Expression and activation levels of 187 proteins, of which 137 were kinases and downstream substrates, were captured using the Reverse Phase Protein Array (RPPA). The DMSO effect was heterogeneous across cell lines and varied based on concentration, exposure time, and cell line. Of the 187 proteins measured, all were statistically different in at least one comparison at the highest DMSO concentration, followed by 99.5% and 98.9% at lower concentrations. Only 46% of the proteins were found to be statistically different in more than 5 cell lines, indicating heterogeneous response across models. These cell line specific alterations modulate response to in vitro drug screening. Ultra-low DMSO concentrations have broad and heterogeneous effects on targetable signaling proteins. Off-target effects need to be carefully evaluated in pre-clinical drug screening and testing.Targetable alterations in cancer offer novel opportunities to the drug discovery process. However, pre-clinical testing often requires solubilization of these drugs in cosolvents like dimethyl sulfoxide (DMSO). Using a panel of cell lines commonly used for in vitro drug screening and pre-clinical testing, we explored the DMSO off-target effects on functional signaling networks, drug targets, and downstream substrates. Eight Non-Small Cell Lung Cancer (NSCLC) cell lines were incubated with three concentrations of DMSO (0.0008%, 0.002%, and 0.004%  The identification of targetable alterations in cancer continues to offer novel opportunities to the drug discovery process and to the development of compounds capable of directly interacting with and modulating activation of malfunctioning proteins ,2. BecauWhile genomic alterations initiate and promote tumorigenesis, phenotypically these alterations manifest as aberrantly activated proteins and kinase-driven signal transduction events ,7. Withiv/v [v/v still modulate gene expression and large-scale miRNA profiles as well as tissue-specific genome-wide methylation patterns [To minimize DMSO-dependent cytotoxicity , its effv/v ,13. Howepatterns ,15. Thus−4 to 4 × 10−3 % v/v) on the signaling network of eight commercially available cell lines commonly used for in vitro drug testing and cell-based assays. Dose- and time-dependent changes in signaling events were captured on a continuous scale using the Reverse Phase Protein Microarray (RPPA), a high throughput antibody-based assay. To fully capture off-target effects and functional deregulation of signaling networks associated with DMSO, emphasis was placed on activation levels of kinases and downstream substrates commonly targeted by FDA approved and investigational anti-cancer compounds.Using lung cancer as a model system, this work explored the effect of ultra-low DMSO doses  cell lines was selected for molecular analysis. Cells were incubated with three different amounts of DMSO, namely 0.0008% (C1), 0.002% (C2), and 0.004% (C3) th rates . Cell lip = 0.01, 0.04, and 0.03 for C1, C2, and C3, respectively), while it promoted signal transduction events in others . The H2122 and H1734 lines were the most affected cell lines and presented a nearly 40% increase in the overall phosphorylation levels; however, the magnitude of change varied greatly across timepoints. On the other hand, the H358 and H23 cell lines were the least affected models within and across concentrations (p > 0.05). However, datapoints trended in similar directions across different concentrations of DMSO, suggesting that DMSO induces similar biological effects regardless of its amount.To explore the effect of DMSO on functional signaling networks and post-translationally modified proteins, broad changes in phosphorylation levels of 125 kinases and downstream substrates were first evaluated . Fold-chIn contrast, DMSO had an inhibitory effect on phosphorylation events in the H522 cells, especially 1 and 6 h after the administration of DMSO. Similarly, a 20% to almost 40% reduction in phosphorylation events was detected after 1 h of incubation in the H1838. The ability of DMSO to change signaling network activities, especially at earlier timepoints after incubation, may be of relevance when testing targeted compounds as these changes may alter a compound’s activity, its ability to engage with its target(s), and its effect on downstream substrates. To further explore the effect of DMSO on the overall signaling network, weighted Pearson’s correlation coefficients were calculated and their distribution was represented as histograms and kernel density plots . This apIn contrast, interconnection in the H358, H2122, and H522 never recovered baseline levels within the 24 h period . Howeverp < 0.05) were found in less than half of the measured analytes in C1 and C2 . However, the proportion of significant endpoints reached 63.6% at the highest DMSO concentration  were statistically different in at least one cell line for the DMSO concentration C1, while the number of statistically different endpoints increased to 186 (99.5%) in C2, and to 187 (100%) C3 . On a centration . Only 64ntration A. The H2ntration . A totalntration A. Of the 187 proteins evaluated, 86 (46.0%) were significantly different in more than 5 cell lines in C1 followed by 120 (64.2%) in C2 and C3 . AmongstIn the Calu-3 and H358 models, ERK 1/2 activation was significantly reduced throughout the incubation period, while in the H1734 line, ERK 1/2 activation levels progressively increased by 2 to 3 times and never recovered to baseline levels. Trends in ERK 1/2 activation in C2 and C3 followed activation patterns resembling those of C1 A.Activation patterns across time also varied from cell line to cell line for AKT, another key regulator of a complex signaling axis. DMSO induced downregulation of AKT S473 in six of the eight cell lines and only the H522 fully recovered after 24 h of incubation D. Increap < 0.05) across timepoints for all eight cell lines in C1, seven cell lines in C2, and five cell lines in C3 , we evaluated broad responses to targeted agents and standard chemotherapeutic compounds across our eight cell lines. Drug susceptibility information was collected on average for 180 compounds with the exception of the H1838 and H1734 . For each compound and cell line, the GDSC database provides IC50-based Z-scores. Cell lines with Z-scores > +2 are considered resistant to treatment while those with Z-scores < −2 were defined as sensitive. Of interest, H2122, the cell line most affected by DMSO in our analysis, showed the greatest sensitivity across all compounds , followed by H23 (2.8%) and Calu-3 (0.5%) , were amongst the models with the highest sensitivity to different anti-ERK compounds. The Calu-3 cells were the only exception to this pattern. Taken together, these data indicated that the DMSO effects on signaling events and drug targets is highly heterogeneous. Cell-dependent response to DMSO may differentially affect activation levels of drug targets and downstream substrates across cell lines and should be carefully evaluated in drug screening and in vitro testing.Finally, using the Genomics of Drug Sensitivity in Cancer (GDSC) database (3 (0.5%) A. The H23 (0.5%) B. In conTo our knowledge, this is the first report exploring broad off-target effects of DMSO on protein signaling networks with a focus on biochemically interconnected, post-translationally modified drug targets and downstream substrates. With a growing number of targeted compounds entering the market every year, discerning the effects of experimental variables from true drug-associated effects is of primary importance to improve the drug discovery process and pre-clinical testing. While DMSO remains the most commonly used cosolvent for water insoluble compounds in pharmacological and toxicological studies, the concentration at which it is used is often unreported . SubsequIt has been previously documented that DMSO is a non-inert substance with significant effect across macromolecules and subsequently cellular functions and molecular profiles ,20,21,22v/v ), selectively induces phosphorylation of tyrosine residues on the EGF receptor in crude membrane fractions of murine erythroleukemia cells [In vitro studies and biochemical assays have previously reported that exposure to DMSO can promote phosphorylation of kinases on multiple phospho-epitopes ,32,33. Eia cells . In lineActivation of ERK 1/2, a central node of the proliferative MAPK pathway and a crossroads between signal transduction networks, is a good example of these diverse adaptation mechanisms as each of the eight models analyzed in this work showed distinct patterns of ERK 1/2 activation and recovery after 24 h of incubation with DMSO. These trends appeared to be independent from expression and activation of cell cycle regulatory molecules or changes in ERK 1/2 overall expression levels, suggesting that DMSO directly affects ERK’s signaling activity. Similarly, Cataldi and colleagues have previously demonstrated that incubation with DMSO for 24 h induces PI3K activation, while the overall expression of the protein remains unaffected within this timeframe . As expeAlthough this work did not directly explore the effect of DMSO on response to treatment(s), data from the “The Genomics of Drug Sensitivity in Cancer” (GDSC) database have provided some insights on their potential interactions. The H2122 cell line, a model whose signaling network was profoundly affected by DMSO, emerged as the most sensitive model across compounds (IC50 Z-score <-2 for 12 of the 180 drugs). In addition, H2122 cells were not resistant to any of the 180 compounds tested. When emphasis was placed on ERK inhibitors exclusively, similar trends were observed. Specifically, cell lines where DMSO induced significant ERK 1/2 downregulation were overall more sensitive to these targeted compounds, while models where ERK 1/2 phosphorylation was unaltered or increased by DMSO showed higher levels of resistance, with only one exception. Although these observations are based on a small number of cell lines, and on a relative short time frame (only up to 24 h), a clear trend was detected between overall drug susceptibility and DMSO effect on cellular signaling networks. This data may have important implications for drug screening and pre-clinical studies, as DMSO may affect drug targets baseline activation levels and susceptibility to treatment. While we specifically designed this analysis to capture short-term adaptation mechanisms associated with DMSO, a few limitations of our work should be addressed. First, this study analyzed in vitro models established from lung malignancies only. Whether these observations hold true across tissue types should be validated in independent analyses. Previous investigations have indicated that the magnitude of the DMSO effects on phospholipids, DNA, RNA and proteins varies from cell type to cell type ,26,27. T2 atmosphere. Cells were passaged using trypsin/EDTA  and sub-cultured at a ratio ranging between 1:3 and 1:10 depending on each cell line’s growth rate.A panel of eight human non-small cell lung cancer cell lines including A549, H1734, H1838, H2122, H21, H358, and H522 were used in this analysis. Calu-3 were purchased from AddexBio , and the remaining seven lines were obtained from American Type Culture Collection . Cell lines were grown following manufacturer’s recommendations. As suggested by the manufacturer, cell lines were grown in media  supplemented with 10% fetal bovine serum (FBS)  at 37 °C and 5% COn = 3) in a 6-well plate and cultured to 80% confluency. Each