Appendix A

Case Studies

This appendix reviews the case studies that the committee examined, including six commercially available omics-based tests, an early single-marker test, and an omics-based test that did not advance to clinical use. These case studies appear in the following order:

  • Human epidermal growth factor receptor 2 (HER2)
  • Oncotype DX
  • MammaPrint
  • Tissue of Origin
  • OVA1
  • OvaCheck
  • AlloMap
  • Corus CAD

The case study focusing on several omics-based tests developed by a Duke University laboratory to predict sensitivity to chemotherapeutic agents appears in Appendix B.

HER2

HER2 is one of the earliest biomarker tests for guiding therapeutic decisions, and is widely used in clinical practice. The development of HER2 as an effect modifier biomarker has transformed breast cancer treatment by identifying the 20-30 percent of patients with overexpression of the HER2 oncogene who are likely to benefit from therapy targeting HER2 (De et



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Appendix A Case Studies This appendix reviews the case studies that the committee examined, including six commercially available omics-based tests, an early single- marker test, and an omics-based test that did not advance to clinical use. These case studies appear in the following order: • Human epidermal growth factor receptor 2 (HER2) • Oncotype DX • MammaPrint • Tissue of Origin • OVA1 • OvaCheck • AlloMap • Corus CAD The case study focusing on several omics-based tests developed by a Duke University laboratory to predict sensitivity to chemotherapeutic agents appears in Appendix B. HER2 HER2 is one of the earliest biomarker tests for guiding therapeutic deci- sions, and is widely used in clinical practice. The development of HER2 as an effect modifier biomarker has transformed breast cancer treatment by identifying the 20-30 percent of patients with overexpression of the HER2 oncogene who are likely to benefit from therapy targeting HER2 (De et 183

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184 EVOLUTION OF TRANSLATIONAL OMICS al., 2010; Phillips et al., 2009). At least seven tests to detect HER2 gene amplification and protein overexpression have Food and Drug Administra- tion (FDA) approval for use as effect modifier markers for tumor response to trastuzumab (reviewed by Allison, 2010, and Shah and Chen, 2010). In addition, some companies have received FDA clearance for imaging analysis tools accompanying FDA-approved HER2 tests (FDA, 2011a). However, despite more than 20 years of research and development, difficulties remain in defining optimal implementation of this single-marker test (De et al., 2010), illustrating some of the profound challenges confronting developers of multianalyte, omics-based tests. These difficulties include the number of modalities for evaluating HER2 (IHC [immunohistochemistry], FISH [fluorescent in situ hybridization], and others), the subjectivity of test results, lab-to-lab variability (central or ref- erence laboratory versus smaller laboratories), laboratory errors leading to false positives and false negatives, differences in cut-off recommendations, and some uncertainty regarding clinical benefit of trastuzumab for patients with borderline HER2-positive results. Accurate selection of patients for therapy targeting HER2, or conversely, identification of those patients who are not likely to benefit from HER2-targeted therapy, depends on reliable HER2 testing and appropriate cut-off criteria (Kroese et al., 2007). HER2 Testing in Clinical Practice In 2007, a panel established by the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) recommended HER2 status determination for all invasive breast cancers (Wolff et al., 2007a,b) and clarified some of the technical limitations of both IHC and FISH (Schmitt, 2009). In 2002, substantial discordance was reported for both IHC and FISH results performed in community laboratories versus a central reference laboratory in the course of two clinical trials (Paik et al., 2002; Roche et al., 2002). In response, the ASCO/CAP panel issued recommendations for the HER2 testing process (e.g., ways to reduce lab- based errors) and interpretation (Wolff et al., 2007a,b). These guidelines alleviated some lab effects within a single HER2 testing modality, though interlab reproducibility continues to be an area of substantial concern for HER2 testing. The choice of HER2 testing modality is also debated (Sauter et al., 2009; Schmitt, 2009). Historically, IHC has been the primary method for HER2 testing, and FISH has been used to confirm these findings, when IHC testing is equivocal. However, some assert that FISH should be the primary HER2 testing platform (Sauter et al., 2009), while others have advised that IHC alone should never be relied on for selecting anti-HER2 treatment (De et al., 2010). The ASCO/CAP recommendations did not recommend one

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185 APPENDIX A method for HER2 testing over another, and the National Cancer Institute (NCI) website currently states that “limitations in assay precision make it inadvisable to rely on a single method to rule out potential Herceptin benefit” (NCI, 2011). New methodologies are also in development for HER2 testing, including quantitative real-time reverse-transcriptase poly- merase chain reaction (qRT-PCR)-based detection of HER2 gene overex- pression, which has been presented as the most quantitative platform to date (Baehner et al., 2010). Differences in cut-off recommendations and equivocal HER2 test results also present challenges. For example, a tumor in which 10 percent of tumor cells show +3 IHC immunoreactivity, and another in which 99 percent of tumor cells display intermediate +2 immunoreactivity might both respond to the same treatment (De et al., 2010). Recently published highly exploratory studies from two of the largest randomized trials of the anti-HER2 therapy trastuzumab have suggested that patients who have some HER2 expression (but below the established cut-off points and not amplified in a FISH test) might benefit from adjuvant trastuzumab (Paik et al., 2008; Perez et al., 2010). A prospective randomized trial (National Surgical Adjuvant Breast and Bowel Project [NSABP] B-47) aims to address this question by randomiz- ing patients with HER2 IHC scores of 1+ or 2+ (but not amplified according to FISH) to chemotherapy plus or minus trastuzumab. False-positive and false-negative results remain a significant concern for HER2 testing as well. False negatives result in a potentially life-saving anti-HER2 therapy being withheld from a patient. False positives result in treatment with anti-HER2 therapies in the adjuvant or neoadjuvant setting, despite a small chance of benefiting from such treatment. This is a concern given trastuzumab’s association with cardiotoxicity, as well as the expense of treatment ($800-$1,000/week for 26-52 weeks) (Sauter et al., 2009). Case Highlights The development of HER2 testing and HER2 targeted therapy rep- resents a significant advance in the treatment of breast cancer and the field of molecularly targeted medicine, but the challenges in implementing HER2 testing in practice have been substantial. Different testing modalities, subjectivity of test results, lab-to-lab variability, false-positive and false- negative results, differences in cut-off recommendations, and some uncer- tainty regarding clinical benefit of trastuzumab for patients with borderline HER2-positive results make it difficult to determine how best to conduct HER2 testing. There is not yet complete consensus on the standardization of HER2 testing, and as new testing methodologies emerge, new questions about HER2 testing will arise. The challenges involved in developing a single-analyte test such as HER2 are informative as the community is mov-

