A novel antiproliferative molecular scaffold was designed by rational modification of known antiandrogens, achieving a significant improvement in anti-cancer activity. 50 values in the low μM range. The cytotoxicity profile was also evaluated for the novel structures in the HEK293 cell line.Prostate cancer is a major cause of male death worldwide and the identification of new efficient treatments is constantly needed. Different non-steroidal androgen receptor antagonists are approved also in the case of castration-resistant cancer forms. Using a rational approach and molecular modelling studies to modify the structure of antiandrogen drug bicalutamide, a new series of phenylsulfonyl-benzamide derivatives was designed and synthesised. Their antiproliferative activities were evaluated in four different human prostate cancer cell lines and several new compounds showed significantly improved IC Unfortulutamide  are two 1, ring B can bend to occupy an inner portion of the ligand-binding domain, thus allowing the closure of the receptor into its agonist conformation. Treatment with enzalutamide induces instead a F876L mutation in the AR, which also confers an antagonist to agonist switch in activity for the drug.7One of the most common mutations found for bicalutamide is W741L in helix 12 of the receptor,4), derived from the natural pigment curcumin, has been reported, in which a central phenyl group is acting as linker connecting two different aromatic rings.9Most non-steroidal antiandrogens are structurally characterised by two differently substituted aromatic rings, named ring A and ring B, connected by a linker, either linear  or cyclic  .8 Recent2 substituent), replacing the classical 4-cyano substituent. The phenolic 4-hydroxy group of 4, previously reported as essential for AR antagonistic activity,With the aim to rigidify the structure of bicalutamide and obtain new anticancer agents, we designed a novel molecular scaffold in which we replaced the flexible bicalutamide methyl-hydroxy-methylene linker with a rigid phenyl group, maintaining the two lateral aromatic rings commonly found in antiandrogen compounds . MoreoveFollowing this approach, a novel family of phenylsulfonyl-benzamides was developed , and the13a, which maintains bicalutamide and enzalutamide aromatic substituents in ring A and is characterised by the fully oxidised sulfonyl linker, was chosen as representative for the novel series of compounds. In a previous work,13a was performed using MOE2015.10,13a aligns well with the binding pose of the parent compounds, with the newly introduced phenyl ring conferring the desired structural rigidity to lock the molecule in the active conformation  similarity score obtained for the alignment with enzalutamide is a further confirmation that the introduction of the phenyl linker rigidifies the novel structure, potentially locking the molecule in an active conformation.A series of flexible alignment and molecular docking studies was performed to evaluate the proposed structural modifications. For these analyses, compound ormation . Moreove13a in the ligand binding domain (LBD) of the open AR homology model, obtained using Plants docking software.13a in the bicalutamide-resistant AR mutant W741L crystal structure ,13a and helix 12 . These two sulfoxides were characterised by low solubility in the reaction solvent, therefore complete oxidation to the corresponding sulfone was much slower and did not go to completion within the reaction time. Using this synthetic approach, 24 novel phenylsulfonyl-benzamides derivatives were prepared, purified and fully characterised.In the first step a copper catalysed C–S coupling reaction was used for the preparation of intermediates Standard bicalutamide and enzalutamide were also prepared following reported procedures.1050 in μM), along with the overall effect in the four cell lines reported as geometric mean. Activity found for bicalutamide and enzalutamide is consistent with previously published data for some of these specific cell lines,All new compounds along with the two standards were tested for their ability to inhibit survival and/or cell proliferation in four human prostate cancer cell lines: LNCaP, 22Rv1, VCaP, androgen-sensitive cell lines, and DU145, a non hormone-sensitive cell line. 50 values ranging from 3.2 μM to >100 μM. Compared with standard enzalutamide, antiproliferative effect was improved up to 8-fold.Considering their overall profile in the four cell lines (geometric mean), almost all the new derivatives performed significantly better than bicalutamide, improving its effect up to 16-fold. The new inhibitors showed concentration-dependent activity with mean IC1 in ring B does not seem to influence the anticancer profile of the scaffold, as no substantial differences were found between the OH group in the para position or the CF3 in meta (i.e.11avs.12a). This finding is in accordance with the proposed binding mode for 13a, in which the R1 substituent is facing outside the AR binding domain without making specific interactions.Substituent R2), with a decrease of effect in the sulfones up to 8-fold. In the new scaffold presented in this work, no particular differences can be observed between thioether and sulfone compounds, with retained activity in most sulfones (i.e.11avs.13a) and only a small increase of IC50 values up to 2-fold in few cases (i.e.11cvs.13c). The partially oxidized derivatives (X = SO) retain the effect of the corresponding thioethers and sulfones (i.e.11e and 13evs.14e).In both our previous work on bicalutamide derivatives and in another published work on analogues of SARMs (selective androgen receptor modulators),2 group in the para position of ring B is associated with retained activity, as found for mono 3-CF3 derivative 11h. Moving the trifluoromethyl group from meta to para position does not have a significant effect on activity (i.e.11hvs.14g), whereas the ortho substitution is characterized by a 3-fold reduction of overall effect in the four cell lines (i.e.11hvs.11i). Replacing the trifluoromethyl group with a bigger –SF5 moiety in para or meta position is associated with a mildly improved activity profile (i.e.11gvs.14e).Removal of the CN or the NO3 in meta , gives the best results in terms of overall activity in the four cell lines (i.e.11a and 13hvs.14c), as we had similarly observed in our previous research efforts.meta to ortho position is associated with activity reduction (i.e.11cvs.11d).Addition of an extra CF50 value (14e). These data suggest that the newly prepared compounds are likely to retain an antagonistic effect on the androgen receptor. An interesting activity profile was also found in the DU-145 cell line (the least sensitive to our new molecules), which expresses low levels of the androgen receptor50 values across the four cell lines), suggesting that besides its canonical anti-androgen receptor action, a different antiproliferative mechanism could also be involved. The new phenylsulfonyl-benzamides, in addition to potentially increase the antagonistic activity on the AR, might also enhance the potential off target effect, even though in some cases they seem more selective for androgen-sensitive cell lines than bicalutamide . As a general observation, the improved antiproliferative activity of the novel compounds should be regarded as the result of the whole structure of each single derivative, since the data found appear to be a combination of the simultaneous effects of the substituent in ring B and linker X.Examining each individual cell line, most of the new compounds showed the best results in the cell lines that highly express the androgen receptor. Among them, LNCaP cells were found to be the most sensitive to the new derivatives, with different compounds active at low μM concentrations and one reaching a sub μM IC11d, 11e and 13e) significantly reduced cell viability if compared to bicalutamide , and only one of them (13e) killed more than 50% of cells. Due to their higher cytotoxicity in comparison with bicalutamide, the antiproliferative effect of these three compounds in the prostate cancer cell lines could also be a consequence of their intrinsic toxicity, besides the canonical AR antagonist activity. Comparing the rest of the compounds with bicalutamide, even if some of them are characterized by an increased cytotoxicity, cell viability reduction is not statistically significant and less than 40% in most cases, therefore the observed cytotoxicity is comparable to the one found for bicalutamide. Moreover, it is worth to note that bicalutamide reaches its antiproliferative IC50 at 50 μM, therefore it would not show reduction of cell viability at 10 μM. On the contrary, most of our new compounds showed antiproliferative activity in the four prostate cancer cell lines at a low μM concentration (<10 μM), and even if some of them show a reduction of cell viability by 35–40%, their anticancer therapeutic window is still wide enough for consideration as a potential treatment for prostate cancer. In particular, compounds 11h, 12h, 13b and 13h are associated with a promising antiproliferative/cytotoxicity profile, with an antiproliferative effect much better than bicalutamide (<16 μM), and with the same cytotoxicity of bicalutamide at 10 μM .A Cell Titer Blue viability assay was performed in the human embryonic kidney cell line HEK293, in order to evaluate the cytotoxicity profile of the newly prepared compounds. 11h, 12h, 13b and 13h have been selected as candidates for further investigations, due to their improved anticancer properties and their cytotoxicity profiles. With the aim to identify a pre-clinical candidate, these compounds will be studied for their binding to the AR, their potential off target effect and their pharmacokinetic properties.A novel family of phenylsulfonyl-benzamides was designed and synthesised after rational modification of the structure of antiandrogen drug bicalutamide, and many of the novel derivatives prepared show improved anti-prostate cancer properties. In particular, most of the new compounds display significantly improved anticancer activities in four different prostate cancer cell lines, with a cell viability reduction effect comparable to the one found for bicalutamide. Differently from previously reported SARMs, no significant difference in antiproliferative activity has been observed between our novel thioether and sulfone derivatives, representing a promising feature for future metabolic stability considerations. Within the 24 novel analogues evaluated, 1H, 13C, 19F) and LRMS. All final compounds were found to be >95% pure by HPLC.All chemistry, biology and molecular modelling experimental procedures, along with compound characterisation, are fully described in the ESI.Supplementary informationClick here for additional data file.


