BRCA-mutated breast cancer (BC), including triple-negative BC (TNBC) and ovarian cancer (OvCa). A key challenge is to identify the factors associated with PARPi resistance; although, previous studies suggest that platinum-based agents and PARPi share similar resistance mechanisms.Poly (ADP-ribose) polymerase inhibitors (PARPi) are approved for the treatment of BRCA-mutated TNBC cell lines. In-silico analysis were performed using multiple databases including The Cancer Genome Atlas, the Genotype-Tissue Expression, The Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer, and Gene Omnibus Expression.Olaparib-resistant (OlaR) cell lines were analyzed using HTG EdgeSeq miRNA Whole Transcriptomic Analysis (WTA). Functional assays were performed in three p = 0.001) as well as in tumor tissues from TNBC patients (p = 0.001). We hypothesized that miR-181a downregulates the stimulator of interferon genes (STING) and the downstream proinflammatory cytokines to mediate PARPi resistance. BRCA1 mutated TNBC cell lines with miR-181a-overexpression were more resistant to olaparib and showed downregulation in STING and the downstream genes controlled by STING. Extracellular vesicles derived from PARPi-resistant TNBC cell lines horizontally transferred miR-181a to parental cells which conferred PARPi-resistance and targeted STING. In clinical settings, STING levels were positively correlated with interferon gamma (IFNG) response scores (p = 0.01). In addition, low IFNG response scores were associated with worse response to neoadjuvant treatment including PARPi for high-risk HER2 negative BC patients (p = 0.001). OlaR TNBC cell lines showed resistance to platinum-based drugs. OvCa cell lines resistant to platinum showed resistance to olaparib. Knockout of miR-181a significantly improved olaparib sensitivity in OvCa cell lines (p = 0.001).High miR-181a levels were identified in OlaR TNBC cell lines (miR-181a is a key factor controlling the STING pathway and driving PARPi and platinum-based drug resistance in TNBC and OvCa. The miR-181a-STING axis can be used as a potential marker for predicting PARPi responses in TNBC and OvCa tumors.The online version contains supplementary material available at 10.1186/s13578-023-01151-y. BRCA-mutated (gBRCA mt) or HER2-negative metastatic breast cancer (BC) who have been treated with chemotherapy either in the neoadjuvant, adjuvant, or metastatic settings [Poly (ADP‑ribose) polymerase inhibitors (PARPi) promote DNA damage accumulation in tumor cells with homologous recombination (HR) deficiency, leading to the concept of synthetic lethality . As a resettings . PARPi asettings . Severalsettings , 7. Althsettings . TherefoBRCA mt ovarian cancer (OvCa) [Three large clinical studies, using PARPi treatment for patients with r (OvCa) , 10, conr (OvCa) ; althougRecently, we reported that the downregulation of the cytosolic DNA sensor stimulator of interferon genes (STING) leads to cisplatin resistance by decreasing the expression of downstream proinflammatory cytokines in triple-negative breast cancer (TNBC) . STING iHomo sapiens (hsa)-miR-181a-5p (or miR-181a-5p from this point on) downregulates STING and thereby allows fallopian tube secretory epithelial cells (FTSEC) to bypass interferon-mediated cell death leading to cancer cell transformation and development of high-grade serous ovarian cancer (HGSOC) [hypothesized that the factors regulating STING levels modulate the downstream signaling in TNBC and OvCa and correlate with PARPi sensitivity as well as cross-resistance to platinum-based drugs.Previously, we reported that tumor tissue  (HGSOC) . High tu (HGSOC) . Thus, win-vitro findings were validated using clinical specimens from TNBC and OvCa patients. This study describes a novel mechanism of PARPi resistance, as well as platinum-based drug cross-resistance; and suggests that the upregulation of miR-181a is a significant factor predicting PARPi responses in TNBC and OvCa.In this study, we obtained the miR profiles of PARPi resistant cell lines using HTG EdgeSeq miRNA Whole Transcriptome Assay (HTG miR WTA) and identified that miR-181a is significantly upregulated in PARPi-resistant TNBC cell lines. Functional assays were then performed to characterize the role of miR-181a in controlling STING pathways as a mechanism of PARPi resistance. Furthermore, the The study was conducted following the Declaration of Helsinki and was approved by the Ethics Committee at Saint John’s Cancer Institute (SJCI) and WIRB: MORD-RTPCR-0995. Six formalin-fixed paraffin-embedded (FFPE) OvCa tissues surgically resected were obtained from patients at the Department of Obstetrics and Gynecology, Perelman School of Medicine, U. of Pennsylvania, PA. Three tissues were treatment naïve at the time of surgery and three tissues were from a second debulking surgery after patients relapsed to adjuvant chemotherapy (Carboplatin/Paclitaxel). All the patient specimens were de-identified. Furthermore, a clinically annotated tissue microarray (TMA) for TNBC (#BR1301) was obtained from US Biomax .BRCA mt TNBC cell lines MDA-MB-436 (BRCA1 mt), HCC1395 (BRCA1 mt), and HCC1937 (BRCA1 mt) were obtained from the American Type Culture Collection  and were cultured as recommended. The establishment of HCC1937 and HCC1395 cell lines olaparib-resistant (OlaR) are described in Additional file Three established human -436 BRCA mt, HCC1Total RNA from cell lines was extracted by the Direct-zol RNA miniprep kit  according to the manufacturer’s instructions. RNA isolation from EV is described in Additional file The HTG miR WTA  was utilized to assess the difference in human miR transcripts between parental and OlaR cell lines using direct next-generation sequencing (NGS) as previously described . HTG miRPurified lentiviral particles for STING (LPP-E1218-Lv103-050), miR-181a (LPP-HmiR0023-MR03-050-S), and their respective controls   were transduced into TNBC cell lines MDA-MB-436, HCC1395, and HCC1937 as previously described [Knockdown experiments were performed as previously described . TNBC ceOlaparib  was dissolved in molecular-grade water at a concentration of 10 mM. For cell viability assays, the measurements were performed after treatment with different concentrations  of olaparib for 48 h, as previously described , 18. ForBC FFPE TMA sections were stained with STING antibody  by immunohistochemistry (IHC) as previously described [In-situ hybridization (ISH) for miR-181a was performed on a FFPE TNBC TMA section and FFPE OvCa sections using the miRNAscope Assay  according to the manufacturer’s instructions and as previously described [escribed . MiR-1813) were seeded in a 96-well culture plate. The number of viable cells was assessed using a Cell Titer-Glo Luminescent Cell Viability assay  according to the manufacturer’s instructions as previously described [TNBC cell lines (1 × 10escribed . For asshttp://imagej.nih.gov/ij/). Western blotting was performed for CD9, CD63, and CD81 in EV derived from HCC1937 and HCC1395 parental cell lines.Traditional western blot was performed as previously described , except Automated western blotting was performed according to the manufacturer’s protocol , and quantified as previously described . ProteinFor a detailed explanation of EV isolation please refer to Additional file https://www.R-project.org/.) in a two-tailed way. The distribution and variation within each group were assessed before statistical analysis. Two groups were compared using Student’s t-test. Multiple groups were analyzed by One-way or Two-way ANOVA followed by a post-hoc Tukey’s multiple comparisons test. The correlation between variables was determined using Pearson’s correlation test. Overall survival (OS), recurrence-free survival (RFS), and distant metastasis-free survival (DMFS) were calculated from the time of taking the first specimen until death/recurrence/distant metastasis or last contact and were analyzed using the Log-rank test. For miRNA analysis, DESeq2 normalization and statistical comparisons were performed using the HTG REVEAL software version 2.0.1. Differentially expressed miRNAs were screened using Log2 fold change (FC) > 1.5 or < − 1.5, median normalized counts > 1000. MiR expression (counts per million) was logarithmically scaled (Log10) for volcano plot data visualization. A two-sided p < 0.05 was considered statistically significant: p < 0.05, **p < 0.01, ***p < 0.001, and ns = not significant. All figures were unified using Adobe Illustrator CC  or CorelDraw graphics suite 8X .Statistical analyses were performed using the GraphPad Prism 8  or R version 4.1.2 (BRCA1 mt olaparib-resistant (OlaR) TNBC cell line was initially established as described in Additional File HCC1937 In-silico analysis using the TCGA BRCA dataset showed that miR-181a levels were significantly higher in primary BC compared to adjacent normal breast tissues   with miR-181a overexpression  but increased PARPi sensitivity in BRCA1 mt TNBC cell lines significantly  was performed in the three TNBC cell lines using lentiviral transduction  compared to normal breast and adjacent normal breast tissues , IFNB, interleukin-12A (IL12A), and interleukin-12 (IL12B) compared to respective control cell lines in RT-qPCR analysis  as the vehicle for miR-181a delivery to non-resistant cell lines. EVs derived from HCC1937 TNBC parental  and OlaR (OlaR-EV) cell lines were isolated using differential ultracentrifugation (DUC) from culture supernatants. EVs were characterized using Western blotting, NTA, FL-NTA, and ACE. By western blot, the three EVs standard markers  were detected, indicating the purity and characteristics of the EV fractions isolated  in both BC and OvCa cell lines  and BRCA1 promoter methylation predicted response to rucaparib [The early detection of drug resistance as well as the understanding of the intrinsic mechanisms that induced PARPi resistance are important for both TNBC and OvCa patients. Thus, the prospects for developing effective strategies for the early detection of PARPi resistance are a growing field. Previous studies have shown that germline and somatic and OvCa , 3. Alteand OvCa –29. A reucaparib .Tumors that are sensitive to platinum-based drugs are also sensitive to PARPi treatment , 32. OurThe present study offers additional insight into miR-181a regulatory functions controlling STING mRNA and protein levels. MiR-181a targets STING and promotes metastasis as well as recurrence in advanced stage HGSOC . Our stuSeveral studies have shown that the activation of STING and the stimulation of type I IFN production are critical for anticancer immune responses. Activation of STING signaling induces the production of type I IFN, which plays critical roles in activating both innate and adaptive immune responses . A studySeveral clinical trials have been conducted to test the efficacy of PARPi plus immune checkpoint inhibitors (ICI) in ovarian, breast, prostate, lung, bladder, gastric cancers, and other solid tumors . MiR-181PIAS3) [Recently, EVs have attracted extensive attention for their role in drug resistance , 38. ThePIAS3) , which tPIAS3) . This stTNBC cell lines resistant to PARPi showed enhanced levels of miR-181a  HCC1937 cell line treated with different concentrations of olaparib (B) or cisplatin (C) (Two-way ANOVA and Sidak’s multiple comparisons test). D Cell viability assays comparing parental and OlaR HCC1937 cell line (Two-way ANOVA and Sidak’s multiple