The organotropism is the propensity of metastatic cancer cells to colonize preferably certain distant organs, resulting in a non-random distribution of metastases. In order to shed light on this behaviour, several studies were performed by the injection of human cancer cell lines into immunocompromised mouse models. However, the information about these experiments is spread in the literature. For each xenograft experiment reported in the literature, we annotated both the experimental conditions and outcomes, including details on inoculated human cell lines, mouse models, injection methods, sites of metastasis, organs not colonized, rate of metastasis, latency time, overall survival and the involved genes. We created MetaTropismDB, a freely available database collecting hand-curated data useful to highlight the mechanisms of organ-specific metastasis. Currently, it stores the results of 513 experiments in which injections of 219 human cell lines have been carried out in mouse models. Notably, 296 genes involved in organotropic metastases have been collected. This specialized database allows the researchers to compare the current results about organotropism and plan future experiments in order to identify which tumour molecular signatures establish if and where the metastasis will develop.Database URL:  http://www.introni.it/Metastasis/metastasis.html Despite the recent therapeutic advances in cancer treatment, the development of metastasis is still the main cause of death. This process, leading to the spread of cancer cells from the primary tumour site to distant anatomical sites, consists of various phases. The phases encompass the local invasion of tumour cells into the adjacent tissue, the intravasation and extravasation phases in the circulatory system and the colonization of the target organs. In this new environment, the circulating tumour cells establish micrometastases, also supported by the primary tumour via secreted factors, including cytokines and extracellular vesicles .An aspect of increasing interest about this exacerbation is the non-random distribution of metastases, which is different for each cancer type, known as organotropism . Indeed,The investigation about the mechanisms of metastasis formation and organotropism is generally performed using mouse models that mimic the various stages of cancer cell homing and spreading. This surrogate system for human organ-specific metastasis is based on the transplantation of human cancer cell lines in immunocompromised mice , 6. In pin vivo selection of clones with a particular tropism, allow the identification of gene expression signatures related to organotropism. For example, genes directing the metastatic breast cancer cell lines towards bone, lung or brain have been identified by xenograft experiments .The experiments about organotropism assessed by the xenograft models are spread in the literature; therefore, there is a need to collect this information in a unique resource, allowing researchers to realize the current knowledge about organ-specific metastasis, compare the results and plan next experiments to highlight the molecular mechanisms underlying the metastatic cell seeding in specific sites. Here, by hand-curated literature analysis, we collected organotropism data from mouse xenograft trans-plantation studies of human cancer cell lines in MetaT-ropismDB database , DSMZ (www.dsmz.de), Cell Model Passports (https://cellmodelpassports.sanger.ac.uk) and CellFinder (www.cellfinder.org). The mouse model table contains the nomenclature of mice, including links to the main companies which develop and supply mouse models and to other databases collecting mouse genomic and phenotypic information, such as The Jackson Laboratory , Charles River Laboratories Inc. (www.criver.com), Envigo (www.envigo.com), Janvier Labs (https://janvier-labs.com) and Taconic Biosciences (www.taconic.com). Moreover, the links to other dat-abases with mouse genomic and phenotypic informa-tion, such as Mouse Genome Informatics (www.informatics.jax.org) and Mouse Phenome Database (https://phenome.jax.org) are stored in this table. Finally, the fourth table stores the references of the articles from which data were extracted, including PubMed IDs.For storage and query of the data, we constructed a MySQL relational database, organized in four tables. The main table stores data regarding each xenograft experiment, including both the experimental conditions and the obtained results. Each record of this table is linked to the records of the other three tables, collecting information about the human cell lines, the mouse models and the references, respectively. In the table of cell lines we collected their official name, a short description, the organ from which they derive, their origin  and links to various cell line databases, including Cellosaurus  or to the literature. In MetaTropismDB database, 18 different mouse models are described, with the BALB/c athymic nude mice being the most frequently adopted model. Other immunocompromised mice collected in this database are the athymic nude, C.B-17 SCID beige, C57BL/6, MF1, NIH-III nude, NMRI nude, NSG, NOD SCID, NOG and Swiss athymic nude mice. Notably, also the nomenclature of these mouse models is defined according to the literature and to the companies which develop and supply the mouse models.The database MetaTropismDB collects organotropism data exclusively obtained from xenograft experiments of human cancer cell lines inoculated, by various techniques, in immunodeficient mouse models. Notably, the database also includes negative results, i.e. the absence of any type of metastasis as a result of inoculation of human cancer cell lines. Overall, it contains 513 records describing both the experimental conditions and the obtained results of each experiment. Since a single experiment usually contained frequency data of metastases in multiple organs, the overall number of metastatic sites collected is 746 and that of the sites without metastases is 404. According to the database data, the main colonized organs were bones, brain, liver, lungs and lymph nodes, whereas the other 31 target organs reported in this database are less frequently colonized. Currently, MetaTropismDB collects metastatic organotropism data of 219 human cancer cell lines, which mainly derive from breast, colorectal, lung, prostate, bladder, pancreatic, thyroid and renal cancers and melanoma. The official names of cell lines were reported according to Cellosaurus database  was the most frequent, with the subcutaneous, the intracardiac or tail vein injection being less adopted. Moreover, among the experimental methods, the most common one was the inoculation into the mouse tail vein. For the visualization and identification of metastases in murine organs, the most commonly adopted method was autopsy and subsequent histological examination; however, bioluminescence and fluorescence The results of xenograft experiments collected in MetaTropismDB include the site of the induced primary tumour, its take rate, the colonized organs, the corresponding metastasis frequency and the development time. Finally, when the authors of the study identified the genes involved in the organotropism in their experimental model, we listed these genes and linked them to the NCBI Gene database. In particular, the association between a gene and a specific tropism is generally identified by assessing its expression by microarray, quantitative PCR or western blot in an organ-specific metastatic cell line. About 300 genes involved in organ-specific metastasis were collected in MetaTropismDB, along with the methods that allowed their identification.The web interface allows the users to browse the experiments or to search Figure  allows tAt the bottom of this card, an image summarizes the effects of different experimental conditions assessed in the same study on organotropism. The results are reported through a yellow/black colour scale, with yellow meaning low frequency of metastases  and black meaning 100% of mice with metastases. This image helps the users to easily realize how the different experimental conditions, which are comparable only within the same study, can affect the metastasis development and organotropism.Finally, a detailed explanation of the search options and result fields is available at the Help page. We plan to continuously update MetaTropismDB, thanks to the suggestions of users.https://amp.pharm.mssm.edu/Enrichr/) in order to perform enrichment analyses and highlight the diseases and organ-specific metastasis pathways (P = 5.79e-23), thus confirming the involvement of these genes in lung metastasis. Moreover, we identified some interesting cellular pathways; among them, the RAGE- and osteopontin-mediated pathways are already known to be involved in lung-specific metastases from different primary tumours. In particular, Receptor for Advanced Glycation Endproducts overexpression in breast cancer cells promoted lung metastasis in vivo (P = 5.04e-20). Moreover, the pathway libraries highlighted the CXCR4-mediated signalling as the most involved pathway (P = 4.77e-08). Indeed, it is known that the osteotropism of CXCR4 + cancer stem cells depends on their ability to follow the CXCL12 gradient towards the bone marrow (By using MetaTropismDB, we selected all the experiments that assessed the metastasis formation exclusively in lungs or in bones of mice injected by human cancer cell lines. Then, the associated genes, collected in the respective records, have been submitted to Enrichr tool (pathways . Regardi in vivo , while o in vivo . Regardie marrow , 18. Note marrow .MetaTropismDB is the first resource that collects information about the organ-specific metastasis of human cancer cell lines engrafted in mouse models. Importantly, data about not colonized organs are also included, when experimentally validated. This database allows the researchers to easily highlight the human cell lines with similar metastatic organotropism or, alternatively, cell lines deriving from the same type of primary tumour but showing different organotropism. For example, commonly expressed genes among different cell lines targeting the same organ, or differentially expressed genes among similar cell lines targeting two different organs, could be investigated in order to identify the genes responsible for the target specificity. For example, as observed in this type of studies collected in our database, lung metastasis from bladder cancer has been shown to be mediated by CD24 , whereasin vivo imaging is increasingly adopted, seems to be very accurate and comparable with histological examination and allows the tracking of the course of metastasis (This database also allows the conscious selection of the most suitable injection methods and murine models for the future xenograft experiments, since they can affect the pattern of metastasis induced by the same cancer cell line. For example, melanoma MDA-MB-435 and prostate PC-3 cancer cell lines formed bone metastasis only through intracardiac injection , 23, andtastasis , 28.In conclusion, since the genes involved in organotropism could represent the new biomarkers of organ-specific metastasis and since the site of metastasis is a predictor of survival, for example in melanoma patients , these mbaaa100_SuppClick here for additional data file.


