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The MYC oncoprotein is activated in a broad spectrum of human malignancies and transcriptionally reprograms the genome to drive cancer cell growth. Given this, it is unclear if targeting a single effector of MYC will have therapeutic benefit. MYC activates the polyamine-hypusine circuit, which post-translationally modifies the eukaryotic translation factor eIF5A. The roles of this circuit in cancer are unclear. Here we report essential intrinsic roles for hypusinated eIF5A in the development and maintenance of MYC-driven lymphoma, where loss of eIF5A hypusination abolishes malignant transformation of MYC-overexpressing B cells. Mechanistically, integrating RNA-seq, Ribo-seq and proteomic analyses revealed that efficient translation of select targets is dependent upon eIF5A hypusination, including regulators of G1-to-S phase cell cycle progression and DNA replication. This circuit thus controls MYC’s proliferative response, and it is also activated across multiple malignancies. These findings suggest the hypusine circuit as a therapeutic target for several human tumor types.

BackgroundAlthough the anti-PD-1+LAG-3 and the anti-PD-1+CTLA-4 combinations are effective in advanced melanoma it remains unclear whether their mechanisms of action and resistance overlap.MethodsWe used single cell (sc) RNA-seq, flow cytometry and IHC analysis of responding SM1 and B16 melanoma flank tumors and SM1 brain metastases to explore the mechanism of action of the anti-PD-1+LAG-3 and the anti-PD-1+CTLA-4 combination. CD4+ and CD8+ T cell depletion and ELISPOT assays were used to demonstrate the unique role of CD4+ T cell help in the anti-tumor effects of the anti-PD-1+LAG-3 combination. Tetramer assays confirmed the loss of CD8+ tumor-reactive T cells in brain tumors resistant to the anti-PD-1+LAG-3 combination.ResultsThe anti-PD-1+CTLA-4 combination was associated with the infiltration of FOXP3+ regulatory CD4+ cells (Tregs), fewer activated CD4+ T cells and the accumulation of a subset of IFNγ secreting cytotoxic CD8+ T cells, whereas the anti-PD-1+LAG-3 combination led to the accumulation of CD4+ T helper cells that expressed CXCR4, TNFSF8, IL21R and a subset of CD8+ T cells with reduced expression of cytotoxic markers. T cell depletion studies showed a requirement for CD4+ T cells for the anti-PD-1+LAG-3 combination, but not the PD-1-CTLA-4 combination at both flank and brain tumor sites. In anti-PD-1+LAG-3 treated tumors, CD4+ T cell depletion was associated with fewer activated (CD69+) CD8+ T cells, impaired IFNγ release and increased numbers of myeloid-derived suppressor cells (MDSCs) but, conversely, increased numbers of activated CD8+ T cells and IFNγ release in anti-PD-1+CTLA-4 treated tumors. Analysis of relapsing melanoma brain metastases from anti-PD-1+LAG-3 treated mice showed an increased accumulation of MDSCs and a loss of gp100+ tumor reactive CD8+ T cells. An analysis of the inferred cell-cell interactions from the scRNA-seq data suggested the MDSCs interacted with multiple subsets of T cells in a bi-directional manner.ConclusionsTogether these studies suggest that these two clinically relevant ICI combinations have differential effects upon CD4+ T cell polarization, which in turn, impacted cytotoxic CD8+ T cell function. Further insights into the mechanisms of action/resistance of these clinically-relevant ICI combinations will allow therapy to be further personalized.

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

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

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

Supplementary Table 1: Clinical characteristics of patients whose tumors were utilized for phosphotyrosine peptide survey. Supplementary Table 2: Phosphorylated Proteins in RCC Cell Line and Tumor Samples. Supplementary Table 3: Phosphopeptides identified using the Andromeda search engine of 10 renal cell carcinoma cell lines. Supplementary Table 4: Supplementary Table 4. Label-free quantification of phosphopeptides identified using the Andromeda search engine of 15 clear cell and 15 papillary renal cell carcinoma tumors. Supplementary Figure 1: Tyrosine kinase inhibitors (0.5 μM) provide functional correlation. Supplementary Figure 2: Tyrosine kinase inhibitors (2.5 μM) provide functional correlation. Please note that this data, due to its large size, is represented best in Microsoft Excel.

Supplementary Figures 3-5 Legends

Immunohistochemistry demonstrates robust DDR1 phosphorylation in papillary RCC tumors.

