DOI:
10.1039/C3LC50741F
(Tutorial Review)
Lab Chip, 2014,
14, 24-31
Rapid translation of circulating tumor cell biomarkers into clinical practice: technology development, clinical needs and regulatory requirements
Received
20th June 2013
, Accepted 10th September 2013
First published on 10th September 2013
Abstract
The great hope in circulating tumor cell (CTC) research lies in the use of these rare cells as an accessible “fluid biopsy” that would permit frequent, minimally invasive sampling of tumor cells for similar molecular assays that are performed on traditional biopsies. Given the rarity of CTCs in peripheral circulation, microscale methods show great promise and superiority to capture and analyze these cells from patients with solid tumors. Novel technologies that produce validated CTC biomarkers may finally provide medical oncologists the tools needed to provide precise, personalized medical care for patients with advanced cancer. However, few CTC technologies demonstrate both experimental and clinical evidence of an accurate, reliable and reproducible assay that also meets the regulatory requirements to enter routine clinical practice. Many opportunities exist to incorporate clinical needs and regulatory benchmarks into technology development to more quickly garner FDA approval to direct decisions on patient care. This review will address: 1) device development tailored to address predictive, prognostic and/or therapeutic needs across the multitude of malignancies and disease stages; 2) validation benchmarks for clinical assay development; 3) early establishment of standard operating procedures for sample acquisition and analysis; 4) demonstration of clinical utility; 5) clinical qualification of a novel biomarker; and 6) integration of a newly validated and qualified technology into routine clinical practice. Early understanding and incorporation of these regulatory requirements into assay development can simplify and speed the integration of these novel technologies into patient care. Meeting these benchmarks will lead to the true personalization of cancer therapies, directing initial and subsequent treatments for each individual based on initial tumor characteristics while monitoring for emerging mechanisms of resistance in these continually evolving tumors.
Introduction
Over the last decade, molecular biomarkers have been used to identify and stratify patients who would most benefit from novel therapeutic agents. The hallmark of such precision medical care is the identification and therapeutic targeting of BCR-Abl with imatinib in patients with chronic myelogenous leukemia (CML).1 Furthermore, FDA approved biomarkers are also utilized to identify those patients most likely to benefit from this agent or one of the more recently developed derivatives of imatinib. The success of this combined diagnostic and therapeutic paradigm has supported numerous research efforts to apply this approach to other malignancies. Recent successes include targeting HER2 in breast cancer,2 BRAF gene mutations in melanoma3 and EML4 and anaplastic lymphoma kinase (ALK) oncogenic fusion genes in non-small cell lung carcinoma.4 However, none of these advances have shown similar dramatic clinical results as in CML. Numerous authors suggest that an underlying complexity exists in advanced solid tumors and metastatic lesions that gives rise to resistant clones that escape these molecular targeted therapies.5 For example, new molecular analysis tools in the field of genomics have provided insight into inter- and intra-tumoral heterogeneity that suggest multiple mechanisms by which tumors recur despite potentially curative therapies.5–7 Further advances in molecular imaging modalities such as FDG and FLT PET imaging suggest that this genomic heterogeneity has functional consequences enabling heterogeneous tumor subpopulations to proliferate, metastasize and ultimately lead to death.8
It's within this complex cellular and molecular environment that oncologists attempt to evaluate patient characteristics to determine the next optimal therapy, as well as develop next generation anti-cancer agents. Practicing clinicians are often left to rely on 1) tumor characteristics (e.g. disease stage); 2) basic imaging modalities (e.g. CT or bone scans) that provide little functional information to predict sensitivity to anti-cancer therapies; 3) blood biomarkers (e.g. secreted proteins such as CEA or PSA) that may not correlate with either treatment response or survival; and 4) individual patient characteristics that predict unacceptable toxicity from anti-cancer therapies (i.e. patients with poor renal function should not receive chemotherapies that are renally excreted). The most clinically relevant cancer biomarkers to date remain tissue biopsies. Biopsy tumor samples undergo morphologic, histologic and, more recently, genomic characteristics to determine the cancer type and identification of predictive markers for treatment response. However, analysis of archived tumor biopsies often do not reflect the dynamic alterations that tumors exhibit in the process of metastases or under the selective pressures of anti-cancer therapies. Recent cases studies suggest that repeat biopsies for molecular analyses can have dramatic impacts on patient care and more institutions are adopting this approach.9 However, the cost of performing tumor biopsies can cost thousands of dollars depending on the complexity of the procedure. And given the likelihood of heterogeneity between different tumor deposits, the clinical utility and diagnostic yield from this approach to sampling tumor cells may not be worth the cost. Efforts to overcome these shortfalls have led to expanding research into circulating tumor cells.
