Oncogene-specific changes in cellular signaling have been widely observed in lung cancer. Here, we investigated how these alterations could affect signaling heterogeneity and suggest novel therapeutic strategies. We compared signaling changes across six human bronchial epithelial cell (HBEC) strains that were systematically transformed with various combinations of TP53, KRAS, and MYC—oncogenic alterations commonly found in non–small cell lung cancer (NSCLC). We interrogated at single-cell resolution how these alterations could affect classic readouts (β-CATENIN, SMAD2/3, phospho-STAT3, P65, FOXO1, and phospho-ERK1/2) of key pathways commonly affected in NSCLC. All three oncogenic alterations were required concurrently to observe significant signaling changes, and significant heterogeneity arose in this condition. Unexpectedly, we found two mutually exclusive altered subpopulations: one with STAT3 upregulation and another with SMAD2/3 downregulation. Treatment with a STAT3 inhibitor eliminated the upregulated STAT3 subpopulation, but left a large surviving subpopulation with downregulated SMAD2/3. A bioinformatics search identified BCL6, a gene downstream of SMAD2/3, as a novel pharmacologically accessible target of our transformed HBECs. Combination treatment with STAT3 and BCL6 inhibitors across a panel of NSCLC cell lines and in xenografted tumors significantly reduced tumor cell growth. We conclude that BCL6 is a new therapeutic target in NSCLC and combination therapy that targets multiple vulnerabilities (STAT3 and BCL6) downstream of common oncogenes, and tumor suppressors may provide a potent way to defeat intratumor heterogeneity. Cancer Res; 77(11); 3070–81. ©2017 AACR.

Lung cancer is a disease of immense complexity with approximately 140 oncogenes and tumor-suppressor gene abnormalities identified (1). These genetic alterations can appear in different combinations even within classically defined cancer subtypes, such as the lung adenocarcinoma subtype of non–small cell lung cancer (NSCLC; ref. 2). It is apparent that no one therapy can fit all oncogenotypes.

The success of targeted therapy directed at EGFR mutant or EML4–ALK fusion lung cancers (despite the noncurative nature of such targeting) has led to a commonly applied approach, namely to search for therapy that is effective for specific oncogenotypes (3). When direct targeting of the oncogenes is not feasible, the goal is to identify vulnerabilities (i.e., molecular alterations crucial for cancer cell survival) within each genetically defined cancer subtype (4). The search for these vulnerabilities arises from the hypothesis that even if the oncogenic alterations themselves are not pharmacologically targetable, downstream, targetable signaling pathways on which cancer cells critically depend may also be altered and represent vulnerabilities. This approach, however, has two major challenges. First, it is difficult to identify specific oncogene-induced signaling alterations without a baseline reference of signaling for a specific cell type. Second, cancer cell populations are notoriously phenotypically heterogeneous (5). In fact, it is possible that even within a well-defined oncogenotype and identified signaling alteration, in an individual tumor, the cancer cell population may be comprised of multiple subpopulations in different signaling states.

Here, we pursue a strategy of investigating changes in signaling for a common oncogenotype found in NSCLC, involving alterations in TP53, KRAS, and MYC using a simplified preclinical model of such changes. Our preclinical model used immortalized human bronchial epithelial cells (HBEC) manipulated to contain various combinations of these three important oncogenic drivers to yield fully tumorigenic derivatives (6). We use this model HBEC system with defined oncogenic changes to search for pathways whose signaling has been differentially altered due to these defined genetic manipulations. Specifically, we first analyzed the pathways at single-cell level to identify intratumor subpopulations in altered signaling states. Next, we searched for targetable vulnerabilities in identified subpopulations. Finally, we tested the ability of small-molecule inhibitors targeting these vulnerabilities—alone or in combination—to eliminate cancer cells both in vitro and in vivo. Thus, in contrast to previous studies that investigated cellular heterogeneity after drug treatment, we studied pretreatment signaling heterogeneity to identify subpopulation-specific therapeutic targets and utilize this information to design a novel combination therapy targeting intratumor heterogeneity.

Cell lines and basal culture conditions

Normal and oncogenically manipulated immortalized HBECs were cultured with Keratinocyte Serum Free Medium (KSFM; Life Technologies Inc.) media containing 50 μg/mL of Bovine Pituitary Extract (BPE; Life Technologies Inc.) and 5 ng/mL (or, 50 pg/mL if otherwise stated) of EGF (Life Technologies Inc.). Parental and oncogenically manipulated HBECs were established between 2003 and 2009. Lung cancer cell lines, established in the laboratories of John Minna and Adi Gazdar between 1987 and 1994, were maintained in RPMI-1640 (Life Technologies Inc.) with 5% FBS. The approximate number of passages for all cell lines between collection and thawing is 10. All cell lines were DNA fingerprinted (PowerPlex 1.2 Kit, Promega) and mycoplasma-free (e-Myco Kit, Boca Scientific).

Viral transfection and transduction of Omomyc construct

MYC target gene knockdown was achieved using the Omomyc construct with pTripZ vector backbone. This construct was originally made by and was a gift from Laura Soucek [Vall d'Hebron Institute of Oncology (VHIO), Edifici Mediterrània, Hospital Vall d'Hebron, Barcelona, Spain; Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Barcelona, Spain]. Cell lines were transduced as described previously (6), and single-cell clones with high level of red fluorescent protein (RFP; inducible by doxycycline) intensity were selected.

qRT-PCR

The mRNA was isolated using a Qiagen kit (Qiagen Inc.), and the cDNA was made by iScript (Life Sciences Research). Quantitative Reverse Transcription PCR (qRT-PCR) was performed using validated Taqman primers and probes (Applied Biosystems) using Applied Biosystems 7500 qRT-PCR machine, and relative expression was calculated using the 2−ΔΔCT method. Number of technical replicates is as described in figure legends. Wells flagged as problematic by the qRT-PCR machine were dropped from the final calculation.

Treatment with small-molecule inhibitors

STAT3 inhibitors BBI-608 (Tocris Bioscience), Stattic (Calbiochem, EMD Millipore), and BCL6 inhibitor FX-1 (provided by Dr. Leandro Cerchietti, Weill Cornell Medical College) were used in varying concentration in MTS drug sensitivity and colony formation assays as previously described (6). For anchorage-dependent liquid colony formation assay, 500 cells were seeded in each well of 6-well-plates (in triplicates for each cell line) for varying concentration of EGF at 0, 0.5, 5, 50, 500, and 5,000 pg/mL in KSFM. The cells were cultured for 2 weeks and then the colonies were stained with crystal violet. Empty wells, defined as having intensity less than one SD below that of blank wells, were omitted.

siRNA-mediated knockdown

siRNA transfections were performed as described in (7). Cells were harvested 48 hours after transfection to seed in in vitro tumorigenicity assays. Six-well siRNA invasion assays were performed with siRNAs at 20 nmol/L RNAi MAX lipid (Life Technologies) and 50,000 cells per well. At 48 hours, cells were washed with PBS, trypsinized, and mRNA was isolated using the RNeasy Mini Kit (Qiagen). After 96 hours, cells were assessed for viability. siRNA oligos were utilized along with positive siPLK1 (positive cell death phenotype), negative (nonsilencing) controls (Qiagen), and C911 controls (Sigma; ref. 7).

