tumor samples from TCGA with gene expression signatures similar to ESC-like ... (a) Schematic illustration of the second chemical screen design and workflow.
Supplementary Results to Cancer stem cell drugs target K-ras signaling in a stemness-context Arafath K Najumudeen et al. Supplementary Figure 1: CSC inhibitors alter Ras distribution and PS nanoclustering. (a) Confocal images of BHK cells transiently expressing mGFP-K-rasG12V or mGFP-HrasG12V treated for 24 h with either DMSO control, 1.3 µM salinomycin, 1.3 µM nigericin, 1.7 µM lasalocid or 10 nM staurosporine (STS). Shown are representative confocal images of cells from three independent experiments. Scale bar, 20µm. (b) Nanoclustering-FRET analysis (illustrated in scheme) in BHK cells co-expressing mGFP- and mCherry-LactC2. Cells were treated for 24 h with either DMSO control, 1.3 µM salinomycin, 1.3 µM nigericin, 1.7 µM lasalocid or 10 nM STS. The apparent FRET-efficiency was calculated from FLIM data (mean ± SEM, n=3). The numbers in the bars indicate the number of analyzed cells. Statistical significance of differences between control and treated cells were examined using one-way ANOVA tests (* p < 0.05, **** p < 0.0001).
Supplementary Figure 2: Hit cross-validation in HEK cells. Validation of hits from the ionophore collection screen was performed with BHK and HEK cells. FRET response of (a) K-ras NANOPS and (b) H-ras NANOPS in BHK and HEK cells treated for 24 h with 1 µg/ml of inhibitors. Block line indicates the average Emax. Bars denote the SEM (n≥4). Statistical significance analyzed using one-way ANOVA tests are indicated. (c) Model of K-ras activity modulation by presence or absence of caveolae, based on work from Ariotti et al. 1. For more explanation, please refer to the main text.
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Supplementary Figure 3: Characteristic gene expression patterns and patient survival analysis. (a) Similarity analysis of expression profiles of the 13 K-ras-nanoclustering-signature genes using ESC and fibroblast expression data from the database ESTOOLS. Fibroblasts show an expression signature that is inverted in primed ESCs. Note that there is a clear difference in the expression pattern of naïve ESC and that there is another set of ESC with expression patterns that seem to be in transit between the naïve and primed ESCs. Scheme on the bottom illustrates the differentiation pathway of ESC. (b) Overall survival analysis between patient tumor samples from TCGA with gene expression signatures similar to ESC-like cancer cells (n=1401) vs. fibroblast-like cancer-cells (n=2631).
Supplementary Figure 4: Gene expression comparison of cancer cells, ESC and fibroblasts. Gene expression comparison of selected cancer cell lines including those from Figure 5 (data extracted from the CCLE database) and ESCs and fibroblasts (data from ESTOOLS database). Only the 13 genes from the K-ras-nanoclustering signature are compared.
Supplementary Figure 5: Additional microbial metabolite screening identifies new candidate CSC inhibitors. (a) Schematic illustration of the second chemical screen design and workflow. The differential chemical screen of the MST metabolite library on H-Ras and K-Ras NANOPS had a Z’ score of 0.60 with a combined hit rate of 10%. (b) Effector-recruitment FRET assay (illustrated in schemes) in BHK cells expressing mGFP-K-rasG12V or mGFP-H-rasG12V and mRFP-RBD. Cells were treated for 24 h with either DMSO control, 1.3 µM of salinomycin, 0.2 µM of avermectin, ivermectin, conglobatin A, ophiobolin A, kazusamycin
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B, leptomycin B or streptonigrin. The apparent FRET-efficiency was calculated from FLIM data (mean ± SEM, n=3). The numbers in the bars indicate the number of analyzed cells. Statistical significance of differences between control and treated cells was examined using one-way ANOVA (***, p < 0.001; ****, p < 0.0001). (c) Confocal images of MDCK cells stably expressing GFP-K-rasG12V or GFP-H-rasG12V treated for 24 h with 0.2 µM of inhibitors. Shown are representative confocal images of cells from three independent experiments. Scale bar, 20 µm.