Purpose:A phase II multi-institutional clinical trial was conducted to determine overall survival (OS) in patients with recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) treated with a combination of cetuximab and nivolumab.Patients and Methods:Patients with R/M HNSCC were treated with cetuximab 500 mg/m2 i.v. on day 14 as a lead-in followed by cetuximab 500 mg/m2 i.v. and nivolumab 240 mg i.v. on day 1 and day 15 of each 28-day cycle. Expression of p16 and programmed cell death-ligand 1 (PD-L1) in archived tumors were determined. Tumor-tissue–modified human papillomavirus (TTMV) DNA was quantified in plasma.Results:Ninety-five patients were enrolled, and 88 patients were evaluable for OS with a median follow-up of 15.9 months. Median OS in the 45 patients who had prior therapy for R/M HNSCC (cohort A) was 11.4 months, with a 1 year OS 50% [90% confidence interval (CI), 0.43–0.57]. Median OS in the 43 patients who had no prior therapy (cohort B) was 20.2 months, with a 1-year OS 66% (90% CI, 0.59–0.71). In the combined cohorts, the p16-negative immunostaining was associated with higher response rate (RR; P = 0.02) but did not impact survival while higher PD-L1 combined positive score was associated with higher RR (P = 0.03) and longer OS (log-rank P = 0.04). In the p16-positive patients, lower median (1,230 copies/mL) TTMV DNA counts were associated with higher RR (P = 0.01) and longer OS compared with higher median (log-rank P = 0.05).Conclusions:The combination of cetuximab and nivolumab is effective in patients with both previously treated and untreated R/M HNSCC and warrants further evaluation.

PDF file - 95KB, Supplementary Table S3-A. Human tumor pY proteome. S3-B. Comparison analysis of unique pTyr sites iden_fy from di_erent samples.

Supplementary Figure from Phase II Multi-institutional Clinical Trial Result of Concurrent Cetuximab and Nivolumab in Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma

Supplemental Figure 1. Expression of T, NK, B and Plasma cell markers by metastatic site. Supplemental Figure 2. Expression of myeloid cell markers by metastatic site and gene expression associated with classical and alternative macrophage activation. Supplemental Figure 3. Baseline and therapy induced changes in the LMM immune landscape. Supplemental Figure 4. Baseline and therapy-mediated changes to the MBM immune landscape. Supplemental Figure 5. Expression of MHC class I and II in melanoma cells from samples with and without DC3s. Supplemental Figure 6. Cell-cell interactions between DC3s and T cells defined by SingleCellR. Supplemental Figure 7. Detection of DC3 cells in clinical samples using multiplexed IHC. Supplemental Figure 8. Validation analysis of samples from melanoma metastases in a publicly available validation dataset. Supplemental Figure 9. Expression of key markers that distinguish the major subsets of myeloid cells. Supplemental Figure 10. Correlation of DC3 gene signature and PC loading for DC3 gene signature in TCGA. Supplemental Figure 11. Overall survival for melanoma patients with LMM metastases whose CSF samples contained high DC3 cells vs. low DC3 cells. Supplemental Figure 12. Correlation of individual genes from the DC3 gene signature to overall survival and their univariate cox proportional-hazards models in TCGA dataset.

Purpose:Melanoma brain metastases (MBM) and leptomeningeal melanoma metastases (LMM) are two different manifestations of melanoma CNS metastasis. Here, we used single-cell RNA sequencing (scRNA-seq) to define the immune landscape of MBM, LMM, and melanoma skin metastases.Experimental Design:scRNA-seq was undertaken on 43 patient specimens, including 8 skin metastases, 14 MBM, and 19 serial LMM specimens. Detailed cell type curation was performed, the immune landscapes were mapped, and key results were validated by IHC and flow cytometry. Association analyses were undertaken to identify immune cell subsets correlated with overall survival.Results:The LMM microenvironment was characterized by an immune-suppressed T-cell landscape distinct from that of brain and skin metastases. An LMM patient with long-term survival demonstrated an immune repertoire distinct from that of poor survivors and more similar to normal cerebrospinal fluid (CSF). Upon response to PD-1 therapy, this extreme responder showed increased levels of T cells and dendritic cells in their CSF, whereas poor survivors showed little improvement in their T-cell responses. In MBM patients, therapy led to increased immune infiltrate, with similar T-cell transcriptional diversity noted between skin metastases and MBM. A correlation analysis across the entire immune landscape identified the presence of a rare population of dendritic cells (DC3) that was associated with increased overall survival and positively regulated the immune environment through modulation of activated T cells and MHC expression.Conclusions:Our study provides the first atlas of two distinct sites of melanoma CNS metastases and defines the immune cell landscape that underlies the biology of this devastating disease.