However, HA induced differing effects on growth, with minimal, non-significant changes among some cell lines, such as SCC4 (+1.7%), CCL-30 (−2.8%), and SCC15 (−2.5%), p = 0.211 and more robust inhibition among other cell lines, SCC9 (−24.4%), SCC25 (−36.6%), and CAL27 (−47.8%), p = 0.0001. Differing effects were also observed with growth and viability under concomitant administration of HA with PD98059 or paclitaxel. Further analysis of these data revealed strong inverse (Pearson’s) correlations between initial baseline growth rate and responsiveness to HA administration, ranging from R = −0.27 to R = −0.883. Conclusion: The results of this study revealed differing responses to HA, which may be inversely correlated with intrinsic characteristics, such as the baseline growth rate. This may suggest that the more rapidly growing cell lines are more responsive to combination therapy with hyaluronic acid; an important finding that may provide insights into the mechanisms responsible for these observations.Introduction: Previous studies have demonstrated that glycosaminoglycan hyaluronic acid (HA) is capable of mediating oral tumor growth. Some clinical evidence has suggested reduced HA expression predicts poor cancer prognosis and that HA-chemotherapy conjugates may function synergistically to inhibit oral tumor growth. Other studies have found conflicting results that suggest enhanced CD44-HA-mediated growth and proliferation. Due to the lack of clarity regarding HA function, the primary goal of this study was to investigate the effects of HA using well-characterized oral cancer cell lines. Methods: Using several commercially available oral squamous cell carcinoma lines , 96-well growth and viability assays were conducted using HA . Results: Different results were observed in each of the cell lines evaluated. HA induced small, non-significant changes in cellular viability among each of the cell lines within a narrow range (1–8%),  Previous studies have demonstrated that the glycosaminoglycan hyaluronic acid (HA) is capable of mediating oral tumor growth ,2,3. ForSome evidence from clinical studies has suggested reduced HA expression may be sufficient to provide diagnostic and prognostic information for different types of cancer, including breast and bladder cancers ,9,10. MoDespite these findings, much remains to be discovered regarding the therapeutic potential of HA for oral cancer treatment. For example, some evidence has suggested improved clinical outcomes for chemotherapeutic delivery with HA , while oSeveral commercially available cell lines were obtained from the American Type Culture Collection , which included the oral squamous cell carcinoma (SCC) lines SCC4 (CRL-1624), SCC9 (CRL-1629), SCC15 (CRL-1623), SCC25 (CRL-1628), and CAL27 (CRL-2095). The nasal septum carcinoma line CCL-30 (RMPI-2650) and normal non-cancerous oral gingival cells HGF-1 (CRL-2014) were also obtained .Available cell lines were verified and cross-checked against the International Cell Line Authentication Committee (ICLAC) database to ensure these were not among the currently known cross-contaminated or misidentified cell lines. Short tandem repeat (STR) profiling results for these cell lines were compared using the eight STR loci from the ATCC database for cell line authentication.In brief, cells were thawed and cultured according to the recommended protocol from the supplier (ATCC). CAL27, CCL-30, and HGF-1 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) modified to contain 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin. The other oral cancer cell lines  were cultured in a 1:1 mixture of Dulbecco’s modified Eagle’s medium and Ham’s F12 medium containing 1.2 g/L sodium bicarbonate, 2.5 mM L-glutamine, 15 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPE)S and 0.5 mM sodium pyruvate. All cells were maintained in a humidified biosafety level-2 (BSL-2) tissue culture chamber. Experiments were performed with cells between passages five and passage ten.Hyaluronic acid (HA) was obtained from MP Biomedicals  with a molecular weight (MW) of 776.651 g/mol. One chemotherapeutic agent used in other HA-combination therapies studies of head and neck cancers , paclita2) were counted and averaged to determine cell number. To verify these results, control and experimental cells were also trypsinized and diluted 1:1 with trypan blue for viability using a BioRad TC20 automated cell counter .The viability of cells was determined at the beginning and end of each experimental assay using the trypan blue exclusion assay. In brief, a trypan blue 0.4% solution (Gibco) was added to control and experimental cells to create a 1:1 dilution, which was incubated at room temperature. Each plate was placed on a hemocytometer grid on a Zeiss Axiovert inverted microscope . Four to six grids , or both in the presence or absence of hyaluronic acid (10 ng/well), which were administered concomitantly at the beginning of each experimental trial. Cells were subsequently fixed with 10% formalin and stained using Gentian violet. Absorbance readings for each plate were measured using a BioTek ELx808  microplate reader at 595 nm to approximate cell number and confluence.Cells were plated in 96-well tissue culture plates at a density of 1.2 × 10t-tests and an alpha level of 0.05 to determine significance. All experiments were performed in triplicate with n = 8 from each experimental trial for a total n = 24 for all experimental conditions.Differences between continuous variables (absorbance readings) were calculated using parametric statistical analysis methods, including two-tailed Student’s Cell cultures were established for each cell line, and viability was determined to establish the baseline before experimentation . In briep = 0.207. More specifically, a few cell lines exhibited small but not significant increases in viability, including HGF-1 (+8%), SCC9 (+3%), CCL-20 (+1%), and CAL27 (+1%), while others exhibited slight decreases in viability .To determine any effects of hyaluronic acid on survival, cells were plated in 96-well assays, and average viability was measured each day over three days . These dp = 0.211. However, some oral cancer cell lines exhibited robust reductions in cellular growth under the experimental conditions, such as SCC9 (−24.4%), SCC25 (−36.6%), and CAL27 (−47.8%), p = 0.0001. Interestingly, the normal oral gingiva cell line HGF-1 also demonstrated a reduction in growth (−31.8%), p = 0.002.Following each measurement of viability, cells were also processed to measure growth to determine any effects of hyaluronic acid on proliferation . These dThe combination of chemotherapeutic agents alone or in combination with hyaluronic acid demonstrated different effects in modulating the viability among various oral cancer cell lines . More spThe combination of chemotherapeutic agents alone or in combination with hyaluronic acid demonstrated different effects in modulating proliferation and growth among various oral cancer cell lines . More spDue to the differing nature of cellular phenotype responsiveness observed in these studies, the changes in growth and viability to hyaluronic acid were assessed for any potential associations with the baseline growth rate of each cell line . This anThe primary goal of this study was to investigate the effects of hyaluronic  using well-characterized oral cancer cell lines to determine any potential effects on cellular phenotypes, such as growth and viability. The results of this study revealed differing responses to both phenotypes, which may be inversely correlated with intrinsic characteristics, such as the baseline growth rate. This may suggest that the more rapidly growing cell lines are more responsive to combination therapy with hyaluronic acid; an important finding that may provide insights into the mechanisms responsible for these observations.For example, one recent study demonstrated synergistic effects similar to those observed in this study with combination cisplatin–hyaluronic acid treatment in advanced metastatic ovarian cancer . AnotherIn addition, these results may suggest that the extracellular matrix (ECM)-mediated responses may, in fact, play significant roles in tumor responsiveness to chemotherapy ,22. ThisAlthough some previous studies have suggested that high expression of HA in both tumor and stromal cells from resected oral cancer patients may correlate with poor prognosis ,14, thesAlthough much remains to be elucidated, these findings suggest that hyaluronic acid may, in fact, exert different effects in oral squamous cell carcinomas. Elucidation of the factors that more accurately determine oral cancer responsiveness, such as CD44, MMP-2, MMP-9, and hyaluronidase expression, may be needed to allow oral healthcare researchers and clinicians to more accurately assess and treat oral cancers with the most efficient and effective means of treatment that may include combination therapy with hyaluronic acid. Because this study revealed differential responses, which may be inversely correlated with intrinsic characteristics, such as baseline growth rate, the analysis and identification of those factors that correlate with responsiveness to combination therapy with hyaluronic acid may provide important insights into the mechanisms responsible for these observations.


