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Z score gene B Z score gene A Z score gene C BT20 BT20 BT20 BT474 BT474 BT474 CAL120 CAL120 CAL120 CAL51 CAL51 CAL51 CAMA1 CAMA1 CAMA1 HCC1143 HCC1143 HCC1143 HCC202 HCC202 HCC202 JIMT1 JIMT1 JIMT1 MCF7 MCF7 MCF7 MDAM134 MDAM134 MDAM134 MDAMB157 MDAMB157 MDAMB157 MDAMB231 MDAMB231 MDAMB231 MDAMB453 MDAMB453 MDAMB453 MDAMB468 MDAMB468 MDAMB468 SKBR3 SKBR3 SKBR3 Gene notessential SUM149 SUM149 SUM149 SUM44 Loss ofviabilityZ score threshold SUM44 SUM44 Gene essential T47D T47D T47D VP229 VP229 VP229 34 breast cancer cell lines Optimizationation for high- throughput RNAi screening 3 replica screens per cell lines LOG transform data Normalize tot platel median Z-score standardize datat using median absolute deviations ! ! Quality control (1) Z’ values calculated per plate using siCON and siPLK1 (2) confirm normal distribution Combine replicate data Functional viabilityib profiles Integration with other profiling platforms " " # $%&% '() *%+ *()( $() ,) -,./ , ,%01 $%&% ,( -,%/ '. 2",$ 3+ 4!((/ , ,%.+ *,$+..( $%&% , ,.& , ,(+ , ,+% -,%% , ,& $%& *.&1$ $" !$# ! # #( ( !"3+ 5 !"3+ !"3+ ( !"3+ + !"3+ $" PIK3CA Pool PIK3CA oligo 1 6 6 PIK3CA oligo 2 PIK3CA oligo 3 6 6 PIK3CA mutant PTEN mutant/null , ,(+ !# , ,+% $ *%+ " 4!((/ -,%% '() 2",$ $%&% 3+ " !"+3) !$# 3$ 3$( 3$+ $" siRNA BT474 CAL120 HCC1143 HCC202 BT20 MDAMB468 SUM149 CAL51 JIMT SKBR3 VP229 MDAMB453 MDAMB231 MDAMB134 SUM44 CAMA MCF7 T47D AKT1 z score -0.5 -0.4 -0.2 -1.8 -1.1 -0.5 -3.2 -0.3 -0.1 -3.6 -1.6 -2.4 0.6 -0.8 0.7 -3.0 0.0 -0.4 AKT2 z score -2.5 0.1 -0.5 -3.2 -0.6 1.4 0.3 -3.0 -0.3 -1.1 -0.4 -1.7 -1.9 0.6 0.1 -1.5 -1.2 -1.2 AKT3 z score -0.2 -0.2 -0.2 -1.4 -0.8 0.3 0.2 -2.1 0.5 -0.1 0.1 -0.8 0.1 0.6 -0.9 0.1 -3.6 -1.1 # 5 #( #( #( ( #( + #( % #( $" ERBB2 oligo 1 ERBB2 oligo 2 ERBB2oligo 3 ERBB2oligo 4 ERBB2 Pool + % 11q13.3 AMP dependencies MYC 8q24.21 AMP dependencies FGFR1 8p12 AMP dependencies AURKA 20q13 AMP dependencies # CDK4 12q14.1 AMP dependencies PPM1D 17q21-q23 AMP dependencies MDM2 12q15 AMP dependencies . PTEN mutant cell line dependencies CDKN2A mutant dependencies KRAS mutant dependencies P53 mutant dependencies # BRCA1 mutant dependencies RB mutant dependencies B. A. Supplementary Figure6. $%&% '.'() *#!$#7$ *%+ *()( $() ,) -,./ , ,%01 $%&% ,( -,%/ '. 2",$ 3+ 4!((/ , ,%.+ *,$+..( $%&% , ,.& , ,(+ , ,+% -,%% , ,& $%& *.&1$ $" !$# # PTEN WT PTEN MUT PTEN WT PTEN MUT PTEN WT PTEN MUT PTEN WT PTEN MUT PTEN WT PTEN MUT PTEN WT PTEN MUT # $$3 ** 5$5' $$3 *** AZ3146: - + - + PTEN WT PTEN MUT * !$#8 !$# 10 μm !$#8 9(,$$3 !$# 9(,$$3 10 μm 10 μm & CAMA1 T47D MDAMB134 MCF7 ZR75.1 SUM44 S68 MDAMB361 BT474 ZR75.30 Normalised Percent Inhibition (NPI) * '. -,%% 5 3( -,%/ *** 3()+ '() p <0.0001 , ,.& 3(). 3+ 3(! Increasing sensitivity to ADCK2 siRNA ADCK2 siRNA Increasing sensitivityto , ,%.+ 3( $" *%+ *()( # 4!((/ ** p p =0.0075 =0.0341 *.