SMARCB1 allele with concurrent loss of the other allele. Biochemical and functional characterization of these models revealed that RMC requires the loss of SMARCB1 for survival. Through integration of RNAi and CRISPR-Cas9 loss-of-function genetic screens and a small-molecule screen, we found that the ubiquitin-proteasome system (UPS) was essential in RMC. Inhibition of the UPS caused a G2/M arrest due to constitutive accumulation of cyclin B1. These observations extend across cancers that harbor SMARCB1 loss, which also require expression of the E2 ubiquitin-conjugating enzyme, UBE2C. Our studies identify a synthetic lethal relationship between SMARCB1-deficient cancers and reliance on the UPS which provides the foundation for a mechanism-informed clinical trial with proteasome inhibitors.Renal medullary carcinoma (RMC) is a rare and deadly kidney cancer in patients of African descent with sickle cell trait. We have developed faithful patient-derived RMC models and using whole-genome sequencing, we identified loss-of-function intronic fusion events in one  Renal medullary carcinoma (RMC for short) is a rare type of kidney cancer that affects teenagers and young adults. These patients are usually of African descent and carry one of the two genetic changes that cause sickle cell anemia. RMC is an aggressive disease without effective treatments and patients survive, on average, for only six to eight months after their diagnosis.Recent genetic studies found that most RMC cells have mutations that prevent them from producing a protein called SMARCB1. SMARCB1 normally acts as a so-called tumor suppressor, preventing cells from becoming cancerous. However, it was not clear whether RMCs always have to lose SMARCB1 if they are to survive and grow.Often, diseases are studied using laboratory-grown cells and tissues that have certain features of the disease. No such models had been created for RMC, which has slowed efforts to understand how the disease develops and find new treatments for it. Hong et al. therefore worked with patients to develop new lines of cells that can be used to study RMC in the laboratory. These RMC cells started dying when they were given copies of the SMARCB1 gene, which supports the theory that RMCs have to lose SMARCB1 in order to grow.Hong et al. then used a set of genetic reagents that can suppress or delete genes that are targeted by drugs, and followed this by testing a range of drugs on the RMC cells. Drugs and genetic reagents that reduced the activity of the proteasome – the structure inside cells that gets rid of old or unwanted proteins – caused the RMC cells to die. These proteasome inhibitor drugs also killed other kinds of cancer cells with SMARCB1 mutations.Proteasome inhibitors are already used to treat different types of cancer. Potentially, a clinical trial could be run to see if they will treat patients whose cancers lack SMARCB1. Further work is also needed to determine the exact link between SMARCB1 and the proteasome. Renal medullary carcinoma (RMC) was first identified in 1995 and is described as the seventh nephropathy of sickle cell disease . RMC is SMARCB1 and occur in young children . The primary tumor tissue from the second patient was obtained at the time of the initial nephrectomy. From this sample, we generated the tumor cell line, CLF_PEDS9001_T1. Cell lines were generated from patients who received 4–8 weeks of neoadjuvant chemotherapy prior to their nephrectomy.From September 2013 until September 2018, three patients who had a diagnosis of renal medullary carcinoma (RMC) were consented to IRB approved protocols . All patients were of African descent and adolescents. We first attempted to create a patient-derived xenograft from each patient by implanting tissue in the sub-renal capsule or subcutaneously in immunodeficient mice but these samples did not form tumors after 6 months of monitoring. We then attempted to develop cell lines from these patients and generated cell lines from two of the three patients (CLF_PEDS0005 and CLF_PEDS9001) . For the first patient (CLF_PEDS0005), we obtained the primary tissue from our local institution at the time of the initial nephrectomy. We generated a short-term culture normal kidney cell line, CLF_PEDS0005_N, and a tumor cell line, CLF_PEDS0005_T1 . In addiSMARCB1 along with fusion events in the second allele of SMARCB1 in RMC patients  or whole blood (CLF_PEDS9001) and compared it to the primary tumor cell lines (CLF_PEDS0005_T1 and CLF_PEDS9001_T) and metastatic cell lines (CLF_PEDS0005_T2A and CLF_PEDS0005_T2B). We found a low mutation frequency  RNA-sequencing data from pediatric renal tumors  or normal kidney tissues with the RMC models using t-distributed stochastic neighbor embedding (tSNE) . The normal cell line, CLF_PEDS0005_N, clustered with TARGET normal kidney tissues and RMC cell lines from both patients clustered with the TARGET Rhabdoid Tumor samples  . Using tSMARCB1 and stably infected our RMC models, G401 (MRT cell line) and HA1E   vectors harboring ll line) . We confll line) .We used these inducible cell lines to assess the biochemical stability of the SWI/SNF complex by using 10–30% glycerol gradient sedimentation followed by SDS-PAGE . In HA1EWe then used the inducible cell lines to measure the consequence of SMARCB1 re-expression on the viability of the cells. We also generated cell lines with inducible expression of a LacZ control to compare with re-expression of SMARCB1. In the HA1E SMARCB1 wild type cells, we found no significant difference in viability between induction of SMARCB1 versus induction of LacZ using direct counting of viable cells . In contSince MRT cell lines arrest and senesce when SMARCB1 is re-expressed , we lookWe then assessed what genes were differentially expressed upon SMARCB1 re-expression in the RMC and MRT cell lines as another way to assess the similarity between these two cancers. We performed RNA-sequencing on the doxycycline-induced SMARCB1 RMC cell lines and compared them to the uninduced cell lines or doxycycline-induced LacZ cell lines. We then re-analyzed our previously published studies of MRT cells with SMARCB1 re-expression and compared them to our RMC cells . We founRRM2, TOP2A) or G2/M  phases of the cell cycle. For PLK1 and CCNB1, we observed a gradual decrease in expression over the course of 120 hr whereas RRM2, UBE2C and TOP2A exhibited a more profound decrease in expression after the first 24 hr and then a modest decrease over the following 96 hr -based Gene Set Enrichment Analysis (GSEA)  and idenng 96 hr .These findings confirm that changes in the transcriptome following SMARCB1 re-expression in RMC cell lines are similar to other SMARCB1 deficient cancer cell lines. In sum, these observations indicate that the RMC cell lines are functionally similar to those derived from other SMARCB1 deficient cancers.MRT, ATRT and RMC are aggressive and incurable cancers. We performed genetic (RNAi and CRISPR-Cas9) and pharmacologic screens to identify druggable targets that would decrease proliferation or survival for these cancers. Specifically, we used the Druggable Cancer Targets (DCT v1.0) libraries and focused on targets that were identified by suppression with RNAi, gene deletion with CRISPR-Cas9-based genome editing, and perturbation by small molecules . We accoRPS6 . We confirmed that the distribution of sgRNAs among biological replicates was highly correlated . When coRPS6 . We usedWe performed a small-molecule screen using a library of 440 compounds that have known targets in the RMC cells (CLF_PEDS0005_T2A and CLF_PEDS0005_T2B) . This liPSMB1, PSMB2, PSMB5, PSMD1, PSMD2, and CUL1), regulators of the cell cycle  and genes involved in nuclear export (KPNB1 and XPO1).We then looked for genes or targets of the small molecules that scored in all three of the RNAi, CRISPR-Cas9 and small-molecule screens. We identified 21 genes in CLF_PEDS0005_T2A and 27 genes in CLF_PEDS0005_T2B  of whichTo eliminate small molecules and targets that affect normal renal tissue, we screened the normal cell line (CLF_PEDS0005_N) with the small-molecule library. We calculated the robust Z-scores for these screens in relationship to the Cancer Cell Line Encyclopedia (CCLE) to normalize the responses to various compounds . We thenSMARCB1 . We found that SMARCB1-deficient cell lines were significantly more sensitive  to treatment with MLN2238 than non-multiple myeloma CCLE cell lines  Rees et. The degWe then performed in vitro studies to confirm the findings from these high throughput small molecule screens. We treated an additional 6 SMARCB1 deficient cell lines (4 MRT and 2 ATRT) with bortezomib. We compared these results to H2172, a lung cancer cell line that was not sensitive to proteasome inhibition in CCLE small-molecule screens, and RPMI8226, an established multiple myeloma cell line that is responsive to proteasome inhibition . We founWe then studied how SMARCB1 loss leads to a dependency on the ubiquitin-proteasome system. Since activation of c-MYC has been observed in SMARCB1-deficient cancers , we asseWe then assessed if the SWI/SNF complex is altered upon treatment with a proteasome inhibitor. We treated uninduced and induced SMARCB1 cells with DMSO or a proteasome inhibitor. We failed to see a consistent significant change in total or nuclear protein when immunoblotting for SWI/SNF complex members  other than increases in SMARCB1 levels upon doxycycline treatment . These fER stress has been implicated as a mechanism by which proteasome inhibitors act on multiple myeloma cells . We saw We then performed GO-based GSEA  on the 5We subsequently assessed the cell cycle by DNA content with cells treated with DMSO or MLN2238 for 24 hr as proteasome inhibitors have been found to cause a G2/M cell cycle arrest in lymphomas, colorectal carcinomas, hepatocellular carcinomas, and glioblastoma multiforme . We obseWe found that treatment with a proteasome inhibitor led to increased