Current drugs may harbor small-cell lung cancer promise

By Lynda Williams, Senior medwireNews Reporter

Scientists have identified several existing non-oncology drug classes that may have potential as a treatment for small-cell lung cancer (SCLC), especially for patients with chemoresistant and metastatic disease.

Julien Sage (Stanford University, California, USA) and co-workers report that both the tricyclic antidepressant (TCA) imipramine and the histamine H1 receptor antagonist promethazine significantly inhibited growth of both mouse and human SCLC lines in vitro and in a mouse tumor model. They also prevented growth of a primary human tumor under mouse skin.

Moreover, the drugs reduced tumor growth and metastases in mice carrying knockout mutations for Rb, p53, and p130, which are commonly lost in SCLC patients. Indeed, imipramine continued to prevent tumor growth in a mouse model for tumors that had developed resistance to first-line platinum chemotherapy.

The researchers explain that imipramine and promethazine bind to multiple receptors on the SCLC surface, triggering both the apoptotic and necrotic cell death pathways.

“Together, these experiments indicate that the expansion of SCLC cells is potently inhibited by imipramine and promethazine and suggest that both chemoresistant tumors and disseminated tumors may respond to treatment in patients with advanced disease,” Sage et al write in Cancer Discovery.

TCAs and related drug inhibition of G protein-coupled receptors may also have potential in other neuroendocrine tumors, such as pancreatic neuroendocrine tumors, retinoblastoma, and brain tumors, they add.

The researchers used a bioinformatics process that scoured a large gene expression profile database for diseases and cell types that may respond to drugs already approved by the US Food and Drug Administration.

They conclude that their study shows the “potential of drug-repositioning approaches, especially computational approaches, in the treatment of cancer.”

“In this specific case, we were able to experimentally expand our findings in SCLC to other neuroendocrine tumors for which few gene expression profile datasets exist and for which the bioinformatics pipeline would not have been possible.”

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