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Overlapped genes between Genes upregulated in Ptenpc-/-Smad4pc-/- tumors as compared to Ptenpc-/- tumors ({greater than or equal to}2 fold) and genes upregulated in GFP+ tumors cells from Ptenpc-/-Smad4pc-/- mice as compared to Tomato+ cells ({greater than or equal to}4 fold).

This file contains supplementary methods, supplementary figure legends, supplementary table legends and supplementary references.

Supplementary Figure 1. Western blots. Supplementary Figure 2. Graphs showing tumor volume from mice following intradermal injection of (A) HMEL-BRAF^V600E cells, (B) WM115 cells, (C) 1205 Lu cells, and (D) pMEL-NRAS^G12D overexpressing GFP, RNF2 wild-type or catalytic mutant derivatives (R70C or I53S). Supplementary Figure 3. (A) RNF expression in the melanoma expression data for normal skin, nevi, and primary tumors. Supplementary Figure 4. (A) Heat map showing clustering of top 10 percent deregulated genes in duplicates of expression data from RNF2^WT overexpressing compared to GFP overexpressing HMEL-BRAF^V600E cells. Supplementary Figure 5. Chromatin state analysis on HMEL-BRAF^V600E cells.

Description of states in 45-state chromatin state model.This table describes the biological annotation of each chromatin state in 50-state chromatin model from HMEL-BRAFV600E cells from ChromHMM program.

Supplementary Figure 1. Immune profiling of pre-treatment, on-treatment and progression CTLA-4 blockade samples by immunohistochemistry. Supplementary Figure 2. Myeloid cell profiling of pre-treatment, on-treatment and progression CTLA-4 blockade samples by immunohistochemistry. Supplementary Figure 3. Increased contact between CD8 T cells and CD68 myeloid cells in non-responding patients to anti-CTLA-4 and anti-PD-1 therapy at pre-treatment CTLA-4 blockade, pre-treatment PD-1 blockade, and on-treatment PD-1 blockade time points. Supplementary Figure 4. Immune profiling of pre anti-PD-1, on-treatment anti-PD-1 and progression anti-PD-1 samples by immunohistochemistry. Supplementary Figure 5. Longitudinal increase in CD8, PD-1, and PD-L1 expression in responders to anti-PD-1 therapy. Supplementary Figure 6. Relative increase in CD8 T cell infiltrate at tumor center in responders to anti-PD-1 on treatment. Supplementary Figure 7. Significant increase in immune infiltrate between responders and non-responders to PD-1 blockade in absence of prior anti-CTLA-4 therapy. Supplementary Figure 8. Immune profiling of myeloid cells atpre-treatment and on-treatment PD-1 blockade time pointsby immunohistochemistry. Supplementary Figure 9. Heatmap of 54 NanoString samples. Supplementary Figure 10. Gene-specific NanoString concordance with immune profiling by IHC in pre-treatment, on-treatment and progression CTLA-4 blockade samples. Supplementary Figure 11. Gene-specific NanoString concordance with immune profiling by IHC in pre-treatment, on-treatment and progression PD-1 blockade samples. Supplementary Figure 12. Prior CTLA-4 blockade is not required for PD-1 early on-treatment profile. Supplementary Figure 13. Hierarchical clustering of gene expression across 54 samples confirms lack of batch effect.

PDF file - 1.1MB, Supplementary Table 1: list of 290 TCGA samples used in the study. Supplementary Table 2: list of miR-mRNA edges identified by CLR. Supplementary Table 3: list of putative direct miR-mRNA edges in the network. Supplementary Table 4: miR-mRNA edges identified as unique to each of the 4 molecular subtypes. (A) Classical (B) Neural (C) Proneural (D) Mesenchymal. Supplementary Table 5: miR and mRNA nodes in regions of copy number aberration in the sub-network corresponding to the 8 discriminatory miRs. Supplementary Table 6: survival analysis results for the 8 discriminatory miRs. Supplementary Table 7: multivariate Cox regression analysis in the TCGA dataset and in the 4 molecular subclasses. Supplementary Table 8: patients' characteristics (validation cohort, n=220). Supplementary Table 9: AQUA values (raw data) for miR-34a expression in Glioblastoma TMAs (validation cohort). Supplementary Table 10: list of transcription factors enriched in the PN subtype

