The Transcriptomic Response of Cells to a Drug Combination Is More Than the Sum of the Responses to the Monotherapies

bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 The transcriptomic response of cells to a drug 2 combination is more than the sum of the responses to 3 the monotherapies

4 5 Jennifer E. L. Diaz 1,7, Mehmet Eren Ahsen 1,2,7,Thomas Schaffter 1,3, Xintong Chen 1, Ronald B.

6 Realubit4,5 , Charles Karan 4,5 , Andrea Califano 4,6 , Bojan Losic 1 , Gustavo Stolovitzky 1,3,4,*

7 1Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai,

8 New York, NY

9 2Department of Business Administration, University of Illinois at Urbana-Champaign,

10 Champaign, IL 11 3 IBM Computational Biology Center, IBM Research, Yorktown Heights, NY 12 4 Department of Systems Biology, Columbia University, New York, NY 13 5 Sulzberger Columbia Genome Center, High Throughput Screening Facility, Columbia 14 University Medical Center, New York, NY 10032, USA 15 6 Department of Biomedical Informatics, Columbia University, New York, NY, USA 16 Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA 17 J.P. Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA. 18 Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA 19 7 These authors contributed equally. 20 * Corresponding author 21 22 Contact information 23 Jennifer E. L. Diaz [email protected] 24 Mehmet Eren Ahsen [email protected]

25 Thomas Schaffter [email protected]

26 Xintong Chen [email protected]

27 Ronald B. Realubit [email protected]

28 Chuck Karan [email protected]

29 Andrea Califano [email protected]

30 Bojan Losic [email protected] 31 Gustavo Stolovitzky [email protected] bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

32 Abstract

33 Our ability to predict the effects of drug combinations is limited, in part by limited understanding

34 of how the transcriptional response of two monotherapies results in that of their combination. 35 We performed the first analysis of matched time course RNAseq profiling of cells treated with 36 both single drugs and their combinations. The transcriptional signature of the synergistic

37 combination we studied had unique gene expression not seen in either constituent 38 monotherapy. This can be explained mechanistically by the sequential activation of transcription

39 factors in time in the gene regulatory network. The nature of this transcriptional cascade

40 suggests that drug synergy may ensue when the transcriptional responses elicited by two 41 unrelated individual drugs are correlated. We used these results as the basis of a simple 42 prediction algorithm attaining an AUROC of 0.84 in the prediction of synergistic drug 43 combinations in an independent dataset. 44

45 46 Introduction

47 Combination therapy has become an increasingly relevant cancer treatment ( Al-Lazikani, 48 Banerji, & Workman, 2012). The complexity of patient-to-patient heterogeneity ( Palmer & 49 Sorger, 2017), intratumor heterogeneity ( Gerlinger et al., 2012) and intracellular pathway 50 dysregulation ( Hyman, Taylor, & Baselga, 2017) provides opportunities for combining drugs to

51 induce responses that cannot be achieved with monotherapy. Effective combinations may target 52 multiple pathways ( Larkin et al., 2015) or the same pathway ( Baselga et al., 2012). They may 53 also reduce the dose of individual drugs, thereby reducing toxicity, or target molecular 54 mechanisms of resistance, thereby prolonging the effective duration of treatment ( Al-Lazikani et 55 al., 2012; Greco, Faessel, & Levasseur, 1996; LoRusso et al., 2012).

56 57 Drug combinations are said to be synergistic if their activity exceeds their expected additive or 58 independent response ( Greco, Bravo, & Parsons, 1995; Palmer & Sorger, 2017). Synergistic 59 behavior is by nature difficult to predict, so rational combinations may not validate 60 experimentally ( Wolff et al., 2013). Hypothesis-driven studies of the mechanisms of synergy and

61 antagonism have focused on a limited set of candidate targets ( Bollenbach, Quan, Chait, & 62 Kishony, 2009; Jiang et al., 2018). Alternatively, unbiased high-throughput screening assays

63 (Borisy et al., 2003; Mathews Griner et al., 2014; Mott et al., 2015) can identify synergistic 64 compounds in a systematic way by assessing cell viability reduction by individual drugs and

65 their combinations. Unfortunately, screening all possible drug-pairs in a panel of N drugs with NC 2 66 cell lines at ND doses requires a large number (½ N (N-1) ND NC ) of experiments, resulting in 67 high costs that limit the practical reach of this approach. Computational methods to predict 68 synergistic combination candidates are needed to improve the experimental cost-benefit ratio 69 (Bansal et al., 2014; Fitzgerald, Schoeberl, Nielsen, & Sorger, 2006).

70 71 To address this need, the DREAM (Dialogue on Reverse Engineering Assessment and 72 Methods) Challenges consortium ( Saez-Rodriguez et al., 2016) conducted a community-wide 73 competition (the NCI-DREAM Drug Synergy Prediction Challenge) that fostered the 74 development and benchmarking of algorithms for drug synergy prediction. The organizers

75 provided a time course of post-treatment transcriptomics data for each of 14 drugs administered 76 to a lymphoma cell line, and asked participants to predict which of the 91 pairwise combinations 77 would be synergistic ( Bansal et al., 2014; Sun et al., 2015). One of the key outcomes of the 78 Challenge and other studies was that synergistic drug combinations could be partially predicted 79 from the transcriptomics of the monotherapies ( Niepel et al., 2017; Sun et al., 2015). The two

80 best-performing teams based their algorithms on the assumption that a concordance of gene 81 expression signatures in drugs with different mechanisms of action often yield synergistic 82 interactions ( Goswami, Cheng, Alexander, Singal, & Li, 2015; Yang et al., 2015). This

83 assumption, while plausible, has little experimental support beyond winning the Challenge. 84 Further, the mechanism behind this phenomenon cannot be ascertained without transcriptomics

85 data from the combination therapy, which was not provided in the Challenge due to cost. We 86 therefore pose a fundamental question that can only be answered with matched

87 monotherapy-combination transcriptomics data: H ow do two different transcriptomics profiles in

88 cells treated with two different drugs combine to give a new transcriptomics profile when the 89 drugs are applied together? If the combinatorial pattern of two gene expression profiles are

90 different in synergistic versus additive drug combinations, then learning to recognize these 91 patterns may enable us to predict synergistic combinations from the gene expression of

92 monotherapies.

93 94 In this paper we explore the relationship between the transcriptional landscape of drug

95 combinations in relation to the profiles of the individual drugs. To our knowledge, we have

96 performed the first systematic, genome-wide analysis of matched time courses of gene

97 expression following perturbation with individual compounds and with their combinations. 98 Deliberately sacrificing breadth for depth, we studied the transcriptional temporal response of a

99 synergistic drug combination, tamoxifen and mefloquine, in breast cancer and prostate cancer 100 cells and compared it to that of additive combinations of withaferin with either tamoxifen or

101 mefloquine. While traditional studies may elucidate specific mechanisms of action for drugs and

102 their combinations, we attempt to examine the transcriptome for molecular indicators of synergy. 103 Our analysis shows that m olecular synergy (measured by the number of genes whose

104 expression changes significantly only in the combination), correlates with the Excess over Bliss 105 independence ( Greco et al., 1995) and increases with time. We used network-based analyses

106 to trace the transcriptional cascade as it unfolds in time in the synergistic combination. We

107 found that transcription factors simultaneously activated by both drugs dominate the cascade. 108 We propose that a pair of drugs with correlated transcriptional programs is likely to trigger a

109 synergistic effect, even when they target different pathways. We contrast this effect with the 110 correlated, by additive, effect of increasing dose of one drug. Correlation of monotherapies

111 predicted synergistic drug interaction with high accuracy (AUROC=0.84) using the independent

112 DREAM dataset. This study represents the first matched monotherapy-combination 113 transcriptomic analysis of synergy, advancing both our understanding of synergy and ability to

114 predict it. 115 116 Results

117 Finding reproducible synergistic and additive combinations

118 To identify drug-pairs for the detailed time course RNA-seq analysis, we leveraged a

119 pre-existing LINCS drug combination dataset collected at Columbia University (Califano’s lab) in

120 the MCF7 cell line, a breast cancer line that is ERalpha positive ( Lee, Oesterreich, & Davidson, 121 2015), and the LNCaP cell line, a prostate cancer cell line that is ERbeta positive ( Takahashi,

122 Perkins, Hursting, & Wang, 2007). This dataset had information on all combinations of 99 drugs 123 against 10 different drugs, each combination assessed in a matrix of 4 by 4 doses at 48 hours

124 after drug application. Among these 990 drug combinations, we found 39 synergistic

125 combinations with a maximum Excess over Bliss (EOB) independence over the 4x4 matrix of 126 more than 30%. From these 39, to select synergistic combinations in which both monotherapies

127 played an important role in eliciting synergy, we chose 13 combinations whose constituent

128 monotherapies showed a variety of combinatorial behaviors across the combinations 129 (antagonism, additivity, and synergy in different combinations) for further testing. We

130 re-assessed the synergy of these 13 pairs using a 6 x 6 dose response matrix, and found that 9 131 of the 13 combinations remained synergistic, while 4 exhibited additive or antagonistic

132 responses, which we removed from further study. Tamoxifen appeared in the greatest number

133 (4) of these 9 combinations. Given this data and the known clinical utility of Tamoxifen in ER+ 134 breast cancer ( Early Breast Cancer Trialists’ Collaborative Group (EBCTCG), 2015; Early

135 Breast Cancer Trialists’ Collaborative Group (EBCTCG) et al., 2011), we focused on these 4 136 combinations. Of those 4, we sub-selected the 2 drug pairs with the highest EOB values,

137 Tamoxifen (T) + Mefloquine (M) and T + Withaferin A (W) (EOB = 49 and 43, respectively). We

138 then further measured viability (using the high throughput Cell Titer Glo) and EOB in triplicate 139 experiments using a 10 x 10 dose matrix at 12 hours, 24 hours and 48 hours (Supplementary

140 Files 1-2) after drug treatment, obtaining results that were consistent with the previously found 141 synergy. Note that these two combinations were tested in at least three independent sets of

142 experiments at this point (99x10 screen, 13 combinations, and 2 combinations). We noted

143 differences in viability, consistent with recent concerns regarding the lack of reproducibility in cell 144 line viability experiments in response to drugs ( Haibe-Kains et al., 2013), and yet TM remained

145 consistently synergistic despite changes in the viability of its constituent monotherapies. We 146 then selected doses of these three drugs that synergized in these two combinations at both time

147 points (20 μM T, 10 μM M, 5 μM W) for subsequent study (Supplementary Files 3,4). T and M

148 are also synergistic at 12 and 24 hours as measured by combination index, but T and W are 149 synergistic only at 24 hours (Figure S1A). For completeness, we included the combination MW

150 in subsequent studies. 151

152 We focused the rest of the study on these three drugs and their combinations (Figure 1A), in an

153 effort to understand how synergy operates at a transcriptomic level (Figure 1B). We studied 154 MCF7 and LNCaP cells under DMSO (vehicle control), T, M and W, and their combinations TM,

155 TW and MW over a 48-hour time course (0, 3, 6, 9, 12, 24 and 48 hours) using nuclei counts as 156 a direct readout of cell viability in relation to DMSO (Figure 1C-H). At these doses, TM (Figure

157 1C,F) synergistically reduced viability as early as 6 hours, with little effect from T and M

158 individually in MCF7 (Figure 1C) and moderate effect in LNCaP (Figure 1F). The synergistic 159 effect of TW and MW was very small compared to TM in MCF7 (Figure 1D,E) and negligible in

160 LNCaP (Figure 1G,H). In relation to TM, we therefore consider the effects of TW and MW to be

161 additive and dominated by W. While W had a strong monotherapy effect, the lack of strong

162 synergy in its combinations does not appear to be a function of dose, as TW was even less 163 synergistic at lower doses of W (Supplementary Files 2,5). 164

165 Finally, we wished to study the effect of drug dose on viability, as a combination treatment

166 exposes the cells to more drug than a monotherapy, and this could mimic the effect of