cleaved caspase-3 levels in addition to changes to Annexin V and PI, suggesting that inhibition of the ubiquitin-proteasome system leads to programmed cell death . We thenTo assess whether RMC cells exhibit an increased proclivity to undergo cell death after cell cycle arrest, we asked whether treatment of SMARCB1-deficient cancers with cell cycle inhibitors led only to cell cycle arrest or arrest followed by cell death. Specifically, we used nocodazole, an anti-mitotic agent that disrupts microtubule assembly in prometaphase, and RO-3306, a CDK1 inhibitor which disrupts the CDK1-cyclin B1 interaction during metaphase . We treaUBE2C, an ubiquitin-conjugating enzyme, for survival. We noted that these cell lines were in the top 5% of cell lines (n = 485) that required UBE2C for survival , or non-small cell lung cancer cell line, A549 (SMARCA4 mutant)  to cyclin B1 for degradation . Cyclin survival , and pri in G2/M . Treatme in G2/M . When weThese studies identify a lethal interaction between suppressing the UPS and SMARCB1-deficient cancers in vitro. The doses used in this study were based on in vitro studies of multiple myeloma or lymphoma cell lines . For pat3 and then treated mice with either vehicle or MLN2238 at the maximum tolerated dose at 7 mg/kg twice a week in the Taconic NCr-nude mouse strain. Treatment with MLN2238 over 26 days induced significant tumor stabilization or regression as compared to vehicle treated tumors as assessed by absolute tumor volume  did not form subcutaneous xenograft tumors in immunodeficient mice  due to the rarity of RMC, the lack of access to SMARCB1 histological stains and unknown sickle cell status . AlthougPatients with RMC and other SMARCB1 deficient cancers have a poor prognosis despite aggressive multi-modal therapy. Using genetic and pharmacologic screens in these RMC models, we identified the ubiquitin-proteasome system as a specific vulnerability in RMC. When we looked more broadly at other SMARCB1 deficient cancers such as MRT and ATRT, we found that these models were similarly sensitive to inhibition of the ubiquitin-proteasome system. Re-expression of SMARCB1 partially rescued the sensitivity to proteasome inhibitors in RMC and MRT models.Prior studies have implicated MYC signaling and downstream activation of ER stress as a mechanism for sensitivity to proteasome inhibitors in Kras/Tp53 mutant pancreatic cancers with Smarcb1 deficiency . HoweverAlthough in multiple myeloma cells, tumor regression has been observed in xenografts following treatment with MLN2238 , we founThere have been case reports of one adult and two children with RMC who exhibited extraordinary responses for 2–7 years following diagnosis after empiric therapy with bortezomib either as monotherapy or in combination with chemotherapy . Our fin3 cubes. CLF_PEDS0005 primary tissue was then dissociated as previously described and cultured in both RPMI media containing 10% FBS (Sigma) or DMEM/F-12 media containing ROCK inhibitor, Y-27632, insulin, cholera toxin, 5% FBS, and penicillin/streptomycin . Following mincing, tumor samples were cultured without further digestion. Tumor samples were grown in culture in the DMEM/F-12 media as described above to yield CLF_PEDS9001_T1, while the normal samples yielded a cell culture that matched the tumor cell line.Patient PEDS9001 whole blood, adjacent normal kidney and tumor tissue were obtained similarly to PEDS0005. Discarded tissue from the nephrectomy following neoadjuvant chemotherapy was sent to our institution within 24 hr of resection. For the normal kidney and tumor tissue, samples were minced to 2–3 mmAll immunohistochemical staining was done in the clinical histopathology laboratory at Boston Children’s Hospital with appropriate positive controls performed with each run. Antibodies used included anti-cytokeratin CAM5.2  and SMARCB1/BAF47 .We developed a custom dual-color breakapart FISH probe, using BAC probes surrounding SMARCB1 at 22q11.23: RP11-662F7  and RP11-1112A23  . The probe set was hybridized to a normal control to confirm chromosomal locations and to determine the frequency of expected fusion signals in normal cells. 50 nuclei were scored by two independent observers (n = 100 per cell line) in the CLF_PEDS0005 and CLF_PEDS9001 models.We performed whole exome sequencing (WES) from genomic DNA extracted from whole blood, normal/tumor tissues, and our patient-derived cell lines as noted in the text. One microgram of gDNA (as measured on a Nanodrop 1000 (Thermo Fisher Scientific)) was used to perform standard  or deep (~150 x mean target coverage for tumor and cell lines) WES. Illumina  chemistry used.We performed PCR-free whole genome sequencing (WGS) from gDNA extracted from whole blood, normal/tumor tissues, and our patient-derived cell lines as noted in the text. Two micrograms of gDNA were used to perform standard  or deep (for tumor and cell lines) coverage. Illumina  HiSeq X Ten v2 chemistry was used. We achieved an average depth of coverage of 38x for the germline DNA and 69x for the tumor cell line DNA.For https://ocg.cancer.gov/programs/target. These normalized samples were then analyzed with t-SNE  v0.2.1 was used to identify large deletions and structural variations. . WES – Sth t-SNE . The folth t-SNE . For theth t-SNE , the pubth t-SNE .chr1 forward (ATAAGACATAACTTGGCCGG), CLF_PEDS0005 SMARCB1 reverse (TTTTCCAAAAGGTTTACAAGGC), CLF_PEDS9001 chr12 forward (AAAAGCATATGTATCCCTTGCT), CLF_PEDS9001 SMARCB1 reverse (CCTCCAGAGCCAGCAGA).gDNA was extracted using QIAamp DNA mini kit (Qiagen). We performed a mixing study of our RMC cell lines with gDNA isolated from the G401 MRT cell line and then performed PCR amplification. We determined that the lower limits of detection of these fusions with our methods were ~1% of tumor cell line gDNA with a minimum 50 ng of gDNA. We subsequently performed the same PCR reactions with 100 ng of gDNA from the tumor tissue samples. Samples were gel purified and submitted for Sanger sequencing (Eton Bio). We found that the sequences from the tumor tissue samples matched those of the cell lines, confirming that the genomic alterations that we found in the cell lines reflect those found in the original tumor. Primers utilized were CLF_PEDS0005 RNA was extracted as above and normalized using the Nanodrop to one microgram. One microgram of RNA was then added to the High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) and PCR reactions were performed as per manufacturer’s recommendations. cDNA was then diluted and added to primers  and PoweWe performed Affymetrix Human Genome U133 Plus 2.0 on our RMC cell lines. We then combined the following GEO datasets using a GenePattern module with robust multi-array (RMA) normalization GSE64019, GSE70421, GSE70678, GSE36133, GSE94321 . We utilNuclear extracts and gradients were performed as previously published . BrieflyPrimary cell lines were authenticated by Fluidigm or WES/WGS sequencing or by qRT-PCR. Cells were tested for mouse antibody production  and mycoplasma using the Lonza MycoAlertPLUS Mycoplasma Detection Kit . Established cell lines were authenticated by Fluidigm SNP testing. Cell lines were refreshed after approximately 20 passages from the frozen stock.in vitro studies. Compounds were resuspended in DMSO and frozen down in 20 microliter aliquots to limit freeze-thaw cycles. Compounds were added as noted in the figure legends. In vitro studies used 15 nM for bortezomib and 100 nM for MLN2238 or are otherwise specified in the text. For the pulse experiments, we used 2.5 micromolar of MLN2238.Bortezomib, MLN2238, nocodazole and RO-3306 were purchased from SelleckChem  for the pDONR223 SMARCB1 was Sanger sequenced (Eton Bio) and aligned to variant 2 of SMARCB1. SMARCB1 was subsequently cloned into the inducible vector pLXI401 or pLXI403  by Gateway Cloning. LacZ was used as a control. Lentivirus was produced using tet-free serum . Cell lines were infected with lentivirus to generate stable cell lines. Cell lines were then confirmed to re-express LacZ or SMARCB1 by titrating levels of doxycycline (Clontech). Parental cell lines were treated with increasing doses of doxycycline to determine the toxicity to cells and measured by Cell-TiterGlo after 96 hr. Cells were then grown with or without doxycycline in a six well plate. Cells were counted by Trypan blue exclusion on a ViCELL XR  every 4–5 days. Results shown are the average of at least three biological replicates.Cells were plated in a six well dish and treated with or without doxycycline for up to 7 days. Senescence was assessed with the Senescence β-Galactosidase Staining Kit without modifications .http://www.broadinstitute.org/rnai/public/). Viruses from both pools were generated as outlined at the GPP portal. As CLF_PEDS0005_T2A and CLF_PEDS0005_T2B were expanded, we performed titrations with the libraries as outlined at the GPP portal. For the sgRNA pool, both cell lines were first transduced with Cas9 expression vector pXPR_BRD111. We screened the DCT v1.0 shRNA library in biological replicates and the Cas9 expressing cell lines with the sgRNA pools at an early passage (<20) and at a multiplicity of infection (MOI) <1, at a mean representation rate above 500 cells per sgRNA or shRNA. gDNA was extracted and was submitted for sequencing of the barcodes. We achieved sequencing depths of at least 500 reads per shRNA or sgRNA.These were performed as previously published . BrieflysgRNAs targeting the genes noted in the manuscript  with protease inhibitors  and phosphatase inhibitors . Antibodies used were as follows: ARID1A , α-tubulin , β-Actin (C-4) , β-Actin , BAF57/SMARCE1 , BAF60a , BAF155 , BAF170 , SMARCA4 , Cleaved Caspase-3 , c-MYC  or c-MYC , cyclin B1 , cyclin D1 , GAPDH , GRP78 , IRE1-alpha , lamin A/C , PSMB5 , SMARCB1/SNF5 (Bethyl A301-087A), UBE2C . Results shown are representative of at least two biological replicates.Cells were treated using the conditions noted in the