Melanoma cells release extracellular vesicles (EVs) subpopulations which differ in size, phenotype and molecular content. Melanoma derived EVs play a role in the development and progression of cancer by delivering surface receptors and bioactive  or signaling molecules to target cells.We applied Fourier Transform Infrared Spectroscopy (FTIR) to compare infrared spectra of absorption for different subpopulations of EVs originating from normal human melanocytes, primary cutaneous melanoma (WM115) and metastatic cutaneous melanoma (WM266-4).FTIR results showed that exosome and ectosome populations differ in content of protein and lipid components. We obtained higher lipid to protein ratio for ectosomes in comparison with exosomes what confirms that exosomes are very densely packed with protein cargo. We identified the lowest value of saturated fatty acids/unsaturated fatty acids parameter in the metastatic WM266-4 cell line and ectosomes derived from WM266-4 cell line in comparison with normal melanocytes and the primary WM115 cell line. We identified the alterations in the content of secondary structures of proteins present in EV subpopulations originating from melanocytes and melanoma cells in different malignancy.Obtained results revealed differences in the molecular composition of melanoma derived EVs subtypes, including protein secondary structure, and showed progressive structural changes during cancer development.  •Fourier-Transformed Infrared spectroscopy allows recognition lipid and protein content in extracellular vesicles (EVs).•Subpopulations of (EVs) from human melanocytes and melanoma cells contain distinct lipid composition and protein structure.•Ectosomes from malignant human melanoma are rich in saturated fatty acids and random coiled proteins.•Exosomes from malignant human melanoma are bigger in compare to those from melanocytes and have higher lipid to amid ratio. The bigMelanoma cells release a variety of extracellular vesicle (EV) subtypes including smaller exosomes (<150 nm) and larger ectosomes >100 nm) diverse phenotype and molecular content changing within melanoma treatment 0 nm dive,12. EVs Previous reports led us to set up a goal to find more comprehensive methodology for recognition differences in melanoma EVs molecular content. We aimed to conform Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy to obtain infrared spectra of absorption for different subpopulations of EVs. The main objective of this study was to find differences in the ATR-FTIR spectral signatures for exosomes and ectosomes which were representative for the human melanoma cell lines showing different levels of metastasis  in comparison with normal human melanocytes.22.12 cell culture flask in M254 medium  supplemented with Human Melanocyte Growth Supplement-2  and antibiotics: 100 U/mL penicillin and 100 μg/mL streptomycin .Three different cell lines: human epidermal melanocytes (HEMa-LP), primary cutaneous melanoma (WM115) and metastatic cutaneous melanoma (WM266-4) were used for the study. HEMa-LP  were cultured on T75cmTwo melanoma cell lines were obtained from the ESTDAB Melanoma Cell Bank . WM115  and WM266-4  originated from the same individual and represented radial/vertical growth phase and lymph node metastasis, respectively [2 atmosphere at 37 °C in a humidified incubator and passaged after reaching 80% confluence.Cells were grown in monolayers in 5% CO2.2g  and 4500×g  to remove remaining cells and cellular debris. Then, supernatants were used for hydrostatic filtration dialysis (HFD) method assisted by a low vacuum pump (0.2 mBar). For EVs concentration, dialysis membrane with 1000 kDa molecular weight cut-off (MWCO) was applied [g for 20 min at 4 °C to obtain ectosomes pellet. Then, the supernatant was collected and ultracentrifuged  using S140-AT fixed angle rotor  at 75 000×g for 30 min at 4 °C and followed by centrifugation at 150 000×g for 1.5 h to get exosomes  method using NanoSight LM10 Malvern system  equipped with a sCMOS camera and a 405 nm laser. Before measurement, the exosomes and ectosomes samples were diluted in a filtered (0.05 μm) deionized water. The assay was performed at room temperature (22 °C) and 5 videos for 30s were recorded for EV samples. Data were captured and analyzed using NTA 3.1 Build 3.1.45 software with a detect threshold of 5 and camera level of 11.2.4For preparation of Transmission Electron Microscopy (TEM) images, exosomes and ectosomes pellet from each cell line was fixed with 2.5% GA  in 0.1 M cacodylic buffer  for 2 h at RT. Then, samples were postfixed in 1% osmium tetroxide solution (1 h) and dehydrated by passing through graded ethanol series and embedded in PolyBed 812 at 68 °C. Ultrathin sections were collected on 300 mesh grids or one slot made from copper, additionally the latter was covered with formvar film. For cutting the Leica EM UC7 microtome was used. Then, sections were contrasted using uranyl acetate and lead citrate. For observation, the JEOL JEM 2100 H T electron microscope  was used at accelerating voltage 80 kV.2.5−1. Collected spectra were postprocessed by smoothing , background substracting and normalization using SpectraGryph v. 1.2.13 software [ATR-FTIR spectra were collected using a Nicolet 6700  spectrometer equipped with diamond ATR accessory. 5 μl of sample  was mounted on the ATR crystal and left to dry. The measurements were performed at room temperature and 256 scans were co-added at a nominal resolution of 4 cmsoftware . All speBased on the COV value, we estimated the precision of spectral parameters , Fig. 7.2.6−1) which corresponds to C PBM data was replaced with SVG by xgml2pxml:<glyph-data id="pc-E00C" format="PBM" resolution="300" x-size="12" y-size="9">000000000000000000000000000000000000111111111111000000000000111111111111000000000000000000000000000000000000</glyph-data><svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="20.666667pt" height="16.000000pt" viewBox="0 0 20.666667 16.000000" preserveAspectRatio="xMidYMid meet"><metadata>Created by potrace 1.16, written by Peter Selinger 2001-2019</metadata><g transform="translate scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z M0 280 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z"/></g></svg>O (70–85%) and C–N (10–20%) vibrations, is the most sensitive spectral region to protein secondary structure compositions [The secondary structure of proteins present in EVs was determined by decomposing the second derivative of the Amid I band using Peak Deconvolution App available in OriginPro 2019b c. Amid Iositions . All sec33.1Size distribution of EV subpopulations corresponds to the hydrodynamic diameters of the EVs in suspension. Both mean and mode of the hydrodynamic diameter were lower for exosomes in comparison with ectosomes . The exo3.2TEM visualization was performed for screening the morphology and size of EV subpopulations originating from studied cell lines. The representative TEM images of EV derived from melanocytes and two melanoma cell lines: WM115 and WM266-4 are shown in 3.3−1, originating mainly from CO vibrations of the protein peptide backbone) and Amid II  bands [To characterize molecular composition of human melanocytes, two melanoma cell lines, and ectosome and exosome subpopulations isolated from these cell lines, the ATR-FTIR spectra were gathered and analyzed. ATR-FTIR spectrum in the mid-infrared range consists primarily of lipid and amid components a. In thes) bands . The aves) bands .Fig. 4Th−1) was much higher for ectosomes in comparison with exosomes, which means that membrane components are very representative for ectosomes. Ectosomes are formed from the cell membrane by blebbing and they are enriched in phosphatidylserine (PS), sphingomyelin (SM), cholesterol or diacylglycerol, which are present in both inner and outer leaflet of cell membrane [The lipid composition of EVs is typical as for the cell plasma membrane but the appearance of their spectra differs from the typical distribution to some extend because they are affected by EV biogenesis process. In our study, the relative absorbance of lipid bands , which means that exosomes are very densely packed with protein cargo. Our observations are in line with results obtained by Mihaly et al. who showed the higher protein to lipid ratio for exosomes compared to ectosomes isolated from Jurkat cell line [−1), Amid B (3078 cm−1) and DNA (970 cm−1) [For the measured exosome samples, we found a higher absorbance for characteristic bands of proteins  ,24,25.3.4−1), headgroup (1300-1000 cm−1) and interfacial region  can be specified in the lipid component present in ATR-FTIR spectrum [−1) and unsaturated (3006 cm−1) fatty acids what has been shown in −1, SFA2: 2840-2861 cm−1, UFA: 2977-3017 cm−1, L: 2950-2966 cm−1  correlated with cancer stage and malignancy [It has been previously reported that lipid composition of cancer cells varies from normal cells due to different lipid metabolism and synthesis under stressing conditions . Such dilignancy . Howeverlignancy .In our study we observed that the SFA/UFA ratio is lower in ectosomes derived from the metastatic WM266-4 cell line in comparison with normal melanocytes and the primary WM115 cell line a. IntereNot only lipid composition is different in ectosomes but also the Lipid/Protein ratio is much higher as compared with representative exosomes and cells c.de novo FAs synthesis [−1) in WM266-4 ectosomes in comparison with those derived from WM115 or normal melanocytes (Phosphatidylcholines (PCs) are the major components of membrane lipids and their contribute to proliferative growth in cancer cells since the synthesis was increased in response to ynthesis . Our datanocytes , Fig. 5aIn addition to changes in the degree of saturation of lipid chains, the changes in the length of acyl chains are also observed. Naguib et al. showed that length of acyl chains is reduced in PIs as a result of mutation of p53 protein which is common in cancers including melanoma ,37. We oProtein-to-lipid ratio parameter that enable discrimination of different EV subpopulations was for the first time proposed by Osteikoetxea et al.  