In the large-scale breeding of conifers, cultivating embryogenic cells with good proliferative capacity is crucial in the process of somatic embryogenesis. In the same cultural environment, the proliferative capacity of different cell lines is significantly different. To reveal the regulatory mechanism of proliferation in woody plant cell lines with different proliferative potential, we used Korean pine cell lines with high proliferative potential 001#–001 (Fast) and low proliferative potential 001#–010 (Slow) for analysis. A total of 17 glutathione-related differentially expressed genes was identified between F and S cell lines. A total of 893 metabolites was obtained from the two cell lines in the metabolomic studies. A total of nine metabolites related to glutathione was significantly upregulated in the F cell line compared with the S cell line. The combined analyses revealed that intracellular glutathione might be the key positive regulator mediating the difference in proliferative capacity between F and S cell lines. The qRT-PCR assay validated 11 differentially expressed genes related to glutathione metabolism. Exogenous glutathione and its synthase inhibitor L-buthionine-sulfoximine treatment assay demonstrated the positive role of glutathione in the proliferation of Korean pine embryogenic cells. Due to the rapid growth of population and sustained growth of the economy, global demand for wood continues to grow . High-quPinus rigida × P. taeda). Businge et al. [Picea abies. In addition, tryptophan was only presented in two cell lines that can differentiate into somatic embryos. They further inferred that exogenous auxin was crucial to EC proliferation and somatic embryos differentiation. Further research on the regulatory mechanism of EC proliferation in conifers needs to be carried out urgently.Transcriptomics ,14,15,16e et al.  reportedPinus koraiensis Sieb. et Zucc.) is a dominant tree species of temperate zone climax community broad-leaved forest. It is also an extremely important high-quality precious timber tree and nut species and economic forest species in East Asia. The somatic embryogenesis technology system of Korean pine has been established previously [Korean pine  and Slow  cell lines was different. The F cell line had abundant filamentous early somatic embryos on the surface a and a rThe F and S cell lines  were sequenced by Illumina Hiseq platform. A total of 40 Gb clean data was obtained. The clean data in each sample were more than 6 Gb, and the base ratio of Q30 was 93.08–95.93%. The impurities of reads were removed to obtain clean reads. The GC content was more than 43.78%, which met the requirements of subsequent transcriptome de novo assembly. A total of 16,027 unigenes was obtained from de novo assembly, with high assembly integrity. The mapped ratio values of the six samples reached above 75.35%, so the result of de novo assembly in six samples was good and met the requirements for further tests.−5) and HMMER . Principal component analysis (PCA) was performed on six samples to understand the correlation of overall DEGs between the two samples of F and S cell lines and the difference of DEGs between F and S cell lines. According to the PCA diagram, there was little difference in the principal components in the samples of F and S cell lines  between F and S cell lines, in which 870 genes were upregulated and 1013 genes were downregulated . A total of 1422 DEGs with annotation information was obtained by BLAST  metabolism, ascorbate and aldarate metabolism, pyrimidine metabolism, and other pathways  trended toward downregulation, and only a few differential genes (3) trended toward upregulation.A total of 17 GSH-related DEGs was significantly changed between F and S cell lines , includiThrough the observation of transcriptome data, it was found that many DEGs were involved in a plant hormone signal transduction pathway, mainly involved in the metabolic pathway of indole-3-acetic acid (IAA) and cytokine in biosynthesis. Therefore, in this study, genes that played a role in the anabolism, transport, and signal transduction of these two hormones were selected for specific analysis.SAUR family , the expression trends of the remaining 11 DEGs were similar to the RNA-Seq results, indicating that the differential genes obtained by sequencing in this study were credible.Twelve DEGs related to GSH and auxin between F and S cell lines of Korean pine were selected for qRT-PCR verification . Principal component analysis (PCA) was performed on six samples to understand the correlation of overall metabolisms between the two samples of F and S cell lines and the difference of metabolites between F and S cell lines. According to the PCA diagram, there was little difference in the principal components in the samples of F and S cell lines , while tp < 0.05, and VIP > 1). All DEMs were divided into 27 categories, mainly 31 kinds of carboxylic acids and their derivatives; 17 kinds of fatty acyl compounds; 11 kinds of organic oxygen compounds; 6 kinds of imidazopyrimidines; 6 kinds of purine nucleosides and organic compounds; 4 kinds of nitrogen compounds; as well as 3 kinds of benzene and its substitutes, indole and its derivatives, phenols; purine nucleotides; pyrimidine and its derivatives, pyrimidine nucleosides, and pyrimidine nucleotide metabolites; 2 kinds of cinnamaldehyde, cinnamic acid and its derivatives and flavonoid metabolites; and a kind of (5’->5’)-dinucleotide, 5’-deoxyribonucleoside, biotin, and its derivatives.Volcano plot revealed 412 differential metabolites (DEMs) between F and S cell lines, in which 183 metabolites were upregulated and 229 were downregulated  were preferentially analyzed. The KEGG co-enrichment results are shown in The selected genes and metabolic pathways  was performed on the differential genes and metabolites related to GSH for further exploring the gene most related to metabolites . It was The intracellular GSH content of F and S cell lines was shown in p > 0.05). In addition, the intracellular zeatin (ZR) (The results of intracellular hormone content measurement in F and S cell lines are shown in tin (ZR) f and abstin (ZR) g contenttin (ZR) h, ZR/ABAtin (ZR) i, and (Itin (ZR) j were siExogenous addition of GSH promoted EC proliferation in F and S cell lines, and exogenous BSO inhibited EC proliferation k,l, whicThe proliferative process of Korean pine EC was the result of the dynamic balance of embryonic cell division and differentiation. This process was closely related to GSH and plant growth regulators, which was consistent with Belmonte et al. . In the GSH is an important antioxidant in plants, which can protect cells from oxidative stress . It playGST family regulated GSH S-transferases. GST is a family of proteins with various forms and similar three-dimensional structures. GSH S-transferase is a key enzyme in the GSH binding reaction and possesses a well-defined GSH binding domain at its active site, which catalyzed the initial step of GSH binding reaction [GST family, most of which were downregulated, and only one gene was upregulated. Therefore, it was speculated that the stress resistance of the F cell line was higher than that of the S cell line.Genes of the reaction . The comDHAR family. Its expression in the F cell line was higher than that in the S cell line, and its expression was downregulated in the S cell line. This further suggested that the stress resistance of the F cell line might be higher than that of the S cell line, which needed to be further investigated.GSH dehydrogenase/transferase family genes are regulated by dehydroascorbate reductase. Dehydroascorbate reductase (DHAR) is an important antioxidant enzyme in plants. It is the key enzyme to promote ascorbic acid (ASA) regeneration in the process of ASA-GSH cycle, and it plays an important role in protecting cell components against oxidative damage . DHAR caCARP is a leucine peptidase family gene. The overexpression of CARP increased the catalytic activity of several aminopeptidase substrates [CARP family gene in the S cell line trended toward downregulation. In the GSH metabolic pathway, γ-glutamylcysteine, glycine (Gly), and ATP were catalyzed by GSH synthase (GSS) to form GSH, ADP, and P1 [bstrates . CARP acbstrates . In this, and P1 . TherefoAUX1 family genes are auxin influx carriers that mediate the transport of amino-acid signaling molecules. AUX1 family gene restored auxin reactivity and promoted growth [TIR1 family proteins, and binding of auxin to TIR1 can stabilize the interaction between TIR1 and Aux/IAA, thus leading to ubiquitination of Aux/IAA and