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The Drug Sensitivity and Resistance Testing (DSRT) Approach

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The Drug Sensitivity and Resistance Testing (DSRT) Approach A phenotypic screening and machine learning platform eciently identifies triple negative breast cancer-selective and readily druggable targets Prson Gautam 1 Alok Jaiswal 1 Tero Aittokallio 1, 2 Hassan Al Ali 3 Krister Wennerberg 1,4 Identifying eective oncogenic targets is challenged by the complexity of genetic alterations in 1Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Finland cancer and their poorly understood relation to cell function and survival. There is a need for meth- Current kinome coverage of kinase inhibitors in TNBC exhibit diverse kinase dependencies MFM-223 is selectively addicted to FGFR2 2Department of Mathematics and Statistics, University of Turku, Finland 3The Miami Project to Cure Paralysis, Peggy and Harold Katz Family Drug Discovery Center, A A Sylvester Comprehensive Cancer Center, and Department of Neurological Surgery and Medicine ods that rapidly and accurately identify “pharmacologically eective” targets without the require- clinical evaluation TN Kinases MFM-223 CAL-120 MDA-MB-231 TNBC TNBC TNBC TNBC TNBC TNBC HER2+ 100 University of Miami Miller School of Medicine, Miami, FL 33136, USA. non- HER2+ FGFR1 0.97 0.00 0.00 MFM-223 BL1 BL2 M MSL IM LAR ER+, PR+ 50 ment for priori knowledge of complex signaling networks. We developed an approach that uses ma- cancerous FGFR2 56.46 0.00 0.00 CAL-120 25 4 MDA-MB-231 Biotech Research & Innovation Centre (BRIC) and Novo Nordisk Foundation Center HCC1937 CAL-85-1 CAL-120 MDA-MB-231 DU4475 CAL-148 MCF-10A SK-BR-3 BT-474 FGFR3 25.10 0.00 0.00 0 chine learning to relate results from unbiased phenotypic screening of kinase inhibitors to their bio- for Stem Cell Biology (DanStem), University of Copenhagen, Denmark HCC1599 HDQ-P1 BT-549 MDA-MB-436 MFM-223 FGFR4 0.00 0.00 0.00 MAXIS*Bk Clinical status MDA-MB-468 CAL-51 Hs578T MDA-MB-453 score chemical activity data. This process, which we call idTRAX (Identification of Drug TaRgets and An- HCC1143 Approved Phase 4 B Erdafitinb AZD4547 Lucitanib Discordance within kinase dependency profiles generated by different ti-targets by Cellular and Molecular Cross-referencing), discovers targets that are pharmacological- 100 100 100 Phase 3 B 100 67 gene silencing approaches and idTRAX ly responsive and readily druggable. Additionally, the identified targets are not typically overcome TKL Phase 2 33 75 75 75 TK 0 -33 Phase 1 -67 -100 50 50 50 Cell lines y inhibition by the robustness of signaling networks, because only targets that eectively induce a phenotype t MAXIS*BK score A 1 6 8 3 bili 9 7 3 3 1 A 8 0 3 a 1 5 i T 1 9 4 14 25 25 25 0.3 1 upon pharmacological engagement are selected. We applied this methodology to triple negative %v CAL-14 STE HCC159 MDA-MB-23 CAL-5 CAL-85- Hs578 HCC SK-BR- MDA-MB-43 HCC193 MFM-22 DU447 MDA-MB-46 CAL-12 MCF-10 BT-54 HDQ-P MDA-MB-45 BT-47 ρ) 0.2 HCC1599 