cell line was treated with three ultra-low DMSO concentrations  0.0008% (C1), 0.002% (C2) and 0.004% (C3) respectively for 5 min, 1 h, 6 h, and 24 h . Baseline untreated control samples were also collected along with the longitudinal samples. At the end of the incubation period, cells were washed twice with PBS  and lysed in TPER  supplemented with 300 mM sodium chloride and a cocktail of protease and phosphatase inhibitors to prevent protein degradation and dephosphorylation including: 1 mM sodium orthovanadate ; 200 mM PEFABLOC ; 5 µg/mL Aprotonin , 5 µg/mL Pepstatin A  (prepared at 1.0 mg/mL in a solution of 10% acetic acid and 90% methanol) and 5 µg/mL Leupeptin  [Cells were seeded in technical replicates  . Proteinn = 3) were averaged so that a single output value was generated for each sample. Coefficient of variations across technical replicates were <5%.Cell lysates were immobilized in technical triplicates onto nitrocellulose coated glass slides  using an Aushon 2470 arrayer  equipped with 185 μm pins. Reference standard curves were printed along with the experimental samples for internal quality control. Selected arrays were stained with Sypro Ruby Protein Blot Stain  following the manufacturer’s directions to quantify the total protein amount for each microarray spot and used for normalization purposes. Immunostaining was performed as previously described . In briehttps://www.cancerrxgene.org, accessed on 1 February 2021). For the work, the GDSC2 dataset released in 2019 was used. As per database instructions, this version of the database drug sensitivity data was collected along with DMSO control data and used for normalization purposes. For each cell line, normalized IC50 are provided for a large panel of targeted compounds and chemotherapeutics. Cell lines were tested with an average of 180 drugs, with the exception of the H1838 and H1734 which were treated with 101 and 29 compounds, respectively. Normalized IC50 scores expressed as Z-scores were retrieved for each cell line. Per database instructions, cell lines with Z-scores > +2 were considered resistant to treatment while those with Z-scores < −2 were classified as sensitive.The effect of DMSO on drug sensitivity was assessed using archived data retrieved from the open-access Genomics of Drug Sensitivity in Cancer (GDSC) database (n = 3) and protein networks were displayed using a graphical presentation where vertices represent individual proteins. Kernel density plots and related histograms were created using R software version 3.4.3. In brief, for each treatment/time pair, replicate measurements were collected . Alpha level for significance was set at 0.05. In order to correct for inflated Type-I error from multiple group comparisons. For each cell line, RPPA values are shown using bar graphs created in GraphPad version v.6.07. RPPA values were first normalized, within each cell line, to baseline level; bar graphs visualize mean and standard error for each group. Venn Diagrams were created in a Gene List Venn Diagram. Pathway enrichment analysis was performed in Reactome Knowledgebase version 75 ,18, and


Canine prostate adenocarcinoma (PAC) and transitional cell carcinoma (TCC) are typically characterized by metastasis and chemoresistance. Cell lines are important model systems for developing new therapeutic strategies. However, as they adapt to culturing conditions and undergo clonal selection, they can diverge from the tissue from which they were originally derived. Therefore, a comprehensive characterization of cell lines and their original tissues is paramount.This study compared the transcriptomes of nine canine cell lines derived from PAC, PAC metastasis and TCC to their respective original primary tumor or metastasis tissues. Special interests were laid on cell culture-related differences, epithelial to mesenchymal transition (EMT), the prostate and bladder cancer pathways, therapeutic targets in the PI3K-AKT signaling pathway and genes correlated with chemoresistance towards doxorubicin and carboplatin.Independent analyses for PAC, PAC metastasis and TCC revealed 1743, 3941 and 463 genes, respectively, differentially expressed in the cell lines relative to their original tissues (DEGs). While genes associated with tumor microenvironment were mostly downregulated in the cell lines, patient-specific EMT features were conserved. Furthermore, examination of the prostate and bladder cancer pathways revealed extensive concordance between cell lines and tissues. Interestingly, all cell lines preserved downstream PI3K-AKT signaling, but each featured a unique therapeutic target signature. Additionally, resistance towards doxorubicin was associated with G2/M cell cycle transition and cell membrane biosynthesis, while carboplatin resistance correlated with histone, m- and tRNA processing.Comparative whole-transcriptome profiling of cell lines and their original tissues identifies models with conserved therapeutic target expression. Moreover, it is useful for selecting suitable negative controls, i.e., cell lines lacking therapeutic target expression, increasing the transfer efficiency from in vitro to primary neoplasias for new therapeutic protocols. In summary, the dataset presented here constitutes a rich resource for canine prostate and bladder cancer research.The online version contains supplementary material available at 10.1186/s12935-021-02422-9. Prostate cancer in dogs can be classified into two histopathological groups: prostate adenocarcinoma (PAC) and transitional cell carcinoma (TCC) . Both arMetastasis involves a complex series of events. Among them, the epithelial-to-mesenchymal transition (EMT) plays a critical role in canine prostate cancer , since iChemotherapy can follow tumor type or patient-specific approaches –19. PatiThe present study aims to contribute to the existing knowledge of prostate cancer with the comprehensive transcriptomic characterization of a panel of four PAC, two PAC metastasis and three TCC cell lines and their original tumor tissues. Basic features of this sample set have been reported previously, including medical patient data, histopathology, doubling time, growth behavior, immunophenotype and chemosensitivity towards doxorubicin, carboplatin and meloxicam . ContinuTissue samples and one fine needle-aspiration biopsy of suspected prostate or bladder carcinomas were collected from ten patient dogs at the Small Animal Clinic, University of Veterinary Medicine Hannover, Foundation, Hannover, Germany between 2003 and 2015 with the consent of the patients’ owners  was fresh frozen in liquid nitrogen and stored at −80 °C until RNA isolation. The second part was histopathologically classified into PAC or TCC by a certified and experienced pathologist. Modified Gleason scores were between 8 and 10; metastasis confirmed in nearly all cases . Cell liEach cell line was seeded in T25 flasks in triplicate beyond passage 60. At 70–80% confluency, cells were detached with TrypLE™Express , pelleted, fresh frozen in liquid nitrogen and stored at −80 °C until further use.In accordance with German national legislation, this study does not require ethical approval, as all tissue samples were collected from: (1) a dog that was euthanized due to poor prognosis; or (2) a bladder cancer that was surgically removed as part of treatment; or (3) leftover material from a diagnostic fine needle aspiration biopsy.RNA from cell pellets was isolated using the RNeasy Mini Kit , in accordance with the manufacturer’s protocols. For tissue samples, the AllPrep DNA/RNA/miRNA Universal Kit (Qiagen GmbH) was utilized as previously described . RNA wasSequencing reads were trimmed and filtered using trimmomatic  with paDifferential expression analysis was conducted with the DESeq2 R package . Only prExpression values were obtained by applying the regularized logarithm transformation implemented in the rlog function of the DESeq2 R package  to the rGene expression profiles (z-scores of the regularized-logarithm transformation) were visualized as heatmaps with the pheatmap function from the homonymous R package . Cluster5 cells per insert in serum-free medium. The lower chambers of the wells were filled in duplicates with medium containing 10% serum as attractant, or with serum-free medium as controls. After 48 h of incubation, non-invasive cells were removed from the upper chamber with moistured cotton swabs and two washing steps with PBS. The remaining invasive cells were fixed in 10% formalin for 10 min and permeabilized by methanol for 20 min, both followed by washing twice in PBS. Subsequently, the cells were stained for 2 min with 1% crystal violet, washed three times and allowed to dry. Pictures of three representative areas on each membrane were taken with a DMI600 B microscope  in 100× magnification using the LAS AF 2.6.0 software. Finally, the area covered by invasive cells was quantified using Image J 2.0.0. The experiment was conducted three times independently. Means were compared by univariate ANOVA followed by a Ryan-Einot-Gabriel-Welsch post-hoc test using SAS Enterprise Guide® 7.15.An invasion assay was performed to study EMT and metastatic potential of the nine cell lines. Thereby, cells which were allowed to migrate through an artificial basement membrane, attracted by a serum-containing medium, were quantified. In preparation, cell culture inserts with 8 µm pores in transparent PET membranes  were placed into 12 well plates and coated over night with 200 µg/ml basement membrane extract  in serum-free Medium 199 (Life Technologies GmbH). Additionally, the cells were starved overnight in serum-free medium. The next day, the starved cells were seeded with a density of 2.5 × 10Functional analysis of DEGs was performed with DAVID  , 54, basFor PAC, PAC metastasis and TCC, a gene was considered expressed if it showed a minimum of ten reads in each cell line sample and/or each tissue sample.The fold-changes of the DEGs in the Kyoto Encyclopedia of Genes and Genomes (KEGG) –58 prostAll nine cell lines were washed three times with ice cold PBS and detached by scraping in RIPA buffer containing cOmpleteTM Mini Protease Inhibitor Cocktail . After cell lysis by ultrasound , protein concentrations were determined by Bradford Assay . Next, 30 µg cell lysate per well was separated by electrophoresis in Criterion™ TGX™ gels and blotted onto PVDF membranes using the Trans-Blot Turbo Transfer System (Bio-Rad Laboratories GmbH). The membranes were blocked in Odyssey blocking buffer  diluted 1:3 in PBST for one hour at room temperature. Afterwards, the membranes were incubated with primary antibodies against PI3K-p110α , PI3K-p85 , Akt  and β-Actin   diluted in 1:5 blocking buffer in PBST at 4 °C overnight. The next day, the membranes were washed again, followed by incubation with fluorescence-labeled secondary antibodies  for one hour at room temperature. After three further washing steps, fluorescence signals were detected by a LI-COR Imager with Image Studio Lite software (LI-COR Biosciences GmbH). The Western blot analysis was repeated three times with subsequent passages. Protein lysates of the lymphoma cell lines GL-1 (canine), NALM-6 and SEM (both human) served as positive controls.Human orthologs of canine genes were identified using the table of orthologous genes retrieved from Ensembl BioMart  on NovemHalf maximal inhibitory concentrations of doxorubicin and carboplatin for all nine analyzed cell lines measured in metabolic activity and cell count in a previous study  were corFurthermore, the top 300 genes