Abstract
The lack of effective treatment options for advanced non-clear cell renal cell carcinoma (NCCRCC) is a critical unmet clinical need. Applying a high throughput drug screen to multiple human kidney cancer cells, we identified the combination of the VEGFR-MET inhibitor cabozantinib and the SRC inhibitor dasatinib acted synergistically in cells to markedly reduce cell viability. Importantly, the combination was well tolerated and caused tumor regression in vivo. Transcriptional and phosphoproteomic profiling revealed that the combination converged to downregulate the MAPK-ERK signaling pathway, a result not predicted by single agent analysis alone. Correspondingly, the addition of a MEK inhibitor synergized with either dasatinib or cabozantinib to increase its efficacy. This study, by employing approved, clinically relevant drugs provides the rationale for the design of effective combination treatments in NCCRCC that can be rapidly translated to the clinic.
Introduction
RCC accounts for more than 400,000 new cancer cases and 175,000 deaths per year worldwide1-3.Compared to other cancers, the pathology and genetics of kidney cancer is uniquely linked4, 5.Approximately 75% of kidney cancers are predominantly composed of clear cells and characterized by increased angiogenesis due to the loss of the VHL tumor suppressor gene. Consequently, clear cell RCC (CCRCC) are responsive to drugs that that directly or indirectly inhibit angiogenesis6-8.The remaining ∼25% of kidney cancers, while pathologically heterogeneous (e.g. papillary, chromophobe, sarcomatoid) have an intact VHL gene, and are broadly classified as non-clear cell RCC (NCCRCC). NCCRCC show minimal responses to antiangiogenics and have no effective treatment options9-16.While recent advances in immunotherapeutic approaches have further improved outcomes for metastatic CCRCC, standard therapies for advanced NCRRCC are lacking and long-term survival is poor4.
We and others identified SRC, an intracytoplasmic tyrosine kinase, as a novel therapeutic target in RCC17,18.Despite its promise, this target has shown minimal efficacy: notably, the FDA-approved SRC inhibitor dasatinib is primarily cytostatic and fails to kill RCC cells18; similarly, clinical activity of SRC inhibitors have been modest with rare durable responses19-22.This latter observation may be because SRC is activated non-mutationally through its interactions with growth factor receptors, where it acts as a rheostat for multiple signaling pathways that mediate proliferation and survival23.Consequently, upfront combinatorial drug therapies that blocks SRC and its key signaling partner(s) could potentially be more effective than single agent dasatinib.
Here, we took a systematic approach toward identifying co-targeting strategies for dasatinib by performing a combination drug screen using a chemogenomic library of mechanistically annotated, clinically-relevant approved and investigational drugs that inhibit pathways involved in growth, metabolism, and apoptosis in eight representative human RCC cell lines (5 VHL Wild Type and 3 VHL Null), resulting in a dataset incorporating 37,888 single agent and dasatinib combination responses. Based on this dataset, we selected 28 promising drugs to undergo further combination matrix screening covering 6,720 distinct drug-dasatinib combinations to enable the identification of potential synergies. These studies revealed cabozantinib as a promising drug combination with dasatinib. Cabozantinib inhibits several tyrosine kinases which are biologically relevant in RCC, including VEGFRs, MET, and AXL24.Cabozantinib is approved for use in advanced renal cell carcinoma, having demonstrated improved progression-free survival (PFS) vs. standard-of-care sunitinib as first-line treatment in patients with intermediate- or poor-risk metastatic CCRCC25, and showing significant improvements in PFS, objective response rate (ORR) and overall survival (OS) when compared with everolimus in patients treated with prior anti-angiogenic therapy26,27.Subsequently, we performed in vivo testing in representative VHL WT RCC models. Strikingly, while single agent dasatinib and cabozantinib recapitulated the clinical responses to restrain tumor growth, the combination caused marked tumor regression. Comprehensive integration of transcriptome and phosphoproteomic analysis of the combination therapy revealed rewiring of the kinome, with inhibition of MAPK signaling required for cytotoxic synergy. Taken together, our studies have identified promising drug combinations that transcend lineage and genetic landscape to induce cytotoxicity, suggesting broad utility across different kidney cancer subgroups.
