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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Biomol Screen. Author manuscript; available in PMC 2011 July 29.
Published in final edited form as:
PMCID: PMC3146390

Identification and preliminary characterization of novel small molecules that inhibit growth of human lung adenocarcinoma cells


Drug treatment for human lung cancers remains unsatisfactory, despite the identification of many potential therapeutic targets (such as mutant KRAS protein) and the approval of agents that inhibit the tyrosine kinase activity of mutant epidermal growth factor receptor (EGFR). To seek new therapeutic strategies against lung tumors, we have screened 189, 290 small molecules for their ability to retard growth of human lung adenocarcinoma cell lines, which harbor mutations in EGFR or KRAS. Four candidates that are structurally different from common tyrosine kinase inhibitors were selected for further study. We describe one small molecule (designated lung cancer screen-1, LCS-1) in detail here. Identification of the targets of LCS-1 and other growth inhibitors found in this screen may help to develop new agents for treatment of lung adenocarcinomas, including those driven by mutant EGFR and KRAS.

Keywords: high throughput drug screen, lung cancer, EGFR, KRAS


Lung cancer is the leading cause of cancer mortality in the United States and world-wide (Jemal et al. 2008). Although surgery cures some patients when the disease is detected in its early stages, the four major categories of lung cancer (small-cell carcinoma, adenocarcinoma, squamous-cell carcinoma and large-cell carcinoma) tend to metastasize readily, and none responds well to other available forms of treatment, such as radiotherapy and chemotherapy.

Efforts to sub-categorize the four histological forms of lung cancers by genetic and biochemical methods have helped to increase our understanding of these diseases and to create opportunities for development of targeted therapies. In particular, about 10% of lung adenocarcinomas in the U.S. and a higher percentage in parts of Asia (Pao and Miller 2005) contain mutant forms of the epidermal growth factor receptor (EGFR; usually due to a deletion mutation in exon 19 of EGFR or a substitution mutation, L858R, in exon 21); these changes confer sensitivity to tyrosine kinase inhibitors such as erlotinib and gefitinib (Lynch et al. 2004; Paez et al. 2004; Pao et al. 2004). Despite sometimes dramatic initial responses to tyrosine kinase inhibitors (TKIs), however, most tumors with EGFR mutations respond only partially, a few show little or no response, and all ultimately develop secondary drug resistance due to an additional mutation in EGFR (most commonly T790M) (Kobayashi et al. 2005; Pao et al. 2005), amplification of MET (Bean et al. 2007; Engelman et al. 2007), or other unknown changes. Mutations in the KRAS gene are found in about 15 to 30% of lung adenocarcinomas (Rodenhuis and Slebos 1990; Suzuki et al. 1990) but no successful means has been developed to target tumors with these mutations. Many other proto-oncogenes and tumor suppressor genes are now known to be mutated or show changes in copy number in lung adenocarcinomas (Ding et al. 2008; Weir et al. 2007), however, rational approaches to these putative targets are, at best, in early stages of development.

While clues from tumor genotyping offer one attractive route to new therapeutics, high throughput methods for assessing cancer cell phenotypes in response to small molecules or after introduction of inhibitory RNAs, offer other potentially fruitful approaches. With these screening methods, it is possible to identify unsuspected vulnerabilities in cancer cells that reflect known or unrecognized mutations, changes in gene expression, or alterations in signaling pathways that create novel dependencies. For instance, mutations in genes such as EGFR or KRAS might sensitize cells to inhibition of a normally dispensable function, a phenomenon called “synthetic lethality.”

We took advantage of well-characterized human lung adenocarcinomas cell lines, large libraries of small molecules, and a robotics-based screening facility to perform a survey of 189, 290 small molecules for their capacity to interfere with the growth of one or more of these cell lines. In our preliminary screen, we used four adenocarcinoma cell lines with some common mutations: either of the two mutations in EGFR that sensitize cells to TKI’s (the exon 19 deletion or L858R), an EGFR mutation that confers drug resistance (T790M) in combination with L858R, and a common mutation that activates KRAS (G12C). We then tested molecules confirmed to inhibit one or more of these cell lines for their ability to inhibit growth of a larger set of adenocarcinoma lines, as well as non-cancerous human lung cells. Based on the phenotypic effects and their chemical properties, we selected four compounds for further study. We have also made a preliminary assessment of the feasibility of using one of the four selected small molecules to identify cellular targets.