HCC1937 MDA-MB-436 MDA-MB-468 MDA-MB-231 HCC1143 HDQ-P1 MCF-10A BT-549 Hs578T CAL-85-1 BT-474 SK-BR-3 MDA-MB-453 MFM-223 CAL-51 CAL-120 CAL-148 DU4475 0 0 0 breast cancer, which still lacks targeted therapy. We screened 19 breast cancer cell lines with ~500 PRKD2 PRKD3 0.1 1 10 100 1000 0.1 1 10 100 1000 1 10 100 1000 10000 0.1 PRKD1 Concentration (nM) PRKX small-molecule kinase inhibitors with annotated kinase activity data and identified cell-line specific PRKACA 0.0 PRKCQ SGK3 PRKCH -0.1 kinase targets, i.e. kinases whose inhibition causes cell line-specific cytotoxicity or cytostaticity. We PRKCG MFM-223 CAL-120 MDA-MB-231 Spearman’s rho ( PRKCA C CMGC AKT3 -0.2 AKT2 100 100 100 also found that triple negative breast cancer cell lines exhibit heterogeneous target patterns, indi- AKT1 RPS6KA1 n **** RPS6KA6 io t 75 75 75 siRNA_shRNA RPS6KA4 shRNA_CRISPR siRNA_CRISPR shRNA_idTRAX siRNA_idTRAX CRISPR_idTRAX cating the need of personalized medicine approach to tackle them. On further analysis, we found CK1 RPS6KA5 PRKG2 PRKG1 50 50 50 ROCK2 that the correlation between targets identified by the pharmacological approach and those identi- ROCK1 B CAMK1D =0.2551, p=0.0006 =0.1147, p=0.1263 =0.1077, p=0.1511 CHEK2 viability inhibi 25 25 25 ρ ρ ρ CAMK2D % 5 5 5 fied by knockdown/knockout-based screens was low, suggesting that gene-silencing approaches PDPK1 A) BRSK1 A) BRSK2 0 0 0 MARK1 A) MARK4 FGFR1 FGFR2 FGFR3 FGFR4 Death FGFR1 FGFR2 FGFR3 FGFR4 Death FGFR1 FGFR2 FGFR3 FGFR4 Death may not be the most ecient at identifying targets for small-molecule drug discovery or repurpos- SIK2 Scramble Scramble Scramble 0 0 0 MARK3 siRNA MELK NUAK1 ing. Our approach provides a platform for rapidly identifying sample-specific drug targets and po- TSSK1B TSSK2 -5 -5 -5 PHKG2 A) Heat map based on MAXIS*Bk score, predicting FGFR2 addiction unique to MFM-223 cell line. Atypical Protein Kinases AGC PHKG1 z-score (siRN tentially guiding personalized therapy regimens. GRK7 B) Validated of FGFR addiction with three individuals FGFR KIs. The dose response curves are color coded z-GARP score (shRN MAPKAPK3 z-GARP score (shRN ABC1 PLK1 respective to cell lines as mentioned in the legend. -10 -10 -10 MAP4K2 -2 -1 0 1 -2 -1 0 1 -10 -5 0 5 STK26 C) Bar plot illustrating the effect of siRNA-based knockdown of four different FGFR isoforms in the three respective Alpha STK3 CERES score (CRISPR) CERES score (CRISPR) z-score (siRNA) STK4 MINK1 cell lines. Brd STK10 The drug sensitivity and resistance testing (DSRT) approach PAK6 ρ=0.1455, p=0.0520 ρ=0.0227, p=0.7629 ρ=0.01629, p=0.8286 CAMK Others PAK5 PDHK PAK3 well established generic SRPK1 0 0 0 PI3K CHEK1 PIKK vulnerabilities ABL1 ABL2 25 25 25 SRC RIO YES1 idTRAX identifies pharmacologically effective targets not FYN score score score TIF1 FGR 50 50 50 Printing of drug plates containing BLK detected by RNAi ~600 oncology drugs in 5 different HCK LCK A 75 75 75 concentrations in a 10,000-fold PIK3CA PIK3CD 100 MFM-223 MAXIS*Bk MAXIS*Bk MAXIS*Bk concentration range. BTK Kinases MFM-223 CAL-148 CAL-120 MDA-MB-231 