Leo Kinarsky

Altered recognition of antigen is a mechanism of CD8+ T cell tolerance in cancer

Antigen-specific CD8+ T-cell tolerance, induced by myeloid-derived suppressor cells (MDSCs), is one of the main mechanisms of tumor escape. Using in vivo models, we show here that MDSCs directly disrupt the binding of specific peptide–major histocompatibility complex (pMHC) dimers to CD8-expressing T cells through nitration of tyrosines in a T-cell receptor (TCR)-CD8 complex. This process makes CD8-expressing T cells unable to bind pMHC and to respond to the specific peptide, although they retain their ability to respond to nonspecific stimulation. Nitration of TCR-CD8 is induced by MDSCs through hyperproduction of reactive oxygen species and peroxynitrite during direct cell-cell contact. Molecular modeling suggests specific sites of nitration that might affect the conformational flexibility of TCR-CD8 and its interaction with pMHC. These data identify a previously unknown mechanism of T-cell tolerance in cancer that is also pertinent to many pathological conditions associated with accumulation of MDSCs.

Dendritic Cell-based Full-length Survivin Vaccine in Treatment of Experimental Tumors

Survivin is a good candidate for cancer immunotherapy since it is overexpressed in most common human cancers, poorly expressed in most normal adult tissues and is essential for cancer cell survival. Previously, we and others have demonstrated that survivin-specific immune responses can be generated in mice and cancer patients. These responses resulted in a substantial antitumor effect. However, the fact that survivin is expressed in normal hematopoietic progenitor cells and endothelial cells may potentially limit the use of vaccination against survivin in the clinic due to possible toxicity. In this study, we have evaluated this risk by using dendritic cells (DC) transduced with an adenovirus encoding mutant human survivin (Ad-surv DCs). Immunization of mice with Ad-surv DCs resulted in generation of CD8+ T cells recognizing multiple epitopes from mouse survivin. These responses provided significant antitumor effect against 3 different tumors EL-4 lymphoma, MC-38 carcinoma, and MethA sarcoma. Survivin-specific T-cells did not affect bone marrow hematopoietic progenitor cells and no autoimmune abnormalities were observed. However, as was the case with other tumor vaccines it provided only partial antitumor effect against established tumors. The existing paradigm suggests that generation of immune response against multiple tumor-associated antigens may provide a better antitumor effect. Here, we directly tested this hypothesis by combining vaccines targeting different tumor-associated proteins: survivin and p53. Despite the fact that combination of 2 vaccines generated potent antigen specific T-cell responses against both molecules they did not result in the improvement of antitumor effect in any of the tested experimental tumor models.

The role of the SEA (sea urchin sperm protein, enterokinase and agrin) module in cleavage of membrane-tethered mucins

The membrane‐tethered mucins are cell surface‐associated dimeric or multimeric molecules with extracellular, transmembrane and cytoplasmic portions, that arise from cleavage of the primary polypeptide chain. Following the first cleavage, which may be cotranslational, the subunits remain closely associated through undefined noncovalent interactions. These mucins all share a common structural motif, the SEA module that is found in many other membrane‐associated proteins that are released from the cell surface and has been implicated in both the cleavage events and association of the subunits. Here we examine the SEA modules of three membrane‐tethered mucins, MUC1, MUC3 and MUC12, which have significant sequence homology within the SEA domain. We previously identified the primary cleavage site within the MUC1 SEA domain as FRPG/SVVV a sequence that is highly conserved in MUC3 and MUC12. We now show by site‐directed mutagenesis that the F, G and S residues are important for the efficiency of the cleavage reaction but not indispensable and that amino acids outside this motif are probably important. These data are consistent with a new model of the MUC1 SEA domain that is based on the solution structure of the MUC16 SEA module, derived by NMR spectroscopy. Further, we demonstrate that cleavage of human MUC3 and MUC12 occurs within the SEA domain. However, the SEA domains of MUC1, MUC3 and MUC12 are not interchangeable, suggesting that either these modules alone are insufficient to mediate efficient cleavage or that the 3D structure of the hybrid molecules does not adequately recreate an accessible cleavage site.

