ATP7B. Chromosomal alterations were characterized by segmented deletions and gains leading to severely altered karyotypes. The few chromosomal changes shared among LTTs included gains involving the anti-apoptotic BCL2L1 gene and losses involving the NRF2 regulator KEAP1. Overall, the extent of genomic changes paralleled cisplatin treatment concentrations. In conclusion, bladder cancer cell lines selected for cisplatin-resistance contain abundant and characteristic drug-induced genomic changes. Cisplatin treatment may therefore generate novel tumor genomes during patient treatment.The DNA-damaging compound cisplatin is broadly employed for cancer chemotherapy. The mutagenic effects of cisplatin on cancer cell genomes are poorly studied and might even contribute to drug resistance. We have therefore analyzed mutations and chromosomal alterations in four cisplatin-resistant bladder cancer cell lines (LTTs) by whole-exome-sequencing and array-CGH. 720–7479 genes in the LTTs contained point mutations, with a characteristic mutational signature. Only 53 genes were mutated in all LTTs, including the presumed cisplatin exporter  It is particularly efficacious in testicular germ cell cancers, whereas its efficacy in other cancer types, such as ovarian or urinary bladder cancer, is often limited by inherent or acquired resistance3 caused by multifaceted mechanisms9.Cisplatin 2-d(GpG), 25% cis-Pt(NH3)2-d(ApG) 1,2-intrastrand adducts and 5–10% 1,3-intrastrand adducts12. In addition, a small, but relevant amount of interstrand crosslinks is created. Intrastrand crosslinks are repaired by nucleotide excision repair (NER) or may be bypassed by translesion synthesis during replication. Resolving interstrand crosslinks requires the crosslink-homologous repair system. The various types of DNA damage caused directly or indirectly by cisplatin may thus lead to base mutations or even double-strand breaks and chromosomal instability. Very recently, a specific mutational signature for cisplatin was deduced from treated mammary and hepatic tumor cells and likewise detected in human tumor samples treated with cisplatin13.Following activation in the cell by aquation, cisplatin forms monoadducts with guanine bases in DNA which react with adjacent purine bases to form intrastrand crosslinks, namely about 65% 14. These mechanisms have been studied in depth in many cancer types, especially in cell line models. A less explored issue is which genomic changes are elicited by cisplatin in cancer cells that acquire resistance to the DNA-damaging drug, and whether and to which extent these contribute to the various mechanisms of resistance.Many mechanisms are involved in the resistance of cancer cells to cisplatin treatment, including changes in transport and metabolism of the drug, altered DNA repair and checkpoint signaling as well as evasion of apoptosis50 values for 72 h cisplatin treatment, as determined by standard viability assays, ranged from 1.9 to 12 µM, but increased in the respective cisplatin-resistant variants to 10 to 210 µM, yielding resistance indices of 5–17 fold7. They are maintained with regular cisplatin treatment, which would be lethal for the parental cells, but from which the LTTs recover within a few days. As described in detail in previous publications, the LTTs presented with a broad spectrum of resistance mechanisms, each to various extents, prominently increased cisplatin detoxification, driven partly by activation of NRF2 signaling8, which increases glutathione biosynthesis and the MRP2 glutathione conjugate exporter, among others, and decreased apoptosis with higher expression of anti-apoptotic factors like survivin7. The LTTs are not enriched for bladder cancer stem cell-like populations, but canonical WNT signaling is partly activated and some markers of epithelial-mesenchymal transition are increased5.We have previously generated cisplatin-resistant variants, named LTTs (for long-term treated), from four different bladder cancer cell lines by repeated treatment with increasing concentrations of cisplatin. In the initial cell lines ICHere, we report on the genomic differences between the parental RT-112, J82, 253J, and T-24 cell lines and their cisplatin-resistant LTT variants, as determined by whole exome sequencing (WES), array comparative genomic hybridization (aCGH) and karyotyping. Based on this data, we addressed several questions. First, to which extent does cisplatin induce point mutations in bladder cancer cell lines? Second, do these mutations conform to the signature proposed by recent studies? Third, is there evidence for induction of chromosomal alterations by cisplatin? Fourth, are mutations and chromosomal alterations gained during initial selection or accrued gradually? Fifth, is it possible to identify genomic changes accounting for the observed mechanisms of resistance in the LTTs?et al.13, namely frequent C > A transversions preferentially in a ACC or GCC context, more frequent C > T transitions preferentially in CCC or CCT and less commonly in CCA or CCT contexts, with rarer transversions of C > G, predominantly in a GCC context and T > A, with a preceding C and any base following  ranged between 0.90 and 0.97  mutations (27.0–59% vs. 25% expected by chance), indicating no positive selection for functionally important mutations in general.15. Among these genes, mutations likely to have a functional impact  were found in NRXN2, encoding the cell adhesion protein neurexin 2, HECW1, encoding the eponymous ubiquitin ligase , FAM205A, encoding a poorly studied transmembrane protein, and ATP7B, a copper transporting ATPase.The mutations in the four LTTs affected between 720 and 7479 genes. Only 53 protein-coding genes were mutated in all four LTTs Fig. , Table 3The variant allele frequency of the new variants extended continuously to 1 in all LTTs Fig. . In factATP7B, and RT-112-LTT additionally contained mutations in ATP7A  and FAM205A , are entirely unexplored in the context of cancer and chemoresistance. NRXN2 encodes Neurexin-2, a plasma membrane and adhesion protein predominantly expressed in the brain. Most of the mutations found in NRXN2 were observed in the first three Laminin domains  or maintenance concentrations (LTTs). Knockdown of ATP7B enhanced the effect of cisplatin in all LTTs, but interestingly also in 253J and T24 parental cells  and cell adhesion molecules were overrepresented among the mutated genes. However, these attributes may rather reflect that larger genes are more likely to be hit. We therefore searched specifically for mutations in genes previously implicated in mechanisms of cisplatin resistance (abbreviated CRG here) as well as for TCGA-SMG. Among the CRG, a KEAP1 mutation previously described in RT-112-LTT8 is very likely to contribute to NRF2 activation in this cell line, as KEAP1 negatively regulates NRF2. A mutation in BACH1, albeit relatively conservative (A678G) might influence the same pathway. Mutations in three genes, CHUK (G289R), NFKB1 (nonframeshift substitution) and SIRT6 (G22A) might alter NFκB survival signaling. A different, presumably heterozygous mutation of CHUK (A42E) was also detected in 253J-LTT. Three LTT lines acquired mutations in HDAC6, a histone deacetylase involved in cell stress responses. Most interestingly, all LTTs contained missense mutations in ATP7B, and RT-112-LTT additionally contained mutations in ATP7A, which encode copper transporters thought to support cisplatin extrusion . It is therefore not clear which mutations functionally contribute to cisplatin resistance. Accordingly, an analysis using the DAVID functional annotation toolon Table .TP53 (R198H), FOXA1 (A83T in the forkhead N-domain) at high allele frequency, ERCC2 (Y16N in the helicase domain), ARID1A (G406R and R1566K) and STAG2 (G667T) at presumable heterozygote frequency. Interestingly, 253J-LTT acquired two mutations in ARID1A (R1566K and P1567A) as well, albeit at moderate allelic frequency. All TCGA-SMG mutations in T-24-LTT occurred at relatively low variant allele frequencies and none were detected in J82-LTT . In contrast, RT-112-LTT cells had a significantly higher number of chromosomes compared to RT-112 parental cells  including several new markers  and decreased on average in J82-LTT vs. J82 , likewise with new markers .To detect karyotype changes in the LTT lines, array-CGH was performed for all four cell line pairs. Since all four cell lines are aneuploid, rather than comparing each cell line against a reference genome and then calculating the differences between parental and LTT variant, DNA from each parental cell line was hybridized against that of its LTT variant Figs . The valers Fig. . The others Fig. . A slighIn the aCGH analyses, chromosomal instability presented as multiple segmental chromosomal gains and losses as well as focal deletions and amplifications, as illustrated exemplarily for RT-112-LTT in Fig. SLC6A11, SLC6A1 (within gain 3p25.3-p24.3), IKKB, POLB, SLC20A2, DKK4 (gain 8p11.23-p11.21), MRE11A, MRE11B (loss 11q14.2-q21), BCL2A1 (gain 15q24.3-q25.3), BLM (gain 15q26.1-q26.3), TOP2A, ERBB2, KRT14, STAT3, BECN1, BRCA1, und WNT3 (gain 17q12q-21.32). Many of these genes have functions relevant for cisplatin resistance, such as cell survival, DNA repair and WNT signaling9. The KEAP1 copy number in the 19p13.3-q13.33 region appeared diminished in all cell lines, including RT-112  cell lines selected for resistance to high concentrations of cisplatin, we observed a large number of mutations with distinctive characteristics by whole exome sequencing and pronounced chromosomal instability by aCGH. Since all parental cell lines can be safely considered as optimally evolved for long-term propagation in culture, mutations and chromosomal changes observed in the resistant LTTs are expected to reflect the genotoxic impact of cisplatin and potentially positive selection for resistance to the drug, but not selection for other properties like faster growth. In fact, the LTTs grow more slowly than their respective parental cellsTwo of our questions were to which extent cisplatin induced point mutations in the cell lines during selection and whether these mutations had a distinct profile. The number of point mutations, ranging from 865 to 12,500, largely paralleled the tolerated drug doses. By comparison, the parental cell lines harbor between 34,400 and 36,800 variants compared to the reference genome, meaning that between 2.3% and 34.1% mutations were additionally induced. These are minimum percentages, as a fraction of the variants in the parental cells will be germ-line variants and as we did not