167 increasing drug dose. Additionally, M has been shown to inhibit the function of MDR1, a

168 multi-drug efflux pump ( Riffkin et al., 1996) and its effect could therefore be simply to increase 169 the intracellular tamoxifen concentration. We performed a separate set of experiments in which

170 we measured the effect of T alone at 5, 10, 20, and 25 μM, and M alone at 2.5, 5, 10, and 15

171 μM at 24 hours in MCF7 cells. Viability in 25 μM T and 15 μM M was similar to TM (Figure S1B),

172 although we continued to observe inter-experiment variability in the efficacy of monotherapies

173 (eg. 20 μM at 24 hours). Treating 25 μM T as a combination of 5 and 20 μM T, and 15 μM M as 174 a combination of 5 and 10 μM M, we observed EOBs of 31.5 and 17.5 respectively

175 (Supplementary File 6), far lower than the EOB of 99.3 in TM (Figure 1C), suggesting that the

176 synergy we observed is a phenomenon distinct from dose response. To study the transcriptional

177 mechanisms of drug combinations, we collected RNA from the same cultures from which we

178 measured viability at each treatment for RNAseq. 179 180 Gene expression of drug combinations in relation to monotherapies

181 For each treatment and time point up to 24 hours we collected samples in triplicate and

182 performed RNAseq studies. The RNAseq replicate data was reliable, with replicates showing a

183 very high concordance (Figure S2A-B). We first examined the gene expression over all

184 treatments and time points in MCF7 (Figure 1I) and LNCaP (Figure S2C). The transcriptional 185 profiles for the monotherapies T and M were similar to each other and to DMSO at all time

186 points, including for the variable dose experiments; yet, the combination treatment TM produced

187 an expression profile distinct from DMSO, T, and M at all times after 0. The transcriptional

188 profiles for W, TW and MW on the other hand, were similar to each other but different from T, M,

189 TM and DMSO over time. However, gene expression profiles from different doses of the same 190 monotherapy were more similar than they were different, with changes evolving gradually with

191 increasing dose (Figure S2D). This pattern mirrors the phenotypic viability profiles (Figure 1C-H,

192 Figure S1B). Figure 1G shows a two-dimensional Principal Component Analysis (PCA) of the

min max

A B Cell Lines Treatment Phenotypic View Molecular View Transcriptomics View of Synergy 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 MCF7 & LNCaP min Viability RNAseq max Tamoxifen treatment Annotation T weak/noTW: Synergistic genes synergy min drug A 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 max9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 TM: treatment Synergistic splicing strong Annotation synergy drug B 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 +9 12 24 0 3 Synergistic6 9 12 24 0 biological3 6 9 processes12 24 MW: treatment M weak/no W combination Temporal activation of synergistic genesAnnotation Mefloquine synergy Withaferin A AB ? Molecular rules of combinations treatmenttime time DMSO T treatment M DMSO C Tamoxifen & Mefloquine D TM E Tamoxifen & Withaferin T Mefloquine & Withaferin '%!" '%!" W '%!" treatment M TW DMSO '$!" '$!" TM MW '$!" T W '#!" M '#!" TW '#!" TM '!!" '!!" MW '!!" W Mefloquine Withaferin A Withaferin A &!" TW &!" &!" Tamoxifen Mefloquine 50.8±1.2 MW Tamoxifen Viability (%) Viability Viability (%) Viability %!" %!" Predicted (%) Viability %!" Predicted Predicted 103.9±2.4 Observed4.6±2.4 9.8±1.5 Observed12.0±1.9 11.7±1.6 Observed $!" $!" $!" 99.3±2.8 33.2±2.3 #!" #!" #!" 35.2±2.0 !" !" !" !" (" '!" '(" #!" #(" )!" )(" $!" $(" (!" !" (" '!" '(" #!" #(" )!" )(" $!" $(" (!" !" (" '!" '(" #!" #(" )!" )(" $!" $(" (!" Time (Hours) Time (Hours) Time (Hours) F G H

44.8±9.0 -7.3±13.1 2.8±5.2 10.1±11.8 59.6±5.8 1.6±4.6

I J

time

Figure 1: The Transcriptomics of Drug Combinations Mirror their Phenotypic Characteristics A) Monotherapies and drug combinations used in the study. B) Workflow of molecular analysis of synergy. Starburst highlights the novel component of RNAseq analysis. Question mark denotes the focus of the study. C-H) Fold change over control of cell count for MCF7 cells (C-E) and LNCaP cells (F-H) treated with Tamoxifen and Mefloquine (C,F), Mefloquine and Withaferin (D,G), and Tamoxifen and Withaferin (E,H). Predicted viability is based on the Bliss model. Excess Over Bliss (EOB) ± ErrorEOB is given for the 12, 24, and 48 hr time points (see Methods). I) Average gene expression for each treatment and time point in the MCF7 combination experiments (covering 108 treatment and 18 DMSO samples). G) Principal component analysis of gene expression for the average over replicates at each treatment and time point in the MCF7 combination and dose experiments. (See also Table S1, Figures S1-3.) bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

193 transcriptional data from the combination and dose experiments in MCF7. The data for T and M

194 monotherapies, from both the combination experiments and the dose experiments, localize

195 slightly but distinctly above DMSO. However, their TM combination goes farther from DMSO

196 than T or M but in the same almost vertical direction. The PCA representation of W progresses 197 in an almost horizontal direction, with TW and MW co-localizing with W, which dominates the

198 combination. Therefore the 2-dimensional PCA representation of the transcriptomes after

199 treatment suggests orthogonal synergistic and additive directions. The PCA representation of

200 the gene expression from treated LNCaP cells indicates very similar dynamics in this distinct

201 cell line (Figure S2E). 202

203 We next examined differential expression relative to DMSO. The high concordance of replicates

204 allowed for clear detection of differentially expressed genes (DEGs) in different conditions

205 (Figure S3A). To determine DEGs in MCF7, we selected a false discovery rate (FDR) cutoff at

206 which the only DEGs at time 0 (~30 min post-treatment; see Methods) over all treatments are 207 well-known immediate early genes ( Sas-Chen, Avraham, & Yarden, 2012; Tullai et al., 2007);

208 Table S1). We then used this cutoff across all time points and treatments of the fixed dose

209 experiments. To achieve consistency in our treatment of the variable dose experiments which

210 were done separately and with fewer replicates, we chose an FDR cutoff resulting in

211 approximately the same number of DEGs in M 10 μM and T 20 μM (Figure S3B). In LNCaP, we 212 selected a false discovery rate (FDR) cutoff at which there were no DEGs at time 0, as we

213 noticed no differentially expressed immediate early genes in this case (see Methods). We then

214 quantified and examined the properties of DEGs in monotherapies and their combinations. The

215 number of DEGs in MCF7 cells treated with TM, W, MW, and TW were 1 to 2 orders of

216 magnitude greater than that of treatments with T and M (Figure 2A-C). We evaluated the 217 presence of synergistically expressed genes (SEGs) which we define as genes that are

218 differentially expressed in a combination therapy but not in either of the constituent

219 monotherapies. Approximately 90% of DEGs in MCF7 cells treated with TM are synergistic, and

220 not differentially expressed in either T or M alone (Figure 2A) at any time point. To test for

221 artifacts related to the chosen FDR cutoff, we calculated the percentage of SEGs over different 222 FDR thresholds and observed that the general trend is independent of the specific cutoff (Figure

223 S3C-D). In contrast, most DEGs in treatments TW and MW were also differentially expressed in

224 W (Figure 2B-C). These molecular signatures parallel the effect of these drugs on viability

225 (Figure 1C-E), reflecting the overall synergistic character of TM, and a mostly additive dominant

226 effect of W. In LNCaP cells, we observed a similar effect of TM: more than 75% of DEGs are

227 SEGs at any time point (Figure S4A). However, we observed that when LNCaP cells were

228 treated with TW or MW, more than a quarter of DEGs were SEGs at any time point, and more

229 than half at 12 and 24 hours (Figure S4B-C); in comparison, in MCF7 cells treated with MW or

230 TW, less than a quarter of DEGs were SEGs at nearly every time point (Figure 2B-C).

231 This highlights the ability of different cells to respond differently to drugs, and we explore it 232 further in the next section. Interestingly, across both cell lines and including the pairs in our dose

233 experiments, the number of SEGs correlates well (r=0.63, p=0.000044; Spearman rs = 0.59, 234 p=0 .00013) with EOB for all treatments and time points, (Figure 2D). The number of SEGs and

235 the EOB of T 25 μM and M 15 μM are similar to TW and MW and considerably smaller than TM,

236 consistent with the interpretations that behavior of TW and MW represent additivity, and that the 237 molecular and phenotypic synergy of TM transcends the expected behavior of a simple increase

238 in dose.

239

240 Finally, we also observed a significant relationship between the EOB of a combination and the

241 correlation of the transcriptional profiles of the constituent monotherapies (Figure 2E; see 242 Methods). Conversely, the monotherapy pairs from our dose experiments (i.e. T 5 and 20 μM, M

243 5 and 10 μM) had high correlation as expected, but low EOB. For this reason, when including

244 the dose experiments, the direct correlation between EOB and the correlation of transcriptional

245 profiles is not significant (r=0.31, p=0.068; Spearman rs = 0.28, p=0.097). While this suggests 246 high correlation alone is not sufficient to generate synergy, it is intuitively unlikely that combining 247 two doses of the same drug would be explored in a search for new synergistic combinations.

248 When we therefore removed the pairs from the dose experiments, we observed a significant

249 relationship between EOB and correlation of transcriptional profiles (r=0.59, p=0.00064;

250 Spearman rs = 0.53, p=0.019). This result suggests that similarity of the transcriptional profiles 251 between two treatments is an important feature of drug synergy, only when the treatments are 252 two distinct drugs, presumably with different targets. The nature of correlation as a necessary

253 but not sufficient condition for synergy will be discussed further in a later section.

254 255 Critical cancer pathways are synergistically enriched

256 We checked for enrichment of gene sets associated with specific biological processes. 257 Candidate gene sets were selected from gene-set libraries retrieved from the Enrichr tool (E. Y.

A B C

Ta Me

D E

time

metabolism*

autophagy† transcription

signaling

immunity*

toll-like receptor

cytokine apoptosis*

ER stress/ unfolded protein

cell response nuclease growth* estrogen† cell cycle*

Treatment

TUM downregulated genes upregulated genes

Figure 2: Synergistically Expressed Genes, Correlated Monotherapies, and Biological Processes are Associated with Synergy A-C) Number of DEGs over time in MCF7. The Venn diagrams in the inset correspond to DEGs at 3 hours in A) T, M, and TM, B) T, W, and TW, and C) M, W, and MW. The area represented in each color is in proportion to the number of genes in the corresponding color of the Venn diagram; blue areas represent SEGs. D-E) Relationship of Excess Over Bliss score with D) the number of SEGs, and E) correlation in gene expression values between each pair of monotherapies. Note that some of the “pairs” from the dose experiments represent the same dataset correlation with itself (i.e. T 10 μM with T 10 μM for the T 20 μM “combination”) and so have correlation = 1.0 as expected, and are shown for clarity. F) Enrichment of DEGs in T, M, and TM with cancer-relevant genes sets. Only gene sets enriched in at least one condition (time point or treatment) are shown. “TUM” indicates the union of DEGs after in T or M. Color intensity reflects degree of enrichment by Fisher’s Exact test. * = hallmark of cancer, † = drug target (see Methods). (See also Figure S4.) bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

258 Chen et al., 2013; Kuleshov et al., 2016), pathways implicated in the hallmarks of cancer

259 (Hanahan & Weinberg, 2011), and likely drug targets of T and M (see Methods). Figure 2F 260 shows the biological processes that are enriched in at least one of the subgroups of DEGs in

261 MCF7 cells under treatment with T, M, TM, as well as in the set TU M , the union of DEGs under

262 T or M, which represents the expected DEGs if T and M acted additively. If T and M act

263 synergistically, we expect that the set of DEGs in TM should be enriched in more functional

264 classes than TU M . Implicated biological processes fell into three classes: 1) endoplasmic 265 reticulum stress, estrogen signaling, and kinase activity were enriched in both TM and

266 monotherapies; 2) apoptosis, toll-like receptor and cytokine signaling, immunity, transcription,

267 metabolic processes, and autophagy were markedly more enriched in the combination TM than

268 in either monotherapy or TU M , an effect no recapitulated by increasing monotherapy dose at 24

269 hours; and 3) downregulation of the cell cycle was present in both the combination and 270 monotherapies at 12 and 24 hours, but began to occur much earlier in the combination (Figure

271 2F). Classes 2 and 3 appear to be synergistically affected in TM but were not synergistic in

272 either TW or MW (Figure S4D).