Streptococcus gallolyticus subsp. gallolyticus (Sgg) is an opportunistic pathogen of the human gut and strongly associated with colorectal cancer (CRC). A unique feature of Sgg is its ability to degrade tannic acids (TA). TA constitute an important part of the human diet with known anti-tumorigenic properties. Here, we examined whether Sgg is able to protect tumor cells from the toxic effect of TA and thus drive tumorigenesis indirectly. Human CRC cell lines (n = 8) were treated with increasing concentrations of TA. We confirmed the cytotoxic activity of TA in a dose-dependent manner. In virtually all cell lines, viability decreased significantly (>60% inhibition). Moreover, pyrogallol, the degradation product of TA, had no effect on the tested cell lines. This suggests a specific effect of TA. Cytotoxicity was due to necrosis and induction of senescence in residual cells. Finally, when TA was degraded by Sgg, the cytotoxic effect could be abolished. Tumor cells even responded with boosted cell proliferation, highlighting the impact of Sgg on CRC progression. We here provide another piece of evidence for the active interplay between Sgg and cancer preventive components. These data will help to move forward in designing concepts for therapeutic and eventually also prophylactic approaches to combat gastrointestinal malignancies.The tannase-producing Gram-positive bacterial species  Streptococcus gallolyticus subsp. gallolyticus (Sgg), previously known as Streptococcus bovis biotype I, is asymptomatically found in the gastrointestinal tract of humans (2.5–15%), ruminants and birds. The bacteria belong to the Streptococcus bovis/Streptococcus equinus complex (SBSEC)1, a highly diverse bacterial group of Gram-positive, non-hemolytic Lancefield group D commensals. The original division of the SBSEC into S. bovis and S. equinus has further changed over the past years, reaching the current splitting into seven main (sub)species, Streptococcus infantarius subsp. infantarius (Sii), Streptococcus lutetiensis, Streptococcus gallolyticus subsp. pasteurianus (Sgp), Streptococcus gallolyticus subsp. macedonicus (Sgm), Streptococcus gallolyticus subsp. gallolyticus (Sgg), Streptococcus alactolyticus and S. equinus3. Sgg is estimated to be the causative agent of endocarditis in 11–14% of cases4. Interestingly, multiple studies have shown that endocarditis due to Sgg is often associated with gastrointestinal malignancy5. Colonization of the gut by Sgg is related to the occurrence of colorectal cancer (CRC), the presence of Sgg in CRC patients with up to 74% is much higher than in healthy people7. It has been suggested that Sgg itself may play a causal role in CRC development, as the bacteria promote tumor proliferation by upregulating β-catenin8.The opportunistic pathobiont Sgg colonization, as secondary bile acids strongly enhanced activity of a bacteriocin  that is produced by the bacteria9. Further on, Sgg is remarkably resistant to bile acids10. Another distinctive characteristic of Sgg is its ability to degrade tannic acids , a property, which led this bacterium to be named “gallolyticus”11. Tannic acid (TA) belongs to the family of hydrolysable tannins. It is found in a variety of fruits and beverages, such as tea, coffee and red wine, thus TA constitutes an important part of the human diet13. Moreover, TA inhibits the proliferation of diverse tumor cells, including CRC, without being toxic to normal cells15. TA constrains the expression of inflammatory genes and cytokines in human lung cancer cells16, and has anti-metastatic potential17. Finally, it was shown recently that TA inhibits telomerase activity in vitro and in vivo20.On the other hand, it was previously shown that CRC-specific conditions – namely the increased concentration of bile acids – promote 21, which allows them to degrade TA. In contrast, the closely related (sub)species Streptococcus infantarius subsp. infantarius (Sii) have lost genes for detoxifying toxic substances such as TA22. Sgg hydrolyze TA by tannase into gallic acid. Gallic acid will be decarboxylated by a gallate decarboxylase to pyrogallol (PG)23. Of note, Jiménez et al. considered Sgg as the best bacterial cellular factories for tannin degradation so far known24.Intriguingly, especially bacteria that selectively colonize tumorous tissues in CRC patients - but not adjacent non-malignant tissues - contain tannase homologous genesSgg might protect the tumor cells from the toxic effect of TA. We could show that all of the investigated low passaged CRC cell lines are susceptible towards TA treatment. Moreover, PG, the degradation product of TA, had no cytotoxic effect on the tested cell lines. Finally, when TA was degraded by Sgg, the toxic effect on the tumor cells could be abolished. This supports the assumption that colonization of the tumor by Sgg protects the tumor cells against otherwise toxic plant components that will be consumed by a normal human diet.We therefore hypothesized whether this special feature of 25. The CRC cell lines HT29 and CaCo2 were originally obtained from the German collection of cell cultures  and routinely cultured in our lab. Cells were maintained in full medium: DMEM/HamsF12 supplemented with 10% fetal calf serum (FCS), glutamine (2 mmol/L) and antibiotics .CRC cell lines HROC24 T1 M1, HROC60, HROC173, HROC183 T0 M2, HROC257 T0 M1, HROC285 T0 M2, HROC324, and HROC370 were described before and obtained from Cell line services Sgg UCN34 was isolated at the Hospital in Caen 26. Sii JIM 9407 was isolated from human origin in Africa27. Bacteria were grown on blood agar plates at 37 °C, aerobically, overnight and subsequently stored at 4 °C. For further use, overnight cultures were cultivated in BHI broth at 37 °C under a 5% CO2–20% O2 atmosphere in the presence or absence of TA . The optical density was determined in a Spectramax  or the bacteria were plated every hour to determine the CFU/ml.CRC cell lines  were treated with increasing TA or PG concentrations (ranging from 2.5 to 100 µM) for 72 h. Thereafter, selected cell lines were used for different experiments. Bacterial supernatants from TA degradation experiments were diluted 1:10 in cell culture medium before applied to the cells.28. Additionally, a Yo-Pro-1/PI-based assay for discriminating (early) apoptotic and necrotic cells was applied as described before25.Cells were grown to about 70% confluence in 24-well culture plates and then incubated in complete medium with or without TA and PG for 72 hours, respectively. Subsequently, cells were prepared for crystal violet staining, as well as flow cytometric apoptosis and cell cycle analysis as reported previously29 Briefly, cells were seeded in 6-well plates and grown to 100% confluency. Monolayers were scratched with a 20 μL pipette tip to induce a wound. Wounded edges were imaged using a Zeiss inverted microscope. Images were taken daily for a period of 5 d under a × 10 objective lens.Tumor cell proliferation after TA or PG treatment was investigated as described before30.Induction of senescence after 5 days of treatment with TA or PG was measured by β-galactosidase, detectable at pH 6.0 with the artificial substrate X-galSgg UCN34 or Sii was determined by a spectrophotometric method31. Briefly, overnight cultures of bacteria were set to 108 CFU/ml in PBS and incubated with 2 mM methylgallate for 24 h at 37 °C. After 24 h of incubation, 0.5 ml of the suspension were alkalinized with equal amounts of saturated NaHCO3 solution (pH 8.6) and then left in the atmosphere at room temperature for 60 min. This alkalization facilitated nonenzymatic oxidation of gallic acid  to form polymerized compounds of o-quinone derivatives, resulting in green to brown coloration of the medium. Absorption was measured at 440 nm.Tannase activity of 8 CFU/ml in PBS and incubated with 1 mM TA at 37 °C for 24–48 h. As control TA solution (1 mM) without bacteria was used. Absorption of TA was measured at 310 nm. Bacterial supernatants for incubations on cells were sterilfiltered and stored at −20 °C until use.Overnight cultures of bacteria were set to 10t-test. If normality failed, the non-parametric Mann-Whitney U-Test was applied. In case of multiple comparisons, one way ANOVA on ranks (Bonferroni’s Multiple Comparison Test or Holm-Šídák) was applied. Statistical evaluation was performed using GraphPad PRISM software, version 5.02 . The criterion for significance was taken to be p < 0.05.All values are reported as mean ± SD. After proving the assumption of normality, differences between controls and treated cells were determined by using the unpaired Student’s To investigate the susceptibility of CRC cell lines against TA, 8 patient-derived low passage CRC cell lines were treated with increasing TA concentrations. Following 24, 48, and 72 hours of incubation, biomass was quantified by crystal violet staining Fig. . TA reduMicroscopic observation confirmed these data with a destroyed cell layer and dead cells after 72 h of incubation with 25 µM TA  were subjected to flow cytometric apoptosis/necrosis analysis. We observed predominantly necrosis in all cell lines Fig. . Late aplls Fig. . Complem32. Thus, we assessed the potential of TA to induce senescence in four selected cell lines  after 5 days of incubation with 50 µM TA or PG. For all cell lines, induction of senescence was observed. Figure TA was previously described to induce senescence in the CRC cell line HCT116data not shown).Next, a wound healing assay was used to examine proliferation in the presence of TA or PG Fig. . RepreseTaken together, TA, but not its degradation product PG, is toxic on CRC cell lines and induces senescence in residual cells.Sgg UCN34 contains two genes for tannase (GALLO_1609 and GALLO_0933) as well as two decarboxylase genes10, important for TA degradation. We confirmed tannase activity of Sgg UCN34 by a spectrophotometric method33, using the Sii strain as a negative control. This method uses methyl gallate as substrate degraded by bacterial tannase, whereby gallic acid will be released and detected at A440 nm. Figure Sgg, but not Sii, produced significant higher amounts of gallic acid, comparing to control samples without bacteria.The strain Sgg and Sii in the presence of high TA concentrations was tested. However, TA had no influence on Sii or Sgg growth at concentrations of 1000 µM  could be detected after 24 hours of incubation with Sgg  widely occur in common food such as berries, grapes, mangoes, and nuts, but also in a variety of beverages such as coffee, tea or red wine for a review see36.In an experimental mouse model, it has been demonstrated that very low dose dietary TA administration prevented animals against spontaneous hepatic neoplasm development. The TA amount consumed daily by each mouse in this study corresponded to the human situation, and further support the strong anti-carcinogenic effect of TA37. TA increases the production of reactive oxygen species by altering the redox balance in the cell. This finally induces cell cycle arrest as described for HCT116 cells38, supported by our own observations.In our study, we examined the direct effects of TA on a panel of patient-derived as well as long-term cultured CRC cell lines. TA induced a biphasic, but dose-dependent growth inhibiting effect in all CRC cell lines tested, supporting recent findings in which TA was described to exert anti-proliferative activity on numerous cancers. TA affected migratory potential in all cell lines, and cytotoxicity was mainly due to necrosis with additional senescence induction in residual cells. Mechanistically, this is likely attributable to local amino acid starvation that triggers cancer cell senescence. This effect is well-described for other amino-acid degrading enzymes, such as the bacterial arginine deiminase41, with additional ability to act as chemosensitizer42. The latter is a characteristic quite common for other polyphenols as well. The selective antitumoral efficacy of TA was further shown here by lacking cytotoxicity of PG, a degradation product of TA. While this substance was found to exert growth-inhibiting and pro-apoptotic against breast cancer cells in vitro, viability as well as migratory activity of CRC was not substantially affected43. Moreover, the natural concentration of PG in fruits and vegetables is generally low, but likely increases by tannin-degrading bacteria, such as Sgg21.TA is thus considered a potential anticancer agent39. Still, clinical evidence of dietary polyphenols as