text. One million cells were spun down and resuspended in PBS. Cells were then subjected to FITC Annexin V and PI staining as described . Another set of cells were subjected to PI/RNAse staining . Samples were analyzed within 1 hr with the SA3800 Spectral Analyzer (Sony Biotechnology). Biological replicates were performed. Data were analyzed with FlowJo v10 .We measured the cell’s ability to cleave Suc-LLVY-aminoluciferin utilizing Proteasome-Glo (Promega) following a one-hour treatment with the noted proteasome inhibitor and measured luminescence. Results shown are from at least two biological replicates.3, mice were randomized into various treatment groups: vehicle control (5% 2-hydroxypropyl-beta-cyclodextrin (HPbCD)) or MLN2238 (7 mg/kg IV twice a week for 4 weeks). MLN2238 (diluted in 5% 2-HPbCD) was purchased from MedChem Express. Randomizations to the treatment arm occurred. Blinding was not performed. Statistical analysis was performed using the two-tailed t-test or Mantel-Cox as noted in the text.This research project has been reviewed and approved by the Dana-Farber Cancer Institute’s Animal Care and Use Committee (IACUC), in compliance with the Animal Welfare Act and the Office of Laboratory Welfare (OLAW) of the National Institutes of Health (NIH). Five million cells of G401 in 100 µL of a 50% PBS/50% Matrigel (BD Biosciences) mixture were injected subcutaneously into flanks unilaterally in Taconic NCr-Nude (CrTac:NCr-Foxn1nu) female mice at 7 weeks of age. When tumors reached approximately 150 mm In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.SMARCB1 loss and are sensitive to proteasome inhibition" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Ross Levine as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jeffrey Settleman as the Senior Editor. The following individuals involved in review of your submission have also agreed to reveal their identity: James Brugarolas (Reviewer #2) and Cory Abate-Shen (Reviewer #3).Thank you for submitting your article "Renal medullary carcinomas depend upon The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.The work is novel and of interest, and the reviewers found merit in the findings and the potential for advancing the field. The observation that SMARCB1 loss induces proteasome inhibitor sensitivity is of clear mechanistic and therapeutic relevance and could be translated to a mechanism-informed clinical trial.Essential revisions:1) The first section of the manuscript describing the genomic characterization of the RMC cell lines and samples is merely using brute force genomics to show what we already know. This should be markedly reduced in description as it is confirmatory. Moreover, the approach to developing the models needs to be discussed here. Not the methods per se but the approach – e.g. general comment about the patients, where are the tissues implanted, take rate etc.2) What are the genes in GSEA which lead to similar gene expression in SMRCAB1 altered tumors in different cancer contexts? Does re-expression of SMRCAB1 lead to reversal of the aberrant gene expression; if so, for which genes and with what kinetic does this occur?3) The major aspect which is of interest is the potential mechanism for increased proteasome inhibitor sensitivity, but this is not given enough emphasis. Is this really all due to differential cell cycle effects, e.g. like PLK1 which is always more dependent in more rapidly dividing cells? Can a Cyclin B1 rescue experiment be used to show this is the key target for proteasome inhibitor sensitivity?4) The in vivo data are underwhelming with only a modest improvement in survival and slowing of tumor growth (not regression). Is this an issue with relative dependency on the target or due to incomplete target inhibition? Alternatively, the survival curves in Figure 5F could be continued to assess whether there is a meaningful difference if that is feasible in the time-frame.5) While some pediatric tumors are characterized by deletion of both SMARCB1 alleles, RMC typically exhibits deletion of one allele and translocation of the other. The authors argue that, at least in their two cell lines, and more broadly, these translocations are simply LOF events. However, why they involve translocations is not clear. It would be helpful to integrate their two translocations with those reported in the literature and analyze for shared features. Essential revisions:1) The first section of the manuscript describing the genomic characterization of the RMC cell lines and samples is merely using brute force genomics to show what we already know. This should be markedly reduced in description as it is confirmatory. Moreover, the approach to developing the models needs to be discussed here. Not the methods per se but the approach – e.g. general comment about the patients, where are the tissues implanted, take rate etc.We agree with the reviewers and have revised the first section of the manuscript by streamlining the genomic characterization and focusing more on the approach in developing models of RMCs (subsection “Derivation and genomic characterization of RMC models” of the revised manuscript).2) What are the genes in GSEA which lead to similar gene expression in SMRCAB1 altered tumors in different cancer contexts? Does re-expression of SMRCAB1 lead to reversal of the aberrant gene expression; if so, for which genes and with what kinetic does this occur?SMARCB1-deficient tumors with matched normal tissues using RNA sequencing data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. We identified 6,311 differentially expressed genes with a q-value <0.1.To address this question, we first performed a comparison of the transcriptomes between the primary SMARCB1-deficient tumors which normalized upon re-expression of SMARCB1, we compared these 6,311 genes with the 527 differentially expressed genes that we had identified when we re-expressed SMARCB1 across multiple SMARCB1-deficient cancer cell lines (Revised Supplementary file 6). We found 257 genes that overlapped between these two datasets. Among these genes, we found that the most significantly enriched gene sets involved the cell cycle using GSEA Gene Ontology (Revised Supplementary file 6).To determine the set of aberrant genes from We then analyzed CLF_PEDS9001_T at time points spanning 24-120 hours after re-expressing SMARCB1 to assess the kinetics of gene expression changes. We first performed qRT-PCR and immunoblotting to identify the time point where SMARCB1 elevated mRNA and protein expression occurs. We found that SMARCB1 mRNA and protein expression occurs as early as 24 hours but persists over the course of 120 hours .RRM2, TOP2A) or G2/M  phases of the cell cycle. Specifically, we determined the kinetics over which changes in expression occurred upon re-expression of SMARCB1. For PLK1 and CCNB1, we observed a gradual decrease over the course of 120 hours whereas RRM2, UBE2C and TOP2A exhibited a more profound decrease in expression after the first 24 hours and then a modest decrease over the following 96 hours .We then analyzed 5 of these 257 genes that are implicated in regulation of the G1/S .3) The major aspect which is of interest is the potential mechanism for increased proteasome inhibitor sensitivity, but this is not given enough emphasis. Is this really all due to differential cell cycle effects, e.g. like PLK1 which is always more dependent in more rapidly dividing cells? Can a Cyclin B1 rescue experiment be used to show this is the key target for proteasome inhibitor sensitivity?To address this question, we first analyzed the experiment described in the comments raised in #2. The genes that were reversed upon SMARCB1 re-expression were those related to the cell cycle. Since we rescued the sensitivity to proteasome inhibitors by re-expression of SMARCB1, we speculated that treatment with proteasome inhibitors led to the observed effects on the cell cycle.A cyclin B1 rescue experiment  is not feasible. Cyclin B2 expression compensates for loss of cyclin B1 in cancer cells, and double knockout leads to complete cell arrest .As an alternative, we asked whether treatment of SMARCB1-deficient cancers with cell cycle inhibitors led only to cell cycle arrest or arrest followed by cell death. For example, nocodazole is an antimitotic agent that interferes with microtubule polymerization, arrests cells in G2/M and leads to accumulation of cyclin B1 . In addition, CDK1 and cyclin B1 form a complex required for mitotic progression, which can be inhibited by a CDK1 small molecule inhibitor, RO-3306 . We treated both G401 and CLF_PEDS9001_T with nocodazole or RO-3306 for 24 hours and observed accumulation of cells in G2/M similar to that of MLN2238 . We also found increased cyclin B1 and cleaved caspase-3 following treatment with these compounds . By 72 hours, we saw that treatment with either nocodazole or RO-3306 induced cell death in the majority of cells (65-90%) , similar to what we observed when we treated cells with MLN2238 . These observations suggest that SMARCB1-deficient cell lines are susceptible to programmed cell death following treatment with a cell cycle inhibitor and that the cell cycle arrest seen from treatment with MLN2238 can lead to programmed cell death.Based on these studies, we conclude that proteasome inhibitors induce the G2/M cell cycle arrest phenotype in our SMARCB1 deficient cell lines. We have incorporated these new experiments, expanded our results in the revised manuscript (see subsection “Proteasome inhibition leads to cell cycle arrest in G2/M and subsequent cell death”).4) The in vivo data are underwhelming with only a modest improvement in survival and slowing of tumor growth (not regression). Is this an issue with relative dependency on the target or due to incomplete target inhibition? Alternatively, the survival curves in Figure 5F could be continued to assess whether there is a meaningful difference if that is feasible