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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.

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.

Supplementary Figure from Tumor Expression Quantitative Trait Methylation Screening Reveals Distinct CpG Panels for Deconvolving Cancer Immune Signatures

Figure S1: Adaptor knockdowns. A) 72hr titration of BKM120 in MET-amplified NSCLC cell lines analyzed by Cell Titer Glo 72 hr. Figure S2: Distribution of tumor types for PDX models used in MET:GRB2 PLA survey. Figure S3: Immunohistochemistry in PDX models and cell line controls A) H&E and IHC for MET and phospho-MET (Tyr1234/5) from non-treated control mice. Figure S4: Intratumoral heterogeneity of MET:GRB2 Signaling complexes in LXFA2165. Red foci represent MET:GRB2 PLA signal. Green = cytokeratin, Blue = DAPI. Figure S5: Regression model of MET:GRB2 PLA to tumor volume change. Supplemental Table 1: Summary of MET TKi sensitivity, phospho-MET western and MET:GRB2 complex presence in 12 cell line panel Supplemental Table 2: Mutations and Mutational Burden* Identified in Top 20 Lung Adenocarcinoma Mutations as Defined by TCGA (2). Supplemental Table 3: METÎ"ex14 Patient Characteristics

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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.

Table S2: Odds ratio and 95% confidence limits for cancers associated with variants with pleiotropic associations. Figure S1: Flowchart showing analysis steps. Figure S2. Manhattan plots by chromosome for individual cancer sites. Figure S3. Results for rs11571833. Figure S4: Conditional QQ-plots.

Supplementary Figures S1-S2; Supplementary Tables S1-S3.

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.

Purpose: The clinical use of BRAF inhibitors is being hampered by the acquisition of drug resistance. This study shows the potential therapeutic use of the HSP90 inhibitor (XL888) in six different models of vemurafenib resistance.Experimental Design: The ability of XL888 to inhibit growth and to induce apoptosis and tumor regression of vemurafenib-resistant melanoma cell lines was shown in vitro and in vivo. A novel mass spectrometry–based pharmacodynamic assay was developed to measure intratumoral HSP70 levels following HSP90 inhibition in melanoma cell lines, xenografts, and melanoma biopsies. Mechanistic studies were carried out to determine the mechanism of XL888-induced apoptosis.Results: XL888 potently inhibited cell growth, induced apoptosis, and prevented the growth of vemurafenib-resistant melanoma cell lines in 3-dimensional cell culture, long-term colony formation assays, and human melanoma mouse xenografts. The reversal of the resistance phenotype was associated with the degradation of PDGFRβ, COT, IGFR1, CRAF, ARAF, S6, cyclin D1, and AKT, which in turn led to the nuclear accumulation of FOXO3a, an increase in BIM (Bcl-2 interacting mediator of cell death) expression, and the downregulation of Mcl-1. In most resistance models, XL888 treatment increased BIM expression, decreased Mcl-1 expression, and induced apoptosis more effectively than dual mitogen-activated protein–extracellular signal–regulated kinase/phosphoinositide 3-kinase (MEK/PI3K) inhibition.Conclusions: HSP90 inhibition may be a highly effective strategy at managing the diverse array of resistance mechanisms being reported to BRAF inhibitors and appears to be more effective at restoring BIM expression and downregulating Mcl-1 expression than combined MEK/PI3K inhibitor therapy. Clin Cancer Res; 18(9); 2502–14. ©2012 AACR.

Supplementary Table 2 from Inherited Variants in Mitochondrial Biogenesis Genes May Influence Epithelial Ovarian Cancer Risk

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

The Akt activation inhibitor triciribine and the farnesyltransferase inhibitor tipifarnib have modest to little activity in clinical trials when used as single agents. In this article, preclinical data show that the combination is more effective than single agents both in cultured cells and in vivo. Combination index data analysis shows that this combination is highly synergistic at inhibiting anchorage-dependent growth of breast cancer cells. This synergistic interaction is also observed with structurally unrelated inhibitors of Akt (MK-2206) and farnesyltransferase (FTI-2153). The triciribine/tipifarnib synergistic effects are seen with several cancer cell lines including those from breast, leukemia, multiple myeloma and lung tumors with different genetic alterations such as K-Ras, B-Raf, PI3K (phosphoinositide 3-kinase), p53 and pRb mutations, PTEN, pRB and Ink4a deletions, and ErbB receptor overexpression. Furthermore, the combination is synergistic at inhibiting anchorage-independent growth and at inducing apoptosis in breast cancer cells. The combination is also more effective at inhibiting the Akt/mTOR/S6 kinase pathway. In an ErbB2-driven breast tumor transgenic mouse model, the combination, but not single agent, treatment with triciribine and tipifarnib induces significant breast tumor regression. Our findings warrant further investigation of the combination of farnesyltransferase and Akt inhibitors. Clin Cancer Res; 17(9); 2852–62. ©2011 AACR.