Circulating tumor cells (CTC) are a population of cells found in peripheral circulation that have the potential to act as surrogate source of tumor cells for use in precision medical strategies.10 Recent studies suggest that CTCs are shed into circulation from primary and metastatic tumor sites and may contribute to the development of metastatic tumor deposits.11,12 CTC's are rare cells with an estimated frequency of one cell for every 108 to 1010 peripheral blood cells.13,14 The great hope in CTC research lies in the potential utility of these rare cells as an accessible “fluid biopsy” that would permit frequent, minimally invasive sampling of tumor cells for molecular assays that are currently performed on traditional biopsy specimens.15,16
As reviewed in this issue of Lab on a Chip and others, many varied approaches have been developed over the last fifteen years to capture and analyze CTCs.10,17–22 Observation of CTCs dates back more than a century, yet as of today there is still no standard definition of what constitutes a CTC.23 Various independent assays have been developed to enrich, characterize and define CTC according to their specific assay.24–32 To date, only the Veridex CellSearch™ platform has been licensed by the FDA for clinical indications in prostate, colon and breast cancer.33 While itself a critical advance in the clinical utilization of CTCs as an accessible source of tumor cells, the overall utility of sole enumeration of CTCs has been questioned by various authors.16,34–37 The process by which this platform has reached clinical qualification is instructive on the process by which emerging technologies can reach broad clinical utility. In an effort to make the qualification process more efficient the FDA has developed a critical path initiative for guidance in the development of novel technologies.38
Defining the context of use
Lesko and Atkinson et al. defined a biomarker as “A characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or biological responses to a therapeutic intervention”.39 Biomarkers are then further categorized for clinical use as either diagnostic, prognostic, predictive, pharmacodynamic, or as a surrogate of a clinical endpoint (Table 1). Further, a biomarker may have utility in more than one of these categories. Within each of these categories, a biomarker must further have a defined context of use (COU), meaning a specified manner of interpretation and clinical application that may extend across a spectrum of cancer stages and phases of treatment.40 For example, analysis of EGFR mutational status from a formalin fixed, paraffin embedded specimen is a qualified biomarker that is predictive for treatment response to erlotinib targeted therapy in patients with metastatic, non-small cell lung cancer. The unique position of the CellSearch™ platform as an FDA cleared technology lies in its rigid, repeatable and robust assay, solely consisting of enumeration, for defined clinical contexts in metastatic prostate, breast and colorectal cancer.
Table 1 Biomarker classificationa
Type |
Definition |
Example |
Adapted from: Qualification Process for Drug Development Tools.
|
Diagnostic |
Identifies the presence of a malignancy and potentially establishes the site of origin. |
Tissue biopsy |
Prognostic |
Baseline patient or disease characteristic that categorized patients by degrees of risk for disease recurrence or progression. A prognostic biomarker informs about the natural history of the disorder in that particular patient in the absence of a therapeutic intervention. |
ECGO performance status; Oncotype Dx in breast cancer. |
Predictive |
Baseline characteristic that categorizes patients by their likelihood for response to a particular treatment. A predictive biomarker is used to identify whether a given patient is likely to respond to a treatment intervention in a particular way It may predict favorable or an unfavorable response. |
EGFR mutation in non-small cell lung cancer and sensitivity to erlotinib/gefitinib, KRAS mutation and insensitivity to cetuximab in colorectal cancer, ER/PR positivity in breast cancer and sensitivity to endocrine therapies. |
Pharmacodynamic (or activity) |
Dynamic assessment that shows that a biological response has occurred in a patient after having received a therapeutic intervention. A pharmacodynamic biomarker may be treatment-specific or more broadly informative of disease response. |
Blood pressure, HbA1C, radiographic measures, and cholesterol. |
Surrogate endpoint |
Biomarker intended to substitute for a clinical efficacy endpoint. They are expected to predict clinical benefit, harm, or lack of benefit or harm. Surrogate endpoints are a subset of pharmacodynamic biomarkers. |
Progression free survival (PFS), overall survival (OS). |
Once an assay has defined the clinical context and COU, the initial steps in advancing a novel platform towards clinical utility include 1) developing an analytically valid assay and 2) clinical studies that generate supportive evidence for use in a specific clinical context. This is not always a linear process, wherein many technologies refine and validate an assay while simultaneously determining the appropriate COU as the performance characteristics of the technology become apparent. One example includes the development of the HIV viral load assay. Due to poor sensitivity, this assay was first used only in the research setting. As the sensitivity, specificity, repeatability and reproducibility were improved, this test began to show utility as a predictive biomarker. Further improvements in the assay have led to the use of the HIV viral load as a pharmacodynamic assay as well as use in the diagnostic setting.41
The development of CTC technologies for specific COUs is far more complicated in cancer than HIV given the sheer range of solid tumor types, stages of disease and therapeutic interventions available. For example, there are many histologic subtypes of breast cancer (Luminal A/Luminal B/Basal/HER2), many therapeutic contexts (neoadjuvant, adjuvant, recurrent/metastatic, treatment types (surgery, radiation, systemic therapy) and pharmacodynamic targets (estrogen receptor, HER2)) that would benefit from improved prognostic, predictive or pharmacodynamic biomarkers. While this complexity in oncology can be daunting, it rather reflects the sheer range of opportunities for biomarker discovery, development and validation to improve the quality of care for patients with cancer. When developing technologies for CTC research, incorporating flexibility into device design can facilitate applicability to a wide range of potential COUs. However, clinical utility is only realized in a platform and assay that targets a relevant clinical endpoint in a manner that is reproducible and easily incorporated into established clinical workflows. Too often have technologies been designed without this clinical perspective, resulting in devices that while being technologically useful, may lack the willingness to adopt from the clinical side. A true translational tool brings together the potential and excitement of the latest technology with an endpoint and interface that can integrate within a clinician's toolbox.