in the time-frame.During our tolerability and dose finding studies, we found that the maximal dose of MLN2238 tolerated by the Taconic NCR-nude mice at 6-8 weeks of age was 7 mg/kg by intravenous injection. Other investigators using multiple myeloma cells in the Taconic CB-17 mouse strain dosed the MLN2238 compound at 11 mg/kg  while in lung and colon cancer xenograft models, investigators used doses ranging from 11-14 mg/kg in other mouse strains . We used the Taconic NCr-Nude mice because the G401 cells form tumors efficiently in this mouse strain. We speculate that the lower dose that we used for these experiments led to inadequate distribution of MLN2238 in a subset of tumors (see below).We agree with the reviewers that the presentation of subpanels in our original submission made the effect of treatment look modest. Specifically, we found that treatment with MLN2238 led to tumor regression or stable disease in 4 animals but failed to affect tumor growth in 3 tumors. We included two plots in our original submission. First was Figure 5E  which represented the average of all tumors treated with vehicle or MLN2238 and second was Supplementary figure 7F  which showed a waterfall plot of response after 26 days of vehicle or MLN2238 treatment. On reflection, the waterfall plot is more representative of the response of the tumors as some tumors responded to treatment and others did not (see below). To better represent our findings, we have replaced Figure 5E (revised submission) to show the individual response of each mouse. We also moved and modified Supplementary figure 7F  to Figure 5F (revised submission) in the revised manuscript.To assess the heterogeneous response to treatment, we performed new experiments in which we assessed whether the tumors showed evidence of response to MLN2238 by analyzing cyclin B1 and cleaved caspase-3 protein expression. We compared two tumors from our vehicle treated mice to one tumor that responded to MLN2238 and one tumor that did not respond to MLN2238. We observed increased cyclin B1 and cleaved caspase-3 by immunoblot in the tumor that responded to MLN2338 treatment but no evidence of accumulation of cyclin B1 or activation of cleaved caspase-3 in the tumor that did not respond to MLN2238 . These observations suggest that for the tumors that failed to respond to treatment with MLN2238, we did not achieve the necessary inhibition of the proteasome to lead to cyclin B1 accumulation and subsequent activation of cleaved caspase-3.Although in multiple myeloma cells, tumor regression has been observed in xenografts following treatment with MLN2238 , we found that treatment of SMARCB1 deficient xenografts led to a cytostatic response. This finding is similar to what has been observed in xenograft models of non-small cell lung cancer (14 tumor models) and colon cancer (6 tumor models) . Nevertheless, these studies still support the importance of testing this hypothesis in patients, particularly since there are no standard therapies for SMARCB1-deficient cancers.In the revised manuscript we now include these revised figures and have revised the text to explain these findings more clearly in the Results section and Discussion section.5) While some pediatric tumors are characterized by deletion of both SMARCB1 alleles, RMC typically exhibits deletion of one allele and translocation of the other. The authors argue that, at least in their two cell lines, and more broadly, these translocations are simply LOF events. However, why they involve translocations is not clear. It would be helpful to integrate their two translocations with those reported in the literature and analyze for shared features.There are two published articles that described the translocations seen in patients with RMC . In Carlo et al., the authors used FISH to identify the fusion event in 8 of 10 patients but were unable to identify the specific translocations either due to the lack of whole genome data or the lack of adequate tissue sample. In Calderaro et al., the authors identified fusion events in 4 of 5 patients. We analyzed the available data in Calderaro et al. in the context of our genomic findings. We failed to find shared featured when we aligned the sequences . We found at most 2 bp of homology on each side of the breakpoints which makes homology-mediated mechanisms unlikely. We then used RepeatMasker 4.0.8 (www.repeatmasker.org), an online database of repetitive DNA elements, to identify shared breakpoint sequences but we were unable to find any matches. As with most fusion events, the molecular basis for these RMC fusion events remains undefined.We have summarized these findings in subsection “Derivation and genomic characterization of RMC models” of the revised manuscript.


Magydaris tomentosa flowers (Desf.) W. D. J. Koch has been analyzed by GC-MS. It is mainly constituted by furanocoumarins such as xanthotoxin, xanthotoxol, isopimpinellin, and bergaptene. Other coumarins such as 7-methoxy-8-(2-formyl-2-methylpropyl) coumarin and osthole also occurred. The antiproliferative activity of Magydaris tomentosa flower extract has been evaluated in vitro on murine monocye/macrophages (J774A.1), human melanoma (A375) and human breast cancer (MCF-7) tumor cell lines, showing a major activity against the latter.The petroleum ether extract of  Magydaris tomentosa (Desf.) W. D. J. Koch ex DC is a plant of Apiaceae family having the synonyms of Magydaris pastinacea (Lam.) Paol. It grows in few regions of Mediterranean area and in Sicily it has been found in some mountain places  and 6-mercaptopurine (6-MP) were purchased from Sigma Chemicals. Cultured cells were grown in adhesion on Petri dishes and maintained with Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% foetal calf serum (FCS), 25 mM HEPES, 2 mM glutamine, 100 U/mL penicillin and 100 µg/mL streptomycin at 37 °C in a Hera Cell humidified CO2 incubator  with 5% CO2.J774A.1, cultured murine monocyte macrophages, A375 human melanoma cell line and MCF-7 human breast cancer cell line were purchased from American Tissue Culture Collection  and used to evaluate the antiproliferative activity of petroleum ether extract from4/well) were placed on 96-well microtiter plates and allowed to adhere for 2 h at 37 °C in 5% CO2 and 95% air. Thereafter, the medium was replaced with 90 µL of fresh medium and a 10 µL aliquot of serial dilutions of Magydaris petroleum ether extract (10–100 µg/mL), dissolved in DMSO and then diluted in DMEM, was added. Cells were incubated for 72 h. Cell viability was assessed through MTT assay [Magydaris petroleum ether extract, was calculated as:Cells (J774.A1, A375 and MCF7, 3.4 × 10TT assay . BrieflyTT assay . The optM. tomentosa can be efficiently extracted with simple infusion of the intact blossoming tops in petroleum ether. This extract is mainly constituted of furanocoumarins with high biological activities. Comparing the literature data, the cytotoxicity of the extract resulted more powerful against MCF-7 cell line than single compounds tested until now. This fact can be explained by synergic effects among compounds constituting the extract.Less polar coumarins occurring in flowers of


For many years, immortalized cell lines have been used as model systems for cancer research. Cell line panels were established for basic research and drug development, but did not cover the full spectrum of leukemia and lymphoma. Therefore, we now developed a novel panel (LL-100), 100 cell lines covering 22 entities of human leukemia and lymphoma including T-cell, B-cell and myeloid malignancies. Importantly, all cell lines are unequivocally authenticated and assigned to the correct tissue. Cell line samples were proven to be free of mycoplasma and non-inherent virus contamination. Whole exome sequencing and RNA-sequencing of the 100 cell lines were conducted with a uniform methodology to complement existing data on these publicly available cell lines. We show that such comprehensive sequencing data can be used to find lymphoma-subtype-characteristic copy number aberrations, mRNA isoforms, transcription factor activities and expression patterns of NKL homeobox genes. These exemplary studies confirm that the novel LL-100 panel will be useful for understanding the function of oncogenes and tumor suppressor genes and to develop targeted therapies. In particular, leukemia-lymphoma (LL) cell lines serve as convenient in vitro tool due to their world-wide accessibility, straightforward manipulability and low culture costs, providing experimental models to address a multitude of questions in the field of LL biology3. Indeed, the scientific benefits of utilizing LL cell lines have definitely boosted our knowledge on a plethora of aspects of these diseases4. Importantly, many studies contoured our appreciation of the suitability of LL cell lines as model systems, replicating faithfully most features of the primary cells6.Human cancer cell lines form a renewable resource and are vital models for studying the cellular and molecular mechanisms underlying tumorigenesis as well as for anti-cancer drug screeningin vitro drug discovery tool intended to supplant animal studies in drug screening (reviewed in7). This screening tool was quickly appreciated as an invaluable source of information about the mechanisms of growth inhibition and tumor cell cytotoxicity7. Later in the 2000s, the NCI-60 panel transitioned from a drug-discovery pipeline to a more general research tool in support of the cancer research community8. Another panel incorporating a reduced number of cell lines of particular interest which had been derived from several solid tumor types was established in Japan9. These two cell line panels did not aim at one single cancer category but were designed to represent a variety of different tumor entities. Nevertheless, these sets have provided the framework for the use of defined panels of cell lines at the same time as keeping with the