CCR Translation for This Article from The HSP90 Inhibitor XL888 Overcomes BRAF Inhibitor Resistance Mediated through Diverse Mechanisms

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

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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.

Supplemental Figure S1. Flowchart for epigenomic annotation of lncRNAs. Supplemental Figure S2. Distribution of ovarian tissue histone marks in lncRNA genes. Supplemental Table S1. Study participants for GWAS meta-analysis of epithelial ovarian cancer. Supplemental Figure S3. Distribution of number of EOC risk SNPs for 10,000 permutations of lncRNA-sized regions. Supplemental Table S2. SNPs associated with risk for less common ovarian cancer subtypes are not enriched in lncRNAs. Supplemental Figure S4. Genomic coverage of GENCODE v19 lncRNA genes (n=13,442 with GWAS SNPs). Supplemental Table S3. Density of EOC-associated SNPs in lncRNA genes versus whole genome (All histologies). Supplemental Table S4. Density of EOC-associated SNPs in lncRNA genes by histologic subtype. Supplemental Table S5. Promoter and transcription factor motifs enriched in EOC-associated lncRNA regions. Supplemental Table S6. Biological processes of putative promoter and transcription factors enriched in 53 EOC-associated lncRNA. Supplemental Figure S5. Regional plots for five novel loci associated with EOC risk and expression of lncRNA genes.

Background: Genome-wide association studies (GWAS) for epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy, have identified novel susceptibility loci. GWAS for survival after EOC have had more limited success. The association of each single-nucleotide polymorphism (SNP) individually may not be well suited to detect small effects of multiple SNPs, such as those operating within the same biologic pathway. Gene set analysis (GSA) overcomes this limitation by assessing overall evidence for association of a phenotype with all measured variation in a set of genes.Methods: To determine gene sets associated with EOC overall survival, we conducted GSA using data from two large GWAS (N cases = 2,813, N deaths = 1,116), with a novel Principal Component-Gamma GSA method. Analysis was completed for all cases and then separately for high-grade serous histologic subtype.Results: Analysis of the high-grade serous subjects resulted in 43 gene sets with P < 0.005 (1.7%); of these, 21 gene sets had P < 0.10 in both GWAS, including intracellular signaling pathway (P = 7.3 × 10−5) and macrolide binding (P = 6.2 × 10−4) gene sets. The top gene sets in analysis of all cases were meiotic mismatch repair (P = 6.3 × 10−4) and macrolide binding (P = 1.0 × 10−3). Of 18 gene sets with P < 0.005 (0.7%), eight had P < 0.10 in both GWAS.Conclusion: This research detected novel gene sets associated with EOC survival.Impact: Novel gene sets associated with EOC survival might lead to new insights and avenues for development of novel therapies for EOC and pharmacogenomic studies. Cancer Epidemiol Biomarkers Prev; 21(3); 529–36. ©2012 AACR.

Background: Mitochondria contribute to oxidative stress, a phenomenon implicated in ovarian carcinogenesis. We hypothesized that inherited variants in mitochondrial-related genes influence epithelial ovarian cancer (EOC) susceptibility.Methods: Through a multicenter study of 1,815 Caucasian EOC cases and 1,900 controls, we investigated associations between EOC risk and 128 single nucleotide polymorphisms (SNPs) from 22 genes/regions within the mitochondrial genome (mtDNA) and 2,839 nuclear-encoded SNPs localized to 138 genes involved in mitochondrial biogenesis (BIO, n = 35), steroid hormone metabolism (HOR, n = 13), and oxidative phosphorylation (OXP, n = 90) pathways. Unconditional logistic regression was used to estimate OR and 95% CI between genotype and case status. Overall significance of each gene and pathway was evaluated by using Fisher's method to combine SNP-level evidence. At the SNP level, we investigated whether lifetime ovulation, hormone replacement therapy (HRT), and cigarette smoking were confounders or modifiers of associations.Results: Interindividual variation involving BIO was most strongly associated with EOC risk (empirical P = 0.050), especially for NRF1, MTERF, PPARGC1A, ESRRA, and CAMK2D. Several SNP-level associations strengthened after adjustment for nongenetic factors, particularly for MTERF. Statistical interactions with cigarette smoking and HRT use were observed with MTERF and CAMK2D SNPs, respectively. Overall variation within mtDNA, HOR, and OXP was not statistically significant (empirical P > 0.10).Conclusion: We provide novel evidence to suggest that variants in mitochondrial biogenesis genes may influence EOC susceptibility.Impact: A deeper understanding of the complex mechanisms implicated in mitochondrial biogenesis and oxidative stress may aid in developing strategies to reduce morbidity and mortality from EOC. Cancer Epidemiol Biomarkers Prev; 20(6); 1131–45. ©2011 AACR.

Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103–14. ©2016 AACR.

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

Supplementary Figure 1 from Inherited Variants in Mitochondrial Biogenesis Genes May Influence Epithelial Ovarian Cancer Risk

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

Supplementary Data from Tumor Expression Quantitative Trait Methylation Screening Reveals Distinct CpG Panels for Deconvolving Cancer Immune Signatures

Supplemental Table 1: Flow Cytometry Antibodies

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Supplementary Table 1 from Inherited Variants in Mitochondrial Biogenesis Genes May Influence Epithelial Ovarian Cancer Risk

Background: Genome-wide association studies (GWAS) have identified multiple loci associated with epithelial ovarian cancer (EOC) susceptibility, but further progress requires integration of epidemiology and biology to illuminate true risk loci below genome-wide significance levels (P < 5 × 10−8). Most risk SNPs lie within non–protein-encoding regions, and we hypothesize that long noncoding RNA (lncRNA) genes are enriched at EOC risk regions and represent biologically relevant functional targets.Methods: Using imputed GWAS data from about 18,000 invasive EOC cases and 34,000 controls of European ancestry, the GENCODE (v19) lncRNA database was used to annotate SNPs from 13,442 lncRNAs for permutation-based enrichment analysis. Tumor expression quantitative trait locus (eQTL) analysis was performed for sub-genome-wide regions (1 × 10−5 > P > 5 × 10−8) overlapping lncRNAs.Results: Of 5,294 EOC-associated SNPs (P < 1.0 × 10−5), 1,464 (28%) mapped within 53 unique lncRNAs and an additional 3,484 (66%) SNPs were correlated (r2 > 0.2) with SNPs within 115 lncRNAs. EOC-associated SNPs comprised 130 independent regions, of which 72 (55%) overlapped with lncRNAs, representing a significant enrichment (P = 5.0 × 10−4) that was more pronounced among a subset of 5,401 lncRNAs with active epigenetic regulation in normal ovarian tissue. EOC-associated lncRNAs and their putative promoters and transcription factors were enriched for biologically relevant pathways and eQTL analysis identified five novel putative risk regions with allele-specific effects on lncRNA gene expression.Conclusions: lncRNAs are significantly enriched at EOC risk regions, suggesting a mechanistic role for lncRNAs in driving predisposition to EOC.Impact: lncRNAs represent key candidates for integrative epidemiologic and functional studies. Further research on their biologic role in ovarian cancer is indicated. Cancer Epidemiol Biomarkers Prev; 26(1); 116–25. ©2016 AACR.

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

Density Measures of lncRNA Enrichment at Independent EOC-associated Regions

DNA methylation signatures in tumors could serve as reliable biomarkers that are accessible in archival tissues for tracking the epigenetic dynamics shaped by both cancer cells and the tumor microenvironment. However, given the ultrahigh dimensionality and noncollapsible nature of the data, it remains challenging to screen all CpG sites to identify the most promising marker panels. In this article, we introduce the concept of tumor-based expression quantitative trait methylation (eQTM) for the prioritization and systematic mining of predictive biomarkers. In melanoma as a disease model, eQTM CpGs and genes represent new and efficient candidate targets to be investigated for both prognostic and immune status monitoring purposes. Three cis-eQTM CpGs (cg07786657, cg12446199, and cg00027570) were strongly associated with and can serve as surrogate biomarkers for the tumor immune cytolytic activity score (CYT). In addition, multiple eQTM genes could be further exploited for predicting immunoregulatory phenotypes. A targeted gene panel analysis identified one eQTM in TCF7 (cg25947408) as a novel candidate biomarker for uncoupling overall T-cell differentiation and exhaustion status in a tumor. The prognostic significance of this eQTM as an independent signature to CYT was validated by both The Cancer Genome Atlas and Moffitt melanoma cohort data. Overall, eQTMs represent a mechanistically distinct class of potential biomarkers that can be used to predict patient prognosis and immune status.Significance:This study provides a novel and promising approach to identify targeted epigenetic biomarkers in cancer and will spur further analysis in tumor immune phenotyping.

Variants selected for replication

Funding sources, acknowledgements, consortia involved in study and bioinformatics tools used

Supplementary Figure S1 from LIN28B Polymorphisms Influence Susceptibility to Epithelial Ovarian Cancer