First, we determined the static concentration-response of DOX and DEX in breast cancer cell lines, JIMT-1 and MDA-MB-468. With a three-dimensional (3D) response surface analysis using a competitive interaction model, we characterized their interaction to be modestly synergistic in MDA-MB-468 or modestly antagonistic in JIMT-1 cells. Second, a cellular-level, pharmacodynamic (PD) model was developed to capture the time-course effects of the two drugs which determined additive and antagonistic interactions for DOX and DEX in MDA-MB-468 and JIMT-1, respectively. Finally, we performed in vitro to in vivo translation by utilizing DOX and DEX clinical dosing regimen that was previously identified to be maximally cardioprotective, to drive tumor cell PD models. The resulting simulations showed that a 10:1 DEX:DOX dose ratio over three cycles of Q3W regimen of DOX results in comparable efficacy based on MDA-MB-468 (additive effect) estimates and lower efficacy based on JIMT-1 (antagonistic effect) estimates for DOX + DEX combination as compared to DOX alone. Thus, our developed cell-based PD models can be used to simulate different scenarios and better design preclinical in vivo studies to further optimize DOX and DEX combinations.Dexrazoxane (DEX) is the only drug clinically approved to treat Doxorubicin-induced cardiotoxicity (DIC), however its impact on the anticancer efficacy of DOX is not extensively studied. In this manuscript, a proof-of-concept  Doxorubicin (DOX) is widely used to treat a variety of solid tumors and hematological malignancies in the clinic . HoweverThe cardioprotective activity of DEX has long been attributed to the iron-chelating activity of its metabolite, ADR-925 . This mein vitro-in vivo predictions indicated that DEX and DOX dose ratio of 10:1 and the Q3W (once every 3 weeks) DOX regimen offer maximal cardio-protection.While the cardioprotective effects of DEX have been well characterized in clinical and preclinical studies , the impin vitro IC50s were determined and DOX and DEX concentrations were selected for subsequent drug combination and time-course studies. A three-dimensional (3D) response surface analysis using a competitive interaction model was conducted to determine the nature of their interactions. Subsequently, a mathematical, cellular response, pharmacodynamic (PD) model was developed to capture the time-course effects of the two drugs as single agents as well as their dynamic drug-drug interaction in cancer cells, and relevant parameters were estimated. Finally, in vitro to in vivo translation was performed by utilizing clinically relevant doses and regimens of DOX and DEX, previously identified to be maximally cardioprotective  was purchased from Selleck Chemicals  while Dexrazoxane (DEX) was from Millipore Sigma-Aldrich Co. . DOX was dissolved in molecular biology grade water while DEX in DMSO as per manufacturer’s instructions. Stock solutions were stored at −80°C for long-term storage while fresh serial dilutions were prepared in cell culture media each time prior to experiments. JIMT-1 cells were procured from AddexBio  and cultured in cell culture media comprising of Dulbecco’s Modified Eagle’s Medium (DMEM) with 10% sterile filtered fetal bovine serum (FBS), 1% sodium bicarbonate, 1% MEM Non-essential amino acids, and 1% penicillin/streptomycin antibiotics. MDA-MB-468 cells were purchased from American Type Culture Collection (ATCC)  and cultured in DMEM with 10% FBS, and 1% penicillin/streptomycin. Both the cell lines were maintained at 37°C in a humidified atmosphere with 5% COlsewhere .3 or 5 × 103 cells per well (100 µL) of a 96-well plate. After overnight incubation, the cell lines were exposed to varying concentrations of DOX (0.005–1 µM), DEX (0.1–400 µM), or their combinations for 72–96 h for various sets of experiments. The CCK8 cell viability assay was subsequently carried out as per the manufacturer’s instructions and as described previously  and concentrations required to achieve 50% of maximal effects (IC50).R is the response to drug treatments (% cell viability), R0 is the baseline response (% cell viability in absence of drug treatments or control), Imax is the maximal effect of drug treatments, IC50 is the drug concentration required to achieve 50% of Imax, C is the drug concentration. The IC50 values were used to select concentrations for single agents and combinations during time-course and subsequent studies.An inhibitory Hill function was used to characterize the concentration-response curves for DOX and DEX in the two breast cancer cell lines, JIMT-1 and MDA-MB-468 at 72 h and to estimate their corresponding maximal effects , Imax,A and Imax,B are the maximal effects of the two drugs , CA and CB are concentrations of the drugs, IC50A and IC50B are the half-maximal inhibitory concentrations of the drugs, γA and γB are the respective Hill coefficients and ψ is the interaction term. All individual drug-related parameters were fixed from the DOX and DEX concentration-response curve fittings and the interaction term, ψ, was estimated using Monolix software version 2016R1 . The apparent interaction between the drugs was antagonistic when ψ > 1, synergistic when ψ <1 and additive when ψ = 1.Where, R is % cell viability, RAdditionally, three-dimensional (3D) response surface plots of cell viability versus DOX and DEX concentrations were constructed to visually evaluate the interaction between the drugs for both cell lines. Briefly, an additive interaction was assumed between both drugs  and 3D response surface plots were generated for both cell lines using Eq. in vitro cellular level pharmacodynamic model (PD) was developed for the single agents, DOX, DEX, as well as their combination effects on the cell viability of human breast cancer cell lines, JIMT-1 and MDA-MB-468 as described below.The First, the degradation kinetics of DOX and DEX in cell culture media described previously  were levR is the % cellular viability (cellular response) at time t, kg is the first-order growth rate constant for cancer cell lines, while R0 is the % cellular viability at time zero.Next, the cell growth of breast cancer cell lines, JIMT-1 and MDA-MB-468, without any treatment (control group) was best described by an exponential growth function as follows:Smax,x is the maximal killing rate constant while SC50,x is the concentration of DOX or DEX required to induce half-maximal effect in JIMT-1 and MDA-MB-468. Kx is the cytotoxicity Hill function, K1x to K3x are transit compartments with Ʈx the mean transit time between compartments and Cx represents the drug concentration.The cellular response or the PD effect of DOX or DEX on the cell viability of JIMT-1 and MDA-MB-468 was described by a stimulatory effect (Hill function) on the cell death of cancer cells. A stimulatory effect on cell death in the JIMT-1 and MDA-MB-468 cancer cells lines was needed to capture the significant reduction in cell viability as compared to the control arm, as suggested by the observed data. The delay between the exposure of DOX or DEX and the non-linear cytotoxic (stimulation of death) effect on JIMT-1 and MDA-MB-468 was captured with the inclusion of three transit compartments for both drugs. This is consistent with previously published reports describing their mechanisms including temporal delay due to intracellular signaling cascade involved in their cytotoxic effects. It is to be noted that several empirical functions for cell-killing such as linear, power or sigmoidal (Hill) functions were initially evaluated to describe the trends in the observed data. Furthermore, the number of transit compartments were also varied for model optimization. The cell-killing function and number of transit compartments in the final models were selected based on visual inspection, goodness-of-fit and precision on parameter estimates. The differential equations for the effect of DOX or DEX as single agents on JIMT-1 and MDA-MB-468 cells in the final model are as follows:As both the drugs induce cytotoxic effects (stimulation of death function) in JIMT-1 and MDA-MB-468, an interaction parameter, ψ, was applied to Eq. In vitro to in vivo translation of PD responses for the joint effect of DOX + DEX in JIMT-1 and MDA-MB-468 cells was performed by utilizing clinically relevant pharmacokinetic (PK) profiles of the two agents to drive the PD models developed in the in vitro setting. In the previous study assessing cardioprotective activity of the DOX + DEX combination (2 administered every 3 weeks (Q3W) alone or in combination with DEX 500 mg/m2 over three cycles was chosen for performing model simulations for cell viability of JIMT-1 and MDA-MB-468 cells using the PD models established in vitro.bination , it was tumor/AUCplasma with the linear trapezoidal method using Phoenix WinNonlin (Version 8.2). Subsequently, this ratio was used as a multiplicative factor (Fac1) to determine DOX tumor concentrations as a fraction of the simulated plasma concentrations of DOX for the 50 mg/m2 Q3W regimen. Similarly, DEX tumor concentrations were determined using a multiplicative factor, Fac2, as a fraction of the simulated plasma concentrations of DEX at a dose level of 500 mg/m2. Due to unavailability of tumor distribution data for DEX, arbitrary values of Fac2  were used to simulate various scenarios for DEX distribution to tumor tissue in humans.First, DOX and DEX plasma PK data was obtained from previously published reports  and charin vitro PD models established for JIMT-1 and MDA-MB-468 cells. Population simulations were conducted for 500 subjects with an arbitrary inter-individual variability (IIV) of 10% introduced on the PD parameters and the Fac1 and Fac2 parameters for each cell model. Evaluation of efficacy was performed by calculating the area under the effect (% cell viability) curve (AUEC) as an integrated measure of PD response for the following scenarios: DOX alone, DOX + DEX (Fac2 = 0.1), DOX + DEX (Fac2 = 1) and DOX + DEX (Fac2 = 10).The determined tumor concentrations were then used to drive the All PK-PD model fittings and simulations were performed using Monolix suites version 2016R1 or higher, while AUEC calculations were performed using RStudio version 1.2.5033.max of 0.988 and 0.959 (both close to 1) for the former compared with that of 0.74 and 0.825 for the latter in JIMT-1 and MDA-MB-468. Overall, MDA-MB-468 was more sensitive as compared to JIMT-1 for both DOX and DEX treatments as evidenced by a lower determined IC50 and a higher maximal killing for both DOX and DEX. The inhibitory Hill model captured the concentration-response data as demonstrated with observations versus individual prediction plots shown in The two breast cancer cell lines, JIMT-1 and MDA-MB-468, were exposed to a wide range of single agent (either DOX or DEX) concentrations for 72 h. The inhibitory Hill model was fitted to the concentration-response curves, each for DOX and DEX as single agents in both the cell lines as shown in To examine the cancer cell killing effects of the drug combinations and to determine the nature of their interactions in the static setting, cells were exposed to a range of DOX and DEX concentrations as single agents as well as their combinations for 72 h as indicated in deg, DOX) and DEX  previously estimated at 0.022 (±0.0004) h−1 and 0.054 (±0.0016) h−1, respectively, were used to describe their loss in cell culture media over time (As shown in ver time . Based over time  to accouThe model fittings for the time-course effects of DOX or DEX as single agents in JIMT-1 and MDA-MB-468 are shown in −1 and 0.0167 (±1.45%) h−1, for JIMT-1 and MDA-MB-468, respectively. The dynamic changes of JIMT-1 and MDA-MB-468 cell viability over 96 h in presence of DOX or DEX were adequately captured with stimulatory effects on cell death characterized with a capacity-limited, Hill function  for DOX was 0.0705 (±15.6%) h−1 and 1.16 (±3.55%) h−1 while the DOX concentration inducing 50% of maximal cell killing rate  was estimated to be 0.315 (±18.5%) µM and 0.761 (±7.48%) µM for JIMT-1 and MDA-MB-468. On the other hand, the estimated, maximal killing rate constant  for DEX was 0.0482 (±20.2%) h−1 