Chemical-genetic interaction profiling in model organisms has proven powerful in providing insights into compound mechanism of action and gene function. However, identifying chemical-genetic interactions in mammalian systems has been limited to low-throughput or computational methods. Here, we develop Quantitative and Multiplexed Analysis of Phenotype by Sequencing (QMAP-Seq), which leverages next-generation sequencing for pooled high-throughput chemical-genetic profiling. We apply QMAP-Seq to investigate how cellular stress response factors affect therapeutic response in cancer. Using minimal automation, we treat pools of 60 cell types—comprising 12 genetic perturbations in five cell lines—with 1440 compound-dose combinations, generating 86,400 chemical-genetic measurements. QMAP-Seq produces precise and accurate quantitative measures of acute drug response comparable to gold standard assays, but with increased throughput at lower cost. Moreover, QMAP-Seq reveals clinically actionable drug vulnerabilities and functional relationships involving these stress response factors, many of which are activated in cancer. Thus, QMAP-Seq provides a broadly accessible and scalable strategy for chemical-genetic profiling in mammalian cells. Identifying chemical-genetic interactions in mammalian cells is limited to low-throughput or computational methods. Here, the authors present QMAP-Seq, a broadly accessible and scalable approach that uses NGS for pooled high-throughput chemical-genetic profiling in mammalian cells. Likewise, the function of a gene can be inferred by comparing its chemical-genetic interaction profile (the quantitative landscape of the effects of this particular gene on the efficacy of a panel of compounds) to the profiles of genes with known functions6.Chemical–genetic interaction profiling in model organisms, such as yeast, has emerged as a powerful strategy to reveal functional insights into compounds, genes, and cellular processes. In these studies, the mechanism of action of a compound can be deduced by comparing its chemical–genetic interaction profile  to the profiles of compounds with known cellular targets to identify the most similar profiles7 and define mechanisms of drug resistance9. Even focused studies interrogating limited numbers of chemical–genetic interactions can reveal critical insights. For example, one recent study demonstrated that the efficacy of a handful of clinical compounds was unaffected by knockout of their putative targets10, highlighting the power of using chemical–genetic approaches to validate on-target activity of drug candidates. Despite their utility, these studies are low-throughput and thus limited to investigating small numbers of compounds.The development of highly specific and efficient genetic perturbation tools based on CRISPR-Cas9 has enabled similar types of chemical–genetic studies in mammalian systems, albeit at much smaller scales than in model organisms. Most often, chemical–genetic studies in mammalian systems involve genome-scale loss-of-function screens against one compound over the course of several weeks to identify drug targets11, describes cell death resulting from the combination of a genetic variant and a chemical perturbation, where each individual perturbation is viable. By exploiting genetic variants  that differentiate tumor from normal tissue, synthetic lethal interactions provide a therapeutic window for selectively targeting cancer cells. The potential of synthetic lethality is best exemplified by the development and FDA approval of PARP inhibitors for patients with BRCA-mutated ovarian, breast, and prostate cancers13. There is also value in identifying synthetic rescue interactions, where a cytotoxic compound has reduced efficacy in the presence of a particular genetic variant, thus providing insights into drug resistance mechanisms14. The only existing strategies for identifying clinically relevant chemical–genetic interactions for more than a handful of genetic variants and compounds rely entirely on predictive approaches. Some of these predictions are based on genetic or chemical–genetic interactions identified in yeast16, while others, such as the Cancer Cell Line Encyclopedia17, Genomics of Drug Sensitivity in Cancer20, Cancer Therapeutics Response Portal23, and PRISM25, are based on computational methods that correlate genetic and molecular features of human cancer cell lines with drug response. While these correlative approaches have been useful, they are limited by the fact that many features are rare and lack sufficient representation—or are not even present—in current cancer cell line collections, reducing the statistical power to detect significant correlations. Related to this, even correlations involving more common features are confounded by the multitude of additional features that also distinguish each cell line. Thus, these approaches still require direct experimental validation, ideally in a manner that tests individual features along with their corresponding isogenic controls in relevant mammalian models.There is a growing interest in identifying synthetic lethal and synthetic rescue chemical–genetic interactions that can serve as the basis for cancer therapeutic strategies. Chemical–genetic synthetic lethality, a concept rooted in classical genetics31, but are not easily druggable32, we reasoned that QMAP-Seq could be used to identify synthetic lethal chemical–genetic interactions dependent on the activation status of these factors. Furthermore, because the individual branches of the proteostasis network are typically studied in isolation, and the functional relationship within and between the branches remains largely unexplored, we postulated that QMAP-Seq could also be used to provide functional insight into the proteostasis network. We first perform QMAP-Seq with one cell line and demonstrate that it generates precise and accurate quantitative measures of compound efficacy that are concordant with established cell viability assays based on live-cell imaging. We then expand QMAP-Seq to multiple cell lines, which enables the parallel measurement of 86,400 cell viability phenotypes in a single experiment. Altogether, we identify 60 sensitivity interactions and 124 resistance interactions and validate a subset of these interactions individually using an established metabolic-based cell viability assay. This work illustrates the power of systematic, high-throughput chemical–genetic profiling in mammalian systems.Here, to systematically and directly measure the contribution of individual genes to acute drug response, we devise quantitative and multiplexed analysis of phenotype by sequencing (QMAP-Seq). Unlike most chemical–genetic strategies in mammalian systems, QMAP-Seq is characterized by short-term compound treatment, which better recapitulates the timing of most high-throughput drug screening assays and enables testing of thousands of compounds in parallel. As proof-of-concept, we apply QMAP-Seq to the protein homeostasis (proteostasis) network, a critical set of cellular stress response factors that maintain proper protein function from synthesis to folding to degradation. Because individual proteostasis factors are activated to varying degrees across tumors to cope with cancer-associated and drug-induced stressERN1), XBP1, ATF3, ATF4, ATF6), oxidative stress response (NRF2 (NFE2L2), KEAP1), and in autophagy (ATG7). As a control to validate that QMAP-Seq can detect known chemical–genetic interactions, our library also contained an sgRNA targeting SLC35F2, a solute carrier required for cellular uptake of the cytotoxic compound, YM15533. We engineered MDA-MB-231 triple-negative breast cancer cells with these 11 single-gene knockouts and a pool of five non-targeting (NT) sgRNA controls. Because constitutive expression of Cas9 can result in off-target effects34 and cell toxicity35, we designed a system with doxycycline-inducible Cas9, providing temporal control over gene knockout , unfolded protein response . We next amplified the 768 samples, corresponding to distinct compound-dose-replicate combinations, using unique sets of i5 and i7 indexed primers  , demonstrating the high degree of accuracy of QMAP-Seq  Fig. . To comp14) Fig. . As expeSeq Fig. . Taken tBecause the genetic and epigenetic background of a cell line can impact therapeutic response, we next expanded QMAP-Seq to multiple cell lines Fig. . We seleTo ensure that each cell line would be similarly represented in our assay despite different doubling times, we measured the relative abundance of each of the five cell lines grown in heterogeneous pools. We prepared five pools, each containing 20% of one cell line expressing ZsGreen and 20% of each of the other four cell lines expressing dTomato Fig. . We co-cAfter pooling the five cell lines each possessing our panel of proteostasis factor knockouts at the optimized pooling ratio, we performed the QMAP-Seq workflow Fig. . Seven dP = 6.3e−3)      using QMAP-Seq. As expected, the ER+ cell line ZR-75-1 was preferentially sensitive to 4-Hydroxytamoxifen, and the HER2+ cell line SKBR3 was preferentially sensitive to Lapatinib Fig. . In addir = 0.9324)  Fig. . Thus, nP < 0.05) and 124 cell line–gene–drug interactions that conferred compound resistance (AUC difference >25 and P < 0.05) compared to sgNT (see “Methods” for details)  Fig. . To charls) Fig.  with thels) Fig. , suggestls) Fig. .43. To validate these pooled screening hits in traditional arrayed format, we treated MDA-MB-231 cells possessing individual gene knockouts with Carfilzomib for 72 h and measured their intracellular reducing potential as a proxy of cell viability. As we observed using QMAP-Seq, knockout of these proteostasis factors further sensitized MDA-MB-231 cells to Carfilzomib   Fig. . This neNFE2L2), the master transcriptional regulator of the oxidative stress response44, we expected a low degree of correlation between these two factors. Indeed, NFE2L2 and KEAP1 displayed the third lowest correlation. In addition, we observed a high correlation between ERN1 and XBP1, which was expected given that the ER stress sensor, IRE1 (ERN1), activates XBP1 through mRNA splicing45.To model the overall structure of the proteostasis network, we assessed the functional similarity of each proteostasis factor’s chemical–genetic interaction profiles. Specifically, we quantified the Spearman correlation between all gene–gene pairs based on AUC difference across all cell line and compound contexts Fig. . Using t46 and retinal pigment epithelium cells47. Furthermore, the paralogs HSF1 and HSF2 have been reported to function cooperatively51, antagonistically52, and in a context-dependent manner53. Interestingly, HSF1 and HSF2 displayed the second highest correlation, providing evidence in support of a cooperative interaction in breast cancer. The lowest correlation genetic interaction was observed between ATF4 and KEAP1, which is not entirely surprising considering KEAP1 has been reported to negatively regulate ATF4 expression in other cancer models54. Altogether, we conclude that chemical–genetic profiling using QMAP-Seq provides insight into the organization of the proteostasis network in breast cancer cells and has the power to reveal genetic relationships.This approach also revealed previously unknown genetic relationships Fig. . For exa24, an approach that uses microsphere bead technology for multiplexing cell lines, but not compound treatments. Another important feature of QMAP-Seq is the use of genetically engineered isogenic cell line pairs, which enables direct assessment of the contribution of a single genetic perturbation to compound sensitivity. It is worth noting, however, that other