The correct identification of pigmented nodular lesions of the scalp is often challenging. Despite the importance of clinical patterns and dermoscopy, important adjuvant tools that are usually helpful, their interpretation sometimes is not clear-cut. Here, the authors discuss a case of sebaceoma mimicking a malignant pigmented neoplasia, with conclusive histopathology. Diagnosis of pigmented nodular lesions of the scalp is often challenging. Clinical findings and dermoscopy are important in the suspicion of malignant melanocytic lesions; nevertheless, histopathology maintains its fundamental role in diagnostic conclusion, since other lesions can mimic them.An 80-year-old female patient, Fitzpatrick phototype II, with a history of endometrial cancer, developed a black, nodular, and asymptomatic lesion with erythematous base and central crust on the vertex of her scalp . DermoscSebaceoma has been called sebomatrixoma or sebaceous epithelioma, when the use of the term epithelioma suggested malignancy. Classified by Troy and Ackerman in 1984 as a benign neoplasm with sebaceous differentiation, it more often affects women, with predominance in the eighth decade of life.Clinically, it appears as a yellowish or orange, solitary, or rarely multiple hemispheric exophytic tumor located in the seborrheic areas of the body, especially on the scalp.2Dermoscopy of sebaceoma may present an amorphous yellowish-erythematous area with or without ulcerations, with centripetally branched arboriform vessels. The amorphous yellowish-erythematous area may be an important finding suggesting the sebaceous etiology of the lesion.4Several benign adnexal tumors may present as a single nonspecific lesion; therefore, histopathological examination is fundamental for definitive diagnosis. There are tumors for which no malignancy is suspected because they lie more deeply in the dermis and may resemble cysts. However, on several occasions, benign tumors are connected to the epidermis or touch it, with the possibility that traumatic ulcerations and thereby malignancies can be mimicked.6Benign and malignant tumors are identified by the type of differentiation they exhibit, the remnants of their origin cells, although malignant tumors lack the richness of findings that benign variants show. Regarding sebaceous tumors, the signs of differentiation are sebaceous cells and sebaceous ducts .5Figure Sebaceous glands are composed of several lobes leading to a duct connected to the hair follicle. There is a peripheral single row of undifferentiated cells and, toward the center, cells with increasing degrees of differentiation by fat synthesis until the well-differentiated sebocytes in which the nucleus is indented by various depressions caused by large fat vacuoles. Near the duct, the sebaceous cells lose their nuclei, and thus the sebaceous secretion known as holocrine is eliminated.Sebaceomas are constituted by masses located in the dermis, connected or unconnected to the epidermis, with histological architecture that suggests benignity: rounded contours, a greater vertical axis, and symmetry, different from the sebaceous carcinomas despite the possible presence of mitosis . They ar6What differentiate the sebaceous adenoma from sebaceoma are the cellular arrangement and the proportion of undifferentiated cells, more numerous in sebaceoma. In sebaceous adenoma, the arrangement of the latter in the periphery of the aggregates and in those with some degree of differentiation in the central portion resembles the normal sebaceous gland, facilitating its identification. However, unlike the normal gland, there are several layers of peripheral undifferentiated cells. This proliferation replaces the epidermis and its secretion is released straight to the surface, instead of draining into the follicular unit through the sebaceous duct where the sebaceous secretion of the normal gland reaches the surface.In sebaceoma, with the two types of cells arranged in a disordered way, the search for sebocytes with more advanced differentiation – and they may be sparse – is necessary. In contrast, the sebaceous ducts, absent in the adenomas, are present in the sebaceomas and serve as a clue for the active search of sebocytes.Although the sebaceous adenoma has the architectural and cytological characteristics closest to the normal gland, some authors considered it as a second type of sebaceous carcinoma due to presence of cell stacking and mitotic figures in undifferentiated peripheral cells.7The distinction between benign and malignant neoplasms considers the degree of differentiation, with the malignant neoplasms having lower degrees, since the cellular apparatus is geared toward cell division rather than synthesizing substances involved in differentiation. Advanced differentiation and malignity are opposing concepts .6Figure It is possible to suppose that the secretion of sebaceous adenomas, being freely eliminated on the surface, does not suffer the effect of a retrograde compression, as should occur with the passage of sebum through a narrow duct in the normal gland. It is a speculation to conclude that secretion retained until its elimination is a factor that inhibits cell division and that its absence in sebaceous adenomas favors cell proliferation. It is important to remember that in the bulbs of anagenic follicles, producing a hair shaft continuously at a reasonable rate, cell stacking and mitosis also occur.Immunohistochemical study in sebaceoma shows multifocal positivity of the neoplastic cells with anti-CK7 and anti-EMA antibodies, and negativity in reactions with anti-CK20 and anti-BerEp4 antibodies .Figure 5Presence of neoplasms with sebaceous differentiation or multiple keratoacanthomas may be revealing of Muir-Torre Syndrome, which also has visceral adenocarcinomas located mainly in the gastrointestinal and genitourinary tracts, endometrium, and larynx. It predominantly affects men in the fifth decade of life, and clinical screening is mandatory, as in the case of the patient in question.9The diagnosis of pigmented nodular lesions of the scalp is challenging. Dermoscopy is an excellent tool for the definition of these cases, having well-established standards for the diagnosis of neoplasms, pigmented or not. However, as in the case reported, the histopathological study remains the gold standard for diagnosis, and was fundamental for the elucidation of the sebaceous nature described above.None declared.Bárbara Catojo Poggi: Elaboration and writing of the manuscript; obtaining, analyzing and interpreting the data.Daniel Fernandes Melo: Approval of the final version of the manuscript; conception and planning of the study; elaboration and writing of the manuscript; intellectual participation in propaedeutic and/or therapeutic conduct of the cases studied; critical review of the manuscript.Juliana Marques da Costa: Approval of the final version of the manuscript; elaboration and writing of the manuscript; intellectual participation in propaedeutic and/or therapeutic conduct of the cases studied; critical review of the manuscript.Maria Auxiliadora Jeunon Sousa: Approval of the final version of the manuscript; elaboration and writing of the manuscript; effective participation in research orientation; intellectual participation in propaedeutic and/or therapeutic conduct of t.he cases studied; critical review of the literature; critical review of the manuscript.The authors declare no conflicts of interest.


Gemcitabine (GEM) and cisplatin (CDDP) combination therapy (GC) is the standard chemotherapy for advanced biliary tract cancer (BTC); however, its pharmacotherapeutic efficacy remains unclear. To investigate the effects of GC, we selected 11 from 17 BTC cell lines, according to their GEM sensitivity, to be assessed using the MTS assay. The presence of synergistic effects of GC was determined using the Bliss additivism model (BM) and the combination index (CI) at a GEM:CDDP molar ratio of 7:1; this ratio was based on the respective human renal clearances of the two drugs. The pharmacotherapeutic effects were evaluated by comparing the IC50 values for administrations of GEM alone and GC in combination. All cell lines showed synergistic effects when analyzed using the BM. Based on the CI values, strong synergism, synergism, and additive effects were seen in four, five, and two cell lines, respectively. For all four GEM-resistant cell lines, on which GC had strong synergistic effects, the pharmacotherapeutic effects of GC were disappointing, with all IC50 values > 1 µM. For the GEM-effective cell lines, on which GC had synergistic or additive effects, the IC50 values were all <1 µM, and the differences were small between the IC50s for administration of GEM alone and GC in combination. Our results suggest that GC has synergistic effects on BTC cell lines but that its pharmacotherapeutic effects are inadequate. Biliary tract cancer (BTC) is a highly malignant disease for which surgical resection is the only curative therapy. Moreover, there is no effective chemotherapy for recurrent or inoperable cases. Consequently, the development of new therapeutic anticancer drugs for BTC is eagerly anticipated. Recently, several studies on potential new therapeutic molecular targets for BTC, including tumors with genomic alterations, were described ,3,4,5,6.The synergistic mechanism of combined GC therapy is considered to be the ability of GEM to become incorporated into DNA, thereby promoting an increase in the accumulation of platinum compounds, such as CDDP, and the formation of CDDP–DNA adducts. Subsequently, GEM is converted to 2’-deoxy-5-azacytidine, and the CDDP–DNA adducts reduce cells’ ability to repair DNA ,9. HowevThe aim of this study was to clarify both the synergistic effect of CDDP administration in combination with GEM and its real pharmacotherapeutic activity on BTC cell lines and to demonstrate the basic premise of the efficacy of GC in the clinical setting.2.In the current study, we used 17 BTC cell lines: seven extrahepatic BTC cell lines, nine intrahepatic BTC cell lines, and one gallbladder carcinoma cell line. Of these cell lines, 13 were our established cell lines: five were derived from extrahepatic BTC  and eight were derived from intrahepatic BTC , NCC-CC6-1, and NCC-CC6-2). Further information regarding these cell lines is presented in GEM (gemcitabine hydrochloride), which was obtained from Eli Lilly Japan K.K. , was dissolved in Dulbecco’s phosphate buffered saline (Sigma-Aldrich). Cisplatin  was dissolved in dimethyl sulfoxide . Solutions of cisplatin in DMSO were freshly prepared before use. The concentration of DMSO in medium was adjusted so as not to exceed 0.2%, which was ascertained as the concentration that had no influence on any cell line.3/ well in 100 μL culture medium. After 24 h, the medium was removed and replaced with GEM and/or CDDP at concentrations in the range 0–100 µM. The data for GEM administration for our 13 established cell lines were obtained from our previous report [The effects of GEM and/or CDDP on 17 BTC cell lines were evaluated using the 3--5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) cell viability assay. Cells were placed in each well of a 96-well plate at 3.0 × 10s report ,16. For s report ,16. The s report . After 7For GEM, the cell viability curves and the 50%, 60%, 70%, and 80% inhibitory concentrations (IC50–80) were then calculated for each cell line; for CDDP, the cell viability curves and the 50% inhibitory concentrations (IC50) were calculated for a subset of cell lines . The BTCWe evaluated the efficacy of GC using the Bliss additivism model (BM). The experimental method was adjusted from that used for the single administrations of GEM and CDDP: following the placement of cells into 96-well plates, an amount of medium similar to that administered in the single-agent experiments was removed from each well. Consequently, 50 μL of each drug was prepared at double concentrations to make up 100 μL of GC. The GEM concentrations were set in the range 0–10 μM , and the CDDP concentrations were set in the range 0–50 μM . Each combination of concentrations of GEM and CDDP were tested. After 72 h of drug administration, we measured absorbances using the MTS assay in a manner similar to that for single-agent administration. The degree of efficacy of GC was determined using the BM ,19,20,21The BM was calculated for each combination of concentration of drugs A and B using the following expression: Ebliss = EA + EB − (EA × EB). EA and EB are the fraction depression effects caused by drugs A and B separately at each concentration , and Ebliss is the inhibition ratio expected if the two drugs exhibited only an additive effect. The BM value is the difference between the observed cell viability data and that expected if the effects of combined administration are purely additive . A BM of 0 indicates additive effects, BM < 0 indicates antagonistic effects, and BM > 0 indicates synergistic effects. To evaluate the effects of GC over the whole concentration range, we carried out experiments for all 36 concentration combinations for each cell line. However, to avoid the influence on the apparent drug efficacy of drug toxicity in the high concentration range of CDDP, we focused on data near the concentration range for which the IC50 of CDDP was calculable. These concentration ranges for each cell line are those inside the black lines in in the BM matrices. 