Benchmarks for clinical application
Demonstration of analytical validity
The initial steps toward developing a validated biomarker begin with the pre-analytical and analytical phases. The scientific integrity of a biomarker is defined by pre-analytical and initial analytical assay evaluations prior to in clinical trials. Pre-analytical assessment includes proper specimen selection, specimen handling, processing, and storage parameters including determining the effects on the sample of storage time and varying storage temperatures. This pre-analytical work leads to the development of robust standard operating procedures (SOP) to ensure measurements can be assessed within the same context each and every time.
Following initial pre-analytical assessment further analytical validation is necessary. Qualification of a new biomarker requires that Clinical Laboratory Improvement Amendments (CLIA) standards are maintained and testing is performed in CLIA certified labs.42 These standards are comprehensive but briefly include: availability of standard reference materials, calibration of equipment, reagent checks, optimization of test protocols, acceptance criteria for standard curves and assessment of reagent matrix effects and interference from other compounds, antibodies, etc. Throughout this course it is necessary to establish a rigorous quality control process for assessing reproducibility within intra-and inter-assay variability. Finally, establishing data management and storage standards needs to be established including data reduction, interpretation and reference intervals. Completion of pre-analytical assessment and initiation of the analytical evaluation begins the process towards assay qualification and the establishment of a reliable assay for use in clinical trials and future patient use. It should be noted that assay qualification is separate and unique from device authorization. Devices that are involved in the actual measuring procedures of CTC are regulated and authorized for use by the Office of In Vitro Diagnostics and Radiologic Health (OIR).43
Demonstration of clinical utility
After completion of pre-analytical and initial analytical biomarker assay development the biomarker can be incorporated into clinical studies in order to generate evidence in support of qualification (Table 2). Similar to drug development, CTC technology development follows a path of prospective trials beginning with phase I trials to refine the assay, determine sensitivity and specificity and generate the first clinical data in patients. Biomarkers in phase I trials are often pharmacodynamic in nature, demonstrating that assay readouts correlate with drug target effects for support of the drug selection and drug dose.44 Prospective phase II trials with biomarkers can be used to provide further evidence that a novel agent affects a specific target or provides data regarding biomarker association and clinical outcomes. Associations that are established can then be further explored in phase III trials. A recognition of the substantial clinical work needed to achieve qualification has led the FDA to recommend formation of collaborative groups to increase efficiency of joint efforts and lessen resource burden on any individual person or group.40,45
Table 2 Biomarker level of evidence
Phase |
Definition |
I |
Exploratory CTC assay trial for development of sensitivity, specificity and initial clinical data and publications. |
II |
Limited size study of a developing CTC assay that may be compared with an existing CTC standard to determine clinical relevance benefit. |
III |
Reproduction of CTC assay results by other specialized CTC laboratories to confirm reproducibility of sensitivity, specificity results. |
IV |
New CTC assay is implemented in large-scale clinical trials with defined endpoints (PFS or OS) for a defined cohort of patients and stage and treatment. |
Advancing to clinical qualification
The Center for Drug Evaluation and Research (CDER) has established a framework for interactions between CDER and biomarker developers that consists of an initial stage of regulatory consultation and advice to determine what data will be necessary for qualification followed by a thorough review of evidence for a qualification decision.21,38 This process begins with a submission of a letter of intent (LOI) to CDER that addresses proposed context of use, overview of available data to support biomarker development and a structured summary of planned studies to generate evidence to support biomarker qualification (Table 2). The role the biomarker will play within clinical trials also needs to be established. For example, the role of an integrated biomarker in clinical trials is intended to validate assays that are planned for ongoing investigation. Integrated biomarkers are often used in phase I and II early drug development trials and may be compared to existing CTC assays. An integral biomarker in a clinical trial is an assay that is done in order for the clinical trial to proceed. For example, the biomarker may be used to determine eligibility or stratify participants within different arms of a trial. The level of evidence necessary to achieve biomarker qualification can be framed in a similar stepwise fashion as drug development trials (Table 2). Once sufficient clinical evidence has been obtained a formal qualification package is submitted to CDER. A Qualification Review Team (QRT) reviews submitted data and may ask for clarification about the submission prior to making a final decision. If CDER accepts the biomarker for qualification a Statement of Qualification will be issued.21
Current clinical applications of CTCS
Despite extensive research over the last decade, the only FDA licensed CTC assay is the Veridex CellSearch™ platform. As reviewed elsewhere, this platform captures and enumerates CTCs defined as cells that are positive for EpCAM (epithelial cell adhesion molecule), cytokeratins, an intact nuclei, and negative for a leukocyte marker (CD45).46 However, it is on the basis of this rigid and defined assay readout, that this platform has been able to reach FDA clearance as a prognostic tool in metastatic prostate, breast and colorectal cancer.