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186 EVOLUTION OF TRANSLATIONAL OMICS ing toward the development of multianalyte, omics-based tests in which these challenges may be magnified. ONCOTYPE DX Oncotype DX (Genomic Health Inc.) is a multigene expression test developed to predict the risk of recurrence for node-negative, estrogen- receptor-positive breast cancer. Oncotype DX estimates the likelihood of distant recurrence at 10 years, and classifies individuals at low (scores less than 18), intermediate (18-30) and high (31-100) risk of breast can- cer recurrence, assuming the use of adjuvant endocrine therapy, such as tamoxifen and/or an aromatase inhibitor, without chemotherapy. Developed as a laboratory-developed test (LDT), the test has not been submitted to FDA for clearance or approval; however, Genomic Health indicated that the company benefited from prior interaction with FDA and the extensive background material FDA provides on its website about assay validation.1 Two ongoing prospective studies (the TAILORx and RxPONDER trials, see section below on Clinical Utility) direct patient management on the basis of Oncotype DX Recurrence Score. For both trials, information required for approval of investigational use of Oncotype DX in the trial was submitted as part of an investigational new drug application to FDA.2 Developers of Oncotype DX sought to identify a subgroup of patients who were at such a low risk of recurrence that even if chemotherapy is active, the risks of chemotherapy would outweigh the benefit. Large randomized trials had previously demonstrated the benefit of adding chemotherapy to tamoxifen therapy for patients with estrogen-receptor-positive tumors (Berry et al., 2005; EBCTCG, 2005; Fisher et al., 1989, 1997, 2004). Adju- vant chemotherapy studies have generally demonstrated that the relative risk reduction from chemotherapy is constant across risk groups, but stud- ies by the developers of Oncotype DX in patients with node-negative and node-positive estrogen-receptor-positive early breast cancer randomized to chemotherapy suggested that the relative risk reduction of chemotherapy in women with low Recurrence Scores was lower (Albain et al., 2010; Paik et al., 2006). This suggested that the absolute benefit of chemotherapy is low- est for those with the smallest risk of recurrence, and many women treated with tamoxifen alone are likely to remain free of distant recurrence with minimal, if any, benefit from the addition of chemotherapy. In this regard, Oncotype DX is used as a prognostic factor. 1 Personal communication, Steven Shak, Genomic Health, December 13, 2011. 2 Personal communication, Lisa McShane, National Cancer Institute, February 9, 2012.

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187 APPENDIX A Discovery Phase The discovery of Oncotype DX is described by Paik et al. (2004) and the Oncotype DX website (Genomic Health, 2011c). Investigators opti- mized the methods using a high-throughput, RT-PCR assay for quantify- ing RNA expression in formalin-fixed, paraffin-embedded (FFPE) tissue (Cronin et al., 2004). Two hundred and fifty candidate genes were selected for assay development based on microarray expression data and informa- tion from genomic databases, published literature, and experiments in molecular and cell biology. The relationship between gene expression and recurrence was analyzed in archival tissue from 447 breast cancer patients in three separate clinical studies (see Table A-1). Investigators generated a 21-gene panel (16 cancer-related genes and 5 reference genes) and compu- tational model for determining the Recurrence Score. Five steps were used to develop the final gene list and Recurrence Score computational model. First, univarible analysis of each gene was performed separately for the three studies. Second, 16 cancer-related genes were selected based on their performance in predicting recurrence across all three studies. Third, based on coexpression by cluster and principal component analysis, 13 of the 16 genes were put into four gene groups (proliferation, estrogen receptor, HER2, and invasion). Fourth, martingale residual analysis was used to identify linear or non-linear functions for each of the gene groups. Fifth, regression analysis performed on each of the three studies was used to select the coefficients for each of the four gene groups and the remaining three individual genes.3 Additional analyses indicated that inclusion of additional cancer-related genes beyond 16 did not increase the robustness of prediction across the three datasets and that inclusion of fewer than 16 reduced the robustness (Paik et al., 2004). Many, but not all, of the 16 cancer-related genes in Oncotype DX were previously well established in the cancer litera- ture for their association with prognosis (Kim and Paik, 2010). Test Validation Phase More than 150 standard operating procedures (SOPs) were developed for the 5-step 21-Gene Recurrence Score, including SOPs for equipment, histopathology, information technology, pre- and postanalytical methods, production and quality control, and quality assurance (Shak, 2011). 3 Personal communication, Steven Shak, Genomic Health, December 13, 2011.

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188 EVOLUTION OF TRANSLATIONAL OMICS TABLE A-1 Archival Tissue Used in the Development of Oncotype DX Computational Model and Gene List Study Paik et al., 2003 Cobleigh et al., 2005 Esteban et al., 2003 Tissue source Tamoxifen arm of Rush Presbyterian- Providence NSABP B-20 St. Luke’s Hospital St. Joseph’s Hospital Sample # 233 78 136 Lymph-node status Negative > 10 positive nodes Positive or negative Estrogen-receptor Positive Positive and negative Positive and negative status Treatment Tamoxifen (100%) Tamoxifen (54%) Tamoxifen (41%) Chemotherapy (80%) Chemotherapy (39%) NOTE: NSABP = National Surgical Adjuvant Breast and Bowel Project. SOURCE: Shak (2011). Analytical Validation The analytical validity of Oncotype DX was assessed in the Agency for Healthcare Research and Quality (AHRQ) report, Impact of Gene Expression Profiling Tests on Breast Cancer Outcomes (AHRQ, 2008). The report noted there is evidence about Oncotype DX’s assay performance and laboratory characteristics as well as some limited information on its reproducibility. Cronin et al. (2007) found that Oncotype DX met accept- able operational performance ranges with minimal assay imprecision due to instrument, operator, reagent, and day-to-day baseline variation. Investiga- tors also conducted technical feasibility studies during assay development, including analysis of preanalytical factors such as variability in preparation, tumor block age, and dissection (Shak, 2011). Paik and colleagues (2004) measured and reported the reproducibility within and between blocks in the clinical validation study. Statistical and Bioinformatics Validation The computational procedures used to determine the Recurrence Score are published, and there is public information that provides an overview of how the computational model was generated (see Discovery Phase) (Paik et al., 2004). The supplementary materials of Paik et al. (2004) note that the investigators weighted the NSABP B-20 results most heavily in select- ing the final gene list and developing the computational model because investigators planned to clinically validate the test in similar archival tissue from NSABP B-14 patients. However, more detailed information on model

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189 APPENDIX A development and the RT-PCR and clinical data used in the development of 21-Gene RS are not publicly available.4 The test was locked down prior to clinical validation. The investigators reported that “the prospectively defined assay methods and endpoints were finalized in a protocol signed on August 27, 2003, and RT-PCR data were transferred to the NSABP for analysis on September 29, 2003” (Paik et al., 2004, p. 2820). Genomic Health was blinded to the clinical outcome data until the RT-PCR data were locked and transferred to NSABP.5 Clinical/Biological Validation Archival tissue from breast cancer patients in three studies was used to clinically validate the prognostic value of Oncotype DX (Table A-2). Paik et al. (2004) found that the Recurrence Score quantified the likelihood of distant recurrence in tamoxifen-treated patients with lymph-node-negative, estrogen-receptor-positive breast cancer. Investigators prospectively defined the endpoints for validation and prespecified the cut-off values for low, intermediate, and high risk of recurrence. They had a large number of patient samples on which to clinically validate the prognostic value of the Recurrence Score, and did not use samples from the discovery phase in the validation studies. Although this study was not a true prospective clini- cal validation, many assert the prospective–retrospective study design has evidentiary value close to a prospective study (AHRQ, 2008; Harris et al., 2007; Simon et al., 2009). The second study, Habel et al. (2006), assessed the prognostic value of the Recurrence Score using archival tissue from patients treated within the Northern California Kaiser Permanente health plan. Investigators found that the Recurrence Score was associated with the risk of breast cancer death among patients with estrogen-receptor-positive breast cancer who were treated with tamoxifen or were not treated with systemic adjuvant therapy. The third study, Esteva et al. (2005), used a smaller number of archival tissue samples and did not find an association between the Recurrence Score and risk of distant recurrence. Investigators hypothesized that this result could be due to potential selection bias or confounding factors. However, investigators did find a high degree of concordance between RT-PCR and immunohistochemical assays for estrogen receptor, progesterone receptor, and HER2. Chemotherapy benefit In an exploratory analysis designed to assess the test’s ability to predict the benefit of chemotherapy treatment, investigators 4 Personal communication, Steven Shak, Genomic Health, December 13, 2011. 5 Personal communication, Steven Shak, Genomic Health, December 13, 2011.

