- Open Access
Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology
- Hideaki Tahara1Email author,
- Marimo Sato1Email author,
- Magdalena Thurin2Email author,
- Ena Wang3Email author,
- Lisa H Butterfield4Email author,
- Mary L Disis5,
- Bernard A Fox6,
- Peter P Lee7,
- Samir N Khleif8,
- Jon M Wigginton9,
- Stefan Ambs10,
- Yasunori Akutsu11,
- Damien Chaussabel12,
- Yuichiro Doki13,
- Oleg Eremin14,
- Wolf Hervé Fridman15,
- Yoshihiko Hirohashi16,
- Kohzoh Imai16,
- James Jacobson2,
- Masahisa Jinushi1,
- Akira Kanamoto1,
- Mohammed Kashani-Sabet17,
- Kazunori Kato18,
- Yutaka Kawakami19,
- John M Kirkwood4,
- Thomas O Kleen20,
- Paul V Lehmann20,
- Lance Liotta21,
- Michael T Lotze22,
- Michele Maio23, 24,
- Anatoli Malyguine25,
- Giuseppe Masucci26,
- Hisahiro Matsubara11,
- Shawmarie Mayrand-Chung27,
- Kiminori Nakamura18,
- Hiroyoshi Nishikawa28,
- A Karolina Palucka12,
- Emanuel F Petricoin21,
- Zoltan Pos3,
- Antoni Ribas29,
- Licia Rivoltini30,
- Noriyuki Sato31,
- Hiroshi Shiku28,
- Craig L Slingluff32,
- Howard Streicher33,
- David F Stroncek34,
- Hiroya Takeuchi35,
- Minoru Toyota36,
- Hisashi Wada13,
- Xifeng Wu37,
- Julia Wulfkuhle21,
- Tomonori Yaguchi19,
- Benjamin Zeskind38,
- Yingdong Zhao39,
- Mai-Britt Zocca40 and
- Francesco M Marincola3Email author
© Tahara et al; licensee BioMed Central Ltd. 2009
Received: 2 June 2009
Accepted: 17 June 2009
Published: 17 June 2009
Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations.
Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD) of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs) likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms.
Other candidate systemic and/or tissue-specific biomarkers were recognized that might be added to the list of known entities applicable in immunotherapy trials. The need for a systematic approach to biomarker discovery that takes advantage of powerful high-throughput technologies was recognized; it was clear from the current state of the science that immunotherapy is still in a discovery phase and only a few of the current biomarkers warrant extensive validation. It was, finally, clear that, while current technologies have almost limitless potential, inadequate study design, limited standardization and cross-validation among laboratories and suboptimal comparability of data remain major road blocks. The institution of an interactive consortium for high throughput molecular monitoring of clinical trials with voluntary participation might provide cost-effective solutions.
The International Society for the Biological Therapy of Cancer (iSBTc) launched in collaboration with the USA Food and Drug Administration (FDA) a task force addressing the need to expeditiously identify and validate biomarkers relevant to the biotherapy of cancer . The task force includes two principal components: a) validation and application of currently used biomarkers; b) identification of new biomarkers and improvement of strategies for their discovery. Currently, biomarkers are either not available or have limited diagnostic, predictive or prognostic value. These limitations hamper, in turn, the effective conduct of biotherapy trials not permitting optimization of patient selection/stratification (lack of predictive biomarkers) or early assessment of product effectiveness (lack of surrogate biomarkers). These goals were summarized in a preamble to the iSBTc-FDA task force ; the results are going to be reported on October 28th at the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting ; a document summarizing guidelines for biomarker discovery and validation will be generated. Several other agencies will participate in the workshop including the National Cancer Institute (NCI), the National Institutes of Health (NIH) Center for Human Immunology (CHI) and the National Institutes of Health Biomarker Consortium (BC).
With the generous support of the Office of International Affairs, NCI, the "US-Japan Workshop on Immunological Molecular Markers in Oncology" included, on the US side, significant participation of the iSBTc leadership, representatives from Academia and Government Agencies, the FDA, the NCI Cancer Diagnosis Program (CDP), the Cancer Therapy and Evaluation Program (CTEP), the Cell Therapy Section (CTS) of the Clinical Center, and the CHI, NIH. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immunogenetic background and the diverse disease prevalence of the two Nations and compare scientific and clinical approaches in the development of cancer immunotherapy.
Emerging biomarkers potentially useful for the immunotherapy of cancer
Carbonic Anhydrase IX
Renal Cell Cancer
STAT-1, CXCL-9, -10, -11, ISGs
IL-1α,-1β, IL-6, TNF-a, CCL3, CCL4
CCL5, CCL11, IFN-γ, ICOS, CD20
T regulatory cells
hTERT pulsed DCs
CCL2, -3, -4, -5 CXCL-9, -10
T cell mulifunctionality
Prognostic Biomarkers (useful for patient stratification/data interpretation)
Oncotype DX, Mamma Print
IFN-γ, IRF-1, STAT-1, ISGs, IL-15, CXCL-9, -10, -11 and CCL5
IFN-γ, IRF-1, STAT-1
Colorectal Cancer, Nasopharyngeal Ca
ARPC2, FN1, RGS1, WNT2
Mechanistic/End Point Biomarkers
IFN-γ, IRF-1, STAT-1, ISGs, IL-15, CXCL-9, -10, -11 and CCL5
IL-2 therapy/TLR-7 therapy
Melanoma/Basal Cell Cancer
IRF-1, STAT-1, ISGs, IL-15, CXCL-9, -10, -11 and CCL5
Vaccinia virus (Xenografts)
Herpes simplex virus (syngeneic model)
Kinetic regression/growth model
Howard Streicher (CTEP, Bethesda, MD, USA) presented an overview of biomarkers useful for patient selection, eligibility, stratification and immune monitoring. CTEP sponsors more than 150 protocols each year across many types of new agents, so that this program is familiar with the need to prioritize trials selection using biomarkers. Biomarkers are important for 1) patient selection and stratification for the best therapy; 2) identification of the most suitable targets of therapy; 3) measurement of treatment effect; 4) identification of mechanisms of drug action; 5) measurement of disease status or disease burden and; 6) identification of surrogate early markers of long-term treatment benefit .
Examples of biomarkers predictive of immunotherapy efficacy (predictive classifiers) [4–7] are telomere length of adoptively transferred tumor infiltrating lymphocytes which is significantly correlated with likelihood of clinical response , serum levels of vascular endothelial growth factor (VEGF), which are negatively associated with response of patients with melanoma to high dose interleukin (IL)-2 administration  or K-ras mutations that predict ineffectiveness of cetuximab for the treatment of colorectal cancer . Recently, the European Organization for Research and Treatment of Cancer (EORTC) reported a signature derived from pre-treatment tumor profiling that is predictive of clinical response to GSK/MAGE-A3 immunotherapy of melanoma. The signature includes the expression of CCL5/RANTES, CCL11/Eotaxin, interferon (IFN)-γ, ICOS and CD20 [11, 12].
Prognostic biomarkers assess risk of disease progression independent of therapy and can be used for patient stratification according to likelihood of survival thus simplifying subsequent interpretation of clinical results; examples include transcriptional signatures such as Oncotype DX or Mamma Print to stratify breast cancer patients  though their usefulness needs further validation . Korn et al  proposed the incorporation of multivariate predictors such as performance status, presence of visceral or brain disease and sex to interpret correlations between response and survival data in early-phase, non-randomized clinical trials. Similarly, body mass and other parameters could predict individual survival probabilities and help stratify patients with prostate cancer in randomized phase III trials . Recently, Grubb et al.  described a signaling proteomic signature based on a comprehensive analysis of protein phosphorylation that could be used for the stratification of patients with prostate cancer. Guidelines for the identification of potential classifiers during explorative, high throughput, discovery-driven analyses were proposed by Dobbin at al. ; they include the assessment of 3 parameters: standardized fold change, class prevalence, and number of genes in the platform used for investigation. Assessment is based on an algorithm that guides the determination of the adequacy of sample size in a training set. A web site is available to assist in the calculations .
Analyses performed during or right after treatment can provide mechanistic explanations of drugs function such as the intra-tumor effects of systemic interleukin (IL)-2 therapy  or local application of Toll-like receptor agonists  (mechanistic biomarkers). End point biomarkers assure that the expected biological goals of treatment were reached. Best examples are the immune monitoring assays performed during active specific immunization [22, 23]. Surrogate biomarkers inform about the effectiveness of treatment in early phase assessment and help go/no go decisions about further drug development . This is important because tumor response rates documented during phase II trials have not been, with few notable exceptions, reliable indicators of meaningful survival benefit. The series of phase II trials of cooperative group studies in North America over the past 35 years have shown little evidence of impact for single agents, but have identified benchmarks of outcome that now may be addressed, including progression at 6 months (18%), and survival at 12 months (25%) that have been unaltered over the interval of the study. These benchmarks may now allow us to accelerate progress by developing adequately powered phase II studies that would serve as the threshold for decision making for new phase III trials . Recently, a new survival prediction algorithm was proposed; tumor measurement data gathered during therapy are extrapolated into a two phase equation estimating the concomitant rate of tumor regression and growth. This kinetic regression/growth model estimates accurately the ability of therapies to prolong survival and, consequently, assist as a surrogate biomarker for drug development .
Steps in biomarker discovery
Since the term "biomarker" is used for a wide variety of purposes, confusion often results when biomarker development, validation and qualification are discussed [7, 25, 26]. During phase I and II clinical trials that are meant to establish dose, schedule and drug activity, biomarkers should primarily show biological effect of the drug (i.e. demonstrate whether a drug reached its target) and do not need to be validated as a surrogate equivalent of long term benefit. As the drug assessment process proceeds the expectations of a given biomarker grow in parallel. Moving from correlative science to clinically applicable biomarkers, validation of the marker and the assay in cohorts need to be performed. At this stage, it is important to separate data used to develop classifiers from data used for testing treatment effects. The process of classifier development can be exploratory, but the process of evaluating treatments should not be. Ultimately, clinical qualification of the marker for clinical use should be based on testing specific hypotheses in prospectively selected patient populations.
This was emphasized by Nora Disis (University of Washington, Seattle, WA, USA) who discussed steps in biomarker validation . Referring to work from Pepe et al [28–31], five phases of biomarker development were described: 1) pre-clinical exploratory phase that identifies promising directions; 2) clinical validation in which an assay can detect and characterize a disease; 3) retrospective longitudinal validation (i.e. a biomarker can detect disease at an early stage before it becomes clinically detectable or has other predictive value); 4) prospective validation of the biomarker accuracy and 5) testing its usefulness in clinical applications to predict clinically relevant parameters. An example of exploratory studies is the identification of a distinct phenotype of functional T cell responses and cytokine profiles that distinguish immune responses to tumor antigens in breast cancer patients . Tumor antigen-specific immune responses in cancer patients were observed to differ from responses to common viruses. In particular, a reduced frequency of IFN-γ-producing CD4 T cells was observed. In this discovery phase, it may be useful to test pre-clinical models to verify the strength of an hypothesis . Following the steps of validation, a retrospective analysis suggested that survival is associated with development of memory immune responses  or that changes in serum transforming growth factor (TGF)-β values are prognostic in breast cancer; an inverse correlation between TGF-β levels and development of immune responses and epitope spreading during immunotherapy was found to be of clinical significance. Similar importance of epitope spreading was previously reported by others in the context of dendritic cell (DC)-based immunization against melanoma [35–38] or antigen-specific, epitope-based vaccination . Important exploratory findings were reported by Hiroyoshi Nishikawa (Mie University, Mie, Japan) , who observed a good correlation between antibody and T cell responses following NY-ESO-1 protein vaccine suggesting that cellular immune responses could be extrapolated following the simpler to measure humoral responses. A detection system was developed to identify antibodies against NY-ESO-1 that was validated by inter-institutional cross validation. The assay was tested in patients with esophageal cancer who expressed NY-ESO-1.
Pre-clinical screening for biomarker identification
Studies in transgenic mice shed insights about the kinetics of activation of vaccine-induced T cells useful for the design of future monitoring studies. DUC18 transgenic mice bearing CMS5 tumors were studied. Adoptive T cell transfer of mERK2-recognizing T cells obtained from mice 2, 4 or 7 days after immunization demonstrated that only those obtained 2 days after immunization could control tumor growth in recipient animals. Cytokine expression analysis suggested that outcome was correlated with the breath of the cytokine repertoire produced by the adoptively transferred T cells (multi-functionality); the multi-functionality was time-dependent and was maximal in T cells harvested 2 days after immunization. Tumor challenge did not restore multi-functionality while ablation of T regulatory cells did. Also peptide vaccination rescued multifunctional T cells in vivo. This pre-clinical model suggests that cytokine secretion panels should be included for immune monitoring of patients with cancer . Bernard Fox (Earle A Chiles Research Institute, Portland, OR, USA) presented a model in which the effect of anti-cancer vaccination was tested in conditions of homeostasis-driven T cell proliferation in lymphocyte depleted hosts . Lymphopenia strongly enhanced the expansion of CD44hiCD62Llo T cells in tumor vaccine-draining lymph nodes which corresponded to higher anti-cancer protection compared with normal mice. This study suggested that vaccination could be performed during immune reconstitution in immunotherapy trials utilizing immune depletion and that a target T cell phenotype could be used as a potential mechanistic/end point biomarker. When the experiments were repeated in mice with established tumor, depletion of T regulatory cells was required for therapeutic efficacy. The design of their current clinical trial translating finding from preclinical studies was discussed. Yutaka Kawakami (Keio University, Tokyo, Japan) presented an animal model in which SNAIL expression (a gene involved in tumor progression) induced resistance of tumors to immunotherapy (see later) and may represent a new predictive biomarker of tumor responsiveness to immune therapy if validated in humans .
Validation and standardization of current biomarker assays – a link to the iSBTc/FDA task force
Lisa Butterfield (University of Pittsburgh, Pittsburgh, PA, USA) and Nora Disis summarized validation efforts on immunologic assay performance and standardization [22, 23, 44–49]. This effort is critical to the selection of true biomarkers over the "noise" of assay variation in order to have reliable, standardized measures of immune response. This is a primary focus of one of the two "iSBTc-FDA Taskforce on Immunotherapy Biomarkers" working groups. Published guidelines for blood shipment, processing, timing and cryopreservation were presented together with examples of standardization of the most commonly used immune response assays; the IFN-γ ELISPOT, intra-cellular cytokine staining and major histocompatiblity multimer staining . Understanding the cryobiology principles that explain cellular function after preservation is becoming extremely important as multi-institutional studies require shipment of specimens across vast distances often following non-standardized procedures. Recent studies illustrate the potential for improving the cryopreservation of stem cells. Standardization of cell processing has led to the study of liquid storage prior to cryopreservation, validation of mechanical (uncontrolled rate freezing) freezing, and cryopreservation bag failure [50, 51].
Extensive discussion about assay validation is beyond the purpose of this report as it was discussed in the previous related manuscript . However, it is important to emphasize the proven need for assay standardization with standard operating procedures utilized by trained technicians (who undergo competency testing), the need for standard and tracked reagents and controls, and more broadly accepted, shared protocols which would allow for better cross-comparisons between laboratories. The guidelines of CLIA (Clinical Laboratory Improvements Amendments), which include definitions of test accuracy, precision, and reproducibility (intra-assay and inter-assay) and definitions of reportable ranges (limits of detection) and normal ranges (pools of healthy donors, accumulated patient samples) are available at the CLIA website . Butterfield included examples of assay standardization performed at the University of Pittsburgh Immunologic Monitoring and Cellular Products Laboratory. A good example is the development of potency assays for the maturation of DCs; recently production of IL-12p70 was shown to represent a useful marker that could distinguish between DC obtained from normal individuals compared to those obtained from individuals with cancer or chronic infections , a similar consistency analysis was reported by others . Use of central laboratories may help overcome the extensive cost and effort of this level of standardization [46, 55].
The Biomarkers Consortium (BC): A Novel Public-Private Partnership Leading the Cutting-edge of Biomarkers Research
Although not active participant in the workshop, the NIH BC deserves mention because it purposes converge toward the issue discussed herein and future efforts in biomarker discovery should taken into account the potential usefulness of this NIH initiative. The promise of biomarkers as indicators to advance and revolutionize many aspects of medicine has become a reality for researchers in all sectors of biomedical research. Biomarkers include molecular, biological, or physical characteristics that indicate a specific, underlying physiologic state to identify risk for disease, to make a diagnosis, and to guide treatment . Given the breadth of utility of biomarkers, the importance of cross-sector and cross-therapeutic research efforts is inevitable and the BC has taken a first step to implement this reality. The BC is a unique partnership among FDA, NIH and Industry, serving the individual missions of each organization while focusing on biomarkers, an area of alignment of the interests of all the consortium's participants. The mission of the BC is to brings together the expertise and resources of various partners to rapidly identify, develop, and qualify potential high-impact biomarkers. The Consortium's founding partners are the NIH, the FDA, and Pharmaceutical Research and Manufacturers of America (PhRMA). Additional partners represent Center for Medicare and Medicaid Services, biopharmaceutical companies and trade organizations, patient and professional groups, and the public, and partners in all categories share a common goal- using biomarkers to hasten the development and implementation of effective interventions for health and fighting disease. The BC was formally launched in late 2006 to identify and qualify new, quantitative biological markers ("biomarkers"), for use by biomedical researchers, regulators and health care providers. Effective identification and deployment of biomarkers is essential to achieving a new era of predictive, preventive and personalized medicine. Biomarkers promise to accelerate basic and translational research, speed the development of safe and effective medicines and treatments for a wide range of diseases, and help guide clinical practice. The BC endeavors to discover, develop, and qualify biological markers or "biomarkers" to support new drug development, preventive medicine, and medical diagnostics.
Operations of the BC are managed by the Foundation for the NIH (FNIH), a free-standing charitable foundation with a congressionally-mandated mission to support the research mission of the NIH. As managing partner, the FNIH is responsible for coordinating both the funding and administrative aspects of the BC and staffs the executive committee, steering committee and project team members with respect to BC operations.