ranked by ProGENI for doxorubicin or carboplatin resistance based on either metabolic activity or cell count were subjected to functional analysis with DAVID , 54. HumThis study characterized seven cell lines of canine urinary tract cancer in direct comparison to their original tumor tissues. Previous histopathological examination  of the s2 fold-changes, greater than 5 or smaller than −5.Separate analyses for PAC, PAC metastasis, and TCC revealed 1743, 3941 and 463 DEGs, respectively, between the cell lines and their original tumor tissues when controlling for patient-specific effects (Methods). Independently of the histological classification of the samples, biological processes (GO_BP terms) enriched among the identified DEGs were associated with immune response, cell adhesion and angiogenesis/endodermal cell differentiation . Consistent with their high expression of the mesenchymal marker rs Figs.  and 6, Trs Figs. . Also TCrs Figs. B–D. The rs Figs. , were amMMP9 and the group of growth factors and their receptors (GFR), also known as RTK. DEGs in the metastasis cell lines were spread over the entire prostate cancer pathway, including the axes of carcinogenesis, RTKs and downstream molecules, and the hormonal axis  in TCC samples, including the urothelial marker K3A Fig. B. In genThe PI3K-AKT signaling pathway (cfa04151) is an important regulator of the cell cycle and the focus of numerous therapeutic approaches , 21, 68.PI3K and AKT, the key players of the pathway, were not differentially expressed. Moreover, their expression profiles could also be validated at protein level by performing a Western blot . Furthermore, only few downstream genes were differentially expressed, almost exclusively in the PAC metastasis cell lines. Although the functionality of the corresponding proteins warrants further investigation, these findings indicate a good model suitability of the analyzed cell lines for the downstream PI3K signaling cascade. This observation is surprising given that numerous RTKs—direct activators of PI3K—were downregulated. In general, each cell line exhibited its own individual therapeutic marker signature. For instance, the RTK FGFR2 is targeted by erdafitinib, an inhibitor for the treatment of human metastatic bladder cancer [FGFR2 was generally downregulated in PAC cell lines, Adcarc0846 and Adcarc1511.1 appear to have similar FGFR2 expression levels relative to their original tissues. Thus, those two PAC-derived cell lines may still represent suitable model systems for an FGFR2-targeted approach. Furthermore, canine and human TCC tissues and cell lines have been found to overexpress the RTKs EGFR and ERBB2 [EGFR a therapeutic target. A first study has shown promising effects of the EGFR and ERBB2 inhibitor lapatinib on canine TCC cell lines [EGFR inhibitor erlotinib reduced proliferation in a subset of human TCCs [EGFR and ERBB2 were not differentially expressed in the present sample set, suggesting that the characterized cell lines are suitable models for EGFR-targeted therapies. Furthermore, the tyrosine kinases and therapeutical targets EPHA2, MET, LYN and PDGFRA were expressed by specific cell lines at the same level compared to the average of the tissues. Claudins participate in the formation of tight junctions and are therefore essential for the maintenance of an epithelial structure. To promote EMT and metastasis, tight junctions are dismantled in favor of a more invasive, mesenchymal cell type [CLDN1, 3, 4 and 7 and the lowest invasive potential and is hence the most suitable for a claudin-targeted therapy approach. In contrast, Adcarc1258 was highly invasive, expressed all four claudin genes at the lowest level among all cell lines and could therefore be used as negative control.Although cell lines are common model systems for targeted chemotherapy, results obtained with cell lines are not always reproducible in clinical trials . The mair cancer . Althougnd ERBB2 , 89–91. ll lines , while tman TCCs , 94. EGFell type , 95. Theell type , 96. Theell type . Among PCorrelating molecular marker signatures with outcome or chemoresistance can help unravel resistance mechanisms and identify predictive markers  for persLast but not least, this analysis is based on the transcriptome and only selected genes were verified on the protein level. Since translation is influenced by multiple regulatory mechanisms, mRNA levels are not unconditionally transferrable to protein levels . AdditioIn essence, compared to their original tumor tissues, the nine canine cell lines characterized in the present study conserved EMT gene expression programs and downstream PI3K-AKT signaling. Due to their individual therapeutic target signatures, they constitute a suitable panel to evaluate a large variety of inhibitors in vitro. Additionally, the panel includes cell lines derived from primary tumors and metastases of the same patient, which are interesting subjects for metastasis research.Enabled by the rare availability of the tissue samples from which they were derived, this study assessed at the transcriptomic level nine canine PAC, PAC metastasis and TCC cell lines as in vitro models for targeted therapies in canine prostate cancer. Adaptation to culturing conditions and clonal selection resulted in the downregulation of genes associated with tumor microenvironment in the cell lines compared to their original tissues, while tumor- and patient-specific characteristics, as well as the extent of EMT, were mostly conserved. Most importantly, all nine cell lines featured unique therapeutic target expression signatures and preserved subsequent signaling cascades. In summary, this dataset constitutes a rich resource for choosing suitable in vitro models for testing future therapeutic approaches for canine prostate cancer.Additional file 1. Functional analysis of differentially expressed genes (DEGs) and categorization of retrieved enriched biological processes.Additional file 2. The ProGENI top 300 genes associated with doxorubicin and carboplatin resistance quantified by metabolic activity and cell count in the characterized cell lines.Additional file 3. Functional analysis of top 300 genes associated with chemoresistance.Additional file 4. Log2 read counts of therapeutically relevant RTK genes.


Ovarian cancer (OC) is known for its poor prognosis, due to the absence of reliable biomarkers and its late diagnosis, since the early-stage disease is almost asymptomatic. Lipid metabolism plays an important role in OC progression due to the development of omental metastasis in the abdominal cavity. The aim of our study was to assess the therapeutic role of various enzymes involved in lipid metabolism regulation or synthesis, in different subtypes of OC represented by cell lines as well as patient-derived cancer cell cultures (PDCs). We show that proprotein convertase subtilisin/kexin type 9 (PCSK9), a cholesterol-regulating enzyme, plays a pro-survival role in OC and targeting its expression impairs cancer cell growth. We also tested a small library of metabolic and mTOR-targeting drugs to identify drug vulnerabilities specific to various subtypes of OC. Our results show that in OC cell lines and PDCs the second generation of mTOR inhibitors such as AZD8055, vistusertib, dactolisib and sapanisertib, have higher cytotoxic activity compared to the first generation mTOR inhibitors such as rapalogs. These results suggest that, in the era of precision medicine, it is possible to target the metabolic pathway in OC and identify subtype-specific drug vulnerabilities that could be advanced to the clinic.n = 11) and mTOR (n = 10) inhibitors using OC cell lines (n = 8) and ex vivo patient-derived cell cultures  to identify clinically suitable drug vulnerabilities. Targeting PCSK9 expression with siRNA or PCSK9 specific inhibitor (PF-06446846) impaired OC cell survival. In addition, overexpression of PCSK9 induced robust AKT phosphorylation along with increased expression of ERK1/2 and MEK1/2, suggesting a pro-survival role of PCSK9 in OC cells. Moreover, our drug testing revealed marked differences in cytotoxic responses to drugs targeting metabolic pathways of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) PDCs. Our results show that targeting PCSK9 expression could impair OC cell survival, which warrants further investigation to address the dependency of this cancer on lipogenesis and omental metastasis. Moreover, the differences in metabolic gene expression and drug responses of OC PDCs indicate the existence of a metabolic heterogeneity within OC subtypes, which should be further explored for therapeutic improvements. Background: Dysregulated lipid metabolism is emerging as a hallmark in several malignancies, including ovarian cancer (OC). Specifically, metastatic OC is highly dependent on lipid-rich omentum. We aimed to investigate the therapeutic value of targeting lipid metabolism in OC. For this purpose, we studied the role of PCSK9, a cholesterol-regulating enzyme, in OC cell survival and its downstream signaling. We also investigated the cytotoxic efficacy of a small library of metabolic ( Ovarian cancer (OC) continues to be the second most common gynecological cancer in the world, due to its late diagnosis and poor prognosis . MoleculAltered cellular metabolism is one of the hallmarks of tumorigenesis, directly or indirectly connected to the accumulated genomic aberrations . The metA common site for metastatic disease arising from intraperitoneal tumors is the omental tissue which favors metastatic tumor growth . Omentumn = 8) and PDCs (n = 5). Our OC models are comprised of subtype-specific cell lines such as HGSOC and endometrioid, including paired cell-lines pre- and post- acquisition of cisplatin resistance, whereas PDCs were also chosen as representative models of HGSOC and LGSOC. We also analyzed the efficacy of a small library of anti-metabolic (n = 11) and first and second generation of mTOR inhibitors (n = 10) in order to identify metabolic vulnerabilities that could be therapeutically useful in OC treatment.In this study, we investigated the role of PCSK9, the proprotein convertase subtilisin/kexin type-9 regulating cholesterol and lipid metabolism, in OC cell survival and its intracellular signaling, in cell lines . JHOS2 cells were cultured in DMEM/F-12  supplemented with 10% fetal bovine serum, 1× MEM-NEAA  and PrimocinTM . Kuramochi and Ovsaho cell lines were cultured in RPMI 1640 supplemented with 10% fetal bovine serum, 2 mM L-glutamine and PrimocinTM. COV362, HEK293T and HeLa cells were cultured in DMEM (Lonza), supplemented with 10% fetal bovine serum, 2mM L-glutamine and PrimocinTM. All cell lines were kept and incubated at 37 °C and 5% CO2.Human ovarian cancer (OC) cell lines OVCAR3 and OVCAR3cis were cultured in RPMI 1640  supplemented with 20% fetal bovine serum , 2 mM L-glutamine (Thermo Fisher Scientific) and penicillin/streptomycin (Thermo Fisher Scientific). The cisplatin resistant phenotype, OVCAR3cis, cells were cultured with 1 µM cisplatin . The endometrial subtype OC cell lines A2780 and A2780cis, were cultured in RPMI 1640, supplemented with 10% fetal bovine serum, 2 mM L-glutamine and PrimocinSmall interference RNA (siRNA) transfections were performed with Transfection Reagent 1 or 4  cells and with small interference RNA (siRNA) ON-TARGETplus Non-targeting Control Pool (GE Dharmacon) acting as a control or ON-TARGET plus Human PCSK9 siRNA—SMART pool 10 nM (GE Dharmacon) were used to silence PCSK9 in ovarian cancer. The final concentration of siRNA used in transfection was 6 nM for OVCAR3 cells and 25 nM for HeLa cellsTM, Thermo Fisher Scientific). pCINEO expression vector  was used as transfection control.Human PCSK9/NARC1 plasmid (HG15813-CM) was transfected to HEK293T or JHOS2 cells with TurboFect™ Transfection Reagent (Thermo Fisher Scientific) and reduced serum media Opti-MEM I (GibcoCellTiter-Glo (CTG) 2.0 (Promega) assay was used to measure cell viability, according to manufacturer’s instructions. Cells were plated to 96-well plates and untreated or treated with increasing concentrations of the PCSK9 inhibitor (PCSK9i) PF-06446846 hydrochloride  and viability was analyzed after 48 h. The luminescence was measured with Envision plate reader .