and 0.29 (±48.5%) h−1 while the DEX concentration inducing 50% of maximal cell killing rate  was estimated to be 21.2 (±18.8%) µM and 17.5 (±23.7%) µM for JIMT-1 and MDA-MB-468. Thus, time-course data analysis confirmed DOX to be far more potent as compared to DEX which is consistent with the concentration-response analysis. Higher maximal killing rate (Smax) as well as lower concentration to induce 50% of Smax (SC50) was consistently estimated for DOX versus DEX in both the cell lines. The delayed effects of DOX and DEX were captured well with the help of three transit compartments on the stimulation of death function, with ƮDOX and ƮDEX representing the mean transit time. ƮDOX was estimated at ∼14 h and ∼ 36 h while ƮDEX was estimated at ∼30 h and ∼ 55 h for JIMT-1 and MDA-MB-468.In the absence of DOX or DEX, the cell viability was characterized with an exponential growth function Eq.  using a tion Eq. -3e. The To characterize the time-course combinatorial effects of DOX + DEX on the cell viability or stimulation of death of breast cancer cell lines, the model structure incorporated an interaction parameter ψ where ψ = 1, ψ < 1, and ψ > 1 indicates additive, synergistic, and antagonistic interactions, respectively -4i. The Methods section, the AUCtumor/AUCplasma ratio (Fac1) was calculated for DOX based on data extracted from literature (2) values of 0.1, 1 and 10 were utilized to describe DEX concentrations at the tumor site. For JIMT-1 cells, the predicted AUEC for % cell viability over three cycles of 3 weeks each was higher with the DOX + DEX combinations (∼1560-fold) as compared to DOX, indicating higher cell killing with DOX alone and antagonistic effects between DOX + DEX on tumor cells, consistent with the in vitro PD model (in vitro (2) did not have a significant impact on AUEC predictions of the DOX + DEX combination, likely due to the dominant effect of DOX on tumor cell killing due to its high potency as compared to DEX in tumor cells.Following establishment of cellular-level PD models for DOX and DEX in JIMT-1 and MDA-MB-468 cells, model-based simulations were performed to investigate the long-term effects of clinically relevant DOX and DEX dosing regimens in tumor cells . Figure terature  and was PD model . For MDAin vitro . Of notein vitro study to examine the nature of drug-drug interaction between DOX and DEX and translated our in vitro findings to the clinical setting. Our stepwise approach consisted of first, quantitatively capturing the static concentration-response and time-course anticancer effects of DOX and DEX as single agents. Then, by means of mathematical modeling, we characterized the nature of DOX + DEX interaction by estimating the interaction parameter ψ. We used breast cancer (BC) as a prototype disease and the cell lines JIMT-1 and MDA-MB-468 as prototypes for two difficult-to-treat breast cancer subtypes, BC refractory to HER2 therapy (JIMT-1) and triple negative BC (MDA-MB-468). Second, we performed the in vitro to in vivo translation of our findings for which we simulated the clinical tumor site concentration-time profiles for DOX and DEX dosing regimens (in vivo changes in cancer cell viability (PD responses) were found to be consistent with our in vitro findings that a 10:1 DEX:DOX dose ratio over three cycles of Q3W regimen of DOX results in comparable efficacy based on MDA-MB-468 (additive effect) estimates and lower efficacy based on JIMT-1 (antagonistic effect) estimates for DOX + DEX combination as compared to DOX alone. While the developed in vitro PD model and in vivo translation remains to be validated with future experiments, our previously developed cell-based TD (toxicodynamic) model  where large preclinical studies are not feasible.In this manuscript, we presented a proof-of-concept regimens  that werc) model  along wiJIMT-1 (HER2 expressing cell line resistant to trastuzumab) and MDA-MB-468 (triple negative breast cancer) were selected as “difficult to treat” breast tumor prototypes and considered representative cell lines for advanced/metastatic breast cancer patients . In suchin vitro IC50 for DOX and DEX in the two cell lines showed ∼500 to 2000 fold higher potency for DOX than DEX  and lower SC50 (∼23 to 68 folds) in the two cancer cell lines  of DOX was mediated by both topoisomerase II alpha (TOP2A) and topoisomerase II beta (TOP2B) isoforms in HTETOP fibrosarcoma cell line  did not interfere negatively with the in vivo activity of DOX in A2780 and MX-1 human tumor xenograft mouse models  . A cliniin vitro to in vivo translational modeling approach accounted for clinically relevant DOX and DEX doses and their pharmacokinetic aspects, an important caveat that remains to be addressed is extrapolation of effects at the pharmacodynamic level, given the differences between a static two-dimensional monolayer cell-culture system versus the three-dimensional tumor microenvironment in an in vivo system. To this end, next steps and ongoing efforts include extending the in vitro assessment of this combination to a three-dimensional and dynamic (3DD) cell culture system, which is more representative of in vivo conditions as compared to a standard two-dimensional (2D) monolayer cell culture system (in vivo using our present in vitro PD model as a starting point, provides some insights into the nature of the in vivo PD interaction between DOX and DEX in breast cancer cells to guide future studies of this combination.While our e system . Neverthin vivo conditions. Such developed PK/PD/TD models can serve as in silico tools to assess DOX and DEX combinations for safety and efficacy and enable better design of preclinical in vivo studies.To summarize, we have developed a proof-of-concept cell-based PD model which can serve as a platform to optimize DOX and DEX combinations for efficacy indices. As with the TD model, efforts are ongoing to extend the PK model to include different tissue compartments including the tumor and the heart and link appropriate tissue concentrations to PD/TD models so that efficacy and safety predictions can be simultaneously estimated. Similarly, evaluation of the dynamic changes in the intracellular signaling pathway for DOX and DEX PD models is underway to enable better quantitative understanding of the underlying mechanisms, as well as evaluation of this combination in a 3DD system to better reflect


Glioblastoma is one of the most common malignant brain tumors. Vitamin D, primarily its hormonally active form calcitriol, has been reported to have anti‐cancer activity. In the present study, we used patient‐derived glioma cell lines to examine the effect of vitamin D3 and calcitriol on glioblastoma. Surprisingly, vitamin D3 showed a more significant inhibitory effect than calcitriol on cell viability and proliferation. Vitamin D receptor (VDR) mediates most of the cellular effects of vitamin D, and thus we examined the expression level and function of VDR via gene silencing and gene knockout experiments. We observed that VDR does not affect the sensitivity of patient‐derived glioma cell lines to vitamin D3, and the gene encoding VDR is not essential for growth of patient‐derived glioma cell lines. RNA sequencing data analysis and sterolomics analysis revealed that vitamin D3 inhibits cholesterol synthesis and cholesterol homeostasis by inhibiting the expression level of 7‐dehydrocholesterol reductase, which leads to the accumulation of 7‐dehydrocholesterol and other sterol intermediates. In conclusion, our results suggest that vitamin D3, rather than calcitriol, inhibits growth of patient‐derived glioma cell lines via inhibition of the cholesterol homeostasis pathway. Vitamin D3 exerts a significantly stronger inhibitory effect on cell viability and cell proliferation of patient‐derived glioma cell lines than calcitriol, and this effect is mediated through inhibition of cholesterol homeostasis and cholesterol synthesis. We used GBM cell lines for research as well, which were isolated from GBM patient tumor samples and cultured with the serum‐free stem cell culture method. These cell lines were called patient‐derived glioma cell lines. Patient‐derived glioma cell lines have similar characteristics to GSC and retain a certain proportion of genomic and transcriptome characteristics from patients.Glioma, a type of central nervous system (CNS) tumor, is considered to derive from neuroglial stem or progenitor cells . More thCYP2R1), cytochrome P450 family 27 subfamily A member 1 (CYP27A1) and cytochrome P450 family 27 subfamily B member 1 (CYP27B1), to form active vitamin D , which then binds to vitamin D receptors (VDR) and regulates downstream genes . Hi. HiP = 0g. PCSK9 , 46, 47.HMGCR mRNA expression or the degradation of HMGCR protein, thereby regulating the cholesterol synthesis pathway [To further explore the changes in the contents of sterols after the treatment of vitamin D3, sterolomics analysis was performed by LC/MS. The results showed that the total sterols in patient‐derived glioma cell lines increased significantly after vitamin D3 treatment, whereas total cholesterol decreased Fig., which w pathway , 49, 50.DHCR7 and DHCR24, which led to excessive accumulation of downstream sterol intermediates. The accumulation of sterol intermediates further strengthened the inhibition of the cholesterol synthesis and cholesterol homeostasis pathways. This was the main mechanism by which vitamin D3 inhibited cell viability and proliferation in patient‐derived glioma cell lines.In summary, vitamin D3 stagnated cholesterol synthesis by inhibiting the expression levels of de novo by astrocytes [Changes in genetic and epigenetic signaling pathways in tumors often promote metabolic reprogramming . Alteredtrocytes . Meanwhitrocytes . TherefoDHCR7 and DHCR24, thereby inhibiting cholesterol synthesis and cholesterol homeostasis pathways. In our view, the effects of vitamin D3 on cholesterol homeostasis have been rarely reported, and its mechanisms have scarcely been studied [Although the research on vitamin D in GBM has focused on the active form of vitamin D, in the present study, we demonstrated that patient‐derived glioma cell lines were more sensitive to vitamin D3 than calcitriol and calcifediol. Interestingly, vitamin D3 obtained more significant inhibitory effects on cell viability and cell proliferation of patient‐derived glioma cell lines by inhibiting the expression levels of  studied , 58. MeaDHCR7 are associated with a disease called Smith–Lemly–Opitz‐syndrome (SLOS), which defects in hedgehog signaling [SMO) and inhibiting the expression level of GLI family zinc finger 1 (GLI1) in some types of cell lines [PTCH1), SMO and GLI1 were measured by a quantitative PCR (Fig. It is worth noting that mutations in gene ignaling , 61. Furll lines , 62. SucPCR Fig. . The resin vivo is more complex. In addition, whether this regulation occurred only on patient‐derived glioma cell lines or is a general feedback mechanism is also an important question. Limited by the lack of instruments, we were not able to examine whether the enzymatic activity of DHCR7 was inhibited after vitamin D3 treatment as previously reported, and only confirmed that the mRNA expression level of DHCR7 was decreased. How vitamin D3 regulated the mRNA expression or/and enzyme activity of DHCR7 at the microscopic level is still a very interesting question. These questions are worthy of further study.A major limitation of the present is that the experimental model used was only cell line, but the environment Although our works focused on vitamin D3, calcifediol and calcitriol also had interesting phenomena. Sterolomics in vitamin D3‐, calcifediol‐ or calcitriol‐treated groups was significantly different from each other Fig. . In the The authors declare that they have no conflicts of interest.https://www.webofscience.com/api/gateway/wos/peer‐review/10.1002/2211‐5463.13679.The peer review history for this article is available at RY and JC conceptualized and designed the study. RY wrote the manuscript. WZ, Y‐PY, GC, ZG and XW provided the human glioma cell lines. All authors read and approved the final version of the manuscript submitted for publication.


