Clinical significance of side population in ovarian cancer cells

Recently, accumulating evidence has suggested that tumors, including ovarian cancer, are composed of a heterogeneous cell population with a small subset of cancer stem cells (CSCs) that sustain tumor formation and growth. The emergence of drug resistance is one of the most difficult problems in the treatment of ovarian cancer, which has been explained recently by the potential of CSCs to have superior resistance against anti-cancer drugs than conventional cancer cells. In this study, we expanded this line of study to examine whether this phenomenon is also observed in clinical specimens of ovarian cancer cells. In total we could analyze 28 samples out of 60 obtained from ovarian cancer patients. The clinical samples were subjected to testing of the expression of side population (SP) as a CSC marker, and according to the presence of SP (SP+) or absence of SP (SP−), clinicopathological significances were analyzed. Although there was no statistical significance, there were more SP+s in recurrent cases as well as in ascitic and peritoneal dissemination than in primary tumor of the ovary. There was no correlation between SP status and FIGO staging. In 19 cases of those who could be followed more than 6 months from initial therapy, there were 8 cases of recurrence or death from disease, and all of these were SP+. On the other hand, in 11 cases of disease-free survivors, 6 were SP+. There was a significant difference in prognosis between SP+ and SP− (p = 0.017). Although this study was limited, it revealed that SP could be contained more in recurrent or metastatic tumors than in primary tumors, and also that the presence of SP could be a risk factor of recurrence in ovarian cancer. Therefore, a novel therapeutic strategy targeting SP could improve the prognosis of ovarian cancer

Global gene expression analysis of canine osteosarcoma stem cells reveals a novel role for COX-2 in tumour initiation

Osteosarcoma is the most common primary bone tumour of both children and dogs. It is an aggressive tumour in both species with a rapid clinical course leading ultimately to metastasis. In dogs and children distant metastasis occurs in >80% of individuals treated by surgery alone. Both canine and human osteosarcoma has been shown to contain a sub-population of cancer stem cells (CSCs), which may drive tumour growth, recurrence and metastasis, suggesting that naturally occurring canine osteosarcoma could act as a preclinical model for the human disease. Here we report the successful isolation of CSCs from primary canine osteosarcoma, as well as established cell lines. We show that these cells can form tumourspheres, and demonstrate relative resistance to chemotherapy. We demonstrate similar results for the human osteosarcma cell lines, U2OS and SAOS2. Utilizing the Affymetrix canine microarray, we are able to definitively show that there are significant differences in global gene expression profiles of isolated osteosarcoma stem cells and the daughter adherent cells. We identified 13,221 significant differences (p = 0.05), and significantly, COX-2 was expressed 141-fold more in CSC spheres than daughter adherent cells. To study the role of COX-2 expression in CSCs we utilized the COX-2 inhibitors meloxicam and mavacoxib. We found that COX-2 inhibition had no effect on CSC growth, or resistance to chemotherapy. However inhibition of COX-2 in daughter cells prevented sphere formation, indicating a potential significant role for COX-2 in tumour initiation

Inhibition of the JAK2/STAT3 pathway in ovarian cancer results in the loss of cancer stem cell-like characteristics and a reduced tumor burden

Background Current treatment of ovarian cancer patients with chemotherapy leaves behind a residual tumor which results in recurrent ovarian cancer within a short time frame. We have previously demonstrated that a single short-term treatment of ovarian cancer cells with chemotherapy in vitro resulted in a cancer stem cell (CSC)-like enriched residual population which generated significantly greater tumor burden compared to the tumor burden generated by control untreated cells. In this report we looked at the mechanisms of the enrichment of CSC-like residual cells in response to paclitaxel treatment. Methods The mechanism of survival of paclitaxel-treated residual cells at a growth inhibitory concentration of 50% (GI50) was determined on isolated tumor cells from the ascites of recurrent ovarian cancer patients and HEY ovarian cancer cell line by in vitro assays and in a mouse xenograft model. Results Treatment of isolated tumor cells from the ascites of ovarian cancer patients and HEY ovarian cancer cell line with paclitaxel resulted in a CSC-like residual population which coincided with the activation of Janus activated kinase 2 (JAK2) and signal transducer and activation of transcription 3 (STAT3) pathway in paclitaxel surviving cells. Both paclitaxel-induced JAK2/STAT3 activation and CSC-like characteristics were inhibited by a low dose JAK2-specific small molecule inhibitor CYT387 (1 μM) in vitro. Subsequent, in vivo transplantation of paclitaxel and CYT387-treated HEY cells in mice resulted in a significantly reduced tumor burden compared to that seen with paclitaxel only-treated transplanted cells. In vitro analysis of tumor xenografts at protein and mRNA levels demonstrated a loss of CSC-like markers and CA125 expression in paclitaxel and CYT387-treated cell-derived xenografts, compared to paclitaxel only-treated cell-derived xenografts. These results were consistent with significantly reduced activation of JAK2 and STAT3 in paclitaxel and CYT387-treated cell-derived xenografts compared to paclitaxel only-treated cell derived xenografts. Conclusions This proof of principle study demonstrates that inhibition of the JAK2/STAT3 pathway by the addition of CYT387 suppresses the ‘stemness’ profile in chemotherapy-treated residual cells in vitro, which is replicated in vivo, leading to a reduced tumor burden. These findings have important implications for ovarian cancer patients who are treated with taxane and/or platinum-based therapies. Keywords: Ovarian carcinoma, Cancer stem cell, Metastasis, Ascites, Chemoresistance, Recurrence, JAK2/STAT3 pathwa