pooled assays could in principle profile isogenic cell line pairs, while QMAP-Seq could be performed with pools of non-isogenic cell lines. QMAP-Seq leverages readily available next-generation sequencing, which itself continues to improve in throughput, and is compatible with common sequencing libraries, such as RNA-Seq. As a result, the costs associated with QMAP-Seq are 1/10th the cost of an ATP-based cell viability assay of the same magnitude performed in 384-well format. The compound requirements are also lower, which is particularly useful when profiling scarce compounds or natural products.Here, we introduce QMAP-Seq, a highly multiplexed chemical–genetic profiling strategy that enables systematic phenotyping of dozens of cell lines with defined genetic perturbations across thousands of individual compound treatments. We also present a bioinformatic analysis pipeline that simplifies the processing of thousands of fastq files into over 10,000 dose–response curves. QMAP-Seq has several major advantages over existing methods for the identification of chemical–genetic interactions in mammalian cells. For one, our pooled approach provides substantial gains in throughput, facilitating over 50 times as many cell viability measurements per sample as established arrayed assays. By introducing sample-specific indexes, thousands of compound treatment samples are further pooled and sequenced together in a single next-generation sequencing run. This distinguishes QMAP-Seq from PRISMImportantly, the short-term nature of QMAP-Seq  enables profiling of thousands of compounds, which would be technically challenging with existing genome-scale chemical–genetic methods that require several weeks of passaging and treating cells prior to readout. The short-term format also minimizes potential secondary effects that may arise due to several weeks of exposure to compounds, and thus identifies the most relevant chemical–genetic interactions. Collectively, these attributes of QMAP-Seq enable accurate mapping of biological network structure, as we demonstrated for the proteostasis network.25 or by profiling one cell line at a time. Additionally, factors secreted by one cell line could conceivably alter the compound sensitivity of another cell line in the pool. Although such paracrine-mediated effects would certainly warrant further studies, we -2A-GFP 58, EcoRI and BamHI restriction sites were introduced flanking the future cell line barcode insertion site by amplifying two overlapping fragments with Barcode Frag1 F/R and Barcode Frag2 F/R . Cell line barcode oligos were inserted within the EcoRI and BamHI restriction sites of the modified lentiGuide-Puro vector.For inserting cell line barcodes downstream of the gRNA scaffold and upstream of cPPT/CTS within the lentiGuide-Puro vector 59 and non-targeting (NT) sgRNAs sourced from ref. 58 and ref. 9 were inserted into the cell line barcoded lentiGuide-Puro vectors by resuspending the top and bottom strands of the sgRNA oligos . sgRNA oligos were cloned into the barcoded lentiGuide-Puro vectors by combining 100 ng vector, 2 μL 1:200 diluted oligo duplex, 2 μL 10x FastDigest Buffer, 1 μL 10 mM DTT, 1 μL 10 mM ATP, 1 μL BsmBI, 0.5 μL Quick Ligase, and ddH2O to a final volume of 20 μL and incubating in thermocycler as follows: 37 °C for 5 min, 21 °C for 5 min, repeat for a total of 6 cycles. Ligation reactions were treated with PlasmidSafe exonuclease by combining 11 μL ligation reaction, 1.5 μL 10x PlasmidSafe Buffer, 1.5 μL 10 mM ATP, and 1 μL PlasmidSafe exonuclease and incubating in thermocycler as follows: 37 °C for 30 min, 70 °C for 30 min. Ligation reactions were transformed into Stbl3 cells. All plasmid sequences were verified by Sanger sequencing.Gene-targeting sgRNAs sourced from the Brunello Human CRISPR Knockout Pooled Library293T, ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 cells were obtained from ATCC. 293T cells were cultured in DMEM medium  supplemented with 10% Tet System Approved Fetal Bovine Serum  and 1% Penicillin/Streptomycin . To facilitate co-culturing cell lines in mixtures, ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 cells were cultured individually or collectively in a common growth medium: RPMI-1640 medium  supplemented with 10% Tet System Approved Fetal Bovine Serum  and 1% Penicillin/Streptomycin . All cell lines were authenticated at the University of Arizona Genetics Core and tested negative for mycoplasma contamination.For engineering Cas9-expressing breast cancer cell lines, virus was produced from pLVX-TetOne Cas9 lentiviral transfer plasmid as specified in the application note for Lipofectamine 3000 Transfection Reagent , but substituting pMD2.G envelope plasmid and psPAX2 packaging plasmid for ViraPower Lentiviral Packaging Mix. ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 cells were transduced with virus and induced with either 100 ng/mL doxycycline  or 10 ng/mL doxycycline  for 7 days (refreshing doxycycline every 3 days). Induced cells were gated into three equal-size bins based on GFP brightness and sorted for medium GFP+ cells.For introducing sgRNA libraries, pooled sgRNA plasmid libraries possessing appropriate cell line barcodes were generated by combining equal amounts of 11 gene-targeting sgRNA plasmids and a pool of five NT sgRNA plasmids (sgNT_0-4). Virus was produced with Lipofectamine 3000 Transfection Reagent  as described above and was functionally titered on its respective cell line using serially-diluted virus. ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 Cas9 cells were transduced with appropriate sgRNA libraries at MOI = 0.3, maintaining coverage of at least 7500 cells per sgRNA. Transduced cells were selected using 2 μg/mL puromycin  for 3 days.g for 5 min, and stored at −20 °C.For generating cell spike-in standards, virus for five NT sgRNA plasmids (sgNT_5-9) possessing the 293T cell line barcode was produced in an arrayed format. 293T cells were transduced with the viruses individually. Transduced cells were selected using 2 μg/mL puromycin  for 3 days. To pool, cell lines were detached, resuspended in PBS, counted, and pooled at the following ratio: 1x sgNT_5, 3x sgNT_6, 9x sgNT_7, 27x sgNT_8, 81x sgNT_9. Pooled cells were aliquoted, pelleted at 425 × For assessing induction of FLAG-Cas9, MDA-MB-231 pLVX-TetOne Cas9 cells were treated with 0, 0.5, 1, 2, 5, or 10 ng/mL doxycycline  for 48 h. Cells were harvested and lysed in buffer containing 50 mM Tris, pH 7.5, 1 mM EDTA, 150 mM NaCl, 1% Triton X-100, 0.1% SDS. Protein concentration was measured using the BCA Protein Assay Kit . Five micrograms of total protein per lane was electrophoresed and transferred using an iBlot 2 Dry Blotting System (Thermo Fisher Scientific). Membrane was probed with 1:1000 Anti-FLAG primary antibody  followed by 1:10,000 Anti-Mouse IgG-Peroxidase secondary antibody , developed with Immobilon Western Chemiluminescent HRP Substrate , visualized using a ChemiDoc Touch Imaging System (Bio-Rad), and analyzed using Image Lab 5.2.1 (Bio-Rad). Membrane was stripped with ReBlot Plus Mild Antibody Stripping Solution  and reprobed with 1:10,000 Anti-Alpha Tubulin primary antibody  followed by 1:10,000 Anti-Mouse IgG-Peroxidase secondary antibody .For confirming whole population knockout of the proteostasis factors, MDA-MB-231 pLVX-TetOne Cas9 cells transduced with appropriate sgRNAs were treated with 10 ng/mL doxycycline  for 96 h (refreshing doxycycline every 2 days) to induce Cas9 expression prior to harvesting. Western blot analysis was performed as described above using the following antibodies: 1:1000 Anti-HSF1 , 1:1000 Anti-HSF2 , 1:1000 Anti-IRE1 , 1:1000 Anti-XBP1 , 1:1000 Anti-ATF3 , 1:1000 Anti-ATF4 , 1:1000 Anti-ATF6 , 1:1000 Anti-ATG7 , 1:1000 Anti-NRF2 , 1:1000 Anti-KEAP1 , 1:10,000 Anti-Alpha Tubulin , 1:10,000 Anti-Beta Actin . All uncropped blots are provided as a Source data file.ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 cells were transduced with pHIV-Luc-ZsGreen  or pUltra-Chili-Luc  and sorted for GFP+ or RFP+ cells, as appropriate.For preparing the five original pools, fluorescently labeled cell lines were counted, pooled, and frozen in liquid nitrogen. Pools were thawed on Day 0 and cultured normally. Six days and 13 days after thawing, the percentages of GFP+ and RFP+ cells were quantified by flow cytometry analysis using a LSRFortessa Cell Analyzer (BD Biosciences).r) of each of the five cell lines, the equation for exponential cell growth was used:xt = percentage at day t, x0 = percentage at day 0, r = growth rate, and t = time [days]. Growth rates (r) were then used to calculate the optimal starting percentage (x0) of each cell line to achieve 20% representation at t = 7 days.For estimating the growth rate . Heat maps were generated using Cluster 3.0 and Java TreeView 1.1.6r4.For preparing cell pools for QMAP-Seq with multiple cell lines (five cell lines × 12 genetic perturbations), individual cell line pools consisting of 12 sgRNAs (11 gene-targeting sgRNAs + 5 pooled NT sgRNAs) were prepared as described in “Cell engineering”. Each of the five individual cell line pools was counted, pooled with the other four cell line pools according to ratios derived from the relative cell abundance competition experiment, and frozen in liquid nitrogen .For confirming the efficacy of the sgRNAs that target SLC35F2, MDA-MB-231 pLVX-TetOne Cas9 cells engineered with either sgNT or one of the four sgRNAs targeting SLC35F2 were induced with 100 ng/mL doxycycline for 6 days (refreshing doxycycline every three days). In total, 1000 cells were seeded in a volume of 50 μL in opaque 384-well plates. The next day, YM155 was added over a nine-point concentration range in quadruplicate using a D300e Digital Dispenser (Tecan). Seventy-two hours later, cell viability was measured using the CellTiter-Glo Luminescent Cell Viability Assay . Luminescence was read using an Infinite M1000 PRO (Tecan) with an integration time of 500 ms.For live-cell imaging, pooled MDA-MB-231 pLVX-TetOne Cas9 cells engineered with 12 sgRNAs were induced with 100 ng/mL doxycycline for six days (refreshing doxycycline every 3 days). In total, 5000 pooled cells were seeded in a volume of 100 μL in 96-well plates. The next day, 89 compounds were added over the indicated four-point concentration range in duplicate from custom compound plates prepared at the High Throughput Analysis Laboratory (Northwestern University). Seventy-two hours later, the percent confluence of the population of cells in each well was measured  using an IncuCyte ZOOM Live-Cell Analysis System GUI version 2015A (Essen BioScience). To calculate relative cell confluence, the percent confluence of a compound-treated well was normalized to the median percent confluence of DMSO-treated wells.For validating QMAP-Seq hits using Resazurin, MDA-MB-231 pLVX-TetOne Cas9 cells engineered with appropriate sgRNAs were induced with 100 ng/mL doxycycline for 6 days (refreshing doxycycline every 3 days). In total, 5000 cells were seeded in a volume of 100 μL in 96-well plates. The next day, Carfilzomib was added over the indicated four-point concentration range in triplicate using a D300e Digital Dispenser (Tecan). Seventy-two hours later, cell viability was measured using the Resazurin Cell Viability Kit . Relative fluorescent units were read  using an Infinite M1000 PRO (Tecan).For all cell viability assays, dose–response curves were fit in GraphPad Prism 8 using the log(inhibitor) vs. response model (three parameters) with the top constrained to 100%.YM155 was obtained from Selleckchem (#S1130). Compounds for QMAP-Seq were obtained from the FDA-Approved Drug Library , the Clinical Compound Library , or from the