Micheliolide (MCL), derived from parthenolide (PTL), is known for its antioxidant and anti-inflammatory effects and has multiple roles in inflammatory diseases and tumours. To investigate its effect on renal disease, we intragastrically administrated DMAMCL, a dimethylamino Michael adduct of MCL for in vivo use, in two renal fibrosis models–the unilateral ureteral occlusion (UUO) model and an ischaemia-reperfusion injury (IRI) model and used MCL in combination with transforming growth factor beta 1 (TGF-β1) on mouse tubular epithelial cells (mTEC) in vitro. The expression of fibrotic markers (fibronectin and α-SMA) was remarkably reduced, while the expression of the epithelial marker E-cadherin was restored after DMAMCL treatment both in the UUO and IRI mice. MCL function in TGF-β1-induced epithelial-mesenchymal transition (EMT) in mTEC was consistent with the in vivo results. Metadherin (Mtdh) was activated in the fibrotic condition, suggesting that it might be involved in fibrogenesis. Interestingly, we found that while Mtdh was upregulated in the fibrotic condition, DMAMCL/MCL could suppress its expression. The overexpression of Mtdh exerted a pro-fibrotic effect by modulating the BMP/MAPK pathway in mTECs, and MCL could specifically reverse this effect. In conclusion, DMAMCL/MCL treatment represents a novel and effective therapy for renal fibrosis by suppressing the Mtdh/BMP/MAPK pathway. Micheliolide (MCL) is known for its antioxidant and anti-inflammatory effects and has multiple effects in inflammatory diseases and tumors. The authors studied the effects of an MCL derivative in two renal fibrosis models. They found that the metastasis adhesion protein metadherin (Mtdh) was activated under fibrotic conditions and that MCL suppressed its expression via the BMP/MAPK pathway. Michelia compressa and Michelia champaca [Micheliolide (MCL) and parthenolide (PTL) are compounds isolated from the herbs champaca . MCL, aschampaca , 3. The champaca . The dimchampaca , 5, 6. Ichampaca –3, 6, 7.Our previous studies on MCL showed its anti-inflammatory effect on adipohepatic damage in diabetic mice  and the Renal fibrosis is the most common feature of chronic kidney diseases (CKD) progressing to end-stage renal disease. This process involves the loss of the renal parenchyma and the excessive accumulation of extracellular matrix (ECM) , 11. RenMetadherin (Mtdh), also known as lysine-rich CEACAM1 co-isolated (LYRIC)/3D3 and astrocyte elevated gene-1 (AEG-1), was initially discovered as an oncogene that is upregulated in various types of cancers, such as breast carcinoma and hepatoma , 20. StuMtdh is expressed in not only the tumour condition but also in healthy organs, and the kidney is one such organ with high Mtdh expression . AccordiIn this study, we highlight the therapeutic efficacy of MCL on renal fibrosis in vivo and in vitro and further investigate the underlying mechanism of Mtdh as a target of MCL in this condition.MCL and DMAMCL were synthesized by Accendatech Co., Ltd. . DMAMCL was freshly dissolved in normal saline (NS) daily for the animal experiments. MCL was prepared in dimethyl sulphoxide  at a concentration of 20 mM and stored at −20 °C.4 cells/ml was seeded in 96-well plates and cultured in a 37 °C incubator with 5% CO2. Upon reaching 70% confluence, the cells were treated with MCL  for 24 h. Then, 5 mg/ml 3--2,5-diphenyltetrazolium bromide  was added to the media (10 μl/well) for 4 h. The supernatant was carefully aspirated, and 150 μl/well of DMSO, was added to the medium to dissolve the remaining crystals. After low-speed vibration for 10 min at room temperature, the optical density of all wells was measured at 490 nm using a microplate Reader . The MTT assay was repeated independently at least 3 times, and the viability curve of mTECs was generated using GraphPad Prism 6 software .An MTT assay was used to detect the cytotoxicity of MCL towards mouse tubular epithelial cells . A total of 1 × 10n = 6 mice per group): (1) sham-operated mice with a normal saline (NS) gavage (sham group); (2) unilateral ureteral occlusion (UUO) mice with an NS gavage (vehicle group); and (3) UUO mice with a DMAMCL gavage . The UUO model was generated using an established procedure, as previously described [For the UUO model, C57BL/6 mice  were purchased from and housed at the Southern Medical University Laboratory Animal Centre . The mice were divided into three groups as follows (escribed . Followin = 6 mice per group): (1) sham-operated mice with a normal saline (NS) gavage (sham group); (2) IRI mice with an NS gavage (vehicle group); and (3) IRI mice with a DMAMCL gavage . The IRI surgery was conducted using an established procedure [For the IRI model, C57BL/6 mice  were purchased from Pengyue Laboratory Animal Breeding Company  and housed at the Southern Medical University Laboratory Animal Center . The mice were divided into three groups as follows , constant humidity (55 ± 15%) and a 12-hour light-dark cycle, and they were provided standard food and water. The animal experimental protocols were approved by the Southern Medical University Ethics Committee  and strictly complied with ethical principles throughout the whole experiment.Kidney tissues were fixed with a 4% paraformaldehyde solution, embedded in paraffin, and sectioned into 4-μm-thick slices. The kidney tissue sections were stained with Masson’s trichrome dyes and then examined under a light microscope.TM III Detection System/Mo & Rb  was used following the manufacturer’s instructions. All antibodies were freshly diluted with 2% goat serum . The LYRIC (Mtdh) antibody  was used at a 1:100 dilution, the fibronectin antibody  was used at a 1:200 dilution, the α-SMA antibody  was used at a 1:250 dilution and the E-cadherin antibody  was used at a 1:100 dilution. Slides were counterstained with haematoxylin followed by a bluing reagent. Negative controls were treated with PBS instead of primary antibodies.Immunohistochemical staining was performed as previously described, with a slight modification . A GTVis2 at 37 °C. Upon reaching approximately 70% confluence, the cells were serum-starved for 12 h and then treated with recombinant human TGF-β1  for the time course or co-incubated with MCL , as indicated.The mTECs were cultured in DMEM/F12  supplemented with 5% FBS  in a humidified incubator with a 5% COThe mTECs were obtained to construct an Mtdh stable overexpression cell line in this study. mTECs were seeded in 12-well plates and cultured as described above before various concentrations of puromycin were added (0–40 μg/ml). The viable count of mTECs was calculated, and the minimum concentration of puromycin that caused complete mTECs death was recorded. The process was repeated twice, and the screening concentration of 10 ng/ml was used in subsequent experiments. Mtdh recombinant packaged lentivirus particles (LV-Mtdh) were constructed by GENECHEM , and a lentivirus vector lacking Mtdh sequences was used as a negative control (LV-NC). The related information regarding the Mtdh lentivirus is shown in the Supplementary Materials (sup1). mTECs were infected with packaged virus particles and polybrene (8 μg/ml) overnight, according to the manufacturer’s instructions. Fresh culture medium containing 10 ng/μl puromycin was added early the next morning. The infected cells continued to grow until they reached 95% confluence and were then reseeded into new 100-mm dishes at a 10–20% density and grow continuously for 2 weeks. The 5% FBS medium containing 10 ng/μl puromycin was replaced every 3 days until drug-resistant clones appeared. Ten clones were carefully picked, and the cloned cells were cultured, passaged, and finally frozen at −80 °C. A stable Mtdh-knockdown mTEC line was established as described above.TM 2000  and a certain volume of plasmid was added to the culture medium. 6 h later, the transfection reagents were removed, fresh complete medium supplemented with 5% FBS ± MCL (10 μM) was added, and the cells were cultured for an additional24 h before harvest.Mtdh sequences were inserted into a pcDNA3.1 vector to construct a plasmid targeting Mtdh (Mtdh vector) by GenePharma . The empty vector was used as a control (Negative control). mTECs were seeded into 6-well plates. Upon reaching 70% confluence, LipofectamineTM Western, Millipore, Massachusetts, USA). Quantification was performed by measuring the grey scale intensity of the bands using Photoshop CS5 software .Cell or the whole kidney tissue lysates were extracted in SDS lysis buffer containing 1 × PMSF  and 1 × phosphatase inhibitor . Western blots were performed as indicated with the following primary antibodies: Mtdh, fibronectin, BMPR1A, and Smad1/5/9 ; Col I ; α-SMA ; E-cadherin ; p-P38 MAPK, P38 MAPK, p-ERK, ERK, and p-Smad1/5/9 , and GAPDH . The blots were subsequently incubated with an HRP-conjugated secondary antibody  for 1–2 h at ambient temperature. Then, the membranes with immobilized antibodies were detected by enhanced chemiluminescence . All data were from at least three independent experiments and presented as the mean ± standard error of the mean (SEM). Student’s t-test was used to analyse the differences between two groups, whereas ANOVA was used for the comparisons in multiple groups, followed by Student’s MCL and DMAMCL Fig.  were synn = 6 animals per group), and NS was used as a vehicle control. As shown in Fig. We initially investigated the effect of DMAMCL, the pro-drug of MCL, on renal fibrosis in a mouse UUO model. DMAMCL was administered the day after the operation via gavage at a dosage of 25 mg/kg per day  and used an empty vector to generate a negative control cell