Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput...

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Identifying Candidate Targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening Phil Lorenzi, Ph.D. Dept. of Bioinformatics & Computational Biology Co-Director, Proteomics and Metabolomics Core Facility MD Anderson Cancer Center ````````´´´´´´´´

description

In this 60 minute webinar, Philip L. Lorenzi Ph.D. talked to us about autophagy, a programmed process in which cell contents are delivered to lysosomes for degradation and which appears to have both tumor-suppressive and tumor-promoting functions. Phillip and his colleagues have compiled a comprehensive, curated inventory of autophagy modulators by integrating information from published siRNA screens, multiple pathway analysis algorithms, and extensive text-mining of the literature and he will provide extensive analysis of their sources of information and their complex relationships with each other.

Transcript of Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput...

Page 1: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Identifying Candidate Targets for Cancer Therapy with Integrated

Text Mining and High-Throughput Screening

Phil Lorenzi, Ph.D. Dept. of Bioinformatics & Computational Biology

Co-Director, Proteomics and Metabolomics Core Facility MD Anderson Cancer Center

````````´´´´´´´´

Page 2: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Rationale for Therapy with L-Asparaginase (L-ASP)

ASNS

L-ASP

Asn Proliferation

Cancer Cell:

Asp Gln Asn +

Gln

ATP

NH3

EIF2AK4

EIF2A

Protein Synthesis

Acute Lymphoblastic Leukemia

ATF4

necrosis apoptosis

Starvation

RED L-ASP Sensitivity BLUE L-ASP Resistance

NARS QARS

DDIT3

CASP3

Page 3: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Rationale for Therapy with L-Asparaginase (L-ASP)

Cancer Cell: Acute Lymphoblastic Leukemia

Starvation

NH3

EIF2A

NARS QARS

EIF2AK4 ATF4 NARS QARS

necrosis apoptosis

DDIT3

CASP3

ASNS Asp Gln Asn +

ATP

Project 1: ASNS as a biomarker

Project 2: ASNS-mediated mechanisms

Project 6: siRNA Screens and pathway analysis

Project 3: Metabolomics Project 4: Computationally-

guided improvement of L-ASP therapeutic index

Project 5: Chemical screens

Protein Synthesis

Page 4: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Rationale for Therapy with L-Asparaginase (L-ASP)

Cancer Cell: Acute Lymphoblastic Leukemia

L-ASP

Asn Proliferation

Gln

Starvation

NH3

EIF2A

NARS QARS

EIF2AK4 ATF4 NARS QARS

necrosis apoptosis

DDIT3

CASP3

ASNS Asp Gln Asn +

ATP

siRNA Screens and Pathway Analysis

Protein Synthesis

Page 5: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Autophagy Mediates Resistance to L-ASP

Page 6: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

The Autophagy Pathway

Lorenzi et al. Autophagy, 2014

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Autophagy Mediates Resistance to Stress

Claerhout S and Lorenzi PL. Drugs Fut 36:919 (2011)

Page 8: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

The Autophagy Pathway

Eng CH and Abraham RT. Oncogene 30:4687 (2011)

prompted us to perform a census of the autophagy pathway due to: 1) its complexity, and 2) lack of a complete picture

Page 9: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

siRNA Screens and Pathway Analysis

• Key Questions – Which genes and

proteins modulate the autophagy pathway?

– Which genes represent candidate targets for cancer therapy?

Page 10: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Building the Autophagy Pathway • siRNA

– siRNA Screen 1 (753 siRNA pools targeting 705 genes) • 7 validated hits

– siRNA Screen 2 (21,121 siRNA pools targeting 16,492 genes) • 148 validated hits

– siRNA Screen 3 (726 individual kinase-targeted siRNAs) • 21 validated hits

– siRNA Screen 4 (21,121 siRNA pools targeting 16,492 genes) • 169 validated hits

Limitations – Only two genome-wide

screens

– Hits exhibited almost no intersection

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Pathway Analysis

• Pathway: a collection of genes, proteins, and/or small molecules that modulate a cellular process or disease state

• Growing demand in biological sciences to perform “pathway analysis” on omic data sets

• Common Goals: – build pathway maps

– obtain insight not provided by specific gene/protein/metabolite-level analyses

– drug development: targeting pathways may improve cancer therapy (since cancer cells can acquire resistance to targeting single nodes)

Page 12: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Under the Hood of Pathway Studio

• MedScan • ResNet Mammalian cartridge

• ChemEffect cartridge

• DiseaseFx cartridge

Database

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Building the Autophagy Pathway

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Building the Autophagy Pathway

“In contrast, mTOR is inhibited in nutrient-poor conditions, leading to the induction of autophagy.”