comparisons test). E Representative images for GFP positive cells with miR-181a-OV in MDA-MB-436, HCC1395, and HCC1937 cell lines using fluorescence microscopy. Scale bars: 100 µm. F–H Cell viability assays comparing miR-181a-OV and empty vector (CTRL) in MDA-MB-436 (E), HCC1395 (F), and HCC1937 (G) cell lines (two-way ANOVA and Sidak’s multiple comparisons test). I Cell viability assays comparing miR-181a-OV and empty vector (CTRL) HCC1937 cell lines treated with different concentrations of cisplatin (two-way ANOVA and Sidak’s multiple comparisons test). Cell viability assays were performed in triplicates. Figure S2. STING overexpression or downregulation does not affect cell proliferation. A Representative images for STING-OV (GFP) in MDA-MB-436, HCC1395, and HCC1937 cell lines using fluorescence microscopy. Scale bars: 100 µm. B–D Cell viability assays comparing STING-OV and empty vector (CTRL) in MDA-MB-436 (B), HCC1395 (C), and HCC1937 (D) cell lines (two-way ANOVA and Sidak’s multiple comparisons test). E–G Cell viability assays comparing si-STING and si-CTRL in MDA-MB-436 (E), HCC1395 (F), and HCC1937 (G) (two-way ANOVA and Sidak’s multiple comparisons test). Figure S3. STING is downregulated in TNBC and relates to outcomes. A STING mRNA levels in normal breast  and primary BC (Primary) tissues in the TCGA and GTEx databases (Student’s t-test). BSTING mRNA levels in tissues from tumor-adjacent normal breast , Luminal (Lum), Luminal-HER2 (Lum-HER2), HER2, and TNBC in the TCGA BRCA dataset (One-way ANOVA and Tukey’s multiple comparisons test). CSTING mRNA levels in tissues from the tumor-adjacent normal breast , Normal-like (Norm-like), Luminal-A (LumA), Luminal-B (LumB), HER2-enriched, and basal-like breast cancer (BLBC) in the TCGA BRCA dataset (One-way ANOVA and Tukey’s multiple comparisons test). D–F Survival analysis of RFS (D), OS (E), and DMFS (F) for patients with TNBC in the TCGA, GEO, and EGA databases combined (Log-rank test). G–I Survival analysis of RFS (G), OS (H), and DMFS (I) for patients with BLBC in the TCGA, GEO, and EGA database combined (Log-rank test). *p<0.05, **p <0.01, ***p <0.001. Figure S4. Analysis of the mRNA levels of the downstream components of the STING pathways. A–E Quantification by RT-qPCR of STING (A), IL6 (B), IFNB (C), IL12A (D), and IL12B (E) mRNA levels in empty vector (CTRL) and STING-OV HCC1395 cell line (Student’s t-test). F–J Quantification by RT-qPCR of STING (F), IL6 (G), IFNB (H), IL12A (I), and IL12B (J) mRNA levels in empty vector (CTRL) and STING-OV MDA-MB-436 cell line (Student’s t-test). K–O Quantification by RT-qPCR of STING (K), IL6 (L), IFNB (M), IL12A (N), and IL12B (O) mRNA levels in miR-181a-OV and CTRL in HCC1395 cell line (Student’s t-test). P-T Quantification by RT-qPCR of STING (P), IL6 (Q), IFNB (R), IL12A (S), and IL12B (T) mRNA levels in miR-181a-OV and empty vector (CTRL) in MDA-MB-436 cell line (Student’s t-test). *p<0.05, **p <0.01, ***p <0.001. Figure S5. Correlation analysis between miR-181a levels and drug activities. A–D Correlation between miR-181a levels and cisplatin (A), rucaparib (B), olaparib (C), talazoparib (D) drug activity in the BC cell lines obtained from CCLE and GDSC BRCA datasets (Pearson’s correlation coefficient). F–I Correlation between miR-181a levels and cisplatin (F), rucaparib (G), olaparib (H), talazoparib (I) drug activity in the OvCa cell lines obtained from CCLE and GDSC BRCA datasets (Pearson’s correlation coefficient). Figure S6. Uncropped western blotting images. A-C Uncropped western blotting images for Figures 3D (A), H (B), and I (C) are shown. Figure S7. Uncropped western blotting images. A–D. Uncropped western blotting images for Figures 3J (A), K (B), 4A (C), and E (D) are shown. Figure S8. Uncropped western blotting images. A Uncropped western blotting images for Figure 6A (A) and G (B) are shown.Additional file 2: Table S1. Reagents and resources utilized in the study.


Artemisinin is a natural compound extracted from Artemisia species belonging to the Asteraceae family. Currently, artemisinin and its derivatives are considered among the most significant small-molecule antimalarial drugs. Artemisinin and its derivatives have also been shown to possess selective anticancer properties, however, there are several limitations and gaps in knowledge that retard their repurposing as effective anticancer agents. Hybridization resulting from a covalent combination of artemisinin with one or more active pharmacophores has emerged as a promising approach to overcome several issues. The variety of hybridization partners allows improvement in artemisinin activity by tuning the ability of conjugated artemisinin to interact with various molecule targets involved in multiple biological pathways. This review highlights the current scenario of artemisinin-derived hybrids with potential anticancer activity. The synthetic approaches to achieve the corresponding hybrids and the structure–activity relationships are discussed to facilitate further rational design of more effective candidates. Cancer is considered to be one of the most life-threatening diseases throughout the world nowadays. World Health Organization estimated 10 million people worldwide to have died from cancer in 2020 ,4,5. To Molecular hybridization is a modern strategy used in drug discovery for rational drug design . Based oTypically, hybrid molecules containing two or more pharmacophoric units can be classified depending on the manner in which they are covalently linked. In the case of directly linked hybrids  and its derivatives were developed into drugs and became the gold standard for malaria treatment. More recent studies showed that artemisinins also exhibit interesting anticancer activities in vitro and in vivo against a wide range of cancer types ..44]. Simter bond A. Moreovnditions B. Compoue itself .12–19 and 22 were tested for their in vitro cyctotoxicity against HL-60 leukemia cells. All hybrids showed enhanced cytotoxicity with respect to unconjugated dihydroartemisinin (2) with a dihydroartemisinin (2)/hybrid ratio ranging between 3 and 10.5 . Th. Th44 waclitaxel .50) and drug concentration resulting in total growth inhibition (TGI). The anticancer agent etoposide was used as a reference standard drug. Both quinoline–bis(dihydroartemisinin) hybrids 49 and 50 showed anticancer activity against all cancer cell lines tested with GI50 values in the range of 0.03–0.08 μM and TGI values in the range of 2.21–18.50 μM. Both hybrids were found to be much more potent than the reference drug etoposide  (Quinoline–bis(dihydroartemisinin) hybrids 49 and 50  were prectively) . Pharmac3) and the appropriate indoloquinoline derivative in the presence of EDCI and 1-hydroxybenzotriazole was reported . A. A86a–c er cells .85b,c in order to obtain hybrids 88b,c characterized by stable ether linkers of different lengths  were found to be 5- and 7-fold more potent than dihydroartemisinin (2) (IC50 = 2 μM), respectively, and showed comparable cytotoxicity to that of reference drug doxorubicin (IC50 = 0.3 μM)  .93a,b were found to be significantly more effective in inducing apoptotic subG0 population and in triggering mitochondrial membrane potential loss than dihydroartemisinin (2).A mechanism insight in HL-60 cells was also reported. Both hybrids 95a–d, prepared by condensation of dihydroartemisinin (2) and a series of chalcone derivatives 94a–d, is depicted in 95a–c were found to be highly cytoselective towards cancer cell lines with respect to normal cells. Hybrid 95a was the most potent of the series against all cancer cell lines tested, showing comparable or higher cytotoxic activity with respect to reference drug parthenolide , A549 and A-460 (lung), MDA-MB-231 (breast), HeLa .Xie et al. reported ,74 two ses 96a–l . Hybrids99a–l were found to be from 2.6- to 40-fold more active than the parent dihydroartemisinin (2) in all cancer cell lines tested, proving the effectiveness of the conjugation with a chalcone moiety. Moreover, the presence of a chalcone moiety also improved the cytoselectivity towards HT-29 and HeLa cell lines with respect to MDA-MB-231, A549 and H460 cell lines. By comparing hybrids 99i–k it can be evidenced that the bioisosteric replacement of phenyl (99i) with a thiophene (99k) and furan (99j) rings significantly decreased the activity against the five tumor cell lines tested  whereas the extension of the length of the alkyl linker between the dihydroartemisinin and triazole moiety increased the activity (hybrid activity 108 < 109) (All hybrids were tested against selected cancer cell lines HCT116 and HT-29 (colon), MDA-MB-231 (breast) and normal MRC-5 fibroblast cells. By comparing hybrids 8 < 109) C.d, suggesting that the different electronic effect of substituents at the C-3 and C-4 skeleton of the coumarin moiety could play a positive role in the cytotoxicity of hybrids. In particular, hybrid 103d was the most potent with IC50 = 0.05 μM, comparable to that of reference drug doxorubicin. Moreover, hybrid 103d showed the best cytoselectivity towards HT-29 cancer cells with respect to normal MRC-5 cells (MRC-5/HT-29 = 1014)  C. Furtheigration .111b–e as well as of hybrids 114a–e and 115a–e conjugated through ether linkers is depicted in 111b–e, 3-chloro-4-methyl-coumarin-derived hybrid 111d showed the best activity towards the HT-29 colon cancer cell line, being 45-fold more potent than unconjugated dihydroartemisinin (2)  (The synthesis of dihydroartemisin-derived fused hybrids inin (2) C. Among inin (2) C 78]..111b–e aTamoxifen belongs to a class of anticancer drugs that are selective estrogen receptor modulators extensively used in hormone-dependent breast cancer therapy over recent decades ,122. Chr3) and deoxoartemisinin derivative 26 were reacted with two metabolites of tamoxifen, 2-hydroxytamoxifen 116 (afimoxifene) and (Z)-N-desmethyl-tamoxifen 119, to give the corresponding hybrids 117, 118 and 120, 121, respectively  and artesunate (3) against both PC-3 and MCF-7. Hybrid 121 was found to be the most potent of the series with EC50 = 2.74 and 3.91 μM in PC-3 and MCF-7 cells, respectively. Moreover, hybrid 121 showed marked improved activity compared to dihydroartemisinin (2), being 93- and 5-fold more potent in PC-3 and MCF-7, respectively.Frohlich et al. recently reported  the syntectively . Hybrids linkers . All hybSteroids are natural organic bioactive compounds present in animals as well as in plants and fungi. Steroids are considered important scaffolds in medicinal chemistry due to their molecular structure as well as to their ability to interact with various biological targets in different pathways .122 and estrone derivative 125 were conjugated with both artesunate (3) and deoxoartemisinin derivative 26 to give the corresponding hybrids with amide linkers 123, 124  and estrogen derivatives 129 and 131a,b followed by the introduction of the sulfamate moiety and deprotection. All hybrids are characterized by ester linkers. Hybrids 132 were obtained as pure anomers α (132a) and β (132b) . Al. Al133  in comparison to parent dihydroartemisinin (2) and acridine 136a (2). Compound 137b is the only hybrid with significant activity towards the HT29-AK colon cancer cell line with 1.6-fold enhanced activity with respect to unconjugated dihydroartemisinin (2). This result may be related to the length of the linker that seems to play a role in the cytoselectivity and