Results
Comprehensive high-throughput drug synergy screen
Hypothesizing that the purely cytostatic response observed with SRC inhibition alone necessitates co-targeting of bypass signaling pathways, we performed a combination drug screen to identify drugs that synergized with dasatinib to kill cancer cells (Figure 1A; Table S1). First, we screened a library of 296 structurally diverse, medicinally active, and cell permeable small molecules (including inhibitors of key cancer-relevant targets, e.g., VEGFR, MET, EGFR, PDGFR, PI3K, CDK and apoptosis inducing molecules, e.g. BCL2, TP53, MDM2, survivin) in eight human RCC cell lines (VHL WT: ACHN, SN12C, TK10, UO31, CAKI-1, and VHL Null: 786-0, A498, 769-P)28.We tested 8 doses of each drug with or without dasatinib and read viability after 5 days of drug treatment, thereby generating 37,888 single agent and drug+dasatinib dose responses. Eighty-one of the drugs passed the “Highest Single Agent” (HSA) filter, where the combination has at least 10% greater inhibition than either dasatinib or the single agent alone at the same dose, for at least three doses29.The viability readings were used to calculate the GI50 (the drug concentration necessary to inhibit growth by 50% compared to the untreated condition), minimum viability, and the AUC (area under the dose-response curve) between drug alone and drug+dasatinib. Next, we determined the leads for secondary screening with the following rationale: a drug should have an effect in multiple cell lines, but an effect across all cell lines is not necessary. Therefore, for a specific measurement such as GI50 or AUC, we considered drugs that were in the top 50% of the measurement for all drugs in more than half of the cell lines (i.e. 5 or more cell lines), and drugs that were in the top 25% of the measurement for all drugs in more than one cell line (i.e. 2 or more cell lines). We applied these criteria to three calculations (GI50 fold change, AUC difference between drug alone and drug+dasatinib, and AUC percent change between drug alone and drug+dasatinib) and noted the drugs that passed the criteria for multiple measurements. We shortlisted the drugs based on the above screen criteria for efficacy, safety considerations and clinical utility, and nominated 28 drugs. Notably, these drugs were active against targets relevant to RCC biology including CDK, mTOR, PI3K, MET and VEGFR. In a composite analysis of all cell lines, cabozantinib emerged as the strongest sensitizer across all parameters (Figure 1B, C, Figures S1-3; see Materials and Methods). These 28 drugs showed strong curve shifts in combination relative to single agents (drug+dasatinib/drug alone log fold change <1; Figure 1D-K)
To confirm the findings of the high throughput screen, we generated dose response curves for cabozantinib alone and with the IC25 (quarter maximal inhibitory concentration) of dasatinib and observed a leftward shift with corresponding decreases in cell viability. (Figure 2A-D). Next, we treated RCC cells with increasing doses of cabozantinib and dasatinib alone and in combination and observed a synergistic interaction in suppressing proliferation (Figure 2E-H). Subsequently, the 28 selected drugs were further subjected to the secondary screening, which involved a dose-matrix of 6×8 in five human RCC cell lines (ACHN, CAKI-1, SN12C, 786-0 and 769-P), generating a further 6,720 dose-response signatures (Figure S4). The growth inhibition values from this secondary screen across different drug doses and combinations were first analyzed for synergy using the Bliss independence model29.Positive Bliss scores indicate combination effects where the effect is greater than additive. Cabozantinib was identified as one the most synergistic combinations with dasatinib (Figure 2I). We additionally analyzed for synergy using the multiple drug dose-median effect model as described by Chou and Talalay (CalcuSyn 2.0, Biosoft)30.Calcusyn calculates Combination Index (CI) for drug combinations: CI < 1 is synergistic; CI = 1 additive and CI > 1 is antagonistic (Figure 2J, K). We calculated CI for 28 drug combinations in five cell lines and ranked the drugs based on their synergistic effects in combination with dasatinib. This demonstrated consistent synergy (CI<1) between dasatinib and the top ranked drugs from the primary screen, validating our selection criteria. In particular, we observed synergy between dasatinib and inhibitors of VEGFR (cabozantinib, PD1703074, foretinib, R406-fostamatinib) and PI3K (GSK1059615, and GDC-0941). In agreement with the Bliss model, cabozantinib was one of the highest ranked synergistic combinations. Notably, we confirmed our prior finding of synergy between SRC and STAT3 inhibition (CYT387), demonstrating the robustness of our screen31.Collectively, these experiments suggested several potential therapeutic partners for dasatinib. We selected cabozantinib for further evaluation because it has been approved for both frontline and second-line treatment in CCRCC25-27,is being actively studied in ongoing single and combination clinical trials (NCT03541902, NCT04022343, NCT03937219, NCT03635892), and its distinction as consistently being a top hit throughout our screens in all cell lines tested.
Because our screening data was performed using CellTiter-Glo, which is adenosine triphosphate-dependent, we additionally examined the dasatinib-cabozantinib combination using alternative assays measuring caspase activation and resazurin metabolism (CellTiter-Blue). In agreement with the CellTiter-Glo data, combined treatment with dasatinib and cabozantinib caused a marked increase in apoptosis and decrease in proliferation in RCC cells in the panel compared to dasatinib alone (Figure S5). Western blot analysis of human NCCRCC ACHN and SN12C cells treated with cabozantinib and dasatinib confirmed the inhibition of their respective targets, as indicated by the de-phosphorylation of MET and SRC (Figure 2L).