Small molecule screen and growth assay

Chemicals were supplied by Chembridge Research Labs, USA (120, 000 synthetic compounds), Biofocus DPI, UK (16, 044 synthetic compounds, Analyticon Discovery, Germany (1, 408 natural products) and SPECS, Netherlands (51, 838 synthetic and natural compounds). Library compounds were pre-plated in 5 μL of 10% DMSO (v/v) into 384-well microtiter plates (Corning; NY) using a liquid dispenser TPS-384 Total Pipetting Solution (Apricot Designs; Monrovia, CA); columns 13 and 14 were left empty for controls. Negative control (vehicle only) wells contained 1% DMSO (v/v) and positive control (lethal compound) wells contained 25 μM staurosporine in 1% DMSO (v/v). Cells were added in 40 μL growth medium using a bulk Multidrop Liquid Dispenser (Thermo, Waltham, MA). After 72 h incubation, 5 μL Alamar blue reagent was added using the Multidrop Dispenser (Shum et al. 2008). The cells were further incubated for 24 h and the fluorescence intensity was read on either a Perkin Elmer Victor3 V multi label plate reader (Ex: 530 nm and Em: 590 nm) or on an Amersham LEADseeker Multimodality Imaging System equipped with Cy3 excitation and excitation filters and FLINT epi-mirror (Antczak et al. 2009; Shum et al. 2008). The screens against the cell lines were performed on a fully automated linear track robotic platform (CRS F3 Robot System, Thermo Electron, Canada) using several integrated peripherals for plate handling, liquid dispensing and fluorescence detection. Data files were loaded into the HTS Core Screening Data Management System, a custom built suite of modules for compound registration, plating, data management, and powered by ChemAxon Cheminformatic tools (ChemAxon, Hungary). The signal inhibition induced by the compounds was expressed as a percentage compared to high and low controls located on the same plate.

Compounds that inhibited growth by 50% or more at a concentration of 10 μM in at least one cell line were tested again in a confirmation screen in duplicates. The solubility and structural integrity of compounds for which growth inhibitory activity was confirmed were then tested as described under Supplementary Methods. Only compounds that were soluble at 100 μM were used in dose-response studies. The dose response was assessed in duplicate using 12 point doubling dilutions with 100 μM compound concentration in 1% DMSO (v/v) as the upper limit. The dose response curve for each set of data was fitted separately and the two IC50 values obtained were averaged. For compounds having an IC50 below 1 μM, the dose response study was repeated using dilutions starting at 10 nM for more accurate determination.

Detection of protein phosphorylation

For phosphorylation studies, cells were treated with compounds for 24 h in the continued presence of 10% serum. Phosphorylation was detected either by Western blotting using phospho-specific antibodies or by using the phospho-proteome profiler antibody arrays from R&D Systems according to the manufacturer’s instructions. For Western blotting whole cell extracts (50 μg) were resolved on 4–20% gradient gels, transferred onto nitrocellulose membranes and then immuno-blotted. For the phospho-protein antibody arrays, 300 μg of whole cell extracts were incubated with either a MAPK or receptor tyrosine kinase (RTK) proteome profiler antibody array overnight.

Statistical analysis

Assay robustness and reproducibility were estimated by the Z′ factor (Zhang et al. 1999). This statistical parameter is a function of the signal-to-noise ratio and is widely used in high throughput screening to validate the robustness and reproducibility of an assay. A robust cell-based assay has a Z′ factor between 0.3 and 1.0. Z′ = 3[(σhi + σlo)/(μ hi − μ lo)] where σhi and σlo are the standard deviation of the high (vehicle-treated) and low (drug-treated) controls, respectively, and μ hi and μ lo are the mean value of the of the high and low controls, respectively. Reproducibility was evaluated by performing a small screen of 3, 000 compounds with a wide range of biological activity on two separate occasions. The pair of data from the two validation sets were subjected to scatter plot analysis and the standard deviation of the difference of the pairs is a measure of the assay variability. The proportion of molecules that are hits in both validation sets versus the hits in at least one set is the reproducibility rate. The assay was considered reproducible if the proportion of molecules that are hits in both validation sets was at least 65% of the hits observed in only one set.