binding to DNA for regulating auxin-related gene expression [GH3 is an early gene regulated by auxin [GH3 family genes catalyzed free IAA into IAA amino acids and regulated the level of endogenous auxin by regulating the conversion of free and conjugated auxin.An auxin signal transduction pathway between F and S cell lines in Korean pine is shown in d growth . Auxin spression . GH3 is by auxin . GH3 famARF) family genes are auxin response factors. A high concentration of endogenous IAA is conducive to binding with receptors and releasing ARF [ARF gene in the S cell line trended toward downregulation, indicating that the receptor role of TIR1 in auxin signal transduction might be weakened, and the gene expression mode of the F cell line was conducive to the accumulation, transportation, and signal transmission of IAA level. Therefore, it was considered that the level of IAA in the F cell line might be higher than that in the S cell line, and IAA was involved in the regulation of EC proliferative ability in Korean pine. However, there was no significant difference in the expression of auxin-related metabolites between F and S cell lines. By measuring the intracellular hormone content, there was no significant difference in the contents of IAA, ZR, and ABA in F and S cell lines, but IAA/ABA, ZR/ABA and (IAA + ZR)/ABA were significant differences. Therefore, it can be seen that the proportion of growth-promoting hormones in the F cell line was higher than that in the S cell line. The proliferation of Korean pine EC was controlled by the synergistic effect of various hormones in cells.The plant hormone auxin modulates cell proliferation and cell expansion in part by changing gene expression. Primary auxin response genes consist of members of three gene families: Aux/IAA, GH3, and SAUR . The IAAsing ARF . The expFull sibling family cones 1# were collected from a Korean pine seed orchard of Lushuihe Forestry Bureau of Heilongjiang Province 1# on 1 July 2018. The cones were washed with 50-fold dilution detergent for 30 min, rinsed continuously with running water for 8 h, and then placed in an ultra-clean workbench for sterilization with 75% alcohol for 50 min. Then, they were taken out, soaked in 75% alcohol for 1 min, and washed 3–5 times with sterile distilled water.−1), sucrose (30 g·L−1), acid hydrolyzed casein (500 mg·L−1), 1-Naphthylacetic acid (2 mg·L−1), 6-benzylaminopurine (1.5 mg·L−1), and gelrite (4 g·L−1) . Five explants were inoculated into each cultural dish . After adjusting pH to 5.8, we carried out high-temperature and high-pressure sterilization , cooled the cultural medium to 55–60 °C, and added L-glutamine by filtration sterilization (0.22 μm filter). After inoculating, the cultural dishes with explant were cultured in the dark at 23 ± 2 °C.Korean pine megametophyte was used as an explant and put in EC induction medium. The EC induction medium was mLV  basic me−1), 2,4-dichlorophenoxyacetic acid (2.26 μmol·L−1), 6-benzylaminopurine (0.44 μmol·L−1), acid hydrolyzed casein (500 mg·L−1), L-glutamine (500 mg·L−1), and gelrite (4 g·L−1). The EC was sub-cultured every two weeks. The initial inoculation amount of EC was 0.2 g, 3 replicates per cell line, and the cultural conditions were the same as those of induction culture.When the EC cluster grew to a diameter of 1–2 cm, it was transferred to the proliferation medium. The medium was mLV basic medium supplemented with sucrose  with high proliferative potential and 001#–010 , with low proliferative potential. The cell lines F and S were used for cytological observation. The method used was as follows: fresh EC was put on the slide, stained with 0.1% safranine dye for 10 min, and covered with a cover glass. The cover glass was gently tapped to spread the plant tissues evenly. Then, it was immediately observed and recorded under an optical microscope .To explore the mechanism that accounted for the difference in proliferative potential of different embryogenic cell lines of Korean pine, we used two EC cell lines F and S as tested materials, performed transcriptomic and metabolomic analyses, and subsequently identified multiple key genes and metabolites regulating EC proliferative potential. After 14 days of culture, EC was collected and quickly frozen in liquid nitrogen and stored in −80 °C refrigerator for transcriptome sequencing analysis. The samples were taken out from the −80 °C freezer and used to extract RNA with the RNA Extraction Kit . Then, 1 μL of the RNA solution was used to detect the concentration using Nanodrop. RNA integrity was assessed using the Agilent Bioanalyzer (2100 RNA Nano 6000).A total of 1 μg of RNA was used for the construction of sequencing libraries with different index barcodes indicating different samples. Finally, the library fragments were purified using the AMPure XP system and the library quality was assessed on the Agilent Bioanalyzer 2100 system. Indexed/barcoded samples were clustered on the cBot cluster generation system using the TruSeq PE Cluster Kit v3-cBot-HS (Illumia). After cluster generation, library preparations were sequenced on the Illumina Hiseq platform and paired-end reads were generated. The experiments were performed in triplicate.The test data were collated by Excel 2003, and BLAST  software−ΔΔCt calculation method, and the calculation was performed according to the method of Livak [The AAE7  gene of Korean pine was used as the internal control gene and twelve DEGs were validated by qRT-PCR. The primers were synthesized by Beijing biomarker technologies corporation. The details of the primer sequences are shown in of Livak , with 3 ECs grown from the F and S cell lines were placed in a lyophilizer (Scientz-100F) for vacuum freeze-drying. After being fully dried, they were thoroughly ground into powder  with a grinder . A total of 100 mg of powder was dissolved in 1.2 mL of 70% methanol solution for extraction . The extracted mixture centrifuged at 12,000 rpm for 10 min, and the supernatant was filtered through a 0.22 μm filter for UPLC-MS/MS analysis . The experiments were performed in triplicate.p < 0.05, and VIP > 1 and the screening criteria for differential expression of genes with fold difference ≥ 2, FDR ≤ 0.01.The above transcriptome and metabolite identification results were analyzed jointly. Set the criteria: the screening criteria for differential expression of metabolites with folded difference (FC) > 1, The content of GSH was determined by a Nanjing Jiancheng kit . The model of glutathione oxidized (GSSG) content kit was purchased from Nanjing Jiancheng Bioengineering Institute, A 061-1. The calculation method of total glutathione (T-GSH) content was the sum of GSH and GSSG. GSH: GSSG was described as the ratio of GSH to GSSG.The contents of indole-3-acetic acid (IAA), zeatin (ZR) and abscisic acid (ABA) in the F and S cell lines were determined by an enzyme-linked immunosorbent assay (ELISA) kit , and the specific assay method was shown in the kit instruction.−1 GSH  or 0.5 mmol·L−1 BSO , the treatment without GSH and BSO was used as the control (CK), the fresh weight was measured on the 14th day of EC proliferation culture, and the EC proliferative rate was calculated.F and S cell lines EC (0.2 g) were inoculated into the mLV basic medium supplemented with 0.5 mmol·Ln = 5. Graphs were constructed in Sigma Plot 12.0 .Experimental calculations, including proliferation parameters, were performed using Excel 2003 . A one-way analysis of variance (ANOVA) and Duncan’s multiple comparisons tests were performed using SPSS 19 , mean ± se, There were differences in proliferative potential among different embryogenic cell lines of Korean pine. Integrated transcriptome–metabolome analysis shows that glutathione-related genes and metabolites play a positive role in the proliferation of embryogenic callus of Korean pine. Through the analysis results, it was found that the glutathione content in the F (Fast proliferative rate) cell line was significantly higher than that in the S (Slow proliferative rate) cell line. When exogenous glutathione was added to the two cell lines, the proliferation rate of both cell lines was promoted, which further confirmed the results of transcriptome and metabolome. Our research laid a foundation for revealing the regulatory mechanism of woody plant cell proliferation and cell totipotency expression, established the technology to improve woody plant embryogenic callus proliferative ability, optimized the woody plant somatic embryogenesis system, and provided a new ground for revealing the molecular regulatory mechanism of conifer somatic embryogenesis.