Dasatinib-cabozantinib co-treatment induces tumor regression in human NCCRCC xenograft models
We next examined the safety and efficacy of dasatinib and cabozantinib cotreatment in vivo in two xenograft tumor models. While dasatinib and cabozantinib exhibited anti-tumor effect on ACHN and CAKI-1 human RCC xenografts, the combination potently inhibited tumor growth and caused tumor regression (Figure 3A, B: ACHN xenograft tumors; Figure 3E, F: CAKI-1 xenograft tumors). Importantly, the combination was well tolerated with no weight loss recorded (Figure S6). Consistent with our prior reports and current in vitro findings, dasatinib alone had a minimal impact on apoptosis18, 31.In marked contrast, combination treatment with dasatinib and cabozantinib resulted in a significant increase in the magnitude of apoptosis (established by an increase in cleaved caspase 3; P < 0.0001) [ACHN xenograft tumors], [CAKI-1 xenograft tumors]) and a reduction in proliferation (demonstrated by a decrease in Ki-67; P < 0.0001) (Figure 3C, D, I: ACHN xenograft tumors; Figure 3G, H: CAKI-1 xenograft tumors). Pharmacodynamic studies demonstrated that combination therapy led to the suppression of SRC and Met-phosphorylation in treated NCCRCC xenograft tumors (Figure 3J). These data support the combination of dasatinib and cabozantinib as an effective strategy for NCCRCC.
Combination treatment of NCCRCC cells reveals deterministic and stochastic signaling outputs
To obtain further insight into the signaling pathways acutely affected by dasatinib and cabozantinib co-treatment, we evaluated the changes in the phosphoproteome of the human NCCRCC cell line ACHN after dasatinib, cabozantinib and the combination treatment via quantitative, label-free quantitative mass spectrometry32-34.Supervised hierarchical clustering revealed duplicate samples clustered together, but that treatment altered phosphorylation levels of phosphopeptides with 3,369 phosphoserine and phosphothreonine (pST) peptides and 81 phosphotyrosine (pY) peptides significantly differed between treated and untreated cells (FDR <0.10, 0.20, respectively) (Figure 4A). As expected based on known dasatinib and cabozantinib targets, the SRC family kinases (including SRC, Fyn, Hck, Lyn, and Fgr) and KDR (VEGF) were predicted to be significantly less active in the combination-treated cells based on pY kinase substrate enrichment analyses (KSEA)33,35,an approach that estimates changes in a kinase’s activity based on the collective phosphorylation changes of its identified substrates. Focusing on the single agents, cabozantinib alone enriches for phosphatase activity of PTPN2/6, tyrosine kinases activity of RET and ERBB3/4, and transcription factor activity of STAT1/3. Dasatinib treatment alone also enriches phosphatase activity of PTPN6 and FLT1 kinase activity, a binding protein to VEGFR for activation. The robustness of the data and analysis was demonstrated by the de-enrichment of Abl1 activity, a known target of dasatinib36.These changes in kinase/phosphatase activity enrichment suggest for potential resistance mechanisms for cell survival when given either single agent (Figure 4 B, C: left panels).
KSEA for the pST motifs for the dasatinib-cabozantinib combination showed striking decreased activity across multiple kinases, including CDKs, NEK2, PKA and PKCs that would not have been predicted from the known kinome single activity profile of dasatinib or cabozantinib. However, it has been shown in previous literature that CDKs are phosphorylated and activated by SRC kinase, increasing the entry to cell cycle via phosphorylation and degradation of p27. In addition, BCR-ABL can also function to activate CDKs via p27 as well. Both of which is inhibited with dasatinib37, 38.Conversely, we observed enrichment of PTK6, ERBB, MAP2K1 pY motifs, all of which may interact with one another, suggesting potential activation of bypass tracks from combination therapy (Figure 4 D: right panels). Collectively, the phosphoproteome data provide strong evidence that the combination treatment, in addition to reducing known SRC and KDR signaling, also impacts multiple unexpected signaling networks that may be direct or indirect targets of these drugs.
Integration of transcriptomic and phosphoproteomic datasets reveals coordinated inhibition of MAPK-ERK pathway
Next, we sought mechanistic understanding for the observed tumor regressions after dasatinib-cabozantinib co-treatment by performing RNA sequencing of ACHN human NCCRCC cells treated with dasatinib, cabozantinib and the combination for 24 hours. Cabozantinib treatment caused differential expression of 4,026 (2,248 up + 1,178 down) genes compared to vehicle treatment. Dasatinib treatment resulted in a much more modest transcriptional response relative to cabozantinib treatment, inducing differential expression of 49 (48 up + 1 down) genes compared to vehicle treatment. Co-treatment induced the greatest transcriptional response, resulting in differential expression of 5,839 (3,048 + 2,791) genes compared to vehicle (Log2FC ≥ 0.5 or ≤ 0.5; q < 0.01), 65% of which were shared with either or both of the individual treatments (Figure 5A). Comparison of overall expression profiles across the treatments revealed a high degree of correlation across treatments (Figure S7; Methods). These results indicate shared molecular programs between each treatment, but also point to molecular changes specifically induced by the combination treatment.
