examined for the expression of 250 genes. Correlating clinical outcome with expression levels yielded many prognostic genes from these 3 studies, and 16 top-performing genes were identified for final model building and validation. Relative expression levels of the 16 genes were measured in relationship to average expression levels of 5 reference genes. While the majority of these 16 genes were found to be estrogen receptor-related (ESR1, PGR, BCL2, SCUBE2) and proliferation-related (Ki67, STK15, Survivin, CCNB1, MYBL2), some genes did not belong to these 2 categories (HER2, GRB7, MMP11, CTSL2, GSTM1, CD68, BACG1). An unscaled recurrence score (RSu) was calculated by using coefficients defined on the basis of regression analysis of gene expression. Recurrence score (RS) was rescaled from RSu as follows: RS = 0 if RSu < 0; RS = 20 X (RSu - 6.7) if 0 [less than or equal to] RSu < 100;and RS [less than or equal to] 100 if RSu > 100. Final validation of the RS was achieved by examining its performance in an independent cohort from NSABP trial B-14, which was not used in the model-building process. The validation study was conducted with a rigorous predefined statistical analysis plan with pre-specified outcome endpoints and cutoffs for RS. The predefined low-risk group (RS < 18) demonstrated a significantly better prognosis than higher-risk groups. (9) Compared to the NCCN or St. Gallen criteria, which assigned less than 10% of these patients from B-14 into the low-risk group, RS allowed the categorization of 50% of these patients into a low-risk group with similar 10-year distant disease-free survival rates as the low-risk groups identified by the NCCN or St. Gallen criteria (S. Paik, MD, and G. Tang, PhD, unpublished data, September 2008).

When reporting on the B-14 study, Paik et al (9) used 3 subgroups arbitrarily chosen prospectively for Kaplan-Meier plot comparisons. However, RS was developed originally as a continuous variable and should be used as such. For example, the prognosis for a patient with an RS of 17 (who is categorized as low risk in the B-14 study) would not be very different from that of a patient with an RS of 19 (categorized as intermediate risk). On the other hand, the prognosis for a patient with an RS of 2 would be fairly different from the prognosis of a patient with an RS of 17, although both are in the low-risk group. As discussed above, because of the inclusion of proliferation- and estrogen receptor-related genes, RS was expected to be also predictive of chemotherapy response; this was tested in NSABP trial B-20 where, indeed, a higher RS was associated with a higher degree of benefit from adjuvant chemotherapy. (15) Although this interaction between RS and chemotherapy has not been validated outside NSABP trial B-20 until recently, because of the low baseline risk of patients with low RS, the oncology community came to the consensus that enough evidence existed not to use chemotherapy in such patients, given the expectation that the benefit from chemotherapy would be low. However, for patients in the intermediate range of RS--for whom the baseline risk is sufficiently high to cause worry, but for whom chemotherapy would have an uncertain degree of benefit on the basis of the B-20 results--it was felt that a randomized prospective comparison between modern antihormonal therapy, including aromatase inhibitor with or without chemotherapy, would be important. This formed the basis for the design of the TAILORx trial, a United States intergroup study that uses upfront testing with OncotypeDX and the assignment of intermediate-risk patients to either hormonal therapy alone or chemohormonal therapy. (17) The tumor tissue bank established from this trial will be highly valuable in the further optimization of gene expression-based or other prognostic and predictive assays.

Which Gene Expression-Based Prognostic Test is Better?

None of these tests is perfect. Performance among the tests is similar, as has been demonstrated elegantly by the North Carolina group. (18) Thus, their utility should be based on the clinical context for which they were developed. (16) However, it may be useful to pathologists and clinicians to have an understanding of the fundamental philosophic differences that researchers exercised in the development of various gene expression assays. The Table summarizes the differences between 2 commercial tests (OncotypeDX and MammaPrint).

From a conceptual viewpoint, there are 2 kinds of gene expression-based tests: those that provide results as a continuous variable and those that provide categoric (usually dichotomous) results. The OncotypeDX assay (9) is an example of the former and MammaPrint (11) and intrinsic subtype assays (19) are examples of the latter.

The OncotypeDX assay assumes a biologic continuum and reports a score ranging from 0 to 100, which is the result of mathematic transformation of Cox model of expression levels of 16 cancer-related genes in the tumor. (9) Each score is associated with an expected distant recurrence rate at 10 years among patients with estrogen receptor-positive, node-negative breast cancer treated with tamoxifen and an expected degree of benefit from adding chemotherapy to tamoxifen. The MammaPrint assay compares expression levels of 70 cancer genes as measured by microarray centroids or averaged expression levels of good or poor prognosis groups from a reference study, and cases are assigned to either group on the basis of distance from each centroid. (11) Although each case is different and represents one in a biologic continuum, the cases are assigned to dichotomous classification on the basis of resemblance. For the intrinsic subtypes developed by the Stanford group, that assignment is to multiple groups (luminal A/B, HER2, and basal-like). As expected, within each subgroup, there is a continuum of expression levels of classification genes. (19)

Physicians often prefer to deal with dichotomous results in the clinic, but many fail to recognize that such dichotomous tests are the results of transforming continuous results into dichotomous ones and that an assessment of risk, based on that dichotomous classification, provides an averaged risk rather than an individual risk. To assume that the MammaPrint assay is better than the OncotypeDX assay because it can assign intermediate-risk patients defined by RS into either a good or a poor prognosis group is a mistake.