chemopreventive compounds is scarce and at least partially attributable to the fact that TA concentrations in the gastrointestinal tract are hardly measurable and backtracked to mediate a direct tumor growth inhibitory effect.Apart from the therapeutic effect of TA, a recent study even proposed this substance as a promising pyruvate kinase isoenzyme M2 inhibitor for CRC prevention44. Here, we show that Sgg, as well as Sii are able to grow in the presence of high TA concentrations (1000 µM ≜ 1701 g/l). However, our data indicate that only Sgg actively degrades TA, supporting earlier reports45. Physiologically, this characteristic is considered an adaptive mechanism to withstand stress conditions associated with the presence of these phenolic compounds.TA possess numerous pharmacological and thus biologically relevant characteristics, such as anti-inflammatory and bacteriostatic activity with MIC values between 0.012 and 1 g/lSgg is a common and selective gut colonizer of CRC patients. This high prevalence of Sgg in CRC patients - compared to healthy persons - might be associated with specific conditions created by the tumor, such as increased concentration of bile acids and a slowed food flow. This leads to accumulation of plant-derived fiber carbohydrates and possibly TA, which may represent a favorable microenvironment for establishment and proliferation of Sgg10.While TA may have potent antitumoral activity, Sgg is a passenger or a driver bacterium has not been completely elucidated, but is just beginning to become clear. Pasquereau-Kotula described a two-hypothesis model, by which Sgg either contributes to CRC development as a consequence of local microbial imbalance or actively accelerates transformation due to high colonization in pre-malignant epithelium along with specific inflammatory responses and increased cell proliferation46. Growing evidence supports the former with Sgg not being a bona fide pathogen, but likely benefits from the microenvironment created by preneoplastic glands in the gut9. A recent study supports these findings by identifying different interactions of Sgg strains with human colon cancer cells. This finding was confirmed in vivo using the azoxymethane-induced CRC model47. The authors suggested a close contact between Sgg and host cells to be important for mediating growth promoting effects. Our study adds a piece to this and thus broadens our current knowledge on the interaction of Sgg with CRC.Whether Lactobacillus plantarum, Lonepinella koalarum and Selenomonas ruminantium, Staphylococcus lugdunensis) almost none of them have been linked to CRC. A recent study identified an active tannase enzyme from the oral pathogen Fusobacterium nucleatum48 – only lately described to promote CRC similar to Sgg. Of note, serum anti-Fn antibody IgA combined with carbohydrate antigen 19-9 and carcinoembryonic antigen are reliable screening tools and just as for F. nucleatum49, efforts to use Sgg antibodies as serum biomarkers are on the way for CRC risk stratification50. Hence, such microbial–based approaches will facilitate diagnostic efficacies prospectively.Yet, while some gastrointestinal microbes were described to preferentially degrade hydrolyzable tannins  the cytotoxic activity of tannic acids against colorectal cancer cells and (II) describes a protective role of the tannic acid-degrading bacteria


The growth of human cancer cells is driven by aberrant enhancer and gene transcription activity. Here, we use transient transcriptome sequencing (TT‐seq) to map thousands of transcriptionally active putative enhancers in fourteen human cancer cell lines covering seven types of cancer. These enhancers were associated with cell type‐specific gene expression, enriched for genetic variants that predispose to cancer, and included functionally verified enhancers. Enhancer–promoter (E–P) pairing by correlation of transcription activity revealed ~ 40,000 putative E–P pairs, which were depleted for housekeeping genes and enriched for transcription factors, cancer‐associated genes, and 3D conformational proximity. The cell type specificity and transcription activity of target genes increased with the number of paired putative enhancers. Our results represent a rich resource for future studies of gene regulation by enhancers and their role in driving cancerous cell growth. TT‐seq measures genome‐wide enhancer transcription with high sensitivity in human cancer cells. The resulting comprehensive annotation of putative enhancers and enhancer‐promoter pairs serves as a valuable resource for studying enhancer function. Finally, we provide a comprehensive catalog of transcribed candidate enhancers with cancer‐associated somatic mutations and putative enhancer–promoter pairs involving cancer‐associated genes. Overall our results serve as a resource to study enhancer activity and gene regulation, and to select candidate enhancers for functional studies.et al, et al, et al, et al, We applied transient transcriptome sequencing  in our data sets, we used the hidden Markov model‐based genome segmentation algorithm GenoSTAN . We then made use of a large set of putative enhancer regions  clearly showed properties characteristic of actively transcribed enhancers Fig . The reget al, et al, Based on our eRNA annotation, we next defined putative enhancer regions, which are regions from which eRNA synthesis is initiated. To define intergenic enhancer regions, we selected the region between the TSSs of each pair of intergenic bidirectional eRNAs and extended it by 500 bp in both directions Fig , of whicet al, et al, et al, et al, et al, et al, et al, et al, et al, et al, The majority of eukaryotic promoter regions are divergently transcribed with the TSS for the protein‐coding gene being accompanied by an upstream, antisense TSS producing often non‐coding, upstream antisense RNAs (uaRNAs) . At these promoters, transcription was generally biased toward the coding direction  cases  is a hallmark of cancer . About 14 and 10% of TT‐seq‐defined enhancers were active in STARR‐seq for HCT 116 and K562, respectively Fig . Next, wet al, et al, P‐values < 0.01). Finally, we took the verified functional enhancer regions from both studies and calculated the overlap with the K562 STARR‐seq peaks. The proportion of CRISPRi‐verified functional enhancers also called active by STARR‐seq was 25 and 14% for Gasperini et al and Schraivogel et al, respectively. Together these results corroborate the putative enhancers defined by TT‐seq in our study.To extend these results, we asked whether ENCODE‐predicted enhancers with validated functionality in K562 cells, as determined by CRISPRi screens  enhancers, or both. Using the binary information of mRNA and eRNA being synthesized versus not synthesized, we found that 58.5% of mRNAs were ubiquitously synthesized in all cell lines, and only 4.8% were exclusively synthesized in a single cell line Fig . This waet al, et al, Although a large fraction of mRNAs was synthesized ubiquitously, their synthesis level across cell lines often varied . Next, we compared our E–P pairing approach with the activity‐by‐contact (ABC) model  data for 19,023 promoter fragments measured in LoVo colorectal cancer cells .Finally, we quantified the effect of multiple enhancers per promoter and observed that both cell type specificity and level of promoter transcription increased with the number of paired enhancers Fig . In addiet al, et al, et al, P‐value < 0.001, simulation). Further, enhancers were more enriched for cancer‐associated SNPs than all other SNP‐disease/trait associations from GWAS . Finally, we selected only enhancers paired to at least one gene by any of the three pairing strategies , we compiled a list of 2,459 distinct SNPs from the NHGRI‐EBI genome‐wide association studies (GWAS) catalog  it is a simultaneous and quantitative analysis of promoter and enhancer transcription, (ii) it provides TT‐seq signal tracks together with putative enhancer and eRNA annotations, (iii) it provides putative enhancer–promoter pairs based on several pairing approaches, and (iv) by using enhancer transcription it increases the fidelity of selecting functional enhancers for further analyses. We anticipate many applications of this resource to further study enhancer activity and gene regulation. The resource will also help to study how somatic mutations in enhancers and transcription‐related factors alter enhancer function and specificity to cause a cancer phenotype.in vivo transcribed candidate enhancers in fourteen human cancer cell lines from seven different types of cancer. The putative enhancers are in accordance with known genomic marks of active enhancers . BT‐20 (ATCC HTB‐19), DU 145 (ATCC HTB‐81), and SK‐UT‐1 (ATCC HTB114) cells were cultured in MEM  supplemented with 0.1 mM NEAA  and 1 mM sodium pyruvate . 769‐P (ATCC CRL‐1933) and 786‐O (ATCC CRL‐1932) cells were cultured in RPMI‐1640 . MDA‐MB‐231 (ATCC HTB‐26) and LoVo (ATCC CCL‐229) cells were cultured in DMEM‐GlutaMAX . U‐118 MG (ATCC HTB‐15) and GP5d  cells were cultured in DMEM . GP5d cells were cultured in cell culture flasks coated with poly‐l‐lysine . M059J (ATCC CRL‐2366) cells were cultivated in DMEM/F‐12 , HCT 116 (ATCC CCL‐247) cells were cultivated in McCoy's 5a Medium Modified , and PC‐3 (ATCC CRL‐1435) cells were cultivated in F‐12K (ATCC 30‐2004). Cells were authenticated by STR profiling at the manufacturer (ATCC and ECACC) and verified to be free of mycoplasma contamination using the MycoAlertTM Mycoplasma Detection Kit . Biological replicates were cultured independently.Human cancer cell lines were obtained from ATCC  and Sigma‐Aldrich , cultured in cell culture media  supplemented with 1% penicillin/streptomycin  and 10% fetal bovine serum  at 37°C in a humidified 5% COet al, 2. Cells were harvested using TRIzol . 