To hone in on the genes and phosphopeptides changes specifically induced by the combination treatment, we developed a statistical and network approach to compare across these treatments (Figure 5B). We used likelihood ratio tests to compare “full” linear models including terms for effects of dasatinib, cabozantinib, and their interaction to “reduced” models, which only included terms for cabozantinib and dasatinib individually. We prioritized genes and phosphopeptides that were significantly better explained by the full model. Three hundred and twenty-four genes were significantly better explained after the inclusion of an interaction term, indicating that these genes are preferentially activated after combination treatment (likelihood ratio test: q < 0.01). To identify molecular nodes that may mediate the response to the combination treatment, we performed a master regulator (MR) analysis of the 324 genes to identify transcription factors or signaling molecules that serve signal integration hubs. Specifically, we used the MARINa (Master Regulator Inference algorithm) and VIPER (Virtual Proteomics by Enriched Regulon analysis) algorithms and a gene regulatory network derived from the TCGA KIRP cohort to infer Master Regulator activity from genes ranked by the effect of interaction of expression39, 40(Figure 5C). The dasatinib-cabozantinib interaction inferred transcriptional MRs were involved in DNA replication and repair, transcriptional regulation, proliferation and mitosis, e.g. ASF1B, RAD51, CENF, TOP2A, BUB1B, AURKB, PTTG1, MCM5 and CCNE141,42. Accordingly, gene set enrichment analysis (GSEA) of the inferred transcriptional MRs demonstrated downregulation for cell cycle, DNA replication, homologous recombination and base excision repair43 (Figure S8).
We used a similar analytical approach to analyze the proteomic data and identified 959 proteins that could be explained by an interaction between dasatinib and cabozantinib. Kinase activity driving the effect of drug interaction was inferred from the phosphoproteomic data using kinase-substrate enrichment analysis (q-value < 0.01), and demonstrated concordant downregulation of cell cycle associated kinases CDK1/CCD2. Notably, KSEA also suggested downregulation of multiple nodes of the MAPK pathway, e.g. SHP, ERK1, ERK2, ERK5 (MAPK7), JNK1 (MAPK8) with the dasatinib-cabozantinib combination44 (Figure 5D).
To integrate across transcriptional and phosphoproteomic datasets, we leveraged the TieDIE algorithm with the Multinet interaction network to combine inferred transcriptional MRs, inferred kinases, and kinases into an integrated network associated with response to the combination drug treatment45. TieDIE analysis predicted a complex regulatory network underlying the response to the cabozantinib-dasatinib combination treatment that converged to downregulate the MAPK signaling pathway. Specifically, it connected downstream nuclear translocating proteins of the MAPK pathway such as MAPK7 (ERK5), RPS6KA1 (P90-RSK1) to the inferred transcriptional MRs involved in DNA replication and proliferation (e.g., BUB1B, MCM5, PTTG1; Figure 5E)46.Critically, in support of the TieDIE “interactome”, western blot analysis of ACHN cells treated with dasatinib or cabozantinib singly demonstrated only minimal effect on MAPK activity (as determined by ERK1/2 phosphorylation), while the combination markedly suppressed MAPK activity (Figure 5F).
To better define the effect of MAPK pathway inhibition, we evaluated the addition of MEK inhibitors to either dasatinib or cabozantinib. First, we observed that trametinib (an approved MEK inhibitor) when combined with dasatinib induced substantial decreases in proliferation in multiple kidney cancer cells, as demonstrated by the shift in GI50 (Figure S9). Since clinical trials testing another MEK inhibitor is already underway for kidney cancer (cobimetinib47: NCT NCT03264066), and with cabozantinib already approved in kidney cancer, we next evaluated the combination of cobimetinib with cabozantinib in ACHN and SN12C human NCCRCC cells. We used a dose matrix to sample a large range of concentrations and concentration ratios and saw that cobimetinib-cabozantinib combination exhibited marked decreases in cell viability at clinically relevant doses (peak plasma levels of 0.51μM and 4.61 μM respectively)48 (Figure 5G, H). We used the Calcusyn analysis to test for synergy. This demonstrated consistent synergy [combination index (CI) < 1] between cobimetinib and cabozantinib (Figure 5 I, J). Accordingly, these findings demonstrate that inhibition of the MAPK pathway contributes to the synergistic effect of the dasatinib-cabozantinib combination in NCRCC, and highlights the potential for additional novel co-targeting strategies.
Discussion
NCCRCC accounts for ∼20% of kidney cancer, with papillary, chromophobe and sarcomatoid variants accounting for the majority of subtypes5.For advanced NCCRCC, the only treatment options, which have been largely extrapolated from agents studied in CCRCC, include antiangiogenics or mTOR inhibitors. However, several large studies have demonstrated that patients with NCCRCC have a worse prognosis with lower response rates to these therapies9-16. Consequently, the NCCN recommends enrolment to a clinical trial for NCCRCC patients as the preferred choice49.Taken together, the lack of treatment options for NCCRCC patients represents a critical unmet clinical need. Here, we sought to identify preclinical synergistic drug combinations that would transcend lineage and genetic landscape to induce cytotoxicity in NCCRCC and ultimately lead to deep and durable responses in patients.