consider rare variants  in the LTTs. Thus, treatment with cisplatin induced a large number of mutations.20. The profiles of new mutations in the three UC LTTs with sufficient numbers of mutations fitted exactly with that defined by Boot et al. for hepatoma HepG2 and breast cancer MCF7A cells13. Thus, across three different human tumor entities, cisplatin appears to elicit the same mutation profile. Moreover, the mutation profile seen by Boot et al.13 and in our analysis is highly similar to a new mutational signature derived by Liu et al.21 from a comparison of UC tissue samples before and after treatment with cisplatin-based chemotherapy, which in turn bears strong similarities to that observed in another study on post-chemotherapy metastatic urothelial carcinomas22. Liu et al. proposed that this signature is likely caused by cisplatin21. Our results strongly support this contention. The newly defined cisplatin mutational signature resembles the cisplatin mutation profile previously described in C. elegans, S. cerevisiae, and DT-40 chicken cells25 in its predominance of C > A and C > T changes, but the profiles differ in their base contexts, suggesting differences in cisplatin mutagenicity between species. Of note, in addition to the distinctive three-base contexts, we observed further characteristics of cisplatin mutagenesis in the LTTs, such as double-base replacements and single base deletions. On a note of caution, the cisplatin mutational signature cannot be identified in J82-LTT. While this is plausibly due to the low number of mutations in this cell line, we cannot formally exclude that it presents a different mutational signature.Currently, five cancer signatures have been identified as prevalent in bladder cancer, more precisely, signatures 1, 2, 5, 10, and 13in vivo, the relative number of mutations differs strongly, as Liu et al. observed only a low percentage of new mutations (<1%)21. Plausible explanations for this difference are (i) the higher drug concentrations used on the cell lines, as supported by the positive association between cisplatin maintenance concentration and number of mutations, and (ii) the longer treatment duration in the cell lines, as supported by the large fraction of subclonal mutations in an overall clonal environment, which suggests accrual over time. Further potential factors accounting for the difference could be the use of drug combinations in patient treatment and more pronounced tumor heterogeneity in vivo. Notably, unlike Liu et al. in patient tissues, we did not observe APOBEC-related mutational signatures for the new variants in the LTTs, even though UC cell lines express APOBEC3B27.Whereas the spectrum of cisplatin-induced point mutations observed in our study in urothelial carcinoma cell lines is very similar to that observed in UC tissues following neoadjuvant chemotherapy et al. in bladder cancer tissues following chemotherapy21. In addition, detection of CNAs by aCGH in the pure tumor cell population in cell lines is likely to be more sensitive and robust than inference of CNA from exome sequencing of cancer tissues with a variable stromal content. Cisplatin is known to induce chromosomal aberrations, presumably mostly as a consequence of interstrand crosslinks. Accordingly, cisplatin sensitivity is related to the cellular capacity for interstrand crosslink and homologous recombination repair. In the bladder cancer cell lines used, interstrand crosslink repair is functional30. In the LTTs, several new variants in DNA repair genes were detected, but all occurred at relatively low allelic frequency and almost all were silent or conservative changes. Most UC cell lines, however, are deficient in DNA damage checkpoint signaling due to inactivation of p53 and RB1 function, a defining property of almost all urothelial cancers31, including those investigated by Liu et al.21. Therefore, the different extent of CNAs is unlikely to result from differences in DNA repair and checkpoint signaling between tumor tissues and cell lines. As for the number of mutations, the intensity and duration of cisplatin treatment may represent the decisive factor. The lower amount of chromosomal aberrations in 253J-LTT cells is in line with this idea. Our results therefore clearly indicate the potential of cisplatin to induce severe chromosomal aberrations, while the comparison to the tissue data suggests that their extent may depend on drug concentration and treatment duration. These dependencies deserve further investigation to better understand the clastogenic effects of cisplatin and their impact in patients.The same factors may also account for the discrepancy between the high chromosome instability detected in the LTTs and the low number of copy number alterations (CNA) reported by Liu 50s of their parental cells, but below their acquired IC50. These concentrations still induce DNA damage as evidenced by accumulation of cells in S-phase and growth arrest, from which the LTTs - unlike parental cells - recover within a few days7. Thus, during the initial selection period, more DNA damage is incurred, whereas during further propagation, cells sustain DNA damage only to an extent with which they can cope. Accordingly, any mutagenic effects of cisplatin will also be less dramatic.Both the results from WES and aCGH argue that the LTT cell lines were generated by the initial selection for a surviving clone with multiple new mutations and chromosomal alterations followed by gradual acquisition of further mutations and - albeit to a limited degree - of copy number changes. From the WES data, this conclusion is supported by the relatively high fractions of homozygous (or near homozygous) new mutations in each cell line Figs  and 3 acin vivo are more likely to be genomically heterogeneous at the start of treatment and populations emerging during treatment are oligoclonal22. In such tumors, point mutations are more easily detected than copy number changes by current deep sequencing techniques.Notably, clonal selection in the LTTs may constitute another factor explaining the higher extent of CNAs in the cell lines compared to tumor tissues. In the cell model, a single clone apparently was selected from a relatively homogeneous cell population. Cancers KEAP1 mutation promoting NRF2 activation in RT-112-LTT8, we detected mutations or copy number changes in genes related to NF-κB-signaling, DNA repair and cell survival regulation, among others. In addition, several point mutations affected large chromatin regulators like ARID1A, which are known to be relevant in UC in general and are important for transcriptional regulation of cell survival. Whether these genomic alterations are functionally important needs to be ascertained individually.Because of the high number of mutations and copy number changes in the LTTs, our last question, namely whether genomic changes may underlie the observed mechanisms of resistance in these cells, is difficult to answer. In addition to the previously reported ATP7A and ATP7B metal ion transporters. Both are capable of exporting platinum compounds and are established as contributing to cisplatin resistance16. Specifically, mutations in ATP7B have been observed in patients with metastatic bladder cancer and may be predictive for the response to cisplatin-based chemotherapy32. In the LTTs, expression of ATP7A and ATP7B is at most moderately changed7, but the present findings raise the possibility that missense mutations, especially in ATP7B, might contribute to cisplatin resistance. These mutations are located within the HMA  domains in the N-terminal metal-binding region where site-directed mutations have been demonstrated to influence cisplatin binding and ATPase activation16. Interestingly, some mutations described in the TCGA data set are also located in HMA domains. Our data thus support the assumption that mutations in ATP7B may be a significant mechanism in the development of cisplatin resistance in UC. As a further support of their functional importance this idea, we observed that siRNA-mediated knockdown of ATP7B sensitized all four LTTs to cisplatin. The precise effect of each missense mutation on cisplatin binding and extrusion would however need to be tested in appropriate models16.Cases in point are the frequent missense mutations observed in the HECW1 deserves further investigation33. Its gene product, NEDL1, is best characterized as an ubiquitin ligase for the WNT/β-catenin pathway signal transducer DVL1 and its loss of function could therefore increase the activity of the pathway. Increased activity of this pathway has previously been implicated in cisplatin resistance34 and, interestingly, we have observed increased expression of some of pathway targets in the LTTs5. NEDL1 is moreover capable of inducing apoptosis in a p53-dependent manner. The UC cell lines used in our study are all p53-mutant, but the influence of NEDL1 on apoptosis in UC cell lines should likewise be investigated in future work.In addition, the functional significance of mutations in 21, which might be used to detect the impact of cisplatin with respect to point mutations in vivo. For several reasons, including polyclonality and tumor heterogeneity, copy number changes are more difficult to detect in vivo. However, if detectable, segmental gains and losses as well as focal alterations of the sort seen in the LTTs could be indicators of cisplatin clastogenic activity in tumor and other tissues.In conclusion, our investigation demonstrates that the emergence of cisplatin-resistant UC cell lines in a continuous treatment model is accompanied by massive genomic changes, both point mutations and chromosomal alterations. These findings underline the potential of cisplatin to cause genomic changes in cancers and patients. Our data confirm a mutational signature proposed by others5.The human UC cell lines (UCCs) RT-112 and T-24 were obtained from the DSMZ , whereas 253J and J82 cell lines were kindly provided by J. Fogh . Parental UCCs and their long-term cisplatin-treated sublines were grown in DMEM GlutaMAX-I  containing 10% heat-inactivated fetal bovine serum. Long-term cisplatin-treated sublines (LTTs) were established by continuously escalating cisplatin  dosages over several months until stable resistant cell lines could be maintained at doses of 50 μM (RT-112-LTT), 3.3 μM (J82-LTT), 6.6 μM (253J-LTT), and 23 μM (T-24-LTT) as described previously5 using primers amplifying ATP7B  or the housekeeping gene SDHA  for normalisation. Relative expression was calculated by the 2−ΔΔCt method Relative cell viability was measured in quadruplicates