Signaling readouts, immunofluorescence, and immunoblot assay

Six signaling readouts were selected (Supplementary Table S1). Hoechst 33342 was used to identify nuclear regions. Cells were fixed with 4% paraformaldehyde for 5 minutes, permeabilized with ice-cold 100% methanol at –20°C for 10 minutes, washed with 0.1% TBST (TBS + 0.1% Tween 20), and blocked with 5% BSA solution in 0.1% TBST at room temperature for 30 minutes. Note that 5% BSA in 0.1% TBST was used for primary and secondary antibody dilutions. Plates stained with primary antibodies were incubated at 4°C overnight. They were washed with 0.1% TBST 3 times. Next, plates were incubated with secondary antibodies in the dark at room temperature for 2 hours and then washed again with TBST 3 times. After the final washing step, 100 μL of TBST containing 0.1% sodium azide was added to each well. Immunoblots were performed as described in ref. 6 with primary antibodies shown in Supplementary Table S2.

Image acquisition, processing, and quality control

All fluorescence images were acquired using a TE-2000 epifluorescence microscope (Nikon) equipped with integrated Perfect-Focus System (PFS), Nikon Plan Apochromat 20x objective lens, and Photometrics Cool SNAP HQ camera. Image acquisition was controlled by NIS-Elements software (Nikon). Image background correction was done using the National Institute of Health ImageJ rolling-ball background subtraction plug-in (8). Cellular regions were determined using a watershed-based segmentation algorithm (9) that first retrieves nuclear regions using DNA staining and then combines multiple cytosolic region markers to identify cellular boundaries. Images were visually inspected, and images with severe focus, staining, or cell-segmentation artifacts were discarded. We identified approximately 1,000 cellular regions per marker/well after applying automated cell segmentation to our image data.

Determination of altered cellular signaling state

To understand the activity in each signaling pathway, we focused on a specific intensity feature for each marker, namely the ratio of the average nuclear to average cytoplasmic intensities. The exception was β-CATENIN, for which we measured total intensity in the cytoplasm to capture its loss in the cell membrane and cytoplasm. For each cell, we extracted this intensity feature. Approximately, 104 cells were analyzed per marker per cell line. For any marker, cells that fall within 5th and 95th percentile of the parental HBEC distribution were defined to constitute the baseline signaling. The top and bottom 5% (total 10% of cells) were defined as outliers. The distribution of oncogenically manipulated HBECs, when superimposed on this control parental distribution, gave us the altered fraction of cells from total population. The altered fractions for a condition were calculated by combining cells across multiple replicate wells.

List of TGFβ downstream target genes used in microarray data analysis

List of TGFβ downstream target genes used in microarray data analysis was as follows: P15, P21, P57, 4EBP1, BIK, BIM, DAPK, FAS, GADD45b, ATG5, ATG7, BECLIN 1/ATG6, THROMBOSPONDIN, FOXP3, FOXC2, FOXK1, CD25A, E2F-1, ID1-3, MYC, BCL-XL, BCL2, BCL6, HGF, MSP, TGFα, GATA-3, T-BET, SOX2, SOX4, SOX15, MMP7, MMP19, MMP2, MMP9, PDGF-B, VEGF, LIF, SNAIL1/2, ZEB1/2, HMGA2, HDM2, TIMP, IFNγ, MICA, NKG2D, NKP30, PERFORIN, and T-BET.

Drug treatment in xenografted tumors

Note that 106 H1993 cells were injected subcutaneously to each of 40 female NON-SCID mice (4–6-week old) following the previously described protocol (6). The tumor volume and body weight were routinely measured. After the tumors reached a volume between 50 and 100 mm3, they were randomized in 4 groups of 10. A total of 20 mg/kg of BBI-608 was administered intraperitoneally with 1x PBS vehicle as described previously (10, 11), and 25 mg/kg of FX-1 was administered intraperitoneally with vehicle (5% Tween-80, 30% PEG-400, 65% dextrose solution 5%; ref 12). The treatment dose and schedule for single agent and combination are described in details in Supplementary Table S3. After the tumors reached approximately 2,000 mm3 in the vehicle-treated group, all the mice were sacrificed following appropriate humane protocols described before (6), and the tumors were surgically resected, measured (1/2 × length × width × width), and weighed. P values were calculated by two-way Anova with Tukey multiple comparison test using GraphPad Prism (version 7.01, GraphPad Software). Error bars represent SD (n = 10 technical replicates). All studies were conducted on approval by the University of Texas Southwestern Medical Center Institutional Animal Care and Research Advisory Committee.

Data availability

The microarray data discussed in this publication have been made available in the National Center for Biotechnology Information's Gene Expression Omnibus (GEO) public repository (http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession number GSE40828. Raw images of a subset or complete dataset are available upon request.

Oncogenically manipulated HBECs provide a unique model system to search for intratumor heterogeneity

To decipher signaling alterations in the context of commonly observed NSCLC oncogenotypes, we used our collection of immortalized HBECs with defined combinations of TP53 knockdown, KRASV12, and MYC overexpression. We previously showed that while each change and various combinations progressed the cells toward malignancy, only after introduction of all three oncogenic changes did HBEC3KT cells become fully tumorigenic (6). We refer to this triply manipulated, transformed derivative as HBECPKM, whereas the other derivatives include TP53 knockdown alone (HBECP), TP53 knockdown and KRASV12 (HBECPK), MYC overexpression alone (HBECM), and TP53 knockdown and MYC overexpression (HBECPM). Consistent with variability in gene expression across tumor cells observed in vivo (13–15), HBECPKM cells show a high degree of cell-to-cell differences in the expression of TP53, KRAS, and MYC (Supplementary Fig. S1). Taken together, the cohort of HBECs provided us with a unique in vitro model system to study single-cell variability in multiple signaling pathways during oncogenic progression of lung cancer.

Oncogenically transformed HBECPKM cells reveal significant alterations in SMAD2/3 and STAT3 signaling

To identify signaling markers altered in different HBEC oncogenotypes, we focused on somatic mutation–related signaling readouts in NSCLC. In particular, to facilitate microscopy-based single-cell studies, we selected six signaling readouts whose levels and/or intracellular localization (cytoplasm vs. nucleus) change upon pathway alteration, namely: β-CATENIN, SMAD2/3, phospho-STAT3, P65, FOXO1, and phospho-ERK1/2 (Fig. 1A; Supplementary Table S1; ref. 16). For β-CATENIN, we measured total intensity in the cytoplasm to capture its loss in the cell membrane and cytoplasm (as in other studies that make use of β-CATENIN as readout of epithelial to mesenchymal transition; refs. 17–19). For the other five markers, we measured the ratio of average nuclear to average cytoplasmic intensities from individually identified cells in the immunofluorescence images (20).

Figure 1.

Oncogenically transformed HBECPKM cells reveal significant alterations in SMAD2/3 and STAT3 signaling. A, Immunofluorescence images of parental and HBECPKM with antibodies against β-CATENIN, SMAD2/3, p-STAT3, P65, FOXO1, p-ERK1/2 (scale bar, 50 μm). B, Definition of downregulated (blue), baseline and upregulated (yellow) fraction of cells in SMAD2/3 signaling in oncogenically manipulated HBECs compared with the parental HBEC. For each cell line, shown are the single-cell distributions of nuclear to cytoplasmic SMAD2/3 intensity. The vertical lines denote 5th (blue) and 95th (yellow) percentiles of the parental HBEC distribution. Cells below and above these lines are considered downregulated and upregulated, respectively. C, Quantification of signaling alteration for total SMAD2/3 in parental and oncogenically manipulated HBECs. Top, Shown are the oncogenic manipulations performed on each cell line. Middle, Sample immunofluorescence images with antibodies against SMAD2/3, with cell nuclei outlined in white. White arrows, lower SMAD2/3 in the nuclei of HBECPKM cells. Bottom, for each cell line, blue and yellow bars indicate the fraction of upregulated (up) and downregulated (down) cells. Error bars, SDs (n = 8 technical replicates) for fractions of altered subpopulation measured across technical replicate wells. D and E, As in B and C for p-STAT3 (n = 6 technical replicates). Growth conditions are as described in text. F, Western blot for parental HBEC and HBECPKM cells with antibodies against p-SMAD3, p-STAT3, and GAPDH (loading control).

