Pancreatic ductal adenocarcinoma (PDAC) patients suffer poor outcomes, including a five-year survival of below 10%. Poor outcomes result in part from therapeutic resistance that limits the impact of cytotoxic first-line therapy. Novel therapeutic approaches are needed, but currently no targeted therapies exist to treat PDAC.act) and CRISPR knock-out (CRISPRko) screens in two common PDAC cell lines (Panc-1 and BxPC3). We used pathway analysis to identify gene sets enriched among our hits and conducted RNA-sequencing and chromatin immunoprecipitation-sequencing (ChIP-seq) to characterize top hits from our screen. We used scratch assays to assess changes in cellular migration with HDAC1 overexpression.To assess cellular resistance mechanisms common to four cytotoxic chemotherapies  used to treat PDAC patients, we performed four genome-wide CRISPR activation . These cells also displayed phenotypic changes in cellular migration consistent with activation of the EMT pathway. The expression changes resulting from HDAC1 activation were also observed with activation of several other co-repressor complex members. Finally, we developed a publicly available analysis tool, PancDS, which integrates gene expression profiles with our screen results to predict drug sensitivity in resected PDAC tumors and cell lines.Our data revealed activation of ABCG2Our results provide a comprehensive resource for identifying cellular mechanisms of drug resistance in PDAC, mechanistically implicate HDAC1, and co-repressor complex members broadly, in multi-drug resistance, and provide an analytical tool for predicting treatment response in PDAC tumors and cell lines.The online version contains supplementary material available at 10.1186/s12885-021-08388-1. Despite decades of clinical trials evaluating dozens of potential therapeutics, pancreatic ductal adenocarcinoma (PDAC) has remained largely refractory to improvement of five-year survival rates, which are still less than 10% . With laKRAS, TP53, SMAD4, and CDKN2A, however these have thus far proven to be poor drug targets [The limited effectiveness of cytotoxic chemotherapy has led to a long effort to identify alternative treatment strategies. Large cohort sequencing efforts have established a relatively short list of commonly mutated genes, including  targets . Moreove targets , 8. An aRNF43-mutations (a recurrently mutated gene in PDAC) [Cellular mechanisms of resistance have been explored by previous insertional mutagenesis- and RNA interference-based screens and have successfully identified genes whose inactivation leads to gemcitabine sensitivity in PDAC cells –11. Genoin PDAC) . These pin PDAC) .ko) [act) [The growing field of precision oncology aims to predict an optimal treatment for a patient based on genomic profiling. One group has begun to look for gene expression signatures associated with drug response in patient-derived PDAC organoids . Howeverko)  and endoo) [act)  screenin2. HEK 293FT cells (ThermoFisher #70007) were cultured in DMEM (ThermoFisher #11965) supplemented with 10% FBS (VWR #16777), 0.5% Penicillin-Streptomycin (ThermoFisher #15140122), 0.5% Non-essential amino acids (ThermoFisher #11140), and 0.5% sodium pyruvate (ThermoFisher #11360070).Panc-1 (CRL-1469), BxPC3 (CRL-1687), and MiaPaca-2 (CRL-1420) cell lines were obtained from ATCC. All cell lines were maintained in DMEM (ThermoFisher #11965) supplemented with 10% FBS (VWR #16777), 0.5% Penicillin-Streptomycin (ThermoFisher #15140122), and 0.5% GlutaMAX (ThermoFisher #A12860). All cell lines were maintained at 37 °C and 5% COLentiCas9-Blast (Addgene #52962) or Lenti-dCAS9-VP46-Blast (Addgene #61425) and lenti-MS2-p65-HSF1-Hygro (Addgene #61426) were used to generate stable cell lines for gene knockout and activation, respectively. We used the GeCKO A pooled sgRNA library (Addgene #1000000049) and the SAM pooled sgRNA library (Addgene #1000000057) for gene knockout and activation screening. We used lentiSAMv2 (Addgene #75112) for single gene activation validation. Guides were cloned as described previously . pMD2.G 2 bioassay plates with LB agar containing ampicillin and grown for 14 h at 35 °C. Transformation efficiency was greater than 1 × 108 for both libraries. Bacterial colonies were collected, pelleted and stored at -20 °C for less than 1 week. Plasmid DNA was extracted from greater than 3 g of pellet using the Qiagen EndoFree Plasmid Maxi Kit (Qiagen #12362).The GeCKO A and SAM library were amplified as described previously (20). Briefly, for each pooled library 8 electroporations were performed using 1uL of library and 25uL of Lucigen Endura Electrocompetent cells (Lucigen #60242). Pooled transformation were plated on 825cm.Primer sequences used for amplification are available in Supplemental Table S12 flasks. To proceed with viral packaging, DMEM was removed and replaced with 13 mL of pre-warmed OptiMEM (ThermoFisher #31985). The pMD2.G, psPAX2, and pooled library plasmids were combined with Lipofectamine PLUS reagent (ThermoFisher #15338) and OptiMEM. The combined solution was added and incubated for 6 h, when OptiMEM was replaced with media supplemented with 1% bovine serum albumin (ThermoFisher #A9647). After 60 h, media was harvested, centrifuged at 3000 rpm for 10 min at 4 °C, filtered through a 0.45um filter (VWR #28145), and stored at 4 °C overnight in Lenti-X concentrator (Clontech #631231) at a 3:1 virus to Lenti-X concentrator volume ratio. The following day the virus was centrifuged at 1500×g for 45 min at 4 °C and the supernatant was poured off. The viral pellet was resuspended in DMEM culture media at one-tenth the original volume and stored at -80 °C. Packaging of all vectors was performed identically to the pooled library, with reagents scaled down proportionally according to the surface area of the flask.HEK293T cells were grown to 60% confluency in 225cmViral packaging of single sgRNAs used for validation was performed in 6-well tissue culture treated plates. The protocol was performed as above except cells were 90% confluent, the incubation after transfection was reduced to 48 h and virus was either frozen as before, or in some cases used fresh.Panc-1 and BxPC3 cell lines were transduced with LentiCas9-Blast or co-transduced with Lenti-dCAS9-VP46-Blast and lenti-MS2-p65-HSF1-Hygro for knock out and activation screening, respectively. 500,000 cells were seeded into each well of a six-well flask with 0.8μg/mL polybrene (Sigma #TR-1003-G). Previously packaged plasmid was added at volumes that ranged from 0 to 500uL of virus. Cells were then centrifuged at 2000 rpm for 2 h at 37 °C. The following day, Panc-1 cells were treated 10μg/mL Blasticidin and 1500μg/mL Hygromycin and BxPC3 cells were treated with 2.5μg/mL Blasticidin and 1250μg/mL Hygromycin for 1 week until control cells (no virus added) were dead. The lowest viral titer that had a sufficient number of cells to carry forward were grown up such that pooled libraries could be transduced at 500x representation at a multiplicity of infection (MOI) of 0.4.6 cells per well with 0.8μg/mL polybrene and sufficient volume of concentrated virus to reach an MOI of 0.4 (approximately 30% of cells surviving after antibiotic selection). Cells were transduced as described above. The following day cells were split into larger 225cm2 flasks and either puromycin  or zeocin  was added to select for presence of knock out and activation plasmids, respectively. Library-transduced cells were under selection for 1 week post-transduction and expanded to 7 × 107 cells per treatment replicate or 1000x representation for each of the 4 drugs  and control conditions per replicate. Drug concentrations were as follows for Panc-1 cells: Gemcitabine (25 nM) Oxaliplatin (2.5uM), Irinotecan (500 nM), 5-FU (7.5uM) and for BxPC3: Gemcitabine (25 nM), Oxaliplatin (2.5uM), irinotecan (250 nM), 5-FU (5uM). A minimum 500x representation was maintained at all times in control cells. Drug treatment doses were optimized to yield ~ 80% cell death relative to untreated control cells after 14 days of culture. After 14 days of drug treatment, cells were pelleted and stored at -80 °C. DNA extraction and library preparation was performed as previously described [Pooled library transduction was performed in at least 30 wells of a 12 well flask at 3 × 10escribed .2 fold changes of the top two sgRNAs targeting each gene. Multi-drug hits were prioritized by computing the mean “L2FC sum” of the four drug treatments.To identify top genes from our genome-wide screen, we prioritized genes by the “L2FC sum” in each cell line, which is the sum of the replicate minimum log6 cells were seeded per well of a 6-well plate for a total volume of 2 mL of cells and media. To that we added, 1.6ul of polybrene (Millipore Sigma #TR-1003-G), and 1 mL of packaged virus. Then, we used spinfection for 30 min at 2000RPM at 37C. All lines were established using antibiotic selection as described above.The top sgRNAs for genes of interest were cloned into either LentiCrispr-v2 or LentiSAMv2 for knock-out or activation screen validation as described previously . Sequen. To geneNon-targeting and targeting sgRNAs were plated in 96-well plates at 750 cells/well and treated with a range of gemcitabine, oxaliplatin, or irinotecan concentrations. Plated cells were grown for 6 and 8 days, for the knockout and activation screen, respectively, with the media changed and drug applied every 2 days. The number of viable cells surviving drug treatment was assayed with CellTiter-Glo (Promega #G7571). ABCG2 inhibition was done under these conditions as well, with the only change being the addition of 3uM sorafenib or 3uM KO143 where indicated.2 fold change was computed for each sgRNA in each drug treated sample relative to the untreated control sample for each replicate. sgRNAs with fewer than 10 counts in the untreated control samples and fewer than 50 counts in a treated control sample were excluded from further analysis (< 1% of sgRNAs). At this step we recognized the first replicate of our knock-out screen correlated relatively poorly with the second two replicates and was excluded from downstream analysis. We ranked each of the sgRNAs targeting each gene by the minimum log2 fold change across each replicate. Top genes were subsequently prioritized for follow up by their “L2FC sum” in each cell line, which is the sum of the replicate minimum log2 fold changes of the top two sgRNAs targeting each gene  for each cell line were sequenced on one lane of Illumina NextSeq resulting in an average of 40 million reads per sample. 