CAL−51 CAL-148 CAL−51 TXK 50 100 100 100 20 40 AKT1 54.96 45.74 0.00 0.00 TEC CAL-120 ITK 25 30 10 AKT2 44.98 46.98 0.00 0.00 MDA-MB-231 -2 -1 0 1 -10 -5 0 5 -10 -5 0 5 FRK 0 SRMS 20 DSS CERES score (CRISPR) z-GARP score (shRNA) z-score (siRNA) 0 idTRAX to de-convolve cell line-selective kinase AKT3 20.86 12.93 0.00 0.00 Selective DSS Selective FER MAXIS*Bk FES 10 −10 dependency from drug sensitivities PTK2B score 0 EPHA3 t b n el in ib i in 75 ib ib cin cin −3 21 di cin ine ine ulin 391 502 iclib ic Q1 sine 128 y atin y in bine ista ene s lone bic − c lin olis halan K −2 −391 d abine oci rinad 91 aenol Stattic dib no 26458 YM155 no cetax staur PIK− AT 101 AT efit T−406 om e pimy ara X2 IN BI 2536 (+)J emetan f BIIB021 Afatinib In GSK−J4 X2 tupi AVN944 UCN−01 A z AUY922 Indibulin MK1775 icam Nut alrub Alv G demetan Olapar K amatinib AZD8055 Linsitinib SNS−032 Paclitaxel Dina AZD2014 Neratinib K 046 Do lumetinib Belinostat Valru − Dact TAK−901 Volasertib TGX V AZD7762 Melp ABT−751 Patupilone UNC0642 Entinostat Bleomycin Ble Pl Clomif Vinblastine Plicamycin emcit Carfilzomib Floxuridine Pa erd Vincristine Trametinib efametinib Cytarabine Dapo Vinorelbine Floxuridine Bortezomib Clo Pravast SB 743921 Tosedostat Clofarab Ixabepilone Mido −ade SK2636771 Bortezomib Ser Dacomitinib Pevonedistat Lestaurtinib Se S o−ade Tubastatin A Tubastatin G R Mitomycin C Hydroxyurea Hydroxyurea Omacetaxine Pevone Panobinostat Dactinomycin Methotrexate Tanes Methotrexate Fost Camptotechin EPHA4 GSK21 G PF− Camptotechin NVP or PF−00477736 Mercaptopurine hl −chloro −chloro−adenosine 8−c (5Z)−7−Oxo 8 mino 8−amino−chloro−adenosine Correlation between kinase dependencies assimilated using different screening approaches (drug, siRNA, shRNA, and CRISPR 8−a EPHB2 EPHB3 inhibitor EPHA2 B Afuresertib Uprosertib Ipatasertib screening). siRNA screening data (z-score), shRNA data (z-GARP scores) and CRISPR data (CERES score) were extracted from Apitolisib EPHB4 FGFR1 PI3K kinase FGFR1 100 100 100 Campbell et al [22], Marcotte et al [23] and from Meyers et al [24] studies, respectively. MAXIS*Bk score represents kinase dependency mTOR FGFR3 Axitinib 100 100 100 Vandetanib Dactolisib FGFR2 CDK JAK profile based on compound screening. Only kinases common to all datasets (n=179) were used in the analysis. RET 75 75 75 RET Incubation for 72 hours Dovitinib Lapatinib Lucitanib 75 75 75 CUDC−102 LTK R KIT A) Dot plot comparing correlation (Spearman rank correlation) between different functional profiling approaches. For each comparison, 100 max 50 50 50 at 37°C, 5% CO Erlotinib ALK Drug response TRK %viability %viability 2 %viability 25 25 25 Danusertib ROS1 Regorafenib AKT only the cell lines that are common between the two data sets were incorporated. Colors indicate individual cell lines. Dasatinib INSR 80 0 0 0 UCN−01 50 50 50 y inhibition INSRR t Midostaurin -10 -9 -8 -7 -6 -9 -8 -7 -6 -5 -8 -7 -6 -5 -4 Aurora VEGFR B) Scatter plot comparing scores from different methods in a representative cell line MDA-MB-231.
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