activity in the series of hybrids 136a–c. Hybrid 136b was further investigated in HL-60 leukemia cells. Flow cytometry analysis demonstrated that this hybrid can promote apoptotic cell death and bind covalently to intraparasitic cellular targets in the presence of iron (II) . M. M144a–e 549/DDP B [85].143a,c, 145a and 146c were selected for further pharmacokinetic studies in a CD-1 mouse model by single intravenous administration (dose = 30 mg/kg). The results are reported in Hybrids 2) with isatin derivatives substituted at C-3 with different groups such as methoxy, fluorine and methyl. The synthesis of the most potent hybrids of the series is reported in 149a [149b [149a and 149b were in turn modified at the C-3 position of the isatin moiety with, among others, hydroxime and thiosemicarbazide moieties, leading to hybrids 150a and 150b, respectively. The new hybrids were tested for their antiproliferative activity against breast cancer cell lines MCF-7 and MDA-MB-231 as well as their corresponding multidrug-resistant cell lines. Most of the hybrids were found to be significantly more potent than parent dihydroartemisinin (2) in all cancer cell lines tested and, in particular, hybrids 149a,b and 150a,b were found to be 4- and 5-fold more potent (50 > 100 μM).Cao et al. reported ,87 a ser in 149a  and 149be potent . Accordi2) was reacted with isatin carboxylic acid homologue derivatives 151a–d (153a–c is reported in 153a [2) towards triple-negative MDA-MB-231 and MDA-MB-231/ADR breast cell lines and 75- and 55-fold more potent than in MCF-7 and MCF-7/ADR cell lines, respectively (153b (R = OBn) [153a (153b (R = OEt) [50 = 1.27 μM (MDA-MB-231/MCF-7 = 11.8) (153c [50 = 2.85–10.18 μM). In particular, hybrid 153c was found to be 29-fold more active than dihydroartemisinin (2) in multidrug-resistant triple-negative MDA-MB-231/ADR (2) with isatin through ester bonds could be helpful to overcome drug resistance and to tackle triple-negative breast cancer.Xu et al. reported ,89 a sers 151a–d  and theiectively . AnalogoR = OBn) , with a n) [153a . On the R = OEt) , with a  = 11.8) . Hybrid -231/ADR . The ove154, Sulfasalazine 154,  is an an155 obtained by condensation reaction of dihydroartemisinin (2) and sulfasalazine 154  and sulfasalazine 154 as well as their 1:1 combination. In particular, treatment of TN22 cells with 5 μM of hybrid 155 led to a 60% reduction of cell viability whereas unconjugated dihydroartemisinin (2) and dihydroartemisinin (2) and sulfasalazine 154 1:1 combinations reduced the cell viability by only 20%. No effect on cell viability was found by treatment with sulfasalazine 154 alone. These data prove a relevant effectiveness of molecular hybridization. A deep mechanism insight demonstrated that the remarkable reduction of cell viability can be attributed to apoptosis and cell cycle arrest. Moreover, hybrid 155 showed significant inhibition of cell migration which is an important property to fight malignancies with invasive growth such as glioma.Hybrid 135a in lung carcinoma [The research on artemisinin’s anticancer activity has demonstrated that despite challenges, repurposing of artemisinin and its derivatives as anticancer drugs is possible. Indeed, artemisinins have shown anticancer activity against different cancer types by acting through various pathways. Anticancer efficacy of artemisinins has also been proved in in vivo studies with evidence of tumor growth inhibition in xenograft models. Human trials reported so far have demonstrated that some artemisinins are safe and have significant anticancer efficacy. However, artemisinins are characterized by poor pharmacokinetic properties such as low tissue distribution and short half-life that limit their applicability in clinical anticancer therapy. To avoid these limitations, different strategies have been implemented, such as the use of combination therapy and nanoformulations, that can overcome the pharmacokinetic barriers of artemisinins, as well as molecular hybridization. This last option can help not only to overcome the intrinsic limitations of artemisinins but also to improve anticancer activity by lowering the toxicity as a result of enhanced cytoselectivity and to improve the efficacy against multidrug-resistant cancers. The discovery of artemisinin’s anticancer activity is relatively recent, therefore, the research on artemisinin moiety molecular hybridization has been developed mainly over the last 10–15 years. Although many efforts have been made, as demonstrated by the numerousness of the hybrids reported in the literature, the lack of systematic studies on the molecular mechanism of action of hybrids as well as the lack of in vivo studies still limit the development of new artemisinin-based hybrid drugs. Indeed, while many hybrids among the herein reported ones showed interesting improved anticancer activity against selected cancer cell lines, few cases have been extensively studied, for instance, ursodeoxycholic bile acid hybrids 13 and 22 in hepatocellular carcinoma ,46, cinnarcinoma  and sulfarcinoma . Up to narcinoma  and quinarcinoma . More in


Background: Osteosarcoma (OS) represents a rare cancer with an unfavorable prognosis that needs innovative treatment. The aim was to isolate a secretome from mesenchymal stem cells (MSCs) that are treated with paclitaxel (PTX)-containing microvesicles as a drug delivery system and analyze its cytotoxic effects on OS cell lines . Methods: Three batches of secretome  were produced from three bone marrow (BM) MSCs samples treated for 24 h with 15 µg/mL of PTX or with a standard medium. The viability of the OS cell lines after 5 days of exposure to SECR-1-2-3 (pure and diluted to 1:2 and 1:4) was analyzed with an MTT assay. The same SECR batches were analyzed with high-performance liquid chromatography (HPLC) and with a nanoparticle tracking assay (NTA). Results: A statistically significant decrease in the viability of all OS cell lines was observed after treatment with SECR-PTX 1-2-3 in a dose–response manner. The NTA analyses showed the presence of nanoparticles (NPs) with a mean size comparable to that of extracellular vesicles (EVs). The HPLC analyses detected the presence of PTX in minimal doses in all SECR batches. Conclusions: This proof-of-concept study showed that the conditioned medium isolated from MSCs loaded with PTX had a strong cytotoxic effect on OS cell lines, due to the presence of EV and PTX.  Sarcomas are rare mesenchymal tumors, occurring in the bones or soft tissues, which affect all ages but are more common in the pediatric age group. In fact, despite their rarity, they constitute significant mortality in around 13% of cancers . The surRecent studies have shown that the therapeutic biological effects of MSCs lie in the release of bioactive factors known as the secretome. This can be used as a possible alternative to cell therapy and also as a physiological carrier of anticancer agents, thanks to the extracellular vesicles (EVs) contained in the secretome itself, which reflects the genetic and proteomic contents of the secreting MSCs . In addiWe aimed to develop a proof-of-concept (POC) study to verify the use of secretome-containing microvesicles loaded with drugs, which could be considered a new therapeutic approach to treating OS patients. For this purpose, we verified if the conditioned medium, harvested from MSCs loaded with PTX, contained a secretome with an antitumor effect on three OS cell lines ; we demonstrated that it had a strong cytotoxic effect and showed the presence of microvesicles and PTX.2 atmosphere. After 7–10 days, the non-adherent cells were removed and the adherent MSC cells were re-fed every 3–4 days. When the cells reached about 80% of confluence, they were detached with trypsin/EDTA (Sigma Aldrich), counted, re-seeded, and expanded. The cells at each passage were frozen in α-MEM with 5% albumin and 10% dimethyl sulfoxide . The cells were cultured up to the third passage, then the MSC surface markers were analyzed by flow cytometry , using the following antibodies: CD90 FITC, CD73 PE, CD105 PC7, CD146 APC, CD45-34–14 FITC, CD19 APC, and HLA-DR PE. At the same time, the cells were double-divided for the 2 conditions: (1) SECR-CTRL in standard medium and (2) SECR-PTX, where the cells were treated with PTX at a concentration of 15 µg/mL for 24 h, at 37 °C in a 5% CO2 atmosphere. After 24 h of PTX uptake, the medium was removed in both conditions and the MSCs were washed three times and cultured in serum-free medium for 48 h to induce EV release. At the end of serum starvation, the MSCs were counted, and their viability was evaluated by staining with Trypan Blue. The conditioned media was collected and then centrifuged at 3500× g for 15 min to eliminate cell debris and apoptotic bodies. Three batches of secretome (SECR-CTRL and SECR-PTX for each batch) were produced from three BM samples. The secretome thus obtained was used for a cytotoxicity assay on the OS tumor cell lines, nanoparticle tracking analysis (NTA), and high-performance liquid chromatography (HPLC).Human bone marrow (BM) samples were collected after receiving a signature giving written consent, in accordance with the “City of Health and Science Hospital of Turin, Pediatric Onco Hematology, Regina Margherita Children’s Hospital” Ethics Committee, as required by the Declaration of Helsinki. Paclitaxel (Kiabi 6 mg/mL) is a Taxol derivative, and its mechanism of action is via interaction with microtubules, altering their polymerization and, thus, inhibiting cell mitosis. The drugs were used according to their drug dossier. BM cells were harvested from unfiltered BM collection waste bags, which are usually discarded after the BM infusion. The bag was washed with phosphate-buffered saline and the cells were collected and washed at 200 g for 5 min. The cells were cultured with Alpha Minimum Essential Medium Eagle , supplemented with 1% L-glutamine, 1% penicillin/streptomycin, and 10% of human platelet lysate (HPL) , and maiFirst, we confirmed the literature data reporting the resistance of MSCs to high doses of paclitaxel. Three batches of BM-MSCs were expanded at the third passage; when they reached semi-confluence, they were treated with PTX at 15 µg/mL for 24 h. We then calculated the viability using Trypan Blue. Subsequently, for SJSA, MG63, and HOS, we calculated the PTX inhibitory concentration (IC50) using an MTT assay. The cells were cultivated and treated with scalar doses of PTX (from 320 µg/mL to 0 µg/mL) for 24 h. At the end of the time period, the cells were treated with 12 nM of MTT stock solution  and incubated at 37 °C for 4 h in the dark. After labeling with MTT, we added 50µL of DMSO and incubated it at 37 °C for 10 min. The absorbance was read at 540 nm, using a GloMax Discover Microplate Reader . The IC50  was calc2 atmosphere. Finally, 12 nM of MTT stock solution was added to each well of the 96-multiwell flask and the flask was incubated for 4 h at 37 °C in the dark, as previously described. The absorbance was read at 540 nm, using a GloMax Discover Microplate Reader. The viability was calculated as a percentage by dividing the absorbance of the sample by the absorbance of the live control cells (cells treated with standard medium) × 100. The data were plotted using the GraphPad software and a statistical analysis was performed using a two-way ANOVA test.The SECR-PTX cytotoxicity was tested using an MTT