We demonstrate that the dasatinib-cabozantinib combination has potent synergy in 2D cell culture models. Importantly, this combination induces tumor regression in vivo, confirming the robustness of our experimental screening approach. We observed that multiple NCCRCC cells appear to respond similarly to the combination, suggesting broad utility across this histologically diverse group of cancers. Mechanistically, the dasatinib-cabozantinib combination converge to suppress MAPK signaling to induce cytotoxicity. Critically, this suggests that combining dasatinib or cabozantinib with MEK inhibitors would be a viable combination. In support of this, we found that the cabozantinib-cobimetinib combination synergized to markedly reduce cell viability. Taken together, our preclinical findings provide rationale for clinical trial design.
What then is the most straightforward path to the clinic? We speculate that these findings have direct clinical implications for cabozantinib, providing rationale for further clinical study of cabozantinib in NCCRC and supporting future combination treatments. Retrospective studies suggest that cabozantinib has activity in patients with papillary RCC (the commonest NCCRCC subtype, accounting for ∼15% of all kidney cancers), has activity in metastatic RCC to bone50, and prospective clinical studies are ongoing (NCT02761057). However, it is highly likely that NCCRCC patients will develop resistance to single agent cabozantinib, either due to short term signaling adaptations (e.g. bypass tracks) or longer-term selection of resistance variants (e.g. gatekeeper mutations). Combination studies with cabozantinib need to be considered, and our data suggest that either dasatinib or cobimetinib would be suitable. Additionally, cobimetinib is currently undergoing combination studies in RCC (+ Azetolizumab, NCT03264066). Importantly, our work provides the mechanistic rationale on how best to develop these combinations for the clinic.
The caveats here are limitations of cell culture and mouse xenograft studies, and how these can be extrapolated to clinical trials. While the dasatinib-cabozantinib combination was well tolerated in mice, the potential clinical toxicities in the setting of patients who have exhausted multiple lines of therapies and with co-morbidities are part of the real-world study considerations. Appropriate dose-finding studies are needed as a first step to explore safety and feasibility, either stand-alone or incorporated into larger efficacy trials.
Material and Methods
Cell lines
ACHN, A498, 769-P, 786-O, CAKI-1, SN12C, TK10 and UO31 were used in this study and were obtained from the NCI and ATCC. Cell lines were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum at 37°C in a 5% CO2 incubator.
High throughput drug screening and workflow on the identification of hits
We did screening using a library of 296 small molecules (that included kinase inhibitors and apoptosis inducing molecules; Table S1) in 8 cell lines (ACHN, A498, 769-P, 786-O, CAKI-1, SN12C, TK10 and UO31). We did a 8 dilutions of drug with or without dasatinib and read viability using cell titer glo after 5 days of drug treatment. The readings were used to calculate the GI50 (using XLfit), minimum viability, AUC (area under curve), AUC difference between drug alone and drug+das and Z score values. We applied multiple criteria to identify the hits from the primary screening that were additionally validated in the secondary screening. The following are the criteria used: 1) GI50 fold change: Drugs that have at least 5-fold change in GI50 ratio between drug alone and drug+dasatinib (ratio GI50 drug/ GI50 combination) in 50% of cell lines (n = 38). 2) AUC difference: Drugs that have AUC difference of more than 0.9 (average is 0.9) between drug alone and drug+dasatinib in at least 50% of cell lines (n = 31). 3) AUC % change: Drugs that have AUC % change of more than 25% (average is 25%) between drug alone and drug+dasatinib in 66% (4 of 6) cell lines (n = 31). 4) GI50 of combination: Combinations that are in top 50% (based on GI50 z-score calculation) in 50% (3 of 6) cell lines and pass through the minimum viability cutoff of 15% across cell lines that (average min viability value is looked at) (n=18). 5) AUC of combination: Combinations that are in the top 50% (based on AUC z-score calculation) and in 50% (3 of 6) cell lines and pass through the minimum viability cutoff of 15% across cell lines that (average min viability value is looked at) (n = 13). We shortlisted the drugs based on the above criteria and picked 28 drugs that were identified by at least 4 of the 5 parameters. These 28 drugs were further subjected to the secondary screening, which involved a dose-matrix of 6×8 (6 doses of dasatinib and 8 doses of the drug). The growth inhibition values from the secondary screening were subjected to the estimation of synergy using Calcusyn. Calcusyn calculates Combination Index (CI) for drug combinations: CI < 1 is synergistic; CI = 1 additive and CI > 1 is antagonistic effects30.We calculated CI for 28 drug combinations and ranked the drugs based on its synergistic effect in combination with dasatinib from highest to lowest.