2 and CDDP 25 mg/m2, giving a dose ratio of 40:1. The human plasma clearances of GEM and CDDP are 85.6 and 15 L/h/m2, respectively, giving a plasma concentration ratio for GEM:CDDP of 5.6:1 [For advanced BTC, based on the drug package inserts, the clinical dose of GC treatment is GEM 1000 mg/mof 5.6:1 ,23,24. Tof 5.6:1   to test the statistical significance of the differences between the dose–response curves of GC and GEM at each concentration for all cell lines. Statistical significance was set at P < 0.05.Student’s This study was approved by the ethics committee of the National Cancer Center (ID: 2007-022).The dose–response analysis for single administrations of GEM and CDDP are given for all BTC cell lines in The results of BM analysis for each cell line are shown in The dose–response curves for combined administration of GEM and CDDP at a molar ratio of 7:1 and for GEM single administration against all BTC cell lines are shown in To evaluate the true pharmacotherapeutic effects of GC, we compared the IC50 values between GEM single administration and GC at a molar ratio of 7:1. All four GEM-resistant cell lines showed measurable IC50 values for GC at a 7:1 molar ratio, but all four IC50 values were >1 µM. In contrast, all four GEM-effective cell lines had measurable IC50 values for GC at a 7:1 molar ratio, but all four IC50 values were <1 µM. In the three GEM-intermediate cell lines, GC at 7:1 molar ratio showed either synergistic or additive effects, with IC50 values in the range 0.04–1.53 .In the present study, we used 11 representative BTC cell lines and multiple methods to measure drug efficacy, allowing us to examine the relationship between the synergistic effects and pharmacotherapeutic activity of combined GC administration. Our results showed a synergistic effect of GC administration in most of the BTC cell lines, even for GEM-resistant cell lines. These results suggested that the addition of CDDP to GEM represents an effective and valuable therapy for BTC. However, interestingly, although GC at a molar ratio of 7:1 showed strong synergism in all four GEM-resistant cell lines, the IC50 values of GC were relatively high (>1 µM). In addition, the average IC50 value for GEM alone was low (0.06 µM) for the GEM-effective cell lines in our study; studies on other types of cancer cell lines have found IC50s for GEM in the range 0.0025–50 µM ,12,27,28Although synergistic effects of GC for other cancer cell lines have been reported ,11,12,14In the current study, most of the BTC cell lines showed synergistic effects of GC compared with GEM single administration. However, in the ABC-02 study of 410 patients with locally advanced or metastatic cholangiocarcinoma, gallbladder cancer, or ampullary cancer, the clinical response rates were 26.1% for GC therapy and 15.5% for GEM monotherapy . As showTo account for the differences between in vitro and in vivo drug metabolism, we adopted a GC molar ratio of 7:1, taking into consideration the human plasma clearances of GEM and CDDP. However, in the clinical setting, liver and/or kidney drug metabolism may vary because of individual differences. Moreover, we carried out several repeat experiments with the same cell lines, and the dose–response curves were almost identical, although the IC50 values sometimes varied a little. However, the IC50 for NCC-CC1 was different between our in house data for GEM and for GEM single administration carried out as part of the combination study, despite the two dose–response curves being similar in shape and position. Therefore, further detailed studies, such as in vivo studies using xenograft models, are necessary. Such investigations may show synergistic effects of GEM and CDDP under conditions that more closely resemble those in humans.In conclusion, the synergistic effects of GC were examined in vitro in this preliminary report using 11 BTC cell lines and two different methodologies. In one approach, we considered the GEM and CDDP plasma clearances and used a molar ratio of 7:1 to reproduce the respective concentrations in the clinical setting as much as possible. GC had synergistic or additive effects for all cell lines. However, in some cell lines for which GEM single administration was effective, the pharmacotherapeutic efficacy of GC did not show much improvement. Furthermore, in cell lines for which GEM single administration was ineffective, GC showed strong synergy but insufficient pharmacotherapeutic activity. We believe that these preclinical-style findings are important to help improve the chemotherapeutic treatment strategy for advanced and recurrent BTC.


Oleanolic (OA) and ursolic (UA) acids are recognized triterpenoids with anti-cancer properties, showing cell-specific activity that can be enhanced when loaded into polymeric nanoparticles. The cytotoxic activity of OA and UA was assessed by Alamar Blue assay in three different cell lines, i.e., HepG2 (Human hepatoma cell line), Caco-2  and Y-79 (Human retinoblastoma cell line). The natural and synthetic mixtures of these compounds were tested as free and loaded in polymeric nanoparticles in a concentration range from 2 to 32 µmol/L. The highest tested concentrations of the free triterpene mixtures produced statistically significant cell viability reduction in HepG2 and Caco-2 cells, compared to the control (untreated cells). When loaded in the developed PLGA nanoparticles, no differences were recorded for the tested concentrations in the same cell lines. However, in the Y-79 cell line, a decrease on cell viability was observed when testing the lowest concentration of both free triterpene mixtures, and after their loading into PLGA nanoparticles.  Pentacyclic triterpenes are known for their multiple pharmacological properties ,2,3, whiCytotoxicity assays are widely used by the pharmaceutical industry to screen the biosafety of compounds, and also to evaluate the potential anti-tumoral effect of chemotherapeutic drugs. Among the methodologies commonly used to evaluate cell viability, the authors can highlight the MTT  ,11, SRB In the last decades, the success of chemotherapy has significantly increased by site specific targeting of anti-cancer drugs when loaded into nanoparticles . These sd,l-lactide-co-glycolide) acid (PLGA) nanoparticles (PLGA-NPs) have been reported. The Alamar blue assay was carried out using three different cell lines, i.e., human hepatoma (HepG2), human epithelial colorectal adenocarcinoma (Caco-2) and human retinoblastoma (Y-79). The characterization of the developed nanoparticle formulations was reported in a previous work  of 98,000 Da of molecular weight (MW) and 75/25 molar ratio of lactide/glycolide was a kind gift from Boehringer . The selected triterpenoids, Oleanolic Acid (OA) (≥97%) and Ursolic Acid (UA) (≥90%) were purchased from Sigma-Aldrich . Lutrol® F68  was offered by BASF . The reagents and consumables for the cell culture were purchased from Gibco . MilliQ water was obtained by a MilliQ® Plus System lab supplied. All other reagents were of analytical or high-performance liquid chromatography (HPLC) grade obtained from Fisher Scientific .ResomerPlumeria obtusa L. var. sericifolia (C. Wright ex Griseb.) Woodson (Apocynaceae), collected in Calakmul, Campeche, Mexico [w/w).The leaves of , Mexico  were usePLGA-NPs loaded with NM-OA/UA or SM-OA/UA were prepared applying the solvent displacement production procedure . Brieflyv/v) FBS , 2 mM l-glutamine, and antibiotics (100 U/mL penicillin and 100 µg/mL of streptomycin), in an incubator with atmosphere of 5% CO2 in air at 37 °C, as previously described [Y-79 (Human retinoblastoma cell line) and Caco-2  were purchased from Cell Lines Service CLS . HepG2 (Human hepatoma cell line) was purchased from ATCC . The adherent cell lines (Caco-2 and HepG2) were maintained in DMEM , whereas Y-79 cell line was maintained in RPMI-1640 . Both media were supplemented with 10%  or 1 × 105 cells/mL (Y-79) density, in 96-well plates (100 µL/well). The stock solutions of NM-OA/UA and SM-OA/UA were prepared in dimethyl sulfoxide (DMSO) at a concentration of 1.0 µg/µL (2.2 mM). For each mixture and formulation , five concentrations  were tested, by diluting the required volume of NM or SM with FBS-free culture media to achieve the final concentrations, or the appropriate volume of NPs that contain the required volume of OA/UA, and added to cells 24 h after seeding (100 µL/well). The cell viability was evaluated, after 24 h or 48 h of cells exposure to above mentioned solutions, by applying the Alamar blue assay . The sample solutions were replaced by a 10% (v/v) Alamar blue solution, diluted in FBS-free culture medium, adding 100 µL to each well. Absorbance was read at 570 nm  and 620 nm . The data were analyzed by calculating the percentage of Alamar blue reduction (according to the manufactures recommendation) and expressed as a percentage of the control (untreated cells), as previously reported [For the cytotoxicity assays, the adherent cells (Caco-2 and HepG2) were handled as described in  and the reported .p-value < 0.05 was accepted as significant.The data presented are the mean ± standard deviation of four determinations, following analysis of variance (ANOVA) in the GraphPad Prism software (version 5.01). The Tukey multiple comparison test was used to compare the significance of the difference between the groups, a 3 factorial design, as reported in Alvarado et al. [Submicron-sized PLGA-NPs were produced by a solvent displacement procedure. A mean particle size of 213.55 ± 1.60 nm was obtained for the NM-OA/UA NPs, with a polydispersity index of 0.090 ± 0.038 and zeta potential of −27.12 ± 0.27 mV. For SM-OA/UA NPs, a mean particle size of 217.98 ± 2.74 nm, polydispersity index of 0.073 ± 0.011 and zeta potential of −26.85 ± 0.49 mV were recorded. The encapsulation efficiency reached 79.01 ± 4.22% for NM-OA/UA NPs and 78.25 ± 3.01% for SM-OA/UA NPs. Both formulations exhibited a Newtonian behavior and high long-term physical stability, without presenting phenomena of destabilization over a period of 6 months. The production of these NPs has been optimized using a 2Alamar blue was selected for the evaluation of the pentacyclic triterpenoids cytotoxicity, as it is a quantitative measure of cell proliferation and metabolic activity, useful in the cytotoxicity screening of tested compounds/formulations and can be used as a baseline for further in vivo studies. Alamar blue is a reliable, simple, non-radioactive, sensitive fluorometric/colorimetric assay commonly used to detect the metabolic activity of cells. This assay combines additional advantages, as it is ready to use, extremely stable, minimally toxic to the cells, and allows for the continuous monitoring of cultures. It may also be useful to predict if formulations cause cellular damage which can result in the loss of the metabolic cell function. Indeed, the oxidation-reduction (REDOX) indicator is incorporated in the cell which, when under a reducing environment of a metabolically active cell, is reduced. Alamar blue becomes therefore fluorescent and changes its color from blue to pink. The reduction of Alamar blue can be mediated by mitochondrial enzymes or cytosolic and microsomal enzymes ,31,32. TThe cell viability in Caco-2 cells is shown in The obtained data demonstrate that these terpenoids have cytotoxic activity against HepG2 and Caco-2 cells, which may translate to anti-tumoral activity. The toxicity of these compounds was significantly reduced or abolished when loaded into PLGA nanoparticles. This was attributed to a controlled release leading to concentrations onto the cells, that were not enough to exhibit cytotoxic effects but that could have a protective role. These results may demonstrate that the developed formulation exhibit some protective effects in the intestine and liver as HepG2 and Caco-2 cells are in vitro models for these organs, respectively. This might be a promising result for the formulation of these drugs in innovative oral dosage forms.RBl) is a molecular marker of retinoblastoma tumors. This gene is located in chromosome 13q14. Y-79 is a human retinoblastoma (RB) cell line that has been firstly established from an explant of a primary tumor of a donor with a family history of retinoblastoma. The cultured tumoral cells had ultrastructural characteristics similar to those seen in the original tumor, such as microtubules, triple membrane structures, nuclear membrane infoldings, centrioles, large coated vesicles, basal bodies and annulate lamellae . The incThe effect of blank-NPs on the cell viability of all the cell lines was tested at the two time-points and showed to be statistically different from the control. However, the observed decrease was only approximately 15% of the control . Thymus plant extracts, rich in UA and OA triterpenoids  were also reported to exhibit high anti-proliferative effects through cell cycle arrest and apoptosis [50  [These decreases on the cell viability, especially pronounced in Y-79 cell line, are possible because these compounds exhibit potential anticancer effects both in vitro and in vivo. They inhibited proliferation and caused apoptosis in the cells of numerous cancers, including melanoma, pancreatic cancer, breast cancer, lung cancer, colon cancer, cervical cancer ,39,40,41poptosis . From thpoptosis  reportedosis [50 , compareNM-OA/UA . Concernvery low , indicatG2 cells  comparedIn summary, oleanolic (OA) and ursolic (UA) acids have some toxicity at high concentrations (anticancer effect), especially pure (non-loaded) synthetic mixtures which show higher toxicity than the natural mixture in all cell lines. This is attributed to the purity or to the presence of other isomer with lower activity than UA or OA. The potent cytotoxic activity against Y-79 cell line exhibited by these compounds is a promising result to develop new formulations for the treatment of retinoblastoma as prospective anticancer agents, as previous studies also showed them to have a good anti-inflammatory and antioxidant activity. The lower toxicity against Caco-2 cells (colon) and HepG2 (liver) is also very important as it opens new perspectives in oral-delivery formulations with low toxicity, and also ensures low enteric toxicity if inadvertently ingested.