assay. The three OS cell lines  were tested with the three secretome lots  and the test was performed in triplicate. The cells were plated at 15,000 cells/well in a 96-multiwell flask containing DMEM supplemented with 1% L-glutamine, 1% penicillin/streptomycin, and 10% of fetal bovine serum  for 24 h. After 24 h, the cells were treated with SECR-CTRL, SECR-PTX, and PTX, diluted in the standard medium with dilutions of 1:2 and 1:4, to study the dose-dependent response. The OS cells were treated with: (I) SECR-CTRL-1; SECR-PTX-1 (II) SECR-CTRL-2; SECR-PTX-2 (III) SECR-CTRL-3; SECR-PTX-3 (IV) PTX 15 µg/mL (V) Standard Medium, and were then incubated for 5 days at 37 °C in a 5% CO® PMX-120  nanoparticle tracking analysis (NTA) system, equipped with a light source wavelength of 488 nm. Before the measurements, the samples were diluted ten times in PBS to avoid changes in the osmolarity of the dispersion and to have an EV concentration suitable for the NTA analysis. After optimization of the instrumental parameters, the sensitivity and the shutter, set in scatter mode, were set at 70 and 100, respectively, in 3 × 33 videos of 1 s, recording a total number of particles of between 2000 and 3600 per analysis. In the fluorescent mode, the sensitivity and the shutter were set at 80 and 100, respectively. To stain the EVs, 1 uL of CellMaskTM Green (CMG) was added to the secretome and left in the dark at RT for 1 h, then the samples were diluted ten times in PBS. As a control, cell culture media with added CMG was used; no particles were detected under the same instrumental conditions.The analysis was performed using a ZetaView® BEH C8 1.7 μm 2.1 × 50 column, using a binary mixture consisting of trifluoroacetic acid at 0.1% in water (eluent A) and trifluoroacetic acid at 0.1% in acetonitrile (eluent B).HPLC-MS/MS analyses were carried out on a Waters UPLC Acquity-TQD system equipped with an ACQUITY UPLCThe analysis was conducted in gradient according to the profile below, using a flow rate of 0.400 mL/min, an injection volume of 5 µL, and a column temperature of 40 °C. The autosampler temperature was kept at 20 °C.Gradient Profile  was: 40, 0; 50, 3.47; 100, 4.34; 100, 6.00The detector was set in MRM mode, following the m/z 854.50 → 286.19 transition with a cone potential of 25 V and a collision energy of 18 eV.2 = 0.999614. The point at the lowest concentration (50 ng/mL) coincides with the LOQ and LOD values.The system was calibrated with a series of PTX standards, with concentrations of between 50 ng/mL and 5 µg/mL, resulting in a straight line where rSample preparation: The sample containing the secretome was frozen, then thawed and vortexed, and 10 mL of the sample was taken. To this sample, 10 mL of acetonitrile was added and the mixture was vortexed for 1 min. The mixture was then placed in an ultrasonic bath for 10 min and centrifuged at 8700 rpm for 5 min.After centrifugation, the supernatant was removed and 10 mL of MTBE was added, vortexed for 1 min, placed in an ultrasonic bath for 5 min, and centrifuged at 8700 rpm for 5 min, then the organic phase was removed and the solvent evaporated. The sample was then diluted to 0.2 mL with methanol. We used three different batches of MSCs isolated from the BM of children who underwent BM collection for sibling transplantation. The characteristics of the donors are reported in 50 after 5 days of treatment, to compare the effect of free PTX with the PTX content on the EVs. 50, obtained after 24 h and after 5 days of treatment with PTX, in the different OS cell lines, reported in µg/mL and pg/mL, respectively  were tested with PTX to verify the drug sensitivity. We calculated the PTX IC50 on the three OS cell lines, using an MTT assay after 24 h, and all cell lines were sensitive to PTX. Using the same test, we also calculated the ICvely see .After having evaluated that the BM-MSCs were able to withstand high doses of PTX and that the OS cell lines were sensitive to PTX treatment, we analyzed the cytotoxic effect of three batches of SECR-PTX and SECR-CTRL in a 5-day MTT -2,5-diphenyl-2-Htetrazoliumbromide) assay on the three OS cell lines, SJSA, MG63, and HOS.p < 0.0001 in all cell lines); (ii) 60.15% ± 9.54 in SJSA (p < 0.0003), 24.28% ± 6.93 in MG63 (p < 0.0001) and 14.49% ± 2.08 in HOS (p < 0.0001). With SECR-PTX diluted to 1:2 (p < 0.0001), the values were 67.13% ± 8.54 in SJSA (p < 0.001), 43.49% ± 13.01 in MG63 (p < 0.0001), and 28.62% ± 14.43 in HOS (p < 0.0001), respectively. We observed, in all three OS cell lines, that there was a significantly high decrease in viability between untreated cells (control condition—CTRL Viability) and cells treated with 30 µg/mL of PTX , showing a viability of <10% in all OS cell lines. This was also seen with pure SECR-PTX-1-2-3 and with diluted SECR-PTX, showing a dose–response effect. Specifically, we obtained the following mean values with standard deviations in viability: (i) 40.70% ± 16.47 in SJSA, 13.19% ± 2.05 in MG63, 9.55% ± 0.36 in HOS with pure SECR-PTX (TM Green). Then, the NTA was set to fluorescent mode to exclude the non-stained NPs. We investigated the contents of the secretome to verify the presence of EVs through an analysis of the three secretome batches , batches without the drug (SECR-CTRL), and after loading with 15 µg/mL of PTX (SECR-PTX), performed in triplicate by NTA. In p-value < 0.0001). SECR-CTRL presented a mean size of 149 ± 1 nm  while those in SECR-PTX exhibited a mean size of 175 ± 4 nm . In addition, the number of NPs/mL decreased in SECR-PTX compared to those in SECR-CTRL. Finally, the Z potential was negative in all samples. No differences were observed in all the analyzed measures between the different batches. The results that we obtained showed that the cells subjected to treatment with the drug for 24 h produced fewer but larger EVs, compared to the MSCs grown with the standard medium, which produced more but slightly smaller EVs. In response to these data, we assumed that the increased size was due to the presence of PTX, a hypothesis that was evaluated and confirmed by the subsequent HPLC analysis.Within the two groups, very similar size distributions were detected. The EVs detected in SECR-PTX had a slightly significantly larger size than those in SECR-CTRL  drug concentration. as showed in Table 4After the HPLC analysis, we compared the drug contents: (i) using the number of cells obtained after secretome collection , dividing the content of PTX (pg/mL)/number of cells, we evaluated the PTX content into a single cell (PTX pg/Cell) via proportional calculation; (ii) the number of EVs produced by MSCs was related to collection volume (PTX(pg/mL)/ vol (mL); and (iii) we evaluated the PTX content related to EVs/mL (PTX pg/EVs). The resulting data are reported in In the last years, the association of chemotherapy with radiotherapy and surgery has considerably ameliorated/improved the survival and quality of life of patients affected by OS; however, prognoses remain poor in patients with advanced disease .Moreover, traditional chemotherapy drugs, such as methotrexate (MTX), cisplatin (CIS), doxorubicin (DOX), and ifosfamide (IFO), which are routinely used for OS treatment have some side effects, such as unstable blood concentrations, rapid clearance, poor targeting, and drug resistance. Their use is also limited because they exhibit side effects on normal cells and variable toxicity . For thiIn fact, we have demonstrated that after treatment with PTX, the MSCs achieved the delivery of PTX in association with EVs (microvesicles and exosomes). The secretome thus isolated showed a cytotoxic effect on different tumors, such as mesothelioma, glioblastoma, oral squamous carcinoma, and breast cancer .In this POC study, we demonstrated that the medium that had been conditioned and isolated from the MSCs after PTX treatment contained a new PTX formulation, based on the presence of EVs with a strong cytotoxic effect on OS-tested cell lines.p > 0.05). Moreover, we observed that all batches showed cytotoxic activity, regardless of the MSC sample used to produce the secretome. However, we noted a difference in the PTX content according to cell number and the presence of EVs and PTX; therefore, further investigations are needed, and the number of samples that are analyzed needs to be increased. We observed a linear correlation between the content of PTX/EVs Tot and the cytotoxic effect on all cell lines, which exhibited a significant correlation in SJSA and MG63 . In the HOS cell line, this correlation was less evident (p = 0.07) because the line was more sensitive to PTX. In fact, it was not possible to calculate the PTX-IC50 value after 5 days of treatment  as showed in However, the complexity of the secretome makes it difficult to study and this requires an appropriate approach. The BM-MSC sample variability and the different techniques and media used for cell culturing and isolation can lead to heterogeneity in the samples. Therefore, using a standard method for each phase of the experimental protocol and the decision to select only children as donors allowed us to reduce the sample variability, meaning that all the analyzed tests showed no significant differences between the three batches of secretome (p < 0.0001) between the three OS cell lines, SJSA, MG63, and HOS, after treatment with SECR-PTX-1-2-3. SJSA was less sensitive to PTX ; these data are due to the high heterogeneity of the tumor subtypes. Interestingly, as evidenced by the equal concentrations observed during the standard curve created to obtain the IC50 after 5 days, we observed a higher cytotoxic effect of the new PTX formulation in comparison with PTX alone, as shown in At the same time, we observed a significant difference , which was higher than that seen in the samples used for testing free PTX. The cytotoxic effect of the secretome compared to the use of the drug alone has also been confirmed in the literature. These data might support the advantageous use of EVs containing a chemotherapy drug, which acts upon the tumor site through intravenous administration or during surgery, operating directly in the tumor site and reducing the drug’s side effects. In fact, free PTX can cause thrombosis, neuropathy, and high mortality ,20; therFinally, regarding the physical analysis of the secretome, we observed that the EVs’ mean size (nm) and negative membrane potential were in line with data reported in the literature for the conventional description of EVs ,22. The MSC-derived secretome, such as in DDS, can be considered an innovative biotechnological product, constituting an advanced cell-free therapy conveying the drug to the area affected by neoplasia, thereby increasing therapeutic efficacy and limiting the side effects. Since pediatric sarcomas and OS are still very aggressive and difficult to treat, it is interesting to study alternative and new therapies that possibly offer multiple advantages over conventional chemotherapy drugs. In this work, we demonstrated that a conditioned medium obtained from PTX-loaded MSCs contained a new formulation of PTX with a strong cytotoxic effect on OS cell lines. This POC study suggests a new therapeutic approach for OS treatment, with the future objective of producing lyo-secretomeas a stable and easily storable product to use in a standardized GMP-compliant process, along with the creation of a biobank of DDSs ready to use in clinical trials.