5′ and 3′ adapters were observed in > 99% of reads and were trimmed using cutadapt . Adapter2 fold changes for the top, second, and third sgRNA targeting each gene within a Reactome pathway to that of all other genes by Wilcox ranked sum test. Reactome [10P-values for the top, second, and third sgRNAs of each pathway . Gemcitabine sensitivity  and gene expression data for 14 PDAC cell lines was obtained from previously published work [Sensitivity to each of the four drugs was computed using a cell line’s treatment naïve gene expression levels and the minimum L2FC Sum for each gene across both cell lines. Raw expression data for each cell line, obtained from sources described below, was processed to obtain raw count tables . Varianched work . A panelhed work . Cell cohttps://gdac.broadinstitute.org/). We calculated a cox proportional hazard ratio and associated p-value DESeq2 VST normalized transcripts using the R ‘Survival’ package ‘coxph’ function.To create an integrated analysis of our screen results (knockout and activation) and patient overall survival, we downloaded survival statistics for 185 PDAC patients who participated in the TCGA study (https://www.encodeproject.org/documents/).Cell lines over-expressing chromatin remodelers, including HDAC1 were characterized using RNA-sequencing (RNA-seq) and ChIP-sequencing (ChIP-seq). Data were generated and analyzed using established, published methods , 29. Bri5 cells. Cell migration was assessed using images captured every 8 h for 64 h on a Lionheart FX (Biomek). Based on merged images collected over 64 h, we calculated the time (hr) to close the gap by half the width as previously described [MiaPaca-2 cells over-expressing ABCG2 or expressing a non-targeting guide were used for scratch assays. Scratch assays were completed using standard growth conditions after plating 1 × 10escribed . Images Raw sequencing data from our screen are available at SRA by referencing the BioProject number PRJNA542321.RNA-sequencing and ChIP-sequencing data are available using the GEO accessions GSE131596 (ABCG2 only) and GSE158541 .2 count fold change (L2FC) of the two most enriched sgRNAs targeting each gene in each cell line. We identified multi-drug resistance genes by computing the mean L2FC sum across all four drugs in each cell line . Based on these results, we focused our analysis on the activation screen, which revealed more genome-wide significant hits and an integrated analysis of the two screens described below.In the CRISPRko screen, we observe minimal overlap between cell lines and drugs. We calculated a combined L2FC Sum representing the combined results of all cell lines and drugs aimed at identifying multi-drug resistance genes. None of these signals reached statistical significance. We also identified overlapping genes from the 5-FU and gemcitabine screens. Since these drugs are both nucleoside analogs, we anticipated we might see similar hits. Again, the overlap was minimal but among the top hits in both screens was the gene VPS25, which is a tumor suppressor known to inhibit the Notch pathway . Inhibitact of the ATP-binding cassette (ABC) transporter, ABCG2, was the only perturbation that persistently induced resistance across each of the drug treatments in both of our cell lines . This script computes predicted drug sensitivity scores from gene expression data. We computed gemcitabine sensitivity scores and successfully stratified gemcitabine-treated PDAC patients into groups with distinct overall survival  and members of the tumor suppressing SWI/SNF complex (SMARCA1 and ARID1A) are recurrently mutated in PDAC tumors [MTA2/3, CHD3/4, HDAC1/2, and GATAD2A), members of the Nuclear receptor CoRepressor (NCoR) complex , and the repressive transcription factor REST and its interacting partner RCOR1, each of which have been demonstrated to regulate gene expression programs important for chemoresistance [act experiments in Panc-1 and MiaPaca-2 cells with HDAC1, which cooperates with trans-acting repressors as a member of several transcriptional repressor complexes. Our top HDAC1 sgRNA induced greater than 10-fold overexpression of HDAC1. Overexpression of this transcriptional regulator also produced several weaker transcriptional changes that we hypothesize to be downstream of HDAC1-mediated transcriptional regulation  and assay gene expression in these cell lines using RNA-seq. These lines showed concordant up-regulation of at least one, but often several, of the same top genes over-expressed upon HDAC1-activation  to identify HDAC1 binding sites in cells with activated HDAC1 compared to the wild-type parent MiaPaca-2 cell line to further understand the impacts of HDAC1 overexpression. We observed a 3-fold increase in the number of sites occupied with HDAC1 after overexpression relative to control cells and identified 17,501 occupied sites specific to HDAC1 activation Fig. . Finallyko and CRISPRact. We observed a relatively weak correlation between replicate reproducibility and gene significance levels across cell lines. While this observation may be partially explained by methodological covariates like selection strength, it is also likely that strong genetic and phenotypic differences observed between these cell lines permit different mechanisms of chemoresistance [Large-scale screening efforts that both inhibit and endogenously activate expression of genes have revolutionized forward genetic screen and are empowering discoveries that were previously not possible . Here, wsistance . A gene sistance , 50 and While large-scale screening efforts like this one represent an important step in the fight to reverse resistance and improve treatment options for PDAC, our study is also limited in that it identifies only cellular mechanisms of resistance. These data should be considered only part of the picture since the tumor microenvironment also plays a critical role in chemoresistance .act of ABCG2 was the highest-confidence perturbation capable of inducing multi-drug resistance. We confirmed ABCG2 overexpression in three pancreatic cancer lines resulted in resistance to chemotherapy and we showed that chemical inhibition of ABCG2 can reverse resistance in vitro, but its clinical relevance is unclear given a lack of strong correlation with patient outcomes. Some reports indicate that ABCG2 is expressed uniquely in cancer stem cells, a minority of the total cell population; thus, ABCG2’s role may be obscured in bulk tumor sequencing [The data we generated demonstrate the value of pooled CRISPR screening as a method for discovering cellular mechanisms of drug resistance. CRISPRquencing .While ABCG2 expression is not strongly linked to patient outcomes, we showed that overall, hits from our screen are relevant to patient outcomes. Moreover, we leveraged our screen data to predict drug sensitivity in cell lines and to predict outcomes in patients based on their respective gene expression profiles demonstrating the potential application of these data, en masse, to direct personalized therapeutic approaches.act of several transcriptional repressor complex members to chemoresistance. Another recent screen also implicated HDAC Class I signaling in resistance to dasatinib, a tyrosine kinase inhibitor is being tested in PDAC cell line [HDAC1, induced a program of gene expression associated with epithelial-to-mesenchymal transition (EMT). EMT has been associated with drug resistance for several decades but the mechanisms by which EMT is induced vary between cancers and cell lines [HDAC1 overexpression significantly increases occupation of HDAC1 at the promoter regions of genes important for the regulation of EMT and those changes likely drive chemoresistance. It is worth noting however that we observe a broad reprogramming that includes altered expression of genes involved in cell proliferation, migration and adhesion  as well as the regulation of apoptosis.Our pathway-based analysis linked CRISPRell line . We obsell lines , 53–55. ll lines . These all lines  includinll lines , 58. We ll lines , 60. OurAn interesting observation from our integration of HDAC1 binding and gene expression was that despite its canonical role as a repressor, HDAC1 was capable of binding and impacting transcription of its targets both as a repressor and as an activator. Recent studies have observed that HDAC complexes can play a variety of roles in gene regulation . In factGiven the link we observed between HDAC1 overexpression and chemoresistance, it follows logically that HDAC inhibition might be a relevant therapeutic strategy. In fact, HDAC inhibitors have exhibited inconsistent results in clinical trials to date, an observation that has been partially attributed to a poor understanding of which HDAC classes are the best targets and which biomarkers indicate sensitivity to HDAC inhibition . For thaOur screens represent a significant resource in the understanding of chemoresistance in pancreatic cancer. The ability to predict patient outcomes based on the results of this screen speaks to the value of the data for both mechanistic understanding and for clinical application. We have explored one significant pathway resulting from this work to further our understanding of HDAC1 specifically and chromatin remodeling more generally with our data pointing to the relevance of chromatin remodeling for the regulation of the EMT program, which contributes drug resistance. The results of our large-scale screen provide additional avenues for exploration in the important quest to improve treatment options for pancreatic cancer patients.Additional file 1: Supplemental Table S1. Primer Sequences for sgRNA Library Construction. Supplemental Table S2. sgRNA sequences for CRISPRact of individual genes and controls. Supplemental Table S3A. Activation (SAM) Screen results. L2FC Sum for individual drug-cell line combinations and combined. Supplemental Table S3B. Knockout (Gecko) Screen results. L2FC Sum for individual drug-cell line combinations. Supplemental Table S4A. Top 10 hits for each drug in each cell line for knockout (GecKO) Screen. Supplemental Table S4B. Top 10 hits for each drug in each cell line for Activation (SAM) Screen. Supplemental Table S5. Results of pathway analysis and Survival p-values used to make Fig. Additional file 2: Figure S1. (A) Schematic detailing the screening workflow. (B) A heatmap of the sgRNA count per million, z-scored by row for the most variable 500 sgRNAs across all of our CRISPRa screen replicates in the PANC-1 cell line. (C-F) Boxplots describing the replicate, non-replicate, and scrambled null permutation Spearman correlations for knockout screens in Panc-1 (C) and BxPC3 (E) and activation screens in Panc-1 (D) and BxPC3 (F). Figure S2. Irinotecan resistance following overexpression of ABCG2 compared to controls in two cell lines. A) BxPC-3 cells over expressing ABCG2 show resistance to irinotecan . B) MiaPaca-2 cells over expressing ABCG2 show resistance to irinotecan . Figure S3. The percentile rank for genes known to be involved in chromatin remodeling. HDAC1 is among the genes in this pathway. Figure S4. ChIP-seq analysis of HDAC1-overexpressing (red) MiaPaca-2 cells compared to non-targeting controls (blue). HDAC1 overexpressing cells show increased HDAC1 peak height at (A) the IGF2BP2 promoter, (B) the WNK1 promoter, and (C) the B4GALT6 promoter.