The Biomarkers Consortium is creating fundamental change in how healthcare research and medical product developments are conducted by bringing together leaders from the biotechnology and pharmaceutical industries, government, academia, and non-profit organizations to work together to accelerate the identification, development, and regulatory acceptance of biomarkers in four key areas: cancer, inflammation and immunity, metabolic disorders, and neuroscience. Results from projects implemented by the consortium will be made available to researchers worldwide.
The special case of array technology – A balance in reproducibility, sensitivity and specificity of genes differentially expressed according to microarray studies
A discussion about biomarkers relevant to the clinics warrants special attention to high-throughput technologies and, among them, the use of global transcriptional analysis platforms [57, 58]. Indeed, in the last decade, microarray technology has arguably offered the most promising tool for discovery-driven, patient-based analyses and, consequently, for biomarker discovery . Several publications claimed that microarrays are unreliable because list of differentially expressed genes are often not reproducible across similar experiments performed at different times, with different platforms, and by different investigators. The FDA has taken leadership in testing such hypothesis through the MicroArray Quality Control (MAQC) project whose salient results have been recently summarized [57, 60]. Comparisons using same microarray platforms and between microarray results were performed and validated by quantitative real-time PCR. The data demonstrated that discordance between results simply results from ranking and selecting genes solely based on statistical significance; when fold change is used as the ranking criterion with a non-stringent significant cutoff filtering value, the list of differentially expressed genes is much more reproducible suggesting that the lack of concordance is most frequently due to an expected mathematical process . Moreover, comparison of identical sample expression profile performed on different commercial or custom-made platforms at different test sites yielded intra-platform consistency across test sites and high level of inter-platform qualitative and quantitative concordance [58, 61]. Quantitative analyses of gene expression comparing array data with other quantitative gene expression technologies such as quantitative real-time PCR demonstrated high correlation between gene expression values and microarray platform results ; discrepancies were primarily due to differences in probe sequence and thus target location or, less frequently, to the limited sensitivity of array platforms that did not detected weakly expressed transcripts detectable by more sensitive technologies. The conclusion, however, was that microarray platforms could be used for (semi-)quantitative characterization of gene expression. When one-color to two color platforms were compared for reproducibility, specificity, sensitivity and accuracy of results, good agreement was observed. The study concluded that data quality was essentially equivalent between the one- and two-color approaches suggesting that this variable needs not to be a primary factor in decisions regarding experimental microarray design .
Raj Puri (FDA, Bethesda, MD, USA), suggested that, the consistency and robustness of high throughput technology, particularly, in the area of transcriptional profiling can be used to evaluate product quality particularly when tissue, cells or gene therapy products are proposed for clinical utilization and potential licensing; these materials may display a consistent phenotype based on standard markers but display different genetic characteristics when examined at the global level. Several examples are emerging that may affect the interpretation of data on cellular products adoptively transferred to patients. David Stroncek (CTS, NIH, Bethesda, Maryland, USA)  showed that different maturation schemes of DCs or stem cells bear quite different results in their transcriptional phenotype even when similar agents are used [65–68]. Similar work has been reported by the FDA on stem cell characterization [69–71]; same principles were followed to address assay reproducibility in freeze and thaw cycles  or changes in culture conditions . By using this validation approaches it will be hopefully possible to enhance the quality of potency assessment for cellular products ; this will provide consistency across clinical protocols performed in different institutions and may facilitate identification of novel clinically-relevant biomarkers. With this purpose, the FDA as developed a web site offering guidance for pharmacogenomic data submission [74–76].
Novel monitoring approaches
Monitoring of tumor specific immune responses to undefined antigens
Some vaccine-therapies target whole proteins or cell extracts which have the advantage of exposing the immune system to a broader antigenic repertoire. However, it is difficult to verify whether antigen-specific responses were elicited by the vaccine since the relevant antigen is often not known. For instance, the utilization of GVAX against prostate follows surrogate end points such as prostate-specific antigen levels or doubling time . However, it is difficult to characterize the immune response because strong allo-reactions are generated by the foreign cancer cells and no clear antigen relevant to the autologous tumor is known. Thus, monitoring strategies need to be designed for these situations. Fox suggested the screening of pre- and post-vaccination sera looking for developing antibodies. This could be done with commercially available protein arrays that allow screening of thousand of proteins. Indeed, increased prostate-specific antigen doubling time correlates with immune responses toward a limited number of tumor-associated antigens. At the same time, T cell responses can be monitored following antigen presentation by autologous antigen presenting cells fed with proteins identified by the analysis of sera on protein arrays. Since it is unknown whether the immune responses are targeting antigens expressed by vaccine, but not tumor, circulating tumor cells might be used to examine whether specific antigens were expressed by tumor.
Anti cytotoxic T lymphocyte antigen (CTLA)-4 antibodies have been used in hundreds of patients confirming a low but reproducible response rate of about 10%. Most responses, however, are long term and 20 to 30% are associated with severe autoimmune toxicities. There is a critical need to understand the mechanism(s) leading to response and/or toxicity. Antoni Ribas (UCLA, Los Angeles, CA, USA) described the characterization of immune responses during anti-CTLA-4 therapy. Following guidelines to define assay accuracy as suggested by Fraser [78, 79], careful analyses were performed taking into account technical (different protocols), analytical (same procedure, variations in replicates) and physiological (same person, different results over time) sources of variance. A true response was defined as a value above the Mean+3SD normal controls [80, 81]. With these stringent criteria, neither expansion nor decrease in circulating T regulatory cells supposed to be primary targets of the treatment was observed. However, post-treatment gene expression profiling demonstrated activation of T cells. Phospho-flow assays using cellular bar-coding, which allows multiplex analysis of different cell subsets suggested that tremelimumab induces activation of pLck, phosphorylated signal transducer and activator of transcription (STAT)-1 in CD4 cells while phosphorylation of STAT-5 decreases. Moreover, a decrease in phospho Erk was observed in both CD4+ and CD14+ cells. Surprisingly, the therapy affected monocytes not previously known to be targets of anti-CTLA-4 therapy. However, subsequent analyses demonstrated that monocytes express CTLA-4 emphasizing the importance to study the immune responses at a multi-factorial and unbiased level [82–84]. In addition, an increase in IL-17-expressing CD4 T cells was observed after treatment that correlated with autoimmune toxicity and inflammation although no direct correlation with clinical response was noted .
Novel cytotoxicity assays
Cell specific assays based on ELISPOT technology or FACS analysis are emerging that directly or indirectly characterize cell capability to carry effector functions. This is important because dissociations have been described between cytokine and cytotoxic molecule expression [86–88]. ELISPOT assays that detect the effector response of cytotoxic T cells to cognate stimulation have been recently described [89–91]. More recently, a flow cytometric cytotoxicity assay was developed for monitoring cancer vaccine trials . The assay simultaneously measures effector cell de-granulation and target cell death. Interestingly, as previously shown using transcriptional analyses and target cell death estimation , this assay demonstrated that vaccine-induced T cells in patients undergoing vaccination with the gp100 melanoma antigen do not display cytotoxic activity ex vivo but the cytotoxic activity could be restored by in vitro antigen recall. These observations are supported also by others findings that IFN-γ and granzyme-B production by recently activated CD8+ memory T cells fades few days after stimulation as the immune response contracts into the memory phase [86, 93–95]. Thus, future monitoring trials should include a broader range of assays testing the expression/secretion of different cytokines and cytotoxic molecules.
Imaging technologies to study trafficking
There are several examples of differences between therapy-induced changes in the tumor microenvironment compared with the peripheral circulation [20, 96–98]. Ribas, proposed the study of the kinetics of anti-tumor immune responses in vivo using PET-based molecular imaging  expanding the analysis of immune conjugate kinetics for pharmacokinetics studies and visualization of lymphoid organs [100, 101]. Tools to evaluate the function of lymphoid tissue or other components of the tumor microenvironment are critical to assess the dynamic of response to anti-CTLA4 therapy and, likely, other forms of immunotherapy. Tumors do not decrease in size and may even increase due to inflammation and necrosis in the early phases of anti-ACTL-4 treatment and, therefore, tumor size is not a reliable predictor of response. However, 18F-FDG was a useful early marker of response demonstrating increased glycolitic activity by activated immune cells .
High throughput reverse phase protein microarrays (RPMA) for signal pathway profiling
Global profiling of protein activation is an important tool for the understanding of the signaling response to immune stimulation. Julia Wulfkuhle (George Mason University, VA, USA) described novel proteomics approaches that could be particularly useful for immune monitoring.
A clear example is the complexity of the response to type I IFNs. It is becoming increasingly appreciated that signaling down-stream of type I IFNs is more complicated than predicted by the reductionist Jak/STAT model [103, 104]. In highly controlled experimental settings we could not demonstrate a direct quantitative relationship between STAT-1 phosphorylation and activation of interferon-stimulated genes (ISGs) (Pos et al. manuscript in preparation); a deeper characterization of interactions among STAT dimers  and among alternative pathways is necessary to fully understand the mechanisms of IFN-induced responses and their relationship with TSD . RPMA provide the opportunity to study the phosphorylation states of hundreds of signaling molecules at the same time and potentially provide better characterization of the mechanisms controlling downstream transcription following cytokine stimulation [17, 106–108]. Although most studies performed with these arrays were limited to the understanding of transformed cell biology, it is possible to apply these technologies to cellular subsets obtained from the peripheral circulation or from tumor tissues during immunotherapy trials. While the RPMA technology allows for the analysis of hundred of proteins at the time, it is not cell-specific and special precautions in the preparation of samples are necessary such as laser capture microdissection or cell sorting for single cell populations. Gary Nolan's group at Stanford, has developed a conceptually similar approach for the study of signaling pathways at the cellular level that utilized multi-color FACS analysis [83, 109, 110]. However, multi-color FACS analysis is limited to the analysis of only a dozen endpoints at once while RPMA analysis provides measurements of 150–200 signaling proteins with the same starting cell number. Either of these approaches is likely to provide comprehensive functional information about the status of activation and responsiveness of immune cells during immunotherapy.
Tissue handling processing can affect the status of phosphoproteins – novel molecular fixatives
Following procurement the tissue remains alive and is subject to hypoxic and metabolic stress while being transported or reviewed by the pathologist prior to freezing or formalin fixation. Time taken to obtain and preserve material, concentration of endogenous enzymes, tissue thickness and penetration time, storage temperature, staining and preparation; all of these factors can directly affect the phosphorylation status of a protein  and the expression of the protein as well as messenger RNA levels . During the delay time prior to molecular stabilization the kinase pathways are active and reactive. Consequently, in order to stabilize phosphoproteins during the pre-analytical period it is necessary to inhibit the activity of kinases as well as phosphatases. Use of permeability enhancers can potentially change the speed of tissue phosphoproteins activation and phosphatase and kinase inhibitors can stop this process ; these novel fixatives are becoming commercially available.
Biomarker harvesting using nano-particles
"Smart" core shell affinity bait nano-porous particles amplify the concentration of a given analyte . The analyte molecule binds to high affinity bait inside the particle. The analyte is concentrated because all of the target analyte is removed from the bulk solution and concentrated in the small volume of nanoparticles. Concentration factors can excide 100 fold. Different chemical "baits" are used to capture different kind of proteins based on charge or other biochemical characteristics. The size of the nanoparticles shell pores determines the protein size cutoff that can enter the particle. Biomarkers, chemokines or cytokines can be separated from larger proteins present at much higher concentrations. In addition, the binding to the bait stabilizes the captured analyte protein against degradative enzymes. This approach may be particularly useful for the study of serum cytokines which are, even at bioactive levels, at concentrations below the threshold of detection of most non antibody-based methods [114, 115].
Computational models of the immune system can provide additional tools for understanding and predicting response to immunotherapy. Doug Lauffenburger developed a set of mechanism-based models to predict in vitro behavior of immune system cells through a quantitative analysis of receptor-ligand binding and trafficking dynamics . Extending this approach to clinical applications, Immuneering Corporation is developing modeling technology to analyze measurements taken from patient samples, and preparing proof of concept trials to assess the responsiveness of melanoma and renal cell carcinoma patients to IL-2 therapy. Advanced techniques for the validation of computational models have also been developed . Among them, the modular analysis of disease-specific transcriptional patterns developed by Chaussabel et al [118, 119] holds promise to represent an important tool to comprehensively follow the modulation of immune responses during therapy (see later).
Emerging concepts in biomarker discovery; the state of the science
Signatures from the tumor microenvironment
Most presentations by US participants discussed the immune biology of cutaneous melanoma as a prototype of cancer immunotherapy; most Japanese presentations (a Country with limited prevalence of melanoma) discussed other cancers. Thus, while cutaneous melanoma provided a paramount model to discuss cancer immune biology, other cancers offered an overview at potential expansion of emerging concepts to other diseases (i.e. common solid cancers) and other ethnic groups (the Asian population) . Though disease- or population-specific patterns were observed, commonalities were identified that support the hypothesis of a constant mechanism that leads to TSD .
From the delayed allergy reaction to the immunologic constant of rejection
In 1969, Jonas Salk suggested that the delayed hypersensitivity reaction of the tuberculin type, contact dermatitis, graft rejection, tumor regression and auto-allergic phenomena such as experimental allergic encephalomyelitis were facets of a single entity that he called "the delayed allergy reaction . Expanding on this argument, we proposed that tumor rejection represents an aspect of a broader phenomenon responsible for TSD that occurs also in autoimmunity, clearance of pathogen-infected cells or allograft rejection [121, 123–125]. Transcriptional studies done in humans at the time when tissues transition from a chronic lingering inflammatory process to an acute one leading to TSD point to common mechanisms that are activated during immunotherapy against cancer or chronic viral infections or dampened when inducing tolerance of self in autoimmunity or of allografts in transplantation. This theory emphasizes the need to deliver potent pro-inflammatory stimuli in the target tissue. Antigen-specific effector-target interactions are not sufficient to induce TSD but rather act as triggers to induce a broader activation of innate and adaptive immune responses. Given a conducive microenvironment, these responses can expand to an acute inflammatory process inclusive of several effector mechanisms. Thus, immunotherapy should amplify the inflammatory processes induced by tumor-specific T cells within the tumor microenvironment.
Interferon-stimulated genes (ISGs) – Some ISGs are more significant than others
Comparisons of transcriptional studies performed by various groups in human tissues undergoing acute (but not hyper-acute) rejection suggests that TSD encompasses at least two separate components: the activation of ISGs and the broader attraction and in situ activation of innate and adaptive immune effector functions (IEF) mediated by a restricted number of chemokines and cytokines. While the ISGs are consistently present during rejection, IEFs may vary according to the model system studied. Examples include the acute inflammatory process inducing regression of melanoma metastases during IL-2 therapy [20, 126] or basal cell cancer by Toll-like receptor-7 agonists . The same signatures are observed in acute but not in chronic HCV infection leading to clearance of pathogen [127–129] and in acute uncontrollable kidney allograft rejection . Furthermore, activation of ISGs is a classic signature associated with systemic lupus erythematosus and tightly correlates with the severity of the disease [118, 131, 132]. Moreover, coordinate expression of specific ISGs such as IRF-1 linked with the induction of adaptive Th1 immune responses with genes mediating cytotoxicity and the CXCL-9 through -11 chemokines has been associated with better prognosis in colorectal cancer [133–135]. Interestingly, similar results are observable in experimental mouse models. According to the linear model of T cell activation, ISGs and IEFs activation is short lasting and is rapidly followed by a contraction phase ; the signatures associated with the acute phase can be observed within the tumor microenvironment during adaptive and/or innate immunity-mediated tumor regression [136, 137].
It should be emphasized that the expression of ISGs is necessary but not sufficient for the induction of TSD as it is observed also in chronic inflammatory processes that do not lead to TSD . However, the definition of ISGs in itself is vague and refers to a large repertoire of genes that may be activated by type I IFNs in various conditions depending upon the type of cell stimulated and the conditions in which the stimulus is provided . Although canonical ISGs (those stimulated by type I IFN) are regularly observed during TSD, it appears that those most specifically associated with TSD but not chronic inflammatory processes are ISGs downstream of IFN-γ stimulation such as interferon-regulatory factor (IRF)-1 [139–141] and STAT-1 . Importantly, IRF-1 specifically promotes IL-15 expression , which is central to the induction of TSD . IRF-3 is also commonly activated during TSD; IRF-3 is responsible for the over-expression of CXCL-9 through -11 and CCL5 chemokines  which also play a central role in TSD. This signature of acute inflammation are in contrast with the indolent inflammatory process that fosters cancer growth and hampers immune responses [123, 142–146]; in particular, the extensive expression of immune-inhibitory mechanisms during tumor progression  dramatically contrast with the picture observed during TSD and emphasizes the need to study the tumor microenvironment at relevant moments when the switch from chronic to acute inflammation occurs [148–150].
Chemokines, cytokines and effector molecules
The comparative approach described so far  suggests that TSD is determined by the expression of a limited number of genes generally associated with Th1 immune responses. Among them IL-15 and its own receptors play a central role in clinical and experimental models of tumor rejection [21, 137, 151]. Together with IL-15 the chemokines CCL5/RANTES and CXCL-9/Mig -10/IP-10 and -11/I-TAC are consistently present during TSD and probably serve as central attractors of CXCR3 and CCR5-expressing effector T and NK cells . In particular, CD8 T cell infiltration to inflamed areas such as the cerebrospinal fluid in multiple sclerosis , atherosclerotic plaques  or allografts [155, 156] is predominantly mediated by CXCR3 ligand chemokines, which also play a central role in tumor rejection. This observation collimates with a recent report suggesting that CXCR3 expression in CTL is associated with survival benefit in the context of melanoma . This finding could be explained by the heavy lymphocyte infiltration present in melanoma metastases expressing of CXCR3 ligand chemokines such as CXCL9/Mig  and CXCL10/Ip-10 . A finding recently confirmed by independent investigators . Interestingly, CCL5/Rantes and IFN-γ were also reported to predict immune responsiveness during GSK/MAGE-A3 immunotherapy . Moreover, the role played by CCL5/RANTES is suggested by the weight that CCR5 polymorphism plays in the prognosis of melanoma . More recently, Kalinski et al  proposed the utilization of DCs conditioned to drive the development of immune responses toward Th-1 immunity by conditioning DC with a mixture of polycytidylic acid (poly-I:C), IFN-α and IFN-γ. These DCs express CXCR3 and CCR-5 ligands that promote the chemotaxis and in situ expansion of effector cytotoxic T cell phenotype. Additionally, these DCs repress the expansion of T regulatory cells since they do not express the CXCR4 ligand chemokine CCL22/MDC [163, 164]. Most importantly, these DC can regulate T cell homing properties. This is explained by the three wave model of myeloid and plasmacytoid DC production of chemokines ; upon viral stimulation, DC secrete in the first 2 to 4 hours chemokines potentially attracting a broad range of innate and adaptive effectors cells such as neutrophils, cytotoxic T cells, and natural killer cells (CXCL1/GROα, CXCL2/GORβ, CXCL3/GROγ and CXCL16); in a second phase lasting between 8 and 12 hours, they secrete chemokines that attract activated effector memory T cells (and to a lesser degree NK cells) (CXCL8/IL-8, CCL3/MIP-1α, CCL4/MIP-1β, CCL5/RANTES, CXCL9/Mig, CXCL10/IP-10 and CXCL11/I-TAC); finally, the third resolving wave occurs 24 to 48 hours following stimulation producing chemokines that attract regulatory T cells (CCL22/MDC) or naïve T and B lymphocytes in lymphoid organs (CCL19/MIP-3β and CXCL13/BCA-1). Possibly, the intensely pro-inflammatory IFN and poly-I:C-based conditioning prolongs the acute phase of DC activation and the same may occur in vivo during the acute inflammatory process leading to TSD.
