Shahid Mian

Serum Proteomic Fingerprinting Discriminates Between Clinical Stages and Predicts Disease Progression in Melanoma Patients

PURPOSE Currently known serum biomarkers do not predict clinical outcome in melanoma. S100-beta is widely established as a reliable prognostic indicator in patients with advanced metastatic disease but is of limited predictive value in tumor-free patients. This study was aimed to determine whether molecular profiling of the serum proteome could discriminate between early- and late-stage melanoma and predict disease progression. PATIENTS AND METHODS Two hundred five serum samples from 101 early-stage (American Joint Committee on Cancer [AJCC] stage I) and 104 advanced stage (AJCC stage IV) melanoma patients were analyzed by matrix-assisted laser desorption/ionisation (MALDI) time-of-flight (ToF; MALDI-ToF) mass spectrometry utilizing protein chip technology and artificial neural networks (ANN). Serum samples from 55 additional patients after complete dissection of regional lymph node metastases (AJCC stage III), with 28 of 55 patients relapsing within the first year of follow-up, were analyzed in an attempt to predict disease recurrence. Serum S100-beta was measured using a sandwich immunoluminometric assay. RESULTS Analysis of 205 stage I/IV serum samples, utilizing a training set of 94 of 205 and a test set of 15 of 205 samples for 32 different ANN models, revealed correct stage assignment in 84 (88%) of 96 of a blind set of 96 of 205 serum samples. Forty-four (80%) of 55 stage III serum samples could be correctly assigned as progressors or nonprogressors using random sample cross-validation statistical methodologies. Twenty-three (82%) of 28 stage III progressors were correctly identified by MALDI-ToF combined with ANN, whereas only six (21%) of 28 could be detected by S100-beta. CONCLUSION Validation of these findings may enable proteomic profiling to become a valuable tool for identifying high-risk melanoma patients eligible for adjuvant therapeutic interventions.

Selective MHC expression in tumours modulates adaptive and innate antitumour responses

Abstract Progress towards developing vaccines that can stimulate an immune response against growing tumours has involved the identification of the protein antigens associated with a given tumour type. Epitope mapping of tumour antigens for HLA class I- and class II-restricted binding motifs followed by immunization with these peptides has induced protective immunity in murine models against cancers expressing the antigen. MHC class I molecules presenting the appropriate peptides are necessary to provide the specific signals for recognition and killing by cytotoxic T cells (CTL). The principle mechanism of tumour escape is the loss, downregulation or alteration of HLA profiles that may render the target cell resistant to CTL lysis, even if the cell expresses the appropriate tumour antigen. In human tumours HLA loss may be as high as 50%, inferring that a reduction in protein levels might offer a survival advantage to the tumour cells. Alternatively, MHC loss may render tumour cells susceptible to natural killer cell-mediated lysis because they are known to act as ligands for killer inhibitory receptors (KIRs). We review the molecular features of MHC class I and class II antigens and discuss how surface MHC expression may be regulated upon cellular transformation. In addition, selective loss of MHC molecules may alter target tumour cell susceptibility to lymphocyte killing. The development of clinical immunotherapy will need to consider not only the expression of relevant CTL target MHC proteins, but also HLA inhibitory to NK and T cells.

Tumor Regression Induced by Intratumor Therapy with a Disabled Infectious Single Cycle (DISC) Herpes Simplex Virus (HSV) Vector, DISC/HSV/Murine Granulocyte-Macrophage Colony-Stimulating Factor, Correlates with Antigen-Specific Adaptive Immunity

Direct intratumor injection of a disabled infectious single cycle HSV-2 virus encoding the murine GM-CSF gene (DISC/mGM-CSF) into established murine colon carcinoma CT26 tumors induced a significant delay in tumor growth and complete tumor regression in up to 70% of animals. Pre-existing immunity to HSV did not reduce the therapeutic efficacy of DISC/mGM-CSF, and, when administered in combination with syngeneic dendritic cells, further decreased tumor growth and increased the incidence of complete tumor regression. Direct intratumor injection of DISC/mGM-CSF also inhibited the growth of CT26 tumor cells implanted on the contralateral flank or seeded into the lungs following i.v. injection of tumor cells (experimental lung metastasis). Proliferation of splenocytes in response to Con A was impaired in progressor and tumor-bearer, but not regressor, mice. A potent tumor-specific CTL response was generated from splenocytes of all mice with regressing, but not progressing tumors following in vitro peptide stimulation; this response was specific for the gp70 AH-1 peptide SPSYVYHQF and correlated with IFN-γ, but not IL-4 cytokine production. Depletion of CD8+ T cells from regressor splenocytes before in vitro stimulation with the relevant peptide abolished their cytolytic activity, while depletion of CD4+ T cells only partially inhibited CTL generation. Tumor regression induced by DISC/mGM-CSF virus immunotherapy provides a unique model for evaluating the immune mechanism(s) involved in tumor rejection, upon which tumor immunotherapy regimes may be based.