® 680RD Donkey anti-Goat IgG, IRDye® 800CW Donkey anti-Mouse IgG, or IRDye® 680RD Donkey anti-Rabbit IgG  were used before detection with Odyssey® CLx Imaging System (LI-COR). Image analysis was performed using Image Studio ™ Lite software (LI-COR).Cells were lysed with 50 mM Tris-HCl (pH 7.5) containing 1 mM EDTA, 50 mM NaF, 150 mM NaCl, 1% Triton X-100, 10% glycerol plus 1% phosphatase and protease inhibitor cocktails . Proteins were separated using SDS-PAGE and were transferred onto nitrocellulose membrane . The following primary antibodies were used: anti-PCSK9 antibody , pERK1/2 (T202/Y204) , ERK1/2 , pAKT (Ser473) , AKT , pMEK Ser217/221 , MEK  and anti β-tubulin . Specific LI-COR secondary antibodies IRDyeDrug testing was performed for OC cell lines and PDCs with dose-dependent concentration of drugs for 72 h. Briefly, 1000–1500 cells were added to wells of 384-well plates with drugs pre-plated over a 10,000-fold concentration range (in five concentrations). After a 3-day incubation at 37 °C, cell viability was measured using the Cell Titer-Glo reagent (Promega) and plates were read with PHERAstar FS . For each drug, dose–response curves were generated and DSS were calculated as previously described . In shorTotal RNA was isolated from PDCs using total RNeasy kit . Quantity and quality of the RNA samples were assessed by Qubit  and Bioanalyzer . RNA with an RNA integrity number (RIN) > 8 was used to derive libraries and the sequencing was performed with Illumina HiSeq system  as previously described . The expPCSK9 increases LDLR degradation and, therefore, decreases the circulating LDL uptake and clearance [Proprotein convertases (PCs) are serine proteases capable of specific proteolysis at the RXR/KR motif in a multitude of substrates such as growth factors and growth factor receptors and pro-angiogenic proteins . This falearance . Since lTo date, there are not any known LGSOC representative cell lines. Moreover, for OVCAR3 and A2780 cell lines, we have included both parental and cisplatin-resistant clones, as platinum chemoresistance usually develops in OC patients and has therapeutic significance. Western blot analysis of OC cell line lysates identified variable expression of PCSK9 isoforms (uncleaved and cleaved) in comparison with cervical carcinoma HeLa cell line, which was previously shown to express PCSK9  Figure . The higAPOA1, APOA4, APOH, APOC3 as well as PCSK2, PCSK6 and PCSK1, suggesting a subtype specific metabolic gene expression profile of OC PDCs.Furthermore, we wanted to evaluate PCSK9 expression in OC tumor samples and for this, we used PDCs derived from HGSOC and LGSOC patient tumors. Personalized cancer therapy using ex vivo pharmacogenomic testing of patient-derived cancer cells has already shown great potential and PDCs are currently used as models for tumor molecular profiling as excellent ex vivo representatives of patients’ tumor . We asseNext, we wanted to assess whether targeting PCSK9 expression has any effect on OVCAR3 and HeLa cell viability. siRNA-mediated knockdown of PCSK9 expression resulted in a statistically significant reduction of cell survival A, and a We also investigated whether overexpression of PCSK9 in JHOS2 cells that typically lack its expression will activate pro-survival signaling pathways such as AKT/ERK. Transient transfection of PCSK9 in JHOS2 cells resulted in increased phospho-AKT, phospho-ERK and phospho-MEK levels, as well as an increase in ERK and MEK basal expression . This co2), an alcohol dehydrogenase inhibitor chelating copper ions selectively accumulated in cancer cells. We also focused on compounds that inhibit DNA or RNA synthesis by blocking the activity of various enzymes required for metabolic reactions, such as daporinad, AVN944, methotrexate, pemetrexed, TH588 and triapine  pathway helped us to understand the molecular mechanisms that regulate eukaryotic cell growth, linking nutrient sensing to regulation of protein synthesis, metabolism, aging and disease development . mTOR isTargeting the mTOR pathway has been extensively explored in cancer therapy and the first mTOR inhibitors approved were a class of rapamycin derivatives known as “rapalogs”. Rapamycin, also known as sirolimus and one of the first generation mTOR inhibitors, exerts its effect by binding to FRBP-12 (12 kDa FK506-binding protein) to form a ternary complex with mTOR, leading to inactivation of mTORC1 . Other rMany human diseases, including cancer, are the consequence of altered metabolic pathways. Cancer cells have distinctive metabolic phenotypes characterized by altered nutrient metabolism that supports the rapid manufacture of biomass required to sustain rapid cell proliferation . OC is pIn this study, we investigated expression levels and an anti-apoptotic role of PCSK9 in OC models including cell lines and PDCs from various subtypes of OC. We found variable PCSK9 expression in our OC cell lines, with OVCAR3 showing the highest level of PCSK9  compared to HeLa cells that are known to express PCSK9 A,B. DeteAPOA1, APOA4, APOH, APOC3 as well as PCSK2, PCSK6, PCSK1 were observed in LGSOC PDCs compared to HGSOC PDCs  is an inhibitor of nicotinamide phosphoribosyltransferase (NAMPT) specifically inhibiting the biosynthesis of NAD+ from niacinamide, which is essential for the cellular metabolism, protein modification and messenger synthesis . DaporinAmong the mTOR targeted drugs, our screen showed that the first generation of rapalogs had overall low DSS values in both OC cell lines and PDCs, except for PDC#4 which displayed good sensitivity to all rapalogs B. PrevioOur study is the first to evaluate the expression and anti-apoptotic role of PCSK9 in OC. By investigating the efficacy of various metabolic and mTOR inhibitors ex vivo using tumor representative OC PDCs, our results showed differences in drug cytotoxic responses in LGSOC compared to HGSOC PDCs, which is in line with previous studies showing that OC subtypes display clear metabolic phenotypes . As such


Endogenous retroviruses (ERVs) are becoming more and more relevant in cancer research and might be potential targets. The oncogenic potential of human ERVs (HERVs) has been recognized and includes immunosuppression, cell fusion, antigenicity of viral proteins, and regulation of neighboring genes. To decipher the role of HERVs in human cancers, we used a bioinformatics approach and analyzed RNA sequencing data from the LL-100 panel, covering 22 entities of hematopoietic neoplasias including T cell, B cell and myeloid malignancies. We compared HERV expression in this panel with hematopoietic stem cells (HSCs), embryonic stem cells (ESCs) and normal blood cells. RNA sequencing data were mapped against a comprehensive synthetic viral metagenome with 116 HERV sequences from 14 different HERV families. Of these, 13 HERV families and elements were differently expressed in malignant hematopoietic cells and stem cells. We found transcriptional upregulation of HERVE family in acute megakaryocytic and erythroid leukemia and of HERVFc family in multiple myeloma/plasma cell leukemia (PCL). The HERVFc member HERVFc-1 was found transcriptionally active in the multiple myeloma cell line OPM-2 and also in the Hodgkin lymphoma cell line L-428. The expression of HERVFc-1 in L-428 cells was validated by qRT-PCR. We also confirm transcriptional downregulation of ERV3 in acute megakaryocytic and erythroid leukemia, and HERVK in acute monocytic and myelocytic leukemia and a depression of HERVF in all malignant entities. Most of the higher expressed HERV families could be detected in stem cells including HERVK (HML-2), HERV-like, HERVV, HERVT, ERV9, HERVW, HERVF, HERVMER, ERV3, HERVH and HERVPABLB. Retroviruses (Retroviridae family) are characterized by a replication cycle in which the viral RNA genome is reverse-transcribed and integrated into a host cell’s nuclear genome to form the provirus. The integration of retroviruses into germline DNA can lead to the formation of vertically transmittable proviral sequences known as endogenous retroviruses (ERVs). Such proviruses maintain the potential to generate multiple germ line copies, either by infectious cycles or retrotransposition , 2. Vertgag (group-specific antigen), pol (transcriptase/polymerase) and env (envelope), which are flanked by long terminal repeat sequences (LTRs). Most of this viral DNA is inactivated by deletions or by mutations that have led to the disruption of open reading frames (ORF). Nevertheless, there are a few HERVs with complete and intact open reading frames for the generation of viral proteins  and widely used in many laboratories  based gene expression data from the LL-100 panel of leukemia and lymphoma cell lines were made available, which covers 22 different entities including T cell, B cell and myeloid malignancies and thus the entire spectrum of these diseases. This dataset includes cell lines from the following entities: acute myeloid leukemia (AML) with myelocytic, monocytic, erythroid, and megakaryocytic differentiation; chronic myeloid leukemia (CML) with myeloid or lymphoid blast crises; multiple myeloma/plasma cell leukemia (PCL); T cell and pre B cell acute lymphoblastic leukemia (ALL); Hodgkin lymphoma; Burkitt lymophoma/B cell ALL; chronic lymphocytic leukemia/prolymphocytic leukemia (CLL/PLL); activated B cell (ABC) and germinal center (GC) subtypes of diffuse large B cell lymphoma (DLBCL); hairy cell leukemia (HCL); mantle cell lymphoma (MCL); primary effusion lymphoma (PEL); primary mediastinal B cell lymphoma (PMBL); anaplastic large cell lymphoma (ALCL). The panel is completed by cell lines from myeloproliferative neoplasm (MPN), NK cell and mature T cell malignancies. The LL-100 panel consists of well-characterized and authenticated cell lines that are publicly available  elements were collected from the nucleotide database from the National Center for Biotechnology Information (NCBI). The corresponding reference publications and accession numbers are listed in the https://bioedit.software.informer.com), the created sequence was saved as a fasta file, which was then used as the HERV reference genome for mapping. We have added this fasta file named Viruses21.fasta in the supplement.To quantify HERV expression, a synthetic virus metagenome was assembled to be used for mapping analysis. Because endogenous viral sequences can influence the development of cancer and immune diseases by transcription of The cell lines of the LL-100 panel are authenticated and free of contamination by mycoplasma or non-inherent viruses. Furthermore, the method of RNA isolation and sequencing are identical in all cell lines. Therefore, this dataset allows comparative studies without methodical impact. The RNA-seq data were downloaded from the NCBI Short Read Archive (SRA) under BioProject PRJEB30312 .HPRT1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and