Pinus nigra cell lines with contrasting embryogenic capacity, accounting for variability between genotypes. Our results showed that cell wall-related and carbohydrate-acting proteins were the most differentially accumulated. Peroxidases, extensin, α-amylase, plant basic secretory family protein (BSP), and basic secretory protease (S) were more abundant in the medium from the lines with high embryogenic capacity. In contrast, the medium from the low embryogenic capacity cell lines contained a higher amount of polygalacturonases, hothead protein, and expansin, which are generally associated with cell wall loosening or softening. These results corroborated the microscopic findings in cell lines with low embryogenic capacity—long suspensor cells without proper assembly. Furthermore, proteomic data were subsequently validated by peroxidase and α-amylase activity assays, and hence, we conclude that both tested enzyme activities can be considered potential markers of high embryogenic capacity.Somatic embryogenesis is an efficient mean for rapid micropropagation and preservation of the germplasm of valuable coniferous trees. Little is known about how the composition of secretome tracks down the level of embryogenic capacity. Unlike embryogenic tissue on solid medium, suspension cell cultures enable the study of extracellular proteins secreted into a liquid cultivation medium, avoiding contamination from destructured cells. Here, we present proteomic data of the secretome of  The process has been initiated in many plant species such as herbal species, angiosperm, and conifer trees. The developmental pattern of SE differs from that of angiosperms (Somatic embryogenesis (SE) is a complex process by which somatic embryos develop from somatic cells under iosperms . Von Arniosperms . Cell suiosperms .Pinus caribaea var. hondurensis). Secreted proteins are involved in the regulation of many biological and physiological processes, such as cell expansion, cell division, cell-to-cell communication, cell adhesion, signal transduction, and response to stress or stimuli (reviewed by Pinus nigra) at the level of the cellular proteome. A comparison of three types of tissues obtained from two cell lines  revealed a higher impact of cell line/genotype on proteome than the type of investigated tissue. An altered accumulation of proteins active in the biosynthesis of cell wall components was also found, comparing the embryogenic and non-embryogenic tissue of both cell lines  in the SE . Previoull lines .P. nigra. We compared the population of proteins secreted into the suspension culture medium from cell lines with different embryogenic capacities by label-free mass spectrometry. In our comparison, we included two cell lines with high embryogenic capacity (HEC) and two with low embryogenic capacity (LEC) to weigh the influence of genotype. Our results contribute to a better understanding of complex regulation of early stages of SE, when bipolar structures are already formed. Finally, we proposed potential markers of early stages of SE in pine species.In the present study, we aimed to obtain comprehensive secretome profiles and to identify possible players in the SE of conifer 22.1P. nigra enclosed in megagametophytes , squashed, and covered by a coverslip . The pre2.3On the eighth to ninth day after the last subculture, the tissues were resuspended in a liquid basal DCR medium without growth regulators and organic additives. Aliquots of 100–120 mg of tissue in the suspension were pipetted on stacked filter paper discs, and after absorbing the liquid, the filter papers with cells on their surface were placed on a maturation medium. The maturation medium was based on basal DCR medium supplemented with ABA , enzymatic casein hydrolysate (500 mg/L), glutamine (50 mg/L), myo-inositol (200 mg/L), glycine (2 mg/L), nicotinic acid (0.5 mg/L), pyridoxine (1 mg/L), thiamine (1 mg/L), and maltose 6%. The maturation medium was solidified with 1% gelrite  and E492, E494 (HEC). On the eighth day after the last subculture, 2.5 g of well-growing embryogenic tissue (taken from different plates) was resuspended in 25 ml of liquid basal DCR proliferation medium. The suspension cultures were cultivated in 100-ml Erlenmeyer flasks on a rotary shaker at 80–90 rpm in the dark. After 1 week of cultivation, the suspension was poured into 25-ml glass cylinders, and the cells were allowed to settle for 30 min. We estimated the sedimented cell volume  as a non-destructive growth parameter . Subsequ2.5Extraction of secreted proteins was performed in duplicate for each cell line. The culture medium (50 ml) was concentrated and partially desalted in two steps by ultrafiltration at 4 °C using Amicon Ultra-15  and Amicon Ultra-0.5  to a final volume of 200 µl. According to the manufacturer’s instructions, concentrated secreted proteins were precipitated with 2-D Clean-Up Kit , and pellets were solubilized in 40 µl of solubilization buffer . Proteins were quantified using the Pierce BCA Protein Assay kit (Thermo Scientific).To estimate potential contamination by cytosolic proteins, the activity assay of cytosolic enzyme glucose-6-phosphate dehydrogenase  was performed using the G6PDH assay kit (Sigma-Aldrich) according to the manufacturer’s instructions.Secreted proteins were separated in 12% polyacrylamide separation gel with 4% upper stacking gel. Prior to loading, protein samples (25 µg) were mixed with loading buffer  in a 1:1 ratio and heated at 70 °C for 10 min. Electrophoresis was performed on the Protean II xi Cell unit (gel dimensions 16 × 20 cm) (Bio-Rad) as follows: constant voltage of 60 V (30 V per gel) until the dye front reached separation gel, then at a constant voltage of 180 V (90 V per gel) until the run was complete. The protein bands were visualized with colloidal Coomassie blue (G-250) and digitalized using the GS-800 Calibrated Densitometer (Bio-Rad).2.6Each gel lane was divided into nine sections of similar size and cut into small pieces. Gel pieces were washed with 50 mM ammonium bicarbonate containing 50% acetonitrile until complete removal of the dye. Subsequently, gel pieces were reduced with 10 mM dithiothreitol in 100 mM ammonium bicarbonate, alkylated with 50 mM iodoacetamide in 100 mM ammonium bicarbonate, and dehydrated with 100% acetonitrile. Finally, the gel pieces were saturated with 10 ng/µl trypsin (Promega) in 10 mM ammonium bicarbonate containing 10% acetonitrile for 2 h at 4 °C and digested at 37 °C overnight. The digestion was stopped by 1% trifluoroacetic acid in 70% acetonitrile and tryptic peptides were extracted by 70% acetonitrile with 1% trifluoroacetic acid. Extracted peptides were stored at −20 °C until analysis.2.7Pinus taeda protein database . The samples were loaded onto a trap column  and separated with a C18 column  on an Ultimate 3000 RSLCnano system (Dionex) in a linear 120-min gradient (3%–43% B) and a flow rate of 300 nl/min. The following two mobile phases were used: 0.1% FA (v/v) (A) and 80% ACN (v/v) with 0.1% FA (B). Eluted peptides were sprayed directly into an Orbitrap Elite mass spectrometer  and spectral datasets were collected in the data-dependent mode using the Top 15 strategy for the selection of precursor ions for the HCD fragmentation . Each ofdatabase . The masXD040247 .2.8Peroxidase activity was measured in the cell culture medium of cell lines E489, E490, and E483 (LEC) and E494, E482, and E486 (HEC) in triplicate. The secreted proteins for the peroxidase activity assay were concentrated as described above, followed by buffer exchange [phosphate-buffered saline ] after the ultrafiltration step. Peroxidase activity was determined spectrophotometrically at 420 nm using the Peroxidase (POD) Assay kit (Elabscience) according to the manufacturer’s instructions. Peroxidase activity was expressed in units per milligram of secreted protein (U/mg protein). One unit was defined as the enzyme activity that catalyzes 1 µg of substrate by the 1 mg of protein extract per minute at 37 °C.The α-amylase activity was measured in the culture medium of cell lines E489, E490, and E483 (LEC) and E456, E477, and E494 (HEC) in triplicate. Secreted proteins were concentrated using Amicon Ultra-15 . The activity was determined spectrophotometrically at 405 nm using the Amylase Activity Assay Kit (Sigma Aldrich) according to the manufacturer’s instructions. The α-amylase activity was expressed in nmol/min·ml. One unit is the amount of α-amylase that cleaves ethylidene-pNP-g7 to generate 1.0 μmol of p-nitrophenol per minute at 25 °C.2.9t-test) on proteomic data was done in Perseus (version 1.5.5.3). Two cell lines with similar embryogenic capacity were treated as replicates. Statistical evaluation of enzymatic activity essays was done in Prism  using ANOVA followed by post-hoc Tukey test. Each of the six cell lines was considered as a separate group.A powerful classification approach, partial least square discriminant analysis (PLS-DA) with pareto scaling, was carried out by script EZinfo 3 for Waters (Umetrics). In essence, PLS-DA discovers a combination of variables (specific proteins), which separate sample groups (cell lines with HEC versus cell lines with LEC). To assess the relative influence of a proteomic variable on the mathematical model, variable importance in the projection (VIP) scores were calculated. VIP score is a weighted sum of the squared correlations between elements of the model. Additionally, conventional statistics  and TargetP-2.0 server . Only proteins predicted to have a signal peptide by both programs were considered. Those proteins were further scanned against PS00014 motif using the ScanProsite server (https://prosite.expasy.org/scanprosite/) to determine if they are retained in the endoplasmic reticulum (ER). Proteins containing ER retention motif were excluded from the list of secreted proteins. The localization prediction was accepted when three out of four used prediction tools  concurred.To predict the subcellular localization of differentially accumulated proteins, we used a set of bioinformatic tools. The presence of signal peptides (SP) was predicted using SignalP 5.0 server (http://gpcr2.biocomp.unibo.it/gpipe/index.htm) was used for the prediction of GPI-anchored proteins (specificity: ≥99.5%). For the prediction of transmembrane helices in proteins, they were queried in TMHMM Server v. 2.0 (http://www.cbs.dtu.dk/services/TMHMM/), CCTOP (http://cctop.enzim.ttk.mta.hu/), and MEMSAT analysis tool on PSIPRED server (http://bioinf.cs.ucl.ac.uk/psipred/). Transmembrane predictions were accepted in the case of the presence in at least two predictors. We assigned the most probable location for each identified protein by cross-referencing data obtained from the bioinformatics tools mentioned above.The PredGPI prediction tool  were classified as HEC cell lines ; moreover, they did not reach the cotyledonary developmental stage. These structures necrotized or were overgrown due to intensive tissue cell proliferation. Two types of cell lines also differed in the structural organization of early stages of bipolar somatic embryos. HEC cell lines contained bipolar somatic embryos with well-formed embryonal parts composed of tightly packed meristematic cells (“head”). The embryonal part was connected with a suspensor composed of a bundle of long vacuolated cells (3.2P. nigra with different regeneration capacity: two LEC (E489 and E490) and two HEC cell lines (E492 and E494) growing in the proliferation basal DCR medium. We analyzed secreted proteins by the label-free LC-MS/MS approach with quality control of protein extract by denaturing gel electrophoresis in two biological replicates per cell line  to the separation of biological groups in the mathematical model Figure 23.3P. nigra, we also included medium from additional cell lines into the analysis. Altogether, peroxidase activity was measured in the medium from six cell lines: E489, E490, and E483 (LEC), and E482, E486, and E494 (HEC). Detected peroxidase activity was in the range 19.7 ± 9.8 to 66.4 ± 11 U/mg protein in medium from LEC cell lines, and differentially higher activity (108.8 ± 4.1 to 144.8 ± 6.1 U/mg protein) was measured in the medium from HEC cell lines  acting on the different components of the cell wall, and their activity is often affected by post-translational modifications . Among t radiata . However radiata . Cell waCyclamen persicum, and it was proposed as a potential morphological marker of SE  activity. The β-glucuronidase indirectly participates in Hmodeling . Eudes emodeling . The autmodeling . Severalmodeling , cell–cemodeling , and celmodeling .Additionally, we identified two isoforms of xyloglucan endotransglycosylase/hydrolase  of the GH16 family, each accumulated in the medium from different types of analyzed cell lines Figure 42O2 and reactive oxygen species, thus causing a wide range of cell wall modifications. As a result of their action, cell wall loosening or cell wall stiffening may occur. Cell wall stiffening through the formation of suberin, lignin, or cross-linking of cell wall components, such as extensins, led to the inhibition of cell elongation growth , hydroxyproline-rich glycoprotein (HRGP) with homology to leucine-rich repeat extensin (EXT) proteins, and dirigent protein (DIR) in the medium from HEC cell lines Figure 4n growth . Overexpn growth , and celn growth . On the n growth  and contn growth . Our resQuercus (Three thaumatin-like proteins (TLPs), all more abundant in the medium from HEC cell lines Figure 4Quercus . TLPs afQuercus . IncreasQuercus , similarAraucaria angustifolia occurred at the later stages of development. Contrastingly, starch accumulated early in the proliferation phase in the blocked line, and starch degradation occurred during the maturation phase , more abundant in the medium from HEC cell lines Figure 4on phase . In accolomerata .Expansin-like A1 protein (EXLA1) was more abundant in the medium from LEC cell lines Figure 44.2We found that LysM-domain containing receptor-like kinase (LysM-RLK) accumulated in the medium from HEC cell lines Figure 4Ginkbilobin-2 and Gnk domain-containing protein both accumulated in the medium from HEC cell lines Figure 44.3Extracellular nuclease S1 and two cysteine proteinases (both papain-like C1 family) accumulated in the medium from LEC cell lines Figure 4Other interesting proteins, plant basic secretory family protein (BSP) and basic secretory protease, accumulated in the medium from HEC lines Figure 45Our study revealed differences in secretome comparing the cell lines with different embryogenic capacities. The majority of altered proteins are involved in changes of the cell wall composition and remodeling, although some proteins such as peroxidases can have, depending on the developmental context, both loosening and stiffening effects on the cell wall. Both proteomic and microscopy results suggest that cell walls of HEC cell lines, in which cotyledonary embryos are developing, undergo rather stiffening or rigidification and, in contrast, LEC cell lines in which cotyledonary somatic embryos are not developing undergo loosening, softening, or even cell separation. In such scenario, higher activity of peroxidase in HEC might indicate feruloylate cross-linking or lignin deposition, rather than radical-mediated polymer degradation. Although further biochemical and biomechanical experiments are needed to reconcile this, changes that we detected clearly point to the protein-mediated cell wall and starch dynamics likely regulating and defining embryogenic capacity in pine cell cultures. Both peroxidase and α-amylase activities in the culture medium can be used as a marker to distinguish cell lines with HEC and LEC.https://www.ebi.ac.uk/pride/archive/, PXD040247.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: KK and TS contributed to the conception and design of the study. MP, TS, JB, PB, MD, and KK performed the experiments. MD carried out the statistical analysis. KK wrote the manuscript. All authors contributed to the article and approved the submitted version.


