Introduction: mTOR inhibitors are anticancer agents affecting mTOR/AKT/PI3K pathway that is one of the most important in human cancer cells. Hyperactivation of mTOR/AKT/PI3K and overexpression of this pathway members are frequently reported in uterine sarcoma and carcinosarcoma.in vitro models.Present study is aimed to assess the activity of the two mTOR inhibitors (rapamycin - RAP and sapanisertib - MLN) as a single agent and combined with gemcitabine  in uterine sarcoma and carcinosarcoma Material and methods: SK-UT-1 and SK-UT1-B (uterine carcinosarcoma), MES-SA (leiomyosarcoma) and ESS-1  cell lines were used. An MTT assay was performed to examine the cytotoxicity of RAP, MLN and mixtures: RAP+MLN, RAP+GEM, MLN+GEM against these cells. The interactions between tested compounds were assessed in isobolographic analysis.Results and conclusions: Carcinosarcoma cell lines (both SK-UT-1 and SK-UT-1B) do not respond to RAP and respond relatively weakly to MLN treatment. Additive and supraadditive effects were noted for combined treatment with GEM and MLN. Endometrial stromal sarcoma cell line (ESS-1) occured to be sensitive to both RAP and MLN, but the response was stronger for MLN. Additive effect of all tested drug combinations was observed for ESS-1. Leiomyosarcoma cell line (MES-SA) was found sensitive to both mTOR inhibitors. Additive effects in combinations of GEM, RAP and MLN were observed, what makes them promising for future preclinical and clinical trials. Additivity with slight tendency towards antagonism between GEM and MLN observed in MES-SA cell line is unexpected finding and might prompt the mechanistic research aimed to explain this phenomenon. It integrates many cell functions including growth, proliferation, translation and transcription Such properties made it one of the most actively investigated molecular target in oncology. Many substances affecting mTOR have been identified so far.The mTOR protein executes its metabolic function by composing complexes with raptor and mLST8/GβLTORC1) and with rictor and mSIN,GβL TORC2)  and with  and witTORC1 regulates mostly protein synthesis by phosphorylation of translation factors, TORC2 controls cell survival and cytoskeleton functions, both complexes are linked by AKT Streptomyces hygroscopicus bacteria. It is widely used as immunosupresant in prevention of transplanted organ rejection and restenosis of coronary vessels (stents coated with rapamycin). Rapamycin binds and deactivates mTOR molecule. It was proved to efficiently inhibit activity of TORC1 complex The most 'classic' mTOR inhibitor is rapamycin (sirolimus), from which the names of protein and pathway were created, it is natural compound isolated from culture of MLN  is novel allosteric and ATP-competitive mTOR inhibitor. Unlike rapamycin, this compound inhibits both TORC1 and TORC2 complexes. The mechanism of overcoming rapamycin resistance by MLN was revealed by Rodik-Outmezguine et al. Gemcitabine is commonly using chemotherapeutic, according to recent recommendations is one of agents to be used in standard systemic treatment of uterine sarcomas Hyperactivation of mTOR/AKT/PI3K and overexpression of this pathway members are frequently reported in uterine sarcoma and carcinosarcoma The majority of studies that investigated the effectiveness of various mTOR inhibitors , in sarcomas, were designed to assess single agent activity There is limited number of studies already finished and ongoing assessing the performance of MLN added to conventional chemotherapy but these do not recruit patients with neither uterine sarcomas nor carcinosarcomas Presently, there is a gap in knowledge about interactions between tested compounds in uterine sarcoma models. In present study we propose to evaluate the activity of both mentioned mTOR inhibitors in combination with gemcitabine as a standard therapeutic agent, that may indicate the directions for future therapeutic regimens.The study is aimed to assess the activity of RAP and MLN as a single agent, in combination and combined with gemcitabine in uterine sarcomas and carcinosarcomas in vitro models.Four cell lines were selected to experiments:- SK-UT-1 and SK-UT1-B- derived from uterine carcinosarcoma - MES-SA- derived from leiomyosarcoma- ESS-1- derived from low grade endometrial stromal sarcoma.Cell lines were obtained from the Laboratorio de Investigación Traslacional, MD Anderson Cancer Centre in Madrid, Spain. The SK-UT-1, SK-UT-1B and MES-SA cells were cultured according to ATCC recommendations 20The methodology of cell cultures and detailed description of used cell lines was described in details previously Cell viability was assessed by performing MTT test. Cells were plated in microplates and incubated in presence of various concentrations of RAP , MLN , GEM  and following 1:1 mixtures: RAP+MLN, RAP+GEM, MLN+GEM for 96 h. Afterwards MTT  -2,5-diphenyltetrazolium bromide) solution was added for 3 h of incubation. Living cells metabolize MTT into purple formazan crystals that are solubilised. The solution was examined optically in light 570 nm length with ELX-800 plate reader  and analyzed with Gen5 software . All experiments were repeated three times and the median value was used for calculations.The IC50 values were calculated by computer-assisted log-probit analysis. Differences in cells viability between control and particular concentrations of tested compounds were evaluated with one-way ANOVA Tukey's post-hoc testing. P<0.05 was considered to indicate a statistically significant difference. The analysis was performed using GraphPad Prism 5.0 .In order to analyze pharmacodynamic interactions between tested substances isoblography was used. The methodology of this analysis was described in our previous paper Detailed results of single agent activity of RAP and MLN measured in MTT test in SK-UT-1, SK-UT-1B, ESS-1 and MES-SA cell lines are presented on Figures 50 values obtained for each agent in monotherapy in all tested cell lines are summarized in table 50 values were not determined.ICThe results of MTT test performed to assess the activity of combinations of tested drugs in SK-UT-1, SK-UT-1B, ESS-1 and MES-SA were presented in Figure The results of isobolographic analysis indicating type of interactions between examined substances are presented in Figures Resistance of carcinosarcoma cell lines  to rapamycin and relatively weaker response to MLN than observed in other investigated cell lines was noticed. Such observation were not reported yet in available literature. The resistance to rapamycin might be explained by feedback activation of AKT and as consequence activation of mTOR pathway. The results of genetic analysis by Jones et al. revealed that the activity of mTOR pathway in carcinosarcomas is less altered, comparing to type I endometrial cancer 50 cell viability was decreased to about 50% in SK-UT-1 and about 30% in SK-UT-1B such results have to be considered as promising and this combination might occur to be useful in clinical practice. No data regarding activity of mTOR inhibitors in combination with chemotherapy in uterine carcinosacoma models or in clinical practice are available in literature up to date. Bae‐Jump et al. revealed synergistic effect of rapamycin and cisplatin combination in endometrial cancer cells, however the cells that were used were derived from type I endometrial cancer Interestingly, we noticed additive and supra-additive effects of combined GEM and MLN in SK-UT-1 and SK-UT-1B. Taking into consideration that we achieved relatively deep inhibition of cell viability in presence of relatively low concentrations of tested compounds. When both substances were mixed in 1/10 of their ICAdditive effect of all tested combinations was observed in endometrial stromal sarcoma cell line (ESS-1), though it responded to treatment with rapamycin moderately. Endometrial stromal sarcoma tumours are, in most cases, hormone-sensitive. In systemic treatment of these tumours anti-oestrogen agents are widely using in combinations with cytostatics and radiotherapy - DNA synthesis alteration by GEM- mTOR pathway inhibition- Oestrogen receptor deactivation.The importance of mTOR signalling in leiomyosarcoma tumours biology is known in vitro as well as in vivo in patients suffering from such conditions as i.a. breast cancers, gliomas There are two main limitations of mTOR inhibitors usefulness in clinical practice. One is acquisition of resistance by cancer cells, second one is toxicity to patients. Acquiring the resistance to mTOR inhibitors (most commonly rapamycin and its derivates) was detected in various tumours' models Multidrug regimens have a chance to overcome both of them. Obviously, these may allow decreased dosing by exploiting additivity and synergy in drugs combinations. Hence, lead to reduce dose-depending toxicity, but targeting multiple cellular pathways at the same time gives, also a chance to avoid formation of cells resistance to anti-neoplastic agent. When many pathways are altered simultaneously then single mutation resulting with resistance to one of agents is not associated with survival handicap and risk of resistance is much lower comparing to monotherapy.The mTOR pathway is considered as one of regulating mechanisms responsible for cell sensitivity to GEM. Such relation was already proved in various cancers including breast and pancreatic Interestingly, the combination of GEM and mTOR inhibitors is already in early phases clinical trials. So far results of the studies are inconsistent. Combination of temsirolimus  and GEM showed promising activity in pancreatic cancer in vivo. Secondly, the reported project do not include any mechanistic research, partially the activity mechanisms of used compounds were previously described The present study is not free of limitations. The most important is using only 2D models of uterine sarcomas and carcinosarcoma, such models are easily reproducible and allow to perform isobolographic analysis of relations between tested substances, but on the other hand do not fully reflect behaviour of tumours 1. Carcinosarcoma cell lines (both SK-UT-1 and SK-UT-1B) do not respond to RAP and respond relatively weak to MLN treatment. Although, additive and supraadditive effects were noted in combined treatment with GEM and MLN.2. Additive effect in all tested combinations was observed in endometrial stromal sarcoma cell line (ESS-1). This suggests that combinations of mTOR inhibitors and GEM might be effective combination in treatment of such tumours, especially in cases of hormone-insensivity.3. Leiomyosarcoma cell line (MES-SA) appeared as the most sensitive to both mTOR inhibitors among all of tested cell lines. Additive effects observed in combinations of GEM, RAP and MLN make them promising for future preclinical and clinical trials.4. Tendency to antagonism between GEM and MLN observed in MES-SA cell line is unexpected finding and might prompt mechanistic research aimed to explain this phenomenon