&1$ $() ,( $%& , ,%01 2",$ $%&% , ,(+ , ,+% ,& , $" ESR1 3( ER+ 1 CAMA1 MDAMB134 SUM44 ' HCC202 SKBR3 MCF7 MDAMB453 T47D BT474 VP229 BT20 JIMT1 MDAMB468 SUM149 HC1143 HS578T MDAMB157 CAL51 MDAMB231 CAL120 Group 1 Group 2 Low expression High expression Z>-2 DEL/lossD Low expression Z<-2 AMP/gainA High expression E. Group 1 Group 2 Functional Breast Cancer Profiles Sup.Info.1 Supplementary Figures Supplementary Figure 1. Functional profiling of a breast cancer tumour cell line panel. a. Cell lines were profiled with a siRNA library targeting 714 kinases and kinase- related genes. Shown here are the effects on cellular viability in the tumour cell panel of siRNAs targeting one of three genes A, B or C. Z score plots for each example are shown, where a Z score of less that -2 signifies that an siRNA is having a significant effect of cell viability. An siRNA causing significant loss of cell viability in all of the cell lines assayed (as for gene A) likely targets a gene that has an essential and ubiquitous function in both normal and tumour cells. Similarly, an siRNA that had no significant effect on viability in any of the cell lines is either not functional or targets a non-essential gene (as for gene B). Finally, an siRNA that caused significant lethality in only some but not all cell lines (as for gene C), identifies a gene that represents a candidate tumour-specific dependency and a candidate therapeutic target. b. Data processing procedure for RNA interference screens. Raw luminescence readings were log2 transformed and then median centred per plate (excluding control wells) and standardised per screen using a robust Z score. Scores from replicate screens were combined using the mean. The separation of positive and negative controls (Z’- factor) was used to estimate screen quality. c. Reproducibility of RNAi viability profiles in breast tumour cell panel. Plot of R2 values for the three screening replicates of each cell line is shown. d. and e. Distribution of viability effects in each cell line. Normalised distributions of RNAi Z scores for each cell line. In this analysis, median Z scores from triplicate screens were used. f. Western blot of lysates from cell line panel showing expression of ERBB2, ER, PR, PTEN and ACTIN. Supplementary Figure 2. PIK3CA and ERBB2 oncogene addiction effects in breast tumour cells. a. Heat map showing the results of a supervised clustering of siRNA Z scores. Breast tumour cell lines were clustered according to PIK3CA gene mutation status and differential effects between PIK3CA mutant and wild type groups identified using the median permutation test. Statistically significant effects (p<0.05) are shown. siRNA targeting PIK3CA is marked by the arrow. b. Western blot of total cell lysates from MCF7 cells 48 hours after transfection with individual and pooled PIK3CA siRNA. c. Functional Breast Cancer Profiles Sup.Info.2 Box/whiskers plots of individual and pooled PIK3CA siRNA effects on cell lines characterised by PIK3CA mutations (*=p<0.05 using Student’s t-test). d. Western blot of total cell lysates from the breast cell line panel showing expression of PI3K p110a, PTEN, AKT1, AKT2, AKT3 and ACTIN. PIK3CA mutant cell lines are indicated in red whilst PTEN deficient cell lines are indicated in blue and double mutants are boxed. Z scores of AKT1, 2 and 3 are shown in a table. e. Heat map showing the results of a supervised clustering of siRNA Z scores. Breast tumour cell lines were clustered according to ERBB2 gene mutation status and differential effects between ERBB2 mutant and wild type groups identified using the median permutation test. Statistically significant