Cell viability and apoptosis analysis
Cell viability assays were performed by plating cells/well in 96-well plates in triplicate and treating the following day with the indicated agent: dasatinib (dose range of 0–400 nM) and cabozantinib (dose range of 0–10 μM). The experiment was continued for 3 days and then the cells were treated with CellTiter-Blue (Promega) and incubated for 1 hour. Fluorescence was measured and quantified and photographs were obtained using a Cytation 5 Cell Imaging Reader. The effect of dasatinib, cabozantinib and the dasatinib+cabozantinib combination on cell number was assessed as fold of DMSO-treated control cells. Experimental results are the average of at least three independent experiments. Apoptosis was determined using Caspase 3/7-Glo assay kit (Promega) following the manufacturer’s instructions. Briefly, 4000 cells per well were plated in 96 well plates and cultured for 24h. Cells were treated with dasatinib, cabozantinib and the combination of dasatinib+cabozantinib for 72h, and then 100 μl reagents were added to each well and incubated for 30 min at room temperature. Caspase 3/7 activity was measured using a luminometer. Luminescence values were normalized by cell numbers. The effect of dasatinib, cabozantinib and the combination of dasatinib+cabozantinib on caspase 3/7 activation was assessed as fold of DMSO-treated control cells.
Western Blotting
Cells were plated in 6 well dishes and treated the following day with the indicated agents. Treatments were for 24 hours, after which cells were washed with ice cold PBS and lysed with RIPA buffer (Sigma). Phosphatase inhibitor cocktail set II and protease inhibitor cocktail set III (EMD Millipore) were added at the time of lysis. Lysates were centrifuged at 15,000g x 10 min at 4 degrees C. Protein concentrations were calculated based on a BCA assay (Thermo Scientific) generated standard curve. Proteins were resolved using the NuPAGE Novex Mini Gel system on 4% to 12% Bis-Tris Gels (Invitrogen). For western blotting, equal amounts of cell lysates (15-20 μg of protein) were resolved with SDS-PAGE, and transferred to membranes. The membrane was probed with primary antibodies, washed, and then incubated with corresponding fluorescent secondary antibodies and washed. The fluorescent signal was captured using LI-COR (Lincoln, NE) Odyssey Imaging System, and fluorescent intensity was quantified using the Odyssey software where indicated. The following antibodies were used for Western blots: p-SRC (Y416), SRC, p-MET (Y1234/1235), p-p44/42 MAPK (Erk1/2) (T202/Y204), ERK and β-actin (AC15) (Abcam). Ki67 (Dako) and cleaved caspase3 (Cell Signaling Technologies) were used for immunohistochemistry. Dasatinib, cabozantinib and cobimetinib were purchased from Selleck chemicals.
In Vivo Xenograft Studies
6-week old mice were utilized for human renal cell carcinoma xenografts. For both ACHN and CAKI-1 cell lines 2×106 cells were diluted in 50 µl of PBS and 50 µl of Matrigel (BD Biosciences) and were injected subcutaneously into the right and left flank of each mouse. Tumors were monitored until they reached an average size of 50-80mm3 (approximately 2 weeks), at which point treatments were begun. dasatinib (25mg/kg/day: ACHN; 35mg/kg/day: CAKI-1) was administered by oral gavage 5 days/week. Cabozantinib (30mg/kg/day: ACHN; 10mg/kg/day: CAKI-1) were administered by oral gavage 3 days/week. Dasatinib and cabozantinib were dissolved in NMP/PEG. Tumors and mouse weights were measured twice weekly. At least 6-8 mice per treatment group were included. All mice were euthanized using CO2 inhalation followed by cervical dislocation per institutional guidelines at Oregon Health and Science University. Experiments were approved by the Institutional Animal Care and Use Committee at OHSU.
Immunohistochemistry
Immunostaining was performed following deparaffinization and rehydration of slides. Antigen retrieval was performed in a pressure cooker using citrate buffer (pH 6.0) for 4 min. Nonspecific binding was blocked using Vector mouse IgG blocking serum 30 min at room temperature. Samples were incubated at room temperature with rabbit monoclonal antibodies cleaved caspase 3 (Cell Signaling Technologies), and Ki67 (Dako). Slides were developed with Vector Immpress rabbit IgG (#MP7401) and Vector Immpress mouse IgG (Vector Laboratories) (#MP7400) for 30 min at room temperature. Chromogenic detection was performed using Vector Immpact DAB (Vector Laboratories) (#SK4105) for 3 min. Slides were counterstained with hematoxylin. A 3DHistech MIDI Scanner (Perkin Elmer) was used to capture whole slide digital images with a 20x objective. Images were converted to into MRXS files and computer graphic analysis was completed using inForm 1.4.0 Advanced Image Analysis Software (Perkin Elmer).