Cancer is the second leading cause of death worldwide, and the number of cases is increasing alarmingly every year. Current research focuses on the development of novel chemotherapeutic drugs derived from natural as well as synthetic sources. The abundance and diversity in natural resources offer tremendous potential for the discovery of novel molecules with unique mechanisms for cancer therapy. Mangrove-derived fungi are rich source of novel metabolites, comprising novel structure classes with diverse biological activities. Across the globe, coastal areas are primarily dominated by mangrove forests, which offer an intensely complex environment and species that mostly remain unexplored. In recent years, many structurally diverse compounds with unique skeletons have been identified from mangrove fungi and evaluated for their antiproliferative properties. These compounds may serve as lead molecules for the development of new anticancer drugs. Mangrove endophytes can be modulated using epigenetic means or culture optimization methods to improve the yield or to produce various similar analogs. The present review provides an insight into the bioactive metabolites from mangrove endophytes reported during the period from 2012 to 2018  along with their cytotoxic properties, focusing on their chemical structures and mode of action, as indicated in the literature. Mangroves are salt-tolerant forest ecosystems, representing a lively ecosystem with an amalgam of land-dwelling and marine habitats with high biodiversity and socio-economic importance . Marine Cancer affects different organs, and is identified by the unchecked proliferation of abnormal cells that invade other healthy tissue. The treatment is primarily confined to chemotherapy. Besides being expensive, chemotherapy is known to cause severe side effects, making treatment problematic. The non-effectiveness of many existing drugs along with multi-drug resistance further aggravates the problem, making cancer treatment difficult. For medicinal chemists, the primary goal remains the discovery and identification of chemotherapeutic agents derived from natural products. Secondary metabolites have opened new avenues for the development of novel therapeutic agents ,7. EndopMost of the endophytes have the potential to produce novel bioactive metabolites, which will undoubtedly boost novel drug discovery. However, higher similarity among microbes leads to the frequent identification of the same compound in the endophytes. During axenic cultivation, a specific portion of the biosynthetic genes are expressed while growing in vitro, and various genes stay masked or silent and do not express in laboratory conditions. For this reason, the routine method of fermentation yields metabolites without chemical diversity. Co-cultivation could help to overcome this problem and is preferred, with two or more microbes allowed to grow together. This approach offers a better competitive environment, allowing the increased production of constitutive as well as cryptic compounds that are not traced out in axenic cultures . SeveralAspergillus niger is cultivated over a two-week period in vermiculite-based semi-solid medium treated with suberoylanilide hydoxamic acid (SAHA), it leads to the isolation of a new fungal metabolite nygerone -dioxine (42), preussomerin EG4 (43), rhytidenone E (44), rhytidenone F (45), palmarumycin C5 (46), (H)-one (47) , , and 4,8one (47) , were isell line .Lasiodiplodia sp. 318#, an endophytic fungus associated with Excoecaria agallocha, collected from Guangdong Province, China, was the source of compound 2,4-Dihydroxy-6-nonylbenzoate (48) —was obtained from the same fungus. Compound 49 exhibited average cytotoxicity against the MDA-MB-435, HepG2, HCT-116, A549, and leukaemia THP1 cell lines with IC50 values of 10.13, 12.50, 11.92, 13.31 and 39.74 µM, respectively . Co. CoStemp8Y cells . Compoun8Y cells . Mishra 8Y cells  reportedactivity . The prootential .68) and B (69) and the known compounds, preussomerin C (70), ymf 1029C (71) and altenusin (72)  [Two new hydroanthraquinones, paradictyoarthrins A (sin (72) , were is0 µg/mL) .73) , which was found in the South China Sea. The IC50s of SZ-685C in nonfunctioning pituitary adenoma (NFPA), MMQ, and RPC cells were 18.76, 14.51, and 56.09 µM, respectively. Hoechst 33342 dye/propidium iodide (PI) double staining and fluorescein isothiocyanate-conjugated Annexin V/PI (Annexin V-FITC/PI) apoptosis assays detected an enhanced the rate of apoptosis in cells treated with SZ-685C. Enhanced expression levels of caspase 3 and phosphate and tensin homologs were determined by Western blotting. The protein expression levels of Akt were decreased when the primary human NFPA cells were treated with SZ-685C. It has been observed that SZ-685C (73) induces the apoptosis of human NFPA cells through the inhibition of the Akt pathway in vitro. These findings suggest that SZ-685C may be a potentially promising Akt inhibitor and anti-cancer agent for the treatment of NFPA [A marine anthraquinone derivative SZ-685C (73)  has been of NFPA .73), was previously reported to inhibit the proliferation of certain tumor cells. SZ-685C inhibited MMQ cell growth in a dose-dependent manner but showed little toxicity toward rat pituitary cells. The IC50 of SZ-685C in MMQ cells and RPCs were 13.2 and 49.1 µM, respectively. Increasing numbers of apoptotic cells were observed in response to escalating concentrations of SZ-685C, and the expression level of prolactin was inhibited. Nevertheless, the level of prolactin mRNA was unchanged. Additionally, miR-200c was upregulated in MMQ cells compared with RPCs, and downregulation of miR-200c was observed in SZ-685C-treated MMQ cells. Furthermore, the overexpression of miR-200c weakened the effect of the SZ-685C-induced apoptosis of MMQ cells. It has been suggested that SZ-685C induces MMQ cell apoptosis in a miR-200c-dependent manner [SZ-685C (t manner .74) and G (75)  , were is 2 µg/mL .76, 77) and known MK3018 (78), palmarumycin CR1 (79)  , were exSporothrix sp. isolated from the bark of an inshore mangrove sample Kandelia candel in the South China Sea was the source of Sporothrin A (80), sporothrin B (81), sporothrin C (82), diaporthin (83)  [hin (83) . Compoun1.05 μM) .84), and one new anthraquinone derivative 9-dehydroxyeurotinone (85), and known compounds, variecolorin G (86), alkaloid E-7 (87), and emodin (88)  , were is30 mg/mL .89) and F (90) (91) and C (92) and verticillin D (93)  —and thren D (93) —were ext.7 µg/mL .94) and B (95)   were obtectively .96) and B (97) ,22-hexadehydro-5α,9α-epidioxy-3,15-dihydroxy sterols, nigerasterols A (d B (97) , were isectively .98–101)  [Four new quinazolinone alkaloids, namely, aniquinazolines A–D (98–101) , were id88.4 µM) .E,24R)-ergosta-7,22-dien-6-one (102), 3β,5α,14α-trihydroxy--ergosta-7,22-dien-6-one (103), and beauvericin (104) (Aspergillus terreus (No. GX7-3B), a mangrove endophytic fungus isolated from the leaves of Rhizophora stylosa. Compounds 102 and 104 exhibited good or moderate cytotoxic activity against MCF-7, A549, HeLa and KB cell lines with IC50 values of 4.98, 1.95, 0.68, 1.50 and 2.02, 0.82, 1.14, 1.10 µM, respectively; compound 103 exhibited poor activity against the cell lines tested, namely MCF-7 (25.4 µM), A549 (27.1 µM), HeLa (24.4 µM) and KB (19.4 µM) [The compounds 3β,5α-Dihydroxy-(22in (104) , were ex19.4 µM) .105), and a known compound, LL-Z1271β (106) (Aspergillus terreus (No. GX7-3B) isolated from the branch of Brugnieria gymnoihiza, growing in a coastal salt marsh of the South China Sea in Guangxi Province, China. Compound 105 showed potent inhibiting activity towards the MCF-7 and HL-60 cancer cell lines with IC50 values of 4.49 and 3.43 µM, respectively, and compound 106 exhibited promising activity against the HL-60 cell line with an IC50 value of 0.6 µM [A new compound, botryosphaerin F (1β (106) , were obf 0.6 µM .107)  [A new eudesmane-type sesquiterpenoid, penicieudesmol B (107) , was iso 9.5 µM) .108)  , was obt.7 µg/mL .109), along with Brefeldin A 7-O-acetate (110)  , were pr S phase .111)  , was extan 10 µM .112–114)  , were obo 3.9 µM .Penicillium chrysogenum V11, a mangrove endophytic fungus was the source of a novel chaetoglobosin named penochalasin I (115), along with chaetoglobosins A (116), and cytoglobosin C (117) (115 exhibited marked cytotoxicity against MDA-MB-435 and SGC-7901 cells (IC50 < 10 µM), and compounds 116 and 117 showed potent cytotoxicity against SGC-7901 and A549 cells (IC50 < 10 µM) [ C (117) . Compoun< 10 µM) .118) and brocazine G (119) , B (121), E (122), F (123)  approach, new diketopiperazines, spirobrocazine C ( G (119)  were chaof 59 µM . Previou F (123)  were iso124), and PR-toxin (125) . Besides, compound 124 selectively exhibited cytotoxic activity toward MOLT-3 and T47D cancer cells with the IC50 values of 1.36 and 1.08 µM and with selectivity index (SI) values of 9 and 11, respectively . Compound 125 exhibited cytotoxicity against the HuCCA-1, HeLa, T47D, and MDA-MB231 cell lines with IC50 values in the range of 0.81–2.19 µM, which was comparable to that of doxorubicin (IC50 0.26–2.24 µM). Interestingly, the compound 125 exhibited cytotoxicity against the HL-60 cell line with an IC50 value of 0.06 µM, which is superior to doxorubicin (IC50 1.21 µM). Compound 125 selectively showed cytotoxic activity against MOLT-3 and HL-60 cancer cell lines with IC50 of 0.09 and 0.06 µM, respectively, with respective SI values of 40 and 61 (IC50 3.66 µM for the MRC-5 cell line) [The fungal metabolites TMC-264 (in (125)  were obtll line) .126–128), and one new xanthone derivative, penixanacid A (129)  , were is–40.5 µM .130    was isolectively .Penicillium 303# obtained from the sea water in Zhanjiang Mangrove National Nature Reserve in Guangdong Province, China, was the source of compounds 133, 134, 135 and 136 -isobenzofuranone (137) -7-methoxy-6-methyl-1(3ne (137) , was isoectively .138, 139) C (140) (141)  , and deh0) (141)  were obtto 10 µM .Penicillium sp. ZH16, a mangrove endophytic fungus, obtained from the South China Sea, was the source of the furanocoumarin 5-methyl-8-(3-methylbut-2-enyl) furanocoumarin (142)  . Compounectively .S)-6-oxo-de-O-methyllasiodiplodin (143), (3R)-de-O-methyllasiodiplodin (144), (3R)-nordinone (145)  with an IC50 value of 6.94 µM. Compound 145 displayed average cytotoxic activity against the GH3 and MMQ cell lines with IC50 values of 12.33 and 10.13 µM, respectively, and against RPC cell lines with an IC50 value of 100.03 µM. Compound 143 was less active against the GH3 and MMQ cell lines with IC50 values of 21.42 and 13.59 µM, respectively, and against the RPC cell lines with an IC50 value of 142.8 µM as a positive control [Zhang et al.  reportedne (145) , by the  control .146)  , by co-cR,10R)-dihydro-harzianone (147)  , was isoectively .Nigrospora sp. MA75 associated with Pongamia pinnata, which led to the isolation of a new compound 2,3-didehydro-19a-hydroxy-14-epicochlioquinone B (148)   when groectively .104) (Fusarium sp. (No. DZ27) an endophytic fungus residing inside the bark of Kandelia candel from Dongzhai mangrove, Hainan, China, in the South China Sea. Compound (104) showed cytotoxic activity against the KB and KBv200 cell lines with IC50 values of 5.76 and 5.34 µM, respectively. It induces apoptosis through the mitochondrial pathway, including the decrease of relative oxygen