by the CellTiter-Glo assay .The siRNA-mediated knockdown of ATP7B in UCCs was performed by transfecting 10 nmol/L siATP7B or a non-targeting control  using Lipofectamine RNAiMAX Reagent  according to the manufacturer’s protocol. RNA isolation, cDNA synthesis, and quantitative real-time PCR were performed as previously described on the Lightcycler 96 system 35.For chromosome preparation the cells were grown to near confluency, and incubated with 10 μg/ml colcemid  at 37 °C for 0.5–2 hours. Cells were harvested by using 0.05% trypsin/ 0.02% EDTA (ethylene diamine tetraacetic acid) in phosphate-buffered saline (PBS) , treated by hypotonic solution (0.56% KCl w/v) for 30 min at room temperature, and fixed in ice cold methanol/acetic acid solution . The fixed cells were dropped onto ice cold wet slides, and air dried. Giemsa banding using Trypsin and Giemsa (GTG-banding) was performed according to standard protocols. For chromosome banding analysis a digital karyotyping system  was used. A total of 20 metaphases of each cell line were analyzed. The karyotype was designated according to the International System for Human Cytogenetic NomenclatureDNA was extracted by a Blood & Cell Culture DNA Midi Kit  according to the manufacturer’s protocol. Comparative genomic hybridization (CGH) was performed using an array of 60mer DNA-oligonucleotide probes with a median overall probe spacing of 13 kb. Array CGH was done essentially according to the protocol of the manufacturer  using 1.0 μg of each cyanine 3-dUTP (Cy3) labelled tumor DNA and cyanine 5-dUTP labeled (Cy5) reference DNA tumor DNA .36, using freely available R packages from Bioconductor (bioconductor.org) and CRAN (cran.r-project.org). The R-script operated as a pipeline implementing the following steps: 1) Conversion of the reference/tumor log10-ratios as returned by the feature extraction software to tumor/reference log2-ratios. 2) Removal of improper oligo-nucleotides, flagged by the feature extraction software as having a too inhomogeneous distribution of pixel intensities throughout the spot representing the oligo (flags gIsFeatNonUnifOL = 1 or rIsFeatNonUnifOL = 1), or as being oversaturated (flags gIsSaturated = 1 or rIsSaturated = 1) or undersaturated or otherwise nonsensical (log-ratio set to zero). 3) Breakpoint detection using three different R packages: DNAcopy37, GLAD 38, and HaarSeg40. For GLAD and HaarSeg the recommended default parameters were used; for DNAcopy, the parameter “UNDO.SD” was calculated as 0.25 / derivative log ratio spread (data), adjusting the approximate minimum log-ratio difference between adjacent segments to our own log-ratio cutoff (see point 4). 4) Assignment of an aberration status  to the regions defined by the breakpoints based on the following criteria: To be called aberrant, a region had to span at least five oligos with an average log2-ratio >0.25 for duplications and <−0.25 for deletions, and it had to be supported by at least two of the three breakpoint methods used. 5) Creation of a detailed report listing the calls, their positions, sizes, log-ratios, and affected genes. For detecting recurrent aberrations, a second R-script was used to combine the results of multiple array analyses into one, reporting the relative number of samples sharing the same aberrations in textual form and as graphics (penetrance plots). For further visual inspection Agilent Workbench Software (Version 7.0) was used.Microarrays were scanned using an Agilent Technologies Scanner G2505C. Agilent’s Feature Extraction Software version 11.5.1.1 was used to extract log-ratios from the scanned images. The bioinformatic analysis was carried out with a custom-built script for the R Statistical EnvironmentTM System (Life Technologies) using Ion PI™ v2 chip with the Ion PI™ Hi-Q™ Sequencing 200 Kit according to the manufacturer’s protocol . Amplicon sequences were aligned to the human reference genome GRCh37(hg19) in the target region of the AmpliSeq™ exome using the Torrent SuiteTM software 5.2.0 with the implemented TMAP algorithm (Life Technologies). Subsequently, variants were detected using the Torrent Variant Caller Plugin v5.2.0.34 and the predefined parameter set ‘germ line low stringency’. Detailed annotations to each detected variant were collected in the databases of the NCBI Reference Sequence Database , Exome Sequencing Project (ESP6500), 1000 Genomes Project (1000 g), ClinVar (2015/06/29) and dbSNP142 using the open source software tool ANNOVAR. Further information was added by the implemented prediction tool MutationTaster. The Integrative GenomicsViewer (IGV 2.4.4) was used to visualize the read alignments and to check possible errors, for example due to homopolymeric stretches. Further information on filtering parameters and according numbers of variant is given in Supplementary Table The exome NGS (next generation sequencing) library was generated from high quality genomic DNA from RT-112, J82, 253J, and T-24 parental and LTT cell lines with the Ion AmpliSeq™ Exome RDY Kit . Amplification and adapter ligation were performed according to the manufacturer’s protocol . The library was barcoded during the ligation reaction using the Ion Xpress™ Barcode Adapter Kit (Life Technologies). Library concentration was determined by Qubit measurement (Life Technologies) and library fragment length was analyzed using the 2100 Bioanalyzer . The library was diluted to a final concentration of 8 pM. Emulsion-PCR and enrichment were performed on the Ion OneTouch™ 2 System (Life Technologies) using the Ion PI™ Hi-Q™ OT2 200 Kit following the manufacturer’s protocol . Sequencing was performed on the Ion Proton42. We calculated the relative contributions of these 96 mutated trinucleotides for each sample using the SomaticSignatures R package version 2.14.043. We closer examined the base substitutions caused by cisplatin long-term treatment in each cell line by removing mutations identical in LTT and control and calculating the relative contributions of mutations that were exclusively present in each LTT cell line. The fraction of mutations at the 96 mutated trinucleotides are represented in bar charts. Cosine similarity among the mutational profiles of the LTT lines and to those of HepG2 and MCF10A . Each of these classes can be subdivided into 16 groups by sequence context if the bases immediately 5′ and 3′ to each mutated base are considered0A Table , which w46.ATP7B, HECW1, NRXN2, and FAM205A mutations in 404 bladder urothelial carcinoma samples (TCGA) were analyzed using the cBioPortal for Cancer GenomicsSupplementary materialsSuppl Table 1Suppl Table 2Suppl Table 3Suppl Table 4Suppl Table 5Suppl Table 6Suppl Table 7Suppl Table 8Suppl Table 9Suppl Table 10Suppl Table 11Suppl Table 12Suppl Table 13Suppl Table 14Suppl Table 15Suppl Table 16Suppl Table 17Suppl Table 18Suppl Table 19Suppl Table 20


Although low-grade serous ovarian cancer (LGSC) is rare, case-fatality rates are high as most patients present with advanced disease and current cytotoxic therapies are not overly effective. Recognizing that these cancers may be driven by MAPK pathway activation, MEK inhibitors (MEKi) are being tested in clinical trials. LGSC respond to MEKi only in a subgroup of patients, so predictive biomarkers and better therapies will be needed.We evaluated a number of patient-derived LGSC cell lines, previously classified according to their MEKi sensitivity. Two cell lines were genomically compared against their matching tumors samples. MEKi-sensitive and MEKi-resistant lines were compared using whole exome sequencing and reverse phase protein array. Two treatment combinations targeting MEKi resistance markers were also evaluated using cell proliferation, cell viability, cell signaling, and drug synergism assays.KRAS mutation status, and EGFR and PKC-alpha protein expression. The biomarkers were validated in three newly developed LGSC cell lines. Sub-lethal combination of MEK and EGFR inhibition showed drug synergy and caused complete cell death in two of four MEKi-resistant cell lines tested.Low-grade serous ovarian cancer cell lines recapitulated the genomic aberrations from their matching tumor samples. We identified three potential predictive biomarkers that distinguish MEKi sensitive and resistant lines: KRAS mutations and the protein expression of EGFR and PKC-alpha should be evaluated as predictive biomarkers in patients with LGSC treated with MEKi. Combination therapy using a MEKi with EGFR inhibition may represent a promising new therapy for patients with MEKi-resistant LGSC.The online version of this article (10.1186/s12935-019-0725-1) contains supplementary material, which is available to authorized users. Each year in Canada and the United States, over 25,000 women are diagnosed with ovarian cancer , 2. Low-TP53 gene are rare (8% in LGSC versus 96% in HGSC) [KRAS (20–40%), NRAS (7–26%) and BRAF (5–33%) genes [It is now recognized that LGSC has unique clinical, pathological, and molecular characteristics compared to other types of ovarian cancers, such as the high-grade serous ovarian carcinoma (HGSC) , 8. Molein HGSC) , and thain HGSC) , 12. LGS%) genes –20. Evid%) genes  led to a%) genes . A secon%) genes . CurrentTo date, preclinical laboratory research in LGSC has been limited to tumor tissues. The low frequency and slow growth rate of these tumors have challenged the development of cell lines and animal xenograft models. In the past 5 years, our laboratory has successfully established a collection of patient-derived LGSC cell lines that are now available for pre-clinical drug testing. Previously, we evaluated the effects of four different MEKi  in eight advanced/recurrent LGSC cell lines. Our results indicated that there were substantial differences in cellular response and on-target drug efficacy between cell lines and drugs . EncouraAdvanced or recurrent LGSC samples (tumor and ascites) were obtained from the OvCaRe gynecologic tumor bank , and the John and Mary Knight Translational Ovarian Cancer Research Unit . Tumor bank protocols, cell line derivation, and all research relating to this study was approved by institutional human ethics review boards at BCCA (H14-02859), the University of British Columbia , and the University of Western Ontario (HSREB 12668E). Clinical information was extracted retrospectively from patient records. Tumor histology was confirmed by a gynecological pathologist.2. No immortalization methods were used. Doubling time of these cells ranged from 30 to 80 h, with an average of 47 h, reflecting the clinical slow growth rate of LGSC.Low-grade serous ovarian cancer patient-derived cell lines were established through continuous in vitro culture of patient material obtained through OvCaRe or the John and Mary Knight Translational Ovarian Cancer Research Unit (cell line iOvCa241) tumor banks. Cultures were established and maintained in M199:MCDB105 (1:1) media  supplemented with 10% defined fetal bovine serum  maintained at 37 °C and 5% COMicrosatellite analysis of short tandem repeats (STRs) was performed on LGSC cell lines and corresponding tumor and buffy coat samples for cell line authentication. STR analyses of 10 loci were performed by Genewiz Inc.  (Data available upon request). STR results confirm that all LGSC cell lines and buffy coat samples match to corresponding tumor samples.Whole exome sequencing (WES): Agilent SureSelect RNA Library All Exons v6 protocol was performed by Beijing Genome Institute, per manufacturer’s guidelines. Quality and quantity of post-capture libraries were assessed using an Agilent 2100 Bioanalyzer. Libraries were sequenced on an Illumina Hiseq 4000 (PE 100). Copy number variation (CNV) analysis: Data analysis was performed using Nexus Copy Number Discovery Edition Version 9.0 . Samples were processed using the Nexus NGS functionality (BAM ngCGH) with the FASST2 segmentation. The log ratio thresholds for single copy gain and single copy loss were set at + 0.18 and − 0.18, respectively. The log ratio thresholds for gain of 2 or more copies and for a homozygous loss were set at + 0.6 and − 1.0, respectively. Tumor sample BAM files were processed with corresponding normal tissue BAM files. Probes were normalized to median. Mutation analysis: Sequence alignment and mutation calling were performed in Partek Flow environment (© 2017 Partek Inc). Sequence reads were aligned to GRCh38/hg38 human genome build using bwa 0.7.2. Variants were called using Strelka 1.0.15 for all cell lines except for VOA-1312 (lacking buffy coat sample). VOA-1312 variant calling was performed using LoFreq 2.1.3.a. The called variants were annotated using the wAnnovar software (reference obtained from: http://jmg.bmj.com/content/49/7/433.citation-tools). Annotated calls were then filtered to show only protein-changing SNVs that were present in cell line DNA at allele frequencies (AF) greater than 0.1 and coverage higher than 16×. For VOA-1312, all calls not present in dbSNP (version 138) were retained, while of the calls that were present in dbSNP, only calls with (average heterozygocity + aveHet standard error) < 0.1 were retained. These were additionally filtered using the same criteria as for the Strelka calls.Whole genome sequencing (WGS): Genomic data from LGSC tumors T7 and T11 were obtained from the personalized oncogenomics (POG) program at the BCCA. Methodology has been previously described in detail [n detail . To summAssessment of MEKi sensitivity using trametinib  and selumetinib , were performed as previously described . Phase c2. Absorbance at 490 nm was measured using a microplate reader (BioTek Epoch SN257811). Viability for each treatment was compared to DMSO treated cells. Wells were subsequently stained with crystal violet (CV) to determine residual cells after treatment. Statistical analysis using t-test were used to compare treatment conditions and differences were considered significant at a p-value < 0.05.Cell viability was measured using MTS-Cell Titer 96R Aqueous Non-Radioactive Cell Proliferation Assay, following manufacturer recommendations  at endpoint of Incucyte™ proliferation assays. Treatment media was replaced with 100 μL of fresh media and 20 μL of MTS. Plates were incubated for 3.5 h at 37 °C in humidified 5% COErlotinib (Cat. No. S7786) were purchased from Selleck Chemicals . Dimethylsulfoxide  was purchased from Sigma-Aldrich . Cells were seeded in 96-well plates at 40–50% confluence and treated after 24 h with DMSO or a range of drug concentrations. The inhibitory concentration  was determined using crystal violet staining after 72 h drug treatment.Cell lysates were prepared according to previously published protocols , then 20Reverse-phase protein array on whole tumor and cell line lysates was performed as previously described , 29. ProPRKCA shRNA sequences and one non-target shRNA (Cat. No. TLHVU1401-5578) was purchased from transOMIC Technologies . VOA-3723 and VOA-6406 were plated at 50% confluence in 6-well tissue culture dishes 24 h prior to lentiviral transduction. 199:105 media supplemented with 1% Hyclone dFBS and polybrene  and lentivirus expressing non-targeting shRNA or PRKCA shRNA  in a total volume of 1.5 mL was added. After 24 h, cells were washed with PBS and complete media was added. Successful transduction was confirmed using confocal microscopy. After an additional 24-h recovery, transduced LGSC cells were selected and maintained using puromycin .shERWOOD-UltramiR shRNA lentiviral target gene set containing three http://www.combosyn.com). This software is based on the median-effect principle and the combination index-isobologram theorem  [Cell proliferation, viability and crystal violet results from in vitro drug testing (single drug and drug combinations) were used to assess drug synergism using CompuSyn software  . Drug doOur laboratory previously established a collection of LGSC cell lines derived from patients with advanced/recurrent disease. Preclinical evaluation of four MEKi in eight different LGSC cell lines resulted in the identification of two distinct phenotypes: MEKi-sensitive (MEKi-Se) cell lines n = 2), and MEKi-resistant (MEKi-Re) cell lines (n = 6). In this first study, MEKi drug concentrations and IC50 values were reported . Recogni, and MEKWES was performed to characterize the genomic profiles of our LGSC cell lines and primary cultures. First, we compared the copy number profiles of two of our cell cultures  with those of their associated tumor samples (from WGS data). As shown in Fig. MTAP and CDKN2A tumor suppressor genes, was found in all samples. As expected, KRAS and NRAS non-synonymous mutations were both found most frequently. Either mutation was present in 28.6% of all cell lines/cultures, and in 36.4% (KRAS) and 27.3% (NRAS) respectively when analyzed by patient (some cell lines were derived from the same patient at different times). KRAS and NRAS mutations co-existed in only one cell line (VOA-8862). Only one BRAF mutation was detected . Additionally we analyzed gene mutations and copy-number changes affecting 61 well-known MAPK-pathway genes is shown in Additional file RAS mutations, copy-number variation (CNV) findings, and MEKi sensitivity in each LGSC cell culture (n = 14) is shown in Table Results from the WES analysis in our LGSC cell lines and primary cultures n = 14) show variable levels of genomic aberration and non-synonymous mutations (NsMs), ranging from 1 to 66% total genome change and 24-111 mutation calls per cell line , while MEKi-Re cell lines were either NRAS mutant , or KRAS/NRAS wt . Of interest, the VOA-8862 cell line (mutations in both KRAS and NRAS) was found to be sensitive to all four MEKi tested. In this line, the KRAS mutation variant detected (G12D) is known to be oncogenic, while the NRAS mutation variant detected (C118Y) was not found in the COSMIC database, therefore its oncogenic potential remains unknown. We did not observe any obvious correlation between the degree of CNV in each cell line (copy number high versus low) and MEKi response.We found that all MEKi-Se cell lines  carried oncogenic mutations in To identify biomarkers of MEKi response, we compared two MEKi-Se  and six MEKi-Re  LGSC cell lines using reverse phase protein array (RPPA) analysis. To do so, lines treated with DMSO, 1 μM selumetinib, or 0.1 μM trametinib were screened using a panel of 91 validated antibodies , with and without MEK inhibition (using selumetinib or trametinib), in four MEKi-Re LGSC cell lines . IC50 values for erlotinib in these cell lines are shown in Additional file With the highest dose of erlotinib treatment alone , all four MEKi-Re cell lines continue to proliferate. Interestingly as shown in Fig. The effects of erlotinib, with and without MEKi treatment, on EGFR and MAPK signaling pathways were evaluated using WB. Levels of total and phosphorylated EGFR and ERK1/2, as well as total and cleaved PARP (c-PARP) were measured after 24 h treatment. Results from these experiments indicated that drug effects on cell signaling were cell line dependent  carried PRKCA copy-number gain and two MEKi-Se cells (VOA-9164 and VOA-8862) had PRKCA copy-number loss. To determine whether PKC-alpha protein expression played a role in mediating MEKi resistance we evaluated the effects of PKC-alpha knockdown using lentiviral shRNA, with and without selumetinib or trametinib in two MEKi-Re LGSC cell lines (VOA-6406 and VOA-3723). As shown in Fig. PRKCA shRNA resulted in a complete PKC-alpha protein knockdown in VOA-6406 cells and a partial knockdown in VOA-3723 cells by WB. Subsequent MEKi treatment (1.0 μM selumetinib or 0.1 μM trametinib) experiments showed no significant changes in cell viability when compared to non-target shRNA transduced lines  are frequently found in cancer. MAPK pathway inhibitors, such as MEK inhibitors, were developed as targeted therapeutics to potentially treat such cancers , 42. MEKMTAP and CDKN2A genes) [KRAS and NRAS genes, in agreement with results from previous studies on LGSC tumor tissues [RAS mutations were often associated with RAS copy number gain. As previously reported [In this study, we utilized genomic and proteomic techniques to molecularly characterize a collection of LGSC cell lines and primary cultures (derived from advanced/recurrent LGSC patients), and identify markers that predict response (sensitivity/resistance) to MEKi treatment in vitro. Genomic profiles of two of these cell models were compared with their corresponding tumor samples from the same patient and showed remarkably similar copy number profiles, supporting the utility of these cell models for preclinical research. Subsequent comparisons of genomic profiles from an additional twelve LGSC cell models showed frequent deletion of Chr9p (including loss of A genes) , 49 and  tissues –15. Addireported , 46, 50 KRAS in all four MEKi-Se lines which were absent in all six MEK-Re lines. Previous results from a clinical trial using selumetinib  in this study. In agreement with our results, two recent case reports on LGSC patients with remarkable and durable clinical responses (> 5 years) to MEKi therapy have reported oncogenic KRAS mutations (both G12V) in their tumors [RAS mutation status as a predictive biomarker. It is not unexpected that