Identification of O-glycosylated decapeptides within the MUC1 repeat domain as potential MHC class I (A2) binding epitopes

The MUC1 glycoprotein is considered a tumor antigen due to its over expression and aberrant glycosylation in cancer tissues. The latter results in appearance of new antigenic tumor specific glycopeptides not found on normal glycoforms of the mucin. MUC1 glycopeptides can be presented by APCs on MHC class II molecules to activate glycopeptide specific helper T-cells. No study has yet reported presentation of MUC1 glycopeptides on MHC class I molecules as stimulators of cytotoxic T-cells. In this study we show that human immunoproteasomes and cathepsin-L can generate octa to undecameric glycopeptides from the MUC1 repeat domain in vitro. We identified glycosylated fragments of which the decameric glycopeptide SAP10 [SAPDT(GalNAc)RPAPG] containing a single sugar binds with comparable strength to the MHC class I allele HLA A*0201 as predicted high-score binding epitopes of the tandem repeat. The same sequence glycosylated with the disaccharide Gal-GalNAc does not bind. The glycan on SAP10 is predicted by molecular modeling to either protrude out or point into the MHC groove. SAPDTRPAPG peptide and the respective glycopeptide stimulated cytotoxic T-cells in vitro. Our findings suggest that MUC1 tandem repeat glycopeptides are capable of activating both helper and cytotoxic T-cells and thus represent good candidates for further development as vaccines.

Different Domains of the Transcription Factor ELF3 Are Required in a Promoter-specific Manner and Multiple Domains Control Its Binding to DNA

Elf3 is an epithelially restricted member of the ETS transcription factor family, which is involved in a wide range of normal cellular processes. Elf3 is also aberrantly expressed in several cancers, including breast cancer. To better understand the molecular mechanisms by which Elf3 regulates these processes, we created a large series of Elf3 mutant proteins with specific domains deleted or targeted by point mutations. The modified forms of Elf3 were used to analyze the contribution of each domain to DNA binding and the activation of gene expression. Our work demonstrates that three regions of Elf3, in addition to its DNA binding domain (ETS domain), influence Elf3 binding to DNA, including the transactivation domain that behaves as an autoinhibitory domain. Interestingly, disruption of the transactivation domain relieves the autoinhibition of Elf3 and enhances Elf3 binding to DNA. On the basis of these studies, we suggest a model for autoinhibition of Elf3 involving intramolecular interactions. Importantly, this model is consistent with our finding that the N-terminal region of Elf3, which contains the transactivation domain, interacts with its C terminus, which contains the ETS domain. In parallel studies, we demonstrate that residues flanking the N- and C-terminal sides of the ETS domain of Elf3 are crucial for its binding to DNA. Our studies also show that an AT-hook domain, as well as the serine- and aspartic acid-rich domain but not the pointed domain, is necessary for Elf3 activation of promoter activity. Unexpectedly, we determined that one of the AT-hook domains is required in a promoter-specific manner.

Multicenter Breast Cancer Collaborative Registry

The Breast Cancer Collaborative Registry (BCCR) is a multicenter web-based system that efficiently collects and manages a variety of data on breast cancer (BC) patients and BC survivors. This registry is designed as a multi-tier web application that utilizes Java Servlet/JSP technology and has an Oracle 11g database as a back-end. The BCCR questionnaire has accommodated standards accepted in breast cancer research and healthcare. By harmonizing the controlled vocabulary with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), the BCCR provides a standardized approach to data collection and reporting. The BCCR has been recently certified by the National Cancer Institutes Center for Biomedical Informatics and Information Technology (NCI CBIIT) as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product. The BCCR is aimed at facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention, treatment, and survivorship strategies against breast cancer. Currently, seven cancer institutions are participating in the BCCR that contains data on almost 900 subjects (BC patients and survivors, as well as individuals at high risk of getting BC).

PCCR: Pancreatic Cancer Collaborative Registry

The Pancreatic Cancer Collaborative Registry (PCCR) is a multi-institutional web-based system aimed to collect a variety of data on pancreatic cancer patients and high-risk subjects in a standard and efficient way. The PCCR was initiated by a group of experts in medical oncology, gastroenterology, genetics, pathology, epidemiology, nutrition, and computer science with the goal of facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention and treatment strategies against pancreatic cancer. The PCCR is a multi-tier web application that utilizes Java/JSP technology and has Oracle 10 g database as a back-end. The PCCR uses a “confederation model” that encourages participation of any interested center, irrespective of its size or location. The PCCR utilizes a standardized approach to data collection and reporting, and uses extensive validation procedures to prevent entering erroneous data. The PCCR controlled vocabulary is harmonized with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). The PCCR questionnaire has accommodated standards accepted in cancer research and healthcare. Currently, seven cancer centers in the USA, as well as one center in Italy are participating in the PCCR. At present, the PCCR database contains data on more than 2,700 subjects (PC patients and individuals at high risk of getting this disease). The PCCR has been certified by the NCI Center for Biomedical Informatics and Information Technology as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product. The PCCR provides a foundation for collaborative PC research. It has all the necessary prerequisites for subsequent evolution of the developed infrastructure from simply gathering PC-related data into a biomedical computing platform vital for successful PC studies, care and treatment. Studies utilizing data collected in the PCCR may engender new approaches to disease prognosis, risk factor assessment, and therapeutic interventions.