line (LV-NC). After screening positive stable cell lines with 10 ng/ml puromycin, the Mtdh level was significantly increased in LV-Mtdh cell lysates compared with LV-NC lysates Fig. . We deteMtdh stable-knockdown cell lines were constructed (sh-Mtdh) as previously described, and sh-NC served as a negative control to further evaluate the effect of Mtdh on renal fibrosis. Dramatic decreases in fibronectin, collagen I and α-SMA expression were observed in the sh-Mtdh group, even in the presence of TGF-β1 stimulation. Consistent with the levels of mesenchymal cell markers, the expression of the epithelial cell marker E-cadherin was increased after Mtdh knockdown Fig. .Fig. 7KnBecause Mtdh plays a pivotal role in renal fibrosis, and DMAMCL/MCL inhibits its expression and exerts anti-fibrotic effects, we hypothesized that the therapeutic effects of DMAMCL/MCL might be attributed to the mechanism by which Mtdh expression is inhibited. mTECs transiently transfected with the Mtdh target plasmid were treated with MCL in vitro for 24 h to verify this hypothesis. As shown in Fig. We next examined whether Mtdh was involved in crucial modulatory pathways, such as the TGF-β and BMP signalling. The BMP/BMPR1A/Smad1/5/9 pathway competitively antagonizes TGF-β/Smad2/3 signalling, and both p-Smad1/5/9 and p-Smad2/3 can combine with Smad4 to form a complex for transport into the nucleus, where this complex functions to regulate gene transcription. As shown in Fig. The MAPK pathway, which includes P38 MAPK and ERK, is a non-classical BMP signalling pathway that does not involve Smads and plays an important role in many aspects of fibrogenesis. Here, Mtdh overexpression increased P38 MAPK and ERK phosphorylation. As shown in Fig. The same experiment was performed in Mtdh knockdown cells with or without TGF-β1 treatment to confirm the effects of Mtdh on TGF-β, BMP and MAPK signalling. As shown in Fig. In the present study, we provide evidence of the effects of DMAMCL/MCL, derivatives of PTL, on alleviating renal fibrosis in vivo and in vitro. Additionally, a novel pro-fibrotic mediator was identified—Mtdh. Coincidentally, DMAMCL/MCL suppressed Mtdh expression in renal fibrotic models in vivo and in vitro. We provide strong evidence that DMAMCL specifically reduced the EMT induced by suppressing Mtdh expression in vitro. Therefore, this study identified a novel pro-fibrotic mediator, Mtdh, and was the first study to reveal the anti-fibrotic potential of DMAMCL/MCL in treating renal diseases by suppressing the Mtdh/BMP/MAPK axis.Michelia, present promising anti-fibrotic efficacy. These compounds are derived from PTL, an anti-inflammatory drug with a long history of use in treating various diseases. With improved water solubility and stability, DMAMCL/MCL are expected to have more extensive applications. MCL has been shown to inhibit glioma cell growth by inducing apoptosis in colitis-associated cancer cells [DMAMCL/MCL, isolates of er cells , activater cells , suppreser cells , and aller cells . This stMCL may reverse fibrosis through many pathways; here, we focused on Mtdh, a novel factor involved in kidney fibrogenesis, as described above. Based on the results from both in vivo and in vitro studies, DMAMCL/MCL suppressed Mtdh expression. Specifically, MCL suppressed EMT in mTECs induced by Mtdh overexpression. Thus, Mtdh might be a selective target of DMAMCL/MCL that mediates the anti-fibrotic effect. However, we were unable to clarify the correlation between the Mtdh level and the severity of fibrosis due to insufficient samples.Certain previous studies have focused on the role of Mtdh in the EMT. A positive correlation between Mtdh overexpression and EMT in cancers has been reported . HoweverEMT has been studied for the past decade to gain a better understanding of its roles in tumourigenesis and organ fibrosis. Intrinsic cells in the kidney undergo EMT during injury, which is an important mechanism in renal fibrosis . At day We constructed two stable mTEC lines to clarify whether Mtdh exacerbates EMT in vitro; one line stably overexpressed Mtdh , and the other was a stable Mtdh-knockdown line . Compared with the LV-NC group, the LV-Mtdh group showed a higher EMT potential. Fibronectin, collagen I, and α-SMA were significantly upregulated in the LV-Mtdh group. Mtdh-knockdown cells presented a low sensitivity to TGF-β1. Compared with the sh-NC group, the sh-Mtdh group did not readily acquire the EMT phenotype upon TGF-β1 stimulation. These in vitro results provided further support for our hypothesis that Mtdh is involved in the promotion of kidney fibrosis. The effect of Mtdh on animals requires further investigation.TGF-β/Smads and BMP/Smads are antagonists of the pathogenesis of renal fibrosis. TGF-β1 produces its downstream effects by binding to TβRII and subsequently activating TβRI, leading to Smad2/3 phosphorylation and the recruitment of Smad4 to form the R-Smad complex that translocates into the nucleus to regulate the transcription of pro-fibrotic genes , 34. BMPAs shown in the present study, Mtdh overexpression reduced BMP7 and BMPR1a levels and increased the levels of phosphorylated Smad1/5/9, but not Smad2/3, suggesting that Mtdh might function upstream of BMP/Smad1/5/9 rather than TGF-β/Smad2/3. The data obtained from Mtdh knockdown cells were consistent with these observations. The P38 MAPK and ERK pathway, a non-canonical BMP signalling pathway, were associated with Mtdh. According to our data, Mtdh increased the phosphorylation of P38 MAPK and ERK. To the best of our knowledge, this study is the first to verify Mtdh expression in the UUO mice and IRI mice and to link Mtdh and the BMP pathway.In conclusion, we identified Mtdh as a novel modulator of fibrogenesis. Mtdh regulates BMP/MAPK signalling and mainly influences Smad1/5/9 phosphorylation, instead of Smad2/3 phosphorylation, suggesting that Mtdh exerts a selective effect on BMP signalling rather than the TGF-β/Smad2/3 pathway. The administration of the DMAMCL/MCL intervention to UUO mice and TGF-β1-treated mTECs exerts a potent anti-fibrotic effect by suppressing Mtdh expression. This study provides a promising lead compound for developing an effective anti-fibrotic therapy.


Patients suffering from high risk stage II colon cancer (CC) may benefit from adjuvant onco-therapy, but additional prognostic markers are needed for better treatment stratification. We investigated the prognostic value of Programmed Death Ligand-1 (PD-L1) in a true population-based cohort of patients with stage II CC.PD-L1 expression on tumour cells was evaluated by immunohistochemistry in 572 colon cancers. Whole sections from tumour blocks representing the deepest invasive front of the primary tumour were used for analysis. A cut-off of 5% positivity was used for dichotomizing the data. The prognostic value was investigated in Cox proportional hazard models for recurrence-free survival (RFS) and overall survival (OS).p = 0.028), high malignancy grade (< 0.001), right side localization (p < 0.001) and microsatellite instability (MSI) (p < 0.001). Thirty-one (18%) of the MSI and 4 (1%) of the microsatellite stable tumours were classified as high PD-L1, respectively. PD-L1 expression provided no prognostic value as a single marker. In patients with MSI tumours, high PD-L1 expression had no significant impact regarding OS or RFS.Overall, 6% of the tumours were classified as high PD-L1. High PD-L1 was related to female gender (PD-L1 expression in tumour cells of stage II CC did not provide any prognostic impact, neither in the entire population-based cohort nor in the group of MSI patients. Additional investigations of the immunogenic microenvironment are needed for evaluating the prognostic information in CC. Colon cancer is one of the most common cancers in the Western world. About 1/3 have stage II disease, and this group of patients are in general having a good prognosis with a 5-year overall survival (OS) of approximately 70–80% after surgery alone , 3. HoweIn recent years, the tumour microenvironment has been investigated, and the role of the interaction between cancer cells and the immune system in cancer surveillance has been emphasized . Tumour-Current results of the prognostic value of PD-L1 in CRC are controversial. Some studies only report on trends towards worse prognosis for high PD-L1 expression , 10, whiWith this motivation, the aim of the present study was to evaluate the prognostic impact of PD-L1 in a nationwide, population-based cohort of stage II CC.This study is reported in accordance with the REMARK guidelines .N = 746). Exclusion criteria were as follows: missing tumour block (N = 11), insufficient tissue for analyses (N = 2), incorrectly staged patients (N = 25), treatment with adjuvant chemotherapy (N = 26)/radiotherapy (N = 1) and death within 90 days after the operation (N = 75). Furthermore patients with loco-advanced disease (N = 8) and patients diagnosed with another malignancy prior to CC were excluded from the study (N = 26), and the final study population comprised 572 patients.The population and sources of data has previously been descreibed in detail . In brieHistopathological data were obtained by microscopic examination and from the national Patobank containing all pathology reports in Denmark. The term “not assessed” was used if the pathological feature was not described. Clinical data were obtained from The National Patient Registry.Recurrence primarily occurred within the first five years of follow-up and to encompass the majority of recurrences a follow-up period of seven years was selected.Formalin-fixed, paraffin-embedded tissue blocks were