a single biomarker, such as KRAS mutation status, will not accurately predict responses to MEKi treatment, recognizing that LGSC harbor other MAPK-pathway gene mutations and significant MAPK copy number changes. Furthermore, KRAS copy-number amplification (described as one activating mechanism) could also play a role in mediating MEKi efficacy [Further evaluation of mutation profiles in eight LGSC cell lines with different sensitivity to MEKi treatment (two MEKi-Se and six MEKi-Re) showed oncogenic mutations in y et al. ) did notr tumors , 47. As efficacy .Using RPPA to compare MEKi-Se and MEKi-Re LGSC cell lines, we found that all MEKi-Re lines had higher levels of EGFR and PKC-alpha expression. These results were subsequently validated in three newly established LGSC cell lines. Using this approach, we also described proteomic changes specific to each MEKi tested (selumetinib or trametinib). The changes we observed may be particularly relevant when evaluating differences in drug efficacy, as MEKi may exhibit differences in MEK isoform specificity or off-target effects . InteresIn colorectal cancer, preclinical studies with BRAF inhibitors have reported adaptive feedback reactivation of MAPK signaling involving EGFR , 51. ThiKRAS mutant status. Though the treatment was tolerated, significant side effects occurred with combination therapy. If these drug treatment combinations are going to be effective in LGSC, optimal drug dosing will be required in order to minimize side effects without loss of treatment efficacy.There is a growing body of evidence supporting the use of combining a targeted therapy with other targeted agents or with traditional chemotherapeutic agents , 52. ComBRAF mutant melanomas, and combining a BRAFi and an EGFRi has improved tumor regression in BRAF mutant colorectal cancer xenografts [KRAS G12D mutation and a CUL1:BRAF fusion) were identified in the tumors of both patients [KRAS G12V [MEK1 (Q56_V60del) gene mutations experienced disease stabilization in response to this drug treatment combination [Combination therapy with BRAFi and MEKi has remarkably improved survival in the adjuvant setting for patients with nografts , 54. In patients . AdditioRAS G12V , 47 and bination .PRKCA copy number gain. PKC-alpha knockdown by itself did not affect cell proliferation in either cell line. The results of our experiments suggest that PKC-alpha protein expression appears to be a predictive biomarker but is not a therapeutic target mediating MEKi resistance.PKC-alpha expression has been implicated in chemotherapy drug resistance in some cancers , 37. To BRAF mutations, they have demonstrated little effect in the treatment of BRAF mutant colon cancers [KRAS are more common than in BRAF [Identifying molecular characteristics to predict drug sensitivity/resistance in individual patients with solid tumors has proved to be challenging. The efficacy of therapies designed to target specific mutations are known to be dependent on the cancer type. For example, while BRAF inhibitors have shown to be effective in melanomas carrying  cancers , 55, 56. in BRAF , 57, 58.KRAS mutations and low levels of EGFR and PKC-alpha protein expression. The confirmation of these results in MEKi treated LGSC tumors samples could lead to better patient selection for MEKi treatment, and further avoid unnecessary treatment and toxicities in patients unlikely to respond. Our study also suggests that a significant portion of those LGSC patients whose tumors are unresponsive to MEKi therapy may benefit from combination therapy with EGFR and MEK inhibition. As LGSC xenografts are not yet available for research, we are currently unable to validate these results in vivo. However, we are now using our LGSC patient-derived cell lines to establish xenograft models. The potential predictive value of the three molecular markers of MEKi response identified in our LGSC cell line models should be considered for further validation in clinical trials using MEKi for the treatment of LGSC.In summary, this proteogenomic study is the first to perform predictive biomarker discovery for MEKi treatment in LGSC cell lines. MEKi-Se cell lines were found to have oncogenic Additional file 1: Table S1. List of antibodies included in the RPPA analysis.Additional file 2: Figure S1. Preclinical MEKi evaluation in novel LGSC cell lines.Additional file 3: Table S2. List of all NsMs and genomic aberrations (CNV and NsMs) affecting 61 MAPK-related genes .Additional file 4: Table S3. List of proteins differentially expressed between MEKi-se and MEKi-re LGSC cell lines .Additional file 5: Table S4. EGFR information on LGSC cell lines.Additional file 6: Figure S2. Biological effects of trametinib and erlotinib drug combination in MEKi-re LGSC cell lines.Additional file 7: Table S5. Compusyn data from selumetinib/trametinib and erlotinib drug combinations in MEKi-re LGSC cell lines.Additional file 8: Figure S3. Effects of trametinib and erlotinib combination on LGSC cell lines by WB analysis.


Camptothecin (CPT) and its derivatives are currently used as second- or third-line treatment for patients with endocrine-resistant breast cancer (BC). These drugs convert nuclear enzyme DNA topoisomerase I (TOP1) to a cell poison with the potential to damage DNA by increasing the half-life of TOP1-DNA cleavage complexes (TOP1cc), ultimately resulting in cell death. In small and non-randomized trials for BC, researchers have observed extensive variation in CPT response rates, ranging from 14 to 64%. This variability may be due to the absence of reliable selective parameters for patient stratification. BC cell lines may serve as feasible models for generation of functional criteria that may be used to predict drug sensitivity for patient stratification and, thus, lead to more appropriate applications of CPT in clinical trials. However, no study published to date has included a comparison of multiple relevant parameters and CPT response across cell lines corresponding to specific BC subtypes.TOP1 gene (i.e. amplification), TOP1 protein expression level, TOP1 activity and CPT susceptibility, activity of the tyrosyl-DNA phosphodiesterase 1 (TDP1), the cellular CPT response and the cellular growth rate across a representative panel of BC cell lines, which exemplifies three major BC subtypes: Luminal, HER2 and TNBC.We evaluated the levels and possible associations of seven parameters including the status of the TOP1 gene copy number, TOP1 activity, and CPT response, although the data were too limited for statistical analyses. In cell lines representing Luminal and TNBC subtypes, we observed a direct correlation between TOP1 protein abundancy and levels of enzymatic activity. In all three subtypes , TOP1 exhibits approximately the same susceptibility to CPT. Of the three subtypes examined, the TNBC-like cell lines exhibited the highest CPT sensitivity and were characterized by the fastest growth rate. This indicates that breast tumors belonging to the TNBC subtype, may benefit from treatment with CPT derivatives.In all BC cell lines analyzed (without regard to subtype classification), we observed a significant overall correlation between growth rate and CPT response. In cell lines derived from Luminal and HER2 subtypes, we observed a correlation between TOP1 activity is not a marker for CPT sensitivity in breast cancer. Breast cancer (BC) is the most frequent form of cancer among women . The incCamptothecin (CPT) is the mother compound of an important class of drugs that specifically target the nuclear enzyme topoisomerase I (TOP1). The members of this class of compounds are currently among the most effective anti-cancer drugs for the treatment of advanced-stage gastrointestinal  malignancies, ovarian cancer, and recurrent small-cell lung cancer (SCL) –19. BiolCPT inhibits the re-ligation step of the TOP1 catalytic cycle, leading to the accumulation of cytotoxic covalent TOP1-DNA cleavage complexes (TOP1cc). The cytotoxic effect of TOP1cc is specific to the S-phase and is thought to reflect collision events between the replication machinery and TOP1cc, leading to DNA fragmentation and cell death . DecreasTOP1 gene copy number may be used as an alternative to TOP1 protein expression as a predictive biomarker for stratification of patients with CPT-responsive colorectal cancer  exhibited greater resistance to the drug, with IC50 of 0.448 ± 0.054 μM and 0.481 ± 0.060 μM, respectively  and HCC1419 (HER2 subtype)] showed high sensitivity toward CPT treatment, exhibiting an ICIn addition to TOP1 activity, the drug susceptibility of the enzyme itself can influence cellular sensitivity to CPT. The susceptibility of TOP1 toward CPT was estimated by measuring TOP1 activity in nuclear extracts prepared from the eight cell lines selected in the presence of 60 μM CPT, using the REEAD assay. Note that CPT susceptibility as measured in terms of inhibition of the (nuclear-extracted) TOP1 re-ligation activity is not dependent on TOP1 activity per se  = 0.0003). Hence, all four TNBC-like cell lines  had a high growth rate  . The aim was to investigate possible associations of cellular response to CPT with various cellular and molecular factors, including TOP1 gene is common in patients with BC and is often considered as a potential biomarker of response to treatment with TOP1 inhibitors [TOP1 was found to be amplified in two of five Luminal cell lines, one of four HER2 cell lines, and nine of 14 TNBC-like cell lines . Jandu and co-authors reported a putative association between TOP1 protein level and gene copy number in nine BC cell lines, but it was not possible to determine the significance of this presumptive correlation, because the level of TOP1 protein expression was evaluated by eye rather than by quantitative densitometry [TOP1 locus or by other aspects of cell metabolism. Current data regarding TOP1 gene status in breast tumor tissue are rather controversial. An investigation of TOP1 gene copy number (including 1033 cases) based on use of the The Cancer Genome Atlas (TCGA) portal revealed that only 2% of breast tumors exhibited increased TOP1 copy number [TOP1 amplification (> 30% of BC patients had gene copy numbers > 4). This discrepancy in the overall analysis of gene copy number may be explained by a dilution effect caused by the presence of normal stromal cells [It has been shown that a gain of the hibitors , 57, 58.l lines  Fig. 1)..TOP1 genitometry . In geneal cells .We observed a direct association between protein level and cleavage-ligation activity of TOP1 in Luminal and TNBC-like subtypes but not for the HER2 subtype. The lack of