Figure 1.

Oncogenically transformed HBECPKM cells reveal significant alterations in SMAD2/3 and STAT3 signaling. A, Immunofluorescence images of parental and HBECPKM with antibodies against β-CATENIN, SMAD2/3, p-STAT3, P65, FOXO1, p-ERK1/2 (scale bar, 50 μm). B, Definition of downregulated (blue), baseline and upregulated (yellow) fraction of cells in SMAD2/3 signaling in oncogenically manipulated HBECs compared with the parental HBEC. For each cell line, shown are the single-cell distributions of nuclear to cytoplasmic SMAD2/3 intensity. The vertical lines denote 5th (blue) and 95th (yellow) percentiles of the parental HBEC distribution. Cells below and above these lines are considered downregulated and upregulated, respectively. C, Quantification of signaling alteration for total SMAD2/3 in parental and oncogenically manipulated HBECs. Top, Shown are the oncogenic manipulations performed on each cell line. Middle, Sample immunofluorescence images with antibodies against SMAD2/3, with cell nuclei outlined in white. White arrows, lower SMAD2/3 in the nuclei of HBECPKM cells. Bottom, for each cell line, blue and yellow bars indicate the fraction of upregulated (up) and downregulated (down) cells. Error bars, SDs (n = 8 technical replicates) for fractions of altered subpopulation measured across technical replicate wells. D and E, As in B and C for p-STAT3 (n = 6 technical replicates). Growth conditions are as described in text. F, Western blot for parental HBEC and HBECPKM cells with antibodies against p-SMAD3, p-STAT3, and GAPDH (loading control).

Close modal

We adopted a high-throughput, single-cell approach that can detect oncogene-induced signaling alterations, even if they are present only in a subpopulation of cells. For each HBEC derivative, we used our quantitative microscopy approach to profile approximately 104 cells per condition, across multiple experimental conditions involving growth factors and pharmacologic inhibitors. Each HBEC derivative provided a distribution of marker expression within a population. For each marker, we defined cells to be in a “baseline signaling” state if their feature values were between the 5th and 95th percentile range of all feature values observed within the parental HBEC population (Materials and Methods). Our analysis of heterogeneity focused on the enrichment or de-enrichment of outlier subpopulations. Cells with feature values below or above the basal signaling range were defined to be in a down- or upregulated state (respectively). We measured the degree of signaling alteration based on the deviation of down- or upregulated cellular subpopulations from 5% in the parental HBECs (Fig. 1B–E).

Three of our signaling readouts (P65, FOXO1, phospho-ERK1/2) showed no appreciable alteration in any oncogenically manipulated HBECs. P65 showed strong cytoplasmic localization in all cell lines, suggesting possible inactivation of the TNFα pathway in HBECs. FOXO1 was distributed mostly in the cytoplasm throughout all the cell lines, suggesting no obvious alteration during oncogenic progression. In addition, compared with parental HBECs, we also did not observe upregulation of phospho-ERK1/2, despite the presence of the constitutively activated (and upstream) oncogenic KRASV12(Supplementary Fig. S2; ref. 21). The unaltered level of ERK signaling is consistent with the similar sensitivity of parental HBEC and HBECPKM cells to the MEK (upstream of ERK) inhibitor (Supplementary Fig. S3).

The three remaining pathway readouts (β-CATENIN, SMAD2/3, and phospho-STAT3) did show substantial signaling alterations, with the most dramatic change occurring for HBECPKM (Fig. 1; Supplementary Fig. S2). β-CATENIN showed dramatic loss of intensity from the cell membrane and cytoplasm, as expected from the (previously observed) mesenchymal nature of HBECPKM (Fig. 1A; Supplementary Fig. S2; ref. 6). SMAD2/3 showed a considerable increase of downregulated cells (from 5% to ∼40% of the total population) in HBECPKM cells (Fig. 1B and C). This downregulation in SMAD2/3 signaling in HBECPKM cells was also observed with phospho-SMAD2/3 antibody (Supplementary Fig. S4). Our initial studies of phospho-STAT3 showed no obvious change in the number of upregulated cells (Supplementary Fig. S5). This was not expected, as MYC-dependent upregulation of phospho-STAT3 has been reported in breast cancer (22–24). We wondered whether this was due to a high concentration of added EGF (5,000 pg/mL) in our defined media. We found by lowering the concentration of EGF (50 pg/mL vs. 5,000 pg/mL), which reduced EGF-related STAT3 upregulation in HBECs, that the fraction of upregulated cells increased from 5% to approximately 30% (Fig. 1D and E; Supplementary Fig. S6). The lowered EGF concentration maintained the altered SMAD2/3 signaling differences between HBEC and HBECPKM cells. Thus, throughout the remainder of our studies, we used the low (50 pg/mL) concentration of EGF. Immunoblots using antibodies against phospho-SMAD2/3 confirmed downregulation in SMAD2/3 signaling in HBECPKM versus parental HBEC (of course, immunoblots cannot detect both the down- and upregulation of signaling observed in single-cell measurements of STAT3; Fig. 1F; Supplementary Table S2). Taken together, we identified two specific signaling pathways that are altered during TP53, KRASV12, and MYC oncogenic progression (SMAD2/3 downregulation and pSTAT3 upregulation). Importantly, we found that these alterations in signaling state are not graded with the addition of oncogenic changes, but are rather “switch-like” occurring only after three oncogenic changes are in place.

Upregulation of STAT3 signaling can be utilized as a targetable vulnerability in HBECPKM

STAT3 is a known druggable target (25). We found that the HBECPKM showed increased sensitivity to STAT3 inhibitor Stattic compared with the nonmanipulated HBECs (Fig. 2A; Supplementary Fig. S7). However, no cells with upregulated STAT3 signaling were present after Stattic treatment, although the fraction of cells downregulated simultaneously for both SMAD2/3 and STAT3 signaling increased (Fig. 2B). Our data suggested we need to further characterize the alterations in SMAD2/3 signaling.

Figure 2.

STAT3 inhibitor diminishes the subpopulation of cells upregulated in STAT3 signaling but leaves the subpopulation of cells downregulated in SMAD2/3 signaling. A, MTS assay of parental HBEC and HBECPKM cells for their response to STAT3 inhibitor Stattic. x-axis, concentration of Stattic in log scale; y-axis, percent viability, with 100% corresponding to DMSO control conditions for each respective cell line. Error bars, SD (n = 8 technical replicates). B, Quantification of downregulated (blue) and upregulated (yellow) fraction of cells in SMAD2/3 and p-STAT3 signaling in HBECPKM cells (relative to parental HBEC) before and after Stattic treatment. Error bars as in Fig. 1C (n = 6 technical replicates).

Figure 2.