Identification of small molecules that reduce growth of human lung adenocarcinoma cell lines

We screened a library of 189, 290 small molecules for agents that would inhibit growth of human lung adenocarcinoma cell lines. The cell lines that were used in the primary screen have been well characterized and are shown in Table 1 (Paez et al. 2004; Pao et al. 2004; Pao et al. 2005; Sordella et al. 2004; Tracy et al. 2004). Three of the cell lines (H1650, H3255 and H1975) contain EGFR alleles found commonly in human lung adenocarcinoma cell lines (delE746-A750, L858R and L858R-T790M, respectively). These cell lines display different sensitivity to the EGFR tyrosine kinase inhibitor gefitinib (Table 1). Growth of H3255 is the most sensitive to gefitinib (IC50 = 10 nM); this cell line also has amplification of EGFR (Tracy et al. 2004). In contrast, growth of H1975 (with the L858R-T790M allele) is inhibited by gefitinib only at high concentrations (IC50 = 10 μM). Introduction of allele-specific EGFR siRNAs into H1975 or H3255 induces apoptosis, suggesting that both lines require continuous expression of the EGFR oncogene for survival ((Sordella et al. 2004); R. S, unpublished). The H1650 cell line shows intermediate sensitivity to gefitinib (IC50 = 1 μM). The H2030 cell line contains the G12C allele of KRAS; growth of this line is inhibited only at high concentration of gefitinib (IC50 = 10 μM). Two lines of non-tumor cells were used to help eliminate compounds that showed general cytotoxicity: primary human bronchiolar epithelial cells (NHBE) and human lung fibroblasts (WI-38). We sequenced exons 18–24 of EGFR and exon 2 of KRAS and did not detect any mutations in either of these two genes in the control cells (results not shown). NHBE and WI-38 cells showed intermediate sensitivity to gefitinib (IC50 = 1 μM).

Table 1
Characteristics of human lung adenocarcinoma cell lines and control cells

Our screening strategy is outlined in Figure 1A. The primary screen was performed at a single drug concentration of 10 μM against H1650, H1975, H2030 and H3255. Cell growth (number of viable cells) remaining at the end of the assay was determined using the vital dye Alamar blue. This growth assay does not differentiate between cytostatic or cytotoxic compounds. Before conducting the screen, we validated the screening platform and assay by screening a library of approximately 3, 000 compounds against 5 cell lines (H1650, H1975, H2030, H3255 and HPL1D) on two separate days. The robustness and reproducibility of the assay were assessed using the Z′ statistical parameter (Zhang et al. 1999). The high signal: noise ratio of the assay and the Z′ values of 0.5–0.8 are consistent with a robust assay (Table S1).

Figure 1Figure 1
Outline and summary of cell-based small molecule screen

Compounds that reduced growth of at least one of the adenocarcinoma cell lines at 10 μM by μ 50% were considered “positives”. Using this criterion, we identified 6, 552 novel small molecules for further analysis (Figure 1B). Out of this group, 3, 112 compounds were re-synthesized and tested again for their ability to reduce growth and the activity of 669 compounds was confirmed. Dose response studies were then performed with the panel of adenocarcinoma cell lines and two control lines. In parallel, this group of 669 compounds was tested for solubility in growth media, and compound purity and structure were tested by LC-MS. Forty nine compounds had either low solubility in growth media or contained impurities that raised questions about the identity and structure of the molecules. We were therefore able to titrate the activity of only 620 of the candidates. Fifty nine compounds inhibited the growth of at least one tumor cell line with IC50 μ ≤ 1 μM. We then selected molecules as candidates for further study based on the following criteria: 10-fold better growth inhibition of at least one adenocarcinoma cell line over control cells; ease of making chemical derivatives and affinity reagents; a lack of structural similarity to ATP; and strong structure-activity relationships (SAR) of analogs that were present in the library. Of the 59 compounds with IC50 μ 1μM for inhibition of growth of at least one lung cancer line, 9 showed a 10-fold selectivity (based on IC50 values) for tumor cells over the control cells. None of these compounds resembled ATP in structure and there were no analogs of 5 of these 9 compounds in the library, preventing an estimate of SAR. Therefore, only 4 compounds met all of the criteria outlined above.