Pre-clinical models also clearly underline the central role that CXCR3 ligand chemokines play in recruiting activated effector T cells and NK cells at the tumor site. In particular, oncolytic viral therapy was recently shown to induce powerful anti-cancer immune responses that are centrally mediated by CXCL-9/Mig, -10/IP-10, -11/I-TAC and CCL5/RANTES. Similar results were obtained delivering oncolytic herpes simplex virus in a syngeneic model of ovarian carcinoma  or by the systemic administration of vaccinia virus colonizing selectively human tumor xenografts .
Location, orientation and organization of the immune infiltrates
Jérôme Galon, Franck Pagès, Marie-Caroline Dieu-Nosjean and Wolf-Hervé Fridman have analyzed the immune infiltrates in large cohorts of colorectal and non small cell lung cancers. High densities of T cells with a TH1 orientation and high numbers of CD8 T cells expressing perforin and granulysin, enumerated at the time of surgery, appear to be the strongest prognostic factor (above TNM staging) for disease free and overall survival, at all stages of the disease [133, 134]. Genes associated with adaptive immunity (i.e. CS3, ZAP70) TH1 orientation (i.e. T-bet, IFNγ, IRF-1) and cytotoxicity (i.e. CD8, granulysin) correlated with low levels of tumor recurrence whereas that of genes associated with inflammation or immune suppression did not . The immune responses needed to be coordinated both in terms of location (center of the tumor and invasive margin (2)) and of orientation with memory and TH1 but not TH2, lack of immune suppression, and in terms of inflammation or angiogenesis . Moreover, in the few patients with high T cells infiltration who presented with metastasis at the time of diagnosis, there was a loss of effector/memory T cells in the tumor . Adjacent to the tumors, some patients presented with tertiary lymphoid structures containing germinal center – like structures composed of mature dendritic cells, CD4 and CD8 lymphocytes and activated B cells, a likely place for a local immune reaction to be generated . This finding supports a potential helper role that B cells may play in the recruitment and activation of effector T cells . The resemblance of tertiary lymph nodes were particularly evident in early stage cancers [133, 168] and the enumeration of memory TH1 (IFNγ-producing) and CD8 (granulysin producing) T cells in the center and invasive margin of human tumors should become part of the prognostic setting of human tumors [167, 170]. This recommendation is also based on concordant observations extended to several other tumors [171–176].
Signatures from circulating immune cells and soluble factors
Bernard Fox emphasized the need for a comprehensive approach to the characterization of immune responses that trespasses the simple enumeration of tumor antigen-specific T cells. Characterization by 8 color flow cytometry of vaccine-induced T cells in patients with melanoma vaccinated with the gp100 melanoma antigen demonstrated a wide range of functionality that spanned from different avidity for target antigen, to different levels of tumor-induced CD107 mobilization . Importantly, it was noted that vaccine-induced T cells do not acquire in the memory phase enhanced functional avidity usually associated with competent memory T-cell maturation; these data suggest that other vaccine strategies are required to induce functionally robust long-term memory T cell function . Concordant results have been previously reported by Monsurró et al.  by profiling the transcriptional patterns of vaccine-induced memory T cells; a quiescent phenotype was observed that required in vitro antigen recall plus IL-2 stimulation to recover full effector function. Similar observations have been also recently reported by others [94, 95]. Thus, vaccination is not sufficient to produce effector cells qualitatively and quantitatively capable to induce cell-mediated TSD unless a secondary reactivation is provided at the receiving end by combination therapy .
Damien Chaussabel (Baylor Institute for Immunology, Dallas, Texas, USA) summarized his work profiling circulating peripheral blood mononuclear cell (PBMC) adopting a modular analysis framework to reduce the multidimensionality of array data. This strategy enhances the visualization through the reduction of coordinately expressed transcripts into functional units [118, 119]. With this approach, PBMCs display a disease-specific pattern; individuals with a given disease bear transcriptional fingerprints that are qualitatively and quantitatively related to the severity of the disease. The modular process has been successfully used to identify patients at high risk for liver transplant rejection. It is interesting that a similar approach was recently described by others to identify patients with HCV infection likely to respond to IFN-α therapy; analysis of PBMC signatures ex vivo and their responsiveness to IFN-α stimulation was a predictor or clinical outcome . More recently the Baylor group, in collaboration with John Kirkwood has expanded this approach to the monitoring of patients with melanoma treated by active specific immunization; preliminary observations identified baseline differences among patients and enhancement of IFN-modular activity following treatment.
Immunologic differences between patients with cancer and non-tumor bearing individuals were conclusively confirmed by the work of Peter Lee (Stanford University, Stanford, California, USA) [181, 182]; PBMCs from patients with melanoma and other solid cancers  display strongly reduced responsiveness to IFN-α stimulation that can be measured by intra-cellular staining for phosphorylated STAT-1 protein. Gene expression profiling of lymphocytes from patients with Stage IV melanoma identified 25 genes differentially expressed in T and B cells of cancer patients compared with carefully selected normal controls; of the 25 genes, 20 were ISGs among which CXCL9–11, STAT-1, OAS and MX-1 were included; all of them are critical component of the immunologic constant or rejection ([121, 137] and were down-regulated in cancer patients. The top 10 genes could separate melanoma patients from healthy individuals in self-organizing clustering. Phosphorilation of STAT-1 is a primary component of IFN-signaling and, therefore, a phospho-assay was developed. Originally T cells were found to be predominantly affected but with more cases studied also B cells were recognized as affected . PBMCs from patients with breast cancer demonstrated the same difference in STAT-1, IFI44, IFIT1, IFIT2, and MX1 expression and were similarly unresponsive to IFN-α stimulation. The same results were observed in patient with gastrointestinal cancers where the same effects could be observed in T, B and NK cells. IFN-γ induced phosphorilation is only affected in B-cells, while very little dynamic response is seen in T cells and NK cells. This may be related to a dynamic alteration of IFN-γ receptor in various stages of T cell activation . These alterations appear already at STAGE II of disease and continue as the disease progresses. It is not known whether other signaling defects are present in these cells. This is possible considering the reported alternations of T cell receptor signaling described in the past by others [185–187] and in general altered T cell function in circulating and/or tumor infiltrating lymphocytes [86, 147, 179, 187]. Indeed, also in Lee's study a decrease in expression of CD25, HLA-DR, CD54 and CD95 was observed. Most recently, STAT-1 phosphorylation analysis was applied to patients undergoing immunotherapy with high-dose IFN-α and preliminary results suggest that responding patients display a modest but significant STAT-1 phosphorylation in CD4 and CD8 T cells. Thus, IFN signaling may predict clinical response to high dose IFN therapy and should be considered a novel tool for patient monitoring during clinical trials. It is surprising to observe that the analysis of a single pathways (STAT-1) is such a powerful biomarker of immune responsiveness considering the complexity of the JAK/STAT family interactions and their mutual modulation [105, 188]. However, it is remarkable that the STAT-1/IRF-1/IL-15 axis is a central component of TSD confirming its relevance to cancer rejection. The general immune suppression of cancer patients had been previously described by other studies, for instance, Heriot et al  observed that monocytes from patients with colorectal cancer produce low levels of IFN-α and TNF-α in response to LPS stimulation compared with matched healthy donors. Interestingly, as observed by Lee at al , such depression of innate immune responses were observed at early stage in patients with Duke's A and B.
Basic insights about cancer immune biology
Much can be learned in human immunology by a comparative method that looks at immunological phenomena with an interdisciplinary approach . The relevance of IFN signatures in the context of various diseases represents a good example. He et al  observed that decreased IFN signaling and decreased ex vivo responsiveness of PBMCs to IFN-α stimulation were harbingers of non-responsiveness of HCV-infected patients to systemic administration of pegylated IFN-α and Ribavarin. These differences were interpreted as related to the genetic background of patients as it was observed that PBMCs from patients of African American (AA) origin were least likely to respond to IFN-α stimulation ex vivo and to recover from hepatitis compared to patients of European American (EA) background. This observation raises the question of whether patients with melanoma or HCV that have better changes to respond to therapy are characterized by a different genetic background compared to those likely to do poorly. A recent analysis performed in our laboratories (Pos et al. in preparation) failed to demonstrated dramatic differences between the responses of the two ethnic groups to IFN-α (see later). Thus, alterations in IFN signaling are likely to represent a secondary effect due to the presence of cancer cells or viral particles that in turn may interfere with the innate immune response of the host. This being the case, it will be likely in the future that more insights about the mechanisms leading to altered IFN signaling in cancer patients will be gathered by a more in depth analysis of cancer biology and the products released by cancer cells that may affect immune cells activity locally and at the systemic level.
Indeed tumors, including melanoma, display strong differences in the expression of ISGs [190, 191], which are coordinately associated with the expression of several chemokines, cytokines, growth and angiogenic factors [190, 192]. Moreover, the presence of immune activation has been associated with the prognosis of melanoma . Thus, it is likely that melanoma and other cancers express an immune modulatory phenotype that may alter not only their own microenvironment but whose effects can reverberate at the systemic level. Whether these differences are due to distinct disease taxonomy  or to disease progression [126, 190] remains to be clarified.
Mohammed Kashani-Sabet proposed a model that may explain the dichotomy observed in the biological pattern of melanomas. Studying check points in the progression of melanoma, it was observed that BRAF mutations occur early in the development of the disease and do not account for the switch to an increasingly more aggressive phenotype. Transcriptional analysis was performed to compare radial to vertical growth, which identified predominantly loss of gene expression [195, 196]. Two subtypes of melanoma were identified that could not be segregated only on account of BRAF mutations. Rather, modifiers associated with the vertical growth phase included immune regulatory genes such as IFI16, CCL2 and 3, CXCL-1, -9 and -10. These genes are up regulated in primary melanoma compared with nevi but become down-regulated in the metastatic phase in some but not all melanomas , a phenomenon we had previously observed comparing the transcriptional profile of melanoma metastases to normal melanocytes  and other cancers . A multi-marker diagnostic assay for melanoma was developed ; a large training set of tissue microarrays with 534 samples including nevi and melanoma biopsies was validated on 4 independent test sets and found ARPC2, FN1, RGS1, SSP1 and WNT2 to be over-expressed in melanoma compared with nevi. Based on the 5 markers, a diagnostic algorithm was developed that could differentiate with high accuracy and specificity benign from malignant lesions . The markers were also evaluated on independent cohorts including the German Cancer Registry (Heidelberg/Kiel cohort). The multi-marker approach tested at several stages of disease could predict sentinel node status and disease specific survival (p < 0.001). The multi-marker score demonstrated higher accuracy than lesion depth or ulceration. A molecular map of melanoma progression is being built from melanocyte to various growth phases and metastatization and will be evaluated in the ECOG data set. Although this algorithm does not directly address the immune responsiveness of tumors, it will be important to include such information for patient stratification in future clinical trials to interpret immunotherapy results.
Constitutive activation of immune regulatory mechanism was also reported by Yutaka Kawakami, who discussed the molecular mechanisms of cancer cell induced immune-suppression and their potential as biomarkers of responsiveness to immunotherapy. In particular, regulatory mechanisms dependent on the MAPK, WNT and BRAF mutations were discussed. BRAF and NRAS mutations occur early in melanoma . Kawakami reported that inhibition of BRAF or STAT-3 depleted the expression of several cytokine including IL-6, CXCL8/IL-8 and IL-10 by cancer cells. Also a MEK inhibitor blocked the expression of IL-10. Finally, VEGF expression was inhibited by small interference RNA (siRNA) for ERK1/2. In vivo studies, observed that inhibition of ERK induced the enhancement of T cell responses and protection of mice from cancer . Considering the recently described role of VEGF as a negative predictor of immune responsiveness of melanoma metastases to high dose IL-2 therapy  and a poor prognostic marker of survival in colorectal cancer , it is possible that this observation may provide an important target for a combination therapy for VEGF expressing melanomas. In particular, the melanoma cell line, 888-MEL previously extensively characterized [200, 201] was found to be sensitive to MEK inhibition. Moreover, Kawakami reported that IL-10 production is strictly dependent (in this cell line) upon the expression of β-catenin a mutation inducing enhanced activation of the WNT pathway . Transfection of β-catenin induced production of IL-10; moreover, culture of DC with supernatant of melanoma cells with high β catenin induces IL-10-producing DC and it was decreased by siRNA blockade of β-catenin. Functionally, T cells produced less TNF-α when stimulated with DC cultured with supernatant from β-catenin positive melanomas and expressed higher levels of FOX P3. In a xenogenic model, the human melanoma cells 397-MEL that do not express constitutively high levels of activated β-catenin, were transfected to produce IL-10. Upon antigen exposure T cells were observed to produce less IFN-γ and display lowered lytic activity in animals implanted with the IL-10 expressing tumors. However, IL-10 blocking antibodies did not reverse the tolerogenic effect suggesting that a more complicated mechanism is responsible for the effect on T cells than the direct activity of IL-10. Of interest is the relationship between IL-10 expression and responsiveness. The high expression of IL-10 by 888-MEL contrasts with the observation that this cell line was derived from a patient who dramatically responded to immunotherapy and was a long-term survivor . However, the perceived immune suppressive role of IL-10 may be more complex than previously reported. We observed, that IL-10 expression by melanoma cells studied in pre-treatment biopsies is a positive predictor of tumor responsiveness to immunotherapy with high-dose IL-2 [126, 204, 205]; moreover, the majority of pre-clinical models in which the effect of IL-10 was evaluated as a modulator of tumor responsiveness identified this cytokine as a factor favoring tumor regression suggesting a dual role of IL-10 promoting growth in natural conditions but favoring tumor rejection upon immune stimulation . Kawakami's work may shed light on this paradoxical observation; screening of siRNA against 800 kinases was done to identify which are involved in immune suppression; it was found that STKX kinase inhibits IL-10 and TGF-β production. Moreover, epithelial-mesenchymal transition is induced by SNAIL transfection, which also induces IL-10, VEGF and TGF-β and, in co-culture with human PBMCs, induces FOX-P3 expression. Co-culture of PBMCs with melanoma cells transfected with SNAIL increases the number of FOX-P3-expressing T cells and this is also reversed by SNAIL/TSP (downstream of SNAIL) blockade. Blocking SNAIL expression by tumors with siRNA induced increase in CD4 and CD8 T cells, thus in vivo SNAIL may be involved in immune suppression. Similar results can be obtained by anti-TSP1 which can induce better T cell infiltrates. SNAIL transfected melanoma is resistant to immunotherapy in mouse models and may represent a new predictive biomarker of tumor responsiveness to immune therapy .
Host's genetics vs cancer genetics; the riddle of tumor immunology
The relative contribution of the genetic background of the host, the genetic instability of cancer and the effects of the environment on the natural history of cancer is complex. A good example is nasopharyngeal carcinoma (NPC), which predominantly affects specific geographic areas and ethnicities, in particular the Asian Population [207–210]. NPC etiology is clearly linked to Epstein-Barr virus (EBV) infection  and the immune response to the EBV infection appears to bear a strong influence in both the natural history of the disease and response to therapy [207, 212–218]. A recent observation linked elevated VEGF secretion by the tumor tissue to outcome; in that study, high VEGF secretion correlated with decreased survival. The reason for the prevalence of NPC in specific ethnic groups remains to be conclusively explained but there is evidence that the genetic background of the host plays an important role in familiar and sporadic cases [209–211, 218–230]. However, as for most disease etiologies that are influenced by numerous genes, the genetic determinants of disease prevalence and clinical outcome are still not fully understood [231–238]. In particular, cancer immune responsiveness can be influenced by either the genetic background of the host's or by disease heterogeneity [1, 239]. Few lines of evidence suggest that the genetic make up of patients may affect the natural history of cancer or its responsiveness to therapy; a polymorphism of the IFN-γ gene was associated with responsiveness to combination therapy with IL-2 therapy and chemotherapy . Others found that variants of CCR5 are predictors of survival in patients with melanoma receiving immunotherapy . More recently, the responsiveness to IFN-α therapy in melanoma was found to be associated with autoimmune disease which in turn could be related to genetic predisposition [241, 242]. Recently, Dudley et al  reported that the adoptive transfer of tumor-infiltrating lymphocytes with shorter telomeres was associated with a strongly decreased chance of clinical response; although this effect has been explained by a senescent phenotype of lymphocytes, it is possible that genetic variations in the ability to conserve telomere length could be responsible for differences among patients as previously observed for other instances [243–245].