Infertility affects 10.7–15.5% of couples worldwide, and it is defined as the inability to conceive after 6–12 months of unprotected sexual intercourse  and the Many research and clinical groups worldwide use various approaches to study, prevent, diagnose and treat reproductive disorders . These tAlso, further advancements in computer and information sciences have been contributing to providing more targeted treatments in reproductive medicine , which iJournal of Translational Medicine named “Reproductive Medicine & Clinical Reproductive Sciences”. This section welcomes new research focusing on the basic mechanisms or the translational and clinical aspects of human reproductive biology. Also, we encourage the submission of preclinical and clinical studies whose results have the potential to prevent, diagnose, and treat reproductive issues, as well as studies that describe and characterize the genes and the mechanisms regulating human reproductive biology in cell lines or animal models. Reviews on existing and novel technologies, and commentaries on scientific breakthroughs in the field are also welcome. We look forward to receiving your contributions.We are delighted to launch a new section in the


Arising from T progenitor cells, T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignant tumor, accounting for 15% of childhood ALL and 25% of adult ALL. Composing of putative enhancers in close genomic proximity, super enhancer (SE) is critical for cell identity and the pathogenesis of multiple cancers. Belonging to the cytosolute linker protein group, FYB1 is essential for TCR signaling and extensively studied in terms of tumor pathogenesis and metastasis. Dissecting the role of FYN binding protein 1 (FYB1) in T-ALL holds the potential to improve the treatment outcome and prognosis of T-ALL. in vitro and/or in vivo.  In this study, SEs were explored using public H3K27ac ChIP-seq data derived from T-ALL cell lines, AML cell lines and hematopoietic stem and progenitor cells (HSPCs). Downstream target of FYB1 gene was identified by RNA-seq. Effects of shRNA-mediated downregulation of FYB1 and immunoglobulin lambda-like polypeptide 1 (IGLL1) on self-renewal of T-ALL cells were evaluated In vitro, knocking down FYB1 led to comprised growth and enhanced apoptosis of T-ALL cells. In vivo, downregulation of FYB1 significantly decreased the disease burden by suppressing tumor growth and improved survival rate. Knocking down FYB1 resulted in significantly decreased expression of IGLL1 that was also an SE-driven gene in T-ALL. As a downstream target of FYB1, IGLL1 exerted similar role as FYB1 in inhibiting growth of T-ALL cells.  As an SE-driven gene, overexpression of FYB1 was observed in T-ALL, according to the Cancer Cell Line Encyclopedia database.  Our results suggested that FYB1 gene played important role in regulating self-renewal of T-ALL cells by activating IGLL1, representing a promising therapeutic target for T-ALL patients. As an aggressive hematological tumor, T-cell acute lymphoblastic leukemia (T-ALL) accounts for 15% and 25% of childhood and adult ALL, respectively, and presented poor sensitivity to chemotherapy drugs and prone to the drug resistance . About 1As a large cluster composed of several adjacent near-ordinary enhancers, super enhancer (SE) is commonly observed in high-density transcription factors, cofactors, and enhancer-related epigenetic modifications. By regulating lineage-specific genes and oncogens, SE is critical for cell identity and the pathogenesis of multiple cancers –10. InhiThe FYN binding protein 1 (FYB1) encodes the adhesion and degranulation-promoting adapter protein (ADAP) and is highly expressed in T cells, NK cells, bone marrow cells, and platelets, responsible for the signal transduction upon T cell receptor (TCR) stimulation with integrin activation .Studies have shown key roles of FYB1 gene in megakaryocyte-specific platelet production  and autoin vitro and in vivo through the downstream effector, IGLL1. Our results proposed FYB1 as a potential vulnerability of T-ALL cells, representing an interesting therapeutic target.In this study, we identified FYB1 as an SE-driven gene using public H3K27ac ChIP-seq data from T-ALL cell lines, acute myeloid leukemia (AML) cell lines and hematopoietic stem and progenitor cells (HSPCs). As a SE-driven gene, FYB1 gene was highly expressed in T-ALL cell lines, suggesting plausible dependance of T-ALL cells on the high expression of FYB1 gene. Downregulating FYB1 blunted the growth of T-ALL cells both 2 gas at 37°C and subjected to routine mycoplasma testing. In 2020 and 2021, all cell lines were confirmed by short tandem replicates.Human T-ALL cell lines, including HUT78, J-gamma1, CCRF-CEM, MOLT-4, Jurkat and 6T-CEM, human AML cell lines, including MV4-11 and NB4, and human CML cell line K562, were all cultured with Roswell Park Memorial Institute (RPMI)-1640 medium  supplemented with 10% FBS  and 1% penicillin–streptomycin . Human neuroblastoma cell lines SK-N-BE2 and IMR32, human gastric cancer cell lines OACP4C and HGC-27, and human osteosarcoma cell line HOS were all cultured with DMEM medium  supplemented with 10% FBS  and 1% penicillin–streptomycin . All cell lines mentioned above were purchased from the Chinese Academy of Sciences Cell Bank and were cultured in a humidified incubator with 5% CO3. Following the instructions of the manufacturer, Cell Counting Kit-8 (CCK8)  was applied to determine the cell viability. Community multiplication was computed as a percentage of cell growth in the control medium. Cell density was measured three times and repeated in at least three separate trials. Plots and statistic analysis were performed by Graph Prism software 8.4.3 .Cells  were plated in a 96-well plate with a density of 1 × 10ΔCT method. Sequences of primers used in this study were shown as below: GAPDH: forward: TGCACCACCAACTGCTTAG, reverse: GATGCAGGGATGATGTTC, FYB1 forward: GGATGTCTCAGTCAATAGCCG, and reverse: GGTTCCTTGTCAGGCTTTTCC.The quantitative real-time polymerase chain reaction (qRT-PCR) was conducted as mentioned previously . TRIzol®μm filter. Leukemia cells were then incubated with lentivirus for 24 hr supplemented with polyurethane (Sigma–Aldrich) of 10 μg/ml. Puromycin (10 μg/ml) selection was used to make stable T-ALL cell lines was prepared with 10 μg/ml puromycin .shRNAs targeting FYB1 and IGLL1 (sequences were shown in P < 0.05 and Log2 (fold change) >0.5 or Log2 (fold change) <−0.5). The RNA sequencing data have been uploaded to the GEO database (https://www.ncbi.nlm.nih.gov/geo) under the session of GSE197450.In accordance with the protocol advised by Novogene , RNA sequencing was carried out as brief described as below. Total RNA was first retro-transcribed into cDNA for library construction, followed by next-generation sequencing. The original reads were then filtered and mapped against HISAT for clean reads. Gene expression levels were then computed (in fragments per kilobase exon model mapped per million reads). The differentially expressed genes (DEGs) were identified by DESeq2 analysis (μg/ml polyurethane (Sigma–Aldrich). Stable cell lines were selected with 10 μg/ml of puromycin . Leukemia cells obtained after 5 days of screening were washed with precooled 1x PBS and suspended in a 1x binding buffer and then treated using the FITC-Annexin V Apoptosis Kit  according to the manufacturer's instructions. Flow cytometry  was used to analyze the apoptosis.Apoptosis was analyzed as mentioned previously . T-ALL cμmol/l; Catalog no.: P4170; Sigma–Aldrich; Merck KGaA) and 25 μg/ml RNase A, and kept in dark for 1 hr. Beckman Gallios™ flow cytometry  was used to evaluate cell cycle distribution. The MultiCycle AV DNA analysis software  was used to analyze the proportion of cells at different stages of the cell cycle.The cell cycle was analyzed as mentioned previously . The celWestern blotting was analyzed as mentioned previously . The RIP6 Jurkat cells via tail vein injection. On the 18th day after cell injection, luciferase was injected into the abdominal cavity of each mouse and followed by immediate anesthesia with isoflurane gas. Then, the NightOWL in vivo imaging system  was used for the mouse of each group on the 25th, 30th, and 35th days. Mice weight and fur were monitored every 5 days. Mice were euthanized when they reached the humane endpoint. The liver, spleen, peripheral blood (PB), and bone marrow of mice in the sh-NC group and the sh-FYB1 group were collected; and the liver and spleen organs were subjected for imaging. The flow cytometry  was used to analyze the expression quantity of antihuman CD45+ after grinding the liver, spleen, PB, and bone marrow of mice and the differences between the sh-NC Group and the sh-FYB1 group were compared. Immunohistochemistry and HE (hematoxylin and eosin) staining were conducted for each organ specimen. Cleaved-Caspase 3  and Ki67  were used according to the manufacturer's instructions.All animal procedures in this study have been approved and licensed by the Animal Care and Use Committee of Children's Hospital of Soochow University (CAM-SU-AP #: JP-2018-1). Female NSG mice aged 4–8 weeks (NOD-Prkdc) Scid II2rgEm1/Smoc, NM-NSG-001) were purchased from Shanghai Model Organizations. Mice were maintained in a standard SPF room and used in all studies of the sh-NC Group and the sh-FYB1 Group with 12 for each. On Day 0, each mouse was injected with 2 × 10http://www.cistrome.org/). The obtained ChIP-Seq H3K27ac datasets  were aligned to the reference genome (UCSC hg38) with alignment parameters -p 4 -q -x [Public ChIP-Seq H3K27ac datasets of T-ALL cell lines, AML cell lines, and HSPCs were downloaded from the Cistrome database ( 4 -q -x . Peaks w 4 -q -x  for the  4 -q -x . Then wehttp://www.broadinstitute.org/ccle).The expression level of FYB1 and IGLL1 mRNA in different types of tumor cell lines was obtained from the Cancer Cell Line Encyclopedia (CCLE) . Supplementary 1. A total of 213 genes were selected which were commonly correlated with SEs in at least 6T-ALL cell lines, including CDK6, CCND3, ETV6, and FYB1 . In this study NB4, MV4-11, and THP-1 were also used as representative AML cell lines. Additionally, we included three HSPC) samples, to compare the H3K27ac signals among those T-ALL, AML, and HSPC cell samples. The principal component analysis (PCA) result and clustering result based on the peak signals clearly distinguished T-ALL samples from AML or HSPC Figures . Putativand FYB1 . As showhttps://portals.broadinstitute.org/ccle) (FYB1 was identified as one of the SE-driven genes in T-ALL cells . SE was rg/ccle) . Furtherrg/ccle) . CollectP < 0.001) . In contrast, overexpression of FYB1 resulted in enhanced proliferation of J-gamma1 and Jurkat cells  with high expression of FYB1 by shRNAs . Knockin Figures . In addi Figures . In cons Figures , which c Figures . Taken tin vitro, we therefore proceeded to evaluate this in vivo. To this end, Jurkat cells with or without knocked-down FYB1 were transplanted into Nod–Scid mice   . No obvi 0.0026) . All theP < 0.05 and Log2 (fold change) >0.5 or Log 2(fold change) <−0.5, Supplementary 3. IGLL1 was among the top 20 downregulated genes (Supplementary 2). All these results point to IGLL1 as a potential downstream target of FYB1 signaling.To reveal the potential targets of FYB1 responsible for T-ALL cell proliferation, we performed RNA-seq in J-gamma1 cells with or without knocking down FYB1 (GEO ID: GSE197450). About 1,051 DEGs were identified after knocking down FYB1 gene, including 757 downregulated