Optimization of Rifamycin B fermentation in shake flasks via a machine-learning-based approach

Rifamycin B is an important polyketide antibiotic used in the treatment of tuberculosis and leprosy. We present results on medium optimization for Rifamycin B production via a barbital insensitive mutant strain of Amycolatopsis mediterranei S699. Machine-learning approaches such as Genetic algorithm (GA), Neighborhood analysis (NA) and Decision Tree technique (DT) were explored for optimizing the medium composition. Genetic algorithm was applied as a global search algorithm while NA was used for a guided local search and to develop medium predictors. The fermentation medium for Rifamycin B consisted of nine components. A large number of distinct medium compositions Eire possible by variation of concentration of each component. This presents a large combinatorial search space. Optimization was achieved within five generations via GA as well as NA. These five generations consisted of 178 shake-flask experiments, which is a small fraction of the search space. We detected multiple optima in the form of 11 distinct medium combinations. These medium combinations provided over 600% improvement in Rifamycin B productivity. Genetic algorithm performed better in optimizing fermentation medium as compared to NA. The Decision Tree technique revealed the media-media interactions qualitatively in the form of sets of rules for medium composition that give high as well as low productivity. (C) 2004

Role of extracellular protease in nitrogen substrate management during antibiotic fermentation: a process model and experimental validation

Kinetics of extracellular protease (ECP) production has typically been studied for processes that involve protease as a product. We argue that ECP is equally important in fermentations where protease is not a product of interest. Industrial fermentations typically use complex nitrogen substrates, which are proteolytically hydrolyzed to amino acids (AA) by ECP before assimilation. However, high AA concentrations may lead to nitrogen catabolite repression (NCR) of the products such as antibiotics. Thus, ECP plays a crucial role in managing the nitrogen substrate supply thereby affecting the antibiotic productivity. Here, we have studied the induction of ECP and its effect on the antibiotic productivity for a rifamycin B overproducer strain Amycolatopsis meditterranei S699. This organism produces ECP at the level of 14 U mL(-1) in complex media, which is sufficient for hydrolysis of proteins in the media but low compared to other ECP overproducers. We find ECP secretion to be repressed by ammonia, AA, and under conditions that support high growth rate. We propose a structured kinetic model which accounts for the kinetics of ECP secretion, amino acid availability, growth, and antibiotic production. In addition to the quantity, the timing of ECP induction was critical in achieving higher rifamycin productivity. We artificially created conditions that led to delayed protease secretion, which in turn led to premature termination of batch and lower productivity. The predictive value of the model can be useful in better management of the available nitrogen supply, minimization of NCR, and in the monitoring of fermentation batches

Model-based optimization of feeding recipe for rifamycin fermentation

Industrial fermentation processes typically use complex media and operate in fed-batch mode to minimize the effects of catabolite repression. However, model-based feeding recipes have not been reported for such processes primarily as a result of the lack of reliable process models. By using a recently published process model, we optimize the feeding recipe for rifamycin B fermentation in complex media. Experimental validation shows a twofold improvement in productivity over an optimized batch. The dynamic optimization problem was posed as a nonlinear program and solved using successive quadratic programming. The feed profiles of four substrates were parameterized to convert the problem into a finite decision space consisting of substrate feed rates and switching intervals. Several distinct recipes, each corresponding to a unique initial guess of decision variables, showed comparable productivity, implying the presence of multiple local optima. The strategy presented here can be applied for optimization of other fermentation processes for which reliable process models are available. (c) 2006 American Institute of Chemical Engineers