273

274 We also interrogated the dysregulated genes in the treated LNCaP cells by the same 275 procedure. As more SEGs had appeared in LNCaP cells treated with MW and TW than in

276 MCF7, especially at 12 and 24 hours (Figure 2D), we compared the synergistically enriched

277 gene sets in LNCaP cells for TM, TW, and MW (Figure S4E). A few biological processes, such

278 as autophagy, were synergistically enriched in all three combinations. Gene sets for which we

279 observed differences between the combinations fell into two broad groups: synergistically 280 enriched more in W combinations (W-enriched) or synergistically enriched more in TM

281 (TM-enriched). The W-enriched gene sets included two main classes: 1) cholesterol

282 biosynthesis and metabolism was only synergistically upregulated in MW and 2) rRNA and

283 ncRNA processing was synergistically upregulated only in TW at 24 hours, while tRNA and

284 mitochondrial RNA processing was synergistically downregulated at some time points in both 285 TW and MW. TM-enriched gene sets fell into three classes: 1) temporal differences:

286 endoplasmic reticulum stress was upregulated at earlier time points in TM than in the

287 monotherapies, whereas it was similarly enriched in W, TW, and MW at all time points except 24

288 hours, and intrinsic apoptosis in response to ER stress was upregulated initially in W, TW, and

289 MW followed by normalization over time, whereas in TM it was synergistically upregulated in an 290 increasing manner over time; 2) certain metabolic processes (generation of precursor

291 metabolites and energy, cofactors, amino acids, and sulfur) were synergistically downregulated

292 only in TM; and 3) genes that are repressed by estrogen receptor were synergistically

293 upregulated only in TM. We hypothesize that the W-enriched classes represent mechanisms by 294 which LNCaP cells counter the effects of the drug combinations and evade cell death, whereas

295 TM-enriched gene sets, particularly class 2, may represent gene sets that function as

296 harbingers of phenotypic synergy, distinguishing synergistic drug combinations from

297 combinations whose effects can be resisted by cells.

298 299 Co-expressed genes show a synergistic temporal pattern

300 We studied temporal patterns of drug response. We used k-means clustering (see Methods) to

301 identify co-expressed genes with similar time evolution in T, M and TM. This unsupervised

302 clustering method identified four distinct temporal patterns (Figure 3A): 1) upregulated in TM

303 (2253 genes), 2) strongly upregulated in TM (421 genes), 3) downregulated in TM (1709

304 genes), 4) strongly downregulated in TM (718 genes). In each cluster, the average differential 305 expression observed in the combination TM was significantly stronger than that in T + M, in

306 which the expression in T and M are added. The trajectory over time for most genes is

307 monotonic and saturates at 9 hours. However, we also tested for genes whose trajectories were

308 significantly different in TM than T and M (data not shown). A minority of genes in each cluster

309 exhibited unique temporal profiles in TM, including mixed transient and monotonic behavior, 310 suggesting the existence of temporal synergy (e.g., Figure 3B).

311

312 We then assessed these gene classes for enrichment in biological processes (Figure 3C).

313 Consistent with enrichment of these processes at each time point (Figure 2F), upregulated

314 genes were enriched in endoplasmic reticulum stress (clusters 1-2), and downregulated genes 315 were enriched in cell cycle and metabolic processes (clusters 3-4). In addition, apoptosis and

316 downregulated targets of estrogen were enriched in genes strongly upregulated in TM (cluster

317 2), highlighting synergistic properties. Metabolic processes and the cell cycle were distinguished

318 by clusters 3 and 4, highlighting the biological significance of the degree of downregulation.

319 Finally, the genes with significantly different trajectories in TM account for a small but distinct 320 subset of these synergistic biological processes (data not shown). Together, these data indicate

321 that monotonic dysregulation dominates gene behavior and triggers important biological

A Cluster 1 (2253 genes) Cluster 2 (421 genes) Cluster 3 (1709 genes) Cluster 4 (718 genes) 5 T 4 M T+M 3 TM 2

-1 Log2 CPM (normalized by DMSO) -2

-3 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 Time (hours) B FTH1 CYP1A1 SCD FASN 6 5

3 2 1 0 −1

Log2 CPM (normalized by DMSO) Log2 CPM (normalized by −2

−3 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 0 3 6 9 12 24 Time (hours)

metabolism*

autophagy†

transcription

signaling

immunity* - log

toll-like receptor 10 ( FDR)

cytokine

apoptosis*

ER stress/ unfolded protein

cell response nuclease growth* estrogen† cell cycle* Cluster 1 2 3 4

Figure 3. Differentially Expressed Genes have Different Time Courses A) Mean and standard deviation of gene expression in four clusters identified according to their similarity in expression in T, M, and TM. B) Examples of genes in each cluster with significantly different trajectories in TM than the monotherapies. C) Enrichment of the same biological processes as in Figure 2F in the clusters. bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

322 processes, which implies that the early transcriptional responses might be sufficient to predict

323 synergy.

324 325 Synergistically spliced and expressed genes are different

326 We studied splicing by examining the relative exon usage for each gene. Combination treatment 327 TM induced unique patterns of relative exon usage, compared to DMSO, T, and M. For

328 example, many exons were less used in TM, consistent with an exon skipping modality of

329 alternative splicing (Figure 4A). As with differential gene expression, most differentially spliced

330 genes in TM were synergistic, i.e., not differentially spliced in either monotherapy (Figure S5A).

331 This was not the case with the combinations involving W (Figure S5B-C) where the differentially

332 spliced genes in MW and TW had substantial overlap with the differentially spliced genes in W. 333 However, these synergistically spliced genes were generally distinct from the SEGs (Figure 4B 334 and Figure S5D-F). Despite this distinction, the number of synergistically spliced genes

335 correlated with the EOB score (r=0.73, p=0.002; Spearman rs = 0.75, p=0.0017) over all 336 treatments and time points (Figure 4C), as with the SEGs (Figure 2D). These data suggest that

337 expression and splicing represent two separate mechanisms driving phenotypic synergy. 338 339 Synergistic activation of transcription factors

340 We next examined how regulation of the transcriptome can be affected synergistically. We 341 focused on the MCF7 data for this analysis as we were able leverage a robust pre-existing

342 MCF7-specific transcriptional network ( Woo et al., 2015). Research has shown that the activity 343 of a transcription factor (TF) can be inferred from expression of its targets ( Alvarez et al., 2016;

344 Lefebvre et al., 2010). Because activity of a TF may be affected in many ways, including 345 post-translational modification, co-factor binding, and cellular localization, this approach is a 346 more robust measure of activity beyond simply measuring expression of the TF itself. We

347 utilized a conservative method for this analysis that distinguishes positive effector and negative 348 effector (repressor) functions of a TF (Figure S6A; see Methods). Of the 1,101 TFs studied,

349 most of the differentially active (DA) ones were uniquely active as a positive effector, suggesting 350 that much of the response to these drugs is the result of upregulation of genes, and positive 351 TF-gene interactions (Figure S6B-C).

352

bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certifiedColor Key by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. and Histogram Top 100 synergistic spliced exons TM 12hrs Count A B 0 4 8 12

−1 0 1 Row Z−Score

hsa−mir−7515 YTHDC2 UBN1 KIFC3 MED16 RUNX1T1

NUP214 NFXL1 PSEN1 FGFR1 FASTKD1FGFR1 FGFR1 CKAP5

MYO15A MYO15A ARMC9 SLC25A17 ANK2

SNRNP200 RUNX1T1 ATG4B SIGLEC10 AP1G1 DNAJC7 TYK2 TEX26−AS1 HDAC9 C11orf49HDAC9 AP1G1 TBL1XR1 DHX29 SMG6 ASTN2 DCTN2 RP4−717I23.3 FGFR1FGFR1 CHID1 DTNB GRM8   C

ANK2 TEX26−AS1 RAI14 AP1G1  ATP13A2 HDAC8HDAC8 PRCP Correlation = 0.73 PRCP ANK2  AC073043.1 LRP1 SSBP2 ARIH2  PAPOLG  ATG13 MIR663A PAPOLG  RUNX1T1 GPS1 WSCD2 ATP13A5  MTFR1 SYNE1 ITGA3 FADS3 MTFR1  ARMC9 

WSCD2 

M T DMSO TM 

M T DMSO TM   Color Key  and Histogram  Top 100 synergistic spliced exons TM 12hrs  Count  0 4 8 12

−1 0 1  Row Z−Score 

hsa−mir−7515 YTHDC2 UBN1 KIFC3 MED16 RUNX1T1

NUP214 NFXL1 PSEN1 Figure 4. New Differential Splicing Emerges in Drug Combination TM and is Distinct from Differential ExpressionFGFR1 A) Top 100 synergistically FASTKD1 FGFR1 spliced exons in combination TM at 12 hours. B) Number of synergistically expressed and synergisticallyCKAP5 spliced genes in TM over time; MYO15A MYO15A ARMC9 shaded areas correspond to the Venn diagram for 3 hours. C) Relationship of Excess Over Bliss scoreSLC25A17 with the number of synergistically ANK2 spliced genes. (See also Figure S5.) SNRNP200 RUNX1T1 ATG4B SIGLEC10 AP1G1 DNAJC7 TYK2 TEX26−AS1 HDAC9 C11orf49 AP1G1 TBL1XR1 DHX29 SMG6 ASTN2 DCTN2 RP4−717I23.3 FGFR1 CHID1 DTNB GRM8

ANK2 TEX26−AS1 RAI14 AP1G1 ATP13A2 HDAC8

PRCP PRCP ANK2

AC073043.1 LRP1 SSBP2 ARIH2 PAPOLG ATG13 MIR663A PAPOLG RUNX1T1 GPS1 WSCD2 ATP13A5 MTFR1 SYNE1 ITGA3 FADS3 MTFR1 ARMC9

WSCD2 M T DMSO TM bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

353 Similarly to the differential expression and differential splicing results, most differentially active 354 transcription factors (DATFs) in TM were not DA in the monotherapies T and M (Figure 5A).

355 Conversely, most DATFs in TW and MW were also DA in W (Figure 5B-C). The majority of 356 DATFs over all treatments and times were produced from genes that were differentially 357 expressed or differentially spliced (Figure 5D). However, some instances of DATFs did not

358 correspond to differential expression or splicing and may represent TFs that become DA by 359 mechanisms not captured by RNA-seq, including some that have a known connection to cancer

360 treatment or to biological processes identified in Figure 2F. For example, ATF4, one of the top 361 DATFs in TM, is not differentially expressed nor spliced, and is a key regulator of the response 362 to endoplasmic reticulum stress ( Pakos-Zebrucka et al., 2016).

363 364 Examining TF activity over time, we found that most DATFs, once DA, tend to remain so at later

365 time points. This time course in TM was distinct from T and M (Figure 5E), whereas those of W, 366 TW, and MW were very similar (Figure S6D). In addition, the patterns of differential TF activity 367 were remarkably similar in T and M, and in fact these two monotherapies had a higher

368 correlation in differential activity values of significant TFs than either of the W pairings 369 (Spearman r at 12 hours: 0.8 in T and M, 0.1 in T and W, 0.4 in M and W), echoing the

370 differential expression data (Figure 2E). Using the set of DATFs in at least one time point in T, 371 M, and TM (Figure 5E), we examined the enrichment of their target sets in the temporal gene 372 clusters identified by k-means clustering (Figure 3). The genes in each cluster are significantly

373 enriched in distinct TF target sets, suggesting that the temporal patterns are regulated by 374 different TFs (Figure 5F).