It has b3.5In the present study an increase abundance in intermolecular β-sheet structure was observed in melanocyte-derived ectosomes and exosomes compared to EVs originating from melanoma cell lines, while the percentage content of β-sheet structure was almost the same in cells from studied cell lines b. We alsThe most significant differences in the present study concerned the content of random coil structure between melanocytes and WM266-4 cells c. The hiThis higher abundance of intrinsically disordered proteins (IDPs) is strongly associated with cancer. IDPs range from fully unstructured to partially structured and include random coils. They are significantly depleted in order-promoting hydrophobic  and aromatic  amino acid residues, and possess a low content of Cys and Asn residues. On the other hand, IDPs are enriched in disorder-promoting polar  and structure-breaking (Gly and Pro) amino acids . In a stRegarding melanoma, typical mutations are observed in genes coding proteins such as B-Raf serine/threonine kinase (BRAF), cyclin-dependent kinase 4 (CDK4), melanocyte inducing transcription factor (MITF), phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase (PTEN), cyclin-dependent kinase inhibitor 2A (CDKN2A), ARF tumor suppressor (p14ARF), and p53. These proteins are present in a few functional proteoforms with disordered structures, adding another route to the disease's etiology . In addiFinally, following changes in protein secondary structure with ATR-FTIR may also be useful to classify blood sera from breast cancer and healthy donors. However, the protein composition  cannot contribute to distinguish serum samples from these groups of patients, because the contribution of protein structure in a total serum protein mixture is too small . Using E4The main goal of the present study was to find differences in the ATR-FTIR spectral signatures for EV subtypes (exosomes and ectosomes) derived from melanoma cell lines showing dissimilar levels of metastasis (WM115 and WM266-4) compared to normal human melanocytes (HEMa-LP).Based on the registered ATR-FTIR spectra for cell lines and EV subtypes derived from parental cells, we calculated parameters that enable differentiation of studied samples such as: Lipid (All)/(Amid I + Amid II), ACL and SFA/UFA. Our experiment confirmed previous observations that exosome and ectosome populations differ in content of protein and lipid components. We obtained higher lipid to protein ratio for ectosomes in comparison with exosomes what confirms that exosomes are very densely packed with protein cargo.Analysis of the lipid component in the ATR-FTIR spectra allowed to find differences in the content of saturated and unsaturated fatty acids (SFA/UFA ratio) in studied samples. We identified the lowest value of SFA/UFA parameter in the metastatic WM266-4 cell line and ectosomes derived from this cell line in comparison with normal melanocytes and the primary WM115 cell line. These results suggest that lipid composition of WM266-4 derived ectosomes can be potential prognostic biomarker for metastasis. Interestingly, we also found reduced value of ACL parameter in WM115 and WM266-4 cell lines compared to melanocytes. The same relationship was observed in ectosomes from WM266-4 cell line. The findings of this study suggest that exosomes are promising source of protein biomarkers for melanoma metastasis, while ectosomes are potential source of lipid biomarkers.One of the more significant findings to emerge from this study is that FTIR analysis allows to identify the alterations in the content of secondary structures of proteins present in EV subpopulations originating from melanocytes and melanoma cells in different malignancy. We found differences in the percentage content of random coil, β-sheet, inter β-sheet and α-helix between studied samples. The most significant differences in the present study concerned the content of random coil structure between melanocytes and WM266-4 cells. The highest percentage of random coil structure was found in WM266-4 cells, in ectosomes from WM266-4 and exosomes from WM115 cell line.The results of this study indicate that the molecular composition of EVs subtypes, including protein secondary structure is changing during melanoma development. These molecular changes can be quickly identified by FTIR analysis and then used as a marker for metastasis development.Jagiellonian University (N17/MNS/000002 106) and from the Polish National Science Centre (2018/31/N/NZ4/03787).This work was supported by grants from the The authors declare that they have no conflicts of interest.


Ixodes spp. cell lines at different passages, their karyotypes differed from 2n = 28 chromosomes for parental Ixodes spp. ticks, and both increase and decrease in chromosome numbers were observed. For example, the highly passaged Ixodes scapularis cell line ISE18 and Ixodes ricinus cell lines IRE/CTVM19 and IRE/CTVM20 had modal chromosome numbers 48, 23 and 48, respectively. Also, the Ornithodoros moubata cell line OME/CTVM22 had the modal chromosome number 33 instead of 2n = 20 chromosomes for Ornithodoros spp. ticks. All studied tick cell lines had a larger genome size in comparison to the genomes of the parental ticks. Thus, highly passaged tick cell lines can be used for research purposes, but possible differences in encoded genetic information and downstream cellular processes, between different cell populations, should be taken into account.Tick cell lines are an easy-to-handle system for the study of viral and bacterial infections and other aspects of tick cellular processes. Tick cell cultures are often continuously cultivated, as freezing can affect their viability. However, the long-term cultivation of tick cells can influence their genome stability. In the present study, we investigated karyotype and genome size of tick cell lines. Though 16S rDNA sequencing showed the similarity between  In the last decade, tick cell lines have been employed increasingly as an easy-to-handle system to study viral and bacterial infections and their influence on tick cell viability and gene expression9, tick biochemistry11 and other aspects of tick cellular processes13. Moreover, successful genetic manipulations in tick cell lines17 have opened up the possibility of expressing recombinant proteins in tick cells in vitro.Ticks are ectoparasites that feed on the blood of mammals, birds, reptiles and amphibians. They carry various pathogenic viruses, bacteria and protists, and thus transmit diseases such as tick-borne encephalitis, Lyme disease, anaplasmosis and babesiosis5. These include seven cell lines derived from embryos of the hard tick Ixodes scapularis19; four from embryos of the hard tick I. ricinus21 and six from embryos and neonate larvae of the soft tick Ornithodoros moubata22. The process of cell line establishment is known to result in changes to the karyotype of cultured tick cells20. Diploid chromosome numbers of Ixodes and Ornithodoros ticks were determined previously: 28 chromosomes with an XX /XY  sex determination system were reported for I. scapularis25 and I. ricinus26, and 20 chromosomes for O. moubata27.Many cell lines have been derived from different tick species to date28. Instead, they may be cultured continuously, and some cell lines can be held for several weeks or months at temperatures between 4 and 15 °C30. Moreover, most argasid tick cell lines are difficult or impossible to cryopreserve successfully27. In our study, we aimed to analyze the genome stability of different tick cell lines during long-term cultivation. Therefore, we performed an analysis of karyotype and genome size of I. scapularis, I. ricinus and O. moubata cell lines and compared these data with the known genome sizes of the corresponding ticks. We noted that long-term continuous passaging of tick cells could increase the probability of genomic changes.Cryopreservation of tick cell lines during short periods of inactivity is not recommended due to unpredictable viability and a possible lengthy recovery period following resuscitationI. ricinus cell lines carried out 10 years previously. For cell line OME/CTVM22, no earlier passages are available because these cells cannot be cryopreserved22. We found that the chromosome numbers differed between passage levels of the same tick cell line  contained the expected diploid number of chromosomes, 28, but numbers ranged from 12 to 98. At passage 442, the majority of the cell population contained between 48 and 52 chromosomes, with a predominance of cells that had 50 chromosomes (22%)  with a range of 13–92 chromosomes per cell. The modal number at passage 436 was still 23 (41%), and 20% of the cell population contained 22 chromosomes , and two other I. scapularis cell lines showed similar profiles during the first 2–3 years in culture25. Meyer and co-workers analyzed chromosome spreads of the ISE18 cell line at passage 31 and found that they typically contained 26–30 chromosomes25 which is in agreement with observations made by Chen and co-authors24. However, karyotyping of a culture of the I. scapularis cell line IDE8 at passage ~ 80, in which 15% of cells were infected with the intracellular bacterium Ehrlichia ruminantium, revealed a range of 14–100 chromosomes per cell, with peaks at 24 and 26 (14% of cells each)32.In the cell line ISE18 at passage 133, almost half of the cell population (49%) had 48 chromosomes, but metaphase spreads with 21–109 chromosomes were also observed  and both lines contained aneuploid cells.The karyotype of cell line OME/CTVM22 was quite similar at passages 151 and 180, being aneuploid with a modal chromosome number of 33 at both passages (15% and 10% of cells respectively). However, the overall range of chromosome numbers in metaphase spreads was highly variable, ranging from 20, matching the diploid chromosome number of Rhipicephalus appendiculatus34 and Rhipicephalus (Boophilus) microplus35, as well as other arthropods such as the beetle Heteronychus arator36, the honey bee Apis mellifera37, the fruit fly Drosophila melanogaster41 and the moth Spodoptera frugiperda42. Therefore, we can conclude that karyotype changes in our studied tick cell cultures are not exceptional and could be the result of the long-term