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1 Supplementary Materials and Methods Gene Set Enrichment Analysis for Gene Expression Data We Compared the Gene Expression Leve Supplementary Materials and Methods Gene set enrichment analysis for gene expression data We compared the gene expression levels from 2 different phenotypes (i.e., drug sensitive versus resistant) and picked up the genes which had significant different expression for Gene set enrichment analysis (GSEA) by using Molecular Signatures Database(V3.0). Gene set enrichment analysis was carried out by computing overlaps with canonical pathways(CP) and gene ontology (GO) gene sets(C5), obtained from the Broad Institute [1]. Genes in Gene Set (K), Genes in Overlap (k), k/K and P value were used to rank the pathways enriched in each phenotype. We used with 10363 genes in 2334 pathways as the gene set in this study. Supplementary References 1.Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005;102:15545– 50. 1 Supplementary Results Other drug response-genotype associations in this study Cells with wild genotype were more sensitive to irinotecan than those with TGFβR2 mutation and deletion together (P=0.03) (Supplementary Table S6). PC cells with wild genotype in ADAM gene family were more sensitive to gemcitabine and docetaxel than those with mutation in this family (p<0.05) (Supplementary Table S6). PC cells with wild genotype were more sensitive to docetaxel than those with DEPDC or TTN mutation (p<0.05) (Supplementary Table S6). PC cells with BAI3 mutation were more sensitive to cisplatin than those with wild genotype (p=0.02) (Supplementary Table S6). We also examined some important members of TGFβ pathway (SMAD3, SMAD4, TGFβR1 and TGFβR2) individually and together for additional alterations. Cells were grouped by whether they had a mutation or deletion in any of these four genes. PC cells with the deletion of SMAD Pathway genes (SMAD3, SMAD4, TGFβR2 or TGFβR3) were less sensitive to triptolide (P=0.03) (Supplementary Table S6). Logistic regression models were set up to analyze the factors related to drug response. Different gene statuses were considered as sensitive or resistant genetic factors for each drug. We found AHNAK or DEPDC mutation were resistant factors for cisplatin, the mutation of DEPDC, TTN, FMN2, BAI3 or MLL were resistant factors for docetaxel, ODZ4, OVCH1 or SMADPath gene inactivation were resistant factors for triptolide, and BAI3 inactivation was a resistant factor for gemcitabine (Supplementary Table S7). CDK.SMAD.P53 inactivation was a sensitive factor for artemisinin (Supplementary Table S7). We compared the IC50 values of each drug between familial and sporadic pancreatic cancer cell lines and didn’t find differences of drug response for anticancer drugs (p>0.05) (Supplementary Table S17). Different gene expression profiles were correlated with drug responses of broad classes of anticancer agents in human PC cells We did the Spearman correlation analysis between IC50 values of each drug and the gene expression levels for 19 cell lines in the discovery screen. According to this rule, we discovered a series of related genes, as shown in Supplementary Table S10. We found that expression levels of some genes were closely correlated to anticancer drug responses in human PC cell lines. NIT2, AKT1S1, STK17A, MAP3K12, MAP4K2 and MAPK11 were closely related to drug response of cisplatin. MYH9, ABL2, GPR125, RCC2, STK17B and TTK were related to chemosensitivity of MMC. RAB6B, RHOC, SNIP1, TUSC1, MED23, MYB and WNT11 were related to drug response of triptolide. FBXL12, LDB1, SIN3B, TSC22D3, ABCG2, CDGAP, 2 MAP3K10, MED16, NOTCH3 and STAT6 were related to drug response of gemcitabine. CDC37, PIM3, TSPAN5 and USP5 were correlated to drug response of Parp1inhibitor. We also compared gene expression levels of 20,661 protein-coding genes from sensitive and resistant cell lines of each drug. The rules to define sensitive versus resistant by IC50 value were as follows: <5 nM versus >20 nM for gemcitabine, <1 nM versus >5 nM for docetaxel and triptolide, <10 nM versus >50 nM for MMC, <200 nM versus >10000 nM for irinotecan, cisplatin and Parp1 inhibitor and <300 nM versus >10000 nM for artemisinin. We selected the genes which had significant different expression levels for Gene set enrichment analysis and results were shown in Supplementary Table S18. We found that genes with significant different gene expression gathered in cell cycle, cell cycle checkpoint, DNA replication, DNA repair, apoptosis and MAPK Pathway for gemcitabine; genes for MMC gathered in DNA damage,P38 signaling, protein deacetylase activity and apoptosis pathway; genes for triptolide were involved in MTA3 and JUN Pathway; genes for Parp1 inhibitor gathered in mitochondrial metabolism; genes for docetaxel were involved