STAT3 inhibitor diminishes the subpopulation of cells upregulated in STAT3 signaling but leaves the subpopulation of cells downregulated in SMAD2/3 signaling. A, MTS assay of parental HBEC and HBECPKM cells for their response to STAT3 inhibitor Stattic. x-axis, concentration of Stattic in log scale; y-axis, percent viability, with 100% corresponding to DMSO control conditions for each respective cell line. Error bars, SD (n = 8 technical replicates). B, Quantification of downregulated (blue) and upregulated (yellow) fraction of cells in SMAD2/3 and p-STAT3 signaling in HBECPKM cells (relative to parental HBEC) before and after Stattic treatment. Error bars as in Fig. 1C (n = 6 technical replicates).

Close modal

SMAD2/3 signaling alteration is MYC-dependent but not related to cell-cycle or cell size changes

Because the HBECPKM cell line shows mesenchymal-like behavior (6), the SMAD2/3 downregulated subpopulation was not expected (26, 27). Thus, we next chose to investigate if alterations in SMAD2/3 signaling are genuinely oncogene-related.

First, we used a previously developed approach to identify cells in the G1 stage of the cell cycle based on the distribution of total DNA intensity (5). We found that oncogene-induced downregulation in SMAD2/3 signaling is apparent even within the subpopulation of G1 cells (Fig. 3A). Thus, the observed changes in signaling are unlikely due to changes in the fractions of cells in different cell-cycle stages. We additionally found that signaling alterations were not simply due to changes in cell size between the parental and oncogenically manipulated cells (Supplementary Fig. S8).

Figure 3.

Downregulation in SMAD2/3 signaling is oncogene-dependent. A, Single-cell quantification of altered fraction of cells across parental and oncogenically manipulated HBEC cell lines in G1 phase of cell cycle. Cells were computationally classified into various cell-cycle phases based on their DNA intensity (5). For each step in the oncogenic progression, only cells belonging to the G1 phase were considered. SMAD2/3 signaling alteration was calculated as in Fig. 1C (n = 8 technical replicates). B, qRT-PCR analysis of well-known MYC target genes (ASS1, RGS16) in HBECPKM cells with nontarget control (NTC) and Omomyc construct. The expression of each gene in HBECPKM cells with Omomyc construct is normalized to its expression in HBECPKM cells with NTC. Error bars, SD (n = 6 technical replicates); P values were computed using a two-sided t test. C, Effect of MYC knockdown on SMAD2/3 signaling. Shown are the single-cell distributions of SMAD2/3 signaling (as in Fig. 1B) for HBECPKM (black dotted line), HBECPKM with nontarget control vector (gray dashed line), HBECPKM with Omomyc vector (solid gray line), compared with parental HBEC (solid black line). The vertical lines denote 5th (blue) and 95th (yellow) percentiles of the parental HBEC distribution. Cells below and above these lines are considered downregulated and upregulated, respectively. Results are obtained from pooling wells (n = 6 technical replicates). D, Quantification of SMAD2/3 signaling heterogeneity showing fraction of upregulated (yellow) and downregulated (blue) cells, in HBECPKM, with nontarget control and Omomyc construct. Error bars as in Fig. 1C (n = 6 technical replicates).

Figure 3.

Downregulation in SMAD2/3 signaling is oncogene-dependent. A, Single-cell quantification of altered fraction of cells across parental and oncogenically manipulated HBEC cell lines in G1 phase of cell cycle. Cells were computationally classified into various cell-cycle phases based on their DNA intensity (5). For each step in the oncogenic progression, only cells belonging to the G1 phase were considered. SMAD2/3 signaling alteration was calculated as in Fig. 1C (n = 8 technical replicates). B, qRT-PCR analysis of well-known MYC target genes (ASS1, RGS16) in HBECPKM cells with nontarget control (NTC) and Omomyc construct. The expression of each gene in HBECPKM cells with Omomyc construct is normalized to its expression in HBECPKM cells with NTC. Error bars, SD (n = 6 technical replicates); P values were computed using a two-sided t test. C, Effect of MYC knockdown on SMAD2/3 signaling. Shown are the single-cell distributions of SMAD2/3 signaling (as in Fig. 1B) for HBECPKM (black dotted line), HBECPKM with nontarget control vector (gray dashed line), HBECPKM with Omomyc vector (solid gray line), compared with parental HBEC (solid black line). The vertical lines denote 5th (blue) and 95th (yellow) percentiles of the parental HBEC distribution. Cells below and above these lines are considered downregulated and upregulated, respectively. Results are obtained from pooling wells (n = 6 technical replicates). D, Quantification of SMAD2/3 signaling heterogeneity showing fraction of upregulated (yellow) and downregulated (blue) cells, in HBECPKM, with nontarget control and Omomyc construct. Error bars as in Fig. 1C (n = 6 technical replicates).

Close modal

We next tested whether the oncogene-induced alteration in SMAD2/3 signaling is reversible. In particular, we wondered whether inhibition of MYC function in HBECPKM would reverse the observed signaling alteration. We stably introduced a MYC-dominant negative construct, Omomyc (28), which allowed us to inducibly inhibit MYC's transcriptional activity in HBECPKM. Upon induction of Omomyc, we observed a significant reduction in the expression of downstream target genes of MYC (RGS16 and ASS1; ref. 29), confirming functionality of the Omomyc construct (Fig. 3B). When assayed for SMAD2/3 signaling, the fraction of downregulated HBECPKM cells decreased from approximately 40% to 20% (Fig. 3C and D). This partial reversion suggests that MYC overexpression plays a role in the observed SMAD2/3 signaling alteration, and that this alteration is not simply due to permanent, genomic changes of our transformed HBECPKM cell line.

Alterations in SMAD2/3 downstream genes can reveal targetable vulnerabilities in HBECPKM

To target the subpopulation of cells with downregulated SMAD2/3 signaling, we searched for genes with two properties. First, we looked for targets that are SMAD2/3-related. To this end, we compiled a list of 50 downstream target genes of TGFβ signaling from the literature (Materials and Methods). Second, we looked for targets that are upregulated in HBECPKM cells primarily due to oncogenic changes rather than culture conditions. To this end, we made use of available microarray mRNA expression data to compare expression of genes in HBECPKM cells +/– serum versus all the other HBECs in normal, serum-free growth conditions. We found 10 differentially expressed genes with significant P values (Fig. 4A). Among these genes were MYC (which was expected and served as a positive control), BCL6, MMP7, FOXK1, and SOX2, which were all confirmed by qRT-PCR (Fig. 4B). However, although the expressions for all of these genes were strongly altered between HBECs and HBECPKM cells, BCL6 provided the most constant level of expression in HBECPKM cells (among the upregulated alterations) independent of whether cells were cultured in short-term (40 minutes) or long-term (2 weeks) serum treatment conditions.

Figure 4.

Alterations in SMAD2/3 downstream genes may reveal novel targetable vulnerabilities. A, mRNA expression analysis of TGFβ downstream genes using microarray. HBECPKM cells grown in presence and absence of 10% FBS is in one group. Other HBECs (with no single or double manipulations) are in the second group. Only differentially and significantly altered (two-sided t test, P value < 0.05) genes are shown. Numbers in the first column represent fold change (Log2) between the median level of gene expression of HBECPKM and other HBECs. B, mRNA expression analysis of top three upregulated genes (SOX2, BCL6, and MMP7) in HBECPKM cells using qRT-PCR. FOXK1 was used as an example of a downregulated gene in HBECPKM. Parental HBEC (black) or HBECPKM (gray) were grown in defined, serum-free growth condition, in 10% FBS treatment for 40 minutes, or in 10% FBS treatment for 2 weeks. y-axis, gene expression relative to human reference (Materials and Methods). Error bars, SE (n = 6 technical replicates).