2.4 ng RNA spike‐ins mix per 106 cells was added to each sample after cell lysis. Spike‐in sequences and production are described in Gressel et al  to span the entire MYC enhancer region and IGH locus, respectively (Eurofins MWG Operon). sgRNAs were cloned into an sgRNA Cloning Vector  using Gibson assembly master mix . DU 145 (2 × 106) cells were transfected  with 10 µg of eight pooled equimolar sgRNA constructs together with hCas9 plasmid . Post‐transfection half of the cultured cells were collected for PCR genotyping, while the other half was re‐plated for culturing. Cells were collected at days 2, 4, and subsequently every fourth day till day 32. DNA from cells was extracted  and genotyped with 300 ng of DNA at following conditions—initial denaturation at 95°C for 5 min; denaturation at 98°C for 15 s, annealing at 60°C for 30 s, extension at 72°C for 30 s ; and final extension at 72°C, 5 min. Note, the combination of the four terminal sgRNAs can lead to different lengths of the MYC enhancer deletion region, which can be subsequently detected with genotyping primers . Each experiment was done in triplicate. Sequences of guide RNAs and primer pairs are given in Appendix Table et al  and the Immunoglobulin Heavy (IGH) gene locus on chromosome 14q32.33  were carried out in DU 145 cells. The single guide RNAs (sgRNAs) were designed  genome assembly (Human Genome Reference Consortium) using STAR 2.6.0c  values over all GENCODE (v31) annotated genes (type = gene). Distributions were generally bimodal, and a suitable cutoff was selected in the valley between the two peaks. Since distributions looked overall similar between samples, we selected normalized FPK ≥ 20 as expression cutoff for all samples. Unless stated otherwise, this cutoff was used to distinguish between transcribed (active) and untranscribed (inactive) genomic regions.To determine a suitable cutoff to distinguish transcribed genomic regions from background, we plotted the densities of replicate‐averaged loget al, et al, et al, Transcription unit annotation and classification were done as described et al  was at least 2‐fold higher than TT‐seq signal over the end (last 1 kbp) of the upstream mRNA/uaRNA.Enhancer annotation ‐seq and all ChIP‐seq data were taken from ENCODE via GEO  within ± 500 bp ‐seq and H3K27ac ChIP‐seq data were downloaded from GEO , for whl, et al  and EV4.l, et al . IndividTo compare eRNA synthesis and enhancer transcription across cell lines, we first generated merged eRNA and enhancer annotations containing all eRNAs and enhancers annotated in at least one of the cell lines. This was done using the reduce function from the GenomicRanges R/Bioconductor package over concatenated eRNA and enhancer annotations from all cell lines. These merged annotations comprised 63,216 eRNAs and 58,457 enhancers Table . To quanTo compare mRNA synthesis across cell lines, we calculated normalized FPK values for all RefSeq protein‐coding genes . 14,627 RefSeq protein‐coding genes were transcribed in at least one cell line.n = 3,468), and Pearson correlation coefficients r were calculated between vectors of TT‐seq log2(counts) representing uaRNA and mRNA synthesis patterns across the cell lines.To investigate transcription directionality at promoters, we took all protein‐coding genes annotated in RefSeq  and selected only those for which all cell lines used the same promoter region and no protein‐coding gene was annotated on the antisense strand within ± 5 kbp of the sense TSS. In each cell line, for each of these promoters, we calculated normalized TT‐seq fragment counts in 1 kbp regions downstream  and upstream  of the protein‐coding TSS. Promoters were classified as bidirectional or unidirectional if at least 10 counts were observed on both strands or the protein‐coding strand only, respectively. For all bidirectional promoters, we defined a transcription directionality score as the ratio of mRNA and uaRNA counts. To compare protein‐coding gene transcription at unidirectional vs bidirectional promoters, we compared normalized TT‐seq fragment counts in 1 kb regions downstream of the protein‐coding TSS. To compare protein‐coding gene transcription at promoters which were unidirectional in all cell lines vs promoters which were bidirectional in at least one of the cell lines, we compared the maximum normalized TT‐seq fragment counts over all cell lines. To investigate whether directionality scores were preserved across cell lines, we correlated uaRNA and mRNA counts at bidirectional promoters over cell lines. Only promoters with observed bidirectional transcription in at least 5 cell lines and varying transcription over cell lines (coefficient of variation > 50%) were considered (n = 715), and Pearson correlation coefficients r were calculated between vectors of TT‐seq log2(counts) representing plus and minus strand transcription patterns across the cell lines.To investigate transcription directionality at enhancers, we took all bidirectional intergenic enhancer regions shorter than 3 kbp from the unified enhancer annotation (see above) and calculated normalized TT‐seq fragment counts in these regions on the plus and minus strand (relative to the plus strand of the genomic reference sequence), only considering cell lines in which the respective enhancers were annotated. Based on this, we defined a transcription directionality score as the ratio of plus and minus strand counts. To investigate whether directionality scores were preserved across cell lines, we correlated plus and minus strand transcription at bidirectional enhancers over cell lines. Only enhancers with observed bidirectional transcription in at least 5 cell lines and varying transcription over cell lines (coefficient of variation > 50%) were considered  transcribed enhancers , not overlapping a functional lncRNA originating from an intergenic promoter DHS, and overlapping an enhancer state annotated by GenoSTAN (see TT‐seq eRNA/enhancer definition for details).et al, n = 28,771; K562: n = 20,552). To also compare STARR‐seq overlap with candidate enhancers defined by other methods, we took HCT 116 and K562 DNase I(DHS), H3K4me1, and H3K27ac peak regions from ENCODE . To calculate enrichments over expected STARR‐seq overlap, we randomly sampled 20 sets of TT‐seq‐matched enhancer regions. To this end, random non‐coding regions (same number as observed TT‐seq enhancers) were chosen to have a similar overall distribution of their widths and distances from nearest gene promoters, which had a similar overall distribution of transcription levels and cell type specificity scores. Such null sets were generated separately for TT‐seq‐defined enhancers in HCT 116 and K562, and STARR‐seq overlaps determined to obtain empirical null distributions and to calculate empirical P‐values. To investigate whether candidate enhancers not called active in STARR‐seq were enriched for having annotated neighboring enhancers in close proximity, we counted the number of neighboring enhancers within ± 12.5 kbp for each candidate enhancer of a conservative (“combined”) set of enhancers annotated by DHS, H3K27ac and TT‐seq. The 12.5 kbp cutoff was chosen as it is also the distance used for stitching together enhancers before super‐enhancer calling (see below) and as we observed that 85% of Med1 called super‐enhancer regions in LoVo  in LoVo cells, we downloaded published Med1 ChIP‐seq data  uniform expression. We calculated overall cell type specificity as described  of a promoter or enhancer in cell line i, and n is the number of cell lines. The specificity score ranged between 0 and 1, where 0 means unspecific  and 1 means cell type‐specific synthesis.We defined two types of entropy‐based cell type specificity scores.2et al, p1, p2, is defined to beSpecificity of a promoter/enhancer's transcription pattern with respect to a particular cell line ranks a promoter/enhancer according to the similarity its transcription pattern has toward synthesis in only that particular cell line. This cell type specificity score was calculated as described , asRelying on the theorem that the square root of the JS divergence is a metric  were kept. For this, Pearson correlation coefficients r were calculated between vectors of TT‐seq log2 values representing transcriptional activity patterns across the 14 cell lines.To obtain putative E–P pairs, we used pairwise across‐cell‐type correlations between enhancer and promoter transcriptional activity. With the “correlated neighboring” (CN) and “correlated window” (CW) approach, enhancers were paired to the nearest upstream and downstream neighboring active promoters, and all active promoters within ± 500 kbp distance, respectively. For E–P pairing, active promoters were defined by having normalized FPK ≥ 30. E–P distances were calculated between enhancer midpoints and gene TSSs. Only pairs with correlated transcriptional activity . Only E–P pairs spanning a distance of less than 500 kbp were considered. This approach was used in addition to also provide putative E–P pairing information in the absence of correlated transcriptional activity. Moreover, this approach was used to show that cell type‐specific enhancers are associated with cell type‐specific gene expression Fig , as suchet al, et al, To show that cell type‐specific enhancers were associated with cell type‐specific gene activity, we paired enhancers either to their nearest active promoter Fig  or to inet al, et al, Published promoter capture Hi‐C data for the LoVo colorectal cancer cell line , almost all of the putative enhancers reported in our study pair to at least one promoter. This resulted in a considerable fraction (59%) of putative enhancers which were paired in our study but not paired by the ABC model. Thus, we took only positive E–P pairs reported by the ABC model and compared them to E–P pairs described in our study for the same enhancers.Published predictions for E–P interactions in K562 cells Fulco n = 3,804  downloaded on April 18, 2020. SNPs were filtered to retain only those located in the non‐coding genome  . We further obtained a list of SNPs associated with cancer (EFO_0000311) by GWAS and from this selected all traits corresponding to the types of cancer cell lines used in this studys . For comparison of the overlap of cancer‐associated SNPs and enhancers to the overlap of SNPs from the 1000 Genomes project (Phase 3v5) and enhancers, we randomly sampled 1,000 matched SNP sets from the EUR population , using SNPsnap . SNPsnap matched the original SNPs based on MAF, gene density, distance to nearest genes, and number of sites within LD of 0.8 of the original site. SNPs were then overlaid with TT‐seq‐defined enhancer regions, and the P‐value was determined based on the empirical background distribution.All SNP‐disease/trait associations were taken from the NHGRI‐EBI GWAS Catalog Welter https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152291).The raw and processed TT‐seq data together with the eRNA and enhancer annotations have been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE152291 (KL designed and carried out TT‐seq experiments, contributed to, and interpreted bioinformatics data analysis. LAJ carried out TT‐seq experiments and contributed to data interpretation. HvdE processed ChIP‐seq and CHi‐C data and conducted Med1 super‐enhancer analysis. KD carried out CRISPR/Cas9‐mediated enhancer deletion. JT provided conceptual input, and contributed data and material. ML designed, conducted, and interpreted bioinformatics analysis. ML and PC supervised research. KL, ML, and PC wrote the manuscript, with input from all authors.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Table EV4Click here for additional data file.Table EV5Click here for additional data file.Table EV6Click here for additional data file.Table EV7Click here for additional data file.Review Process FileClick here for additional data file.Source Data for Figure 7Click here for additional data file.Source Data for Expanded ViewClick here for additional data file.


