American Association for Cancer Research

November 15 Clinical Cancer Research Highlights

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Selected Articles from the November 15, 2005 Issue

The articles referenced in this Highlights section will be available online in HTML and PDF formats to all interested users at no charge until the next issue of Clinical Cancer Research is published. Click on the article title to view the complete article.

View the Table of Contents for the November 15 issue of Clinical Cancer Research.


Estrogen Linked to Breast VEGF Content, Angiogenesis

Dabrosin
Page 8036

Angiogenesis may be involved in sex steroid–dependent breast cancer development. Vascular endothelial growth factor (VEGF) and fibroblast growth factor-2 (FGF-2) induce angiogenesis. Posttranslational cleavage of sequestered matrix proteins is involved in the regulation of bioactive proteins in vivo. Dabrosin used microdialysis to sample extracellular VEGF and FGF-2 in normal human breast tissue in situ in pre- and postmenopausal women. Breast tissue VEGF was found to correlate significantly with estradiol, but not progesterone, whereas FGF-2 exhibited no correlation with either sex steroid. Estrogen induction of free extracellular VEGF may be one mechanism involved in sex steroid–dependent breast carcinogenesis.


Bcl-2 Antisense, Ionizing Radiation Therapy Combine to Make Strategic Sense

Yip et al.
Page 8131

CCR 11-15-05 Yip 8131In this issue, Yip et al. demonstrate the benefit of combining Bcl-2 antisense oligonucleotide (ASO) with ionizing radiation (RT) as a successful therapeutic strategy in treating nasopharyngeal cancer in xenograft models. In particular, when Bcl-2 ASO and RT were readministered, such treated mice survived 80 days beyond that of control mice. Detailed bio-distribution studies were also conducted, demonstrating the apparent colocalization of the ASO molecule with the tumor vasculature, which differs in different tumors, which could also influence tumor response. This study establishes the possibility that combining RT with Bcl-2 ASO is an effective therapeutic strategy for human malignancies.


Progress, Promise and Potential of Molecular Imaging Probes Assessed

Kelloff et al.
Page 7967

CCR 11-15-05 Kelloff 7967In this Review, collaborators from the NCI, FDA, and academia describe the scientific basis of oncology imaging probes and presents examples of state-of-the art molecular imaging probes and their high promise as oncologic drug development tools. The current regulatory opportunities for new and existing probe development and testing are also reviewed, with a focus on recent FDA guidance to facilitate early clinical development of promising probes. This effort is in concert with the FDA Critical Path Initiative, which has called for the development of tools to increase the speed, efficiency, and cost-effectiveness of drug development for cancer and other diseases.

 

 

 


Neural Markers Featured in Pediatric Kidneys

Cutcliffe et al.
Page 7986

Clear cell sarcomas of the kidney (CCSK) are the second most common pediatric renal tumor. The molecular and cellular pathobiology of these tumors has remained elusive, and there are no positive diagnostic markers. Using microarray analysis followed by protein verification, Cutcliffe et al. present novel findings that neural markers are upregulated in CCSK, and that the sonic hedgehog and the AKT pathways are activated. Potential therapeutic targets are also identified. This work suggests mechanisms for proliferation in CCSK and identifies a potential link between neuroectodermal tumors and CCSK.


Proteomics Predict Chemoradiosensitivity of Esophageal Cancers

Hayashida et al.
Page 8042

Establishment of a reliable method of predicting the efficacy of chemotherapy and radiotherapy is necessary to provide the most suitable treatment for each cancer patient. Hayashida et al. investigated whether proteomic profiling of serum samples of esophageal cancer patients are capable of being used to predict the efficacy of preoperative chemoradiotherapy. A prediction model of the proteomic pattern was built from 15 pathologically diagnosed responders and 12 nonresponders by machine learning algorithms. They report that this model correctly diagnosed chemoradiosensitivity in 93.3% (14 of 15) of the separate cases in a blinded manner.