Figure 4.

Alterations in SMAD2/3 downstream genes may reveal novel targetable vulnerabilities. A, mRNA expression analysis of TGFβ downstream genes using microarray. HBECPKM cells grown in presence and absence of 10% FBS is in one group. Other HBECs (with no single or double manipulations) are in the second group. Only differentially and significantly altered (two-sided t test, P value < 0.05) genes are shown. Numbers in the first column represent fold change (Log2) between the median level of gene expression of HBECPKM and other HBECs. B, mRNA expression analysis of top three upregulated genes (SOX2, BCL6, and MMP7) in HBECPKM cells using qRT-PCR. FOXK1 was used as an example of a downregulated gene in HBECPKM. Parental HBEC (black) or HBECPKM (gray) were grown in defined, serum-free growth condition, in 10% FBS treatment for 40 minutes, or in 10% FBS treatment for 2 weeks. y-axis, gene expression relative to human reference (Materials and Methods). Error bars, SE (n = 6 technical replicates).

Close modal

Next, we observed that MYC and BCL6 gene expressions are strongly correlated (Pearson correlation coefficient 0.83) across 20 single-cell clones of HBECPKM (Fig. 5A). Such correlation of BCL6 expression with the expression of other genetic manipulations (TP53 and KRAS) was relatively lower. Further, we found that inhibition of MYC function with Omomyc reduced BCL6 level by 2-fold (Fig. 5B). This low level of BCL6 expression after MYC inhibition is comparable with the level of BCL6 in the parental HBEC. Taken together, BCL6, a downstream gene of SMAD2/3 signaling (30–32), is significantly upregulated in a serum-independent manner and strongly correlated to MYC expression in HBECPKM cells.

Figure 5.

BCL6 is a targetable vulnerability in HBECPKM cells. A, Correlation of relative BCL6 gene expression and TP53, KRAS, and MYC gene expression in 20 single-cell clonal populations of HBECPKM using qRT-PCR. The coefficients of determination R2 (0.6283, 0.4326, and 0.8466, respectively) for simple linear regression as shown. B, qRT-PCR of BCL6 gene expression for (left to right) parental HBEC (black), HBECPKM, HBECPKM transfected with nontarget control (NTC), and HBECPKM transfected with Omomyc construct. Error bars are as in Fig. 3B (n = 2 technical replicates); normalization is with respect to the parental HBEC. C, Confirmation of BCL6 knockdown based on gene expression. Relative BCL6 gene expression using qRT-PCR for (left to right) parental HBEC transfected with nontarget control, parental HBEC transfected with siRNA against BCL6, HBECPKM transfected with nontarget control, and HBECPKM transfected with siRNA against BCL6. Error bars are as in Fig. 3B (n = 6 technical replicates); the expression of BCL6 in each cell line with siBCL6 is normalized to BCL6 expression with siNTC. D, Confirmation of BCL6 knockdown based on protein expression. Western blot showing BCL6 protein expression in HBECPKM transfected with nontarget control and siRNA against BCL6. E, Effect of BCL6 knockdown on cell viability for parental HBEC and HBECPKM transfected with (left to right): nontarget control, siRNA against PLK1 (positive toxic control), siRNA against BCL6, and C911 control for siBCL6. Error bars, SE (n = 2 technical replicates), and P values were calculated using a two-sided t test.

Figure 5.

BCL6 is a targetable vulnerability in HBECPKM cells. A, Correlation of relative BCL6 gene expression and TP53, KRAS, and MYC gene expression in 20 single-cell clonal populations of HBECPKM using qRT-PCR. The coefficients of determination R2 (0.6283, 0.4326, and 0.8466, respectively) for simple linear regression as shown. B, qRT-PCR of BCL6 gene expression for (left to right) parental HBEC (black), HBECPKM, HBECPKM transfected with nontarget control (NTC), and HBECPKM transfected with Omomyc construct. Error bars are as in Fig. 3B (n = 2 technical replicates); normalization is with respect to the parental HBEC. C, Confirmation of BCL6 knockdown based on gene expression. Relative BCL6 gene expression using qRT-PCR for (left to right) parental HBEC transfected with nontarget control, parental HBEC transfected with siRNA against BCL6, HBECPKM transfected with nontarget control, and HBECPKM transfected with siRNA against BCL6. Error bars are as in Fig. 3B (n = 6 technical replicates); the expression of BCL6 in each cell line with siBCL6 is normalized to BCL6 expression with siNTC. D, Confirmation of BCL6 knockdown based on protein expression. Western blot showing BCL6 protein expression in HBECPKM transfected with nontarget control and siRNA against BCL6. E, Effect of BCL6 knockdown on cell viability for parental HBEC and HBECPKM transfected with (left to right): nontarget control, siRNA against PLK1 (positive toxic control), siRNA against BCL6, and C911 control for siBCL6. Error bars, SE (n = 2 technical replicates), and P values were calculated using a two-sided t test.

Close modal

BCL6 is a targetable vulnerability in HBECPKM cells

To test if BCL6 is a targetable vulnerability in HBECPKM, we made use of siRNA-mediated knockdown of BCL6 and searched for a differential change in viability of HBECPKM cells versus HBEC cells. First, we confirmed that our siRNA knocked down both BCL6 mRNA and protein expression (Fig. 5C and D; Supplementary Fig. S9). Next, we found that the BCL6 knockdown resulted in significantly higher cell death (4-fold) in HBECPKM cells than the parental HBECs (Fig. 5E). Using a C911 BCL6-siRNA construct (7), we showed that the siRNA knockdown was an “on target” effect. Thus, we found BCL6 to be a potential targetable vulnerability in the HBECPKM cells.

BCL6 can be a targetable vulnerability in a subset of NSCLC lines

To examine the generality of BCL6 as a vulnerability, we tested 5 NSCLC cell lines: H1693, H1819, H1993, HCC827, and H2009. We compared SMAD2/3 signaling in these cells with parental HBECs (Fig. 6A). We utilized parental HBEC cells treated with 10% serum for 40 minutes as a positive control for SMAD2/3 upregulation. Our analysis showed that the majority of cells (>50%) in H1693, H1993, and HCC827 show SMAD2/3 downregulation compared with the parental HBEC. In contrast, H2009 and H1819 showed a smaller fraction of cells with SMAD2/3 downregulation. Further, we found that H1693 and H1993 have significantly higher levels of BCL6 gene expression compared with the parental HBEC in our qRT-PCR and Western blot assays (Fig. 6B and C). The high levels of BCL6 decreased significantly after MYC target gene knockdown using the Omomyc construct similar to the result we observed in the HBECPKM cells (Fig. 3C and D). After siRNA-mediated knockdown of BCL6, we observed that H1693 and H1993 showed significantly higher cell death than H2009 (Fig. 6D). Hence, our data suggest that BCL6 can be a targetable vulnerability in a subset of NSCLC cell lines that exhibit SMAD2/3 downregulation coupled with increased BCL6 expression.

Figure 6.