The growth-inhibitory profiles of these compounds are shown in Table 2. To simplify discussion, the four compounds selected for further study were given the acronym LCS (lung cancer screen)-1 to 4. All four impaired the growth of at least two adenocarcinoma cell lines with an IC50 < 1 μM. Each of the lung cancer lines contains a known mutation affecting either EGFR or KRAS, yet the pattern of growth inhibition does not correlate with these mutations. This is most evident in the cases of LCS-2 and LCS-4. LCS-2 shows no activity against the H3255 line but it is highly active against both the H1650 and H1975 lines, which also have EGFR mutations, and it is quite active against the KRAS mutant line, H2030. Similarly, LCS-4 is moderately active against H1975 and H2030 but shows no activity against the two other cell lines with EGFR mutations (H1650 and H3255). LCS-1 and LCS-3 inhibited growth of all four lines, but with different patterns of activity: LCS-1 is at least ten-fold more active against H1975 than against H1650 and shows intermediate strength against H3255 and H2030; LCS-3 is most active against H1650. LCS-1 has moderate inhibitory effect on the control cell lines, but LCS-2, and -3 have at least ten-fold lower activity on NHBE cells. Since LCS-4 is inactive against two tumor cell lines, it was presumed not to have generalized activity against common growth mechanisms and was not tested with the control cells.

Table 2
Cellular activity and chemical properties of candidate small molecules

Taken together, these findings are most compatible with the idea that the active compounds are targeting the products of genes, either wild-type or mutant, on which the growth-inhibited cancer cell lines have become dependent on as a result of still unidentified mutations specific to each line. The affected proteins might or might not be components of the EGFR or KRAS signaling machinery. A satisfactory explanation of the observed patterns of sensitivity of tumor cell lines to these small molecules will probably require a fuller description of the genotypes of the cell lines and identification of the molecular targets of the compounds.

As shown in Table 2, the four candidates are all of low molecular weight and are predicted to cross the plasma membrane readily, based on the membrane partition coefficient (cLogP). The cLogP value of a compound is the logarithm of its partition coefficient between n-octanol and water ((log Coctanol/Cwater)) and is a well-established measurement of the hydrophilicity of a compound (Ghose et al. 1999). Compounds that readily permeate the plasma membrane have a cLogP value of < 5.

2-phenylpyridazin-3(2H)-ones as growth inhibitors of human lung adenocarcinoma cell lines

The primary HTS data provided useful information about the relationship of structure to activity (SAR) in the vicinity of the 2-phenylpyridazin-3(2H)-one scaffold of LCS-1. The SAR data are based on the differential activity of a cluster of 123 derivatives tested on the four cell lines. The SAR data strongly suggest that the pyridazine-3(2H)one moiety of LCS-1 requires an electron-deficient environment on the pyridazine ring with closely packed substitutions such as halogens and halogen mimetics as electron withdrawing groups tolerated at positions R2 and R3. Substitutions of electron-rich donors at these positions eliminated biological activity. Surprisingly, methoxy group substitutions on the R1 position did not affect activity, whereas a hydroxyl group at the same position completely abolished activity. This observation offered the first potential point of attachment for a linker that joins an active compound to a solid support for affinity chromatography. The phenyl moiety of LCS-1 was found to tolerate the most substitutions found within the cluster identifying the R5 position as a second potential point of attachment for a linker. The other candidates LCS-2, LCS-3 and LCS-4, were not represented by as many analogs in the library. As shown in Figure 2A, LCS-1 is not related to ATP in structure and is therefore unlikely to be an ATP-competitive inhibitor. Based on its chemical structure, LCS-1 appeared to be easily amenable to chemical modification. LCS-1 was therefore the first compound chosen for further study.