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190 EVOLUTION OF TRANSLATIONAL OMICS TABLE A-2 Clinical/Biological Validation Studies for Oncotype DX Study Paik et al. (2004) Habel et al. (2006) Esteva et al. (2005) Tissue source NSABP B-14 Kaiser Permanente MD Anderson Cancer Center Study Does 21-Gene RS Does 21-Gene RS Does 21-Gene RS question correlate with predict risk of breast predict risk of likelihood of distant cancer-specific recurrence in women recurrence? mortality in women not treated with treated and not systemic therapy? treated with tamoxifen? Study design Prospective– Retrospective; Retrospective with retrospective matched case control case inclusion criteria Cases = patients who died from breast cancer Controls = breast cancer patients individually matched to cases alive at the date of death of their matched case Patient Lymph-node-negative; Lymph-node-negative; Lymph-node-negative; characteristics ER+ ER +/– ER +/– Treatment Tamoxifen; no +/–Tamoxifen; no No systemic therapy chemotherapy chemotherapy Sample # 668 220 cases, 570 149 controls Blinding Yes Yes Yes Independence Different specimens Different specimens Different specimens than used in discovery than used in discovery than used in discovery NSABP control of Kaiser Permanente MD Anderson control clinical outcome data control of clinical of clinical outcome outcome data data

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191 APPENDIX A TABLE A-2 Continued Study Paik et al. (2004) Habel et al. (2006) Esteva et al. (2005) Results Rate of recurrence RS associated with No association significantly lower (p risk of breast cancer between RS and < 0.001) with low-risk death in ER+, distant recurrence-free RSs compared to tamoxifen-treated and survival in ER+/– high-risk RSs; RS tamoxifen-untreated patients with no provided significant patients (p = 0.003 adjuvant systemic predictive power and p = 0.03, therapy independent of age respectively) and tumor size; RS was predictive of overall survival NOTE: ER = estrogen receptor, NSABP = National Surgical Adjuvant Breast and Bowel Proj- ect, RS = Recurrence Score. used archival tissue from the NSABP B-20 study, in which patients were randomized to tamoxifen or tamoxifen plus chemotherapy. There were two chemotherapy arms: the cyclophosphamide, methotrexate, and fluorouracil (CMF) arm and the methotrexate and fluorouracil (MF) arm (Paik et al., 2006). In this study, there appeared to be a relative treatment modifier effect independent of the prognostic role of Oncotype DX (p = 0.038 for interac- tion between Recurrence Score and chemotherapy treatment). Prognosis was more favorable in patients with a low Recurrence Score who received only tamoxifen, and chemotherapy did not appear to be active in this sub- group. In contrast, patients with a high risk of recurrence based on their Recurrence Score had a worse prognosis if treated with tamoxifen only, and achieved a large benefit from chemotherapy. Tissue from the tamoxifen plus chemotherapy arms were not previously used in the development of Oncotype DX, but tissue from the tamoxifen-only arm had been previously used in the test discovery phase. As noted in Chapter 2, the use of discovery phase tissue samples to assess test performance is not ideal because it can lead to overfitting. The TAILORx trial (see below) will provide higher qual- ity evidence to assess the benefit from chemotherapy treatment in a subset of patients with Recurrence Scores of 11-25 because it will prospectively evaluate the impact of Oncotype DX on treatment outcome within a large, randomized clinical trial population and will not use tissue samples from the discovery phase. Lymph-node-positive patients Tumor samples from lymph node-positive patients were used to help develop Oncotype DX (Cobleigh et al., 2005),

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192 EVOLUTION OF TRANSLATIONAL OMICS and a recent prospective–retrospective analysis of a large trial found that the Recurrence Score is prognostic for tamoxifen-treated patients with posi- tive nodes and, as expected, their prognosis was worse than for patients with negative lymph nodes (Albain et al., 2010). The analysis also evaluated the effect modifier role of Oncotype DX and indicated that node-positive patients with low Recurrence Scores did not benefit from chemotherapy treatment, but node-positive patients with high Recurrence Scores had an improvement in disease-free survival when treated with chemotherapy. The relative effects of chemotherapy rose with increasing Recurrence Scores. The RxPONDER trial (see below) will provide more information on the clinical utility of Oncotype DX in lymph-node-positive patients. Clinical Utility TAILORx Trial NCI initiated the Trial Assigning IndividuaLized Options for Treat- ment (TAILORx) in 2006 (now fully accrued) to assess the performance of Oncotype DX in a large, prospective, randomized clinical trial. The primary objective of TAILORx is to assess the effect of chemotherapy, in addition to hormonal therapy, in women with Recurrence Scores between 11 and 25.6 The benefit of chemotherapy for women in this mid-range risk group is currently unclear. The study involves more than 10,000 women recently diagnosed with estrogen-receptor-positive and/or progesterone-receptor- positive, HER2-negative breast cancer without lymph node involvement at 900 sites in the United States, Canada, and several other countries outside of North America. Women with a low Recurrence Score received hor- monal therapy alone while women with a high Recurrence Score received hormonal therapy and chemotherapy. Women with Recurrence Scores in the mid-range risk group were randomized to receive either chemotherapy plus hormonal therapy or hormonal therapy alone. Women will be studied for 10 years, with additional follow-up 20 years after initial therapy (NCI, 2006, 2010a,b). 6 While the Oncotype validation studies prespecified cut-off values of 0-18, 18-30, and 31 and above for low, intermediate, and high risk of recurrence, the TAILORx investigators defined a mid-range risk of recurrence as scores of 11-25 to roughly correlate with a 10 to 20 percent risk of distant recurrence at 10 years. In TAILORx, patients classified at mid-range risk will be randomized to receive either hormonal therapy or hormonal therapy and chemo- therapy, while patients at very low risk (below 11) will be assigned to hormonal therapy only and those at high risk (above 25) will receive hormonal therapy and chemotherapy.

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193 APPENDIX A RxPONDER Trial Oncotype DX is also being evaluated in lymph node-positive patients in a prospective trial, RxPONDER (Rx for POsitive NoDe, Endocrine Responsive breast cancer), which will recruit 4,000 patients with Recur- rence Scores of 25 or less who have estrogen-receptor-positive tumors and 1-3 positive lymph nodes. Patients will be randomly assigned to treatment with chemotherapy plus hormonal therapy or hormonal therapy alone. The trial seeks to determine whether these women may safely forego chemo- therapy treatment and whether there is an optimal Recurrence Score cut- point for recommending chemotherapy or not (SWOG, 2011). Clinical Use As an LDT, Oncotype DX is performed in Genomic Health’s clinical laboratory that is certified under the Clinical Laboratory Improvement Amendments of 1988 (CLIA). Oncotype DX has been incorporated into guidelines from ASCO and the National Comprehensive Cancer Network (NCCN). The ASCO 2007 Update of Recommendations for the Use of Tumor Markers in Breast Cancer stated that “Oncotype DX may be used to identify patients who are predicted to obtain the most therapeutic ben- efit from adjuvant tamoxifen and may not require adjuvant chemotherapy. In addition, patients with high recurrence scores . . . appear to achieve relatively more benefit from adjuvant chemotherapy (specifically [C]MF) than from tamoxifen” (Harris et al., 2007, p. 5299). The guidelines specify that there are insufficient data to suggest whether these conclusions can be generalized to other hormonal therapies (e.g., aromatase inhibitors) or other chemotherapy regimens. However, a recent study using specimens from the Arimidex, Tamoxifen, Alone or in Combination (ATAC) trial found that the Recurrence Score was an independent predictor of distant recurrence in women with node-negative and node-positive, hormone- receptor-positive patients treated with anastrozole (Arimidex), an aroma- tase inhibitor (Dowsett et al., 2010). NCCN guidelines note that Oncotype DX is an option when evaluating certain patients with breast cancer, and assert that “the Recurrence Score should be used for decision-making only in the context of other elements of risk stratification for an individual patient” (NCCN, 2011a, p. 85). More than 7,500 physicians have ordered the Oncotype DX test for more than 175,000 patients (Genomic Health, 2011a), with 55,000 Oncotype DX tests ordered in 2011.7 Oncotype DX is covered by almost all private insurers and is a covered benefit for Medicare beneficiaries and some Medicaid ben- 7 Personal communication, Steven Shak, Genomic Health, December 13, 2011.