a significant correlation for HER2-like cells may be partially explained by the small number of cell lines of this subtype available for experimentation or, alternatively, by specific cellular metabolic features associated with HER2 amplification .TOP1 gene copy number, protein expression, enzyme activity, and CPT-induced cytotoxicity. This is in contrast to results obtained from cell lines originating from tumors other than breast cancer, which demonstrated a direct correlation between TOP1 gene copy number and IC50 [Given the labor-intensity of experiments involving the analysis of multiple parameters  across numerous cell lines, we selected eight cell lines that represented three main BC subtypes. A Spearman correlation test applied to all cell lines investigated showed that there was no correlation between and IC50 –62. Howeand IC50 , 46. Forand IC50 .In addition to the TOP1 activity, drug susceptibility of the enzyme itself may influence cellular sensitivity to CPT , 55. We TDP1 is an enzyme that catalyzes the repair of several types of DNA damage, including CPT-induced DNA damage , 63. Conp < 0.05) lower growth rates, when compared to their parental and DMSO controls [It is well established that when a replication fork is encountered by CPT-stabilized TOP1cc, DNA fragmentation and cell death follow. It can be assumed that rapidly dividing cells are more drug sensitive than slowly dividing cells. We therefore investigated whether the differential drug response observed among the cell lines analyzed correlated with growth rate. When dividing the cell lines into two broad categories slow growing (double time above 89 h) and fast growing (doubling time below 34 h) we identified a direct association between the rate of cell division and sensitivity to CPT. This finding is in agreement with previous studies reporting that the cell-killing mechanism of CPT is S-phase dependent , 65. Ourcontrols .It is striking to note that four of the cell lines  characterized by a high growth rate and high CPT sensitivity have low TOP1 activity. This may suggest off target effects of CPT, but is more likely explained by the fact that the cytotoxicity of CPT depend on collision of replication forks with CPT induced TOP1cc resulting in the generation of lethal DNA damage. It is possible that in very fast-growing cells even a low activity of TOP1 is sufficient to generate sufficient TOP1cc in the presence of CPT for efficient cell killing. Indeed, in cells with defect repair pathways, which is a key mark of cancer cells, even a single TOP1cc at the right place and time may kill the cells . Hence, TOP1 gene copy number is a poor indicator for CPT response in BC cell lines. More comprehensive studies are needed to confirm this hypothesis for each breast cancer subtype. Indeed, for cell lines derived from the HER2 and Luminal subtypes, our data point to a putative direct correlation between gene copy number, TOP1 activity, and cellular sensitivity to CPT, suggesting that TOP1 at either the genomic or enzymatic level may be a predictive marker for response to CPT in these BC subtypes. For all BC subtypes, there is a direct correlation between growth rate and cellular response to CPT. Collectively, our results suggest that more aggressive and quickly dividing malignant tumors may be targeted more effectively by CPT. The extremely powerful response of cell lines derived from TNBC tumors treated with CPT appears promising, suggesting that it may be worthwhile to investigate the possibility of treating patients with this subtype of BC with CPT derivatives.Taken together, our results suggest that Additional file 1: Table S1. description of the cell lines used in the study. Table S2. Shows the result of the quantifications of the TOP1 protein levels. Figure S1. Nuclear TOP1 IHC staining. Figure S2. Shows the correlation between TOP1 protein expression level and TOP1 gene copy number in cell lines of the luminal subtype. Figure S3. Shows the correlation between TOP1 protein expression level and TOP1 gene copy number in cell lines of the HER2 subtype. Figure S4. Shows the correlation between TOP1 protein expression level and TOP1 gene copy number in cell lines of the TNBC subtype. Figure S5. Shows the correlation between TOP1 activity and protein expression level in cell lines of the luminal subtype. Figure S6. Shows the correlation between TOP1 activity and protein expression level in cell lines of the TNBC subtype. Figure S7. Shows the correlation between TOP1 activity and protein expression level in cell lines of the HER2 subtype. Figure S8. Shows a graphically depiction of TOP1 susceptibility to CPT in nuclear extracts. Table S3. Summary of the results obtained for all investigated parameters.


NSCLC), we previously demonstrated that mesenchymal NSCLC was sensitive to polo‐like kinase (Plk1) inhibitors, but the mechanisms of resistance in epithelial NSCLC remain unknown. Here, we show that cMet was differentially regulated in isogenic pairs of epithelial and mesenchymal cell lines. Plk1 inhibition inhibits cMet phosphorylation only in mesenchymal cells. Constitutively active cMet abrogates Plk1 inhibitor–induced apoptosis. Likewise, cMet silencing or inhibition enhances Plk1 inhibitor–induced apoptosis. Cells with acquired resistance to Plk1 inhibitors are more epithelial than their parental cells and maintain cMet activation after Plk1 inhibition. In four animal NSCLC models, mesenchymal tumors were more sensitive to Plk1 inhibition alone than were epithelial tumors. The combination of cMet and Plk1 inhibition led to regression of tumors that did not regrow when drug treatment was stopped. Plk1 inhibition did not affect HGF levels but did decrease vimentin phosphorylation, which regulates cMet phosphorylation via β1‐integrin. This research defines a heretofore unknown mechanism of ligand‐independent activation of cMet downstream of Plk1 and an effective combination therapy.To address the need for improved systemic therapy for non–small‐cell lung cancer ( We observed differential regulation of cMet phosphorylation after Plk1 inhibition in epithelial and mesenchymal NSCLC. We confirmed cMet's role in Plk1 inhibition–induced apoptosis by inhibiting, silencing, and activating cMet in NSCLC et al, https://portals.broadinstitute.org/ctrp/) database : 33 proteins were associated with sensitivity to BI2536, 36 with sensitivity to GSK461364, 37 with sensitivity to BRD‐K70511574, and 26 with sensitivity to GW‐843682X  and β‐catenin (P < 0.001) was higher and expression of the mesenchymal protein Snail (P < 0.01), as well as ATM and thymidylate synthase, was lower in cell lines resistant to Plk1 inhibitors than in those sensitive to Plk1 inhibitors. We also found that cMet protein expression correlated with drug sensitivity for all Plk1 inhibitors  protein and phosphoprotein expression measured using reverse phase protein array (RPPA) from the MD Anderson Cell Line Project database ] to MET gene copy number in NSCLC cell lines. MET gene copy number was obtained from the MD Anderson Cell Line Project database, CTRPv2, and Kubo et al  with Spearman's rho coefficient values that ranged from −0.428 to 0.430 and associated P‐values that ranged from 0.078 to 0.872. However, this analysis was limited by the fact that there were drug sensitivity data for only two NSCLC cell lines with MET copy number > 5.The correlation of drug sensitivity with cMet protein expression motivated us to compare Plk1 inhibitor sensitivity [area under the curve (AUC) and effective dose 50  with 5 ng/ml TGF‐β for at least 14 days, which led to the expected changes in the expression of vimentin, Snail, Slug, ZEB1, Twist, E‐cadherin, β‐catenin, and claudin 7 Fig . Given tin vitro . The Plket al, P < 0.05, we observed that the mean expression of 33 proteins or phosphoproteins was differentially regulated . The combination therapy led to tumor regression in nine of 10 epithelial and nine of 10 mesenchymal PDX mice  than in the epithelial xenograft models (TC202 and H1975). Co‐treatment with Plk1 and cMet inhibitors significantly increased the percentage of TUNEL‐positive cells in all mouse models. Taken together, these results support cMet as a driver of Plk1 inhibitor resistance in epithelial NSCLC in vivo, and the findings suggest that co‐inhibition of Plk1 and cMet increases apoptosis, leading to tumor regression in NSCLC.To asses for apoptosis, we next performed TUNEL staining in paraffin‐embedded tissues Fig G. SimilaTo test cMet as a driver of resistance, we used constitutively active TPR‐Met, which is a fusion protein that lacks the extracellular, transmembrane, and juxtamembrane domains of the cMet receptor and includes the TPR dimerization motif, which allows constitutive and ligand‐independent activation of the kinase. Expression of the TPR‐Met chimeric protein was biologically active in three mesenchymal cell lines , which in turn increased downstream Akt (S473) and Erk (p42/44 T202/204) phosphorylation Fig A.Compared with the control vector–transfected cells, TPR‐Met–expressing cells showed less apoptosis, as measured by Apo‐BrdU, cleaved PARP, and cleaved caspase 3, after treatment with volasertib Fig A and B. To determine whether we could overcome the effects of cMet activation by using a cMet inhibitor, we combined tepotinib and volasertib. Tepotinib inhibited cMet universally but did not completely abrogate cMet signaling in the TPR‐Met–expressing cell lines. The addition of tepotinib significantly increased apoptosis in control vector–transfected cells, but apoptosis was only moderately increased in TPR‐Met–transfected cells, consistent with a lack of full cMet inhibition Fig A and B. MET amplification and a MET exon 14 skipping mutation  and the one with an activating MET mutation (H596), volasertib did not lead to a decreased in pMet. Both H596 and H920 were resistant to the Plk1 inhibition GSK461364  and a decrease in the mesenchymal genes VIM (0.1‐fold), ZEB1 (0.001‐fold), TWIST (0.3‐fold), and SNAIL  compared with the parental cell line , as measured by Apo‐BrdU and cleaved PARP  in both parental and Calu6‐VAR cells  in conditioned medium and in cellular lysates after Plk1 inhibition in both mesenchymal and epithelial cell lines. Neither the secreted nor cellular HGF levels were affected by Plk1 inhibition in all tested NSCLC cell lines Fig A. LikewiITGB1), and vimentin (VIM) because this is a ligand‐independent pathway that involves proteins associated with EMT. Plk1 maintains cell surface levels of β1‐integrin via phosphorylation of vimentin on S82  and VIM  phosphorylation. Inhibition or silencing of Plk1 