In a broader sense, the heterogeneous response to IFN-α observed among patients with either cancer [182, 183] or HCV [180, 246, 247] can be plausibly explained by inherited genetic predispositions that determine the responsiveness to this cytokine. It has been proposed that single nucleotide polymorphisms in the IFN pathway are associated with the response to IFN-α therapy of HCV . Moreover, ISG polymorphisms have been associated with other immune pathologies and differences in the prevalence of IRF and STAT gene polymorphisms have been associated with the prevalence of systemic lupus erythematosus in AA [249, 250]. Alternatively, racial differences in the responsiveness to a given treatment may come from effects that the disease exerts on the host's immune cells, and from differences to environmental exposures. Thus, AA may be genetically less protected against HCV infection for reasons unrelated to IFN-α activity; yet, the higher viral load or other factors associated with worse disease may, in turn, affect IFN-related pathways [180, 246, 251, 252]. Whether the genetic background determines the responsiveness to IFN-α or whether acquired differences in the disease status are responsible for differences in the disease phenotype among populations, can only be answered by studying normal volunteers not bearing a disease, like cancer or HCV, that are known to affect the immune response . Based on the observation that AA patients with HCV infection are the least likely to respond to IFN-α stimulation, we tested whether immune cells from 48 AA and 48 EA normal volunteers matched for age and sex responded differently to IFN-α. We compared the levels of STAT-1 phosphorylation and global transcriptional profile of T cells between the two ethnic groups. The same subjects were genetically characterized by genome wide single nucleotide polymorphism analysis to determine the racial deviation of the two groups. This is an important task considering the genetic diversity of AA and their potential admixture with other ethnic groups  Although there was clear separation among AA and EA at the genomic levels, no clear differences could be identified at the functional level (phospho-assays or transcriptional profiling, Pos et al. manuscript in preparation). Thus, it is likely that differences observed in IFN-α responsiveness among different individuals of distinct genetic background or within the same ethnic group affected by cancer or HCV may be secondary to a difference in the disease itself or a difference in the response of the host to the disease, which may affect secondarily the host's immune response. This observation may help interpret differences in tumor immune biology according to race/ethnicity reported by other groups.
Stefan Ambs (NCI, Bethesda, Maryland, USA) reported a comparison of transcriptional patterns between AA and EA in prostate and breast cancer [254, 255]. It is noteworthy that AA have higher death rates from all cancer sites combined than other US populations . Ambs also presented an example for race/ethnic differences in the prevalence of a genetic susceptibility locus from published reports. Several genetic variants at the 8q24 cancer locus are most common among subjects with African ancestry and these differences can explain some of the excess risk of AA to develop prostate cancer. In their study, Ambs and coworkers compared 33 AA and 36 EA macro-dissected tumors by transcriptional analysis. Numerous genes were differently expressed between the two patient groups, but the biggest differences were found to be related to genes involved in the immune response and in particular associated with IFN signaling: IFN-γ, STAT1, CXCL9–11 CCL5 CCL4 CCR7, IL-15 and -16, USG15, Mx1, IRF-1, – 8, -2, OAS2, TAP1 and 2. These genes were over expressed in AA suggesting that in those tumors the cancer cells are in an anti-viral state. Interestingly, the expression of these genes in prostate and breast cancer was associated with resistance to chemotherapy and radiation and in general with a worse prognosis  bearing the opposite significance than the expression of similar signatures in colorectal cancer [134, 135, 141]. Their expression is associated with a poor prognostic connotation in the former and a good one in the latter. An explanation for this discordant and opposite observation is lacking. Similar differences in the tumor microenvironment were observed by Ambs studying breast tumors and comparing tumor stroma and micro-dissected tumor epithelium. Those data were further validated by immunohistochemistry in an extended set of tissues . In tumors from AA, an increased macrophage infiltration was observed, using CD68 as marker, and also a higher micro vessel density, as judged by CD31 expression, when compared with EA tumors
Xifeng Wu (MD Anderson Cancer Center, Houston, Texas, USA) emphasized the need for a systematic evaluation of genetic variants in inflammation-associated pathways as predictors of cancer risk and clinical outcome. The evolution of epidemiologic research from traditional to molecular and even more integrative epidemiology has rapidly changed the paradigm of cancer research. The integration of information at the pathway level is necessary because multiple inherited alterations in gene function can have additive effects as part of a pathway and different pathways can act synergistically or in antagonism. Additional assessment of the predicted or documented functional effects of genetic variants in the biology of disease should also be considered in these models. Wu's hypothesizes that the inflammatory response that plays a role in carcinogenesis is modulated by genetic variability. Fifty-nine SNPs in 36 genes were analyzed. SNPs were selected at promoter UTR or coding region segments according to the literature. Several cytokines were selected and were studied in 1,500 lung cancer cases and 1,700 matched controls. Comprehensive epidemiologic information was obtained and 7 SNPs were found to be relevant. Among them, IL-1α and IL-1β positively correlated with lung cancer prevalence in heavy smokers suggesting that deregulated inflammatory response to tobacco-induced lung damage promotes carcinogenesis . Five SNPs were associated with increased risk of developing bladder cancer including MCP1 and IFNAR2 and two variants of COX2 and IL4r (the COX-2 allele was observed to be associated with reduced mRNA expression) . Interestingly, an IL-6 polymorphism was associated with an increased risk of recurrence after treatment with BCG and with poor survival. In another study of about 400 cases of bladed cancer of whom half experienced recurrence after treatment, Wu and coworkers observed that the genes that were associated with risk of developing bladder cancer were also predictor of response; a survival analysis based on a combination of SNPs including those related to IFN genes could predict with a much higher accuracy risk of recurrence compared to clinical parameters and this observation is now under validation studying a 10,000 SNPs of which 400 belong to the already investigated inflammation-related pathways.
Predictors of responsiveness
Although the IFN pathways seem to be central to TSD, the large experience gained treating patients with adjuvant melanoma with IFN-α has shown limited success. John Kirkwood (University of Pittsburgh Cancer Center, Pittsburgh, Pennsylvania, USA) summarized the long term experience with this treatment emphasizing the importance of sufficiently large randomized studies to obtain conclusive information about usefulness of therapeutics and related biomarkers [15, 242, 260]. An extensive meta analysis including all phase II trials suggested that while in various trials different outcome biomarkers are identified these are most likely to fail validation as larger patient cohorts are treated . A recent analysis looking for predictive biomarkers in melanoma and renal cell carcinoma  suggested that the ex vivo ability of IFN-α to revert STAT-1 phosphorylation signaling defects in melanoma patients may be useful [182, 183]. In addition, development of autoimmunity during IFN-α therapy is a clear predictor of a 50-fold reduction in frequency of relapse . Finally, the concentration of various soluble factors in pretreatment sera of patients undergoing IFN-α therapy suggested that the pro-inflammatory cytokines IL-1β, IL-1α, IL-6, TNF-α and chemokines CCL2/MIP-1α and CCL3/MIP-1β are elevated in patients with longer relapse-free survival . Together with VEGF and fibronectin potentially predictive of immune responsiveness to high-dose IL-2 therapy , these biomarker represent candidate parameters for validation in future trials. High VEGF, together with high IL-6 levels have also been reported as negative predictor of response to bio-chemotherapy [263, 264].
This is advancement from previous analyses in which the majority of putative predictors of IL-2 response were related to post-treatment parameters [265, 266]. In renal cell carcinoma an additional biomarker has been described, carbonic anhydrase IX, whose expression in pre-treatment lesions may be associated with higher likelihood of response ; interestingly, carbonic anhydrase IX is not expressed by melanomas although they display a similar ranges of responsiveness to IL-2 therapy, suggesting, that this molecule may be a biomarker of a particular phenotype associated with responsive lesions but not the determinant of responsiveness . In any case, further validation, together with a better understanding of the biology of these tumors will hopefully enhance the usefulness of these candidate biomarkers.
It has recently been shown that treatment with anti CTLA-4 antibodies can induce clinical responses in few patients previously vaccinated with irradiated, autologous granulocyte-macrophage colony-stimulating factor (GM-CSF)-secreting cancer cells . However, a large phase III study on hormone refractory prostate cancer-bearing patients treated with the same vaccine (but not anti-CTLA-4 antibody) failed to demonstrate effectiveness leading to early termination of the clinical protocol [270, 271].
Masahisa Jinushi (The University of Tokyo, Tokyo, Japan) reported the mechanisms hampering vaccine effectiveness and the potentials for combining anti-CTLA-4 therapy. It was observed that GM-CSF-deficient mice are defective in apoptotic cell phagocytosis and develop autoimmune manifestations including pulmonary alveolar proteinosis, SLE, insulitis and diabetes . GM-CSF transduction restores the production of cytokines that regulate T helper cell differentiation (TGF-β, IL-1b IL-4 IL-12p70 and IL-23p19) in response to apoptotic cells. GM-CSF regulates the phagocytosis of apoptotic cells by antigen presenting cells and modulates the function of the phagocyte receptors milk fat globule EGF 8 (MGF-E8), a protein secreted at high levels by melanomas during the vertical growth phase. MGF-E8 has pleiotropic functions in the tumor microenvironment including promoting cancer cell survival, invasion and immune suppression. While GM-CSF regulates T helper cell differentiation by MFG-E8, TLR stimulation suppresses MFG-E8 production by antigen presenting cells resulting in increased allo-mixed lymphocyte reaction in apoptotic cell loaded macrophages-driven splenocytes proliferation . Blockade of MFG-E8 in tumor cells potentiates GVAX therapeutic immunity in the B16 mouse melanoma model. GVAX/RGE (inhibitor of MFG-E8) vaccines decreases Tregs and decreases tumor specific CD8+ T cell effectors with decrease of FoxP3 and increase in CD69 expressing CD8 T cells . MFG-E8 expression in melanoma patients with advanced stage is high and not detected in non advanced stage melanoma and nevi . Thus, MFG-E8 might be considered a negative regulator of GVAX induced immunity by regulating Treg/Teff balance. It is a prognostic factor and may predict response to GVAX and possibly other types of immunotherapy as recently shown by Aloysius el al  with various cancers vaccinated with hTERT peptide-pulsed DCs and by Tatsumi et al.  in the context of renal cell carcinoma and melanoma.
The NCI has shown strong interest in developing a systematic approach to the prioritization of agents to be tested in immunotherapy trials including the type of immune response modifier ()()[277, 278] or target cancer antigen . Criteria were developed for the selection of each agent with a non-parametric approach receiving feed back from several investigators; however, the ideal antigen and/or biologic modifier and their combination remain to be defined. An ideal candidate target could be considered a protein expressed consistently by cancer initiating cells. Sato et al.  described their efforts in identifying such cells among which they describe sperm mitochondrial cystein rich protein and sex determining region Y box-2 protein as potential candidate targets of immunotherapy. They may be used against breast cancer as their expression correlates with poor prognosis and resistance to chemotherapy. Identification of epitopes is underway for HLA alleles common in the Asian population and this novel target could be considered a potential biomarker for patient selection. Another important target expressed by several tumors and potentially associated with the oncogenic process is NY-ESO, a prototype cancer/testis antigen, which induces strong antibody and T cell responses. Extensive work has been done in Japan on patients with esophageal and other solid cancers . NY-ESO was delivered as cholesterol-bearing hydrophobized pullulan nano-particles that absorb the protein and express it in the antigen presenting cells. Humoral and cellular immune responses were elicited in 9 of 13 treated patients and clinical responses were observed in 4 of 5 evaluable patients. Several examples of antigen spreading were observed and a restricted region of the NY-ESO protein was found to be most immunogenic; it is suggested that, for the future, only this region should used for immunization. This is an example of the relevance of careful immune monitoring related to a specific target antigen that provides insights for the design of future clinical trials.
For gastrointestinal tumors, EpCAM, a tumor associated antigen was proposed as a useful target in gastrointestinal cancers. Use of anti-EpCAM may affect tumor stage and progression. Recently a technique was developed to isolate circulating tumor cells using magnetic beads based on EpCAM expression. Cancer cells were isolated from 130 cancer patients and 40 normal controls. Highly significant differences in extractable cells were observed between cancer and normal patients and between patients with or without metastatic disease. The identification of ≥ 2 circulating cancer cells was associated with tumor stage, survival and pleural or peritoneal dissemination. In esophageal cancer cell lines a proliferation assay was performed showing that introduction of EpCAM increases the expression of cyclins suggesting that EpCAM expression accelerates cell cycle and may be an important novel target for the immunotherapy of gastrointestinal tumors. Indeed, anti-EpCAM antibodies decrease tumor growth in animal models and recent clinical trials have been initiated [282, 283]. More recently, antibody-mediated targeting of adenoviral vectors modified to contain a synthetic immunoglobulin g-binding domain in the capsid was described that could be used to target tumor-specific antigens expressed on the surface of cancer cells .
Furthermore, attention should be put to the status of methylation or acetylation patterns of various genes that may directly or indirectly affect immune function either by down-modulating the expression of putative tumor antigens, or by interfering with immune-regulatory pathways [285–287].
It is becoming increasingly apparent that recurrent themes related to the diagnosis, prognosis and responsiveness to therapy are emerging in the context of cancer immunotherapy. Although relatively unrefined, these concepts appear to be valid as they have been reported in concordance by various groups and several of the observed biomarkers represent conceptually similar pathways involved in tissue rejection or tolerance (Table 1). Although, this is only a beginning, it is encouraging to see that among the thousands of biological permutations that could be considered at the theoretical level, direct human observation is providing a tool to restrict the inquisitive mind of scientists to a much more defined circle of possibilities to be explored in the future.
We would like to thank Dr Raj Puri, Director, Division of Cellular and Gene Therapies, FDA, Center for Biologics Evaluation and Research for his participation to the meeting and the useful comments on the proceedings.