A Generalized Beta Model for the Age Distribution of Cancers: Application to Pancreatic and Kidney Cancer

The relationships between cancer incidence rates and the age of patients at cancer diagnosis are a quantitative basis for modeling age distributions of cancer. The obtained model parameters are needed to build rigorous statistical and biological models of cancer development. In this work, a new mathematical model, called the Generalized Beta (GB) model is proposed. Confidence intervals for parameters of this model are derived from a regression analysis. The GB model was used to approximate the incidence rates of the first primary, microscopically confirmed cases of pancreatic cancer (PC) and kidney cancer (KC) that served as a test bed for the proposed approach. The use of the GB model allowed us to determine analytical functions that provide an excellent fit for the observed incidence rates for PC and KC in white males and females. We make the case that the cancer incidence rates can be characterized by a unique set of model parameters (such as an overall cancer rate, and the degree of increase and decrease of cancer incidence rates). Our results suggest that the proposed approach significantly expands possibilities and improves the performance of existing mathematical models and will be very useful for modeling carcinogenic processes characteristic of cancers. To better understand the biological plausibility behind the aforementioned model parameters, detailed molecular, cellular, and tissue-specific mechanisms underlying the development of each type of cancer require further investigation. The model parameters that can be assessed by the proposed approach will complement and challenge future biomedical and epidemiological studies.

extension of cox proportional Hazard Model for estimation of Interrelated Age-period-cohort effects on cancer survival

In the frame of the Cox proportional hazard (PH) model, a novel two-step procedure for estimating age-period-cohort (APC) effects on the hazard function of death from cancer was developed. In the first step, the procedure estimates the influence of joint APC effects on the hazard function, using Cox PH regression procedures from a standard software package. In the second step, the coefficients for age at diagnosis, time period and birth cohort effects are estimated. To solve the identifiability problem that arises in estimating these coefficients, an assumption that neighboring birth cohorts almost equally affect the hazard function was utilized. Using an anchoring technique, simple procedures for obtaining estimates of interrelated age at diagnosis, time period and birth cohort effect coefficients were developed. As a proof-of-concept these procedures were used to analyze survival data, collected in the SEER database, on white men and women diagnosed with LC in 1975–1999 and the age at diagnosis, time period and birth cohort effect coefficients were estimated. The PH assumption was evaluated by a graphical approach using log-log plots. Analysis of trends of these coefficients suggests that the hazard of death from LC for a given time from cancer diagnosis: (i) decreases between 1975 and 1999; (ii) increases with increasing the age at diagnosis; and (iii) depends upon birth cohort effects. The proposed computing procedure can be used for estimating joint APC effects, as well as interrelated age at diagnosis, time period and birth cohort effects in survival analysis of different types of cancer.

Conformational Studies of O-Glycosylated 15-Residue Peptide from the Human Mucin (MUC1) Protein Core

Certain MUC1 epitopes are detected on MUC1 glycoproteins from malignant cells as opposed to normal cells. The expression of the mucin epitopes appears to be due to the aberrant glycosylation in the tumors resulting in the excessive exposure of the MUC1 protein core on the cell surface. The truncated oligosaccharides of the tumor-derived mucin facilitate antibody binding to this epitope by unmasking the portion of the protein core that is involved in the antibody recognition. A polypeptide fragment APDTRP is known as an immunodominant (ID) region of the MUC1 protein core. To establish a structural rationale for the development of peptide-based tumor markers and vaccines, conformations of the 15-residue peptide PPAHGVTSAPDTRPA and its glycosylated counterpart with GalNAc residue attached at T7 position were studied by molecular dynamics (MD) simulations. The MD simulations in explicit water with and without NMR-derived constraints [1] were used to elucidate the effect of O-glycosyl-ation on conformational propensities of a peptide backbone. Structural propensities of the peptide backbone for the APDTRP fragment were compared with a published crystal structure [2] of the breast tumor-specific antibody SM3 complexed with a 13-residue MUC1 peptide antigen that included this ID region.