in serine/threonine/tyrosine kinase activity, chromatin assembly and disaseembly, p38 and JUN Signaling and nucleosome assembly; genes for artemisinin were involved in PI3K Pathway. We listed discovered genes of cell cycle, apoptosis, autophagy, DNA repair, tumor suppressor gene and anticancer drug metabolism pathway between sensitive and resistant human pancreatic cancer cells in Supplementary Figure S4 and Supplementary Table S16. Genes were selected for sensitive versus resistant based on P<0.05 of the median IC50s. Cell cycle genes (CDC2L1,CDC37L1,CDC73,E2F1), apaoptosis and autophagy genes(GADD45B,ATG10, ATG4B), DNA repair genes(ERCC3, PARP2,PLK2, RAD17,RAD54L2,TP53BP2,TSC2) and some MAPK pathway genes were upregulated in triptolide sensitive PC cell lines (Supplementary Figure S4A and Supplementary Table S16). It was also observed that cell cycle genes (CDC20,CDC25A,CDC27,CDC45L,CDC6, CDK5R1,CDKN2B), apaoptosis and autophagy genes (ATG10,ATG4C,CASP5), and DNA repair genes (AURKA, AURKB, BLM,BRCA1MSH2,RAD21,RAD23A, RAD51, RAD51AP1,SMC2,XRCC5,XRCC6) were upregulated in gemcitabine sensitive PC cell lines (Supplementary Figure S4C,S4D and Supplementary Table S16). Apaoptosis and autophagy genes (ATG9A,BCL3) were upregulated, but cell cycle genes (CDC2,CDC40) and DNA repair genes (MDM2, MLH1,MSH3,BRCA1) were downregulated in MMC sensitive pancreatic cell lines (Supplementary Figure S4E and Supplementary Table S16). Different gene expression profiles were correlated with DPC4/SMAD4, TP53 or P16/CDKN2A inactivations 3 In order to assess the gene expression profile under DPC4 deficiency, we compared gene expression levels of 20,661 protein-coding genes from 2 different genotypes (DPC4.md versus DPC4.wt) and then discovered the genes in cell cycle, apoptosis and DNA repair pathway which had statistically significantly different expression levels. Compared to cells with DPC4.wt, cells with DPC4.md showed these genes were mainly involved in pathways related to cell cycle checkpoint (CDKN1B, CDC2L6, CDCA4), DNA repair (FANCD2, TP73, XRCC3), apoptosis (BCL2L1,BCL2L13,BCL7B) and MAPK signaling (Supplementary Figure S5A and Supplementary Table S19). Overall, these results suggest that under DPC4.md genetic background, cellular decreased cell cycle checkpoint arrest, apoptosis and DNA repair pathway change may confer sensitivity to cisplatin and irinotecan and nonresponse to gemcitabine. In order to know the gene expression profile under TP53 deficiency, we compared gene expression levels from 2 different genotypes (TP53.md versus TP53.wt) and then discovered the genes with P value less than 0.05. Compared to cells with TP53.wt, cells with TP53.md showed upregulated genes gathered in pathways related to cell cycle checkpoint (CDKN1C,CDKN2A,CDKN2B, CDKN2C,CDC25B), DNA repair (RAD51,RAD54L,BLM,MLF1), apoptosis (CASP7,CASP8AP2,CASP9,BCL2L11,CYCS), autophagy (ATG12, ATG4B,ATG4D,ATG7) and MAPK signaling (Supplementary Figure S5B,S5C and Supplementary Table S20). These results suggest that increased cell cycle G1/S checkpoint arrest, increased exacerbated spontaneous HR observed in p53 defective cells, upregulation of apoptosis and autophagy genes may contribute to the sensitivity of triptolide and Parp1 inhibitor. We compared gene expression levels from 2 different genotypes (P16.md deletion versus P16.wt) and then found the genes with P value less than 0.05. Compared to cells with P16 wild type, cells with P16 deletion showed changed genes gathered in pathways related to cell cycle checkpoint (AKT1,CHEK2,CDC25C,CCNB1IP1,CDC14A, CDCA2,CDK10,CDK2AP1), apoptosis (FADD,FAIM,FAS,BAG5, BMF),DNA repair(PLK1,RAD21,RAD9B,WRN,XRCC2, PCNA) and autophagy (ATG9A) pathway (Supplementary Figure S5D and Supplementary Table S21). Overall, these results suggest that underregulation of DNA repair genes and cell cycle checkpoint change may confer nonresponse to gemcitabine and MMC under P16.md. 4 Supplementary tables: Table S1 Characteristics of human pancreatic cancer cell lines used in this study Table S2 Characteristics of human DPC4/SMAD4 isogenic pancreatic cancer cell lines Table S3 Characteristics of human TP53 isogenic colon cancer cell lines Table S4 IC50 values (nM) of Gemcitabine, Triptolide, Docetaxel, MMC, Cisplatin, Irinotecan, Parp1 inhibitor and Artemisinin for human PC cell lines Table S5 IC50 values (nM) of Gemcitabine, Triptolide, Docetaxel, MMC, Cisplatin, Irinotecan, Parp1 inhibitor and Artemisinin for human pancreatic cancer cell lines Table S6 Correlation analysis between chemosensitivity and somatic mutations alone, homozygous deletion alone or both in pancreatic cancer cells identified in the discovery and prevalence screen arranged by drugs Table S7 Logistic regression analysis for drug response to genetic status Table
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