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The Autophagy Pathway

Lorenzi et al. Autophagy 2014

Gene Symbol /

Entity Name

Text

Rank

Gene Symbol /

Entity Name

Text

Rank

Gene Symbol /

Entity Name

Text

RankADIPOQ -1 FGF2 -1 MTOR -160

AGTR2 -93 FKBP1A -1 NFE2L2 -122

AKT1 -158 FLT1 -1 NGF -1

AKT2 -1 FYN -1 NLRC4 -93

ALDH2 -93 GATA4 -1 NPC1 -93

ATF4 -93 GGPS1 -93 NQO1 -1

BCL2A1 -1 GH1 -1 OPA1 -93

BID -1 GRIN1 -1 PDPK1 -1

BRCA1 -1 GSTA4 -1 PIK3CD -1

BSG -1 HDAC1 -134 PNPLA8 -1

BTRC -1 HDAC2 -1 PPP2R1A -93

C12orf5 (TIGAR) -122 high density lipoprotein -1 PRKDC (DNA-PK) -134

CASP2 -1 HMOX1 -93 PTK6 -1

CASP3 -152 HSPB1 -1 RAP2B -1

CASP7 -134 ICMT -1 RHEB -93

CASP8 -153 IL10 -1 RPTOR -122

CAST -1 IL13 -142 RTN3 -1

CAT -142 IL3 -146 RUNX1 -122

CAV1 -122 IL4 -134 SGK2 -1

CCDC88A -1 IL8 -1 SGPP1 -1

CDKN1A -93 INS -155 SKP2 -1

CISD2 -93 IRS1 -93 SLC1A5 -93

CLIC4 -1 JUN -93 SNCA -145

COPB2 -1 JUNB -1 SOD2 -142

CSF1 -1 KAT2B -1 SPINK1 -122

CSF3 -1 KDR -1 SPP1 -93

CTSA -1 KIAA0226 (RUBICON) -134 SRC -1

CXCR3 -1 KIT -1 TGM2 -122

CYP2E1 -1 LEP -1 TMEM192 -1

DGKE -1 LRRK2 -148 TNIP1 -1

DLC1 -1 MAPT -93 TORC1 complex -157

DUSP1 -1 MCL1 -1 TORC2 complex -1

EGF -122 MDH2 -1 TP53BP2 -1

EGFR -93 MIR106B -93 TTI1 -1

EIF4EBP1 -93 MIR199A1 -1 UCN -93

EIF4G1 -1 MIR20A -93 VEGFA -134

EIF4G2 -1 MIR375 -122 XBP1 -1

EPAS1 -93 MTMR14 -1 YWHAZ -93

ERBB2 -1 MTMR3 -93 ZFYVE1 -1

FEZ1 -1 MTMR8 -1

Table S1A. Genes, proteins, and protein complexes that negatively regulate autophagy

(hits by text mining only)

Gene Symbol /

Entity Name

Text

Rank

Gene Symbol /

Entity Name

Text

Rank

Gene Symbol /

Entity Name

Text

Rank

ABL1 193 CASP9 1 exocyst 1

actin filament 170 CCR2 1 FADD 170

actin related protein 2/3 complex1 CD14 1 FAM176A 119

ACTR2 1 CD40 209 FAS 244

AGER 232 CD46 170 FLT3 1

AGT 193 CD82 1 FNBP1L 1

AGTR1 119 CDKN1B 193 focal adhesion 1

AMBRA1 272 CDKN2A 221 FOXO1 232

APOL1 119 CFLAR (cFLIP) 119 FOXO3 275

APP 221 CFTR 1 GABARAPL1 119

ARIH1 1 CHMP2B 1 GAPDH 193

ASAH1 1 CLDN5 209 GCG 268

ATF4 232 COL18A1 170 GFAP 1

ATG10 209 collagen type I 1 GLUL 1

ATG12 278 COPI vesicle-CoAt 221 GNAI1 1

ATG13 193 COPII complex 1 Golgi complex 1

ATG14 262 CST3 1 GOPC 119

ATG16L1 270 CTGF 1 GPR37 1

ATG3 193 CYB561D2 119 G-protein 1

ATG4B 232 CYP27B1 1 GPSM1 209

ATG4C 1 DAPK1 273 GRID2 221

ATG4D 1 DEDD 1 GSK3B 170

ATG9A 261 DIRAS3 193 HDAC6 257

ATG9B 1 DNA-PK 119 HIF1A 270

ATM 170 DNM1L 1 HMGB1 248

BAD 170 DNM2 1 HRAS 193

BAG3 221 DRAM1 240 HSF2 1

BBC3 238 dynactin complex 119 HTT 257

BECN1 (ATG6) 285 DYNLL1 1 IFITM3 1

BIK 1 EEF1G 1 IFNG 277

BIRC2 1 EEF2 1 IGFBP3 119

BNIP3 281 EEF2K 257 IKBKB 1

BNIP3L 262 EGR1 1 IKBKG 119

BRAF 119 EIF2A 170 IL1B 1

C12orf44 (ATG101) 221 EIF2AK2 238 IL2 119