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229 APPENDIX A Baggerly, K. A., J. S. Morris, S. R. Edmonson, and K. R. Coombes. 2005b. Signal in noise: Evaluating reported reproducibility of serum proteomic tests for ovarian cancer. Journal of the National Cancer Institute 97(4):307-309. Baraldi-Junkins, C., H. R. Levin, E. K. Kasper, B. K. Rayburn, A. Herskowitz, and K. L. Baughman. 1993. Complications of endomyocardial biopsy in heart transplant patients. Journal of Heart and Lung Transplantation 12(1 Pt 1):63-67. BCBS (Blue Cross and Blue Shield Association). 2008. Gene expression profiling of breast cancer to select women for adjuvant chemotherapy. Technology Evaluation Center 22(13):1-51. BCBS. In press. Gene expression profiling as a noninvasive method to monitor for cardiac allograft rejection. Technology Evaluation Center. Berry, D. A., C. Cirrincione, I. C. Henderson, M. L. Citron, D. R. Budman, L. J. Goldstein, S. Martino, E. A. Perez, H. B. Muss, L. Norton, C. Hudis, and E. P. Winer. 2005. Estrogen- receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer. Journal of the American Medical Association 295(14):1658-1667. Bonislawski, A. 2011. Vermillion Buys Correlogic’s Assets for $435K; Corre - logic Settles with LabCorp, Quest. http://www.genomeweb.com/proteomics/ vermillion-buys-correlogics-assets-435k-correlogic-settles-labcorp-quest. Buyse, M., S. Loi, L. J. van ‘t Veer, G. Viale, M. Delorenzi, A. M. Glas, M. S. d’Assignies, J. Bergh, R. Lidereau, P. Ellis, A. Harris, J. Bogaerts, P. Therasse, A. Floore, M. Amakrane, F. Piette, E. T. Rutgers, C. Sortiriou, F. Cardoso, and M. J. Piccart. 2006. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. Journal of the National Cancer Institute 98(17):1183-1192. Check, E. 2004. Proteomics and cancer: Running before we can walk? Nature 429(6991): 496-497. ClinicalTrials.gov. 2011a. Cardiac Allograft Rejection Gene Expression Observa- tional (CARGO) II Study (CARGO II). http://www.clinicaltrials.gov/ct2/show/ NCT00761787?term=CARGO&rank=1 (accessed November 15, 2011). Clinicaltrials.gov. 2011b. Genetic Testing or Clinical Assessment in Determining the Need for Chemotherapy in Women with Breast Cancer That Involves No More Than 3 Lymph Nodes. http://clinicaltrials.gov/ct2/show/NCT00433589?term=mindact&rank=1 (ac- cessed March 27, 2011). Cobleigh, M. A., B. Tabesh, P. Bitterman, J. Baker, M. Conin, M. L. Liu, R. Borchik, J. M. Mosquera, M. G. Walker, and S. Shak. 2005. Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes. Clinical Cancer Research 11(24 Pt 1):8623-8631. Correlogic. 2004. Re: Correlogic Systems Inc. Reference Laboratory—OvaCheck Testing Service. http://www.correlogic.com/pdfs/July14SteveGutmanLetter.pdf (accessed Decem- ber 2, 2011). Cronin, M., M. Pho, D. Dutta, J. C. Stephans, S. Shak, M. C. Kiefer, J. M. Esteban, and J. Baker. 2004. Measurement of gene expression in archival paraffin-embedded tissues: Development and performance of a 92-gene reverse transcriptase-polymerase chain reac- tion assay. American Journal of Pathology 164(1):35-42. Cronin, M., C. Sangli, M.-L. Liu, M. Pho, D. Dutta, A. Nguyen, J. Jeong, J. Wu, K. C. Langone, and D. Watson. 2007. Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node- negative, estrogen receptor-positive breast cancer. Clinical Chemistry 53(6):1084-1091. CTAF (California Technology Assessment Forum). 2010. Gene Expression Profiling for the Diagnosis of Heart Transplant Rejection. http://ctaf.org/content/assessment/detail/1208 (accessed January23, 2012).

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230 EVOLUTION OF TRANSLATIONAL OMICS De, P., B. R. Smith, and B. Leyland-Jones. 2010. Human epidermal growth factor receptor 2 testing: Where are we? Journal of Clinical Oncology 28(28):4289-4292. Deng, M. C., H. J. Eisen, M. R. Mehra, M. Billingham, C. C. Marboe, G. Berry, J. Kobashigawa, F. L. Johnson, R. C. Starling, S. Murali, D. F. Pauly, H. Baron, J. G. Wohlgemuth, R. N. Woodward, T. M. Klingler, D. Walther, P. G. Lal, S. Rosenberg, and S. Hunt. 2006. Non- invasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. American Journal of Transplantation 6(1):150-160. Diamandis, E. 2004. Mass spectrometry as a diagnostic and a cancer biomarker discov- ery tool: Opportunities and potential limitations. Molecular and Cellular Proteomics 3(4):367-378. Dowsett, M., J. Cuzick, C. Wale, J. Forbes, E. A. Mallon, J. Salter, E. Quinn, A. Dunbier, M. Baum, A. Buzdar, A. Howell, R. Bugarini, F. L. Baehner, and S. Shak. 2010. Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: A TransATAC study. Journal of Clinical Oncology 28(11):1829-1834. Dumur, C. I., M. Lyons-Weiler, C. Sciulli, C. T. Garrett, I. Schrijver, T. K. Holley, J. Rodriguez- Paris, J. R. Pollack, J. L. Zehnder, M. Price, J. M. Hagenkord, C. T. Rigl, L. J. Buturovic, G. G. Anderson, and F. A. Monzon. 2008. Interlaboratory performance of a microarray- based gene expression test to determine tissue of origin in poorly differentiated and undifferentiated cancers. Journal of Molecular Diagnostics 10(1):67-77. Dumur, C. I., C. E. Fuller, T. L. Blevins, J. C. Schaum, D. S. Wilkinson, C. T. Garrett, C. N. Powers. 2011. Clinical verification of the performance of the Pathwork Tissue of Origin test. American Journal of Clinical Pathology 136(6):924-933. EBCTCG (Early Breast Cancer Trialists Collaborative Group). 2005. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet 365(9472):1687-16717. Elashoff, M. R., J. A. Wingrove, P. Beineke, S. E. Daniels, W. G. Tingley, S. Rosenberg, S. Voros, W. E. Kraus, G. S. Ginsburg, R. S. Schwartz, S. G. Ellis, N. Tahirkheli, R. Waksman, J. McPherson, A. J. Lansky, and E. J. Topol. 2011. Development of a blood- based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients. BMC Medical Genomics 4(1):26. Esteban, J., J. Baker, M. Cronin, M. L. Liu, M. G. Llamas, M. G. Walker, R. Mena, and S. Shak. 2003. Tumor gene expression and prognosis in breast cancer: Multi-gene RT-PCR assay of paraffin-embedded tissue. Proceedings of the American Society of Clinical Oncology 22:Abstract 3416. Esteva, F. J., A. A. Sahin, M. Cristofanilli, K. Coombes, S.-J. Lee, J. Baker, M. Cronin, M. Walker, D. Watson, S. Shak, and G. N. Hortobagyi. 2005. Prognostic role of a multigene reverse transcriptase-PCR assay in patients with node-negative breast cancer not receiving adjuvant systemic therapy. Clinical Cancer Research 11(9):3315-3319. FDA (Food and Drug Administration). 2007a. 510(k) Substantial Equivalence Determina- tion Decision Summary (k070675). http://www.accessdata.fda.gov/cdrh_docs/reviews/ K070675.pdf (accessed September 20, 2011). FDA. 2007b. FDA Clears Breast Cancer Specific Molecular Prognostic Test. http://www. fda.gov/NewsEvents/Newsroom/PressAnnouncements/2007/ucm108836.htm (accessed March 14, 2011). FDA. 2008a. 510(k) Substantial Equivalence Determination Decision Summary Assay and Instrument Combination Template (k073482). http://www.accessdata.fda.gov/cdrh_docs/ reviews/K073482.pdf (accessed November 23, 2011). FDA. 2008b. 510(k) Substantial Equivalence Determination Decision Summary (K080896). http://www.accessdata.fda.gov/cdrh_docs/reviews/K080896.pdf (accessed November 15, 2011).