following vendors: 4-Hydroxytamoxifen was obtained from Sigma-Aldrich (#H7904), and Bortezomib was obtained from Cayman Chemical (#10008822). For validation experiments, Carfilzomib was obtained from a different vendor  than where it was sourced for QMAP-Seq.60. The existence of specific genetic alterations in breast cancer was confirmed using cBioPortal62. Up to two proteostasis-modulating compounds per target were incorporated.Compounds for QMAP-Seq with multiple cell lines spanned six categories: positive controls, chemotherapeutics, targeted therapies (NCI), targeted therapies (OncoKB), diverse compounds (Informer Set), and proteostasis-modulating compounds. Relevant positive controls  were included. For all remaining compound categories, compounds that were not part of the FDA-Approved Drug Library  or the Clinical Compound Library  were filtered out. One compound per compound class was selected from breast cancer chemotherapeutics  in duplicate using one of two dose ranges. The standard dose range encompassed doses from 10 nM to 10 μM, and the low dose range covered doses from 1 nM to 1 μM. Dose ranges were selected by referencing doses previously used for treating cancer cell lines, including the Informer Set63. For QMAP-Seq with multiple cell lines, pooled cell lines (five cell lines × 12 genetic perturbations) were thawed in media containing 100 ng/mL doxycycline . Three days later, cells were expanded and doxycycline was refreshed. Three more days later, 5000 pooled cells were seeded in a volume of 100 μL in 96-well plates using an EL406 Microplate Washer Dispenser (BioTek Instruments), while maintaining doxycycline induction. The next day, 180 compounds were added over a four-point concentration range in duplicate from custom compound plates prepared at the High Throughput Analysis Laboratory (Northwestern University). Briefly, compound plates containing 500 nL of compound at 1000× were resuspended in 250 μL media per well to achieve 2× compound concentration, and 100 μL of 2× compound was distributed to replicate wells using multichannel pipet. Cells were treated for 72 h.A step-by-step protocol describing the QMAP-Seq protocol can be found at Protocol ExchangeCell spike-in standards were thawed at room temperature for 5 min and resuspended in Lysis Buffer , 0.45% IGEPAL CA-630 , 0.45% TWEEN 20 , 10% Proteinase K , 79.1% Nuclease-Free Water ) to achieve a concentration of 36.3 total cell spike-in standards/μL. Cells were homogenized using a 5 mL syringe and a 21G × 1″ needle three times followed by a 27G × ½″ needle three times. Cells were incubated in 60 °C water bath for 1 h, pipetting up and down every 20 min.After 72 h of compound treatment, compound-treated cells were washed with 100 μL PBS. 50 μL Lysis Buffer containing 36.3 total cell spike-in standards/μL (1815 spike-in cells/well) was added using multichannel pipet. Plates were covered with foil adhesive and incubated in 60 °C oven for 1 h. Cell lysates were transferred to PCR plates using multichannel pipet. Proteinase K from Lysis Buffer was inactivated at 95 °C for 15 min using thermocycler.2, 0.6 μL DMSO , 6 μL 1 μM P5 Primer Mix (mix of 0-8nt staggered primers), 6 μL 1 μM P7 Primer, 8 μL Lysate (Proteinase K inactivated again just prior to PCR), 30.16 μL Water, 0.24 μL Platinum Taq DNA Polymerase . To reduce the likelihood of PCR jackpot effects, half of the PCR volume from each reaction was transferred to a second set of PCR plates. PCRs were run in thermocycler as follows: 94 °C for 4 min, , 72 °C for 15 min, 4 °C hold.A 359–367 bp fragment (depending on stagger length) containing the sgRNA and cell line barcode was amplified with a unique set of P5 and P7 primers for each well by combining the following reagents: 6 μL 10x Taq DNA Polymerase Buffer, 1.2 μL 10 mM dNTP Mix, 1.8 μL 50 mM MgClTechnical duplicates were recombined. Library size was verified by running 5 μL of PCR products on E-Gel 96 2% Agarose gels . An equal volume (6 μL) of each PCR product was pooled together. The concentration of the pooled PCR products was measured using the Qubit dsDNA HS Assay Kit . Pooled PCR products were purified using the QIAquick PCR Purification Kit  with sufficient PCR purification columns to avoid exceeding the maximum binding capacity of each column. The concentration of the purified sample was measured using the Qubit dsDNA HS Assay Kit . The purified sample was purified a second time using the Purification Module with Magnetic Beads . The final library concentration was measured using the Qubit dsDNA HS Assay Kit .The sequencing library was diluted to 2.5 nM, combined with PhiX  to achieve 25% PhiX (to increase nucleotide diversity), and denatured. The sequencing library was loaded on a NovaSeq 6000 (Illumina) using a NovaSeq 6000 S1 Reagent Kit, 200 cycles, 1.3B Reads . Single-end sequencing was performed using the following run parameters: Illumina Read 1 Primer: 164 cycles , Illumina Index 1 Primer: 6 cycles (to sequence i7 index), Illumina Index 2 Primer: 6 cycles (to sequence i5 index).Individual samples were demultiplexed based on i5 and i7 index sequences (Supplementary Data Relative cell numbers were plotted as heat maps and dose–response curves. Heat maps were generated using Cluster 3.0 and Java TreeView 1.1.6r4. Dose–response curves were fit in GraphPad Prism 8 using the log(inhibitor) vs. response model (three parameters) with the top constrained to 100%. The area under the curve (AUC) was calculated using the equation: AUC = 0.5(dose 1 cell number) + dose 2 cell number + dose 3 cell number + 0.5(dose 4 cell number).2(cell number) > 0.7) were excluded, which included these cell line-sgRNA pairs: ZR-75-1 sgHSF1 and ZR-75-1 sgKEAP1. Third, compounds with an AUC > 550 for any cell line-sgRNA pair were excluded, which included these compounds: Ruxolitinib and STF083010 . Fourth, noncytotoxic cell line-compound pairs (relative cell number of sgNT with the highest dose ≥ 90%) were excluded, which included 225 cell line-compound pairs. Fifth, cell line-compound pairs without a significant dose-dependent reduction in cell viability (difference in relative cell number of sgNT between lowest and highest dose < 25%) were excluded, which included 366 cell line-compound pairs (union between fourth and fifth exclusion criteria = 380 cell line-compound pairs).Data were filtered using the following exclusion criteria. First, samples with low read counts were excluded, which included these compounds: ABT199, Belinostat, BMS345541, Dexamethasone, Fingolimod, and RacRotigotine. Second, cell line-sgRNA pairs with high variation  compared to in the five cell line experiment (5000 cells ÷ 60 perturbations ≈ 83 cells per perturbation), five times as many cells for each of the cell spike-in standards were added per sample for the one cell line experiment. The data from Plate 8 was excluded from analysis due to a technical problem with the addition of cell spike-in standards for that plate. The sequencing library was loaded on a NextSeq 500 (Illumina) using a NextSeq 500/550 High Output Reagent Kit, 400M Reads .P < 0.05) and that had a large effect size (absolute magnitude of AUC change >60). These interactions were visualized as an unweighted network using a standard force-directed layout in Cytoscape v3.7.2 (cytoscape.org).For constructing a chemical–genetic interaction network, data were filtered as described above. All interactions were then filtered for those considered significant . For correlation analysis of AUCs between live-cell imaging and QMAP-Seq, Pearson r was reported, and statistical significance of Pearson correlation was determined using a two-tailed test (n = 89 compounds). For correlation analysis of AUCs between two independent QMAP-Seq experiments, Pearson r was reported, and statistical significance of Pearson correlation was determined using a two-tailed test (n = 220 compound-sgRNA combinations). For comparing the variation between plates for reads versus cell number, statistical significance was determined using an unpaired, two-tailed F test to compare variances (n = 16 plates).For correlation analysis of replicates, Pearson t test . The chemical–genetic interactions with an Absolute AUC Difference > 25 and P < 0.05 were designated as hits.For identifying the chemical–genetic interactions with the greatest effect, a volcano plot was produced after data filtering as described above. The magnitude was determined by calculating the difference in mean AUC between sgRNA and sgNT for every cell line-compound combination. The statistical significance was determined using an unpaired, two-tailed n = 180 compounds).For determining pathway enrichment, statistical significance was determined using a one-tailed binomial test to compare observed distribution to expected distribution (Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Peer Review FileReporting Summary


These mutations cause an increase in the levels of the oncometabolite D-2-hydroxyglutarate (D-2-HG), which leads to widespread changes in several cellular processes, including the epigenetic landscape. The aim of our study was to explore whether the methylome of IDH mutant cartilage tumors is altered upon tumor progression and whether these underlying epigenetic vulnerabilities could be used as a target for therapy in both IDH wildtype and IDH mutant high-grade chondrosarcomas. As surgery is nowadays the only treatment option for chondrosarcoma patients, the identification of novel therapeutic strategies remains an important endeavor. The findings in this study show that histone deacetylase (HDAC) inhibition may represent a promising therapeutic strategy for all chondrosarcoma patients, since sensitivity towards this therapeutic option seems independent of the IDH mutation status and the chondrosarcoma subtype.Cartilage tumors frequently harbor mutations in the isocitrate dehydrogenase IDH1 or IDH2) genes are common in enchondromas and chondrosarcomas, and lead to elevated levels of the oncometabolite D-2-hydroxyglutarate causing widespread changes in the epigenetic landscape of these tumors. With the use of a DNA methylation array, we explored whether the methylome is altered upon progression from IDH mutant enchondroma towards high-grade chondrosarcoma. High-grade tumors show an overall increase in the number of highly methylated genes, indicating that remodeling of the methylome is associated with tumor progression. Therefore, an epigenetics compound screen was performed in five chondrosarcoma cell lines to therapeutically explore these underlying epigenetic vulnerabilities. Chondrosarcomas demonstrated high sensitivity to histone deacetylase (HDAC) inhibition in both 2D and 3D in vitro models, independent of the IDH mutation status or the chondrosarcoma subtype. siRNA knockdown and RNA expression data showed that chondrosarcomas rely on the expression of multiple HDACs, especially class I subtypes. Furthermore, class I HDAC inhibition sensitized chondrosarcoma to glutaminolysis and Bcl-2 family member inhibitors, suggesting that HDACs define the metabolic state and apoptotic threshold in chondrosarcoma. Taken together, HDAC inhibition may represent a promising targeted therapeutic strategy for chondrosarcoma patients, either as monotherapy or as part of combination treatment regimens.Mutations in the isocitrate dehydrogenase ( Chondrosarcomas are bone malignancies characterized by the production of cartilage and account for 20% of all malignant bone tumors ,2. BasedIDH1 or IDH2, collectively referred to as IDH) genes , occurring in ~50% of the cases [2 in the tricarboxylic acid cycle, while the IDH1 enzyme is located in the cytosol where it performs a similar reaction. The hotspot mutations induce