IL3RA  is a cell membrane protein frequently expressed in acute myeloid leukemia (AML) and Hodgkin lymphoma; therefore, it is a promising therapeutic target for cancer treatment. Here, we introduce BAY-943, a novel IL3RA-targeting antibody–drug conjugate that shows potent and selective efficacy in IL3RA-positive AML and Hodgkin lymphoma cell lines. In IL3RA-positive AML mouse models, BAY-943 improved survival and reduced tumor burden. Impressively, treatment with BAY-943 induced complete tumor remission in 12 out of 13 mice in an IL3RA-positive HL model. BAY-943 showed a favorable safety profile without any signs of toxicity in rats and monkeys. Overall, these preclinical results support the further development of BAY-943 for the treatment of IL3RA-positive hematologic malignancies.IL3RA (CD123) is the alpha subunit of the interleukin 3 (IL-3) receptor, which regulates the proliferation, survival, and differentiation of hematopoietic cells. IL3RA is frequently expressed in acute myeloid leukemia (AML) and classical Hodgkin lymphoma (HL), presenting an opportunity to treat AML and HL with an IL3RA-directed antibody–drug conjugate (ADC). Here, we describe BAY-943 (IL3RA-ADC), a novel IL3RA-targeting ADC consisting of a humanized anti-IL3RA antibody conjugated to a potent proprietary kinesin spindle protein inhibitor (KSPi). In vitro, IL3RA-ADC showed potent and selective antiproliferative efficacy in a panel of IL3RA-expressing AML and HL cell lines. In vivo, IL3RA-ADC improved survival and reduced tumor burden in IL3RA-positive human AML cell line-derived (MOLM-13 and MV-4-11) as well as in patient-derived xenograft (PDX) models (AM7577 and AML11655) in mice. Furthermore, IL3RA-ADC induced complete tumor remission in 12 out of 13 mice in an IL3RA-positive HL cell line-derived xenograft model (HDLM-2). IL3RA-ADC was well-tolerated and showed no signs of thrombocytopenia, neutropenia, or liver toxicity in rats, or in cynomolgus monkeys when dosed up to 20 mg/kg. Overall, the preclinical results support the further development of BAY-943 as an innovative approach for the treatment of IL3RA-positive hematologic malignancies. Interleukin 3 receptor subunit alpha  is the α subunit of the heterodimeric IL-3 receptor. Together with the β subunit, it forms a functional high-affinity receptor for IL-3 . IL3RA i+CD38− AML blasts as compared to expression in normal cells. Furthermore, these IL3RA-overexpressing cells have been shown to be able to initiate and maintain the leukemic process in immuno-deficient mice and thus act as leukemic stem cells [Several studies have indicated that IL-3 and its receptor play important roles in the progression of AML , and indem cells . Consequem cells ,21. Takeem cells .Here, we exploited a novel pyrrole subclass payload that potently inhibits the kinesin spindle protein (KSP/KIF11/Eg5) in biochemical and cellular assays to develop an ADC to target IL3RA on cancer cells ,23,24,25KSP is active in all proliferating cells and therefore, KSP inhibitors (KSPi) representing various structural classes have resulted in neutropenia, mucositis, and stomatitis in clinical trials ,30,31,32To generate the IL3RA-ADC BAY-943, a non-cell-permeable KSPi was conjugated randomly to the lysine residues of a humanized derivative of the anti-IL3RA antibody 7G3 , TPP-947The binding affinity of the IL3RA-targeting antibody TPP-9476 (IL3RA-Ab) to human and cynomolgus monkey IL3RA was assessed by surface plasmon resonance (SPR) and flow cytometry. IL3RA-Ab showed high affinity to both the human and the cynomolgus monkey IL3RA protein with dissociation constants (KD) of 11 nmol/L and 16 nmol/L, respectively, as determined by SPR. No binding to murine IL3RA was observed. Furthermore, IL3RA-Ab bound specifically to the IL3RA-expressing human hematologic cancer cell lines MOLM-13, MV-4-11, and KG-1 as determined by flow cytometry A.As the prerequisite for ADC activity is to effectively deliver the cytotoxic payload into the cells, we next studied the ability of IL3RA-Ab to internalize upon target binding. The fluorescently labeled IL3RA-Ab showed highly specific, target-dependent internalization in the IL3RA-positive MOLM-13 and MV-4-11 cell lines with >3.5-fold enhancement as compared with the non-specific internalization of the isotype control antibody B,C. In f50 18–21 nmol/L in MOLM-13 cells; Since IL3RA-Ab demonstrated the essential properties of an effective ADC antibody, we conjugated a non-cell permeable KSPi to the lysine residues of the IL3RA-Ab TPP-9476 via a novel legumain-cleavable peptide linker  to produ50) values at the subnanomolar to nanomolar range in the IL3RA-positive AML  and HL  derived cell lines, whereas little activity was observed in the tumor cell lines with low levels of or negative for IL3RA membrane expression . Moreover, in IL3RA-positive AML and HL cell lines, a 10 to 100-fold higher sensitivity to IL3RA-ADC compared to the non-targeted isotype control ADC was observed  cell line-derived xenografts: MOLM-13 human AML and MV-4-11 human biphenotypic leukemia models in mice. Both the MOLM-13 and MV-4-11 cell lines harbor s in AML . The medIn the vehicle and the isotype control ADC groups, nearly all mice had symptoms of leukemia, i.e., splenomegaly and paralysis of hind limbs. In addition, the mean body weights decreased in these treatment groups, indicating that the mice suffered from leukemia . HoweverThe antitumor efficacy of IL3RA-ADC was also tested in the subcutaneous MOLM-13 and MV-4-11 xenograft models in mice. Repetitive dosing with IL3RA-ADC resulted in a significant suppression of tumor growth in both models compared to the isotype control ADC, while the standard-of-care cytarabine showed no activity in these models . FurtherNMP1, FLT3-ITD, IDH1, IDH2, and DNMT3A  reduced tumor burden compared to the vehicle or isotype control ADC, as indicated by a decreased number of human CD45 (hCD45)/human IL3RA (hIL3RA)-positive cells in blood on day 56 (both p < 0.001). In addition, treatment with IL3RA-ADC using either the preventive or therapeutic setting resulted in prolonged MSTs of 107 or 108 days, respectively  or a therapeutic (treatment started on day 34) setting. IL3RA-ADC administered at 10 mg/kg inhibited the growth of IL3RA-positive AML cells, as indicated by temporarily reduced numbers of hCD45-positive cells in blood compared to vehicle or isotype control ADC in both settings .The safety, including possible changes in the hematologic cell populations, of IL3RA-ADC was evaluated in the cynomolgus monkey in two range-finding studies with single or repeated dosing. IL3RA-ADC was well-tolerated without adverse events typically observed with ADCs containing other payload classes, such as thrombocytopenia, neutropenia, or signs of liver toxicity. In addition, mucositis, a dose-limiting toxicity for small molecule KSP inhibitors in clinical studies , was notThe effect of the toxophore metabolite BAY-716 was also analyzed after a single dose of 0.25 mg/kg in rats (data not shown). No laboratory or histopathology findings were observed, indicating that this non-cell-permeable toxophore metabolite does not induce toxic effects thereby underlining the good safety profile of IL3RA-ADC.Despite the recent progress in the treatment of AML, clinical outcomes have improved only minimally over the past three decades. Therefore, novel therapeutic agents with a larger therapeutic window and a favorable tolerability profile are urgently needed to improve the therapeutic outcome for AML patients. Increasing evidence indicates that IL3RA is highly expressed in leukemic stem cells but not in normal hematopoietic stem cells, and it associates in AML with treatment response, minimal residual disease detection, and prognosis ,17. ConsHere, we explored a novel concept to improve the therapeutic window and safety of KSP inhibition by targeting a non-cell-permeable KSP inhibitor as ADC to AML cells, and thereby, sparing fast-dividing healthy cells from KSP inhibition. This provides a payload with a novel mode of action and would be a new therapeutic option for the treatment of IL3RA-positive malignancies. The investigated IL3RA-targeting ADC  consists of a humanized anti-IL3RA antibody conjugated with a stable lysine linkage to a potent proprietary KSPi via a protease-cleavable linker, producing a non-cell-permeable payload metabolite.KSP is an ATP-dependent plus-end directed motor protein, which generates force and moves along microtubules, and it is involved in the separation of the centrosomes, the generation of the bipolar spindle, and thereby plays an important role in mitosis . The inhAntibody–drug conjugates (ADCs) are one solution that has been proposed to mitigate the toxic side effects of anti-mitotic therapies and to broaden their therapeutic window. In fact, currently more than 60 ADCs against multiple targets in solid and hematologic tumors are in clinical trials ,46, and The IL3RA-ADC BAY-943 demonstrated the