375 376 TF activation in monotherapies can account for synergistic gene 377 expression in combinations via a TF activation cascade

378 We next asked how the combination of T and M gives rise to the synergistic activity of TFs in 379 TM. We hypothesized that DATFs in T and/or M could alter the activity of other TFs when both

380 drugs are administered together. We examined two possible mechanisms by which this could 381 happen in combination TM. First, d istinct D ATFs in each monotherapy may converge as 382 regulators of other TFs when the two monotherapies are combined. This is an “AND” model for

383 the activation of a TF, in that both TFs need to be active in the combination for the activation of 384 their targets. Alternatively, t he same DATF in T and M may be more strongly DA in TM due to

A B C

Mefloquine Withaferin

fen MW Withaferin TW

D F

Adjusted Transcription Time p-value Factor State (hours) (lowest) ZC3H11A 3, 6 5.76E-15 ATF4 3, 6 3.02E-12 TSC22D3 12, 24 6.67E-09 CREBBP 3, 9 1.48E-08 Di MORC3 3 4.26E-08 MYBL2 6 3.14E-07 GABPB1 9, 12 4.07E-07 BAZ1B 12 7.93E-07 SLC2A4RG 12 1.32E-06 ZNF267 6, 12 1.52E-06 ZNF185 6 1.56E-06 UBP1 6, 12 1.62E-06 HMGA1 24 1.62E-06 ELF4 24 2.34E-06 BAZ2B 6 3.56E-06

E Activators Active FDR) ( 10 log - Repressors Inactive Cluster 1 2 3 4

Treatments T M TM Time (hours) 0 3 6 3 24 -log10(FDR)

Figure 5. New Differentially Active Transcription Factors Emerge in Combination TM A-C) Number of DATFs over time with Venn diagrams at 3 hours in A) T, M, and TM, B) T, W, and TW, and C) M, W, and MW. Area represented in each color matches the number of genes in the corresponding color of the Venn diagram; blue areas represent synergistic TFs. D) All instances of DATFs according to the differential expression or splicing status of each TF in the corresponding treatment and time point. The top 20 most significant DATFs not differentially expressed nor spliced are listed. All 20 are positive effectors. Arrows: up = activated, down = inactivated. E) Heatmap of DATFs over time in T, M, and TM at 3-24 hours. Color intensity reflects the degree and direction of enrichment by Fisher’s Exact test with red for activation and blue for inactivation. Only significant instances are shown. F) Enrichment of gene clusters from Figure 2F with sets of TF targets. Color intensity reflects the degree of enrichment by Fisher’s Exact test. (See also Figure S6.) bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

385 the combined activating effects of the two monotherapies. This dose enhancement mechanism 386 in the combination will be called the “double-down” model. We also assessed TFs that are

387 linked through the network to those explained by these AND and double-down models in the 388 same time point, as multiple rounds of transcriptional effects could occur within 3 hours (Figure 389 S6; L ópez-Maury, Marguerat, & Bähler, 2008).

390 391 We examined the potential effect of these two mechanisms on synergistic TFs and SEGs

392 (Figure 6, Figure S6). At each time point, we identified the synergistic TFs that could have 393 resulted from the AND mechanism (converging red and blue arrows in Figure 7), and the 394 double-down mechanism (magenta arrows). At time 3 hours, for example, there are two TFs

395 that are active in T, M, and TM: M YC and K LF10. These TFs are connected through the network 396 to 9 TFs (Figure 6) active in TM (but not in T or M). These TFs are in turn connected to 16

397 additional TFs (Figure S6) active in TM (but not in T or M), giving a total of 25 TFs accounting 398 for 42% of all synergistic TFs. Because there was no active TF in T alone, there was no AND 399 mechanism at work. At 9 hours, 2 new TFs become active in T alone, 18 in M alone and 3 in

400 both T and M, accounting for 12 new synergistic TFs: 6 via the AND mechanism (purple), 4 via 401 the double-down mechanism, and 2 additional TFs due to a combination of AND and

402 double-down models (Figure S6). At each time point after 3 hours we identified TFs connected 403 to TFs identified at the immediately previous time point (vertical arrows). Over all time points, 404 the double-down model alone can explain 83 synergistic TFs, the AND model only explains 12

405 TFs, and mixed AND and double-down explain 4. In total, this cascade of TF activation 406 accounted for the majority of synergistic TFs at all time points, with 88% of the synergistic TFs

407 at 24 hours explained by the cascade of activation initiated by the 2 TFs activated at 3 hours in 408 both T and M. The number of TFs arising from TFs synergistically activated at previous time 409 points was substantial and accounted for the majority of identified TFs after 3 hours.

410 411 We next asked how the AND and double-down mechanisms, along with the activation of

412 synergistic TFs resulting from them, affected the larger group of SEGs. Here, we identified 413 genes potentially affected by the AND and double-down mechanisms, as well as those 414 connected to the newly identified TFs at the current and previous time point. At 3 hours, 29

415 SEGs can be ascribed to the double-down mechanism, and 146 are direct targets of the newly 416 explained TFs (Figure 6). In all, this accounts for 175 (46%) of all SEGs. By 12 and 24 hours,

417 the vast majority (78% and 79% respectively) of SEGs were explained by this cascade.

Time (hours) Differentially Active Transcription Factors Synergistic Genes

0 3 T 2 9 25 (42%) 29 175 (46%) 2 M 146

11 1 12 63 (53%) T 4 65 715 (53%)

1 6 20

M 640

56 3 5 50

2 93 (65%) T 6 40 1218 (65%) 9 3 6 36 18

M 1084

62 3 22 2 22 10

2 T 22 139 (85%) 12 10 168 1779 (78%) 11 15

M 1546

48 2 39 10 54 79 8 T 131 (88%) 277 24 14 26 1886 (79%) 1 6

5 M 1431 TM

Figure 6. Transcription Factors Become Differentially Active in a Time-Dependent Cascade in TM The number of DATFs or SEGs at 3-24 hours are shown as bubbles. Blue, red, and white bubbles represent DATFs in T, M, and TM, respectively. TFs (gray bubbles) and SEGs (green bubbles) shown are “explained” by the following mechanisms: double-down mechanism at the same (magenta arrow and number) or previous (angled magenta arrow) time point, the AND mechanism at the same (converging blue and red arrows and purple number) or previous (angled converging blue and red arrows) time point, or by connection to another TF “explained” by one of these mechanisms at the same (see Figure S7), or previous (vertical arrows) time point. The total number and percentage of TFs or SEGs in TM meeting any of these criteria is shown. bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

418 Together, these data suggest that T and M act in concert, mostly through the double-down 419 mechanism, to trigger a transcriptional cascade that results in substantial differential activation

420 of synergistic TFs and genes not seen in either monotherapy. 421 422 Predicting drug synergy in an independent dataset

423 We have observed that correlation of gene expression of monotherapies is associated with 424 phenotypic synergy in MCF7 cells treated with our three combinations (Figure 2E). We next

425 wished to test the generalizability of this association by leveraging the independent DREAM 426 Challenge dataset ( Bansal et al., 2014), which utilized microarray data from LY3 DLBCL cells

427 treated. Of the 91 drug pairs, none of the synergistic combinations have a correlation smaller 428 than approximately 0.4 (Figure 7A). Indeed the average correlation for the pairs with EOB > 2.5 429 (at which the average EOB in three replicates is larger than zero by more than the standard

430 error) is 0.48 which is statistically significantly (t-test p=1.0E-8) larger than the average 431 correlation of 0.3 for pairs with EOB < -2.5. These data suggest that correlation of

432 monotherapies is a necessary, but not sufficient, condition for synergy in multiple contexts. We 433 note that unlike in our dataset that is more limited in breadth, correlation of monotherapies and 434 EOB are not linearly correlated in the DREAM data. Figure 7B outlines a hypothetical model for

435 this relationship. Finally, we used correlation to predict synergy and compared these results to 436 those of DIGRE, the best performing method in the DREAM Challenge ( Goswami et al., 2015),

437 in predicting the 16 synergistic drug pairs out of the total 91 pairs in the DREAM dataset. 438 Correlation outperforms DIGRE in AUROC (Figure 7C) and AUPR (Figure 7D), as well as the 439 Probabilistic Concordance Index used in the DREAM Challenge paper (correlation: 0.63;

440 DIGRE: 0.61). This simple calculation can robustly predict synergistic drug combination pairs 441 given transcriptomics information of the monotherapies.

442 443 Discussion

444 In this paper we report the first analysis of gene expression data taken from cells after treatment 445 with monotherapies and their combinations in a detailed time course, to elucidate the 446 transcriptional mechanisms underlying synergistic drug interactions. We studied three drug

447 combinations on MCF7 breast cancer cells: tamoxifen and mefloquine (TM), tamoxifen and 448 withaferin (TW), and mefloquine and withaferin (MW). Of these three combinations, TM was

449 dramatically synergistic (Figure 1C,F). This combination was selected from a high-throughput

A C

B drug 1 drug 2 D

Additive Gene ? Figs. 2E, 7A Expression Correlated Gene Expression

“double-down” Fig. 6 mechanism No Phenotypic Synergy transcriptional Figs. 5, 6 cascade

Pro-Survival or ? Synergistic Gene Other Gene Figs. 2A-D, 4 Expression and Splicing Expression synergistic pathway activation

Pro-Cell Death Figs. 2F, 3C Gene Expression synergistic cell death

Phenotypic Synergy Figs. 1C-E, S1A-G

Figure 7. Correlation of Monotherapies is Associated with Synergy in an Independent Dataset A) Relationship between Excess Over Bliss (EOB) for 91 drug pairs and the correlation between the gene expression of LY3 DLBCL cells treated with corresponding monotherapies in the DREAM dataset. The inset indicates the distribution of correlations for pairs with EOB < -2.5 and EOB > 2.5. B) Hypothetical model for the relationships between monotherapy correlation, SEGs, and synergy. Boxed nodes represent phenomena we directly measured in this study. C-D) ROC (C) and PR (D) for classification of synergistic drug pairs using expression correlation and DIGRE. bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

450 screen. A mechanistic rationale for its efficacy is not obvious and the known target processes of

451 T (estrogen signaling; S hiau et al., 1998) and M (autophagy; S harma et al., 2012) are different. 452 However, the effect of M on estrogen receptor target gene sets (Figure 2F, Figure S4E) 453 indicates a moderate anti-estrogen effect, akin to the effect of recently developed novel

454 quinolone derivative estrogen receptor antagonists ( X. Li et al., 2019; Tang et al., 2014, 2016). 455 This suggests an unexpected overlap in the targets of T and M, even if estrogen receptor

456 represents an “off-target” of M, rather than a primary target, and may explain the high gene 457 expression correlation we observed. Although these data and the i n vitro s ynergy of TM makes 458 it an attractive candidate for further i n vivo studies, we are not aware of clinical studies on it in

459 breast or other cancers. The experiments required to validate the targets and synergistic 460 mechanisms of TM and its i n vivo effect would be beyond the scope of this study. Our aim here

461 was to shed light on the transcriptional response of the combination in terms of the 462 monotherapies. 463

464 We have explored the regulation of synergy by integrating our gene expression data with an 465 MCF7-specific transcriptional network which allowed us to estimate the differential activation of

466 TFs. Possibly due to overlapping target sets of T and M, we find that TF activity is remarkably 467 correlated between T and M treatments at all time points, resulting in a considerably higher 468 correlation in gene expression for this drug pair than the other two drug pairs we examined. This

469 correlated TF activation in response to the T and M results in a “double-down” effect in the 470 combination, where a TF activated by both drugs is reinforced in its activation in the

471 combination at early time points, beginning with early response TFs MYC and KLF10 ( Tullai et 472 al., 2007). M YC is a proto-oncogene that has been implicated in regulating the unfolded protein 473 response after prolonged tamoxifen treatment ( Shajahan-Haq et al., 2014), suggesting it may

474 play a role in the ER stress we observed in response to tamoxifen treatment. Conversely, 475 KLF10 is a tumor suppressor that represses MYC expression in healthy cells and is involved in

476 repressing proliferation and inducing apoptosis ( Ellenrieder, 2008). It may therefore act to check 477 unregulated M YC expression and facilitate the induction of apoptosis. 478

479 The action of these TFs triggers a transcriptional cascade that expands over time and results in 480 the emergence of a massive number of SEGs (DEGs in the combination but not in either

481 monotherapy) not recapitulated by increasing monotherapy dose. In our study, we find that a 482 high number of SEGs is strongly associated with the synergistic combination in MCF7, whereas

483 all combinations produced SEGs in LNCaP, only one of which resulted in phenotypic synergy

484 (Figure 2D). The data suggest that SEGs are a sensitive, but not specific, molecular indicator of 485 synergistic processes in the combination, which in the case of TM includes pro-cell death 486 processes. This phenomenon is distinct from the relationship between differential expression

487 and cell death, which can reflect processes triggered by single agents, unrelated to the behavior 488 of combinations.