a gain-of-function of these enzymes, resulting into abnormal cellular concentrations of the oncometabolite D-2-hydroxyglutarate (D-2-HG) [IDH1R132C mutation, which is one of the most efficient D-2-HG producers [The most common genetic alteration in central conventional chondrosarcoma is the hotspot mutation affecting the arginine residues encoded by the isocitrate dehydrogenase 1 and -2  and histone demethylases (family of Jumonji enzymes) [IDH mutations interfere with the dynamic processes that regulate the accessibility of the DNA and could thereby permanently change gene expression, which could ultimately lead to tumor formation and growth. Indeed, the introduction of an IDH mutation induces the formation of benign cartilage tumors  in mice [IDH mutated enchondromas, which are considered as the benign pre-cursor lesions of chondrosarcoma, are characterized by a CpG island methylator phenotype (CIMP)-positive status [IDH mutation is an early genetic event which highly alters the epigenetic landscape in early phases of cartilage tumor formation. The characteristic hypermethylation phenotype is also observed in primary IDH mutant chondrosarcomas [IDH mutations might be beneficial for chondrosarcoma patients, and could potentially help to overcome the intrinsic chemotherapy resistance in chondrosarcoma. Due to the high structural similarity between α-KG and its antagonist D-2-HG, α-KG dependent enzymes are inhibited by the high levels of the oncometabolite ,11, causenzymes) ,13, lead in mice . IDH mute status . The mute status ,16. Togesarcomas . TherefoIDH mutant enchondroma towards chondrosarcoma using a methylation array on primary tumor samples. In fact, high grade tumors demonstrated an increased number of hypermethylated genes as compared to low grade tumors, suggesting that epigenetic mechanisms play an important role in chondrosarcoma progression. Therefore, a broad compound screen containing 128 compounds which target different epigenetic key players (including histone deacetylases (HDACs), sirtuins (SIRTs), histone demethylases (HDMs), histone acetyltransferases (HATs), histone methyltransferases (HMTs), and DNA methyltransferases (DNMTs)) was performed on IDH wild type and mutant chondrosarcoma cell lines to explore whether these epigenetic changes can be used as a target for novel anti-cancer therapy. No synthetic lethal interactions with IDH mutations were identified, but several general interesting targets were determined, and interesting compound classes included HDAC and bromodomain and extra-terminal motif (BET) protein inhibitors. Additionally, we explored if one of the most promising hits, the HDAC inhibitor romidepsin, could help to sensitize chondrosarcoma cells to chemotherapy or small molecule inhibitors. To address this question, a drug screen with a panel of non-epigenetic drugs was designed to explore if a combination treatment could be a promising therapeutic strategy for chondrosarcoma patients.In this study, we confirmed that the hypermethylation phenotype is retained upon progression from IDH mutant cartilage tumors were CIMP-positive, except for one enchondroma and one low-grade chondrosarcoma   . Hierarcsarcomas A. Thus, 89 genes . Of note89 genes . Biologi89 genes . Hence, The primary epigenetics compound screen was performed as described in IDH mutation status, as only one potential synthetic lethal interaction was observed in JJ012 cells   . Howevertubacin) . Hence, The human genome encodes for eleven subtypes of HDAC enzymes, which can be divided into four different classes: class I , class IIA , class IIB (HDAC6 and HDAC10), and class IV (HDAC11). To identify the most potent and specific HDAC inhibitor for follow-up studies, all HDAC inhibitors were screened at a lower concentration  and ordered based on HDAC class specificity . As show50 values in the range of 0.89 to 1.96 nM after 72 h of treatment  were treated with a high dose of romidepsin  for 24 h or 48 h. Caspase 3/7 activity was significantly induced in SW1353 cells after 24 h, and in all cell lines after 48 h C. A concAs HDAC inhibitors induce the acetylation of histones, the level of acetylated histone 3 (acH3K9) was determined after 24 h and 48 h treatment with romidepsin. An induction of acH3K9 was observed in all three chondrosarcoma cell lines D. HoweveTwenty non-epigenetic drugs were selected for the HDAC inhibitor combination drug screen . These dThe general Bcl-2 family member inhibitor, ABT-737, and the specific Bcl-2 inhibitor, venetoclax, showed the highest synergistic effect when combined with romidepsin, especially at higher drug concentrations . InductiIDH mutant enchondroma towards chondrosarcoma. We showed that the CIMP-positive status is retained in high-grade IDH mutant chondrosarcoma. Moreover, the number of highly methylated genes seemed to increase upon tumor progression. This suggests that methylation patterns change upon chondrosarcoma progression, and that an increased number of methylated genes is associated with aggressiveness of the disease. Since we chose to include a homogeneous IDH mutant subgroup of chondrosarcomas, it remains to be established whether methylation is equally important in IDH wildtype chondrosarcomas. The epigenetics compound screen results, indicating sensitivity to the same epigenetic modulators, suggest that the epigenetic landscape is comparable between IDH mutant and wildtype chondrosarcomas. This is further supported by the lack of a synthetic lethal interaction between the IDH mutation and a broad spectrum of epigenetic regulators, even though these kinds of synthetic lethal interactions  were previously described in IDH mutant acute myeloid leukemia (AML) and glioma [IDH mutation on the methylome. In this study, we explored whether the methylome is altered upon progression from d glioma ,26,27. TIDH mutant chondrosarcoma tumors, we expected that targeting of DNMTs by for instance decitabine and azacytidine would be effective in chondrosarcoma. However, none of these DNMT inhibitors had a pronounced effect on chondrosarcoma cell viability. Nevertheless, the epigenetics compound screens identified several other classes of interesting drug targets for both IDH wildtype and IDH mutant chondrosarcoma, among which inhibitors against Aurora kinases, FLT3, HDAC, and JAK showed the most pronounced effect on chondrosarcoma cell growth. Most of these targets play a major role in growth signaling pathways (FLT3 and JAK) or cell cycle control (Aurora kinases). We have previously shown that targeting cell cycle regulators, especially Checkpoint Kinase 1 (CHK1), could be a promising therapeutic strategy for chondrosarcoma patients [Based on the retained CIMP-positive status and the increasing amount of hypermethylated genes in high-grade patients . Hence, COL2A1), leading to the disruption of normal cartilage development [The HDAC enzymes play a prominent role in skeletal development and aberrant expression is associated with a wide variety of bone-related abnormalities. HDAC1 and HDAC3 are most abundantly expressed in healthy human cartilage , and we elopment , while iIDH mutation status and the chondrosarcoma subtype , indicating that HDAC inhibition could be a promising therapeutic strategy for all chondrosarcoma patients. Romidepsin is clinically approved for the treatment of cutaneous T-cell lymphoma and other peripheral T-cell lymphomas. In vitro studies of romidepsin treatment in T-cell lymphomas report IC50 values in the low nanomolar range (1 to 11 nM) [50 and IC50 values in our 2D and 3D chondrosarcoma cell culture models. The currently approved dosing regimen of romidepsin in patients with T-cell lymphomas reaches maximum plasma concentrations of 700 nM [IDH mutation status independent manner. As in vitro and in vivo models differ in nutrient availability, metabolism and cell types involved in tumor growth, the downstream effects of IDH mutations on the epigenetic landscape and the metabolism might also vary between these two types of models.Previously, it was shown by other groups that the HDAC inhibitors romidepsin, trichostatin A, and sodium valproate affect cell proliferation in 2D in vitro models of chondrosarcoma ,32. In to 11 nM) ,34, whicf 700 nM , which ff 700 nM . Additiof 700 nM  should bWe also explored if sub-optimal concentrations of romidepsin could be used in a combination treatment strategy, to either sensitize chondrosarcoma to non-epigenetic therapies or to reduce HDAC inhibitor toxicity. However, HDAC inhibition could not sensitize chondrosarcoma cells to four different types of chemotherapy , although this has been extensively described in other tumor types . Only thIDH mutation status  and of all twenty compounds included in the custom designed compound library for the HDAC inhibitor combination drug screen are available in the IDH wild type (IDHWT)) [IDH1R132G) [IDH2R172S) [IDHWT) [IDH1R132C) [IDHWT) [IDH1R132C) [IDH2R172W) [IDHWT) [IDHWT) [2 at 37 °C. PCR-based mycoplasma tests and STR profiling  were performed regularly.The central conventional chondrosarcoma cell lines CH2879 ((IDHWT)) ,46, JJ01H1R132G) , SW1353  [IDHWT)  and L835H1R132C) ; the ded [IDHWT) , HT1080 H1R132C) ,51, L297H2R172W)  and L325 [IDHWT) ; and the [IDHWT)  were cul [IDHWT) . Long-te [IDHWT)  were culThe chondrosarcoma cell lines CH2879, JJ012, and SW1353 were seeded in optimized cell densities  in black 96-well plates. After overnight attachment, cells were treated with the epigenetics compound library at a concentration of 2 µM. After 72 h of treatment, cells were fixed with 4% formaldehyde and stained with 2 µg/mL Hoechst 33342 . Plates were imaged using a BD Pathway 855 microscope  and the number of Hoechst 33342 positive nuclei was quantified using the Image-Pro software . All plates were included in the analysis and data were normalized to the negative controls  to obtain percent of control values. All compounds that reduced the nuclei count ≥50% in at least one of the cell lines were selected for the secondary epigenetics compound screen. The screen was performed in triplicate. A schematic overview of the primary epigenetics compound screen can be found in IDH1 mutant cell lines  were pre-exposed to 10 µM AGI-5198 for 72 h [IDH1 mutant cell lines were seeded in black 96-well plates  in normal growth medium supplemented with 10µM AGI-5198 or 0.1% DMSO as a control. To mimic D-2-HG production, CH2879 cells were seeded (5000/well) in normal growth medium supplemented with 250 µM D-2-HG or 1% PBS as a control. After overnight attachment, cells were treated with the selected compounds (n = 61) from the primary screen at a concentration of 2 µM. Compounds were diluted in medium supplemented with 0.1% DMSO, 10µM AGI-5198, 1% PBS or 250 µM D-2-HG. After 72 h of treatment, a nuclei count was performed as described under the methods section of the primary epigenetics compound screen. All plates were included in the analysis and data were normalized to the negative controls  to obtain percent of control values. Compounds that reduced the nuclei count ≥50% in all cell lines were considered as potential therapeutic options for both IDH wildtype and IDH mutant chondrosarcomas. A ≥75% difference in nuclei count between cells treated with or without 10 µM AGI-5198 or cells treated with or without 250 µM D-2-HG was considered as a potential synthetic lethal interaction with the IDH mutation. The screen was performed in triplicate. A schematic overview of the secondary epigenetics compound screen can be found in To reduce the D-2-HG levels, CH2879 cells (7000/well), JJ012 cells (3000/well), and SW1353 cells (3000/well) were seeded in 96-well plates in normal growth medium supplemented with 0.1% DMSO or romidepsin (1 nM (JJ012 cells) and 0.75 nM (CH2879 and SW1353 cells)). After overnight attachment, cells were treated with the custom designed compound library. For each library compound, five concentrations were tested, which were based on previous experience . CompounFor the 2D cell cultures, the cell viability assays, nuclei count assays and data analysis were performed as previously described . Cells wFor the 3D cell cultures, the cell viability assays and the immunohistochemical stains (Ki-67 and cleaved caspase 3) were performed as previously described . Cells w(GAPDH) (Dharmacon) and an siKinasePool  were used as negative controls. siRNA SMARTpools against Kinesin Family Member 11 (KIF11) and Polo Like Kinase 1 (PLK1) (Dharmacon) were used as positive controls. To reduce toxicity, the medium was refreshed 24 h after the transfection. Three days after medium refreshment, cells were fixed, stained with Hoechst 33342 and imaged with the Cellomics ArrayScan VTI HCS 700. Data were normalized to the negative controls to obtain percent of control values. The experiment was performed in triplicate. Reverse siRNA transfection was performed for CH2879 cells  and JJ012 cells (5000/well) in 96-well plates. To achieve transient knockdown of all HDAC subtypes, cells were transfected with 0.1 µL/well DharmaFECT 3  and 50 nM siRNA SMARTpools (Dharmacon), which consist of four individual siRNAs targeting a gene of interest. The mock condition, the siRNA SMARTpool against Glyceraldehyde-3-Phosphate Dehydrogenase RNA expression of all HDAC subtypes in chondrosarcoma cell lines was extracted from a next generation RNA sequencing dataset we previously described . Reads pThe apoptosis and cell cycle assays were performed and analyzed as previously described . Cells wSample preparation, western blotting and quantification were performed as previously described . Cells wTo determine significant changes between experimental groups, a nonparametric Kruskal–Wallis test followed by a Dunn’s post-hoc test was performed. A Grubbs’ test was performed to detect outlier values in the dose–response curve datasets. All statistical tests were performed in GraphPad Prism 8. The Z’-factor was used as a measure for compound screen quality and was calculated to determine the effect size between negative and positive controls . To calcIDH mutant cartilage tumors are associated with tumor progression and this study shows that targeting of epigenetic regulators could be a potential therapeutic strategy for both IDH wildtype and IDH mutant chondrosarcoma patients. Our study establishes that HDAC enzymes play a prominent role in the epigenetic landscape and survival of chondrosarcoma cells, especially class I HDACs. Chondrosarcoma cell lines are highly sensitive to the class I HDAC inhibitor romidepsin, irrespective of the chondrosarcoma subtype or the IDH mutation status. In addition, our study identifies glutaminolysis and Bcl-2 family member inhibitors as potential candidates to be used in combination with HDAC inhibition, suggesting that romidepsin influences the metabolic state and apoptotic threshold of chondrosarcoma cells. Further studies are needed to elucidate the exact mechanisms underlying HDAC inhibitor sensitivity in chondrosarcoma and to confirm romidepsin efficacy in an orthotopic chondrosarcoma mouse model. Taken together, pharmacological inhibition of HDAC enzymes may represent a promising targeted therapeutic strategy for chondrosarcoma patients. In spite of the fact that the mechanisms underlying HDAC inhibitor sensitivity are currently unknown, the identification of novel treatment strategies for chondrosarcoma remains an important objective, especially for patients with unresectable or high-grade tumors.In summary, alterations in the methylome of


CEN‐tools), a website and python package, in which users can interrogate the essentiality of a gene from large‐scale genome‐scale CRISPR screens in a number of biological contexts including tissue of origin, mutation profiles, expression levels and drug responses. We show that CEN‐tools is suitable for the systematic identification of genetic dependencies and for more targeted queries. The associations between genes and a given context are represented as dependency networks (CENs), and we demonstrate the utility of these networks in elucidating novel gene functions. In addition, we integrate the dependency networks with existing protein–protein interaction networks to reveal context‐dependent essential cellular pathways in cancer cells. Together, we demonstrate the applicability of CEN‐tools in aiding the current efforts to define the human cellular dependency map.An emerging theme from large‐scale genetic screens that identify genes essential for cell fitness is that essentiality of a given gene is highly context‐specific. Identification of such contexts could be the key to defining gene function and also to develop novel therapeutic interventions. Here, we present Context‐specific Essentiality Network‐tools ( CEN‐tools allows users to navigate public large‐scale gene essentiality datasets and extract context‐specific essentiality networks, where context refers to the genetic background, gene expression, CNVs and other cell line features. Genetic perturbation experiments enable identification of genes that are essential for the survival and fitness of the cell. It is now widely accepted that the binary nature of gene essentiality as defined in classical genetics is too simplistic and gene essentiality is highly context‐specific. There are genes, often termed as core fitness genes, which are required for core functioning and housekeeping of the cells and their knockout causes loss of fitness, in principle, in all cell types in all conditions. However, a large number of genes, termed context‐specific essential genes, play important roles for cell fitness only in a particular genetic or environmental context . CEN‐tools is an integrated database and set of computational tools to explore context‐dependent gene essentiality from pooled CRISPR data sets, obtained from the two largest publicly available essentiality screening projects: the DepMap project ‐tools , both in pancancer and within tissue/cancer type, and represent them in Context‐specific Essentiality Networks or CENs. Each edge in this network represents the type of association used to annotate the underlying contexts. To demonstrate the value of CENs, we collected the co‐essentiality networks from the PICKLES database  TP53. This is an observation often made in pooled CRISPR screens as cells harboring the WT tumor suppressor genes are often dependent on their repressors for proliferation  promoter containing multiple binding sites for SRF  and SHOC2  become available to ultimately aid designing better therapeutics.Using CEN‐tools for group‐wise testing for significant association we identified many previously described molecular markers that are associated with essentiality, including, but not limited to mutational dependence on major cancer drivers and tissue‐specific lineage dependency markers ).Tissue annotations were obtained from both CCLE website and Cell Model Passports. In case of four cell lines , discrepancies between the two websites were found in terms of their annotations, hence their expression profiles were examined on a t‐sne plot and based on these cells were manually annotated as “Bone” tissue. In case of discrepancies in cancer type annotations, the Cellosaurus Bairoch,  under thet al, 2 fold change of 1 and non‐essential genes a median log2 fold change of 0.Corrected log fold changes from Project Score and DepMap projects were retrieved from Project Score, in which the same preprocessing pipeline was applied. “Essentiality scores” were calculated by computing the scaled log2 fold change. For this, gene‐level fold changes were first quantile normalised per sample and then median scaled according to BAGEL essential and non‐essential genes  matrix calculated using the MAGeCK : Tissue refers to the tissue of origin of the cell lines. Cancer refers to the further classification of the cell lines into the cancer type within each tissue. 2et al, et al, et al, Mutation (discrete context): For mutation analysis, two different mutational annotations obtained from CCLE database : For expression analysis, cell line expression values were obtained from the Cell Line Passport and CCLE database as normalised FPKM values : Drug responses for the cell lines were obtained as normalised Z‐scores from the CancerRxGene database (Yang U (two‐samples Wilcoxon) tests were used with default parameters  in R  to only include the genes represented in the PICKLES database as BRAF co‐essential genes . Further subsetting was then performed on the network file as follows:Project = ‘BROAD’comparision_in = ‘Skin’effector = ‘expression’ or effector = ‘tissue’ or effector = ‘hotspot_mutation’ or effector = ‘hotspot_co_mutation’.higher = ‘high_in_group’ or is.na(higher)median_group > 0 | is.na(median_group)width ≥ 2dashes = FALSEet al, The resulting subnetwork was imported in Cytoscape Advanced edge filter optionsFor Expression context correlations show edges corresponding to: Positive correlationsFor Tissue/Cancer context comparisons show edges corresponding to: Increase in essentiality/expressionFor Mutation context comparisons show edges corresponding to: Both Increase and decrease in essentiality/expressionSelect mutation annotations: CCLE: hotspot mutationsOnly show tissue/cancer edges in which the median essentiality score of the essential context is higher than: 0.3Only show mutation edges in which the median essentiality score of the essential context is higher than: 0.4Perform context comparisons: Within a tissue of origin/cancer type.Confidence level of association (Tissue/Cancer type): 1:LowConfidence level of association (Mutation): 2:MediumSelect: Group APress “Initialise network”et al, Download the network and the node attributes by clicking the download buttons in the left sidebar. Open the network file in Cytoscape Advanced edge filter optionsFor Expression context correlations show edges corresponding to: Positive correlationsFor Tissue/Cancer context comparisons show edges corresponding to: Increase in essentiality/expressionFor Mutation context comparisons show edges corresponding to: Increase in essentiality/expressionSelect mutation annotations: CCLE: hotspot mutationsOnly show tissue/cancer edges in which the median essentiality score of the essential context is higher than: 0.2Only show mutation edges in which the median essentiality score of the essential context is higher than: 0.4Perform context comparisons: Within a tissue of origin/cancer type.Confidence level of association (Tissue/Cancer type): 1: LowConfidence level of association (Mutation): 2: MediumSelect: Group APress “Initialise network”. The network will have too many nodes to be displayed but will still be generated (you will see a warning).Download the network and the node attributes by clicking the download buttons in the left sidebar. Open the network file in Cytoscape.For visualisation, only display nodes with “TF” attribute and any other nodes directly associated with these “TF” nodes.et al, To elucidate the cellular essential processes of the NRAS‐mutant skin cancer cells, we integrated the NRAS‐mutant skin CEN, comprising the statistical associations between the gene dependencies, with the STRING protein interaction network, comprising biological associations in the human cells Toggle below to display edges corresponding to either Tissue or Cancer subtype: TissueAdvanced edge filter optionsFor Expression context correlations show edges corresponding to: Positive correlationsFor