High aldehyde dehydrogenase activity identifies tumor-initiating and metastasis-initiating cells in human prostate cancer

Metastatic progression of advanced prostate cancer is a major clinical problem. Identifying the cell(s) of origin in prostate cancer and its distant metastases may permit the development of more effective treatment and preventive therapies. In this study, aldehyde dehydrogenase (ALDH) activity was used as a basis to isolate and compare subpopulations of primary human prostate cancer cells and cell lines. ALDH-high prostate cancer cells displayed strongly elevated clonogenicity and migratory behavior in vitro. More strikingly, ALDH-high cells readily formed distant metastases with strongly enhanced tumor progression at both orthotopic and metastatic sites in preclinical models. Several ALDH isoforms were expressed in human prostate cancer cells and clinical specimens of primary prostate tumors with matched bone metastases. Our findings suggest that ALDH-based viable cell sorting can be used to identify and characterize tumor-initiating and, more importantly perhaps, metastasis-initiating cells in human prostate cancer.

Endosonographic radial tumor thickness after neoadjuvant chemoradiation therapy to predict response and survival in patients with locally advanced esophageal cancer: a prospective multicenter phase ll study by the Swiss Group for Clinical Cancer Research (SAKK 75/02)

EUS response assessment in patients with locally advanced esophageal cancer undergoing neoadjuvant chemoradiation therapy (CRT) is limited by disintegration of the involved anatomic structures.

Tumor lymphangiogenesis and metastasis to lymph nodes induced by cancer cell expression of podoplanin

The membrane glycoprotein podoplanin is expressed by several types of human cancers and might be associated with their malignant progression. Its exact biological function and molecular targets are unclear, however. Here, we assessed the relevance of tumor cell expression of podoplanin in cancer metastasis to lymph nodes, using a human MCF7 breast carcinoma xenograft model. We found that podoplanin expression promoted tumor cell motility in vitro and, unexpectedly, increased tumor lymphangiogenesis and metastasis to regional lymph nodes in vivo, without promoting primary tumor growth. Importantly, high cancer cell expression levels of podoplanin correlated with lymph node metastasis and reduced survival times in a large cohort of 252 oral squamous cell carcinoma patients. Based on comparative transcriptional profiling of tumor xenografts, we identified endothelin-1, villin-1, and tenascin-C as potential mediators of podoplanin-induced tumor lymphangiogenesis and metastasis. These unexpected findings identify a novel mechanism of tumor lymphangiogenesis and metastasis induced by cancer cell expression of podoplanin, suggesting that reagents designed to interfere with podoplanin function might be developed as therapeutics for patients with advanced cancer.

Synthesis and biological activity of new functionalized epothilones for prodrug design and tumor targeting

Epothilones are potent antiproliferative agents, which have served as successful lead structures for anticancer drug discovery. However, their therapeutic efficacy would benefit greatly from an increase in their selectivity for tumor cells, which may be achieved through conjugation with a tumor-targeting moiety. Three novel epothilone analogs bearing variously functionalized benzimidazole side chains were synthesized using a strategy based on palladium-mediated coupling and macrolactonization. The synthesis of these compounds is described and their in vitro biological activity is discussed with respect to their interactions with the tubulin/microtubule system and the inhibition of human cancer cell proliferation. The additional functional groups may be used to synthesize conjugates of epothilone derivatives with a variety of tumor-targeting moieties.

Isolated GH deficiency type II: knockdown of the harmful Delta3GH recovers wt-GH secretion in rat tumor pituitary cells

Isolated GH deficiency type II (IGHD II) is the autosomal dominant form of GHD. In the majority of the cases, this disorder is due to specific GH-1 gene mutations that lead to mRNA missplicing and subsequent loss of exon 3 sequences. When misspliced RNA is translated, it produces a toxic 17.5-kDa GH (Delta3GH) isoform that reduces the accumulation and secretion of wild-type-GH. At present, patients suffering from this type of disease are treated with daily injections of recombinant human GH in order to maintain normal growth. However, this type of replacement therapy does not prevent toxic effects of the Delta3GH mutant on the pituitary gland, which can eventually lead to other hormonal deficiencies. We developed a strategy involving Delta3GH isoform knockdown mediated by expression of a microRNA-30-adapted short hairpin RNA (shRNA) specifically targeting the Delta3GH mRNA of human (shRNAmir-Delta3). Rat pituitary tumor GC cells expressing Delta3GH upon doxycycline induction were transduced with shRNAmir-Delta3 lentiviral vectors, which significantly reduced Delta3GH protein levels and improved human wild-type-GH secretion in comparison with a shRNAmir targeting a scrambled sequence. No toxicity due to shRNAmir expression could be observed in cell proliferation assays. Confocal microscopy strongly suggested that shRNAmir-Delta3 enabled the recovery of GH granule storage and secretory capacity. These viral vectors have shown their ability to stably integrate, express shRNAmir, and rescue IGHD II phenotype in rat pituitary tumor GC cells, a methodology that opens new perspectives for the development of gene therapy to treat IGHD patients.

Disabling c-Myc in childhood medulloblastoma and atypical teratoid/rhabdoid tumor cells by the potent G-quadruplex interactive agent S2T1-6OTD

We investigated here the effects of S2T1-6OTD, a novel telomestatin derivative that is synthesized to target G-quadruplex-forming DNA sequences, on a representative panel of human medulloblastoma (MB) and atypical teratoid/rhabdoid (AT/RT) childhood brain cancer cell lines. S2T1-6OTD proved to be a potent c-Myc inhibitor through its high-affinity physical interaction with the G-quadruplex structure in the c-Myc promoter. Treatment with S2T1-6OTD reduced the mRNA and protein expressions of c-Myc and hTERT, which is transcriptionally regulated by c-Myc, and decreased the activities of both genes. In remarkable contrast to control cells, short-term (72-hour) treatment with S2T1-6OTD resulted in a dose- and time-dependent antiproliferative effect in all MB and AT/RT brain tumor cell lines tested (IC(50), 0.25-0.39 micromol/L). Under conditions where inhibition of both proliferation and c-Myc activity was observed, S2T1-6OTD treatment decreased the protein expression of the cell cycle activator cyclin-dependent kinase 2 and induced cell cycle arrest. Long-term treatment (5 weeks) with nontoxic concentrations of S2T1-6OTD resulted in a time-dependent (mainly c-Myc-dependent) telomere shortening. This was accompanied by cell growth arrest starting on day 28 followed by cell senescence and induction of apoptosis on day 35 in all of the five cell lines investigated. On in vivo animal testing, S2T1-6OTD may well represent a novel therapeutic strategy for childhood brain tumors.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.