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Background: Preclinical studies in myeloid neoplasms have demonstrated efficacy of Bromodomain and Extra-Terminal protein inhibitors (BETi). However, BETi demonstrate poor single agent activity in clinical trials. Several studies suggest that combination with other anti-cancer inhibitors may enhance the efficacy of BETi. Experimental Design: To nominate BETi combination therapies for myeloid neoplasms, we used a chemical screen with therapies currently in clinical cancer development and validated this screen using a panel of myeloid cell line, heterotopic cell line models, and PDX models of disease. We utilized standard protein and RNA assays to determine the mechanism responsible for synergy in our disease models. Results: We identified PIM inhibitors (PIMi) as therapeutically synergistic with BETi in myeloid leukemia models. Mechanistically, we show that PIM kinase is increased after BETi treatment, and that PIM kinase upregulation is sufficient to induce persistence to BETi and sensitize cells to PIMi. Further, we demonstrate that miR-33a downregulation is the underlying mechanism driving PIM1 upregulation. We also show that GM-CSF hypersensitivity, a hallmark of chronic myelomonocytic leukemia (CMML), represents a molecular signature for sensitivity to combination therapy. Conclusion: Inhibition of PIM kinases is a potential novel strategy for overcoming BETi persistence in myeloid neoplasms. Our data supports further clinical investigation of this combination.

Immunotherapy (IT) and targeted therapy (TT) are both effective against melanoma, but their combination is frequently toxic. Here, we investigated whether the sequence of IT (anti–PD-1)→ TT (ceritinib–trametinib or dabrafenib–trametinib) was associated with improved antitumor responses in mouse models of BRAF- and NRAS-mutant melanoma. Mice with NRAS-mutant (SW1) or BRAF-mutant (SM1) mouse melanomas were treated with either IT, TT, or the sequence of IT→TT. Tumor volumes were measured, and samples from the NRAS-mutant melanomas were collected for immune-cell analysis, single-cell RNA sequencing (scRNA-seq), and reverse phase protein analysis (RPPA). scRNA-seq demonstrated that the IT→TT sequence modulated the immune environment, leading to increased infiltration of T cells, monocytes, dendritic cells and natural killer cells, and decreased numbers of tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells. Durable responses to the IT→TT sequence were dependent on T-cell activity, with depletion of CD8+, but not CD4+, T cells abrogating the therapeutic response. An analysis of transcriptional heterogeneity in the melanoma compartment showed the sequence of IT→TT enriched for a population of melanoma cells with increased expression of MHC class I and melanoma antigens. RPPA analysis demonstrated that the sustained immune response induced by IT→TT suppressed tumor-intrinsic signaling pathways required for therapeutic escape. These studies establish that upfront IT improves the responses to TT in BRAF- and NRAS-mutant melanoma models.

S1: IT monotherapy in SW1 model S2: IT->dabrafenib-trametinib therapy in BRAF mutant melanoma. S3: IT->TT sequence in NRAS#5 model S4: Expanded heatmap of all immune cells S5: IT->TT increases accumulation of activated CD8+ T cells S6: The TT->IT sequence on T cells, Tregs and MDSCS S7: Expanded heatmap of T and NK cells S8: IT->TT increases accumulation of activated CD8+ T cells, pathway analysis. S9: Expanded heatmap of myeloid cell data S10: IT->TT sequence increases expression of MHC I mRNAs S11: TT affects melanoma antigens S12: IC50 curves showing resistance to TT S13: Model showing mechanism of IT->TT sequence ST1: tumor weights at the end of the experiment