in vitro cultivation. Moreover, it is important to consider that even if individual cells display the expected modal diploid chromosome number of the parent tick, they do not necessarily contain the parental chromosome complement. The ability of tick cells to apparently gain and lose chromosomes, without affecting their in vitro viability, suggests that individual cells could undergo an initial reduction in chromosome number, followed by duplication of one or more of the remaining chromosomes, thereby returning the total to the original diploid number, but not the original complement.Karyotype changes have been reported in cell lines of other tick species, Ixodes ticks. For this purpose, we performed amplicon analysis of the 16S rDNA sequences derived from the cell lines maintained at the University of South Bohemia for 8–12 years and compared them with the publicly available sequences of I. ricinus and I. scapularis from the NCBI database. We included additional DNA samples from the same cell lines maintained independently and continuously for the same amount of time in the Tick Cell Biobank: IRE/CTVM19 at passage 2  and passage 229, and ISE18 at passage 55. The resultant alignment and phylogenetic reconstruction showed that 16S rRNA sequences from IRE/CTVM19 and IRE/CTVM20 cell lines clustered with those from I. ricinus, and 16S rRNA sequences from the ISE18 cell line clustered with those from I. scapularis  for the ticks and tick cell lines (blue peaks) and measured their mean fluorescence intensity (MFI) Fig. .Figure 345 and compared them with the chromosome numbers 45. The genome size of IRE/CTVM19 cells at passage 246 was also determined in the same study and found to be 3.80 Gbp, about 1.4-fold larger than the size of the average parent tick genome45. However, in our study, we determined the genome size of female I. ricinus as 2.99 Gbp. The genome size of our highly-passaged IRE/CTVM19 cell line was larger than previously reported45, reaching 6.67 Gbp at passage 459 and 5.96 Gbp at passage 478. The genome size of the IRE/CTVM20 cell line was 5.28 Gbp at passage 442, but at passage 465 it was 4.47 Gbp.The average size of the haploid genome of I. ricinus cell lines, we observed a tendency for genome size to decrease after approx. 20 passages at intervals of 12–14 days and split ratio of 1:4. Because all tick cell lines are heterogeneous, derived from multiple individuals and multiple tissues within those individuals, there can be many possible reasons for a decrease in genome size. For tick cell lines, that can be maintained for many months without subculture20 the passage interval used in our study was relatively short and the split ratio was quite high. Passaging the cells regularly and intensively over a period of time could have favoured cells with fewer chromosomes, enabling them to divide more quickly and come to predominate in the cultures. This was indeed reflected in the chromosome counts, which showed a slight overall reduction in both cell lines between the first and second time points , passage 30  and passage 133  , the genome size was found to be 3.55 Gbp, while at passage 133 it had reached 6.83 Gbp. The haploid genome size of O. moubata tick in our laboratory and found that it was 0.72 Gbp. This was smaller than the genome size of its closest relative for which data is available—Ornithodoros turicata with 1.09 Gbp44. Due to their fragility and tendency to clump22, it was not possible to obtain an accurate count of the number of cells in aliquots of the O. moubata cell line OME/CTVM22 prior to processing for FACS measurement, and some cells were damaged during the subsequent procedures, so the final number of cells assessed by FACS was much lower than for the Ixodes spp. cell lines 25, and in a diplotene bivalent chromosome from a male I. ricinus46. In our study, we detected a hybridization signal at each terminal region of all chromosomes of male I. ricinus nymphs In cells of the IRE/CTVM19 and ISE18 lines, fluorescent signals were detected at two foci in each terminal region of all chromosomes Fig. B,C,F,G, 47.The possible explanation of the differences in chromosome numbers between cell lines and passage levels could be structural rearrangements of the genome. Considering the FISH results, the possible reasons for different telomere size in IRE/CTVM20 cells could be gradual telomere shortening, which usually occurs during cell division, spontaneous telomere loss or amplification, fusion, and fission, or it could be a result of stochastic events in which large blocks of telomeric repeat sequences are lost in a single rapid deletion event48. Such gene imbalance is often harmful at the organism level but is common in immortalized cell lines and tumours, where it can be an advantage for cells49. Another conclusion that can be drawn from the results of FISH hybridization on telomeres, is that the decrease of DNA content in IRE/CTVM19 cells at passage 478 and IRE/CTVM20 cells at passage 465 revealed by FACS analysis is not due to telomere loss.In the case of IRE/CTVM19 and ISE18 cells, which have nicely visible telomeres, localized on both sides of the chromosomes, the possible explanations of high chromosome number could be polyploidization or whole chromosome duplications. At the same time, amplification of telomeres (de novo synthesis) can also occur, as a mechanism of chromosome stabilization after breaks50. Therefore, the use of tick cell lines at similar and identified passage levels will yield better reproducibility of results. Possible changes and differences in the chromosome number and genomic content should also be considered when conducting experiments studying gene expression in tick cell lines.Thus, due to their phenotypic and genotypic heterogeneity, tick cell lines cannot be considered as “stable” a model as mammalian clonal cell lines. This could have serious consequences for the reproducibility of experimental findings and possible misinterpretation of results18, IRE/CTVM19 and IRE/CTVM2020, and OME/CTVM2222 were maintained at 28 °C in tightly-capped (non-vented) flasks in Leibovitz’ L-15 medium  supplemented with 20% fetal bovine serum , 10% tryptose phosphate broth , 2 mM L-Alanyl-L-glutamine , 100 units/ml penicillin G, 100 μg/ml streptomycin and 0.25 μg/ml amphotericin B 32. All cell cultures were passaged every 12–14 days at split ratios of 1:4 for Ixodes spp. cell lines and 1:2 for the Ornithodoros cell line and sampled at passage levels as indicated. In addition, an aliquot of ISE18 cells cryopreserved at passage 26 was resuscitated as described previously51 and maintained as above.Tick cell lines ISE1824, with slight modifications as follows. Cells were cultivated for five days (seven days for OME/CTVM22 cells) at 28 °C in fresh growth medium prior to treatment to arrest dividing cells in metaphase. To achieve this, 0.1 µg/ml colchicine  was added to the supernatant medium, and cells were incubated for 20 h at the same temperature. Next, cells were suspended and centrifuged at 275 × g for 8 min at room temperature (RT). The cell pellet was resuspended in 8 ml of 0.075 M KCl and incubated for 30 min at 28 °C. After incubation, 1 ml of freshly prepared, ice-cold fixative  was added drop by drop. The suspension was centrifuged at 275 × g for 8 min at 4 °C. The pellet was gently resuspended in 5 ml of fixative, which was added drop by drop, and kept for 30 min on ice followed by centrifugation at 275 × g for 8 min at 4 °C. This step was repeated one more time. Finally, the pellet was gently resuspended in 0.5 ml of fixative and stored at − 20 °C.Karyotyping of all cells apart from low-passage IRE/CTVM19 and IRE/CTVM20 was done as described previouslyIxodes sp. cell line; 35 metaphase spreads were analyzed for the Ornithodoros cell line.For the preparation of slides, 10 µl of the lysed and fixed cell suspension was dropped onto a microscope slide (lying at an angle of 45 degrees) from a height of ~ 20 cm at RT and air-dried. Slides were mounted with a drop of Roti-Mount FluorCare DAPI , covered with a coverslip, and sealed with nail varnish to prevent evaporation. Slides were analyzed using an Olympus BX53 fluorescence microscope equipped with a Xenon lamp and a DAPI filter cube (U-FUN). These slides were used for counting chromosomes in metaphase spreads from tick cell lines. One hundred metaphase spreads were analyzed for each In the case of slides for fluorescence in situ hybridization (FISH), 10 µl of the lysed and fixed cell suspension was macerated in a drop of 60% acetic acid using a wolfram needle. The spreading was performed on a slide using a heating plate at 45 °C and switching to a frozen plate for more efficient spreading of metaphase nuclei and better detection of hybridized probes.Low-passage IRE/CTVM19 and IRE/CTVM20 cells were seeded into flat-sided tubes  and incubated at 28 °C for 24 h; 625 μl of colcemid  was added to arrest cell development at metaphase, and the cells were incubated for a further 18 h. Cells were then harvested, centrifuged at 400 × g for 5 min and the pellet re-suspended in 5 ml 0.75% sodium citrate and incubated for 35 min at 37 °C. The cell lysate was centrifuged as before, the pellet was re-suspended in 5 ml ice-cold fixative as above and held on ice for 5 min. This process was repeated, and finally the cell lysate was centrifuged as before, and the pellet was gently resuspended in an equal volume of ice-cold fixative. Single drops were dripped onto ice-cold, wet, clean microscope slides from a height of approximately 90 cm. The slides were air-dried and then stained with 3% Giemsa for 1 h. The slides were examined under a light microscope at a magnification of × 250 to count the chromosomes in at least 100 spreads per cell line.I. ricinus nymphs, as described previously for I. scapularis23 with some modifications. Ticks were maintained in the tick rearing facility of the Institute of Parasitology, Biology Centre CAS; these were the second generation of ticks originating from regular field collections in forest and park areas of Ceske Budejovice. Tick nymphs were fed on laboratory guinea pigs obtained from the animal rearing facility at the Institute of Parasitology, Biology