Chemotherapy mistreatment is partially due to a lack of rapid and reliable tools to discriminate between sensitive and resistant phenotypes. In many cases, the resistance mechanism is not fully understood, contributing to the diagnostic tools' absence. This work aims to determine the capacity of MALDI-TOF-MS profiling to discriminate between chemotherapy-resistant and sensitive phenotypes in leukemia and glioblastoma cells. A multivariate analysis of two therapy-resistant leukemia cell lines (Ki562 and Kv562) and two TMZ-resistant glioblastoma cell lines (U251-R and LN229-R) and their sensitive counterparts was performed. In this work, we first show MALDI-TOF-MS patterns analysis ability to differentiate these cancer cell lines by their chemotherapy-resistant status. We present a rapid and inexpensive tool that would guide and complement the therapeutic decision. Survival and quality of life for cancer patients remain poor despite treatment improvements made over the last few years. It is well known that many chemotherapy drugs have adverse effects that impair the patient quality of life. Currently, patients receive chemotherapy based on the standard of care established, in many cases without a study of their particular tumor sensitivity. Consequently, sometimes patients are treated with ineffective drugs, which give poor benefits and diminish their quality of life. Therefore, developing strategies that guide therapeutic decisions in a personalized manner is required.3. Notably, this technology is highly cost-effective in clinical microbiology for identifying bacteria4, and it is becoming increasingly available in the clinical diagnosis ground, even in resource-constrained countries.Up to our knowledge, there are no rapid and point-of-care diagnostic methods to assess chemotherapy resistance. Over the past years, the emergence of "omics" approaches has improved the study of multiple diseases, including cancer. Matrix-assisted light desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS) is a robust tool for studying proteins in complex biological samples with high fidelity7. Taking this into account, we decided to use human chronic myeloid leukemia (CML) and glioblastoma (GBM) cell lines as models of malignancies in which the absence of personalized medicine tools directly impacts patient survival and quality of life.Interestingly, only three groups analyzed mammalian cells through this procedure. In these works, the MALDI-TOF-MS tool was employed with different aims as the exclusion of cross-contamination in cell lines or the classification of cancer cells8. This enzyme is responsible for leukemogenesis; thus, the first-line therapies are based on tyrosine kinase inhibitors (TKI) such as imatinib, nilotinib, dasatinib, bosutinib and ponatinib11. Although imatinib is the first-line treatment, in the face of therapeutic failure, the recommendation is to continue with other TKI. Therefore, the ability to anticipate the imatinib-resistant phenotypes would aid in the decision-making process regarding which drug to be implemented, which could positively impact the overall response to treatment. It is worth noting that, even though these drugs are highly effective, their long-term use favors the selection of resistant cells conducing to therapeutic failure12. The resistant mechanisms have been classified into BCR-ABL dependent and independent. The first includes active site mutations, gene amplification, and enzyme overexpression, while the second is mediated by the over-activation of PI3K and MAPK signaling pathways and efflux bombs activity16. The Kv562 cell line used in this work present BCR-ABL independent mechanisms as we previously demonstrated18.CML is a myeloproliferative syndrome with an incidence of 1–2 cases over 100.000 people/year, representing about 15% of all adult leukemia cases. This pathology has different phases  with its own clinical characteristics. The main genomic feature is the Philadelphia chromosome, the product of a reciprocal translation between chromosomes 9 and 22 [t], which generates a fusion gene that encodes a constitutively activated kinase BCR-ABL19. The current therapy includes surgical resection, radiotherapy, and Temozolomide (TMZ) as first-line chemotherapy21. Notably, over 50% of patients are refractory to TMZ, mainly due to overexpression of O-6-methylguanine-DNA methyltransferase (MGMT) enzyme, although other players were found to be involved in this therapeutic resistance24. Moreover, the myeloid and hepatic toxicity caused by TMZ limit its use and negatively impact several patients quality of life26. Even though the FDA has approved three other agents  for GBM treatment, patient survival remains at 14.6 months after diagnosis21.Regarding GBM, this neoplasm is the most frequent and aggressive type of primary brain tumorIn this work, we evaluate the protein profile of two therapy-resistant leukemia cell lines (Ki562 and Kv562) and two TMZ-resistant glioblastoma cell lines (U251-R and LN229-R) and their sensitive counterparts and perform a multivariate analysis. As a result, we aim to analyze if MALDI-TOF-MS analysis allows the differentiation between chemotherapy-sensitive and resistant cancer cell lines based on their protein profiles.Drugs used were imatinib  and temozolomide . RPMI 1640, DMEM, L-glutamine, streptomycin, penicillin, and XTT were purchased from Invitrogen . Phenazine methosulfate (PMS), formic acid, acetonitrile, matrix solution , and trifluoroacetic acid were purchased from Sigma-Aldrich .−5 M 2-mercaptoethanol, 100 µg/ml streptomycin and 100 IU/ml penicillin (RPMI-C). Kv562 cells were previously obtained by incubating K562 cells with increasing doses of vincristine and were grown in the presence of 150 ng/ml (162 nM) of this drug17. The resistance developed is mediated by both P-glycoprotein (Pgp) and PI3K over-activation18. It is worth noting that such mechanisms are also involved in imatinib resistance, being BCR-ABL independent mechanisms27. Ki562 cells were cultured in the presence of 1 µM Imatinib and were obtained as described in the results section , 2 mM L-glutamine, 10 mM HEPES buffer, 5 × 1030. Both resistant cell lines U251-R and LN229-R and both aged controls U251-V and LN229-V were obtained as described in the results section  were grown in adherent cultures at 37 °C in a 5% CO2 atmosphere with DMEM supplemented with 10% heat-inactivated FBS, 2 mM L-glutamine, 100 µg/mL streptomycin and 100 IU/mL penicillin (DMEM-C), as we previously described34. Briefly, 3 × 103 GBM cells/well or 2.5 × 105 CML cells/ml were seeded in 96-well plates and treated with TMZ for 72 h or Imatinib for 48 h. After treatment, 25 μl of an XTT solution (1 mg/ml) containing PMS (7.5 µg/ml) was added to the culture medium (100 μl), and cells were incubated for two additional hours at 37 °C in a 5% CO2 atmosphere. After incubation, the absorbance (Ab) was read at 450 and 620 nm using a microplate reader .An XTT assay was performed for metabolic activity as we previously described34.The corrected absorbance was calculated as Ab (treated)450 – Ab (treated)620, as we previously described35. The model was used to estimate four parameters: the maximum value, the minimum value; the EC50; and the Hill coefficient. Finally, we used these estimated parameters to calculate the fitted viability values for each concentration value.Cell lines were processed by biological and experimental triplicate. The metabolic index was calculated for each drug concentration as a percentage of the treated averaged absorbance relative to the basal averaged absorbance. Half-maximal effective concentration (EC50) was used because not all entities ultimately reached 50% of inhibition in the range of drug concentration used. To determine EC50s using our data, we perform a non-linear (log-logistic function) regression using the drc R package, specifically for analyzing dose–response curves37.It is worth noting that this assay was not carried out in Kv562 cells since they were previously established and their resistance to imatinib has already been demonstrated in our previous reports6 cells were seeded in a 100 mm plate and incubated for 24 h for the correct attachment of cells. Then, the medium culture was changed, and cells were incubated for an additional 24 h, harvested, centrifuged, and resuspended in 500 ml of PBS. This procedure was carried out in triplicate , and MALDI-TOF-MS analysis was performed for each. Aliquots were centrifuged , 5 min), then the supernatant was discarded, and cell pellets were homogenized with 25 μl of 100% formic acid (Sigma Aldrich) and 25 μl of 100% acetonitrile (Sigma Aldrich). After centrifugation , 1 μl of the supernatant was loaded by quadruplicate  onto each spot of the MALDI metal plate and overlaid with 1 µl of matrix solution ). Duplicates or triplicates were acquired for each spot . Continuous mass spectra (Ms) were obtained with a Microflex LT mass spectrometer   within a mass range of 2000–20,000 Da. Ms were internally calibrated and controlled daily using Escherichia coli ribosomal proteins .A fast MALDI-TOF-MS approach was applied to mammalian cell lines based on whole-cell extract analysis. 1 × 1038 and processed using MALDIquant (v1.16.2) R package39. Spectra were square root-transformed and smoothed, employing the Savitzky-Golay algorithm. Then, they were baseline-corrected, applying the Statistics-sensitive Non-linear Iterative Peak-clipping algorithm (SNIP) process across 50 iterations40. The peak detection was carried out by a function that estimates the noise of mass spectrometry data by computing the median absolute deviation (MAD). The signal-to-noise ratio (SNR) was set up in 3, with a half window size of 40 and a tolerance of 0.2. Peaks that occur in less than 50% of spectra were discarded. Finally, spectra were averaged by their technical replicates. MALDI-TOF-MS data were categorized with the Binda R package41. Programmed peak selection was performed in the entire dataset seeking biomarkers of chemotherapy susceptibility status by the Binary Discriminant Analysis (BDA) algorithm41. The best-extracted peaks were then used to run a hierarchical k-means clustering-Principal Component Analysis (PCA) with the Factoextra R package42. The binary distance was used to measure dissimilarity between observations, ward.D2 was the agglomeration method used, and k was set to 2 or 3, considering the displayed levels in comparison43.Ms were read as fid/aqus files with MALDIquantForeign (v0.10)Establishment of the resistant cell lines29. Control cells derived from K562, Ko562, were cultured in parallel but without the selection pressure of imatinib , obtaining Ki562 cells according to previous reports32. Control cells derived from both cell lines, U251-V and LN229-V, were cultured in parallel but without the selection pressure of TMZ , obtaining U251-R and LN229-R cells , demonstrating that the IC50 of imatinib in Kv562 cells is 5.11 folds higher than K562Figure Figure 3.MALDI-TOF-MS analysisThese results confirm that we successfully obtained a new imatinib-resistant CML cell line and two TMZ-resistant GBM models. In addition, the aging of the cultures, even without the selection pressure of TMZ, seems to induce a low resistance to the first-line drug in the in vitro GBM models analyzed.A statistical analysis pipeline was implemented to test if MALDI-TOF-MS data could discriminate the chemotherapy susceptibility status of different cancer cell lines. First, a supervised classification by the Binary Discriminant Analysis (BDA) was implemented, searching for peaks that best differentiated the groups. A subset of the best-ranked peaks was used for conducting a hierarchical k-means clustering-PCA. The results for each model are described below.ACML modeli)Comparison between sensitive K562, Ko562, and resistant CML cells Cell lines Ms patterns of K562-Ko562-Kv562 and K562-Ko562-Ki562 were compared separately. Additionally, both resistant phenotypes were analyzed against the K562 and Ko562 cell lines. Figure Four leukemia cell lines were considered for MALDI-TOF-MS analysis: K562, Ko562, Kv562, and Ki562. As previously described, K562 is the Imatinib-sensitive cell line, Ko562 is the aged control cell line , while Ki562 and Kv562 are two different chemotherapy-resistant cells to Imatinib. Table ii)Comparison between resistant CML cell lines Then we decided to analyze and compare Kv562 and Ki562 resistant cell lines Ms patterns. Clusters appeared as two non-superimposed groups, and the first two principal components described more than 70% of the deviation, representing a robust cluster split Comparison between sensitive, low,and high-resistant GBM cells U251 (TMZ sensitive), U251-R (high resistance to TMZ), and U251-V (low resistance to TMZ) were analyzed by biological triplicate, experimental quadruplicate, and technical triplicate , LN229-R (high resistance to TMZ), and LN229-V (low resistance to TMZ) were analyzed by biological triplicate, experimental quintuplicate, and technical triplicate Table . In summii)Comparison between resistant GBM cell lines Then we also analyze and compare U251-R and LN229-R cell lines' Ms patterns. Clusters appeared as two non-superimposed groups, and