BCL6 in a targetable vulnerability in a subset of NSCLC cell lines. A, Quantitation of the fraction of cells with altered SMAD2/3 signaling in NSCLC cell lines H1693, H1819, H1993, HCC827, and H2009 compared with parental HBEC and parental HBEC treated with 10% FBS for 40 minutes (positive control) based on immunofluorescence microscopy. Error bars (n = 7 technical replicates) as in Fig. 1C. B, Relative BCL6 gene expression from qRT-PCR assay of NSCLC cell lines transfected with nontarget control and Omomyc construct compared with the level in parental HBEC (horizontal dotted line). Error bars and P values are as in Fig. 3B (n = 6 technical replicates). C, Protein expression of BCL6 and phospho-STAT3 in NSCLC cell lines H1693, H1819, H1993, HCC827, and H2009 compared with parental HBEC as measured by Western blot. GAPDH was used as a loading control. D, Relative viability of NSCLC cell lines H1693 and H1993 compared with H2009 after siRNA-mediated BCL6 knockdown. Error bars and P values are as in Fig. 3B (n = 2 technical replicates).

Figure 6.

BCL6 in a targetable vulnerability in a subset of NSCLC cell lines. A, Quantitation of the fraction of cells with altered SMAD2/3 signaling in NSCLC cell lines H1693, H1819, H1993, HCC827, and H2009 compared with parental HBEC and parental HBEC treated with 10% FBS for 40 minutes (positive control) based on immunofluorescence microscopy. Error bars (n = 7 technical replicates) as in Fig. 1C. B, Relative BCL6 gene expression from qRT-PCR assay of NSCLC cell lines transfected with nontarget control and Omomyc construct compared with the level in parental HBEC (horizontal dotted line). Error bars and P values are as in Fig. 3B (n = 6 technical replicates). C, Protein expression of BCL6 and phospho-STAT3 in NSCLC cell lines H1693, H1819, H1993, HCC827, and H2009 compared with parental HBEC as measured by Western blot. GAPDH was used as a loading control. D, Relative viability of NSCLC cell lines H1693 and H1993 compared with H2009 after siRNA-mediated BCL6 knockdown. Error bars and P values are as in Fig. 3B (n = 2 technical replicates).

Close modal

Combined treatment with BCL6 inhibitor and STAT3 inhibitor can achieve better response than treatment with each inhibitor alone in NSCLC lines

After identifying BCL6 and phospho-STAT3 as targetable vulnerabilities, we tested the effect of small-molecule inhibitors of these components alone and in combination using colony formation assays in characterized NSCLC lines. To pharmacologically block the transcriptional function of BCL6, we utilized FX-1 (12). Among our panel of lung cancer cell lines, H1693 was highly sensitive to FX-1 (Fig. 7A and B). By contrast, HCC827 and H2009 were relatively resistant while H1993 had an intermediate response. To block the transcriptional function of STAT3, we utilized the highly potent inhibitor BBI-608 (10, 11) that is known to downregulate expression of STAT3 target genes. Unlike our previously used inhibitor (stattic), BBI-608 can be used in a more clinically relevant setting. Among our panel of lung cancer cell lines, HCC827 was highly sensitive to BBI-608 (Fig. 7A and B). By contrast, H1693 and H2009 were relatively resistant. H1993 showed an intermediate response. We observed a correspondence between higher sensitivities to FX-1 with higher levels of BCL6 protein expression but no such correspondence of sensitivity to BBI-608 with phospho-STAT3 levels (Fig. 6C). As might be expected based on their response to single treatments of BBI-608 or FX-1, H1693 and HCC827 (which each responded to one of the two treatments) were sensitive to the combination treatment but 2009 (which was resistant to both treatments) continued to be resistant. Crucially, H1993, which only showed a partial response to each of the two individual treatments was highly sensitive to the combination.

Figure 7.

In vitro or in vivo combination treatment with BCL6 and STAT3 inhibitors eliminates more cancer cells than single-agent treatments. A, Liquid colony formation assay of four NSCLC cell lines H1693, HCC827, H2009, and H1993 with their responses to STAT3 inhibitor, BBI-608 alone (left), FX-1 alone (middle), and the combination treatment (right). Image shows crystal violet–stained colonies after 2 weeks of drug treatment. For both single-agent and combination treatments, BBI-608 concentration (mmol/L) ranges in the 6-well plates are 0, 0.05, and 0.15 (top row left to right), and 0.44, 1.33, and 4 (bottom row left to right). FX-1 concentration (mmol/L) ranges in the 6-well plates are 0, 0.31, and 0.93 (top row left to right), and 2.77, 8.3, and 25 (bottom row left to right). B, Quantification of liquid colony formation assay of four NSCLC cell lines H1693, HCC827, H2009, and H1993 to BBI-608 alone (left), FX-1 alone (middle), and the combination treatment (right). The number of colonies in each experimental condition was normalized to vehicle controls. Error bars, SD (n = 8 technical replicates). Solid curves were constructed using a sigmoidal curve fit. C, Volume of H1993 cell line–derived subcutaneous, xenografted tumors measured (1/2 × length × width × width) and averaged in each of the four treatment groups (vehicle, BBI-608 alone, FX-1 alone, and BBI-608+FX-1 combination) over time. BBI-608 was administered at 20 mg/kg. FX-1 was administered at 25 mg/kg. Gray arrow, the start day of treatment. P values were calculated by two-way Anova with Tukey multiple comparison test. Error bars, SD (n = 10 technical replicates).

Figure 7.

In vitro or in vivo combination treatment with BCL6 and STAT3 inhibitors eliminates more cancer cells than single-agent treatments. A, Liquid colony formation assay of four NSCLC cell lines H1693, HCC827, H2009, and H1993 with their responses to STAT3 inhibitor, BBI-608 alone (left), FX-1 alone (middle), and the combination treatment (right). Image shows crystal violet–stained colonies after 2 weeks of drug treatment. For both single-agent and combination treatments, BBI-608 concentration (mmol/L) ranges in the 6-well plates are 0, 0.05, and 0.15 (top row left to right), and 0.44, 1.33, and 4 (bottom row left to right). FX-1 concentration (mmol/L) ranges in the 6-well plates are 0, 0.31, and 0.93 (top row left to right), and 2.77, 8.3, and 25 (bottom row left to right). B, Quantification of liquid colony formation assay of four NSCLC cell lines H1693, HCC827, H2009, and H1993 to BBI-608 alone (left), FX-1 alone (middle), and the combination treatment (right). The number of colonies in each experimental condition was normalized to vehicle controls. Error bars, SD (n = 8 technical replicates). Solid curves were constructed using a sigmoidal curve fit. C, Volume of H1993 cell line–derived subcutaneous, xenografted tumors measured (1/2 × length × width × width) and averaged in each of the four treatment groups (vehicle, BBI-608 alone, FX-1 alone, and BBI-608+FX-1 combination) over time. BBI-608 was administered at 20 mg/kg. FX-1 was administered at 25 mg/kg. Gray arrow, the start day of treatment. P values were calculated by two-way Anova with Tukey multiple comparison test. Error bars, SD (n = 10 technical replicates).

Close modal

Combination of BBI-608 (STAT3 inhibitor) and FX-1 (BCL6 inhibitor) increased H1993 cell line–derived xenografts' response to therapy

We wondered whether this observed benefit of combined treatment would also hold in vivo for H1993-derived subcutaneous xenografts (see Supplementary Table S3 for doses and schedules). As with the in vitro results, we found that both single-agent therapies significantly reduced (P < 0.0001) the growth in tumor volume as compared with the vehicle-treated controls (Fig. 7C; Supplementary Table S3). More importantly, the combination therapy significantly increased (P < 0.005) the response of the xenografted tumors compared with tumors from the single-agent therapies. Taken together, the growth rate of the tumor supported the hypothesis that combined treatment was more effective than either treatment alone.

