Biomedical subjects

Selman Ali

Publications and source records attributed to Selman Ali.

3 recordsLinked to original sources

PubMed2004

Trafficking of tumor peptide-specific cytotoxic T lymphocytes into the tumor microcirculation.

The major histocompatibility complex class I-restricted CD8(+) cytotoxic T-lymphocyte (CTL) effector arm of the adaptive immune response can specifically recognize and destroy tumor cells expressing peptide antigens. Although adoptive T-cell therapy has been successfully used for the treatment of viral and malignant diseases, little is known of the trafficking and fate of adoptively transferred antigen-specific T cells. In the present study, splenocytes derived from mice that rejected their tumors (CT26 or CT26-clone 25 tumors) in response to direct intratumor injection of disabled infectious single-cycle herpes simplex virus (DISC-HSV) encoding murine GM-CSF were restimulated with peptide in vitro. CTLs specific for the AH-1 and beta-gal peptides expressed by CT26 and CT26-clone 25 tumor cells, respectively, were generated and used for adoptive cellular therapy and trafficking studies. Intravenous administration of AH-1-specific CTLs 3 days following i.v. injection of CT26 cells resulted in significant tumor growth inhibition, whereas administration of control CTLs generated against a bacterial beta-gal peptide did not inhibit the growth of tumors. Trafficking of AH-1-specific lymphocytes and their interaction with the CT26 tumor microcirculation was analyzed using real-time in vivo microscopy (IVM). AH-1-specific but not beta-gal-specific CTLs adhered and localized in the CT26 tumor microvasculature, but neither population adhered to the endothelium of the normal microcirculation. This study provides direct visual evidence suggesting that AH-1-specific CTLs that mediate a therapeutic response traffic to and localize within the tumor microenvironment.

Animals↗

PubMed2003

Disabled infectious single cycle herpes simplex virus (DISC-HSV) is a candidate vector system for gene delivery/expression of GM-CSF in human prostate cancer therapy.

BACKGROUND: DISC-HSV is a replication incompetent herpes simplex virus that is a highly efficient vector for the transduction of genes in vivo and in vitro. We examine the ability of DISC-HSV to infect human prostate cancer cell-lines and xenograft tumor models, and induce expression of reporter and therapeutic cytokine genes. METHODS: Infection was confirmed by cellular staining for the beta-galactosidase reporter gene product, and by EM. Human GM-CSF production following DISC-hGMCSF infection was measured using ELISA. The metabolic activity of infected cells was determined by NADP/NADPH assay. Cell death was estimated by cell-cycle analysis using flow cytometry with propidium iodide staining. RESULTS: Infection of DU145, PC3 and LNCaP cells with DISC-HSV was dose dependent. Cells infected with DISC-hGM-CSF released significant levels of hGM-CSF for 3 days. NADP/NADPH assay suggested that infected cells continued to be metabolically active for 3 days post-infection, which was consistent with flow cytometry findings that cell death did not occur within 7 days of infection. Tumor xenografts injected with DISC-HSV expressed beta-galactosidase, and intracellular viral particles were demonstrated using EM. CONCLUSIONS: We have previously reported the rejection of established tumors following intra-tumoral injection of DISC-GMCSF. This study demonstrates the ability of DISC-HSV to infect prostate cancer and express GMCSF at significant levels. We suggest that prostate cancer is a potential target for therapy using DISC-HSV containing GM-CSF.

Animals↗

PubMed2003

A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions.

An ability to predict the likelihood of cellular response towards particular chemotherapeutic agents based upon protein expression patterns could facilitate the identification of biological molecules with previously undefined roles in the process of chemoresistance/chemosensitivity, and if robust enough these patterns might also be exploited towards the development of novel predictive assays. To ascertain whether proteomic based molecular profiling in conjunction with artificial neural network (ANN) algorithms could be applied towards the specific recognition of phenotypic patterns between either control or drug treated and chemosensitive or chemoresistant cellular populations, a combined approach involving MALDI-TOF matrix-assisted laser desorption/ionization-time of flight mass spectrometry, Ciphergen protein chip technology and ANN algorithms have been applied to specifically identify proteomic 'fingerprints' indicative of treatment regimen for chemosensitive (MCF-7, T47D) and chemoresistant (MCF-7/ADR) breast cancer cell lines following exposure to Doxorubicin or Paclitaxel. The results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign 'class' for treatment regimen (e.g. control/drug treated or chemosensitive/chemoresistant) with a high degree of accuracy using boot-strap statistical validation techniques and that biomarker ion patterns indicative of response/non-response phenotypes are associated with MCF-7 and MCF-7/ADR cells exposed to Doxorubicin. We have also examined the predictive capability of this approach towards MCF-7 and T47D cells to ascertain whether prediction could be made based upon treatment regimen irrespective of cell lineage. Models were identified that could correctly assign class (control or Paclitaxel treatment) for 35/38 samples of an independent dataset. A similar level of predictive capability was also found (> 92%; n = 28) when proteomic patterns derived from the drug resistant cell line MCF-7/ADR were compared against those derived from MCF-7 and T47D as a model system of drug resistant and drug sensitive phenotypes. This approach might offer a potential methodology for predicting the biological behaviour of cancer cells towards particular chemotherapeutics and through protein isolation and sequence identification could result in the identification of biological molecules associated with chemosensitive/chemoresistance tumour phenotypes.

Algorithms↗