Tissue/Cancer context comparisons show edges corresponding to: Increase in essentiality/expressionFor Mutation context comparisons show edges corresponding to: Increase in essentiality/expressionSelect mutation annotations: CCLE: hotspot mutationsOnly show tissue/cancer edges in which the median essentiality score of the essential context is higher than: 0.2Only show mutation edges in which the median essentiality score of the essential context is higher than: 0.4Perform context comparisons: Within a tissue of origin/cancer type.Confidence level of association (Tissue/Cancer type): 1: LowConfidence level of association (Mutation): 1: LowSelect: Group APress “Initialise network”. A network will appear.On the main panel, press the toggle button “Filter 1: Hide edges corresponding to change only in expression”. This will update the networkOn the side panel, press the “Load tissues represented in the current network”.Upon clicking the button in step 5 a dropdown menu with “Filter to only show edges corresponding to following tissues (press ‘refresh’ to update the list if filtering parameters are changed)” will appear. Select “Skin” on from this dropdown menu.Download the networks and the node attribute files for the shown networks. These files will be used later for network annotation.Press “Map the current nodes to a PPI network” button. This will automatically trigger switching to a new tab “Integration with PPI network”.On the Integration with PPI network tab, select the following options: Display Network from STRING: Map CEN onto STRING network.Select both output options: Show CEN‐mapped STRING network: Upon clicking this button a purple button “Retrieve PPI network of CEN from STRING” will appear. Press this button to visualise the network on the main panel. Use, adjust STRING interaction score: 400.Perform enrichment analysis and further choose the following options. Choose the enrichment category to display: KEGGChoose enrichments FDR cut‐off: 0.05et al, Download the network and the node attributes by clicking the download buttons in the left sidebar. Open the network file in Cytoscape , growth property, microsatellite stability, tissue and cancer subtype. Only cell lines for which we had essentiality data, mutation information and expression data are available for selection.Selected cell lines are presented in a table and highlighted in a T‐distributed Stochastic Neighbour Embedding (t‐SNE) plot for further interactive selection. In the t‐SNE plot, each cell line is represented by a dot. Cell lines with similar gene expression profiles will be closer together. The t‐SNE plot was generated with the following steps:Rtsne package  in R (version: 3.6.1). Default parameters were used except for perplexity, which was set to 70 and the number of maximal iterations, which was changed to 500.The gene expression matrix of each project  containing FPKM values with genes as columns and cell lines as rows was reduced to two dimensions using the ggplot2 package using the scatterplot function geom_point (version: 3.3.0)  or “Add points to selection” (add the choice to previously selected cell lines) button. More information on the selected cell lines is shown in the corresponding table.To support the decision, the dots can be highlighted by tissue of origin or subtype.The choice of selectable dots can be restricted by using the above‐described dropdown menus for mutations, CNV's etc. on the left panel of the app. Cell lines that do not adhere to the chosen criteria will appear as faint grey dots in the t‐SNE plot and cannot be clicked. Cell lines that adhere to the chosen criteria but are not part of the selection appear as grey dots and can be clicked to add them to the selection.As soon as the user is satisfied, the selected cell lines can be submitted for further Context analysis to CEN‐tools by hitting the “Click here to confirm your selection” button. The user should also give their selection a name that will be later on used as a label in plots of subsequent analysis.Advanced python tool users of CEN‐tools are also able to restrict the analysis to their choice of cell lines. For detailed guidance on the python tool refer to the CEN‐tool python documentation.RPL22 mutation and the essentiality of its paralog RPL22L1 was investigated in the “BROAD” project. The following steps were taken:The association of Navigate to the “Context analysis” tab and “Tissue/Coessentiality” subtab.On the left menubar select “Advanced selection” and then “Interactive Cell Line Selector”Hit the “Launch the interactive Cell Line Selector” button. You will be redirected to a new tab. Wait a little while until the interface is fully loaded.On the left menubar choose: Show cell lines with mutations from: Input genesType “RPL22” in the appearing text boxHit the “Submit” buttonShould cell lines contain a mutation in all (‘AND’) or at least one (‘OR’) of the above chosen genes?: ORSelection based on Copy Number Variation (CNV)?: No selection based on CNVCell culture growth properties: AllGenome stability: AllTissue of origin : AllCancer type : AllColour cell lines by: TissueChoose a name for your selection: e.g. “RPL22 non‐silent mutation”Hit the “Click here to confirm your selection” button. You will be redirected to the “Context analysis” tabOn the left menubar select: Start typing to select gene: RPL22L1Cells not chosen will be labelled as “Pancancer”, do you wish to subset Pancancer list by tissue type?: NoERBB2 amplification with its essentiality in breast and oesophagus cell lines was investigated in the “BROAD” project. The following steps were taken:The association of Navigate to the “Context analysis” tab and “Tissue/Coessentiality” subtab.On the left menubar select “Advanced selection” and then “Interactive Cell Line Selector”Hit the “Launch the interactive Cell Line Selector” button. You will be redirected to a new tab. Wait a little while until the interface is fully loaded.On the left menubar choose: Show cell lines with mutations from: No selection based on mutationSelection based on Copy Number Variation (CNV)?: YesWait until you see a black box below the slider. This might take a while.Choose a range of relative copy numbers to restrict the choice of genes with CNV: 1.1‐7.22Pick 1 or more genes, whose relative CN lies within the selected range: ERBB2Should cell lines contain a CNV in all (‘AND’) or at least one (‘OR’) of the above chosen genes?: ORCell culture growth properties: AllGenome stability: AllTissue of origin : Breast, OesophagusCancer type : AllColour cell lines by: TissueChoose a name for your selection: e.g. “ERBB2 amplification”Hit the “Click here to confirm your selection” button. You will be redirected to the “Context analysis” tab.On the left menubar select: Start typing to select gene: ERBB2Cells not chosen will be labelled as “Pancancer”, do you wish to subset Pancancer list by tissue type?: YesSubset Pancancer to a specific tissue of origin: Breast, OesophagusWRN helicase in colorectal cell lines was investigated in the “SANGER” project. The following steps were taken:1Navigate to the “Context analysis” tab and “Tissue/Coessentiality” subtab.2On the left menubar select “Advanced selection” and then “Interactive Cell Line Selector”.3Hit the “Launch the interactive Cell Line Selector” button. You will be redirected to a new tab. Wait a little while until the interface is fully loaded.4On the left menubar choose: aShow cell lines with mutations from: No selection based on mutationbSelection based on Copy Number Variation (CNV)?: No selection based on CNVcCell culture growth properties: AlldGenome stability: MSIeTissue of origin : Colon/RectumfCancer type : AllgColour cell lines by: TissuehChoose a name for your selection: e.g. “MSI in colorectal”5Hit the “Click here to confirm your selection” button. You will be redirected to the “Context analysis” tab.6On the left menubar select: aStart typing to select gene: WRNbCells not chosen will be labelled as “Pancancer”, do you wish to subset Pancancer list by tissue type?: YescSubset Pancancer to a specific tissue of origin: Colon/RectumThe association of microsatellite instability (MSI) with the essentiality of the P < 0.01). It is possible to add these edges; however, users should avoid adding confidence 1 edges  for tissues that also have low median score to avoid including high number of false‐positive edges.Lenient threshold  is generally applied for “Tissue” context in which there is likelihood of a large number of confounding factors , therefore a higher dynamic range of essentiality score distributions. In some cases, tissue contexts will have a very high dynamic range and median essentiality score can be less than 0.2 even though the association is of high statistical confidence  is generally applied for “Cancer type” contexts .Stringent threshold  is applied when the tested context is a single mutation in a single gene in a single tissue/cancer type.CENs that are used in consort with PPI integration are generated with a medium threshold to identify all possible enriched complexes.The network tab of CEN‐tools offers advanced options to users, which can be used to apply a range of thresholds to identify the most CENs for their study. In this study, we have used a multiple thresholding system depending on how many confounding factors one would expect within the group being tested and the purpose of the study as detailed below:Users should note that these thresholds are recommendations and they may choose to use a different thresholding method depending on the requirement.https://cancer.sanger.ac.uk/cell_lines). The cell line was cultured in DMEM/F12 media (Life Technologies) supplemented with 10% heat‐inactivated (50°C for 20 min) FBS, 20 μg/ml blasticidin and penicillin–streptomycin at 37°C with 5% CO2. Logarithmic growth phase of the cells was maintained by passing the cells every 2–3 days. Cells were tested and found to be mycoplasma free.The Cas9‐expressing A375 cell line was obtained from the Sanger Institute Cancer Cell Line Panel  Kit: CLS‐010G). To establish a clonal reporter line, 8 days post‐transduction, cells were individually sorted into 96‐well plates using FACS (MoFlo XDP). The clonal cells were further cultured for 3 weeks, and 48 individual clones were tested for their expression of GFP. Five clones exhibiting different levels of GFP were selected for expansion. The selected clone exhibited the highest constitutive expression of GFP when grown in culture supplemented with 10% serum and the highest loss of GFP signal when transduced with a single gRNA targeting 6 cells/well. Treated cells were detached from culture plates using EDTA, washed twice with PBS supplemented with divalent ions and analysed using flow cytometry. All flow cytometry was performed on a Cytoflex flow cytometer. 10,000 events per sample was acquired for each sample. Live cells were gated using forward and side scatter. GFP was excited at a wavelength of 488 nm and emission detected using a 530/30 band pass filter; BFP was excited at 405 nm and the emission detected using a 450/50 band pass filter. Analysis was performed using FlowJo software (Treestar).Drug treatments and gRNA transductions were performed on six‐well culture plates with 1 × 10Conceptualisation: SS, EP; Data curation, methodology, formal analysis, investigation, validation: SS, CD; Visualisation, software: SS, PW; Software: CD; Writing—original draft: SS, CD, PW; Writing—review & editing: EP, GJW. Supervision: SS, EP, GJW; Resources and funding acquisition: GJW, EP; Project administration: SS, EP.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Table EV4Click here for additional data file.Review Process FileClick here for additional data file.