Supplementary Figure 6: Ophiobolin A is a highly potent CSC inhibitor that targets CaM. (a) Expression of indicated stem cell markers in MDA-MB-231, MDA-MB-436, Hs-578T and MCF7 cells grown in adherent and non-adherent sphere forming conditions. β-actin was used as a loading control. (b) Drug efficacy comparison of ophiobolin A on MDA-MB-231 cells grown under adherent and non-adherent, mammosphere forming conditions. Cells were treated with different concentrations of ophiobolin A for 72 h (n=3). Percentage viability of adherent was measured using alamarBlue and mammospheres were counted as described in the Methods section. The values were expressed relative to the DMSO control treated cells. (c) Analysis of siRNA mediated calmodulin 1 knockdown efficiency in MDA-MB-231 cells used for mammosphere analysis. β-actin was used as a loading control. (d) Left, nanoclustering-FRET analysis on HEK cells treated with 0.2 µM conglobatin A and otherwise performed like in Figure 7b. The apparent FRET-efficiency (mean ± SEM, n=3) was calculated from FLIM-FRET data. Statistical significance of differences between control and treated cells was examined using one-way ANOVA (ns, not significant; ****, p < 0.0001). Right, Analysis of siRNA mediated calmodulin 1 knockdown efficiency from n=5 independent measurements in HEK293 cells. β-actin was used as a loading control. (e)
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Correlation of individual genes in the K-ras nanoclustering signature set with the drug response to salinomycin, staurosporine and ophiobolin A across the screened cell lines (n=15 for salinomycin and staurosporine, n=14 for ophiobolin A). *, p < 0.05, Spearman rank correlation coefficent.
Page 4 of 11
Compound number 1 2 3 4
Compound name
MW
Activity
Solvent of stock
Reveromycin A Antibiotic RK-682 Nigericin sodium Deethylindanomycin
660.8 775.1 746.9 465.6
100% DMSO 100% DMSO 100% DMSO 100% DMSO
5
Antibiotic UK-1
386.4
6
Moenomycin complex
1583.6
7
Lasalocid sodium
590.8
8 9 10 11 12 13 14
Monensin A Salinomycin Enniatin complex Indanomycin Ionomycin Valinomycin
670.8 751 639.8 493.7 709 1111.3
N/A N/A K+, Rb+, Cs+, Na+ divalent cations Mg2+, Zn+ dependent DNA binding agent phosphoglycolipid antibiotic complexes with mono& di-valent cations Na+, K+, Li+, Ag+, Tl+ K+ K+ divalent cations Ca2+ K+
Compactin
390.51
HMG-CoA inhibitor
Ethanol – PBS (1:1)
100% DMSO 100% DMSO 100% DMSO 100% DMSO 100% DMSO 30% Methanol - Water 100% DMSO 30% Methanol - Water 30% Methanol - Water
Supplementary Table 1: Collection of ionophores used in the first compound screen with K- and H-ras-NANOPS. Compound numbers are the same as in Figure 1b. Activity: Known ionophoric activity of the compounds is indicated. Compactin (compound 14), an HMG-CoA inhibitor that blocks farnesylation and prevents Ras membrane anchorage, was used as the positive control. MW denotes molecular weight. Final solvent concentration in cells was typically less than 0.01%.