Vaccinium macrocarpon containing 54.2% v/v proanthocyanidins (CystiCran®). The solvents ethanol, methoxyethanol and polyethylene glycol were the least cytotoxic to all cell lines, with a maximum tolerated concentration (MTC) between 1 and 2% v/v. Ethanol, methanol and polyethylene glycol were mostly suitable for antimicrobial susceptibility testing, with minimum inhibitory concentrations (MICs) ≥ 25% v/v. The MTC values of the solvents dimethyl sulfoxide, dimethoxyethane and dimethylformamide varied from 0.03% to 1.09% v/v. The MICs of dimethyl sulfoxide, methoxyethanol and dimethoxyethane were in the range of 3.125–25% v/v. The cytotoxic effects of CystiCran® on eukaryotic cell lines were directly proportional to the superimposed effect of the solvents used. The results of this study can be useful for selecting the appropriate solvents for in vitro estimation of the cytotoxic and growth inhibitory effects of bioactive molecules in eukaryotic and prokaryotic cells.The cytotoxicity and microbicidal capacity of seven organic solvents commonly applied for studying plant extracts and bioactive compounds were systematically investigated based on international standards. Four cell lines of normal  or tumor  tissue origin, seven bacterial and one fungal strain were used. The impact of the least toxic solvents in the determination of in vitro cytotoxicity was evaluated using a standardized extract from  Cytotoxicity testing and evaluation of antimicrobial activity, following robust international standards ,2,3, areAiming to fill the gap of information about cytotoxicity and microbicidal effects of solvents on non-tumorigenic and tumorigenic cell lines, test bacterial or fungal strains, recommended by the international standards for toxicity testing and AST, we focused our study on the effects of the following seven widely used organic solvents: dimethoxyethane (DME), 2-methoxyethanol (MEtOH), dimethyl sulfoxide (DMSO), polyethylene glycol (PEG-400), dimethylformamide (DMF), ethanol (EtOH) and methanol (MeOH). These solvents are mostly applied for the dissolution of polar and nonpolar compounds or extracts in studies evaluating antineoplastic, antimicrobial and cytotoxic activities.v/v [DMSO, EtOH and PEG have been tested on a large panel of cell lines (mostly tumorigenic) and microorganisms ,12,13,14v/v . The effEscherichia coli and Bacillus subtilis were able to grow on agar plates in the presence of up to 4.8% of the solvents [E. coli than the other solvents. B. subtilis was shown to tolerated higher concentrations of DMF (≥10% v/v), suggesting that it can use DMF as a source of carbon and nitrogen. A study performed with DMSO, EtOH and MeOH on Staphylococcus epidermidis, Salmonella paratyphi A, Vibrio cholerae, Shigella flexneri and Pseudomonas oleovorans found that EtOH was mostly toxic to the listed microorganisms, inhibiting their growth by 19% at a concentration of 1% v/v, followed by MeOH. In contrast, DMSO was the least cytotoxic, with MTC of 3% volume fraction [Concerning the sensitivity of microorganisms, a diverse solvent tolerance on the growth of different bacterial species was observed for MeOH, EtOH, DMF and DMSO. solvents . DMF wasVaccinium macrocarpon  containing 54.2% proanthocyanidins (PAC) as a test sample. Proanthocyanidins are widely used as components of different food additives due to various health effects associated with them [The available literature concerning the sensitivity of animal and human cells to the selected seven organic solvents reveals that there are no comprehensive data about their in vitro cytotoxicity on the CCL-1 and HaCaT cell lines, which are commonly used in experimental chemotherapy. CCL-1 is recommended as a standard in Annex C of ISO 10993-5 , and HaCith them . Thus thEscherichia, Yersinia, Pseudomonas, Streptococcus, Enterococcus and Staphylococcus and one fungal strain  were also investigated for their susceptibility to the solvents. Four of these strains are recommended by ISO 20776-1 to estimate antibacterial activity [Seven pathogenic bacterial strains belonging to the genera activity . Some phVaccinium macrocarpon, depending on the solvents used and cell lines tested.With this in mind, a systematic study on cytotoxicity and microbicidal activity of the most commonly used organic solvents was carried out using the MTT and AST tests. The results could help select appropriate solvents and their relevant concentrations, thus guiding experimental studies of in vitro effects of biologically active molecules or extracts. In addition, our study provides experimental data on the cytotoxic effects of the standardized extract from the plant x (#PS-B), trypsin , and Accutase® (#ACC-1B). HCOOH was from Chimspektar OOD, Sofia, Bulgaria. The following media were used for the in vitro experiments with bacteria: brain heart infusion broth/agar , Mueller–Hinton broth/agar  and Luria–Bertani (LB) broth/agar . CystiCran®, a standardized concentrated extract from Vaccinium macrocarpon with 54.2% content of PAC A , was delivered by NATUREX-DBS, LLC .The following chemicals were purchased from Merck : dimethyl sulfoxide , ethanol  and methanol , 3--2,5-diphenyltetrazolium bromide , disodium ethylenediamine-tetraacetate dihydrate , L-glutamine (#G7513), and Dulbecco’s phosphate-buffered saline . N,N-dimethylformamide , polyethylene glycol of average molecular weight 400 ; 2-methoxyethanol  and 1,2-dimethoxyethane  originated from Acros Organics . The media, enzymes and sera used for the in vitro experiments with human cell lines originated from Capricorn Scientific, Ebsdorfergrund, Germany: DMEM (#DMEM-HPA), MEM (#MEM-A), fetal bovine serum (#FBS-HI-12A), horse serum (#HOS-1A), pen/strep 100TM . The first three cell lines were obtained from CLS Cell Lines Service . CCL-1TM was purchased from the American Type Culture Collection . Cells were maintained in a controlled environment—sterile cell culture flasks in a CO2 incubator Panasonic MCO-18AC  at 37 °C and a humidified atmosphere containing 5% CO2. CCL-1 was cultured in MEM with 10% heat-inactivated horse serum and 2 mM L-glutamine, and the cell culture medium for the rest of the cell lines was DMEM-HG with 4.5 g/L glucose with the addition of 10% heat-inactivated fetal bovine serum and 4 mM L-glutamine. Pen/strep (concentration of penicillin G sodium 105 Units/L and of streptomycin sulfate 100 mg/L) was added to both culture media. The cell lines were maintained as an adherent monolayer in a log phase. The cells were sub-cultivated about 1–3 times per week after the monolayer reached 80–90% confluence. The medium was removed from the flask, and the cell monolayer was washed with 3–5 mL PBS. The cells were incubated with 0.05% EDTA in PBS for 8–10 min, followed by the next step where all cell lines were subjected to enzyme cell detachment. HaCaT and CCL-1 monolayers were detached with 1 mL solution of trypsin/EDTA (0.1%/0.05% for HaCaT and 0.25% (w/v)/0.53 mM for CCL-1) and incubated for 2–5 min at 37 °C (for HaCaT) or at room temperature (for CCL-1 cells), whereas Accutase® was used for A-375 and A-431 lines. The incubation lasted until the separation and dispersion of the cells in the monolayer were observed using light microscopy. In the end, all cell lines were resuspended in 10 mL medium and centrifuged for 3 min at 300× g. The cell pellet was resuspended in fresh medium and sub-cultivated at a seeding density of 1.0 × 104 cells/cm2.The cell lines utilized in this study included the human tumor lines A-375  and A-431 (non-melanoma epidermoid squamous skin carcinoma) as well as the models for non-tumorigenic cells—HaCaT  and CCL-1Staphylococcus aureus (ATCC 29213), Staphylococcus aureus—MRSA , Streptococcus pyogenes , Enterococcus faecalis (ATCC 29212), Escherichia coli (ATCC 35218), Pseudomonas aeruginosa (ATCC 27853), and Yersinia enterocolitica . The selected strains are the most often investigated ones in the AST research studies in correspondence with the standard ISO 20776-1 Part 1: Reference method for testing the in vitro activity of antimicrobial agents against rapidly growing aerobic bacteria involved in infectious diseases [Candida albicans . The strains S. aureus, MRSA, E. coli and E. faecalis were grown on Mueller–Hinton broth/agar (MHB/MHA); S. pyogenes, P. aeruginosa, Y. enterocolitica and C. albicans were grown on brain heart infusion (BHI) broth/agar; E. coli was also grown on Luria–Bertani broth/agar (LBB/LBA).The bacterial strains used for AST of the solvents included: ATCC 2921, Staphyl50) and MTC concentrations of the solvents and PAC according to Annex C, ISO 10993-5 [5 cells/mL) were seeded per well in 96-well plates with a flat bottom. The cells were incubated for 24 h, allowing them to enter the log phase of their growth. On the 24th hour, the cells were treated with the solvents or CystiCran® solutions in concentrations ranging between 0.004% and 2% volume fraction (v/v) and incubated further for 72 h. The incubation periods are following the recommendations of the FDA guide [The MTT test was performed in order to determine the median inhibitory , and 50 µL were resuspended in 10 mL of the appropriate culture medium for each strain. The bacterial inocula were added to microtiter trays containing equivalent volumes of medium loaded with the relevant solvent in concentrations varying from 0.025 to 25% volume fraction. The final volume of each sample was 100 µL/well. The plates were incubated at 37 °C for 24 h. The pure culture medium served as a negative control. Every MIC was evaluated visually as the lowest concentration without visible growth. The minimum bactericidal concentration (MBC) was determined by incubating the samples treated from ½ MIC up to the highest concentration on MHA for 24 h at 37 °C. MBC was defined as the lowest compound’s concentration reducing the colony growth of the initial bacterial inoculum by ≥ 99.9%.The minimum inhibitory concentration (MIC) for each solvent was estimated by the broth microdilution method (BMD) in 96-well microplates, following ISO 20776/1-2006  and CLSI+-translocation). The protocol of Wang et al. was applied after minor modifications [The cell dehydrogenase  activity of all samples was measured at the end of the BMD assay, based on reducing the MTT dye to formazan by the membrane located bacterial enzyme NADH: ubiquinone reductase  against the respective blanks—water, EtOH, DMSO and MEtOH. The analytical calculations