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231 APPENDIX A FDA. 2008c. OvaSure Manufacturer Letter. http://www.fda.gov/MedicalDevices/DeviceRegu- lationandGuidance/IVDRegulatoryAssistance/ucm125130.htm (accessed November 23, 2011). FDA. 2008d. Laboratory Corporation of America 29-Sep-08. http://www.fda.gov/ICECI/ EnforcementActions/WarningLetters/2008/ucm1048114.htm (accessed November 23, 2011). FDA. 2009. 510(k) Substantial Equivalence Determination Decision Summary (K081092). http://www.accessdata.fda.gov/cdrh_docs/reviews/K081092.pdf (accessed September 20, 2011). FDA. 2010. 510(k) Substantial Equivalence Determination Decision Summary (K092967). http://www.accessdata.fda.gov/cdrh_docs/reviews/K092967.pdf (accessed November 16, 2011). FDA. 2011a. 510(k) Premarket Notification CDRH SuperSearch. http://www.accessdata.fda. gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm (accessed October 18, 2011). FDA. 2011b. 510(k) Substantial Equivalence Determination Decision Summary (K101454). http://www.accessdata.fda.gov/cdrh_docs/reviews/K101454.pdf (accessed September 19, 2011). FDA. 2011c. Substantial Equivalence Determination Decision Summary (K081754). http:// www.accessdata.fda.gov/cdrh_docs/reviews/K081754.pdf (accessed October 11, 2011). Fisher, B., J. Costantino, C. Redmond, R. Poisson, D. Bowman, J. Couture, N. V. Dimitrov, N. Wolmark, D. L. Wickerham, and E. R. Fisher. 1989. A randomized clinical trial evaluat- ing tamoxifen in the treatment of patients with node-negative breast cancer who have estrogen-receptor-positive tumors. New England Journal of Medicine 23(320):479-484. Fisher, B., J. Dignam, N. Wolmark, A. DeCillis, B. Emir, D. L. Wickerham, J. Bryant, N. V. Dimitrov, N. Abramson, J. N. Atkins, H. Shibata, L. Deschenes, and R. G. Margolese. 1997. Tamoxifen and chemotherapy for lymph node-negative, estrogen receptor-positive breast cancer. Journal of the National Cancer Institute 89(22):1673-1682. Fisher, B., J. Jeong, J. Bryant, S. Anderson, J. Dignam, E. R. Fisher, and N. Wolmark. 2004. 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 364(9437):858-868. Fung, E. T. 2010. A recipe for proteomics diagnostic test development: The OVA1 test, from biomarker discovery to FDA clearance. Clinical Chemistry 56(2):327-329. Genomic Health. 2011a. Oncotype DX Breast Cancer Assay. http://www.oncotypedx.com/ en-US/Breast/HealthcareProfessional/Overview.aspx (accessed January 20, 2011). Genomic Health. 2011b. Oncotype DX Breast Cancer Assay: Insurance Information. http:// www.oncotypedx.com/en-US/Breast/HealthcareProfessional/InsuranceInformation.aspx (accessed January 31, 2011). Genomic Health. 2011c. The Development and Clinical Validation of Oncotype DX. http:// www.oncotypedx.com/en-US/Breast/HealthcareProfessional/Overview.aspx (accessed July 21, 2011). Glas, A. M., A. Floore, L. Delahaye, A. T. Wittereveen, R. C. F. Pover, N. Bakx, J. S. T. Lahti- Domenici, T. J. Bruinsma, M. O. Warmoes, R. Bernards, L. F. A. Wessels, and L. J. van ‘t Veer. 2006. Converting a breast cancer microarray signature into a high-throughput diagnostic test. BMC Genomics 7(278). Goldhirsch, A., J. N. Ingle, R. D. Gelber, A. S. Coates, B. Thurlimann, and H.-J. Senn. 2009. Thresholds for therapies: Highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009. Annals of Oncology 20(8):1319-1329. Grenert, J. P., A. Smith, W. Ruan, R. Pillai, and A. H. Wu. 2011. Gene expression profiling from formalin-fixed, paraffin-embedded tissue for tumor diagnosis. Clinica Chimica Acta 412(15-16):1462-1464.