Figure 2Figure 2Figure 2
Structure and growth inhibitory activity of LCS-1, LCS-1.28 and LCS-1.34

Synthesis of a focused library of LCS-1 derivatives

Based on the SAR profile obtained from primary HTS data, we synthesized a focused library of 41 LCS-1 analogs to further explore the effects of substitutions on the R2 and R3 positions on the pyridazine ring and to assess the feasibility of linker attachments at the R1 position of the pyridazine ring and at the R5 position of the phenyl moiety of the molecule. Supplementary Table 2 summarizes the screening data with 10 cell lines, including two control cell lines. All analogs were soluble within the range of concentrations used for dose-response studies. One of the new analogs showed slightly increased potency compared to the parental molecule, 13 were inactive and the rest inhibited growth with IC50 values with a potency similar to LCS-1 (Supplementary Table 2). Chemical modifications on the R2, R3, R4 and/or R8 positions were found to tolerate only halogens or halogen mimetics, consistent with the preliminary SAR data.

The growth-inhibitory activity profile of two active analogs compared with the parental compound LCS-1, is shown Figure 2C. Chloro-to-bromo substitutions on the R2 and R3 positions on the pyridazine ring resulted in the analog LCS-1.28 (4,5-dibromo-2-m-tolylpyridazin-3(2H)-one). This analog had a similar activity profile to the parental molecule. Chlorination of the ortho and the meta positions on the phenyl ring of LCS-1 produced analog LCS-1.34, which was slightly more potent than LCS-1 and LCS-1.28 in inhibiting growth of H1975.

Reduction of growth of additional lung adenocarcinoma cell lines by LCS-1, LCS-1.28 and LCS-1.34

We expanded the panel of human lung adenocarcinoma cell lines used to test LCS-1 to include a total of 16 cell lines (8 with EGFR mutations and 8 with KRAS mutations). LCS-1 inhibited the growth of 9 of these cell lines (some with EGFR and some with KRAS mutations) at concentrations 10- to 40-fold lower than levels required to inhibit growth of NHBE or WI-38 cells (Table 3). When tested against one or two of the LCS-1 derivatives, the tumor cell lines were, in general, similarly sensitive to LCS-1, LCS-1.28 and LCS-1.34. However, NHBE and WI-38 cells were two- to three-fold more sensitive to LCS-1.28 and LCS-1.34 than to LCS-1.

Table 3
Growth inhibitory profile of LCS-1, LCS-1.28 and LCS-1.34

Determination of the half-life of LCS-1, LCS-1.28 and LCS-1.34 in human liver microsomes

To determine which of the three most active substituted phenyl pyridazinone compounds would be the best for in vitro and in vivo assays, we measured their stability in human liver microsomes. LCS-1 had a half-life of 6.6 min and this was similar to the half-life of LCS-1.28 (7.1 min). LCS-1.34 exhibited a 13-fold increase in half-life (82.2 min) in human liver microsomes over the parental molecule, and it was therefore chosen for all further experiments.

Inhibition of growth by the LCS-1.34 may be due to both inhibition of proliferation and induction of apoptosis

To determine the cellular mechanism by which compounds related to LCS-1 reduced the number of cells, we examined the effect of the most stable analog, LCS-1.34, on DNA synthesis and apoptosis. H358, H1975 and H3255 cells were treated with increasing concentration of LCS1.34 for 24 h then DNA synthesis was assessed by thymidine incorporation for the final 2 h. DNA synthesis was reduced in a concentration-dependent manner to 47 ± 3% and 42 ± 17% of control values (0.25 μM LCS-1.34) in H358 and H1975, respectively (Figure 3A). H3255 was the least sensitive of the three cell lines, showing only a 16% reduction in thymidine incorporation (84 ± 9% of control) after treatment with 0.25 μM LCS-1.34 (Figure 3A). However, at 1 μM, there was a further reduction in DNA synthesis to 30 ± 8% of control in H3255 cells, with more profound reductions in the other two cell lines.