information-rich character of screens7.The National Cancer Institute (NCI) tumor cell line panel (known as NCI-60 as 60 cancer cell lines were assembled) was developed in the 1980s as an 10. We have assembled a panel of 100 authenticated LL cell lines that reflects the heterogeneity of the entities under the umbrella category of LL. In addition to well-known and commonly analyzed cell lines, this invaluable and publicly available platform includes additional cell lines assigned unequivocally to the various entities but with specific characteristics. It is hoped that this focused LL-100 cell lines panel may enhance the current scientific momentum, helping to fully elucidate the underlying pathology of these LL malignancies and providing an important and unique resource for the testing of novel therapeutic agents.The majority of studies in the arena of LL focus on a narrow number of cell lines. We realized that there is a need for a reference panel specialized on LL cell lines to facilitate hypothesis-driven research efforts11. This approach is being steadily replaced by next generation sequencing technologies which comprise the sequencing of complete transcriptomes, exomes and whole genomes. These applications are used in cancer research to identify aberrations in the genome, deregulated and mutated genes, and alternative splicing. The obtained data are helpful to classify malignancies, to improve existing therapies, and to identify new targets for novel therapeutic approaches12. Here, we present transcriptome and exome sequencing data of a panel of 100 authenticated LL cell lines (LL-100) and selected examples of their utilization.Based on data of the human genome project, high-throughput methods have boosted the knowledge of processes in normal and malignant cells. The microarray technology showed for the first time simultaneous activities of thousands of genes and allowed the classification of tissues and diseasesWe performed whole exome sequencing (WES) and mRNA-sequencing (RNA-seq) on a panel of 100 LL cell lines representing 22 subtypes  cell lines and of cell lines from various other B-non Hodgkin lymphoma (B-NHL) entities as well as from Hodgkin lymphoma (HL).For many years, expression profiling has been applied to classify tumors including LLUnsupervised cluster analysis showed that all PEL cell lines grouped together, separate from cell lines derived from activated-B-cell-like (ABC) and germinal center (GC) diffuse large B-cell lymphoma (DLBCL), mantle cell lymphoma (MCL), primary mediastinal B-cell lymphoma (PMBL) and from cell lines derived from HL Fig. . NotablyCCND2 and the absence of B-cell markers in PEL and HL cell lines. CD19, CD20 (MS4A1), CD24, CD79A and CD79B were expressed in all tested lymphoma entities beside PEL and HL , IL-10, IL2RB, and PRDM1, all described as PEL-characteristic genes  has recently been approved for treatment of patients with multiple myeloma23. RQ-PCR analysis showed that SLAMF7 is comparably expressed in PEL and multiple myeloma cell lines  regulate normal hematopoiesis and their activities are precisely controlled during hematopoietic stem cell self-renewal and their differentiation into the diverse blood cell lineages. Consequently, many of these TFs emerged as proto-oncogenes or tumor suppressors because deregulation of these TFs alters the cellular transcriptional program eventually impairing differentiation and thus fostering malignant transformation. Aberrant activities of TFs which can be caused by a variety of direct or indirect mutations and epigenetic alterations, are a hallmark of cancer, including hematological malignancies27, activities of TFs were predicted via the expression levels of their direct target genes. TF activities were estimated via so-called consensus TF regulons (CTFRs) which have been defined recently by Garcia-Alonso et al. on the basis of diverse sources for human TF-target interactions29. For each cell line from the LL-100 panel relative TF activities were computed from RNA-seq data applying DoRothEA  for CTFRs from 289 single TFs  are critical for B-cell development30. Accordingly, the CTFRs of these TFs showed strong activity in cell lines from B-cell derived malignancies but were inactive in myeloid-derived leukemias  and GATA2. However, in general hematopoietic TF activities determined via CTFRs did hardly correlate with gene expression levels of the TFs . Other TFs (e.g. GATA1) reflect the differentiation status of the respective normal counterpart and a third group of TFs (e.g. TAL1) depicts aberrant activities highly characteristic for specific entities. In general, TF activities across all studied cell lines did rarely correlate with their gene expression levels. Thus, analyses of CTFR activities from RNA-seq data are a suitable tool to measure and evaluate the relevance of a specific TF in hematological cell lines.40. These genes are classified according to differences in their conserved homeobox and ordered into classes and subclasses41. The NKL subclass comprises 48 members which are involved in fundamental differentiation processes like NKX2-1 in that of the lung and the thyroid, and NKX2-5 in the heart43. Normal expression patterns of nine NKL homeobox genes in early hematopoiesis and subsequent lymphocyte development have been identified and termed hematopoietic NKL-code45. According to this code, T-cells silence all NKL homeobox genes during their thymic development while mature NK-cells maintain expression of MSX1 and mature B-cells HHEX or NKX6-346. Alterations of the NKL-code may underlie the generation of particular hematopoietic malignancies. According to this notion, 24 NKL homeobox genes are reported to date for aberrant activity in T-cell acute lymphoblastic leukemia (T-ALL), mediating differentiation arrest and transformation48.Homeobox genes encode TFs which show basic impacts in developmental processes including embryonal development and cell differentiation in the adult. Therefore, deregulation of homeobox genes generates developmental disturbances or cancerMSX1 is an oncogene in T-ALL and a tumor suppressor in NK-cells49, showing accordingly reduced activity in NK-cell leukemia cell lines . Therefore, this cell line may represent a model to examine alternative upstream and novel downstream factors of NKX2-1 in T-ALL.Using the LL-100 transcriptome dataset we here screened NKL homeobox gene activities in cell lines and show some results for selected lymphoid entities. To discriminate active and inactive genes we have set a cutoff at 500 normalized counts. Accordingly, aberrant activation of particular subclass members was detected in immature T-ALL but not in mature T-cell lines Fig. . This fines Fig. . Thus, t402 Fig. . Aberranine Fig. . Of noteHLX and MSX145. Our data show that malignant BCP-ALL cell lines lack activity of HLX, MSX1 and NKX6-3 (except SEM)  may represent NKL homeobox genes primarily deregulated in this type of B-cell malignancy, serving as diagnostic marker and/or therapeutic target.In normal B-cell development NKL homeobox genes HHEX and NKX6-3 are the only subclass members active in developing naïve and germinal center B-cells, and in mature memory B-cells and plasma cells while B-cell progenitor (BCP) cells additionally express EM) Fig. , showingnes Fig. . Moreove533 Fig. , supportHHEX (in OCI-LY7 and RI-1) and NKX6-3 (except DOHH-2) and aberrant activation of HLX (NU-DHL-1) and NKX3-1 (OCI-LY3)  Fig. . Of noteY3) Fig. . TherefoY3) Fig. 15. Toget54.DLBCL shows a high degree of genetic diversity with unique molecular patterns including varying occurrences of copy number alterations (CNAs), resembling the different states of B-cell maturation they derive from and which is also reflected by the diverse clinical outcome55. Of those CNAs, we find 38 (84%) in at least one cell line, including all focal amplifications  and 129 (+/−12) CNAs, respectively, with a size >10 kb Fig. . While aons Fig. .Figure 456 and found 83% of patient-derived events in our cell lines  to 252 (OCI-LY3) of the COSMIC genes located in regions of CNA  characterizes CNAs in cell lines of the two major DLBCL-subtypes and shows how they recapitulate recurring events from patients, (ii) allows the identification of those genes that are deregulated by CNAs and likely have a disease-relevant function and (iii) by doing so enables the selection of appropriate models for further molecular research related to DLBCL. Furthermore, we believe that this kind of analysis is applicable to other entities and that thereby valuable models for those entities can be obtained.58. More than 90% of human genes are affected by alternative splicing60. Tissue-specific splice factors together with ubiquitious RNA binding factors cooperate to generate tissue-specific RNA isoforms59. The existence of different promoters can also lead to different N-terminal RNA variants.Allowing different combinations of exons, alternative splicing leads to the production of multiple mRNA isoforms of the same gene, often resulting in proteins of different functionalityLIMS1 in myeloid vs T-cell lines  with a sensitivity of 1 and a specificity of 1.The RNA-seq data of the LL-100 panel allowed us to find RNA isoforms that specify different hematopoetic lineages. Bioinformatic analysis identified genes with tissue-specific exons, e.g. nes Fig. . Two N-tnes Fig. . These rnes Fig. , S10. AlIn sum, our data show that RNA-seq analysis allows detection of cell lines from different lineages on the basis of alternatively expressed exons.One goal of personalized medicine in cancer medicine is the development of targeted therapies aiming to reverse detrimental effects of mutated or deregulated genes. The costs of sequencing technologies will presumably soon be low enough to allow routine diagnostics detecting genetic alterations for classification of the patient’s tumor and determining treatment strategies.9. Thus, the NCI-60 human cell line panel developed for use in drug