Colorectal cancer (CRC) is a heterogeneous disease with a complex biology and a wide number of altered genes such as BRAF, KRAS and PIK3CA. Advances with new-targeted therapies have been achieved and available treating options have prolonged patient’s survival. However, BRAF-mutated CRC patients remain unresponsive to available therapies with RAF inhibitors (RAFi) alone or combined with ErbB inhibitors (ErbBi). These unmet needs require further exploitation of oncogenic signaling in order to set up individualized treatments.To this end, we tested the efficacy of single agent or combined treatments using the BRAFi, vemurafenib and two different ErbBi: panitumumab and afatinib in CRC cells characterized by different molecular phenotypes.V600E mutated cells expressing high levels of ErbB2.Combination strategies with BRAFi and ErbBi achieved a better response in BRAFOur findings support the importance of ErbB2 evaluation in BRAF-mutated CRC patients and its role as a positive predictor factor of response to BRAFi/ErbBi combination. Colorectal carcinoma (CRC) is the third leading cause of death from cancer worldwide . Recent V600E, which signals as a monomer in the absence of upstream signaling through the RAS signaling pathway. This explains at least in part the resistance of BRAFV600E CRCs to EGFR pathway blockade [BRAF  belongs to a family of genes that encode serine–threonine kinases, which heterodimerize and are activated by several receptor tyrosine kinases (RTKs), such as EGFR. Together with downstream MEK and ERK proteins, RAF kinases constitute a powerful mitogen-activated protein kinase (MAPK) pathway , 4. The blockade , 3.Selective inhibitors of RAF kinases, such as vemurafenib, regorafenib , and dabV600E mutant colon cancers [V600E CRCs [This issue has been addressed in several preclinical and clinical studies, which indicate that EGFR blockade and BRAF targeting act synergistically to inhibit ERK pathway signaling in BRAF cancers , 16, 19.00E CRCs . The the00E CRCs .V600E mutant colon cancer cell lines with different mutational profiles  (ATCC CCL-231), Colo-205  (ATCC CCL-222) and HT-29  (ATCC HBT-38) human colorectal cancer cells  were both purchased from SelleckChem . Each was dissolved in DMSO to obtain a 10-mM stock solution. Panitumumab  was kindly provided by GPS in a stock solution of 20 mg/ml.  and cell count by Trypan blue exclusion. Each sample was measured in triplicate and repeated at least three times.The dye trypan blue exclusion test was used to determine the number of viable or death cells present in a cell suspension .rsb.info.nih.gov), after verification of non-saturation and background subtraction. Values are expressed as the integrals of each band normalized to weakest band and a ratio between target protein and calibrator proteins (housekeeping–HK: HSP70) is shown.Protein lysates were prepared using RIPA buffer  with fresh protease inhibitors (Roche). Lysates were separated on 6% SDS polyacrylamide gels. Proteins were transferred to nitrocellulose membranes (0.45 μm) (PerkinElmer). Membranes were blocked for 1 h at room temperature in 5% nonfat dry milk and incubated overnight at 4 °C with the following antibodies: rabbit anti-human ErbB2 (HER2-Neu) , rabbit anti-HSP70 . HRP-conjugated secondary antibodies (Santa Cruz Biotechnology) were used in combination with enhanced chemiluminescence (ECL Amersham). Densitometry calculations were made with Image-J software . Two-way ANOVA test for multiple comparison was used unless otherwise specified.For our investigations, we selected colorectal carcinoma cell lines according to their mutational status Table . In partV600E mutation with wild-type EGFR; the third (SW-48) was BRAF wild-type (wt) with an EGFR mutation.Two  harbored the BRAFV600E inhibitor, vemurafenib (VEM). As expected, the BRAF wt SW-48 cells were unaffected by the drug , a small-molecule receptor tyrosine kinase inhibitor (TKI), which irreversibly blocks signaling activity from all ErbB-family homo- and heterodimeric receptors . AfatiniV600E cell lines—both of which are EGFR-wildtype—differed: in Colo205 cells significant changes in both viability and death rates were already evident after exposure to the lowest concentration tested (0.01 μM) . As expected SW-48 cells, which harbor an EGFR mutation targeted by afatinib , were sensitive to low doses of the drug (0.1 and 1 μM) Fig. a, b. TheμM) Fig. c and d, μM) Fig. e and f.We then wanted to investigate if the combination of vemurafenib with afatinib, which is able to target ErbB2 and ErbB4 receptors in addition to EGFR, could achieve better results than the combination of vemurafenib and panitumumab.We then exposed the cells to vemurafenib and afatinib— the latter at doses of 1 μM or 10 μM .V600E lines (Colo205 and HT-29), the two drugs produced additive growth-limiting effects that were afatinib dose-dependent  expression levels in these tumors should also be used to inform treatment decision-making.Indeed, in clinical settings, vemurafenib alone proves to be insufficient in CRC tumors owing in many cases to feedback-mediated reactivation of RAS. ErbB2 (HER2-Neu) has been identified as a possible mediator of this feedback. Our experiments showed that the small molecule pan-ErbB family inhibitor afatinib effectively reduces the viability of CRC cells. Moreover, responses to afatinib treatment were heterogeneous. Based on the ErbB2 protein levels we demonstrated in the cell lines we used, it seems reasonable to speculate that high-level expression of ErbB2 might predict an effective response to AFA at lower doses. We therefore propose that combined treatment with vemurafenib and afatinib could be used for BRAFV600E oncoprotein by the small-molecule drug vemurafenib (VEM), which is highly effective in melanoma [V600E cell lines, with the aim to shed light on the possible mechanisms driving resistance to biological therapy in BRAF mutant patients. We found that the BRAFV600E cell lines we tested differed markedly in their sensitivity to short-term exposure to the BRAF inhibitor VEM, as reflected by assays of cell viability and cell death. All cell lines were unresponsive to the anti-EGFR blockade with panitumumab, whereas all displayed sensitivity to afatinib (Table V600E mutated cell lines (Table CRC includes different categories of tumors based on their specific mutational spectra and molecular phenotype, driving distinct oncogenic signaling pathways. The main goal of molecular oncology is the development of personalized medicine, and this is particularly urgent for BRAF mutant CRC patients since effective therapies are still lacking. Indeed, the inhibition of the BRAFmelanoma , showed melanoma . In BRAFmelanoma , mostly melanoma , 29. Indmelanoma . Similarmelanoma . Our stuib Table , which ces Table . LookingWe suggest screening tumors for the HER2-Neu expression since its high levels could be considered as positive predictive factor of treatment response using afatinib or using afatinib+vemurafenib.V600E mutation, other signaling pathway activations could be responsible for therapy failure. Therefore, BRAF mutant patients should not be considered as having a unique underlying biology but heterogeneous paths [Our work presents new molecular aspects of BRAF mutated CRC cells which can occur in resistant patients and support the notion that, besides the specific BRAFus paths , that maus paths .


We treated two cancer cell lines with or without CKI and assessed the resulting phenotypes by employing cell viability and proliferation assays. Based on these results, we carried out high-throughput transcriptome data analysis to identify genes and candidate pathways perturbed by CKI. We integrated these differential gene expression results with previously reported results and carried out validation of selected differentially expressed genes. CKI induced cell-cycle arrest and apoptosis in the cancer cell lines tested. In these cells CKI also altered the expression of 363 core candidate genes associated with cell cycle, apoptosis, DNA replication/repair, and various cancer pathways. Of these, 7 are clinically relevant to cancer diagnosis or therapy, 14 are cell cycle regulators, and most of these 21 candidates are downregulated by CKI. Comparison of our core candidate genes to a database of plant medicinal compounds and their effects on gene expression identified one-to-one, one-to-many and many-to-many regulatory relationships between compounds in CKI and DE genes. By identifying genes and promising candidate pathways associated with CKI treatment based on our transcriptome-based analysis, we have shown that this approach is useful for the systematic analysis of molecular changes resulting from complex mixtures of bioactives.Traditional Chinese Medicine (TCM) preparations are often extracts of single or multiple herbs containing hundreds of compounds, and hence it has been difficult to study their mechanisms of action. Compound Kushen Injection (CKI) is a complex mixture of compounds extracted from two medicinal plants and has been used in Chinese hospitals to treat cancer for over twenty years. To demonstrate that a systematic analysis of molecular changes resulting from complex mixtures of bioactives from TCM can identify a core set of differentially expressed (DE) genes and a reproducible set of candidate pathways. We used  The treatments of choice for cancer are often radiotherapy and/or chemotherapy, and while these can be effective, they can cause quite serious side-effects, including death. These side-effects have driven the search for adjuvant therapies both to mitigate side-effects and/or potentiate the effectiveness of existing therapies. Traditional Chinese Medicine (TCM) is one of the adjuvant therapies, particularly in China, but increasingly so in the West. Although clinical trial data describing the effectiveness of TCM are currently limited, TCM remains an attractive option because its potential effectiveness is believed to result from the cumulative effects of multiple compounds on multiple targets . HoweverSophora flavescens) and Baituling (Smilax Glabra) and contains more than 200 different chemical compounds including alkaloids and flavonoids such as matrine, oxymatrine, and kurarinol that have been reported to have anti-cancer activities [CKI is an herbal extract from two TCM plants, Kushen . Both cell lines have also been shown to undergo apoptosis in response to compounds found in CKI , 7, 11. However, these and other reports did not evaluate the entire range of molecular changes from treatment with a multi-component mixture such as CKI , 17. WhiIn contrast to previous studies, our strategy was to carry out comprehensive transcriptome profiling and pathway identification from cancer cells treated with CKI. Instead of focusing on specific genes or pathways in order to design experiments, we have linked phenotypic assessment and RNA-seq analysis to CKI treatment. This allows us to present an unbiased, comprehensive analysis of CKI specific responses of biological networks associated with cancer. Our results indicate that different cancer cell lines that undergo apoptosis in response to CKI treatment can exhibit different CKI induced gene expression profiles that nonetheless implicate similar core genes and pathways in multiple cell lines.in vivo. To this end, we primarily use concentrations of CKI higher than used in vivo in order to be able to detect effects in the short time frames available to tissue culture experiments. We also combine our current analysis with previously published data to focus on a shared, much smaller set of candidate genes and pathways.The current study presents the effects of CKI on gene expression in cancer cells with an aim to identify candidate pathways and regulatory networks that may be perturbed by CKI 2.CKI  in 5 ml ampoules was provided by Zhendong Pharmaceutical Co. Ltd.  