Page 5 of 11
Name (Gene symbol)
Nanoclustering (nc) associated activity
Galectin-1 (LGALS1)
increase of GTP-H-ras nc (no binding to inactive H-ras), 2-5 but decrease of GTP-K-ras nc (DA unpublished observation) 6,7 increase of K-ras nc
Galectin-3 (LGALS3) Nucleophosmin (NPM1) Nucleolin (NCL) Caveolae: Caveolin-1 (CAV1) and Cavin-1 (PTRF) H-RasG12V (HRAS)
increase of GDP/GTP-K-ras nc
References
8
binding partner of NPM1; increases membrane affinity 8 of K-ras (not nc) decrease K-ras nc by quenching plasma membrane 1 phosphatidylserine (PS) decreases K-ras nc remotely, via PS perturbation
9
Supplementary Table 2: List of seven known K-ras nanocluster modulators and their activity References: 1 Ariotti N, Fernández-Rojo MA, Zhou Y, Hill MM, Rodkey TL, Inder KL et al. Caveolae regulate the nanoscale organization of the plasma membrane to remotely control Ras signaling. J Cell Biol 2014; 204: 777–792. 2 Rotblat B, Belanis L, Liang H, Haklai R, Elad-Zefadia G, Hancock JF et al. H-Ras nanocluster stability regulates the magnitude of MAPK signal output. PLoS ONE 2010; 5: e11991. 3 Prior IA, Muncke C, Parton RG, Hancock JF. Direct visualization of Ras proteins in spatially distinct cell surface microdomains. J Cell Biol 2003; 160: 165–170. 4 Paz A, Haklai R, Elad-Sfadia G, Ballan E, Kloog Y. Galectin-1 binds oncogenic H-Ras to mediate Ras membrane anchorage and cell transformation. Oncogene 2001; 20: 7486– 7493. 5 Guzmán C, Solman M, Ligabue A, Blazevitš O, Andrade DM, Reymond L et al. The efficacy of Raf kinase recruitment to the GTPase H-ras depends on H-ras membrane conformer-specific nanoclustering. J Biol Chem 2014; 289: 9519–9533. 6 Shalom-Feuerstein R, Plowman SJ, Rotblat B, Ariotti N, Tian T, Hancock JF et al. K-ras nanoclustering is subverted by overexpression of the scaffold protein galectin-3. Cancer Res 2008; 68: 6608–6616. 7 Levy R, Biran A, Poirier F, Raz A, Kloog Y. Galectin-3 mediates cross-talk between KRas and Let-7c tumor suppressor microRNA. PLoS ONE 2011; 6: e27490. 8 Inder KL, Lau C, Loo D, Chaudhary N, Goodall A, Martin S et al. Nucleophosmin and Nucleolin Regulate K-Ras Plasma Membrane Interactions and MAPK Signal Transduction. J Biol Chem 2009; 284: 28410–28419. 9 Zhou Y, Liang H, Rodkey T, Ariotti N, Parton RG, Hancock JF. Signal Integration by Lipid-Mediated Spatial Cross Talk between Ras Nanoclusters. Mol Cell Biol 2014; 34: 862–876.
Page 6 of 11
Query Gene
Correlated Gene
CAV1
ANXA1, ANXA2, CAV2, CD44, COL5A2, COL8A1, EMP3, FRMD6, GLIPR1, GNG11, GREM1, LMNA, MTMR11, MYOF, NT5E, PARVA, PEA15, RFTN1, SERPINE1, SYNJ2, TAX1BP3, TGFB1I1, TRAM2, VIM
EGFR KRAS LGALS1
ANPEP, LTBP2, MGLL, SQRDL CCNB2, PAICS ANXA1, ANXA2, CAV2, CD44, CD59, COL1A1, COL5A2, COL8A1, CREB3L1, EMP3, FRMD6, GLIPR1, GNG11, ITGA5, LOX, MTMR11, MYOF, PARVA, PEA15, RFTN1, TGFB1I1, TRAM2, VIM ANPEP, FRMD6, GREM1, LOX, LTBP2, MGLL, NT5E, OPTN, PLP2, RFTN1, SQRDL, TGFB1I1, WIPI1 HNRNPAB CCNB2, HNRNPAB, PAICS CD44, CD59, COL1A1, CREB3L1, EMP3, GREM1, ITGA5, LMNA, OPTN, PLP2, SERPINE1, SYNJ2, TAX1BP3, TGFB1I1, TRAM2, WIPI1
LGALS3 NCL NPM1 PTRF
Supplementary Table 3: List of 32 most co-expressed genes with the 10 genes in ESCs and fibroblasts extracted from ESTOOLS database. Genes that were coregulated with more than one of the 10 genes in the K-ras-nanoclustering signature were retained.