Figure 3Figure 3
LCS-1.34 inhibits DNA synthesis and induces apoptosis

To determine the effect of LCS-1.34 on cell death, H358, H1975 and H3255 cells were treated for 48 h with LCS-1.34, and annexin-FITC conjugates bound to the surface of treated cells was measured by FACS. LCS-1.34 induced apoptosis in the three cell lines to different extents (Figure 3B): LCS-1.34 at 0.5 μM increased the number of H358 and H1975 cells undergoing apoptosis by three-fold (from 8 to 24 % in H358 cells and from 3.5 to 11% in H1975 cells). This concentration of LCS-1.34 did not affect the number of apoptotic cells in the H3255 cultures; however, at a higher concentration (1 μM), the number increased 2-fold, from 9 to 19%. At this higher concentration, LCS-1.34 induced an approximately 6-fold increase in the number of apoptotic cells in H358 and H1975 cultures.

MAPK and PI 3-kinase pathways are inhibited by LCS-1.34

To gain additional insight into the mechanism by which the LCS-1-related compounds induced cell death, we analyzed the phosphorylation status of several signaling proteins that mediate proliferation and survival. We used a proteome profiler antibody array (R&D Systems) that allowed us to measure the phosphorylation status of 18 kinases, including three major MAPK family members (ERK, JNK and p38) and components of the PI 3-kinase pathway (AKT, GSK-3 and p70 S6 kinase). H1975 cells were treated for 24 h with 0.5 μM LCS-1.34. Whole cell-extracts were prepared and protein phosphorylation analyzed. Phosphorylation of ERK2, the three AKT isoforms, and p70 S6 kinase was reduced in cells that were treated with LCS-1.34 (Figure 4A). Similar results were obtained in H3255 cells (data not shown). LCS-1.34 did not, however, alter phosphorylation of any of the p38 or JNK isoforms (data not shown).

Figure 4
LCS-1.34 blocks phosphorylation of cytoplasmic components of signaling pathways but not phosphorylation of EGFR family members

EGFR family members are not direct targets of the phenyl pyridazinone compounds

Given the effect of LCS-1 and its derivatives on some cell lines with EGFR mutations, we asked if phosphorylation of EGFR or other members of the EGFR family (ERBB2 and ERBB3) was affected by these compounds using a human phospho-RTK antibody array. For these experiments, we used H3255 cells, which have relatively higher levels of phospho-EGFR. Treatment of cells for 24 h with 0.5 μM LCS-1.34 did not affect total phosphorylation of EGFR, ERBB2 or ERBB3 whereas erlotinib dramatically reduced phosphorylation of all three receptors (Figure 4B). In addition, we used a phosphorylation site-specific antiserum to show that treatment of H1975 cells with LCS-1.34 did not alter the phosphorylation of EGFR at Y1068 (Figure 4C).


Several methods are being developed to identify new therapeutic targets in many types of cancer. These include screening with small interfering RNAs (Whitehurst et al. 2007), direct sequencing of genes in tumor samples (Weir et al. 2007; Wood et al. 2007), and analysis of tyrosine phosphorylated proteins by mass spectrometry (Guha et al. 2008; Rikova et al. 2007). Here we used another approach---a cell-based, high throughput screen of large chemical libraries---to identify novel small molecules that reduce growth of human lung adenocarcinoma cell lines and could lead to the identification of unexpected therapeutic targets.

Out of nearly 200, 000 compounds, over 6, 000 showed activity in our initial screen, and those were winnowed to a group of 59 compounds that had reproducible activity at sub-micromolar concentrations. We then selected four novel small molecules, LCS-1 to LCS-4, for further study, based on physical, chemical, and inhibitory properties, including selective activity against cancer lines as opposed to non-tumor lines.

Our efforts to exploit the results of the screen were focused on characterizing LCS-1. This 2-phenylpyridazin-3(2H)-one based compound was amenable to chemical modification, allowing us to make a derivative (LCS-1.34) that was 13-fold more stable in human liver microsomes. We tested LCS-1, LCS-1.34, and one other derivative for growth inhibitory activity against a panel of 16 human lung adenocarcinoma lines with EGFR or KRAS mutations, and observed anti-tumor cell activity in over half of the lines. However, there was no correlation of the growth-restricting activity with known mutations in EGFR or KRAS. Until more is known about the genetic and biochemical features of these cell lines, we will be unable to make any definitive correlation between genotypes or signaling activity and sensitivity to LCS-1 and its derivatives.