species generation, the loss of mitochondrial membrane potential, the release of cytochrome c, the activation of Caspase-9 and -3, and the cleavage of PARP. Additionally, the regulation of Bcl-2 or Bax was not involved in the apoptosis induced by beauvericin in KB and KBv200 cells [A known cyclic peptide, beauvericin (104) , was obt00 cells .149)  , was isolymerase . Apicidi78 µg/mL .150), fusamine (151), and 3-(1-aminoethylidene)-6-methyl-2H-pyran-2,4(3H)-dione (152) (Fusarium incarnatum (HKI0504), an endophytic fungus of the mangrove plant Aegiceras corniculatum. Compound 150 exhibite weak antiproliferative activity against HUVEC and the K-562 cell line with GI50 values of 41.1 and 33.3, respectively. Compounds 151 and 152 exhibit anti-proliferative activity against HUVEC and the GI50 K-562 cell line with GI50 values of 37.3, 37.6, and 41.1, 33.3, respectively. The compounds 150, 151 and 152 displayed cytotoxic activity against HeLa cells with a CC50 value of 23.8, 23.3, and 23.8 µM, respectively. The standard imatinib exhibited anti-proliferative activity against HUVEC and the GI50 K-562 cell line with a GI50 value of 18.5 and 0.17, respectively, and cytotoxic activity against HeLa cells with a CC50 value of 65.8 µM [An unusual alkaloid 2-acetyl-1,2,3,4-tetrahydro-β-carboline (ne (152) , were is 65.8 µM .Acremonium sp., an endophytic fungus residing inside the leaves of Sonneratia caseolaris collected from Hainan, China, was the source of a dimeric anthracene derivative torrubiellin B (153)  . Compoun–38.1 µM .154), pestalotiopene A (155), and cytosporone E (156)  and resistant A2780 cell lines  [Waol A ( E (156)  were obtctively) .157)  , a knownectively .158) (158 showed cytotoxic activity against the hepG2 cell line (IC50 = 25 µg/mL) [The compound 2--5-hydroxy-3-methoxybenzoate (158) , was ext5 µg/mL) .159)  . Compounectively .160) and methyl marinamide (161)   were obtectively .162)  . Compoun 12.0 µM .H-xanthene-1-carboxylate (163) and lichenxanthone (164)  , were isectively .165, 166)  , were isectively .167, 168, 169 and 170 . Compound 167 displayed cytotoxicity against the HuCCA-1, A549, MOLT-3, HepG2, HL-60, MDA-MB231, and T47D cancer cell lines with IC50 values in the range of 0.28–37.46 µM and against the MRC-5 normal cell line with an IC50 value of 17.92 µM. Compound 167 selectively showed activity against the HL-60 cell line with an IC50 value of 0.28 µM and a selectivity index (SI) value of 64.0. Compound 168 showed activity against the A549, MOLT-3, HepG2, HL-60, MDA-MB231, T47D, HeLa and MRC-5 cells IC50 values in the range of 12.09–170.08 µM. Compounds 169 and 170 were active against the cell lines tested, IC50 15.20–76.97 µM for 169 and IC50 18.31–154.51 µM for 170. Compound 170 also selectively exhibited cytotoxic activity toward the HL-60 cell line (IC50 18.31 µM) with an SI value of 4.4 [Compounds  and 170  were ext-epi-Steperoxide A (171), along with the known sesquiterpenes steperoxide A (172), merulin B (173), and merulin C (174)   were iso75 µg/mL .175–177), penitrems A, C, F (178–180) and 3β-hydroxy-4β-desoxypaxilline (181)   were obtectively .Salinispora bacteria with proteasome inhibitory activity is expected to be a future anti-cancer drug, and is presently under clinical trials [Recent studies in the marine-based microorganisms have shown that these microorganisms are a rich source for novel bioactive compounds. Salinosporamide A (marizomib), a microbial compound isolated from marine l trials . Howeverl trials ,97. It hl trials , mixing l trials ,100 to eAspergillus species derived from mangroves produced the new alkaloid aspergicin and the previously recognized compounds neoaspergillic acid and ergosterol, with antibacterial activity [Pestalotia sp. when grown in the presence of the marine-derived bacterium Thalassopia sp., which belongs to the Gram-negative group. [Libertella sp., a marine-based fungi, were cultured in the presence of the bacteria Thalassopia sp., it resulted in the production of diterpenoid libertellenones of fungal origin [Sphingomonas sp. was grown in the presence of Aspergillus fumigatus, a novel compound glionitrin A, a diketopiperazine disulfide, was identified and appeared to show strong cytotoxicity against HCT-116, A549, AGS and DU145 cells [Microbes in natural ecosystem conditions always harbor and flourish in co-existence with a variety of microbes. Antagonism and competition for limited resources often lead to high competition among species, and microbes adopt various defense strategies, which favor the production of important bioactive secondary metabolites . The co-activity . Li et aactivity  co-cultuactivity . Pestaloe group. . When Lil origin . In anot45 cells . These sA. niger when grown with suberoylanilide hydoxamic acid (SAHA). Wang et al. [Penicillium citreonigrum when grown in the presence of methyl transferase inhibitor, 5-azacytidine (5-AZA). When Hypoxylon sp., an endophytic fungi, was treated with the epigenetic modifiers SAHA and AZA it enhanced the production of volatile organic compounds (VOCs) [Leucostoma persoonii from Rhizophora mangle enhanced the production of cytosporones B, C, E and R in HDAC inhibited fermentation [The addition of epigenetic modifiers to fungi would allow us to induce cryptic fungal gene clusters. This technique can be applied to any fungal strain and does not require strain-dependent genetic manipulation. Williams et al.  reportedg et al.  reporteds (VOCs) . The marentation . These s27) and peptide molecules  as well as complex stereospecific structures such as cytochalasin H (18), phomopsichalasin G (19), aniquinazolines A–D (98–101) and penitrem A, B and F (178–180). Chemical diversity plays an important role in the drug discovery pipeline, as this provides structurally diverse scaffolds that display similar activity via different modes and/or mechanisms of action. This phenomenon is also observed in mangrove fungal metabolites, as they show potent anticancer activity via different mechanisms of action such as apoptotic cell death , the inhibition of kinase proteins involved in signal transduction pathways . Although many metabolites demonstrated moderate cytotoxic activities against cancer cell lines, only a few displayed superior activity than the standard anticancer drugs . It can be suggested that the rational derivatization of metabolites may provide molecules with better activity against a wide range of cancer cell lines. In addition, the identified metabolites with broad-spectrum anticancer activity need to be investigated to establish their mechanisms of action and to develop as novel anticancer therapeutics.Mangrove fungi are a ubiquitous source of novel bioactive metabolites with the potential to display anticancer properties. It is interesting to observe the chemical diversity in these metabolites, which include simple glycoside (


Metastatic melanoma is one of the most aggressive forms of cancer in humans. Among its types, mucosal melanomas represent one of the most highly metastatic and aggressive forms, with a very poor prognosis. Because they are rare in Caucasian individuals, unlike cutaneous melanomas, there has been fewer epidemiological, clinical and genetic evaluation of mucosal melanomas. Moreover, the lack of predictive models fully reproducing the pathogenesis and molecular alterations of mucosal melanoma makes its treatment challenging. Interestingly, dogs are frequently affected by melanomas of the oral cavity that are characterized, as their human counterparts, by focal infiltration, recurrence, and metastasis to regional lymph nodes, lungs and other organs. In dogs, some particular breeds are at high risk, suggesting a specific genetic background and strong genetic drivers. Altogether, the striking homologies in clinical presentation, histopathological features, and overall biology between human and canine mucosal melanomas make dogs invaluable natural models with which to investigate tumor development, including tumor ætiology, and develop tailored treatments.We developed and characterized two canine oral melanoma cell lines from tumors isolated from dog patients with distinct clinical profiles; with and without lung metastases. The cells were characterized using immunohistochemistry, pharmacology and genetic studies.Ocr_OCMM1X & Ocr_OCMM2X) were produced through mouse xenografts originating from two clinically contrasting melanomas of the oral cavity. Their exhaustive characterization showed two distinct biological and genetic profiles that are potentially linked to the stage of malignancy at the time of diagnosis and sample collection of each melanoma case. These cell lines thus constitute relevant tools with which to perform genetic and drug screening analyses for a better understanding of mucosal melanomas in dogs and humans.We have developed and immunohistochemically, genetically, and pharmacologically characterized. Two cell lines  contains supplementary material, which is available to authorized users. In humans, melanoma is one of the most aggressive types of cancer. Whereas cutaneous forms represent the most common cases, melanoma of the oral cavity is rare but very aggressive, highly metastatic  and assoIn dogs, although local and regional control of oral canine malignant melanomas (CMMs) using surgery followed by adjuvant radiation may be successful in preventing tumors from spreading, the preventive use of poly-chemotherapeutic protocols using cytotoxic drugs has previously been considered non-productive, with no real benefit nor any extension of survival time –8.CMMs are one of the most frequently diagnosed malignancies of the oral cavity. These cancers account for 7% of all malignant tumors in dogs, with 160,000 cases reported worldwide every year , 10 and Human and canine oral melanomas share multiple morphological, cytogenetic, molecular and signaling pathway similarities –16  of various origins  from donors with different stages of development (T3 N1 M0 and T4 N1 M0). Of the three cases, no distant metastases were reported and only morphological characterization was performed   as described by Rütgen et al [Population doubling times (PDT) were evaluated for each cell line Table . Cells . Alongside haematoxylin–eosin staining (HE), the following commercially available antibodies were used for the phenotypical characterization of melanocytes: Melan-A , S-100 protein , Vimentin, and cytokeratin   and paraffin embedded for routine pathological analysis. Immunohistochemical staining was carried out on 4- to 6-μm-thick slides using an automated protocol developed for the Discovery XT automated slide staining system  were grown on pre-coated poly-lysine 4-well chamber slides  and stained as previously described . The cel2 at each passage, with 2 or 3 passages per week. Prior to any treatment, the cells  were seeded in flat bottomed 96-well plates at a density of 2 × 103 cells per well with complete medium. Cells were treated the following day with 1% DMSO vehicle control or with a four-fold serial dilution of chemotherapeutic agent ranging from 24 nM to 400 μM dacarbazine, an alkylating agent , 6.1 nM to 100 μM vemurafenib, a BRAF inhibitor  and 6.1 nM to 100 μM LY294002, an inhibitor of PI3Kα/δ/β and autophagosome formation . To quantify cellular proliferation, MTS assays  were performed by adding 20 μL of MTS solution to each well and incubating at 37 °C for 1 to 2 h. The optical density of each