capability of delivering a novel cytotoxic payload to IL3RA-positive cells. The IL3RA antibody TPP-9476 to which the KSP inhibitor payload is linked via a legumain-cleavable peptide linker showed high binding affinity and specificity to IL3RA and bound specifically to IL3RA-expressing human AML and HL cell lines. The IL3RA antibody internalized into the lysosomes of IL3RA-positive MOLM-13 and MV-4-11 AML cell lines, and IL3RA-ADC demonstrated high cytotoxic potency in IL3RA-positive MV-4-11 and MOLM-13 AML and HDLM-2 and L-428 HL derived cell lines. Furthermore, in the IL3RA-positive cell lines tested, IL3RA-ADC showed 10 to 1000-fold cytotoxicity compared with the isotype control ADC, indicating high target selectivity. The less prominent selectivity observed in MOLM-13 and THP-1 cells may be explained by a non-specific uptake of the isotype control ADC to AML cells differentiated along the macrophage lineage.In an in vivo setting, IL3RA-ADC administered at 10 mg/kg increased survival in both the IL3RA-positive MOLM-13 and MV-4-11 cell line-derived and IL3RA-positive AM7577 and AML11655 patient-derived AML xenograft models harboring molecular alterations associated with poor prognosis in AML. The increased survival was accompanied by a reduction in the growth of IL3RA-positive AML cells and tumor size in the systemic and subcutaneous models, respectively. In the IL3RA-positive HDLM-2 subcutaneous Hodgkin lymphoma model, IL3RA-ADC treatment also resulted in significant antitumor efficacy with most animals being tumor-free at the end of the study.The body weights of the animals decreased over the course of the study, indicating that they suffered from leukemia. However, no IL3RA-ADC treatment-related body weight losses were observed, suggesting good tolerability , particu+Lin− cell population, and transient decreases in basophils and IL3RA-positive basophils. Importantly, liver toxicity, thrombocytopenia, and neutropenia, which are frequently observed with ADCs in the clinic and in cynomolgus monkey preclinical studies [The good tolerability of the IL3RA-ADC was confirmed by repeated dose safety and immunotoxicity assessments in the cynomolgus monkey with no changes in the portion of the CD34 studies ,49, wereCell lines were acquired from DSMZ  unless otherwise noted and cultured according to the provider’s instructions. Human MDA-MB-231 breast cancer, NCI-H292 non-small cell lung cancer, MV-4-11 and THP-1 acute monocytic leukemia, KG-1 acute myelogenous leukemia, Rec-1 mantle cell lymphoma, and Ramos Burkitt’s lymphoma cells were obtained from ATCC . Human OVCAR-8 ovarian cancer cells were acquired from the NCI-60 Human Tumor Cell Line Panel . The human diffuse B cell lymphoma cell line HBL-1 was obtained from Dr. Georg Lenz  and cultivated in RPMI 1640 supplemented with 10% fetal calf serum (FCS). The human Hodgkin lymphoma cell line L-428 (source not known) was cultivated in RPMI 1640 supplemented with 10% FCS. Cancer cell lines were obtained between 2002 and 2012, authenticated using short tandem repeat DNA fingerprinting at DSMZ , and subwww.syncom.eu; Groningen, the Netherlands), cytarabine (HT0476) from Accord Healthcare GmbH , and staurosporine (#S4400) from Sigma-Aldrich .The anti-IL3RA antibodies TPP-9476 and TPP-8988 (recognizes a different epitope in the extracellular domain of IL3RA than TPP-9476) and the isotype control antibody TPP-754, the IL3RA-ADC BAY-943, the isotype control ADC BAY-229 (with TPP-754), the non-cell-permeable toxophore metabolite BAY-716 (active toxophore metabolite of IL3RA-ADC), and the cell-permeable small molecule KSPi BAY-331 were manufactured at Bayer AG. Ispinesib (SYNT1009) was purchased from Syncom  was generated by humanization of the murine anti-IL3RA antibody 7G3  as descrThe generation and characterization of the IL3RA-Ab is described in the ® FlowSight® or the Guava easyCyteTM flow cytometers  and analyzed using the IDEAS® software or the guavaSoft 2.6 software .Internalization and colocalization experiments were performed in MOLM-13 and MV-4-11 AML cell lines using flow cytometry-based imaging. The IL3RA-specific antibody TPP-9476, IL3RA-ADC BAY-943, corresponding isotype control antibody TPP-754, and isotype control ADC BAY-229 were lysine-conjugated with a ten-fold molar excess of CypHer 5E mono NHS ester  at pH 8.3. The reaction mixture was purified by chromatography , followed by centrifugation . Alexa 488  was utilized as a constitutive dye. The fluorescence was measured using the Amnis4/well) were incubated with the labeled antibodies (10 µg/mL) at 37 °C, 5% CO2 for 0, 1, 2, and 6 h. The fluorescence was measured using the Amnis® FlowSight® or the Guava easyCyteTM flow cytometers and analyzed using the IDEAS® or the guavaSoft 2.6 software. The kinetics of the internalization were determined based on the analysis of the median fluorescence intensity (MFI) over time.For the internalization assay, the tumor cells (5 × 104/well) were incubated with the labeled antibodies (20 µg/mL) at 37 °C, 5% CO2 for 0 h, 0.5 h, 2 h, and 6 h. The lysosomal compartment marker CytoPainter LysoGreen  was added 30 min before the end of the incubation period. After incubation, the cells were washed and resuspended in ice-cold FACS (fluorescence-activated cell sorting) buffer consisting of phosphate-buffered saline (PBS) and 3% FCS. The lysosomal colocalization was analyzed with FACS image analysis using the IDEAS® software.For the colocalization studies, MOLM-13 and MV-4-11 tumor cells . Cells (2000–5000 cells/well) were incubated at 37 °C, 5% CO2 for 24 h and the compounds were added at concentrations of 3 × 10−11–3 × 10−7 M  in triplicates. Cell viability was determined at the beginning (day 0) and after 72 h incubation in the presence or absence of ADCs. The IC50 of the growth inhibition was calculated in comparison to day 0. The IL3RA antigen density was determined with the QIFI  quantitative flow cytometry assay using the murine anti-IL3RA antibody clone 7G3 .The antiproliferative activity of IL3RA-ADC was determined in a panel of human tumor cell lines using the CellTiter-GloAll animal experiments were conducted in accordance with the German Animal Welfare Law and approved by Berlin authorities . When a body weight loss of >10% was observed, treatment was ceased until recovery. In the systemic models, mice were sacrificed individually when signs of leukemia were observed . The molecular alterations of the tested in vivo models are described in 6 or 5 × 106 cancer cells in 0.9% NaCl, respectively. The mice were treated with i.p. injection of IL3RA-ADC at 2.5 or 10 mg/kg once weekly (Q7D), every two weeks (Q14D), or every three weeks (Q21D). In the MOLM-13 model, treatments were started on day 10, and the study was terminated on day 124. In the MV-4-11 model, treatments were started on day 3, and the study was terminated on day 174 after tumor cell injection.For the systemic MOLM-13 and MV-4-11 models, female CB17-SCID  or NOD SCID  mice were injected intravenously (i.v.) with 200 µL of 7.5 × 106 cancer cells in PBS at CrownBio . The development of AML was monitored by flow cytometric analysis of the percentage of hCD45 cells in blood. On day 38 after tumor cell injection, when approximately 4% of hCD45-positive cells were present, the mice were randomized, and treatments were started. The mice were treated with i.v. injections of IL3RA-ADC at 2.5 or 10 mg/kg, Q7D or the isotype control ADC at 10 mg/kg, Q7D. In the first IL3RA-ADC treatment group, the initial dose of 2.5 mg/kg, Q7D was increased to 10 mg/kg, Q14D from the second administration onwards (indicated as 2.5 + 10 mg/kg). The study was terminated on day 104 after tumor cell injection.For the systemic AM7577 PDX model, female NOD/SCID mice  were injected i.v. with 100 µL of 1.4 × 10®  were injected i.v. with 400 µL of 1 × 107 cancer cells in PBS at EPO Berlin-Buch GmbH . The development of AML was monitored by the percentage of hCD45-positive cells in blood as determined by flow cytometry. Treatments were initiated on day 5 after tumor cell injection (preventive setting) or day 34 after tumor cell injection (therapeutic setting) when approximately 46% of hCD45-positive cells were detected in blood. The mice were treated with i.v. injections of IL3RA-ADC at 10 mg/kg, Q7D or the isotype control ADC at 10 mg/kg, Q7D. The study was terminated on day 118 after tumor cell injection.For the systemic AML11655 PDX model, female CIEA NOG mice7 cancer cells suspended in 30% Matrigel/70% medium. Tumor volume (0.5 × length × width2) was determined based on twice weekly measurement of tumor area by a caliper (length and width). Treatments with IL3RA-ADC  or ispinesib  were started on day 49 when the tumors had reached a mean size of 100 mm3. The study was terminated on day 84 after tumor cell injection.For the HDLM-2 Hodgkin lymphoma model, female CB17-SCID mice  were injected subcutaneously (s.c.) with 100 µL of 1 × 10The subcutaneous MOLM-13 and MV-4-11 models as well as safety studies are described in the p values < 0.05 were considered significant.Statistical analyses were performed using R  . Flow cyThe novel IL3RA-ADC with a differentiated mode-of action demonstrates selective binding and internalization to IL3RA-positive cells, which translates into selective and efficacious antitumor activity in IL3RA-positive AML and Hodgkin lymphoma models. By employing a KSP inhibitor, a stable lysine linkage between the payload and the antibody, and a legumain-cleavable linker resulting in a non-cell-permeable payload metabolite, IL3RA-ADC presents a new alternative for the treatment of IL3RA-positive malignancies. Using the KSPi as a payload in an ADC is expected to result in manageable toxicity and a broader therapeutic window compared to that reported for the systemic application of KSPi in clinical trials. Our data support further development of the IL3RA-ADC BAY-943 as an innovative approach for the treatment of patients with IL3RA-positive AML.