PDF file - 104KB, Supplementary Figure 1A-B: PC9GR cells acquired drug resistance to both reversible and irreversible EGFR-TKI. A: PC9 and PC9GR cells were treated for 72 h with increasing concentrations of erlotinib or CL387,785. Data generated by cell viability assay (CellTiter-Glo) are expressed as a percentage of the value for untreated cells. Determinations were done in triplicate. B: PC9 and PC9GR cells were incubated for 1 hour in the absence or presence of erlotinib (1 �M) as indicated. Cell lysates were subjected to protein expression analysis with antibodies to pEGFR, pSrc, pAkt, and pErk along with antibodies to β-actin as a loading control. C-D: Gene status in PC9 and PC9GR cells. C: PC9GR cells acquired T790M while retaining exon 19 deletion as determined by PCR-invader assay. Values of red bars above zero represent positive results with the detection probe for indicated mutation sequences of EGFR, while values of light blue bars are quality controls showing good PCR quality, as previously described (1). The value of red bars = (fluorescence of the samples with detection probe for mutation sequences) - (fluorescence of the normal control with the same probe � 2). The value of light blue bars = (fluorescence of the samples with detection probe for wild type sequences) - (fluorescence of the normal control with the same probe � 0.8). DEL1 represents E746-A750del type1 (2235-2249del GGAATTAAGAGAAGC). DEL2 represents E746-A750del type2 (2236-2250del GAATTAAGAGAAGCA). DEL3 represents L747-P753del insS. D: FISH analysis (CEP7 (green)/D7S522 (red)) was done in PC9 and PC9GR cells as previously described (2). No additional copy of D7S522 compared to CEP 7 was found in these cells, explaining that neither PC9 nor PC9GR cells has MET amplification.

Supplementary Figure from Single-cell Characterization of the Cellular Landscape of Acral Melanoma Identifies Novel Targets for Immunotherapy

Supplementary Materials and Methods

Supplemental Table 2: Sample and Patient Clinical Correlative Information

Supplemental Table 4: top genes that differentiate each cluster of myeloid cells

PDF file - 139KB, Effects of ligands exposure with or without EGFR-TKI in PC9 and PC9GR cells. A-F: PC9 (A, C, and E) or PC9GR cells (B, D, and F) were incubated for 1 hour in the absence or presence of 100 nM erlotinib (A, C, and E) or afatinib (B, D, and F) with indicated concentrations of HGF (A and B), IGF1 (C and D), or Gas6 (E and F). Cell lysates were subjected to protein expression analysis with antibodies to pMET (A and B), IGF1R (C and D), or pAXL (E and F) along with pEGFR, pAkt, pErk, and antibodies to β-actin as a loading control.

Internal (Supplementary Figure 3) and external (Supplementary Figure 4) tumor LC-MS/MS controls.

Supplementary Table from Phase II Multi-institutional Clinical Trial Result of Concurrent Cetuximab and Nivolumab in Recurrent and/or Metastatic Head and Neck Squamous Cell Carcinoma

PDF file - 54KB, A: Example of peak area (EIC) for EGFR pY 1172. B: Example of peak area (EIC) for MET pY 1234.

Purpose:Acral melanoma is a rare subtype of melanoma that arises on the non–hair-bearing skin of the palms, soles, and nail beds. In this study, we used single-cell RNA sequencing (scRNA-seq) to map the transcriptional landscape of acral melanoma and identify novel immunotherapeutic targets.Experimental Design:We performed scRNA-seq on nine clinical specimens (five primary, four metastases) of acral melanoma. Detailed cell type curation was performed, the immune landscapes were mapped, and key results were validated by analysis of The Cancer Genome Atlas (TCGA) and single-cell datasets. Cell–cell interactions were inferred and compared with those in nonacral cutaneous melanoma.Results:Multiple phenotypic subsets of T cells, natural killer (NK) cells, B cells, macrophages, and dendritic cells with varying levels of activation/exhaustion were identified. A comparison between primary and metastatic acral melanoma identified gene signatures associated with changes in immune responses and metabolism. Acral melanoma was characterized by a lower overall immune infiltrate, fewer effector CD8 T cells and NK cells, and a near-complete absence of γδ T cells compared with nonacral cutaneous melanomas. Immune cells associated with acral melanoma exhibited expression of multiple checkpoints including PD-1, LAG-3, CTLA-4, V-domain immunoglobin suppressor of T cell activation (VISTA), TIGIT, and the Adenosine A2A receptor (ADORA2). VISTA was expressed in 58.3% of myeloid cells and TIGIT was expressed in 22.3% of T/NK cells.Conclusions:Acral melanoma has a suppressed immune environment compared with that of cutaneous melanoma from nonacral skin. Expression of multiple, therapeutically tractable immune checkpoints were observed, offering new options for clinical translation.