Supplementary Figure Legends

Extended Experimental Methods

Supplemental Figure S1. Integrative Analysis of Functional Genomics and Copy Number Variation in Melanoma Cells and Tissues, Supplemental Figure S2. Elastic Net Derived Biomarker Results for Melanoma Survival Genes and Detection of These Biomarkers in TCGA SKCM, Supplemental Figure S3. The SOX10 Regulatory Network Supporting Cell Autonomous Melanoma Cell Growth and Survival, Supplemental Figure S4. SOX10 Addiction Specifies Sensitivity of BRAF Mutant Melanomas to BRAF and MEK Inhibitors In Vitro, Related to Figure 1, Supplemental Figure S5. SOX10 Addiction Specifies Sensitivity of BRAF Mutant Melanomas to BRAF and MEK Inhibitors In Patients, Related to Figure 2, Supplemental Figure S6. Bicluster of melanoma cell lines and chemical compounds in McDermott/Benes GDSC dataset, Related to Figure 3, Supplemental Figure S7. Nomination of TBK1 as a Therapeutic Target for Drug-Resistant Melanoma, Related to Figure 3, Supplemental Figure S8. TBK1/IKKε-Addiction is Conserved In Vivo, Related to Figure 4, Supplemental Figure S9. TBK1/IKKε-Addiction Corresponds to a Cell Autonomous Innate Immune Melanoma Subtype, Related to Figure 5, Supplemental Figure S10. TBK1/IKKε Activate AKT and YAP to Support Survival of the Cell-autonomous Immune Melanoma Subtype, Related to Figure 6, Supplemental Figure S11. 13C glucose and 13C glutamine metabolism at 30m and 2h, Related to Figure 7 and Supplemental Fig. S7, Supplemental Figure S12. Distinct Epigenetic Cell Fate Programs Specify TBK1/IKKε Addiction, Related to Figure 7.

PDF file - 854K, Supplementary Figure 1: miR-mRNA relationship (miR-34a; PDGFRA) detected by CLR in all GBM samples. Supplementary Figure 2: loss of p53 and Pten in murine neural stem cells and progenitor cells promotes the formation of proneural malignant gliomas. Supplementary Figure 3: expression levels of the subtype-specific miRNAs in p53/Pten-/- E13 embryonic neural stem cells infected with lentiviral miRNA precursors. Supplementary Figure 4: miR-34a expression in an independent cohort of GBM by in situ hybridization. Supplementary Figure 5: miR-34a levels in human and mouse proneural GBM. Supplementary Figure 6: miR-34a impairs tumor formation in orthotopic transplants. Supplementary Figure 7: miR-34a impairs the self-renewal of human proneural malignant spheroids. Supplementary Figure 8: miR-34a loss induces the expression of its oncogenic targets. Supplementary Figure 9: validation of PDGFRA as a direct target of miR-34a. Supplementary Figure 10: PDGFRA rescue on the self-renewal phenotype caused by miR-34a over-expression. Supplementary Figure 11: inverse correlation between the expression levels of miR-34a, PDGFRA and Smad4 in the GBM TCGA dataset. Supplementary Figure 12: validation of Smad4 as a direct target of miR-34a. Supplementary Figure 13: Smad4 rescue on the self-renewal phenotype caused by miR-34a over-expression

Supplementary Figure S1. CyTOF analysis of biological samples from Ptenpc-/-Smad4pc-/- mice (Related to Figure 2). Supplementary Figure S2. Strategy used for MDSCs Isolation (Related to Figure 3). Supplementary Figure S3. Treatment scheme for Gr-1 antibody, peptibody, and Cxcr2 inhibitor SB225002. Supplementary Figure S4. IHC staining of Ki67, CD45, Vimentin, Smooth muscle actin (SMA) and Trichrome staining of mouse prostate tissues treated with IgG control or Gr1 antibody. Supplementary Figure S5. The top 10 differentially expressed genes in Ptenpc-/-Smad4pc-/- tumors as compared to Ptenpc-/- tumors, identified by microarray analysis (n=5). Figure S6. Top 10 activated oncogenic signatures identified by GSEA analysis in Ptenpc-/- Smad4pc-/- tumors as compared to Ptenpc-/- tumors (n=5). Figure S7. Clustering of primary prostate tumors from Wallace et al into MDSC-high and MDSC-low subtypes.

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Supplementary Tables 1-2, Figures 1-8 from PTEN Is a Major Tumor Suppressor in Pancreatic Ductal Adenocarcinoma and Regulates an NF-κB–Cytokine Network

