The A6 peptide (acetyl-KPSSPPEE-amino) has antitumor activity in the absence of significant adverse events in murine tumor models and clinical trials. A6 shares sequence homology with CD44, an adhesion receptor involved in metastasis that is also a marker of cancer stem cells and drug-resistant phenotypes. We investigated the mechanism of action of A6 by examining its effects on CD44 activity, cell migration, and metastasis. A6 inhibited the migration of a subset of ovarian and breast cancer cell lines, exhibiting IC 50 values of 5 to 110 nmol/L. The ability of A6 to inhibit migration in vitro correlated with CD44 expression. Immunopreciptation studies showed that CD44 binds A6 and that biotin-tagged A6 can be cross-linked to CD44. The binding of A6 altered the structure of CD44 such that it was no longer recognized by a monoclonal antibody to a specific epitope. Importantly, A6 potentiated the CD44-dependent adhesion of cancer cells to hyaluronic acid and activated CD44-mediated signaling, as evidenced by focal adhesion kinase and MAP/ERK kinase phosphorylation. In vivo, A6 (100 mg/kg delivered s.c. twice daily) reduced the number of lung foci generated by the i.v. injection of B16-F10 melanoma cells by 50% (P ¼ 0.029 in an unpaired t test). We conclude that A6 potently blocks the migration of CD44-positive cells in vitro through an interaction with CD44 that alters its structure and activates CD44 to enhance ligand binding and downstream signaling. The concurrent ability of A6 to agonize the CD44 receptor suggests that CD44 activation may represent a novel strategy for inhibiting metastatic disease.

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Motivation: Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy.Results: Three different machine learning techniques were used: generative–discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome.Availability: The combined prediction engine will be available from July 2008, see http://engine.euresist.orgContact: rosen@il.ibm.com

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