Quality control documentation in in vitro assays, 4 × 105 cells were seeded in 6-well trays and cultured overnight before being treated with either CKI  or 5-FU (150 μg/ml for Hep G2 and 20 μg/ml for MDA-MB-231) based on the cell viability assay [For all The assay was performed as previously described . For eacThe treated cells were harvested, and the cell pellets were snap frozen with liquid nitrogen and stored at -80°C. Three biological replicates for each group were collected and total RNA was isolated with PureLink™ RNA Mini Kit (Thermo Fisher Scientific) according to the manufacturer’s protocol. After quantified using a NanoDrop Spectrophotometer ND-1000 (Thermo Fisher Scientific), the quality of the total RNA was verified on a Bioanalyzer by Cancer Genome Facility  ensuring all samples had RINs>7.0.v1.0.0.Libraries were prepared according to the TruSeq Stranded mRNA-seq with dual-indexes protocol and the sequencing was performed on the NextSeq500 v2 platform with 75bp paired-end reads by the Ramaciotti Centre for Genomics . The fastq files were generated and quality control trimmed through Basespace with FASTQ Generation https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124715).The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus  and are The clean Hep G2 reads were aligned to the human genome reference (hg38) using STAR v2.5.1 with following parameters: –outFilterMultimapNmax 20 –outFilterMismatchNmax 10 –outSAMtype BAM SortedByCoordinate –outSAMstrandField intronMotif . The cleThe DE genes in common for both Hep G2 and MDA-MB-231 cell lines at 24 hours and 48 hours after CKI treatment were selected as “shared” genes. These shared genes were utilized to describe the major anti-cancer functions and principal mechanisms of CKI.p values less than 0.01. Specific over-represented terms/pathways and gene expression status mapping in KEGG pathways were visualized with the R package “Pathview” [Gene Ontology (GO), and Kyoto Encyclopedia of Gene and Genomes (KEGG) over-representation analyses of both cell lines were carried out using the online database system ConsensusPathDB  with theathview” .To integrate with previous data from MCF-7 cells , all theRT-qPCR was performed as previously described . The lisIn our previous study, CKI significantly perturbed/suppressed cancer cell target genes/networks. In the current study, we present results that confirm and generalize our previous work. We had previously determined that low concentrations of CKI in our short-term cell assay showed no/little phenotypic effect within 48 hours, and very high doses resulted in excessive cell death at 48 hours precluding the isolation of sufficient RNA for transcriptome analysis . TherefoWe used flow cytometric analysis of propidium iodide-stained cells to assess the effect of CKI on cell cycle and apoptosis. In Hep G2 cells, CKI treatment resulted in an overall increase in the proportion of cells in G1 phase and a decrease in S phase . SimilarIn order to elucidate the molecular mechanisms of action of CKI on these cancer cells, we carried out transcriptome analysis. As mentioned above, RNA samples from two cell lines were sequenced with 75 bp paired-end reads. We had previously sequenced transcriptomes from CKI treated MCF-7 cells  and haveThe mapping rates for all samples were around 90% , which iWe identified a set of 6852 shared DE genes by identifying common DE genes between CKI-treated groups and untreated groups from both Hep G2 and MDA-MB-231 cell lines, at 24 hours and 48 hours . These sBased on our previous results  and analBecause low dose treatment with CKI did not cause significant gross phenotypic effects in either cell line, we decided to use gene expression as a more sensitive measure of phenotype to look at the effect of lower doses of CKI. We used 0.125 mg/ml, 0.25 mg/ml, 0.5 mg/ml and 1 mg/ml concentrations to look for dose dependency of gene expression. Our results showed an obvious dose-dependent expression trend  in both To identify candidate mechanisms of action of CKI, we carried out functional enrichment analysis. We used ConsensusPathDB  and ClusGO over-representation was determined based on Biological Process at level 3 and q value <0.01. The results for both cell lines at both time points were summarized and visualized based on semantic analysis of terms in We then used KEGG pathways to determine the specific pathways altered by CKI in cancer. The most regulated over-representative KEGG pathways are summarized according to KEGG Orthology (KO) . Cell cyBecause the KEGG enrichment revealed many pathways relating to diseases, most of which were cancers, we decided to explore the enrichment of DE genes with respect to DO terms . In the In addition to cell line specific functional enrichment of DE genes, we also analyzed the over-represented GO terms for shared DE genes . The mosSimilar results were observed from KEGG analysis  sheet 14We also carried out an over-representation analysis of DO terms  for all Specific to the therapeutic potential of CKI for cancer treatment, we applied our data set mapping to KEGG cancer pathways: pathways in cancer—homo sapiens . The R pCollectively, these results suggest a direct anti-cancer effect of CKI and implicate specific candidate mechanisms of action based on the perturbed molecular networks. The most obvious example is the cell cycle, where the G1-S phase is significantly altered, resulting in the induction of apoptosis. The downstream process triggered by CKI is the suppression of gene expression of cell cycle regulators, including TP53 and CCND1. The other perturbed cancer pathways provide additional candidate mechanisms of action for CKI. In the following section, we integrate these results with previous results reported in the literature to refine the core set of genes and pathways perturbed by CKI.Although Hep G2  and MDA-MB-231  are different cancer types, they shared a large number of CKI DE genes with similar expression profiles, presumably, these shared genes include CKI response genes that are essential to the apoptotic response triggered by CKI. However, the number of shared CKI DE genes is too high to allow straight forward identification of genes critical to the CKI response. We, therefore, decided to combine these data with previously reported CKI DE genes from MCF-7 cells  in orderAmong the 363 core CKI DE genes, cytochrome P450 family 1 subfamily A member 1 (CYP1A1) gene is the most over-expressed. This gene is consistently up-regulated by CKI in all three cell lines and showed a significant dose response. In liver cancer cells, over-expression of CYP1A1 induced by plant natural products has been associated with Aryl-hydrocarbon Receptor transformation . Furtherhttps://www.broadinstitute.org/cancer/cga/target) identified 7 DE genes that were shared across the three cell lines and two time points (Comparison of the 363 core genes to the 135 Tumor Alterations Relevant for Genomics-driven Therapy (TARGET) genes (version 3) from The Broad Institute  has also been shown to regulate apoptosis in cancer [The TP53 gene encodes a tumor suppressor protein, that can induce apoptosis . Howevern cancer . Therefoin vivo, possibly altering angiogenesis which is critical for tumor progression [The GO  and KEGGgression . In addigression . In a prgression .We have examined the effect of a complex mixture of plant natural products (CKI) on different cancer cell lines and have identified specific, consistent effects on gene expression resulting from this mixture. However, the complexity of CKI makes it difficult to determine the mechanism of action of individual components, and often testing of individual components has resulted in either no effect or contradictory results in the research literature. In spite of this complexity, it is possible to map our results on to a pre-existing corpus of work that links individual natural compounds to changes in gene expression. We have used BATMAN , an onliin vitro. These effects show that CKI can suppress the expression of cell cycle regulatory genes and other well-characterized cancer-related genes and pathways. Validation of a subset of DE genes at lower doses of CKI has shown a dose-response relationship which suggests that CKI may have similar effects in vivo at clinically relevant concentrations. Our results provide a molecular basis for further investigation of the mechanism of action of CKI.Our systematic analysis of gene expression changes in cancer cells caused by a complex herbal extract used in TCM has proven to be effective at identifying candidate molecular pathways. CKI has consistent and specific effects on gene expression across multiple cancer cell lines and it also consistently induces apoptosis S1 Fig(TIF)Click here for additional data file.S2 Fig(A-D) Each small square represents a GO biological process function at level 3. Size of the squares positively correlates with the statistical significance of related biological process. Different colours distinguish biological process clusters that are described by the top shaded functional representatives.(PDF)Click here for additional data file.S3 FigIn the cell cycle pathway, each coloured box is separated into 4 parts, from left to right representing 24h CKI treated Hep G2, 48h CKI treated Hep G2, 24h CKI treated MDA-MB-231 and 48h CKI treated MDA-MB-231.(TIF)Click here for additional data file.S4 FigHeatmap showing the expression fold changes of core genes in three cell lines at two time points. All the core genes can be separated into the following 3 clusters: genes up regulated in all three cell lines, genes down regulated in all three cell lines and DE genes that are uncorrelated in terms of expression change across the three cell lines.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Tablep value<0.01, cut off q value<0.01. Sheet 5-8: KEGG enrichment of each cell line at two time points. Selection standard: cut off p value<0.01, cut off q value<0.01. Sheet 9-12: DO enrichment of each cell line at two time points. Selection standard: cut off p value<0.01, cut off q value<0.01. Sheet 13: GO enrichment of shared genes by both cell lines. Selection standard: cut off p value<0.01. Sheet 14: KEGG enrichment of shared genes by both cell lines. Selection standard: cut off p value<0.01.Sheet 1-4: GO enrichment of each cell line at two time points. Selection standard: cut off (XLSX)Click here for additional data file.S1 FileBoth original Chinese document and English translation included.(PDF)Click here for additional data file. 28 May 2020PONE-D-20-11981The effect of compound kushen injection on cancer cells: integrated identification of candidate molecular mechanismsPLOS ONEDear Dr. Adelson,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.Both reviewers have raised some issues but those of Reviewer 2 are particularly of concern and need to be addressed if the authors plan to submit a revised manuscript.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Jul 12 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocolsIf applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: We look forward to receiving your revised manuscript.Kind regards,Salvatore V PizzoAcademic EditorPLOS ONEJournal requirements:When submitting your revision, we need you to address these additional requirements:http://www.plosone.org/attachments/PLOSOne_formatting_sample_main_body.pdf and http://www.plosone.org/attachments/PLOSOne_formatting_sample_title_authors_affiliations.pdf1.    Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at http://journals.plos.org/plosone/s/submission-guidelines#loc-cell-lines.2. Please provide additional information about the MDA-MB-231 and HepG2 cells used in this work, including any quality control testing procedures . For more information, please see 3. We understand that you obtained Compound Kushen Injection (CKI)  from Zhendong Pharmaceutical Co. Ltd for this study. For purposes of reporting, we request that you provide additional details as to the source of this material. Please provide any quality assessments and chemical assessments that were supplied when you obtained the product.https://journals.plos.org/plosone/s/submission-guidelines.#loc-statistical-reporting.4.  