ubiquitin B (UBB) were used. Analogous to the HERV family specific FPKM, the housekeeping gene specific FPKM was calculated and HERV family specific FPKM were normalized to this value (see Supplement).To analyze the sequencing data they were uploaded to the Galaxy platform . With FaThree paired-end RNA-seq datasets  from human pluripotent embryonic stem cells (ESCs) from the BioProject PRJNA296379 were analyzed . The rea2 atmosphere in a humidified 37°C incubator and cultured in RPMI , supplemented with 10% (v/v) fetal calf serum (FCS), 100 U/mL penicillin and 100 μg/mL streptomycin . The cells were passaged in 75cm2 flask twice a week.The five Hodgkin lymphoma cell lines L-1236, L-428, L-540, KM-H2 and HDLM-2 were obtained from the German Collection of Microorganisms and Cell Cultures GmbH . Cell lines were grown in 5% COTotal RNA was extracted using the NucleoSpin RNA Mini kit for RNA purification  according to manufacturer’s instructions. Quality and concentration of RNA were determined by visualization of rRNA bands in agarose gel electrophoresis and with a NanoDrop 2000 spectrophotometer .To quantify HERVFc-1 transcripts, 2 μg of total RNA and 100 μM Oligo-dT primer were used for cDNA synthesis  following the manufacturer’s instructions. A sample without reverse transcription was also prepared to check for genomic DNA contamination (noRT control).For the qRT-PCR 5 μL 5xGreen GoTaq buffer, 0.2 μl GoTaq polymerase , 16.8 μl nuclease-free water, 0.5 μl of 10 mM dNTPs , 0.25 μl forward primer (25 μM), 0.25 μl reverse primer (25 μM) and 2 μl cDNA were prepared. The amplification was performed under the following conditions: 94°C for 30 s, 60°C for 30 s, 72°C for 45 s (40 cycles). The experiment was repeated four times for the gene expression analysis.-ΔΔCt method (HPRT1) as reference gene for normalization. noRT controls were used for exclusion of genomic DNA amplifications. The following primers from Eurofins Genomics GmbH (Ebersberg/Germany) were used: HERVFc-1_forward: 5’-CTC CCC ATC TCT CTG GTG C-3’ and HERVFc-1_reverse: 5’-TGA GGA GGC TGG TTT CTC TAA G-3’; HPRT1 _forward: 5’-ACC AGT CAA CAG GGG ACA TAA-3’ and HPRT1_reverse: 5’-CTT CGT GGG GTC CTT TTC ACC-3’.Relative gene expression was calculated with the 2t method  using hyData visualization and One-way ANOVA followed by Dunnett’s multiple comparisons test for statistical analysis was performed using GraphPad Prism version 8.0.0 for Windows, GraphPad Software, San Diego, CA, USA. Statistical significances are symbolized as asterisks: * = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001).We analyzed the RNA-seq data of 100 cell lines from the LL-100 panel covering 22 entities of human leukemia and lymphoma . Eight REBNA-1 in the EBV-positive cell lines DAUDI, RAJI, VAL, YT, HG-3, JVM-3, JVM-13, MEC-1, PGA-1, BONNA-12, HAIR-M, HC-1, GRANTA-519 and JVM-2 , Human herpes virus 8 (HHV8) or xenotropic MuLV-related virus (XMRV). To demonstrate the functionality of our method we first analyzed all cell lines for the presence of these exogenous viruses. As expected, we could detect reads mapping against the EBV gene pression . In the oduction . PEL casoduction . XMRV wagag, pol and env genes to our virus metagenome. We found only one known full-length HERVE sequence known as ERVE-1 in the human NCBI nucleotide database. Within the human genome there is a large number of HERVE related sequences that are 90-93% identical to the known full-length ERVE-1 sequence  virus , of whicThe endogenous retrovirus group 48 member 1 (ERVH48-1/ERV-Fb) is part of the HERVF family, whose family members are preferentially expressed in the human placenta . Our datThe activation of endogenous retroviral LTRs plays a fundamental role in the maintenance of pluripotency and induction of an antiviral state in stem cells . TherefoIn both stem cell types, nine HERV families and elements are each significantly upregulated compared to PBMCs. The HERVK family, HERV-like family, HERVV family, HERVT family were all highly significantly upregulated  in both stem cell types 3. The hThe HERVFc subfamily is part of the larger HERVF family . The humTo confirm the results obtained in the mapping analyses and to further verify the functionality of our mapping approach, the expression of HERVFc-1 in the Hodgkin lymphoma cell lines L-428, L-540, L-1236, KM-H2 and HDLM-2 was analyzed by RT-PCR. According to the mapping results for HERVFc-1 , suggesting that the HERVE family is expressed according to the transcriptional program of human tissues and human cancer cells , respectively, is regulated by upstream LTRs belonging to the ERV9 group of HERVs, which have an anti-oncogenic effect  and ZS/2018/12/96169 (HC) from European Regional Development Fund within the local program “Sachsen-Anhalt WISSENSCHAFT Schwerpunkte”. We acknowledge the financial support of the Open Access Publication Fund of the Martin Luther University Halle-Wittenberg.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Long noncoding RNAs (lncRNAs) emerge as essential roles in the regulation of alternative splicing (AS) in various malignancies. Serine- and arginine-rich splicing factor 1 (SRSF1)-mediated AS events are the most important molecular hallmarks in cancer. Nevertheless, the biological mechanism underlying tumorigenesis of lncRNAs correlated with SRSF1 in esophageal squamous cell carcinoma (ESCC) remains elusive. In this study, we found that lncRNA DiGeorge syndrome critical region gene 5 (DGCR5) was upregulated in ESCC clinical samples, which associated with poor prognosis. Through RNA interference and overexpression approaches, we confirmed that DGCR5 contributed to promote ESCC cell proliferation, migration, and invasion while inhibited apoptosis in vitro. Mechanistically, DGCR5 could directly bind with SRSF1 to increase its stability and thus stimulate alternative splicing events. Furthermore, we clarified that SRSF1 regulated the aberrant splicing of myeloid cell leukemia-1 (Mcl-1) and initiated a significant Mcl-1L (antiapoptotic) isoform switch, which contributed to the expression of the full length of Mcl-1. Moreover, the cell-derived xenograft (CDX) model was validated that DGCR5 could facilitate the tumorigenesis of ESCC in vivo. Collectively, our findings identified that the key biological role of lncRNA DGCR5 in alternative splicing regulation and emphasized DGCR5 as a potential biomarker and therapeutic target for ESCC. Disease carcinogenesis and prevention substantially distinctive between esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), with obvious differences in histological types2. ESCC accounts for up to 90% of esophageal cancer in the Asian population, which is characterized by high invasion or distant metastasis3. Although advances in ESCC treatments approaches such as surgery, chemotherapy, and radiotherapy, the overall 5-year survival rate is still unsatisfied4. Therefore, it is urgent to investigate the new biomarkers and treatments for ESCC.Esophageal cancer (EC) is one of the most aggressive cancer worldwide and contributes the sixth highest cancer-related mortality rate5. Accumulating studies have reported lncRNAs are aberrantly expressed in human cancers and participate critically roles in diverse biological processes7. Mechanistically, lncRNAs function as important regulators in gene expression networks in multiple ways, such as chromatin modification, transcription, and posttranscriptional regulation, which mainly depend on their subcellular localization8. For example, HNF1A-AS1 in gastric cancer9 and TTN-AS1 in non-small cell lung cancer (NSCLC) progression10 functioning as the ceRNA molecular is widely reported in cancer development. Besides, lncRNAs regulate their target gene expressions via binding with RNA-binding proteins (RBPs)12. DiGeorge syndrome critical region gene 5 (DGCR5), also known as linc00037, is a lncRNA located on chromosome22 (22q11.21), which is first reported in Huntington’s disease. Emerging studies indicate that higher expression of DGCR5 is particularly important in the progression of laryngeal carcinoma13 and gallbladder cancer14, suggesting DGCR5 may be an oncogenic lncRNA in cancers. By contrast, DGCR5 acts as a tumor suppressor in cervical cancer15, gastric cancer16, and bladder cancer17. All these seemingly contradictory findings prove the heterogeneity roles of DGCR5 in different biological scenarios. As for ESCC, research works have confirmed that some aberrantly expressed lncRNAs related to ESCC development19. However, the functions and mechanisms of DGCR5 in ESCC remain unknown.Long noncoding RNAs (lncRNAs) are a class of transcripts longer than 200 nucleotides, most of which lack valid coding capacity20. Importantly, aberrant AS events are frequently observed in a diverse spectrum of tumor types, which is independent of the already characterized genetic alterations21. Serine- and arginine-rich splicing factor 1 (SRSF1) belongs to the splicing factor of the SR family, and participates in posttranscriptional gene regulation through mRNA alternative splicing22. Our previous research has demonstrated that SRSF1 could mediate the aberrant AS of bridging integrator-1 (BIN1) to neutralize its tumor-suppressing functions in NSCLC23. Nevertheless, the concrete function and upstream regulatory mechanisms of SRSF1 in ESCC still remain elusive. Recently, the interactions between lncRNAs and splicing factors are regarded to be important for initiating and maintaining the alternative splicing process24. Nevertheless, the potential function of DGCR5 on ESCC and whether it relates to the AS events via SRSF1-regulated have never been discovered.Alternative splicing (AS) is a posttranscriptional process enabling to regulate the generation of various mRNA and protein products, playing a vital role in the process of development and differentiationThe aim of this study is to characterize the biological functions of DGCR5 in ESCC and investigate the underlying mechanisms associated with alternative splicing. In this study, we first identified an upregulated expression of DGCR5 in ESCC. Gain-of-function and loss-of-function experiments investigated the carcinogenesis of DGCR5 on ESCC cells in vitro and in vivo. Then, the interaction between DGCR5 and SRSF1 was analyzed to explore the impact of DGCR5 on the alternative splicing process of myeloid cell leukemia-1 (Mcl-1). Taken above, our results shed new light on the role of DGCR5 in the tumorigenesis and progression of ESCC.https://cancergenome.nih.gov/) and found that DGCR5 was significantly upregulated in ESCC tissues compared with normal tissues , more lymph node metastasis (P = 0.0008) and higher TNM stage (P = 0.0008), while not related with age and gender (Table P = 0.045), lymph node metastasis (P = 0.047), and DGCR5 expression (P = 0.003) were significantly associated with the prognosis of ESCC patients  online database to identify proteins that interacted with DGCR5. As showed SRSF1 mediating the aberrant AS of BIN1 in NSCLC in our previous study, SRSF1 protein caught our attention for its role in splicing regulation in cancers (Supplementary Table P < 0.01)  and subcellular fractionation. The results showed DGCR5 primarily localized in the nucleus of TE1 and Kyse170 cells Fig. . Since lIn order to explore whether oncogenic splicing factor SRSF1 is regulated by DGCR5, we detect SRSF1 expression on ESCC cells when DGCR5 overexpression or knockdown. The consequence showed that there was no correlation between DGCR5 and SRSF1 mRNA level Fig. . However27. In addition, considering Mcl-1 has been related to SRSF128, we focused on Mcl-1 as a potential target. Then we confirmed Mcl-1 mRNA expression was significantly upregulated in ESCC samples . Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis identified that DGCR5 had a great correlation with the apoptosis signaling pathway Fig. . Moreoveles Fig. . Furtherls Figs.  and 2F, ls Figs. . Then wels Figs. . Therefo31. To analyze whether the alternative splicing of Mcl-1 regulated by SRSF1 protein in ESCC cells, we successfully constructed SRSF1 overexpressed or knockdown in the TE1 and Kyse170 cells  and a full-length isoform Mcl-1L (antiapoptotic) Fig. . We then32, we then detect the function of the DGCR5/SRSF1/Mcl-1 axis on caspase-3 proteins. The results showed no obvious change was observed in the cleaved-caspase-3  model by subcutaneously injecting Kyse170 cells with vector or interfered DGCR5 into 5-week-old nude mice. Tumor volumes were observed 23 days after injection. We found that tumor volumes and weights in silencing the DGCR5 group were significantly suppressed compared with the control Fig. . DGCR5 e33 In this study, we confirmed DGCR5 was highly expressed in ESCC tissues, which was an unfavorable prognosis. Then, we examined that elevated expression of DGCR5 was positively correlated with deeper invasion range, more lymph node metastasis, and higher TNM stage. Knockdown of DGCR5 significantly decreased the proliferation, migration, and invasion ability, while promoted apoptosis of ESCC cells in vitro and in vivo. In addition, we defined the novel mechanism that DGCR5 could connect with the oncogenic splicing factor SRSF1 protein and increase its stability, thus involving the alternative splicing of Mcl-1 mRNA in ESCC cells  and Mcl-1L (antiapoptotic), in which the latter variant is predominant in cancers. In particular, Mcl-1 is highly expressed in ESCC and correlates with poor prognosis49. As previous studies provided, Mcl-1 could be alternatively spliced to exert antiapoptotic functions by SRSF1 protein in breast cancer50. In this study, we analyzed the intensive correlation between SRSF1 and Mcl-1 expression in ESCC. Our data revealed that SRSF1 regulated the apoptosis signaling pathway by participating in the alternative splicing of Mcl-1 and triggered the elevation of Mcl-1L subtype in ESCC, providing the novel insight into the role of SRSF1 in Mcl-1 alternative splicing of ESCC cells.Aberrant AS is demonstrated as a key mechanism that generates multiple RNA variants to regulate protein diversity and enlarge the complexity regulation in many diseases, including cancers51. However, the number of current studies on the correlation of lncRNAs with the regulation of AS events in ESCC is limited. Through bioinformatics analysis, we found that DGCR5 was related to the apoptosis-related signaling pathway. In addition, our research indicated that DGCR5 regulated ESCC cell apoptosis through the Mcl-1 pathway. Then, we explored the influence of SRSF1 on Mcl-1 alternative splicing through DGCR5-mediated in ESCC cells. Our results found that SRSF1 protein was responsible for the regulation of the malignant biological behavior of DGCR5, thereby facilitating the splicing of Mcl-1. These findings demonstrated that the role of the regulatory network between DGCR5/SRSF1/Mcl-1 in ESCC. Moreover, caspase-3 is a downstream target gene of the apoptosis pathway that playing a crucial role in the caspase family52. Then we detected the expression of the cleaved-caspase-3 protein in ESCC cells to determine whether it was essential for the DGCR5/SRSF1/Mcl-1axis. Nevertheless, we found that DGCR5 promoted apoptosis of ESCC cells through a caspase-3-independent pathway. Collectively, our findings showed that the interaction between DGCR5 and SRSF1 increased the expression of Mcl-1L via mediating alternative splicing and promoted ESCC cells apoptosis via a caspase-3-independent pathway.LncRNAs have been confirmed to directly participate in posttranslational regulation of splicing factors in cancersIn summary, we determined upregulated expression of DGCR5 in ESCC and verified its great significance for ESCC patients. DGCR5 led to the progression of ESCC cell tumorigenesis both in vitro and in vivo. Moreover, our findings revealed the molecular mechanism that DGCR5 involved in tumorigenesis of ESCC via SRSF1-mediated alternative splicing of Mcl-1. Overall, our study certificated DGCR5/SRSF1/Mcl-1axis could act as a novel regulatory factor in the carcinogenesis of ESCC.Seventy pairs of ESCC tissues and matched normal tissues were obtained from radical surgery of ESCC patients in the Fourth Hospital of Hebei Medical University  between March 2017 and April 2018. None of the ESCC patients received radiotherapy, chemotherapy, or immunotherapy before surgery. The samples were diagnosed independently by two pathologists. We collected and updated patients’ clinical information every 3 months by follow-up. This study was approved by the ethics committee of the Fourth Hospital of Hebei Medical University.2 incubator. All experiments were performed with mycoplasma-free cells.All human ESCC cell lines  were obtained from the Research center of the Fourth hospital of Hebei Medical University . ESCC cell lines were cultured in RPMI-1640  supplemented with 10% fetal bovine serum  in the humidified atmosphere of the 5% COTotal RNA was extracted using the Trizol reagent . PARIS Kit  was used to isolate the nuclear and cytoplasmic RNA. In total, 2 μg of the total RNA performed to reverse transcription with reverse transcriptase . The cDNA was subjected to PCR amplification  using primers designed for human DGCR5 and Mcl-1, then were separated using 2% agarose gels and visualized with ethidium bromide. SYBR Green Master Mix  was used for qRT-PCR in ABI Quant Studio TM 6 Flex. The relative expression of lncRNA and mRNA levels were normalized to GAPDH or small nuclear U6, respectively, and then analyzed by the 2 − ΔCt or 2 − ΔΔCt method. All primers are listed in Supplementary Tables 10. The siRNA sequences are as follows Supplementary Table DGCR5, SRSF1, or Mcl-1 were, respectively, cloned into pCDNA3.1 plasmid for DGCR5, SRSF1, or Mcl-1 overexpression, which were purchased from GenePharma . DGCR5 and SRSF1 siRNAs were also synthesized by GenePharma. Transfection was conducted with Lipofectamine 2000 (Invitrogen). Cell transfection was performed as we previously described3 cells/well for CCK-8 assays, then was performed as we previously described10. For the colony-formation assays, 1 × 103 transfected ESCC cells were inoculated into six-well plates and cultured at 37 °C for 14 days. The colonies were fixed with 4% paraformaldehyde, then stained with 1% crystal violet and counted.Transfected cells were seeded into 96-well plates at a density of 1 × 105 transfected cells suspended in 200 μl of serum-free RPMI-1640 medium were seeded in the upper chamber , and 600 μl RPMI-1640 medium supplemented with 10% FBS (BI) was placed in the lower chamber. After incubation for 36 h, the cells were fixed with 4% paraformaldehyde for 20 min at room temperature, then stained with 0.1% crystal violet for 20 min. Cells on the lower side of the filters were defined as migration or invasion cells and counted at ×200 magnification in three random fields of each filter. We performed invasion and migration assays as previously described10.For invasion and migration assays, the upper chamber of each insert coated with 20 μl Matrigel to form a matrix barrier or not, 2 × 1010.Apoptosis assays were assayed with flow cytometry (FCM) using the FITC Annexin V Apoptosis Detection Kit I , which was performed as previously describedTE1 and Kyse170 cells were seeded until cell confluence reached 60–70%, fixed in 4% paraformaldehyde for 20 min, and then added proteinase K (20 μg/ml) to cover cells and incubate for 30 min at 37 °C. Rabbit serum was added for blocking after hybridization with RNA probe . Then anti-DIG-HRP was incubated at 37 °C for 40 min. TSA chromogenic reagent added and reacted in the dark for 5 min at room temperature. DAPI was incubated for 8 min in the dark, and then a fluorescence microscope was mounted.10.The EZ-Magna RIP kit  was used for RNA immunoprecipitation (RIP) according to the manufacturer’s instructions. TE1 cells were lysed in RIP buffer consisting of 150 mM KCL, 25 mM Tris-HCl (pH 7.4), 5 mM EDTA, 5 mM DTT, 0.5% Triton X-100, supplemented with RNase inhibitor Ribolock and proteinase inhibitor cocktail. The lysate was mixed with SRSF1 antibody  or normal IgG coupled beads and left under rotation at 4 °C. Beads were subsequently washed in lysis buffer and the input RNA was purified, and thus analyzed by RT-PCR and qRT-PCR using the primer of DGCR5. RIP assay was performed as we previously describedTotal proteins were extracted and lysed with RIPA . Nuclear and cytoplasmic proteins were extracted with the nuclear and cytoplasmic protein extraction kit . Proteins (30 μg) were separated by 15% SDS-PAGE and transferred onto PVDF membranes , then blocked with 5% skimmed milk and incubated overnight at 4 °C with the following antibodies: anti-SRSF1 , Mcl-1 , Mcl-1 long-form for the molecular weight of 40 kDa and Mcl-1 short-form was 32 kDa. Bcl-2 , BAX , Caspase-3 , Histone-H3 , or β-actin . Then the species-matched secondary antibodies were incubated for 1 h at 37 °C and the proteins were detected using BeyoECLPlus .53. The rabbit polyclonal antibody against human SRSF1  and the mouse polyclonal antibody against human Mcl-1  were used for detection. The staining was visualized according to the 0–4 semi-quantitative system, which based on the percentage of positive cells and the intensity of staining. The total scores were determined by multiplying the percentage score and intensity score and considered as a low expression for the score of 0–4 and high expression for the score of 5–12. The immunostaining score was assessed by two pathologists, independently.IHC analysis was performed as we previously describedn = 5/group). In total, 4 × 106 Kyse170 cells with different transfected were suspended in 200 μl serum-free RPMI-1640, then subcutaneously injected into the right flank of each mouse. The tumor was measured every 3 days. The mice were sacrificed after 23 days, then tumors were weighed and processed for further analysis. Tumor volume was calculated as follows: V (volume) = (length × width2)/2. All animal experiments were performed under approval by the Fourth Hospital of Hebei Medical University Animal Care Commission.Four-week-old male BLAB/c nude mice were randomly divided into two groups . The measurement data are presented as mean ± standard error. The Student’s Supplementary figure legendsUnivariate Cox regression analysis of the relationship between clinicopathological features and survival rate of ESCC patientsMultivariate Cox regression analysis of the relationship between clinicopathological features and survival rate of ESCC patientsList of DGCR5 target proteins by starBase V2.0Expression of SRSF1 staining between ESCC tissues and matched adjacent tissues n (%)Real Time PrimersRT-PCR primerssiRNA sequencesDGCR5 overexpression promoted ESCC progressionDGCR5 promotes ESCC cells migration in vitro, and the subcellular location of DGCR5 in ESCC cells.DGCR5 had no effect on the expression of SRSF1 protein in cytoplasm.The expression of SRSF1 at mRNA and protein levels were examined by transfection of si-SRSF1 or SRSF1 on TE1 and Kyse170 cells.DGCR5 promotes the expression of Mcl-1 by activation of SRSF1on TE1 and Kyse170 cells.