Ewing's sarcoma is a pediatric cancer of the bone that is characterized by the expression of the chimeric transcription factor EWS-FLI1 that confers a highly malignant phenotype and results from the chromosomal translocation t(11;22)(q24;q12). Poor overall survival and pronounced long-term side effects associated with traditional chemotherapy necessitate the development of novel, targeted, therapeutic strategies. We therefore conducted a focused viability screen with 200 small molecule kinase inhibitors in 2 different Ewing's sarcoma cell lines. This resulted in the identification of several potential molecular intervention points. Most notably, tozasertib (VX-680, MK-0457) displayed unique nanomolar efficacy, which extended to other cell lines, but was specific for Ewing's sarcoma. Furthermore, tozasertib showed strong synergies with the chemotherapeutic drugs etoposide and doxorubicin, the current standard agents for Ewing's sarcoma. To identify the relevant targets underlying the specific vulnerability toward tozasertib, we determined its cellular target profile by chemical proteomics. We identified 20 known and unknown serine/threonine and tyrosine protein kinase targets. Additional target deconvolution and functional validation by RNAi showed simultaneous inhibition of Aurora kinases A and B to be responsible for the observed tozasertib sensitivity, thereby revealing a new mechanism for targeting Ewing's sarcoma. We further corroborated our cellular observations with xenograft mouse models. In summary, the multilayered chemical biology approach presented here identified a specific vulnerability of Ewing's sarcoma to concomitant inhibition of Aurora kinases A and B by tozasertib and danusertib, which has the potential to become a new therapeutic option. Mol Cancer Ther; 10(10); 1846–56. ©2011 AACR.

Supplementary Table 1: Table provides information on the siRNA sequences and/or chemical structures of the non-FDA approved inhibitors for bromodomain proteins, transcription factors and kinases described in the current study. Supplementary Table 2: Sequences of primers used for qRT-PCR analysis of various targets described in the study. Supplementary Table 3: Sequences of primers used for ChIP analysis of various targets described in the study. Supplementary Table 4: Analysis of HOXB13 target gene expression in the GSE92721 data set. Supplementary Table 5: Analysis of HOTBIN10 gene expression in the GSE92721data set. Supplementary Table 6: Analysis of HOTBIN10 gene expression in the Moffitt TCC data set. Supplementary Table 7: Clinical parameters of the primary cancers in the Moffitt TCC data set. Supplementary Table 8: Clinical parameters of the metastatic cancers in the Moffitt TCC data set. Supplementary Table 9: Analysis of HOTBIN10 genes in MSKCC gene expression data set (GSE21034). Supplementary Table 10: Analysis of HOTBIN10 gene expression in the GSE67980 CTC data set.

Figure S1. Compounds screen across SCLC cell lines Figure S2. Aurora kinase B identified by drug profiling in H82 cells and drive cell survival in MYC amplified SCLC Figure S3. TBK1 identified by drug profiling in SW210.5 cells

(A) TMA containing 68 patient-derived xenograft (PDX) tumor models stained with PAXIP1 and WEE1 (B-C) Two lung adenocarcinoma PDXs with PAXIP1 and WEE1 staining were used to perform ex vivo 3-D clonogenic assays with various concentrations of AZD1775 and cisplatin and tumor/control - T/C was measured by image analysis.

Figure S2. Global tissue expression profiles of selected kinases.

Table S1. Mutational status of all specimens regarding KRAS and EGFR.

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Peptides used for LC-MRM based protein quantification for each kinase

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Legends for Supplementary Figures and Tables

Supplemental methods and legends for supplemental figures S1 to S3 and supplemental tables S1 to S3

Cell viability screen in SCLC cells against Roche published kinase inhibitor set

On-target resistance to next-generation TRK inhibitors in TRK fusion–positive cancers is largely uncharacterized. In patients with these tumors, we found that TRK xDFG mutations confer resistance to type I next-generation TRK inhibitors designed to maintain potency against several kinase domain mutations. Computational modeling and biochemical assays showed that TRKAG667 and TRKCG696 xDFG substitutions reduce drug binding by generating steric hindrance. Concurrently, these mutations stabilize the inactive (DFG-out) conformations of the kinases, thus sensitizing these kinases to type II TRK inhibitors. Consistently, type II inhibitors impede the growth and TRK-mediated signaling of xDFG-mutant isogenic and patient-derived models. Collectively, these data demonstrate that adaptive conformational resistance can be abrogated by shifting kinase engagement modes. Given the prior identification of paralogous xDFG resistance mutations in other oncogene-addicted cancers, these findings provide insights into rational type II drug design by leveraging inhibitor class affinity switching to address recalcitrant resistant alterations.Significance:In TRK fusion–positive cancers, TRK xDFG substitutions represent a shared liability for type I TRK inhibitors. In contrast, they represent a potential biomarker of type II TRK inhibitor activity. As all currently available type II agents are multikinase inhibitors, rational drug design should focus on selective type II inhibitor creation.This article is highlighted in the In This Issue feature, p. 1