well was acquired with a spectrophotometric plate reader  at a wavelength of 490 nm in accordance with the supplier’s instructions. Experiments were performed in 6 replicates. The results for each treatment were reported as the mean of the percent surviving cells ± SEM (standard error of the mean). IC50 values were obtained using GraphPad Prism™ software, version 6.1 .The cells used for MTS cytotoxicity assay, A375, Sk-Mel28, Ocr_OCMM2X and Ocr_OCMM1X, were seeded at 5000 cells/cm6 cells per flask 24 h prior to treatment. Either the control solution or 5 μM vemurafenib was applied for 24 and 48 h. Cells were washed twice with PBS and then harvested, and the protein extracts were prepared for Western blotting. Briefly, protein extracts were isolated by lysis of the harvested cell pellets with RIPA buffer  supplemented with a protease inhibitor cocktail . The protein concentration was determined by the Pierce™ BCA protein assay kit . Twenty micrograms (20 μg) of protein was subjected to 10% SDS-NuPAGE precast polyacrylamide gel electrophoresis and then transferred to a nitrocellulose membrane . Next, the protein was incubated with antibodies directed against MEK, phosphorylated-MEK (pMEK), AKT, phosphorylated-AKT  and HSC-70 , according to the supplier’s instructions  were seeded in 60 mm petri dishes at a final concentration of 5 × 10Total tissue and cellular DNA were isolated from each corresponding tumor sample and cell line pellet using the Qiagen DNeasy Kit according to the manufacturer’s instructions . The samples were assessed for quality and quantity by spectrophotometry using a NanoDrop spectrophotometer  and for genomic DNA integrity by agarose gel electrophoresis, showing no significant degradation.Braf, Nras, Gna11, Gnaq, Mitf and p53  analysis was carried out on the DNA previously extracted from Dog_1 and Dog_2, using pan-genomic arrays of 60-mer oligonucleotides, comprising a total of 180,000 probes . The arrays were scanned on an Agilent G2505C scanner, the images were quantified using Agilent Feature Extraction software (vA.8.5.1.1), and the acquired images were analyzed using Agilent Genomic Workbench software (V7.0.4.0). The canine genome build CanFam3.1 was used for data analysis. Copy number aberrations (CNA) were identified using aberration detection method 2 (ADM2) segmentation and the R script DNA library. A CNA was called as either a gain or deletion according to the log2 ratio distribution. Probes on chromosomes X or Y were not analyzed because of sex mismatching. Regions of heterozygous gain (amplification) were defined as having a log2 ratio > + 0.25, while regions suggestive of heterozygous loss (deletion) were defined as having a log2 ratio < − 0.25. (The threshold was fixed at 6.0). The array data presented here are publicly accessible through the Gene Expression Omnibus (GEO) Series, accession number GSE88724.P < 0.05 .Results are expressed as the mean ± SEM, and statistical analyses were performed using GraphPad Prism® and StatView® 5.0 software . Data were analyzed using ANOVA followed by Fisher’s PLSD post hoc test with a significance level of Melanoma tumor tissues were obtained from a 14-year-old Yorkshire Terrier (Dog_1) and an 11-year-old German Shepherd (Dog_2). The histopathological reports confirmed the diagnosis of malignant melanoma while CT scan of oral cavity and thorax completed the tumor staging. Both tumors were in the oral cavity but presented different WHO stage at the time of diagnosis and samples collection Table . Unfortucells) tend to cycle very slowly (75.1 ± 8.0 h), whereas the xenograft-derived cell line Ocr_OCMM2X grows slightly faster (61.6 ± 4.0 h) (Table After a minimum of 5 passages (two months), population doubling times were evaluated, and showed that the primary derived cells from Dog_2 , with 59.9 ± 6.0 h and 41.8 ± 8.0 h for the first and second passages, respectively.3 were observed in the group engrafted with the cells isolated from Dog_2’s tumor (TS2) . A rapid onset of the tumor engraftment of TS2 (from Dog_2) compared to TS1 (from Dog_1) during the first passage reflected higher stage of malignancy at the time of diagnosis and samples collection the natural aggressiveness of the tumor. Tumors arising from Dog_2 grew faster, with a median tumor volume of 1600 ± 295 mm3 in the period between 50 and 65 days  and two human cutaneous melanoma cell lines as controls . Dose-response curves and IC50 values were determined 72 h after treatment.Dacarbazine has been used as the standard chemotherapy for metastatic melanoma since 1972. The rate of response to dacarbazine ranges from 5 to 15% .The IC50 values for dacarbazine of the human cell lines were 202.4 ± 60.40 μM for A375 and 261.04 ± 31.55 μM for Sk-Mel-28, which are similar to values reported in the literature . The IC5These values show a difference of response between the two cell-lines that might be explained by the more aggressive nature of the primary tumor higher stage of malignancy of a tumor collected from Dog_2 (TS2). Indeed, the Ocr_OCMM2X cell line originates from a highly metastatic melanoma, whereas Ocr_OCMM1X cells were obtained from a less aggressive tumor 32.39 ± 2.01 μM.The IC50 values for vemurafenib of the human cell lines were 0.54 ± 0.05 μM for A375 and 10.78 ± 3.29 μM for Sk-Mel28, which are similar to the values reported in the literature . The IC5The IC50 values for LY294002 were 32.39 ± 2.01 μM and 37.74 ± 9.6 μM in canine cell lines Ocr_OCMM2X and Ocr_OCMM1X, respectively. Those values were slightly lower than the IC50 values of 61.63 ± 16.85 μM for A375 and higher than the IC50 values of 26.56 ± 0.81 μM for Sk-Mel28, which were similar to the values reported in the literature , 28 , Braf (V595), Gna11, Nras (G12 and Q61), Gnaq and p53. However, no amplification of the 11 exons of TP53 was found in Dog_1-derived tumor and Ocr_OCMM1 cells, which was unexpected. We thus suspected a deletion of this region, which was further confirmed by CGH analysis.All the samples were wild type for To order to identify the Copy Number Abbreviations (CNA) possibly involved in the tumorigenesis of these melanomas, we performed CGH analyses on the tumor DNA and derived cell lines of the 2 melanomas  gene in both tumors, and the loss of CFA26 within PTEN (phosphatase and tensin homologue) gene associated with the loss of a region of CFA11, including CDKN2A (cyclin-dependent kinase inhibitor 2A) in the Dog_2 tumor. Moreover, the Dog_2 tumor displayed a sigmoidal pattern of copy number loss followed immediately by a gain of CFA30, as already described in humans, specifically for oral melanomas.The two tumors present distinctive genome-wide CGH profiles  that can be used as tools allowing the further validation and use of the canine model in the study of melanoma.CMec-1 CMec-2 KMec and LMec) of various origins  from donors with different stages of development (T3 N1 M0 and T4 N1 M0). Of the three cases, no distant metastases were reported and only morphological characterization was performed [Only a few canine melanoma cell lines have already been reported in the literature. For instance, Inoue et al. established four canine melanoma cell lines  in both cell lines, confirming the melanocytic origin of these malignant cells.The pharmacological sensitivity of the Ocr_OCMM1X and Ocr_OCMM2X cell lines to conventional chemotherapeutic agents has been evaluated. Continuous exposure to these agents led to the emergence of resistance in the Ocr_OCMM2X cell line, which correlated with the aggressiveness of the primary tumor. Dacarbazine constitutes the most widely used molecule in the treatment of melanomas, but the response rates associated with this drug are very poor. In fact, only 15–25% of treated human melanomas show signs of improvement with this agent , 30.Braf, Nras, Gna11, Gnaq, Mitf and p53. Mutations in these genes are described in human cutaneous melanoma , mucosal melanoma (NRAS), blue nevi and uveal melanomas  and in numerous other cancers (TP53) [BRAF-mutated [BRAF V600E mutation inhibitor, and we observed inhibited cell proliferation. According to Tahiri et al., some melanoma tumors harboring wild-type BRAF could be sensitive to vemurafenib [Several oncogenes are known to be linked to melanomagenesis, such as s (TP53) . While m-mutated , this al-mutated –34. Alth-mutated , 33, 34.urafenib   cascade, the PI3K/Akt pathway constitutes an important signaling pathway in the progression, proliferation and survival of malignant melanocytes. To evaluate the extent of inhibition of the MAPK and PI3K/Akt pathways in our cell lines, Western blots were performed on native and phosphorylated MEK and AKT proteins.The pharmacological results regarding vemurafenib inhibition were confirmed by Western blot analyses and showed an inhibition of the MAPK pathway and a significant decrease in the levels of phosphorylated MEK protein, as previously reported . The resInterestingly, the PI3K/Akt pathway was shown to be highly active in the Ocr_OCMM1X and Ocr_OCMM2X canine cell lines, confirming the importance of this pathway in melanomagenesis –39. ThisINK4A and PTEN, is known to be a major element driving the development of melanoma pathogenesis. These cytogenetic signatures have been identified in dog melanomas, including in our canine cell lines, and correlate with the potential aggressiveness of each parent tumor [Poorman et al. noted a remarkable similarity between canine melanoma genetic profiles and human orthologous variations with this complex copy number signature on CFA30/HSA15 [Genetic profiling revealed different mutational landscapes of the two cell lines that were in accordance with the parent tumor profiles. Indeed, most CNAs observed in the primary tumors were detected in the related cell lines. The differences in the CNAs observed between primary tumors and the corresponding cell lines are potentially linked to the noise in the primary tumor due to the presence of wild-type non-tumorigenic cells but could also be the result of sub-clone selection during primary culture or cell line genetic drift though passages Additional file 2:List of the primers used for gene sequencing. (TIF 362 kb)Additional file 3:A, C) and Dog_2  engrafted tumors using Hematoxilin & Eosin  and Melan-A  (magnification X400). (PPTX 188530 kb)Immunohistochemical analysis of Dog_1  and Ocr_OCMM2X  cell lines. (PDF 242 kb)Additional file 5:IC75 values of the isolated human (A375 and Sk-Mel28) and canine melanoma (Ocr_OCMM1X and Ocr_OCMM2X) cell lines, 72 h after treatment with Dacarbazine, Vemurafenib and LY294002. (TIFF 564 kb)Additional file 6:CGH profiles of canine chromosomes 11, 22, 26 and 30 in Dog_1. Comparative analysis between the primitive tumor, xenograft tissue, Ocr_OCMM1X Passage 1 and Ocr_OCMM1X. The diagrams were generated using a specific algorithm with R statistical computing software. (PDF 2368 kb)Additional file 7:CGH profiles of canine chromosomes 11, 22, 26 and 30 in Dog_2. Comparative analysis between the primitive tumor, Ocr_OCMM2 primary and Ocr_OCMM2X. The diagrams were generated using a specific algorithm with R statistical computing software. (PDF 3075 kb)Additional file 8:Comparative analysis in CGH profiles of canine chromosomes 11, 22, 26 and 30 in Dog_2 vs. Dog_1 derived cells. Comparative analysis between Dog_2 primitive and xenograft derived tumors, Ocr_OCMM2 primary and Ocr_OCMM1X Passage 1. The diagrams were generated using a specific algorithm with R statistical computing software. (TIF 4433 kb)Additional file 9:Comparative analysis in CGH profiles of canine chromosomes 11, 22, 26 and 30 in Dog_2 vs. Dog_1 derived cells. Comparative analysis between Dog_2 primitive and xenograft derived tumors, Ocr_OCMM2X and Ocr_OCMM1X derived cells. The diagrams were generated using a specific algorithm with R statistical computing software. (TIF 4864 kb)


Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures.R2) respectively and 0.909, 0.912 for the mean and top quartile of the Pearson correlation (Rp) respectively, which are significantly better than those of the previous works , , . However, when the training set and the testing set are divided exclusively based on drugs or cell lines, the performance of tCNNS decreases significantly and Rp and R2 drop to barely above 0.In this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract features for cancer cell lines from the genetic feature vectors respectively. After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines. When the training set and the testing set are divided based on the interaction pairs between drugs and cell lines, tCNNS achieves 0.826, 0.831 for the mean and top quartile of the coefficient of determinant  contains supplementary material, which is available to authorized users. Historically, drug discovery was phenotypic by nature. Small organic molecules exhibiting observable phenotypic activity (e.g. whole-cell activity) were detected, a famous example being penicillin, which was serendipitously found. Phenotypic screening, an original drug screening paradigm, is now gaining new attention given the fact that in recent years the number of approved drugs discovered through phenotypic screens has exceeded those discovered through molecular target-based approaches. The latter, despite being the main drug discovery paradigm in the past 25 years, can potentially suffer from the failure in identifying and validating the therapeutic targets. In reality, most FDA approvals of first-in-class drugs actually originated from phenotypic screening before their precise mechanisms of actions or molecular targets were elaborated. A popular example of this is aspirin , for which it took nearly a century to elucidate the mechanism of its actions and molecular targets.There are some public phenotypic screening datasets online to support the study of the pharmacological functions of drugs. Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) are the most popular datasets in the field .A pioneer work using machine-learning approaches to predict drug response on cancer cell lines was by Menden et al. . The autThe first wave of applications of deep learning in pharmaceutical research has emerged in recent years. Its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples cover bioactivity prediction , de novoConvolutional neural network (CNN) is a machine learning model that can detect relevant patterns in data and support classification and regression . CNN hasInspired by the achievements of CNN in these areas, we are interested to see if CNN, compared to conventional machine-learning techniques –4, couldthe decisive step in the model development process.Erik et al.  stated tRecently, in drug discovery, researchers started to use the molecular structure of drugs directly as features –20 inste50. Wang et al.  appears in all the 223 drugs. Meanwhile, there is only one drug containing [Au] and only one drug containing [Cl]. Suppose the rows are used to represent different symbols, and the columns are used to represent positions in the SMILES format, then each drug in SMILES format can be converted into a 72∗188 one-hot matrix which only contains 0 and 1. In the one-hot matrix for a drug, a value 1 at row The structure of the proposed model tCNNS is shown in Fig. First stage: A model with two CNN branches is built to distil features for drugs and cell lines separately. A 1D CNN is used for the cell-line branch since the input data are 1D feature vectors for cell lines. Another 1D CNN is used for the drug branch and treat different symbols as different channels in the CNN. The convolution is applied along the length of the SMILES format. The structures for the two branches are the same. For each branch, there are three similar layers: each layer with convolution width 7, convolution stride 1, max pooling width 3, and pooling stride 3. The only difference between the layers is that their number of channels are 40, 80 and 60, respectively. The choices of these parameters for the CNN are inspired by the model in , in whic50 values. There are three hidden layers in the FCN, each with 1024 neurons. The dropout probability is set to be 0.5 for the FCN during the training phase [Second stage: After the two branches of the CNN, there is a fully connected network (FCN), which aims to do the regression analysis between the output of the two branches and the ICng phase .tCNNS is implemented using TensorFlow v1.4.0 , which iR2), Pearson correlation coefficient (Rp), and root mean square error (RMSE). This is the same as that in the benchmark paper [Three metrics are adopted to measure the performance of our model: the coefficient of determination R, PearsonR2 measures variance proportion of the dependent variables that is predictable from the independent variables. Let yi be the label of a sample xi, and our label prediction on xi is fi. The error of our prediction, or residual, is defined as ei=yi−fi. Let the mean of yi be R2 is defined as: Rp measures the linear correlation between two variables. Y is used as the true label and F as the corresponding prediction for any sample. Let the mean and standard deviation of Y be σY respectively, and those for the prediction F be σF respectively. Rp is defined as: Y and F, and RMSE is defined as: Rediscovering Known Drug-Cell Line Responses. In this part, the drug-cell line interaction pairs are divided into a training set, a validation set and a testing set. tCNNS is trained on the training set and the result on the test set is reported. The validation set is used to decide when to stop training.Predicting Unknown Drug-Cell Line Responses. In this part, tCNNS is trained on the known drug-cell line interaction pairs in GDSC and is used to predict the missing pairs in GDSC.Retraining Without Extrapolated Activity Data. In this part, tCNNS is trained and tested on a subset of GDSC data. The subset is called max_conc data, and it is more accurate than the rest of the data in GDSC.Blind Test For Drugs And Cell lines. In this part, drugs and cell lines, instead of the interaction pairs, are divided into the training set, the validation set and the test set.Cell Lines Features Impacts. In this part, the performance of tCNNS is tested with respect to the different sizes of the feature vectors for the cell lines.Biological Meaning v.s Statistical Meaning. In this part, the input data are transformed in various ways to check whether tCNNS can capture the biological meaning in the data.Eliminating Outliers. The 223 drugs are visualized in different feature spaces to show that the features extracted from SMILES can solve the problem of outliers in traditional feature space.In this section, the performance of our model tCNNS is demonstrated under various data input settings. The titles and the meaning of these experiments are summarized as follows: 50 for 172,114 of them. To compare to the results of previous studies [50 values in GDSC.In the 223×948  drug-cell line interaction pairs, GDSC provides the ICExperiments were set in this way to stimulate those real situations in which the models can only be trained on known interaction pairs between drugs and cell lines, and the models will be useful only if it can predict unknown interaction pairs. The validation set was separated from the training set so that it would be possible to choose a suitable time to stop training independently and avoid the problem of over-fitting.tCNNS was tested 50 times, and an example of the regression result is displayed in Fig. R2 was increased from 0.72 to 0.826 for the mean and 0.831 for the top quartile. Rp was increased from 0.85 to 0.909 for the mean and 0.912 for the top quartile, and RMSE was reduced from 0.83 to about 0.027.In the 50 repeated experiments, R2 of around 0.65 and Rp of around 0.81, which is shown in Additional file These results clearly showed that tCNNS outperformed the previous work reported in  in many R2 was further increased to 0.92 and the top quartile Rp was further increased to 0.96. The cost of this enhancement was that the network would become unstable and diverge [Many hyper-parameters affected the performance of tCNNS, such as the number of layers and the filter size. It was found that a smaller pooling size and more numbers of channels could further enhance the performance, but with a decrease in stability. For example, when the pooling size was reduced from 3 to 2, the top quartile  diverge  during tIn this part, tCNNS was trained on all the known interaction pairs in GDSC and then it was used to predict the values for those missing pairs in GDSC. The known pairs were split into 90% as the training set, and 10% as the validation set. Again, if the RMSE on the validation set did not decrease in 10 recent epochs, the training process would stop and the trained tCNNS was used to predict the values for the missing items. The results are shown in Fig. 50 values for missing items grouped by drugs. For each drug, the box represents the distribution of the values with its related cell lines. Drugs were sorted by the median of the distribution: the 20 drugs with highest median and 20 drugs with the lowest median value were plotted. As the real values for these missing pairs were not known, the accuracy of our prediction was obtained by survey and analysis as follows.Figure Bortezomib was the best drug in our prediction. In fact, the top 40 pairs with the lowest IC50 value were all from Bortezomib with some other cell lines. The outstanding performance of Bortesomib in missing pairs was consistent with that in the existing pairs. There is some supporting information in [Bortezomib can make cell lines to be sensitive to many other anti-cancer drugs.ation in  that theAica ribonucleotide and Phenformin have the poorest performance in tCNNS prediction. Based on our survey, the former one was initially invented to stop bleeding, and the later one was initially used as an anti-diabetic drug. These two drugs have the potential to cure cancer because they can inhibit the growth of cell (Aica ribonucleotide) or inhibit the growth of Complex I (Phenformin), but their effects are limited since anti-cancer is only the side effect of them, and not their main function.50 of drug Bortezomib with cell line NCI-H2342 was 1.19∗10−4μg. The small value indicated that there may be a good therapeutic effect. This prediction was supported by the findings reported in [Bortezomib is able to control Phosphorylation that causes lung cancer and NCI-H2342 is a lung cell line. Similar evidence to support this prediction can also be found in Cell Signaling Technology’s 2011 published curation set (https://www.phosphosite.org/siteAction.action?id=3131).Based on the tCNNS predictions, the ICorted in , 49, in 50 value verified by biological experiments, and maximum screening concentration (max_conc), which is the maximum IC50 value