were based on multicomponent analysis calculations, derivative order 0, polynomial degree 0, smoothing points 1, data interval 2 nm and analytical wavelength zone from 190 nm to 820 nm.The stock solutions of CystiCran50 values from the MTT assay were evaluated with a non-linear regression analysis  using sigmoidal concentration–response curves. The cell viability is presented as a percentage of the untreated control. At least four wells were seeded for every concentration. Three independent assays were performed (n = 3). The maximum tolerated concentration (MTC) for the cell lines was determined from three independent assays according to ISO 10993-5 as the maximum concentration at which at least 70% of the cells were viable  GraphPad Software Inc., San Diego, CA, USA). To compare the MTC values of the solvents for each cell line, the ordinary One-way ANOVA statistical analysis based on Tukey’s multiple comparisons test with a single pooled variance (GraphPad Prizm 6.01 software) GraphPad Software Inc., San Diego, CA, USA) was utilized, and p ≤ 0.05 was set as a significance level. The respiratory activity of the bacterial cultures was calculated as a percentage against the untreated control.The ICwww.acdlabs.com) and ChemAxon’s Chemicalize platform . The following physicochemical properties were calculated: the octanol-water partition coefficient (logP), the distribution coefficient (logD) at pH = 7.4, and the topological polar surface area (TPSA). TPSA calculation is based on the summation of tabulated surface contributions of polar fragments in the chemical structures, and it is conformationally independent [Molecular property calculations were performed on the Percepta Platform of ACD/Labs, v. 2020.2  on four cell lines with the MTT test are summarized in 50 values and MTC of the solvents, respectively, whereas the values are presented as numbers in 50 of the solvents varied from 0.12 to >2% v/v and the MTCs were in the range of 0.03 to >2% v/v  and 0.89% v/v (PEG-400 on A-431). As expected, EtOH was the least cytotoxic solvent, with MTC and IC50 > 2% v/v determined on HaCaT, A-375 and A-431 cell lines. It was followed by MEtOH with MTCs ranging from 0.56 to 1.31% and IC50 0.17 to 2.60%. Next were DMSO and PEG-400, which exhibited similar toxicity—MTCs 0.15 to 1.09% and IC50 0.63 to 2.60%. The most toxic solvents were DME and DMF—their MTCs were in the range of 0.03–0.67% and IC50 varied in the interval 0.12–0.67%. >2% v/v . The IC5® are presented in The results for the in vitro cytotoxicity of the concentrated PAC extract CystiCran50 values for CCL-1 when the PAC extracts were dissolved in water, EtOH or MEtOH were not calculated because the values were higher than the highest concentration applied. The remaining IC50 values varied between 0.07 and 1.12 mg/mL. The MTC ranged between 0.02 and 1 mg/mL. The lowest IC50 value was that of CystiCran® in DMSO or EtOH on A-375 cells, and the highest (except CCL-1 cells) was determined for HaCaT cells when water was used as a solvent. The least sensitive cell line was CCL-1, and the most sensitive—A-375. The DMSO solution was less cytotoxic for HaCaT (IC50 = 0.17 mg/mL) than for A-431 (IC50 = 0.09 mg/mL) and A-375 (IC50 = 0.07 mg/mL) cells. The EtOH solution was the least cytotoxic to CCL-1 cells, more cytotoxic to A-431 (IC50 = 0.38 mg/mL) and HaCaT (IC50 = 0.19 mg/mL) and the most cytotoxic—to A-375 (IC50 = 0.07 mg/mL) cells. The IC50 values for MEtOH were between 0.27 and 0.64 mg/mL, whereby HaCaT was the most sensitive, followed by A-431 and A-375.All four cell lines  were incubated with different concentrations of the extract (0.004–1 mg/mL) for 72 h. The ICmax) as follows: 1) water—216, 242, 280 and 538 nm without an isolated peak at 366 nm; 2) absolute EtOH—242, 280, 366 and 538 nm; 3) DMSO—280, 366 and 538 nm; 4) MEtOH—242, 280, 366 and 538 nm. The absorbance values at λmax 242, 280 and 366 nm in the different solvents are presented in Based on the above-described cytotoxicity studies, spectrophotometric measurements were performed using EtOH, DMSO and MEtOH. The solvents DME, DMF and PEG-400 were not tested due to their high cytotoxicity. The results obtained in the course of the study in the analytical zone of 190–820 nm wavelength (λ) showed the presence of several absorption maxima (λY. enterocolitica and E. coli in LBB) and 12.5% v/v , followed by DMSO with MICs between 3.125 (for Y. enterocolitica) and 25% v/v (for the Staphylococcus aureus strains). The MICs determined for MEtOH were from 6.25 to 25% v/v for P. aeruginosa and the staphylococcal strains, respectively. The least toxic were EtOH and MEtOH, with MICs higher than 25% v/v for all tested bacterial strains. The solvents EtOH and MEtOH did not also inhibit the respiratory activity of the treated bacteria, whereas the other four solvents showed a varying inhibition potential on the tested strains. There was no proportional dose-dependent correlation between the MIC values and the DEHA activity. The MBC was higher than the MIC for most of the strains and solvents, with some exceptions .C. albicans was inhibited at a concentration of 6.25% v/v MEtOH or DMSO  than MEtOH (>70% active cells). The MBCs were higher than the MICs, except for DMSO, which exhibited bactericidal effect at the same concentration determined as MIC—6.25% v/v.The growth of  or DMSO . RegardiYersinia strains regarding all studied solvents or the effects of DME, MEtOH and PEG-400 on the bacterial test strains presented here. To determine better the applicability of the tested solvents and their maximum nontoxic concentrations, we applied bioassays recommended for evaluation on in vitro cytotoxicity in cell lines and AST of microorganisms by international standard protocols or widely used laboratory assays [50 values and MTCs, whereas MICs, MBCs and respiratory activity were measured for the bacterial cultures.There are few research models on cytotoxicity of vehicle solvents by using transformed non-tumorigenic cell lines ,12,24. Iy assays ,21,24. T50 and MTCs values (v/v). However, the MTC of PEG-400 in HaCaT (0.41% v/v) was twofold higher than that in CCL-1 (0.21% v/v), confirming the general trend of the higher sensitivity of fibroblasts. A plausible explanation for these results is the function and distribution of both cell types in human or animal tissues. Keratinocytes are present in all four epidermal layers, and they function as a protective barrier against adverse environmental factors [Generally, the non-tumorigenic cell lines tested in our study were more sensitive than the tumorigenic ones. The mouse fibroblasts were more sensitive than the immortalized keratinocytes, as seen from the lower ICs values  of all s factors ,28, wher factors ,30. Our  factors .v/v, which was determined as MTC for this solvent in this cell line, and the concentration of 1.25% v/v led to 50% inhibition, which is consistent with the results of [50 value of 13 mg/mL (1.16% v/v) for CCL-1 cells, which is lower than the cytotoxicity estimated by [In our study, EtOH was among the solvents with the weakest cytotoxicity towards mouse carcinoma cells, in agreement with a study performed by . Similarsults of . In anotsults of . Studiessults of ,18. The mated by , due to,®, containing a high concentration of PAC, was tested on four cell lines. PACs, the oligomers and polymers of flavan 3-ols, belong to the flavonoid family. PACs found in cranberry (Vaccinium macrocarpon) appear to be of primary importance for preventing uropathogenic bacterial adhesion [® as a suitable test product to assess the importance of different solvents in the planning and conducting of bioassays. As seen in ® when dissolved in different solvents. The trend showed a dependence on the cytotoxicity of the solvents themselves. Namely, the substance was more cytotoxic when dissolved in a more cytotoxic solvent, such as DMSO. This dependence varied among the different cell lines, probably due to the cell type. The sensitivity of the non-tumorigenic cells to the water solution of CystiCran® was significantly lower (IC50 = 1.12 mg/mL for HaCaT and > 2 for CCL-1) than that of the tumor cell lines (IC50 = 1 for A-375 and 0.77 for A-431), which was not the case for EtOH and MEtOH. To investigate if the observed differences are due to changes in the physicochemical features of the solutions, we measured their absorption spectrophotometrically. UV spectroscopy is considered to be one of the methods in the structural analysis of PAC: the compounds absorb principally in the range of 240 to 400 nm, and the UV spectra of most of them are composed of main absorption maxima between 300 and 400 nm and 240–285 nm. During the studies, changes were reported in both analytical zones. For all solutions except water, the absorption maxima disappeared at 216 nm, and for the solution in DMSO—at 242 nm. Again at 242 nm, the absorbance values for the other solutions decreased, and a hypochromic effect was observed. Only for MetOH the absorbance values at 280 and 366 nm were higher (hyperchromic effect). At 538 nm, the absorbance values of all solutions were below 0.190 AU (absorption units). All spectral profiles reflected changes in the structure and composition of the CystiCran® components. The polar solvents used have a negligible effect on λmax regardless of their interaction with the PAC molecules. Hypochromic and hyperchromic effects only are available compared to water, which mainly influences the quantitative content of PAC. A comparison between the IC50 values of the solvents  and difference in the culture media. For the BMD test, we cultured the P. aeruginosa strain in BHI broth, which is richer in nutrition components than the MHB used in [Staphylococcus. The S. epidermidis strain investigated by Wadhwani et al. [S. aureus strains tested in our experiments. Only DMSO in concentration 25% v/v inhibited the metabolism of both strains up to 12–13% metabolically active cells, but did not exert any bactericidal effect, showing full restoration of the bacterial growth 48 h later and after culturing on agar plates. Interestingly, the Y. enterocolitica strain was the most sensitive to the solvents tested, compared to the other strains. Its growth was inhibited at 3.125% v/v DMSO or DME, and at 12.5% v/v PEG-400 or MEtOH. Only EtOH and MeOH were well tolerated, exerting no toxicity on this strain at concentrations of 25% v/v or higher.Regarding the bacterial