effects (p<0.05) are shown. f. Western blot of total cell lysates from BT474 cells 48 hours after transfection with individual and pooled ERBB2 siRNA. g. Box/whiskers plots of individual and pooled ERBB2 siRNA effects. *p<0.05 measured by t-test. NPI = normalised percent inhibition Supplementary Figure 3. a. Waterfall plot of BRAF Z scores in the breast tumour cell line panel. The breast tumour cell line MDAMB231 containing a BRAF mutation is highlighted in red. b. Z score plot of the BRAF mutant COLO-829 cell line profiled with an siRNA viability screen targeting 714 kinases and kinase-related genes. Supplementary Figure 4. Candidate genetic dependencies for amplification events commonly found in breast cancer. Waterfall plots of kinases that selectively kill breast cancer cell lines containing a. an amplification of 11q13.3. b. MYC amplified breast cancer cell lines. c. FGFR1 amplified breast cancer cell lines. d. AURKA amplified breast cancer cell lines. e. CDK4 amplified breast cancer cell lines. f. PPM1D amplified breast cancer cell lines. g. MDM2 amplified breast cancer cell lines. p<0.05 for a-g, using the Student’s t-test. Supplementary Figure 5. Candidate tumour suppressor and oncogene mutation dependencies. Waterfall plots of kinases that selectively kill breast cancer cell lines containing a. PTEN mutations. b. CDKN2A mutations. c. KRAS mutations. d. p53 mutations. e. BRCA1 mutation. f. RB mutation. p<0.05 for a-f, using the Student’s t-test. Functional Breast Cancer Profiles Sup.Info.3 Supplementary Figure 6. Inhibition of TTK is PTEN selective. a. Western blot of total cell lysates of the breast cell line panel showing the expression of PTEN. b. PTEN FISH analysis of CAL51 and HEC1b (PTEN wildtype) cells using the Vysis LSI PTEN (10q23) (red) and CEP 10 (green) dual colour probes. Red arrows indicate PTEN probe hybridization in the HEC1b cell line. The Cal51 image shows a signal pattern indicative of a PTEN (10q23) deletion. c. Waterfall plots of mitotic/checkpoint kinases that selectively inhibit PTEN mutant/null cell lines (p<0.05). d. Heat map showing the results of a supervised clustering of siRNA Z scores. Breast tumour cell lines were clustered according to PTEN gene mutation status and differential effects between PTEN mutant and wild type groups identified using the median permutation test. Statistically significant effects (p<0.05) are shown. e. Box/whiskers plots showing the effect of pooled siRNA of non-silencing siRNAs (siControl 1-3) and other non-mitotic/checkpoint kinases in PTEN mutant and wild type breast cancer cell lines. f. Western blot of total cell lysates from MCF7 cells 48 hours after transfection with individual and pooled TTK siRNA. g. Histograms plot showing the percentage of abnormal chromosomes for HCT116 PTEN wt and HCT116 PTEN null cells with and without 2uM AZ3146 treatment for 24 hours. Chromosome counts from 60 metaphase spreads for each treatment were analysed (**p=0.003, ***p<0.0001, fishers exact test). h. Metaphase spread images of HCT116 PTEN wt/null cells with and without 2uM AZ3146 treatment for 24 hours. Supplementary Figure 7. ADCK2 is a candidate estrogen receptor (ER) selective target. a. Heat map showing the results of a supervised clustering of siRNA Z scores.