PAC in dogs is infrequent, difficult to differentiate from TCC and usually characterized by poor prognosis, enhancing the value of the few available cell lines. However, as cell lines adapt to culturing conditions, a thorough characterization, ideally compared to original tissue, is indispensable. Herein, six canine PAC cell lines and three TCC cell lines were profiled by immunophenotype in comparison to respective original tumor tissues. Three of the six PAC cell lines were derived from primary tumor and metastases of the same patient. Further, two of the three TCC cell lines were derived from TCCs invading into or originating from the prostate. Cell biologic parameters as doubling times and chemoresistances to commonly used drugs in cancer treatment  were assessed. All cell lines were immunohistochemically close to the respective original tissue. Compared to primary tumor cell lines, metastasis-derived cell lines were more chemoresistant to doxorubicin, but equally susceptive to carboplatin treatment. Two cell lines were multiresistant. COX-2 enzyme activity was demonstrated in all cell lines. However, meloxicam inhibited prostaglandin E2 production in only seven of nine cell lines and did neither influence metabolic activity, nor proliferation. The characterized nine cell lines represent excellent tools to investigate PAC as well as TCC in prostate and urinary bladder of the dog. Furthermore, the profiled paired cell lines from PAC primary tumor and metastasis provide the unique opportunity to investigate metastasis-associated changes PAC cells undergo in tumor progression. The combination of nine differently chemoresistant PAC and TCC cell lines resembles the heterogeneity of canine lower urinary tract cancer.Canine prostate adenocarcinoma (PAC) and transitional cell carcinoma (TCC) of prostate and urinary bladder are highly invasive and metastatic tumors of closely neighbored organs. Cell lines are valuable tools to investigate tumor mechanisms and therapeutic approaches  For resIn general, cell lines are established from tumor-burdened individuals. Ideally, investigated features of the primary tumor are representative for the tumor type or subtype and stay preserved in the derived cell line . Howeverp53 have also been detected and correlated with poor survival , n0 the cell count after 24 h, t the time [h] and r the specific growth rate of each cell line. Doubling times (DT) [h] were calculated using the formula DT [h] = ln(2) / ln(r).Cell lines were seeded in 6-well plates 2*102 supply achieved constant cultivation conditions as described above.Cell lines were seeded in 96-well plates 7,500 cells/well. Morphology and growth behavior were surveyed with a live cell Imaging microscope  over 72 h in 100x magnification. Pictures of the same position were taken every 15 minutes with the program LAS AF 2.6.0. A heating unit, a humidifier and regulated CODoxorubicin  was diluted to 100 μM in PBS and carboplatin  was diluted to 10 mM in cell culture medium. Therefore, solvent controls in cell culture medium used for controls in doxorubicin and carboplatin inhibition were not necessary. Meloxicam was purchased from Selleck Chemicals  and solved in dimethyl sulfoxide (DMSO) to a concentration of 75 mM. Solutions were sterile filtered and aliquots were stored at -20 °C.® AQueous One Solution Cell Proliferation Assay, Promega, Mannheim, Germany). All cell lines were seeded in 96-well plates 7,500 cells/well. The next day, medium was exchanged, and cells were exposed in quadruplets to increasing concentrations of doxorubicin  or carboplatin . Dilutions were chosen based on doxorubicin serum concentrations in patient dogs [® AQueous One Solution Cell Proliferation Assay, Promega) was conducted according to manufacturer’s protocol. The absorbance was recorded at 490 nM using a Synergy2 plate reader . Blanks were subtracted.The inhibitory effect of doxorubicin and carboplatin on metabolic activity was ascertained by MTS assay  were calculated using GraphPad Prism Software 3.0 .5 cells/well and exposed to the respective concentrations of doxorubicin or carboplatin used in MTS assay the next day. For carboplatin, 0.5 and 1000 μM were omitted again, as these concentrations showed little differences to 0 and 2000 μM in MTS assays. Controls were supplied with fresh medium. After 72 h of exposure, medium including floating cells and debris was collected. Adherent cells were detached with 1 ml TrypLE™Express (Life Technologies GmbH), pooled with the respective medium and centrifuged for 10 min at 160 g. The obtained pellets were stained with Annexin V-FITC Detection Kit plus  according to manufacturer’s protocol and fluorescence signals were measured by flow cytometry using a MACSQuant® Analyzer 10 . Data analysis was performed with FlowJo 10.3 software. Debris was excluded and proportions of vital, apoptotic and late apoptotic cells were determined.Additionally, proportions of vital, apoptotic and late apoptotic cells were assessed after doxorubicin and carboplatin exposure. Cells were seeded in 6-well plates 1.5*104 cells/well and in 96-well plates 7,500 cells/well. The next day, medium was exchanged and cells were exposed to 1μM, which resembles the therapeutic steady state plasma concentration in dogs [For meloxicam response regarding total cell number and metabolic activity, cells were seeded in 24-well plates 4*10 in dogs , and 10 4 cells/well in 12-well plates. The next day, medium was changed and cells were treated in duplicates with 1 and 10 μM meloxicam. Controls were incubated with 0.13% (v/v) DMSO. After 24 h of exposure, medium was collected, centrifuged for 10 min at 160 g for removal of cells and debris and stored at -80 °C. PGE2 concentrations in medium samples were measured in duplicates using a competitive ELISA  according to manufacturer’s protocol.The functionality of COX-2 enzyme was ascertained by production of prostaglandin E2 (PGE2). Each cell line was seeded in a density of 7.5*10Statistical analysis of data was performed with SAS software 7.1 . Means and standard deviations are displayed. For comparison to untreated controls in metabolic activity, cell count and proportions of vital, apoptotic and late apoptotic cells, Dunnett’s test for multiple comparisons was applied. Additionally, Student’s t-tests were performed for comparison between PGE2 concentrations at 1 and 10 μM meloxicam. The confidence levels were set to 95% , 99%  and 99.9% . All experiments were conducted three times independently, except from cell count and apoptosis rate of doxorubicin and carboplatin response, which were conducted four times and from PGE2 concentrations, which were measured once.FFPE prostate tissue samples P1, P2, P3 and P4.1 were histopathologically classified as infiltrative PAC . All fouIn both metastasis-derived cell lines Metadcarc1511.2 and Metadcarc1511.3, cells were distinctly larger than cells of the primary tumor cell line Adcarc1511.1 and showed numerous cytoplasmic vacuoles, which were also observed in Adcarc1508 and TCC0840 cells  between 0.9151 and 0.9999.Doubling times of cell lines varied between 18.2 and 38.5 h . The lonTCC0840, Adcarc0846, TCC1506, Adcarc1508, Adcarc1511.1, Metadcarc1511.2 and Metadcarc1511.3 grew in monolayers in cobblestones-like patterns. Adcarc0846 and Adcarc1508 tended to form piles when reaching confluence. Adcarc1258 and TCC1509 had low anchoring potential and did not establish uniform monolayers in culture. Instead, cells formed prominent protrusions when attached to the well bottom and tended to form aggregates with increasing cell density. Videos of growth behavior are provided as After 72 h of exposure to doxorubicin, all cell lines showed decreased metabolic activity at 200 nM and higher Figs  and 4. RCell count IC50 values for doxorubicin varied between 3.6 nM in Metadcarc1511.3 and 95.7 nM in Adcarc1258. At 2000 nM doxorubicin, total cell numbers in all PAC and TCC derived cell lines were reduced below seven percent, total cell numbers of cell lines originating from metastases were reduced below 16%.Carboplatin metabolic activity IC50 values varied between 33.6 and 129.3 μM in all cell lines with Metadcarc1511.2 and Metadcarc1511.3 ranging in the lower half . ReductiCell count IC50 values for carboplatin ranged from 3.8 μM in TCC1509 and Adcarc1511.1 to 10.9 μM in Metadcarc1511.2 . ReductiDoxorubicin and carboplatin induced dose-dependent increases of apoptotic cell proportions and decreases of vital cells in nearly all cell lines Figs  and 8. NAfter 72 h exposure to carboplatin, Adcarc1511.1 was the cell line reacting first with increased apoptosis rates at 20 μM. At 200 and 500 μM carboplatin, all cell lines except from TCC0840 showed increased apoptotic rates.Metabolic activities and total cell numbers did not decrease in any cell line treated with meloxicam in both concentrations .Basal PGE2 concentrations in medium ranged beyond 1000 pg/ml in all cell lines except from Adcarc1258, Adcarc0846 and TCC1509 . MeloxicThe presented study provides nine thoroughly characterized cell lines of canine lower urinary tract cancer. Therefore, combined in a panel, these cell lines offer a great opportunity to evaluate therapeutic strategies in any case of lower urinary tract cancer in dogs, including TCC located in the prostate and PAC metastasis.When comparing initial characterizations of different human ,45,67,68in vitro models are required for repeatable results in further experiments, cell lines were herein characterized in later passages.Some of the previously mentioned human and canine PAC and TCC cell lines were characterized in early and some in later passages. However, subculturing-induced gene expression changes were reported to happen within the first ten passages , particuMetastasis research is an important part in human and veterinary cancer research. Various studies were carried out using metastasis-derived prostate cancer cell lines like LNCaP and PC-3  or metasThe broad range of the herein analyzed cell lines’ IC50 values for doxorubicin are comparable with human prostate and bladder cancer cells  and withIn most cell lines, cell counts decreased before apoptotic proportions of cells increased, indicating that doxorubicin and carboplatin first inhibit proliferation and induce apoptosis at higher concentrations. However, in Adcarc1258 after exposure to doxorubicin and in TCC1509 after exposure to both drugs, apoptosis began at lower concentrations compared to the other cell lines, which makes these cell lines more sensitive to apoptosis inducing stimuli.Exposed to high concentrations of doxorubicin, still a number of cells survived in both metastasis-derived cell lines and in TCC0840. Especially in those cell lines, apoptotic proportions of cells did not increase until 2000 nM, indicating that doxorubicin inhibits proliferation, but does not induce apoptosis. Apoptotic proportions of TCC0840 were relatively high in controls and in all treated samples. It remains uncertain if the detachment of cells or the staining procedure induced apoptosis in this cell line or if a certain proportion of this cell line constantly underwent apoptosis. Changes of vital and apoptotic proportions of cells in cell lines Adcarc1511.1 and Metadcarc1511.3 that were observed at 200 and 100 μM doxorubicin but not at higher concentrations might be of artificial origin. Prominently in both metastasis-derived cell lines after treatment with doxorubicin and in Adcarc1258, TCC0840, Adcarc0846 and Adcarc1511.1 after carboplatin