CALCOCO2 1 EIF2AK3 257 IL24 240

CAMK4 1 EIF2AK4 170 IRGM 268

CAMKK2 119 EIF2S1 1 IRS2 1

CAMP 1 EPM2A 119 JAK3 1

CAPN10 119 ESR2 1 KIAA1324 1

CAPNS1 1 ETF1 1 KRAS 1

Table S1B. Genes, proteins, and protein complexes that positively regulate autophagy

(hits by text mining only)

LAMP1 119 PLG 119 STK11 (LKB1) 193

LCN2 1 PML 1 SUMF1 1

LDL 119 PPARGC1A 119 TBC1D7 1

LMNA 1 PPP1R15A (GADD34) 1 TFEB 1

LRP1 119 PRCP 1 TGFB1 (TGF-β) 244

MAP1B 193 PREB 170 TLR1 170

MAP1LC3B (ATG8) 267 PRKAA1 (AMPK) 1 TLR2 119

MAP2K4 1 PRKCQ 209 TLR3 1

MAP2K7 119 PRND 1 TLR4 252

MAP3K7 (ASK1) 252 PRODH 1 TLR5 1

MAPK1 (ERK2) 273 PSEN1 119 TLR7 209

MAPK3 (ERK1) 244 PTEN 248 TLR9 119

MAPK8 (JNK) 279 PTH 1 TM9SF1 170

MAPK9 1 PTPN2 1 TNFRSF10B 119

MCOLN1 170 RAB1A 209 TNFRSF1A (TNFR1) 119

MCOLN3 119 RAB23 1 TNFRSF1B (TNFR2) 119

microtubule 248 RAB7A 170 TNFSF10 (TRAIL) 252

MLH1 119 RAB9A 1 TP53INP1 1

MSH2 1 RAC1 1 TP53INP2 209

MTDH 1 RAF1 170 TP73 170

MYD88 240 RB1CC1 (ATG11) 244 TRAF2 1

myosin II 119 RGS16 1 TRAF6 119

NBR1 119 RGS19 119 TRIB3 119

NGFR 1 RIPK1 193 TRPV1 1

NLRX1 1 RIPK2 119 TSC1 1

NMDA-selective glutamate receptor1 RPS6KB1 119 TSC2 119

NOD1 221 S100A8 1 TUFM 1

NOD2 255 S1PR5 1 UCP3 1

NPEPPS 1 SCARB2 1 ULK2 193

OLR1 119 SERPINB2 119 ULK3 1

oxidized LDL 232 SERPINE1 119 UVRAG 266

P2RX7 1 SESN2 1 V-ATPase 119

PACRG 1 SH3GLB1 221 VCP 170

PARK2 276 SIRT1 262 VIM 1

PARK7 193 SLC33A1 1 VLA-3 receptor 119

PDGFRB 1 SMAD2 1 VMP1 240

PEA15 170 SMAD3 1 VPS4A 1

PGLYRP1 1 SMAD4 1 WDFY3 1

PIK3C3 (Vps34) 280 SPARC 1 WIPI1 (ATG18) 232

PIK3CB 1 SPHK1 1 WIPI2 (ATG21) 119

PIK3R4 221 SPNS1 1 YWHAQ 1

PLCE1 1 SQSTM1 (p62) 209 ZC3H12A 119

Table S1B (continued)

1a 2b 3c 4d

AGER 232 -126

BAK1 -134 1

BAX -148 221

BCL2 -159 170

BCL2L1 (BCLXL) -156 119

BCL2L11 (BIM) -93 193

BIRC5 (survivin) -1 119

CCL2 -1 119

CTSD -1 209

CXCR4 -1 119

DDIT3 -1 170

E2F1 -1 255

ERN1 -1 170

FBXL20 -124 58

GNAI1 1 -80

GNAI3 -1 170

HSPA5 -1 209 -38

HSPB8 -1 1

IL6 -1 1

LAMP2 -1 193

MAPK14 (p38 MAPK) -150 209

MYC -93 193

NAMPT -1 119

NUPR1 -93 1

PARP1 -1 221

PINK1 -150 1

PLD1 -1 49

PPARG -1 1

PRKCD -122 119

RB1 -93 1

RICTOR -1 1

TLR3 1 -16

TNF 262 -46

TP53 -154 248

siRNA Screen

Table S1C. Genes and proteins that both

negatively and positively regulate autophagy

Gene Symbol

Text

Rank (-)

Text

Rank (+)

1a 2b 3c 4d 1 2 3 4 1 2 3 4

ABCB7 -47 CRABP2 -43 HLA-DRB1 -83

ADRA1A -74 CRISPLD1 -66 HMGCL -47

APLP2 -20 CSNK1A1 -21 HRC -136

ARSE -79 CSNK1G2 -1 HS3ST4 -41

ASB15 -82 CSNK2A2 -50 HSFY2 -62

ASCC3 -21 CSTB -3 ICT1 -22

ATG16L2 -50 CYP27A1 -48 IGSF9 -62

BAI1 -40 DAPK2 -1 IRAK3 -43

BAI3 -6 DBX1 -17 ITGAV -109

BAIAP2 -20 DDX24 -87 IWS1 -69

BCAN -73 DND1 -33 KIAA0196 -96

BDNF -60 DYSF -17 KIF4B -36