genes and 294 upregulated genes  and found that the expression of IGLL1 was highly expressed in T-ALL (Supplementary 2). Besides, we also determined the effects of knocking down IGLL1 on the apoptosis and found enhanced apoptosis in J-gamma1 and 6T-CEM cells as a result of knocking down IGLL1 (To further explore the role of IGLL1 in T-ALL, we analyzed the CCLE dataset (in T-ALL . To evalin T-ALL  were usein T-ALL . In agrein T-ALL , at leasin T-ALL . Moreovewn IGLL1 . In addiwn IGLL1 . Taken tAs an invasive hematological malignancy, T-ALL was characterized of heterogenous phenotypes and genetics. The past decades have seen improved treatment outcome, thanks to intensified combination therapy. However, around 20% of T-ALL patients would experience relapse and relapsed/refractory T-ALL patients have even worse prognosis –26. TherOver the past decade, mounting evidence has shown that SE, associated with the pathogenesis of various solid tumors and hematological malignant tumors, plays a significant role in the regulation of key oncogenes –31. In tActing as an essential adapter of the FYN and LCP2 signaling network, FYB1 is critical in bridging T-cell signaling to remodeling of the actin cytoskeleton and important for the fitness of normal T cell. However, whether FYB1 is essential for the fitness of T-ALL cells is largely unknown , 38.in vitro and in vivo by inhibiting cell proliferation and promoting apoptosis (Figures Crucial for TCR signaling transduction, FYB1 has been reported for its involvement in the pathogenesis, invasion, and metastasis of various solid tumors –17. Howe Figures  and 4. IAmong the top genes that were downregulated due to knocking down FYB1 and IGLL1 was also driven by T-ALL specific SE . In consGenerally, our results indicate that FYB1-IGLL1 axis might play important roles in the pathogenesis of T-ALL, providing new insights for the biology of T-ALL. Given its role in controlling cell proliferation and apoptosis, FYB1-IGLL1 represents an interesting target for T-ALL therapy.


There is a clear need to expand the toolkit of adequate mouse models and cell lines available for preclinical studies of high-grade neuroendocrine lung carcinoma  and large cell neuroendocrine carcinoma (LCNEC)). SCLC and LCNEC are two highly aggressive tumor types with dismal prognoses and few therapeutic options. Currently, there is an extreme paucity of material, particularly in the case of LCNEC. Given the lack of murine cell lines and transplant models of LCNEC, the need is imperative. In this study, we generated and examined new models of LCNEC and SCLC transplantable cell lines derived from our previously developed primary mouse LCNEC and SCLC tumors. RNA-seq analysis demonstrated that our cell lines and syngeneic tumors maintained the transcriptome program from the original transgenic primary tumor and displayed strong similarities to human SCLC or LCNEC. Importantly, the SCLC transplanted cell lines showed the ability to metastasize and mimic this characteristic of the human condition. In summary, we generated mouse cell line tools that allow further basic and translational research as well as preclinical testing of new treatment strategies for SCLC and LCNEC. These tools retain important features of their human counterparts and address the lack of LCNEC disease models. High-grade neuroendocrine lung carcinomas are among the most malignant lung tumors. They consist of small cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC) and account for >15% and 2–3% of all lung tumors, respectively ,5 and twThe prognosis of patients is dismal. Most SCLC or LCNEC patients are diagnosed at advanced stages, when surgery is not a therapeutic option and survival rates at 5 years are as low as 7–8%  or even Rb1, Rbl1, Pten, and Trp53 in basal epithelial airway keratin K5 expressing cells led to SCLC with a remarkable resemblance to its human counterpart [Rb1, Rbl1, Pten, and Trp53 in a wide variety of lung epithelial cells (using an adeno-CMVcre virus). Therefore, there is an urgent need to expand the availability of experimental preclinical in vitro and in vivo models for this lung cancer tumor type.Researchers have developed and analyzed several genetically engineered mouse models (GEMMs) of SCLC ,4,13,14,nterpart ,21. MeanIn this study, we developed novel in vitro and in vivo models of high-grade neuroendocrine carcinoma of the lung. We established, from the abovementioned spontaneous tumors, new SCLC and LCNEC cell lines that can be successfully transplanted into immunodeficient or immunocompetent mice and develop the corresponding SCLC or LCNEC tumor allografts. The derived cells and tumors maintained neuroendocrine properties and transcriptomic characteristics of the tumors from which they originated and displayed strong similarities to their human counterparts. Moreover, the SCLC syngeneic model showed the ability to develop metastases, mimicking this characteristic of the human disease. These cell lines might become robust tools for basic and translational research of high-grade neuroendocrine lung cancers as well as preclinical studies of novel therapeutic strategies. In addition, the syngeneic immunocompetent mice may be particularly suitable for testing novel immunotherapeutic and anti-metastatic approaches. The intratracheal administration of adenovirus Ad-CMVcre to adult QKO mice produced LCNEC in lungs . An examRb1, Rbl1, Pten, and Trp53 loci  or immunocompetent (QKO) backgrounds and observed that both the 46LCNEC and 54SCLC subcutaneous tumors developed from the same cell passage reached a similar size irrespective of the recipient mice  and showed the ability of these cell-derived tumors to colonize distant organs, thus mimicking the primary tumors arisen in the lungs and the human condition. As tumors injected with 54SCLC grew very rapidly in the syngeneic mouse . Immunohistochemical detection of the neuroendocrine markers chromogranin A or CGRP as well as the expression of TTF-1 or the absence of p63 that were observed in subcutaneous tumors (Given the high metastatic rates reported in human SCLC patients (about 70% at diagnosis) , we sougnd Trp53 ,21, the ouse see , we surgs tumors  corrobors tumors .These data show the ability to form metastases of both the 54 and 57SCLC cell lines in the syngeneic model, which reproduces the pattern observed in human SCLC and makes these cell lines particularly suitable to address anti-metastatic therapeutic approaches in defined molecular settings.To further characterize our models, we performed RNA sequencing from LCNEC and SCLC cell lines , their corresponding derived syngeneic allograft tumors , and normal lung tissue from littermates used for 57SCLC and 46LCNEC syngeneic allografts (lung). As expected, principal component analysis (PCA) showed that triplicates from each sample type grouped together in the bi-dimensional space A. To anaWe evaluated whether our cell and allograft syngeneic models resembled the spontaneous tumors from which they originated. In a previous work , we charThis study presents new mouse LCNEC and SCLC cell lines, which were generated from spontaneous tumors arisen in our previously generated GEMMs ,21. SubcGenetically engineered mouse models (GEMMs) faithfully reproduce human tumors in a physiological and immunocompetent environment . HoweverThe relevance of mouse cell lines for basic, translational, and preclinical research depends on how closely they resemble their human tumors’ counterparts. The comparative transcriptomic analyses performed here shows that LCNEC and SCLC cells and subcutaneous tumors retain the transcriptomic profile of the original spontaneous tumors and share strong similarities with those of human LCNEC and SCLC ,21,28. TSCLC is a highly metastatic and recalcitrant carcinoma, with metastasis as a major cause of mortality. While human SCLC cells fail to metastasize when transplanted into mice, few murine SCLC cell lines have been reported to generate metastasis after subcutaneous engraftment ,29 to seIn summary, we report the establishment and precise characterization of high-grade neuroendocrine lung carcinoma (LCNEC and SCLC) cell lines and transplanted tumors in different backgrounds. To our knowledge, these are the first murine LCNEC cell lines that have been generated. Our data support the neuroendocrine properties of these novel cell lines and their transcriptomic similarities among cells, allograft, and spontaneous and human tumors of the same cancer type (LCNEC and SCLC). Importantly, the SCLC cell lines showed metastatic potential with the ability to grow metastatic lesions. In summary, we showed that these cell lines might serve as reliable model systems for further neuroendocrine lung cancer research and identification of novel molecular targets and immunotherapies for the treatment against highly aggressive SCLC and LCNEC.All the animal work was approved by the Animal Ethical Committee (CEEA) and conducted in compliance with Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) guidelines.Rbl1, Pten, and Trp53 alleles along with a null Rbl1 gene and have been described elsewhere [nu/nu immunodeficient 6–8-week-old mice were obtained from Janvier (Saint-Berthevin). QKO mice bear floxed lsewhere . PrimaryFor the isolation and culture of tumor cells, CMV-QKO and K5-QKO mice  were sacCells grew either in suspension (SCLC cells from K5-QKO tumors) or in adherent monolayer (LCNEC cells from CMV-QKO tumors). To split the cell cultures, suspending aggregates were harvested and mechanically disaggregated by pipetting with micropipette. Adherent cultures were washed (PBS) and detached by incubating the cells in trypsin-EDTA solution (MilliporeSigma) for 3 min at 37 °C.2 flasks and cultured for 5 days. Every day, the cell number was counted in a Countess 3 Automated Cell Counter . To assess cell growth kinetics, 100,000 LCNEC or SCLC cells were seeded into 75 cmAll experiments were performed with mycoplasma-free cells.2) × (short side/2))/3. Tumors were allowed to grow until a maximum volume of 1500 mm3; then, mice were euthanized and processed for histology. Complete necropsies were performed in search of metastases. For mice injected with 54SCLC, subcutaneous tumors were surgically excised and allowed to regrow. Organs were processed for histology, as described below  and injected subcutaneously into the flanks of recipient (immunodeficient NMRI nude or immunocompetent QKO) mice. Tumors were measured twice a week with a caliper, and tumor volume was calculated with the formula (4 π ((long side/2)elow see .F/FRb1, -/-Rbl1, F/FTrp53, and F/FPten control lungs, cells, and tumors using DNeasy Blood & Tissue Kit . Primer sequences, amplified fragments, and PCR amplification product sizes appear in Genomic DNA was isolated from Adherent cell lines (LCNEC) were seeded in chamber slides and grew there for 48 h. Floating cell lines (SCLC) were spun in Shandon CytoSpin 2 centrifuge at 400 rpm for 5 min. For the immunofluorescence analyses, the cells were (1) fixed with MetOH-acetone (1:1) at −20 °C for 10 min; (2) incubated with horse serum 10% for 1 h at room temperature; and (3) washed three times with sterile phosphate-buffered saline (PBS) (pH 7.5) prior to incubation with the appropriate primary antibodies diluted in horse serum 10%. Primary antibodies were used as follows: 1/200 dilution of anti-NCAM CD56 ; 1/100 dilution of anti-SSTR2 ; and 1/2000 dilution of anti-CGRP . Secondary antibodies were used as follows: 1/1000 anti-rabbit AlexaFluor488  and 1/1000 anti-mouse AlexaFluor488 (A11001Molecular