Natural products are the ideal basis for the design of novel efficient molecular entities. Podophyllotoxin, a naturally occurring cyclolignan, is an example of natural product which displays a high versatility from a biological activity point of view. Based on its unique chemical structure, different derivatives have been synthesized presenting the original antitumoral properties associated with the compound, i.e., the tubulin polymerization inhibition and arising anti-topoisomerase II activity from structural modifications on the cyclolignan skeleton. In this report, we present a novel conjugate or hybrid which chemically combines both biological activities in one single molecule. Chemical design has been planned based in our lead compound, podophyllic aldehyde, as an inhibitor of tubulin polymerization, and in etoposide, an approved antitumoral drug targeting topoisomerase II. The cytotoxicity and selectivity of the novel synthetized hybrid has been evaluated in several cell lines of different solid tumors. In addition, these dual functional effects of the novel compound have been also evaluated by molecular docking approaches. Podophyllum, presents a tubulin interaction at the colchicine binding site that inhibits its polymerization, destabilizing and preventing the formation of microtubules required for cell division [In recent decades, the potential bioactivity of natural products has become well-known and well-characterized. Hence, the singularity of these compounds on the mechanisms of actions is based on three main characteristics: chemical skeletons, chiral structures and molecular complexity ,2. Moreodivision . The effdivision .As occurs with most natural products, structure–activity relationship (SAR) studies on the cyclolignan skeleton have been previously reported. From these reports, etoposide emerged as a new drug derived from podophyllotoxin that presented a particularity in its activity. The structural changes performed on the cyclolignan skeleton to obtain etoposide modified its mechanism of action, with it becoming an inhibitor of DNA-topoisomerase II (Topo-II) . The cheHowever, it is not only these structural changes that attribute novel properties to podophyllotoxin derivatives. The chemical skeleton of this cyclolignan has been widely modified, including different functional groups and heterocycles ,9. From Exploring the properties of the inversion of the configuration at position C-7 of podophyllotoxin structure, a huge structure–activity relationship has been carried out, taking hybridization as a tool and combining the cyclolignan skeleton with other compounds with different pharmacological activities, and obtaining interesting results on the bioactivity ,25,26,27Drug discovery must be firstly supported by rational design, but also by a complete subsequent evaluation of the activity. In this context, cytomic and proteomic techniques are essential to confirm the success of the design ,29,30 anAccording to the previous experience of the group with cyclolignans and taking into account that protein–drug interaction requires a difficult optimization, we have planned a new family of hybrids, named biscyclolignans, that combine, in a single molecule, both structure requirements to directly target tubulin and Topo-II, taking advantage of the duality of the skeleton of podophyllotoxin.O-demethylepipodophyllotoxin as Topo-II inhibitor and the other from podophyllic aldehyde as tubulin polymerization inhibitor  T.S.Ying, family Berberidaceae) by a previous procedure optimized by us [1, was purified (40%), both cyclolignan fragments could be obtained.Podophyllotoxin, the natural product, is the starting material of both cyclolignan fragments present in the final conjugate. The first step in the synthesis is the isolation of podophyllotoxin from commercial resin of ed by us . Once th2 [On one side, for the podophyllic aldehyde rest, the γ-lactone ring of podophyllotoxin was opened in basic conditions and the carboxylic acid was esterified with a methyl group. Oxidation under Swern conditions led to the podophyllic aldehyde, 2 ,34.O-demethylation at the C-4′ and epimerization of the C-7 position of podophyllotoxin, 1, followed by replacement by an aromatic diamine-type linker, gave rise to the other fragment of the hybrid, 3, in a one-pot reaction [2 was condensed with the 7β-cyclolignan 3 through an imine-type bond, obtaining the final 7β,9-biscyclolignan 4 , colorectal (HT-29) and breast cancer (MCF-7) cell lines. The cytotoxicity against them was evaluated at different concentrations. Several observations about the behavior of the compounds can already be highlighted in this assay, as shown in For this study, the podophyllic aldehyde, 2, was more potent in MG-63 cell line, while the MCF-7 cell line was slightly affected by this compound, and HT-29 was practically unaffected after 24 h. These results of our lead compound are in accordance with previous works and confirm the selectivity of this compound in comparison with the natural product podophyllotoxin, 1 [3, there were two cell lines very sensitive to this compound, HT-29 and MCF-7, while the osteosarcoma cell line MG-63 presented a reduced affectation compared with the other two. These differences in behavior show that both precursors are well-differentiated in their activity, as expected from the structural–activity relationship of the cyclolignans [4. This compound showed remarkable activity in the osteosarcoma cell line, being more potent than its precursors in this cell line. With these initial results, we observed the efficiency of the hybridization in cyclolignans and the potential of the new conjugate 4, which improved the activity of the precursors in the osteosarcoma cell line.The evaluation of cytotoxicity across the three human tumoral cell lines displayed differences at 24 h incubation for all the tested compounds. Podophyllic aldehyde, toxin, 1 . Regardiolignans . MoreoveApoptosis triggering is an important approach for determining the pattern of cell death mediated by the compounds. In this case, annexin V was used as a marker in flow cytometry for the determination of the cells in apoptosis stage, in combination with propidium iodide (PI), which detects late apoptosis and cell necrosis . The ana2, showed a modest apoptosis effect, which is in accordance with our previously reported studies where we observed that the initiation of inhibition of tubulin polymerization in this compound and podophyllotoxin, 1, did not show total cell death until longer incubation times [These analyses gave interesting results for the tested compounds. The first compound, podophyllic aldehyde, on times .+ cells. The other cell lines tested only increased apoptotic cells by approximately 10% compared to the control, although dot-plots with different cell distributions were observed on the HT-29 and MCF-7 cell lines.The most affected cell line by this precursor was MG-63 with a 63.85% of annexin3, since a strong variation compared to the control was observed, as shown in 2. Finally, the most outstanding results were obtained by the final conjugate 4. MCF-7 cell line, whose viability was not affected by compound 4, showed a cell distribution related to that of compound 3 by flow cytometry. This fact indicates that activity related to topoisomerase inhibition is present in this conjugate. Although this result is important, the values obtained for the MG-63 cell line draw much more attention where almost all the cells are in apoptosis (94.68% annexin+ cells), with a behavior closer to that of aldehyde 2. This particular value shows that the combination of both cyclolignan moieties in the same structure triggers an intense cellular response that could be mediated by the addition of the two mechanisms of action involved in cyclolignan structures.Interesting results were obtained for the 7β-cyclolignan precursor Since the inhibition of tubulin polymerization and the inhibition of Topo-II have a critical role in the cell cycle, we evaluated this to get more insights about the potency of the compounds tested.Bearing in mind previously reported studies, the cell cycle arrest by podophyllotoxin and non-lactonic cyclolignans such as podophyllic aldehyde, is reached in the range of µM and sub-µM concentrations ,12. The As can be observed in 2, and this tendency is confirmed in this study. The final conjugate also shows increased biological activity in this colorectal cancer cell line, which may be attributed to tubulin inhibition activity.HT-29 cell line was usually the most affected by other compounds synthetized by our group ,13,31, l4 was the most potent in arresting the cycle at the G2/M phase. These results suggest that the final conjugate 4 is able to act simultaneously in both targeted proteins, reaching our objective of merging both mechanisms of action in a single molecule.Finally, the MG-63 cell line seems to present a higher disruption of the cell cycle even under µM range. In this cell line, both precursors have shown activity below μM range and the final hybrid 4 can act as an inhibitor for the targeted proteins. As described in the methods section  and docking calculations have been carried out in order to obtain structural evidence that compound  section , we set ock-VINA .Podophyllotoxin has been described to occupy the colchicine binding site. This pocket is embedded in the β-subunit, close to the interface with α−tubulin and GTP binding site. It is thought that ligands that occupy the colchicine binding site inhibit tubulin dimerization via dimer destabilization, thus preventing the required contacts of the protomers from forming the microtubule ,38.4 for which ΔG = −12.5 kcal/mol. Interestingly, our results predict that the binding energy of 4 is even higher than that of podophyllotoxin, the reference compound.In 4 occupies the colchicine site. These results suggest that the compound 4 is able to hinder the formation of the dimer and the subsequent polymerization of the tubulin to form microtubules better than the precursors. This is a consequence of the