Purpose: Irreversible EGFR-tyrosine kinase inhibitors (TKI) are thought to be one strategy to overcome EGFR-TKI resistance induced by T790M gatekeeper mutations in non–small cell lung cancer (NSCLC), yet they display limited clinical efficacy. We hypothesized that additional resistance mechanisms that cooperate with T790M could be identified by profiling tyrosine phosphorylation in NSCLC cells with acquired resistance to reversible EGFR-TKI and harboring T790M.Experimental Design: We profiled PC9 cells with TKI-sensitive EGFR mutation and paired EGFR-TKI–resistant PC9GR (gefitinib-resistant) cells with T790M using immunoaffinity purification of tyrosine-phosphorylated peptides and mass spectrometry–based identification/quantification. Profiles of erlotinib perturbations were examined.Results: We observed a large fraction of the tyrosine phosphoproteome was more abundant in PC9- and PC9GR-erlotinib–treated cells, including phosphopeptides corresponding to MET, IGF, and AXL signaling. Activation of these receptor tyrosine kinases by growth factors could protect PC9GR cells against the irreversible EGFR-TKI afatinib. We identified a Src family kinase (SFK) network as EGFR-independent and confirmed that neither erlotinib nor afatinib affected Src phosphorylation at the activation site. The SFK inhibitor dasatinib plus afatinib abolished Src phosphorylation and completely suppressed downstream phosphorylated Akt and Erk. Dasatinib further enhanced antitumor activity of afatinib or T790M-selective EGFR-TKI (WZ4006) in proliferation and apoptosis assays in multiple NSCLC cell lines with T790M-mediated resistance. This translated into tumor regression in PC9GR xenograft studies with combined afatinib and dasatinib.Conclusions: Our results identified both codrivers of resistance along with T790M and support further studies of irreversible or T790M-selective EGFR inhibitors combined with dasatinib in patients with NSCLC with acquired T790M. Clin Cancer Res; 20(15); 4059–74. ©2014 AACR.

Supplementary Data from Single-cell Characterization of the Cellular Landscape of Acral Melanoma Identifies Novel Targets for Immunotherapy

Supplemental Table 5: Cox proportional hazard model analysis identifies immune cell subpopulations associated with better survival outcomes

PDF file - 91KB, A: Effects of dasatinib combined with afatinib on apoptosis in HCC4006-T790M and HCC827-T790M cells. Apoptosis assay was carried out using PE-conjugated caspase-3 antibody, following incubation of HCC4006-T790M or HCC827-T790M cells for 72 hours with DMSO, erlotinib (E), dasatinib (D), afatinib (A), and afatinib + dasatinib. Values are expressed as a percentage of caspase-3-positive cells. Determinations were done in triplicate. Bars, SD. *P < 0.001 versus DMSO or each single agent (Student's t-test). B: Validation of active pTyr sites from PC9 cell lines on lung cancer patient. Venn diagram was used to compare the pTyr sites identified from PC9 and human lung tumor tissue with positive EGFR mutation. Total 83 overlap active pTyr sites were obtained.

PDF file - 387KB, Effects of ligands exposure with or without EGFR-TKI in PC9 and PC9GR cells. A-F: PC9 (A, C, and E) or PC9GR cells (B, D, and F) were incubated for 1 hour in the absence or presence of 100 nM erlotinib (A, C, and E) or afatinib (B, D, and F) with indicated concentrations of HGF (A and B), IGF1 (C and D), or Gas6 (E and F). Cell lysates were subjected to protein expression analysis with antibodies to pMET (A and B), IGF1R (C and D), or pAXL (E and F) along with pEGFR, pAkt, pErk, and antibodies to β-actin as a loading control.

Supplemental Table 1: Flow Cytometry Antibodies

Supplemental Table 3: top genes that differentiate each cluster of T/NK cells

PDF file - 67KB, Supplementary Table S1: Quantitation of pY changes in between PC9 and PC9GR cell lines with and without erlotinib treatment. The cutoff was set as P value1.5; Supplementary Table S2: Cell viability IC50 in PC9GR cells and H1975 cells.