the first two principal components described more than 70% of the deviation, representing a robust cluster separation  and unsupervised (Hkmc-PCA) algorithms result in prominent profiling of the chemotherapy susceptibility status. The first algorithm ranks the peaks in order of their group discrimination importance, and the second one visualizes the power of the former best-selected peaks to discriminate observations in separate and homogeneous clusters.The development of tools that facilitate and promote the personalization of therapy in oncology is a necessity. In this work, we demonstrated that the MALDI-TOF-MS pattern analysis, in a standard experimental setting for microbiological bio-typing, helped discriminate between chemotherapy-sensitive and resistant cell lines in both CML and glioblastoma models. Other works have reported using MALDI-TOF-MS to classify human tumor cells or to evaluate the cross-contamination of different cell linesOur results show that chemotherapy-resistant and sensitive cell lines present a differentiable MALDI-TOF-MS pattern, making this technology a potential tool for evaluating chemotherapy susceptibility. Currently, two of the critical issues in cancer are the failure in the diagnosis and treatment. Therefore, these results would be the basis for translating this technology towards improving the prognosis and quality of life of patients with tumor pathologies. In this way, knowing each patient's susceptibility to chemotherapy would make it possible to administer the best chemotherapy option, with better results and fewer unnecessary adverse effects.Furthermore, the methodology could also differentiate two distinctive leukemia-resistant phenotypes (Ki562 and Kv562) and two GBM-resistant phenotypes suggesting the potential use of this technology as personalized medicine tool. As we previously introduced, the mechanisms of resistance to imatinib can be dependent or independent of BCR-ABL. Kv562 cells present high Pgp activity and overactivation of PI3K, while Ki562 cells present an overexpression of BCR-ABL . Therefore, each resistant cell line represents the different types of resistance to imatinib. This observation gives relevance to the in vitro model used.Interestingly, aged culture cell lines  exhibit characteristic Ms patterns, different than their sensitive and resistant counterparts, showing EC50s higher than the sensitive cells in both tumor models. This effect is more evident in the GBM model. This biological advantage could be due to the long-term culture and the elevated mutational rate characteristic of tumor cells. This observation could be in accordance with the bad prognosis of the patients after late diagnoses. In this sense, the longer growth time would give an opportunity for the accumulation of mutations and selection of cells with better fitness, favoring the malignancy.4. Therefore, its aid in the decision-making process in cancer treatment would pose an unprecedented repositioning of this technology. Its good availability and quickness could directly impact therapeutic success rate and thus improve patients' health and quality of life. In this way, in the case of GBM, obtaining a resistant cell pattern could re-direct the treatment to radiotherapy or try the few therapeutic alternatives available, avoiding the TMZ negative health cost. In the case of CML, an imatinib-resistant pattern could confer a signal to employ another TKI drug improving the therapeutic response of the patient, as well as preventing selective pressure enhancement.This high throughput technology is currently available in several healthcare facilities for microbiological biotypingAll in all, this work paves the way for future studies considering this technology's use in the oncology therapeutic decision. Although the tests were carried out using cell line models, the results were consistent, and we hope to continue the study in patient samples in order to translate this tool to the clinic. The ensemble between supervised (BDA) and unsupervised (Hkmc-PCA) algorithms results in a prominent profile of susceptibility status to first-line chemotherapy. In addition, the equipment used in this work is the same that is already used for bacterial biotyping, facilitating its potential translation since many clinical laboratories already have the equipment. Another advantage is the brief processing time which is in the hour scale. In this way, our work opens the possibility of improving therapy using this technique, promoting personalized medicine, and impacting the quality of life of cancer patients.MALDI-TOF-MS spectra patterns analyzed by multivariate strategies have proved to be a valuable tool for discriminating leukemia and glioblastoma cell lines regarding their chemotherapy susceptibility status. This work is an ideal starting point for further research and validation of a possible point-of-care MALDI-TOF-MS protocol for tumor profiling.


Our results will help design strategies to improve STARR-seq methods and interpret the results.Self-transcribing active regulatory region sequencing (STARR-seq) and its variants have been widely used to characterize enhancers. However, it has been reported that up to 87% of STARR-seq peaks are located in repressive chromatin and are not functional in the tested cells. While some of the STARR-seq peaks in repressive chromatin might be active in other cell/tissue types, some others might be false positives. Meanwhile, many active enhancers may not be identified by the current STARR-seq methods. Although methods have been proposed to mitigate systematic errors caused by the use of plasmid vectors, the artifacts due to the intrinsic limitations of current STARR-seq methods are still prevalent and the underlying causes are not fully understood. Based on predicted  In partn 500 bp , the vasn 500 bp ,34, resun 500 bp . First, n 500 bp . Second,n 500 bp . It was n 500 bp . Althougn 500 bp  as well n 500 bp  in multicis-regulatory modules (http://cci-bioinfo.uncc.edu)  the WHG-STARR-seq (We downloaded from the database of predicted ncc.edu)  1 426 94ncc.edu) ,37. We dncc.edu)  and CTCTncc.edu) . We downTARR-seq  peaks inTARR-seq  and the TARR-seq . If therTARR-seq . More deTARR-seq , we exclTARR-seq  the epigTARR-seq .We used our previously trained logistic regression-based universal functional state predictor (UFSP) to predict the enhancer functional state (active or non-active) of all the CRMs  in the cell lines/tissues using signals of four active enhancer epigenetic marks CA (ATAC-seq), H3K4me1, H3K4me3 and H3K27ac, as previously described . BrieflyAs accurate prediction of target genes to distal CRMs is still a highly challenging task, and the existing methods are not necessarily superior to the simple ‘closest gene assignment’ , we assiIn each cell/tissue, we divided each nucleotide position in 85% of the human genome from which we were able to predict CRMs and non-CRMs into one of six categories A–F, based on whether the position falls in a CRM or a non-CRM, overlaps with a STARR peak or not in the cell/tissue, and falls in an active CRM or a non-active CRM in the cell/tissue type Figure . SpecifiTo measure the structural complexity of a CRM or a STARR peak, we mapped our previously predicted TFBSs  into theD is the average degree of the TFBSs’ corresponding unique motifs in the interaction network.where We randomly selected DNA sequences from the whole genome with the matched number and length distribution of the CRMs/STARR peaks in categories A–E. We defined enrichment as follows:N is the total length (bp) of CRMs/STARR peaks covered by a state and N is the total length of randomly selected sequences covered by the state. For TE enrichment, N is the number of TEs in the CRMs/STARR peaks and M is the number of TEs in randomly selected sequences.For ChromHMM state  enrichmeTo make the heatmap and density plots, we used all CRMs in category C and all STARR peaks in categories D and E. We randomly sampled 10 000 CRMs in category A and B regions. Moreover, we sampled the same number of non-CRMs in category F as the STARR peaks in category E. We extracted a 6 kb segment of genome DNA centered on the middle of each sequence, and for each 100 bp sliding window, we calculated the mean of fold enrichment of each chromatin signal  to input signal  using EnrichedHeatmap (w0 mode) .U test was used to compare the gene expression levels, GC content, CRM/STARR peak lengths and CRM complexity. The Kolmogorov–Smirnov (K-S) test was used to compare the distributions of data (density graphs).A two-tailed Mann–Whitney Based on combinatory patterns of TFBSs found in 11 348 TF ChIP-seq datasets for 1360 TFs in 722 cell/tissue types, whose 1000 bp binding peaks cover 85% of the human genome, we predicted a DNA segment in the genome regions to be either a CRM or a non-CRM, thereby partitioning the genome regions into two exclusive sets: 1.43M CRMs and 1.76M non-CRMs ,37 Figu. After fWe predicted roughly the same number of active CRMs as STARR peaks in two cell lines (A549 and GM12878), 68 times more active CRMs than the CapSTARR peaks in the pancreatic organoid and 2–10 times as many active CRMs as the STARR peaks in the remaining six cell lines Figure . With anTo see whether we overpredicted active CRMs or STARR-seq methods underdetermined active CRMs, we analyzed the relation of STARR peaks to our predicted active CRMs, non-active CRMs in a cell line/tissue and the non-CRMs in the 85% of human genome regions. The vast majority (98.8% on average) of nucleotide positions of the STARR peaks in a cell line/tissue fell in the 85% of the genome regions, so we ignored the remaining small portion (1.2% on average) that were located in the 15% of the genome regions where we were not able to predict CRMs and non-CRMs due to the lack of TF binding data in these regions . Based oP= 2.7 × 10−5) in pooled nine cell lines/tissues  than those associated with categories A, D, E and F in pooled nine cell lines/tissues  had similarly high gene expression levels to those associated with category B (positions of active CRMs that do not overlap STARR peaks in the cell line/tissue), although they are significantly different ] of all STARR peak positions and 2.1% (C/(B + C)] of active CRM positions in the cell lines/tissues Figure . NotablyP< 5.03 × 10−115) than those associated with the other four categories  in pooled nine cell lines/tissues  had similarly high gene expression levels to those associated with category C (positions of active CRMs that overlap STARR peaks in the cell line) Figure ; like ges Figure  and in es Figure . In conts Figure  to form s Figure , suggests Figure ,37. Conss Figure . Moreoves Figure . They ars Figure . These as Figure . They has Figure .These predicted active CRMs comprise an average of 97.9% [B/(B + C)] of all predicted active CRM positions in a cell line/tissue Figure . These rP< 1.76 × 10−214) expression levels than genes associated with categories B and C in pooled nine cell lines/tissues  (Both genes associated with category A (positions of non-active CRMs that do not overlap STARR peaks in the cell line/tissue) and genes associated with category D (positions of non-active CRMs that overlap STARR peaks in the cell line/tissue) had significantly lower  , indicat × 10−6)  as expec × 10−6) ,37, but  × 10−6) . However × 10−6) .Moreover, STARR-seq signals in category A were too weak Figure . They weFurthermore, STARR-seq signals in category D were stronger than those in categories A and B, but weaker than those in category C in pooled cell lines/tissues and in seven out of nine cell lines/tissues Figure , indicatWe further reason that if non-active CRMs in category D in a cell line/tissue are masked by repressive histone marks as shown in Figure P< 1.04 × 10−3) in pooled nine cell lines/tissues and in eight out of nine cell lines/tissues , suggesting that the STARR peaks in category E might not be CRMs as we argued earlier (Genes associated with category E (positions of non-CRMs that overlap STARR peaks in the cell line/tissue) also had significantly lower expression levels than genes associated with categories B and C ] of all STARR peak positions in the cell lines/tissues Figure . With anP< 5.03 × 10−115) in pooled cell lines/tissues and eight out of the nine cell lines/tissues (P< 3.38 × 10−2)  in pooled cells/tissues and eight out of the nine cell lines/tissues (P< 2.51 × 10−2) , suggesting that non-CRMs in category F are not CRMs as we argued earlier (P< 0.001) higher density of TFBS-like sequences than randomly selected non-CRM sequences with matched lengths and GC content  had significantly lower expression levels than those associated with categories B and C  Figure . However) Figure . As expe) Figure  ] of genomic positions of our predicted active CRMs (category B) Figure . Since wEqually importantly, we identified two types of STARR peaks that are located in repressive chromatin in the cell lines/tissues but display enhancer activity in episomal expression vectors. The first type is those in category D Figure . They ovThe second type of STARR peaks in repressive chromatin are in category E Figure . They ovlqad085_Supplemental_FilesClick here for additional data file.