Deregulation of tight junction (TJ) proteins and the associated disruption of TJ function has been demonstrated to play a role in the development of endometrial cancer. In the current study, we have shown overexpression of claudin-3 and -4 mRNA (by RT-PCR) and protein (by immunoblotting) in a panel of 9 human endometrial cancer cell lines. To further expand our understanding of the complex role of TJ deregulation in endometrial cancer, we also investigated the expression of 84 TJ and TJ-associated genes  by microarray analysis. Consistent with the claudin-3 and -4 RT-PCR and immunoblot findings described above, we observed overexpression of the claudin-3 and -4 genes by microarray analysis. Further, we observed overexpression of an additional three genes in 8 of the 9 endometrial cancer cell lines: OCLN (occludin), F11R (JAM-A) and TJP3 (ZO-3). OCLN and F11R encode integral membrane proteins whereas TJP3 encodes a cytosolic scaffolding protein that indirectly links membrane TJ proteins to the actin cytoskeleton and cell signaling pathways. Our data suggest that the structural disruption of TJs coupled with the downstream deregulation of signaling pathways involved in cellular proliferation and migration may contribute to the development of endometrial cancer. Endometrial cancer is the most common female reproductive cancer in the United States and so far has accounted for an estimated 12,550 deaths in 2022 Over the last few years, we and others have demonstrated a role for tight junction (TJ) deregulation in the development of endometrial cancer The TJ protein complex consists of the integral membrane proteins: claudins (CLDNs), occludin (OCLN), junctional adhesion molecules (JAMs) and the coxsackievirus and adenovirus receptor (CAR). Investigations into the role of TJ deregulation in endometrial cancer have primarily focused on the expression of CLDNs and OCLN. Indeed, several studies have shown overexpression of CLDNs-3 and -4 in uterine serous papillary carcinoma In the current study we have demonstrated the deregulated expression of CLDNs-3 and -4 in a panel of human endometrial cancer cell lines. In addition, to gain a more global understanding of the role of TJ deregulation in endometrial cancer development we have evaluated those same cell lines for the expression of 84 TJ and TJ-associated genes through microarray analysis.Our study found that all but one of the cell lines showed overexpression of an additional three TJ genes: OCLN, F11R (JAM-A) and TJP3 (ZO-3), suggesting a more complex role for TJ deregulation in endometrial cancer involving proteins that function at different levels within the TJ network from cell to cell adhesion to cell signaling.2 humidified atmosphere at 37oC.The normal endometrial cell line THESCs (CRL-4003) and the endometrial cancer cell lines, AN3-CA (HTB-111), KLE (CRL-1622), MES-SA (CRL-1976), RL95-2 (CRL-1671), and SK-UT-1 (HTB-114) were obtained from the American Type Culture Collection . The endometrial cancer cell lines EFE-184 (ACC-230), MFE-280 (ACC-410), MFE-296 (ACC 419) and MFE-319 (ACC 423) were purchased from Deutsche Sammlung von Mikroorganismen und Zellkulturen DSMZ . The cancer cell lines were cultured in their respective medium as stipulated by ATCC or DSMZ supplemented with 10-20% fetal bovine serum (FBS) , 1% penicillin/streptomycin/2mM-glutamine . The normal endometrial cell line (THESCs) was cultured in phenol-free medium per ATCC instructions supplemented with 1.5 g/l sodium bicarbonate, 1% ITS+ Premix , 500ng/ml puromycin, 10% charcoal/dextran treated fetal bovine serum (CSFBS) . All cell lines were maintained in a 5% COoC for 3 min, sheared and stored at -80°C.Cells were harvested by washing with PBS, followed by treatment with trypsin/EDTA (0.25%/1mM). Protein extracts were obtained by resuspending the cell pellet in Laemmli sample buffer/5% β-mercaptoethanol . The extracts were boiled at 1006 cells/ml) using the Qiagen RNeasy Mini Kit  according to the manufacturer's instructions. RNA extracts were stored at -80°C.RNA was isolated from endometrial cells  according to the manufacturer's instructions, then the signals were visualized using the Invitrogen™ iBright™ FL1500 . As a loading control, membranes were re-probed with 4.0µg/ml mouse monoclonal anti-human GAPDH .Approximately 30 µg of each of the protein extracts was subjected to electrophoresis on precast 10% SDS-polyacrylamide gels  and electrophoretically transferred to Immobilon-P PVDF membranes . The membranes were probed for 1 h at room temperature with 2 μg/ml rabbit polyclonal anti-human claudin-3  or 3 μg/ml mouse monoclonal anti-human claudin-4  primary antibodies in 5% milk/PBS solution. The membranes were washed in PBS for 10 min, PBS/0.1% TWEEN for 5 min (twice) then PBS for 10 min. The membranes were then incubated with 1:3000 HRP-conjugated goat anti-rabbit  or goat anti-mouse  secondary antibodies for 1 h at room temperature then washed as described above. Membranes were incubated with the enhanced chemiluminescence ECL® RT-PCR kit according to the manufacturer's instructions . Briefly, a 20 μl reaction contained 2 μg of RNA, 1X Buffer RT, 5 mM dNTPs, 10 U/ml of RNase inhibitor, and 1 U/ml of reverse transcriptase. The SYBR Green I assay and Applied Biosystems StepOnePlusTM were used to detect RT-PCR products . Each 20 μl RT- PCR reaction contained 0.1 μg of cDNA; 400 nM each of the following primer sequences taken from Pan et al. . The melting curve was measured at 65-95oC with a heating rate of 0.1oC per sec and a continuous fluorescence measurement. Each reaction was performed in triplicate. The relative expression was calculated using the 2 -△△Ct method . Statistical significance was evaluated for each target gene separately using one-way ANOVA and post-hoc Tukey HSD test compared to THESCs (n = 4); *p<0.05; ** p<0.01 was considered to indicate a statistically significant result.Prior to reverse transcription, RNA samples were treated with DNase I . RNA was then reversed transcribed into cDNA using the Q-OMNISCRIPT2 Profiler PCR Array  and the Applied Biosystems StepOnePlusTM (Applied Biosystems), the expression of 84 tight junction genes was determined. In addition to the tight junction genes, the 96-well RT2 profiler microplate contained primers for 5 housekeeping genes, one DNA contamination control, three reverse transcription controls and three positive PCR controls. Briefly, the RNA from endometrial cancer cell lines and the normal endometrial cell line (THESCs) was isolated using the RNeasy Mini Kit  according to the manufacturer's instructions. RNA samples were reverse transcribed into cDNA using the RT2 first strand kit . cDNA was mixed with the RT2 SYBR Green mastermix  and 25 μl of the PCR mix was aliquoted into each well of the RT2 profiler PCR array microplate. The PCR program consisted of an initial denaturation period (10 min at 95oC) followed by 40 cycles of amplification and quantification . The relative expression was based on the expression ratio of each of the TJ genes and was determined using the 2 -△△Ct method as described above .Using a SYBR green-based quantitative real-time PCR Human Tight Junctions RTThe expression of CLDN-3 and -4 proteins in a panel of nine endometrial cancer cell lines was determined by immunoblot analysis Figure B and 2B.To investigate the mechanism of CLDN-3 and -4 protein overexpression, we subjected RNA isolated from the same panel of endometrial cancer cell lines to RT-PCR analysis using primers targeted to the CLDN-3 and -4 genes Figure A and 2A.2 Profiler PCR microarray  using the RTay Table . As expeay Table . In addiIn the current study, we investigated the contribution of deregulated TJ gene expression to endometrial cancer development. Using immunoblot analysis, we demonstrated overexpression of the CLDN-3 and -4 integral membrane proteins in 4 of 9 and 5 of 9 endometrial cancer cell lines, respectively. RT-PCR analysis of the same cell lines indicated increased mRNA transcription as the underlying mechanism for the protein overexpression. In the case of two cell lines, there were inconsistencies between the patterns of CLDN protein and mRNA expression. AN3-CA, showed overexpression of the CLDN-3 protein but no associated overexpression of the CLDN-3 mRNA. This finding could be due to stabilization of the CLDN-3 protein in this cell line. In contrast, the MFE-280 cell line showed overexpression of CLDN-4 mRNA but no associated protein overexpression. It is possible that defective RNA processing may be responsible for this discrepancy.Consistent with our findings, multiple groups have shown deregulated expression  of CLDN-3 and -4 integral membrane proteins To-date, much research has focused on the role of deregulated claudin and occludin proteins in cancer development. However, these integral membrane proteins constitute just one component of the complex network of TJ proteins that contribute to a wide and diverse array of cellular processes. To gain a greater understanding of the role of TJ deregulation in cancer development, we expanded our analysis beyond integral membrane proteins to investigate the expression of genes encoding TJ-associated proteins found in the cytosol and cytoskeleton. Using a TJ microarray, we were able to analyze the expression of 84 different TJ genes. In addition to the expected frequent overexpression of the much studied CLDN-3, CLDN-4 and OCLN genes, we also observed overexpression of an additional 21 TJ genes in at least one of the endometrial cancer cell lines. Notably, a further two genes - F11R (JAM-A) and TJP3 (ZO-3) were overexpressed in 8 of the 9 cell lines. These findings are consistent with a study by Colas et al. (2011) By studying the expression of all of the known TJ and TJ-associated genes in a panel of endometrial cancer cell lines, our microarray analysis indicates a more complex role for TJ deregulation in tumor etiology. Specifically, we have confirmed the role of deregulated CLDN and OCLN genes  and provided strong additional evidence for deregulated F11R (JAM-A) and TJP3 (ZO-3) in the development of endometrial cancer. These data provide potential gene candidates for the development both of diagnostic markers and novel gene-based therapeutic approaches to the treatment of endometrial cancer.