exposure, total cell numbers decreased much faster than metabolic activities. Various transporters and other avoidance mechanisms are involved in doxorubicin and carboplatin resistance ,80. Assuin vitro, which was also demonstrated for human prostate cancer cell lines [in vitro studies [Daily administered COX-2 inhibitors increase survival times in dogs with prostate and bladder tumors ,10,81. Hll lines  and canill lines –85. Consll lines , supportll lines ,85. In A studies ,87 mightp53 mutation cannot be excluded. Specific mutations affecting the binding site of the herein used antibody might prevent it from binding to p53 [Due to a short half-life of its wild type, p53 is almost exclusively detectable in accumulated and mutated forms by immunohistochemistry ,14. MutaEpithelial to mesenchymal transition is a common pathological process in canine metastatic prostate cancer ,91. It iAll tumors apart from P1 possess basal cell features by expressing pan-CK and luminal cell characteristics by staining positive for CK7 and CK8/18, which supports the involvement of basal and luminal cells in canine prostate cancerogenesis ,95. UPIILike in the human prostate cancer cell line LNCaP , Adcarc1Transcriptomic analysis of canine PAC tissues referred to non-cancerous controls will be the next step to identify upregulated cancer-specific targets for promising inhibitors . Furtherin vitro tools that are immunohistochemically close to the respective tumor tissue. Adcarc1511.1, Metadcarc1511.2 and Metadcarc1511.3 provide the unique opportunity to investigate functional cellular features associated with canine PAC metastasis, as they were derived from primary tumor and metastasis of the same patient. TCC0840 and TCC1509 represent the uncertain percentage of TCC with invasion or emergence in prostate tissue. Adcarc1258 and TCC0840 represent multi-resistant canine PAC and TCC, while TCC1506 and TCC1509 are chemosensitive. Both metastasis derived cell lines Metadcarc1511.2 and Metadcarc1511.3 are resistant to apoptotic effects induced by doxorubicin, but susceptive to carboplatin treatment.Summarized, this thorough characterization enriches canine lower urinary tract cancer research with nine valuable S1 Tablem = male; f = female; n = neutered; original tissue ; cell line names are explained as institution (Tiho = University of Veterinary Medicine Hannover); species (D = dog); tissue origin ); diagnosis ; abbreviations of cell lines written in bold; none = cell lines have not been published yet; n.a. = tissue of patient no. 5 is missing, as the patient owners declined surgery and necropsy; *diagnosis by cytology of cells obtained by fine needle aspiration biopsy; **in total remission after a combination protocol of vincristine, asparaginase, cyclophosphamide, doxorubicin, prednisone and lomustin.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fig(TIFF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Video(MP4)Click here for additional data file.S2 Video(MP4)Click here for additional data file.S3 Video(MP4)Click here for additional data file.S4 Video(MP4)Click here for additional data file.S5 Video(MP4)Click here for additional data file.S6 Video(MP4)Click here for additional data file.S7 Video(MP4)Click here for additional data file.S8 Video(MP4)Click here for additional data file.S9 Video(MP4)Click here for additional data file.


During the development of cell lines for therapeutic protein production, a vector harboring a product transgene is integrated into the genome. To ensure production stability and consistent product quality, single-cell cloning is then performed. Since cells derived from the same parental clone have the same transgene integration locus, the identity of the integration site can also be used to verify the clonality of a production cell line. In this study, we present a high-throughput pipeline for clonality verification through integration site analysis. Sequence capture of genomic fragments that contain both vector and host cell genome sequences was used followed by next-generation sequencing to sequence the relevant vector-genome junctions. A Python algorithm was then developed for integration site identification and validated using a cell line with known integration sites. Using this system, we identified the integration sites of the host vector for 31 clonal cell lines from five independent vector integration events while using one set of probes against common features of the host vector for transgene integration. Cell lines from the same lineage had common integration sites, and they were distinct from unrelated cell lines. The integration sites obtained for each clone as part of the analysis may also be used for clone selection, as the sites can have a profound effect on the transgene’s transcript level and the stability of the resulting cell line. This method thus provides a rapid system for integration site identification and clonality verification. After transfection, the resulting cells are heterogeneous and have a wide range of productivity and other properties. Single cell cloning is performed, and high producing cell lines are isolated as candidates to be the production cell line. To ensure the consistency and quality of the products produced by the cells over time, regulatory agencies require the demonstration of clonality, i.e. that cells originated from a single transfected ancestor cell.2Chinese hamster ovary (CHO) cells are one of the most commonly used cell lines used to produce therapeutic proteins.3 and even stability of the region.4After transfection and entry into the nucleus, the vector integrates into the genome of the host cell randomly. One cell may have one or more integration events depending on the vector used and its dose. The chance of two integration events occurring on the same site, either on both alleles of the same chromosome or in the genome of two different cells, is extremely low. A characteristic of clonally derived cells is thus that all cells within the population should have the same integration site(s) of the vector on the genome. Identifying the integration site of the product gene and demonstrating that two sublines of cells have the same integration site can thus be taken as evidence that the two originated from the same ancestor. Furthermore, the genome context of the integration site can be explored to reveal information on epigenetic accessibility, transcriptional activity,5 splinkerette-PCR,6 linear amplification mediated PCR (LAM-PCR),7 and targeted locus amplification (TLA)8 have been developed to identify integration sites of transgenes. These methods can be applied to demonstrate clonality. LAM-PCR for integration site identification has been used to analyze and track clonal lineages of blood cells following lentiviral gene therapy,10 while TLA has been used in CHO cells for identification of clones with the same integration sites after pool selection and single cell cloning.11Several PCR-based assays such as inverse PCR,In this study, we established a high throughput method that can be used to support monoclonality in different production cell lines generated using random integration of plasmids. By designing one set of probes for the common features of the host vector used to introduce the gene of interest, selective capture was utilized to simultaneously sequence vector-containing DNA from dozens of clonal cell lines from multiple independent vector integration events with different product genes. This method also allowed for a relatively simple bioinformatic analysis to identify these integration sites through the use of a Python algorithm. Cell lines were separable based on their integration sites, and we were able to demonstrate the clonality of the original cell populations. This method thus provides a rapid and cost-efficient tool for clonality verification and integration site identification in product producing CHO cells.13 The organization of the tricistronic vector used for introducing the transgene is illustrated in The monoclonal antibody producing cell line, SH-87, has been described previously.Cell lines used for clonality analysis were all derived from the same CHO-K1 host cell line using the vector shown in For integration site analysis of cell line SH-87, probes (120bp in length) were focused on the ends of the linearized vector . The firFor clonality analysis of platform cell lines, probes (120bp in length) were designed to capture the regions of the linearized vector that are common in all transfected cell lines at 5x coverage with tiling . SequencAn overview of the experimental methods is shown in For all other cell lines, gDNA was extracted using a Qiagen Blood and Cell Culture DNA Max Kit . 100ng of gDNA from each cell line was sheared using a Covaris E220 ultrasonicator  to obtain fragments with an average length of 250bp. Further library preparation was performed using an Agilent SureSelect HS Reagent Kit . Libraries for all cell lines were pooled prior to sequence capture following the Agilent SureSelect system instructions. Captured DNA was eluted, amplified, and sequenced on one lane of Illumina MiSeq  using 100bp paired end reads for a total of 25 million reads.14 (version 0.33). Reads were then mapped to the CriGri-PICR release of the Chinese hamster genome15 using BWA-MEM16 (BWA release 0.7.17). The host vector sequence was added to the genome sequence as an additional scaffold. Duplicate reads were removed using the MarkDuplicates command in Picard tools 2.18.16. The resulting SAM (Sequence Alignment/Map) file, which describes how reads are aligned to the genome, was used for further analysis. Samtools17 (version 1.9) was used to create BAM files for visualization in IGV18 (version 2.4.19).Fastq files from sequencing were trimmed to remove adapter sequences using Trimmomatichttps://doi.org/10.13020/9wgm-mj51). Sequence capture data from cell line SH-87 was used for validation of the method, as integration sites from this cell line had been previously determined using whole genome sequencing and confirmed by PCR/Sanger sequencing.13An analysis pipeline was written in Python 3.6.3 to identify integration sites from mapped sequencing data  of the SAM file and the SA:Z tag added by BWA-MEM for chimeric alignments to identify reads which contain a split alignment between the vector and genome . These aNext, the CIGAR (Concise Idiosyncratic Gapped Alignment Report) tag for each alignment was used to find the exact vector-genome junction position. The CIGAR string is a compact method to report how bases within a read align to the reference genome, specifying which bases match, are deleted/inserted, or are clipped  in the case of split reads. Column 4 of the SAM file format reports the leftmost  position that is aligned for the read alignment. If the CIGAR string begins with an alignment match (M), the length of the match is added to the position to obtain the vector-genome junction . Otherwi13 All six junctions were found by the algorithm  were used in this study. Cell lines characterized from lineages A, B, and E consisted of a clonal parental cell line and a set of subclones. Unrelated cell lines C-1 and D-1 were added to the analysis to increase the diversity of cell lines examined. Cell line A-1 was run in duplicate (labeled A-1 and A-1D) for a total of 32 samples. gDNA from these samples was sheared, and fragments containing probed vector regions were captured and sequenced.5  for each sample ranged from 0. 46 - 5.6 x 105 . The per5 . This coThe library size varied with cell lineage, with the cell lines from lineage E having on average significantly more unique reads after sequencing than those from lineage A or B , had an additional vector-genome junction on the same scaffold as the integration site, either 1kbp  would suggest possible non-clonality.The method presented here allows for rapid verification of clonality or common lineage between different cell lines. Use of sequence capture increases the relevant information extracted from sequencing, and the bioinformatic analysis is rapid for processing the low number of reads required from each sample. Additionally, through the use of capture probes targeting common regions of the host vector, future clones can easily be added to the analysis without the need for new probe design. In general, a longer sequencing read length and an increased library size for each sample may have improved this dataset and would be helpful for detecting rarer integration events. The library size may be increased either through reducing the number of samples pooled before capture, or by increasing the input DNA from each sample to reduce PCR duplicates from library preparation. Despite some of these issues in sequencing, the pipeline was robust in its ability to distinguish cell lines of different lineages based on their integration sites. This method is thus a valuable, accessible tool to address clonality verification.Supplementary Information