CONCLUSION

The field of gene expression-based prognostic assays is rapidly evolving and much experience has been gained in the field. It is important to stress that context-specific markers are the ones most useful to clinicians. Prespecifying clinical context will be critical in the development and validation of new assays.

Supported in part by Public Health Service Grants U10CA12027, U10CA-69974, U10CA-37377, and U10CA-69651 from the National Cancer Institute, Department of Health and Human Services, and by Genomic Health, Inc, Redwood City, California.

References

(1.) Fisher B, Jeong JH, Bryant J, et al. Treatment of lymph-node-negative, oestrogen-receptor-positive breast cancer: long-term findings from National Surgical Adjuvant Breast and Bowel Project randomised clinical trials. Lancet. 2004;364: 858-868.

(2.) Carlson RW, Edge SB, Theriault RL, et al. NCCN: breast cancer. Cancer Control. 2001;8:54-61.

(3.) Goldhirsch A, Wood WC, Gelber RD, Coates AS, Thurlimann B, Senn HJ. Meeting highlights: updated international expert consensus on the primary therapy of early breast cancer. J Clin Oncol. 2003;21:3357-3365.

(4.) Harbeck N, Kates RE, Schmitt M, et al. Urokinase-type plasminogen activator and its inhibitor type 1 predict disease outcome and therapy response in primary breast cancer. Clin Breast Cancer. 2004;5:348-352.

(5.) Simon JA, Szankasi P, Nguyen DK, et al. Differential toxicities of anticancer agents among DNA repair and checkpoint mutants of Saccharomyces cerevisiae. Cancer Res. 2000;60:328-333.

(6.) Amat S, Penault-Llorca F, Cure H, et al. Scarff-Bloom-Richardson (SBR) grading: a pleiotropic marker of chemosensitivity in invasive ductal breast carcinomas treated by neoadjuvant chemotherapy. Int J Oncol. 2002;20:791-796.

(7.) Ravdin PM, Siminoff LA, Davis GJ, et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol. 2001;19:980-991.

(8.) Olivotto IA, Bajdik CD, Ravdin PM, et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol. 2005; 23:2716-2725.

(9.) Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351:2817-2826.

(10.) Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995; 270:467-470.

(11.) van't Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530-536.

(12.) van de Vijver MJ, He YD, van'tVeer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999-2009.

(13.) Masuda N, Ohnishi T, Kawamoto S, Monden M, Okubo K. Analysis of chemical modification of RNA from formalin-fixed samples and optimization of molecular biology applications for such samples. Nucleic Acids Res. 1999;27: 4436-4443.

(14.) Cronin M, Pho M, Dutta D, et al. Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Am J Pathol. 2004;164:35-42.

(15.) Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006;24:3726-3734.

(16.) Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst. 2003;95:14-18.

(17.) Sparano JA. TAILORx: trial assigning individualized options for treatment (Rx). Clin Breast Cancer. 2006;7:347-350.

(18.) Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med. 2006;355:560-569.

(19.) Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747-752.

Chungyeul Kim, MD; Yusuke Taniyama, MD; Soonmyung Paik, MD

Accepted for publication November 10, 2008.

From the Division of Pathology, National Surgical Adjuvant Breast and Bowel Project Foundation, Pittsburgh, Pennsylvania.

The authors have no relevant financial interest in the products or companies described in this article.

Reprints: Soonmyung Paik, MD, NSABP, Division of Pathology, Four Allegheny Center 5th Floor, East Commons Professional Bldg, Pittsburgh, PA 15212 (e-mail: soon.paik@nsabp.org).
Table. Summary of Differences Between 2 Commercially Available
Prognostic Tests for Breast Cancer in the United States

                                              Test

Characteristic          OncotypeDXa              MammaPrintb

Sample requirement      3 to 6 unstained         Snap-frozen or fresh
                          sections,                tissue procured in
                          10-[micro]m thick,       RNARetain solution
                          made from routine        (Asuragen Inc,
                          formalin-fixed,          Austin, Texas)
                          paraffin-embedded
                          tumor block
Technology              Real-time                Oligonucleotide
                          quantitative             microarray
                          polymerase chain
                          reaction
Number of genes         16 (plus 5 reference     70
                          genes)
What is reported?       Continuous risk score    Dichotomous
                          from 0 to 100 and        classification (low
                          associated risk of       or high risk)
                          distant recurrence
                          in 10 years; HER2,
                          estrogen and
                          progesterone
                          receptor mRNA
                          levels
Web link to sample      http://www.oncotypedx    http://row.agendia
  reports                 .com/Flash/              .com/en/your_test_
                          ResultsFlashDemo.htm     results_3.html
                          (accessed April          (accessed November
                          2009)                    10, 2008)
Algorithm               Based on Cox model       Based on distance of
                                                   each case to
                                                   centroids derived
                                                   from reference
                                                   study
Clinical context        Node-negative,           Any
                          estrogen
                          receptor-positive
                          breast cancer
                          treated with
                          tamoxifen
Independently           Yes                      Yes
  validated
  prognostic utility
Predictive of degree    Yes                      Most likely yes, but
  of benefit from                                  has not been tested
  chemotherapy                                     formally
Cost                    About US $3400           About US $4000
Agency clearance        CLIA                     FDA
Utilization in United   > 65 000 cases tested    Not known (claimed to
  States                                           be more than 15 000
                                                   worldwide)

Abbreviations: CLIA, Clinical Laboratory Improvement Amendments; FDA,
US Food and Drug Administration.

(a) Genomic Health Inc, Redwood City, California.

(b) Agendia, Amsterdam, the Netherlands.
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