Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling

Abstract

Kinase inhibitors constitute an important new class of cancer drugs, whose selective efficacy is largely determined by underlying tumor cell genetics. We established a high-throughput platform to profile 500 cell lines derived from diverse epithelial cancers for sensitivity to 14 kinase inhibitors. Most inhibitors were ineffective against unselected cell lines but exhibited dramatic cell killing of small nonoverlapping subsets. Cells with exquisite sensitivity to EGFR, HER2, MET, or BRAF kinase inhibitors were marked by activating mutations or amplification of the drug target. Although most cell lines recapitulated known tumor-associated genotypes, the screen revealed low-frequency drug-sensitizing genotypes in tumor types not previously associated with drug susceptibility. Furthermore, comparing drugs thought to target the same kinase revealed striking differences, predictive of clinical efficacy. Genetically defined cancer subsets, irrespective of tissue type, predict response to kinase inhibitors, and provide an important preclinical model to guide early clinical applications of novel targeted inhibitors.

Fig. 1.

Profiling 500 tumor cell lines with a variety of selective kinase inhibitors reveals a wide range of sensitivities for most compounds. (A) Pie chart representation of the sensitivity of 500 cancer cell lines to inhibitors of the indicated kinases after treatment for 72 h. Sensitivity is calculated as the fraction of viable cells relative to untreated controls. The complete set of sensitivity data is presented in SI Fig. 18. (B) Hierarchical clustering of the sensitivity of 500 human cancer cell lines to a single concentration of each of the 14 protein kinase inhibitors indicated in A. The concentration selected for each compound was that which most clearly discriminated between sensitive and resistant cell lines. The “subclusters” with the highest correlation coefficients are displayed adjacent to the clustering profile and are further explored in Results. The kinase inhibitors are numbered as indicated in A. The subclusters highlight (from top to bottom) those compounds targeting MET, EGFR, HER2, and BRAF, with correlation coefficients of 0.85, 0.92, 0.86, and 0.87, respectively. The sensitivity of each cell line is indicated by the increasing intensity of the green signal. Resistant cell lines are indicated in black. Cell lines not screened with a particular compound are indicated in gray.

Fig. 2.

Profiling for erlotinib sensitivity reveals EGFR genotype-correlated sensitivity in multiple tissue types. (A) Pie chart representation of the sensitivity of 131 human lung cancer cell lines to treatment with 200 nM EGFR kinase inhibitor erlotinib. The drug effect was calculated as the fraction of untreated cells present after 72 h of treatment. Details regarding the most sensitive cell lines identified are shown in the chart. EGFR mutations are highlighted in the table in yellow, and amplifications (copy no. > 4) are highlighted in blue. The mutation status of K-Ras (the Lower chart) was obtained from the Wellcome Trust Sanger Institute web site, and K-Ras mutants were present only in the erlotinib-insensitive lines. (B) Pie chart representation of the sensitivity of 500 human cancer cell lines to treatment with 200 nM erlotinib. The drug effect was calculated as the fraction of untreated cells present after 72 h of treatment. Details regarding the most sensitive cell lines identified are shown in the chart, and the cell lines are shown in order of decreasing sensitivity (from top to bottom) (*, copy numbers were estimated from SNP array profiles). EGFR mutations are highlighted in the table in yellow, and amplifications (copy no. > 4) are highlighted in blue.

Fig. 3.

Sensitivity to a MET kinase inhibitor is well correlated with MET gene amplification. Pie chart representation of sensitivity of 500 human cancer cell lines to treatment with 200 nM MET-selective kinase inhibitor PHA665752. The drug effect was calculated as the fraction of untreated cells present after 72 h of treatment. The most sensitive cell lines are detailed in the chart together with their MET gene copy number (*, copy numbers were estimated from SNP array profiles). Those cell lines exhibiting MET amplification (copy no. > 4) are highlighted in blue.

Fig. 4.

Sensitivity to a selective BRAF kinase inhibitor is well correlated with BRAF mutational status in tumor cell lines, irrespective of tissue of origin. (A) Pie chart representation of the sensitivity of 500 human tumor cell lines to treatment with 200 nM BRAF inhibitor AZ628 and 2 μM sorafenib for 72 h. The drug effect was calculated as the fraction relative to untreated controls, and values for the top 7% of responders were tabulated, as shown in the chart. The mutational status of BRAF, where known, is indicated. The tumor type is also indicated. For comparison, the sorafenib table also shows the sensitivity of cell lines to 200 and 2 μM sorafenib. The only sorafenib-sensitive cell lines with a V600E mutation in BRAF (melanoma G-361) were also sensitive to AZ628. However, the sensitivity of G-361 placed it at position 57, which falls outside of the top 34 AZ628-sensitive cell lines shown in the table. (B) Sensitivity to AZ628 of nonmelanoma cell lines harboring BRAF or NRAS mutations.

Fig. 5.

Sensitivity to a Src/ABL kinase inhibitor demonstrates overlap with erlotinib sensitivity and EGFR mutation status. Pie chart representation of the sensitivity of 500 human cancer cell lines to treatment with 2 μM Src/ABL kinase inhibitor AZD0530. The drug effect was calculated as the fraction of untreated cells present after 72 h of treatment. The most sensitive cell lines are detailed in the chart together with their sensitivity to a similar duration of treatment with 200 nM EGFR inhibitor erlotinib, including their EGFR kinase domain mutation status where known.