Centre CAS. Nymphs from one cohort were visually inspected, and males were sorted from females according to their size as male nymphs are approximately one third smaller . The dissection was performed exactly 5 days after the bloodmeal when the highest number of mitoses would be recovered from the dissected testes. The dissection was carried out in cold Ringer physiological solution52 as described previously46. In brief, testes were washed free of blood in a drop of Ringer solution and swollen in hypotonic solution for 20 min. Tissue was fixed in Carnoy’s fixative for 15 min and macerated in a drop of 60% acetic acid using a wolfram needle. The tissue was spread on a slide using a heating plate at 45 °C and switching to a frozen plate for more efficient spreading of metaphase nuclei. The preparations were dehydrated in an ethanol series , air-dried, and stored at − 20 °C until further use to prevent chromosome aging.All animal experiments presented in this study were in accordance with the Animal Protection Law of the Czech Republic  and with the approval of the Czech Academy of Sciences . Metaphase spreads were prepared from testes of fully fed Ixodes species. Q5 Hot Start High-Fidelity DNA Polymerase  was used for the PCR reaction and PCR products were visualized by agarose gel electrophoresis. The NucleoSpin Gel and PCR Clean-up Kit  was used for elution of PCR products from the gel, and ethanol-3 M sodium acetate precipitation was used to increase the purity of DNA. DNA was diluted in 5 mM Tris–HCl, pH 8.5. The purity of DNA samples was measured using NanoPhotometer Pearl  and concentration was measured using the Qubit dsDNA HS Assay Kit .Genomic DNA from different passage levels of cell lines ISE18, IRE/CTVM19 and IRE/CTVM20 was obtained using either a NucleoSpin Tissue Kit  or a DNeasy blood and tissue kit  following the manufacturers’ instructions. The following primers: IR_16S_F2: 5′-ATGAGTGCTAAGAGAATGATT-3′ and IR_16S_R1: 5′-CTTCTTCACCAAAAAAGAATCC-3′ were used to amplify a 334 bp fragment of the tick 16S rDNA gene. Primers were designed to recognize the conserved region of 16S rDNA in different Sequencing of 16S rDNA was performed by Admera Health Biopharma Services . In brief, amplicon sequencing was performed using the MiSeq Reagent kit v2 and 250PE protocol with Illumina’s MiSeq sequencing platform to a 0.1 M PE read depth per sample.53. Trimmed reads were mapped to I. ricinus and I. scapularis reference 16S rDNA sequences from NCBI GenBank using HISAT2 v2.1.054. The details of sequencing data processing, quality filtering and mapping are summarized in Supplementary Table 55 with default settings and the resultant contigs were BLAST-searched against the NCBI nt database.Raw reads were quality- and adapter-trimmed using trimmomatic v0.36Ixodes spp. cell lines and 16S rRNA sequences from the NCBI nt database was performed using Muscle56 with default parameters. The phylogenetic tree was constructed by the UPGMA method57 with MEGA X software58. Branch supports were tested by bootstrap resampling .The alignment of the identified 16S rRNA sequences of g for 5 min at RT. Pellets were resuspended in phosphate buffered saline (PBS) and centrifuged twice under the same conditions. Cells were counted using a Bürker's chamber and fixed by adding 1 ml ice-cold 96% ethanol to 0.5 ml of cell suspension containing 1 × 106 cells. The cell suspension was mixed by inversion and stored at 4 °C.Cultured tick cells were centrifuged at 500 × I. ricinus ticks and partially fed female O. moubata were provided by Jan Erhart . Preparation of nuclei was adapted from a previously-described protocol44. Nine ticks per species, divided into three biological replicates, were separately dissected under modified Galbraith buffer 59 to remove the midgut and wash off blood (if present). Whole tick bodies, including the cuticle but excluding the midgut, were ground in a 7 mL Dounce homogenizer containing 1 mL of ice-cold Galbraith buffer ten times with steady pressure using an “A” pestle to separate nuclei from cells. The suspension was centrifuged at 80 × g for 3 min at 4 °C to remove particulate matter. The supernatant was collected and centrifuged at 300 × g for 3 min at 4 °C. The nuclei in the pellet were washed twice in ice-cold PBS at 300 × g for 3 min at 4 °C, counted in a Bürker's chamber and fixed in ethanol, as described above.Unfed adult female Gallus gallus domesticus were used as an internal cytometry standard. Blood from female G. g. domesticus was collected into 50 ml centrifuge tubes containing heparin as anticoagulant and stored overnight at RT before processing. Blood was centrifuged for 10 min at 250 × g at RT, and the plasma and buffy coat were aspirated completely and discarded. The erythrocyte fraction was washed three times in PBS, each for 10 min at 250 × g at RT. The resultant cells were suspended in 2 volumes of PBS, and a 5% cell suspension was prepared from the stock. Cells were counted using a Bürker's chamber and fixed by adding 1,000 µl ice-cold 96% ethanol to 500 µl of cell suspension containing 1 × 106 cells. The cell suspension was mixed by inversion and stored at 4 °C.Chicken red blood cells (CRBC) from Prior to measurement, fixed cells were equilibrated to RT, gently suspended by inverting the tube, and centrifuged at 500 × g for 5 min. The supernatant was carefully aspirated, and cells were washed twice with PBS by centrifugation at 500 × g for 5 min at RT, suspended in 1 ml of PBS and counted. All samples were treated with 100 µg/ml Ribonuclease A  in PBS for 20 min at 37 °C and stained with propidium iodide  for 20 min at RT.6 cultured cells or nuclei from three ticks. At least 10,000 cells were counted per replicate. The results were further analyzed with the Flowing Software 2 and visualization of the results was done using FlowJo software, v.10.5.0.Samples of PI-stained tick cell suspensions were mixed with PI-stained CRBC in a 3:1 ratio and analyzed using a FACS Canto II flow cytometer and FACS Diva Software, v.5.0 (BD Biosciences). Red fluorescence of samples was detected in the Texas Red channel and exported to histograms of fluorescence intensity, which is directly equivalent to the amount of DNA in cells. For each sample, we used three replicates, each of which contained 1 × 1045. Diploid genome size was calculated based on the following conversion Eq.44Two peaks corresponding to CRBC and tick cells were separately gated, and their mean fluorescence intensity (MFI) was used for calculation. The amount of nucleic material in each tick sample was estimated by comparison of MFI to the CRBC internal standard. A total of 2.5 pg of DNA was used as the DNA weight of a diploid CRBC , as described inI. ricinus and O. moubata ticks.Haploid genome size was then calculated for all tick cell lines and female n specific for insects, as suggested46, was generated by non-template PCR60 as described25 using primers (TTAGG)5. The synthesized probe was labeled with biotin using either a random priming method  according to the manufacturer’s instructions or nick translation as described previously61.The telomeric probe (TTAGG)62. Freshly prepared or stored (at − 20 °C) slides were dehydrated in an ethanol series  and air-dried. The RNA digestion was done by incubation of slides with 100 μg/mL Ribonuclease A  in 2X saline-sodium citrate (SSC) buffer  under a coverslip for 1 h at 37 °C in a humid chamber containing paper towels moistened in 2X SSC. Slides were washed three times in 2X SSC for 5 min each at RT on a rocking platform. Slides were incubated in 3.7% formaldehyde diluted in wash-blocking buffer  for 10 min followed by washing three times in 2X SSC for 5 min each at RT and dehydration in an ethanol dilution series (see above) and air-drying. Hybridization mixture (15 µl per slide) was prepared from 100 ng biotin-labelled telomeric probe, deionized formamide , SSC , and dextran sulphate . The mixture was denatured at 95 °C for 10 min, immediately chilled on ice for 5 min, and then applied to the chromosome preparation, covered with a coverslip, and incubated at 70 °C for 1 min. Slides were incubated overnight in a humid chamber at 37 °C. The following day, preparations were washed in a series of washing solutions in a rocking bath as follows: twice in 2X SSC at 42 °C for 5 min each, twice in 0.1X SSC at 42 °C for 5 min each, once in 2X SSC at 42 °C for 5 min, and once in 2X SSC at RT for 10 min. The preparations were blocked in wash-blocking buffer at RT for 15 min. Streptavidin-DyLight 549  was diluted in wash-blocking buffer (1:50), applied to the slides, covered with a coverslip, and incubated at 37 °C for 1 h in darkness. Slides were washed three times in wash-blocking buffer at 45 °C in a rocking bath for 5 min each. Roti-Mount FluorCare DAPI  was used as a mounting medium for the slides. Slides were analyzed using an Olympus BX53 fluorescence microscope equipped with a Xenon lamp, red filter and DAPI filter cube (U-FUN).The modified protocol of Cabral-de-Mello and co-workers was usedIxodes spp. ticks and 2n = 20 chromosomes for Ornithodoros spp. ticks, became altered in tick cell lines, and both increase and decrease in chromosome number were observed. However, the genome sizes of all tick cell lines were larger in comparison to those of the parent ticks. We hypothesize that these changes were caused by adaptation of the cells to growing in culture. Therefore, tick cell lines can be used confidently for research purposes such as testing of chemical or biological agents, genome editing, recombinant protein production, pathogen propagation, etc.; however, scientists should be aware of and understand the potential differences in the internal cellular processes between different cell populations resulting from karyotype changes.Long-term continuous cultivation of tick cell lines can dramatically influence their karyotype and genome size. The modal chromosome number was different from that of the parent ticks in the studied cell lines IRE/CTVM19, IRE/CTVM20, ISE18 and OME/CTVM22, even in those cell lines which originated from the same tick species. The tick cell karyotype, which is 2n = 28 chromosomes for Supplementary Information.