development comprises sixty human cancer cell lines derived from nine different tissues7. Only six cell lines  represent tumors of the blood.Immortalized tumor cell lines have been used for a long time to understand the molecular and cellular function of mutated genes and to develop new drugs. However, the cell line panels established hitherto did not represent most forms of leukemia and lymphomaCovering 22 leukemia and lymphoma entities we present the novel LL-100 panel, 100 cell lines for use in basic research and drug development. The selected cell lines of this panel are authenticated and free of contamination by mycoplasma or non-inherent viruses. Furthermore, the methods of RNA- and DNA isolation and sequencing are identical in all cell lines. Therefore, this dataset allows comparative studies without methodical impact. We performed WES and RNA-seq analysis for all 100 cell lines. In exemplary studies, we show that lymphoma entities can be identified by gene expression analysis and splice variant analysis. WES analysis documented that copy number aberrations in DLBCL cell lines reflects the situation in primary tumor cells and may lead to the identification of potential oncogenes. RNA-seq analysis identified tumor entity-specific activities of CTFRs, demonstrating the usefulness of cell lines as model systems for transcription factor research. Finally, RNA-seq analysis specified aberrant activities of NKL homeobox genes.All data and cell lines are publicly available. As demonstrated exemplarily in this study, the sequencing data can be used for various approaches. We hope that the novel LL-100 panel described here will stimulate many studies in the field of leukemia and lymphoma research.3.Cell lines were taken from the stock of the cell lines bank (Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures). Cell lines were authenticated by DNA profiling and cytogenetics. Detailed references and cultivation protocols have been described previouslywww.bioinformatics.babraham.ac.uk/projects/fastqc). Reads were aligned by STAR (2.5.3a)61 to the Gencode Homo sapiens genome (v26) and converted/sorted via samtools (0.1.19)62. Counting the reads to each gene was done via HTSeq-count python script (0.8.0)63. Data was processed and analyzed in the R/Bioconductor environment . Normalization, estimation of dispersions, and testing for differentially expressed genes based on a test assuming negative binomial data distribution was computed via DESeq264.Total RNA was extracted via miRNeasy Mini Kit  including DNase digestion. Library preparation and sequencing steps were commissioned to GATC Biotech . The GATC pipeline included the production of strand-specific (fr-first strand) mRNA libraries, quality control via Applied Biosystems Fragment Analyzer and Nanodrop, concentration measurement via Qubit fluorometer. The libraries were sequenced on Illumina HiSeq2500  with >29 million reads per sample and deposited at ENA (PRJEB30312). Reads were trimmed via fastq-mcf (ea-utils 1.04.807). Reads were quality controlled via FastQC 66.DNA was isolated with the High Pure PCR Template Preparation Kit . Library preparation  and sequencing steps  were commissioned to Genewiz  and deposited at ENA (PRJEB30297). Insert lengths were aimed to be higher than 250 bp in order to increase coverage and uniformity in coding regions61 to the human gencode genome (v26). Subsequently, alignment files were processed (samtools 0.1.19), duplicates removed , and variants called via GATK tools 67 and overlapping VarScan (v2.4.3)68 results. Mutation effects were annotated via Ensembl VEP 69. Data were processed and analyzed in the R/Bioconductor environment (3.3.2/3.3). Overlapping single nucleotide variations via Haplotypecaller and VarScan were filtered for >=20 quality, >=10 depth, >=0.2 allele frequency, <0.01 MAF, missense/frameshift/stop gained mutations.Reads were aligned by STAR (2.5.3a)70 (v11.0) was applied on the duplicate-clean alignment files with NC-NC as normal cell control. Neighboring regions with identical CN in the disperse whole exome data were fused to one region and CN regions below 10 kb were omitted and visualized via circos71 (0.67–7). Individual regions and genes of interest were plotted with the R/bioconductor packages ggplot2 (3.1.0) and Gviz (1.22.3).For identifying copy number alterations (CNA) in DLBCL cell lines control-FREEC73. Differentially expressed genes between PEL and Non-PEL cell lines were identified via moderated t-statistics  setting p < 0.05 and log fold changes >1. For hierarchical cluster analysis (average linkage) 30% of the most varying probe sets served as basis for the distance matrix (1-correlation) and visualised via the R package hdust.Profiling of gene expression was commissioned to the Genome Analytics Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany. 500 ng total RNA were used for biotin labelling according to the 3′ IVT Express Kit . 7.5 µg of biotinylated cDNA were fragmented and placed in a hybridization cocktail containing four biotinylated hybridization controls . Samples were hybridized to an identical lot of Affymetrix GeneChip HG-U133 Plus 2.0 for 16 h at 45 °C. Steps for washing and SA-PE staining were processed on the fluidics station 450 using the recommended FS450 protocol (Affymetrix). Image analysis was performed on GCS3000 Scanner and GCOS1.2 Software Suite (Affymetrix). For data analysis spot intensities were RMA-background corrected and quantile normalized. Data processing was done via R/Bioconductor (3.2.2) using limma and affy packageswww.ncbi.nlm.nih.gov). This type of profiling gene chip contains 37 probes of the 48 known human NKL homeobox genes, excluding HMX3. We analyzed datasets GSE26713 and GSE79533 for T-ALL and BCP-ALL patients, respectively. Expression values were given as boxplots using R-packages. Outliers were interpreted as samples showing aberrant overexpression.Public expression profiling datasets used in this study were generated by U133 Plus 2.0 gene chips from Affymetrix and obtained from Gene Expression Omnibus , likely confidence (B), and medium confidence (C)29. For this, gene expression data were prepared similarly as described28: (i) lowly expressed genes were discarded, defined as those with an average CPM < 1 (counts per million) and prior count of 3; (ii) data were TMM  normalized using calcNormFactors function described in limma; (iii) fitted log2 CPM were extracted and finally tested for transcriptional activity via VIPER in R/Bioconductor (3.4.4/3.6). Transcriptional activity and gene expression heatmaps were vizualised via gplots (R-package).Relative TF activities were computed from RNA-seq data with DoRothEA v2 via VIPERCCND2 (Hs00153380_m1) CD20/MS4A1 (Hs00544819_m1), CD96 (Hs00175524_m1), CD138 (Hs00896424_g1), HMX2 (Hs01394375_m1), HMX3 (Hs01392772_m1), IL-10 (Hs00961622_m1), NKX2-1 (Hs00163037_m1), PRDM1 (Hs00153357_m1), S100A2 (Hs00195582_m1), S100A4 (Hs00243202_m1), S100A6 (Hs00170953_m1) and SLAMF7 (Hs00900280_m1) using TBP as endogenous control. Relative expression levels were calculated using the ΔΔCt method.RNA was prepared using the RNeasy Mini kit (Qiagen). For mRNA quantification, reverse transcription was performed using the SuperScript II reverse transcriptase kit . PCR was performed on a 7500 Applied Biosystems . TaqMan probes (Applied Biosystems) were used to quantify human LIMS1, RABGAP1L and TFEC are shown in Table Primers and RT-PCR conditions for detection of splice variants of For immunophenotyping, cells were washed and incubated for 30 min at 4 °C with antibodies (Ab) against CD319/SLAMF7 , CD138 Ab  and appropriate isotype controls (BD Biosciences). Subsequently, cells were treated with FITC conjugated anti-mouse secondary Ab  and propidium iodide (PI) (Sigma-Aldrich). Labeled cells were analyzed on a FACSCalibur (BD Biosciences) using CellQuest Pro software.75. Anti BLIMP1 (#9115), CCND2 (#3741S), S100A4 (#13018S), S100A6 (#13162S) Abs were purchased from Cell Signaling . Anti GAPDH (ab8245) and anti S100A2 (ab 109494) Abs were from Abcam . Specific bands on nitrocellulose membranes were visualized with the biotin/streptavidin-horseradish peroxidase system  in combination with the “Renaissance Western Blot Chemoluminescence Reagent” protocol .Samples were prepared as described previouslyDataset 1Dataset 2Dataset 3Dataset 4Dataset 5


As proof of concept, we have developed such a model and showed that different phenotypes had different responses to the same challenge  both in terms of sensitivity and phenotypic plasticity. We suggest that similar model-systems could be developed and used when designing novel therapeutic strategies, to address the potential impact of phenotypic heterogeneity and plasticity of cancer on the development of successful therapies. Specifically, the effect of a therapy should be considered on more than one cancer cell phenotype (to increase its effectiveness), and both cell viability as well as changes in phenotypic state  should be evaluated. Although we are aware of the limitations of in vitro systems, we believe that the use of established cell lines that express multiple phenotypes can provide invaluable insights into the complex interplay between therapies and cancer's heterogeneous and plastic nature.The main challenges in developing effective anti-cancer therapies stem from the highly complex and heterogeneous nature of cancer, including the presence of multiple genetically-encoded and environmentally-induced cancer cell phenotypes within an individual. This diversity can make the development of successful treatments difficult as different phenotypes can have different responses to the same treatment. The lack of model-systems that can be used to simultaneously test the effect of therapies on multiple distinct phenotypic states further contributes to this problem. To mitigate these challenges, we suggest that  Cancer is a complex disease that manifests itself in a