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232 EVOLUTION OF TRANSLATIONAL OMICS H. Con. Res. 385, 107th Cong., 2nd sess. (July 22, 2002). Expressing the sense of the Congress that the Secretary of Health and Human Services should conduct or support research on certain tests to screen for ovarian cancer, and Federal health care programs and group and individual health plans should cover the tests if demonstrated to be ef- fective, and for other purposes. Habel, L. A., S. Shak, M. Jacobs, A. Capra, C. Alexander, M. Pho, J. Baker, M. Walker, D. Watson, J. Hackett, N. T. Blick, D. Greenberg, L. Fehrenbacher, B. Langholz, and C. P. Quesenberry. 2006. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Research 8(3):R25. Harris, L., H. Fritsche, R. Mennel, L. Norton, P. Ravdin, S. E. Taube, M. R. Somerfield, D. F. Hayes, and R. C. Bast, Jr. 2007. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. Journal of Clinical Oncology 25(33):5287-5312. Hillen, H. F. 2000. Unknown primary tumours. Postgraduate Medical Journal 76(901):690-693. Hornberger, J., and R. Chien. 2010. P2-09-06: Meta-Analysis of the Decision Impact of the 21-Gene Breast Cancer Recurrence Score in Clinical Practice. Poster presented at the 33rd Annual San Antonio Breast Cancer Symposium, San Antonio, Texas, December 8-12. Hornberger, J. C., M. Amin, G. R. Varadhachary, W. D. Henner, and J. S. Nystrom. 2011. Effect of a gene expression-based tissue of origin test’s impact on patient management for difficult-to-diagnose primary cancers. Journal of Clinical Oncology 29(Suppl 4; abstr 459). ISHLT (International Society of Heart and Lung Transplantation). 2010. The International Society of Heart and Lung Transplantation Guidelines for the Care of Heart Trans- plant Recipients. Task Force 2: Immunosuppression and Rejection. http://www.ishlt.org/ ContentDocuments/ISHLT_GL_TaskForce2_110810.pdf (accessed January 23, 2012). Kim, C., and S. Paik. 2010. Gene-expression-based prognostic assays for breast cancer. Nature Reviews 7(6):340-347. Knauer, M., S. Mook, E. J. Rutgers, R. A. Bender, M. Hauptmann, M. J. van de Vijver, R. H. T. Koornstra, J. Bueno-de-Mesquita, S. C. Linn, and L. J. van ‘t Veer. 2010. The predictive value of the 70-gene signature for adjuvant chemotherapy in early breast cancer. Breast Cancer Research and Treament 120(3):655-661. Kroese, M., R. L. Zimmern, and S. E. Pinder. 2007. HER2 status in breast cancer—an example of pharmacogenetic testing. Journal of the Royal Society of Medicine 100(7):326-329. Laouri, M., M. Halks-Miller, W. D. Henner, and S. Nystrom. 2011. Potential clinical util- ity of gene expression profiling in identifying tumors of uncertain origin. Personalized Medicine 8(6):615-622. Leek, J. T., R. B. Scharpf, H. C. Bravo, D. Simcha, B. Langmead, W. E. Johnson, D. Geman, K. Baggerly, and R. A. Irizarry. 2010. Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics 11(10):733-739. Liotta L. A., E. F. Petricoin, III, T. D. Veenstra, and T. P. Conrads. 2004. High-resolution serum proteomic patterns for ovarian cancer detection. Endocrine-Related Cancer 11(4): 585-587. Liotta, L. A., M. Lowenthal, A. Mehta, T. P. Conrads, T. D. Veenstra, D. A. Fishman, and E. F. Petricoin, III. 2005. Importance of communication between producers and consum- ers of publicly available experimental data. Journal of the National Cancer Institute 97(4):310-314. Marboe, C. C., M. Billingham, H. Eisen, M. C. Deng, H. Baron, M. Mehra, S. Hunt, J. Wohlgemuth, J. Prentice, and G. Berry. 2005. Nodular endocardial infiltrates (quality lesions) cause significant variability in diagnosis of ISHLT grade 2 and 3A rejection in cardiac allograft recipients. Journal of Heart and Lung Transplant 24(7 Suppl.): S219-226.

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233 APPENDIX A Mehra, M., J. Kobashigawa, M. Deng, K. Fang, T. Klingler, P. Lal, S. Rosenberg, P. Uber, R. Starling, and S. Murali. 2007. Transcriptional signals of T-cell and corticosteroid- sensitive genes are associated with future acute cellular rejection in cardiac allografts. Journal of Heart and Lung Transplantation 26(12):1255-1263. Mehra, M. R., J. A. Kobashigawa, M. C. Deng, K. C. Fang, T. M. Klingler, P. G. Lal, S. Rosenberg, P. A. Uber, R. C. Starling, S. Murali, D. F. Pauly, R. Dedrick, M. G. Walker, A. Zeevi, and H. J. Eisen. 2008. Clinical implications and longitudinal alteration of peripheral blood transcriptional signals indicative of future cardiac allograft rejection. Journal of Heart and Lung Transplantation 27(3):297-301. Miller, R. W., A. Smith, C. P. DeSimone, L. Seamon, S. Goodrich, I. Podzielinski, L. Sokoll, J. R. van Nagell, Jr., Z. Zhang, and F. R. Ueland. 2011. Performance of the American College of Obstetricians and Gynecologists’ ovarian tumor referral guidelines with a multivariate index assay. Obstetrics & Gynecology 117(6):1298-1306. Monzon, F. A., M. Lyons-Weiler, L. J. Buturovic, C. T. Rigl, W. D. Henner, C. Sciulli, C. I. Dumur, F. Medeiros, and G. G. Anderson. 2009. Multicenter validation of a 1,550-gene expression profile for identification of tumor tissue of origin. Journal of Clinical Oncol- ogy 27(15):2503-2508. Monzon, F. A., F. Medeiros, M. Lyons-Weiler, and W. D. Henner. 2010. Identification of tissue of origin in carcinoma of unknown primary with a microarray-based gene expression test. Diagnostic Pathology 5(3):10.1186/1746-1596-5-3. Mook, S., M. K. Schmidt, G. Viale, G. Pruneri, I. Eekhout, A. Floore, A. M. Glas, J. Bogaerts, F. Cardoso, M. J. Piccart-Gebhart, E. T. Rutgers, and L. J. van ‘t Veer. 2008. 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 Research and Treament 116(2):295-302. Mulcahy, N. 2010. NCCN Guideline on Occult Cancer Show Immunohistochemistry Is “Rapidly Changing.” http://www.medscape.com/viewarticle/718870 (accessed September 26, 2011). NCCN (National Comprehensive Cancer Network). 2011a. NCCN Guidelines Version 2.2011 Breast Cancer. http://www.nccn.org/professionals/physician_gls/pdf/breast.pdf (accessed August 30, 2011). NCCN. 2011b. NCCN Guidelines Version 2.2012 Ovarian Cancer. http://www.nccn.org/ professionals/physician_gls/pdf/ovarian.pdf (accessed December 17, 2011). NCI (National Cancer Institute). 2006. Personalized Treatment Trial for Breast Cancer Launched. http://www.cancer.gov/newscenter/pressreleases/TAILORxRelease (accessed January 27, 2011). NCI. 2010a. Phase III Randomized Study of Adjuvant Combination Chemotherapy and Hormonal Therapy Versus Adjuvant Hormonal Therapy Alone in Women with Previ- ously Resected Axillary Node-Negative Breast Cancer with Various Levels of Recurrence (TAILORx Trial). http://www.cancer.gov/clinicaltrials/ECOG-PACCT-1 (accessed Janu- ary 27, 2011). NCI. 2010b. TAILORx: Testing Personalized Treatment for Breast Cancer. http://www.cancer. gov/clinicaltrials/noteworthy-trials/tailorx (accessed January 27, 2011). NCI. 2011. FDA Approval for Trastuzumab. http://www.cancer.gov/cancertopics/druginfo/ fda-trastuzumab (accessed September 26, 2011). Nielsen, H., F. B. Sorensen, B. Nielsen, J. P. Bagger, P. Thayssen, and U. Baandrup. 1993. Reproducibility of the acute rejection diagnosis in human cardiac allografts. The Stanford Classification and the International Grading System. Journal of Heart and Lung Trans- plantation 12(2):239-243.