Cutaneous T-cell lymphoma (CTCL) is a severe lymphoid malignancy with a worse prognosis lacking curative treatment regimens. Several gene mutations and deregulated pathways, including NFkB signaling, have been implicated in its pathogenesis. Accordingly, CTCL cell line HUT-78 reportedly contains mutated NFKB2, which is constitutively activated via partial gene deletion, also demonstrating that genomic rearrangements cause driving mutations in this malignancy. Here, along with HUT-78, we analyzed CTCL cell line HH to identify additional aberrations underlying gene deregulation. Karyotyping and genomic profiling of HH showed several rearrangements worthy of detailed investigation. Corresponding to the established karyotype, RNA-seq data and PCR analysis confirmed the presence of t, generating a novel fusion gene, FOXK2::TP63. Furthermore, chromosomal rearrangement t was connected to amplification at 4q24–26, affecting aberrant NFKB1 overexpression thereat. Transcription factor binding-site analysis and knockdown experiments demonstrated that IRF4 contributed to NFKB1 expression. Within the same amplicon, we identified amplification and overexpression of NFkB signaling activator CAMK2D (4q26) and p53-inhibitor UBE2D3 (4q24). Genomic profiling data for HUT-78 detailed a deletion at 10q25 underlying reported NFKB2 activation. Moreover, amplifications of ID1 (20q11) and IKZF2 (2q34) in this cell line drove overexpression of these NK cell differentiation factors and possibly thus formed corresponding lineage characteristics. Target gene analysis for NFKB1 via siRNA-mediated knockdown in HH revealed activation of TP63, MIR155, and NOTCH pathway component RBPJ. Finally, treatment of HH with NFkB inhibitor demonstrated a role for NFkB in supporting proliferation, while usage of inhibitor DAPT showed significant survival effects via the NOTCH pathway. Collectively, our data suggest that NFkB and/or NOTCH inhibitors may represent reasonable treatment options for subsets of CTCL patients. Cutaneous T-cell lymphoma (CTCL) covers a heterogeneous group of lymphomas presenting in the skin. This malignancy becomes manifest in subtypes with distinct histopathologies and clinical features and includes mycosis fungoides, Sézary syndrome, lymphomatoid papulosis, and primary cutaneous anaplastic large cell lymphoma . CTCL isAnalyses of chromosomal and genomic aberrations in CTCL patients and cell lines have shown the presence of translocations, fusion genes, and copy number alterations ,5,6,7,8.Deregulated signaling pathways in CTCL patients include the JAK-STAT pathway, MAP kinase signaling, p53 pathway, and NFkB signaling . The NFkChromosomal aberrations and copy number alterations underlying activation of oncogenes or inhibition of tumor suppressor genes have been frequently described in CTCL ,4,5,6,7.Cell lines HH, JURKAT, NK-92, KHYG-1, and DERL-2 were obtained from the DSMZ , HUT-78 from the ATCC , and the cell lines MAC-1 and MAC-2A kindly provided by Marshall E. Kadin . Cell culture conditions and backgrounds of each cell line are described elsewhere . Cell li6 cells by electroporation using the EPI-2500 impulse generator  at 350 V for 10 ms. Transfected cells were harvested after 20 h cultivation. Transfection experiments were performed twice, generating similar results.Gene-specific siRNA oligonucleotides and AllStars negative control siRNA (siCTR) were purchased from Qiagen . SiRNAs (80 pmol) were transfected into 1 × 10Additional cell treatments were performed with 14 µM NFKB inhibitor  or with 10 µM DAPT -L-alanyl]-S-phenylglycin-tert-butylester; Sigma), both dissolved in dimethyl sulfoxide (DMSO). Treated cells were functionally analyzed with the IncuCyte S3 Live-Cell Analysis System . This system allows continuous documentation of living cells within the incubator. Subsequent analysis of the microscopical images using the associated software allows adapted examination of the data, graphical presentation, and statistical calculations. For detection of apoptotic cells, we additionally used the IncuCyte Caspase-3/7 Green Apoptosis Assay diluted at 1:2000 . This assay stains apoptotic cells detectable by integrated fluorescence microscopy. Thus, proliferating and apoptotic cells are detectable at once. Live-cell imaging experiments were performed twice with four-fold parallel tests.Karyotyping, spectral karyotyping (SKY), and fluorescence in situ hybridization (FISH) were performed as described previously . BAC cloFor genomic profiling, the genomic DNA of CTCL cell lines was prepared by the Qiagen Gentra Puregene Kit (Qiagen). Labeling, hybridizatin, and scanning of HD Cytoscan arrays were performed at the Genome Analytics Facility of the Helmholtz Centre for Infection Research , using HD arrays according to the manufacturer´s protocols . Data were visualized and interpreted using the Chromosome Analysis Suite software version 2.0.1.2 (Affymetrix).www.ebi.ac.uk/ena; accessed on 1 March 2022) using data set E-MTAB-7721 [https://github.com/pinin4fjords/shinyngs; accessed on 1 March 2022) for bar plots and Heatmapper for heatmaps [www.proteinatlas.org; accessed on 1 March 2022) [Transcriptome data from 100 leukemia/lymphoma cell lines (LL-100) were obtained from the European Nucleotide Archive  .https://www.ncbi.nlm.nih.gov/gds/; accessed on 1 March 2022) as reported previously [Expression profiling data sets from cell lines were generated by Dr. Robert Geffers  using HG U133 Plus 2.0 gene chips  and obtained from GEO . cDNA was synthesized by random priming from 5 µg RNA using Superscript II (Invitrogen).https://blast.ncbi.nlm.nih.gov/Blast.cgi; accessed on 2 March 2022).To analyze fusion genes in cell lines, we used the following oligonucleotides: FOXK2-for 5′-GGTCTTCAGGGTACAAGGTGG-3′, FOXK2-rev 5′-GCCCTTGTCCGCAGTCCTGTAG-3′, TP63-for1 5′-TGAACCATCAGAAGATGGTGCG-3′, TP63-rev1 5′-GCATCGAAGGTGGAGCTGGGC-3′, TBL1XR1-for 5′-GAAAGCCATATCAGTCAGTCC-3′, TBL1XR1-rev 5′-TCTATCAGGGACAGAGACTCT-3′, TP63-for2 5′-GTTCAGCCCATTGACTTGAAC-3′, TP63-rev2 5′-CTGCTGGTCCATGCTGTTC-3′. To analyze a described mutation in NFKB1, we amplified the according gene fragment using cDNA as a template and oligonucleotides NFKB1-for 5′-CTTCAGAATGGCAGAAGATGATCC-3′ and NFKB1-rev 5′-CAGGTAGTCCACCATGGGATG-3′. PCR products were generated using taqpol (Qiagen) and thermocycler TGradient , analyzed by gel electrophoresis and documented with the Azure c200 Gel Imaging System . Oligonucleotides were obtained from Eurofins MWG . PCR products were cloned into pCR4-TOPO (Invitrogene) and sequenced at Eurofins MWG. The sequences were analyzed using nucleotide BLAST  PCR analysis was performed with the 7500 Real-time System, using commercial buffer and primer sets . For normalization of expression levels, we analyzed the transcript of TATA box binding protein (TBP). We used the ddCT method, and the obtained values are indicated as fold expressions in relation to one selected sample that was set to 1. For quantification of genomic copy numbers for ID1, we used the following oligonucleotides: ID1-1 5′-CTACGACATGAACGGCTGTTACTC-3′ and ID1-2 5′-TTCGGATTCCGAGTTCAGCTCC-3′. The MEF2C locus was used as a control: MEF2C-1 5′-AGAAGGCTTATGAGCTGAGC-3′ and MEF2C-2 5′-AGACTGGCATCTCGAAGTTG-3′. Genomic DNA was prepared as described above.t-test and derived p-values indicated by asterisks .Quantitative PCR analyses were performed in triplicate. Standard deviations are calculated for each experiment and presented in the figures as error bars. Statistical significance was assessed by Western blots were generated by the semi-dry method, and protein lysates from cell lines were prepared using SIGMAFast protease inhibitor cocktail (Sigma). Proteins were transferred onto nitrocellulose membranes  and blocked with 5% dry milk powder dissolved in phosphate-buffered saline buffer (PBS). The following antibodies were used: alpha-Tubulin (Sigma), NFKB1 , and NFKB2 . The used antibodies were diluted as follows: alpha-Tubulin (TUBA) 1:1000, NFKB1 1:1000, NFKB2 1:500. For loading controls, blots were reversibly stained with Poinceau (Sigma), and detection of TUBA was performed thereafter. Secondary antibodies were linked to peroxidase for detection by Western-Lightning-ECL . Western blot analyses were performed twice. Documentation was performed using the digital system ChemoStar Imager .Chromosomal and genomic aberrations may result in the deregulation of specific genes contributing to the pathogenesis of hematopoietic malignancies. To identify novel oncogenes that may potentially serve as therapeutic targets in CTCL, we used cell line HH as a model and searched for chromosomal aberrations by performing classical karyotyping, SKY, and FISH analyses A. The coFusion genes form a special class of oncogenes with proven pathological and diagnostic potential. To check if cytogenetic aberrations in cell line HH generated fusion genes, we analyzed our published LL-100 RNA-seq data set bioinformatically. This procedure revealed a potential fusion between FOXK2 at 17q25 and TP63 at 3q28, highlighted by the cytogenetic data showing explicit der(17) partners of the semi-cryptic t. According to the RNA-seq data and verified by RT-PCR and Sanger sequencing analyses, we observed the expression of a FOXK2::TP63 fusion transcript in HH, joining FOXK2 exon 4 and TP63 exon 4 B. In addInterestingly, T-cell large granular lymphocyte leukemia (T-LGL) cell line MOTN-1 also showed elevated TP63 levels . AccordiWe also performed genomic profiling of CTCL cell lines HH and HUT-78, revealing copy number alterations that may underlie specific gene deregulations. The results for all chromosomes are shown in To investigate transcriptional consequences of the genomic amplification at 4q24–26, we analyzed LL-100 RNA-seq data of HH in comparison to 11 leukemia/lymphoma control cell lines comprising DERL-7, MOTN-1 (T-cell lymphoma), CCRF-CEM, JURKAT, RPMI-8402, MOLT-4, HPB-ALL, DND-41 (T-cell leukemia), and YT, NK-92, KHYG-1 (NK-cell leukemia/lymphoma). We assigned 49 amplified genes and