Supplementary Materials and Methods

Gene set enrichment analysis for expression data

We compared the levels from 2 different phenotypes (i.e., drug sensitive versus resistant) and picked up the 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 (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.

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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,

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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 -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

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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.

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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 S8 IC50s of DPC4/SMAD4 isogenic cells tested with Cisplatin, Irinotecan and Gemcitabine

Table S9 Correlation analysis between IC50s of Cisplatin, Irinotecan and Gemcitabine and TGFB pathway activity of DPC4 isogenic cells

Table S10 Genes whose expression levels were correlated with chemosensitivity in pancreatic cancer cell lines

Table S11 Combination indices (CI) for csplatin+irinotecan combination in DPC4 isogenic cell lines

Table S12 IC50s of TP53 isogenic colon cancer cell lines tested with Parp1 inhibitor and Triptolide

Table S13 Comparisons of gene mutation statuses of cell lines having IC50<200nM with those having IC50>10000nM for Parp1 inhibitor

Table S14 Genes with significant different gene expression in cell cycle and DNA damage pathway between TP53 defective PC cell lines having IC50<200nM and those having IC50>10000nM for Parp1 inhibitor

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Table S15 P16/CDKN2A deleted cell rank ordered by their sensitivity to gemcitabine and MMC investigated for association with CDKN2B, DMRTA1, MTAP and TUSC1 in pancreatic cancer cells

Table S16 Genes with significant different gene expression in cell cycle, apoptosis, autophagy, DNA repair, tumor suppressor gene and anticancer drug metabolism pathway between sensitive and resistant human pancreatic cancer cells

Table S17 Chemosensitive differences of anticancer drugs between familial and sporadic pancreatic cancer cell lines

Table S18 Sensitivity-related gene sets for each drug by employing Gene set enrichment analysis for sensitive versus resistant cell lines

Table S19 Genes with significant different gene expression in cell cycle, apoptosis, autophagy, DNA repair, tumor suppressor gene and anticancer drug metabolism pathway between DPC4.md and DPC4.wt human pancreatic cancer cells

Table S20 Genes with significant different gene expression in cell cycle, apoptosis, autophagy, DNA repair, tumor suppressor gene and anticancer drug metabolism pathway between TP53.md and TP53.wt human pancreatic cancer cells

Table S21 Genes with significant different gene expression in cell cycle, apoptosis, autophagy, DNA repair, tumor suppressor gene and anticancer drug metabolism pathway between P16.md and P16.wt human pancreatic cancer cells

Table S22 Genes with significant different gene expression in PC cells clustering in a triangle network

Supplementary figure legends:

Figure S1: Correlations of drug response to DPC4 genotype. Cytotoxic effects of cisplatin (A) and irinotecan (B) were related to DPC4/SMAD4 deletion. Gemcitabine (C, E) and cisplatin (D) (not significant) are included for the DPC4 mutation status because they have significant different drug responses in DPC4 isogenic pairs. Wilcoxon rank sum tests were used to compare differences in the median IC50 by mutation status. “md” indicates either mutation or deletion and the “wt” indicates wildtype.

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Figure S2: Correlations of drug response to P16 genotype. Cytotoxic effect of gemcitabine (A) was related to P16 deletion. MMC (B) was included for the P16 mutation status since it was significantly correlated with P16 status when P16 inactivation was compared to wild types. Gemcitabine (C) was included since it was significantly correlated with P16 status when only deletions were compared to widtypes. PC cells with wildtype DPC4 and P16 were 4-fold more sensitive to gemcitabine than DPC4 and P16 defects, although this difference was not statistically significant (D). Wilcoxon rank sum tests were used to compare differences in the median IC50 by mutation status. “md” indicates either mutation or deletion and the “wt” indicates wildtype.

Figure S3: Reponses of isogenic cancer cell lines to anticancer drugs. TGFβ pathway activities were tested for DPC4 isogenic pairs using luciferase assay (A).Correlations between IC50s of cisplatin (B) or gemcitabine (C) and TGFβ pathway activity of DPC4 isogenic cells were performed by Spearman analysis. Three pairs of isogenic colon cancer cell lines for TP53 demonstrated differences in response for Parp1 inhibitor (D).

Figure S4: Gene expression profiles correlated with chemotherapeutic response. Gene expression profile related to sensitivity of triptolide (A, B), Parp1 inhibitor (C, D), gemcitabine (E,F,G) and MMC (H,I) in cell cycle, apoptosis, autophagy, DNA repair, tumor suppressor gene and anticancer drug metabolism pathway was shown. Genes were selected for sensitive versus resistant based on P<0.05 of the median IC50s (Wilcoxon rank sum test). The rules to define sensitive versus resistant by IC50 value were as follows: <1 nM versus >5 nM for triptolide, <200 nM versus >10000 nM for Parp1 inhibitor, <5 nM versus >20 nM for gemcitabine and <10 nM versus >50 nM for MMC. Y axes of A, C, E and H represent fold of median gene expression level of sensitive versus insensitive PC cells, whereas Y axes of B, D, F and I represent fold of median gene expression level of insensitive versus sensitive PC cells.

Figure S5: Gene expression profiles correlated with DPC4/SMAD4.md, TP53.md and P16/CDKN2A.md. Gene expression profile related to DPC4/SMAD4.md (A,B), TP53.md (C,D) and P16/CDKN2A.md (E,F) in cell cycle, apoptosis, autophagy, DNA repair, tumor suppressor gene and anticancer drug metabolism pathway was shown. Genes were selected for DPC4/SMAD4.md versus DPC4/SMAD4.wt, TP53.md versus TP53.wt and P16/CDKN2A.md versus P16/CDKN2A.wt based on P<0.05 of the median IC50s (Wilcoxon rank sum test). Y axes of A, C and E represent fold of median gene expression level of gene inactivation versus wild type of PC cells, whereas Y axes of B, C and F represent fold of median gene expression level of gene wild type versus inactivation of PC cells. “wt” indicates a wildtype; “md” indicates either a mutation or deletion.

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