489 490 These SEGs contributed to specific biological processes that are likely responsible for the killing 491 effect of the combination TM, including activation of intrinsic apoptosis in response to

492 endoplasmic reticulum stress and cell cycle arrest. While T promotes apoptosis ( H. Chen, 493 Tritton, Kenny, Absher, & Chiu, 1996), cells are rescued in part by the pro-survival activation of

494 autophagy ( Samaddar et al., 2008), which degrades and recycles metabolites and other cellular 495 constituents including depolarizing mitochondria ( Klionsky & Emr, 2000). Autophagy is enriched 496 in T treated cells, likely in response to the unfolded protein response triggered by ER stress

497 (Klionsky & Emr, 2000; Figure 2F, Figure S4E), and perhaps accounting for the poor efficacy of 498 T in the first 24 hours (Figure 1C,F). While we did not observe transcriptomic evidence of this in

499 our analysis, research indicates that treatment of with M (an antimalarial agent) alters regulation 500 of autophagy in a cell-type specific manner ( Sharma et al., 2012; Shin et al., 2015, 2012). This 501 effect may compromise mitochondrial recycling resulting in lower ATP levels ( Wang et al., 2011).

502 503 When treating cells with T and M simultaneously, we expect that the pro-survival effects of the

504 autophagy pathway will be abrogated by M, leading to a synergistic shutdown of metabolic 505 processes, and cell death by apoptosis (Boya et al., 2005). Indeed, we observed synergistic 506 changes in apoptosis and autophagy in both cell lines upon TM treatment (Figure 2F, Figure

507 S4E). The large numbers of SEGs we observed in all combinations in LNCaP cells (Figure 2E) 508 allowed us to study the distinction between SEGs in combinations with phenotypic synergy and

509 those without. W, TW, and MW all quickly downregulated biogenesis of RNA and protein, TW 510 upregulated RNA metabolism, and MW upregulated cholesterol metabolism, which may be 511 physiologic responses to ER stress (Christian & Su, 2014; Faust & Kovacs, 2014), whereas

512 cells in TM regulated these processes more slowly. Perhaps through the combined protective 513 effects of synergistically upregulating autophagy and downregulating biogenesis, cells treated

514 with the W combinations were able to recover from an initial upregulation of intrinsic apoptosis 515 in response to ER stress. In TM however, the upregulation of autophagy and downregulation of

516 biogenesis were not as robust, and cells gradually and synergistically downregulated

517 metabolism and upregulated intrinsic apoptosis in response to ER stress (Figure S4E). These 518 data indicate that drug combinations can induce SEGs that represent either protective

519 responses or cell death, only the latter of which results in phenotypic synergy. This suggests 520 SEGs are a necessary, but not sufficient, condition for synergy. 521

522 In MCF7, cell competition induced by varying levels of the synergistically active TF MYC may 523 activate the synergistically upregulated IRAK2 and its NFkB effectors (N FKB1, CASP8, and

524 NFKBIA are upregulated in TM), triggering apoptosis in the less fit cells (Meyer et al., 2014). 525 Cells that are dying or undergoing ER stress produce damage-associated molecular patterns 526 (Schaefer, 2014), which activate TLR3 signaling through a MYD88- independent (M YD88 and its

527 adaptor IRAK1 are synergistically downregulated in TM) and TRIF- mediated pathway leading to 528 the activation pro-inflammatory NFkB signaling (Pandey et al., 2015). All these biological

529 processes (T LR3 signaling, MYD88- independent and TRIF- dependent regulation of cytokines, 530 NFkB signaling) are synergistically enriched in TM. The crosstalk between cellular stress 531 response and innate immune signaling likely accounts for the enrichment of immunity functional

532 classes (Muralidharan & Mandrekar, 2013). The emergence of these synergistic functional gene 533 classes was not recapitulated by increasing monotherapy dose (Figure 2F). Rather, they are

534 unique to the combination and this suggests that a focus on known targets of monotherapies is 535 insufficient to predict the effects of combinations. 536

537 The utilization of RNAseq technology allowed us to examine synergistic effects on RNA splicing, 538 an approach not previously available to studies of synergy that used microarrays. Splicing is an

539 important aspect of cancer biology as inclusion or exclusion of an exon may alter the proteomic 540 function by adding or deleting a key regulatory protein domain ( X. Chen et al., 2017). In the 541 present study, we found considerable activation of alternatively spliced genes in the combination

542 TM but not in either monotherapy. Similarly to synergistic gene expression, synergistic splicing 543 was dramatically higher in the synergistic combination TM than in the other combinations TW

544 and MW. As the genes affected by this synergistic alternative splicing were largely distinct from 545 the SEGs, this likely represents a separate molecular mechanism of synergy (Figure 4). This is 546 consistent with previous work on transcription and alternative splicing ( Pan et al., 2004). The

547 biological outcomes of this phenomenon likely depend on the specific activities of the alternative 548 protein isoforms.

549

550 We observed that there is a relationship between the number of SEGs and synergy as 551 measured by the EOB independence, and likewise between correlation between monotherapies

552 and synergy. We propose that genes affected similarly by two monotherapies at a low level are 553 likely to be synergistically expressed in the combination, by what we term the “double-down” 554 transcriptional mechanism. Thus, two monotherapies with correlated gene expression are more

555 likely to combine to cause synergistic transcriptional activity, gene expression, and cell death. 556 While previous research has indicated that synergy is context-specific ( Held et al., 2013;

557 Holbeck et al., 2017), our finding that monotherapy correlation is associated with EOB in the 558 independent DREAM dataset suggest that these mechanisms may constitute general features 559 of synergy (Figure 7A). Machine learning methods on cell line characteristics and drug

560 combination features can also be effective in predicting synergy ( Menden et al., 2018). However 561 to our knowledge, only the DREAM dataset we used has m atched post-treatment expression

562 and viability data, limiting our ability to validate our findings regarding the gene expression 563 patterns associated with synergy in other contexts. 564

565 The data indicate that while all synergistic combinations have correlated monotherapies, the 566 converse is not true: some drug pairs are correlated in gene expression, but do not generate a

567 synergistic effect. Indeed, when we combine two doses of the same drug, tamoxifen, whose 568 gene expression profiles are by nature correlated, this does not result in synergy. Correlated 569 monotherapies and SEGs appear to be related phenomena that are required for synergy, but

570 insufficient to generate it in all cases. We have generated a hypothetical model of the 571 relationship to account for these findings (Figure 7B). In the model, correlated monotherapies

572 that act through the “double-down” mechanism generate a transcriptional cascade resulting in 573 expression of many SEGs. We hypothesize that where SEGs are enriched in pro-cell death 574 genes as in our MCF7 data (Figure 2F), this leads to phenotypic synergy. However, there may

575 exist correlated drug pairs that only generate additive gene expression, or where SEGs appear 576 but represent pro-survival programs or processes unrelated to cell viability. Under these

577 conditions, correlated monotherapies would not result in a synergistic combination. We hope to 578 explore the mechanisms that distinguish non-synergistic, correlated drug pairs from synergistic

579 ones in future studies. 580

581 We have shown that gene expression correlation can be used to predict synergy with higher

582 accuracy than the best performing algorithm to date (Figure 7C-D). As the DREAM dataset

583 consists of microarrays in a lymphoma cell line, the importance of correlation appears to be

584 independent of both cell type and gene expression measurement technology. Additionally, the

585 monotonicity of our time course data in the combination (Figure 3A) indicates gene expression

586 at later time points can be predicted from earlier ones, and synergy can therefore be predicted

587 from a single time point. These rules of synergy could therefore be used as the basis for in silico

588 screening of drug pairs for synergy using existing gene expression datasets. This approach may

589 be an efficient and cost-effective precursor to preclinical studies of drug synergy.

590 591 Acknowledgements

592 We thank Mukesh Bansal, James Bieker, Ross Cagan, Jing He, and Carlos Villacorta for useful 593 discussions. We also acknowledge NIH T32 GM007280. 594 595 Author Contributions

596 Conceptualization: GS, Methodology: MEA, JELD, TS, XC, RR, CK, AC, BL, GS, Software Programming: 597 MEA, JELD, TS, XC, BL, Experiments: RR, CK, Formal Analysis: MEA, JELD, TS, XC, RR, BL, GS, 598 Writing: JELD, MEA, AC, BL, GS, Visualization Preparation: JELD, MEA, Supervision Oversight: BL, GS, 599 Funding Acquisition: AC, GS 600 601 Declaration of Interests

602 Dr. Califano is founder, equity holder, consultant, and director of DarwinHealth Inc., a company that has 603 licensed some of the algorithms used in this manuscript from Columbia University. Columbia University is 604 also an equity holder in DarwinHealth Inc. 605 The other authors have no competing interests to declare. 606 607 Methods

608 Cell Culture

609 MCF-7 (ATCC HTB-22) cells were obtained from ATCC. Cells were cultured according to 610 manufacturer’s recommendations in ATCC-formulated Eagle's Minimum Essential Medium 611 (Catalog No. 30-2003) with 10% heat-inactivated fetal bovine serum, and 0.01 mg/ml human bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

612 recombinant insulin. LNCaP cells were purchased from ATCC (Cat No CRL-1740) and stored in 613 liquid nitrogen until use. Frozen vial was quickly thawed in 37C bath, and then cells were

614 washed from DMSO by spinning in 15ml vial filled with 10mls of PBS. Cells were re-suspended 615 in RPMI media (ATCC, Cat No 30-2001) supplemented with 10% Fetal Bovine Serum (ATCC, 616 Cat No 30-2021) and plated into 75cm cell culture flask (Corning. Cat No 430641). Growth

617 media was changed every 3-4 days. After reaching confluence, cells were split at a ratio 1:6. 618

619 To split, media was removed, cells were washed with PBS, and trypsin-EDTA mix was added for 620 5 min. After detachment, cells were washed with growth media, collected into 50ml vial, spin 621 down at 1000RPM, suspended in fresh media and plated into 75cm flasks. Cells were treated

622 with Withaferin A (Enzo Life Sciences BML-CT104-0010), Mefloquine hydrochloride 623 (Sigma-Aldrich M2319-100MG) or Tamoxifen citrate (Tocris 0999) in 0.3% DMSO for the viability

624 time courses (Figure 1C-H) or 0.4% DMSO for dose-response curves (Figure S1B). 625 626 Viability

627 The cells were plated at 10,000 cells per well in a clear bottom black 96w plate (Greiner Cat. 628 No. 655090) and a white 96w plate (Greiner Cat. No. 655083) then they were placed in an

629 incubator. After 24hrs, the plates were removed from the incubator and treated with drugs using 630 the HP D300 Digital Dispenser. After the targeted drug treatment times, 100uL of Cell-Titer-Glo 631 (Promega Corp.) was added to the wells in the white 96w plate and shaken at 500rpm for 5

632 minutes. The plate was then read by the Perkin Elmer Envision 2104 using an enhanced 633 luminescence protocol to count the number of raw luminescent units per well. For the black

634 clear bottom 96w plates, the plate was spun at 300g for 5 minutes and all the cell media was 635 removed. Methanol was then added at 200ul per well and let sit at room temperature for 15 636 minutes. The methanol was removed from the wells and 200uL of PBS with Hoechst 33342

637 nucleic acid stain at a final concentration of 1 uG/mL was then added to the wells. The plates 638 were then imaged with the GE Healthcare IN Cell Analyzer 2000 that is equipped with a CCD

639 camera. The IN Cell Analyzer software was used to count the number of cells detected in each 640 well to calculate the viability. Three replicates were used for the combination experiments and 641 two replicates for the dose experiments.