illustrated their activities in a heatmap A. For HHExpression data for NFKB1 using the complete LL-100 RNA-seq data set and RQ-PCR analysis of NFKB1 in CTCL and T-ALL cell lines showed high transcript levels exclusive to HH B,C. ReceFinally, Western blot analysis of NFKB1 and NFKB2 in CTCL and control cell lines showed results corresponding to the RNA data. Accordingly, NFKB1 was highly expressed in HH, while NFKB2 was elevated in all analyzed CTCL cell lines, including HH, HUT-78, MAC-1, and MAC-2A C. Taken www.genome.cse.ucsc.edu/index.html; accessed on 1 March 2022). This exercise revealed several IRF sites . Fusion genes containing TP63 have been reported in various T-cell malignancies, including CTCL ,12. ThesAdditionally, we detected the fusion gene TBLXR1::TP63 in cell line MOTN-1. Both FOXK2 and TBLXR1 are ubiquitously expressed in hematopoietic cell lines. In contrast, TP63 is expressed in selected cell lines at various levels. Thus, wild-type-configured TP63 is expressed in HH but not in MOTN-1. However, in both cell lines, the level of TP63 transcript was enhanced, possibly due to the fusion to FOXK2 or TBLXR1, respectively.Chromosomal rearrangement t in HH was intimately connected with the complex amplification at 4q24–26, targeting the NFKB1 locus. NFKB1 and NFKB2 are functionally related and encode TFs, which execute gene regulation via the NFkB signaling pathway. This pathway plays an oncogenic role in several hematopoietic malignancies, including CTCL ,49. Our Target gene analysis of NFKB1 revealed transcriptional activation of MIR155 and RBPJ. MIR155 encodes the micro-RNA miR155, which affects the suppression of specific genes and operates as an oncogene in several hematopoietic malignancies, including CTCL ,34. The RBPJ encodes a TF that interacts with the activated intracellular NOTCH receptor to regulate NOTCH target genes. The aberrantly activated NOTCH pathway plays an oncogenic role in T-cell malignancies, including CTCL ,36,37. OThe amplicon at 4q24–26 also contained the genes CAMK2D and UBE2D3, which are involved in NFkB signaling and p53 degradation, respectively. CAMK2D encodes a protein kinase that binds and inhibits the repressor-protein IKKB, resulting in activation of NFkB signaling . DownregFinally, we identified enhanced expression of ID1 and IKZF2 in CTCL cell line HUT-78. These TFs are involved in NK-cell differentiation and may, thus, shift the phenotype of HUT-78 toward NK-cells as indicated by cluster analysis ,41. CTCLWe have identified widespread genomic alterations in CTCL cell line HH, separately generating the fusion gene FOXK2::TP63, overexpression of NFKB1, and three different modes of p53 suppression. Our data highlight the pathogenic impact of NFkB and NOTCH signaling in CTCL, recommending therapeutic targeting of these pathways. CTCL cell lines HH and HUT-78 differ in mode and type of aberrantly activated NFkB factors and may, thus, serve to develop adjusted treatment protocols for affected patient subsets. However, additional analyses are required to support our findings in patients.


H-3,6-diazaphenothiazine (DPT-1) and pentacyclic 7-(3′-dimethylaminopropyl)diquinothiazine (DPT-2), have recently been shown to exhibit promising anticancer activities in vitro. In this report, we demonstrated that DPT-1 and DPT-2 could be pro-apoptotic agents in lung carcinoma, the human lung carcinoma A549 and non-small lung carcinoma H1299, in the range of IC50 = 1.52–12.89 µM, with a protective potential to healthy cell lines BEAS-2B and NHDF. The compounds showed higher activity in the range of the tested concentrations and low cytotoxicity in relation to normal healthy cells than doxorubicin, used as the reference drug. The cytostatic potential of DPT-1 and DPT-2 was demonstrated with the use of MTT assay. Cell cycle analysis via flow cytometry using Annexin-V assay showed the pro-apoptotic and pro-necrotic role of the studied diazaphenothiazines in the cell cycle. DPT-1 and DPT-2 initiated a biological response in the investigated cancer models with a different mechanism and at a different rate. Based on these findings, it can be concluded that DPT-1 and DPT-2 have potential as chemotherapeutic agents.The phenothiazine derivatives, tricyclic 10 Worldwide, lung cancer has been the most common diagnosed carcinoma for the last several decades. It is the world’s leading cause of cancer death ,2. This The heterocyclic ring system plays an important role in the development of novel scaffolds with improved pharmaceutical properties in anticancer research . TricyclDPT) series, two derivatives deserve special attention: tricyclic 10H-3,6-diazaphenothiazine   and pentacyclic 7-(3′-dimethylaminopropyl)diquinothiazine   exhibited an extremely strong action against the glioblastoma SNB-19, melanoma C-32, and breast cancer MCF-7 cell lines with IC50 values of 0.46 and 0.72 μg/mL and non-toxic action against the normal fibroblast HFF-1 cell line   ,27,29.5 in 2 mL of completed fresh DMEM-F12 medium, 24 h before drugs addition. An Aria III flow cytometer  was used. The flow cytometry analysis was done on a free Flowing Software 2.5.1 program  [Many drugs work by inhibiting cell proliferation and causing cell cycle blockage. Using flow cytometry, the impact of new compounds on the cell cycle were determined. Cells incubated with compound were also analyzed for cellular death pathways, apoptosis induction, followed by Annexin-V assay. Annexin-V is a protein that binds phosphatidylserine and allows early detection of apoptosis; additionally, iodium propide staining distinguishes it from necrosis. For apoptosis and cell cycle assays the cell cultures were plated in 6-well plates at a confluence of 1 × 10Finland) ,27,29.Microscopic observation and images acquisition with Live Cell Analyzer  was used to directly observe on-plate confluence, density, and cell viability and images acquisitions ,31.t-test, and a p-value < 0.05 is indicated with a star [The results are expressed as means ± SD from three independent experiments. Results were analyzed in MS Excel 2010. Statistical significance was calculated with a h a star ,29,30,31In silico analyses of the molecular descriptor and parameters of Lipinski’s, Ghose’s and Veber’s rules and an ADME profile were carried out using a Swiss internet server SwissADME . PredictH-3,6-diazaphenothiazine  and pentacyclic 7-(3′-dimethylaminopropyl)diquinothiazine , demonstrating significant in vitro anticancer activity against lung cancer cell lines, the human lung carcinoma A549 and non-small lung carcinoma H1299, and protective potential to healthy cell lines, BEAS-2B and NHDF. The reference compound in the conducted studies was doxorubicin. Using a 72 h MTT viability assay (Promega) we observed a strong cytotoxic activity of DPT-1 and DPT-2.In this publication, we presented two synthetic diazaphenothiazines, tricyclic 1050 doses against A549 or H1299 cells confirms promising selectivity against cancer. The analysis of the cell cycle revealed different pathways of the mechanism of anticancer activity induced by the studied diazaphenothiazines. DPT-1 activated the process of apoptosis, in contrast to DPT-2, which activated the necrosis phase. Both tested compounds showed selectivity of action. Preliminary in silico analyses of ADME parameters and the biological profile confirmed the validity of our research.Both compounds appear to be good candidates for in vitro tests, especially in 3D cultures, where a mixture of sensitive healthy cell lines is used together with chemoresistant cancer cell lines. A low lethal effect observed in NHDF or BEAS-2B cells at ICDPT-1 and DPT-2 possess potential as chemotherapeutic agents. Further advanced in vivo and enzymatic studies are planned.Based on these primary findings, it can be concluded that


Though immune checkpoint inhibition has recently shown encouraging clinical efficacy in mesothelioma, most patients do not respond. Combining immune checkpoint inhibition with radiotherapy presents an attractive option for improving treatment responses owing to the various immunomodulatory effects of radiation on tumors. However, the ideal dosing and scheduling of combined treatment remains elusive, as it is poorly studied in mesothelioma. The present study characterizes the dose- and time-dependent changes to expression of various immune markers and cytokines important to antitumor responses following irradiation of mesothelioma cell lines.Two murine  and two human  mesothelioma cell lines were treated with titrated gamma-radiation doses (1-8 Gy) and the expression of MHC class-I, MHC class-II and PD-L1 was measured over a series of post-irradiation timepoints (1-72 hours) by flow cytometry. Levels of cytokines IL-1α, IL-1β, IL-6, IL-10, IL-12p70, IL-17A, IL-23, IL-27, MCP-1, IFN-β, IFN-γ, TNF-α, and GM-CSF were measured by multiplex immunoassay in murine cell lines following 8 Gy radiation.Following irradiation, a dose-dependent upregulation of MHC-I and PD-L1 was observed on three of the four cell lines studied to varying extents. For all cell lines, the increase in marker expression was most pronounced 72 hours after radiation. At this timepoint, increases in levels of cytokines IFN-β, MCP-1 and IL-6 were observed following irradiation with 8 Gy in AB1 but not AE17, reflecting patterns in marker expression.Overall, this study establishes the dose- and time-dependent changes in immune marker expression of commonly studied mesothelioma cell lines following radiation and will inform future study into optimal dosing and scheduling of combined radiotherapy and immune checkpoint inhibition for mesothelioma. Mesothelioma is an aggressive, incurable cancer in urgent need of more effective therapies. Mesothelioma may develop in any mesothelial surface of the body, but most commonly it arises in the lung pleura following inhalation of asbestos, which acts as a carcinogen . The lonImmune checkpoints such as cytotoxic T-lymphocyte-associated protein-4 (CTLA-4), and the programmed