Reduced cell growth was associated with partial inhibition of DNA synthesis and with partial induction of apoptosis in the lines tested, but the major physiological actions of LCS-1 or other agents have not been established. In addition, we do not know whether inhibition of one or more of the well-characterized signaling pathways is responsible for the effects observed on DNA synthesis and apoptosis. In some initial surveys of biochemical activities, LCS-1 impaired phosphorylation of MAPK, AKT isoforms, and p70 S6 kinase, enzymes that act in pathways known to regulate cell growth and survival. But until we have undertaken a more extensive investigation of the effects of our four compounds, we cannot draw any conclusions about whether the effects on phosphorylation reported here are responsible for the effects on cell growth that allowed us to select these compounds. Notably, LCS-1 does not appear to affect the activation of EGFR family members, as measured by phosphorylation status; consequently, if inhibition of the MAPK and PI 3-kinase reflects interference with the EGFR signaling pathway, the effects likely occur at some step downstream of the receptor. Alternatively, these effects could occur in a manner independent of the EGFR pathway.

Finally, we propose that the compounds we have identified here as tumor cell-specific inhibitors of growth could lead to identification of their cellular targets by affinity chromatography (Godl et al. 2003) or by methods based on perturbation of gene expression profiles (Lamb et al. 2006). Knowledge of such targets could deepen our understanding of how oncogenic EGFR and KRAS transform lung epithelial cells, provide specific targets for cancer drug development, and predict how cells might escape the growth-inhibitory effects of the candidate small molecules described here. Indeed, small molecules have been used to identify targets of activated KRAS in smaller screens (Yang and Stockwell 2008). Because the small molecules that we selected for further study do not resemble ATP structurally, they may not be ATP-competitive inhibitors. However, this will have to be tested experimentally. It possible that the targets of our compounds are non-traditional drug targets, such as non-enzymatic components of cytoplasmic signaling networks or transcription factors.

Supplementary Material

Suppl Table S2

Suppl Text and table


This work was supported by grants from the NIH (5PO1CA129243-03), and the Experimental Therapeutics Center of Memorial Sloan-Kettering Cancer Center (supported by the William H. Goodwin and Alice Goodwin and Commonwealth Foundations for Cancer Research). The HTS Core Facility is partially funded by the Lilian S. Wells Foundation and by an NIH/NCI Cancer Center Support Grant 5P30CA008748-44. RS was funded by a postdoctoral fellowship from the Canadian Institutes for Health Research. We are grateful to Dr. Yixuan Gong (in Dr. William Pao’s lab) for help with the annexin binding assay. We also thank members of the HTS Core Facility for their help during the course of this study.