larger size of compound 4, which fits in the dimerization interface, interacting with both α-tubulin and β-tubulin  fits in the β-tubulin strand placing the trimethoxyphenyl residue in the inner part of the pocket while the four fused rings are located closer to the interface of the two protomers , thus justifying the decreased dimerization energy where 4 is bound to tubulin, and also its higher binding energy.Podophyllotoxin .Additionally, DNA-topoisomerase αII isoform has been studied to further explore the complementary activity of the new conjugate  studies , two eto4 was highly similar to the one of etoposide. Likewise, for these compounds, 1 and 2, lower values were obtained for the interaction with the DNA and, especially, with the protein. When the simulation of etoposide was carried out, a higher value for the interaction with the DNA was obtained. This result agreed with previously reported studies of this compound, where it was described that etoposide presents a high affinity to the nicked DNA strand [As expected, podophyllotoxin and podophilic aldehyde have obtained lower energy values (ΔG) than etoposide, since the inhibition of Topo-II is not their pharmacological target; however, the energy found for the new hybrid A strand . The strTaking etoposide as the reference for Topo-II inhibition, it can be observed that the molecule is located in the middle of the DNA break. In addition, the dimethoxyphenyl ring interacts with the protein through a hydrogen bond between the main-chain of S462 and the hydroxyl at C-4 position. This interaction seems to be responsible for the interaction with the protein. As expected, podophyllotoxin and podophyllic aldehyde do not present an optimal disposition for interaction either with DNA or with protein, which justifies the lower values obtained in the calculations carried out . In addi4, whose binding free energy is equivalent to that for etoposide, adopts an optimal disposition for interaction with DNA, remaining perfectly intercalated between the two strands resulting from the break. Regarding the interactions with the protein, it interacts with two basic residues of the protein , thus leading to a stabilization of the complex. This fact justifies the considerable increase in the energy of the ligand–protein interaction that compound 4 presents with respect to the other compounds.Conjugate O-demethylation in C-4′ [2 and 3 integrated in the structure of the final hybrid 4, the new conjugate presented the best values in viability, improving the precursors’ results on the osteosarcoma cell line. This is an important result that displays how the two structures may contribute to the final activity of the conjugate and shows the potential of the novel dual conjugate. The extensive flow cytometry analysis confirms the expectations of the viability results.A combination of two different podophyllotoxin derivatives in a single entity has originated a novel hybrid compound which shows, simultaneously, the dual mechanisms of action associated with cyclolignans. The chemical requirements for tubulin and Topo-II inhibition were successfully included in the same molecule. This novel hybrid combines the best modifications carried out in the non-lactonic podophyllotoxin derivatives  with the in C-4′ . The che+), the anti-topoisomerase activity is represented by an alteration in the cellular distribution in the cytometer graphic. Final compound 4 presented a total distortion in the distribution of cells in the dot plot according to the dual behavior implied in the conjugate, reaching the highest annexin+ value among the tested compounds. Furthermore, cell cycle inhibition was an important parameter to determine the activity of the compounds and the dual behavior of conjugate 4, showing the highest potency in cell cycle arrest with a disruption mediated by the addition of two mechanisms: tubulin and topoisomerase inhibition. From the in c evaluation, the remarkable selectivity found in the tumor lines tested should be noted, where the best results were obtained in the osteosarcoma line compared to the other solid tumor lines tested. These results are interesting because osteosarcoma is particularly aggressive, presenting a high risk of metastasis [The evaluation of cell death effect reveals two different behaviors for these compounds. While tubulin inhibition is reflected by an increment in apoptotic cells  spectrometers in CDCl3 using the residual solvent signal as reference. Chemical shift (δ) values are expressed in ppm, followed by multiplicity and coupling constants (J) in Hz.1) was isolated from commercial resin of P. emodi following the protocol previous described by us [Podophyllotoxin (ed by us .2) was synthesized from podophyllotoxin as described in our previous works [Podophyllic aldehyde  in dry CH2Cl2 (2.5 mL) stirring with NaI  for 15 min at room temperature (rt) and N2 atmosphere. MeSO3H  was dropped while the reaction was maintained in an ice bath. The mixture was stirred at rt for 5 h, then N2 was bubbled and the solvent was evaporated. The raw reaction was dissolved in dry THF (6 mL) and p-phenylenediamine  and Et3N  were added, stirring the mixture for 19 h at rt. The crude product was diluted with EtOAc and the solution was washed with saturated aqueous NaCl, dried over Na2SO4, filtered, and the solvent was removed under vacuum. The crude product was chromatographed on silica gel, eluting with 30% EtOAc/CH2Cl2 to yield compound 3 .7β-Cyclolignan 4: A mixture of aldehyde 2 , anhydrous MgSO4  and amine 3  was dissolved in CH2Cl2 (3 mL) and stirred at rt for 3 d. The reaction mixture was diluted with CH2Cl2, filtered and concentrated under vacuum. The crude product was chromatographed on silica gel, eluting with 20% EtOAc/CH2Cl2 to yield compound 4 . See 7β,9-Biscyclolignan 2, 37 °C). All media and supplements were purchased from Sigma .The human colorectal adenocarcinoma , the human breast cancer  and the human osteosarcoma  cell lines were grown in adherent monolayer culture in Dulbecco’s Minimum Essential Medium (DMEM) medium for HT29 and MCF7 cells and RPMI medium for MG-63, and both were supplemented with 10% heat-inactivated fetal bovine serum (FBS), 1% L-glutamine and 1% penicillin/streptomycin in a humidified atmosphere  and 5 μL of the propidium iodide and incubated for 15 min at room temperature in the dark. After this time, 400 μL of 1X annexin-binding buffer was added. For data analysis of the annexin+/− and PI+/− ratios, the Infinicyt™ software  was used. By representing viability (% annexin− cells) on the vertical and concentrations of the compound on the horizontal one, curves of viability were obtained.To determine cell growth inhibition, early and late apoptosis, as well as cell death, the annexin V/propidium iodide assay  was applied. A total of 200,000 cells were seeded in 35-mm cell culture dishes  and allowed to settle for 24 h. Then, serial dilutions of the chemical compounds  were added to expose the cells for 24 h. After the incubation period, cells were detached by trypsinization and collected into flow cytometry tubes together with the supernatants. After three washing steps with PBS , 1X annexin-binding buffer was added to re-suspend the cells at a 1 × 10To study the cell cycle of HT-29, MCF-7 and MG-63 cells, a solution containing ribonuclease (RNase), propidium iodide (PI) and detergent was added to the cells. As described for the apoptosis assay, cells were collected into flow cytometry tubes and washed three times with PBS . Then, 200 μL of the solution mentioned above was added to each tube and incubated for 10 min at room temperature in the dark.In both flow cytometry assays, data acquisition was performed in a FACS Canto II flow cytometer  using the FACS Diva software . For data analysis, the Infinicyt™ software  was used.4 in the pockets occupied by podophyllotoxin and etoposide in 1SA1 and 5GWK, respectively. For the latter case, two ligand molecules were included in the simulation, replacing the two molecules of etoposide resolved in 5GWK.Our models for tubulin dimer and Topo-II were based on the PDB 1SA1 and 5GWK ,38, resp+ and Cl- were added to the system to account for a 0.15 mol/L KCl concentration. Coordinates of the Hydrogen atoms were generated with CHARMM [Once the complexes were prepared, they were placed in the center of a cubic water box large enough to contain the protein and at least 10 Å of solvent on all sides. To mimic the intracellular conditions, Kh CHARMM  using sth CHARMM , the CHAh CHARMM ,47, and h CHARMM  to accouh CHARMM  algorith4 were assigned using ParamChem [To calculate the docking energy corresponding to each of the ligands, the coordinates of the protein and DNA chains of last frames of the MD simulations were extracted for a subsequent docking stage and carried out using Autodock-Vina . As expearamChem ,51. The aramChem .4, was synthesized and evaluated. Using this natural product as a starting material for preparing the two fragments of the compound, we obtained a novel conjugate that combines the structural requirements for both anti-tubulin polymerization and inhibition of Topo-II activities. The extensive biological analysis carried out with the compounds showed a different behavior for each precursor, and those different behaviors were also observed in the conjugate. The final hybrid 4 achieved the best profile in the viability studies and the highest values in apoptosis induction and in cell cycle arrest at G2/M phase, improving the precursors’ properties. Additionally, the final conjugate had a cytotoxic selectivity in osteosarcoma cells compared to other tumor cell lines. These assays were complemented with molecular docking studies to confirm that the two protein inhibition activities can be involved in the mechanisms of action of the novel conjugate. These results are promising, showing the potential of this 7β,9-biscyclolignan 4. Further studies that include more structure–activity relationships are currently in progress.In this study, a new conjugate derived from podophyllotoxin, the 7β,9-biscyclolignan