multitude of ways, partly due to its diverse genetic and phenotypic nature—both among and within individuals. This diversity can also pose significant challenges to cancer therapies –5. For iThe dynamic interactions between the diverse and continuously evolving genotypes and their fluctuating environments can add difficulty to developing successful therapies. Increased levels of genetic diversity can allow selection for clones with phenotypes that confer adaptive advantage in specific settings, including the presence of various drugs. This unescapable evolutionary aspect of cancer hinders the development of successful therapies as drug resistance is always expected to evolve –15. Consin vitro studies directed at developing new therapeutic strategies generally employ established cell lines with an adherent phenotype  (ATCC® CRL5985™); ATCC) that was initially derived from a metastatic site  of a stage 4 adenocarcinoma lung cancer patient. The line grows as a mixture of adherent cells  and cells in suspension—both as single cells and grape-like clusters that are loosely adherent or remain in suspension. This cell line  was used to experimentally evolve two additional lines: one line selected for adherent growth (referred to as the Adherent-Selected or AS line) and one line selected for growth in suspension (referred to as the Suspension-Selected or SS line), by selectively passaging only the adherent or the suspension cell populations, respectively  that express phenotypic plasticity , this line could be useful to test the effect of the same challenge on systems that comprise multiple phenotypes, such as during advanced disease. On the other hand, the AS line is strictly adherent (like most established cell lines used in cancer research) and, functionally, can be viewed as analogous to a non-disseminating tumor . Lastly, as the clusters in both the ANC and SS lines share a series of features with real CTC clusters, including cluster size, morphology, cell-cell connections and the expression of both epithelial and mesenchymal markers , these lin vivo  in two cancer cell lines exposed to acidic pH (6.6) for 48 h , in terms of both overall sensitivity and phenotypic plasticity . Extracellular pH was chosen as the experimental variable because it is known to affect cancer cell behaviors and metastatic potential of tumors in vivo ; for insin vivo , whereasin vivo , 36. Recfor 48 h . Neverth5 cells were seeded in individual wells of a 12-well plate and grown in regular RPMI overnight, to allow the adherent population to attach to the substrate before the experimental treatments. Next day, the suspension cell population (ANC_Suspension population) was removed from these wells and re-suspended in media at pH 6.4 (acidic), 7.4 (control), or 8.4 , and then placed into new wells. The cells adherent to the wells (ANC_Adherent population) were also subjected to the same three media. For the AS cell line, 2.0 × 105 cells per well were seeded in RPMI, allowed to adhere overnight and then the medium was replaced with either control, acidic or alkaline media. For the SS line, 2.0 × 105 cells were seeded in individual wells containing media at each of the three pH levels. Twenty-four hours later, the suspension and adherent cell fractions from each well were collected separately and the number of live cells in each fraction was assessed (p < 0.03) between each treatment and the control (GraphPad Prism7).Cultures were maintained in RPMI-1640 medium (ATCC) supplemented with 10% fetal bovine serum (ATCC) and 1% GIBCO™ Penicillin-Streptomycin , from here on referred to as RPMI. Acidic and alkaline media were prepared by adjusting the pH of RPMI (pH 7.4) with 1M HCl (to pH 6.4) and 1M NaOH (to pH 8.4), respectively. For the ANC line, 2.0 × 10assessed . The totp < 0.001), the acidic and alkaline treatments drastically affected the phenotypic composition (relative to the control pH) of three of the four populations  to media of three pH levels—acidic, control, and alkaline, and assessed differences in overall population size and phenotypic state , but had no effect on the phenotypic composition of the AS and SS populations. On the other hand, alkaline media affected both the adherent and suspension populations of the ANC line as well as the SS line, while the AS line was unaffected. Taken together, these findings argue for the potential benefit of testing the effect of therapies on different phenotypes using systems similar to the one we developed.Because of the short length of the treatment (24 h) and the doubling time of this cell line (estimated as at least 48 h), the drastic differences in the proportion of phenotypes we observed  can only be explained by invoking phenotypic switches. For instance, relative to their controls, in both ANC populations (ANC_Adherent and ANC_Suspension), acidic pH resulted in a higher proportion of cells in suspension  to highlight the different responses exhibited by different cancer cell populations that are subjected to the same microenvironmental challenge, in terms of both their overall sensitivity and phenotypic state, and (iii) to suggest that these model-systems can be used to both test the effect of therapies on more than one phenotype (to ensure effectiveness and avoid unexpected effects) and design therapies specific for different phenotypes (to improve their effectiveness).The specific goal of this Perspective is 3-fold: (i) to argue that the use of As proof of concept that such lines can be valuable and useful complementary systems for testing and developing new therapies, we used media of two different pH levels and investigated the response of four genetically-related but phenotypically distinct populations. Our data showed clearly that not all phenotypes are sensitive to the same challenge, and that the same challenge can have different effects on both the sensitivity and the phenotypic composition of distinct populations. We believe these findings provide a strong case for the usefulness of such model-systems to investigate the ways in which therapies can affect the range of cancer cell phenotypes likely to be present in the same individual. This would both increase the effectiveness of therapies (as different phenotypes might show different sensitivities to the same treatment) and address potential negative effects . For instance, the same challenge  that may induce an apparent decrease in the size of a population . Similar findings have been reported for two sublines (with distinct phenotypes) derived from a small cell lung line (NCI-H69); the adherent subline showed increased resistance to etoposide, cyclophosphamide, and gamma radiation when compared to the original line growing in suspension , 39. RegThe model-system we presented here used an established non-small lung cancer cell line that we experimentally evolved into two additional lines. The ANC line is highly heterogeneous, containing a mixture of cells expressing one of two stable phenotypes (strictly adherent or strictly suspension) as well as cells expressing some level of phenotypic plasticity (being able to express both phenotypes). Its heterogeneity and plasticity could be valuable in addressing the effect of therapies on multiple plastic phenotypes. The cell clusters in this line are also heterogeneous. They consist of a mixture of cells expressing epithelial or mesenchymal markers—a feature shared with CTC clusters isolated from cancer patients, and thus could be used in developing therapies directly targeted against CTC clusters .Many other cancer cell lines available from ATCC  grow as mixtures of adherent cells and cells in suspension, and thus can be used as proxies for genetically heterogeneous and phenotypically plastic tumors with dispersal potential. Selection experiments similar to the ones we presented here can also be performed on such lines to evolve specific phenotypes. In fact, a small cell lung cell line (NCI-H69) that grows mostly as tightly-packed floating aggregates has been previously used to derive an adherent subline. The suspension and adherent lines were shown to differ in their sensitivity to drugs (discussed above) as well as expression of EMT markers, with the ancestral suspension line being positive for E-cadherin while the derived adherent line was negative for E-cadherin and positive for vimentin . Many otin vitro models (including the system we presented here), we believe that the use of experimental evolution and established cell lines that express phenotypic plasticity can provide invaluable general insights into the complex responses that therapies can trigger in a cancer patient.Based on the growing evidence regarding the impact of phenotypic heterogeneity and plasticity on the success of current therapies (discussed in the Introduction) we argue that alternative model-systems that can better capture cancer's heterogeneity and plasticity could be increasingly integrated in the development of new therapeutic strategies, in three important ways. First, since responses to therapies are known to differ among cancer cell phenotypes (with some phenotypes—such as CTCs and CTC clusters being more refractory to standard treatments), model-systems expressing heterogeneous phenotypes could assist in the development of phenotype-specific therapies. Second, changes in phenotypic state should also be evaluated to address potential unforeseen effects. Third, to increase the efficiency of therapies, strategies directed at altering the tumor microenvironment  to All datasets generated for this study are included in the manuscript.EJ designed and performed research, acquired, analyzed and interpreted data, and contributed to the writing of the manuscript. IC designed and performed research, acquired, analyzed and interpreted data, and edited the manuscript. AN designed research, interpreted data, wrote the manuscript, and supervised the study.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.