One of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. The availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational models for predicting anticancer drug response. In this study, we propose ADRML, a model for Anticancer Drug Response Prediction using Manifold Learning to systematically integrate the cell line information with the drug information to make accurate predictions about drug therapeutic. The proposed model maps the drug response matrix into the lower-rank spaces that lead to obtaining new perspectives about cell lines and drugs. The drug response for a new cell line-drug pair is computed using the low-rank features. The evaluation of ADRML performance on various types of cell lines and drug information, in addition to the comparisons with previously proposed methods, shows that ADRML provides accurate and robust predictions. Further investigations about the association between drug response and pathway activity scores reveal that the predicted drug responses can shed light on the underlying drug mechanism. Also, the case studies suggest that the predictions of ADRML about novel cell line-drug pairs are validated by reliable pieces of evidence from the literature. Consequently, the evaluations verify that ADRML can be used in accurately predicting and imputing the anticancer drug response. One of the highly critical problems in precision medicine is predicting anticancer drug response for each patient4. Due to the heterogeneity of tumors, the patients with the same type of cancer may show various therapeutic responses toward similar drugs5. Therefore, providing computational methods to discover the relationship between genomic information and drug sensitivity is of high importance and can be beneficial in precision medicine6.Precision medicine aims to finely select treatments for cancer based on the genetic information of individual patients7 and Cancer Cell Line Encyclopedia (CCLE)8 are two projects that have provided molecular profiles and drug response values for hundreds of cancer cell lines against several anticancer drugs. These large datasets facilitate the development of computational methods for anticancer drug sensitivity prediction. Numerous computational methods have been proposed to predict drug response using gene expression profile, or other molecular information of cell lines. Some of the computational methods have considered drug information such as chemical substructure of drugs, besides made use of cell line information. In the proposed computational methods, various machine learning methods have been utilized such as sparse linear regression11, random forest13, kernel-based methods17, matrix factorization20, neural networks and deep learning24.Genomics of Drug Sensitivity in Cancer (GDSC)1. They also conducted drug-repurposing and suggested new potential treatments for cell lines with Non-small Cell Lung Cancer (NSCL). It is suggested that patients who have similar genomic properties reveal similar responses to similar drugs1. Based on the SRMF study, Suphavilai et al. have proposed a recommender system called “CaDRReS” that can predict drug response for unseen cell lines19. Furthermore, they showed that latent space features are correlated with associated pathways of drugs. They did not consider any features of drugs for predicting the drug response values. Afterwards, Chang et al. have devised “CDRscan”, an ensemble model containing five Convolutional Neural Networks (CNNs)21. They made use of mutational profiles of cell lines and chemical substructure of drugs as the input features to these CNNs. The drug response values were measured by averaging the output of five CNNs. Moreover, they have repurposed multiple non-oncology drugs as the potential therapeutic agents for cancer cell lines. Recently, Wei et al. have suggested a simple cell line-drug complex network called “CDCN”25. They constructed a complex network from various information, including cell line similarities, drug similarities, and drug responses, and inferred unknown drug response from the network. They also proposed a generalized version that can predict the drug response for new drugs and new cell lines. Despite its simple structure, CDCN had satisfying results in imputing missing drug responses.Wang et al. have proposed a Similarity Regularized Matrix Factorization (SRMF) method, which utilizes the similarity of cell lines based on gene expression profiles and chemical substructure similarity of drugs to predict anticancer drug sensitivity26 and drug–drug interaction prediction27, which yielded accurate performance.Nevertheless, the proposed methods had moderate performance and do not analyze several types of features for cell lines and drugs. Thus, investigating the influence of various features for cell lines and drugs in predicting therapeutic response is still in need and challenging. We investigate three types of cell line features, namely gene expression, mutation profile, and copy number variation, in addition to three types of drug features, including chemical substructure, target proteins, and associated KEGG pathways. In this work, we propose ADRML, Anticancer Drug Response Prediction, by using Manifold Learning. ADRML constructs a bipartite graph between drug and cell lines, and then decompose its adjacency matrix using similarity-constrained manifold learning into two lower-dimensional latent matrices. The proposed method is capable of predicting therapeutic response for new cell lines and new drugs. The similarity-constrained manifold learning previously has been used in the context of drug-disease association predictionThe performance of ADRML is measured using various types of cell line similarities and drug similarities and is compared to the recently proposed methods on both GDSC and CCLE datasets. Moreover, the rationality of ADRML predictions is confirmed by analyzing the association between the predicted drug response values and activity scores of Biocarta pathways. Finally, conducting case studies on the predictions of ADRML for unknown drug response in literature and reliable databases verifies its capability in predicting unknown drug response and admits that ADRML obtains accurate results for new pairs of cell line-drug.7 and Cancer Cell Line Encyclopedia (CCLE)8. Among several types of data in these datasets, various information including the half-maximal inhibitory concentration (IC50), the gene expression profile, copy number variation, and mutation profile was downloaded by using PharmacoGx R package28. The collected genes were accessible in the COSMIC database29, and the collected drugs were restricted to the drugs with a Compound ID (CID) in the PubChem database30.In this work, we used two pharmacogenomic datasets, namely the Genomics of Drug Sensitivity in Cancer (GDSC)2 to impute the missing values, which is fully described in “31, and the CCLE dataset contained 24 drugs and 363 cell lines from 22 cancer types as defined by TCGA. Furthermore, several types of information about drugs were obtained from the following databases:30.The fingerprints of canonical simplified molecular-input line-entry (SMILES) were obtained from PubChem32, and literature.The target proteins were collected from GDSC, DrugBank33.The KEGG-pathways related to the drugs were downloaded from the STiTCH databaseA brief description of the collected data is presented in Table Some values of IC50, copy number variation, and mutation profiles in both datasets were missing. A pre-processing procedure was applied, according to Lu et al.k” is the dimension of latent space, “k” value was considered to be less than the number of cell lines and drugs. For simplicity, we considered fitness in the following).PCC), and Root Mean Square Error (RMSE) are completely explained in “PCC, and the lowest value of RMSE. ADRML achieved the best results when ADRML model is fully described in “mentclass2pt{minimPCC, and k, while RMSE declines as k grows larger. However, the criteria value do not change or have subtle changes after Moreover, the values of mentclass2pt{minimWe investigated the effects of using different similarity constraints on ADRML performance. Several cell line similarities based on gene-expression, mutation, and copy number variation, and multiple drug similarities based on chemical substructure, target proteins, and KEGG pathways were considered as the constraints of manifold learning.Table PCC and validate that ADRML can predict drug responses with high correlation to the real responses. The majority of data in these scatter plots are centered near the fitted line. It is notable that the outliers are natural due to the technical noises in gene expression data, or inconsistency of drug responses in CCLE and GDSC35. Further plots for drug-wise PCC of GDSC are shown in Supplementary Figs. PCC of CCLE are shown in Supplementary Figs. In order to investigate ADRML performance on each drug, we depicted the drug-wise correlation plots. Figures 21, CDCN25, SRMF1, and CaDRReS19. The implementations of all methods were obtained from the available codes referred to in their papers. In order to have a fair comparison, we conducted all evaluations in the same setting and using the same datasets. The comparison was made on the average performance of the models over 30 repetitions of fivefold cross-validation with tuned hyper-parameters.For comprehensive evaluation of ADRML’s performance, we compared it to other recent state-of-the-art methods, namely, CDRscanIt should be noted that the hyper-parameters of CaDRReS cannot be fully tuned, due to its high time complexity. The hyper-parameters for CaDRReS is assumed according to its paper and authors’ suggestion.The features used for cell lines and drugs are different in these methods. For each method, the required features, as mentioned in their paper, are provided from the benchmark datasets described in "36. For executing KNN, the input feature vector for each pair of cell line ith row of simC and jth column of simD. All types of cell line similarities and drug similarities were considered as simC and simD, respectively. The complete report of KNN performance on various types of similarities are provided in Supplementary Table In addition to the mentioned methods, K-nearest neighbor (KNN) with Tables RMSE and favorable PCC; however, it achieved PCC were less than the baseline. CDRscan obtained the favorable PCC but it had the highest RMSE. Therefore, its prediction values have a high correlation and far distance to the real responses, simultaneously. CDCN revealed a satisfying performance but with lower PCC, and higher RMSE than the results of ADRML. Therefore ADRML outperformed other methods.The results of baseline method (KNN) in both datasets were not too far from the state-of-the-art methods, which means that improving the results is challenging. In CCLE dataset, SRMF achieved the best RMSE and moderate PCC. The performance of CaDRRS was satisfying, but PCC were worse than the baseline. CDRscan showed good performance but with high RMSE, similar to its performance on the CCLE dataset. Moreover, CDCN’s performance was satisfying; however, its PCC were lower than ADRML, and its RMSE was higher than ADRML. Consequently, ADRML outperformed other methods with regard to PCC.In the case of the GDSC dataset, SRMF obtained the best In addition to the mentioned analysis, we investigated whether using other types of cell line similarities and dug similarities would aid in improving the results of other methods. To this aim, we executed CDCN, SRMF, CaDRReS, and KNN on all types of similarities. It is worth mentioning that CDRscan receives binary feature matrices as the input and the dimension of binary feature vectors of drugs in CCLE and GDSC datasets were not appropriate for the designed CNNs in CDRscan; therefore, it is not applicable to perform CDRscan on other types of similarities. Other methods  receive the similarity matrices as the input. Moreover, CaDRReS gets only the cell line similarity, and it does not