Prostate cancer
In 2008 de Bono et al. published data that led to FDA approval of the CellSearch platform.47 In this study, 231 patients with metastatic castrate resistance prostate cancer (mCRPC) underwent CTC enumeration with CellSearch technology before and after cytotoxic cancer treatment. Those patients with a defined cutoff of ≥5 CTC per 7.5 mL of whole blood had an overall survival of 11.5 months compared to 21.7 months for those patients with <5 CTC per 7.5 mL. Those who had a pre-treatment unfavorable CTC measurement (≥5 CTC per 7.5 mL) and crossed over to a post treatment favorable CTC measurement (<5 CTC per 7.5 mL) had significantly improved overall survival (OS) (6.8 vs. 21.3 months) compared to patients who remained in the unfavorable post treatment CTC group. Additionally, patients with a baseline favorable CTC measurement who converted to an unfavorable post treatment CTC measurement had a poorer OS (9.3 vs. >26 months). Further studies have found that CTC enumeration in patients with metastatic hormone sensitive prostate cancer is predictive of duration of androgen responsiveness, suggesting an additional COU for this assay.35,48
Breast cancer
In patients with metastatic breast cancer (MBC), Cristofanilli et al., showed the presence of ≥5 CTC per 7.5 mL, using CellSearch, before initiation of a new treatment regimen was predictive of progression free survival (PFS) and OS.49 The CTC counts at later assessments also correlated with PFS and OS, measured from the time of subsequent measurement. Univariate and multivariate analyses found that CTC count (<5 or ≥5) independently correlated with prognosis. However, these analyses did not incorporate radiographic imaging (standard of care assessments) and it is unclear if CTC analysis is independent of the information obtained from imaging modalities. Studies comparing the CellSearch system to current radiographic imaging have shown superior prediction of response to treatment and OS in patients with MBC.50 While these studies provided key information towards qualifying CTC enumeration as a biomarker in metastatic breast cancer, they did not answer the key question regarding therapeutic decision making based on CTC enumeration.
Colorectal cancer
Cohen et al. analyzed CTC counts with the CellSearch platform in 430 patients with metastatic colorectal cancer receiving systemic chemotherapy.51 These authors established a cutoff of ≥3 CTC per 7.5 mL of blood as an independent predictor of PFS (4.5 vs. 7.9 months) and OS (9.4 vs. 18.5 months) compared to CTC counts <3 CTC per 7.5 mL of blood. Interestingly, patients whose CTC counts dropped to less than 3 CTCs per 7.5 mL after chemotherapy were found to have experience a significantly longer PFS and OS compared with patients with unfavorable CTCs at both time points (PFS, 6.2 vs. 1.6 months; OS, 11.0 vs. 3.7 months).
Other solid tumors
As previously discussed, the Veridex CellSearch™ platform is currently the only FDA licensed CTC assay and is approved for clinical use in prostate, breast and colorectal cancers. Despite this narrow clinical FDA indication for CellSearch, CTC research of other malignancies identified by CTC continues to progress. Lung cancer remains the most lethal cancer within the United States with ~160000 deaths annually.52 Current directed treatments of lung cancer in patients with EGFR mutations or ALK-gene rearrangement evidenced by tissue biopsy have shown significant improvement in both overall and progression free survival.53–55 Recent work into detection of both EGFR and ALK-gene rearrangement by circulating tumor cells appear promising and is ongoing with the aim of reliable and repeatable CTC capture and analysis to guide clinical care.56,57 Other malignancies are also being evaluated in the pre-clinical setting using CTC technologies including melanoma,58 pancreatic adenocarcinoma,59 urothelial carcinoma60 and gastric cancers,61 among others.
Directing clinical care
To date, no CTC platform is approved to guide clinical care. Under the definitions and regulatory processes described above, phase III trials must be performed to demonstrate how this biomarker can impact clinical outcomes. One such study sponsored by the NCI is currently enrolling patients with metastatic breast cancer prior to beginning first line chemotherapy (NCT00382018). This study randomizes women who have persistently elevated CTC counts as assessed by the CellSearch platform (5 or more cells per 7.5 mL of blood) after their first round of chemotherapy to continue on their current treatment or switch to a different treatment regimen at the treating physicians discretion. Patients will be followed for progression free survival and overall survival and is currently enrolling patients. A second trial conducted in Europe is enrolling patients with metastatic breast cancer going on to receive third line chemotherapy (NCT01349842). Those patients with detectable CTCs (based on CellSearch results) are randomized to either continue therapy based on standard radiographic responses or changes in CTC counts after one cycle of therapy. Patients will be followed for 4 years with the primary outcome is overall survival. Both of these studies seek to determine if the presence of CTCs is not only a relevant biomarker, but one that should dictate therapeutic decision making.