Probes). Diamidinophenylindole (DAPI) was used to counterstain the nuclei or chromosomes.Necropsies were performed at the end of the experiments. Upon necropsy, tumors and tissues were fixed in formalin for 24 h. Fixed tissues were dehydrated and embedded in paraffin wax. Sections (5 μm) were stained with hematoxylin and eosin (H&E) for histological analysis or processed for immunohistochemistry. Immunohistochemical analyses were performed as follows: the sections were (1) deparaffinized; (2) incubated with 10% horse serum for 30 min at 37 °C to block non-specific binding; and (3) washed three times with sterile phosphate-buffered saline (PBS) (pH 7.5) prior to incubation with the appropriate primary antibodies diluted in horse serum 10%. Primary antibodies were used as follows: 1/100 dilution of anti-chromogranin A ; 1/2000 dilution of anti-CGRP ; 1/200 dilution of anti-TTF1 ; 1/50 dilution of anti-p63 ; and 1/100 dilution of anti-CC10 . Secondary antibodies were used as follows: 1/1000 biotin anti-mouse  and 1/1000 biotin anti-rabbit .Cells and tissues were embedded in RNALater  according to the manufacturer’s instructions. RNA yield and quality were determined using an Agilent 2100 Bioanalyzer.n = 3 57SCLC cell line samples, passage 11, from 3 different cell flasks; n = 3 46LCNEC cell line samples, passage 17, from 3 different cell dishes; n = 3 57SCLC syngeneic allograft tumors (from transplanted cell passage 11); n = 3 46LCNEC syngeneic allograft tumors (from transplanted cell passage 17); and n = 3 normal lung tissue from littermates of mice used for 57SCLC and 46LCNEC syngeneic allografts. RNA sequencing was performed after poly-A selection using Illumina NovaSeq 2 × 150 bp sequencing. Reads were aligned to GRCm39 mouse genome with “Rbowtie2”, counts extracted with featureCounts function from “Rsubread” and gencode annotation release 27 (gencode.vM27.annotation.gtf), and differential expression with DESeq2. Principal component analysis graph was plotted after variance stabilizing transformation (vst function in DESeq2). Transcriptome sequencing was performed from www.broadinstitute.org/gsea (accessed on 5 May 2023)) [We developed signatures of genes specifically up-regulated in tumors from our CMV-QKO LCNEC (moLCNEC) and K5-QKO SCLC (moSCLC) models . Brieflyy 2023))  was usedp-values for each SA. The SubMap module uses permutations to compute p-values. Number of permutations for nominal-p of Fisher’s statistics: 1000. The p-value correction method: FDR and Bonferroni.We analyzed the association between our cells and allograft RNAseq expression data using the SubMap module of Gene Pattern ,25. SubMThe 300-gene LCNEC/SCLC classifier from George et al. (2018)  was convp-value threshold of ≤0.01 and an expression fold change (FC) ≥2 were selected. DAVID [https://david.ncifcrf.gov/home.jsp (accessed on 5 May 2023)) from the NIAID/NIH was used to identify Gene Ontology Biological Process (GOBP) functional categories (GOTERM_BP_DIRECT). GOBP terms were listed by p-value based on EASE score [DESeq2  was usedd. DAVID  web servSE score  and manuData were analyzed with GraphPad Prism 9 software . Statistical analysis was performed using U Mann–Whitney test.Patients with SCLC or LCNEC have very poor prognoses, low survival rates, and few effective treatments. Surgery is not usually an option and, as a consequence, human material for research is scarce. In an attempt to address these issues, this study described newly generated mouse cell lines and in vivo models of SCLC and LCNEC that closely resemble their human counterparts. These constitute relevant tools for further basic and translational research as well as preclinical testing of potential antitumor compounds, including immunotherapies. It is of note that no mouse LCNEC cell lines have been previously described.


Calendula officinalis flower, and Aloe vera on breast cancer vs. epithelial cell lines. Various extraction methods were used, and the proliferative effect of the resulting extracts was assessed by proliferation assay on breast cancer and epithelial cell lines. Carrot, Aloe leaf, and Calendula flower extracts were extracted by hexane and methanol methods, and their semi-purified extracts were able to specifically inhibit the proliferation of breast cancer cell lines. The extract composition was investigated by colorimetric assays, UHPLC-HRMS, and MS/MS analysis. All the extracts contained monogalactosyl-monoacylglycerol (MGMG), while digalactosyl-monoacylglycerol (DGMG) and aloe-emodin were found in Aloe, and glycerophosphocholine (GPC) derivatives were identified in Calendula, except for the isomer 2 detected in carrot, suggesting that their observed different anti-proliferative properties may be associated with the different lipid compounds. Interestingly, Calendula extract was able to strongly inhibit the triple negative breast cancer MDA-MB-231 cell line proliferation , supporting MGMG and GPC derivatives as potential drugs for this BC subtype treatment.Breast cancer (BC) remains the leading cause of mortality in women, despite significant advancements in diagnosis. Thus, the identification of new compounds for its treatment is critical. Phytochemicals are known to exhibit anti-cancer properties. Here, we investigated the anti-proliferation potential of extracts from carrot,  The first fragmentation, detected in ToF-MS/MS spectra, arose from the neutral loss of methyl formate (60 Da). This behavior has been described as pivotal for the identification of the GPC lipid class and seems to involve a gas-phase SN2-like reaction, in which the formate ion acts as the nucleophile and the methyl group of the quaternary ammonium polar head as the electrophile [m/z 224.069 , while the one at m/z 295 could be ascribed to its hydroxy-derivative, already described in plants belonging to Calendula genus [m/z 279), oleic acid , and saturated palmitic acid .Calendula sample was mainly composed of glycerophosphocholine (GPC) derivatives, differently substituted. Their molecular ions shared even trophile . Therefo 224.069 . In partla genus . The lat® Structure Database (LMSD) [Lipid Mapse (LMSD)  was consm/z 699.3803) was found only in Aloe semi-purified extract. Additionally, these metabolites appeared as formate adducts, which in MS/MS experiments immediately gave rise to deprotonated structures. Then, ions at m/z 415.1470, assigned to the digalactosylglycerol moiety, and the corresponding dehydrated fragment at m/z 397.1363, were pivotal for their characterization.Apart from GPCs, one monogalactosyl-monoacylglycerol (MGMG) was tentatively identified in all the samples, whereas a DGMG  was identified as aloe-emodin according to the literature [Finally, in terature .Calendula, Aloe, and carrot were dried, weighted, solubilized in DMSO at a final concentration of 1 mg/mL, and tested at the final concentration of 0.5 µg/mL (assay concentration of DMSO 1.0% v/v). These semi-purified extracts were tested on four different cell lines: MCF10A as breast cell line of control and MCF7, SKBR3 and MDA-MB-231 breast cancer cell lines, representing oestrogen receptor positive, and Her-2 positive and triple negative breast cancer subtypes. All cell lines were treated upon 48 h with all the semi-purified extracts, and cell survival was evaluated by CYQUANT® cell proliferation assay.Semi-purified extracts from v/v of DMSO; the carrot semi-purified extract  isomer 2 as well as the hydroxy-octadecadienoic acid compound also detected in carrot. Instead, all the extracts contained one monogalactosyl-monoacylglycerol , while a digalactosyl-monoacylglycerol  and aloe-emodin were only identified in Aloe leaves semi-purified extract  derivatives, differently substituted in m/z 277.2174) detected in the extracts from all vegetables. Previously, it has been reported that the monogalactosyldiacylglycerol (MGDG), another type of glycoglycerolipid found in the plant cell membrane, showed therapeutic anti-cancer activity [Calendula flowers was higher, compared to the other extracts from carrot and Aloe, on the MDA-MB231 breast cancer cell line. This effect may be due to the presence of the different glycerophosphocholine (GPC) derivatives detected only in Calendula flowers, except for the GPC (16:0) isomer 2 which was also found in carrots.All together, these data suggest that the anti-proliferative effect may be associated with the presence of the glycoglycerolipid compound . BC includes several distinct subtypes based only on genetic, morphologic, and clinical features [Lipid compounds are getting attention for their anti-tumoral, anti-inflammatory, and anti-viral activities. It has been reported that they are able to induce greater apoptosis in various cancer cells  than in normal cells, both in vitro and in vivo, by generating reactive oxygen species, suppressing angiogenesis via the Akt cell signalling pathway, and inducing apoptosis ,40,41,42features . Among a


Globally cells adapted by decreasing expression of genes involved in metabolism, proliferation, and protein maturation, and increasing alternative splicing. They accumulated mutations, especially frameshift insertions, and harbored increased copy number alterations, indicating increased genomic instability. Cells showing highest TMB simultaneously experienced a significant downregulation of DNA replication and repair and chromosomal maintenance pathways. A sixteen-gene common response to chronic hypoxia was put forth, including genes regulating angiogenesis and proliferation. Our findings show that chronic hypoxia enables survival of tumor cells by metabolic reprogramming, modulating proliferation, and increasing genomic instability. They additionally highlight key adaptive pathways that can potentially be targeted to prevent cancer cells residing in chronically hypoxic tumor areas from thriving.Hypoxia afflicts the microenvironment of solid tumors fueling malignancy. We investigated the impact of long hypoxia exposure on transcriptional remodeling, tumor mutational burden (TMB), and genomic instability of cancer cells that were grouped based on their inherent sensitivity or resistance to hypoxia. A hypoxia score was used as a metric to distinguish between the most hypoxia-sensitive (hypoxia high (HH)), and most resistant (hypoxia low (HL)) cancer cells. By applying whole exome sequencing and microarray analysis, we showed that the HH group was indeed more sensitive to hypoxia, having significantly higher TMB ( Hypoxia is common to solid tumors manifesting upon the drop in oxygen below physiological levels. Moving away from the functional vasculature within the growing tumor mass, different levels of oxygenation levels exist creating sites of intermittent, or cyclic, hypoxia experiencing fluctuations in perfusion leading to episodes of hypoxia and reoxygenation; sites of acute hypoxia due to transient disruption of perfusion lasting minutes to hours; and areas of chronic hypoxia where there is continuously limited oxygen diffusion . HypoxiaIn vitro investigations have shown that hypoxia could lead to genomic instability through its downregulation of DNA repair genes and their respective pathways of MMR . Hypoxis of MMR  (reviewes of MMR ). In a ss of MMR . How proin vitro and in vivo; an effect that was not observed in cells proficient for MMR  cells not only resulted in increased TMB, but also dynamic mutational profiles that led to neoantigens renewal  for MMR . This in for MMR . A longigen load . Xenogragen load . We havegen load . While tgen load . In thatgen load .in vitro by applying different