death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) signaling axis are co-inhibitory pathways that physiologically prevent inappropriate activation of immune responses, but are often upregulated by cancers to prevent or dampen T-cell activation and thus suppress antitumor activity . HoweverThe administration of other treatment modalities in conjunction with ICI, such as radiotherapy (RT), may increase both the rate and durability of responses. In mesothelioma, RT is used routinely during surgical resection as well as palliatively to assist in symptoms of pain and dyspnoea . Though Preclinical studies in some cancers have shown improved efficacy of combined RT and ICI over either treatment alone –20, and Murine mesothelioma cell lines AB1 and AE17 were generated as previously described . Human mMurine mesothelioma cell lines were treated with 10 ng/mL recombinant mouse interferon-gamma (IFN-γ)  and incubated for 48 h to induce MHC-I/II and PD-L1 expression. Similarly, human mesothelioma cell lines were subject to a 48 h stimulation with 100 ng/mL recombinant human IFN-γ .6 cells/mL. Cells were irradiated in separate 50 mL tubes by a caesium-137 source at a dose rate of 3.78 Gy/min  at room temperature with doses of 1, 2, 4, 6, and 8 Gy. As the actual radiation dose administered may vary by ± 15% depending on position in the rotating irradiation cannister, tubes were positioned centrally for all irradiations. Immediately after radiation 1 × 106 cells were seeded into separate T175 culture flasks for each timepoint. Cells were then incubated for 1, 6, 24, 48 or 72 h post-irradiation. At the completion of each timepoint, 0.5 × 106 cells were collected per dose and cryopreserved at -80°C until analysis. For cytokine studies, cells were irradiated at 8 Gy and incubated for 72 h, after which cell culture supernatant was collected, centrifuged  to remove debris, and stored at -80°C until analysis. All irradiations were conducted in triplicate.At 80% confluency, cells in culture were harvested and resuspended in R10 at 1 × 10Surface expression of MHC-I, MHC-II and PD-L1 was assessed on irradiated cell lines by flow cytometry. Frozen samples were thawed in a 37°C water bath for one minute, then transferred to fresh 15 mL tubes. Cells were washed in 10 mL R10 by centrifugation . Each sample was resuspended in 200 µl R10, transferred to a 96 well plate, and washed by centrifugation . Samples were washed twice with 200 µL/well PBS . Fixable Viability Dye eFluorTM 506 (eF506) diluted 1/1600 in 20 µL PBS was added to the appropriate wells, and samples were incubated for 20 minutes in the dark at room temperature. Samples were then washed twice in 200 µL flow buffer (PBS/2% NCS/5mM EDTA)/well. Samples were stained with 20 µL of appropriate antibody cocktail . Gating was optimized using FMO controls for each marker  and FACSDiva software (BD Biosciences), and a minimum of 10,000 live events were recorded per sample. Data were analyzed using FlowJo software, version 10.8.0  to generate values for the percentage of cells positive for MHC-I, MHC-II and PD-L1 expression and median fluorescence intensity (MFI). Normalized MFI (nef) was generated by dividing the MFI of each sample with MFI of an unstained control. All radiation doses and timepoints within any individual irradiation experimental repeat were stained and data acquired in the same session.Levels of inflammatory cytokines  were measured in murine mesothelioma cell lines using the LEGENDplex™ 13-plex Mouse Inflammation Panel with Filter Plate . The assay was performed according to manufacturer’s instructions. Standards and samples were assayed in duplicate. Samples were acquired with the FACSCantoII and FACSDiva software (BD Biosciences). Standard curves and cytokine data were generated using the free cloud-based LEGENDplex™ Data Analysis Software Suite (Biolegend).n = 3 independent experimental repeats in all figures. P < 0.05 was considered statistically significant.Data were analyzed and visualized using GraphPad Prism software (version 9.1.2). Immune marker expression was analyzed by two-way analysis of variance (ANOVA), followed by multiple pairwise comparisons with Tukey adjustment. The non-parametric Mann-Whitney U test was used to compare concentrations of a given cytokine in un-irradiated (0 Gy) versus irradiated (8 Gy) samples. Results are presented as mean ± one standard deviation of Prior to irradiation, we first assessed our mesothelioma cell lines for surface expression of three markers commonly found on tumor cells, and of particular relevance to cancer immunotherapy: MHC-I, MHC-II and PD-L1. These were present at varying levels of expression at baseline Figure 1We previously showed that effects of radiation on cell proliferation and survival saturate at doses of 8 Gy in the mesothelioma cell lines studied ; therefoA dose-dependent upregulation of surface MHC-I on both murine mesothelioma cell lines was observed following irradiation , and on the other hand constitutive elevation of PD-L1 to dampen the activity of tumor-specific T cells (as in AB1). Previous preclinical studies in other cancers including hepatocellular carcinoma , pancreain vitro for this cell line. In contrast, both MHC-I and PD-L1 were constitutively expressed by JU77 cells and could be further upregulated by IFN-γ, but expression was unchanged by any dose of radiation administered. It is possible that radiation treatment may have resulted in other immune-related phenotypic or transcriptional changes in this cell line that were beyond the scope of this study.We also sought to compare murine mesothelioma cell lines to human cell lines. Interestingly, though MHC-I was initially poorly expressed by the BYE cell line, radiation strongly induced MHC-I expression in a dose-dependent manner. MHC-I expression was saturated at 6 Gy, showing that such effects can be achieved in humans with clinically used radiation doses. Interestingly, IFN-γ treatment did not induce MHC-I expression on BYE, suggesting an IFNγ-independent mechanism of upregulation Optimizing the timing of delivering ICI with respect to RT is essential to improve antitumor immune responses. However, few studies have measured the dynamic changes to marker expression in response to RT; the present study therefore assessed expression at early (1-6 h) and late (24-72 h) stages post-irradiation. Importantly, we show that where modulation of immune markers is observed, this occurs maximally and, in most cases, solely at the 72 h timepoint, regardless of the level of radiation. This differs from a study in a melanoma cell line, where increased expression of cells irradiated with 10 and 25 Gy was apparent as early as 18 h after radiation; this may have been due to the higher doses administered in this study . Our finin vivo.We hypothesized that observed increases in MHC-I and PD-L1 would associate with changes in the profile of inflammatory cytokines secreted by our cell lines. Although increased levels of MCP-1, IL-6 and IFN-β in AB1 following irradiation with 8 Gy were observed, this did not reach the set level of significance for this study. Interestingly however, the lack of change to these cytokines in AE17 corresponds with the relatively lower increases in MHC-I and PD-L1 expression observed. The increase in MCP-1 observed in AB1 is consistent with a study in a breast carcinoma cell line, where irradiation with 9 and 23 Gy significantly increased levels of this cytokine. Furthermore, IL-6 –35 and Iin vitro, only single doses were administered in this study. Fractionation aims to affect a greater proportion of proliferating tumor cells by delivering multiple low doses at different times, while minimizing toxicity to healthy cells. Future work should establish whether different levels of fractionation result in more pronounced or prolonged antitumor effects compared to single doses. Nevertheless, the studied doses reflect those used in conventional fractionation (2 Gy), as well as hypo-fractionated (> 2 Gy) or hyper-fractionated (< 2 Gy) schedules.One limitation of this study relates to our use of single-dose RT. While fractionated doses of RT are commonly used in the clinic, due to difficulties of studying fractionation in vivo. Another avenue for study is the search for biomarkers to predict likelihood of response to RT and ICI, and dynamic changes to tumor or immune cell expression following radiation or ICI may be one such biomarker  (APP1163065) and the National Health and Medical Research Council (NHMRC) Centres of Research Excellence Scheme (APP1197652 and APP1107043). This material is the responsibility of the authors and does not reflect the views of these funding bodies.The authors acknowledge the facilities, and the scientific and technical assistance of the Centre for Microscopy, Characterization & Analysis, The University of Western Australia, a facility funded by the University, State and Commonwealth Governments. We thank staff managing the NCARD biobank for access to human cell lines in this study. We also thank Dr Tamara Abel, Dr Blake Klyen and Dr Don Heng at the Telethon Kids Institute for training and assistance with the gamma irradiator.