- Butterfield LH, Disis ML, Fox BA, Lee PP, Khleif SN, Thurin M, Trinchieri G, Wang E, Wigginton J, Chaussabel D: A systematic approach to biomarker discovery; Preamble to "the iSBTc-FDA taskforce on Immunotherapy Biomarkers". J Transl Med. 2008, 6: 81-PubMed CentralPubMedGoogle Scholar
- iSBTc: iSBTC/FDA Immunotherapy Biomarker Taskforce. 2008, [http://www.isbtc.org/news/enews.php#Taskforce]Google Scholar
- Chaussabel D: Tracking Scientific Content in Knol. Knol. 2009, [http://knol.google.com/k/damien-chaussabel/tracking-scientific-content-in-knol/39zp8hfjpxrb8/5#]Google Scholar
- Simon R: Development and evaluation of therapeutically relevant predictive classifiers using gene expression profiling. J Natl Cancer Inst. 2006, 98: 1169-1171.PubMedGoogle Scholar
- Simon R: Validation of pharmacogenomic biomarker classifiers for treatment selection. Cancer Biomark. 2006, 2: 89-96.PubMedGoogle Scholar
- Simon R: Development and Validation of Biomarker Classifiers for Treatment Selection. J Stat Plan Inference. 2008, 138: 308-320.PubMed CentralPubMedGoogle Scholar
- Simon R: Lost in translation: problems and pitfalls in translating laboratory observations to clinical utility. Eur J Cancer. 2008, 44: 2707-2713.PubMed CentralPubMedGoogle Scholar
- Dudley ME, Yang JC, Sherry R, Hughes MS, Royal R, Kammula U, Robbins PF, Huang J, Citrin DE, Leitman SF: Adoptive cell therapy for patients with metastatic melanoma: evaluation of intensive myeloablative chemoradiation preparative regimens. J Clin Oncol. 2008, 26: 5233-5239.PubMed CentralPubMedGoogle Scholar
- Sabatino M, Kim-Schulze S, Panelli MC, Stroncek DF, Wang E, Tabak B, Kim D-W, DeRaffele G, Pos Z, Marincola FM: Serum vascular endothelial growth factor (VEGF) and fibronectin predict clinical response to high-dose interleukin-2 (IL-2) therapy. J Clin Oncol. 2008, 27: 2645-2652.Google Scholar
- Karapetis CS, Khambata-Ford S, Jonker DJ, O'Callaghan CJ, Tu D, Tebbutt NC, Simes RJ, Chalchal H, Shapiro JD, Robitaille S: K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med. 2008, 359: 1757-1765.PubMedGoogle Scholar
- Brichard VG, Lejeune D: GSK's antigen-specific cancer immunotherapy programme: pilot results leading to Phase III clinical development. Vaccine. 2007, 25 (Suppl 2): B61-B71.PubMedGoogle Scholar
- Brichard VG, Lejeune D: Cancer immunotherapy targeting tumour-specific antigens: towards a new therapy for minimal residual disease. Expert Opin Biol Ther. 2008, 8: 951-968.PubMedGoogle Scholar
- Habermann JK, Doering J, Hautaniemi S, Roblick UJ, Bundgen NK, Nicorici D, Kronenwett U, Rathnagiriswaran S, Mettu RK, Ma Y: The gene expression signature of genomic instability in breast cancer is an independent predictor of clinical outcome. Int J Cancer. 2009, 124: 1552-1564.PubMed CentralPubMedGoogle Scholar
- Recommendations from the EGAPP Working Group: can tumor gene expression profiling improve outcomes in patients with breast cancer?. Genet Med. 2009, 11: 66-73.Google Scholar
- Korn EL, Liu PY, Lee SJ, Chapman JA, Niedzwiecki D, Suman VJ, Moon J, Sondak VK, Atkins MB, Eisenhauer EA: Meta-analysis of phase II cooperative group trials in metastatic stage IV melanoma to determine progression-free and overall survival benchmarks for future phase II trials. J Clin Oncol. 2008, 26: 527-534.PubMedGoogle Scholar
- Halabi S, Small EJ, Vogelzang NJ: Elevated body mass index predicts for longer overall survival duration in men with metastatic hormone-refractory prostate cancer. J Clin Oncol. 2005, 23: 2434-2435.PubMedGoogle Scholar
- Grubb RL, Deng J, Pinto PA, Mohler JL, Chinnaiyan A, Rubin M, Linehan WM, Liotta LA, Petricoin EF, Wulfkuhle JD: Pathway Biomarker Profiling of Localized and Metastatic Human Prostate Cancer Reveal Metastatic and Prognostic Signatures (dagger). J Proteome Res. 2009, 8: 3044-3054.PubMed CentralPubMedGoogle Scholar
- Dobbin KK, Zhao Y, Simon RM: How large a training set is needed to develop a classifier for microarray data?. Clin Cancer Res. 2008, 14: 108-114.PubMedGoogle Scholar
- Dobbin KK, Zhao Y, Simon RM: Sample size planning for developing classifiers using high dimensional data. 2009, [http://linus.nci.nih.gov/brb/samplesize/samplesize4GE.html]Google Scholar
- Panelli MC, Wang E, Phan G, Puhlman M, Miller L, Ohnmacht GA, Klein H, Marincola FM: Gene-expression profiling of the response of peripheral blood mononuclear cells and melanoma metastases to systemic IL-2 administration. Genome Biol. 2002, 3: RESEARCH0035-PubMed CentralPubMedGoogle Scholar
- Panelli MC, Stashower M, Slade HB, Smith K, Norwood C, Abati A, Fetsch PA, Filie A, Walters SA, Astry C: Sequential gene profiling of basal cell carcinomas treated with Imiquimod in a placebo-controlled study defines the requirements for tissue rejection. Genome Biol. 2006, 8: R8-Google Scholar
- Keilholz U, Weber J, Finke J, Gabrilovich D, Kast WM, Disis N, Kirkwood J, Scheibenbogen C, Schlom J, Maino V: Immunologic monitoring of cancer vaccine therapy: results of a Workshop sponsored by the Society of Biological Therapy. J Immunother. 2002, 25: 97-138.PubMedGoogle Scholar
- Xu Y, Theobald V, Sung C, DePalma K, Atwater L, Seiger K, Perricone MA, Richards SM: Validation of a HLA-A2 tetramer flow cytometric method, IFNgamma real time RT-PCR, and IFNgamma ELISPOT for detection of immunologic response to gp100 and MelanA/MART-1 in melanoma patients. J Transl Med. 2008, 6: 61-PubMed CentralPubMedGoogle Scholar
- Stein WD, Figg WD, Dahut W, Stein AD, Hoshen MB, Price D, Bates SE, Fojo T: Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data. Oncologist. 2008, 13: 1046-1054.PubMed CentralPubMedGoogle Scholar
- Mankoff SP, Brander C, Ferrone S, Marincola FM: Lost in translation: obstacles to Translational Medicine. J Transl Med. 2004, 2: 14-PubMed CentralPubMedGoogle Scholar
- Simon R: The use of genomics in clinical trial design. Clin Cancer Res. 2008, 14: 5984-5993.PubMedGoogle Scholar
- Disis ML, Bernhard H, Jaffee EM: Use of tumour-responsive T cells as cancer treatment. Lancet. 2009, 373: 673-683.PubMed CentralPubMedGoogle Scholar
- Pepe MS, Etzioni R, Feng Z, Potter JD, Thompson ML, Thornquist M, Winget M, Yasui Y: Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. 2001, 93: 1054-1061.PubMedGoogle Scholar
- Huang Y, Pepe MS: Biomarker evaluation and comparison using the controls as a reference population. Biostatistics. 2009, 10: 228-244.PubMed CentralPubMedGoogle Scholar
- Pepe MS, Feng Z, Janes H, Bossuyt PM, Potter JD: Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J Natl Cancer Inst. 2008, 100: 1432-1438.PubMed CentralPubMedGoogle Scholar
- Pepe MS, Feng Z, Longton G, Koopmeiners J: Conditional estimation of sensitivity and specificity from a phase 2 biomarker study allowing early termination for futility. Stat Med. 2009, 28: 762-779.PubMed CentralPubMedGoogle Scholar
- Inokuma M, dela RC, Schmitt C, Haaland P, Siebert J, Petry D, Tang M, Suni MA, Ghanekar SA, Gladding D: Functional T cell responses to tumor antigens in breast cancer patients have a distinct phenotype and cytokine signature. J Immunol. 2007, 179: 2627-2633.PubMedGoogle Scholar
- Lu H, Knutson KL, Gad E, Disis ML: The tumor antigen repertoire identified in tumor-bearing neu transgenic mice predicts human tumor antigens. Cancer Res. 2006, 66: 9754-9761.PubMedGoogle Scholar
- Salazar LG, Coveler AL, Swensen RE, Gooley TA, Goodell V, Schiffman K, Disis ML: Kinetics of tumor-specific T-cell response development after active immunization in patients with HER-2/neu overexpressing cancers. Clin Immunol. 2007, 125: 275-280.PubMedGoogle Scholar
- Ribas A, Timmerman JM, Butterfield LH, Economou JS: Determinant spreading and tumor responses after peptide-based cancer immunotherapy. Trends Immunol. 2003, 24: 58-61.PubMedGoogle Scholar
- Butterfield LH, Ribas A, Dissette VB, Amarnani SN, Vu HT, Oseguera D, Wang HJ, Elashoff RM, McBride WH, Mukherji B: Determinant spreading associated with clinical response in dendritic cell-based immunotherapy for malignant melanoma. Clin Cancer Res. 2003, 9: 998-1008.PubMedGoogle Scholar
- Ribas A, Glaspy JA, Lee Y, Dissette VB, Seja E, Vu HT, Tchekmedyian NS, Oseguera D, Comin-Anduix B, Wargo JA: Role of dendritic cell phenotype, determinant spreading, and negative costimulatory blockade in dendritic cell-based melanoma immunotherapy. J Immunother. 2004, 27: 354-367.PubMedGoogle Scholar
- Butterfield LH, Comin-Anduix B, Vujanovic L, Lee Y, Dissette VB, Yang JQ, Vu HT, Seja E, Oseguera DK, Potter DM: Adenovirus MART-1-engineered autologous dendritic cell vaccine for metastatic melanoma. J Immunother. 2008, 31: 294-309.PubMed CentralPubMedGoogle Scholar
- Lally KM, Mocellin S, Ohnmacht GA, Nielsen M-B, Bettinotti M, Panelli MC, Monsurro' V, Marincola FM: Unmasking cryptic epitopes after loss of immunodominant tumor antigen expression through epitope spreading. Int J Cancer. 2001, 93: 841-847.PubMedGoogle Scholar
- Gnjatic S, Nishikawa H, Jungbluth AA, Gure AO, Ritter G, Jager E, Knuth A, Chen YT, Old LJ: NY-ESO-1: review of an immunogenic tumor antigen. Adv Cancer Res. 2006, 95: 1-30.PubMedGoogle Scholar
- Hiasa A, Nishikawa H, Hirayama M, Kitano S, Okamoto S, Chono H, Yu SS, Mineno J, Tanaka Y, Minato N: Rapid alphabeta TCR-mediated responses in gammadelta T cells transduced with cancer-specific TCR genes. Gene Ther. 2009, 16: 620-628.PubMedGoogle Scholar
- Ma J, Urba WJ, Si L, Wang Y, Fox BA, Hu HM: Anti-tumor T cell response and protective immunity in mice that received sublethal irradiation and immune reconstitution. Eur J Immunol. 2003, 33: 2123-2132.PubMedGoogle Scholar
- Kudo-Saito C, Shirako H, Takeuchi T, Kawakami Y: Cancer metastasis is accelerated through immunosuppression during Snail-induced EMT of cancer cells. Cancer Cell. 2009, 15: 195-206.PubMedGoogle Scholar
- Walker EB, Disis ML: Monitoring immune responses in cancer patients receiving tumor vaccines. Int Rev Immunol. 2003, 22: 283-319.PubMedGoogle Scholar
- Landay AL, Fleisher TA, Kuus-Reichel K, Maino VC, Reinsmoen NL, Weinhold KJ, Whiteside TL, Altman JD: Performance of single cell immune response assays; approved guideline. NCCLS, IFCC. 2004, 24: 1-70.Google Scholar
- Maecker HT, Moon J, Bhatia S, Ghanekar SA, Maino VC, Payne JK, Kuus-Reichel K, Chang JC, Summers A, Clay TM: Impact of cryopreservation on tetramer, cytokine flow cytometry, and ELISPOT. BMC Immunol. 2005, 6: 17-PubMed CentralPubMedGoogle Scholar
- Disis ML, dela RC, Goodell V, Kuan LY, Chang JC, Kuus-Reichel K, Clay TM, Kim LH, Bhatia S, Ghanekar SA: Maximizing the retention of antigen specific lymphocyte function after cryopreservation. J Immunol Methods. 2006, 308: 13-18.PubMedGoogle Scholar
- Ghanekar SA, Bhatia S, Ruitenberg JJ, dela RC, Disis ML, Maino VC, Maecker HT, Waters CA: Phenotype and in vitro function of mature MDDC generated from cryopreserved PBMC of cancer patients are equivalent to those from healthy donors. J Immune Based Ther Vaccines. 2007, 5: 7-PubMed CentralPubMedGoogle Scholar
- Maecker HT, Hassler J, Payne JK, Summers A, Comatas K, Ghanayem M, Morse MA, Clay TM, Lyerly HK, Bhatia S: Precision and linearity targets for validation of an IFNgamma ELISPOT, cytokine flow cytometry, and tetramer assay using CMV peptides. BMC Immunol. 2008, 9: 9-PubMed CentralPubMedGoogle Scholar
- Fleming KK, Hubel A: Cryopreservation of hematopoietic and non-hematopoietic stem cells. Transfus Apher Sci. 2006, 34: 309-315.PubMedGoogle Scholar
- Hubel A, Darr TB, Chang A, Dantzig J: Cell partitioning during the directional solidification of trehalose solutions. Cryobiology. 2007, 55: 182-188.PubMedGoogle Scholar
- Clinical Laboratory Improvements Amendements (CLIA). 2009, [http://www.cms.hhs.gov/CLIA]
- Butterfield LH, Gooding W, Whiteside TL: Development of a potency assay for human dendritic cells: IL-12p70 production. J Immunother. 2008, 31: 89-100.PubMedGoogle Scholar
- Zobywalski A, Javorovic M, Frankenberger B, Pohla H, Kremmer E, Bigalke I, Schendel DJ: Generation of clinical grade dendritic cells with capacity to produce biologically active IL-12p70. J Transl Med. 2007, 5: 18-PubMed CentralPubMedGoogle Scholar
- Lehmann PV: Image analysis and data management of ELISPOT assay results. Methods Mol Biol. 2005, 302: 117-132.PubMedGoogle Scholar
- Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001, 69: 89-95.Google Scholar
- Shi L, Jones WD, Jensen RV, Harris SC, Perkins RG, Goodsaid FM, Guo L, Croner LJ, Boysen C, Fang H: The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies. BMC Bioinformatics. 2008, 9 (Suppl 9): S10-PubMed CentralPubMedGoogle Scholar
- Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de LF, Kawasaki ES, Lee KY: The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006, 24: 1151-1161.PubMedGoogle Scholar
- Marincola FM: In support of descriptive studies: relevance to translational research. J Transl Med. 2007, 5: 21-PubMed CentralPubMedGoogle Scholar
- Casciano DA, Woodcock J: Empowering microarrays in the regulatory setting. Nat Biotechnol. 2006, 24: 1103-PubMedGoogle Scholar
- Shippy R, Fulmer-Smentek S, Jensen RV, Jones WD, Wolber PK, Johnson CD, Pine PS, Boysen C, Guo X, Chudin E: Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat Biotechnol. 2006, 24: 1123-1131.PubMed CentralPubMedGoogle Scholar
- Canales RD, Luo Y, Willey JC, Austermiller B, Barbacioru CC, Boysen C, Hunkapiller K, Jensen RV, Knight CR, Lee KY: Evaluation of DNA microarray results with quantitative gene expression platforms. Nat Biotechnol. 2006, 24: 1115-1122.PubMedGoogle Scholar
- Patterson TA, Lobenhofer EK, Fulmer-Smentek SB, Collins PJ, Chu TM, Bao W, Fang H, Kawasaki ES, Hager J, Tikhonova IR: Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat Biotechnol. 2006, 24: 1140-1150.PubMedGoogle Scholar
- Stroncek DF, Jin P, Wang E, Jett B: Potency analysis of cellular therapies: the emerging role of molecular assays. J Transl Med. 2007, 5: 24-PubMed CentralPubMedGoogle Scholar
- Stroncek DF, Basil C, Nagorsen D, Deola S, Arico E, Smith K, Wang E, Marincola FM, Panelli MC: Delayed Polarization of Mononuclear Phagocyte Transcriptional Program by Type I Interferon Isoforms. J Transl Med. 2005, 3: 24-PubMed CentralPubMedGoogle Scholar
- Jin P, Wang E, Ren J, Childs R, Shin JW, Khuu H, Marincola FM, Stroncek DF: Differentiation of two types of mobilized peripheral blood stem cells by microRNA and cDNA expression analysis. J Transl Med. 2008, 6: 39-PubMed CentralPubMedGoogle Scholar
- Han TH, Jin P, Ren J, Slezak S, Marincola FM, Stroncek DF: Evaluation of 3 Clinical Dendritic Cell Maturation Protocols Containing Lipopolysaccharide and Interferon-gamma. J Immunother. 2009, 32: 399-407.PubMed CentralPubMedGoogle Scholar
- Ren J, Jin P, Wang E, Marincola FM, Stroncek DF: MicroRNA and gene expression patterns in the differentiation of human embryonic stem cells. J Transl Med. 2009, 7: 20-PubMed CentralPubMedGoogle Scholar
- Bhattacharya B, Cai J, Luo Y, Miura T, Mejido J, Brimble SN, Zeng X, Schulz TC, Rao MS, Puri RK: Comparison of the gene expression profile of undifferentiated human embryonic stem cell lines and differentiating embryoid bodies. BMC Dev Biol. 2005, 5: 22-PubMed CentralPubMedGoogle Scholar
- Luo Y, Bhattacharya B, Yang AX, Puri RK, Rao MS: Designing, testing, and validating a microarray for stem cell characterization. Methods Mol Biol. 2006, 331: 241-266.PubMedGoogle Scholar
- Player A, Wang Y, Bhattacharya B, Rao M, Puri RK, Kawasaki ES: Comparisons between transcriptional regulation and RNA expression in human embryonic stem cell lines. Stem Cells Dev. 2006, 15: 315-323.PubMedGoogle Scholar
- Shin JW, Jin P, Fan Y, Slezak S, vid-Ocampo V, Khuu HM, Read EJ, Wang E, Marincola FM, Stroncek DF: Evaluation of gene expression profiles of immature dendritic cells prepared from peripheral blood mononuclear cells. Transfusion. 2008, 48: 647-657.PubMedGoogle Scholar