oxygenation levels and exposure times, but a comprehensive study on the prolonged exposure of tumor cells to hypoxia is yet to be conducted. This is highly relevant, since the actual time tumor cells can remain in a hypoxic state within the tumor can range from hours to even weeks  values for the eight signature genes corresponding to three interdependent experiments carried out in duplicate were obtained from our previously published work . There whttps://www.cbioportal.org/) to download RNA sequencing data  for the 8 signature genes for the following datasets: Breast Invasive Carcinoma ; Colorectal Adenocarcinoma ; Ovarian Serous Cystadenocarcinoma  and Pancreatic Adenocarcinoma . Subsequently the patients in each dataset were given a hypoxia score as previously described . They were cultured in appropriate Dulbecco’s Modified Eagle Medium/F-12 (DMEM/F-12), GlutaMAX Supplement  or in Roswell Park Memorial Institute Medium 1640 (RPMI 1640) Medium, GlutaMax supplement , supplemented with 10% Fetal Bovine Serum (FBS) , 1% Sodium pyruvate  and 1% Penicillin - Streptomycin , as previously described . Cells were passaged twenty times in the two conditions and DNA and RNA was isolated and processed as described below.The three highest scoring cell lines, H226, MCF-7 and HT-29, as well as the three lowest scoring cell lines, Capan-1, SKOV-3, MIA PaCa-2 were maintained in a 5% COescribed . Two ind® Hybridization oven 640 (Affymetrix). Arrays were subsequently washed and stained using appropriate reagents from the GeneChip™ WT PLUS Reagent Kit (Applied Biosystems™) as well as the GeneChip Fluidics Station 450 (Affymetrix/Thermo Scientific inc) following manufacturer’s recommendations. The arrays were immediately scanned with the GeneChip™ Scanner 3000 7G (Affymetrix/Thermo Scientific inc).Total RNA was first isolated using EasyBlue  following manufacturer’s instructions, and subsequently purified with repeated washes and in 75% ethanol. After drying, isolated pellets were resuspended in nuclease free DEPC treated water and concentration and purity were evaluated using the Nanodrop. The integrity of RNA was determined by running 300 ng on 1% agarose gel and the 28S/18S bands were observed. Only RNA with acceptable integrity (28S twice the intensity of 18S and without smearing), as well as purity ratios  were processed for transcriptome analysis with the Human Clariom™ D Assay . Aliquots of 50 ng/μL were prepared and 100 ng of total RNA was used to prepare fragmented and labeled single stranded cDNA. The steps were conducted with the GeneChip™ WT PLUS Reagent Kit (Applied Biosystems™) following manufacturer’s instructions. Each sample was hybridized to one array respectively by placing the arrays in specialized trays and incubating them for 17 h at 45°C and 60 rotations per minute using the Genechipp-value ≤0.05 were considered significant. The lists of significant differentially expressed TCs per comparison per cell line can be found in The images and raw data files from scanned arrays were first checked to ensure correct alignment and that there were no bubbles or debris affecting the image’s clarity. Subsequently, cell probe defined files (.CEL) files were processed using the Transcriptome Analysis Console (TAC) 4.2 software (Applied Biosystem). Gene + Exon summarization using the Signal Space Transformation-Robust Multi-Array Average (SST-RMA) algorithm was first applied to summarize, background-subtract, and normalize the CEL files. Further QC checks were applied to ensure that all samples were properly labeled and hybridized and had acceptable positive signal intensity. The resulting CHP files were imported back to TAC to carry out differential gene expression analysis using ANOVA method for statistical testing with ebayes  correction for small sample size. The hypoxia samples in each cell line were compared to their respective normoxia samples from passage zero and passage twenty. Only transcript clusters (TCs) that showed a fold change (FC) of ≤ −2 or ≥2 with a false discovery rate (FDR) Pathway analysis on the differentially expressed genes per cell line was conducted using GSEA v4.2.2. Default settings were used , except that the permutation type was gene_set and not phenotype, given that the sample numbers per group were less than 7. The metric for ranking genes was the ratio of classes, which is equivalent to the fold change. Curated gene sets from HALLMARK , KEGG , REACTOME , BIOCARTA  and WIKIPATHWAYS (//pub/gsea/gene_sets/c2. cp.wikipathways.v7.5.1. symbols.gmt) were used for enrichment analysis. Enriched pathways are given with a normalized enrichment score (NES) that is an enrichment score which has been normalized across the analyzed gene-sets. The enrichment score itself, represents the degree to which a particular gene-set is over-represented at the top or bottom ranked list of genes present in the expression data. Significantly enriched pathways were considered as those having a NES with an FDR q-value of at least 0.05.p-value of less than 0.05 are considered differentially expressed. In addition, based on the signals of the PSRs and JUCs of a gene, the event of alternative splicing is determined as being an exon cassette (splicing out of exon from primary transcript), intron retention , alternative 5′ donor site , alternative 3′ acceptor site , mutually exclusive exons (2 back to back exons excluded from the mature mRNA transcript), alternative last exon and alternative first exon, as well as complex events that do not fit in these categories. How well the event explains the splicing pattern visualized is given by an event score with values from 0 – 1. The event with the highest value is considered as the most possible mode of alternative splicing. Alternatively spliced TC without any reported event score were excluded from the analysis.Potential alternative splicing was assessed due to the presence of probe selection regions (PSRs) that represent gene exons and junctions (JUCs), which are used to probe the inter-exon boundary. Eventpointer algorithm is applied to predict the presence of alternative splicing events considering the signals of the JUCs and PSRs upon their normalization to the expression levels of the gene itself. The summary expression signals of JUCs and PSRs are then used to determine the splicing index in condition 2 (hypoxia) compared to condition 1 (normoxia). TC with exon splicing index of greater than 2 or less than −2 and an exon FDR ® Genomic DNA Kit  following manufacturer’s recommendation. In brief, DNA pellets were lysed by vortexing in 200 μL PureLink® Genomic Lysis/Binding Buffer following treatment with Proteinase K and RNase A. DNA was subsequently bound on the PureLink® Spin Columns upon addition of 200 μL absolute ethanol. The DNA was washed with appropriate buffers, which were removed by centrifugation and finally eluted using 40–60 μL low binding TE buffer . The purity of gDNA was confirmed with the Nanodrop and the integrity was checked by running 500 ng on agarose gel electrophoresis. The Ion S5 System  was used for whole exome sequencing. Before starting, gDNA was accurately quantified by fluorometry using the Qubit dsDNA HS Assay Kit  and the Qubit 4 Fluorometer (Invitrogen). Subsequently 100 ng of DNA was used for exome library preparation with Ion AmpliSeq™ Exome RDY Kit and Ion Xpress™ Barcode Adapters 17–32 Kit  or Ion Xpress™ Barcode Adapters 33–48 Kit  following manufacturer’s protocols. For every sample, target regions were amplified by PCR and the resulting amplicons were pooled, partially digested then ligated with suitable adaptors to achieve barcoded libraries. Libraries were purified using the CleanNGS system  then finally quantified by qPCR using the Ion Library TaqMan Quantification Kit  and diluted to 100pM. The Ion 540™ Kit–Chef  was used to implement the sequencing reactions following recommended protocols. The Ion template preparation on 540 Ion Chip™ was done using the automated Ion Chef™ System (Ion Torrent™) and the sequencing reaction was carried out in the Ion S5™ sequencer (Ion Torrent™). Two differentially barcoded libraries could be sequenced simultaneously at 25 pM per library.Genomic DNA was isolated using the PureLinkvcf2maf, as described in (n = 2 across both the files) were removed as a part of the data cleaning process from further analysis. Furthermore, two MIA PaCa-2 files were removed from the downstream analyses as they generated TMB levels that were 300 orders of magnitude higher than all values achieved for this cell line, as well as the other cell lines. The TMB values were also higher than those previously reported for the cell line as per the cancer cell line encyclopedia on cbioportal . Therefore, there were 44 files that were analyzed further. The full information of these samples can be found in the Raw sequencing data was first aligned against hg19 with Ion Torrent Suite software to generate Binary Alignment Map (BAM) files. Low stringency somatic variant and indel calling was then conducted with the Torrent Variant Caller by applying default settings to obtain Variant Call Format (VCF) files. VCF files were converted to MAF files using ribed in . Two Capmaftoolsv2.6.05 was used to analyze MAF files. Mutational landscape across the samples HL was analyzed after normalizing the variants called in the hypoxia-treated cancer cells against the normoxia cancer cells used as the control. In this way only variants that emerged in the cancer cells after exposure to long term hypoxic conditions could be identified. The mutational landscape was then reported as follows: 1. Tumor Mutational Burden, 2. Gene mutation summary and 3. Sample mutational summaries. Next, Copy Number Variations (CNVs) were calculated using the command line version of Control-FREEC. The analysis was run on. bam files. Control-FREEC requires an additional custom config file with all the parameter values to be run according to the data input and the desired output. CNVs were computed, normalized, and segmented for each hypoxia tumor sample against a corresponding normoxia sample as a control dataset using a window size of 50kb. breakPointThreshold was set to 0.8, contamination parameter was set to 0, minMappabilityPerWindow was set to 0.85 and ploidy was set to multiple values of 2, 3, 4 and the ploidy was allowed to be chosen according to the value that explains most observed CNVs across all samples. All the supporting files including input, output, config and codes written to carry out the analysis can be found at the following github link: https://github.com/Mohak91/hypoxia_pan_cancer_types_genomics_study.R package −ΔΔCq method  following manufacturer’s instructions. Twenty nanograms of cDNA were subjected to 10 μL qPCR reactions run in triplicate with the Maxima SYBR Green/ROX qPCR Master Mix (2X) . Select genes were quantified by RT-qPCR using specifically designed primers . Quantitq method . Beta acp-value ≤0.05. Heatmaps of expression were generated using Rstudio version 1.3.1073  by applying the gplots package. Hierarchical clustering was performed using Euclidean distance and ward. D2 method.Statistical analysis was conducted using GraphPad Prism 9.3.1. Unpaired two-sided t-test was applied for comparisons between only two groups, otherwise 2-way ANOVA with the recommended correction for multiple comparisons was used. Statistical significance was considered as in vitro 8-gene hypoxia signature   identified on the chip, 14′799 unique transcript clusters were differentially expressed in at least 1 cell line. The bulk of response