ARF−/− cell line to be resistant to one of four classical chemotherapy agents: doxorubicin, vincristine, paclitaxel, and cisplatin. We then surveyed collateral responses to acquisition of resistance to these agents. Although numerous collateral sensitivities have been documented for antibiotics and targeted cancer therapies, we observed only one collateral sensitivity: half of cell lines that acquired resistance to paclitaxel also acquired a collateral sensitivity to verapamil. However, we found that the mechanism of this collateral sensitivity was unrelated to the mechanism of paclitaxel resistance. Interestingly, we observed heterogeneity in the phenotypic response to acquisition of resistance to most of the drugs we tested, most notably for paclitaxel, suggesting the existence of multiple different states of resistance. Surprisingly, this phenotypic heterogeneity in paclitaxel resistant cell lines was unrelated to transcriptomic heterogeneity among those cell lines. These features of phenotypic and transcriptomic heterogeneity must be taken into account in future studies of treated tumor subclones and in design of chemotherapy combinations.Chemotherapy resistance is a major obstacle to curing cancer patients. Combination drug regimens have shown promise as a method to overcome resistance; however, to date only some cancers have been cured with this method. Collateral sensitivity—the phenomenon whereby resistance to one drug is co-occurrent with sensitivity to a second drug—has been gaining traction as a promising new concept to guide rational design of combination regimens. Here we evolved over 100 subclones of the Eµ-Myc; p19 The antibiotic resistance field is facing a similar problem—rates of antibiotic resistance doubled between 2002 and 2014, and there are 23,000 deaths in the US each year due to antibiotic-resistant infections, according to the Centers for Disease Control and Prevention2. In response, researchers in the antibiotic resistance field have studied cases where resistance to one antibiotic causes, or is co-occurrent with, sensitivity to a second antibiotic—termed collateral sensitivity3. In theory, using collaterally sensitive drug pairs in combination or sequentially could select against the emergence of resistant microbes and thus reduce or prevent the emergence of antibiotic resistance4.Since the 1950s, clinicians have combated intrinsic chemotherapy resistance by treating patients with combinations of chemotherapies that have non-overlapping toxicities thereby allowing for higher doses. However, this method has resulted in an overall 5-year survival rate across all cancers of only 68%5. The following year, Andrew Dhawan and colleagues characterized collateral sensitivities to several tyrosine kinase inhibitors (TKIs) in ALK-positive non-small cell lung cancer (NSCLC). They found that cell lines resistant to first-line TKIs are often sensitized to non-TKIs including the classical chemotherapies etoposide and pemetrexed6. In 2020 a study from the same group showed that resistance to combinations of classical chemotherapies in Ewing’s sarcoma causes resistance to the drugs in the combinations, as well as to actinomycin D, and potentially sensitivity to SP-2509, a lysine-specific demethylase 1 inhibitor7. Recently, Mueller et al. found that resistance to PRMT5 inhibition causes collateral sensitivity to paclitaxel due to upregulation of Stmn28.Recently, the concept of collateral sensitivity has begun to be explored in the context of cancer chemotherapy as well. For example, in 2016, Zhao et al. found that in Philadelphia chromosome-positive acute lymphoblastic leukemia (ALL), resistance to dasatinib, a BCR-ABL1 inhibitor, can cause collateral sensitivity to crizotinib, foretinib, vandetanib, and cabozantinib—cMET and/or VEGFR inhibitors9. Additionally, there is experimental and clinical data suggesting that, at least in some cases, cisplatin resistance can cause sensitivity to paclitaxel, and vice versa12. The mechanism behind this effect remains unclear, however, this combination has been found to be effective to treat patients with ovarian, breast, lung, skin, and head and neck tumors13.While the growing knowledge of collateral sensitivities to targeted therapies is encouraging, most patients today are treated with at least one classical chemotherapy. A close review of the literature reveals several examples of likely collateral sensitivities between classical therapies. Vinblastine and paclitaxel have opposing mechanisms of action—the former destabilizes microtubules, and the latter stabilizes microtubules. They also have opposing mechanisms of resistance, vinblastine resistance can result from stabilizing mutations in α- and β-tubulin, while paclitaxel resistance can result from destabilizing mutations in α- and β-tubulin. When these resistance mechanisms are present, vinblastine-resistant cell lines are sensitive to paclitaxel, and vice versaARF−/− cell line which is a murine model for human Burkitt’s lymphoma16. We chose to investigate classical therapies which are commonly used in the clinic, several of which are used to treat Burkitt’s lymphoma, and several of which have collateral sensitivities described in the literature17. The drugs we studied are doxorubicin, a topoisomerase II poison, vincristine, a microtubule destabilizing agent, paclitaxel, a microtubule stabilizing agent, and cisplatin, a DNA crosslinker20.While there are individual examples of collateral sensitivities to classical chemotherapies, there has not been a comprehensive survey of collateral sensitivities to single-agents in this class of drug, as has been done for antibiotics and targeted therapies. Furthermore, the dearth of known mutations that predict resistance to classical chemotherapies motivated us to define a proxy of resistant state by using the collateral effect of resistance to classical chemotherapies on sensitivity to other agents. Here, we survey collateral responses to acquired resistance to several classical therapies in the Eµ-Myc; p19Using a 96-well plate-based drug pulse and stepwise dose increase protocol, we were able to create > 15 evolutionary replicates of stable cell lines resistant to each of these four drugs. We created a protocol to measure the resulting lines’ sensitivities to a panel of 20 chemotherapies with high precision. We found only one collateral sensitivity in a subset of the cell lines we evolved. This work also revealed marked heterogeneity in collateral changes in drug sensitivity between cell lines evolved to be resistant to the same drug. Surprisingly, transcriptomic profiling of cell lines resistant to paclitaxel was unable to explain the phenotypic heterogeneity. This heterogeneity in resistance acquisition needs to be taken into account when designing classical and salvage combination regimens in order to optimize patient outcome. Similarly, potential discordance between phenotype and transcriptome must be considered when studying transcriptome as a proxy for functional biology.ARF−/− cell line as a tractable model because it is chemo-sensitive, has a short doubling time of ~ 12 hours, and grows in suspension so it can be easily manipulated in small culture wells15.To study collateral effects of resistance to classical chemotherapies, we developed a protocol allowing us to evolve many cell lines to resistance to one therapy in parallel. In these experiments we used the murine Eµ-Myc; p19To evolve resistance to classical chemotherapies, 10,000 cells were treated with a dose of drug expected to kill 85% of cells over the course of three days in 96-well plates. After three days, killing was assessed using PI staining via flow cytometry and drug was removed from cells. Cells were allowed to recover for four days in untreated media and wells were split as they became confluent. On day four, the plate was split to leave approximately 10,000 cells in each well which were treated again with a dose expected to kill 85% of cells based on the killing achieved the previous week. Remaining cells from the split were frozen and saved at −80 °C  , regulation of apoptosis via Bcl2 (with venetoclax), and glutathione levels (with buthionine sulfoximine). We first performed dose response curves with these compounds alone to identify the highest non-toxic dose to use in the combination treatment. Unexpectedly, we found that Paclitaxel Resistance Group 2 cell lines are significantly more sensitive to verapamil as a single agent than Paclitaxel Resistance Group 1 cell lines. In other words, Paclitaxel Resistance Group 2 cell lines are collaterally sensitive to verapamil Fig. B,C.FigurIn addition to blocking Abcb1-mediated drug efflux, verapamil is also a calcium channel blocker. Therefore, we tested the hypothesis that calcium flux or signaling may differ between the two groups of paclitaxel resistant cell lines. We compared expression of calcium channels and calcium-dependent kinases between the paclitaxel resistant groups. We found that the calcium channel Cacna1e is more highly expressed in Paclitaxel Resistance Group 2 Fig.  and S4.Finally, to assess the role of Abcb1 drug efflux, Bcl2 signaling, and glutathione levels on paclitaxel resistance, we next performed paclitaxel dose response curves in the presence or absence of the highest non-toxic dose of the three compounds that inhibit these mechanisms of resistance. Treatment with 0.5 μM venetoclax or 10 μM buthionine sulfoximine did not affect paclitaxel sensitivity in any of our cell lines  did not exhibit the broad cross-resistance to other chemotherapeutics expected in cell lines with higher expression of Abcb1. Specifically, cell lines in Paclitaxel Resistance Group 1 had either no or only moderate decreases in sensitivity to Abcb1 substrates including etoposide, doxorubicin, vincristine, 5-FU, and cisplatin, and increased sensitivity to the Abcb1 substrate actinomycin D. Interestingly, verapamil did not fully ameliorate paclitaxel resistance in the 2nd group of paclitaxel resistant cell lines. This group of cell lines (Paclitaxel Resistance Group 2) has increased expression of class III β-tubulin, an alteration that has been widely implicated in paclitaxel resistance both in the lab and in the clinic35. Although these cell lines do not have different levels of MRP-1  is collaterally sensitive to verapamil. We suspect that this collateral sensitivity is not due to drug efflux per se, because both paclitaxel resistance groups have similar levels of Abcb1 expression and similar effects of verapamil co-treatment with paclitaxel. While determination of the mechanism of this collateral sensitivity is beyond the scope of this study, we have several hypotheses that could explain these observations. The first is that the cell lines in Paclitaxel Resistance Group 2 may be creating an endogenous toxic compound that is normally effluxed by Abcb1. The second is that