collected from the departments of pathology in Denmark. The tissue blocks were stored and transported at room temperature. One tumour block representing the deepest invasive margin, was selected from each patient. Prior to inclusion, all histological slides from each tumour were evaluated by first a trainee and afterwards an experienced pathologist.From the selected tumour blocks serial 4 μm thick tissue sections were cut and mounted on FLEX IHC Microscope Slides . One whole tumour section per patient was used for the evaluation of PD-L1 expression. Staining was performed using a Ventana BenchMark ULTRA  automated immunohistochemistry (IHC) slide staining system. Tissue sections were heated and deparaffinised in EZprep . Pre-treatment and demasking were carried out using ULTRA CC1 (no. 950–224) and ULTRA CC2 (no. 950–223), and endogenous peroxidase activity was blocked by Optiview Peroxidase Inhibitor (no. 760–700 Ventana). The slides were incubated with a rabbit monoclonal anti-PD-L1  for 16 min. This clone was chosen based on a pilot study. For amplification Optiview HQ Universal Linker  and Optiview HRP Multimer  was each used for 8 min.2O2 and DAB , followed by Optiview Copper . Counterstain was done using Hematoxylin II  and bluing Reagent . Finally the histological slides were cover slipped with Tissue-Tek PERTEX .The primary antibody was visualized using Optiview H4 in TBS buffer pH 7.6 for 10 min to enhance the immunohistochemical staining. Sections were counterstained with Meyer’s hematoxylin .Evaluation of microsattellite instability (MSI) was performed using IHC. Tumours displaying loss of one or more of the 4 mismatch repair proteins  were considered as MSI, whereas tumours with intact mismatch repair proteins were considered as microsatellite stable (MSS). Staining of mismatch repair proteins was performed using a DAKO Autostainer Link 48 (DAKO) with monoclonal mouse antibody against MLH1 , MSH2 , MSH6 , and PMS2 . Tissue sections were incubated for 30 min at room temperature with the primary antibodies diluted in Envision Flex antibody diluent (code S2022 DAKO). The antibody signal was amplified using Envision Flex+ Mouse(Linker) (DAKO) for 20 min. Bound antibodies were detected using Envision FLEX/HRP (DAKO) and visualized by Envision FLEX DAB (DAKO) and chromogene diluted in Envision Flex Substrate Buffer (DAKO). The sections were incubated in 0.5% CuSOTumour PD-L1 expression was evaluated based on immunostaining of the cell membrane of the epithelial tumour cells. The immunostaining of the stromal cells were not evaluated. Tumour cells were considered positive when any cell membrane staining  was present. Staining intensity was not evaluated and cytoplasmatic immunoreaction was not considered.The percentage of positive tumour cells were scored semi-quantitatively as 0 (no positive tumour cells), 1 (≤1% positive), 2 (> 1 and ≤ 5% positive), 3 (> 5 and ≤ 20% positive), 4 (> 20 and ≤ 50% positive) and 5 (> 50% positive)  values were calculated, and agreement was described according to Landis et al  as moderN = 102). The median age was used as cut-off to dichotomize the parameter age. Survival curves were generated according to the Kaplan-Meier method and the log-rank test was used to test for differences between groups. The multivariable Cox-regression model was used to test for independent prognostic value with hazard ratio (HR) of 1.0 as reference and a 95% confidence interval (CI). A cut-off significance level of 0.10 was pre-specified for a variable to be included in the multivariable Cox regression model.The endpoint OS was defined as time from operation to death of any cause or last follow-up. RFS was defined as time from operation to death of any cause or recurrence of CC. Patients later diagnosed with another cancer were censored at the date of diagnosis , and all statistical tests were two-sided.ChiPatient characteristics are summarized in Table The IHC staining of PD-L1 often had a highly heterogeneous expression both between the central part of the tumour and the invasive margin and along the invasive tumour front Fig. .Fig. 2Exp = 0.028), high malignancy grade (< 0.001), right side localization (p < 0.001) and MSI (p < 0.001).Results regarding PD-L1 expression are displayed in Table The inter-observer agreement for the semi-quantitative evaluation of PD-L1 expression was moderate with κ = 0.418 and weighted κ = 0.573. The agreement improved to substantial, when categorizing the data into high PD-L1 expression (> 5%) or low PD-L1 expression (≤5%), κ = 0.691.p = 0.256 , p = 0.149.The 5-year RFS for the population with low PD-L1 was 69.2% versus 67.7% in the group with high PD-L1 expression, and OS was 74.7% versus 70.5%, respectively. When considering patients with MSI tumours the 5 year RFS for low PD-L1 was 77.4% versus 67.5% in the group of high PD-L1, and OS was 79.4% versus 70.7%. No significant differences in survival rates were observed, considering the entire cohort Fig. . In the p = 0.748 and HR = 1.429 (0.769–2.653), p = 0.258, respectively. Age ≥ 73 years, T4 tumour and perforation were significantly related to an adverse outcome of both OS and RFS. Patients with MSI T3 tumours and high PD-L1 expression did neither have a significant worse OS or RFS, HR = 1.531 (0.757–3.100), p = 0.236 and HR = 1.637 (0.833–3.216), p = 0.153, respectively.Outcomes from the corresponding univariable Cox regression analyses are shown in Table p-value > 0.10 in the univariable Cox-regression analysis.Multivariable Cox regression analyses were not performed, as PD-L1 had In this study, we investigated the prognostic value of PD-L1 expression on tumour cells in an unbiased, nationwide and population-based cohort of patients with stage II CC, treated exclusively with surgery. PD-L1 expression as a single marker did not provide any significant prognostic value regarding OS or RFS, neither in the the entire cohort nor in the subgroup of patients with MSI tumours.In the entire cohort we found 6% of the tumours to have a high expression of PD-L1 on tumour cells, which is in accordance with other studies of CRC, reporting 5% positivity . LikewisThe association between MSI and high PD-L1 expression may be explained by the abundant infiltration of TILs in these tumours. Deficiency of the mismatch repair proteins results in a number of mutations. Therefore MSI tumours have a high load of tumour specific neo-antigens, which can induce an immunological response with recruitment and activation of T-cells . One wayIn the group of patients with MSI tumours, the Kaplan-Meier curves were clearly separated regarding RFS, with a worse RFS related to a high PD-L1 expression, but statistical significance was not reached. This is in accordance with studies of tumour expression of PD-L1 in MSI stage I-IV CRC. Kim et al  reportedIn contrast to our data, Koganemaru et al  reportedLee et al  documentFurthermore, previous studies reporting independent prognostic impact of PD-L1, differ in investigated cohorts and evaluation methods. Lee et al  includedA standardized scoring system for PD-L1 expression in CC is missing and several unvalidated methods are in use. In the present study, the proportion of PD-L1 positive tumour cells was evaluated considering only membranous staining as positive. Intensity of the staining and cytoplasmic immunoreaction were not considered. Whether to take cytoplasmic staining of PD-L1 into account differs among studies. Some investigations use a combination of membranous staining and staining intensity  while otAs mentioned above, we did not consider intensity of the staining. Intensity of IHC may be difficult to interpret. Poor reproducibility of IHC staining intensities of various proteins has been documented, while excellent inter-observer reproducibility was found estimating the fraction of positive tumour cells . PD-L1 wThe lack of a standardized IHC method challenges the assessment of PD-L1 expression, and moreover, different trials use different antibodies and assays. In melanoma, diverse assays have been found to variate in staining sensitivity of tumour cells . For futThe present study is limited by the retrospective design, as we had no influence on the pre-analytical phase of the IHC. However, we used a validated antibody on a fully automatic platform, and only considered membranous staining according to the manufactures recommendation. The PD-L1 antibody used in this study stained both the malignant epithelial cells and immune cells in the stroma, which made it difficult to discriminate these cellular populations when the density of immune cells in the tumour stroma interface was high.The prognostic value of PD-L1 expression on tumour cells is controversial. We investigated the expression of PD-L1 exclusively in stage II CC in a well-defined and unbiased population, and did not find any prognostic value of PD-L1 as a single biomarker. The PD-L1 expression in tumour cells should be seen in the context of the entire immune tumour microenvironment. The expression of PD-L1 is influenced by TILs, which is related to MSI. We did not prove any prognostic value of PD-L1 in patients with MSI tumours, however this interaction should be taken into consideration in future studies.In this nationwide population-based cohort of stage II colon cancer, we found membranous PD-L1 expression (cut-off 5%) in tumour cells of stage II colon cancer to be associated with female gender, high malignancy grade, right side localisation and MSI. The expression of PD-L1 was often highly heterogenous. No prognostic information was detected of PD-L1 as a single biomarker in this cohort of stage II colon cancer.