642 643 Calculation of Phenotypic Synergy

644 Excess over Bliss

645 Suppose a given drug combination XY inhibits IXY percent of the cells, and the X and Y 646 monotherapies inhibit IX and IY percent of the cells respectively. Note that V XY = (1 IXY ) is 647 the viability of the cells, i.e. the percentage of cells that survive after administration of drugs X 648 and Y. Then according to the Bliss model of no interaction between drugs X and Y , the

649 percentage of viable cells in the cell culture treated with combination XY is expected to be 650 V X V Y = (1 IX )(1 IY ) . This value is used for the “predicted” viability in Figure 1C-H. As a 651 result, the Excess over Bliss (EOB) independence ( Bliss, 1939) is given as 652 EOB = 100 * (V X V Y V XY ) = 100 * (IXY (IX + IY IX IY )), 653 which is the difference between the observed and expected inhibitions. EOB can take any value 654 in the interval [-100,100] and a positive EOB implies synergy, a negative EOB implies

655 antagonism and a value close to zero EOB implies additivity. By propagation of errors, the error 656 of EOB is given as:

657 2 2 2 2 Error EOB = √SEM X * (1 + IY – 2IY ) + SEM Y * (1 + IX – 2IX )

658 where SEM represents the standard error of the mean of the inhibition by a given drug. 659 660 Combination Index

661 Although it is simple to calculate, the EOB described above have some limitations as a measure 662 of synergy. For example, it may classify the combination of a drug with itself as synergistic. An

663 alternative method to quantify synergy uses as a null hypothesis the Loewe additivity model and 664 the associated quantity combination index (CI) ( T.-C. Chou & Talalay, 1984). The calculation of 665 CI index requires fitting a dose response curve to monotherapies. Therefore, one needs the

666 inhibition values for different doses of monotherapies. As a result, we could only calculate CI for

667 12, 24 and 48 hours for the TM combination and 12 and 24 hours for the TW combination 668 (Figure S1A) and only for viability measured using CTG.

669

670 Mathematically, the combination index CI is computed as 671

672 CI = Dx 1/ D1 + Dx 2/ D2 , 673

674 where D1 a nd D2 a re the required dosage of Drug 1 and Drug 2 to reach certain effect 675 (percentage cell death in this case) when both drugs administered independently. On the other

676 hand, Dx 1 a nd Dx 2 a re the dosage required to attain the same percentage of cell death when both 677 drug are given in combination. Accordingly, a CI<1 suggests synergism, CI =1 suggests additive

678 and CI>1 suggests antagonism between the drugs. We used the ComboSyn software ( T. C.

679 Chou & Martin, 2005) to compute CI. 680 681 Processing of the RNA-seq Data

682 The cells were plated at a density of 8,000 cells per well in a 96 well plate (Greiner Cat. No.

683 655083) and placed in an incubator. After 24 hours, the plates were removed from the

684 incubator and treated with drugs using the HP D300 Digital Dispenser. The cells were then 685 collected at the targeted time point by removing the media and pipetting 150uL of Qiagen Buffer

686 RLT into each well. The plates were then frozen and stored at -80C. For RNA extraction, the

687 Qiagen RNeasy 96 kit (Cat. No. 74181) was used with the Hamilton ML STAR liquid handling 688 machine equipped with a Vacuubrand 96 well plate vacuum manifold. A Sorvall HT 6 floor

689 centrifuge was used to follow the vacuum/spin version of the RNeasy 96 kit protocol. The

690 samples were treated with DNAse (Rnase-Free Dnase Set Qiagen Cat. No. 79254) during RNA

691 isolation. The RNA samples were then tested for yield and quality with the Bioanalyzer and the 692 Agilent RNA 6000 Pico Kit. The TruSeq Stranded mRNA Library Prep Kit

693 (RS-122-2101/RS-122-2102) was then used to prepare the samples for 30 million reads of

694 single end sequencing (100bp) with the Illumina HiSeq2500. Three replicates were used for the 695 combination experiments and two replicates for the dose experiments.

696 697 Generation of Gene Level Count Matrix

698 We aligned raw reads to hg19 reference genome (UCSC) using the STAR aligner (version

699 2.4.2a) ( Dobin et al., 2013). We use the featureCounts ( Liao, Smyth, & Shi, 2014) module from 700 subread package (version 1.4.4) to map the aligned reads to genes in the hg19 reference

701 genome, which provided us a gene count matrix with 38 samples and 23228 genes. To reduce

702 the noise due to low count genes, we kept genes with at least one count in at least three control 703 (DMSO) samples at any time point. We normalized the resulting count matrix using Trimmed

704 Mean of M-values (TMM) method ( Robinson & Oshlack, 2010). We output log base 2 of count

705 per million (cpm) after adjusting plates as covariates. We used the voom package ( Law, Chen,

706 Shi, & Smyth, 2014) to model the mean variance trend in our data (Figure S2A-B). For the 707 DREAM Challenge microarray data, we quality controlled and normalized it with the RMA

708 normalization methods using the R Bioconductor package affy ( Gautier, Cope, Bolstad, & 709 Irizarry, 2004).

710 711 Differential Expression Analysis

712 We used limma ( Ritchie et al., 2015) pipeline for differential expression analysis to compare

713 treatment with DMSO at respective time points. We corrected the p-values into a false discovery 714 rate (FDR) using BH procedure ( Benjamini & Hochberg, 1995) for multiple testing. To determine

715 an appropriate false discovery rate (FDR) cutoff for differential expression, we examined the

716 data for each treatment at time 0+. Time “0+” represents a treatment of less than 30 minutes, 717 during which drug is added and the cells are then immediately prepared for RNA collection. This

718 time delay between treatment and RNA collection is likely long enough to allow transcription of

719 immediate early genes. Immediate early gene expression has been shown to be induced within 720 minutes following an external stimulus ( Sas-Chen et al., 2012). In the MCF7 combination

721 experiments, the majority of the genes with low p-values at time 0+ in our data are known

722 immediate early genes ( Sas-Chen et al., 2012; Tullai et al., 2007). We selected an FDR cutoff of

723 1.0e-18 for differential expression (Figure S3A), at which the only DEGs at time 0+ over all 724 treatments are well-known immediate early genes (Table S1). For the dose experiments, in

725 which there were two replicates instead of three and thus lower p values, we selected 1.0e-5 as

726 the lowest FDR cutoff which produced at least as many DEGs as the combination experiments 727 at 24 hours in both T and M (Figure S3B). For the LNCaP combination experiments, we

728 selected 1.0e-15 as the lowest FDR cutoff for which there were no DEGs at time 0+ for any

729 treatments. We did not observe any immediate early genes with low p values at this time point 730 in any treatments in LNCaP.

731 732 Generation of Exon Level Count Matrix

733 We mapped the aligned reads to an in-house flattened exon feature file in hg19 reference

734 genome build using featureCounts from subread package (1.4.4). Flattened exon feature file

735 was generated based on gtf (hg19) downloaded from UCSC with overlapping exons from the 736 same gene removed. 737 738 Time Course Gene Expression Clustering

739 To identify the sets of genes that exhibit similar responses to T, M and TM, we clustered their 740 RNA-seq expression profiles. We considered 5101 genes that are differentially expressed for at

741 least one time point (0, 3, 6, 9, 12, or 24 hours) in at least one of the conditions (T, M and TM).

742 First, we computed the mean expression profile of each gene from the expression values of its 743 three replicates A, B, and C (log2(cpm)). We then normalized the mean expression profiles of

744 the 5101 genes by their respective response to DMSO.

745

746 Because we wanted to cluster genes that have similar response in T, M and TM, we joined the 747 vector of normalized expression values in T, M and TM for every gene. Thus, we obtain a vector

748 for each gene that contains 18 values (3 drugs * 6 time points). In order to introduce information

749 about the derivative of the expression profiles, we also joined the delta expression value 750 between each pair of consecutive time point (t3-t0, t6-t3, … t24-t12) for the three conditions.

751 Therefore, the vectors to cluster contains each 33 values.

752 753 We applied a k-means clustering algorithms to group the expression vectors into k groups. In

754 order to identify a suitable value for k, we computed the total within-cluster sum of squares for

755 values of k running from 1 to 20. We then selected k equal to 4 clusters as we observed that the

756 gain in information obtained with larger values of k was becoming considerably small. We ran 757 10’000 times the Hartigan-Wong implementation of the k-means algorithm ( Hartigan & Wong,

758 1979) provided by Matlab with a maximum number of iterations set to 1000 before selecting the

759 partitioning of the vectors that achieved the smallest total within-cluster sum of squares. 760 761 Gene Set Enrichment Analyses

762 In each treatment and time point, we separately analyzed the sets of upregulated and

763 downregulated genes for pathways and processes using the built-in Fisher’s exact test of the

764 Python package Scipy ( Jones, Oliphant, & Paterson, 2001). We assessed enrichment in all 765 gene sets of the GO_Biological_Process database downloaded from the Enrichr ( E. Y. Chen et

766 al., 2013) library repository (h ttp://amp.pharm.mssm.edu/Enrichr/#stats) and, to assess the known

767 effects of tamoxifen, we used the gene set of estrogen receptor related genes downloaded from 768 the Broad Molecular Signatures Database ( Bhat-Nakshatri et al., 2009; Liberzon et al., 2011;

769 Subramanian et al., 2005). We only performed the test where both gene sets contained at least

770 three genes and the overlap contained at least two genes; if either criterion was not met, no

771 p-value was returned, and a p-value of 1 was used for display in the figures. We then calculated 772 the false discovery rate (FDR)-adjusted p-values using the Benjamini-Hochberg method

773 available in the Python package Statsmodels ( Seabold & Perktold, n.d.). To select the gene sets

774 that may explain the synergistic gene expression seen in Figure 2 and suggest biological 775 processes involved in the synergistic drug response (Figure 1), we applied four criteria:

776 1. Synergistic gene sets were defined as those with an FDR less than 0.00001 in at least

777 one time point in TM and less than 0.01 in all time points in TM, but greater than 0.00001 in all

778 time points in TU M or greater than 0.01 in any time point in TU M . TU M refers to the union of 779 genes from T and M that are either upregulated or downregulated.

780 2. Additive gene sets were defined as those with an FDR less than 0.00001 in at least

781 one time point in TU M and less than 0.01 in all time points in TU M . 782 3. GO: Keyword searches for terms associated with each of the ten 2011 hallmarks of

783 cancer ( Hanahan & Weinberg, 2011) were performed in the Gene Ontology online database

784 (Ashburner et al., 2000). For each hallmark of cancer, the highest level ontology relevant to it 785 was selected, followed by 1-2 levels of children of that ontology that were connected by the

786 relation “is_a”, “regulates”, ”positively_regulates”, or “negatively_regulates”. From the gene sets

787 associated with all these ontologies, those with an FDR less than 0.01 in at least one time point

788 in TM were selected. Five hallmarks remained after applying this filter: metabolism, immunity, 789 cell death (only ‘apoptosis’ was significant), growth, and proliferation (only ‘cell cycle’ was

790 significant).