BEGAIN -18 EPHA6 -31 KREMEN2 -83

BLK -17 EVL -94 KRT18 -115

BUB1 -1 F12 -69 KRT81 -30

C18orf8 -64 FABP1 -32 LARP1 -39

C1orf198 -75 FABP4 -31 MAN2A2 -5

CAMK1D -1 FAM122A -64 MAPKAPK2 -12

CAMK2D -1 FANCC -92 MAST2 -20

CAMKV -2 FBXL20 -124 MATN3 -61

CDCA8 -54 FFAR1 -4 MCCC1 -18

CDH18 -71 FOXO4 -46 MECOM -22

CDH19 -56 GAB1 -84 MMACHC -78

CDK5RAP3 -35 GABBR2 -44 MMP10 -103

CDK8 -2 GHRH -34 MMP17 -118

CDKL3 -12 GHSR -91 -19 MMP24 -42

CDKN2D -12 GJA4 -71 MRPS7 -79

CEACAM8 -37 GJB4 -29 MSI2 -26

CECR2 -68 GLB1L2 -10 MUC3A -65

CEND1 -56 GNG11 -7 MYL3 -90

CENPJ -59 GNG5 -68 MYO1H -55

CHAF1B -86 -6 GNRH2 -120 NAA25 -54

CHID1 -81 GPR182 -37 NAGK -67

CHRND -10 GPX2 -89 NCR3 -13

CHST7 -81 GRK6 -3 NEK4 -1

CHST8 -80 GTF2IRD2 -60 NFIL3 -93

CNKSR2 -23 GTPBP4 -42 NNMT -113

COL14A1 -128 HERC4 -15 NPTX1 -138

COPE -127 HIST2H3C -21 NR0B2 -58

CPNE6 -137 HIVEP2 -102 NRBP2 -3

Table S1D. Genes that negatively regulate autophagy (hits by siRNA screening only)Gene

Symbol

siRNA Screen Gene

Symbol

siRNA ScreensiRNA ScreenGene

SymbolNSA2 -135 PXK -1 SUPT5H -61

NUDT1 -77 RAB22A -24 TAB1 -129

OGDH -123 RAB28 -11 TACR2 -29

P2RX1 -73 RBBP8 -53 TASP1 -53

PA2G4 -133 RFX1 -66 TECTB -36

PACSIN1 -1 RIOK1 -1 TGFBI -38

PAFAH1B2 -23 ROCK1 -1 THBS2 -57

PAK6 -88 RPRD1A -28 THBS4 -49

PAQR6 -28 RPS6KA4 -1 TMOD2 -67

PCGF1 -72 RPS6KL1 -12 TMPRSS5 -116

PCNX -35 S100A9 -51 TNFRSF14 -78

PDCD5 -30 SACS -45 TNFRSF17 -134

PFDN2 -84 SAV1 -77 TOMM20 -9

PFKL -52 SCAMP4 -40 TRAPPC1 -74

PGAP2 -107 SCOC -8 TRIM69 -19

PHB -18 SCYL1 -12 TRNT1 -33

PHB2 -97 SDHB -130 TRPA1 -27

PIGY -11 SEL1L -14 TSPAN4 -119

PKLR -12 SEMA4B -121 TTC33 -32

PLDN -25 SETDB1 -75 TUBGCP6 -14

PLVAP -57 SF3A2 -98 UBA6 -34

PLXNA2 -114 SFSWAP -52 UBE2D1 -5

PLXNA4 -76 SHISA5 -99 UNC13B -63

POLR3G -111 SHPK -39 URB2 -140

PPFIA4 -108 SIDT1 -45 USP19 -15

PRAF2 -117 SIX2 -95 USP24 -132

PRDX3 -65 SLC25A19 -24 USP27X -72

PRKAG3 -1 SLC27A1 -13 UST -44

PRKCZ -8 SMARCD1 -112 WAC -25

PROSC -9 SOCS2 -70 WASF1 -104

PRRG3 -12 SORCS2 -55 WFDC2 -110

PSD -82 SOX14 -63 WNK2 -1

PTGER2 -26 SP140 -76 XPO1 -142

PTMA -105 SPECC1L -2 ZFY -131

PTPLB -48 STATH -16 ZMAT5 -27

PTPRH -49 STK32C -1 ZNF28 -7

PTPRU -141 STK40 -18 ZNF35 -59

Table S1D (continued)

1a 2b 3c 4d 1 2 3 4 1 2 3 4

AATK 61 HHLA3 13 PRPF40B 83

ABCC4 63 HOXA9 46 RASGRF2 33

ACVR1 57 HOXB3 75 RASIP1 28

ADHFE1 19 HRG 23 RBM12 54

AKAP5 81 HSD11B2 66 RING1 18

AMBP 78 HSFX1 11 RPL22 25

ARPP21 15 HUNK 3 SCGB1D2 80

ASPHD2 76 KCNG4 32 SEPT12 22

C8orf33 62 KCTD5 21 SGK1 2

CCDC27 71 KIF25 60 SLC26A1 16

CDK4 78 KIF5C 1 SLC35C1 3

CDX2 72 KLK12 36 SNX20 5

CNOT1 82 KLRG1 67 SNX9 31

CRLF3 27 KNG1 20 STAT2 38

CTBS 42 LOC388276 37 SUCNR1 59

CXorf40A 6 LRRN4 84 TCL1B 1

DMPK 64 MAP2K6 4 TESK1 51