Future directions
Moving beyond sole CTC enumeration, an expansive use of CTC technologies lies in the use of these cells as an accessible source of tumor cells for serial analysis with the same, or similar, molecular assays currently used archived tissue biopsies. The applications of such an assay extend from stratifying patients most likely to experience therapeutic benefit but also the identification and therapeutic targeting of resistance mechanisms, prior to evidence of radiographic progression. This approach would only have clinical utility if CTCs prove to be a more representative/relevant sample of tumor cells compared to either remote archived tissue samples (that likely exhibit significant molecular variance compared to the multitude of metastatic sites) or as a surrogate endpoint for radiographic outcomes or overall survival. For example, immunohistochemical analysis for HER2 on archived tumor samples is the standard methodology for determining patients who should be treated with trastuzumab in both the adjuvant and metastatic setting. However, nearly all patients with metastatic cancer progress on trastuzumab and we remain limited in our ability to identify mechanisms of resistance that emerged under the selective pressure of this targeted therapy without performing invasive and painful tumor biopsies. Can the ready access to tumor cells with novel CTC platforms provide greater insight into the dynamic process of tumor biology and tumor resistance? Can such an assay finally lead oncology care into the realm of personalized and precision medicine? These questions remain to be answered and the regulatory process for molecular assays on such serial “biopsies” has yet to be clarified.
Summary
Similar to development of drugs for a specific indication, biomarker development requires qualification through the Food and Drug Administration (FDA) for a specific COU. Once a biomarker has been qualified, the results of the evaluation with the biomarker within the context of use can be used in the regulatory filings. Through this process, qualified biomarkers can serve as a more efficient link to bring new therapeutic treatments into clinical use. The CellSearch™ platform stands as the only FDA-licensed CTC platform for sole CTC enumeration. While itself a critical advance, the greater potential to analyze CTCs for molecular assays in predictive, prognostic and pharmacodynamic niches remains untapped. Especially given the near endless contexts of use for these in the multitude of tumor types and stages of disease. In order to advance current CTC research into clinical practice, emerging technologies need to demonstrate experimental and clinical evidence for providing an accurate, reliable and reproducible assay that meets strict qualification regulatory requirements for patient care. Through coordination of device and biomarker development with a clear plan toward biomarker qualification, an efficient path towards integration into clinical and patient decisions can be established. This concerted effort offers the potential to bring about a fundamental change in the treatment of cancer patients. Through the use of CTC technology we may be able to realize a true “fluid biopsy” to improve patient care across all solid tumors.
Disclosure of interests
The authors have no relevant disclosures.
Acknowledgements
This work was supported by a Challenge Award from the Prostate Cancer Foundation and Movember, a Prostate Cancer Foundation Young Investigator award to Dr. Lang, DOD award W81XWH-12-1-0052, a UW Institute for Clinical and Translational Research-Type 1 Research Pilot Award, and a UW Carbone Cancer Center Investigator Initiated Pilot grant.
References
- B. J. Druker, M. Talpaz, D. J. Resta, B. Peng, E. Buchdunger, J. M. Ford, N. B. Lydon, H. Kantarjian, R. Capdeville, S. Ohno-Jones and C. L. Sawyers, N. Engl. J. Med., 2001, 344, 1031–1037 CrossRef CAS PubMed.
- E. H. Romond, E. A. Perez, J. Bryant, V. J. Suman, C. E. Geyer Jr., N. E. Davidson, E. Tan-Chiu, S. Martino, S. Paik, P. A. Kaufman, S. M. Swain, T. M. Pisansky, L. Fehrenbacher, L. A. Kutteh, V. G. Vogel, D. W. Visscher, G. Yothers, R. B. Jenkins, A. M. Brown, S. R. Dakhil, E. P. Mamounas, W. L. Lingle, P. M. Klein, J. N. Ingle and N. Wolmark, N. Engl. J. Med., 2005, 353, 1673–1684 CrossRef CAS PubMed.
- P. B. Chapman, A. Hauschild, C. Robert, J. B. Haanen, P. Ascierto, J. Larkin, R. Dummer, C. Garbe, A. Testori, M. Maio, D. Hogg, P. Lorigan, C. Lebbe, T. Jouary, D. Schadendorf, A. Ribas, S. J. O'Day, J. A. Sosman, J. M. Kirkwood, A. M. Eggermont, B. Dreno, K. Nolop, J. Li, B. Nelson, J. Hou, R. J. Lee, K. T. Flaherty, G. A. McArthur and B.-S. Group, N. Engl. J. Med., 2011, 364, 2507–2516 CrossRef CAS PubMed.
- E. L. Kwak, Y. J. Bang, D. R. Camidge, A. T. Shaw, B. Solomon, R. G. Maki, S. H. Ou, B. J. Dezube, P. A. Janne, D. B. Costa, M. Varella-Garcia, W. H. Kim, T. J. Lynch, P. Fidias, H. Stubbs, J. A. Engelman, L. V. Sequist, W. Tan, L. Gandhi, M. Mino-Kenudson, G. C. Wei, S. M. Shreeve, M. J. Ratain, J. Settleman, J. G. Christensen, D. A. Haber, K. Wilner, R. Salgia, G. I. Shapiro, J. W. Clark and A. J. Iafrate, N. Engl. J. Med., 2010, 363, 1693–1703 CrossRef CAS PubMed.