Page 7 of 11
Number0of0samples Code
ACC BLCA BRCA CESC COAD DLBC GBM HNSC KICH KIRC KIRP LAML LGG LIHC LUAD LUSC OV PAAD PRAD READ SARC SKCM THCA UCEC UCS
Cancer0Types
Adrenocortical-carcinomaBladder-Urothelial-CarcinomaBreast0invasive0carcinoma0 Cervical-squamous-cell-carcinoma-andendocervical-adenocarcinomaColon0adenocarcinoma0 Lymphoid-Neoplasm-Diffuse-Large-B?cellLymphomaGlioblastoma-multiformeHead-and-Neck-squamous-cell-carcinomaKidney-ChromophobeKidney-renal-clear-cell-carcinomaKidney-renal-papillary-cell-carcinomaAcute0Myeloid0Leukemia0 Brain0Lower0Grade0Glioma0 Liver-hepatocellular-carcinomaLung-adenocarcinomaLung-squamous-cell-carcinomaOvarian0serous0cystadenocarcinoma0 Pancreatic-adenocarcinomaProstate-adenocarcinomaRectum-adenocarcinomaSarcomaSkin-Cutaneous-MelanomaThyroid-carcinomaUterine0Corpus0Endometrial0Carcinoma0 Uterine0Carcinosarcoma0
ESC$like
Fibro$like
P$values0(Fishers's0exact0test)0vs0 Total0TCGA0samples
Percentage0of0samples
Total0 TCGA0 samples
ESC$like
Fibro$like
Total0TCGA0 samples
ESC$like
Fibro$like
Enrichment0for0 P$value0(Fisher's0 ESC/0Fibro$like0 exact0test) vs0the0other0
4 10 111
6 6 17
79 241 1041
0.66 1.65 18.35
1.49 1.49 4.23
1.05 3.20 13.81
0.53 0.04 0.01
0.45 0.07 2.42E?08
2.26 1.11 4.34
0.21 1.00 4.42E$10
6 26
3 3
185 435
0.99 4.30
0.75 0.75
2.46 5.77
0.02 0.17
0.03 9.12E?07
1.33 5.76
1.00 8.06E$04
8 6 1 0 0 1 98 22 2 23 16 78 0 2 10 0 43 0 128 10
0 15 10 3 57 3 0 0 1 40 16 6 3 2 3 15 144 31 18 0
28 161 426 66 518 172 173 463 191 489 490 263 85 297 164 103 372 498 539 57
1.32 0.99 0.17 0 0 0.17 16.20 3.64 0.33 3.80 2.65 12.89 0 0.33 1.65 0 7.11 0 21.16 1.65
0 3.73 2.49 0.75 14.18 0.75 0 0 0.25 9.95 3.98 1.49 0.75 0.50 0.75 3.73 35.82 7.71 4.48 0
3.72E?01 2.14 5.65 0.88 6.87 2.28 2 6 2.54 6.49 6.50 3.49 1.13 3.94 2.18 1.37 4.94 6.61 7.15 1
4.40E?03 0.07 9.02E?13 1.45E?02 2.60E?17 4.47E?05 3.69E?39 0.01 1.09E?04 0.01 1.19E?04 4.34E?18 2.66E?03 7.38E?08 0.47 8.16E?04 0.04 9.52E?17 1.50E?20 0.03
0.40 0.05 0.01 1.00 5.38E?06 5.04E?02 2.59E?04 1.36E?10 1.09E?03 0.02 0.06 0.03 0.63 5.58E?05 0.07 1.07E?03 2.04E?57 0.41 0.05 0.12
100% 3.76 15.08 NA $1000% 4.52 100% 100% 1.33 2.62 1.50 8.64 NA 1.50 2.22 NA 5.04 NA 4.72 100%
0.03 0.01 7.74E$04 NA 0.00 0.31 4.66E?21 2.14E?05 1.00 3.13E$04 0.27 9.07E$11 0.06 0.65 0.26 NA 3.20E$21 NA 6.34E$12 0.01
Supplementary Table 4: Cancer-types from the TCGA database with ESC- or fibroblast-like K-ras nanoclustering expression signature. Table shows the analysis of the different cancer tissue-types from the TCGA tumor database with ESC- or fibroblast (Fibro)-like K-rasnanoclustering signature. The number and percentage of samples with ESC-like (n=605), fibroblast-like signature (n=402) and total TCGA data (n=7536) for each cancer-type is provided. P-values for difference of percentages of each cancer type against the total TCGA data from Fisher’s exact test are provided. Differential enrichment for ESC-like denotes the ratio of ESC-like- / Fibro-like-percentages and enrichment for Fibro-like (italics) for the inverse. P-values for this difference in percentage of cancer types in ESC-like vs. fibroblast-like signature from Fisher’s exact test are provided in the final column. Notable values are in bold. 100% or -100% denotes that only samples of ESC- or Fibro-like signature, respectively, were found. Cancer types are color coded with those significantly dominated by ESC-like in green and fibroblast-like in grey. Page 8 of 11
No.