791 4. To assess known drug targets including estrogen signaling as a target of tamoxifen 792 (Shiau et al., 1998) and autophagy as a target of mefloquine ( Sharma et al., 2012), we included

793 four gene sets related to estrogen signaling from Broad Molecular Signatures Database and any

794 gene sets from the GO Process database containing the words “autophagy” or “estrogen”. 795 Similarly to the hallmarks of cancer, any of these sets with an FDR less than 0.01 in at least one

796 time point in TM were selected.

797 Together, the results of these four approaches comprise the gene sets shown in Figure 2F,

798 Figure 3, Figure S4D-E. 799 800 Synergistic Splicing and Exon Expression

801 We used short read splicing caller: diffSplice ( Hu et al., 2013) in the limma package (version

802 3.24.3) ( Ritchie et al., 2015) as framework to detect synergistic spliced genes at each drug

803 combination. We kept exons that have at least one read in at least one sample, and normalized 804 the expressed exon counts using the TMM method ( Robinson & Oshlack, 2010). F or each

805 combination treatment i at a given time point j, we tested synergistic exon expression (SEE) in

806 the generalized linear model of

807 SEEij = comboij + DMSOj - singlet1ij - singlet2ij 808 We performed two statistical tests to detect synergistic exons expression and synergistic spliced

809 genes. For the former, we performed exon level t-statistic test to detect differences between

810 each exon and other exons from the gene, and defined exons with FDR<0.05 as synergistically 811 expressed (the differential exon expression heatmap). For synergistic splicing, we performed

812 Simes test ( Simes, 1986) for each gene to test hypothesis of whether usage of exons from the

813 same gene differed, genes with Simes-adjusted p-values<0.05 are defined as synergistically 814 spliced genes.

815 816 Generation of the MCF-7 Gene Regulatory Network

817 The original MCF-7 network has been generated by ( Woo et al., 2015) using the network

818 inference method ARACNE2 ( Margolin et al., 2006) and 448 expression profiles for MCF-7 cell 819 line from the connectivity map database (CMAP2) ( Lamb, 2007). The original network includes

820 20,583 probes, 1,109 of which are transcription factors, and 148,125 regulatory interactions.

821 The interactions predicted by ARACNE2 are directed, unless an interaction is found between

822 two TFs, in which case two edges are included in the list (TF1 to TF2 and TF2 to TF1). 823 To obtain a network at the gene level, we applied a one-to-one HG-U133A probe to gene

824 mapping ( Lamb, 2007; Q. Li, Birkbak, Gyorffy, Szallasi, & Eklund, 2011). The mapping file used

825 has last been updated on July 2015 (v3.1.3). We filtered out edges that don’t have both nodes 826 present in the mapping list. Finally, in order to determine positive (activation) and negative

827 (repression) interactions, we calculated the Spearman correlation and the corresponding

828 p-value between each TF-target pair in the network. We then corrected the p-values for multiple 829 hypothesis testing and removed edges with low confidence level (FDR <0.05), which gave us

830 the final network with 9,760 genes, 1,101 TFs, and 48,059 regulatory interactions. For each TF

831 in the network, we defined its positively/negatively regulated targets as the genes to which there

832 exist an outgoing edge in the final network with a positive/negative Spearman correlation 833 coefficient.

834

835 Quantifying Transcription Factor Activity

836 To calculate differential activity for each TF in our network, we examined its putative targets as

837 determined by our network. We utilized a conservative method for this analysis that 838 distinguishes positive effector and negative effector (repressor) functions of a TF (Figure S6A).

839 These functions have been found to be distinct ( Foulkes & Sassone-Corsi, 1992; Partridge,

840 MacManes, Knapp, & Neff, 2016; Wray, 2003). With this analysis in mind, we performed four

841 comparisons in each treatment and time point: 842 1. Positively regulated targets of TF and upregulated genes

843 2. Positively regulated targets of TF and downregulated genes

844 3. Negatively regulated targets of TF and upregulated genes 845 4. Negatively regulated targets of TF and downregulated genes

846 For each of these comparisons, we performed Fisher’s exact test as described above for gene

847 set enrichment analysis. This resulted in two p-values for each transcription factor: one for its 848 positive effector function and one for is negative effector function. We then applied the

849 Benjamini/Hochberg false discovery rate (FDR) adjustment to all the resulting p-values over all

850 time points for the treatment.

851 We used an FDR cutoff of 0.05 to determine differential activity, and we determined the direction 852 of differential activity of each regulon type (Figure S6A) as follows:

853 1. Positively regulated targets of TF enriched in upregulated genes → positive effector

854 function activated 855 2. Positively regulated targets of TF enriched in downregulated genes → positive

856 effector function inactivated

857 3. Negatively regulated targets of TF enriched in upregulated genes → negative effector 858 function inactivated

859 4. Negatively regulated targets of TF enriched in downregulated genes → negative

860 effector function activated

861 We then analyzed each set of two FDR values for the same transcription factor, treatment and 862 time point. If positive and negative effector functions were both activated or both inactivated, the

863 transcription factor was labelled concordant. If one effector function was activated and the other

864 inactivated, the transcription factor was labelled discordant. If only one effector function was 865 differentially active, the transcription factor was labelled unique. For thirty-three transcription

866 factors in 142 cases across all treatments and time points, the same effector function was found

867 to be both activated and inactivated by the above criteria. These nonsensical results were

868 removed from further analysis, and may be due to transcription factors with an unusually large 869 number of targets.

870

871 For each of 1,101 transcription factors ( Woo et al., 2015), we identified positively regulated 872 targets and negatively regulated targets using an MCF7-specific transcriptional regulatory

873 network generated by the ARACNe network inference algorithm ( Margolin et al., 2006). These

874 two sets represent the distinct targets of each TF’s positive effector function and negative 875 effector function. We then assessed the enrichment of each set of targets in the lists of

876 upregulated and downregulated genes in each treatment with respect to DMSO. These

877 enrichment results were used to determine whether transcription factors were activated or

878 inactivated with respect to DMSO (Figure S6A). Most transcription factors were uniquely 879 differentially active in their positive effector or negative effector functions, but not both (Figure

880 S6B-C).

881 882 To account for some transcription factors having very similar sets of targets, we performed

883 Fisher’s exact test as above for all possible pairs of transcription factor target sets among those

884 that were significant at each treatment and time point. We then applied the Benjamini/Hochberg 885 FDR adjustment to all the resulting p-values. For each case where the FDR was significant, we

886 then performed Fisher’s exact test to assess enrichment of the relevant dysregulated genes in

887 each of three sets: the intersection of the two transcription factor target sets, and each of the

888 target sets individually with the intersection excluded. We then applied the Benjamini/Hochberg 889 FDR adjustment to all the resulting p-values over all cases. Finally, where one effector target set

890 was significantly enriched but the other was not, with their intersection excluded, the significant

891 target set was retained as differentially active, using the original FDR values. All the rest were 892 removed from further analysis.

893 894 Graphical Representation of the Transcriptional Cascade

895 Figure 6 shows the evolution of the active transcriptional network after introducing the two drugs

896 T and M. Figure S7 provides a more detailed representation of the mechanisms responsible for 897 the activation of the synergistic TFs. In Figure S7, each oval represents the set of TFs that are

898 activated under T, M and/or TM : (101) indicates the set of TFs active in T and TM but not in M,

899 (011) indicates the set of TFs active in M and TM but not in T, (111) indicates the set of TF active

900 in T, M and TM. Finally, (001) are TF active in TM but not in T or M. The number next to each 901 oval indicates the number of TFs in that set. In this way we can keep track of the activation of

902 synergistic TFs (001) in TM in terms of the activation of a pair of parent TFs, one in T (101) and

903 one in MM (010), or of one parent TF active also in T and M (111), and doing so at each time 904 point. For example, for the time point at 3h (Figure S7), there 2 TF active in T, M and TM that

905 belong to set (111), 2 TF active only in M and TM that belong to set (011), and no TF active in T

906 and TM but not in M (101). The middle layer of this representation contains 3 sets of synergistic

907 TFs that are only active in TM, but not in T or M (001) (grey ovals). Each of these three sets 908 include TFs whose regulators (i.e., their own TFs) are at least one TF in (111) (TF activated

909 through the double-down mechanism: left grey oval in the middle layer), or has two or more

910 parents one in (101) and the other in (011) (TF activated through the AND mechanism: right 911 grey oval in the middle layer), or has three of more parents from sets (101), (111) and (011) (TFs

912 activated through both double down and AND mechanisms: middle grey oval in the middle

913 layer). At 3 h only the double-down mechanism can explain 9 synergistic TFS, which in turn are 914 parents and can explain the activation of 16 additional synergistic TFs, as indicated in the third

915 layer of Figure S7. To the right of the construct we just discussed, there are two more pairs of

916 ovals. The first pair of contains an oval with dashed border, indicating the synergistic TFs that

917 were active at the previous time point (t=0 for 3 h) and the arrow points to the grey oval that 918 indicates how many synergistic TF ative at 3 h can be ascribed to the activation of its regulators

919 in the earlier time point. At 3 hours, both sets are empty. The rightmost pair of ovals, the bottom

920 one represents the set of TFs whose activation can not be explained using any of the above 921 mechanisms. Finally, the diagrams for 12 and 24 hours show additional sets of ovals

922 representing TFs that belong to set (101), (011) and (111) at the previous time point, and that

923 are needed to explain some of the synergistic TFs at the current time point, and whose numbers 924 are indicated in italics in the middle layer of the diagram.

925 926 Drug Synergy Prediction

927 For the correlation-based classifier, for each monotherapy pair, we calculated the Pearson

928 correlation between expression of genes that are differentially expressed in either monotherapy 929 (FDR < 0.05) in the NCI-DREAM data. We ranked the resulting correlations in descending order

930 to calculate a ranking of drug synergy. To test performance by the same measures as the

931 NCI-DREAM challenge, we used the PC-index as well as the AUCROC and AUPR for

932 synergistic drug combination. We used the code provided by the Challenge organizers to 933 calculate the PC-index. For the AUC analysis, we used the same criteria as in the dream

934 challenge for the definition of phenotypic synergy resulting in 16 synergistic drug pairs out of the

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1 Figure Legends

2 Figure 1: The Transcriptomics of Drug Combinations Mirror their Phenotypic Characteristics 3 A) Monotherapies and drug combinations used in the study. B) Workflow of molecular analysis 4 of synergy. Starburst highlights the novel component of RNAseq analysis. Question mark 5 denotes the focus of the study. C-H) Fold change over control of cell count for MCF7 cells (C-E) 6 and LNCaP cells (F-H) treated with Tamoxifen and Mefloquine (C,F), Mefloquine and Withaferin 7 (D,G), and Tamoxifen and Withaferin (E,H). Predicted viability is based on the Bliss model.

