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Cancer Res. 2010 May 1;70(9):3677-86. doi: 10.1158/0008-5472.CAN-09-3788. Epub 2010 Apr 20.

Molecular target class is predictive of in vitro response profile.

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1
Cancer Metabolism Drug Discovery, GlaxoSmithKline, Collegeville, Pennsylvania 19426, USA.

Abstract

Preclinical cellular response profiling of tumor models has become a cornerstone in the development of novel cancer therapeutics. As efforts to predict clinical efficacy using cohorts of in vitro tumor models have been successful, expansive panels of tumor-derived cell lines can recapitulate an "all comers" efficacy trial, thereby identifying which tumors are most likely to benefit from treatment. The response profile of a therapy is most often studied in isolation; however, drug treatment effect patterns in tumor models across a diverse panel of compounds can help determine the value of unique molecular target classes in specific tumor cohorts. To this end, a panel of 19 compounds was evaluated against a diverse group of cancer cell lines (n = 311). The primary oncogenic targets were a key determinant of concentration-dependent proliferation response, as a total of five of six, four of four, and five of five phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway, insulin-like growth factor-I receptor (IGF-IR), and mitotic inhibitors, respectively, clustered with others of that common target class. In addition, molecular target class was correlated with increased responsiveness in certain histologies. A cohort of PI3K/AKT/mTOR inhibitors was more efficacious in breast cancers compared with other tumor types, whereas IGF-IR inhibitors more selectively inhibited growth in colon cancer lines. Finally, specific phenotypes play an important role in cellular response profiles. For example, luminal breast cancer cells (nine of nine; 100%) segregated from basal cells (six of seven; 86%). The convergence of a common cellular response profile for different molecules targeting the same oncogenic pathway substantiates a rational clinical path for patient populations most likely to benefit from treatment. Cancer Res; 70(9); 3677-86.

PMID:
20406975
DOI:
10.1158/0008-5472.CAN-09-3788
[Indexed for MEDLINE]
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