strains, EtOH, MeOH and PEG-400 proved to be least cytotoxic, which makes them attractive solvents for plant extracts and other substances in AST experiments. As seen in  E. coli . In our a et al. . In our d in MHB . WadhwanC. albicans showed that the fungal cells were the most sensitive to DMSO, followed by MEtOH and DME, which made them not suitable for further evaluation of antifungal activity on this strain. Moreover, MEtOH and DME suppressed the metabolic activity of this strain and could interfere with experiments on enzymatic activity. The other three solvents, PEG-400, EtOH and MeOH, were tolerable in concentrations up to 25% volume fraction.The analysis of data on 50, MTC, MIC, MBC and DEHA values. In the summary presented in v/v [A comparative analysis of the cytotoxicity demonstrated by various solvents depending on the cell line and bacterial/fungal strain was performed by following the ICd in v/v , we coulHaving in mind that the studied solvents are organic compounds with their own pharmacokinetics profile, we also looked for possible relationships between some physicochemical properties of the solvents and their cytotoxicity and microbicidal activity recorded in our experiments. Comparing the reported physicochemical parameters and the ranking of the solvents , no cleav/v than DMSO, DME and DMF. The most sensitive cell line is CCL-1. The cytotoxicity of the solvents influenced the cytotoxic effects of the extract CystiCran®, which confirms the need to predetermine the most suitable solvent when planning bioassays to evaluate in vitro cytotoxicity and to develop the experimental design. Additionally, EtOH and MeOH do not affect bacterial and fungal growth in a concentration up to 25%. PEG-400 and MEtOH are moderately tolerated by bacteria, but the latter has more pronounced antifungal activity. DME is the most toxic to all tested bacterial species, except for S. pyogenes and C. albicans (≥25%). DMSO shows microbicidal effect in concentration 6.25% only against Y. enterocolitica and C. albicans. The presented study facilitates the appropriate solvent selection that is extremely important when evaluating the effects of biologically active molecules or extracts in MTT and AST assays. It can help guide researchers to decide on the most relevant solvent and concentrations to be used in in vitro experiments that deal with the herewith examined cell lines and bacterial strains.To the best of our knowledge, the presented study is among the very few ones that investigate systematically the cytotoxic and microbicidal effects of the most used organic solvents and compares their effects on a panel of non-tumorigenic and malignant cells, bacteria and fungi. The non-tumorigenic cells prove to be more sensitive than the tumorigenic ones to all studied solvents. EtOH, MEtOH and PEG-400 are better tolerated by the cell lines in concentrations between 1 and 2%


Protein expression profiles of 10 HDAC/Sirtuin isoenzymes in two panels of human cancer cell lines were compared with each other and with the potencies of various anticancer drugs by Pearson and Spearman correlation analysis to identify patterns of enzyme expression and anticancer activity. Furthermore, the NCI COMPARE database was used to identify possible correlations between the mRNA expression in a 60 cancer cell panel and the potency of the same anticancer drugs. While several interesting correlations were found within both data sets, none of these correlations were identical in the two sets of data, suggesting that protein and mRNA expression profiles are not comparable. Combination treatments with several HDAC inhibitors with a number of the anticancer drugs revealed interesting synergistic effects that were in keeping with some of the correlations predicted by our protein expression analysis.Inhibiting the activity of histone deacetylase (HDAC) is an ongoing strategy in anticancer therapy. However, to our knowledge, the relationships between the expression of HDAC proteins and the antitumor drug sensitivity of cancer cells have not been studied until now. In the current work, we investigated the relative expression profiles of 10 HDAC isoenzymes comprising the classes I–III  in a panel of 17 cancer cell lines, including the breast, cervix, oesophageal, lung, oral squamous, pancreas, as well as urinary bladder carcinoma cells. Correlations between the data of mRNA expression for these enzymes obtained from the National Cancer Institute (NCI) 60 cancer cell line program were also examined. Next, we performed univariate analysis between the expression patterns of HDAC/Sirt isoenzymes with the sensitivity of a 16 cell panel of cancer cell lines towards several antitumor drugs. In a univariate correlation analysis, we found a strong relation between Sirt2 expression and cytotoxicity caused by busulfan, etoposide, and hydroxyurea. Moreover, it was identified that Sirt5 correlates with the effects exerted by oxaliplatin or topotecan, as well as between HDAC4 expression and these two drugs. Correlations between the data of mRNA expression for enzymes with the potencies of the same anticancer agents obtained from the NCI 60 cancer cell line program were also found, but none were the same as those we found with our protein expression data. Additionally, we report here the effects upon combination of the approved HDAC inhibitor vorinostat and one other known inhibitor trichostatin A as well as newer hetero-stilbene and diazeno based sirtuin inhibitors on the potency of cisplatin, lomustine, and topotecan. For these three anticancer drugs, we found a significantly enhanced cytotoxicity when co-incubated with HDAC inhibitors, demonstrating a potentially beneficial influence of HDAC inhibition on anticancer drug treatment. The epigenetic modulation of protein expression can wield a broad influence on tumor progression. It may cause recurrence of cancer, as well as malfunction of therapy ,2,3. EpiThe super-family of HDAC enzymes is subdivided into four classes according to sequence homologies and cofactor dependencies . The cla+. Interestingly, these enzymes do not use NAD+ as a redox active cofactor. Rather, during the enzymatic reaction the acetyl group is transferred to NAD+, releasing nicotinamide and a mixture of 2′- and 3′-O-acetyl-ADP-ribose (OAADPR) as by-products.Class III HDACs, the so-called sirtuins (Sirt), distinguish themselves from classical HDACs by their dependency on the cofactor NADIn humans, seven sirtuin isoenzymes have been identified (Sirt1–7) and were assigned to four different groups, according to their phylogenetic relationship. Class I includes the isoenzymes Sirt1–3, whereas Sirt4 and Sirt5 are assigned to class II and class III, respectively. The class IV covers the isoenzymes Sirt6–7. The various sirtuins differ in their subcellular localisation, enzymatic activities, as well as substrate specificities. Sirt1, Sirt6, and Sirt7 are usually localised in the nucleus, and Sirt2 is mainly cytosolic. Except for Sirt4 and Sirt5, in vivo deacetylation of core histone proteins is reported for all human sirtuins. Additionally, other acyl groups than acetyl are recognized and cleaved by certain isotypes. In this regard, Sirt5 is associated with the removal of acyl groups derived from dicarboxylic acids  and Sirt6, as well as Sirt2 with the cleavage of long-chain fatty acyl groups, such as myristoyl. Besides their function as epigenetic regulators via histone deacetylation, nearly all HDAC/Sirt isoenzymes possess additional non-histone targets, for example, p53, NF-ϰB, or HIF-1α, which explains their overarching role in apoptosis, cell cycle progression, and ultimately, tumorigenesis ,9,10.HDAC inhibitors are currently under investigation because of their promising potential in antineoplastic chemotherapy. Inhibitors of the classical HDAC isotypes can be divided into four groups according to their structural features: (i) hydroxamic acids, such as vorinstat, belinostat, panabinostat, or trichostatin A  represents the most advanced sirtuin-targeting drug candidate and is currently being tested in phase III clinical trials to serve as a disease-modifying agent in Huntington’s disease [In recent years, the HDAC inhibitors vorinostat, panabinostat, belinostat, and romidepsin have been approved as drugs for the treatment of haematological malignancies, as well as various solid tumors ,7. Addit disease ,11,12.Recently, we reported on the design of photoswitchable sirtuin inhibitors based on the structure of the moderately active, but unselective stilbenoid lead GW435821X. By structural modifications, the bioactivity of the parent compound was increased to the lower micro-molar range and isotype selectivity towards Sirt2 and Sirt3 was improved . FurtherDue to the importance of HDAC/Sirt in the epigenomic development of cancer, we asked the question as to how important the expression of specific HDAC/Sirt is on the anticancer action of commonly used chemotherapeutic drugs. Herein, we present a comprehensive study of the HDAC/Sirt expression profiles in a panel of 17 human cancer cell lines from various tumor origins, along with univariate correlation analyses to anticancer drug potency. Furthermore, we investigated the effects of inhibiting the HDAC/Sirt upon combination with selected chemotherapeutics, that is, the approved anticancer drugs cisplatin, lomustine, and topotecan.1a–5c were synthesised as recently described [The compounds escribed ,14. All 2/95% air. At near confluency, cell cultures were passaged weekly and routinely tested to exclude a potential contamination with mycoplasma. The doubling times of cancer cell lines were determined during the weekly transfer of cells into new cell culture flasks by counting the cells and seeding out a specific cell number to the new flasks. The doubling time was calculated via the following equation, where t, which are characterised by their particular cell numbers.With one exception, cell lines were obtained from Deutsche Sammlung von Mikroorganismen und Zellkulturen ; cervical (SISO), breast , bladder , pancreas , lung , oral (BHY), and esophageal  cancers. The A2780 human ovarian cancer line was provided by Dr. Julie A. Woods, Ninewells Hospital, Dundee, UK. All cells were cultured in RMPI 1640 cell culture medium supplemented with 10% of fetal bovine serum and 1% of penicillin/streptomycin in a humidified atmosphere at 37 °C with 5% CO50) was determined by the crystal violet