obtain any drug similarity matrix from the input.RMSE. At the same time, ADRML achieves best PCC.The entire report of the performance criteria measured for the performance of the mentioned methods is presented in Supplementary Table PCC. These achievements further substantiate ADRML performance.All in sum, ADRML performed better than other state-of-the-art methods on both CCLE and GDSC in terms of CCLE dataset contains 363 cell lines from 22 different tissue types. The number of cell lines in each tissue type is shown in Fig. PCC.The detailed procedure of removing redundant cell lines is described in “RMSE, CDCN achieved the best PCC.Moreover, the GDSC dataset comprises 555 cell lines from 19 tissue types. Various tissue types have different numbers of cell lines which are shown in Fig. It can be inferred from the comparison results in Tables Moreover, we applied the redundancy removal procedure with different thresholds PCC values of ADRML assessed using 5-fold cross-validation on the purified datasets. These figures verify that the trend of ADRML performance is almost the same on purified datasets based on copy number variation and gene expression. ADRML achieves the best PCC on the strictest threshold which removes a lot of cell lines and adding other cell lines declines its PCC. Moreover, the ADRML’s PCC on purified CCLE datasets first increased sharply and then decreased by lowering the level of strictness in redundancy removal.ADRML performance was evaluated on each of the resulting datasets after redundancy removal based on various levels of strictness. Figure 37. The detailed procedure is described in “To demonstrate that the prediction of ADRML is meaningful and rational, we investigated the correlation between the predicted drug responses and pathway activity scores for several Biocarta Pathways from MsigDBPaclitaxel drug and Paclitaxel is one of the agents that have been frequently reported for the activation of 41. Thus, the higher consumption of Paclitaxel leads to more activation of Paclitaxel and Paclitaxel positively associated with P53 pathway. It has been verified that Paclitaxel activates P53 signaling pathway42 and the cell lines with disrupted P53 are resistant to Paclitaxel43. Furthermore, HSP90 inhibitor P53 pathway. It has been suggested in the previous studies that P5344, that admits the positive association of P53 pathway.Irinotecan response has a very significant positive correlation with the activity score of P53. Irinotecan is a topoisomerase I inhibitor, which is frequently used for anticancer therapy. The previous study on human hepatocellular carcinoma (HCC) cell lines for the investigation of the apoptotic mechanisms of Irinotecan has revealed that it significantly activates P5345. Additionally, the positive correlation of Irinotecan response and EGFR pathway is supported by several pieces of research. They have shown the resistance to Irinotecan is connected with the increased expression of EGFR46 and have admitted that Irinotecan upregulates the EGFR pathway47. Also, Panobbinostat which is a potent inhibitor of deacetylases and HSP9048, revealed a high significant positive correlation with Panobinostat increased the level of 48.IC50 matrix and trained ADRML with all known drug responses. For each drug, the predictions of ADRML on unknown pairs were partitioned into four quantiles, and the cell lines in the first and last quantiles were considered as the sensitive and resistant cell lines for that drug, respectively. The complete list of sensitive and resistant predicted associations are provided in Supplementary Tables IC50 values in the previous data extracted from GDSC, and now the drug response value for these pairs are available in the latest release of GDSC.We conducted case studies on GDSC cell-line-drug pairs with unknown IC50 values. To do this, we did not impute the missing values in the In this study, we proposed a computational model for predicting anticancer drug response, using manifold learning, called ADRML. The model combines three sets of information, including known drug responses, cell line similarity, and drug similarity, to infer the novel predictions. The main contribution of this paper is evaluating the influence of various types of cell line similarities and drug similarities on the prediction performance. We collected various features for cell lines and drugs from CCLE, GDSC, STiTCH, PubChem, and Drugbank. Here, we investigated nine different scenarios using three cell line similarities based on gene expression, mutation, and copy number variation, and three drug similarities based on the chemical substructure, target proteins, and KEGG pathways. The performance of ADRML was investigated using fivefold cross-validation on cell line-drug pairs. The best performance was obtained using gene expression data about cell lines and target protein data about drugs, which was more accurate than the previously proposed methods. We also investigated the performance of other state-of-the-art methods and KNN (with k = 1) as the baseline method on various types of similarities and showed that their best performance was achieved using the similarities that were suggested in their papers.Another contribution of this paper was the purification of CCLE and GDSC benchmarks via removing redundant cell lines. The purified benchmarks were also used for assessing the methods’ performance. The results showed that excluding redundant cell lines declines the methods’ performance, which may be due to the reduction of sample size or removing bias from the database.21 have provided CDRscan with more cell lines and drugs than used in this paper and have trained CDRscan with 95% of its data (despite 80% of data in this paper). Therefore, the reported 21 is better than the results reported in this paper. One can conclude that providing more informative data may enrich the training data and lead to better training the complex models. It is noteworthy that due to the challenging inherent of the problem, little improvements in results is welcome and useful.It was interesting that KNN with k = 1 as a simple baseline method shows favorable results and outperforms some more complicated methods, especially on the purified datasets. However, it should be noted that sophisticated methods’ performance declines when the data size is not sufficient. A complicated method needs a massive amount of data to train well and gets a good grasp of predicting outputs from inputs. For example, Chang et al.PCC in most comparison scenarios. The predicted drug response values revealed high correlations with observed drug responses and suggested meaningful clues about drug mechanisms in activation/inhibition of pathways. Moreover, the reliable literature evidence supports the predictions of ADRML about novel cell line-drug pairs. As a consequence, the promising results of ADRML verified its efficiency in predicting anticancer drug prediction and imputation.The proposed method in this study outperformed other methods in terms of two criteria Pre-processing to impute missing dataCalculating various types of similarity matrices for cell lines and drugsNormalizing the similarity matricesSimilarity-constrained manifold learning to factorize the IC50 matrix into low-rank latent matricesEstimating Unknown IC50 values using the latent matricesThe overall workflow of ADRML is illustrated in Fig. The proposed method includes five steps:g in cell line c. Furthermore, For the convenience, define IC50 value of drug d or CNV value of gene g in cell line c.c, and g in cell line c, the majority vote of 10 nearest cell lines is used, i.e. Several steps were done to impute the missing data. First, the features that were missed in the majority of cell lines are removed. Second, the cell lines that contain missing values for more than half of the features were excluded. The other missing values were imputed using a k-nearest neighbor approach. To this aim, the distance measure between cell lines was defined as the Euclidean distance of their expression profiles because there is no missing in expression features of the cell line; thus, the distance can be calculated for each pair of cell lines. The distance between PCC and Jaccard-index (JI) were regarded as the similarity function, which are elaborated in the following.ith element of these vectors, and PCC is used to calculate the similarity of two continuous vectors, while JI is appropriate to measure the similarity of two discrete vectors. Therefore, we considered this rationality in the calculation of similarity matrices. The dimensions of cell line, and drug similarity matrices are n denotes the number of cell lines and m denotes the number of drugs. Consequently, we constructed the three types of similarity matrices for cell lines, based on EXPR, CNV, and MUT. Since EXPR and CNV features are real-valued, PCC was used to measure their similarity, while MUT is binary-valued and JI was used to measure mutation similarity.Furthermore, three types of similarity matrices for drug based on Pubchem SMILES (CHEM), target proteins (TRGT), and KEGG pathways (KEGG) were calculated as follows. It is notable that all drug features are binary-valued; thus, JI was used for measuring the similarity of drugs based on each type of information.Then all of the computed similarity matrices were normalized by computing the symmetric normalized Laplacian51. Let S be a similarity matrix, the normalized similarity matrix D is a diagonal matrix with diagonal elements equal to the summation of each row in S, i.e. For computing the similarity score of two cell lines (or drugs), the IC50 drug response matrix R in two latent matrices k, i.e. P and Q are the cell line latent matrix and drug latent matrix, respectively. The ith row of P . After convergence, an estimated matrix is obtained by The latent matrices  to Eqs. , 23 untiQ and P in the first run, respectively. After the convergence, the second predicted matrix was constructed by Moreover, the manifold learning was applied on the transpose of response matrix, i.e. all the above procedure was repeated for factorizing We measured the performance ADRML using 5-fold cross-validation on cell line-drug pairs. To do this, each pair of RMSE, PCC as follows.Test| is the number of samples in the test set. Each criterion evaluates the model performance from a different point of view. Therefore, it is possible to obtain results which led to promising values of one criterion and unfavorable values for other criteria.To avoid randomness and reducing variance, the model performance was averaged over 30 randomly repetition of 5-fold cross-validation. The evaluation criteria include t  ). The cell line c in tissue type t was excluded if it had the similarity higher than Q3 with more than t.In order to eliminate the redundancy from the dataset, the cell lines in each tissue type that have high similarity to the majority of cell lines in that tissue type were considered as the most redundant cell lines and excluded from the dataset. To do this, the minimum (Q0), first quantile (Q1), second quantile (Q2), third quantile (Q3), and maximum (Q4) values for each type of cell line similarity in all tissue type were calculated, which are shown in Supplementary Tables PCC of drug response values and pathway activity scores. To do this, we considered all Biocarta signaling pathways and eliminated the pathways that the gene expression data of more than 10% of its genes were not provided. Therefore, we considered 107 Biocarta pathways for CCLE dataset. The pathway activity score for cell line et. al.20, by summing up the fold change of gene expressions for all genes The association between drug and pathway was computed by the AS stands for the activity score vector of pathway The correlation of drug Supplementary material 1Supplementary material 2


