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 1.

Examples of biomarkers predictive of immunotherapy efficacy (predictive classifiers) 4567 are telomere length of adoptively transferred tumor infiltrating lymphocytes which is significantly correlated with likelihood of clinical response 8, 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 9 or K-ras mutations that predict ineffectiveness of cetuximab for the treatment of colorectal cancer 10. 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 1112.

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 13 though their usefulness needs further validation 14. Korn et al 15 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 16. Recently, Grubb et al. 17 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. 18; 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 19.

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 20 or local application of Toll-like receptor agonists 21 (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 2223. Surrogate biomarkers inform about the effectiveness of treatment in early phase assessment and help go/no go decisions about further drug development 1. 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 15. 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 24.

Since the term "biomarker" is used for a wide variety of purposes, confusion often results when biomarker development, validation and qualification are discussed 72526. 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 27. Referring to work from Pepe et al 28293031, 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 32. 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 33. Following the steps of validation, a retrospective analysis suggested that survival is associated with development of memory immune responses 34 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 35363738 or antigen-specific, epitope-based vaccination 39. Important exploratory findings were reported by Hiroyoshi Nishikawa (Mie University, Mie, Japan) 40, 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.

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 41. 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 42. Lymphopenia strongly enhanced the expansion of CD44^hiCD62L^lo 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 43.

Lisa Butterfield (University of Pittsburgh, Pittsburgh, PA, USA) and Nora Disis summarized validation efforts on immunologic assay performance and standardization 2223444546474849. 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 45. 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 5051.

Extensive discussion about assay validation is beyond the purpose of this report as it was discussed in the previous related manuscript 1. 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 52. 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 53, a similar consistency analysis was reported by others 54. Use of central laboratories may help overcome the extensive cost and effort of this level of standardization 4655.

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 56. 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.

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 5758. 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 59. 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 5760. 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 57. 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 5861. 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 62; 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 63.

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) 64 showed that different maturation schemes of DCs or stem cells bear quite different results in their transcriptional phenotype even when similar agents are used 65666768. Similar work has been reported by the FDA on stem cell characterization 697071; same principles were followed to address assay reproducibility in freeze and thaw cycles 72 or changes in culture conditions 73. By using this validation approaches it will be hopefully possible to enhance the quality of potency assessment for cellular products 64; 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 747576.

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 77. 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 7879, 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 8081. 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 828384. 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 85.

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 868788. ELISPOT assays that detect the effector response of cytotoxic T cells to cognate stimulation have been recently described 899091. More recently, a flow cytometric cytotoxicity assay was developed for monitoring cancer vaccine trials 92. The assay simultaneously measures effector cell de-granulation and target cell death. Interestingly, as previously shown using transcriptional analyses and target cell death estimation 86, 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 86939495. Thus, future monitoring trials should include a broader range of assays testing the expression/secretion of different cytokines and cytotoxic molecules.

There are several examples of differences between therapy-induced changes in the tumor microenvironment compared with the peripheral circulation 20969798. Ribas, proposed the study of the kinetics of anti-tumor immune responses in vivo using PET-based molecular imaging 99 expanding the analysis of immune conjugate kinetics for pharmacokinetics studies and visualization of lymphoid organs 100101. 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 102.

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 103104. 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 105 and among alternative pathways is necessary to fully understand the mechanisms of IFN-induced responses and their relationship with TSD 103. 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 17106107108. 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 83109110. 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.

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 111 and the expression of the protein as well as messenger RNA levels 112. 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.

"Smart" core shell affinity bait nano-porous particles amplify the concentration of a given analyte 113. 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 114115.

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 116. 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 117. Among them, the modular analysis of disease-specific transcriptional patterns developed by Chaussabel et al 118119 holds promise to represent an important tool to comprehensively follow the modulation of immune responses during therapy (see later).

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) 120. Though disease- or population-specific patterns were observed, commonalities were identified that support the hypothesis of a constant mechanism that leads to TSD 121.

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 122. 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 121123124125. 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.

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 20126 or basal cell cancer by Toll-like receptor-7 agonists 21. The same signatures are observed in acute but not in chronic HCV infection leading to clearance of pathogen 127128129 and in acute uncontrollable kidney allograft rejection 130. Furthermore, activation of ISGs is a classic signature associated with systemic lupus erythematosus and tightly correlates with the severity of the disease 118131132. 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 133134135. 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 93; the signatures associated with the acute phase can be observed within the tumor microenvironment during adaptive and/or innate immunity-mediated tumor regression 136137.

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 121. 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 138. 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 139140141 and STAT-1 105. Importantly, IRF-1 specifically promotes IL-15 expression 139, which is central to the induction of TSD 137. IRF-3 is also commonly activated during TSD; IRF-3 is responsible for the over-expression of CXCL-9 through -11 and CCL5 chemokines 139 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 123142143144145146; in particular, the extensive expression of immune-inhibitory mechanisms during tumor progression 147 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 148149150.

The comparative approach described so far 124 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 21137151. 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 152. In particular, CD8 T cell infiltration to inflamed areas such as the cerebrospinal fluid in multiple sclerosis 153, atherosclerotic plaques 154 or allografts 155156 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 157. This finding could be explained by the heavy lymphocyte infiltration present in melanoma metastases expressing of CXCR3 ligand chemokines such as CXCL9/Mig 158 and CXCL10/Ip-10 159. A finding recently confirmed by independent investigators 160. Interestingly, CCL5/Rantes and IFN-γ were also reported to predict immune responsiveness during GSK/MAGE-A3 immunotherapy 12. Moreover, the role played by CCL5/RANTES is suggested by the weight that CCR5 polymorphism plays in the prognosis of melanoma 161. More recently, Kalinski et al 162 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 163164. 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 165; 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 166 or by the systemic administration of vaccinia virus colonizing selectively human tumor xenografts 137.

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 133134. 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 134. 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 167. 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 141. 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 168. This finding supports a potential helper role that B cells may play in the recruitment and activation of effector T cells 169. The resemblance of tertiary lymph nodes were particularly evident in early stage cancers 133168 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 167170. This recommendation is also based on concordant observations extended to several other tumors 171172173174175176.

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 177. 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 178. Concordant results have been previously reported by Monsurró et al. 86 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 9495. 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 179.

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 118119. 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 180. 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) 181182; PBMCs from patients with melanoma and other solid cancers 183 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 (121137 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 183. 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 184. 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 185186187 and in general altered T cell function in circulating and/or tumor infiltrating lymphocytes 86147179187. 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 105188. 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 189 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 183, such depression of innate immune responses were observed at early stage in patients with Duke's A and B.

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 15242260. 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 15. A recent analysis looking for predictive biomarkers in melanoma and renal cell carcinoma 261 suggested that the ex vivo ability of IFN-α to revert STAT-1 phosphorylation signaling defects in melanoma patients may be useful 182183. In addition, development of autoimmunity during IFN-α therapy is a clear predictor of a 50-fold reduction in frequency of relapse 241. 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 262. Together with VEGF and fibronectin potentially predictive of immune responsiveness to high-dose IL-2 therapy 9, 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 263264.

This is advancement from previous analyses in which the majority of putative predictors of IL-2 response were related to post-treatment parameters 265266. 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 267; 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 268. 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 269. 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 270271.

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 272. 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 272. 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 273. MFG-E8 expression in melanoma patients with advanced stage is high and not detected in non advanced stage melanoma and nevi 274. 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 275 with various cancers vaccinated with hTERT peptide-pulsed DCs and by Tatsumi et al. 276 in the context of renal cell carcinoma and melanoma.