Current Developments in the Analysis of Proteomic Data: Artificial Neural Network Data Mining Techniques for the Identification of Proteomic Biomarkers Related to Breast Cancer

Artificial Neural Network (ANN) techniques are becoming increasing popular in many areas of the biological sciences for the analysis of complex data. Careful selection of key parameters when developing ANN models and algorithms is extremely important in order to create generalised models with real-world applicability. This study applies these approaches to the analysis of proteomic data generated using Surface Enhanced Laser Desorption/Ionisation mass spectrometry profiling of cell lines from patients with breast cancer. Examples of these approaches include constrained architecture, Correlated Activity Pruning (CAPing), appropriate training termination methods and other, more advanced methodologies such as parameterisat ion by weightings analysis and stepwise additive approaches. These approaches, when applied to breast cancer cell lines from actual patients, resulted in the identification of 8 protein/peptide molecular ions which were capable of classifying samples into their respective groups to an accuracy of 94.8 % with an area under the curve value of 0.993 when examined with a receiver operating characteristic curve. Several ions which appear to show a significant up or down-regulation with regards to treatment regimen have also been identified. These results indicate that when coupled with other powerful techniques, the development of these novel methodologies and algorithms using ANNs allows for the development of effective data mining tools in order to analyse complex, non-linear, noisy data. This paper will consider current methodologies for the analysis of proteomic data using Artificial Neural Network (ANN) based methodologies, their advantages, disadvan- tages and limitations, and then will describe an application of novel methodologies developed using actual patient data. ANN techniques have been widely applied to many areas of the physical sciences for the analysis of complex systems. As such, extensive knowledge exists on the application and limitations of these methods. Similarly, methodologies exist to overcome many of these limitations and enhance the pre- dictive capabilities and real-world applicability of developed models. This study applies these approaches to the analysis of proteomic data generated using Surface Enhanced Laser Desorption/Ionisation (SELDI) mass spectrometry (MS) profiling with the aim of identifying candidate biomarkers indicative of treatment regimen for chemosensitive (MCF-7 and T47-D) breast cancer cell lines, in order to develop ANN algorithms to correctly assign samples into their appropriate class of either control or drug treated. Examples of these approaches and important parameters which need to be considered when developing ANN models will be discussed, followed by methodologies employed in order to create generalised models with real- world applicability.

Peptide immunisation of HLA-DR–transgenic mice permits the identification of a novel HLA-DRβ1*0101– and HLA-DRβ1*0401–restricted epitope from p53

Because of the central role of CD4+ T cells in antitumour immunity, the identification of the MHC class II–restricted peptides to which CD4+ T cells respond has become a priority of tumour immunologists. Here, we describe a strategy permitting us to rapidly determine the immunogenicity of candidate HLA-DR–restricted peptides using peptide immunisation of HLA-DR–transgenic mice, followed by assessment of the response in vitro. This strategy was successfully applied to the reported haemaglutinin influenza peptide HA(307–319), and then extended to three candidate HLA-DR–restricted p53 peptides predicted by the evidence-based algorithm SYFPEITHI to bind to HLA-DRβ1*0101 (HLA-DR1) and HLA-DRβ1*0401 (HLA-DR4) molecules. One of these peptides, p53(108–122), consistently induced responses in HLA-DR1– and in HLA-DR4–transgenic mice. Moreover, this peptide was naturally processed by dendritic cells (DCs), and induced specific proliferation in the splenocytes of mice immunised with p53 cDNA, demonstrating that immune responses could be naturally mounted to the peptide. Furthermore, p53(108–122) peptide was also immunogenic in HLA-DR1 and HLA-DR4 healthy donors. Thus, the use of this transgenic model permitted the identification of a novel HLA-DR–restricted epitope from p53 and constitutes an attractive approach for the rapid identification of novel immunogenic MHC class II–restricted peptides from tumour antigens, which can ultimately be incorporated in immunotherapeutic protocols.