We investigated whether oncogene-induced signaling alterations can provide insight into vulnerabilities of cancer cell populations and if these changes are homogeneous or heterogeneous in a defined, genetically manipulated, HBEC preclinical model. Our study utilized HBECs, a simplified cellular model of defined oncogenesis (with TP53 knockdown, KRASV12 and MYC overexpression), and focused on signaling pathways commonly affected in lung cancer. We found that SMAD2/3 signaling becomes downregulated, and phospho-STAT3 signaling becomes upregulated after all three oncogenic manipulations. Interestingly, in HBECPKM cells, SMAD2/3 downregulation and phospho-STAT3 upregulation occur in two different subpopulations. Thus, heterogeneity in signaling states is present, even within a defined oncogenotype. We found that the drug Stattic, designed to target STAT3, eliminated cells with STAT3 upregulation, but left a large surviving subpopulation of cells with downregulated SMAD2/3 signaling. There is currently no obvious druggable target for such SMAD2/3 downregulated cells. To target the subpopulation with downregulated SMAD2/3 signaling, we searched for downstream genes of SMAD2/3 that are correspondingly upregulated in a serum-independent manner. This strategy identified the transcription factor BCL6 as such a SMAD2/3 downstream gene, making it a potential genetic vulnerability. This vulnerability was validated both in our HBECPKM model and NSCLC cell line models that exhibited dysregulated SMAD2/3 signaling. Our ability to knock down only a subpopulation of cells in the HBEC series with the STAT3 drug led us to design a combination therapy with small-molecule inhibitors targeting both STAT3 and BCL6, which provided increased response in vitro and in vivo in subcutaneous xenografted tumors.

BCL6 has recently been characterized as a vulnerability in a subset of breast cancers but to our knowledge has not been described as a therapeutic target in lung cancer (33). BCL6 was also identified as a target of miR-187-3p in NSCLC cell lines A549 and SPC-A-1 (34). Of course classically, BCL6 has been widely studied in the context of B-cell lymphoma (35) and its connection to TGFβ resistance (36). In particular, for a subgroup of lymphoma cases, known as “double-hit lymphoma”, BCL6 and MYC aberrations together are considered as prognostic factors (37). Interestingly, we find the analogous result that MYC regulates BCL6 expression in a subset of NSCLC cell lines. Our results point to a possible new strategy to target MYC-related BCL6 overexpressing lung cancers. Further clinical translation of such phospho-STAT3, BCL6-targeted therapy will require both development of small-molecule inhibitors with appropriate characteristics for therapeutic delivery and information if and how lung cancers develop resistance to such combined phospho-STAT3 and BCL6-targeted therapy.

The paucity of "clinically actionable" targets for the majority of driver oncogenes and tumor-suppressor genes in lung cancer indicates the need to find ways to therapeutically target these potential vulnerabilities. In the present work, we show that single-cell analysis of oncogene-induced signaling alterations can reveal targetable vulnerabilities for multiple cellular subpopulations, each having potentially distinct signatures of signaling alteration. Heterogeneity in cancer cell populations has been traditionally viewed as an impediment to effective diagnosis and treatment. Our work suggests that analysis of heterogeneity may, in fact, help identify molecular vulnerabilities and suggest rational, effective therapeutic combination strategies.

No potential conflicts of interest were disclosed.

Conception and design: D. Deb, S. Rajaram, J.D. Minna, S.J. Altschuler, L.F. Wu

Development of methodology: D. Deb, S. Rajaram, J.E. Larsen, L.S. Li, J.D. Minna, S.J. Altschuler, L.F. Wu

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): D. Deb, J.E. Larsen, L.S. Li, K. Avila, F. Xue, J.D. Minna

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): D. Deb, S. Rajaram, P.D. Dospoy, R. Marullo, L.S. Li, L. Cerchietti, J.D. Minna, S.J. Altschuler, L.F. Wu

Writing, review, and/or revision of the manuscript: D. Deb, S. Rajaram, J.E. Larsen, F. Xue, L. Cerchietti, J.D. Minna, S.J. Altschuler, L.F. Wu

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): D. Deb, R. Marullo, J.D. Minna

Study supervision: D. Deb, J.E. Larsen, L. Cerchietti, J.D. Minna, S.J. Altschuler, L.F. Wu

Other (Foster communications and collaborations among authors from 5 different universities and research institutes): D. Deb

We thank Dr. Laura Soucek for kindly providing us with the Omomyc construct, Dr. Luc Girard for his help with the microarray data analysis, Dr. Mitsuo Sato for helping create the panel of oncogenically progressed HBEC lines, and Dr. Ari Melnick for advice and drug for the BCL6 studies. We also thank all the members of Altschuler lab, Wu lab, and Minna lab for useful discussions.