- Han J, Farnsworth RL, Tiwari JL, Tian J, Lee H, Ikonomi P, Byrnes AP, Goodman JL, Puri RK: Quality prediction of cell substrate using gene expression profiling. Genomics. 2006, 87: 552-559.PubMedGoogle Scholar
- Frueh FW: Impact of microarray data quality on genomic data submissions to the FDA. Nat Biotechnol. 2006, 24: 1105-1107.PubMedGoogle Scholar
- FDA: Guidance for Pharmacogenomic Data Submission. 2009, [http://www.fda.gov/cder/guidance/6400fnl.pdf]Google Scholar
- FDA: CBER/Guidances/Guidlines/Points to consider. 2009, [http://www.fda.gov/cber/guidelines.htm]Google Scholar
- Harzstark AL, Ryan CJ: Therapies in development for castrate-resistant prostate cancer. Expert Rev Anticancer Ther. 2008, 8: 259-268.PubMedGoogle Scholar
- Fraser CG: Biological Variation: from Principles to Practice. 2001, Washington, DC: AACCPressGoogle Scholar
- Fraser CG: Reference change values: the way forward in monitoring. Ann Clin Biochem. 2009, 46: 264-265.PubMedGoogle Scholar
- Comin-Anduix B, Gualberto A, Glaspy JA, Seja E, Ontiveros M, Reardon DL, Renteria R, Englahner B, Economou JS, Gomez-Navarro J: Definition of an immunologic response using the major histocompatibility complex tetramer and enzyme-linked immunospot assays. Clin Cancer Res. 2006, 12: 107-116.PubMedGoogle Scholar
- Comin-Anduix B, Lee Y, Jalil J, Algazi A, de la RP, Camacho LH, Bozon VA, Bulanhagui CA, Seja E, Villanueva A: Detailed analysis of immunologic effects of the cytotoxic T lymphocyte-associated antigen 4-blocking monoclonal antibody tremelimumab in peripheral blood of patients with melanoma. J Transl Med. 2008, 6: 22-PubMed CentralPubMedGoogle Scholar
- Davis MM: A prescription for human immunology. Immunity. 2008, 29: 835-838.PubMed CentralPubMedGoogle Scholar
- Aebersold R, Auffray C, Baney E, Barillot E, Brazma A, Brett C, Brunak S, Butte A, Califano A, Celis J: Report on EU-USA workshop: how systems biology can advance cancer research (27 October 2008). Mol Oncol. 2009, 3: 9-17.PubMed CentralPubMedGoogle Scholar
- Berg M, Lundqvist A, McCoy P, Samsel L, Fan Y, Tawab A, Childs R: Clinical-grade ex vivo-expanded human natural killer cells up-regulate activating receptors and death receptor ligands and have enhanced cytolytic activity against tumor cells. Cytotherapy. 2009, 11: 341-355.PubMed CentralPubMedGoogle Scholar
- von Euw E, Chodon T, Attar N, Jalil J, Koya RC, Comin-Anduix B, Ribas A: CTLA4 blockade increases Th17 cells in patients with metastatic melanoma. J Transl Med. 2009, 7: 35-PubMed CentralPubMedGoogle Scholar
- Monsurro' V, Wang E, Yamano Y, Migueles SA, Panelli MC, Smith K, Nagorsen D, Connors M, Jacobson S, Marincola FM: Quiescent phenotype of tumor-specific CD8+ T cells following immunization. Blood. 2004, 104: 1970-1978.Google Scholar
- Kuerten S, Nowacki TM, Kleen TO, Asaad RJ, Lehmann PV, Tary-Lehmann M: Dissociated production of perforin, granzyme B, and IFN-gamma by HIV-specific CD8(+) cells in HIV infection. AIDS Res Hum Retroviruses. 2008, 24: 62-71.PubMedGoogle Scholar
- Monsurro' V, Nagorsen D, Wang E, Provenzano M, Dudley ME, Rosenberg SA, Marincola FM: Functional heterogeneity of vaccine-induced CD8+ T cells. J Immunol. 2002, 168: 5933-5942.Google Scholar
- Shafer-Weaver K, Sayers T, Strobl S, Derby E, Ulderich T, Baseler M, Malyguine A: The Granzyme B ELISPOT assay: an alternative to the 51Cr-release assay for monitoring cell-mediated cytotoxicity. J Transl Med. 2003, 1: 14-PubMed CentralPubMedGoogle Scholar
- Shafer-Weaver K, Rosenberg S, Strobl S, Gregory AW, Baseler M, Malyguine A: Application of the granzyme B ELISPOT assay for monitoring cancer vaccine trials. J Immunother. 2006, 29: 328-335.PubMedGoogle Scholar
- Malyguine A, Strobl S, Zaritskaya L, Baseler M, Shafer-Weaver K: New approaches for monitoring CTL activity in clinical trials. Adv Exp Med Biol. 2007, 601: 273-284.PubMedGoogle Scholar
- Zaritskaya L, Shafer-Weaver KA, Gregory MK, Strobl SL, Baseler M, Malyguine A: Application of a flow cytometric cytotoxicity assay for monitoring cancer vaccine trials. J Immunother. 2009, 32: 186-194.PubMedGoogle Scholar
- Kaech SM, Hemby S, Kersh E, Ahmed R: Molecular and functional profiling of memory CD8 T cell differentiation. Cell. 2002, 111: 837-851.PubMedGoogle Scholar
- Nowacki TM, Kuerten S, Zhang W, Shive CL, Kreher CR, Boehm BO, Lehmann PV, Tary-Lehmann M: Granzyme B production distinguishes recently activated CD8(+) memory cells from resting memory cells. Cell Immunol. 2007, 247: 36-48.PubMed CentralPubMedGoogle Scholar
- Schlingmann TR, Shive CL, Targoni OS, Tary-Lehmann M, Lehmann PV: Increased per cell IFN-gamma productivity indicates recent in vivo activation of T cells. Cell Immunol. 2009,Google Scholar
- Panelli MC, Martin B, Nagorsen D, Wang E, Smith K, Monsurro' V, Marincola FM: A genomic and proteomic-based hypothesis on the eclectic effects of systemic interleukin-2 administration in the context of melanoma-specific immunization. Cells Tissues Organs. 2003, 177: 124-131.Google Scholar
- Wang E, Panelli MC, Marincola FM: Gene profiling of immune responses against tumors. Curr Opin Immunol. 2005, 17: 423-427.PubMedGoogle Scholar
- Wang E, Panelli M, Marincola FM: Autologous tumor rejection in humans: trimming the myths. Immunol Invest. 2006, 35: 437-458.PubMedGoogle Scholar
- Dubey P, Su H, Adonai N, Du S, Rosato A, Braun J, Gambhir SS, Witte ON: Quantitative imaging of the T cell antitumor response by positron-emission tomography. Proc Natl Acad Sci USA. 2003, 100: 1232-1237.PubMed CentralPubMedGoogle Scholar
- Wu AM, Senter PD: Arming antibodies: prospects and challenges for immunoconjugates. Nat Biotechnol. 2005, 23: 1137-1146.PubMedGoogle Scholar
- Radu CG, Shu CJ, Nair-Gill E, Shelly SM, Barrio JR, Satyamurthy N, Phelps ME, Witte ON: Molecular imaging of lymphoid organs and immune activation by positron emission tomography with a new [18F]-labeled 2'-deoxycytidine analog. Nat Med. 2008, 14: 783-788.PubMed CentralPubMedGoogle Scholar
- Tumeh PC, Radu CG, Ribas A: PET imaging of cancer immunotherapy. J Nucl Med. 2008, 49: 865-868.PubMedGoogle Scholar
- Platanias LC: Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat Rev Immunol. 2005, 5: 375-386.PubMedGoogle Scholar
- Kaur S, Sassano A, Dolniak B, Joshi S, Majchrzak-Kita B, Baker DP, Hay N, Fish EN, Platanias LC: Role of the Akt pathway in mRNA translation of interferon-stimulated genes. Proc Natl Acad Sci USA. 2008, 105: 4808-4813.PubMed CentralPubMedGoogle Scholar
- Schindler C, Plumlee C: Inteferons pen the JAK-STAT pathway. Semin Cell Dev Biol. 2008, 19: 311-318.PubMed CentralPubMedGoogle Scholar
- Wulfkuhle JD, Liotta LA, Petricoin EF: Proteomic application for the early detection of cancer. Nature Reviews Cancer. 2003, 3: 267-275.PubMedGoogle Scholar
- Wulfkuhle JD, Paweletz CP, Steeg PS, Petricoin EF, Liotta LA: Proteomic approaches to the diagnosis, treatment and monitoring of cancer. Adv Exp Med Biol. 2003, 532: 59-68.PubMedGoogle Scholar
- Wulfkuhle JD, Speer R, Pierobon M, Laird J, Espina V, Deng J, Mammano E, Yang SX, Swain SM, Nitti D: Multiplexed cell signaling analysis of human breast cancer applications for personalized therapy. J Proteome Res. 2008, 7: 1508-1517.PubMedGoogle Scholar
- Nolan GP, Fiering S, Nicolas JF, Herzenberg LA: Fluorescence activated cell analysis and sorting of viable mammalian cells based on beta-D-galactosidase activity after transduction of Escharichia coli lacZ. Proc Natl Acad Sci USA. 1998, 85: 2603-2607.Google Scholar
- Marks KM, Nolan GP: Chemical labeling strategies for cell biology. Nat Methods. 2006, 3: 591-596.PubMedGoogle Scholar
- Espina V, Edmiston KH, Heiby M, Pierobon M, Sciro M, Merritt B, Banks S, Deng J, VanMeter AJ, Geho DH: A portrait of tissue phosphoprotein stability in the clinical tissue procurement process. Mol Cell Proteomics. 2008, 7: 1998-2018.PubMed CentralPubMedGoogle Scholar
- Dash A, Maine IP, Varambally S, Shen R, Chinnaiyan AM, Rubin MA: Changes in differential gene expression because of warm ischemia time of radical prostatectomy specimens. Am J Pathol. 2002, 161: 1743-1748.PubMed CentralPubMedGoogle Scholar
- Longo C, Patanarut A, George T, Bishop B, Zhou W, Fredolini C, Ross MM, Espina V, Pellacani G, Petricoin EF: Core-shell hydrogel particles harvest, concentrate and preserve labile low abundance biomarkers. PLoS ONE. 2009, 4: e4763-PubMed CentralPubMedGoogle Scholar
- Panelli MC, White RLJr, Foster M, Martin B, Wang E, Smith K, Marincola FM: Forecasting the cytokine storm following systemic interleukin-2 administration. J Transl Med. 2004, 2: 17-PubMed CentralPubMedGoogle Scholar
- Rossi L, Martin B, Hortin G, White RLJr, Foster M, Stroncek D, Wang E, Marincola FM, Panelli MC: Inflammatory protein profile during systemic high dose interleukin-2 administration. Proteomics. 2006, 6: 709-720.PubMedGoogle Scholar
- Sarkar CA, Lauffenburger DA: Cell-level pharmacokinetic model of granulocyte colony-stimulating factor: implications for ligand lifetime and potency in vivo. Mol Pharmacol. 2003, 63: 147-158.PubMedGoogle Scholar
- Apgar JF, Toettcher JE, Endy D, White FM, Tidor B: Stimulus design for model selection and validation in cell signaling. PLoS Comput Biol. 2008, 4: e30-PubMed CentralPubMedGoogle Scholar
- Chaussabel D, Quinn C, Shen J, Patel P, Glaser C, Baldwin N, Stichweh D, Blankenship D, Li L, Munagala I: A modular framework for biomarker and knowledge discovery from blood transcriptional profiling studies: application to systemic lupus erythemathosus. Immunity. 2008, 29: 150-164.PubMed CentralPubMedGoogle Scholar
- Wang E, Marincola FM: Bottom up: a modular view of immunology. Immunity. 2008, 29: 9-11.PubMed CentralPubMedGoogle Scholar
- Jin P, Wang E: Polymorphism in clinical immunology. From HLA typing to immunogenetic profiling. J Transl Med. 2003, 1: 8-PubMed CentralPubMedGoogle Scholar
- Wang E, Worschech A, Marincola FM: The immunologic constant of rejection. Trends Immunol. 2008, 29: 256-262.PubMedGoogle Scholar
- Salk J: Immunological paradoxes: theoretical considerations in the rejection or retention of grafts, tumors, and normal tissue. Ann N Y Acad Sci. 1969, 164: 365-380.PubMedGoogle Scholar
- Mantovani A, Romero P, Palucka AK, Marincola FM: Tumor immunity: effector response to tumor and the influence of the microenvironment. Lancet. 2008, 371: 771-783.PubMedGoogle Scholar
- Wang E, Albini A, Stroncek DF, Marincola FM: New take on comparative immunology; relevance to immunotherapy. Immunotherapy. 2009, 1: 355-366.PubMed CentralPubMedGoogle Scholar
- Wang E, Monaco A, Monsurro' V, Sabatino M, Pos Z, Uccellini L, Wang J, Worschech A, Stroncek DF, Marincola FM: Antitumor vaccines, immunotherapy and the immunological constant of rejection. IDrugs. 2009, 12: 297-301.PubMed CentralPubMedGoogle Scholar
- Wang E, Miller LD, Ohnmacht GA, Mocellin S, Petersen D, Zhao Y, Simon R, Powell JI, Asaki E, Alexander HR: Prospective molecular profiling of subcutaneous melanoma metastases suggests classifiers of immune responsiveness. Cancer Res. 2002, 62: 3581-3586.PubMed CentralPubMedGoogle Scholar
- Bigger CB, Brasky KM, Lanford RE: DNA microarray analysis of chimpanzee liver during acute resolving hepatitis C virus infection. J Virol. 2001, 75: 7059-7066.PubMed CentralPubMedGoogle Scholar
- Bowen DG, Walker CM: Adaptive immune responses in acute and chronic hepatitis C virus infection. Nature. 2005, 436: 946-952.PubMedGoogle Scholar
- Bigger CB, Guerra B, Brasky KM, Hubbard G, Beard MR, Luxon BA, Lemon SM, Lanford RE: Intrahepatic gene expression during chronic hepatitis C virus infection in chimpanzees. J Virol. 2004, 78: 13779-13792.PubMed CentralPubMedGoogle Scholar
- Sarwal M, Chua MS, Kambham N, Hsieh SC, Satterwhite T, Masek M, Salvatierra O: Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N Engl J Med. 2003, 349: 125-138.PubMedGoogle Scholar
- Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau J, Pascual V: Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med. 2003, 197: 711-723.PubMed CentralPubMedGoogle Scholar
- Pascual V, Farkas L, Banchereau J: Systemic lupus erythematosus: all roads lead to type I interferons. Curr Opin Immunol. 2006, 18: 676-682.PubMedGoogle Scholar
- Pages F, Berger A, Camus M, Sanchez-Cabo F, Costes A, Molidor R, Mlecnik B, Kirilovsky A, Nilsson M, Damotte D: Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med. 2005, 353: 2654-2666.PubMedGoogle Scholar
- Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C, Tosolini M, Camus M, Berger A, Wind P: Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006, 313: 1960-1964.PubMedGoogle Scholar
- Galon J, Fridman WH, Pages F: The adaptive immunologic microenvironment in colorectal cancer: a novel perspective. Cancer Res. 2007, 67: 1883-1886.PubMedGoogle Scholar
- Shanker A, Verdeil G, Buferne M, Inderberg-Suso EM, Puthier D, Joly F, Nguyen C, Leserman L, uphan-Anezin N, Schmitt-Verhulst AM: CD8 T cell help for innate antitumor immunity. J Immunol. 2007, 179: 6651-6662.PubMedGoogle Scholar
- Worschech A, Chen N, Yu YA, Zhang Q, Pos Z, Weibel S, Raab V, Sabatino M, Monaco A, Liu H: Systemic treatment of xenografts with vaccinia virus GLV-1h68 reveals the immunologic facets of oncolytic therapy. BMC Genomics. 2009,Google Scholar
- Abati A, Sanford JS, Fetsch P, Marincola FM, Wolman SR: Fluorescence in situ hybridization (FISH): a user's guide to optimal preparation of cytologic specimens. Diagn Cytopathol. 1995, 13: 486-492.PubMedGoogle Scholar
- Honda K, Taniguchi T: IRFs: master regulators of signalling by Toll-like receptors and cytosolic pattern-recognition receptors. Nat Rev Immunol. 2006, 6: 644-658.PubMedGoogle Scholar
- Paun A, Pitha PM: The IRF family, revisited. Biochimie. 2007, 89: 744-753.PubMed CentralPubMedGoogle Scholar
- Camus M, Tosolini M, Mlecnik B, Pages F, Kirilovsky A, Berger A, Costes A, Bindea G, Charoentong P, Bruneval P: Coordination of intratumoral immune reaction and human colorectal cancer recurrence. Cancer Res. 2009, 69: 2685-2693.PubMedGoogle Scholar
- Balkwill F, Mantovani A: Inflammation and cancer: back to Virchow?. Lancet. 2001, 357: 539-545.PubMedGoogle Scholar
- Balkwill F, Charles KA, Mantovani A: Smoldering and polarized inflammation in the initiation and promotion of malignant disease. Cancer Cell. 2005, 7: 211-217.PubMedGoogle Scholar
- Mantovani A: Cancer: inflammation by remote control. Nature. 2005, 435: 752-753.PubMedGoogle Scholar
- Coussens LM, Werb Z: Inflammation and cancer. Nature. 2002, 420: 860-867.PubMed CentralPubMedGoogle Scholar
- De Visser KE, Korets LV, Coussens LM: De novo carcinogenesis promoted by chronic inflammation is B lymphocyte dependent. Cancer Cell. 2005, 7: 411-423.PubMedGoogle Scholar
- Gajewski TF, Meng Y, Blank C, Brown I, Kacha A, Kline J, Harlin H: Immune resistance orchestrated by the tumor microenvironment. Immunol Rev. 2006, 213: 131-145.PubMedGoogle Scholar
- Wang E, Marincola FM: A natural history of melanoma: serial gene expression analysis. Immunol Today. 2000, 21: 619-623.PubMedGoogle Scholar
- Wang E, Panelli MC, Monsurro' V, Marincola FM: Gene expression profiling of anti-cancer immune responses. Curr Op Mol Ther. 2004, 6: 288-295.Google Scholar
- Wang E, Selleri S, Sabatino M, Monaco A, Pos Z, Stroncek DF, Marincola FM: Spontaneous and tumor-induced cancer rejection in humans. Exp Opin Biol Ther. 2008, 8: 337-349.Google Scholar
- Worschech A, Haddad D, Stroncek DF, Wang E, Marincola FM, Szalay AA: The immunologic aspects of poxvirus oncolytic therapy. Cancer Immunol Immunother. 2009,Google Scholar
- Rivino L, Messi M, Jarrossay D, Lanzavecchia A, Sallusto F, Geginat J: Chemokine receptor expression identifies Pre-T helper (Th)1, Pre-Th2, and nonpolarized cells among human CD4+ central memory T cells. J Exp Med. 2004, 200: 725-735.PubMed CentralPubMedGoogle Scholar
- Sorensen TL: Targeting the chemokine receptor CXCR3 and its ligand CXCL10 in the central nervous system: potential therapy for inflammatory demyelinating disease?. Curr Neurovasc Res. 2004, 1: 183-190.PubMedGoogle Scholar