by volasertib or siRNA markedly decreased the S82 vimentin phosphorylation in two mesenchymal cell lines. The resulting increase in total vimentin may be a compensatory change. Vimentin levels were very low in epithelial cell lines without consistent effects by Plk1 inhibition , leading to cMet activation in NSCLC.The current study yielded five main findings that follow from our prior discovery that mesenchymal NSCLC is more sensitive to Plk1 inhibitors than epithelial NSCLC  expressing KRASG12D, Narvaez, et al investigated the combination of MET knockdown and cMet inhibition with Poloppin. Both cMet inhibition and knockdown did sensitize five KRAS mutant cell lines and one pancreatic organoid to Poloppin in vitro.Recently published work using a novel drug (Poloppin) that inhibits phosphopeptide binding by the Plk1 polo‐binding domain supports our findings  is underway. MET is also amplified in 5% of newly diagnosed lung adenocarcinomas  in various cancers, including NSCLC. Co‐targeting of these kinases potentially produces synergistic antitumor effects  were used. Human TGF‐β1 was purchased from Cell Signaling Technology . Human fibronectin was purchased from Sigma‐Aldrich . Constitutive active TPR‐Met plasmid and control pBABE plasmid were obtained from Addgene . Antibodies and dilutions used in the study are listed in All drugs were purchased from Selleck Chemicals  and prepared as 10 mmol/l stock solutions in dimethyl sulfoxide. Predesigned sets of four independent siRNA sequences of the target genes et al, et al, Human NSCLC cell lines were obtained, maintained, and genotyped by STR profiling as previously described  and 1 μg of retroviral plasmid pBABE–TPR‐Met or the control vector using Lipofectamine 3000. The HEK293 cell medium was changed 24 h after transfection, and the cells were incubated at 37°C for 48 h to allow for virus production. After 48 h, HEK293 medium containing viral particles was filtered and transferred onto NSCLC cell culture plates and incubated at 37°C for 48 h. After transduction, fresh RPMI 1640 medium with 10% fetal bovine serum was added to the cell culture plates, and the NSCLC cells were allowed to recover for 24 h. NSCLC cells were selected using 3 μg/ml puromycin.et al, For knockdown experiments, NSCLC cells were transfected with predesigned sets of four independent siRNAs against Plk1 and cMet using Lipofectamine RNAiMAX Transfection Reagent , as we previously described  after 72 h of exposure to serial fold drug dilutions, as described previously  according to the manufacturer's protocol. Briefly, control and treated cell lysates were incubated with the indicated antibody–Dynabeads conjugate followed by purification of antigen–antibody–Dynabeads conjugate and separation on SDS–PAGE for immunoblotting.et al, et al, Apoptosis was measured by TUNEL  staining  as described previously  and as we previously described , and complementary DNAs (cDNAs) were synthesized using iScript Reverse Transcription Supermix . Quantitative real‐time PCR assays were done with iTaq Universal SYBR Green Supermix (Bio‐Rad Laboratories) and the CFX96 Real‐time System (Bio‐Rad Laboratories) as described previously  according to the manufacturer's protocol. Briefly, NSCLC cell lines were treated with 50 nM volasertib in serum‐free medium for 24 h, and HGF levels were measured in conditioned medium.et al, 2)/2. When tumor volumes reached 150 mm3, the mice were randomized and treated with 30 mg/kg volasertib intravenously weekly, 25 mg/kg tepotinib daily (5 days on and 2 days off) by oral gavage, both agents, or vehicle control for 5 weeks. Mice were then euthanized and tumors excised.In conducting research using animals, the investigators adhered to the laws of the United States and regulations of the US Department of Agriculture. All animal experiments were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at The University of Texas MD Anderson Cancer Center and conducted in accordance with MD Anderson's Office of Research Administration and IACUC guidelines. Two million NSCLC cells per mouse were injected subcutaneously, or logarithmically growing PDXs  and an autocorrelation structure of order 1 in residuals, given that we had a single grouping variable of mouse identification. The Tukey test was used for pairwise comparisons between treatment groups, and therefore, Harvested tumor tissues were fixed in 10% formalin, embedded in paraffin, and sectioned at 5 μM. Briefly, after deparaffinization and rehydration, tissue sections were subsequently processed for DNA labeling by TdT dNTP followed by Strep‐HRP/DAB detection using the TUNEL Apoptosis Detection Kit . Images were taken at 40× magnification, and image analysis was performed to calculate the percentage of TUNEL‐positive cells using ImageJ software .https://www.r-project.org/), a publically available and widely used statistical computing tool. RPPA protein expression and Plk1 inhibitor  sensitivity data were downloaded from the MD Anderson Cell Line Project  and CTRPv2  databases and curated for NSCLC cell lines on March 5, 2017. RPPA data for 71 cell lines were available for BI2536, 69 cell lines for GSK461364, 68 cell lines for BRD‐K70511574, and 64 cell lines for GW‐843682X. To identify correlation between drug sensitivity and protein expression, we applied Spearman's correlation using R. For RPPA proteins to be compared between cell lines after treatment with a Plk1 inhibitor, it is necessary to treat cell line as a random effect because the cell line variation is dominant in principal component analysis. Further contrasts were conducted to compare changes in epithelial cells after treatment with the Plk1 inhibitor, changes in mesenchymal cells after treatment with the Plk1 inhibitor, and changes between the two, which is the interaction term. The analysis was carried out by the limma package in R. To correct for multiple hypotheses testing, we adjusted the resulting P‐values using the Benjamini–Hochberg method.All statistical analyses were performed using R packages  with three or more technical replicates. Analysis of variance was applied, and various contrasts were set up to test the significance of difference between groups of interest and of the interaction of two factors. Bonferroni or Benjamini–Hochberg correction was applied to adjust for multiple testing. For tumor growth curve analysis, linear modeling using generalized least squares with two variables (treatment and day) and an autocorrelation structure of order 1 in residuals were applied. The Tukey test was used for pairwise comparisons among treatment groups, and P‐values corrected for multiple comparisons were produced. For survival analysis, log‐rank tests were conducted to assess the association of the treatment type with survival. Benjamini–Hochberg correction was applied to P‐values from pairwise comparisons between treatment groups to adjust for multiple comparisons. In addition, Kaplan–Meier curves were generated. A summary of statistical methods for each specific Figure is provided in There are a variety of group comparisons with respect to different study designs, including two‐factor and three‐factor factorial designs. All RS designed and performed most of the experiments and wrote the first draft of the manuscript. SP, PV, and VS assisted with experimental design and conduct of the animal experiments. BF provided and characterized several PDX models. LS, XR, and JW performed the bioinformatics and statistical analyses. FMJ oversaw the study design and wrote the final draft of the paper.Faye M. Johnson has received research funding from PIQUR Therapeutics and Trovagene. Other authors have no conflicts of interest to declare.in vitro and in vivo. However, mechanisms of resistance to Plk1 inhibitors have not been elucidated and this unknown is a major gap in knowledge.Most NSCLC remains incurable despite survival benefits from targeted and immunotherapy. Thus, there is still an urgent need for effective systemic therapy. While Plk1 inhibitors are well tolerated by patients and some striking clinical responses were observed, response rates were generally low. Likewise, Plk1 inhibitors lead to diverse biological effects in cancer cells. Understanding the basis for Plk1 inhibitor–induced apoptosis is essential to maximizing their antitumor efficacy. To address this need, our laboratory previously discovered that mesenchymal NSCLC cell lines are more sensitive to Plk1 inhibitors than epithelial cell lines We used isogenic pairs of epithelial and mesenchymal NSCLC cell lines to measure changes in the expression of 301 proteins after Plk1 inhibition. We observed differential regulation of the cMet/FAK/Src axis, which is intact in both mesenchymal and epithelial cells. However, Plk1 inhibition inhibits cMet phosphorylation only in mesenchymal NSCLC cells, leading to subsequent inhibition of FAK and Src. Constitutively active cMet abrogates Plk1 inhibitor–induced apoptosis. Likewise, cMet silencing or inhibition enhances Plk1 inhibitor–induced apoptosis. Additionally, cells with acquired resistance to Plk1 inhibitors are more epithelial than their parental cells and maintain cMet activation after Plk1 inhibition. In both patient‐derived and cell line xenografts, mesenchymal NSCLC was more sensitive to Plk1 inhibition alone than was epithelial NSCLC. The combination of cMet and Plk1 inhibition led to regression of tumors in three models and marked tumor size reduction in the fourth model. When drug treatment was stopped, tumors treated with the combination did not regrow. Plk1 regulates cMet via the vimentin protein that is only expressed in mesenchymal NSCLC.de novo and acquired resistance to Plk1 inhibitors in patients with NSCLC.NSCLC cell lines have diverse sensitivities to Plk1 inhibition, which is consistent with the results of clinical trials of Plk1 inhibitors in solid tumors. This study reveals a novel mechanism of non‐canonical cMet activation in resistant/epithelial NSCLC after Plk1 inhibition. The addition of cMet inhibitors is a promising therapeutic strategy to overcome AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Source Data for Expanded View and AppendixClick here for additional data file.Review Process FileClick here for additional data file.Source Data for Figure 2Click here for additional data file.Source Data for Figure 3Click here for additional data file.Source Data for Figure 5Click here for additional data file.Source Data for Figure 6Click here for additional data file.Source Data for Figure 7Click here for additional data file.Source Data for Figure 8Click here for additional data file.