in all cell lines, except for HT-29, was cell-line specific. Indeed, the biggest contributing factor to the variation seen among samples was their cell line-dependent characteristics . The celAK4, the pro-angiogenic gene VEGFA and the cell adhesion gene DSG2 were upregulated in all 3 cell lines, while the translation regulation gene, EIF4G2 and the mitochondrial gene PGAM5 were downregulated. Among the six HL specific genes, two showed variable expression , while one was consistently downregulated (PSAT1P1) and three upregulated  in all 3 cell lines. The upregulation of the cell adhesion gene CEC2B and the glycoprotein hormone STC1 were validated by RT-qPCR , were downregulated in hypoxia. The expression of five of these genes was validated by RT-qPCR . In part RT-qPCR .PGK1, NDRG1 and P4HA1, maintained significant differential expression in all cell lines. Significant VEGFA increased expression was also maintained in the HH group.In terms of the common response to hypoxia that was achieved in all cell lines, three genes were downregulated, two of which are involved in metabolism, while the remaining thirteen genes were consistently upregulated across all 6 cell lines. Those included long non-coding RNAs, genes involved in glycolysis, apoptosis, extracellular matrix (ECM) remodeling, proliferation, and angiogenesis . Of intePDK1, NDRG1, LDHA, and SLC3A2 were alternatively spliced in all the cell lines. In addition, HIF-1α was significantly spliced in all cell lines . There were 175 pathways in H226 (51 specific), 183 pathways in MCF-7 (63 specific), 26 pathways HT-29 (2 specific). With respect to the HL group, there were 58 pathways in Capan-1 (6 specific), 34 pathways in SKOV-3 (16 specific) and 47 pathways in MIA PaCa-2 (3 specific) (Supplementary File 3). Taken together, the HH group observed a higher number of enriched pathways compared to the HL group.The enriched pathways could be grouped into six major subsets: cell signaling and response; metabolism; DNA/RNA/protein processing; cell cycle; DNA replication and repair; and immune response . The onlvia NFκB, among others. On the other hand, MCF-7 demonstrated hypoxia-specific enrichment of the P53 transcriptional network and pathway, while HT-29 was enriched for glycolysis. MIA PaCa-2 and Capan-1 did not show any specific increased enrichment in hypoxia, while SKOV-3 showed the highest number of increased pathways in hypoxia, including pathways of EMT, Extracellular matrix degradation and organization, apical junction, and focal adhesion among others. Indeed, this cell line had quite a unique response to hypoxia, while most of the cell-cycle, and cell-signaling pathways were downregulated in hypoxia for the cell lines, in SKOV-3, eukaryotic translation initiation, Dectin1-mediated non-canonical NFκB signaling, and RHO GTPase related signaling were upregulated; so were pathways of the cell-cycle and cell-cycle checkpoints and control.Other pathways positively enriched in hypoxia were seen in a cell line dependent manner . H226 deBased on these results, it can be concluded that long passaging in hypoxia leads to cell cycle inhibition, decreased metabolic activity as well as nucleic acid and protein processes. Our data also indicate that cells seem to switch to an energy conserving and indolent state.2. Mutational landscape across the samples was analyzed after normalizing the variants called in the hypoxia-treated cancer cells against the normoxia cancer cells used as the control.To determine the relationship between the sensitivity of cancer cell lines to hypoxia with their ability to accumulate mutations, the three highest scoring cell lines, H226, MCF-7 and HT-29, and three lowest scoring cell lines, Capan-1, SKOV-3, and MIA PaCa-2 were cultured in chronic hypoxic conditions, indicated as twenty passages at 1% OTMB achieved across cell lines was variable, ranging from a mean of 1.040 mutations/Mb in SKOV-3 (95%CI: 0.3649–2.394) to 5.37 mutation/Mb (95%CI: 3.801–6.939) in MCF-7 . All celp = 0.03), suggesting that cells that are more sensitive to hypoxia are also more mutable. Looking into the classification of identified variants, the majority were frame-shift insertions, followed closely by missense mutations in both hypoxia groups with the most common form being C>T and T>C transitions  to the hypoxia low (HL) groups, HH displayed a mean of 3.007 mutations per megabase of genome (95%CI: 1.805–4.209), while the HL group accumulated 1.588 mutations (95%CI: 0.9353–2.241) . Indeed,nsitions . Among t samples .FLG, GPRIN2 and PCDHB5, no effects could be reported for most of the genes, however, in the case of MCF-7, seven genes were affected. One was differentially spliced (BRIP1), two were only differentially expressed  and four were both differentially expressed and spliced  (ZNF431) was differentially expressed in H226, and one (ZNF273) differentially spliced in MIA PaCa-2  . With reA PaCa-2 . These rin vitro were also altered in patient samples, we applied our signature to stratify tumors of TCGA datasets based on their hypoxia score into high hypoxia and low hypoxia. For each cancer type, the percent alteration of the top ten in vitro mutated genes were compared within the two groups. Results showed a good concordance level between the genes that were reported as affected in vitro and their mutation in more hypoxic patient tumors . Therefore, while the effect of long passaging in hypoxia on chromosomal instability is cell-line dependent, cells that are more sensitive to hypoxia are more prone to experiencing copy number losses. These changes did not give rise to any discernable phenotypic changes, as cells presented with similar morphology when comparing early and late passages in hypoxia , number of gains (CNG) and losses (CNL) for each cell line . RegardiL groups . Upon ex hypoxia .via ATR as well as DNA damage response were downregulated in H226; REACTOME pathways of G1-S DNA damage checkpoints and transcription coupled NER were downregulated in MCF-7; and REACTOME gene sets of HDR, activation of ATR in response to replication stress and DNA strand elongation, as well as KEGG DNA replication were downregulated in Capan-1. No effects on such pathways could be reported for HT-29 , transcription or translation regulation , and biogenesis of ribosomes  and purines (ATIC), it can be deduced that cells in the HH group mounted a bigger attenuation of key cellular processes when compared to those in the HL group. When considering results of pathway analysis at the cell-line level, the only commonly enriched pathway was that curated for hypoxia. This gene-set includes 200 genes reported to be upregulated in hypoxia and including HIF-1α targets. In addition, all the cell lines, except SKOV-3, showed significant downregulation of DNA/RNA/protein processing-, and cell cycle-related pathways. It seems like SKOV-3 showed bigger resistance to hypoxia than estimated based on the hypoxia score. Nonetheless, all the cell lines experienced a downregulation of pathways of metabolism. Interestingly, a common response to hypoxia could be determined that included sixteen genes. Three of these genes were downregulated, and among them two IDH3A and COA7 are involved in metabolism. While IDH3A has been associated with stabilization and transactivation of HIF-1α  (TP53). It is important to note that through our experiment, we cannot distinguish between the effect of passaging in hypoxia on accumulation of mutations and hypoxia alone as a genotoxic stress. This is relevant given the findings of Ben-David et al., which eloquently highlight the genetic heterogeneity and extensive clonal diversity that exists in cancer cell lines, wherein selection pressure could lead to genetic instability and genetic diversification  , which cnt (HRD)  and can fication . Regardification . Nonethe hypoxia . How theGenomic instability could be due to replication stress and deregulation of DNA repair pathways, both of which have been reported by us  and otheFLG, GPRIN2 and PCDHB5. Based on literature findings  cells, while promoting the inhibitory role of regulatory T-cells . Furtherolecules . Of inteversus hypoxia alone. One limitation of this work is that we did not directly compare the response of these cells in the given condition with other hypoxic conditions, such as acute or cycling hypoxia. This could have firmly underlined chronic hypoxia-specific processes. In addition, the issue faced with the sequencing of two MIA PaCa-2 samples meant we only had two hypoxia samples for this cell line instead of four, as with the other cell lines. Another limitation is that by not carrying out RNA sequencing, we are only capturing a fraction of the altered transcriptome in hypoxia. Functional assays implicating the roles of the reported genes in the adaptation to hypoxia would be required to confirm their relevance to chronic hypoxia. Finally, without neoantigen load data it is difficult to make a complete conclusion on the effect of hypoxia on immunogenicity across the investigated cells.This study provides a global view on the reactivity to hypoxia of different tumor cells with varying inherent sensitivity to the same condition. The choice of focusing on the three highest and three lowest scoring cell lines was to ensure the presence of more than 2 cell lines per group to carry out proper comparative analysis. It would be interesting to see how the findings vary if all the scored cell lines are analyzed. This step would be necessary to determine the robustness of the signature in classifying the cells as HH and HL, and validate the association seen for higher TMB in HH compared to HL. Furthermore, clonality analysis would be necessary for distinguishing between the consequence of high passaging in hypoxia on clonal selection in vitro in hypoxia were frequently mutated in patients with more hypoxic tumors, compared to those with less hypoxic ones, shedding light on their potential relevance to this condition. A global response to chronic hypoxia could also be observed, wherein there was an increase in alternative splicing of both HIF-1-target and non-target genes across cells, and we could report a common signature composed of 16 genes, suggesting the presence of a core response to chronic hypoxia. This core response included well-acknowledged hypoxia-response genes, such as PGK1 and NDRG1, as well as much less investigated genes, including COA7 and the oxygen sensing gene, PAM, whose roles in chronic hypoxia deserve further attention. Finally, our results also hint at the role of chronic hypoxia in depressing tumor cells’ inherent immunogenicity through the downregulation of antigen processing and presentation related pathways. Therefore, chronic hypoxia could influence the adaptation of tumor cells, enabling their survival by promoting metabolic reprogramming, modulating proliferation, and increasing genomic instability.In conclusion we have shown that in chronic hypoxia cancer cells are reprogrammed to an indolent state, and yet they incur genomic instability in the form of high tumor mutational burden, mainly in the form of frameshift insertions, and copy number alterations. This could be due to the downregulation of pathways related to DNA damage response and repair, replication stress, as well as chromosomal maintenance. Of interest, cells that were ranked as being more sensitive to hypoxic stress accumulated a mean TMB that is significantly higher than the more resistant cells. They further experienced a higher rate of copy number loss. We also found that most genes altered