8 Excess Over Bliss (EOB) ± ErrorE OB is given for the 12, 24, and 48 hr time points (see Methods). 9 I) Average gene expression for each treatment and time point in the MCF7 combination 10 experiments (covering 108 treatment and 18 DMSO samples). G) Principal component analysis 11 of gene expression for the average over replicates at each treatment and time point in the 12 MCF7 combination and dose experiments. (See also Table S1, Figures S1-3, and Source Data 13 Files 1 and 3.) 14 15 Figure 2: Synergistically Expressed Genes, Correlated Monotherapies, and Biological 16 Processes are Associated with Synergy A-C) Number of DEGs over time in MCF7. The Venn 17 diagrams in the inset correspond to DEGs at 3 hours in A) T, M, and TM, B) T, W, and TW, and 18 C) M, W, and MW. The area represented in each color is in proportion to the number of genes in 19 the corresponding color of the Venn diagram; blue areas represent SEGs. D-E) Relationship of 20 Excess Over Bliss score with D) the number of SEGs, and E) correlation in gene expression 21 values between each pair of monotherapies. Note that some of the “pairs” from the dose 22 experiments represent the same dataset correlation with itself (i.e. T 10 μM with T 10 μM for the 23 T 20 μM “combination”) and so have correlation = 1.0 as expected, and are shown for clarity. F) 24 Enrichment of DEGs in T, M, and TM with cancer-relevant genes sets. Only gene sets enriched 25 in at least one condition (time point or treatment) are shown. “TU M ” indicates the union of 26 DEGs after in T or M. Color intensity reflects degree of enrichment by Fisher’s Exact test. * = 27 hallmark of cancer, † = drug target (see Methods). (See also Figure S4.) 28 29 Figure 3. Differentially Expressed Genes have Different Time Courses A) Mean and standard 30 deviation of gene expression in four clusters identified according to their similarity in expression 31 in T, M, and TM. B) Examples of genes in each cluster with significantly different trajectories in 32 TM than the monotherapies. C) Enrichment of the same biological processes as in Figure 2F in 33 the clusters. 34 35 Figure 4. New Differential Splicing Emerges in Drug Combination TM and is Distinct from 36 Differential Expression A) Top 100 synergistically spliced exons in combination TM at 12 hours. 37 B) Number of synergistically expressed and synergistically spliced genes in TM over time; 38 shaded areas correspond to the Venn diagram for 3 hours. C) Relationship of Excess Over Bliss 39 score with the number of synergistically spliced genes. (See also Figure S5.) 40 41 Figure 5. New Differentially Active Transcription Factors Emerge in Combination TM A-C) 42 Number of DATFs over time with Venn diagrams at 3 hours in A) T, M, and TM, B) T, W, and bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

42 TW, and C) M, W, and MW. Area represented in each color matches the number of genes in the corresponding color of the Venn diagram; blue areas represent synergistic TFs. D) All instances of DATFs according to the differential expression or splicing status of each TF in the corresponding treatment and time point. The top 20 most significant DATFs not differentially expressed nor spliced are listed. All 20 are positive effectors. Arrows: up = activated, down = inactivated. E) Heatmap of DATFs over time in T, M, and TM at 3-24 hours. Color intensity reflects the degree and direction of enrichment by Fisher’s Exact test with red for activation and blue for inactivation. Only significant instances are shown. F) Enrichment of gene clusters from Figure 2F with sets of TF targets. Color intensity reflects the degree of enrichment by Fisher’s Exact test. (See also Figure S6.)

1 Supplementary Information

2 Supplementary File 1: Viability data and calculated EOB for TM dose matrices at 12, 24, and 48 3 hours in MCF7. 4 5 Supplementary File 2: Viability data and calculated EOB for TW dose matrices at 12, 24, and 48 6 hours in MCF7. 7 8 Supplementary File 3: Time courses of TM, TW, and MW in MCF7. 9 10 Supplementary File 4: Time courses of TM, TW, and MW in LNCaP. 11 12 Supplementary File 5: Viability data and calculated EOB for TM, TW, and MW at 48 hours in 13 LNCaP. 14 15 Supplementary File 6: Viability data and calculated EOB for T and M individual dose 16 combinations in MCF7. 17 18 Supplementary Table 1: Selection of Adjusted P-Value Cutoff for Differentially Expressed Genes 19 The six most differentially expressed genes with respect to DMSO in each treatment at 0 hours

20 are shown in ascending order of their Voom score (l og10 ( FDR)). Immediate Early Genes are 21 marked in red. Differentially expressed genes according to the 10E-18 cutoff for FDR corrected 22 p-value are marked in bold. 23 24 Supplementary Figure 1: The response of MCF7 to TM is more synergistic than to TW A) 25 Combination Index for the combinations at the selected doses. B) Viability of MCF7 cells in 26 response to increasing doses of T and M compared to TM. 27 28 Supplementary Figure 2. Transcriptomic Profiles of MCF7 and LNCaP Cells Treated with 29 Combinations A) Similarity between the gene expression data of two replicates treated with TM 30 at 12 hours. B) The gene expression of one replicate treated with TM at 12 hours and another 31 replicate treated with TM at 3 hours shows differential expression beyond the replicates of (A). 32 C) Average gene expression for each treatment and time point in the LNCaP combination 33 experiments (covering 108 treatment and 18 DMSO samples). D) Average gene expression for 34 each treatment and time point in the MCF7 dose experiments. E) Principal component analysis 35 of gene expression for the average over replicates at each treatment and time point in the 36 MCF7 combination and dose experiments. See also Source Data File 2. 37 38 Supplementary Figure 3. Gene Expression Characteristics of Differential Expression and 39 Synergy A) Comparison between gene expression in treatment (y-axis) and control (x-axis), for 40 all genes over all treatments. Upregulated and downregulated genes as determined by Limma 41 with Voom are in red and blue respectively corresponding to a FDR < 1E-18, and green lines 42 represent fold change over control of 2. B) Choice of FDR cutoff for the dose experiments. C-D). 43 Percentage of differentially expressed genes that are synergistic in each combination according 44 to FDR corrected p-value at C) 3 hours and D) 12 hours. bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

45 46 47 Supplementary Figure 4. Biological Processes are Synergistic in TM in LNCaP but not in 48 Withaferin Combinations in either cell line A-C) Number of DEGs over time in LNCaP. The Venn 49 diagrams in the inset correspond to DEGs at 3 hours in A) T, M, and TM, B) T, W, and TW, and 50 C) M, W, and MW. The area represented in each color is in proportion to the number of genes in 51 the corresponding color of the Venn diagram; blue areas represent SEGs. D) Enrichment scores 52 of differentially up and down regulated genes at different time points in W, M, T, TW, and MW, in 53 MCF7 cells with the same cancer-relevant gene sets shown in figure 3. E) Hierarchical 54 clustering of enrichment scores of differentially up and down regulated genes at different time 55 points in the combination experiments in LNCaP cells with significant gene sets (see Methods). 56 In D-E, “U ” indicates the union of two genes sets, and represents the expected differentially 57 expressed genes if the interaction between drugs was additive. Color intensity reflects the

58 degree of enrichment by Fisher’s Exact test and is shown as –log1 0( FDR corrected p-value). 59 Red: M, Blue: T, Yellow: W, Orange: MW, Green: TW, Magenta: TM. 60 61 Supplementary Figure 5. Differential and Synergistic Splicing A-C) Number of differentially 62 spliced genes over time with Venn diagrams of differentially spliced genes at 3 hours in a) T, M, 63 and TM, b) T, W, and TW, and c) M, W, and MW. The area represented in each color is in 64 proportion to the number of genes in the corresponding color of the Venn diagram; blue areas 65 represent synergistic genes. d-f) Number of synergistically expressed and synergistically spliced 66 genes in d) TM, e) TW, and f) MW over time; shaded areas correspond to the Venn diagrams for 67 3 hours. 68 69 Supplementary Figure 6. Transcription Factors with Altered Activity a) Four cases of 70 transcription factor activity that were assessed to determine whether a transcription factor was 71 activated or inactivated. b) Differentially active transcription factors for each combination 72 according to the status of the positive and negative effector of each transcription factor. Unique: 73 either positive or negative effector, but not both, is differentially active; concordant: both 74 effectors are activated or both are inactivated; discordant: one effector is activated and the other 75 is inactivated. c) Differentially active transcription factors for each combination according to 76 each of the four cases in panel A. d) Heatmap of differentially active transcription factors over 77 time in Withaferin, MW, and TW at 3-24 hours. Color intensity reflects the degree and direction

78 of enrichment by Fisher’s Exact test and is shown as –log1 0( FDR corrected p-value), with 79 positive values for activation and negative values for inactivation. 80 81 Supplementary Figure 7. Cascade of Differential Transcription Factor Activity Connections 82 between differentially active transcription factors in TM based on the MCF7 network. Each 83 bubble represents a set of transcription factors that are differentially active in TM at a given 84 timepoint. The codes on each bubble represent their differential activity status in Tamoxifen (first 85 digit), Mefloquine, (second digit), and TM (third digit), where 1 is differentially active and 0 is not. 86 Synergistic transcription factors in TM (001), are categorized into “explained” bubbles (gray) or 87 not explained (white). At each timepoint, synergistic transcription factors can be “explained” by a bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

network connection to a transcription factor that is differentially active in Tamoxifen and Mefloquine (111, magenta), or to at least one transcription factor in each of Tamoxifen alone (101, blue) and Mefloquine alone (011, red), or both, resulting in the left-hand, right-hand, and middle gray bubbles, respectively, in the middle layer. The fourth gray bubble in the lowest layer represents transcription factors which have connections to transcription factors in the middle “explained” layer, but not to transcription factors in the top layer. Numbers in italics represent synergistic transcription factors that can be explained by connections to transcription factors that were active in monotherapies at the previous time point (blue, magenta, and red bubbled with dashed outlines at top of each timepoint). At the right in each time point, the dashed-outline bubble represents “explained” transcription factors in the gray bubbles at the previous time point. Synergistic transcription factors not explained by other means which have a connection to any “explained” transcription factors at the previous time point are shown in the gray bubble below the dashed-outline bubble. Finally, synergistic transcription factors that cannot be explained by any network connections are shown in the white bubble resulting from the “null” set at each timepoint. The colors in this figure correspond to figure 7, and the sum of all gray bubbles at each timepoint in this figure correspond to the single gray bubble shown at each timepoint in figure 7.

Supplementary Table 1: Selection of Adjusted P-Value Cutoff for Differentially Expressed Genes.

T_0 TM_0 M_0 TW_0 MW_0 W_0 FOS -6 BCAN -17 ATXN2 -3 EGR1 -54 EGR1 -53 EGR1 -53

MYC -3 FOS -17 JUN -2 JUN -26 JUN -28 IER2 -20

TOB1 -3 VIM -17 ZNF592 -1 IER2 -23 IER2 -17 JUN -19

KLF4 -2 ETS1 -15 ZHX2 -1 JUNB -17 PDCD -17 C17O -16 7 SGK1 -2 MSN -13 SCAF4 -1 PDCD7 -16 ZFP36 -15 ZFP36 -16

PRDM1 -2 NCAN -10 NAT8L -1 C17ORF91 -16 JUNB -14 PDCD7 -15

Supplementary Figure 1 A B Combination Index Tamoxifen Drug 2 12 hours 24 hours 48 hours Concentration (Concentration)

20.00 µM Mefloquine (10.00 µM) 0.73 0.61 0.23 Withaferin (4.96 µM) 2.38 0.72 NA bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Supplementary Figure 2

A B Gene expression in TM at 12 hours, replicate A TM at 12 hours, replicate Gene expression in Gene expression in TM at 12 hours, replicate A TM at 12 hours, replicate Gene expression in

Gene expression in TM at 12 hours, replicate B Gene expression in TM at 3 hours, replicate B C D

time

G bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Supplementary Figure 3 A B DEGs in dose experiments by FDR

C D bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Supplementary Figure 4 A B C

time

time bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Supplementary Figure 5

A B C

Ta e

rin in Tam Mef

D E bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Supplementary Figure 6 A Positively regulated targets of TF Negatively regulated targets of TF C

Activated Positive Effector Inactivated Negative Effector

TF TF Genes upregulated vs. DMSO

Inactivated Positive Effector Activated Negative Effector

TF TF Genes downregulated vs. DMSO

D bioRxiv preprint doi: https://doi.org/10.1101/846915; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Supplementary Figure 7

0 2 2 0 1 1 6

101 111 011 001 Ø 101 111 011

2 10 15 93

101 111 011 001 Ø

001 9 001 0 001 0 001 001 3 hours 0 34 12 hours

2 0 3 001 22 001 001 0 001 001

001 62 24

001 1 4 20 25 6 101 111 011 001 Ø 44

001 12 001 0 001 0 001 001 1 5 11 6 hours 24 55 101 111 011

8 14 5 139 001 7 101 111 011 001 Ø 27 011

2 3 18 63 24 hours 8 2 2 101 111 011 001 Ø 001 26 001 0 001 1 001 001

48 17

001 4 001 2 001 6 001 001 001

9 hours 3 56 51 41

001