  • Antczak C, Shum D, Radu C, Seshan VE, Djaballah H. Development and validation of a high-density fluorescence polarization-based assay for the trypanosoma RNA triphosphatase TbCet1. Comb Chem High Throughput Screen. 2009;12:258–268. [PMC free article] [PubMed]
  • Bean J, Brennan C, Shih JY, Riely G, Viale A, Wang L, et al. MET amplification occurs with or without T790M mutations in EGFR mutant lung tumors with acquired resistance to gefitinib or erlotinib. Proc Natl Acad Sci U S A. 2007;104:20932–20937. [PubMed]
  • Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008;455:1069–1075. [PMC free article] [PubMed]
  • Engelman JA, Zejnullahu K, Mitsudomi T, Song Y, Hyland C, Park JO, et al. MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Science. 2007;316:1039–1043. [PubMed]
  • Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J Comb Chem. 1999;1:55–68. [PubMed]
  • Godl K, Wissing J, Kurtenbach A, Habenberger P, Blencke S, Gutbrod H, et al. An efficient proteomics method to identify the cellular targets of protein kinase inhibitors. Proc Natl Acad Sci U S A. 2003;100:15434–15439. [PubMed]
  • Gong Y, Firestone GL, Bjeldanes LF. 3,3′-diindolylmethane is a novel topoisomerase IIalpha catalytic inhibitor that induces S-phase retardation and mitotic delay in human hepatoma HepG2 cells. Mol Pharmacol. 2006;69:1320–1327. [PubMed]
  • Guha U, Chaerkady R, Marimuthu A, Patterson AS, Kashyap MK, Harsha HC, et al. Comparisons of tyrosine phosphorylated proteins in cells expressing lung cancer-specific alleles of EGFR and KRAS. Proc Natl Acad Sci U S A. 2008;105:14112–14117. [PubMed]
  • Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, et al. Cancer statistics, 2008. CA Cancer J Clin. 2008;58:71–96. [PubMed]
  • Kobayashi S, Boggon TJ, Dayaram T, Janne PA, Kocher O, Meyerson M, et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N Engl J Med. 2005;352:786–792. [PubMed]
  • Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–1935. [PubMed]
  • Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 2004;350:2129–2139. [PubMed]
  • Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science. 2004;304:1497–1500. [PubMed]
  • Pao W, Miller V, Zakowski M, Doherty J, Politi K, Sarkaria I, et al. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci U S A. 2004;101:13306–13311. [PubMed]
  • Pao W, Miller VA. Epidermal growth factor receptor mutations, small-molecule kinase inhibitors, and non-small-cell lung cancer: current knowledge and future directions. J Clin Oncol. 2005;23:2556–2568. [PubMed]
  • Pao W, Miller VA, Politi KA, Riely GJ, Somwar R, Zakowski MF, et al. Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med. 2005;2:e73. [PubMed]
  • Rikova K, Guo A, Zeng Q, Possemato A, Yu J, Haack H, et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell. 2007;131:1190–1203. [PubMed]
  • Rodenhuis S, Slebos RJ. The ras oncogenes in human lung cancer. Am Rev Respir Dis. 1990;142:S27–30. [PubMed]
  • Shum D, Radu C, Kim E, Cajuste M, Shao Y, Seshan VE, et al. A high density assay format for the detection of novel cytotoxic agents in large chemical libraries. J Enzyme Inhib Med Chem. 2008;23:931–945. [PMC free article] [PubMed]
  • Sordella R, Bell DW, Haber DA, Settleman J. Gefitinib-sensitizing EGFR mutations in lung cancer activate anti-apoptotic pathways. Science. 2004;305:1163–1167. [PubMed]
  • Suzuki Y, Orita M, Shiraishi M, Hayashi K, Sekiya T. Detection of ras gene mutations in human lung cancers by single-strand conformation polymorphism analysis of polymerase chain reaction products. Oncogene. 1990;5:1037–1043. [PubMed]
  • Tracy S, Mukohara T, Hansen M, Meyerson M, Johnson BE, Janne PA. Gefitinib induces apoptosis in the EGFRL858R non-small-cell lung cancer cell line H3255. Cancer Res. 2004;64:7241–7244. [PubMed]
  • Weir BA, Woo MS, Getz G, Perner S, Ding L, Beroukhim R, et al. Characterizing the cancer genome in lung adenocarcinoma. Nature. 2007;450:893–898. [PMC free article] [PubMed]
  • Whitehurst AW, Bodemann BO, Cardenas J, Ferguson D, Girard L, Peyton M, et al. Synthetic lethal screen identification of chemosensitizer loci in cancer cells. Nature. 2007;446:815–819. [PubMed]
  • Wood LD, Parsons DW, Jones S, Lin J, Sjoblom T, Leary RJ, et al. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318:1108–1113. [PubMed]
  • Yang WS, Stockwell BR. Synthetic lethal screening identifies compounds activating iron-dependent, nonapoptotic cell death in oncogenic-RAS-harboring cancer cells. Chem Biol. 2008;15:234–245. [PMC free article] [PubMed]
  • Zhang JH, Chung TD, Oldenburg KR. A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays. J Biomol Screen. 1999;4:67–73. [PubMed]