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    Advancing a Clinically Relevant Perspective of the Clonal Nature of Cancer

    Advancing a clinically relevant perspective of the clonal nature of cancer Christian Ruiza,b, Elizabeth Lenkiewicza, Lisa Eversa, Tara Holleya, Alex Robesona, Jeffrey Kieferc, Michael J. Demeurea,d, Michael A. Hollingsworthe, Michael Shenf, Donna Prunkardf, Peter S. Rabinovitchf, Tobias Zellwegerg, Spyro Moussesc, Jeffrey M. Trenta,h, John D. Carpteni, Lukas Bubendorfb, Daniel Von Hoffa,d, and Michael T. Barretta,1 aClinical Translational Research Division, Translational Genomics Research Institute, Scottsdale, AZ 85259; bInstitute for Pathology, University Hospital Basel, University of Basel, 4031 Basel, Switzerland; cGenetic Basis of Human Disease, Translational Genomics Research Institute, Phoenix, AZ 85004; dVirginia G. Piper Cancer Center, Scottsdale Healthcare, Scottsdale, AZ 85258; eEppley Institute for Research in Cancer and Allied Diseases, Nebraska Medical Center, Omaha, NE 68198; fDepartment of Pathology, University of Washington, Seattle, WA 98105; gDivision of Urology, St. Claraspital and University of Basel, 4058 Basel, Switzerland; hVan Andel Research Institute, Grand Rapids, MI 49503; and iIntegrated Cancer Genomics Division, Translational Genomics Research Institute, Phoenix, AZ 85004 Edited* by George F. Vande Woude, Van Andel Research Institute, Grand Rapids, MI, and approved June 10, 2011 (received for review March 11, 2011) Cancers frequently arise as a result of an acquired genomic insta- on the basis of morphology alone (8). Thus, the application of bility and the subsequent clonal evolution of neoplastic cells with purification methods such as laser capture microdissection does variable patterns of genetic aberrations. Thus, the presence and not resolve the complexities of many samples. A second approach behaviors of distinct clonal populations in each patient’s tumor is to passage tumor biopsies in tissue culture or in xenografts (4, 9– may underlie multiple clinical phenotypes in cancers.
  • Profiling Data

    Profiling Data

    Compound Name DiscoveRx Gene Symbol Entrez Gene Percent Compound Symbol Control Concentration (nM) JNK-IN-8 AAK1 AAK1 69 1000 JNK-IN-8 ABL1(E255K)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317I)-nonphosphorylated ABL1 87 1000 JNK-IN-8 ABL1(F317I)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317L)-nonphosphorylated ABL1 65 1000 JNK-IN-8 ABL1(F317L)-phosphorylated ABL1 61 1000 JNK-IN-8 ABL1(H396P)-nonphosphorylated ABL1 42 1000 JNK-IN-8 ABL1(H396P)-phosphorylated ABL1 60 1000 JNK-IN-8 ABL1(M351T)-phosphorylated ABL1 81 1000 JNK-IN-8 ABL1(Q252H)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(Q252H)-phosphorylated ABL1 56 1000 JNK-IN-8 ABL1(T315I)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(T315I)-phosphorylated ABL1 92 1000 JNK-IN-8 ABL1(Y253F)-phosphorylated ABL1 71 1000 JNK-IN-8 ABL1-nonphosphorylated ABL1 97 1000 JNK-IN-8 ABL1-phosphorylated ABL1 100 1000 JNK-IN-8 ABL2 ABL2 97 1000 JNK-IN-8 ACVR1 ACVR1 100 1000 JNK-IN-8 ACVR1B ACVR1B 88 1000 JNK-IN-8 ACVR2A ACVR2A 100 1000 JNK-IN-8 ACVR2B ACVR2B 100 1000 JNK-IN-8 ACVRL1 ACVRL1 96 1000 JNK-IN-8 ADCK3 CABC1 100 1000 JNK-IN-8 ADCK4 ADCK4 93 1000 JNK-IN-8 AKT1 AKT1 100 1000 JNK-IN-8 AKT2 AKT2 100 1000 JNK-IN-8 AKT3 AKT3 100 1000 JNK-IN-8 ALK ALK 85 1000 JNK-IN-8 AMPK-alpha1 PRKAA1 100 1000 JNK-IN-8 AMPK-alpha2 PRKAA2 84 1000 JNK-IN-8 ANKK1 ANKK1 75 1000 JNK-IN-8 ARK5 NUAK1 100 1000 JNK-IN-8 ASK1 MAP3K5 100 1000 JNK-IN-8 ASK2 MAP3K6 93 1000 JNK-IN-8 AURKA AURKA 100 1000 JNK-IN-8 AURKA AURKA 84 1000 JNK-IN-8 AURKB AURKB 83 1000 JNK-IN-8 AURKB AURKB 96 1000 JNK-IN-8 AURKC AURKC 95 1000 JNK-IN-8