A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub ( Tumor tissues are mixtures of different cell types. Identification and quantification of constitutional cell types within tumor tissues are important tasks in cancer research. The problem can be readily solved using regression-based methods if reference signals are available. But in most clinical applications, only partial references are available, which significantly reduces the deconvolution accuracy of the existing regression-based methods. In this paper, we propose a partial-reference based deconvolution model, PREDE, integrating the non-negative matrix factorization framework with an iterative optimization strategy. We conducted comprehensive evaluations for PREDE using both simulation and real data analyses, demonstrating better performance of our method than other existing methods. PLOS Computational Biology Methods paper.This is a Tumor tissues are heterogeneous and consist of different cell types including tumor cells (or sub-clones) and various microenvironmental cell types such as infiltrating immune cells and stromal cells –3. The iWith the rapid development of high-throughput sequencing technology, a large number of genome, epigenome, transcriptome and proteome data of tumor samples have been profiled. Such biomedical big data provide a possibility to study tumor heterogeneity from the molecular perspective by using computational methods. Although the recent emerging single-cell sequencing technology strives to tackle these problems by measuring expression profiles of thousands to millions of cells simultaneously, it is yet not feasible to be conducted for large cohort studies due to, for example, expensive cost and extensive dropout events . TherefoIn recent years, many computational methods have been proposed for bulk data deconvolution –10. At pHowever, in real clinical practice, only a fraction of cell types is known while the rest are unknown so the deconvolution problem should be subject to partial reference. A straightforward way to deal with this problem is to use current available information of known cell types as the reference to deconvolve the whole mixture signals, or assume all unknown proportion to be from one cell type . Howeverpartial-reference based deconvolution (PREDE) model based on the non-negative matrix factorization (NMF) framework using an iterative optimization strategy to address the above challenges. Using the expression profiles of the available cell types as input, PREDE could simultaneously estimate both the proportions of all cell types and the expression profiles of unknown cell types. We performed comprehensive evaluations for the proposed deconvolution method in comparison with other existing methods using both simulated data and real dataset of tumor samples. The results demonstrated that PREDE could effectively deconvolve mixture tumor samples from partial reference signals and could reveal novel insights into tumor heterogeneity and clinical prognosis.In this paper, we proposed a Y = WH+ϵ, where Y represents expression matrix of heterogeneous samples, W is basis matrix representing the quantitative expression profiles of constitutional cell types and H is the proportion matrix. If quantitative profiles of cell types (i.e. W) are known, it is so-called reference-based deconvolution. Alternatively, if both basis matrix W and proportion matrix H are unknown, it is so-called reference-free deconvolution.As the previous methods did , we assuW as W1, and the unknown portion as W2, i.e., W = . Given expression matrix Y for all tumor samples and basis matrix W1, PREDE infers the basis matrix W2 for unknown cell types and overall proportion matrix H. The main workflow of PREDE is briefly illustrated in W1 fixed in each iteration , PREDE is actually the typical reference-based deconvolution method. On the other hand, if expression profile of any cell type is unavailable , PREDE then becomes the typical reference-free method.Our PREDE method can be viewed as a generalization of previous reference-based and reference-free deconvolution algorithms. If known reference W. Gene expression matrix Y for mixture samples is then obtained by multiplying W with a randomly proportion matrix H generated from the Dirichlet distribution, followed by an additional error matrix with Gaussian distribution. Y and available reference matrix W1 were used as inputs of PREDE and Akaike information criterion (AICc) was employed to determine the optimal number of cell types.We conducted a series of simulations to comprehensively evaluate the performance of PREDE, by considering three factors in the simulations: noise ratio, expression similarity between cell lines and proportion of rare cell types. To this end, we downloaded gene expression profiles of 91 lung cancer cell lines from the CCLE dataset and selected some of them as reference c in determining the number of cell types from mixture samples. Following the above simulation procedure, we randomly selected 3, 6 and 10 lung cancer cell lines to generate 100 mixture samples respectively, and assumed only a fraction of cell lines to be known. c scores when W consists of 3, 6 and 10 cell types, but only 1, 4 and 8 of them are supposed to be known. As expected, AICc decreased first and then gradually increased with the increase of numbers of cell types, and correctly achieved the minimum values at K = 3, 6 and 10 respectively. We further investigated the accuracy of AICc in determining the total number of cell types when different numbers of known cell types were used for the input. When the total number of cell types was 6, AICc successfully reaches the minimum value at K = 6, regardless of the numbers of known cell types (K1 = 1), the predicted number of cell types is slightly underestimated  between true and predicted proportions of available cell types for all four methods at different levels of noise . PREDE obtained the lowest biases and relatively stable results at all levels of noise, compared to qprog, dcq and CIBERSORT  , dcq  is also capable of inferring gene expression profiles of unknown cell types. We also evaluated the performance of our method when one unknown cell type is highly similar to known cell types. To this end, we constructed two simulation datasets by selecting different sets of lung cancer cell lines from the CCLE dataset. The first is ‘low similarity set’, which consists of 6 cell lines (4 known and 2 unknown) with relatively low Pearson correlation coefficient (PCC) (0.75~0.8) between each pair of cell lines. The second is ‘high similarity set’, which also consists of 4 known and 2 unknown cell types but one unknown cell line is highly correlated with a known one (with PCC about 0.95). For both datasets, PREDE and RF were used to infer proportions of unknown cell lines, where prediction accuracies were measured by MAE and Pearson correlations between predicted and true proportions. We found that for both criteria, PREDE showed relatively lower biases and higher Pearson correlations in recovering the proportions of unknown cell type compared with RF .K = 5 (K = 6). Then we sought to evaluate the performance of the PREDE and RF in inferring proportion and expression profile of the unknown rare population when K was given. PREDE showed a consistently lower proportion bias and higher profile correlation than RF when proportions of the rare cell type changed from 0.07 to 0.10 , AICc achieved its minimal value at K = 5 . This in to 0.10 . Similar methods .c curves . If the number of known cell types is less than K-1, they will treat the remaining cell types (>1) as a merged single cell type. Thus, both DeMixT and ISOpure work when only one tissue in the above rat tissue mixture data is unknown , but only PREDE works even when two tissues are unknown .We compared our method with two state-of-the-art methods for the same problem, i.e., DeMixT  and ISOpWhen the profile of one tissue was assumed to be unknown, PREDE outperformed DeMixT and ISOpure in proportion estimation in the case that liver or lung profile was unknown  and in pSince DeMixT and ISOpure are designed specifically for tumor tissue deconvolution, in addition to rat-tissue mixture data, we further evaluated DeMixT, ISOpure and our method based on a synthetic dataset used in n = 20) downloaded from the GEO database (GSE65136) where the corresponding flow cytometry measurement of proportions are available [W), and 3, 5 and 7 of them are selected as the known reference W1 to test PREDE as well as four other immune cell deconvolution tools, i.e., RF, CIBERSORT [We further evaluated our method on a gene expression dataset of PBMC samples  between the predicted and the true proportions were shown in Another superiority of PREDE is the ability to infer proportions of unknown cell types . When onc values. As expected, different subtypes of tumor samples showed distinct immune cell infiltrating patterns . In addition, our method (as well as other deconvolution methods) fails to separate cell types that evolve on a continuum. It is our future work to integrate time-course data or to incorporate single-cell expression profiles as pseudo-time reference for more reliable deconvolution. Third, our method (as well as qprog and RF) assumes the error to be independently and identically Gaussian distributed across different genes, which may not hold for other types of biological data such as DNA methylation or RNA-seq counts data. So further attention should be paid on developing new methods free of such error assumption for partial reference-based deconvolution.Despite its merit, our study still suffers from the following limitations. First, our method needs the number of cell components as input, which can be correctly inferred by minimizing the Akaike information criterion in the simulation study. However, for tumor mixture tissues, the problem was far more complicated because every two cells in tumor tissue can be different. Cells in a tumor tissue can be classified into different numbers of groups at different