Supplementary Table S1a. Patient Cohort Clinical Summary. Supplementary Table S1b. Patient clinical characteristics. Supplementary Table S1c. Immune profiling by IHC sample log. Supplementary Table S1d. Nanostring 54 sample log. Supplementary Table S2. Summary of immune profiling by immunohistochemistry. Supplementary Table S3. Summary of immune profiling by 4 additional myeloid markers. Supplementary Table S4. Summary of immune profiling by IHC of additional CTLA-4 blockade-naïve samples. Supplementary Table S5. Nanostring Gene List. Supplementary Table S6a. Nanostring summary 54 samples. Supplementary Table S6b. Differentially upregulated and downregulated genes in pre-treatment anti-CTLA-4 samples. Supplementary Table S6c. Differentially upregulated and downregulated genes in on-treatment anti-CTLA-4 samples. Supplementary Table S6d. Differentially upregulated and downregulated genes in pre-treatment anti-PD-1 samples. Supplementary Table S6e. Differentially upregulated and downregulated genes in on-treatment anti-PD-1 samples. Supplementary Table S7. Differentially upregulated and downregulated genes from pre- to on-treatment anti-CTLA-4. Supplementary Table S8. Differentially upregulated and downregulated genes from pre- to on-treatment anti-PD-1. Supplementary Table S9a. Nanostring gene list - chip used for comparison of CTLA-4 blockade-experienced vs -naive anti-PD1 samples (28 samples). Supplementary Table S9b. NanoString additional 28 samples to compare CTLA-4 blockade-experienced vs -naive anti-PD1 samples. Supplementary Table S9c. NanoString normalized data of additional 28 samples to compare CTLA-4 blockade-experienced vs -naive anti-PD1 samples. Supplementary Table S10. Commonly differentially regulated genes between pre- to on-treatment CTLA-4 blockade and PD-1 blockade. Supplementary Table S11a. Fold changes of significant change by anti-PD-1 therapy for paired samples. Supplementary Table S11b. Frequency of significant change by anti-PD1 therapy for paired samples.

Binding sites for RNF2 in HMEL-RNF2WT cells. This file contains list of all chromosomal locations that show enrichment by MACS for RNF2 binding by V5 ChIP-Seq in HMEL-RNF2 cells.

Supplementary Fig. S1. Heatmap of mRNA expression for individual melanosomal differentiation antigens and a MITF transcriptional signature using a published dataset of melanoma samples (2). Supplementary Fig. S2. GPNMB expression is induced in multiple BRAF-mutant cells in response to MAPK pathway inhibition. Supplementary Fig. S3. Gene expression of MITF and GPNMB in melanoma cells following MAPK pathway inhibition. Supplementary Fig. S4. Dabrafenib and Trametinib stimulate MITF nuclear localization. Supplementary Fig. S5. Correlation of MITF and GPNMB mRNA expression in melanoma samples from patients that were on-treatment or that progressed on therapy with MAPK pathway 7 inhibitors. RT-qPCR data for MITF and GPNMB was expressed as the ratio relative to pretreatment levels. Supplementary Fig. S6. Correlation between the expression of individual melanosomal genes with MITF mRNA expression in melanoma samples from patients that were on-treatment or that progressed on therapy with MAPK pathway inhibitors. RT-qPCR data for each gene and MITF was normalized to GAPDH and expressed as the ratio relative to pre-treatment levels. Supplementary Fig. S7. MAPK pathway inhibition results in elevated levels of soluble GPNMB released from melanoma cells. Table S1: Clinical characteristics of patients biopsied for evaluation of MITF and GPNMB mRNA expression Table S2: Correlation coefficients between individual gene expression and MITF or a MITF Signature Table S3: Complete Responses Following Trametinib Treatment in Cohorts Injected with A375 or WM-2664 Melanoma Cells. Table S4: List of Primers for RT-qPCR Analyses

Contains supplementary methods, references and figure legends.

Purpose: To determine if BRAF and/or MEK inhibitor–induced GPNMB expression renders melanomas sensitive to CDX-011, an antibody-drug conjugate targeting GPNMB.Experimental Design: The Cancer Genome Atlas melanoma dataset was interrogated for a panel of MITF-regulated melanosomal differentiation antigens, including GPNMB. BRAF-mutant melanoma cell lines treated with BRAF or MEK inhibitors were assessed for GPNMB expression by RT-qPCR, immunoblot, and FACS analyses. Transient siRNA-mediated knockdown approaches were used to determine if MITF is requirement for treatment-induced GPNMB upregulation. GPNMB expression was analyzed in serial biopsies and serum samples from patients with melanoma taken before, during, and after disease progression on MAPK inhibitor treatment. Subcutaneous injections were performed to test the efficacy of MAPK inhibitors alone, CDX-011 alone, or their combination in suppressing melanoma growth.Results: A MITF-dependent melanosomal differentiation signature is associated with poor prognosis in patients with this disease. MITF is increased following BRAF and MEK inhibitor treatment and induces the expression of melanosomal differentiation genes, including GPNMB. GPNMB is expressed at the cell surface in MAPK inhibitor–treated melanoma cells and is also elevated in on-treatment versus pretreatment biopsies from melanoma patients receiving MAPK pathway inhibitors. Combining BRAF and/or MEK inhibitors with CDX-011, an antibody-drug conjugate targeting GPNMB, is effective in causing melanoma regression in preclinical animal models and delays the recurrent melanoma growth observed with MEK or BRAF/MEK inhibitor treatment alone.Conclusions: The combination of MAPK pathway inhibitors with an antibody-drug conjugate targeting GPNMB is an effective therapeutic option for patients with melanoma. Clin Cancer Res; 22(24); 6088–98. ©2016 AACR.

