Use of Bayesian networks to probabilistically model and improve the likelihood of validation of microarray findings by RT-PCR

Novel Anti-TM4SF1 Antibody-Drug Conjugates with Activity against Tumor Cells and Tumor Vasculature

Antibody-drug conjugates (ADC) represent a promising therapeutic modality for managing cancer. Here, we report a novel humanized ADC that targets the tetraspanin-like protein TM4SF1. TM4SF1 is highly expressed on the plasma membranes of many human cancer cells and also on the endothelial cells lining tumor blood vessels. TM4SF1 is internalized upon interaction with antibodies. We hypothesized that an ADC against TM4SF1 would inhibit cancer growth directly by killing cancer cells and indirectly by attacking the tumor vasculature. We generated a humanized anti-human TM4SF1 monoclonal antibody, v1.10, and armed it with an auristatin cytotoxic agent LP2 (chemical name mc-3377). v1.10-LP2 selectively killed cultured human tumor cell lines and human endothelial cells that express TM4SF1. Acting as a single agent, v1.10-LP2 induced complete regression of several TM4SF1-expressing tumor xenografts in nude mice, including non-small cell lung cancer and pancreas, prostate, and colon cancers. As v1.10 did not react with mouse TM4SF1, it could not target the mouse tumor vasculature. Therefore, we generated a surrogate anti-mouse TM4SF1 antibody, 2A7A, and conjugated it to LP2. At 3 mpk, 2A7A-LP2 regressed several tumor xenografts without noticeable toxicity. Combination therapy with v1.10-LP2 and 2A7A-LP2 together was more effective than either ADC alone. These data provide proof-of-concept that TM4SF1-targeting ADCs have potential as anticancer agents with dual action against tumor cells and the tumor vasculature. Such agents could offer exceptional therapeutic value and warrant further investigation. Mol Cancer Ther; 14(8); 1868-76. ©2015 AACR.

Marco Ramoni: an appreciation of academic achievement

We review the scholarly career of our colleague, Marco Ramoni, who died unexpectedly in the summer of 2010. His work mainly explored the development and application of Bayesian techniques to model clinical, public health, and bioinformatics questions. His contributions have led to improvements in our ability to model behavior that evolves in time, to explore systematic relationships among large sets of covariates, and to tease out the meaning of data on the role of genetic variation in the genesis of important diseases.