DNAJC22 44 MCTP2 47 TGIF2LX 8

DSTN 73 MEA 4 THY1 12

EPB41L3 69 MEGF10 3 TLK1 68

EPHB3 49 MFAP5 9 TLK2 52

EPT1 48 MYBPC3 43 TOM1L1 35

FAM135A 41 NFKB1 5 TRAM2 7

FAM38B 45 NUP160 56 TRIM51 14

FBXL14 65 PAGE4 55 TSPO2 39

FBXL20 58 PI4K2B 74 VPS16 40

FGD4 2 PLBD1 85 WDR6 10

FZD2 70 PLEKHG4 26 ZNF397 30

GNG3 34 PREX1 17 ZSWIM6 29

Gene

Symbol

siRNA Screen

Table S1E. Genes that positively regulate autophagy (hits by siRNA screening only)Gene

Symbol

siRNA Screen Gene

Symbol

siRNA Screen

Text

Rank 1a 2b 3c 4d

AURKA -1 -58

CAPN1 -1 -106

CASP1 -134 -41

CLCF1 -1 -70

COX5A -1 -122

CXCL12 -1 -100

EP300 -122 -125

FGFR1 -1 -4

IGF1 -146 -85

LIF -1 -101

SOD1 -1 -139

STAT3 -122 -51

Table S1F. Genes that negatively regulate

autophagy (hits by text mining and siRNA

screening)

Gene Symbol

siRNA Screen

Text

Rank 1a 2b 3c 4d

AEN 1 24

ATG5 284 4

ATG7 283 6

KIF5B 1 53

RELA 170 2

SREBF2 1 77

ULK1 (ATG1) 282 1

Table S1G. Genes that positively regulate

autophagy (hits by text mining and siRNA

screening)

Gene Symbol

siRNA Screen

Compound

Text

Rank Compound

Text

Rank Compound

Text

Rank

3-methyladenosine -1 epinephrine -69 oxygen -102

acetylcholine -1 erbstatin -1 pactamycin -1

acetylcysteine -102 ethionine -1 panaxoside A -1

adenosine -110 fluridone -1 pepstatin -1

ADP -1 GABA -1 phenylalanine -1

amebicide -69 galactose -1 phosphatidic acid -83

amino acids -116 glutathione -92 phosphatidylinositol 3,4-diphosphate -69

ammonium sulfate -1 glutathione ethylester -1 phosphatidylinositol 3,4,5-triphosphate -94

AMP -106 glycerol -1 polyinosinic-polycytidylic acid -1

anisomycin -1 HCN -69 polyphenols -1

arginine -69 hydroxychloroquine -102 porfimer -1

asparagine -94 hydroxytyrosol -1 porphine -1

atropine -1 imidazole -1 puromycin -83

bafilomycin A1 -113 inositol -83 pyrazolanthrone -69

BAPTA-AM -83 isoproterenol -83 pyrimidine -1

benzoic acid -1 leucine -69 raffinose -1

benzyl alcohol -1 lindane -69 rhapontin -1

bicyclam -1 lipoic acid -1 rifampicin -1

butylated hydroxyanisole -1 L-NAME -69 salvianolic acid B -1

calyculin A -1 lucanthone -1 SB 203580 -83

candesartan -1 lysophosphatidic acid -1 SB 216763 -69

carbohydrates -1 macrofusine -1 streptomycin -1

chelerythrine -1 melatonin -83 sucrose -94

chloroquine -115 menadione -1 sulfur -1

cis-isoelemicin -1 methyl pyruvate -1 suramin -1

clarithromycin -1 mevalonate -69 tempol -1

concanamycin a -1 microcystin-LR -1 tiron -1

cycloheximide -94 myriocin -1 trichostatin A -69

cystamine -1 N-ethylmaleimide -1 tyrphostin A46 -1

desmethylclomipramine -1 nitrogen -112 ubiquinone -1

dibenziodolium -1 N-methylarginine -1 vitamin E -94

D-sorbitol -1 okadaic acid -102

Table S1H. Chemical inhibitors of autophagy from literature searches

Compound

Text