Percentage deviation from control FRET Emax K-ras-NANOPS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
27% 14% 13% 12% 10% 25% 17% 13% 14% 11% 11% 10% 10% 10% 10% 10% 8% -2% 3% 8% -38% -31% -24% -19% -16% -14% -11% -12% -12% -12% -12% -10% 2% -8% -8% -7% -6% -8% -8%
Compound Name
Class
Stauprimide Leptomycin analogue 7-Oxostaurosporine Leptomycin, aminolysed Leptomycin A UCN-01 Leptomycin analogue Cyclosporine D Doxorubicin Leptomycin B Kazusamycin B Staurosporine Leptomycin A Avermectin Idarubicin HCl Unidentified peptide Leptomycin B Chromomycin A3 Epothilone B Elaiophylin Simvastatin Lovastatin Prodigiosin Ophiobolin A Ophiobolin B 111070-14.LA5.38-63/66 8-beta-Hydroxyzearalenone Neoantimycin Pravastatin sodium Ivermectin Conglobatin A Streptonigrin Nonactin Kigamicin C Ophiobolin C Trinactin Indanomycin Leptomycin co-metabolite Antimycin A
Staurosporine Leptomycin Staurosporine Leptomycin Leptomycin Staurosporine Leptomycin
H-ras-NANOPS 18% 11% 13% 12% 10% 7% 5% 1% 7% 1% 3% 5% 9% 2% 1% 5% 13% 11% 10% 10% -33% -31% -18% -15% -15% -12% -14% -11% -16% -3% -7% 1% -13% -13% -12% -10% -10% -10% -10%
Doxorubicin Leptomycin Leptomycin Staurosporine Leptomycin Avermectin Idarubicin Leptomycin
Statin Statin Ophiobolins Ophiobolins Leptomycin
Statin Avermectin Conglobatin Streptonigrin Nactin Ophiobolin Nactin Leptomycin
Supplementary Table 5: List of hits from the MST library screen with K- and H-rasNANOPS using cytometry-FRET. Table shows percentage change of FRET Emax value for hit compounds as compared to the respective non-treated control values for H- and K-rasNANOPS. Hits that increase FRET Emax ≥10% are marked in red and those that decrease FRET Emax ≥-10% are marked blue. Substance/chemical classes of the hits are indicated. Hits from the same substance class are color-coded. Shown are hits satisfying the selection threshold 10% change in FRET Emax for either H or K-ras-NANOPS compared to the respective non-treated control.
Page 9 of 11
1
Compound Name Leptomycin B
2
Kazusamycin B
3
Ophiobolin A
4
Conglobatin A
5
Ivermectin
6
Avermectin
7
Streptonigrin
No.
Structure
Supplementary Table 6: Chemical structures of validated hits from the MST metabolite screen.
Page 10 of 11
Cell line
MDA-MB231
MDA-MB436
Hs578T
MCF7
Mutations
EGFR K-rasG13D B-Raf NF2 P53
PIK3C2B PIK3R1 P53 BRCA1 RB
H-rasG12D RASGRF1 PIK3R1 P53
PIK3CA PIK3AP1 CDKN2A
Basal-like, claudin-low Triple negative
Luminal Triple negative
Basal-like claudin-low Triple negative
Luminal A
Percentage of CD44+/CD24− cells 85 ± 5 (Sheridan et al., 2006)
72 ± 5
86 ± 5
0
Number of mammospheres/2000 cells (mean ± S.D.)
136 ± 28
134 ± 11
133 ± 22
Classification
202 ± 15
Supplementary Table 7: Characteristics of breast cancer cell lines used for mammosphere assays. Table shows the mutation status of typical oncogenes, breast cancer type classification, reported percentage of CD44+/CD24− population and mammosphere formation capacity in our experiments.
Page 11 of 11
Supplementary Figure 1
STS
Salinomycin
Nigericin
K-rasG12V
control
H-rasG12V
BHK21
a
b
PS-clustering FRET
LactC2
PS
FRET
control 112 STS 108 * Salinomycin 90 Nigericin 72 **** Lasalocid 130 4 6 8 10 apparent FRET efficiency, %
Lasalocid
0.4
K-ras
0.6
p