Arabidopsis thaliana, previous forward screens have likely identified almost all the meiotic genes that when mutated lead to a pronounced decrease in fertility. However, the increasing number of genes identified in reverse genetics studies that play crucial roles in meiosis, but do not exhibit strong phenotypes when mutated, suggests that there are still many genes with meiotic function waiting to be discovered. In this study, we produced 897 A. thaliana homozygous mutant lines using Ethyl Methyl Sulfonate (EMS) mutagenesis followed by either single seed descent or haploid doubling. Whole genome sequencing of a subset of lines showed an average of 696 homozygous mutations per line, 195 of which (28%) modify a protein sequence. To test the power of this library, we carried out a forward screen looking for meiotic defects by observing chromosomes at metaphase I of male meiosis. Among the 649 lines analyzed, we identified 43 lines with meiotic defects. Of these, 21 lines had an obvious candidate causal mutation, namely a STOP or splicing site mutation in a gene previously shown to play a role in meiosis . Interestingly, this was the first time that six of these genes were identified in a forward screen in Arabidopsis . These results illustrate the potential of this mutant population for screening for any qualitative or quantitative phenotype. Thus, this new mutant library is a powerful tool for functional genomics in A. thaliana. The HEM (Homozygote EMS Mutants) lines are available at the Versailles Arabidopsis stock center.Genetic screens have been crucial for deciphering many important biological processes, including meiosis. In  This is achieved by screening the phenotype of a population with random mutations throughout the genome, which alter gene function and thus give an identifiable phenotype. On the other hand, the principle of reverse genetics is to test the function of a specific candidate gene by analysing the consequences of its disruption on the process of interest.Genetic screens have improved our knowledge of the genetic basis of biological processes. However, to identify all the genes involved in a biological pathway screening strategies must be sensitive, specific and feasible. In this context, the two classic approaches, which are still widely used, are forward and reverse genetics. On one hand, forward genetics can be used to decipher specific pathways without Arabidopsis  lines that can be used to detect either qualitative or quantitative phenotypes. Thus this new mutant collection is a very useful resource for functional genomics and applied research in A. thaliana.Here, we produced a total of 897 homozygous Figures 1A,B).To produce the HEM lines we generated two collections of almost fully homozygous lines using two different strategies: (i) single seed descent (SSD) and (ii) doubled haploids (DH) . Six parallel rounds of mutagenesis followed by SSD were carried out, giving a total of 698 independent mutant lines (Table 1). With this approach, we expected to obtain a level of homozygosity of 87% in the 4th generation (M4) that was screened (see below). The M5 seeds are available at the Versailles Arabidopsis stock center.For the SSD subset we applied EMS to wild type Col-0 seeds and produced four successive generations by self-fertilization using a single seed in each generation (SSD) . We applied EMS to Col-0 seeds with a homozygous mutation in the GALBRA-1 gene (GL1), which is characterized by the absence of trichomes .For the second population, the strategy was to generate homozygous mutagenized lines by haploidization followed by genome doubling   and 22 DH lines. Among these, 41 lines showed a meiotic defect (see section below) and 6 lines without meiotic defects were randomly chosen among the DH lines.Figure 2A). Of these 897 mutations, 99% were G > A or C > T transitions. When this value was compared for each of the subsets obtained, the SSD lines had more mutations  than the DHs .The HEM populations showed a total mean number of 897 mutations per line, ranging from 500 to 1,500, with a normal distribution : in the SSD lines 70% of the mutations were fixed (676 mutations per line on average), which was lower than the 87% expected for those lines, perhaps due to counter-selection. In the case of the DH lines, we obtained an average of 94% homozygous mutations (720 mutations per line on average). The six randomly chosen lines had a similar number of mutations than the lines having a meiotic phenotype .The percentage of fixed mutations, was different for each of the subsets due to the different approaches used . However, in six lines the percentage of homozygous mutations was 80% (on average) suggesting that the haploidization was not successful or that cross-pollination occurred during their production. Regardless, these lines still show a very high level of homozygosity, equivalent to that in the SSD lines.Figure 2C and Table 2). This category includes: non-synonymous coding mutations that change a single AA ; small indels leading to frame shifts ; new stop codons ; new start codons  and splice site changes . Considering only the mutations resulting in frameshifts, new stop codons or splice site changes, 14 mutations per line should severely disrupt gene functions .In all the HEM lines (SSD and DH), 28% of the fixed mutations (an average of 195 mutations per line) modified a protein sequence  Alexander staining to detect dead pollen grains  and thenAll of the 199 M5 mutant lines from the DH subset were first pre-screened by Alexander staining. The meiotic chromosomal behavior was then examined in lines with more than 10% dead pollen grains. In the case of the SSD lines, we screened 539 M4 mutants  by directly observing meiotic chromosomal behavior at metaphase I. Of these, the lines with a minimum of 20 cells at metaphase I captured were considered as screened, which resulted in a total of 450 mutant lines. For each of the mutants with a meiotic defect at metaphase I, we verified that the same phenotype was observed in the next generation. To optimize the screening procedure, we focused on metaphase I: (i) cells at that stage are relatively easy to find and (ii) most meiotic defects can be detected at metaphase I . A drawback is that some defects cannot be observed at that stage  and would be missed in this screen. However, we occasionally detected meiosis II defects that were included in the study.Table 1): 10% of the lines  had meiotic defects in series 10, whereas no meiotic defects were observed in the other series produced . This variability could reflect differences in the efficiency of the mutagenesis due to slight variations in experimental conditions  that may influence the final outcome of EMS mutagenesis. Therefore, the high number of meiotic mutants observed in the SSD series 10 and the HD lines suggests that these series are especially suitable for carrying out other forward genetic screens.We identified a total of 43 lines with various meiotic defects (18 DH lines and 25 SSD lines), representing 9% (18/199) of the screened DH lines and 6% (25/450) of the SSD lines. However, we observed an important difference among the six different series of mutagenesis used to produce the SSD subset : (i) Different levels of fragmentation , (ii) bivalent shape defects , (iii) the presence of univalent chromosomes at different frequencies suggesting a lack of crossovers , and (iv) bivalent alignment abnormalities .Overall, after screening 80% of the HEM lines the rate of meiotic mutant was high, with 9.4% of the lines showing different types of meiotic phenotypes in the DH and SSD series 10 (43 mutants with a robust meiotic phenotype among 199 DH lines + 261 series 10 SSD lines). The phenotypes described in the 43 HEM lines identified cover a variety of meiotic defects at metaphase I, compared to wild type (Table 3). We considered a candidate mutation to be causal when the mutation was predicted to strongly affect a protein  with a described role in meiosis. Additionally, the observed phenotype had to be consistent with the previously described phenotype. An additional missense mutation in ATM was shown to be causal by genetic mapping (Table 3). In all these lines, the presence of the candidate mutation was confirmed by Sanger sequencing. In addition, Sanger sequencing of the candidate gene SGO1 in two lines that showed premature loss of sister chromatid cohesion identified a stop and a splice site mutation .Of the 41 lines sequenced with meiotic defects, 18 had an obvious candidate mutation (ATM (found in five different mutant lines) plays a role in DSBs repair , and HEI10 are involved in class I crossover formation (FLIP is both an anti- and pro-crossover factor (FANCL promotes crossover formation (PRD2 is required for DSBs formation (REC8 and SGO1 (found in 3 different mutant lines) are both involved in sister chromatid cohesion . Interestingly, most of these mutations have only a mild impact on fertility .The genes described in the 21 lines are involved in a wide range of meiotic mechanisms: s repair , 2003; Mormation , FLIP isr factor ; FANCL pormation ; PRD2 isormation ; REC8 ancohesion ; ASY4 isormation ; and PSSribution ; Table 3In addition, among the 43 lines identified with meiotic phenotypes, in 22 there was no obvious candidate mutation, according to the criteria described above. These lines displayed different meiotic phenotypes and further work is needed to identify their causal mutation.Arabidopsis EMS mutants. These mutants show a high mutation rate per line, 897 mutations per line on average, of which most are fixed. Thus, due to the fixed nature of the mutations, these libraries can be used to repeatedly screen for a specific phenotype and therefore, to analyze either quantitative or qualitative traits.We have described the HEM collections, two libraries of almost fully homozygous Of all the mutations produced, we estimate that the HEM lines contain, >170,000 mutations  with an effect in the protein sequence. Among these >12,000  are mutations that likely knock out the protein’s function .In this study, the HEM lines were used in a forward screen targeting subtle meiotic phenotypes as a new approach to identify novel meiotic mutants. Nine Percent of the mutant lines screened in the DH and SSD series 10 collections showed defects in meiosis (43 lines), which is a direct evidence of the efficiency of mutagenesis in the HEM lines.ATM, MLH3, MLH1, MER3, HEI10, SGO1, ASY4, FLIP, FANCL, PRD2, REC8, and PSS1) involved in different meiotic mechanisms. Interestingly, six of these identified genes  have been found here for the first time in a forward genetic screen. These mutants have only moderate defects in chromosome distribution at meiosis, leading to a subtle reduction in fertility, which is under the threshold of detection by visual examination of fruit length.Within these mutants, there are 12 clear candidate genes  collection, we applied ethyl methanesulfonate (EMS) to wild type ribed in . Seeds wGLABRA1 (GL1). Mutagenized seeds were grown and then crossed as male to the tailswap line (TS) to obtain haploid plants that could be identified due to their lack of trichomes. Diploids were obtained by self-fertilization. M4 seeds were multiplied to obtain the final mutant population of the collection. All plants were cultivated in greenhouses with a 16 h/day and 8 h/night photoperiod, at 20°C and 70% humidity.To generate the DH collection, mutagenesis was performed as in the SSD in Col-0 plants with an existing T-DNA insertion in 1.Alexander staining for pollen viability was performed as described in Genome sequencing was performed with Illumina Hiseq3000 HWIJ00115 with > 8X coverage. The resulting fastq files were analyzed using the Mutdetect pipeline (version 0.0.6-e3ef10e) 1http://www.graphpad.com using TALC-P, VP, MG, and RM contributed to the conception and design of the study. LC-P, VS, AC, AH, AG, VP, DV, and LC performed the experiments. LC and RM analyzed the sequencing data. LC-P wrote the first draft of the manuscript.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.