assay, as previously described [0 control) was subtracted from treated cells and related to untreated control (T/C). The GI50 value was calculated with Prism 6.0 Software  by interpolation of the inhibited proliferation at 50%. All GI50 values are averages of four or more independent determinations.Fifty percent growth inhibition  and using precast “Criterion TGX Stain-Free Gels” and the corresponding “Trans-Blots Turbo Pack Midi” PVDF membranes. All antibodies were purchased from Cell Signaling Technology (United Kingdom): anti-HDAC1 (#5356), anti-HDAC2 (#5113), anti-HDAC4 (#7628), anti-HDAC6 (#7558), anti-Sirt1 (#9475), anti-Sirt2 (#12650), anti-Sirt3 (#5490), anti-Sirt5 (#8782), anti-Sirt6 (#12486), anti-Sirt7 (#5360), anti-mouse IgG HRP-linked (#7076), and anti-rabbit IgG HRP-linked (#7074).75 flasks and samples were collected when confluency of 80% was reached and lysed on ice for 30 3VO3, 0.1% Triton-X, and freshly added 1% protease inhibitor cocktail , followed by a sonication for 10 g, 10 The culture cells were grown in TFor the electrophoretic separation, 30 in bands  were detThe used TGX Stain-Free Gels include unique trihalo compounds that allow a rapid fluorescent detection of proteins. The trihalo compounds react with tryptophan residues in a UV-induced reaction to produce fluorescence. The fluorophores remain covalently bound to the proteins and were detected after the blotting procedure by a Gel Doc EZ imager . The use of stain-free imaging allows the normalisation of bands to the total protein on a blot, eliminating the use of housekeeping proteins, such as β-actin or GAPDH ,19. The The band intensities of the target proteins were related to the signal of the corresponding band column on the stain-free image. The relative protein expression was calculated by the corrected band intensity of the target protein related to the mean band intensity over all cell lines. All results are averages of four or more independent Western blot determinations.https://dtp.cancer.gov/mtweb/search.jsp, accessed on 15 May 2020. For the expression of mRNA, the following experiment ID numbers were used: HDAC1 #GC29185, HDAC2 #GC15516, HDAC4 #GC28681, HDAC6 #GC12671, Sirt1 #GC64431, Sirt2 #GC16536, Sirt3 #GC91754, Sirt5 #GC13785, Sirt6 #GC78925, and Sirt7 #GC46856.The data provided by the National Cancer Institute (NCI) were downloaded from dtp.cancer.gov, R were calculated by using ordinary least squares (OLS) and false discovery rate (FDR) corrections using the Benjamini-Yekutieli procedure [For statistical evaluation and visualisation, the Python packages statsmodels v0.11.1 , Matplotrocedure . The corA representative set of corresponding Western blots showing the expression of the HDAC/Sirt in a panel of 17 human cancer cell lines is pictured in For Sirt1, the highest expression was identified in A427 cells, whereas EFM-19, DanG, 5637, RT-4, and RT-112 cells exhibited low relative protein expression with values less than or equal to 0.65. The total Sirt2 protein consists of two isoforms, one with 43 than 2.0 . The panThe lung carcinoma cell line A427 is distinguished in that it exhibits greater expression of HDAC/Sirt isoenzymes compared to the other cancer cell lines included in this analysis. However, the profile of the cervix carcinoma cell line SiSo was most balanced considering the variance among the other cells.R correlation analysis was first performed to evaluate potential correlations between the protein expression of various HDAC/Sirt isoenzymes. R and p-values are compiled in Pearson-R and p-values) are given in Moreover, we performed a Spearman correlation analysis to identify possible trends in the rankings of the various HDAC/Sirt isoenzymes. The correlation matrix considering the Spearman correlation coefficients is depicted in R-values of 0.354 and 0.364, respectively, and between HDAC4 and HDAC6 (0.258). There were also correlations between Sirt1 with HDAC1 and HDAC2 (0.269), as well as with HDAC6 (0.316). Further significant correlations between the mRNA expression of the isoenzymes were not identified. However, as mentioned above, analysing the correlation of the protein expression seems to better represent the epigenomic functionality as compared to the data from the NCI mRNA analysis. Therefore, the lack of revealing outcomes when screening the NCI mRNA 60 cell line database is not too surprising. Nevertheless, the correlations we identified for Sirt1/HDAC1 and Sirt1/HDAC6 are in agreement with those of NCI.Using the data available from the NCI 60 cancer cell line program, we performed analogous correlation analysis with the data of mRNA expression for the corresponding HDAC/Sirt isoenzymes. The NCI ID numbers of the adduced enzymes are listed in the Methods section. The univariate Pearson correlation matrix of mRNA expression is shown in 50).The 16 cell line panel differed from the previous 17 cell line panel by the removal of the EFM-19 and EPLC-272H cell lines and the addition of the A2780 human ovarian cancer cell line. 50 values, as well as the relative GI50 .To evaluate whether the antiproliferative activity of the HDAC inhibitors SAHA and TSA correlates with the expression of zinc dependent HDAC isoenzymes, the potency of both compounds in a panel of 16 human cancer cell lines was determined, expressed as 50% growth inhibitory values  is shown in R and p-values compiled in 50 values (direct correlation), whereas a negative relation (inverse correlation) implies that high protein levels are accompanied by low GI50 values (greater potency).The matrix for the correlation of the relative protein expression and the corresponding anticancer drug potency . The univariate correlation matrix of mRNA expression is shown in R-values were below −0.370 and only representatives of inverse correlations showed significance, while the positive ones were not statistically significant. However, the very large number of cell lines still makes such correlations significant. Remarkably, only inverse correlations were detected between enzyme mRNA expression and GI50 values—that is, mRNA expression of HDAC1 with 5-fluorouracil and methotrexate, HDAC2 with colchicine, HDAC6 and Sirt1 with lomustine, Sirt2 with camptothecin, chlorambucil, oxaliplatin, thiotepa, and topotecan and between Sirt6 with etoposide. This is in contrast to our data in the 17 cancer cell line panel with protein expression, where both negative and positive correlations were found. The corresponding correlation matrix considering the Spearman correlation coefficients is given in Analogous correlation analysis of HDAC isoenzyme mRNA expression and the potency of anticancer drugs was performed with data from the NCI 60 cancer cell line program , which is the value of the quotient of the GI50 in combination (drug + the HDAC inhibitor) divided by the control GI50 . A To assess whether HDAC inhibitors can affect the potency of the anticancer drugs in combination treatments, the SiSo human cervical cancer cell line was chosen for further experimentation because of a balanced protein expression among all analyzed HDAC/Sirt isoenzymes . SiSo cewith TSA . The GI550 and CGI values are shown in 50 with CGI values of 0.81 and 0.91, respectively. Similar results were obtained in combinations with 2a (0.88), 4a (0.86), and 5c (0.89). For the combination of cisplatin with 1a, 3a, and 5a, a significantly enhanced potency of cisplatin was detected, with decreased GI50 values from 238 to 135, 190, and 121 50 of lomustine decreased significantly in combination with 1a, 3a, and 5a to values of 2.4, 2.3, and 2a and 4a, the CGI were only slightly decreased (0.85 and 0.83) and left unaffected in the case of 5b and 5c (1.07 and 0.93).The potency of lomustine was not enhanced by either SAHA or TSA. As with cisplatin, the GI50 of topotecan from 20.2 to 16.3 and 1a or the diazeno-based sirtuin inhibitors with the exception of 5b led to a significantly enhanced potency of topotecan. The GI50 value of topotecan decreased significantly, in some cases by half, to 13.5 (1a), 10.0 (2a), 11.2 (3a), 14.6 (4a), 12.8 (5a), and 5c) upon co-incubation. Corresponding CGI values ranged from 0.52 to 0.79.The potency of topotecan was significantly enhanced in combination with SAHA and TSA, resulting in decreased GIIn our previous studies, we sought associations between the antiproliferative activity of 19 anticancer drugs with the activity of various antioxidative enzymes in a similar panel of human cancer cell lines . In the HDACs represent a family of key epigenetic modulators. It has been reported that the expression of zinc dependent HDACs can be significantly increased in neoplastic compared to healthy tissues ,28. HoweFor our studies, we expected an irregular protein expression of HDAC isoenzymes across the tested cell lines and assumed that a low protein expression of a certain isoenzyme could be compensated by an increase in the expression of another isoenzyme, reflected by a significant inverse correlation (negative correlation). However, no evidence was found to confirm this hypothesis. Except for HDAC2 with Sirt2, no such negative correlations were apparent. Instead, a few positive correlations were detected. Positive correlations were found between HDAC1 with Sirt1, Sirt3 and Sirt7, HDAC6 with Sirt1, Sirt1 with Sirt3, Sirt6, and Sirt7, as well as Sirt6 with Sirt3. The genes for the HDAC/Sirt isoenzymes are spread widely across the chromosomes; an overview of the localisation is given in 50) of a large number of small molecules such as anticancer drugs in a panel of 60 various cancer cells lines of differing tumor origin. Compiled datasets are accessible free of charge to the public by an Internet-based data bank. On the other hand, data with protein expression profiles are not available, probably because of the high expense and effort such protein analysis by Western blotting would entail. Here, we have performed correlation analysis with protein expression, which we believe more accurately mirrors the cellular epigenomic functionality comparable to data obtained from mRNA analysis. Interestingly, none of the correlations we found between the HDAC/Sirt enzyme expression in our 17 cancer cell line panel were found in the corresponding NCI data with 60 cancer cell lines, and vice versa. Likewise, none of the correlations we found between HDAC/Sirt enzyme expression and anticancer drug potencies in the 17 cell line panel were present in