Gastric cancer (GC) is the fourth most common cancer worldwide, with the highest incidence and mortality regardless of sex. Despite technological advances in diagnosing and treating gastric cancer, GC still has high incidence and mortality rates. Therefore, continuous research is needed to overcome GC. In various studies, cell lines are used to find and verify the cause of specific diseases. Large-scale genomic studies such as ENCODE and Roadmap epigenomic projects provide multiomics data from various organisms and samples. However, few multi-omics data for gastric tissues and cell lines have been generated. Therefore, we performed RNA-seq, Exome-seq, and ChIP-seq with several gastric cell lines to generate a multi-omics data set in gastric cancer.Multiomic data, such as RNA-seq, Exome-seq, and ChIP-seq, were produced in gastric cancer and normal cell lines. RNA-seq data were generated from nine GC and one normal gastric cell line, mapped to a human reference genome (hg38) using the STAR alignment tool, and quantified with HTseq. Exome sequence data were produced in nine GC and two normal gastric lines. Sequenced reads were mapped and processed using BWA-MEM and GATK, variants were called by stralka2, and annotation was performed using ANNOVAR. Finally, for the ChIP-seq, nine GC cell lines and four GC cell lines were used in two experimental sets; chip-seq was performed to confirm changes in H3K4me3 and H3K27me3. Data was mapped to human reference hg38 with BWA-MEM, and peak calling and annotation were performed using the Homer tool. Since these data provide multi-omics data for GC cell lines, it will be useful for researchers who use the GC cell lines to study. According to Global Cancer Statistics, gastric cancer (GC) is the fourth most common cancer worldwide, with the highest incidence and mortality regardless of gender, excluding female breast cancer, and more than 700,000 deaths annually from gastric cancer . Over thCell lines are a population of cells that represent the functions of specific tissues and can be cultured stably for a long period. They are the most convenient tools used for biology research. Cell lines provide many advantages, such as easy cultivation and use, low cost, and the absence of ethical concerns associated with the use of animal and human tissues , 5.In various studies, cell lines are used to find and verify the cause of specific diseases. The data described in this work were initially produced to confirm the genomic and epigenomic landscape of GC cell lines. However, it has not been published because the data set contains samples of low depth or insufficient quality. Large-scale genomic studies such as ENCODE  and RoadData Description.https://cellbank.snu.ac.kr) and American Type Culture Collection (https://www.atcc.org). A normal gastric cell line, HFE145, was previously established by H. Ashktorab and D. T. Smoot . The Exome sequencing data include mapping rate, genome coverage, scores of the mapping quality scores, and duplicate reads, as shown in Data file 3. Each sample produced about 43 million to a maximum of 73 million reads, except for HFE145, which produced about 26 million reads, and showed an average mapping rate of more than 99% and a mapping quality score of more than 29. Duplicate reads were about 34%, and the genome coverage was about 1.7 on average. The human exome represents less than 2% of the genome; therefore, that coverage is sufficient to detect copy number variation (CNV) and structural variation in the genome [http://hudsonalpha.org/myers-lab/protocols) with modifications. Specifically, cells were fixed with 1% formaldehyde, lysed, and sonicated using a Covaris M220 (Covaris). For ChIP-seq analysis, the sonicated lysates for GC cells were used by dividing the same amount into three tubes and 10% input. Normal Rabbit IgG , anti-trimethyl-Histone H3 (Lys4) , and anti-trimethyl-Histone H3 (Lys27)  were prebound to 20 µl Dynabeads coupled with protein A or protein G (Invitrogen). Genomic libraries were prepared at 250 to 400 bp sizes with input and immunoprecipitated fragments using the TruSeq ChIP Sample Prep kit (Illumina). The ChIP-Seq library was sequenced using NextSeq_500 (Illumina), generating 76-bp single reads. The sequenced reads of the ChIP-seq were aligned with the human reference genome (hg38) using BWA-MEM (v0.7.12-r1039) [The chromatin immunoprecipitation (ChIP) assay was performed with nine GC cells in the ChIP set1 and four GC cells in the ChIP set2 for ChIP sequence analysis following a protocol from the Myers lab (2-r1039)  and annoChIP-sequencing data have two experiment sets, and quality and quantity are summarized in Data File 5 and Data file 6. In the ChIP set1, each sample showed about 24 million to a maximum of 69 million reads, an average mapping rate of more than 96%, and a mapping quality score of more than 23.77. The duplication rate ranged from 3 to 71.17%, and the genome coverage ranged from about 0.5 to 1.5. In the ChIP set2, each sample showed about 16 million to a maximum of 41 million reads, an average mapping rate of more than 95% and a mapping quality score of more than 25.16. The duplication rate ranged from 3 to 20.30%, and the genome coverage ranged from about 0.3 to 0.9. The approximate IP efficiency is summarized in Data file 7. Data file 8 shows total tags in peaks / total tags after Homer peak calling, which shows a wide range for each sample but was generally more than 10% in the H3K4me3 immunoprecipitated samples. The immunoprecipitated H3K27me3 samples were generally less than 2%. The excessively high IP efficiency is due to the high duplication levels , and the other cell lines miss one or two data types.


Chemotherapy is pivotal in the multimodal treatment of pancreatic ductal adenocarcinoma (PDAC). Technical advances unveiled a high degree of inter- and intratumoral heterogeneity. We hypothesized that intratumoral heterogeneity (ITH) impacts response to gemcitabine treatment and demands specific targeting of resistant subclones.Using single cell-derived cell lines (SCDCLs) from the classical cell line BxPC3 and the basal-like cell line Panc-1, we addressed the effect of ITH on response to gemcitabine treatment.Individual SCDCLs of both parental tumor cell populations showed considerable heterogeneity in response to gemcitabine. Unsupervised PCA including the 1,000 most variably expressed genes showed a clustering of the SCDCLs according to their respective sensitivity to gemcitabine treatment for BxPC3, while this was less clear for Panc-1. In BxPC3 SCDCLs, enriched signaling pathways EMT, TNF signaling via NfKB, and IL2STAT5 signaling correlated with more resistant behavior to gemcitabine. In Panc-1 SCDCLs MYC targets V1 and V2 as well as E2F targets were associated with stronger resistance. We used recursive feature elimination for Feature Selection in order to compute sets of proteins that showed strong association with the response to gemcitabine. The optimal protein set calculated for Panc-1 comprised fewer proteins in comparison to the protein set determined for BxPC3. Based on molecular profiles, we could show that the gemcitabine-resistant SCDCLs of both BxPC3 and Panc-1 are more sensitive to the BET inhibitor JQ1 compared to the respective gemcitabine-sensitive SCDCLs.Our model system of SCDCLs identified gemcitabine-resistant subclones and provides evidence for the critical role of ITH for treatment response in PDAC. We exploited molecular differences as the basis for differential response and used these for more targeted therapy of resistant subclones. Pancreatic cancer is one of the most aggressive and lethal cancers worldwide, and the fourth leading cause of cancer-associated deaths . It has In recent years, several studies have suggested the classification of pancreatic ductal adenocarcinomas (PDACs) into different subgroups based on their molecular signature –8. CurreAt the single-cell level, it became evident that tumor cells of both subtypes coexist within one tumor. The entirety of these co-existing subpopulations make up the expression profile of the tumor mass. It can therefore be concluded that the genomic and transcriptomic profiles are determined by a continuum of gene expressions derived from a mixture of subpopulations within a pancreatic tumor , 10. ThiITH has become apparent to play an important role in tumor biology and thus also determines the response to the selected therapy options and ultimately overall survival as shown in various tumor entities . Genomicin vitro model. Subsequently, we aim to uncover the molecular preconditions of tumor cell subclones that correlate with their distinct response to therapy using transcriptomic and proteomic profiling. Understanding subclonal resistance mechanisms in heterogeneous tumor cell populations might ultimately help to develop new clinical treatment strategies in PDAC.In our present study, we pursue the hypothesis that ITH influences the treatment response of PDAC since resistant subclones are already present within the tumor cell population before treatment. By establishing single cell-derived cell lines (SCDCLs) of the classical cell line BxPC3 and the basal-like cell line Panc-1, we aim to uncover heterogeneity of treatment response of distinct tumor cell subpopulations in an 2 in a humidified atmosphere. Mycoplasma contamination was excluded in both cell lines by PCR . Cell line authentication was performed by short tandem repeat (STR) profiling using the PowerPlex® 21 System  according to the manufacturer’s instructions.The PDAC-derived cell line BxPC3  and Panc-1  were cultured in DMEM high glucose with 10% fetal bovine serum and 1% Penicillin-Streptomycin-Glutamine  at 37°C, 5% COTo generate single cell-derived cell lines (SCDCLs), limited dilution of both parental cell lines was performed in 96-well plates. For Panc-1 two and for BxPC3 four 96-well plates were seeded initially. Each well was examined by phase-contrast microscopy three hours after plating to ensure that only wells harboring a single cell were used for further cultivation. Once the single-cell clones reached approximately 80% confluency in a 96-well plate, they were transferred to a 6-well plate. Upon reaching 80% confulency in the 6-well-plate, the SCDCLs were further transferred to a T-25 flask. After reaching 80% confluency in the T-25 flask, both RNA and protein samples were collected.50 and ICmax of parental cell populations were determined using dose-response curves. Subsequently, each SCDCL was treated with gemcitabine in the same experimental set-up at the IC50 and ICmax of the respective parental cell population. Finally, for selected SCDCLs, complete dose-response curves were generated, as performed for the parental cell lines. Each individual measurement was carried out in triplicates and repeated three times.To measure the sensitivity to gemcitabine , cells of the