Rank Compound

Text

Rank Compound

Text

Rank

1-(p-hydroxyphenoxy)ethylenebisphosphonate 191 butyric acid 215 doxorubicin 252

17-AAG 1 cadmium 252 epirubicin 1

1-methyl-4-phenylpyridinium 252 cadmium chloride 1 estradiol 1

2-aminooctadecane-1,3-diol 215 caffeine 1 ethacrynic acid 1

2-chloro-N6-cyclopentyladenosine 1 calcipotriol 1 etoposide 258

2'-deoxy-5-azacytidine 1 calcitriol 228 everolimus 272

2-deoxyglucose 245 calcium 287 evodiamine 1

2-methoxyestradiol 245 calcium phosphate 191 fasudil 1

2-methoxyethylmercury chlorate 1 cAMP dibutyrate 1 fatty acids 144

3-hydroxybutyric acid 1 camptothecin 144 fenretinide 191

3-nitropropionic acid 144 cannabinoids 144 fingolimod 144

4-hydroxynonenal 1 capsaicin 144 flavones 1

5-fluorouracil 228 carvedilol 1 flavonoids 215

5-iodotubercidin 144 celecoxib 191 fullerene C60 144

7-hydroxystaurosporine 1 ceramides 288 furosemide 1

abscisic acid 1 ceric oxide 144 gangliosides 1

alisol A 1 cerivastatin 144 gemcitabine 144

alisol B 144 ceruletide 1 glidobactin A 1

alisol B 23-acetate 1 cetuximab 191 glucocorticoids 215

aloe-emodin 1 ClCCP 1 glucosamine 1

alpha-ecdysone 228 clioquinol 1 glucose-6-P 1

aminolevulinic acid 1 clonazepam 1 glycosides 191

amiodarone 144 clonidine 1 gossypol 191

ammonia 228 combretastatin 1 H2O2 280

ammonium chloride 144 combretastatin A4 1 helenalin 1

anthocyanins 258 compound 17 1 hormone 228

antibiotic FR 901228 1 crustecdysone 1 hydrogen fluoride 1

AR-12 144 curcumin 264 imatinib 279

arsenic 191 DAMPP 1 imiquimod 191

arsenite 276 dasatinib 228 ionomycin 191

ascorbate 272 deferoxamine 228 iron 228

aspirin 1 dexamethasone 245 iron monooxide 1

atorvastatin 144 D-fructose 1 isothiocyanic acid 1

aurintricarboxylic acid 144 diacylglycerol 1 lactacystin 1

azithromycin 1 didanosine 1 L-glutamate 1

bafetinib 144 dihydrocapsaicin 191 licochalcone a 1

BCG vaccine 1 dihydroceramide 1 lipopolysaccharide 268

benzalkonium chloride 1 dihydrotestosterone 1 lithium 286

benzyl isothiocyanate 144 dihydroxyacetone 1 lithium chloride 191

berberine 144 diptheria toxin 144 lonafarnib 1

bile acids 1 diquat 1 loperamide 1

bisbenzimide 144 disodium selenite 215 L-proline 1

bortezomib 278 dithiothreitol 144 L-sulforaphane 1

brefeldin A 191 DMSO 144 lysine acetylsalicylate 1

bromodeoxyuridine 1 dopamine 228 magnolol 1

Table S1I. Chemical stimulators of autophagy from literature searches

mangostin 1 platonin 1 sphingolipids 228

manumycin 1 plumbagin 1 staurosporine 1

matrine 144 polyglutamine 144 steroids 215

melphalan 1 pramipexole 1 sulfaphenazole 1

methamphetamine 144 progesterone 1 sulforaphane 268

methylglyoxal 1 propachlor 1 superoxide radical 215

methylpyruvate 1 propionic acid 144 tamoxifen 280

methylselenic acid 1 protopanaxadiol 1 temozolomide 283