verified by biological experiments. In GDSC, any IC50 beyond these two thresholds is extrapolated, and not verified by experiments. In general, IC50 value within min_conc and max_conc are more accurate than those outside of the thresholds.For each drug in GDSC, there are two important thresholds called minimum screening concentration (min_conc), which is the minimum IC50 values below max_conc, which is about 37% of the whole existing 172,114 IC50 values.In the GDSC data that we used in this paper, only max_conc is provided, and there are 64,440 IC50 values below the max_conc threshold, which were randomly divided into 10% data for validating, 10% data for testing. The remaining 80% data is used for training and the size is reduced to 1% while the experiment was repeated 20 times. The regression result is shown in the Additional file In this part, tCNNS was trained on the IC50 values, R2 can be almost 0.5 and Rp be around 0.7. Secondly, and more importantly, tCNNS performed better with less and more accurate data. The dash lines (results on data below max _conc) were always above the solid lines , and the final performance on max _conc data was almost as good as that on the total data, although the amount of data for the former was only 37% of the latter. To further compare the best performance on all data and max _conc data only, the distribution of the 20 times experiments are shown in Additional file From Additional file There are three experimental results shown in Additional file blind test was designed.In previous experiments, interaction pairs between drugs and cell lines were randomly selected to be in the training set, the validation set, or the testing set, which meant that a specific drug or a specific cell line can exist in training and testing at the same time. This experimental setting corresponds to the problem of predicting the effect of a certain drug on a new cell line when its effect on another cell line is given. The problem becomes more challenging if the tested drug is a brand new one, and its effect on any cell lines is not known. To evaluate the performance of tCNNS on this challenging problem, a new experimental setting called 50 values were kept for testing. For the remaining 90% drugs, 90% of their related IC50 values were randomly selected for training and 10% for validating.In the blind test for drugs, drugs were constrained from existing in training and testing at the same time. The interaction pairs were divided based on drugs. 10% (23/223) drugs were randomly selected and their related IC50 values were kept for testing. For the remaining 90% (904/948) cells, 90% of the related IC50 were used for training and 10% for validating.In the blind test for cell lines, cell lines were prevented from existing in the training set and the testing set at the same time. The interaction pairs were divided based on cell lines. Similar to the case for drugs, 10% (94/948) cell lines were randomly selected and their related ICThe blind test for drugs on all data and on the data below max _conc were repeated for 150 times respectively to check the distribution of the results. The same number of experiments for the cell lines were also conducted. The results on all data are shown in Fig. From Fig. Comparing the results of the blind tests for drugs and for cell lines, the blind test for cell lines is slightly better, and the reason is that there is more common information shared among different cell lines and less among drugs. For example, cell lines share similar genetic information, but drugs can be very diversified. To reduce the information sharing among cells lines, another experimental setting was designed in which cell lines from the same tissue cannot exist in training and testing at the same time. The result was shown in Table R2 and Rp in all tissues, which indicated that blood is the most different tissue from other tissues.In GDSC, the 948 cell lines belong to 13 tissue types and 49 sub-tissue types. The 13 tissue types were used instead of 49 sub-tissue types because it can increase the distances and reduce the similarities among different tissues. Each time one tissue type was selected as testing data. For the rest of the tissues, they were mixed together and split into 90% for training and 10% for validation. From Table In GDSC, the 735 features for cell lines after preprocessing belongs to 310 gene mutation states, and 425 copy number variations. As different laboratories may use different methods to extract the features for cell lines, in reality, it is not easy to have the complete 735 features for all cells. Besides, researchers may also have smaller and different feature groups for cell lines. It is attractive if tCNNS can have good performance with fewer features for cell lines. In this part, tCNNS performance was tested with different smaller numbers of features for cell lines to check the change of the performance with respect to the change of numbers of features for cell lines. The corresponding results in this part are shown in Fig. The order of the symbols was randomly shuffled, which equals shuffling the rows in the one-hot representation.The SMILES format was cut into two pieces, and the positions of which were switched. It is equivalent to shift the columns in the one-hot representation.The positions in the SMILES format were shuffled, which equals to shuffling the columns in the one-hot representation.tCNNS takes the one-hot representation of the SMILES format as the features for drugs. Initially, in the one-hot representation of the SMILES format, each row represents a symbol, and each column represents a position in the SMILES format, which is left aligned. For researchers, the SMILES format is a well-defined concept with biological meaning. However, tCNNS may lack the ability to comprehend the biological meaning of the SMILES format and it instead relies on the statistical pattern inside the data. To verify this hypothesis, the one-hot representation of the SMILES format was modified in three ways as follows: 50 values.The experiments were repeated 10 times in the three settings respectively and the results were compared with those obtained by using the SMILES format without any modification. The comparison is shown in Additional file In the last column of the Additional file To check the differences between the features extracted using PaDEL and the features extracted from the SMILES descriptions using CNN, the distribution of the drugs were visualized in different feature spaces. In a deep neural network, the fully connected layer is responsible for regression analysis, and CNN is used for extracting high-level features from the drug features. The input data for the fully connected network is the output of CNN tranche. Hence when drawing the distribution of drugs using CNN, the output of the last layer of CNN tranche was used for drugs.The distribution of cell lines in genetic features space of GDSC was also compared with that found in the output space of the last layer in CNN. The visualization tool used was t-SNE , which wR2, Rp respectively, and 0.831, 0.912 for top quartile of R2, Rp respectively.Rediscovering Known Drug-Cell Line Responses. In this part, tCNNS was trained on 80% data as the training set and tested on the other 10% data. The remaining 10% data were used as a validation set to decide when to stop training. The experiment was repeated 50 times and tCNNS achieves 0.826, 0.909 for mean Predicting Unknown Drug-Cell Line Responses. tCNNS was used to predict the missing interaction pairs in GDSC. A literature survey was carried out and some published works that support the predictions of tCNNS were found and discussed.50 values below max_conc are divided as 80% for training, 10% for validation and 10% for testing. Figure Retraining Without Extrapolated Activity Data. tCNNS was trained on max_conc data. Those ICRp drops to 0.2 and the mean of R2 drops to barely above 0.Blind Test For Drugs And Cell lines. Drugs and cell lines were restricted from existing in the training set and the testing set at the same time. In the blind test, the performance of tCNNS drops significantly, especially in the drug blind test where the mean of R2 remains above 0.80 when the number of features drops to 300, and it is still above 0.72 even when the number of features drops to 50.Cell Lines Features Impacts. The number of features for cell lines was reduced from about 700 to about 50. The mean of Biological Meaning v.s Statistical Meaning. As the results remain almost the same with different modifications to the input data, it can be concluded that tCNNS relies on the statistical pattern, instead of capturing the biological meaning in the data.Eliminating Outliers. 7 outliers exist in the traditional drug feature space. However, this problem does not exist in the feature space extracted from SMILES by tCNNS.To conclude, there are seven subsections in this section, and they are summarized as follows: 50 values are small but the predictions of tCNNS are incorrectly large. In the top right corner, the input IC50 values are large but the predictions of tCNNS are incorrectly small. This means that tCNNS can be further optimized if it can enhance its performance in these two areas.In Fig. 50 values and the SMILES format of drugs are stronger than those observed between the IC50 values and the features extracted using PaDEL.Based on these results, it is concluded that the connections between the ICR2 drops to 0.33 and Rp drops to 0.6. When tCNNS is trained on all data, it learns general knowledge which is useful on the whole dataset. That is why its performance remains stable when tested on data below max_conc. On the other hand, when tCNNS is trained on data below max_conc, it learns knowledge that only be applicable to this specific subset of all data. Its performance is dragged down by data above the max_conc when tested on all data. Although the performance of tCNNS on all data and data below max_conc is similar, the paths they achieve the performance are different.Although the extrapolated data cannot enhance the accuracy of the model, they can help to improve scalability. The tCNNS model trained on all data performs well when tested on data below max_conc, which is natural because the later is a subset of the former. However, the model trained on data below max_conc performs poorly when tested on all data. R2 were almost the same, and the mean of Rp on all data was only a bit better than that on the data below mac _conc. Moreover, the variations of R2 and Rp on data below max _conc was a bit bigger than those on all data. To conclude, the contribution from low quality extrapolated data was limited, and they can only reduce variation and improve Rp a bit.Comparing the result on data below max _conc with that on all data, it can be seen that the means of in silico predicting power.The results in the blind test give us some hint that with limited budgets, the in vivo experiment should be carefully arranged to cover a wider range of drugs and cells from different tissues to get better 50 value, the performance will be significantly improved. For example, NCI-H378 is a special cell line for lung cancer in GDSC, and there are only two IC50 values records for it. For other cell lines, all of them have at least 20 IC50 values. tCNNS can still make accurate predictions one of the IC50 values for NCI-H378 if the