levels of similarity thresholds. In other words, all cells in a tumor tissue form a hierarchical structure where one can get any number of clusters depending on ‘similarity’ between cells within each group. Therefore, we encourage the users to try different https://portals.broadinstitute.org/ccle) to generate mixed samples with different mixing proportions. Second, a benchmark dataset for cancer immunology study is generated by mixing 3 breast cancer cell lines from CCLE, 3 types of immune cells  from GEO with accession number GSE22886 [We simulated three synthetic datasets to comprehensively evaluate our method. First, gene expression profiles of 91 lung cancer cell lines are downloaded from the Cancer Cell Line Encyclopedia database .In addition, we employed gene expression data of peripheral blood mononuclear cells (PBMCs) of 20 samples (GSE65136) as well as the corresponding flow cytometry measurements  to benchhttps://gdc.nci.nih.gov). Expression profiles of seven immune cells  are available from the Human Primary Cell Atlas (HPCA) database [Furthermore, for real data application, we downloaded level 3 gene expression data of all Breast invasive carcinoma (BRCA), Skin cutaneous melanoma (SKCM) and Bladder urothelial carcinoma (BLCA) samples from GDC data portal , a standardized measure of dispersion of a probability distribution, has been commonly used in feature selection for various high-throughput data ,44. In tn×m matrix Y as the expression profiles of n genes in m tumor samples, we assume that these tumor samples are mixtures of K cell types with different mixing proportions. Denote the basis matrix W as expression profiles for these n genes in K cell types, and the proportion matrix H as proportions of the K cell types in m samples. The observed data Y is assumed to be a linear combination of cell type-specific expression profiles, i.e., Y = WH+ϵ, where ϵ is an n×m error matrix. We aim to solve W and/or H from Y. If the basis matrix W is known, the problem is the typical reference-based deconvolution, which can be readily solved by the constrained linear regression . If botlgorithm ,W as W1, and unknown portion as W2, i.e., W = . Given expression matrix Y for all tumor samples and known reference matrix W1, we aim to infer the overall proportion matrix H and unknown basis matrix W2. Thus the above partial-reference based deconvolution problem can be formulized toW2, H1 and H2; (b) column sum of H1 and H2 is less than 1.However, in real clinical practice, only a fraction of the cell types in the tumor samples might be known. We denote the known portion of basis matrix We term the above problem  as an iW2;Start with a random initialization of W1 and estimate hij≤1 and Fix wij≥0;Estimate Repeat steps (ii) and (iii) until convergence or a specific number of times.c). The basic formula of AICc is [N is the sample size, p is the number of model parameters, and SSR is the sum of squared residuals between true and estimated gene expression profiles for all mixture samples. In the NMF framework (include PREDE as well), the samples size should be counted in the level of genes, i.e., N = n×m, and p = K(n+m)−nK1, where n, m, K, K1 are numbers of mixture samples, features, all cell types and known cell types, respectively. Compared with the original AIC, the AICc imposes a higher penalty when sample size is small, and approximates AIC when samples size increases. We calculated AICc for a reasonable range of potential cell type numbers  and the predicted optimal number of cell types were determined by the minimum AICc value.We used Akaike information criterion (AIC)  to deter AICc is :AICc=NlW) and the estimation accuracy of cellular proportion matrix (H), which were assessed by Pearson correlation coefficient and the mean absolute error (MAE) between true and predicted cell type proportions.We evaluated the performance of PREDE from the following two aspects: the estimation accuracy of basis expression matrix  at different levels of noise and (B) at different proportions of unknown cell types.(TIF)Click here for additional data file.S2 FigAccuracies of (A) proportion estimation and (B) profile estimation of our method based on ‘low similarity set’ and ‘high similarity set’.(TIF)Click here for additional data file.S3 FigK when proportion of rare cell types increases from 0.01 to 0.15.AICc values at different numbers of (TIF)Click here for additional data file.S4 FigAccuracies of (A) proportion and (B) profile estimations by PREDE and RF when proportion of rare cell type increase from 0.07 to 0.10.(TIF)Click here for additional data file.S5 FigProportion estimations for rare cell type by four methods at (A) different proportions of the rare cell type and (B) noise ratios.(TIF)Click here for additional data file.S6 Figc values at different numbers of cell types when cancer cells or immune cells are unavailable, respectively.Blue line or red line show AIC(TIF)Click here for additional data file.S7 FigEight cell lines including three breast cancer cell lines, three immune cell lines and two normal cell lines were mixed together with proportions 60%, 20% and 20% respectively. (A-B) Accuracies of profile and proportion estimations when cancer cell lines are unknown; (C-D) Accuracies of profile and proportion estimations when immune cell lines are unknown.(TIF)Click here for additional data file.S8 FigEight cell lines including 3 breast cancer cell lines, 3 immune cell lines, and 2 normal cell lines were mixed to simulate 100 tumor samples. Mean expression profiles of cancer cell lines, normal cell lines and immune cell lines were respectively treated as unknown cell components to validate all three methods using the rest cell lines as input. Estimations of (A) cellular proportion and (B) expression profile for the unknown cellular component by the three methods.(TIF)Click here for additional data file.S9 Fig(TIF)Click here for additional data file.


Gemcitabine is a drug of choice in the treatment of human pancreatic cancer. Chemo-resistance to this drug is common and has been attributed to a variety of distinct mechanisms, involving > 100 genes. A recently developed small-molecule G-quadruplex ligand, the trisubstituted naphthalene diimide compound CM03, has previously been shown to have equivalent potency to gemcitabine in the pancreatic cancer cell line MIA PaCa-2. We report here on cell lines of increased resistance to gemcitabine that have been generated from this line, with the most resistant having 1,000-fold reduced sensitivity to gemcitabine. These resistant lines retain nM sensitivity to CM03. The molecular basis for the retention of potency by this G-quadruplex ligand has been examined using whole transcriptome data analysis with RNA-seq. This has revealed that the pattern of pathways down regulated by CM03 in the parental MIA PaCa-2 cell line is largely unaffected in the gemcitabine-resistant line. The analysis has also shown that the expression patterns of numerous genes involved in gemcitabine sensitivity are down regulated in the resistant line upon CM03 treatment. These results are supportive of the concept that G-quadruplex small molecules such as CM03 have potential for clinical use in the treatment of gemcitabine-resistant human pancreatic cancer. Pancreatic ductal adenocarcinoma (PDAC: ca 85% of cases), is the most common form, and is also one of the most intractable of cancers to treatment. It has a bleak prognosis that has barely changed in over 20 years, with < 5% of patients surviving for five years7.The standard chemotherapy for PDAC has been the nucleoside analogue gemcitabine  in their promoter regions31, and which have been identified as playing roles in PDAC36. By contrast, gemcitabine affects, in large part, a quite distinct set of genes, which are often not quadruplex-containing. It is also notable that CM03 shows in vivo anti-tumour activity27 in the KPC genetic mouse model, in which gemcitabine does not produce significant responses37. In the light of this evidence that CM03 and gemcitabine have distinct and orthogonal targets of action, we hypothesised that CM03 would show significant anti-proliferative activity in PDAC cell lines in which gemcitabine resistance has been generated. We report here the results of a study that addresses this concept. We have used the same approach as before27, examining data from RNA-seq analyses in order to be able to compare results with those from our previous study. We also discuss, in the light of the results presented here, the role that CM03 and related compounds could play in the clinic for the treatment of drug resistant PDAC.We have recently reported that several small-molecule naphthalene diimide derivativesM03 Fig. b having Gemcitabine-resistant (GemR) pancreatic cancer MIA PaCa-2 cell lines were generated for three different gemcitabine concentrations , as described in the Methods section. The parental MIA PaCa-2 cells were designated as gemcitabine sensitive cells. The GemR cell lines were maintained at the three gemcitabine concentrations at which resistance was established. Prior to any experiment, gemcitabine was withdrawn from the culture medium for seven days. The resistant line at the highest gemcitabine concentration (3.0 µM), is the primary focus of the present study, and was designated as GemMIA-R3.38 and as modified for use with quadruplex-binding small-molecule compounds39. The dose–response curves for CM03 are closely similar in both parental MIA PaCa-2 and GemMIA-R3 cells . The CM03 GI50 values remained comparable in all three GemMIA resistant cell lines to that in the parental cell line. This indicates that the mechanism of gemcitabine resistance does not influence the effects of CM03 on cell viability and by implication, on its mechanism of action. We have also examined the ability of the quadruplex ligand CX-546140 to reduce growth in these two cell lines, in order to ascertain whether this structurally very dissimilar compound retains activity in both cell lines. We find that it has reduced cell-growth inhibitory activity compared to CM03 but similarly retains its activity in the GemMIA-R3 line -based method with a treatment period of 96 h, as described previouslyne Table , whereasne Table .Figure 227 in the former would remain largely unaltered in GemMIA-R3 cells. In order to validate this hypothesis, a series of RNA-seq experiments were undertaken in order to profile transcriptome changes in the parental MIA PaCa-2 and GemMIA-R3 cell lines after 6 h and 24 h treatment with CM03.Since CM03 has equivalent potency in the parental MIA PaCa-2 and derived GemMIA-R3 cell lines Table , it was 2FC < − 0.5, FDR < 0.1) and about 1,640 genes upregulated  in gem-resistant cells relative to the gem-sensitive parental line  increased in both cell lines with duration of CM03 treatment although the numbers themselves varied . The definitions of these limits are given in the figure legend Fig. . Overall27. Cells were treated with 400 nM CM03 for 6 h and 24 h, i.e. using the same conditions as in the RNA-seq experiments. In general, the expression pattern for this panel of genes is strikingly similar for both parental and GemMIA-R3 cell lines, although the level of down-regulated expression is consistently greater in the GemMIA-R3 line, with most changes having **P < 0.01, using Student’s t-test. Notably, several genes highlighted in Fig. MAPK11, TP73 and BCL-2 had expression changes which are not statistically significant or *P < 0.05 in parental cells yet were more significantly