MG-132 191 prunin 1 temsirolimus 258

minocycline 1 pseudolaric acid B 1 tetrahydrocannabinol 1

minoxidil 1 pterostilbene 1 thalidomide 1

monensin 1 quercetin 1 thapsigargin 272

morphine 144 quinacrine 1 thioguanine 215

MPTP 1 R59022 1 thyroid hormones 1

muramyl dipeptide 144 ranpirnase 1 topotecan 1

N-acetylsphingosine 252 rapamycin 291 tozasertib 1

naringin 144 resveratrol 289 trastuzumab 1

nelfinavir 228 retinoic acid 1 trehalose 280

neodymium oxide 144 rilmenidine 144 trichokonin VI 1

neutral red 1 reactive oxygen species (ROS) 290 triciribine 1

niclosamide 144 roscovitine 1 trifluoperazine 1

nicotinamide 1 rose bengale 1 triptolide 144

nitrosocysteine 1 rosiglitazone 1 triterpenes 1

NO 228 rotenone 245 triterpenoids 228

nortriptyline 1 saponins 1 troglitazone 144

N-phosphonacetyl-l-aspartate 1 saquinavir 1 tryptamine 144

obatoclax 144 SB 202190 215 tunicamycin 272

oligomycin A 1 selective estrogen receptor modulator 144 ursolic acid 1

olomoucine 1 selenium 1 valproic acid 191

oridonin 284 selenous acid 191 vinblastine 268

oroxylin A 1 seocalcitol 228 vincristine 1

oxaliplatin 1 sesamin 1 vinorelbine 1

oxalylglycine 1 shogaol 1 vitamin D 228

oxidopamine 191 sialic acid 144 vitamin D3 258

palmitic acid 215 silibinin 191 vitamin K2 215

pancratistatin 1 simvastatin 191 voacamine 1

paraquat 245 sodium 1 vorinostat 268

perhexiline 144 sodium 5,6-benzylidene-L-ascorbate 1 wogonin 1

perifosine 191 sodium arsenite 1 xestospongin B 245

phenethyl isothiocyanate 144 sodium chloride 144 XK-469 144

phenylephrine 1 sodium phenylacetate 1 Y-27632 1

phorbol myristate acetate 191 sorafenib 258 YM-155 1

phosphatidylethanolamine 228 soraphen A 1 zinc 144

phosphatidylinositol 3,5-diphosphate 1 spermidine 264

phosphatidylinositol 3-phosphate 284 spermine 1

Table S1I (continued)

Compound

Text Rank

(-)

Text Rank

(+)

3-methyladenine -117 1

acadesine -92 1

AICAR -106 191

ATP -109 245

cAMP -1 258

cholesterol -94 1

cisplatin -1 276

cyclosporine -101 215

cysteamine -1 1

ethanol -69 191

genistein -1 1

glucose -108 264

glutamine -83 191

HDAC inhibitor -1 267

LY 294002 -111 228

metformin -69 252

NAD+ -83 215

nitroprusside -1 144

paclitaxel -1 144

rottlerin -1 252

U0126 -94 1

wortmannin -114 1

Table S1J. Chemicals that both inhibit and

stimulate autophagy from literature

searches

Page 16: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Building the Autophagy Pathway

• Pathway Analysis – Pathway Studio (Elsevier)

• 421 genes + 385 small molecules – Ingenuity Pathway Analysis (Ingenuity Systems)

• 218 genes + 123 small molecules – MetaCore (Thomson Reuters)

• 38 genes

Page 17: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Quality Assessment – How Accurate is PS?