File containing 12 Supplemental Figures. Figure 1 shows a kinome array on WM209 cells, Figure 2 shows detail from the kinome array. Figure 3 shows the effects of the drugs upon p-c-JUN in the BRAF/NRAS WT cell lines. Figure 4 is a time course of pS6 and pERK inhibition following drug treatment. Figure 5 is the effects of drug upon SPRY-2. Figure 6 shows the structure of c-ceritinib. Figure 7 is the IGF1R expression in the cell line panel. Figure 8 is an RTK array following ceritinib treatment. Figure 9 is the effects of IGF1R siRNA upon cell growth. Figure 10 shows the effects of ceritinib and trametinib upon FAK expression. Figure 11 shows the effects of FER silencing upon the growth of the melanoma cells. Figure 12 is the effects of an ACK1 inhibitor and a MEK inhibitor on cell growth.

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A growing number of gene mutations, which are recognized as cancer drivers, can be successfully targeted with drugs. The redundant and dynamic nature of oncogenic signaling networks and complex interactions between cancer cells and the microenvironment, however, can cause drug resistance. While these challenges can be addressed by developing drug combinations or polypharmacology drugs, this benefits greatly from a detailed understanding of the proteome-wide target profiles. Using mass spectrometry-based chemical proteomics, we report the comprehensive characterization of the drug–protein interaction networks for the multikinase inhibitors dasatinib and sunitinib in primary lung cancer tissue specimens derived from patients. We observed in excess of 100 protein kinase targets plus various protein complexes involving, for instance, AMPK, TBK1 (sunitinib), and ILK (dasatinib). Importantly, comparison with lung cancer cell lines and mouse xenografts thereof showed that most targets were shared between cell lines and tissues. Several targets, however, were only present in tumor tissues. In xenografts, most of these proteins were of mouse origin suggesting that they originate from the tumor microenvironment. Furthermore, intersection with subsequent global phosphoproteomic analysis identified several activated signaling pathways. These included MAPK, immune, and integrin signaling, which were affected by these drugs in both cancer cells and the microenvironment. Thus, the combination of chemical and phosphoproteomics can generate a systems view of proteins, complexes, and signaling pathways that are simultaneously engaged by multitargeted drugs in cancer cells and the tumor microenvironment. This may allow for the design of novel anticancer therapies that concurrently target multiple tumor compartments. Mol Cancer Ther; 13(11); 2751–62. ©2014 AACR.

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Supplementary Materials and Methods

Supplementary Figures

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Legends for Supplementary Figures and Tables

Supplementary Fig. 1. Epigenetic landscape and the transcription cofactor recruitment at the HOXB13 enhancer region under various treatments; Supplementary Figure 2. BRD4 is a target of the dual BET-kinase inhibitors in CRPCs; Supplementary Figure 3. Sensitivity of normal and AR negative cell lines to the novel BET-kinase inhibitors; Supplementary Figure 4. HOXB13 but not FOXA1 is under the epigenetic control of BRD4 in prostate cancer cells; Supplementary Figure S5. Haploinsufficiency of HOXB13 sensitizes CRPCs to the novel BET-kinase inhibitors; Supplementary Figure S6. AURKB expression correlates with HOXB13 in human CTCs from mCRPC patients following Abiraterone therapy; Supplementary Fig. S7. AURKB is a therapeutic vulnerability of HOXB13 positive prostate cancers