Neurotrophins are related to survival, growth, differentiation and neurotrophic maintenance as well as modulation of synaptic transmission in different regions of the nervous system. BDNF effects have been studied in the striatum due to the trophic role of BDNF in medium spiny neurons; however, less is known about the effects of NT‐4/5, which is also present in the striatum and activates the TrkB receptor along with BDNF. If both neurotrophins are present in the striatum, the following question arises: What role do they play in striatal physiology? Thus, the aim of this study was to determine the physiological effect of the sequential application and coexistence of BDNF and NT‐4/5 on the modulation of corticostriatal synapses. Our data demonstrated that neurotrophins exhibit differential effects depending on exposure order. BDNF did not modify NT‐4/5 effect; however, NT‐4/5 inhibited the effects of BDNF. Experiments carried out in COS‐7 cells to understand the mechanisms of this antagonism, indicated that NT‐4/5 exerts its inhibitory effect on BDNF by upregulating the TrkB.T1 and downregulating the TrkB‐FL isoforms of the TrkB receptor. Neurotrophins are involved in neural activities, such as synapse formation, synapse modulation and neuronal plasticity.In the striatum, BDNF, NT‐4/5, and TrkB receptors are present.2s committee.Male C57BL/6 mice  35 days old at the beginning of the experiments were used. The mice were housed in groups of five in Plexiglas boxes at room temperature (24‐26°C) under a 12:12 hours light/dark cycle with free access to food and water. The experimental procedures followed the national and international regulations for the care and use of experimental animals and were approved by the local bioethic2.1BDNF and NT‐4/5  were used in a concentration of 50 ng/mL , and the rest of reagents were purchased from SIGMA‐Aldrich Co. LLC  unless otherwise stated.2.22, 5% CO2) low calcium saline solution . Sagittal brain slices (400 µm) containing the striatum, were obtained in the low calcium saline with a vibroslicer . The slices were maintained at room temperature (25°C) in oxygenated physiological saline for 1 hour prior to recording.The mice were anesthetized with halothane; then, they were decapitated, and their brains removed and placed in ice‐cold (4°C) and oxygenated , constantly bubbled . (−)‐Bicuculline methiodide (10 µmol/L) was used in all experiments to block GABAergic inhibitory responses. A stimulus  generated by an isolated voltage stimulator  was administered through a tungsten bipolar electrode  placed in the corpus callosum. The amplitude and duration of the stimulus were held at 45%‐50% of the maximum amplitude response of the population spike. The population spikes were recorded in the dorsal striatum at 32‐34°C using borosilicate glass micropipettes  filled with isotonic solution . The signal was amplified  and filtered (1 kHz) with an interface coupled to a PC running program in Lab View 6.1 .The electrophysiological experiments were performed in a recording chamber perfused with physiological saline  with the same duration and intensity was administered through the bipolar electrode with 30‐50 ms intervals. The population spikes were recorded for 10 minutes before neurotrophin treatment. Data were digitized, stored and analyzed offline with the aid of Microcal Origin, 9.1 .2.52‐5% CO2) in the physiological saline in the presence of Bicuculline; then, the slices were exposed to (a) BDNF (50 ng/mL), (b) BDNF (50 ng/mL) → NT‐4/5 (50 ng/mL), (c) NT‐4/5 (50 ng/mL), or (d) NT‐4/5 (50 ng/mL) → BDNF (50 ng/mL) for 10 or 30 minutes. Then, the striatum was obtained and manually homogenized in lysis buffer  and phosphatase inhibitor complete mini tabs . The homogenate samples were collected, centrifuged (5 minutes × 2812.5 g), and the supernatant was stored at −70°C. A total of 10‐30 µg of protein quantified by the Bradford method was loaded onto 10% polyacrylamide gels for electrophoresis. The proteins were transferred to polyvinylidene fluoride (PVDF) membranes and incubated with primary antibodies against phosphor‐Tyr816‐TrkB (1:1000), TrkB (1:1000), phospho‐Tyr783‐PLC‐γ1 (1:1000), PLC‐γ1 (1:1000), phospho‐Tyr759‐PLC‐γ2 (1:1000), PLC‐γ2 (1:1000), and actin (1:1000) for 12 hours at 4°C. The membranes were then incubated with hydrogen peroxidase‐conjugated (HRP) secondary antibodies for 1‐2 hours at room temperature. All primary antibodies were purchased from Cell Signaling , and peroxidase‐goat antimouse IgG and peroxidase‐goat antirabbit IgG secondary antibodies were purchased from Invitrogen  and ThermoFisher Scientific , respectively. Protein detection was performed via the chemiluminescence method , and images were analyzed with ImageJ software (NIH) for densitometry.To evaluate TrkB receptors and PLC signaling, cerebral slices containing the striatum were incubated at RT and bubbled . When the cells reached 50%‐60% confluence, the medium was changed to FBS‐free Optimem specialized medium (GIBCO), and the cells were transiently cotransfected with 1 μg of DNA from the TrkB constructs (plasmid pGFP‐N1‐TrkB [Addgene #32500] and pRc/CMV HA‐TrkB.T1 [Addgene #39980]) by using the Lipofectamine® 2000 reagent, following the manufacturer's instructions (Invitrogen). After transfection (4 hours), the cells were postincubated in fresh 10% FBS supplemented with DMEM medium and kept at 37°C for 48 hours. Under this transfection protocol, the efficiency was 40%. Thereafter, transfected cells were washed in ice‐cold phosphate‐buffered saline  and processed for Western blotting.The COS‐7 cell line obtained from the ATCC  was grown in DMEM; glucose (1/1) supplemented with 10% fetal bovine serum ; 2 mmol/L L‐glutamine; 100 U/mL penicillin; and 100 μg/mL streptomycin, and the cells were maintained under a humidified atmosphere  and centrifuged (12 000 g × 10 minutes). The supernatant was collected, and protein content determined by the mini‐Bradford assay . A total of 30 μg of protein was mixed in 5× sample buffer  10%, bromophenol blue 0.5%, β‐mercaptoethanol 12.5%, and glycerol 50%) and boiled . Proteins were separated by electrophoresis on a 10% SDS‐polyacrylamide gel (SDS‐PAGE) and transferred onto a nitrocellulose membrane for immunoblotting analysis. Membranes were blocked in 10% nonfat dried milk in PBS or TBS‐0.1% Tween 20 (PBS‐tw or TBS‐tw) overnight at 4°C and incubated for 12 hours in primary antibodies diluted in PBS‐tw or TBS‐tw. After washing, incubation with the corresponding HRP‐conjugated secondary antibodies to either mouse or rabbit was carried out for 1‐2 hours (RT). Bands of immunoreactive proteins were visualized as described above.After cell transfection and neurotrophin treatment, cell cultures were washed twice with PBS, scraped, lysed in radioimmunoprecipitation assay (RIPA) buffer containing a cocktail of protease inhibitors  using parametric or nonparametric one‐way ANOVA, Data followed by post hoc analysis. Significance was set at 33.1.With a stable recording, either BDNF or NT‐4/5 was applied to the recording bath. Figure 3.2Next, we sought to evaluate whether BDNF was able to antagonize the effect of NT‐4/5 on corticostriatal transmission. The application of NT‐4/5 to the bath increased the spike amplitude compared to the spike amplitude in control condition. The addition of BDNF to the bath did not change the modulatory effect of NT‐4/5, suggesting that once NT‐4/5 activated the TrkB receptor, BNDF did not have any further modulatory effect Figure E‐F. NT‐43.3Neurotrophins exert their effects through the activation of Trk receptors; these receptors, in turn, trigger several intracellular pathways. To understand the intracellular mechanisms that mediate the effects of BDNF and NT‐4/5, Trk receptors and the PI3K, MAPK and PLC‐γ signaling pathways were inhibited with K252a, LY294002, U0126, and U73122, respectively. Our data indicated that the inhibition of all of these factors impedes the modulatory effect of BDNF on corticostriatal transmission. Figure Analyzing the role of PLC in BDNF, we found that U73122, a PLC inhibitor, increased the spike amplitude  and NT‐4/5 treatments strengthen the expression of p‐TrkB, TrkB‐FL, TrkB.T1, and TrkB‐FL+T1 receptors compared to untreated cells. No basal expression of any receptor was obtained in control nontransfected cells, and, no expression of p‐TrkB was found in cells expressing TrkB.T1, which is the truncated isoform that lacks tyrosine kinase activity Figure A.The BDNF → NT‐4/5 treatment induced the occurrence of p‐TrkB and the normal expression of TrkB‐FL but upregulated TrkB.T1 expression compared to BDNF treatment alone Figure B. The NT4The main finding in our study was the discovery that NT‐4/5 inhibits BDNF activity by stimulating the TrkB.T1 isoform and downregulating the TrkB‐FL receptor. Previously, we showed that BDNF and NT‐4/5 potentiate corticostriatal transmission to different degrees.To understand the mechanisms underlying our results, we explored signaling pathways and receptor involvement. The inhibition of the TrkB, PI3K, and MAPK signaling pathways impeded BDNF or NT‐4/5 modulation on corticostriatal synapses, demonstrating that these signaling pathways are essential for glutamatergic transmission modulation by neurotrophins, as seen in other cerebral areas.2+ intracellular stores,The U73122 increased the spike amplitude by itself compared to the control. This result was surprising since the U73122 is a specific inhibitor of PLC‐γ. Nonetheless, several studies have suggested that U73122 has unrelated effects on the inhibition of PLC, including the depletion of CaThe experiments in which the treatment sequence was BDNF → PLC inhibitor → NT‐4/5, NT‐4/5 kept antagonizing BDNF effect, indicating that NT‐4/5 inhibition of BDNF was independent from PLC‐γ activation. Nonetheless, in the NT‐4/5 → PLC inhibitor → BDNF treatment, BDNF did not decrease the spikes amplitude as it did in the experiments preincubated only with the PLC blocker. It is possible that U73122 changed the neurotrophic effect by modifying calcium influx.4.1Y816 was estimated for each neurotrophin 10 or 30 minutes after treatment. BDNF and NT‐4/5 independently induced similar p‐TrkB levels; however, when the treatment order was BDNF → NT‐4/5, p‐TrkB was reduced. If the treatment order was NT4/5 → BDNF, p‐TrkB increased. These results suggested that NT‐4/5 antagonism resulted in the interaction of NT‐4/5 with one of the TrkB receptor isoforms after BDNF has phosphorylated TrkB receptor. TrkB‐FL and TrkB.T1 are present in striatal neurons,We speculated that BDNF and NT‐4/5 had differential interactions with the TrkB receptor isoforms in the electrophysiology experiments. Thus, the phosphorylation of p‐TrkBAlthough occlusive effects among neurotrophins have been described previously in the nervous system,Our results in COS‐7 cells indicate that NT‐4/5 upregulates TrkB.T1 and downregulates TrkB‐FL. TrkB.T1 was evaluated in COS‐7 cells because it is the predominant isoform in the adult brainFinally, our study shows that BDNF and NT‐4/5 stimulate TrkB receptors differently under certain physiological conditions. There is evidence of a different interaction between BDNF and NT‐4/5 on the TrkB receptor; for example, the substitution of cystein 354 for serine 354 on the second immunoglobulin motif of the extracellular domain prevents the morphological transformation of NIH3T3 cells in the presence of NT‐4/5 but not BDNF.What is the physiological meaning of the antagonistic effect of NT‐4/5 on BDNF in the striatum? Perhaps, differential modulation is necessary to reduce the excess excitation produced by the increase in synaptic spikes in the presence of BDNF and to maintain synaptic activity at basal levels. In terms of the circuitry, cortical influences first activate interneurons and medium spiny neurons laterThe authors declare no conflict of interest.