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234 EVOLUTION OF TRANSLATIONAL OMICS Paik, S., J. Bryant, E. Tan-Chiu, E. Romond, W. Hiller, K. Park, A. Brown, G. Yothers, S. Anderson, R. Smith, D. L. Wickerham, and N. Wolmark. 2002. Real-world performance of HER2 testing—National Surgical Adjuvant Breast and Bowel Project experience. Journal of the National Cancer Institute 94(11):852-854. Paik, S., S. Shak, G. Tang, C. Kim, J. Baker, M. Cronin, R. Baehner, M. Walker, D. Watson, and T. Park. 2003. Multi-Gene RT-PCR Assay for Predicting Recurrence in Node Nega- tive Breast Vancer Patients—NSABP Studies B-20 and B-14: Abstract #16. Paper pre- sented at San Antonio Breast Cancer Symposium, San Antonio, TX. Paik, S., S. Shak, G. Tang, C. Kim, J. Baker, M. Cronin, F. L. Baehner, M. G. Walker, D. Watson, T. Park, W. Hiller, E. R. Fisher, D. L. Wickerham, J. Bryant, and N. Wolmark. 2004. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. New England Journal of Medicine 351(27):2817-2826. Paik, S., G. Tang, S. Shak, C. Kim, J. Baker, W. Kim, M. Cronin, F. L. Baehner, D. Watson, J. Bryant, J. Costantino, C. E. Geyer, Jr., D. L. Wickerham, and N. Wolmark. 2006. Gene expression and benefit of chemotherapy in node-negative, estrogen receptor-positive breast cancer. Journal of Clinical Oncology 24(23):3726-3734. Paik, S., C. Kim, and N. Wolmark. 2008. HER2 status and benefit from adjuvant trastuzumab in breast cancer. New England Journal of Medicine 358(13):1409-1411. Pathwork Diagnostics. 2010. Pathwork Tissue of Origin Test for FFPE Cleared by U.S. Food and Drug Administration. http://www.pathworkdx.com/News/M129_FDA_Clearance_ Final.pdf (accessed November 17, 2011). Pathwork Diagnostics. 2011a. Pathwork Reimbursement Assistance Program (RAP). http:// www.pathworkdx.com/patient_information/reimbursement1/ (accessed November 22, 2011). Pathwork Diagnostics. 2011b. The Pathwork Tissue of Origin Test. http://www.pathworkdx. com/TissueOfOriginTest/IVDKit/ (accessed November 16, 2011). Pavlidis, N., and Y. Merrouche. 2006. The importance of identifying CUP subsets. In Carcinoma of an Unknown Primary Site, edited by K. Fizazi. New York: Taylor & Francis Group. Pavlidis, N., E. Briasoulis, J. Hainsworth, and F. A. Greco. 2003. Diagnostic and thera- peutic management of cancer of unknown primary. European Journal of Cancer 39(14):1990-2005. Perez, E. A., M. M. Reinholz, D. W. Hillman, K. S. Tenner, M. J. Schroeder, N. E. Davidson, S. Martino, G. W. Sledge, L. N. Harris, J. R. Gralow, A. C. Dueck, R. P. Ketterling, J. N. Ingle, W. L. Lingle, P. A. Kaufman, D. W. Visscher, and R. B. Jenkins. 2010. HER2 and chromosome 17 effect on patient outcome in the N9831 adjuvant trastuzumab trial. Journal of Clinical Oncology 28(28):4307-4315. Petricoin, E. F., A. M. Ardekani, B. A. Hitt, P. J. Levine, V. A. Fusaro, S. M. Steinberg, G. B. Mills, C. Simone, D. A. Fishman, E. C. Kohn, and L. A. Liotta. 2002. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359(9306): 572-577. Petricoin, E. F., III, D. A. Fishman, T. P. Conrads, T. D. Veenstra, and L. A. Liotta. 2004. Proteomic pattern diagnostics: Producers and consumers in the era of correlative sci- ence. Comment on Sorace and Zhan. BMC Bioinformatics (http://www.biomedcentral. com/1471-2105/4/24/comments). Pham, M. X., J. J. Teuteberg, A. G. Kfoury, R. C. Starling, M. C. Deng, T. P. Cappola, A. Kao, A. S. Anderson, W. G. Cotts, G. A. Ewald, D. A. Baran, R. C. Bogaev, B. Elashoff, H. Baron, J. Yee, and H. A. Valantine. 2010. Gene-expression profiling for rejection surveillance after cardiac transplantation. New England Journal of Medicine 362(20):1890-1900.

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235 APPENDIX A Phillips, K. A., D. A. Marshall, J. S. Haas, E. B. Elkin, S. Y. Liang, M. J. Hassett, I. Ferrusi, J. E. Brock, and S. L. Van Bebber. 2009. Clinical practice patterns and cost effectiveness of human epidermal growth receptor 2 testing strategies in breast cancer patients. Cancer 115(22):5166-5174. Pillai, R., R. Deeter, C. T. Rigl, J. S. Nystrom, M. H. Miller, L. Buturovic, and W. D. Henner. 2011. Validation and reproducibility of a microarray-based gene expression test for tumor identification in formalin-fixed, paraffin-embedded specimens. Journal of Molecular Diagnostics 13(1):48-56. Pollack, A. 2004. New cancer test stirs hope and concern. New York Times. http://www. nytimes.com/2004/02/03/science/new-cancer-test-stirs-hope-and-concern.html?src=pm (accessed November 23, 2011). Pollack, A. 2008a. Cancer test for women raises hope, and concern. New York Times. http:// www.nytimes.com/2008/08/26/health/26ovar.html?pagewanted=all (accessed November 23, 2011). Pollack, A. 2008b. Sales of test for ovarian cancer halted. New York Times. http://www. nytimes.com/2008/10/25/business/25cancer.html (accessed November 23, 2011). PR Newswire. 2009. U.S. Food and Drug Administration clears Vermillion’s OVA1(TM) test to determine likelihood of ovarian cancer in women with pelvic mass. http:// www. prnewswire.com/news-releases/us-food-and-drug-administration-clears- vermillions-ova1tm-test-to-determine-likelihood-of-ovarian-cancer-in-women-with- pelvic-mass-62150212.html (accessed December 17, 2011). Quest Diagnostics. 2011. Licenses and Accreditation. http://www.questdiagnostics.com/brand/ company/b_comp_licenses.html (accessed November 21, 2011). Ransohoff, D. F. 2003. Gene-expression signatures in breast cancer. New England Journal of Medicine 348(17):1716. Ransohoff, D. F. 2004. Rules of evidence for cancer molecular-marker discovery and valida- tion. Nature Reviews Cancer 4(4):309-314. Ransohoff, D. F. 2005. Lessons from controversy: Ovarian cancer screening and serum proteomics. Journal of the National Cancer Institute 97(4):315-319. Roche, P. C., V. J. Suman, R. B. Jenkins, N. E. Davidson, S. Martino, P. A. Kaufman, F. K. Addo, B. Murphy, J. N. Ingle, and E. A. Perez. 2002. Concordance between local and central laboratory HER2 testing in the breast intergroup trial N9831. Journal of the National Cancer Institute 94(11):855-857. Rosenberg, S., M. R. Elashoff, P. Beineke, S. E. Daniels, J. A. Wingrove, W. G. Tingley, P. T. Sager, A. J. Sehnert, M. Yau, W. E. Kraus, L. K. Newby, R. S. Schwartz, S. Voros, S. G. Ellis, N. Tahirkheli, R. Waksman, J. McPherson, A. Lansky, M. E. Winn, N. J. Schork, and E. J. Topol. 2010. Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Annals of Internal Medicine 153(7):425-434. Sauter, G., J. Lee, J. M. Bartlett, D. J. Slamon, and M. F. Press. 2009. Guidelines for human epidermal growth factor receptor 2 testing: Biologic and methodologic considerations. Journal of Clinical Oncology 27(8):1323-1333. Schmitt, F. 2009. HER2+ breast cancer: How to evaluate? Advanced Therapeutics 26(Suppl 1):S1-S8. Shah, S., and B. Chen. 2010. Testing for HER2 in breast cancer: A continuing evolution. Pathology Research International 2011:903202. Shak, S. 2011. Case Study: Oncotype DX Breast Cancer Assay. Presentation at Meeting 2 of the Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, Washington, DC, March 30.