This study was supported by University of Texas Southwestern Medical Center SPOREP50CA70907 (J.D. Minna), CPRIT RP110708 (J.D. Minna), U01 CA176284 (J.D. Minna), National Health and Medical Research Council of Australia Overseas-Based Biomedical Training Fellowship (494511) and TSANZ/Allen & Hanburys Respiratory Research Fellowship (J.E. Larsen), NCI-NIH RO1 CA133253 (S.J. Altschuler), NSF PHY-1545915 (S.J. Altschuler), Stand Up to Cancer Convergence Award (S.J. Altschuler), NCI-NIH RO1 CA185404 and CA184984 (L.F. Wu), and the Institute of Computational Health Sciences (ICHS) at UCSF (S.J. Altschuler and L.F. Wu).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Lauro
S
,
Onesti
CE
,
Righini
R
,
Marchetti
P
. 
The use of bevacizumab in non-small cell lung cancer: An update
.
Anticancer Res
2014
;
34
:
1537
45
2.
Larsen
JE
,
Minna
JD
. 
Molecular biology of lung cancer: Clinical implications
.
Clin Chest Med
2011
;
32
:
703
40
3.
Devarakonda
S
,
Morgensztern
D
,
Govindan
R
. 
Genomic alterations in lung adenocarcinoma
.
Lancet Oncol
2015
;
16
:
e342
51
4.
Kim
HS
,
Mendiratta
S
,
Kim
J
,
Pecot
CV
,
Larsen
JE
,
Zubovych
I
, et al
Systematic identification of molecular subtype-selective vulnerabilities in non-small-cell lung cancer
.
Cell
2013
;
155
:
552
66
5.
Singh
DK
,
Ku
CJ
,
Wichaidit
C
,
Steininger
RJ
 3rd
,
Wu
LF
,
Altschuler
SJ
. 
Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities
.
Mol Syst Biol
2010
;
6
:
369
6.
Sato
M
,
Larsen
JE
,
Lee
W
,
Sun
H
,
Shames
DS
,
Dalvi
MP
, et al
Human lung epithelial cells progressed to malignancy through specific oncogenic manipulations
.
Mol Cancer Res
2013
;
11
:
638
50
7.
Buehler
E
,
Chen
YC
,
Martin
S
. 
C911: A bench-level control for sequence specific siRNA off-target effects
.
PLoS One
2012
;
7
:
e51942
8.
Rasband
WS
. 
Image J
.
Maryland, USA
:
National Institutes of Health B
; 
1997–2009
.
Available from
: http://rsb.info.nih.gov/ij/.
9.
Loo
LH
,
Wu
LF
,
Altschuler
SJ
. 
Image-based multivariate profiling of drug responses from single cells
.
Nat Methods
2007
;
4
:
445
53
10.
Li
Y
,
Rogoff
HA
,
Keates
S
,
Gao
Y
,
Murikipudi
S
,
Mikule
K
, et al
Suppression of cancer relapse and metastasis by inhibiting cancer stemness
.
Proc Natl Acad Sci USA
2015
;
112
:
1839
44
11.
Zhang
Y
,
Jin
Z
,
Zhou
H
,
Ou
X
,
Xu
Y
,
Li
H
, et al
Suppression of prostate cancer progression by cancer cell stemness inhibitor napabucasin
.
Cancer Med
2016
;
5
:
1251
8
12.
Cardenas
MG
,
Yu
W
,
Beguelin
W
,
Teater
MR
,
Geng
H
,
Goldstein
RL
, et al
Rationally designed BCL6 inhibitors target activated B cell diffuse large B cell lymphoma
.
J Clin Invest
2016
;
126
:
3351
62
13.
de Bruin
EC
,
McGranahan
N
,
Mitter
R
,
Salm
M
,
Wedge
DC
,
Yates
L
, et al
Spatial and temporal diversity in genomic instability processes defines lung cancer evolution
.
Science (New York, NY)
2014
;
346
:
251
6
14.
Govindan
R
,
Ding
L
,
Griffith
M
,
Subramanian
J
,
Dees
ND
,
Kanchi
KL
, et al
Genomic landscape of non-small cell lung cancer in smokers and never-smokers
.
Cell
2012
;
150
:
1121
34
15.
Zhang
J
,
Fujimoto
J
,
Zhang
J
,
Wedge
DC
,
Song
X
,
Zhang
J
, et al
Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing
.
Science (New York, NY)
2014
;
346
:
256
9
16.
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
75
17.
Heuberger
J
,
Birchmeier
W
. 
Interplay of cadherin-mediated cell adhesion and canonical Wnt signaling
.
Cold Spring Harbor Perspect Biol
2010
;
2
:
a002915
18.
Thiery
JP
,
Acloque
H
,
Huang
RY
,
Nieto
MA
. 
Epithelial-mesenchymal transitions in development and disease
.
Cell
2009
;
139
:
871
90
19.
Zeisberg
M
,
Neilson
EG
. 
Biomarkers for epithelial-mesenchymal transitions
.
J Clin Invest
2009
;
119
:
1429
37
20.
Perlman
ZE
,
Slack
MD
,
Feng
Y
,
Mitchison
TJ
,
Wu
LF
,
Altschuler
SJ
. 
Multidimensional drug profiling by automated microscopy
.
Science (New York, NY)
2004
;
306
:
1194
8
21.
Courtois-Cox
S
,
Genther Williams
SM
,
Reczek
EE
,
Johnson
BW
,
McGillicuddy
LT
,
Johannessen
CM
, et al
A negative feedback signaling network underlies oncogene-induced senescence
.
Cancer Cell
2006
;
10
:
459
72
22.
Tran
PT
,
Fan
AC
,
Bendapudi
PK
,
Koh
S
,
Komatsubara
K
,
Chen
J
, et al
Combined Inactivation of MYC and K-Ras oncogenes reverses tumorigenesis in lung adenocarcinomas and lymphomas
.
PloS One
2008
;
3
:
e2125
23.
Xiong
A
,
Yu
W
,
Liu
Y
,
Sanders
BG
,
Kline
K
. 
Elimination of ALDH+ breast tumor initiating cells by docosahexanoic acid and/or gamma tocotrienol through SHP-1 inhibition of Stat3 signaling
.
Mol Carcinog
2015
;
55
:
420
30
.
24.
Zhang
X
,
Yue
P
,
Page
BD
,
Li
T
,
Zhao
W
,
Namanja
AT
, et al
Orally bioavailable small-molecule inhibitor of transcription factor Stat3 regresses human breast and lung cancer xenografts
.
Proc Natl Acad Sci USA
2012
;
109
:
9623
8
25.
Bendell
JC
,
Hong
DS
,
Burris
HA
 3rd
,
Naing
A
,
Jones
SF
,
Falchook
G
, et al
Phase 1, open-label, dose-escalation, and pharmacokinetic study of STAT3 inhibitor OPB-31121 in subjects with advanced solid tumors
.
Cancer Chemother Pharmacol
2014
;
74
:
125
30
26.
Kaowinn
S
,
Kim
J
,
Lee
J
,
Shin
DH
,
Kang
CD
,
Kim
DK
, et al
Cancer upregulated gene 2 induces epithelial-mesenchymal transition of human lung cancer cells via TGF-beta signaling
.
Oncotarget
2017
;
8
:
5092
5110
.
27.
Zhou
Y
,
He
Z
,
Gao
Y
,
Zheng
R
,
Zhang
X
,
Zhao
L
, et al
Induced pluripotent stem cells inhibit bleomycin-induced pulmonary fibrosis in mice through suppressing TGF-beta1/smad-mediated epithelial to mesenchymal transition
.
Front Pharmacol
2016
;
7
:
430
28.
Soucek
L
,
Helmer-Citterich
M
,
Sacco
A
,
Jucker
R
,
Cesareni
G
,
Nasi
S
. 
Design and properties of a Myc derivative that efficiently homodimerizes
.
Oncogene
1998
;
17
:
2463
72
29.
Dang
CV
. 
c-Myc target genes involved in cell growth, apoptosis, and metabolism
.
Mol Cell Biol
1999
;
19
:
1
11
30.
Huret
J-L
,
Ahmad
M
,
Arsaban
M
,
Bernheim
A
,
Cigna
J
,
Desangles
F
, et al
Atlas of genetics and cytogenetics in oncology and haematology in 2013
.
Nucleic Acids Res
2013
;
41
:
D920
4
31.
Mullen
AC
,
Orlando
DA
,
Newman
JJ
,
Loven
J
,
Kumar
RM
,
Bilodeau
S
, et al
Master transcription factors determine cell-type-specific responses to TGF-beta signaling
.
Cell
2011
;
147
:
565
76
32.
Liu
Z
,
Lin
X
,
Cai
Z
,
Zhang
Z
,
Han
C
,
Jia
S
, et al
Global identification of SMAD2 target genes reveals a role for multiple co-regulatory factors in zebrafish early gastrulas
.
J Biol Chem
2011
;
286
:
28520
32
33.
Walker
SR
,
Liu
S
,
Xiang
M
,
Nicolais
M
,
Hatzi
K
,
Giannopoulou
E
, et al
The transcriptional modulator BCL6 as a molecular target for breast cancer therapy
.
Oncogene
2015
;
34
:
1073
82
34.
Sun
C
,
Li
S
,
Yang
C
,
Xi
Y
,
Wang
L
,
Zhang
F
, et al
MicroRNA-187–3p mitigates non-small cell lung cancer (NSCLC) development through down-regulation of BCL6
.
Biochem Biophys Res Commun
2016
;
471
:
82
8
35.
Lindsley
RC
,
LaCasce
AS
. 
Biology of double-hit B-cell lymphomas
.
Curr Opinion Hematol
2012
;
19
:
299
304
36.
Wang
D
,
Long
J
,
Dai
F
,
Liang
M
,
Feng
X-H
,
Lin
X
. 
BCL6 represses Smad signaling in transforming growth factor-beta resistance
.
Cancer Res
2008
;
68
:
783
9
37.
Cinar
M
,
Rosenfelt
F
,
Rokhsar
S
,
Lopategui
J
,
Pillai
R
,
Cervania
M
, et al
Concurrent inhibition of MYC and BCL2 is a potentially effective treatment strategy for double hit and triple hit B-cell lymphomas
.
Leukemia Res
2015
;39:730–8.