This binds hTS allosterically and, unlike classical inhibitors that bind at the catalytic pocket, causes cell growth inhibition without inducing hTS overexpression. The dual drug combinations showed schedule-dependent synergistic antiproliferative and apoptotic effects. We observed that the simultaneous treatment or 24h pre-treatment of OC cells with the peptide followed by either agent produced synergistic effects even in resistant cells. Similar synergistic or antagonistic effects were obtained by delivering the peptide into OC cells either by means of a commercial delivery system (SAINT-PhD) or by pH sensitive PEGylated liposomes. Relative to non-PEGylated liposomes, the latter had been previously characterized and found to allow macrophage escape, thus increasing their chance to reach the tumour tissue. The transition from the SAINT-PhD delivery system to the engineered liposomes represents an advancement towards a more drug-like delivery system and a further step towards the use of peptides for in vivo studies. Overall, the results suggest that the association of standard drugs, such as cDDP and/or 5-FU and/or RTX, with the novel peptidic TS inhibitor encapsulated into PEGylated pH-sensitive liposomes can represent a promising strategy for fighting resistance to cDDP and anti-hTS drugs. There is currently no effective long-term treatment for ovarian cancer (OC) resistant to poly-chemotherapy regimens based on platinum drugs. Preclinical and clinical studies have demonstrated a strong association between development of Pt-drug resistance and increased thymidylate synthase (hTS) expression, and the consequent cross-resistance to the hTS inhibitors 5-fluorouracil (5-FU) and raltitrexed (RTX). In the present work, we propose a new tool to combat drug resistance. We propose to treat OC cell lines, both Pt-sensitive and -resistant, with dual combinations of one of the four chemotherapeutic agents that are widely used in the clinic, and the new peptide, hTS inhibitor, [D-Gln Ovarian cancer is the fifth cause of mortality among women of all ages in the Western world. Although higher response rates have been achieved using the poly-chemotherapy regimens, mainly based on the combination of cyclophosphamide or paclitaxel and a platinum compound, such as cisplatin (cDDP) or carboplatin , many paCisplatin is an alkylating agent that interacts with the DNA double strain structure and causes formation of adducts that prevent transcription and cellular replication and induces apoptotic cell death. In the multifactorial process of the acquired resistance to cDDP and its derivatives, the phenotypes show overexpression of DNA-repair enzymes and enzymes necessary for the synthesis of thymidine, such as thymidylate synthase (TS) and dihydrofolate reductase (DHFR), since they cause a rapid turnover of DNA . As a coIt is well known that tumours express higher TS levels than normal tissues and that the ovarian tissue is among those with the highest levels of TS expression, a feature that correlates with poorer patients’ overall survival . 5-FluorExposure of cancer cells to 5-FU or other antifolate TS inhibitors acutely upregulates TS, probably due to the inhibition of a negative-feedback mechanism in which the TS protein binds its own mRNA and inhibits its translation ,12,13. I4]LR) displayed the best growth inhibition of both cDDP-sensitive and -resistant OC cell lines LR peptide delivered using the SAINT-PhD delivery system was unaltered when the latter was replaced by these liposomes. While the commercial delivery system proved useful in the early cellular studies, the PEGylated liposomes strategy is deemed necessary to implement further in vivo studies of the [D-Gln4]LR peptide. Recently, the hydrophilic peptide LR has been delivered into OC cells by exploiting such biocompatible and efficient tools as solid lipid nanoparticles (SLNs)  and pH s4]LR octapeptide, delivered into OC cells by either the SAINT-PhD delivery system or by PEGylated pH-sensitive liposomes. More specifically, we investigate their cytotoxicities and abilities to perturbate the cell cycle phase distribution. The microtubule polymer stabilizer paclitaxel was also included in the described combination list, because the drug is largely used in ovarian cancer chemotherapy and shows a different mechanism of action. To this aim, three different tumour-cell treatment schedules were tested. Special attention was focused on the synergistic effects exhibited by the combined cell treatments and the role of sequentially thereon.In the present study, we test the efficacy of combinations of RTX, cDDP and 5-FU with the  analysis. Among the different cell lines examined the IC50 values for the drugs alone varied depending on the resistance phenotype  obtained with the drug alone on the tested cell line. As shown in the diagrams in The [D-Gln4]LR and then to the drugs did not enhance cell killing with the cDDP-sensitive 2008 cells with respect to the concurrent administration. On the contrary, with the resistant C13* cells both 5-FU and RTX increased their cytotoxicities when administered after [D-Gln4]LR; in particular, RTX exhibited a synergistic efficacy at the lower dose. A general enhancement of cytotoxicity, with SQ values higher than unity, was obtained with both combined treatments of IGROV-1 cells, at almost all drug doses employed  RTX-high dose in concurrent administration with the [D-Gln4]LR peptide has a synergistic activity towards all the tested cell lines; (ii) low-dose RTX and high-dose 5-FU administered after the [D-Gln4]LR peptide show synergistic activity towards all cell lines, except the 2008 cells; (iii) high-dose RTX and paclitaxel in the concurrent administration and high-dose paclitaxel administered after the [D-Gln4]LR peptide showed synergistic activity towards all cell lines, except C13*.With the second pair of cell lines, 2008 and C13*, concurrent administration was additive for cDDP, 5-FU, and paclitaxel at the lower dose, supra-additive at the higher dose of paclitaxel and for RTX at both doses  on the 2008 and C13* cell lines. The percentage of cells in the different phases of the cell cycle is reported in Looking for a possible mechanism underlying the antiproliferative activity and correlating with the additive or synergistic effects of the combination between LR peptide in combination with standard drugs. Next, we move on, and pass from the SAINT-PhD delivery system to the engineered PEGylated liposomes, which represent an advancement towards a more drug-like delivery system. The PEGylated liposomes strategy is deemed necessary to implement further in vivo studies of the LR-PpHL) according to the optimised protocol reported, using the unloaded carriers as a control. Moreover, the free [D-Gln4]LR peptide at the equivalent concentration was used for comparison. The elaborated results are reported in In the first experiment, the cells were exposed to increasing concentrations of the peptide-loaded liposomes  , while the reversed schedule  produced an antagonistic effect; the combination sequences leading to the synergistic antiproliferative effect are the same observed with the SAINT-PhD delivery system  following a 24 h exposure of colon carcinoma cells to RTX . BecauseIn addition, the synergistic effect of combining the peptides with cDDP or the other drugs may derive from mis-incorporated genomic uracil, resulting from TS inhibition, which induces DNA damage and early cell-cycle arrest as a result of BER activity, and is a critical determinant of sensitivity to antifolate-based TS inhibition .Our findings on the dependence of the effects of the combination of peptides and RTX on the administration schedule correlate with previous reports where synergistic effects, were obtained by a sequential exposure to the DHFR inhibitor methotrexate (MTX) followed by RTX . ActuallIn our case, the observed sequence-dependent synergy might be accounted for in connection with the finding that HSP90 and TRAP1, that are critical regulators of survival of tumour cells, are among the proteins down-regulated by the peptides. TRAP1 has been described as a mitochondrial chaperone of HSP90AA1. Effective cytoprotection may require TRAP-1 phosphorylation by the mitochondrial-localized kinase, PINK1, which associates with TRAP-1 in vivo. The high expression of TRAP-1 in cancer has been implicated in the inhibition of mitochondrial apoptosis, suppression of ROS production and acquisition of resistance to standard chemotherapeutics . On the The combination synergy of the peptide with paclitaxel, that like the other mentioned chemotherapeutic drugs, is frequently used in OC therapy, is justified by several experiments that report, already at the clinical level, on the positive effects of the combination of this microtubule stabilizer with folate-cycle inhibitors and/or platinum drugs ,58,59. Raltitrexed (RTX) and paclitaxel were purchased from Selleckchem  and were dissolved in DMSO immediately before addition to the cell cultures. Cisplatin (cDDP) was obtained from Santa Cruz Biotechnology. All other chemicals were purchased from Sigma–Aldrich S.r.L. , except otherwise indicated. 4]LR and the internal standard (IS) for quantitative analysis, LR-Ala7, were synthesized as previously described -1,2-distearoyl-sn-glycero-3-phosphoethanolamine sodium salt (DSPE_PEG) purchased from Lipoid . The active peptide LR-loaded liposomes at concentrations of 0.05, 0.075, 0.125 and 0.250 mg/mL; moreover, cells were treated, for comparison, with equivalent amounts of naked peptide. Cells were then incubated in complete medium suspension in 5% CO2 incubator at 37 °C for 15 h. Then, they were washed with PBS, added with 500 μL of complete RPMI-1640 medium and incubated for additional 48 h. After incubation, a final concentration of 0.5 mg/mL MTT was added to the medium and then incubated at 37 °C for 1 h. The medium was removed and the dark blue formazan crystals dissolved in DMSO were determined spectrophotometrically at 535 nm by the multi-plate reader Genios Pro  with Magellan 6 software, and the results are expressed as percentage of cell growth with respect to the control (untreated cells).The cytotoxicity assay was performed by the -2,5-diphenyl tetrazolium bromide; Sigma, Milan, Italy) assay (MTT assay) . Briefly4]LR-PEG liposomes and drugs (namely cDDP or RTX), combined at a fixed ratio either simultaneously or sequentially, was quantified by synergism quotient (SQ) LR loaded liposomes was given 24 h before cDDP or RTX; drugs before: the drug (cDDP or RTX) was given 24 h before liposomes (sequence III). C13*, 2008 and IGROV-1 cell lines were exposed to the combinations using 3 different modalities. Simultaneous drug-exposure (sequence I): cDDP or RTX were given simultaneously with liposomes for 3 days; liposomes before (sequence II): LR peptide, showed a schedule-dependent synergistic antiproliferative effect against OC cells in combination with cDDP, RTX, 5-FU and paclitaxel. More precisely, simultaneous exposure as well as 24 h pre-treatment with the peptide, produced a synergistic cell killing, while a reversed schedule that consists in pre-treating with the non-peptidic drugs was always antagonistic. Furthermore, for the first time, we have provided evidence that this peptide can overcome resistance to cDDP, as its combination with this alkylating agent was synergistic also against the cDDP-resistant cell lines. Also, we have demonstrated a synergistic antiproliferative effect of the peptide in combination with 5-FU, or with RTX even against the C13* and A2780/CP cell lines that had been selected for their resistance to cDDP and cross-resistance to 5-FU and RTX, and feature amplified expression of the folate cycle enzymes [This work has demonstrated that the TS allosteric inhibitor, the [D-Gln enzymes . Most ofOverall, the results of this preclinical study suggest that the association of standard drugs with this novel peptidic hTS inhibitor encapsulated into PEGylated pH-sensitive liposomes is a promising strategy to impact the drug resistance problem and, combined with intracellular mechanistic analysis, may represent a rational basis for future clinical applications in ovarian tumor patients.