Targeted therapy options are currently lacking for the heterogeneous population of patients whose melanomas lack BRAF or NRAS mutations (∼35% of cases). We undertook a chemical biology screen to identify potential novel drug targets for this understudied group of tumors. Screening a panel of 8 BRAF/NRAS-WT melanoma cell lines against 240 targeted drugs identified ceritinib and trametinib as potential hits with single-agent activity. Ceritinib enhanced the efficacy of trametinib across the majority of the BRAF/NRAS-WT cell lines, and the combination showed increased cytotoxicity in both three-dimensional spheroid culture and long-term colony formation experiments. Coadministration of ceritinib and trametinib led to robust inhibition of tumor growth in an in vivo xenograft BRAF/NRAS-WT melanoma model; this was not due to ALK inhibition by ceritinib. Mechanistic studies showed the ceritinib–trametinib combination to increase suppression of MAPK and TORC1 signaling. Similar results were seen when BRAF/NRAS-WT melanoma cells were treated with a combination of trametinib and the TORC1/2 inhibitor INK128. We next used mass spectrometry–based chemical proteomics and identified known and new ceritinib targets, such as IGF1R and ACK1, respectively. Validation studies suggested that ceritinib could suppress mTORC1 signaling in the presence of trametinib through inhibition of IGF1R and/or ACK1 in a cell line–dependent manner. Together, our studies demonstrated that combining a specific inhibitor (trametinib) with a more broadly targeted agent (ceritinib) has efficacy against tumors with heterogeneous mutational profiles. Mol Cancer Ther; 17(1); 73–83. ©2017 AACR.

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This file contains the raw data from the drug screen

Supplementary Tables

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Purpose:The clinical use of MEK inhibitors in uveal melanoma is limited by the rapid acquisition of resistance. This study has used multiomics approaches and drug screens to identify the pan-HDAC inhibitor panobinostat as an effective strategy to limit MEK inhibitor resistance.Experimental Design: Mass spectrometry–based proteomics and RNA-Seq were used to identify the signaling pathways involved in the escape of uveal melanoma cells from MEK inhibitor therapy. Mechanistic studies were performed to evaluate the escape pathways identified, and the efficacy of the MEK-HDAC inhibitor combination was demonstrated in multiple in vivo models of uveal melanoma.Results:We identified a number of putative escape pathways that were upregulated following MEK inhibition, including the PI3K/AKT pathway, ROR1/2, and IGF-1R signaling. MEK inhibition was also associated with increased GPCR expression, particularly the endothelin B receptor, and this contributed to therapeutic escape through ET-3–mediated YAP signaling. A screen of 289 clinical grade compounds identified HDAC inhibitors as potential candidates that suppressed the adaptive YAP and AKT signaling that followed MEK inhibition. In vivo, the MEK-HDAC inhibitor combination outperformed either agent alone, leading to a long-term decrease of tumor growth in both subcutaneous and liver metastasis models and the suppression of adaptive PI3K/AKT and YAP signaling.Conclusions:Together, our studies have identified GPCR-mediated YAP activation and RTK-driven AKT signaling as key pathways involved in the escape of uveal melanoma cells from MEK inhibition. We further demonstrate that HDAC inhibition is a promising combination partner for MEK inhibitors in advanced uveal melanoma.

Activating mutations in BRAF, a key mediator of RAS signaling, are present in approximately 50% of melanoma patients. Pharmacologic inhibition of BRAF or the downstream MAP kinase MEK is highly effective in treating BRAF-mutant melanoma. In contrast, RAS pathway inhibitors have been less effective in treating epithelial malignancies, such as lung cancer. Here, we show that treatment of melanoma patients with BRAF and MEK inhibitors (MEKi) activated tumor NF-κB activity. MEKi potentiated the response to TNFα, a potent activator of NF-κB. In both melanoma and lung cancer cells, MEKi increased cell-surface expression of TNFα receptor 1 (TNFR1), which enhanced NF-κB activation and augmented expression of genes regulated by TNFα and IFNγ. Screening of 289 targeted agents for the ability to increase TNFα and IFNγ target gene expression demonstrated that this was a general activity of inhibitors of MEK and ERK kinases. Treatment with MEKi led to acquisition of a novel vulnerability to TNFα and IFNγ-induced apoptosis in lung cancer cells that were refractory to MEKi killing and augmented cell-cycle arrest. Abolishing the expression of TNFR1 on lung cancer cells impaired the antitumor efficacy of MEKi, whereas the administration of TNFα and IFNγ in MEKi-treated mice enhanced the antitumor response. Furthermore, immunotherapeutics known to induce expression of these cytokines synergized with MEKi in eradicating tumors. These findings define a novel cytokine response modulatory function of MEKi that can be therapeutically exploited.Significance:Lung cancer cells are rendered sensitive to MEK inhibitors by TNFα and IFNγ, providing a strong mechanistic rationale for combining immunotherapeutics, such as checkpoint blockers, with MEK inhibitor therapy for lung cancer.See related commentary by Havel, p. 5699