Immunofluorescence
H&E slides of formalin fixed, paraffin embedded tissue was used to assess morphological integrity of tumor samples. Once integrity was confirmed, immunofluorescent analysis was performed for p-SRC (Y416), p-MET (Y1234/1235) (Cell Signaling Technologies). Four μ sections were cut, de-paraffinized and rehydrated. Antigen retrieval was performed using citrate for 4 min in a pressure cooker. Slides were blocked using 2.5% normal goat serum for 30 min then incubated in primary antibody for 1hr followed by secondary antibody mouse anti-rabbit Alexa 488 (1:1000, Molecular Probes) for 30 min. Slides were rinsed in PBS, air dried, and coverslipped using Dako mounting media with Dapi.
RNA-Seq preprocessing and analysis
RNA-Seq libraries were prepared from triplicate 24-hour treated cell lines using the RNeasy extraction kit (Qiagen). Single-end 100bp reads were sequenced with an Illumina HiSeq 2500. Reads were trimmed of low-quality bases and adapter sequences using TrimGalore (v0.4.4) a wrapper for cutadapt (v1.10)51.Reads were aligned to the human reference genome (GRCh38) and summarized to gene-level abundances using STAR (v2.4.0.1)52.Gene expression heatmaps were created with the R package Complex Heatmap (v1.17.1)53.
Differentially expressed genes in drug-treated samples were identified with DESeq2 (v1.18.1)54 to compare each treatment (dasatinib, cabozantinib, and dasatinib + cabozantinib co-treatment) to the vehicle-treated condition. Genes with an FDR-corrected q-value ≤ 0.01 and a log2FoldChange ≥ 0.5 or ≤ -0.5 were considered differentially expressed.
Significance of the cabozantinib-dasatinib interaction effect on gene expression was assessed using a likelihood ratio test to compare two generalized linear models. Both models feature logarithmic link functions where parameter qij is proportional to the predicted true concentration of transcripts in sample j, xjr are the design matrix components for sample j, and βir are the coefficients (log2 fold changes). The full model contains design matrix components and coefficients for the dasatinib effect (xjdβid), the cabozantinib effect (xjcβic), and an interaction effect (xjdcβidc). The reduced model contains terms only for the dasatinib effect (xjdβid) and the cabozantinib effect (xjcβic).
We considered genes to be affected by the interaction of the drugs if the full model explained the observed effects significantly better than the reduced model (FDR-corrected q-value < 0.01).
Gene Set Enrichment Analysis
Enrichment of specific pathways and ontological terms in the gene most affected by the drug interaction was assessed using the Broad Gene Set Enrichment Analysis (GSEA) (v3.0) Java application43and the Broad MSigDB (v6.2) Hallmark gene set collection. Preranked input for GSEA was generated by ranking genes by the log2 fold change of the by the cabozantinib-dasatinib drug interaction coefficient. P-values were calculated by gene label permutation and were FDR-corrected for multiple testing. Gene sets with an FDR-corrected q-value < 0.1 were considered significant.
Master Regulator Analysis
Master Regulator analysis was used to infer transcription factors responsible for the observed drug interaction-induced changes in gene expression. The R package VIPER (v1.12)39 was used to test for enrichment of Master Regulators in a list of all genes ranked by the coefficient of the interaction effect (βidc), using a network generated from gene expression profiles of the TCGA kidney renal papillary cell carcinoma cohort40. P-values were calculated by gene label permutation and were FDR-corrected for multiple testing. Master Regulators with an FDR-corrected q-value < 0.1 were considered significant.
Phosphoproteomic drug interaction analysis
Interaction effects in the phosphoproteomic data were analyzed in R using the limma (3.36.3)55 package. Significance of the cabozantinib-dasatinib interaction effect on phosphoprotein levels was assessed using an empirical Bayes method to compare a full model, which models individual drug and drug interaction effects, to a reduced model, which models only the effects of the individual drugs. The drug interaction was considered to have a significant effect on phosphopeptide abundance if the FDR-corrected q-value was < 0.05.
Data Integration using TieDie
An integrated network of inferred drug interaction effects on transcription and phosphoprotein levels was created using TieDie (v1.0)45 software. TieDie was used to map enriched transcription factors inferred from the RNA-seq data, kinases significantly affected by the drug interaction effect, and enriched kinases inferred from the phosphoproteomic data onto nodes of the Multinet reference network derived from protein-protein interaction, phosphorylation, metabolic, and gene regulatory networks. Inferred master regulators (FDR-corrected q-value < 0.05, NES ≥ 5) were used as the gene expression (“downstream”) input nodes. KSEA-inferred kinases (FDR-corrected q-value ≤ 0.05) were used as the kinase (“upstream”) input nodes. Additionally, directly measured kinases significantly affected by the drug interaction (FDR-corrected q-value ≤ 0.5 for pY phosphopeptides; FDR-corrected q-value ≤ 0.01 for pST phosphopeptides) were included as upstream input. A more stringent q-value threshold was used for pST phosphopeptides to reduce the number of input nodes. The input weight of inferred transcription factors and kinases was equal to their enrichment score; the input weight of directly measured kinases affected by drug interaction was equal to the interaction coefficient βidc. A heat diffusion algorithm was then run to model diffusion of “heat” from the input nodes to nearby nodes in the network, and a subnetwork of agreement between the data types was identified by identifying nodes that receive heat from both data sources.