down-regulated in GemMIA-R3 cells. It is notable that the sole gene that is not down regulated in either cell line, KRAS, has the lowest number of PQs , providing further evidence of the validity of the GemMIA-R3 line as authentically gemcitabine-resistant. Some genes that promote gemcitabine resistance were slightly up-regulated  and others were highly up-regulated . The effect of CM03 has been to down-regulate the expression of a number of these genes such as SHH, GLI1, RRM2 and to a greater extent the MAPK11, MAPK12 and AKT1 genes. These genes are highly enriched in PQs and are consequently also important players in CM03 action in the parental line.Many genes have been implicated in gemcitabine resistance in MIA PaCa-2 cells, and in clinical gemcitabine resistance. The differential expression of a number of these genes has been examined here Table . The dat10 is highly down-regulated (log2FC = − 2.99) in GemMIA-R3 cells, in accordance with the high level of resistance in this line. In addition, expression of the cytidine deaminase gene CDA , further increases chemo-resistance to gemcitabine  in gemcitabine metabolism, which metabolizes and activates gemcitabinene Table . CM03 ha14, as well as GLI transcription factors, by upregulating SOX2 signalling23. Two key genes in the Hh pathway, sonic hedgehog ligand (SHH) and transcription factor GLI1 are highly up-regulated in GemMIA-R3 cells, with log2FC values of 2.5 and 3.6, respectively. These two genes, which have high PQ representation, are slightly downregulated by CM03 but not statistically significantly. The MAPK11, MAPK12 and AKT1 genes are not upregulated in the GemMIA-R3 cell line, whereas their expression is highly down-regulated upon CM03 treatment (Table The hedgehog (Hh) signalling pathway has also been found to play a role in promoting gemcitabine resistanceST6GAL1 gene (Table 40 and it notable that CM03 treatment results in an increase in the number of DNA damage foci27. The ST6GAL1 gene is highly over-expressed (log2FC = 2.84) in the GemMIA-R3 cell line, presumably contributing to its high level of gemcitabine resistance. CM03 treatment results in significant down-regulation of ST6GAL1 (log2FC = − 1.16) in GemMIA-R3 cells compare to its small effect in parental cells (log2FC = − 0.11). This large difference may be because that the ST6GAL1 gene is more accessible in gem-resistant cells than in sensitive cells. CM03-mediated downregulation of ST6GAL1 should reduce or reverse the effect of overexpressed ST6GAL1 on increasing chemo-resistance.Analogous behaviour has been found for the ne Table , which e24. Expression of the FOXM1 gene did not significantly alter in the GemMIA-R3 cell line compared to the parental line, but CM03 treatment results in some downregulation (log2FC = − 0.52).The Forkhead box protein M1 (FOXM1) is an oncogenic transcription factor and its elevated expression is associated with gemcitabine resistance in patients with pancreatic cancer20. In accord with this, FASN gene expression level is slightly increased in the GemMIA-R3 cell line. The FASN gene contains 40 PQs .Elevated expression of fatty acid synthase (FASN) has been correlated with reduced response to gemcitabine and inhibition of FASN results in synergistic effects on gemcitabine treatmentDCK, CDA, GLI1 and ST6GAL1.A cell line (GemMIA-R3) has been derived from the parental MIA PaCa-2 PDAC cell line, with ca 1,000- fold resistance to the clinically used drug gemcitabine, using a repeated passage approach. Its validity as a resistant line is supported by the changes in expression for several genes associated with gemcitabine resistance, notably 50 for cell growth inhibition) in parental MIA PaCa-2 cells. Its potency is fully retained in the GemMIA-R3 line, as well as in an equivalent gemcitabine resistant line derived from the PANC-1 PDAC line, indicating that potency is not restricted to a single PDAC resistant line, and may be a more general phenomenon. The structurally unrelated G-quadruplex compound CX-5461 is also equipotent in parental and resistant lines, suggesting that the mechanism of action of this G-quadruplex compound also involves distinct genes and pathways to gemcitabine and its resistance.The G-quadruplex small-molecule CM03 compound has ca 10 nM potency , as well as some that are in common with those involved in CM03-induced down-regulation of proliferation and resistance (such as GLI1), is down-regulated by CM03 treatment. This suggests that CM03 treatment of gemcitabine-resistant cells could result in a reduction in gemcitabine resistance. This concept has yet to be evaluated.Several other quadruplex ligands have been previously reported as having activity in chemo-resistant cell lines27 and was used as the > 95% pure hydrochloride/formate salt. This has a pH of 6.95 in H2O. Gemcitabine, CM03 salts and CX-5461 were dissolved in PBS and stocks of 1 mM were prepared and kept frozen and away from light prior to use. Drugs were filtered through 0.22 µm pore-size filter units before addition to appropriate cell culture media.Gemcitabine as the hydrochloride salt was purchased from Sigma-Aldrich (cat no. G6423). CX-5461 was purchased from Adooq Bioscience (cat no. A11065). Compound CM03 was synthesised in-house2 in humidified incubators and routinely passaged.MIA PaCa-2 and PANC-1 cell lines were purchased from ATCC (cat no. CRL-1420 and CRL-1469) and maintained in Dulbecco's Modified Eagle's Medium (DMEM) with 4,500 mg/L glucose and 2 mM L-glutamine  supplemented with 10% foetal bovine serum , 0.1 mg/ml streptomycin and 100 U/ml penicillin  and only for MIA PaCa-2 cells: 2.5% horse serum . Cells were maintained at 37 °C and 5% CO39. 50% growth inhibition  were determined by taking the mean absorbance at 540 nm for each drug concentration expressed as a percentage of the absorbance of untreated control wells. All experiments were performed in triplicate and the mean ± SD values were determined from at least three independent experiments.Cellular growth inhibition was measured using the sulforhodamine B (SRB) assay in 96 well plates as described previouslyGemcitabine-resistant cell lines were generated from the parental MIA PaCa-2 pancreatic cancer line by incrementally increasing the gemcitabine concentration in the culture medium over extended periods of time. A cycle of concentration increase and selection was repeated until reaching a particular target concentration . Each increment of gemcitabine concentration took around a week for cells to gain resistance to and resume cell proliferation. The whole process took several months to achieve the most resistant cell line for the highest gemcitabine concentration (3 µM). At each point, the cells were amplified, cryopreserved and maintained continuously in medium containing gemcitabine at the concentration of interest. The gemcitabine selection pressure was withdrawn for 7 days before a particular gemcitabine-resistant cell line was used in any experiment, in order to avoid any interference from gemcitabine.6 and 24 h = 1.0 × 106 cells/well) and incubated overnight. Then, cells were treated with 0.40 µM CM03 or vehicle (PBS) for 6 h and 24 h. This is the lowest concentration of CM03 that can achieve minimal cell death (10%) and was chosen for these short-term exposures, in order to investigate its direct gene targets and not collateral gene expression changes. It was the same as previously used27, maintaining consistency with the previous study in which it had been shown to cause substantial gene expression changes even after 6 h treatment. Parental cells were also included without CM03 treatment for GemMIA-R3 vs parental gene expression comparison. Total RNA was extracted from 2–4 × 106 cells using the RNeasy mini kit  and on-column DNase1 digestion  as per the manufacturer’s instructions. RNA quality (RIN > 7.0) was checked with an Agilent 2,100 Bioanalyser RNA 6,000 Nano Chip and RNA concentration was quantified using a Qubit fluorometer (ThermoFisher) and Qubit RNA HS Assay Kit . RNA-seq libraries were then generated using the NEBNext mRNA Ultra II with IDT xGen UMI adapters kit for Illumina as per the manufacturer’s instructions and sequenced using an Illumina NextSeq 500 instrument (undertaken at the UCL Genomics Facility).GemMIA-R3 cells were seeded in 100 mm plates (6 h = 2.5 × 10https://www.ncbi.nlm.nih.gov/geo/). RNA-seq data (GEO accession GSE105083) from our previous study with CM03 and the parental cell line MIA PaCa-2 was used for comparison purposes during the analyses27. Illumina run data were demultiplexed and converted to fastq files using Illumina’s bcl2fastq Conversion Software (v2.19). Then, fastq files were pre-processed to remove adapter contamination and poor quality sequences using the program Trimmomatic (v0.36)48 before being mapped to a recent human genome build (UCSC hg38: https://genome.ucsc.edu/) using the RNA-seq alignment tool STAR . Duplication levels were estimated using JE-Suite49, a Unique Molecule Identifier program to filter out duplicates and reads that are the result of PCR amplification were marked. Next, reads per transcript were counted using the program FeatureCounts50 (v1.4.6p5) before normalisation, modelling and differential expression analysis using the SARTools (v1.3.2) package51.The raw and processed sequencing data has been deposited in the GEO public functional genomics data repository with GEO accession no GSE148200 (≥3N1−7G≥3N1−7G≥3N1−7G≥3), was used as previously reported27, in gene promoters (defined for this purpose as being up to 2 kilobases upstream of the transcription start site (TSS) and 100 bases downstream) and in exons and introns. These numbers of putative quadruplexes (PQs) were taken from the earlier study27, computed as reported previously53.For the numbers of putative G4 sequences in an individual gene (PQs in Table 2 fold changes (Log2FC) and false discovery rate (FDR) for drug treatment versus untreated, with the following assignment: Down Strong = genes with Log2FC ≤ − 1 and FDR < 0.05; Down = genes with Log2FC < − 0.5 and FDR < 0.1; Strong Up = genes with Log2FC ≥ 1 and FDR < 0.05; Up = genes with Log2FC > 0.5 and FDR < 0.1. The signalling pathway enrichment analysis was done using the DAVID functional annotation tool (https://david.ncifcrf.gov/)54 on the Up and Down gene lists. The Pathview maps tool (https://pathview.uncc.edu/)55 was used to visualise top affected signalling pathways containing coloured DEGs from Up and Down gene lists to indicate their level of expression.Differentially expressed genes (DEGs) were split into different subsets according to their logACTB, GAPDH, and TUBB, and the fold change was determined using 2−ΔΔCt.Parental MIA PaCa-2 and GemMIA-R3 cell lines were seeded and treated exactly same as in RNA-seq experiment. After extracting total RNA, the RNA concentrations were measured with a NanoDrop 2000/2000c spectrophotometer (ThermoFisher). The cDNA libraries were then prepared from a determined amount of RNA using a SuperScript III First-Strand Synthesis System  as per the manufacturer’s instructions. RT-qPCR was performed using Power SYBR Green Master Mix  with a 50 ng template and 150 nM primers in an AriaMx Realtime PCR System (Agilent). Primers were purchased from Sigma (KiCqStart SYBR Green Primers) and Eurofins Genomics. At least three independent experiments were carried out in triplicate. The data was used to determine the Ct values, which were normalized to the geometric mean of three housekeeping genes Supplementary information