other value is used during the training. Based on the results, the best drug for NCI-H378 is Bortezomib, which has been previously recalled [It is very important to have the specific drug or cell line in the training stage before the performance is predicted. Experimental results support that even with only one or two related ICrecalled , 49.Docetaxel for them. The predicted IC50 value between Docetaxel and NCI-H378 is 0.03μg , and the predicted IC50 between Docetaxel and NCI-H250 is 0.04μg . It is reported in [APR-246 is a potential useful drug on lung cancer because of its synergy with TP53 mutations in lung cells, and there is an "additive effects" between APR-246 and Docetaxel.Moreover, tCNNS predicts another potential drug orted in  that APRThe visualization result of outliers highlights that CNN can distribute the drugs and cell lines more uniformly than features using PaDEL and features of GDSC. For drugs, those seven outliers in the PaDel space are exactly the seven drugs that are composed of multiple parts. The structures of the outliers are shown in the Additional file In this paper, a model called tCNNS has been illustrated for phenotypic screening between cancer cell lines and anti-cancer drugs. tCNNS has been tested on a new version of GDSC with more data compared to previous works. It has achieved a much better coefficient of determinant and Pearson correlation than previous works and has made predictions for missing values in GDSC with trustful evidence. tCNNS can also converge with a very small set of training data and fewer features for cancer cell lines, which is economically efficient. tCNNS can take SMILES as input data for drugs, and this can solve the outlier problem occured in previous works where drug fingerprints are used as features.Additional file 1Supplementary. Some experiments results and figures are in the supplementary file of this paper. (PDF 378 kb)


Human cancer cell lines are fundamental models for cancer research and therapeutic strategy development. However, there is no characterization of circular RNAs (circRNAs) in a large number of cancer cell lines.Here, we apply four circRNA identification algorithms to heuristically characterize the expression landscape of circRNAs across ~ 1000 human cancer cell lines from CCLE polyA-enriched RNA-seq data. By using integrative analysis and experimental approaches, we explore the expression landscape, biogenesis, functional consequences, and drug response of circRNAs across different cancer lineages.https://hanlab.uth.edu/cRic), to benefit the biomedical research community.We revealed highly lineage-specific expression patterns of circRNAs, suggesting that circRNAs may be powerful diagnostic and/or prognostic markers in cancer treatment. We also identified key genes involved in circRNA biogenesis and confirmed that TGF-β signaling may promote biogenesis of circRNAs. Strikingly, we showed that clinically actionable genes are more likely to generate circRNAs, potentially due to the enrichment of RNA-binding protein (RBP) binding sites. Among these, circMYC can promote cell proliferation. We observed strong association between the expression of circRNAs and the response to drugs, especially those targeting chromatin histone acetylation. Finally, we developed a user-friendly data portal, CircRNAs in cancer cell lines  contains supplementary material, which is available to authorized users. RNAs were isolated from the MDA-MB-231 that were transfected with either circMYC or control plasmids. Two micrograms of RNAs was incubated with RNase R (VWR) for 3 h to degrade linear RNAs, followed by TRIzol™-chloroform-based RNA purification. cDNAs were generated from the RNAs and were used for PCR reactions. No reverse transcriptase (No RT) was used as a negative control and the circMYC was used as a positive control to localize the appearance of circMYC. The treatment with RNase R and without circMYC overexpression was send out for Sanger sequencing with the primer as 5′-CATCAGCACAACTACGCAGC-3′.cDNA of MYC was amplified via PCR from the human cDNA using the following primers: F 5′-GGTCAGAGTCTGGATCACCTTC-3′ and R 5′-ACTGTCCAACTTGACCCTCTTG-3′. After the PCR amplification and purification, the cDNA part that undergoes circularization was cloned into the pcDNA3.1 circRNA mini vector which induces circularization of mRNA via the Gibson cloning . MDA-MB-The MDA-MB-231 cells were plated onto TTP culture dishes. The cells were transfected with control siRNA or siRNA specific targets the junction of circular MYC for 8 h (INTERFERin® from PolyPlus-transfection®). The siRNA sequence is 5′ ACAGUGUCAGAGUCUGGAUCACCTT 3′+ 5′ AAGGUGAUCCAGACUCUGACACUGUCC 3′−. One day after the siRNA transfection, the cells were further transfected with control or circMYC plasmids for 6 h (JetPRIME™ from PolyPlus-transfection®).g for 5 min followed by the removal of precipitates. The supernatant was subjected to BCA protein assay to determine the protein concentration. For western blot analysis, 30 μg of protein was used.After 1 day with overexpression of circMYC, the RIPA buffer with protease inhibitor (Thermo Scientific) from Boston BioProducts was used to harvest protein lysates from the transfected MBA-MD231 cells. The cell lysate was centrifuged at 13,000The transfected cells were harvested, and the RNAs were purified at day 4 via the TRIzol™-chloroform extraction. For one well in a six-well plate, 500 μL of TRIzol™ was added into the well to lyse cells and stabilize RNAs, followed by addition of chloroform. The upper layer of the phenol-chloroform was used for column-based RNA purification (RNeasy from Qiagen). One microgram of total RNA was used for cDNA synthesis by iScript™ cDNA synthesis kit from Bio-Rad, which contains both oligo dT and random hexamers. The levels of circMYC were detected by the following divergent primers which detect only circularized MYC transcript using RT-qPCR: F 5′-CATCAGCACAACTACGCAGC-3′ and R 5′-TCCAGCAGAAGGTGATCCAG-3′. 18s rRNA was used as internal control: F 5′-GTAACCCGTTGAACCCCATT-3′ and R 5′-CCATCCAATCGGTAGTAGCG-3′. For the cell proliferation assay, WST-1 from Sigma Aldrich was used from days 1, 2, 3, and 4.We analyzed matched cell lines with both circRNA profiling and drug response data. For each individual circRNA, we first classified the cell lines into two groups based on the expression pattern of the circRNAs, based on if it exceeded the median value of backsplicing reads of all cell lines. We applied the Wilcoxon rank-sum test to identify individual drugs associated with the circRNA expression profile and quantify the difference in the mean AUC between the two groups. To correct the outcome of multiple tests, we used the Benjamini-Hochberg corrections. We defined a significant association between drug sensitivity and the circRNA profile with FDR < 0.05.To systematically investigate the global circRNA landscape in cancer cell lines, we combined four well-established, user-friendly computational algorithms , 32, CIRWe further examined the expression pattern of circRNAs across cancer cell lineages. We identified 1108 circRNAs (1.20%) ubiquitously expressed in ≥ 15 cancer lineages. For example, circDNMT1 derived from DNMT1, which is a DNA methyltransferase with essential roles in mammalian development , was expRs = 0.08 and p = 0.70; Additional file To examine whether lineage-specific circRNAs are introduced by their linear mRNA genes, we obtained lineage-specific mRNA genes as previously described . We obsecis-regulatory elements and trans-acting proteins [n = 51) [n = 22) [n = 71) [n = 104) [Rs| > 0.3, FDR < 0.05), suggesting their significant roles in the biogenesis of circRNAs  , 3′ end [n = 22) , RNA hel[n = 71) , and a sn = 104) . Nine gen = 23,714) available in the gene expression matrix of CCLE are used as background for GSEA. We identified 16 significant related biological processes that may contribute to the biogenesis of circRNAs, such as EMT, which is consistent with a previous report [Rs = 0.37, p < 2.2 × 10− 16; Additional file p = 5.5 × 10− 16; Additional file t test, p = 0.022; Fig. t test, p = 0.048; Fig. To further explore other potential biological processes involved in circRNA biogenesis, we performed gene set enrichment analysis (GSEA) using 50 “hallmark” gene sets from the Molecular Signature Database that represent major biological processes . All pros report  , intron length (p < 1 × 10− 4), number of exons (p < 1 × 10− 4), or gene expression level , exon length (p < 1 × 10− 4), or number of exons  and protein level (Student’s t test p = 0.005) are significantly higher in the group with circMYC expression , which is derived from one of the most important cancer genes. Interestingly, circMYC is significantly more abundant than its paralogous genes  with 265 drugs for 644 human cancer cell lines , and Cann > 30) and examined the pharmaceutical targets of these drugs. Interestingly, we found that the top drug category is drugs that target chromatin histone acetylation. For example, 6 of 11 HDAC inhibitors are associated with circRNAs . For example, cellular sensitivity to the drug Vorinostat, a HDAC inhibitor, is positively associated with 48 circRNAs and negatively associated with 25 circRNAs . In this portal, we provide circRNA expression data and the association between circRNA expression and multi-omic data, including mRNA expression, proteomic, mutation, and drug sensitivity data. CircRiC enables users to examine the features of circRNAs in cancer cell lines in a flexible and interactive way. CircRiC provides four interactive modules: expression landscape, biogenesis, drug response, and integrative analysis  and protein levels   . Furthermore, the recent release of next-generation CCLE facilitates the development of novel therapeutic strategies . With thn = 28) [To facilitate circRNA research in the community, we constructed an interactive and user-friendly data portal, CircRiC, which provides comprehensive analysis of circRNAs with multidimensional data across ~ 1000 cancer cell lines. Our data resource is a complement to MiOncoCirc, which covered circRNA expression from more than 2000 tumor samples and a few cancer cell lines (n = 28) . We expeIn conclusion, we systematically characterized circRNAs in ~ 1000 cancer cell lines and demonstrated the lineage specificity of circRNAs across multiple human cancer lineages. We identified potential factors involved in circRNA biogenesis and confirmed that TGF-β signaling promotes circRNA formation. We observed that clinically actionable genes are likely to generate more circRNAs, potentially due to the enrichment of RBP binding sites. We further characterized the functions of circMYC in promoting cell proliferation and revealed the potential link between circRNA expression and pharmaceutical profiling. Finally, we developed a user-friendly data portal CircRic that integrates our data to benefit the research community.Additional file 1:Figure S1. Expression landscape of circRNAs across cancer cell lines. Figure S2. Potential regulation of EMT on biogenesis of circRNAs. Figure S3. Enrichment of circRNAs in clinically actionable genes. Figure S4. Characterization of functional effects and drug response of circMYC in cancer. Figure S5. Therapeutic liability of circRNAs in CTRP. Figure S6. Examples of modules in CircRiC. Table S1. Summary of circRNAs identified across different cancer cell lines. (PDF 7931 kb)