Osimertinib, a third-generation irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI), has shown marked clinical benefit for non-small cell lung cancer (NSCLC) patients with EGFR activating mutations. However, resistance to osimertinib inevitably develops and heterogeneous mechanisms of acquired resistance have been documented. Therefore, new strategies to bypass resistance are urgently needed. In this study, we investigated the potential activity of abemaciclib as second-line therapeutic approach after osimertinib progression and the effect of combining abemaciclib with osimertinib on the appearance of resistance in osimertinib-sensitive models. Abemaciclib is an inhibitor of cyclin-dependent kinases (CDK) 4 and 6 that inhibits the transition from the G1 to the S phase of the cell cycle by blocking downstream CDK4/6-mediated phosphorylation of Rb. The effects of abemaciclib alone or combined with the third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) osimertinib were examined in a panel of PC9 and HCC827 osimertinib-resistant non-small cell lung cancer (NSCLC) cell lines carrying EGFR-dependent or -independent mechanisms of intrinsic or acquired resistance. Differently from sensitive cells, all the resistant cell lines analyzed maintained p-Rb, which may be considered as a biomarker of osimertinib resistance and a potential target for therapeutic intervention. In these models, abemaciclib inhibited cell growth, spheroid formation, colony formation, and induced senescence, and its efficacy was not enhanced in the presence of osimertinib. Interestingly, in osimertinib sensitive PC9, PC9T790M, and H1975 cells the combination of abemaciclib with osimertinib significantly inhibited the onset of resistance in long-term experiments. Our findings provide a preclinical support for using abemaciclib to treat resistance in EGFR mutated NSCLC patients progressed to osimertinib either as single treatment or combined with osimertinib, and suggest the combination of osimertinib with abemaciclib as a potential approach to prevent or delay osimertinib resistance in first-line treatment. Osimertinib is the first mutant-selective third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) that has been approved for patients with EGFR oncogene-addicted advanced non-small cell lung cancer (NSCLC) . OsimertDespite the efficacy of osimertinib in first- and second-line EGFR mutated NSCLC settings, patients inevitably develop acquired resistance and chemotherapy remains the only therapeutic option for these patients . DiffereAbemaciclib LY835219) is an orally available, small molecule inhibitor of cyclin-dependent kinases (CDK) 4 and 6 that blocks the transition from the G1 to the S phase of the cell cycle with consequent growth arrest. Based on the data from MONARCH-1–2–3 trials, abemaciclib received FDA approval as monotherapy or in combination with fulvestrant or with an aromatase inhibitor for hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer patients 35219 is . Beyond Here we investigated the potential efficacy of abemaciclib in a panel of PC9, PC9T790M, and HCC827 resistant cells with EGFR-dependent or –independent, intrinsic or acquired mechanisms of resistance. We explored the status of the CDK4/6-cyclin D-Rb pathway, and analyzed the effects of the CDK4/6 inhibitor either alone or combined with osimertinib on cell proliferation, colony formation, growth of spheroids, cell death, and cell senescence. Moreover, we tested the efficacy of combining abemaciclib and osimertinib in preventing the acquisition of resistance in osimertinib sensitive cell models.INK4a in a panel of osimertinib-resistant NSCLC cells originated from EGFR-mutated PC9, PC9T790M, and HCC827 cell lines.Firstly, we tested the phosphorylation and expression of the cell cycle related proteins Rb, Cyclin D1, and p16The resistant clones tested showed different mechanisms of intrinsic or acquired resistance to osimertinib, as described in the Materials and Methods Section  and repoINK4a, while all the other cells, either sensitive or resistant, expressed this protein. We then tested the responsiveness to abemaciclib in terms of cell proliferation inhibition and cell cycle arrest. All the resistant cell lines showed sensitivity to abemaciclib, with IC50 values ranging from 200 nM to 1 µM, comparable to those of the respective parental cells  in absence or in presence of 500 nM abemaciclib. The treatment with increasing doses of osimertinib alone led to the emergence of resistant colonies after 9 weeks, and abemaciclib was effective in postponing and reducing the emergence of acquired resistance to osimertinib. Indeed, as shown in These results suggest that a high concentration of osimertinib from the beginning, when combined with abemaciclib, is more effective than a strategy of dose increase, and completely prevents resistance. Altogether, our results indicate the potential value of this combined administration for mutated NSCLC patients in preventing osimertinib resistance.ClinicalTrials.gov NCT04545710), started in September 2020, that has been evaluating abemaciclib combined with osimertinib in resistant EGFR mutated lung cancer patients post progression on osimertinib.In this study we demonstrated that the CDK4/6 inhibitor abemaciclib alone or combined with osimertinib may be an effective strategy for NSCLC osimertinib-resistant patients progressed after either first- or second-line therapy. Interestingly, the combination is undergoing clinical evaluation in a phase II trial  have been associated with worse outcome ,27. In aIn this study, we evaluated the activation of CDK4/6-Cyclin D-Rb pathway in NSCLC cell lines resistant to osimertinib, with acquired resistance to first-line (PC9BRAFG469A cell line) or second-line therapy  and intrinsically resistant (HCC827EMT and HCC827GR5 cell lines).Rb has been found lost in 100% of NSCLC patients underwent histological transformation to small-cell lung cancer (SCLC) after gefitinib treatment , but no INK4a as a resistance factor to CDK4/6 inhibitors  (MTT) assay as previously described . Cell deThe nature of interaction between abemaciclib and osimertinib was calculated using the Bliss additivism model as previously described . BrieflyTo further investigate the interaction between osimertinib and abemaciclib cells were treated with different concentrations of the two compounds using a TECAN D300e Digital Dispenser . After 72 h, cell proliferation was assessed by crystal violet staining. Heatmap and statistics were generate using Combenefit on MATLAB .Spheroids were generated using LIPIDURE-COAT PLATE A-U96  according to the manufacturer’s instruction and as described previously .3 cells per well. Cells were incubated at 37 °C in 5% CO2 incubator and the medium was changed every 3 days, and at the end of the experiment, cells were fixed with ice-cold methanol, stained with 0.1% crystal violet (Sigma Aldrich). The unbound dye was removed by washing with water. The bound crystal violet was solubilized with 0.2% TritonX-100 in PBS and the absorbance of the solution was measured at a wavelength of 570 nm.Cells were seeded in 6-well culture plates at a density of 5 × 103 cells were plated in 24 well-plates and after 4–12 weeks of treatment (depending on cell model) colonies were fixed, stained and counted. Colonies containing at least 20 cells were scored and data were given as colony number per 24-well plate.In the experiments testing resistance acquisition, 15 × 10The evaluation of Senescence Associated β-Galactosidase  expression was performed using the Senescence β-Galactoside Staining kit  as previously described . The numser780, p-Rbser807/ser811, Rb, Cyclin D1, p16INK4a, p-CDK6, CDK6, p-AKTser473, AKT, pEGFRtyr1068, EGFR, p53, p-MDM2, p21, c-Myc, actin, and HRP-conjugated secondary antibodies was from Cell Signaling Technology; the chemiluminescence system (ImmobilionTM Western Chemiluminescent HRP Substrate) was from Millipore . Reagents for electrophoresis and blotting analysis were from BIO-RAD . The whole Western blots are shown in Procedures for protein extraction, solubilization, and protein analysis by 1-D PAGE are described elsewhere . Antibodt-test or by one-way ANOVA followed by Bonferroni’s post-test, and p values are indicated where appropriate in the figures and in their legends. Adjusted p values of less than 0.05 were considered significant.Statistical analyses were carried out using GraphPad Prism version 6.0 software . Results are expressed as mean values ± standard deviations (SD). Differences between the mean values recorded for different experimental conditions were evaluated by Student’s For advanced NSCLC EGFR-mutated patients with T790M positive tumors progressing after second-line and for patients progressing after first-line osimertinib, chemotherapy remains the standard of care. Our preclinical data strongly support the ongoing clinical evaluation of abemaciclib in combination with osimertinib in osimertinib-resistant patients regardless of the type of resistance developed, additionally suggesting that abemaciclib might be equally effective as monotherapy. Moreover, the combination of osimertinib with abemaciclib strongly reduced the appearance of resistance and may represent a potential future strategy for the first-line therapy.