Graphene, an innovated 2D material with atomic thickness, is a very promising candidate and has drawn great attentions in various applications. Graphene metasurface enables dynamic control of various wavefronts, achieving distinguished functionalities. The flexibility of graphene metasurface makes it possible to implement multifunctional devices with ease. In this work, a novel design of multifunctional graphene metasurface, which can combine the functionalities of generating and steering vortex waves, has been proposed. The multifunctional graphene metasurface consists of a large array of graphene reflective unit cells. Each unit cell is controlled independently by its size and external static gate voltage. By scrutinizing the reflective property of the graphene cell, the graphene metasurface is designed to realize multi-functionalities. Simulation results show that vortex wave can be generated and steered. This work can establish a methodology to design multifunctional graphene metasurfaces, and the tunability of graphene opens the gate to the design and fabrication of reconfigurable graphene devices. Graphene, an innovated 2D innovated material with atomic thickness, is drawing more and more attention in biology, optoelectronics, terahertz communication, etc . In teraIt is of great importance to design and fabricate reconfigurable metamaterials to control the behaviour of electromagnetic waves , 13. TheIn telecommunication, orbital angular momentum (OAM) is important to enhance the channel capacity since it can provide infinite state, 29. Thr∘ reflection phase range is calibrated as reference to design a graphene metasurface to combine the functionalities of vortex wave generation and anomalous reflection. The combined metasurface is realized by large array of reflective graphene cells. The simulated results show that vortex waves can be generated and steered by a certain angle of reflection.In this work, based on our previous research on metasurface in micro-nano optics –41, we sThe conductivity of graphene consists of interband and intraband transition. The intraband transition dominates the terahertz and infrared regime, while the interband transition dominates visible optical regime. In terahertz and infrared region, the conductivity can be modelled by Drude model ,\documekB is Boltzmann constant, T is the temperature, τ is the relaxation time, and Ef is Fermi energy.where Ef≫kBT; hence, the equation can be simplified asIn this work, the device operates in the terahertz regime, where T=300K, and the relaxation time of graphene τ=1 ps. In this work, the Fermi energy Ef is controlled by external static gate voltage. In the simulation, graphene is not modelled as 3D metamaterial blocks but 2D surface conductive conditions due to the atomic thickness.assuming the typical value of room temperature λ/10 [Graphene metasurface is composed of large array of graphene cells, which results in collective plasmonic behaviour excited on the surface, realizing extraordinary electromagnetic properties. The frequency is 1.3 THz; thus, due to the slow-wave propagation associated with the plasmonic mode, the resonance can occur at very small sizes, i. e., below λ/10 , 42. In wx×wy is mounted at the center on top of a stack of layered square substrates with side lengths p of 14 µm. A quartz substrate  of 25-µm thickness is placed on top of the metallic ground layer at the bottom. An external biasing DC voltage is applied between the graphene patch and a polycrystalline silicon layer of 50-nm thickness. A 10-nm-thick Al2O3  describes the phase discontinuity along the interface.where ni=1), as shown in Fig. 1 and MS2, Eq. Consider the simplified case that the wave impinges normally, and the upper space is freespace ni=, as showϕt of MSt, we choose a segment Dx along the interface, and the problem becomes the following: assume in x∈Dx, holds −π/2<θr1(x)+θr2(x)<π/2, find ϕt, s. t. for ∀x∈Dx, thatTo obtain It can be derived from Eqs. which leads toθri(x) and can mostly be calculated numerically. Equation where the integration term calculates the contribution of the variance of Furthermore, if the steering angle is constant, the integration term in Eq. This is the governing equation to combine metasurfaces, and the phase distribution can be calculated to combine vortex wave generation and anomalous reflection.1 is the metasurface that generates vortex waves, while MS2 is the metasurface that steers the waves.In this paper, MSl can be generated by a plate of N sectors with successive increment of phase shift. The phase shift of the nth sector ϕn can be calculated as ϕn=ϕ0+2πnl/N, where ϕ0 is the phase shift of the initial sector. Moreover, in order to generate vortex wave, it should be satisfied that −N/2<l<N/2. Therefore, N=4 is sufficient to generate modes l=0, ±1.As illustrated in , vortex l=1, the plate is subdivided into four sectors as shown in Fig. ϕ1 is a piece-wise constant function that decreases by 90∘ through sectors, counterclockwisely.To generate vortex wave with x-polarized wave is impinging normally from above, vortex wave with l=1 will be reflected. It should be noted that the wave is reflected vertically; therefore, the deflection angle is 0∘, i.e., θr1(x)=0∘.When θr, Eq. θi=0∘ and ni=1, Eq. To generate anomalous reflection with deflection angle θr=30∘. From the equation above, by knowing that the period of unit cell is 14 µm, the difference of phase shift between adjacent patches is calculated as 10.9∘. The phase distribution is shown in Fig. In this work, the deflection angle is set as 1 and MS2, we take θr1(x)=0∘ and θr2(x)=30∘ into Eq. t,To combine MSμc and the patch size wx of each cell, a 32×32 graphene metasurface is configured. Figure wx and μc is assigned.From this formula, one can calculate the phase distribution, which is shown in Fig. x-polarized wave impinging from top. The electric field of the incident wave is normalized, i.e., The plate is excited by an l=1 and deflects by 30 ∘ towards x-axis.Figure ∘ phase shift. A graphene metasurface consisting of 32×32 unit cells is designed to realize anomalous reflection and generate vortex THz wave simultaneously. Simulation results show that a vortex wave with l=1 is generated and steered. Graphene exhibits many extraordinary behaviour in terahertz regime, such as supporting SPP, high efficiency, and tunability; therefore, it is a promising candidate in terahertz technology. This research investigate the approach to combine the functionalities of different metasurfaces implemented by graphene, which opens the gate of dynamically controlled multifunctional metasurfaces in terahertz regime.In summary, we have studied the design principle of multifunctional graphene metasurfaces. The methodology of combining two metasurfaces is proposed. As an example, a graphene metasurface is designed to combine the functionality of generating vortex wave and steering the waves. Graphene is a two-dimensional atomic thick material, which can dynamically tune the phase condition by applying external gate voltages. Its parameters are scrutinized to calibrate the reflective behaviour of a single graphene cell and obtain coverage of 360


The surfactant sodium dodecyl benzene sulfonate (SDBS) was used as a dispersant during the reduction process to produce dispersion-stabilized graphene suspensions. Then, to investigate the characteristics of the graphene suspensions, X-ray diffraction (XRD), SEM, TEM, Fourier transform infrared (FT-IR), Raman, XPS, TG, and N2 adsorption–desorption tests were conducted. Finally, analytical models for hydrogen adsorption were investigated with Langmuir and Freundlich fittings. The results show that the application of SDBS can effectively reduce the agglomeration among graphene monolayers and increase the specific surface area of graphene, and that the adsorption behavior is consistent with the Freundlich adsorption model, and is a physical process.As the depletion of traditional fossil fuels and environmental pollution become serious problems for human society, researchers are actively looking for renewable energy sources. Since hydrogen energy is considered a clean, efficient, and renewable alternative energy source, it is regarded as the most promising option. In this context, how to store hydrogen safely and efficiently has become the major challenge that hinders the actual application. To fill this gap, this paper proposes to utilize surfactant-modified graphene for hydrogen storage. Through a modified Hummers’ method and ultrasonic stripping, this study proposes to prepare graphene from graphite oxide with NaBH Energy and the environment are two major issues that mankind needs to confront. With the depletion of traditional fossil fuels and serious environmental pollution, researchers have come to an agreement on the necessity of alternative clean and renewable sources. Hydrogen energy is recognized as one of the ideal options for its cleanness, efficiency, and renewability ,3,4,5,6.Researchers usually use mass storage density and volume storage density as two major indicators for hydrogen storage. Many researchers have carried out extensive and profound studies on hydrogen storage technologies in past decades ,12. One The preparation of graphene has made great breakthroughs in past decades. Recent studies have mainly focused on increasing the specific surface area of graphene materials. Srinivas et al. oxidized graphite with a modified Hummers’ method and reduced hydrated hydrazine to produce graphene . Under ao-phenylenediamine and p-phenylenediamine improved the kinetics of the hydrogen adsorption [Graphene has low hydrophilicity and lipophilicity, which tends to cause agglomeration. Without preprocessing, it is difficult to obtain monolayers and aqueous solutions or organic reagents. The dispersion of graphene may form lumps and agglomerates, which further deteriorate its performance after drying. Therefore, it is necessary to improve the hydrophilicity or lipophilicity of graphene so that the formation of composites and other substances can be reduced. In recent years, researchers found that the surface of graphite oxide contains many active groups, such as carbonyl groups, carboxyl groups, epoxy groups, etc. These groups can react with other substances, making the modification of graphene feasible, and thus leading to an active graphene surface. In general, the modification methods for graphene mainly include surface modification, chemical doping, functionalization of a polymer base, and physical modification. Bourlino et al. utilized amino siloxane and amino acid for its modification, and the modified graphene can remain stable in water . Niyogi sorption . Zhao etsorption . This mesorption . Niyogi sorption . Based uThe experiment materials and equipment are summarized in following A modified Hummers’ method was used to prepare graphite oxide. The experimental method is shown in 3/h. After the pulverization, 300 mg of graphite oxide was dispersed in 60 mL of deionized water to obtain a brownish-yellow suspension. A stable colloidal suspension can be obtained after ultrasonic dispersion for 1 h. Then, the suspension was transferred to a four-necked flask, and 600 mg of sodium borohydride and 50 mg of sodium dodecyl benzene sulfonate (SDBS) were added at 80 °C. After refluxing for 16 h, the mixture was centrifuged and washed with acetone and deionized water until a pH of 7 was reached. The obtained filter cake was vacuum dried and stored for future use. This product was denoted as GS1. The other graphene that was prepared without a dispersing agent was denoted as GS.