- Heller EA, Liu E, Tager AM, Yuan Q, Lin AY, Ahluwalia N, Jones K, Koehn SL, Lok VM, Aikawa E: Chemokine CXCL10 promotes atherogenesis by modulating the local balance of effector and regulatory T cells. Circulation. 2006, 113: 2301-2312.PubMedGoogle Scholar
- Hancock WW, Gao W, Csizmadia V, Faia KL, Shemmeri N, Luster AD: Donor-derived IP-10 initiates development of acute allograft rejection. J Exp Med. 2001, 193: 975-980.PubMed CentralPubMedGoogle Scholar
- Zhang Z, Kaptanoglu L, Tang Y, Ivancic D, Rao SM, Luster A, Barrett TA, Fryer J: IP-10-induced recruitment of CXCR3 host T cells is required for small bowel allograft rejection. Gastroenterology. 2004, 126: 809-818.PubMedGoogle Scholar
- Mullins IM, Slingluff CL, Lee JK, Garbee CF, Shu J, Anderson SG, Mayer ME, Knaus WA, Mullins DW: CXC chemokine receptor 3 expression by activated CD8+ T cells is associated with survival in melanoma patients with stage III disease. Cancer Res. 2004, 64: 7697-7701.PubMedGoogle Scholar
- Kunz M, Toksoy A, Goebeler M, Engelhardt E, Brocker E, Gillitzer R: Strong expression of the lymphoattractant C-X-C chemokine Mig is associated with heavy infiltration of T cells in human malignant melanoma. J Pathol. 1999, 189: 552-558.PubMedGoogle Scholar
- Monteagudo C, Martin JM, Jorda E, Llombart-Bosch A: CXCR3 chemokine receptor immunoreactivity in primary cutaneous malignant melanoma: correlation with clinicopathological prognostic factors. J Clin Pathol. 2007, 60: 596-599.PubMed CentralPubMedGoogle Scholar
- Harlin H, Meng Y, Peterson AC, Zha Y, Tretiakova M, Slingluff C, McKee M, Gajewski TF: Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res. 2009, 69: 3077-3085.PubMedGoogle Scholar
- Ugurel S, Schrama D, Keller G, Schadendorf D, Brocker EB, Houben R, Zapatka M, Fink W, Kaufman HL, Becker JC: Impact of the CCR5 gene polymorphism on the survival of metastatic melanoma patients receiving immunotherapy. Cancer Immunol Immunother. 2007, 57: 685-691.PubMedGoogle Scholar
- Kalinski P, Urban J, Narang R, Berk E, Wieckowski E, Muthuswamy R: Dendritic cell-based therapeutic cancer vaccines: what we have and what we need. Future Oncol. 2009, 5: 379-390.PubMed CentralPubMedGoogle Scholar
- Muthuswamy R, Urban J, Lee JJ, Reinhart TA, Bartlett D, Kalinski P: Ability of mature dendritic cells to interact with regulatory T cells is imprinted during maturation. Cancer Res. 2008, 68: 5972-5978.PubMed CentralPubMedGoogle Scholar
- Mailliard RB, Wankowicz-Kalinska A, Cai Q, Wesa A, Hilkens CM, Kapsenberg ML, Kirkwood JM, Storkus WJ, Kalinski P: alpha-type-1 polarized dendritic cells: a novel immunization tool with optimized CTL-inducing activity. Cancer Res. 2004, 64: 5934-5937.PubMedGoogle Scholar
- Piqueras B, Connolly J, Freitas H, Palucka AK, Banchereau J: Upon viral exposure, myeloid and plasmacytoid dendritic cells produce 3 waves of distinct chemokines to recruit immune effectors. Blood. 2006, 107: 2613-2618.PubMed CentralPubMedGoogle Scholar
- Benencia F, Courreges MC, Conejo-Garcia JR, Mohamed-Hadley A, Zhang L, Buckanovich RJ, Carroll R, Fraser N, Coukos G: HSV oncolytic therapy upregulates interferon-inducible chemokines and recruits immune effector cells in ovarian cancer. Mol Ther. 2005, 12: 789-802.PubMedGoogle Scholar
- Pages F, Kirilovsky A, Mlecnik B, Asslaber M, Tosolini M, Bindea G, Lagorce C, Wind P, Bruneval P, Zatloukal K: The in situ cytotoxic and memory T cells predict outcome in early-stage colerectal cancer patients. J Clin Oncol. 2009,Google Scholar
- Dieu-Nosjean MC, Antoine M, Danel C, Heudes D, Wislez M, Poulot V, Rabbe N, Laurans L, Tartour E, de CL: Long-term survival for patients with non-small-cell lung cancer with intratumoral lymphoid structures. J Clin Oncol. 2008, 26: 4410-4417.PubMedGoogle Scholar
- Deola S, Panelli MC, Maric D, Selleri S, Dmitrieva NI, Voss CY, Klein HG, Stroncek DF, Wang E, Marincola FM: "Helper" B cells promote cytotoxic T cell survival and proliferation indepdently of antigen presentation through CD27–CD70 interactions. J Immunol. 2008, 130: 1362-1372.Google Scholar
- Pages F, Galon J, Dieu-Nosjean MC, Tartour E, Sautes-Fridman C, Fridman WH: Immune infiltration in human tumors, a prognostic factor that should not be ignored. Oncogene. 2009,Google Scholar
- Clemente CG, Mihm MCJ, Bufalino R, Zurrida S, Collini P, Cascinelli N: Prognostic value of tumor infiltrating lymphocytes in the vertical growth phase of primary cutaneous melanoma. Cancer. 1996, 77: 1303-1310.PubMedGoogle Scholar
- Naito Y, Saito K, Shiiba K, Ohuchi A, Saigenji K, Nagura H, Ohtani H: CD8+ T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer. Cancer Res. 1998, 58: 3491-3494.PubMedGoogle Scholar
- Zhang L, Conejo-Garcia JR, Katsaros D, Gimotty PA, Massobrio M, Regnani G, Makrigiannakis A, Gray H, Schlienger K, Liebman MN: Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N Engl J Med. 2003, 348: 203-213.PubMedGoogle Scholar
- Sato E, Olson SH, Ahn J, Bundy B, Nishikawa H, Qian F, Jungbluth AA, Frosina D, Gnjatic S, Ambrosone C: Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc Natl Acad Sci USA. 2005, 102: 18538-18543.PubMed CentralPubMedGoogle Scholar
- Badoual C, Hans S, Rodriguez J, Peyrard S, Klein C, Agueznay NH, Mosseri V, Laccourreye O, Bruneval P, Fridman WH: Prognostic value of tumor-infiltrating CD4+ T-cell subpopulations in head and neck cancers. Clin Cancer Res. 2006, 12: 465-472.PubMedGoogle Scholar
- Salama P, Phillips M, Grieu F, Morris M, Zeps N, Joseph D, Platell C, Iacopetta B: Tumor-infiltrating FOXP3+ T regulatory cells show strong prognostic significance in colorectal cancer. J Clin Oncol. 2009, 27: 186-192.PubMedGoogle Scholar
- Walker EB, Miller W, Haley D, Floyd K, Curti B, Urba WJ: Characterization of the class I-restricted gp100 melanoma peptide-stimulated primary immune response in tumor-free vaccine-draining lymph nodes and peripheral blood. Clin Cancer Res. 2009, 15: 2541-2551.PubMedGoogle Scholar
- Walker EB, Haley D, Petrausch U, Floyd K, Miller W, Sanjuan N, Alvord G, Fox BA, Urba WJ: Phenotype and functional characterization of long-term gp100-specific memory CD8+ T cells in disease-free melanoma patients before and after boosting immunization. Clin Cancer Res. 2008, 14: 5270-5283.PubMed CentralPubMedGoogle Scholar
- Monsurro' V, Wang E, Panelli MC, Nagorsen D, Jin P, Smith K, Ngalame Y, Even J, Marincola FM: Active-specific immunization against melanoma: is the problem at the receiving end?. Sem Cancer Biol. 2003, 13: 473-480.Google Scholar
- He XS, Ji X, Hale MB, Cheung R, Ahmed A, Guo Y, Nolan GP, Pfeffer LM, Wright TL, Risch N: Global transcriptional response to interferon is a determinant of HCV treatment outcome and is modified by race. Hepatology. 2006, 44: 352-359.PubMedGoogle Scholar
- Lee PP, Yee C, Savage PA, Fong L, Brockstedt D, Weber JS, Johnson D, Swetter S, Thompson J, Greenberg PD: Characterization of circulating T cells specific for tumor-associated antigens in melanoma patients. Nat Med. 1999, 5: 677-685.PubMedGoogle Scholar
- Critchley-Thorne RJ, Yan N, Nacu S, Weber J, Holmes SP, Lee PP: Down-regulation of the interferon signaling pathway in T lymphocytes from patients with metastatic melanoma. PLoS Med. 2007, 4: e176-PubMed CentralPubMedGoogle Scholar
- Critchley-Thorne RJ, Simons D, Yan N, Miyahira A, Dirbas F, Johnson D, Swetter S, Carlson R, Fisher G, Koong A: Impaired interferon signaling is a common immune defect in human cancer. Proc Natl Acad Sci USA. 2009, 106: 9010-9015.PubMed CentralPubMedGoogle Scholar
- Selleri S, Deola S, Pos Z, Jin P, Worschech A, Slezak S, Rumio C, Panelli MC, Maric D, Stroncek DF: GM-CSF/IL-3/IL-5 receptor common B chain (CD131) as a biomarker of antigen-stimulated CD8+ T cells. J Transl Med. 2008, 6: 17-PubMed CentralPubMedGoogle Scholar
- Zea AH, Curti BD, Longo DL, Alvord WG, Strobl SL, Mizoguchi H, Creekmore SP, O'Shea JJ, Powers GC, Urba WJ: Alterations in T cell receptor and signal transduction molecules in melanoma patients. Clin Cancer Res. 1995, 1: 1327-1335.PubMedGoogle Scholar
- Rodriguez PC, Ochoa AC: T cell dysfunction in cancer: role of myeloid cells and tumor cells regulating amino acid availability and oxidative stress. Semin Cancer Biol. 2006, 16: 66-72.PubMedGoogle Scholar
- Norian LA, Rodriguez PC, O'Mara LA, Zabaleta J, Ochoa AC, Cella M, Allen PM: Tumor-infiltrating regulatory dendritic cells inhibit CD8+ T cell function via L-arginine metabolism. Cancer Res. 2009, 69: 3086-3094.PubMed CentralPubMedGoogle Scholar
- Leonard WJ, O'Shea JJ: Jaks and STATs: biological implications. Annu Rev Immunol. 1998, 16: 293-322.PubMedGoogle Scholar
- Heriot AG, Marriott JB, Cookson S, Kumar D, Dalgleish AG: Reduction in cytokine production in colorectal cancer patients: association with stage and reversal by resection. Br J Cancer. 2000, 82: 1009-1012.PubMed CentralPubMedGoogle Scholar
- Marincola FM, Wang E, Herlyn M, Seliger B, Ferrone S: Tumors as elusive targets of T cell-based active immunotherapy. Trends Immunol. 2003, 24: 335-342.PubMedGoogle Scholar
- Monsurro' V, Beghelli S, Wang R, Barbi S, Coin S, Di Pasquale G, Bersani S, Castellucci M, Sorio C, Eleuteri S: Anti-viral status segregates two pancreatic adenocarcinoma molecular phenotypes with potential relevance for adenoviral gene therapy. 2009,Google Scholar
- Wang E, Panelli MC, Zavaglia K, Mandruzzato S, Hu N, Taylor PR, Seliger B, Zanovello P, Freedman RS, Marincola FM: Melanoma-restricted genes. J Transl Med. 2004, 2: 34-PubMed CentralPubMedGoogle Scholar
- Mandruzzato S, Callegaro A, Turcatel G, Francescato S, Montesco MC, Chiarion-Sileni V, Mocellin S, Rossi CR, Bicciato S, Wang E: A gene expression signature associated with survival in metastatic melanoma. J Transl Med. 2006, 4: 50-PubMed CentralPubMedGoogle Scholar
- Bittner M, Meltzer P, Chen Y, Jiang E, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A: Molecular classification of cutaneous malignant melanoma by gene expression: shifting from a countinuous spectrum to distinct biologic entities. Nature. 2000, 406: 536-840.PubMedGoogle Scholar
- Haqq C, Nosrati M, Sudilovsky D, Crothers J, Khodabakhsh D, Pulliam BL, Federman S, Miller JR, Allen RE, Singer MI: The gene expression signatures of melanoma progression. Proc Natl Acad Sci USA. 2005, 102: 6092-6097.PubMed CentralPubMedGoogle Scholar
- Houghton AN, Coit DG, Daud A, Dilawari RA, Dimaio D, Gollob JA, Haas NB, Halpern A, Johnson TM, Kashani-Sabet M: Melanoma. J Natl Compr Canc Netw. 2006, 4: 666-684.PubMedGoogle Scholar
- Kashani-Sabet M, Rangel J, Torabian S, Nosrati M, Simko J, Jablons DM, Moore DH, Haqq C, Miller JR, Sagebiel RW: A multi-marker assay to distinguish malignant melanomas from benign nevi. Proc Natl Acad Sci USA. 2009, 106: 6268-6272.PubMed CentralPubMedGoogle Scholar
- Hocker TL, Singh MK, Tsao H: Melanoma genetics and therapeutic approaches in the 21st century: moving from the benchside to the bedside. J Invest Dermatol. 2008, 128: 2575-2595.PubMedGoogle Scholar
- Kawakami Y, Sumimoto H, Fujita T, Matsuzaki Y: Immunological detection of altered signaling molecules involved in melanoma development. Cancer Metastasis Rev. 2005, 24: 357-366.PubMedGoogle Scholar
- Wang E, Voiculescu S, Le Poole IC, el Gamil M, Li X, Sabatino M, Robbins PF, Nickoloff BJ, Marincola FM: Clonal persistence and evolution during a decade of recurrent melanoma. J Invest Dermatol. 2006, 126: 1372-1377.PubMedGoogle Scholar
- Sabatino M, Zhao Y, Voiculescu S, Monaco A, Robbins PF, Nickoloff BJ, Karai L, Selleri S, Maio M, Selleri S: Conservation of a core of genetic alterations over a decade of recurrent melanoma supports the melanoma stem cell hypothesis. Cancer Res. 2008, 68: 222-231.Google Scholar
- Rubinfeld B, Robbins P, el Gamil M, Albert I, Porfiri E, Polakis P: Stabilization of beta-catenin by genetic defects in melanoma cell lines. Science. 1997, 275: 1790-1792.PubMedGoogle Scholar
- Robbins PF, el-Gamil M, Kawakami Y, Stevens E, Yannelli JR, Rosenberg SA: Recognition of tyrosinase by tumor-infiltrating lymphocytes from a patient responding to immunotherapy [published erratum appears in Cancer Res 1994 Jul 15;54(14):3952]. Cancer Res. 1994, 54: 3124-3126.PubMedGoogle Scholar
- Mocellin S, Ohnmacht GA, Wang E, Marincola FM: Kinetics of cytokine expression in melanoma metastases classifies immune responsiveness. Int J Cancer. 2001, 93: 236-242.PubMedGoogle Scholar
- Mocellin S, Wang E, Marincola FM: Cytokine and immune response in the tumor microenvironment. J Immunother. 2001, 24: 392-407.Google Scholar
- Mocellin S, Panelli MC, Wang E, Nagorsen D, Marincola FM: The dual role of IL-10. Trends Immunol. 2002, 24: 36-43.Google Scholar
- Li YH, Hu CF, Shao Q, Huang MY, Hou JH, Xie D, Zeng YX, Shao JY: Elevated expressions of survivin and VEGF protein are strong independent predictors of survival in advanced nasopharyngeal carcinoma. J Transl Med. 2008, 6: 1-PubMed CentralPubMedGoogle Scholar
- Lo KW, To KF, Huang DP: Focus on nasopharyngeal carcinoma. Cancer Cell. 2004, 5: 423-428.PubMedGoogle Scholar
- McDermott AL, Dutt SN, Watkinson JC: The aetiology of nasopharyngeal carcinoma. Clin Otolaryngol. 2001, 26: 82-92.PubMedGoogle Scholar
- Simons MJ: HLA and nasopharyngeal carcinoma: 30 years on. ASHI Quarterly. 2003, 27: 52-55.Google Scholar
- Burgos JS: Involvement of the Epstein-Barr virus in the nasopharyngeal carcinoma pathogenesis. Med Oncol. 2005, 22: 113-121.PubMedGoogle Scholar
- Simons MJ, Day NE, Wee GB, Shanmugaratnam K, Ho HC, Wong SH, Ti TK, Yong NK, Darmalingam S, De-The G: Nasopharyngeal carcinoma V: immunogenetic studies of Southeast Asian ethnic groups with high and low risk for the tumor. Cancer Res. 1974, 34: 1192-1195.PubMedGoogle Scholar
- Lee SP, Chan ATC, Cheung ST, Thomas WA, Croom-Carter D, Dawson CW, Tsai CH, Leung SF, Johnson PJ, Huang DP: CTL control of EBV in nasopharyngeal carcinoma: EBV-specific CTL responses in the blood and tumours of NPC patients and teh antigen-processing function of the tumor cells. J Immunol. 2000, 165: 573-582.PubMedGoogle Scholar
- Chua D, Huang J, Zheng B, Lau SY, Luk W, Kwong DL, Sham JS, Moss D, Yuen KY, Im SW: Adoptive transfer of autologous Epstein-Barr virus-specific cytotoxic T cells for nasopharyngeal carcinoma. Int J Cancer. 2001, 94: 73-80.PubMedGoogle Scholar
- Lin C-L, Lo W-F, Lee T-H, Yi R, Hwang S-L, Cheng Y-F, Chen C-L, Chang Y-S, Lee SP, Rickinson AB: Immunization with Epstein-Barr virus (EBV) peptide-pulsed dendritic cells induces functional CD8+ T-cell immunity and may lead to tumor regression in patients with EBV-positive nasopharyngeal carcinoma. Cancer Res. 2002, 62: 6952-6958.PubMedGoogle Scholar
- Budiani DR, Hutahaean S, Haryana SM, Soesatyo MH, Sosroseno W: Interleukin-10 levels in Epstein-Barr virus-associated nasopharyngeal carcinoma. J Microbiol Immunol Infect. 2002, 35: 365-368.Google Scholar
- Straathof KC, Bollard CM, Popat U, Huls MH, Lopez T, Morriss MC, Gresik MV, Gee AP, Russell HV, Brenner MK: Treatment of nasopharyngeal carcinoma with Epstein-Barr virus – specific T lymphocytes. Blood. 2005, 105: 1898-1904.PubMedGoogle Scholar
- Fang W, Li X, Jiang Q, Liu Z, Yang H, Wang S, Xie S, Liu Q, Liu T, Huang J: Transcriptional patterns, biomarkers and pathways characterizing nasopharyngeal carcinoma of Southern China. J Transl Med. 2008, 6: 32-PubMed CentralPubMedGoogle Scholar
- Mokni-Baizig N, Ayed K, Ayed FB, Ayed S, Sassi F, Ladgham A, Bel HO, El May A: Association between HLA-A/-B antigens and -DRB1 alleles and nasopharyngeal carcinoma in Tunisia. Oncology. 2001, 61: 55-58.PubMedGoogle Scholar
- Hildesheim A, Apple RJ, Chen C-J, Wang SS, Cheng Y-J, Klitz W, Mack SJ, Chen I-H, Hsu M-M, Yang C-S: Association of HLA class I and II alleles and extended haplotypes with nasopharyngeal carcinoma in Taiwan. J Natl Cancer Inst. 2002, 94: 1780-1789.PubMedGoogle Scholar