Table showing frequency distribution of cell cycle phases (G1, S, G2) from 3 biological replicates in indicated cell lines (A549, LKR, PC-9, HCC44, H23).

Supplementary Table S4. The complete list of identified phosphopeptides after (pY) enrichment and TMT-based quantitative phosphoproteomics analysis in KRASG12C mutant lung cancer cell lines (n=8) with mean-centered log abundance values.

Table 1: Cell lines, fusions, tyrosine kinase inhibitors and targets. Table 2: IC50 values for Figure 1C and relative fold change.

Supplementary Table S1- Antibodies used for protein expression in western blotting experiments Supplementary Table S2 - Assays used for gene expression in RT-qPCR experiments Supplementary Table S3 - Antibodies used for protein expression in immunofluorescence experiments Supplementary Table S4 - Inhibitory concentration 50% (IC50) of MEKi after 72h in uveal melanoma cell lines (nM) Supplementary Table S5 - Members of the GSEA GO_REGULATION_OF_PHOSPHATIDYLINOSITOL_3_KINASE_SIGNALING family Supplementary Table S6 - Members of the GSEA GO_TRANSMEMBRANE_RECEPTOR_PROTEIN_KINASE_ACTIVITY family Supplementary Table S7 - Members of the GSEA GO_TRANSMEMBRANE_RECEPTOR_PROTEIN_TYROSINE_KINASE_ACTIVITY family Supplementary Table S8 - Members of the GSEA HIPPO_TARGETS family Supplementary Table S9 - Members of the GSEA REACTOME_GPCR_LIGAND_BINDING Family Supplementary Table S10 - Compounds used in the drug screening Supplementary Table S11 - Members of the GSEA HALLMARK_PI3K_AKT_MTOR_SIGNALING

Supplementary Methods for Xie et al, MEK inhibition modulates cytokine response to mediate therapeutic efficacy in lung cancer

Supplemental Figure 1: Gene ontology network analysis of uveal melanoma cells following MEKi treatment (24h) and ABPP. Supplemental Figure 2: ET-3 increases nuclear accumulation of YAP in 92.1, Mel270, MP41 and OMM1 cells. Supplemental Figure 3: ET-1 does not induce YAP reporter activity in 92.1 or Mel270 cells. Supplemental Figure 4: HDAC inhibitors increase the cytotoxic effects of MEK inhibition. Supplemental Figure 5: MEK inhibition leads to an increase in global protein acetylation in uveal melanoma cells. Supplemental Figure 6: MEKi, HDACi and the MEKi-HDACi combination do not affect the survival of primary uveal melanocytes. Supplemental Figure 7: HDACi suppresses MEKi-induced release of ET-3 from uveal melanoma cells. Supplemental Figure 8: Negative modulation of PI3K-AKT pathway by combinatory treatment with MEKi+HDACi. Supplemental Figure 9: The MEK-HDACi combination delivers durable responses in the 92.1 uveal melanoma xenograft model. Supplemental Figure 10: Macroscopic findings of liver tumor formation after inoculation of MP41 by tail vein injection. Supplemental Figure 11: Uncropped blots Supplemental Figure 12: Flow cytometry plots