−10 m line was used as the ray source with a tube flow of 30 mA . The infrared absorption spectrum of the samples was measured with a Vector 33 Fourier Transform Infrared Spectrometer  with a scanning range of 4000–400 cm−1. KBr tablets were used for sample preparation. Then, the samples were tested with a Raman spectrometer  at room temperature. The excitation line was a 632.8-nm line of an Ar ion laser. One measurement included 20 scans with 20 mW of power. The X-ray photoelectron spectroscopy  was performed with Al Kα radiation  as the excitation source. The water content and composition of samples were examined by a TG analyzer . The test was carried out under an atmosphere of oxygen (gas flow: 30 mL/min) and nitrogen (gas flow: 28 mL/min), and a temperature between 20–800 °C with a heating rate of 5 °C/min. The TG test aims to verify whether the sample was sufficiently dry and the graphite oxide was completely reduced. The morphology and roughness of the sample surface were inspected with an atomic force microscope . For the inspection, 1 mg/L graphene suspensions with water dispersion medium were prepared. The surface morphology and structure of graphene were also inspected with a scanning electron microscope . Gold films were sprayed on the surface of the sample with 5 min of gold plating time. The ultrastructure of the graphene surface was observed with a transmission electron microscope . The measurement of an N2 adsorption–desorption isotherm was studied with a specific surface area analyzer , which had liquid nitrogen as a dispersion medium (77 K). According to the characteristics of the graphene sample N2 absorption capacity, the surface area of the sample was calculated based on the Brunauer–Emmett–Teller (BET) equation [An X-ray diffraction (XRD) test was conducted with a D8 polycrystalline powder diffractometer  to analyze the crystal structure of the prepared graphite oxide and graphene samples. In the test, with Cu Ka radiation, the λ = 1.54 × 10equation .High pressure (0–3000 kPa) adsorption tests of hydrogen gas were carried out on a magnetic suspension balance . The balance contains an automatic gas sampling system, pressure control system, and temperature control system. In the experiment, the purge gas was high-purity helium (99.999%), and the adsorption gas was high-purity hydrogen (99.999%). First, the sample with the 1/3–1/2 sample frame volume was placed in a stainless steel sample box. Then, the pretreatment of samples was carried out in an electric oven at 150 °C under vacuum for 12 h. Thereafter, the adsorption test was conducted with a controlled flow rate of 30 mL/min for both the adsorption gas and the purge gas. The adsorption temperatures were set to 25 °C and 55 °C, and different measurement points were selected within the pressure range of 100–2500 kPa. The adsorption instrument recorded the sample weights at each predefined pressure level. After data processing, the hydrogen adsorption isotherm under different temperatures can be obtained.θ = 26.58°, indicating that the graphite sheets are laid neatly through the space. The graphite oxide crystal plane has a diffraction peak at 2θ = 10.60°, with a relatively low intensity. This result suggests that the graphite interlayer spacing increases from 0.336 nm to 0.779 nm due to the oxidation process. When the graphite oxide was reduced to graphene, a diffraction peak appeared at 26.08°, and the diffraction peak became broader with decreased intensity. This can be explained by the decreased gap between the graphene sheets and higher disorder level. In −1 and a weak absorption peak (D-peak) at 1332 cm−1, indicating that the atomic structure sp2 hybrid carbon of graphite is very tight. After the graphite was oxidized, the G-peak of the graphite oxide becomes boarder and blue-shifts to 1578 cm−1, while the intensity of the D-peak increases. This result indicates that after oxidization, the structural symmetry decreased, the vibration mode increased, part of the sp2 hybrid carbon atoms were converted into sp3 hybrid structures, and C=C double bonds were destroyed. In addition, the intensity ratio of the D-band to the G-band  suggests that the plane length of the sp2 hybridized carbon layer in graphite oxide is shorter than that of graphite. The ID/IG of graphite is 0.052, which is obviously smaller than that of graphite oxide and graphene. Due to the supersonic effect, after the graphite was oxidized, the graphite layers were broken down, and the graphite layer area had shrunk considerably. After graphite oxide is reduced, the D-peak and G-peak were red-shifted. Its intensity ratio (ID/IG = 1.28) is higher than that of the graphite oxide (ID/IG = 1.06), indicating that after the graphite has been fully oxidized and ultrasonically exfoliated, the average size of the sp2 hybridized carbon layers in graphene was larger than that of graphite oxide. At the same time, only a part of the sp3 hybrid carbon atoms were reduced to the sp2 hybrid carbon atom, that is, the structure of the graphite oxide cannot be completely restored to its original graphite structure.−1), C=O (1723 cm−1), aromatic ring C=C (1627 cm−1), and epoxy CO (1233 cm−1) appeared on the infrared spectrum of graphite oxide. The OH vibration peak was generated due to the vibration of hydroxyl groups in the water molecules between the graphite layers. The carbonyl, carboxyl, and epoxy groups result from graphite oxidation. The carbonyl peak is very weak, as it can only be formed close to the graphite layer edge. The epoxy and the alkoxy groups exhibit strong peaks, since the graphite oxide contains a large amount of hydroxyl and epoxy groups. After the graphite oxide was reduced, the oxygen-containing functional group peak of the graphene sample significantly weakened or even disappeared, while the aromatic ring C=C (1627 cm−1) peak was enhanced. Most of the oxygen-containing functional groups in the graphene were removed, but the crystal structure is different from that of the original graphite.2, and H2O. The GS1 had a slight mass loss below 160 °C due to the small amount of adsorbed water molecules. Between 160–800 °C, the mass loss was significantly reduced compared to curve (a), indicating that the GS1 sample has better thermal stability.2 adsorption–desorption isotherms and pore size distributions of graphene samples GS1 and GS at liquid nitrogen temperature, respectively. From the figure, the isotherm shows a type I adsorption curve, and a hysteresis loop appears on the isothermal adsorption–desorption curve at a relative pressure of about 0.4, which results in a narrow gap between graphene layers. The specific surface area of GS1 (about 1206 m2∙g−1) is much larger than that of GS (605 m2∙g−1), but smaller than the theoretical surface area of single-layer graphene (2630 m2∙g−1). This shows that the graphene sheet GS1 has both single-layer graphene and double-layered structures. However, compared with previous studies, the specific surface is still significantly improved [improved . The porT1 and T2 can be calculated by the Clausius–Clapeyron equation ) [When the hydrogen pressure is relatively low, hydrogen molecules are mainly adsorbed in the micropores. As GS1 has more micropores, its adsorption capacity is better than GS in the early stages. With the increase of pressure, the hydrogen is mainly adsorbed by the mesopores and macrospores, and the adsorption amount increases dramatically. For example, at 25 °C and 2500 kPa, the amount of hydrogen adsorbed by GS1 is as high as 1.7 wt %. The heat of adsorption of hydrogen for graphene at temperatures ion (1)) , which iion (1)) .stQ is the heat of adsorption (kJ/mol), R is the gas constant (8.314 kJ/(mol K)), and TP1 and TP2 are the hydrogen pressures (kPa) at temperatures T1 and T2, respectively.Clausius–Clapeyron equation:In order to analyze the rule of adsorption properties of graphene, two kinds of hydrogen adsorption models are discussed.Langmuir isothermal adsorption mode (Equation (2)) is the first model to describe the adsorption mechanism vividly, which lays a foundation for the establishment of other adsorption models. The model assumes that the adsorbent surface is uniform and there is no interaction between the adsorbents. Also, the adsorption is considered as only occuring on the external surface of the adsorbent.Freundlich isothermal adsorption model (Equation (3)) can be applied to both single-layer adsorption and non-uniform surface adsorption, which can describe the adsorption mechanism of non-uniform surface and is more suitable for low concentration adsorption.aV is the actual amount of adsorption (mL/mg) under pressure, K is the reaction rate constant, n is the empirical constant, and p is the adsorption pressure. A smaller n > 2, adsorption is more difficult. K and n can be calculated through the above model. The adsorption isotherms of hydrogen on graphene samples were fitted with Langmuir adsorption isotherm equations and Freundlich adsorption isotherm equations, as shown in Form This paper proposed preparing graphite oxide through a liquid phase oxidation method. After ultrasonic peeling, a high-quality graphene sheet can be prepared by adding the dispersant sodium dodecyl benzene sulfonate (SDBS) during the reduction process. The structure, morphology and composition of the prepared samples were inspected by XRD, Raman, Fourier transform infrared (FT-IR), TG, SEM, TEM, and AFM. The hydrogen adsorption properties of graphene then were investigated by conducting hydrogen adsorption experiments. Through characterization analysis and experimental results, this study concludes:(1) Adding the dispersant sodium dodecylbenzene sulfonate can greatly improve the quality of the prepared graphene sheets. The final prepared product is mainly composed of single-layer graphene with a thickness of 1.1 nm and a small amount of double-layer graphene.2 adsorption–desorption experiments show that the graphene sheet involves a large specific surface area with a rich pore structure. The application of dispersant sodium dodecyl benzene sulfonate (SDBS) can effectively reduce the agglomeration among graphene monolayers and increase the increase the specific surface area of graphene (from unmodified graphene 605 m2∙g−1 to improved 1206 m2∙g−1).