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Despite decades of clinical use and detailed understandings of their mechanisms of action, clinically useful predictive biomarkers for approved microtubule targeting agents such as eribulin, paclitaxel and vinorelbine have eluded development. Our results now provide the basis for gene expression-based, drug-specific predictive biomarkers for eribulin and vinorelbine, as well as deeper understandings of the molecular pathways associated with eribulin and vinorelbine response.p-values and drug overlaps, yielding 52, 29 and 80 genes uniquely associated with eribulin, paclitaxel and vinorelbine, respectively. Further restriction to minimum 2-fold ratios followed by reintroducing data from the middle two quartiles identified 9 and 13 drug-specific unique fingerprint genes for eribulin and vinorelbine, respectively; surprisingly, no gene met all criteria for paclitaxel. Interactome and Reactome pathway analyses showed that unique fingerprint genes of both drugs were primarily associated with cellular signaling, not microtubule-related pathways, although considerable differences existed in individual pathways identified. Finally, four-gene  and five-gene  multivariate regression models for eribulin and vinorelbine showed high statistical correlation with drug-specific responses across the 100 cell lines and accurately calculated predicted mean IC50s for the most and least sensitive cell line quartiles as surrogates for responders and nonresponders, respectively. Collectively, these results provide a foundation for developing drug-specific predictive biomarkers for eribulin and vinorelbine.Eribulin, a natural product-based microtubule targeting agent with cytotoxic and noncytotoxic mechanisms, is FDA approved for certain patients with advanced breast cancer and liposarcoma. To investigate the feasibility of developing drug-specific predictive biomarkers, we quantified antiproliferative activities of eribulin versus paclitaxel and vinorelbine against 100 human cancer cell lines from the Cancer Cell Line Encyclopedia, and correlated results with publicly available databases to identify genes and pathways associated with eribulin response, either uniquely or shared with paclitaxel or vinorelbine. Mean expression ratios of 11,985 genes between the most and least sensitive cell line quartiles were sorted by  Microtubule targeting agents (MTAs), including eribulin, several taxanes, vinca alkaloids, and at least one epothilone, are important clinical anticancer drugs for several cancer types ,2,3,4. DEribulin, a synthetic analog of the marine sponge natural product halichondrin B , is an M®. Paclitaxel and vinorelbine tartrate  were obtained as dry powders from Selleckchem . Eribulin, paclitaxel and vinorelbine were prepared as 10 mM stock solutions in 100% (v/v) DMSO, aliquoted into small volumes and stored at −20 °C until day of use. Although not a designated test agent for this study, cisplatin was included in all assays as an internal reference control for assay performance; cisplatin was obtained as a laboratory grade liquid formulation from Hospira Australia Pty Ltd. , with storage per manufacturer’s instructions and dilutions for cell culture studies on day of use.Eribulin mesylate  was supplied by Eisai Inc.  as laboratory-grade dry powder active pharmaceutical ingredient (API) obtained from the same manufacturing stream used for Eisai’s branded clinical product, HalavenTM in vitro cell line screening service (see One hundred established human cancer cell lines were selected based on inclusion in both the CCLE and Crown Bioscience’s OmniPanelvice see . A list TM in vitro cell line screening service in their Beijing (China) laboratories. All OmniPanelTM cell lines are routinely tested for mycoplasma and authenticated using short tandem repeat (STR) DNA profiling. Testing was conducted using the CellTiter-Glo® Luminescent Cell Viability Assay  following 72 h compound exposures in 96-well plate assay formats. Compounds were added 24 h after cell seeding into plates, with initial seeding densities for individual cell lines having been previously optimized for OmniPanelTM screening. Based on the authors’ prior experience with the test agents, assays employed 9-step half-log test concentrations of 30 pM–300 nM for eribulin and 100 pM–1 μM for paclitaxel and vinorelbine. Concentrations of test agents inhibiting 50% of viable cell densities in vehicle-treated control wells compared to wells with the highest drug concentrations were defined as IC50s and were determined using GraphPad Prism software . Dose–response curve fitting used a nonlinear regression model with sigmoidal dose response, fixed 100% top y-axis values defined by vehicle and floating bottom y-axis values at either the highest concentration tested or another concentration that resulted in a minimum y-axis reading, ensuring that calculated IC50s represent actual antiproliferative biological responses occurring during the 72 h treatment period without plateau artifacts based on different growth rates of different cell lines.Measurement of antiproliferative activities of test agents against the selected 100 CCLE cell lines was performed by Crown Bioscience under contract from Eisai Inc., using Crown’s OmniPanelIC50s for 5 cell lines for eribulin  and 1 cell line for paclitaxel (HCT-15) exceeded the 300 nM and 1000 nM top concentrations used for these 2 drugs, respectively, so surrogate values of 301 nM eribulin and 1001 nM paclitaxel were assigned for purposes of defining cell lines in the bottom (least sensitive) quartile and calculating means/medians of bottom quartiles. Although using such surrogates does not change assignment of bottom quartiles or median IC50 values, it probably results in minor underestimations of mean bottom quartile IC50s, SDs and SEMs for eribulin and paclitaxel, although such underestimations are likely to be small due to the large overall number of cell lines tested.https://depmap.org/portal/ accessed on 3 August 2021; release version “DepMap Public 18Q2”, 2 May 2018). DepMap data using the symbol SEPT10 were replaced with the currently accepted symbol SEPTIN10. Expression values are log2 gene-level reads per kilobase million (RPKM) derived from RNA Sequencing (RNA-Seq), aligned using TopHat version 1.4 and quantified using the pipeline developed for the Genotype-Tissue Expression (GTEx) project as described by Tsherniak et al. | ≥ 1) and (iii) after reintroducing data from the middle two cell line quartiles, retaining only those genes with statistical significance (p < 0.05) for the cognate drug in full linear regression analysis with all 100 cell lines, but lacking statistical significance for both noncognate drugs. Genes meeting all the above criteria for a given drug were designated as unique fingerprint genes or UFGs for that drug. No gene met all criteria for paclitaxel; thus, no paclitaxel UFGs were identified in this study.Ratios of mean expression levels for each gene between the most and least sensitive cell line quartiles  for each drug were calculated. Genes with top/bottom quartile ratios with ® Cloud  ≥ 1). Following this restriction, data from the middle two cell line quartiles were then reintroduced, and each gene in the three subsets was subjected to full linear regression analysis including expression data and IC50s from all 100 cell lines. Only genes with expression showing statistical significance for a given cognate drug but lacking statistical significance for the other two noncognate drugs were designated as UFGs for each drug. This selection process yielded 9 UFGs for eribulin and 13 UFGs for vinorelbine  not utilizing gene expression information from the 50 cell lines in the middle two quartiles, (ii) accepting a risk that small numbers of cell lines in the top and bottom quartiles might have outlier expression patterns that could disproportionately influence gene selection, and (iii) failing to provide a basis for identifying drug-specific UFGs based on the informational power available from all 100 cell lines. To address these limitations, a final curation of the 52, 29 and 80 ectively F,G, was orelbine . UnexpecC5ORF38 and GPR157, were not themselves captured by network propagation. Interestingly, despite sharing mechanistic similarity as MTAs, only four genes overlapped between the eribulin and vinorelbine 100-gene propagated networks . We speculate that this may relate to the fact that the original UFGs were selected, in part, based on lack of correlations to noncognate drugs.We next asked what mechanisms and pathways were associated with eribulin and vinorelbine UFGs. To this end, network propagation  analysesTo further elucidate the pathways associated with the eribulin and vinorelbine UFG interactomes, the eribulin and vinorelbine 100-gene propagated networks were used as query inputs for Reactome pathway enrichment analyses. The resulting Reactome maps indicate that most pathways for both drugs fall into three main Reactome groupings or ‘islands’: the Signal Transduction, Immune System and Cell Cycle islands C,D. Whilp < 0.05 significance plus q < 0.1 to account for FDR, 26 and 122 Reactome pathways were identified for eribulin and vinorelbine UFG interactomes, respectively  by first calculating each cell line’s predicted IC50 based on individual expression of each of the 9 eribulin or 13 vinorelbine UFGs for that cell line, averaging the resulting 9 or 13 predicted IC50s for each cell line, then comparing the averaged predicted IC50s to actual measured IC50s for each cell line for all three drugs. Although no gene met the criteria for assignment as a paclitaxel UFG, predicted IC50s based on expression of eribulin or vinorelbine UFGs are conceptually drug agnostic, so paclitaxel was included as a drug specificity comparator. Not surprisingly, the resulting MVR models built from all 9 or 13 eribulin or vinorelbine UFGs, respectively, successfully predicted cognate drug IC50s with high statistical significance . Unexpec2 values) yielded a four-gene eribulin MVR model  and a five-gene vinorelbine MVR model ; predicted IC50s from both models correlated with measured IC50s across all 100 cell lines with high statistical significance for cognate but not noncognate drugs  whose expression is uniquely associated with response to eribulin versus the other two drugs, with four eribulin UFGs being sufficient for a multigene panel that accurately predicts high versus low eribulin response. Separately, 13 UFGs were identified as uniquely associated with vinorelbine response, with 5 of these being sufficient for a predictive vinorelbine multigene panel. Notably, no genes uniquely associated with paclitaxel response were identified using the same stringent criteria used to identify eribulin and vinorelbine UFGs.In addition to its cytotoxic antimitotic activity ,6,7, eri−7 