Code Availability
All analysis scripts can be found in the following GitHub repository: https://github.com/danielderrick/thomas_kidney_drug_analysis
Phosphoproteomic screen and analysis
Phosphopeptides were enriched and analyzed by mass spectrometry as previously described in detail32.Briefly, cells were lysed and proteins extracted using a guanidinium-based lysis buffer. Lysates were digested using Lys-C and trypsin. Phosphotyrosine peptides were enriched via immunoprecipitation (4G10 antibody) and titanium dioxide. Phosphoserine/threonine peptides were enriched by strong cation exchange and titanium dioxide. After desalting with C18 columns, the phosphopeptide samples were subjected to liquid chromatography-tandem mass spectrometry.
Mass spectrometry data analysis
MS raw files were analyzed via MaxQuant version 1.5.3.30 56 and MS/MS fragmentation spectra were searched using Andromeda57 against human canonical and isoform sequences in Swiss-Prot (downloaded in August 2017 from http://uniprot.org)58.Quantitative phosphopeptide data were log10 transformed and missing data were imputed before applying quantile normalization as previously described33.Hierarchical clustering was performed on the Cluster 3.0 program59, using distance that is based on the Pearson correlation and applying pairwise average linkage analysis. Java Treeview was used to visualize clustering results60.
Kinase substrate enrichment analysis
Kinase substrate enrichment analysis (KSEA) was performed as previously described33, 35.Briefly, the phosphopeptides were rank ordered by fold change, on average, between combination (dasatinib + cabozantinib) treatment vs control and the enrichment score was calculated using the Kolmogorov-Smirnov statistic. Permutation analysis was conducted to calculate statistical significance. The normalized enrichment score was calculated by dividing the enrichment score by the average of the absolute values of all enrichment scores from the permutation analysis (Tables S2-S7).
Statistical analysis
Mouse tumor size was analyzed by 2-way ANOVA with time and drug as factors, using GraphPad Prism. Mouse weight during treatment was analyzed by repeated measures 2-way ANOVA, with time and drug as factors. A P value less than 0.05 was considered statistically significant. Immunohistochemistry: P-values were calculated using one-way ANOVA, with Bonferroni’s multiple comparison test. * denotes P < 0.05, ** denotes P < 0.01, and *** denotes P < 0.001. The plots here were created using R version 3.6.1. Packages needed include ggplot2 and dplyr.
Grants
This study was supported by NIH grants R01 CA169172, P30 CA069533 and P30 CA069533 13S5 through OHSU-Knight Cancer Institute, The Hope Foundation (SWOG), OTRADI, and Kure It Cancer Research (GVT); and in part by the NIH grant R21 CA222455 (GVT, LMH). These studies were supported in part by NIH research grants 1U54CA209988, U54-HG008100 and the Prospect Creek Foundation (LMH). LCC is supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number T32 GM008339. JMD is supported by the Department of Defense Prostate Cancer Research Program W81XWH-15-1-0236 and W81XWH-18-1-0542, Prostate Cancer Foundation Young Investigator Award, and by a grant from the New Jersey Health Foundation.
Author contributions
HWL, DSD, SR, AVG, JP, CHX, and SL performed the experiments and analyzed the data. DSD and LMH performed the computational analyses. LC, RW-III, and JMD performed and analyzed the phosphoproteomics data. AR, AVG, SR, and DG analyzed the HTS data. HWL, DSD, CRW, JMD, AR, LMH, and GVT wrote the paper. GVT conceived the project. LMH and GVT supervised all aspects of the work.
Supplementary Figures
Supplementary Tables
Table S1. Listing of screen drugs
Table S2. Dasatinib Vs Control_KSEA_pY
Table S3. Dasatinib Vs Control_KSEA_pST
Table S4. Cabozantinib Vs Control_KSEA_pY
Table S5. Cabozantinib Vs Control_KSEA_pST
Table S6. Dasatinib+Cabozantinib Vs Control_KSEA_pY
Table S7. Dasatinib+Cabozantinib Vs Control_KSEA_pST
Acknowledgements
We thank Mandy Burns and Amanda Jones for administrative support, Moya Costello for artwork, the Histopathology Shared Resource for pathology support, the Massively Parallel Sequencing Shared Resource and Integrated Genomics Shared Resource for genomics support, and the Oregon Translational Research and Development Institute (OTRADI) for high throughput drug screening support.
Footnotes
↵11 Lead contact