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.
Materials and Methods
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.
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 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.
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).
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.
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).
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.
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.
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.
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.
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.
Disclosure of Potential Conflicts of Interest
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).
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