Supplemental Figure 1. (A) Calu-1 cells were treated with VEGF (50 ng/ml) for 15 minutes and phospho-p38 levels were evaluated by Western blotting. VEGF treatment resulted in activation of p38, and this was blocked with the addition of VEGFR TKIs axitinib or sorafenib. (B) A549, H23, and Calu-1 cells express minimal levels of VEGFR1 as determined by ELISA assay. SKNAS neuroblastoma cells serve as a positive control.

Top 100 downregulated genes after anti-PD-L1 treatment.

PDF file - 27K, SGI-7079 and erlotinib do not adversely affect animal body weight

Supplementary Fig. S7: Effect of AXL and ATR inhibitor combination on cell cycle progression of NSCLC and LCNEC cells.

PDF file, 95K, Antibodies used in reverse-phase protein microarrays

PDF file, 125K, Western blot of total PARP1.

PDF file - 129K, Sweave report depicting complete codes use for microarray analysis of paired adjacent and contralateral airways

Supplementary Material 9 from Gene Expression Profiling Predicts the Development of Oral Cancer

Supplementary title page. Figure S1: CD38 on anti-PD-L1 resistant tumor cells is up-regulated, which is associated with tumor progression. Figure S2: anti-PD-L1 resistant tumors demonstrate the distinct mRNA and protein profiling for immune signature, reflecting the upregulation of CD38. Figure S3: PD-1/PD-L1 blockade results in CD38 up-regulation and acquired resistance in KP-derived lung and melanoma transplantation tumors. Figure S4: Tumor-associated PD-L1 promotes tumor growth but PD-L1 knockout cancer cells still form tumors. Figure S5: PD-L1 knockout effect on tumor growth is CD8 T cell-dependent. Figure S6: CD38 up-regulation after anti-PD-L1 treatment is associated with all-trans retinoic acid signaling. Figure S7: IFN-b, which is enriched in anti-PD-L1 treated tumors, up-regulates CD38 expression on multiple cancer cell lines. Figure S8: IRF1, which links ATRA and IFN-b, is upregulated after anti-PD-L1 treatment. Figure S9: IFN-g, TNF-a, IL-2, and IL-1b don''t regulate CD38 expression on lung cancer cells. Figure S10: The anti-PD-L1 resistant tumors demonstrate an immune suppressive microenvironment. Figure S11: CD38 substantially changes in vivo tumor formation of PD-L1KO cancer cells, but does not change in vitro cell growth rate and cell cycle. Figure S12: The elimination of PD-L1KOCD38negative cancer cells is CD8+ T cell dependent. Figure S13: CD38 on tumor cells inhibits CD8+ T cell function and protects tumor cells from CD8+ T cell killing. Figure S14: CD38-mediated CD8+TIL dysfunction is not affected by tumor growth rate/tumor size. Figure S15: CD38 expression in cancer cell lines and patient tissues, which is associated with the differentiated immune features. Figure S16: CD38 or PD-L1 expression is not correlated with overall survival in early-stage lung cancer. Figure S17: Pre-treatment levels of CD38 and PD-L1 expression in NSCLC patients who received anti-PD-1/PD-L1 therapy, as divided by clinical outcome. Figure S18: The effect of cancer immunotherapy by anti-CD38 is CD8 T cell dependent. Figure S19: Combined inhibitors of CD38 and PD-L1 inhibits tumor growth and metastases. Figure S20: The co-inhibition of PD-L1 and CD38 leads to a favorable antitumor immune microenvironment. Figure S21: Sequential treatment of anti-PD-L1 and anti-CD38 results in enhanced immune response in tumor microenvironment. Figure S22: anti-mouse CD38 antibody (NIMR-5) does not directly kill tumor cells through ADCC and CDC, but causes CD38 internalization. Table S2: Tumor immune markers with the greatest differential transcriptional levels after anti-PD-L1 treatment. Table S6: The most changed immune-related genes after anti-PD-L1 treatment. Table S9:The correlation between CD38 and suppressive immune markers in LUAD dataset. Table S10: The correlation between CD38 and suppressive immune markers in LUSC dataset. Table S11: The available NSCLC patients with CD38 and PD-L1 tumor cell IHC staining and responses to anti-PD-1 therapy. Table S12: The co-inhibition effect of PD-L1 and CD38 on tumor growth and metastasis. Supplementary Material and Methods: Reagents, Cells and Mice, CRISPR/Cas9 Editing, Antibody-mediated Cell Depletion, CD8+ T Cell Adoptive Transfer, mRNA Profiling of Murine Tumors, Flow Cytometry, Nanostring Analysis, qRT-PCR and Western Blotting, Liquid Chromatography-Mass Spectrometry (LC-MS) Analysis, Histologic Analysis, ELISA and RPPA, ADCC/CDC and Internalization Assays, Human Samples, Statistics. Supplementary references

PDF file - 96K, Structure of pyrrolopyrimidine AXL inhibitor SGI-7079

PDF file - 3533K, Sweave report depicting complete codes used for quality control of microarray analysis

Supplementary Material 8 from Gene Expression Profiling Predicts the Development of Oral Cancer

Supplemental Figure 4. DNA repair score trends higher in TTF1-low LUAD cell lines.

In the TCGA NSCLC dataset there was a significantly greater prevalence of p53 mutations in patients with KDR CNG compared to KDR CNG negative patients (p = 0.001).

PDF file - 51K, EMT status of commonly mutated genes in NSCLC

Supplementary Material 2 from Gene Expression Profiling Predicts the Development of Oral Cancer

Supplementary Figure S1. Functional modules among 384 genes selected for the NMF algorithm. Supplementary Figure S2. Co-occurring somatic mutations in KRAS-mutant LUAC subgroups. Supplementary Figure S3. Distribution of bi-allelic deletion/amplification events in the three KRAS-mutant LUAC subsets. Supplementary Figure S4. Alterations in genes involved in G1/S transition in the three KRAS-mutant LUAC subgroups. Supplementary Figure S5. Clonal fractions for somatically mutated genes in the 68 KRAS-mutant LUACs (TCGA). Supplementary Figure S6. Co-mutation plot for the combined PROSPECT/CHITALE validation dataset of chemotherapy -naïve KRAS-mutant LUACs. Supplementary Figure S7. Co-occurring genetic events in PI3K-AKT-MTOR pathway genes in the three KRAS-mutant LUAC subsets. Supplementary Figure S8. LKB1-loss-dependent up-regulation of adaptive branches of the unfolded protein response in KRAS-mutant LUAC cell lines. Supplementary Figure S9. Enumeration of distinct immune cell populations in the KRAS LUAC sub-groups from PROSPECT. Supplementary Figure S10. Analysis of overall survival (OS) in KRAS-mutant LUAC subsets. Supplementary Figure S11. Integrated analyses for a higher pre-determined number of KRAS-mutant LUAC subsets.

XLSX file - 102K, Supplementary table depicting 263 genes comprising a cluster of genes with highest average expression in adjacent airways

PDF file - 233K, Plots correlating protein expression for components of the DNA Repair Score not shown in Figure 3.

Supplementary Tables 1-4 from Epidermal Growth Factor Receptor Expression and Gene Copy Number in the Risk of Oral Cancer