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236 EVOLUTION OF TRANSLATIONAL OMICS Simon, R., M. D. Radmacher, K. Dobbin, and L. M. McShane. 2003. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute 95(1):14-18. Simon, R. M., S. Paik, and D. F. Hayes. 2009. Use of archived specimens in evaluation of prognostic and predictive biomarkers. Journal of the National Cancer Institute 101(21):1446-1452. Sorace, J. M., and M. Zhan. 2003. A data review and re-assessment of ovarian cancer serum proteomic profiling. BMC Bioinformatics 4(24):10.1186/1471-2105-4-24. Stancel, G. A., D. Coffey, K. Alvarez, M. Halks-Miller, A. Lal, D. Mody, T. Koen, T. Fairley, and F. A. Monzon. 2011. Identification of tissue of origin in body fluid specimens using a gene expression microarray assay. Cancer Cytopathology doi: 10.1002/cncy.20167. Starling, R. C., M. Pham, H. Valantine, L. Miller, H. Eisen, E. R. Rodriguez, D. O. Taylor, M. H. Yamani, J. Kobashigawa, K. McCurry, C. Marboe, M. R. Mehra, A. Zuckerman, M. C. Deng, and Working Group on Molecular Testing in Cardiac Transplantation. 2006. Molecular testing in the management of cardiac transplant recipients: Initial clini- cal experience. Journal of Heart and Lung Transplantation 25(12):1389-1395. SWOG (Southwest Oncology Group). 2011. Spotlight: RxPONDER Trial Will Evaluate Whether Gene Expression Test Can Drive Chemotherapy Choice. http://swog.org/ visitors/newsletters/2011/04/index.asp?a=spotlight (accessed May 5, 2011). Thomas, G. S., S. Voros, J. A. McPherson, A. J. Lansky, F. L. Weiland, S. C. Cheng, S. A. Bloom, H. Salha, M. R. Elashoff, B. O. Brown, H. D. Lieu, A. Johnson, S. E. Daniels, and S. Rosenberg. 2011. The Compass trial (NCT01117506): A prospective multi-center, double- blind study assessing a whole blood gene expression test for the detection of obstructive coronary artery disease In symptomatic patients referred for myocardial perfusion imag- ing. Abstract presented at American Heart Association Meeting, November 15, 2011. Ueland, F. R., C. P. Desimone, L. G. Seamon, R. A. Miller, S. Goodrich, L. Podzielinski, L. Sokoll, A. Smith, J. R. van Nagell, and Z. Zhang. 2011. Effectiveness of a multivariate index assay in the preoperative assessment of ovarian tumors. Obstetrics and Gynecol- ogy 117(6):1289-1297. van de Vijver, M. J., Y. D. He, L. J. van ‘t Veer, H. Dai, A. A. M. Hart, D. W. Voskuil, G. J. Schreiber, J. L. Peterse, C. Roberts, M. J. Marton, M. Parrish, D. Atsma, A. Witteven, A. M. Glas, L. Delahaye, T. van der Velde, H. Bartelink, S. Rodenhuis, E. T. Rutgers, S. F. Friend, and R. Bernards. 2002. A gene-expression signature as a predictor of survival in breast cancer. New England Journal of Medicine 347(25):1999-2009. Van ‘t Veer, L. J., H. Dai, M. J. van de Vijver, Y. D. He, A. A. M. Hart, M. Mao, H. L. Peterse, K. van der Kooy, M. J. Marton, A. T. Wittereveen, G. J. Schreiber, R. M. Kerkoven, C. Roberts, P. S. Linsley, R. Bernards, and S. F. Friend. 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(31):530-536. Vermillion. 2011. Payor Information. http://ova-1.com/resources/payor-information (accessed October 11, 2011). Visintin, I., Z. Feng, G. Longton, D. C. Ward, A. B. Alvero, Y. Lai, J. Tenthorey, A. Leiser, R. Flores-Saaib, H. Yu, M. Azori, T. Rutherford, P. E. Schwartz, and G. Mor. 2008. Diagnostic markers for early detection of ovarian cancer. Clinical Cancer Research 14(4):1065-1072. Wagner, L. 2004. A test before its time? FDA stalls distribution process of proteomic test. Journal of the National Cancer Institute 96(7):500-501. Wingrove, J. A., S. E. Daniels, A. J. Sehnert, W. Tingley, M. R. Elashoff, S. Rosenberg, L. Buellesfeld, E. Grube, L. K. Newby, G. S. Ginsburg, and W. E. Kraus. 2008. Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis. Circula- tion Cardiovascular Genetics 1:31-38.

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237 APPENDIX A Wittner, B. S., D. C. Sgroi, P. D. Ryan, T. J. Bruinsma, A. M. Glas, A. Male, S. Dahiya, K. Habin, R. Bernards, D. A. Haber, L. J. van ‘t Veer, and S. Ramaswamy. 2008. Analysis of the MammaPrint breast cancer assay in a predominantly postmenopausal cohort. Clinical Cancer Research 14(10):2988-2993. Wolff, A. C., M. E. Hammond, J. N. Schwartz, K. L. Hagerty, D. C. Allred, R. J. Cote, M. Dowsett, P. L. Fitzgibbons, W. M. Hanna, A. Langer, L. M. McShane, S. Paik, M. D. Pegram, E. A. Perez, M. F. Press, A. Rhodes, C. Sturgeon, S. E. Taube, R. Tubbs, G. H. Vance, M. van de Vijver, T. M. Wheeler, and D. F. Hayes. 2007a. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. Journal of Clinical Oncology 25(1):118-145. Wolff, A. C., M. E. Hammond, J. N. Schwartz, K. L. Hagerty, D. C. Allred, R. J. Cote, M. Dowsett, P. L. Fitzgibbons, W. M. Hanna, A. Langer, L. M. McShane, S. Paik, M. D. Pegram, E. A. Perez, M. F. Press, A. Rhodes, C. Sturgeon, S. E. Taube, R. Tubbs, G. H. Vance, M. van de Vijver, T. M. Wheeler, and D. F. Hayes. 2007b. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. Archives of Pathology and Laboratory Medicine 131(1):18-43. Wu, A. H., J. C. Drees, H. Wang, S. R. VandenBerg, A. Lal, W. D. Henner, and R. Pillai. 2010. Gene expression profiles help identify the tissue of origin for metastatic brain cancers. Diagnostic Pathology 5(26):10.1186/1746-1596-5-26. Zhang, Z., and D. W. Chan. 2010. The road from discovery to clinical diagnostics: Lessons learned from the first FDA-cleared in vitro diagnostic multivariate index assay of proteomic biomarkers. Cancer Epidemiology, Biomarkers, and Prevention 19(12):2995-2999. Zhang, Z., R. C. Bast Jr., Y. Yu, J. Li, L. J. Sokoll, A. J. Rai, J. M. Rosenzweig, B. Cameron, Y. Y. Wang, X. Y. Meng, A. Berchuck, C. Van Haaften-Day, N. F. Hacker, H. W. de Bruijn, A. G. van der Zee, I. J. Jacobs, E. T. Fung, and D. W. Chan. 2004. Three bio- markers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Research 64(16):5882-5890.

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