Supplementary Figures 1-5 from A Genome-Wide Screen Reveals Functional Gene Clusters in the Cancer Genome and Identifies EphA2 as a Mitogen in Glioblastoma

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Supplementary Figure 2 from Mutations in BRAF and KRAS Converge on Activation of the Mitogen-Activated Protein Kinase Pathway in Lung Cancer Mouse Models

Supplementary Figure 1 from Common and Distinct Genomic Events in Sporadic Colorectal Cancer and Diverse Cancer Types

PDF file - 59KB, RAS/RAF mutations correlate with chr 10 status.

Supplementary Materials and Figure Legends 1-4 from Mutations in BRAF and KRAS Converge on Activation of the Mitogen-Activated Protein Kinase Pathway in Lung Cancer Mouse Models

Supplementary Table 3 from Marked Genomic Differences Characterize Primary and Secondary Glioblastoma Subtypes and Identify Two Distinct Molecular and Clinical Secondary Glioblastoma Entities

Supplementary Methods from Marked Genomic Differences Characterize Primary and Secondary Glioblastoma Subtypes and Identify Two Distinct Molecular and Clinical Secondary Glioblastoma Entities

Supplementary Table 4 from Marked Genomic Differences Characterize Primary and Secondary Glioblastoma Subtypes and Identify Two Distinct Molecular and Clinical Secondary Glioblastoma Entities

Colorectal cancer (CRC) is a major cause of cancer morbidity and mortality, and elucidation of its underlying genetics has advanced diagnostic screening, early detection, and treatment. Because CRC genomes are characterized by numerous non-random chromosomal structural alterations, we sought to delimit regions of recurrent amplifications and deletions in a collection of 42 primary specimens and 37 tumor cell lines derived from chromosomal instability neoplasia and microsatellite instability neoplasia CRC subtypes and to compare the pattern of genomic aberrations in CRC with those in other cancers. Application of oligomer-based array-comparative genome hybridization and custom analytic tools identified 50 minimal common regions (MCRs) of copy number alterations, 28 amplifications, and 22 deletions. Fifteen were highly recurrent and focal (EGFR and MYC with the remaining 10 containing a total of 65 resident genes with established links to cancer. Furthermore, comparisons of these delimited genomic profiles revealed that 22 of the 50 CRC MCRs are also present in lung cancer, glioblastoma, and/or multiple myeloma. Among 22 shared MCRs, nine do not contain genes previously shown genetically altered in cancer, whereas the remaining 13 harbor 35 known cancer genes, of which only 14 have been linked to CRC pathogenesis. Together, these observations point to the existence of many yet-to-be discovered cancer genes driving CRC development, as well as other human cancers, and show the utility of high-resolution copy number analysis in the identification of genetic events common and specific to the development of various tumor types. [Cancer Res 2007;67(22):10736–43]

Figures, Legends and Tables

Supplementary Table 1. (Top) IC50 (nM), fold changes (FC) and (Bottom) % viability at 10uM drug treatment compared to DMSO control for BRAF mutant cell lines, A375, MALME-3M, 451Lu and IGR1 (expresses RAC1 P29S) are shown.

PDF file - 328KB, Correlation of DNA copy number aberrations to survival statistics.

Cancer represents the phenotypic end point of multiple genetic lesions that endow cells with a full range of biological properties required for tumorigenesis. Among the hallmark features of the cancer genome are recurrent regional gains and losses that, upon detailed characterization, have provided highly productive discovery paths for new oncogenes and tumor suppressor genes. In this study, we describe the use of an oligonucleotide-based microarray platform and development of requisite assay conditions and bioinformatic mining tools that permits high-resolution genome-wide array-comparative genome hybridization profiling of human and mouse tumors. Using a commercially available 60-mer oligonucleotide microarray, we demonstrate that this platform provides sufficient sensitivity to detect single-copy difference in gene dosage of full complexity genomic DNA while offering high resolution. The commercial availability of the microarrays and associated reagents, along with the technical protocols and analytical tools described in this report, should provide investigators with the immediate capacity to perform DNA analysis of normal and diseased genomes in a global and detailed manner.

PDF file, 634K, BamBam output of peaks and rearrangements for TCGA-GBM-0145, TCGA-GBM-0152 and TCGA-GBM-0648 (Whole Genome Sequence analysis).

XLSX file - 131KB, Genotypes and characteristics of human melanoma cell lines used in this study.