the corresponding NCI data, and vice versa. While the two panels of cancer cell lines have different sizes and different cancer cell lines, in a previous study where we compared relative potencies of anticancer drugs in these two panels, many of the same correlations were confirmed in both panels [Analogous correlation analyses were performed with data from the NCI 60 cancer cell line program. The NCI collects data of mRNA expression for genes as well as antiproliferative potencies  increased the potency of topotecan. The effect to potentiate standard anticancer drugs can be related to the loosening of the chromatin structure. Due to the “open” chromatin structure, the accessibility of the DNA as the drug target may be promoted and cytotoxicity of DNA-damaging chemotherapeutics such as cisplatin, lomustine, and topotecan can be increased [Besides the correlation analysis of HDAC protein expression and anticancer drug potency, we performed combination cytotoxicity testing of HDAC inhibitors with several anticancer drugs. These studies focussed on the effect of SAHA and TSA as well as the newer sirtuin inhibitors on the potency of cisplatin, lomustine, and topotecan. For the combination analysis, the GIll lines . As mentll lines ,52. Evenncreased ,54. Howe50 values of the drug was detected, indicating that cancer cells with low enzyme expression are especially sensitive to anticancer drug treatment. Furthermore, we demonstrated that the inhibition of Sirt2 significantly increased the potency of cisplatin, lomustine, and topotecan in vitro. Based on this observation, a set of recently discovered Sirt2 inhibitors was used to gain first insights into their potential benefit in combination with such anticancer drugs.In summary, insights have been gained into the connection between HDAC/Sirt expression and anticancer drug sensitivity. These studies provide possible clues for the optimisation of the anticancer drug therapy. With the current work, it has been shown that the expression of several HDAC/Sirt isoenzymes correlates with the sensitivity towards a number of anticancer drugs, such as platinum compounds, topoisomerase inhibitors, or other cytostatic agents of natural origin. In most cases, a positive correlation to the relative protein expression with the GI


We created a first-generation metastasis map (MetMap) that reveals organ-specific patterns of metastasis, enabling these patterns to be associated with clinical and genomic features. We demonstrate the utility of MetMap by investigating the molecular basis of breast cancers capable of metastasizing to the brain—a principal cause of death in patients with this type of cancer. Breast cancers capable of metastasizing to the brain showed evidence of altered lipid metabolism. Perturbation of lipid metabolism in these cells curbed brain metastasis development, suggesting a therapeutic strategy to combat the disease and demonstrating the utility of MetMap as a resource to support metastasis research.Most deaths from cancer are explained by metastasis, and yet large-scale metastasis research has been impractical owing to the complexity of in vivo models. Here we introduce an in vivo barcoding strategy that is capable of determining the metastatic potential of human cancer cell lines in mouse xenografts at scale. We validated the robustness, scalability and reproducibility of the method and applied it to 500 cell lines A method in which pooled barcoded human cancer cell lines are injected into a mouse xenograft model enables simultaneous mapping of the metastatic potential of multiple cell lines, and shows that breast cancer cells that metastasize to the brain have altered lipid metabolism. However, large-scale characterization of cell lines has been limited to rudimentary readouts such as viability in cell culture, because more complex phenotypes—such as behaviours in vivo—have not been tractable at scale. By contrast, most studies of metastasis rely on only a small number of experimental models9, thereby making it difficult to extrapolate findings to genetically diverse human tumours10.Human cancer cell lines have been a driving force in cancer research, leading to the discovery of oncogenic mechanisms and therapeutic targetsIdeally, it would be possible to construct a map of organ-specific metastatic potential of hundreds of human cancer cell lines using xenograft models, so that the molecular features of the cell lines could be related to their ability to survive and proliferate in organ-specific microenvironments. However, the prospect of in vivo testing of each cell line individually is unattractive, because it is labour-intensive and expensive, as well as because of the difficulty in sufficiently controlling for variability between animal experiments. We proposed that if cell lines were labelled with molecular barcodes and injected into recipient mice as a pool, internally controlled, metastatic potential could be assessed in a highly scalable manner.To test the feasibility and reliability of in vivo barcoding to monitor growth in different tissues in mice, we performed a pilot study using four breast cancer cell lines Fig. . Each ce11. Reflecting this diversity, the cell lines showed disparate metastatic patterns: pan-metastatic, metastatic preferentially to particular organs or not metastatic   Fig. . We simi13. By contrast, cell lines derived from brain tumours were generally non-metastatic, reflective of their tendency to not undergo haematogenous spread15. Similarly, the DU145 prostate cancer cell line, derived from a brain metastasis lesion16, exhibited brain metastasis in our experimental system. Cell lines derived from metastases showed higher metastatic potential than lines derived from primary tumours, although lines derived from primary tumours known to later give rise to metastases in patients were metastatic in the MetMap  = 0.0034) , which itself has been reported to be associated with brain metastasis in humans20. Indeed, two of the brain metastatic cell lines (JIMT1 and HCC1954) also contain typical ERBB2 gene amplifications  Fig. . A fifthAt the level of DNA copy number, we observed an association between metastatic potential and deletions of chromosome 8p12–8p21.2 (referred to as 8p) (FDR = 0.0017) Fig. . Five ou18. We observed a strong correlation between 8p copy number and gene expression in the METABRIC and TCGA datasets23  in brain metastatic cells   Fig. . SREBF1 ans Fig. . This SRial Fig. .SREBF1 in affecting the lipid phenotype observed in brain metastatic cells, we performed lipidomics after knocking out SREBF1 in JIMT1 and HCC1806 cells using CRISPR–Cas9. SREBF1 knockout resulted in a marked shift in intracellular lipid content, including a decrease in levels of cholesterol, membrane lipids and diacylglycerols  was unique to brain compared with other sites of metastasis. Similar upregulation was also observed in brain metastases from patients compared with extracranial metastases or their matched primary tumours36 , compared with a modest defect in other organs (9–21 fold)  Fig. . Histololls Fig. .SREBF1 requirement for breast cancer growth in the brain, we knocked out SREBF1 in additional brain metastatic lines including HCC1954, MDAMB231 and HCC1806 using CRISPR–Cas9. As with JIMT1, a significant inhibition in brain metastatic growth was also observed in these lines, although the magnitude and duration of growth inhibition varied  currently includes metastasis profiles of 500 cell lines spanning 21 tumour types, providing a large repertoire of models for exploration of metastasis mechanisms. A limitation of the use of human cell lines for such experiments is that they require the use of immunodeficient mice. The extent to which the immune system has a role in mediating patterns of metastasis remains to be determined37.This work describes MetMap as an approach for large-scale in vivo characterization of human cancer cell lines. The MetMap resource . Cell line identities were confirmed by SNP fingerprinting as well as RNA-seq, and compared to the CCLE results. All cell lines were tested negative for mycoplasma. The fluorescence-luciferase-barcode (FLB) construct was engineered using the FUW lentiviral vector backbone . Barcodes 26 nucleotides in length were designed using barcode_generator.py , and cloned into the landing pad C-terminal to the TGA stop codon of fluorescence luciferase using Gibson assembly (New England Biolabs). Lentivirus preparation and cell infection were performed according to published protocols available at http://www.broadinstitute.org/rnai. Infected cells were analysed by FACS with a fixed gate for GFP or mCherry, using a Sony SH4800 sorter.Breast cell lines were cultured under the recommended conditions from CCLE . In vivo metastasis progression was monitored via real-time BLI using the IVIS SpectrumCT Imaging System (PerkinElmer) on a weekly basis. Mice were anaesthetized with inhaling isoflurane, injected intraperitoneally with D-Luciferin (150 mg kg−1), and imaged with the auto exposure setting in prone and supine positions. At the end point, ex vivo BLI was performed by submerging the excised organs in DMEM/F12 medium (Thermo Fisher Scientific) containing D-Luciferin for 10 min and imaged with the auto exposure setting. BLI analysis was performed using Living Image software . In the case of breast cancer cohort study  support, at a matching density to their intracardiac assays, respectively . NSG female mice (The Jackson Laboratory) at 5–6 weeks of age were used. Cancer cells were suspended in PBS, 0.4% BSA and 100 μl of cell suspensions were injected into the left ventricle of anaesthetized mice . The optimized dissociation solutions and programs (Miltenyi Biotec) are listed in Supplementary Table 44.Individual cell lines, cell line pools before injection, and cells isolated from metastases were analysed by RNA-seq. RNA extraction was performed using Quick-RNA MicroPrep according to the manufacturer’s instructions (Zymo Research). RNA was quantified using an RNA 6000 Pico Kit on a 2100 Bioanalyzer (Agilent). RNA samples from cell numbers lower than 500 were not measured but all were used as input for library preparation. cDNA was synthesized using Clontech SmartSeq v.4 reagents from up to 2 ng RNA input according to the manufacturer’s instructions (Clontech). Full-length cDNA was fragmented to a mean size of 150 bp with a Covaris M220 ultrasonicator and Illumina