- Goldsmith DB, West TM, Morton R: HLA associations with nasopharyngeal carconoma in Southern Chinese: a meta-analysis. Clin Otolaryngol. 2002, 27: 61-67.PubMedGoogle Scholar
- Chan ATC, Teo PML, Johnson PJ: Nasopharyngeal carcinoma. Ann Oncol. 2002, 13: 1007-1015.PubMedGoogle Scholar
- Yu MC, Yuan JM: Epidemiology of nasopharyngeal carcinoma. Semin Cancer Biol. 2002, 12: 421-429.PubMedGoogle Scholar
- Feng BJ, Huang W, Shugart YY, Lee MK, Zhang F, Xia JC, Wang HY, Huang TB, Jian SW, Huang P: Genome-wide scan for familial nasopharyngeal carcinoma reveals evidence of linkage to chromosome 4. Nat Genet. 2002, 31: 395-399.PubMedGoogle Scholar
- Tsai MH, Chen WC, Tsai FJ: Correlation of p21 gene codon 31 polymorphism and TNF-alpha gene polymorphism with nasopharyngeal carcinoma. J Clin Lab Anal. 2002, 16: 146-150.PubMedGoogle Scholar
- Huang Z, Desper R, Schaffer AA, Yin Z, Li X, Yao K: Construction of tree models for pathogenesis of nasopharyngeal carcinoma. Genes Chromosomes Cancer. 2004, 40: 307-315.PubMedGoogle Scholar
- Chan AT, Teo PM, Huang DP: Pathogenesis and treatment of nasopharyngeal carcinoma. Semin Oncol. 2004, 31: 794-801.PubMedGoogle Scholar
- Lu CC, Chen JC, Tsai ST, Jin YT, Tsai JC, Chan SH, Su IJ: Nasopharyngeal carcinoma-susceptibility locus is localized to a 132 kb segment containing HLA-A using high-resolution microsatellite mapping. Int J Cancer. 2005, 115: 742-746.PubMedGoogle Scholar
- Li X, Wang E, Zhao YD, Ren JQ, Jin P, Yao KT, Marincola FM: Chromosomal imbalances in nasopharyngeal carcinoma: a meta-analysis of comparative genomic hybridization results. J Transl Med. 2006, 4: 4-PubMed CentralPubMedGoogle Scholar
- Li X, Ghandri N, Piancatelli D, Adams S, Chen D, Robbins FM, Wang E, Monaco A, Selleri S, Bouaouina N: Associations between HLA class I alleles and the prevalence of nasopharyngeal carcinoma (NPC) among Tunisians. J Transl Med. 2007, 5: 22-PubMed CentralPubMedGoogle Scholar
- Ioannidis JP, Ntzani EE, Trikalinos TA: 'Racial' differences in genetic effects for complex diseases. Nat Genet. 2004, 36: 1312-1318.PubMedGoogle Scholar
- Huang RS, Duan S, Kistner EO, Zhang W, Bleibel WK, Cox NJ, Dolan ME: Identification of genetic variants and gene expression relationships associated with pharmacogenes in humans. Pharmacogenet Genomics. 2008, 18: 545-549.PubMed CentralPubMedGoogle Scholar
- Kurian AK, Cardarelli KM: Racial and ethnic differences in cardiovascular disease risk factors: a systematic review. Ethn Dis. 2007, 17: 143-152.PubMedGoogle Scholar
- Zhang W, Duan S, Bleibel WK, Wisel SA, Huang RS, Wu X, He L, Clark TA, Chen TX, Schweitzer AC: Identification of common genetic variants that account for transcript isoform variation between human populations. Hum Genet. 2009, 125: 81-93.PubMed CentralPubMedGoogle Scholar
- Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG, Spielman RS, Cheung VG: Genetic analysis of genome-wide variation in human gene expression. Nature. 2004, 430: 743-747.PubMed CentralPubMedGoogle Scholar
- Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, Lyle R, Hunt S, Kahl B, Antonarakis SE, Tavare S: Genome-wide associations of gene expression variation in humans. PLoS Genet. 2005, 1: e78-PubMed CentralPubMedGoogle Scholar
- Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, Ingle CE, Dunning M, Flicek P, Koller D: Population genomics of human gene expression. Nat Genet. 2007, 39: 1217-1224.PubMed CentralPubMedGoogle Scholar
- Tishkoff SA, Kidd KK: Implications of biogeography of human populations for 'race' and medicine. Nat Genet. 2004, 36: S21-S27.PubMedGoogle Scholar
- Lengauer C, Kinzler KW, Vogelstein B: Genetic instabilities in human cancers. Nature. 1998, 396: 643-649.PubMedGoogle Scholar
- Liu D, O'Day SJ, Yang D, Boasberg P, Milford R, Kristedja T, Groshen S, Weber J: Impact of gene polymorphisms on clinical outcome for stage IV melanoma patients treated with biochemotherapy: an exploratory study. Clin Cancer Res. 2005, 11: 1237-1246.PubMedGoogle Scholar
- Gogas H, Ioannovich J, Dafni U, Stavropoulou-Giokas C, Frangia K, Tsoutsos D, Panagiotou P, Polyzos A, Papadopoulos O, Stratigos A: Prognostic significance of autoimmunity during treatment of melanoma with interferon. N Engl J Med. 2006, 354: 709-718.PubMedGoogle Scholar
- Kirkwood JM, Tarhini AA, Panelli MC, Moschos SJ, Zarour HM, Butterfield LH, Gogas HJ: Next generation of immunotherapy for melanoma. J Clin Oncol. 2008, 26: 3445-3455.PubMedGoogle Scholar
- Yamaguchi H, Calado RT, Ly H, Kajigaya S, Baerlocher GM, Chanock SJ, Lansdorp PM, Young NS: Mutations in TERT, the gene for telomerase reverse transcriptase, in aplastic anemia. N Engl J Med. 2005, 352: 1413-1424.PubMedGoogle Scholar
- Xin ZT, Beauchamp AD, Calado RT, Bradford JW, Regal JA, Shenoy A, Liang Y, Lansdorp PM, Young NS, Ly H: Functional characterization of natural telomerase mutations found in patients with hematologic disorders. Blood. 2007, 109: 524-532.PubMedGoogle Scholar
- Calado RT, Young NS: Telomere maintenance and human bone marrow failure. Blood. 2008, 111: 4446-4455.PubMed CentralPubMedGoogle Scholar
- Gaglio PJ, Rodriguez-Torres M, Herring R, Anand B, Box T, Rabinovitz M, Brown RS: Racial differences in response rates to consensus interferon in HCV infected patients naive to previous therapy. J Clin Gastroenterol. 2004, 38: 599-604.PubMedGoogle Scholar
- Conjeevaram HS, Fried MW, Jeffers LJ, Terrault NA, Wiley-Lucas TE, Afdhal N, Brown RS, Belle SH, Hoofnagle JH, Kleiner DE: Peginterferon and ribavirin treatment in African American and Caucasian American patients with hepatitis C genotype 1. Gastroenterology. 2006, 131: 470-477.PubMedGoogle Scholar
- Su X, Yee LJ, Im K, Rhodes SL, Tang Y, Tong X, Howell C, Ramcharran D, Rosen HR, Taylor MW: Association of single nucleotide polymorphisms in interferon signaling pathway genes and interferon-stimulated genes with the response to interferon therapy for chronic hepatitis C. J Hepatol. 2008, 49: 184-191.PubMed CentralPubMedGoogle Scholar
- Kelly JA, Kelley JM, Kaufman KM, Kilpatrick J, Bruner GR, Merrill JT, James JA, Frank SG, Reams E, Brown EE: Interferon regulatory factor-5 is genetically associated with systemic lupus erythematosus in African Americans. Genes Immun. 2008, 9: 187-194.PubMedGoogle Scholar
- Namjou B, Sestak AL, Armstrong DL, Zidovetzki R, Kelly JA, Jacob N, Ciobanu V, Kaufman KM, Ojwang JO, Ziegler J: High-density genotyping of STAT4 reveals multiple haplotypic associations with systemic lupus erythematosus in different racial groups. Arthritis Rheum. 2009, 60: 1085-1095.PubMed CentralPubMedGoogle Scholar
- Ahlenstiel G, Nischalke HD, Bueren K, Berg T, Vogel M, Biermer M, Grunhage F, Sauerbruch T, Rockstroh J, Spengler U: The GNB3 C825T polymorphism affects response to HCV therapy with pegylated interferon in HCV/HIV co-infected but not in HCV mono-infected patients. J Hepatol. 2007, 47: 348-355.PubMedGoogle Scholar
- Sarrazin C, Berg T, Weich V, Mueller T, Frey UH, Zeuzem S, Gerken G, Roggendorf M, Siffert W: GNB3 C825T polymorphism and response to interferon-alfa/ribavirin treatment in patients with hepatitis C virus genotype 1 (HCV-1) infection. J Hepatol. 2005, 43: 388-393.PubMedGoogle Scholar
- Tishkoff SA, Reed FA, Friedlaender FR, Ehret C, Ranciaro A, Froment A, Hirbo JB, Awomoyi AA, Bodo JM, Doumbo O: The Genetic Structure and History of Africans and African Americans. Science. 2009Google Scholar
- Wallace TA, Prueitt RL, Yi M, Howe TM, Gillespie JW, Yfantis HG, Stephens RM, Caporaso NE, Loffredo CA, Ambs S: Tumor immunobiological differences in prostate cancer between African-American and European-American men. Cancer Res. 2008, 68: 927-936.PubMedGoogle Scholar
- Martin DN, Boersma BJ, Yi M, Reimers M, Howe TM, Yfantis HG, Tsai YC, Williams EH, Lee DH, Stephens RM: Differences in the tumor microenvironment between African-American and European-American breast cancer patients. PLoS ONE. 2009, 4: e4531-PubMed CentralPubMedGoogle Scholar
- Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, Thun MJ: Cancer statistics, 2008. CA Cancer J Clin. 2008, 58: 71-96.PubMedGoogle Scholar
- Weichselbaum RR, Ishwaran H, Yoon T, Nuyten DS, Baker SW, Khodarev N, Su AW, Shaikh AY, Roach P, Kreike B: An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer. Proc Natl Acad Sci USA. 2008, 105: 18490-18495.PubMed CentralPubMedGoogle Scholar
- Engels EA, Wu X, Gu J, Dong Q, Liu J, Spitz MR: Systematic evaluation of genetic variants in the inflammation pathway and risk of lung cancer. Cancer Res. 2007, 67: 6520-6527.PubMedGoogle Scholar
- Leibovici D, Grossman HB, Dinney CP, Millikan RE, Lerner S, Wang Y, Gu J, Dong Q, Wu X: Polymorphisms in inflammation genes and bladder cancer: from initiation to recurrence, progression, and survival. J Clin Oncol. 2005, 23: 5746-5756.PubMedGoogle Scholar
- Ascierto PA, Kirkwood JM: Adjuvant therapy of melanoma with interferon: lessons of the past decade. J Transl Med. 2008, 6: 62-PubMed CentralPubMedGoogle Scholar
- Kirkwood JM, Tarhini AA: Biomarkers of Therapeutic Response in Melanoma and Renal Cell Carcinoma: Potential Inroads to Improved Immunotherapy. J Clin Oncol. 2009, 27: 2583-2585.PubMedGoogle Scholar
- Yurkovetsky ZR, Kirkwood JM, Edington HD, Marrangoni AM, Velikokhatnaya L, Winans MT, Gorelik E, Lokshin AE: Multiplex analysis of serum cytokines in melanoma patients treated with interferon-alpha2b. Clin Cancer Res. 2007, 13: 2422-2428.PubMedGoogle Scholar
- Soubrane C, Mouawad R, Rixe O: Changes in circulating VEGF-A levels related to clinical response during biochemotherapy in metastatic malignant melanoma. J Clin Oncol. 2004, 22: 717s-Google Scholar
- Soubrane C, Rixe O, Meric JB, Khayat D, Mouawad R: Pretreatment serum interleukin-6 concentration as a prognostic factor of overall survival in metastatic malignant melanoma patients treated with biochemotherapy: a retrospective study. Melanoma Res. 2005, 15: 199-204.PubMedGoogle Scholar
- Phan GQ, Attia P, Steinberg SM, White DE, Rosenberg SA: Factors associated with response to high-dose interleukin-2 in patients with metastatic melanoma. J Clin Oncol. 2001, 19: 3477-3482.PubMedGoogle Scholar
- Moschos SJ, Edington HD, Land SR, Rao UN, Jukic D, Shipe-Spotloe J, Kirkwood JM: Neoadjuvant treatment of regional stage IIIB melanoma with high-dose interferon alfa-2b induces objective tumor regression in association with modulation of tumor infiltrating host cellular immune responses. J Clin Oncol. 2006, 24: 3164-3171.PubMedGoogle Scholar
- Atkins MB, Regan M, McDermott D, Mier J, Stanbridge E, Youmans A, Febbo P, Upton M, Lechpammer M, Signoretti S: Carbonic anhydrase IX expression predicts outcome in interleukin-2 therapy of renal cancer. Clin Cancer Res. 2005, 11: 3714-3721.PubMedGoogle Scholar
- Panelli MC, Wang E, Marincola FM: The pathway to biomarker discovery: carbonic anhydrase IX and the prediction of immune responsiveness. Clin Cancer Res. 2005, 11: 3601-3603.PubMedGoogle Scholar
- Hodi FS, Mihm MC, Soiffer RJ, Haluska FG, Butler M, Seiden MV, Davis T, Henry-Spires R, MacRae S, Willman A: Biologic activity of cytotoxic T lymphocyte-associated antigen 4 antibody blockade in previously vaccinated metastatic melanoma and ovarian carcinoma patients. Proc Natl Acad Sci USA. 2003, 100: 4712-4717.PubMed CentralPubMedGoogle Scholar
- Doehn C, Bohmer T, Kausch I, Sommerauer M, Jocham D: Prostate cancer vaccines: current status and future potential. BioDrugs. 2008, 22: 71-84.PubMedGoogle Scholar
- Lassi K, Dawson NA: Emerging therapies in castrate-resistant prostate cancer. Curr Opin Oncol. 2009, 21: 260-265.PubMedGoogle Scholar
- Jinushi M, Nakazaki Y, Dougan M, Carrasco DR, Mihm M, Dranoff G: MFG-E8-mediated uptake of apoptotic cells by APCs links the pro- and antiinflammatory activities of GM-CSF. J Clin Invest. 2007, 117: 1902-1913.PubMed CentralPubMedGoogle Scholar
- Jinushi M, Nakazaki Y, Carrasco DR, Draganov D, Souders N, Johnson M, Mihm MC, Dranoff G: Milk fat globule EGF-8 promotes melanoma progression through coordinated Akt and twist signaling in the tumor microenvironment. Cancer Res. 2008, 68: 8889-8898.PubMedGoogle Scholar
- Jinushi M, Hodi FS, Dranoff G: Enhancing the clinical activity of granulocyte-macrophage colony-stimulating factor-secreting tumor cell vaccines. Immunol Rev. 2008, 222: 287-298.PubMedGoogle Scholar
- Aloysius MM, Mc Kechnie AJ, Robins RA, Verma C, Eremin JM, Farzaneh F, Habib NA, Bhalla J, Hardwick NR, Satthaporn S: Generation in vivo of peptide-specific cytotoxic T cells and presence of regulatory T cells during vaccination with hTERT (class I and II) peptide-pulsed DCs. J Transl Med. 2009, 7: 18-PubMed CentralPubMedGoogle Scholar
- Tatsumi T, Kierstead LS, Ranieri E, Gesualdo L, Schena FP, Finke JH, Bukowski RM, Brusic V, Sidney J, Sette A: MAGE-6 encodes HLA-DRbeta1*0401-presented epitopes recognized by CD4+ T cells from patients with melanoma or renal cell carcinoma. Clin Cancer Res. 2003, 9: 947-954.PubMedGoogle Scholar
- Hawk ET, Matrisian LM, Nelson WG, Dorfman GS, Stevens L, Kwok J, Viner J, Hautala J, Grad O: The Translational Research Working Group developmental pathways: introduction and overview. Clin Cancer Res. 2008, 14: 5664-5671.PubMedGoogle Scholar
- Cheever MA, Schlom J, Weiner LM, Lyerly HK, Disis ML, Greenwood A, Grad O, Nelson WG: Translational Research Working Group developmental pathway for immune response modifiers. Clin Cancer Res. 2008, 14: 5692-5699.PubMedGoogle Scholar
- Cheever MA, Allison JP, Ferris AS, Finn OJ, Hastings BM, Hecht TT, Mellman I, Prindiville SA, Steinman RM, Viner JL: The prioritization of cancer antigens: a National Cancer Institute pilot prioritization project for the acceleration of tranlsational research. Clin Cancer Res. 2009,Google Scholar
- Sato N, Hirohashi Y, Tsukahara T, Kikuchi T, Sahara H, Kamiguchi K, Ichimiya S, Tamura Y, Torigoe T: Molecular pathological approaches to human tumor immunology. Pathol Int. 2009, 59: 205-217.PubMedGoogle Scholar
- Wada H, Sato E, Uenaka A, Isobe M, Kawabata R, Nakamura Y, Iwae S, Yonezawa K, Yamasaki M, Miyata H: Analysis of peripheral and local anti-tumor immune response in esophageal cancer patients after NY-ESO-1 protein vaccination. Int J Cancer. 2008, 123: 2362-2369.PubMedGoogle Scholar
- Fields AL, Keller A, Schwartzberg L, Bernard S, Kardinal C, Cohen A, Schulz J, Eisenberg P, Forster J, Wissel P: Adjuvant therapy with the monoclonal antibody Edrecolomab plus fluorouracil-based therapy does not improve overall survival of patients with stage III colon cancer. J Clin Oncol. 2009, 27: 1941-1947.PubMedGoogle Scholar
- Chaudry MA, Sales K, Ruf P, Lindhofer H, Winslet MC: EpCAM an immunotherapeutic target for gastrointestinal malignancy: current experience and future challenges. Br J Cancer. 2007, 96: 1013-1019.PubMed CentralPubMedGoogle Scholar
- Volpers C, Thirion C, Biermann V, Hussmann S, Kewes H, Dunant P, von der MH, Herrmann A, Kochanek S, Lochmuller H: Antibody-mediated targeting of an adenovirus vector modified to contain a synthetic immunoglobulin g-binding domain in the capsid. J Virol. 2003, 77: 2093-2104.PubMed CentralPubMedGoogle Scholar
- Hoshino I, Matsubara H, Hanari N, Mori M, Nishimori T, Yoneyama Y, Akutsu Y, Sakata H, Matsushita K, Seki N: Histone deacetylase inhibitor FK228 activates tumor suppressor Prdx1 with apoptosis induction in esophageal cancer cells. Clin Cancer Res. 2005, 11: 7945-7952.PubMedGoogle Scholar
- Shen L, Toyota M, Kondo Y, Lin E, Zhang L, Guo Y, Hernandez NS, Chen X, Ahmed S, Konishi K: Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer. Proc Natl Acad Sci USA. 2007, 104: 18654-18659.PubMed CentralPubMedGoogle Scholar
- Suzuki H, Toyota M, Kondo Y, Shinomura Y: Inflammation-related aberrant patterns of DNA methylation: detection and role in epigenetic deregulation of cancer cell transcriptome. Methods Mol Biol. 2009, 512: 55-69.PubMedGoogle Scholar
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