(2) The N(3) In the hydrogen adsorption experiments, the adsorption capacity of high-quality graphene at 25 °C and 55 °C with pressure of 2500 kPa reached 1.7 wt % and 1.1 wt %, respectively. The adsorption isotherms of hydrogen on graphene samples were fitted by the Langmuir adsorption isotherm equation and the Freundlich adsorption isotherm equation. The fitting results show that the linearity of the Freundlich adsorption isotherm equation performs better.(4) The Freundlich fitting model also shows that when hydrogen is adsorbed on graphene at low pressure, it first occurs in the micropore and hollow structures of graphene. With the increase of adsorption pressure, the adsorption of hydrogen molecules gradually occurs on the outer surface of graphene.


However, it is unclear whether tumor aggressiveness and patient survival are influenced more by the proliferative or the migratory properties of cancer cells. To address this question, we experimentally measured proliferation and migration phenotypes across more than 40 breast cancer cell-lines. Based on the latter, we built and validated individual predictors of breast cancer proliferation and migration levels from the cells’ transcriptomics. We then apply these predictors to estimate the proliferation and migration levels of more than 1000 TCGA breast cancer tumors. Reassuringly, both estimates increase with tumor’s aggressiveness, as qualified by its stage, grade, and subtype. However, predicted tumor migration levels are significantly more strongly associated with patient survival than the proliferation levels. We confirmed these findings by conducting siRNA knock-down experiments on the highly migratory MDA-MB-231 cell lines and deriving gene knock-down based proliferation and migration signatures. We show that cytoskeletal drugs might be more beneficial in patients with high predicted migration levels. Taken together, these results testify to the importance of migration levels in determining patient survival.The efficacy of prospective cancer treatments is routinely estimated by  Only 1 out of 5000 drugs from pre-clinical studies enter the market after successful clinical testing2. Cancer drugs show the highest proportion of failures on the road to clinics3. Currently, the prevailing experimental method to initially estimate the pre-clinical efficacy of cancer drug candidates is by measuring their effects on in vitro proliferation rates10. However, even after filtering these findings in animal models, only a fraction of emerging candidates has successfully translated into human trails13. Many factors contribute to the failure of drugs that are effective in pre-clinical systems. For starters, in vitro and in vivo systems are obviously only approximate models of patients that do not capture many aspects of human biology. However, another naturally arising possibility is that other cellular phenotypes, such as migration or invasion, may be better indices of tumor response in patients than cellular proliferation. Addressing this question, we aimed here to quantify the relative weight of proliferation versus migration in determining cancer aggressiveness and patient survival.Drug development risk is a major contributing factor for spiraling drug pricesin vivo to study their association with patient survival and treatment response. However, regrettably, such measurements are yet infeasible. We, therefore, set out to build and validate predictors of proliferation and migration levels in breast cancer cell-lines based on gene expression information. With such predictors, we applied them to predict the proliferation and migration levels of TCGA14 breast cancer patient samples from their gene expression. We tested and verified that the predicted levels of these phenotypes in the tumors are indeed strongly associated with patient survival in the direction expected and that they are associated with cancer aggressiveness as expected. We find that migration is more strongly associated with breast cancer aggressiveness and more importantly, patient survival, than proliferation.Ideally, one would have liked to directly measure proliferation and migration levels directly in tumors CellToPhenotype predictors, using least absolute shrinkage and selection operator (LASSO) based regression15. The predictors were tested on the cell-line data using a standard cross-validation procedure  with patient survival (in an independent dataset – METABRIC)16. Second, CellToPhenotype uses LASSO regressor to regularize the predictor that enables a data-driven feature selection using a cross-validation. A five-fold cross validation procedure to compute the minimum λ value for LASSO. Applying the predictor learned above to the gene expression of breast cancer tumor samples, we can predict migratory and proliferation level for each sample/individual. For each test sample, we iterate this procedure 50 times (to obtain a robust estimate) and take the median value of the migration and proliferation levels as the final estimates.CellToPhenotype predictors (Supplementary Note) consists of two expressions based supervised regression – one for predicting cell migration and other for predicting cell proliferation. Each predictor was trained on Doubling times for 46 breast cancer cell lines were estimated by plating a known number of cells and measuring the total number of cells once the culture reached an estimated 80% confluency. Proliferation rates were then calculated using the doubling time measurements. Cell line migration was estimated in 43 breast cancer cell lines using a live cell imaging-based random cell migration assay. The mean speed of cell migration was then quantified as the final migration estimate.CellToPhenotype predictors, which given the expression of a given cell-line, predict its migration and proliferation levels. These predictors were constructed using least absolute shrinkage and selection operator (LASSO) based regression15, considering as features the genes whose expression is significantly associated with survival in the METABRIC breast cancer collection  than that obtained by the CellToPhenotype predictor. Second, as a control, MIB-1 expression, a marker of tumor cell proliferation and determinant of patient survival in prostate cancer22, shows no correlation with experimental measured proliferation in breast cancer cell lines . Finally, TPX2 expression, a well-known marker of migration in breast cancer23, is significantly correlated with the experimental measurements of migration in breast cancer cell lines . Again, this correlation is smaller compared to correlation via the CellToPhenotype migration predictor. Overall, these results show that the latter provides a better estimate of in vitro proliferation and migration than known marker genes. We also check that the data used in training the CellToPhenotype predictors do not suffer from multicollinearity (Supplementary Note).Next, we compared CellToPhenotype predictions with the expression of three reported gene markers of migration and proliferation (expression data obtained from CCLE project)24, P < 5.5e-20 and P < 4.4e-20 respectively, Fig. We then applied the CellToPhenotype proliferation and migration predictors to analyze breast cancer TCGA tumor data. Given an input tumor sample, each predictor (migration or proliferation) receives as input the levels of expression of its feature genes in that sample and outputs the predicted migration or proliferation levels. We first tested if the CellToPhenotype predicted proliferation and migration levels are higher in the TCGA breast tumors than the matched adjacent normal tissues . Reassuringly, the predicted migration and proliferation levels are significantly higher in the tumors than in the matched non-cancerous tissues  exhibit the highest predicted migration and proliferation levels amongst subtypes. Luminal A patients (n = 224) exhibit the lowest predicted proliferation amongst subtypes and significantly lower migration levels than TNBC (Supplementary Note), consistent with the observation that they have low metastasis levels and respond relatively well to treatment. Luminal B (n = 127) patients have higher predicted proliferation levels than Luminal A patients, consistent with the observation that Luminal B has larger tumor size and higher cellular proliferation index25, and higher rates of lymph node involvement than Luminal A26 . For this analysis, we first built new predictors of migration and proliferation by analyzing the 40 breast cancer cell lines for which we have both migration and proliferation measurements to enable a head-to-head comparison of the effects of migration and proliferation on survival, when built from exactly the same