respective parental cell population growing in log phase  were seeded in 96-well plates. After a period of 24 hours, the cells were treated with gemcitabine concentrations ranging from 2.5 to 320 nmol/L for 72 hours. After this time, the survival fraction of the cells was determined. For this purpose, cell metabolism was used as a surrogate parameter for viability by using the CellTiter-Blue assay . Normalization was based on untreated controls. ICCells were seeded at 20,000 cells per well in a 6-well plate. Cells were detached from a well by trypsination and counted with a Neubauer chamber  every 24 hours. Counting results were normalized to day 1. For each proliferation curve, three independent biological replicates were performed for each time point.RNA was extracted from each SCDCL using the AllPrep RNeasy Mini Kit  as indicated by the manufacturer in the instruction manual. RNA samples were sequenced at Novogene Europe, Cambridge, United Kingdom. A poly-A enrichment and strand-specific library preparation were used. Sequencing was performed on an Illumina Novaseq6000 with S4 flowcell and PE150 length aiming for 30 million reads per sample. The data have been deposited to GEO Accession with the data set identifier GSE232549.Raw sequencing data (fastq format) was mapped against the human transcriptome (Ensembl GRCh38.103) using kallisto (v0.46.1) and differential expression analysis was performed using sleuth (v0.30.0) , 19. GenProtein from each SCDCL was extracted using the EasyPep™ Mini MS Sample Prep Kit . Extracted protein was analyzed using label-free micro-LC tandem mass spectrometry  using data-independent acquisition (DIA). After digestion of non‐labeled protein samples with trypsin, transmitted ions were fragmented and analyzed in the TOF MS Analyzer at high resolution. The raw SWATH data were processed using the software tool DIA-NN v1.7.16  developed by Vadim Demichev et al. . The sofThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD042256 , 25.https://cran.r-project.org/). Using the random forest (RF) algorithm implemented in the caret package the protein data obtained from the MS experiments were used to fit a regression model. Measured response to the IC50 value of gemcitabine of the respective parental tumor population was used as target variable of the model. ‘Backward Feature Elimination’ was utilized to select the most important proteins for the best fit of the regression model. To this end, the ‘Root Mean Square Error’ (RMSE) served as performance measure and the protein set that yielded the best RMSE value was selected from each model run. Using different seed values we took advantage of the random characteristics of the RF algorithm and performed several replicates for both cell lines  of the model runs. In our model, a higher ranking of a protein within a list of the respective run corresponded to a higher relevance for explaining heterogeneous therapy response to IC50 of gemcitabine of the respective parental tumor population. As each run computed an at least slightly different set of ‘optimal’ proteins the results of all model runs were integrated by calculating a total score for each protein as follows: For calculation of the most important proteins that were related to the heterogeneity of SCDCLs we used the R software (v. 4.1.2) along with the packages caret and klaR –28. BothTo measure the sensitivity to JQ1 , cells of the respective parental cell population or SCDCLs growing in log phase  were seeded in 96-well plates. After a period of 24 hours, the cells were treated with concentrations ranging from 1.92 to 30,000.00 nmol/L for 72 hours. After this time, the survival fraction of the cells was determined. For this purpose, cell metabolism was used as a surrogate parameter for viability by using the CellTiter-Blue assay . Normalization was based on untreated controls. Each individual measurement was carried out in triplicate and repeated three times.If not stated differently, all analyses were performed using R version 4.1. Responder stratification was performed using stratifyR (v1.0-3) with 2 strata and a fixed total sample size of 0.9 . Data haWe hypothesized that molecular preconditions of tumor cell subclones within heterogeneous pancreatic cancers correlate with differential response to therapy and could be targeted to modify treatment response  and a concentration close to the maximal inhibitory concentration (ICmax) of the BxPC3 and Panc-1 parental cell populations. Dose-response curves to gemcitabine were generated for both parental cell lines in the concentration range from 2.5 to 320 nmol/L. The parental Panc-1 population (IC50 = 43 nmol/l) proved to be much more resistant to gemcitabine than parental BxPC3 population (IC50 = 9.6 nmol/L) as measured by their respective IC50  and a concentration close to the maximal inhibitory concentration (ICmax) of the respective parental population. We observed a highly variable response of distinct SCDCLs of both cell lines BxPC3 and Panc-1 . There was a continuum of response rates to gemcitabine treatment between these extremes, although one might suspect a greater increase from the fourth most resistant SCDCL onwards , when treated with ICFrom the most sensitive and the most resistant SCDCL of both parental cell lines, complete dose-response curves were subsequently established, corresponding to the experimental design described above. These curves showed significant differences in the resistance profile between BxPC3 SCDCls B2D9 and B3C10 in concentration ranges from 5 nM to 80 nM (p<0.05)   on the 1,000 most variably expressed genes showed a clear clustering of the SCDCLs according to their respective sensitivity to gemcitabine treatment for BxPC3, while this clustering was less clear for Panc-1  and a more sensitive group (sensG). The cut-off value of the survival rate for the assignment of each SCDCL to the respective group was calculated for treatment at the respective ICy et al. . For the50 value, the SCDCLs of BxPC3 showed 159 differentially expressed genes      were enriched in the sensitive SCDCL of BxPC3 across all comparisons. Interestingly, exactly these pathways were enriched in Panc-1 in the group of the resistant SCDCLs across all comparisons. In contrast, enrichment of EMT correlated with resistance to gemcitabine in BxPC3 SCDCLs across all comparisons, while Panc-1 SCDCLs showed no resistance-associated enrichment of this pathway.50  with the highest selectivity for BRD4 .In our pathway enrichment analyses based on transcriptomics described above, the more gemcitabine-resistant SCDCLs of the classical cell line BxPC3 showed enrichment of EMT, TNF signaling via NfKB, and IL2STAT5 signaling. The proteome analyses of the same SCDCLs identified two pivotal proteins in the extracted protein signature, i.e., (i) BRD3 which is a family member of the BET proteins and (ii) the TNF receptor TNFRSF6B whose gene possess a super-enhancer in multiple myeloma cells .Pathway enrichment analyses based on transcriptomics of the gemcitabine-resistant SCDCLs of the basal-like cell line Panc-1 showed enrichment of MYC signaling, i.e., gene sets MYC targets V1 and MYC targets V2.JQ1 suppresses cell proliferation through several signaling pathways such as TNFA_Signaling_via_nfkb, L2_STAT5_SIGNALING, MYC signaling as well as multiple inflammatory transcriptional programs in pancreatic cancer –37.As described above, we intended to identify molecular preconditions of tumor cell subclones that could be targeted to overcome treatment resistance of current clinical standard therapy .50: 679 nM CI95%: 496.5 – 929.1) was more resistant to JQ1 treatment compared to the parental BxPC3 population . Next, we generated dose-response curves for JQ1 of the parental cell population, the most gemctabine-resistant SCDCL and the most gemcitabine-sensitive SCDCL for both cell lines, BxPC3 and Panc-1.In general, the parental population of Panc-1 . compared to the most gemcitabine-sensitive SCDCL B3C10 (IC50: 95.63 nM CI95%: 73.16 - 125)  compared to both derived SCDCLs.In BxPC3 the most gemcitabine-resistant SCDCL B2D9 was the substantially more sensitive to treatment with JQ1 (IC50: 471.8 nM CI95%: 338.4 – 658) in comparison to the most gemcitabine-sensitive SCDCL P4B9 (IC50: 1590 nM CI95%: 667 – 3791). The differential response was significant in concentration ranges from 240 nM to 6000 nM (p<0.02). The parental population Panc-1 was in-between these two SCDCLs (IC50: 679 nM CI95%: 496.5 – 929.1) .We observed a highly variable response to gemcitabine of distinct SCDCLs of both cell lines  MYC targets V1, (ii) MYC targets V2, (iii) G2M checkpoints, and (iv) E2F targets. Indeed, low MYC RNA levels are associated with sensitivity to gemcitabine and c-MYC overexpression correlates with gemcitabine resistance , 45. Uprc cancer . On the c cancer –50. In oStrikingly, also MYC targets V1, MYC targets V2, and the G2M checkpoint pathway that were enriched in the gemcitabine-resistant SCDCLs of Panc-1, were enriched in the gemcitabine-sensitive SCDCLs of BxPC3. Whether this is due to a hierarchical functional relevance of these pathways with respect to resistance to gemcitabine or whether this is due to the different molecular subtypes of the two parental cell lines can only be speculated at this point.In the resistant SCDCLs of BxPC3, we observed enrichment of numerous signaling pathways such as (i) EMT, (ii) TNFA via NfkB, and (iii) IL2STAT5. It is well known that EMT in pancreatic cancer cells contributes to gemcitabine resistance and decreases overall survival in mouse models . In addiAs it is generally accepted that mRNA expression levels do not necessarily reflect the respective protein expression levels , we addiWe identified protein signatures for both BxPC3 and Panc-1 SCDCLs using a machine-learning approach, which were associated with the treatment response of individual SCDCLs. We subsequently extracted individual proteins that were associated with the signaling pathways that were enriched in the transcriptomic analyses. For BxPC3 (i) the TNF receptor TNFRSF6b, (ii) the nuclear protein HMGN5, and (iii) the BET protein BRD3 were extracted among others.In both colon and gastric cancers, TNFRSF6b induces EMT via various signaling pathways and affects the growth and metastasis potential in colon carcinoma –61. In Pin vitro with gemcitabine and BET inhibitors  can be found below: Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used.Conception and design: RB and BF. Development of methodology: BF, MK, and AK. Acquisition of data: BF, OL, KB, and JW. Analysis and interpretation of data: BF, RB, AK, MK, TS, MtW, and HU. Writing, review and/or revision of the manuscript: BF, RB, KH, LB, AK, BH, HB, UW, TK, TG, and HU. Administrative, technical, or material support: HB, TG, and TK. Study supervision: RB. All authors contributed to the article and approved the submitted version.