• False positives – manual curation of all extracted sentences indicated 1181/6215 (19%) of

the relations were incorrect (false positives)

• reduced to ~0% by manual curation

– Example: “The ability of the excess Atg4B mutant to inhibit autophagy...”

• Change direction of regulation from NEGATIVE to POSITIVE

• False negatives – Example: “We show that ATF4 and LC3B play a critical role in activating

autophagy and protecting cells from Bortezomib-induced cell death.”

• Change direction of regulation from UNKNOWN to POSITIVE

– (see next slide)

Page 18: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Integrated Analysis of siRNA + Pathway Data

1: siRNA Screen 1

2: siRNA Screen 2 3: siRNA Screen 3

4: siRNA Screen 4

5: IPA

6: MetaCore

7: Pathway Studio (raw)

8: Pathway Studio (curated)

No single method enables comprehensive pathway interrogation

Lorenzi et al. Autophagy 2014

Page 19: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Integration of Proteomic Data

No single method enables comprehensive pathway interrogation

Behrends et al. Nature 466:68 (2010)

Page 20: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

The Autophagy Pathway

Lorenzi et al. Autophagy 2014

Page 21: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Novel Autophagy-Based Therapeutic Strategies

• Clinical insight – Temsirolimus (MTOR inhibitor/autophagy stimulator) – why is there not a

clear clinical response? • Answer: induces mitophagy, which degrades mitochondrial proteins

(e.g., RIPK1, RIPK3) that promote cell death

• Our proposal – Combination of an autophagy inhibitor + autophagy stimulator

• e.g., hydroxychloroquin (HCQ) + temsirolimus (TEM; rapamycin analog) • Hypothesized outcome: inhibition of mitophagy (by HCQ) would

preserve death-promoting mitochondrial proteins that are activated in response to the autophagy stimulator (TEM)

– Novel targets for autophagy inhibition • RELA (targeted by N-acetylcysteine) • Pharmacological delivery of oxygen (e.g., using RBC carriers)

– Novel targets for autophagy stimulation • IGF1 (targeted by UO126) • Amino acids (targeted by L-asparaginase)

Page 22: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Conclusions

• We constructed an extensive inventory of autophagy-modulating genes, proteins, complexes, and small molecules

• Novel features of our analysis

– direction (positive or negative) of autophagy modulation – semi-quantititive / quantitative indices for strength of evidence – hypothesis: combination of autophagy inhibitor + stimulator will be

effective in the treatment of cancer

• Comprehensive interrogation of a pathway using a single approach is not currently possible

– siRNA screening is particularly problematic

Page 23: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

A Parting Message

• To facilitate text-mining of your own publications:

– Regulation has direction

• Not good writing: “Protein X regulates Protein Y.” • Good writing: “Protein X positively regulates Protein Y.”

Page 24: Identifying candidate targets for Cancer Therapy with Integrated Text Mining and High-Throughput Screening with Phil Lorenzi, PhD

Acknowledgements and Collaborators

MD Anderson • Paul Goldsmith • Natasha Caplen • Bill Reinhold

NCI CCR John Weinstein

• Wai Kin Chan • Preeti Purwaha • Leslie Silva • Tom Horvath • Amos Zimmermann • Michael Pontikos

Gordon Mills • Yiling Lu • Tracy Guo • Jennifer Dennison • Sofie Claerhout

David Hawke Ken Aldape Gautam Borthakur Steve Kornblau Marina Konopleva Jing Wang

• Shelley Herbrich

• Cliff Stephan

Texas A&M • Susan Rempe • David Rogers

Sandia National Lab

ERYtech Pharma • Yann Godfrin • Emmanuelle Dufour • Fabien Gay • Willy Berlier

• Chris Beecher

IROA Technologies

MD Anderson Bradley Broom

• Jiexin Zhang

Rehan Akbani Kevin Coombes

• Ganiraju Manyam

Keith Baggerly

• Sergei Sukharev • Andriy Anishkin

University of Maryland

Elsevier • Virginia Heatwole • Travis Vaught • Travis Glennon • Frank White • Dave Arndt

• The Michael and Susan Dell Foundation • The Chapman Foundation • NCI grant CA143883 (TCGA GDAC) • NCI grant CA083639 (Ovarian SPORE) • CPRIT grant RP130397

Funding