- M. Gerlinger, A. J. Rowan, S. Horswell, J. Larkin, D. Endesfelder, E. Gronroos, P. Martinez, N. Matthews, A. Stewart, P. Tarpey, I. Varela, B. Phillimore, S. Begum, N. Q. McDonald, A. Butler, D. Jones, K. Raine, C. Latimer, C. R. Santos, M. Nohadani, A. C. Eklund, B. Spencer-Dene, G. Clark, L. Pickering, G. Stamp, M. Gore, Z. Szallasi, J. Downward, P. A. Futreal and C. Swanton, N. Engl. J. Med., 2012, 366, 883–892 CrossRef CAS PubMed.
- N. Navin and J. Hicks, Genome Med., 2011, 3, 31 CrossRef CAS PubMed.
- N. Navin, J. Kendall, J. Troge, P. Andrews, L. Rodgers, J. McIndoo, K. Cook, A. Stepansky, D. Levy, D. Esposito, L. Muthuswamy, A. Krasnitz, W. R. McCombie, J. Hicks and M. Wigler, Nature, 2011, 472, 90–94 CrossRef CAS PubMed.
- G. Liu, R. Jeraj, M. Vanderhoek, S. Perlman, J. Kolesar, M. Harrison, U. Simoncic, J. Eickhoff, L. Carmichael, B. Chao, R. Marnocha, P. Ivy and G. Wilding, Clin. Cancer Res., 2011, 17, 7634–7644 CrossRef CAS PubMed.
- L. V. Sequist, B. A. Waltman, D. Dias-Santagata, S. Digumarthy, A. B. Turke, P. Fidias, K. Bergethon, A. T. Shaw, S. Gettinger, A. K. Cosper, S. Akhavanfard, R. S. Heist, J. Temel, J. G. Christensen, J. C. Wain, T. J. Lynch, K. Vernovsky, E. J. Mark, M. Lanuti, A. J. Iafrate, M. Mino-Kenudson and J. A. Engelman, Sci. Transl. Med., 2011, 3, 75ra26 CrossRef PubMed.
- G. Attard and J. S. de Bono, Curr. Opin. Genet. Dev., 2011, 21, 50–58 CrossRef CAS PubMed.
- V. Muller, N. Stahmann, S. Riethdorf, T. Rau, T. Zabel, A. Goetz, F. Janicke and K. Pantel, Clin. Cancer Res., 2005, 11, 3678–3685 CrossRef PubMed.
- M. Y. Kim, T. Oskarsson, S. Acharyya, D. X. Nguyen, X. H. Zhang, L. Norton and J. Massague, Cell, 2009, 139, 1315–1326 CrossRef PubMed.
- A. A. Ross, B. W. Cooper, H. M. Lazarus, W. Mackay, T. J. Moss, N. Ciobanu, M. S. Tallman, M. J. Kennedy, N. E. Davidson and D. Sweet,
et al.
, Blood, 1993, 82, 2605–2610 CAS.
- J. S. Ross and E. A. Slodkowska, Am. J. Clin. Pathol., 2009, 132, 237–245 CrossRef CAS PubMed.
- B. P. Casavant, D. J. Guckenberger, S. M. Berry, J. T. Tokar, J. M. Lang and D. J. Beebe, Lab Chip, 2013, 13, 391–396 RSC.
- J. M. Lang, B. P. Casavant and D. J. Beebe, Sci. Transl. Med., 2012, 4, 141ps113 CrossRef PubMed.
- D. C. Danila, A. Anand, C. C. Sung, G. Heller, M. A. Leversha, L. Cao, H. Lilja, A. Molina, C. L. Sawyers, M. Fleisher and H. I. Scher, Eur. Urol., 60, 897–904 CrossRef CAS PubMed.
- D. C. Danila, M. Fleisher and H. I. Scher, Clin. Cancer Res., 17, 3903–3912 CrossRef CAS PubMed.
- C. Alix-Panabieres and K. Pantel, Clin. Chem., 2013, 59, 110–118 CAS.
- E. S. Lianidou and A. Markou, Clin. Chem. Lab. Med., 2011, 49, 1579–1590 CrossRef CAS PubMed.
- D. R. Parkinson, N. Dracopoli, B. G. Petty, C. Compton, M. Cristofanilli, A. Deisseroth, D. F. Hayes, G. Kapke, P. Kumar, J. Lee, M. C. Liu, R. McCormack, S. Mikulski, L. Nagahara, K. Pantel, S. Pearson-White, E. A. Punnoose, L. T. Roadcap, A. E. Schade, H. I. Scher, C. C. Sigman and G. J. Kelloff, J. Transl. Med., 2012, 10, 138 CrossRef PubMed.
- D. F. Hayes and J. B. Smerage, Prog. Mol.
Biol. Transl. Sci., 2010, 95, 95–112 CAS.
- T. R. Ashworth, Med. J. Aust., 1869, 14, 146–147 Search PubMed.
- T. Fehm, O. Hoffmann, B. Aktas, S. Becker, E. F. Solomayer, D. Wallwiener, R. Kimmig and S. Kasimir-Bauer, Breast Cancer Res., 2009, 11, R59 CrossRef PubMed.