verapamil has been shown to induce apoptosis by increasing glutathione efflux via MRP-1Examination of the changes in drug sensitivity across our DMSO control cell lines revealed several curious effects of our culturing system. First, we observed heterogeneity in sensitivity to several drugs including statins, YM155, sorafenib, 5-FU, chlorambucil, and topotecan. We also observed heterogeneity in gene expression among the DMSO control cell lines, although differences between control cell lines were not concordant in transcriptomic and phenotypic space, echoing what we observed when comparing the paclitaxel resistant cell lines. Specifically, the DMSO control lines only shared nearest neighbors in drug sensitivity and transcriptome PCA space in two out of 12 cases Fig. .40.We observed that some aspect of the growth conditions during the evolution of resistance, such as the extended culturing time, or culturing in the presence of DMSO caused increased sensitivity to two statins and to YM155 in many of the control cell lines. The mechanism behind the control cell lines’ ‘collateral’ sensitivities to the statins and YM155 is unknown. Recently there has been evidence in the literature that statins may be effective as cancer therapies; however, these results need to be carefully considered if culturing techniques can affect cell lines’ statin sensitivity42. Current combination regimens are effective largely because the drugs within them act independently of each other, and drug interactions like collateral sensitivity are rare.After removing artifact drugs for which the DMSO control cell lines exhibited changes in sensitivity, we observed many collateral resistances in our resistant cell lines, but only one collateral sensitivity in eight out of 112 cell lines. Intriguingly, this result supports a recently revived theory termed independent action which posits that drug combinations are effective not because of synergy or other interaction between drugs, but rather because one drug will be effective for each individual cell or tumor. Under this paradigm, drug combinations are effective simply because multiple drugs represent multiple ‘shots on goal’ for an individual in a population with heterogeneous and difficult-to-predict drug sensitivitiesThe fact that collateral sensitivity to classical chemotherapies is rare even in the single agent setting tested here underlines the reality that our understanding of the short- and long-term effects of these drugs is lacking. In contrast, resistance to targeted therapies is much better understood, and in fact targeted therapies induce collateral sensitivities. However, in the clinic, poor response to 1st-line treatment with classical chemotherapy is predictive of failure of 2nd-line therapies. This observation predicts the result we observed but is complicated by the fact that most patients are treated with more than one agent. Perhaps the early use of classical chemotherapies in treatment is problematic and should instead be a last resort. Instead, to extend patient survival, frontline therapy should consist of combinations of targeted therapies which have more predictable resistance mechanisms and can result in treatable collateral sensitivities.45, as well as to targeted chemotherapy resistance46. While antibiotics and targeted therapies are thought to interact relatively specifically with their targets, classical chemotherapies are thought to be more promiscuous and thus have a wider distribution of binding affinities for entities within the cell49. Our results lead us to hypothesize that collateral sensitivities result from strong evolutionary pressure on one or a small number of specific targets within the cell. In these cases, resistance can result from on-target mutations or target bypass, both of which can produce novel and exploitable sensitivities52. In contrast, the diffuse evolutionary pressure exerted by a compound with many binding partners would not be addressed by any single mutation or bypass mechanism as many cellular processes are affected. In this case, resistance may instead result from a more general state change, for example, to an anti-apoptotic state which would not be vulnerable to inhibition of any specific node within the cell. This paradigm leads to our observed result—rare collateral sensitivities to classical chemotherapies, while others have observed collateral sensitivities to antibiotics and targeted therapies.The sparsity of collateral sensitivities to classical chemotherapies is surprising, as collateral sensitivity has been widely documented in response to antibiotic resistanceOur results, showing the sparsity of collateral sensitivities to classical chemotherapy resistance, motivates further work to better understand mechanisms of action and resistance to these agents. Furthermore, efforts are needed to understand which combinations of classical chemotherapies work well together in order to rationally design novel combination regimens. This work is complicated by our observation of heterogeneity in phenotypic response to acquisition of classical chemotherapy resistance which is not explained by differences in gene expression. The discussion of heterogeneity in cancer has previously focused on inter-patient heterogeneity, heterogeneity between metastases within a patient, and intra-tumor heterogeneity. We have uncovered a new dimension of heterogeneity which needs to be further explored—heterogeneity in response and resistance to chemotherapy Fig. .Figure 7ARF−/−-B-cell lymphomas16  were cultured in B-cell medium . All cell lines were routinely tested for mycoplasma contamination using MycoAlert (Lonza). All drugs were obtained from LC Laboratories, Sigma-Aldrich, Calbiochem, or Tocris Biosciences. All drugs were dissolved in DMSO except cisplatin, which was dissolved in 0.9% saline.Murine Eµ-Myc; p1910,000 cells were seeded into each well of 96-well plates , then treated with a dose of a chemotherapeutic to kill 85% of cells after three days. After treatment, viability was assessed via PI staining and FACS. Cells were spun out of drug and allowed to recover in drug-free media for 4 days. During recovery, cells were split as individual wells’ media color started to change. On day 4, all wells were split to approximately 10,000 cells per well and the removed cells were frozen at −80 °C in PCR plates. The cycle was repeated for 12–18 weeks.Cells were seeded in cell-culture treated 384-well plates , then cells were treated as indicated. Cell viability was measured after 72 h using resazurin sodium salt (Sigma). Resazurin was used at 0.008 mg/mL and plates were analyzed 6 h after addition. Fluorescence was measured using the Tecan M200 Pro at an excitation of 550 nm and emission of 600 nm.inf is the viability at high doses of the drug, and A0 is the viability with low or no drug. Sensitivity metrics were generated for each replicate individually, which were then used for further statistical analyses.The following analysis was performed in Matlab: median fluorescence of wells without cells was subtracted from all wells on the same plate. Viability was then normalized to cell growth by dividing all corrected fluorescence values by the median corrected fluorescence of wells containing untreated cells. Viability data was then fit to the following four-parameter hill curve:Total RNA was prepared from the paclitaxel resistant cell lines, the DMSO control cell lines, and the parental cell line using the RNeasy Mini Kit (Qiagen), per the manufacturer’s protocol, and included the on-column DNAse step (Qiagen).6]N10T30VN-3' where 5Biosg = 5’ biotin, [BC6] = 6 bp barcode specific to each sample/well, N10 = Unique Molecular Identifiers, Integrated DNA technologies), to generate two subpools of 15 samples each.RNA samples were quantified and quality assessed using an Advanced Analytical Fragment Analyzer. The initial steps were performed on a Tecan EVO150.10 ng of total RNA was used for library preparation. 3’DGE-custom primers 3V6NEXT-bmc#1-15 were added to a final concentration of 1 µM.  followed by incubation at 42 °C for 90 min and inactivation at 80 °C for 10 min.Following the template switching reaction, cDNA from 15 wells containing unique well identifiers were pooled together and cleaned using RNA Ampure beads at 1.0×. cDNA was eluted with 17 µl of water followed by digestion with Exonuclease I at 37 °C for 30 min, and inactivated at 80 °C for 20 min.54.Second strand synthesis and PCR amplification was done by adding the Advantage 2 Polymerase Mix (Clontech) and the SINGV6 primer  directly to the exonuclease reaction. 8 cycles of PCR were performed followed by clean up using regular SPRI beads at 0.6×, and eluted with 20 µl of EB. Successful amplification of cDNA was confirmed using the Fragment AnalyzerIllumina libraries were then produced using standard Nextera tagmentation substituting P5NEXTPT5-bmc primer  in place of the normal N500 primer.Final libraries were cleaned using SPRI beads at 0.7× and quantified using the Fragment Analyzer and qPCR before being loaded for paired-end sequencing using the Illumina NextSeq500 in paired-end mode (26/50 nt reads).55. Single-end reads were aligned to the GRCm38 mm10 assembly with gencode M25 annotation using STAR version 2.5.1b56. Gene counts from STAR were used to perform differential gene expression analysis with R version 3.6 using edgeR version 3.32.058. Gene expression differences at the total gene level were considered significant at an FDR of < 0.01.Quality control was done with FastQC version 0.11.459 using the hallmark gene sets in MSigDB60 with an FDR correction and a p-value cutoff of 0.05. enrichplot was used to make the GSEA plots61.GSEA was performed on the described differentially expressed genes using clusterProfilerRNA was prepared from the indicated cell lines using the Quick-RNA Miniprep Kit (Zymo Research) and reverse transcribed with SuperScript IV Reverse Transcriptase (Thermo Fischer). Real-time PCR reactions were performed using PrimeTime Gene Expression Master Mix (IDT) on a LightCycler 480 II (Roche). Data were analyzed using the ΔΔCT method, and relative mRNA levels were normalized to Pgk1 levels. Primers used were IDT Predesigned Taqman Gene Expression Arrays for for Bcl-xL (Mm00437783_m1), Mcp-1 (Mm00441242_m1), Abcb1a (Mm00440761_m1), Abcb1b (Mm00440736_m1), and Pgk1 (Mm.PT.39a.22214854).The indicated cell lines were seeded in 6-well tissue culture plates for 24H. The media from those plates was centrifuged to remove cells, supernatant collected, and stored at −80 °C until measuring of IL-6 levels with a kit from Thermo Fisher (88-7064-88).Statistics were performed using GraphPad Prism 5 (GraphPad Software Inc) and R version 3.6. All error bars represent standard error of the mean.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Information.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.