Immunity to Tumour Antigens

During the last decade, a large number of human tumour antigens have been identified. These antigens are classified as tumour-specific shared antigens, tissue-specific differentiation antigens, overexpressed antigens, tumour antigens resulting from mutations, viral antigens and fusion proteins. Antigens recognised by effectors of immune system are potential targets for antigen-specific cancer immunotherapy. However, most tumour antigens are self-proteins and are generally of low immunogenicity and the immune response elicited towards these tumour antigens is not always effective. Strategies to induce and enhance the tumour antigen-specific response are needed. This review will summarise the approaches to discovery of tumour antigens, the current status of tumour antigens, and their potential application to cancer treatment.

Regulation of CTL responses to MHC-restricted class i peptide of the gp70 tumour antigen by splenic parenchymal CD4+ T cells in mice failing immunotherapy with DISC-mGM-CSF

Direct intratumour injection of the disabled infectious single‐cycle–herpes simplex virus–encoding murine granulocyte/macrophage colony‐stimulating factor (DISC‐HSV–mGM‐CSF) into established colon carcinoma CT26 tumours induced complete tumour rejection in up to 70% of treated animals (regressors), while the remaining mice developed progressive tumours (progressors). This murine Balb/c model was used to dissect the cellular mechanisms involved in tumour regression or progression following immunotherapy. CTLs were generated by coculturing lymphocytes and parenchymal cells from the same spleens of individual regressor or progressor animals in the presence of the relevant AH‐1 peptide derived from the gp70 tumour‐associated antigens expressed by CT26 tumours. Tumour regression was correlated with potent CTL responses, spleen weight and cytokine (IFN‐γ) production. Conversely, progressor splenocytes exhibited weak to no CTL activity and poor IFN‐γ production, concomitant with the presence of a suppressor cell population in the progressor splenic parenchymal cell fraction. Further fractionation of this parenchymal subpopulation demonstrated that cells inhibitory to the activation of AH‐1‐specific CTLs, restimulated in vitro with peptide, were present in the nonadherent parenchymal fraction. In vitro depletion of progressor parenchymal CD3+/CD4+ T cells restored the CTL response of the cocultured splenocytes (regressor lymphocytes and progressor parenchymal cells) and decreased the production of IL‐10, suggesting that CD3+CD4+ T lymphocytes present in the parenchymal fraction regulated the CTL response to AH‐1. We examined the cellular responses associated with tumour rejection and progression, identifying regulatory pathways associated with failure to respond to immunotherapy.

Immunogenicity of tumour associated antigens

The continuing discovery of new tumor-associated/specific antigens, many of which are discussed in succeeding chapters in the first half of this book, document that at least some (most?) types of cancer are antigenic. Within the past five years, the number of molecular and cellular immunological techniques for identifying tumour-associated antigens has increased to such extent that over 100 distinct genes have now been associated with the transformation process. These antigens have been classified into several sub-groups and include for example proteins that are either mutated [1, 2], over-expressed [3, 4], associated with embryo-genesis [5] or differentiation [6]. They also include novel products that arise due to genetic translocations such as BCR-Abl [7, 8]. Melanoma is particularly interesting from an immunological perspective because it contains a wide spectrum of tissue-restricted proteins (e.g., MART-1, MAGE, gp100, tyrosinase, TRP-1, and TRP-2) that serve as targets of effector T cells in vitro [9–13]; see also Chapters 3 and 4. However, in vivo, adequate spontaneous activation of tumor-specific lymphocytes either does not occur or it results in inefficient tumor protection. Why tumors that are clearly antigenic are so clearly nonimmunogenic has puzzled investigators for years. Aspects of this paradox will be considered in this introductory chapter, including tolerance, antigen processing and the role of dendritic cells, and the nature of the response induced in terms of the balance between cellular immunity (Thl type response) and antibody responses (Th2 type response).