To comply with PLOS ONE submission guidelines, in your Methods section, please provide additional information regarding your statistical analyses. For more information on PLOS ONE's expectations for statistical reporting, please see 5. Thank you for stating the following in the Competing Interests section:"We wish to draw the attention of the Editor to the following facts which may beconsidered as potential conflicts of interest and to significant financial contributions tothis work. While a generous donation was used to set up the Zhendong Centre byShanxi Zhendong Pharmaceutical Co Ltd, they did not determine the research directionfor this work or influence the analysis of the data. JC: no competing interests, ZQ: nocompeting interests, YHL: no competing interests, HS: no competing interests, TNA: nocompeting interests, WW: is an employee of Zhendong Pharma seconded to ZhendongCentre to learn bioinformatics methods, RDK: no competing interests, DLA: Director ofthe Zhendong Centre which was set up with a generous donation from the ZhendongPharmaceutical Co Ltd. Zhendong Pharmaceutical has had no control over theseexperiments, their design or analysis and have not exercised any editorial control overthe manuscript."We note that you received funding from a commercial source: Shanxi Zhendong Pharmaceutical Co Ltd.,relevant declarations relating to employment, consultancy, patents, products in development, marketed products, etc.Please provide an amended Competing Interests Statement that explicitly states this commercial funder, along with any other http://journals.plos.org/plosone/s/competing-interests).  If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.Within this Competing Interests Statement, please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” . The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This is a good conduced experiment that made a comprehensive exploration of the mechanism of compound kushen injection in two cancer cell lines. Overall, the methodology used is very adequate. However, some points should be taken in consideration to improve the manuscript.(1) There are some mistakes in English and grammatical errors. The authors should made a critical review in language;(2) Both MCF-7 and MDA-MB-231 are breast cancer cell lines but they have the different endocrine genotype. Is it reasonable to integrate the data of MCF-7 and MDA-MB-231 cell lines together?(3) Maybe the authors can prove theirs hypothesis in vivo the next step.Reviewer #2: CKI is so broadly utilized that understanding it's anticancer function is critical to the world's population, however, due to concerns with the experimental design of this study the anticancer activity of CKI will not be revealed here. 1.The comparison of gene expression between two cell lines that originated from very different tissues (breast vs. liver) without including immortalized (BUT NOT TRANSFORMED) tissue type cell line matches are likely to highlight a subset of DNA damage and repair genes common across all tissues and species. It is unclear that CKI drives apoptosis in vivo. However att doses of 1 mg/ml and 2 mg/ml in an in vitro setting where aryl hydrocarbon receptors could be activated cell death will be induced. This is seen in manuscript Figure 1. Therefore the experimental design biases the outcome of the study toward.2. PRS13 is regulated by cell cycle arrest Fannie W. Chen & Yiannis A. Ioannou (1999) Ribosomal Proteins in Cell Proliferation and Apoptosis, International Reviews of Immunology, 18:5-6, 429-448, DOI: 10.3109/08830189909088492, yet it's expression is the reference gene for this study - See Figure 3.3. Figure 4 does not address the question being asked. Some genes that are altered by CKI treatment of HEPG2 and MDA MB 231 cells correlate with other cancer types. What does that suggest about the anticancer mechanism of CKI?4. It is unclear that the mutant form of TP 53 expressed by MDA MB 231 and the wild type form of TP 53 expressed by HEPG2 should be equally regulated by CKI. Figure 3 shows temporal regulation of TP53 in both cell types that is reversed at later time points. If real, this could be exciting but the how and why of this is not explored nor is it convincing due to the differences (or lack there of) the two methods to quantify the results.Thus while there is a great deal of reasonable bioinformatics data generated in this study, due to the experimental design, it does not reveal a mechanism for CKI anti-cancer activity in an unbiased way.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  23 Jun 2020Reviewer #1: This is a good conduced experiment that made a comprehensive exploration of the mechanism of compound kushen injection in two cancer cell lines. Overall, the methodology used is very adequate. However, some points should be taken in consideration to improve the manuscript.(1) There are some mistakes in English and grammatical errors. The authors should made a critical review in language;RESPONSE. We have made a critical review of the manuscript and corrected mistakes or grammatical errors. (2) Both MCF-7 and MDA-MB-231 are breast cancer cell lines but they have the different endocrine genotype. Is it reasonable to integrate the data of MCF-7 and MDA-MB-231 cell lines together?RESPONSE. Our purpose in comparing these three cell lines was to reveal the shared anticancer mechanisms of CKI in different cancer types. A previously conducted assessment of CKI effects on an array of human cancer cell lines (NCI60 panel) indicated that these three cell lines were amongst the most sensitive to CKI treatment. Therefore, we elected evaluate the molecular mechanisms of CKI effects within the same cancer type (but with different genotypes) or across different cancer types. As a result, our study has shown that the anticancer mechanisms of CKI are centered on arresting the cell cycle and include other pathways that trigger cancer apoptosis. This provides a common set of molecular candidates across varied endocrine genotypes of cancer cell lines and varied cancer tissue types. (3) Maybe the authors can prove theirs hypothesis in vivo the next step.RESPONSE. We agree. Indeed, our next research strategy is to carry out studies in vivo, such as in drosophila or mice, and we have already carried out preliminary work in this regard. This will be a story for another manuscript.Reviewer #2: CKI is so broadly utilized that understanding it's anticancer function is critical to the world's population, however, due to concerns with the experimental design of this study the anticancer activity of CKI will not be revealed here.  1. The comparison of gene expression between two cell lines that originated from very different tissues (breast vs. liver) without including immortalized (BUT NOT TRANSFORMED) tissue type cell line matches are likely to highlight a subset of DNA damage and repair genes common across all tissues and species. It is unclear that CKI drives apoptosis in vivo. However att doses of 1 mg/ml and 2 mg/ml in an in vitro setting where aryl hydrocarbon receptors could be activated cell death will be induced. This is seen in manuscript Figure 1. Therefore the experimental design biases the outcome of the study toward.RESPONSE. We do not believe the inclusion of immortalised cell lines as controls would provide a good comparison as they are genetically modified to behave more like cancer cells than primary cell lines. First, we would like to clarify that we are not directly comparing transcriptomes between two cell lines that originated from two different tissues (MDA-MB-231 from breast vs. Hep G2 from liver). In our study, the transcriptome of CKI-treated cells was compared with corresponding untreated cells to generate a list of DE  genes and pathways that are significantly affect by CKI treatment. This was done separately for each cell line. Subsequently, we compared the DE genes/pathway list of three cell lines and to look for common DE genes and pathways that CKI affects in all three cell/tissue types. We believe that these common DE genes and altered pathways across three cancer cell lines is the real reflection of molecular changes caused by CKI at gene level in different cancer types. Therefore, the result of this study showed in an unbiased fashion that CKI can affect cell cycle arrest of not only one type of cancer but various types of cancers. We do not believe the inclusion of immortalised cell lines as controls would provide a good comparison as they are genetically modified to be immortal, like cancer cells. See below for more on this in the response to point 4. The possibility of aryl hydrocarbon receptors being activated by CKI does not invalidate our approach. It may be that this response is part of the effect CKI has on cancer cells, but that it cooperates with other mechanisms that are also pro-apoptotic. At this point we cannot answer this question, but it is beyond the scope of our present study, which aimed to identify conserved DE gene responses to CKI.  2. PRS13 is regulated by cell cycle arrest Fannie W. Chen & Yiannis A. Ioannou (1999) Ribosomal Proteins in Cell Proliferation and Apoptosis, International Reviews of Immunology, 18:5-6, 429-448, DOI: 10.3109/08830189909088492, yet it's expression is the reference gene for this study - See Figure 3.RESPONSE. We selected RPS13 as the reference gene for our study based on the findings from Hendrik J. M. de Jonge et al., 2007, PLoS One and Francis Jacob et al., 2013, PLoS One. According to these reports, RPS13 is one of the most stably expressed genes in the most commonly used 50 cell lines and is the 2nd or 3rd most stably expressed gene across 25 cell lines, including cancer cell lines. No reference gene will be perfect for every condition, but RPS13 is a good choice in this instance. RPS13 is used by many investigators as a “housekeeping” gene for RT-qPCR in studies focusing on cancer cells.  3. Figure 4 does not address the question being asked. Some genes that are altered by CKI treatment of HEPG2 and MDA MB 231 cells correlate with other cancer types. What does that suggest about the anticancer mechanism of CKI?RESPONSE.Many cancer-associated genes play important roles in multiple types of cancers. As we clarified in Question 1, the gene set that we used for functional enrichment analysis in Figure 4 is common DE genes in both HepG2 and MDA-MB-231 cell lines due to CKI treatment, which should be mostly under the category of generalized/essential cancer-associated genes across multiple cancer types. The results suggest that CKI can alter the expression of many essential genes that are not cancer-type specific, but found in cancer cells. This is consistent with the clinical use of CKI for treating different types of cancers.  4. It is unclear that the mutant form of TP 53 expressed by MDA MB 231 and the wild type form of TP 53 expressed by HEPG2 should be equally regulated by CKI. Figure 3 shows temporal regulation of TP53 in both cell types that is reversed at later time points. If real, this could be exciting but the how and why of this is not explored nor is it convincing due to the differences (or lack there of) the two methods to quantify the results.RESPONSE. Based on our results in Figure 3, TP53 was regulated by CKI in a similar fashion in both cell lines , which indicates the expression change of TP53 altered by CKI is independent of its mutation status. With respect to the statement of “temporal regulation of TP53 in both cell types that is reversed at later time points”, we don’t see this reversed regulation in Figure 3. Thus while there is a great deal of reasonable bioinformatics data generated in this study, due to the experimental design, it does not reveal a mechanism for CKI anti-cancer activity in an unbiased way.RESPONSE. We believe this approach is unbiased in that it looks at all genes in the same way. Their differential expression then identifies them as important in terms of CKI activity and mechanism. The inclusion of non-cancer cell lines would not necessarily make the study more “unbiased” as similar pathways might be affected in those cells. It would not be reasonable to rule out those pathways just because they are also identified by DE genes in non-cancer cells. We know that many cancer therapies affect cancer cells and non-cancer cells, but that does not mean they are not useful therapies. 8 Jul 2020The effect of compound kushen injection on cancer cells: integrated identification of candidate molecular mechanismsPONE-D-20-11981R1Dear Dr. Adelson,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Salvatore V PizzoAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 13 Jul 2020PONE-D-20-11981R1 The effect of compound kushen injection on cancer cells: integrated identification of candidate molecular mechanisms Dear Dr. Adelson:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Salvatore V Pizzo Academic EditorPLOS ONE