- M. G. Krebs, R. Sloane, L. Priest, L. Lancashire, J. M. Hou, A. Greystoke, T. H. Ward, R. Ferraldeschi, A. Hughes, G. Clack, M. Ranson, C. Dive and F. H. Blackhall, J. Clin. Oncol., 2011, 29, 1556–1563 CrossRef PubMed.
- P. Pinzani, B. Salvadori, L. Simi, S. Bianchi, V. Distante, L. Cataliotti, M. Pazzagli and C. Orlando, Hum. Pathol., 2006, 37, 711–718 CrossRef CAS PubMed.
- S. Riethdorf, H. Fritsche, V. Muller, T. Rau, C. Schindlbeck, B. Rack, W. Janni, C. Coith, K. Beck, F. Janicke, S. Jackson, T. Gornet, M. Cristofanilli and K. Pantel, Clin. Cancer Res., 2007, 13, 920–928 CrossRef CAS PubMed.
- A. Schoenfeld, K. H. Kruger, J. Gomm, H. D. Sinnett, J. C. Gazet, N. Sacks, H. G. Bender, Y. Luqmani and R. C. Coombes, Eur. J. Cancer, 1997, 33, 854–861 CrossRef CAS.
- S. L. Stott, C. H. Hsu, D. I. Tsukrov, M. Yu, D. T. Miyamoto, B. A. Waltman, S. M. Rothenberg, A. M. Shah, M. E. Smas, G. K. Korir, F. P. Floyd Jr., A. J. Gilman, J. B. Lord, D. Winokur, S. Springer, D. Irimia, S. Nagrath, L. V. Sequist, R. J. Lee, K. J. Isselbacher, S. Maheswaran, D. A. Haber and M. Toner, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 18392–18397 CrossRef CAS PubMed.
- N. Xenidis, M. Ignatiadis, S. Apostolaki, M. Perraki, K. Kalbakis, S. Agelaki, E. N. Stathopoulos, G. Chlouverakis, E. Lianidou, S. Kakolyris, V. Georgoulias and D. Mavroudis, J. Clin. Oncol., 2009, 27, 2177–2184 CrossRef CAS PubMed.
- N. Xenidis, V. Markos, S. Apostolaki, M. Perraki, A. Pallis, G. Sfakiotaki, D. Papadatos-Pastos, L. Kalmanti, M. Kafousi, E. Stathopoulos, S. Kakolyris, D. Mavroudis and V. Georgoulias, Ann. Oncol., 2007, 18, 1623–1631 CrossRef CAS PubMed.
- S. M. Yie, B. Lou, S. R. Ye, X. He, M. Cao, K. Xie, N. Y. Ye, R. Lin, S. M. Wu, H. B. Xiao and E. Gao, Lung Cancer, 2009, 63, 284–290 CrossRef PubMed.
- M. C. Miller, G. V. Doyle and L. W. Terstappen, J. Oncol., 2010, 617421 Search PubMed.
- M. Balic, H. Lin, A. Williams, R. H. Datar and R. J. Cote, Expert Rev. Mol. Diagn., 2012, 12, 303–312 CrossRef PubMed.
- O. B. Goodman Jr., J. T. Symanowski, A. Loudyi, L. M. Fink, D. C. Ward and N. J. Vogelzang, Clin. Genitourin. Cancer, 2011, 9, 31–38 CrossRef PubMed.
- D. F. Hayes and J. Smerage, Clin. Cancer Res., 2008, 14, 3646–3650 CrossRef CAS PubMed.
- H. Iinuma, T. Watanabe, K. Mimori, M. Adachi, N. Hayashi, J. Tamura, K. Matsuda, R. Fukushima, K. Okinaga, M. Sasako and M. Mori, J. Clin. Oncol., 2011, 29, 1547–1555 CrossRef PubMed.
-
U. S. F. a. D. Administration, Critical Path Initiative, Accessed May 26, 2013 Search PubMed.
- L. J. Lesko and A. J. Atkinson Jr., Annu. Rev. Pharmacol. Toxicol., 2001, 41, 347–366 CrossRef CAS PubMed.
-
FDA, http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM267449.pdf, 2011.
-
FDA, Roche Aplicor HIV-1 Monitor Test, http://www.fda.gov/BiologicsBloodVaccines/BloodBloodProducts/ApprovedProducts/PremarketApprovalsPMAs/ucm091626.htm, Accessed August 15, 2013 Search PubMed.
-
FDA, Recommendations: Clinical Laboratory Improvement Amendments of 1988 (CLIA) Waiver Applications for Manufacturers of In Vitro Diagnostic Devices, Accessed May 26, 2013 Search PubMed.
-
OIR, Office of In Vitro Diagnostics and Radiological Health, Accessed May 26, 2013 Search PubMed.
- J. E. Dancey, K. K. Dobbin, S. Groshen, J. M. Jessup, A. H. Hruszkewycz, M. Koehler, R. Parchment, M. J. Ratain, L. K. Shankar, W. M. Stadler, L. D. True, A. Gravell and M. R. Grever, Clin. Cancer Res., 2010, 16, 1745–1755 CrossRef CAS PubMed.
-
FDA, Guidance for Industry Qualification Process for Drug Development Tools, Accessed May 26, 2013 Search PubMed.