to 10−6 M range for all three drugs, eribulin’s steeper slope resulted primarily from increased responses in its most sensitive cell lines, which collectively dropped into the sub-nM IC50 range. In addition, several nonoverlapping cell lines existed for each drug in their respective top and bottom quartiles, suggesting that aggregating response data across large numbers of cell lines might yield information on drug-specific pathways and mechanisms, a concept first established by the well-known NCI60 cell line screen . Coincidentally, the NCI60 screen was first used to predict a tubulin-based mechanism for eribulin’s natural product parent, halichondrin B [Responses of 100 CCLE human cancer cell lines to eribulin and comparator MTAs paclitaxel and vinorelbine were first quantified by IC50 values and ordered by response. While sensitivities across the 100 cell lines trended in the same direction for the three drugs, the range of eribulin’s response from most to least sensitive was considerably greater than the other two drugs, reflected by a much steeper IC50 slope for eribulin compared to the shallower and parallel slopes for paclitaxel and vinorelbine. Importantly, while IC50s for the least sensitive cell lines were in the same 10ondrin B ,25. We sp-value and gene expression ratios between top and bottom quartiles provided the first indications that the three drugs could be distinguished from each other for biomarker development. Notably, at p < 0.0025 stringency, 52 and 80 genes were unique to eribulin and vinorelbine, respectively, with no genes shared between eribulin and paclitaxel, and only nine genes shared between eribulin and vinorelbine. At the same stringency, vinorelbine and paclitaxel shared only one gene. Thus, paclitaxel, a microtubule stabilizer, showed the least similarity with eribulin and vinorelbine, both microtubule destabilizers, yet even eribulin and vinorelbine could be distinguished from each other based on many more genes uniquely associated with each drug compared to shared genes between them.Using top and bottom quartiles as surrogates for responders and nonresponders, respectively, IC50s were correlated with DepMap gene expression data for 11,985 genes. Z-score heat mapping and filtering on p < 0.0025 genes to include only those with at least 2-fold expression difference between top and bottom quartiles, followed by reintroduction of data from the 50 cell lines in the middle two quartiles, resulted in identification of 9 and 13 UFGs for eribulin and vinorelbine, respectively. Interestingly, no gene met these final UFG criteria for paclitaxel. Notably, seven of the nine eribulin UFGs were upregulated with increased response, while 11 of the 13 vinorelbine UFGs were downregulated with increased response, supporting our hypothesis that increased eribulin response may be driven by upregulation of pathways governing sensitivity, whereas increased vinorelbine response may depend more on downregulation of general resistance pathways.Further restriction of the To elucidate biological pathways associated with eribulin and vinorelbine UFGs, network propagation  was usedFinally, subsets of eribulin and vinorelbine UFGs prioritized by highest individual correlation coefficients were used to build drug-specific MVR models that correlate with responses across all 100 cell lines and accurately predict average response levels of the most and least sensitive quartiles based solely on expression levels of only four or five genes, respectively. While others have reported gene or pathway signatures for MTAs, these have been mainly for paclitaxel ,29,30,31IRX2 is a homeobox gene important in normal embryonic development and has been implicated in cancer, while C5ORF38 is coordinately regulated with IRX2, suggesting both are involved in cellular differentiation and growth regulation [DAAM1 is involved in Wnt signaling and early embryonic gastrulation, both processes associated with cellular differentiation [CD70 codes for a ligand for the immune costimulatory molecule CD27 and thus is involved in the activation and proliferation of T cells, including regulatory T cells [Considering the importance of signaling-related Reactome pathways in eribulin and vinorelbine responses, it is reasonable to ask if the four eribulin and five vinorelbine UFGs in the predictive MVR models directly control responses to the two drugs. Strictly speaking, UFG expression levels were only correlated with response, so firm conclusions of direct involvement cannot be drawn. However, with that caveat, information on known roles of the genes permits some speculation. For eribulin, gulation . Similarntiation . Finally T cells . Thus, wEPHA2 codes for EPH receptor A2, a tyrosine kinase involved in cancer-related signaling, while NGEF codes for a guanine nucleotide exchange factor associated with signaling from EPH receptor A2, RhoA, Rac1 and CDC42 as well as cellular transformation and tumorigenesis [SEPTIN10 is associated with B cell leukemias [TRIP10 is involved in tumorigenesis and cancer progression [VSIG10 codes for an immunoglobulin-related protein associated with both cell adhesion and macrophage involvement in colonic pathologies including colon cancer [For vinorelbine, igenesis ,47. SEPTeukemias , while Tgression . Finallyn cancer . Thus, wIn summary, we have combined response data for eribulin, paclitaxel and vinorelbine from 100 human cancer cell lines together with gene expression data to identify small numbers of genes (UFGs) that are uniquely associated with eribulin and vinorelbine responses. Reactome pathway analyses based on UFG-seeded propagated gene networks revealed that responses to both eribulin and vinorelbine are dominantly associated with cellular signaling processes as opposed to canonical MT-based antimitotic processes, perhaps also explaining our inability to identify paclitaxel UFGs using the same criteria. Despite top-level dependence on cellular signaling for response to both eribulin and vinorelbine, detailed analyses of the specific Reactome pathways identified reveal considerable discrimination between the two drugs. Finally, we show that small subsets of eribulin and vinorelbine UFGs can be successfully combined into MVR models that accurately predict a high versus low response to the two drugs based on expression of only four or five genes. Our results indicate that further investigation of the genes, pathways and MVR panels identified here is warranted, with further validation in both preclinical tumor models and in the clinical setting being of highest priority. Ultimately, our hope is that the current results together with such future validation work will lead to the development of drug-specific, gene expression-based predictive biomarker panels for eribulin and vinorelbine that support improved therapeutic decision-making in clinical settings.


P = 0.04). In contrast, grade III–IV acute  as well as extensive chronic  GVHD correlated with higher nonrelapse mortality leading to lower LFS . In conclusion, these data suggest a dissociation of graft-versus-leukemia effects from GVHD in patients with active AML treated with PTCy-based Haplo-HCT.The association between graft-versus-host disease (GVHD) occurrence and acute myeloid leukemia (AML) relapse in patients treated with HLA-haploidentical allogeneic hematopoietic stem cell transplantation (Haplo-HCT) with post-transplant cyclophosphamide (PTCy)-based GVHD prophylaxis has remained debated. Here, we addressed this issue in patients with active AML at transplantation. 2-year cumulative incidences of relapse and leukemia-free survival (LFS) were 49% and 32.3%, respectively. There were no associations between acute nor chronic GVHD of any grade and lower relapse incidence. However, grade I acute GVHD was associated with better LFS (HR = 0.71, 95% CI 0.51–0.99, The online version contains supplementary material available at 10.1186/s13045-023-01403-x. To the EditorAllogeneic hematopoietic stem cell transplantation  has remained the best option for patients with relapsed/refractory acute myeloid leukemia (AML) . This apPost-transplant cyclophosphamide (PTCy)-based GVHD prophylaxis has revolutionized the field of human leukocyte antigen (HLA)-haploidentical hematopoietic cell transplantation (Haplo-HCT) , 6. ConsP = 0.001) and 16% (P = 0.008), respectively, in PBSC recipients. The 2-year cumulative incidences of chronic and extensive chronic GVHD were 26.8% (95% CI 23.4–30.3%) and 13% (95% CI 10.5–15.8%), respectively. There was no impact of stem cell source on chronic GVHD incidence. However, in vivo T-cell depletion was associated with a lower incidence of chronic GVHD .The analyses were carried out in a total of 670 patients  and a statistical trend for lower LFS   and better LFS  and OS . We do not have a biological explanation for the lower NRM in patients with grade 1 acute GVHD. Future studies needed to evaluate whether this is due to a better immune reconstitution in patients with grade I acute GVHD. Finally, extensive chronic GVHD was associated with higher NRM  and lower LFS  and OS   [n = 805) [Our results differ from what has been observed by the Baltimore group in patients receiving Haplo-HCT with PTCy-based GVHD prophylaxis after nonmyeloablative conditioning as treatment of various hematological malignancies (n = 340) . Indeed,n = 805)  and withn = 805) .The absence of association between GVHD occurrence and the risk of relapse might suggest that in vivo T-cell depletion could be particularly suitable in the Haplo-HCT PTCy setting. However, we observed that ATG was associated with higher relapse incidence in multivariate analysis, without significantly affecting OS and LFS.In conclusion, we demonstrated in a cohort of patients with active AML at transplantation treated with PTCy-based T-cell repleted Haplo-HCT that occurrence of GVHD did not decrease the risk of relapse suggesting a dissociation of GvL effects from GVHD in this transplantation setting.Additional file 1. Supplemental data.