libraries were prepared from 2 ng of sheared cDNA using Rubicon Genomics Thruplex DNaseq reagents according to the manufacturer’s protocol. The finished double-stranded DNA (dsDNA) libraries were quantified by Qubit fluorometer, Agilent TapeStation 2200, and RT–qPCR using the Kapa Biosystems library-quantification kit. Uniquely indexed libraries were pooled in equimolar ratios and sequenced on Illumina NextSeq500 runs with paired-end 75-bp reads at the Dana-Farber Cancer Institute Molecular Biology Core Facilities. RT–qPCR quantification of barcodes was performed using Maxima First Strand cDNA Synthesis Kit, Taqman Fast Advanced Master Mix, custom synthesized Taqman probes, and QuantStudio 6 PCR System (ThermoFisher Scientific). Single-cell RNA-seq was performed as previously described45 for barcode detection and quantification. Mapped reads were filtered with the criteria that reads (either 5′ or 3′) must cover over 50% of the barcodes from either end, and counted using samtools. Barcode percentage corresponding to cell composition was calculated for single cell lines, pre-injected cell mixtures and in vivo metastasis samples.Because the RNA-seq library preparation sheared the cDNA randomly into small pieces, demultiplexed RNA-seq reads were mapped to the barcode references using Bowtie 2 local modej targeting organ i, Mi,j was calculated as: ci is the total cancer cell number isolated from organ i, pj is the fractional proportion of cell line j estimated by barcode quantification, and n is the number of replicates of mice. To identify features that associate with brain metastatic potential, a two-class comparison method was used46. The analysis was performed on mutation, copy number, expression, metabolite, and CRISPR-gene dependency . Copy number data were binarized using a cutoff of ≤−1 (loss) and ≥1 (gain).For breast cohort study, metastatic potential of cell line https://sourceforge.net/projects/bbmap/). Reads that uniquely mapped to the human genome were then used as input for mapping and gene-level counting with the RSEM package47. Gene count estimates were normalized using the TMM method in edgeR48. For differential analysis, to properly account for the cancer cell composition differences in each in vivo sample, an in silico modelled in vitro mixture was generated first. For each in silico metastasis model, the estimated expression ĝ of gene i is computed as a weighted average of the cell lines present in the corresponding in vivo sample: gi,j is the baseline in vitro expression of gene i in cell line j and pj is the fractional proportion of cell line j in the in vivo sample, as estimated by barcode quantification, and M is the number of cell lines present in the in vivo sample. The in vivo and in silico counterpart were then compared using a paired design for each organ in voom-limma46. GSEA was performed using camera or GSEA-preranked method implemented in fgsea49. Single sample GSEA signature projection was performed using gsva package50. Gene-signature datasets were from MSigDB (https://www.gsea-msigdb.org/).Potential mouse contaminating reads were removed by competitive mapping to the human/mouse hybrid genome using BBSplit  were adapted to the same culture conditions in phenol-red-free RPMI1640 medium (ThermoFisher Scientific) and barcoded as previously described12. SNP fingerprinting authentication was performed before and after barcoding. Mycoplasma contamination was examined  and only negative lines were used for experiments. These included eight oestrogen-receptor-positive breast cancer cell lines. Despite the lack of phenol red (a weak oestrogen) these breast cell lines maintained ESR1 positivity and expression of a downstream marker of its activity, FOXA1. This is probably explained by the remaining oestrogens in the fetal bovine serum (FBS). PRISM cell lines were pooled on the basis of their in vitro doubling bins, at equal number, in the format of 25 lines per pool, and cryopreserved until use. Cells were thawed and recovered for 48 h before in vivo injection. To form the large pool of 498 cell lines, 20 PRISM pools were mixed at equal total number immediately before injection.PRISM cell lines  were lysed in 200 μl freshly prepared lysis buffer with proteinase K, heat digested at 60 °C, and denatured at 95 °C for 10 min. Twenty microlitres of the lysates was used for barcode amplification per 100 μl PCR volume . PCR was performed using the following conditions: 95 °C for 3 min; 98 °C for 20 s, 57 °C for 15 s, 72 °C for 10 s (30 cycles); 72 °C for 5 min; 4 °C stop. PCR libraries were pooled, purified using Select-a-Size DNA Clean & Concentrator Kit (Zymo Research), and quantified using Qubit dsDNA HS Assay Kit (ThermoFisher Scientific) and a 2100 Bioanalyzer (Agilent). The purified 2 nM of libraries with 20% spike-in PhiX DNA were sequenced on Illumina MiSeq or HiSeq at 800 K mmj targeting organ i, rMi,j, was defined as: ci,j is the read counts of cell line j from organ i, pj is the read counts of cell line j from pre-injected population, n is the number of replicate samples of mice, m is the number of replicates of pre-injected population. Confidence intervals were calculated using bootstrap resampling.Demultiplexed sequencing reads were mapped to the barcode reference to generate a table of cell line barcode counts for each sample/condition. Sequencing-depth normalized read counts were used for calculation of relative metastatic potential. Relative metastatic potential of cell line −1 blasticidin for 10 days with continuous passaging until non-infected controls were killed. For pooled in vivo screen, JIMT1–Cas9 cells were infected with a CRISPR guide library . Extracts were centrifuged for 10 min at 10,000g to remove residual cellular debris. After centrifugation, supernatants were injected directly onto a 100 × 2.1 mm, 1.7-μm ACQUITY BEH C8 column (Waters). The column was eluted isocratically with 80% mobile phase A (95:5:0.1 v/v/v 10 mM ammonium acetate/methanol/formic acid) for 1 min followed by a linear gradient to 80% mobile phase B (99.9:0.1 v/v methanol/formic acid) over 2 min, a linear gradient to 100% mobile phase B over 7 min, then 3 min at 100% mobile phase B. Mass spectrometry analyses were performed using electrospray ionization in the positive ion mode using full scan analysis over 200 to 1,000 m/z at 70,000 resolution and 3 Hz data acquisition rate. Other mass spectrometry settings were as follows: sheath gas 50, in source collision-induced dissociation 5 eV, sweep gas 5, spray voltage 3 kV, capillary temperature 300 °C, S-lens RF 60, heater temperature 300 °C, microscans 1, automatic gain control target 106, and maximum ion time 100 ms. Lipid identities were determined on the basis of comparison to reference standards and reference plasma extracts and were denoted by the total number of carbons in the lipid acyl chain(s) and total number of double bonds in the lipid acyl chain(s).Positive ion mode analyses of polar and nonpolar lipids (C8-pos) were conducted using a liquid chromatography–mass spectrometry (LC–MS) system composed of a Shimadzu Nexera X2 U-HPLC (Shimadzu) coupled to an Exactive Plus orbitrap mass spectrometer (ThermoFisher Scientific). Cellular extracts were collected from 6-well plate culture, in LC-MS-grade isopropanol (Sigma-Aldrich) containing an internal standard 1,2-didodecanoyl-51.Protein lysates were prepared in RIPA lysis buffer (ThermoFisher Scientific) with cOmplete Mini EDTA-free Protease Inhibitor Cocktail (Roche). Western blot was performed using NuPAGE gel (ThermoFisher Scientific) with wet tank blotting (Bio-Rad) and Odyssey detection system (LI-COR). SREBF1 primary antibody , SCD (CD.E10) antibody , GAPDH (D16H11) XP rabbit monoclonal antibody , β-actin (8H10D10) mouse monoclonal antibody , and IRDye 800CW goat anti-mouse IgG , IRDye 680RD goat anti-rabbit IgG  secondary antibodies were used. Western blot was performed for cells cultured in different medium conditions. These include RPMI1640 with 10% FBS, with 10% delipidated FBS, with 10% human cerebrospinal fluid , or with 1% SM1 supplement , or brain-slice-conditioned medium. Brain-slice-conditioned medium was prepared by submerging brain slices (150 μm) in RPMI1640 (no serum) for 48 h. Delipidated FBS was prepared as described52. The EMC-MSK dataset including 615 primary tumours  and the 65-metastasis-sample dataset (GSE14020) were collected and processed as previously described18. Paired primary breast tumour and brain metastasis RNA-seq was obtained from ref. 36. To exclude the confounding effect of brain stroma contamination in this dataset, a contamination indicator generated from GSE52604 was applied, and the contaminating effect was regressed out, generating a corrected gene matrix. PI3K-response signatures were from refs. 26. Signature analysis was conducted as described7. Hierarchical clustering and heatmaps were generated using gplots package. Other plots were generated using ggplot2. log-rank tests of survival curve difference were calculated using survival package. A multivariate Cox proportional hazards model was built using the coxph function (Extended Data Fig. METABRIC, TCGA and MSK targeted-sequencing breast cancer datasets were downloaded from cBioPortalFurther information on research design is available in the Nature Research Reporting Summary linked to this paper.Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-020-2969-2.Supplementary InformationSupplementary Notes 1-4.Life Sciences Reporting SummarySupplementary Figure 1Uncropped raw western blot images.Supplementary Table 1Basal-like breast cancer cell line and barcode information.Supplementary Table 2Metastatic potential of basal-like breast cancer.Supplementary Table 3MetMap cell line, pooling scheme, and updated annotation.Supplementary Table 4Metastatic potential in MetMap500.Supplementary Table 5Metastatic potential in MetMap125.Supplementary Table 6Signature gene list of Chr 8p genes, PI3K-response signatures, and brain stroma indicator.Supplementary Table 7Differential analysis of RNA-Seq of SREBF1-KO versus -WT cells.Supplementary Table 8in vivo metastases versus in-silico-modeled in vitro profiles from breast cancer pools.Differential analysis of RNA-Seq of Supplementary Table 9Tissue dissociation buffers and programs.Supplementary Table 10Guide sequences used for gene perturbation in CRISPR screen and validation; data count of mini-pool screen; CRISPR-seq primers.