- D. C. Danila, K. Pantel, M. Fleisher and H. I. Scher, Cancer J., 2011, 17, 438–450 CrossRef CAS PubMed.
- J. S. de Bono, H. I. Scher, R. B. Montgomery, C. Parker, M. C. Miller, H. Tissing, G. V. Doyle, L. W. Terstappen, K. J. Pienta and D. Raghavan, Clin. Cancer Res., 2008, 14, 6302–6309 CrossRef CAS PubMed.
- T. Okegawa, K. Nutahara and E. Higashihara, J. Urol., 2008, 180, 1342–1347 CrossRef PubMed.
- M. Cristofanilli, G. T. Budd, M. J. Ellis, A. Stopeck, J. Matera, M. C. Miller, J. M. Reuben, G. V. Doyle, W. J. Allard, L. W. Terstappen and D. F. Hayes, N. Engl. J. Med., 2004, 351, 781–791 CrossRef CAS PubMed.
- G. T. Budd, M. Cristofanilli, M. J. Ellis, A. Stopeck, E. Borden, M. C. Miller, J. Matera, M. Repollet, G. V. Doyle, L. W. Terstappen and D. F. Hayes, Clin. Cancer Res., 2006, 12, 6403–6409 CrossRef CAS PubMed.
- S. J. Cohen, C. J. Punt, N. Iannotti, B. H. Saidman, K. D. Sabbath, N. Y. Gabrail, J. Picus, M. Morse, E. Mitchell, M. C. Miller, G. V. Doyle, H. Tissing, L. W. Terstappen and N. J. Meropol, J. Clin. Oncol., 2008, 26, 3213–3221 CrossRef PubMed.
-
U. S. C. S. W. Group, United States Cancer Statistics: 1999–2009 Incidence and Mortality Web-based Report, Atlanta: U.S., Department of Health and Human Services, Centers for Disease Control and Prevention and National Cancer Institute, 2013. Available at: http://www.cdc.gov/uscs, Accessed August 15, 2013 Search PubMed.
- A. T. Shaw, B. Y. Yeap, B. J. Solomon, G. J. Riely, J. Gainor, J. A. Engelman, G. I. Shapiro, D. B. Costa, S. H. Ou, M. Butaney, R. Salgia, R. G. Maki, M. Varella-Garcia, R. C. Doebele, Y. J. Bang, K. Kulig, P. Selaru, Y. Tang, K. D. Wilner, E. L. Kwak, J. W. Clark, A. J. Iafrate and D. R. Camidge, Lancet Oncol., 2011, 12, 1004–1012 CrossRef CAS.
- F. A. Shepherd, J. Rodrigues Pereira, T. Ciuleanu, E. H. Tan, V. Hirsh, S. Thongprasert, D. Campos, S. Maoleekoonpiroj, M. Smylie, R. Martins, M. van Kooten, M. Dediu, B. Findlay, D. Tu, D. Johnston, A. Bezjak, G. Clark, P. Santabarbara and L. Seymour, National Cancer Institute of Canada Clinical Trials, N. Engl. J. Med., 2005, 353, 123–132 CrossRef CAS PubMed.
- C. Zhou, Y. L. Wu, G. Chen, J. Feng, X. Q. Liu, C. Wang, S. Zhang, J. Wang, S. Zhou, S. Ren, S. Lu, L. Zhang, C. Hu, C. Hu, Y. Luo, L. Chen, M. Ye, J. Huang, X. Zhi, Y. Zhang, Q. Xiu, J. Ma, L. Zhang and C. You, Lancet Oncol., 2011, 12, 735–742 CrossRef CAS.
- M. Ilie, E. Long, C. Butori, V. Hofman, C. Coelle, V. Mauro, K. Zahaf, C. H. Marquette, J. Mouroux, P. Paterlini-Brechot and P. Hofman, Ann. Oncol., 2012, 23, 2907–2913 CrossRef CAS PubMed.
- R. Ran, L. Li, M. Wang, S. Wang, Z. Zheng and P. P. Lin, Anal. Bioanal. Chem., 2013, 405(23), 7377–7382 CrossRef CAS PubMed.
- S. Hoshimoto, T. Shingai, D. L. Morton, C. Kuo, M. B. Faries, K. Chong, D. Elashoff, H. J. Wang, R. M. Elashoff and D. S. Hoon, J. Clin. Oncol., 2012, 30, 3819–3826 CrossRef PubMed.
- K. Tjensvoll, O. Nordgard and R. Smaaland, Int. J. Cancer, 2013 DOI:10.1002/ijc.28134.
- M. Rink, F. K. Chun, R. Dahlem, A. Soave, S. Minner, J. Hansen, M. Stoupiec, C. Coith, L. A. Kluth, S. A. Ahyai, M. G. Friedrich, S. F. Shariat, M. Fisch, K. Pantel and S. Riethdorf, Eur. Urol., 2012, 61, 810–817 CrossRef CAS PubMed.
- L. Tang, S. Zhao, W. Liu, N. F. Parchim, J. Huang, Y. Tang, P. Gan and M. Zhong, BMC Cancer, 2013, 13, 314 CrossRef PubMed.
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