cell-lines (the construction itself followed the same predictor generation procedure described above). Given these CellToPhenotype predictors, we estimated the migration and proliferation levels of the 1043 TCGA breast tumors from their expression data, as before. We then employed Cox regression to examine the association between the predicted values and the patients’ survival, controlling for various covariates including age, race, and genomic instability. We find a stronger association of predicted migration levels with patient survival  than the association between proliferation and survival  Fig. . In bothTo estimate the significance of the difference of predictability, we additionally performed a likelihood ratio test comparing the survival predictive power of combined migration and proliferation compared to only proliferation or only migration. We see that the migration + proliferation model is significantly better than a proliferation only model  but not significantly better from a migration only model . Thus, adding migration to a proliferation-only model improves the survival prediction, however adding proliferation does not add significant predictive power to a migration-only model., A migration only model is significantly better than a proliferation only model  in predicting patient survival Fig. . Finallymigration-suppressive genes. Similarly, we also carried out siRNA KD experiments on 227 proteins whose gene expression levels are significantly positively correlated with experimentally determined migration values in these 40 breast cancer cell lines, and determined the effect of each knockdown on cell migration. Among these, we identified all genes whose knockdown significantly decreased cellular migration in MDA-MB-231 cell line and termed them as migration-enhancer genes. Migration suppressive genes  experiments that we have conducted in a highly migratory breast cancer cell line, MDA-MB-231. Keeping translational goals in mind, we considered around 4600 druggable proteins. Most of these genes come from protein kinases and GPCRs (G-protein-coupled receptors). (Kinases which are often over expressed and activated in cancer are easily targetable through their ATP binding sites.) Among these, we knocked down 248 proteins whose gene expression levels are significantly negatively correlated with experimentally determined migration values in the 40 breast cancer cell lines that we studied above. We experimentally measured the effect of each knockdown on cell migration using a 2D migration assay. We termed the genes whose knockdown significantly enhances cellular migration The number of migration-suppressive genes that had low expression were downregulated in a given breast cancer cell-line (S-count) was highly correlated with its CellToPhenotype predicted migration levels  and also with its experimentally measured migration values . This suggests that the S-count could be considered as an approximation of cellular migration. Similarly, the number of migration-enhancer genes that had high expression in a given breast cancer cell-line denotes their migration-enhancer scores (E-count). The E-score is also highly correlated with the predicted migration levels  and the experimentally measured migration values , suggesting that E-count also provide approximate estimates of cellular migration. The sum of S-count and E-counts, termed the KD-migration-score, has a slightly higher correlation with the predicted migration levels  and proliferation levels of these tumors . We then examined the association of the KD-migration and KD-proliferation scores of the TCGA breast cancer tumors and patient survival, after controlling for age, race, and genomic instability via Cox regression. The results reinforce the trend observed previously with the CellToPhenotype analysis, as we find a significant association of KD-migration-scores with patient survival  but a lower association between KD-proliferation-scores and survival   Fig. . Analysi32. Accordingly, our working hypothesis has been that tumors with high migratory estimates may respond better to cytoskeletal drugs and conversely, tumors with high proliferation estimates will respond better to cytotoxic drugs.We next asked if predicted migration or proliferation levels determine patient response to cytoskeletal vs cytotoxic drugs. Although migration and proliferation occur via partially overlapping cellular processes, as a first approximation cytotoxic drugs mainly target proliferation by inhibiting nucleotide synthesis or inducing DNA break while cytoskeletal drugs are known to affect cell migration by targeting microtubules To test this hypothesis, we analyzed data of TCGA breast cancer patients, out of which 389 patients were given at least one of the 7 cytoskeletal drugs and 331 patients were given at least one of 31 cytotoxic drugs Table . In the 33 in Cox regression  gives similar results as before, i.e. migration is a stronger predictor of breast cancer patient survival than proliferation . We also find that patients with high predicted migration levels respond better to cytoskeletal drugs than patients with low predicted levels. siRNA-based predictors of migration and proliferation that we additionally built further testify that migration is indeed a better predictor of survival and the association may even have a causal basis. The choice of MDA-MB-231 cell-line (for the siRNA-based predictor) is unlikely to bias this conclusion because MDA-MB-231 is both highly proliferative and migratory cell line , and since the expression levels of the selected genes (features) are significantly correlated with the experimentally measured migration values in the 40 breast cancer cell lines. Our conclusion about the relative importance of migration over proliferation is robust to a few different migration like wound-healing assays  that the predicted migration levels are higher in circulating tumor cells than in normal samples or TCGA cancer samples  identified many additional genes  in the early stages of cancer drug development, which currently focusses on proliferation screens.In summary, our analysis highlights the importance of tumor migration in determining its aggressiveness and patients’ survival. It puts forward the need to put more effort on 6, Number of cells at harvest = 20 × 106, days in culture = 4 days, then the doubling time would be 2 days or 48 hours (4 days/2 PDL). The proliferation rate for each cell lines was computed using the equation 70/(doubling time). We did this for 46 breast cancer cell lines.Breast cancer cell line population doublings were estimated by plating a known number of cells at day 0 and measuring the total number of cells once the culture reached an estimated 80% confluency . Cell numbers were calculated using a hemocytometer and population doublings (PDL) were determined using the following formula: PDL = 3.32 ). The doubling time was calculated by dividing the number of days or hours between harvest and seeding by the PDL. Example: Number of cells plated at day 0 = 5 × 1038. Before imaging, the cells were pre-exposed for 30–45 min to 0.1 µg/ml Hoechst 33342  to visualize the nuclei. The plates were placed on a Nikon Eclipse TE2000-E microscope fitted with a 37 °C incubation chamber and 5% CO2 supplier, a 20x objective , an automated stage and perfect focus system. Up to four positions per well were automatically defined and nuclei (stained with live Hoechst) were imaged overnight every 10 to 20 minutes using NIS controlling software (Nikon) and a CCD camera (Pixel size: 0.78 or 0.32 µm). The.nd2 files acquired from NIS were exported to.tiff files as mono image for each channel and then converted to.avi files and analyzed using custom made ImagePro Plus macros as previously described39. The mean speed of cell migration was quantified per time-lapse by tracking each nucleus separately over time. This was done for 43 breast cancer cell lines.Cells were seeded on 96-well glass bottom plates  coated with 10 µg/ml collagen type I (isolated from rat tails) in PBS for 1 hour at 37 °C40. ‘Transient siRNA knockdown was achieved by reverse transfection of 50 nM single or SMARTpool siRNA in 2,500-5,000 cells/well in a 96-well plate format (PKT assay) using the transfection reagent INTERFERin  according to the manufacturer’s guidelines. The medium was refreshed after 20 h and transfected cells were used for various assays between 65 to 72 h after transfection’40. .Human siRNA of 475 proteins  were coated with 10 µg/ml fibronectin  for 1 h at 37 °C. Plates were washed twice with PBS, using a HydroFlex platewasher . Subsequently, the plates were coated with white carboxylate modified latex beads  for 1 h at 37 °C, after which the plate was washed 7 times with PBS. 65 h after siRNA transfection, transfected cells were washed twice with PBS-EDTA and trypsinized. Cells were resuspended into single cell suspensions, then diluted, and finally seeded at low density (~100 cells/well) in the beads-coated plate. Cells were allowed to migrate for 7 h, after which the cells were fixed for 10 min with 4% formaldehyde and washed twice with PBS. For each transfection, duplicate bead plates were generated ; transfection of each siRNA library was also performed in duplicate . Procedures for transfection, medium refreshment and PKT assay were optimized for laboratory automation by a liquid-handling robot ’40. .‘PKT assays were performed as described before40. . Major Axis score (Z-score) as a measure of cell speed was further used and computed in the modeling.‘Migratory tracks were visualized by acquiring whole well montages (6 × 6 images) on a BD Pathway 855 BioImager  using transmitted light and a 10x objective (0.40 NA). A Twister II robotic microplate handler  was used for automated imaging of multiple plates. Montages were analyzed using WIS PhagoTracker. Migratory tracks without cells or with more than 1 cell were excluded during image analysis. The quantitative output of PhagoTracker was further analyzed using KNIME. Wells with <10 accepted tracks were excluded. Next, data was normalized to mock to obtain a robust Z-score for each treatment and each parameter. After normalization, an average Z-score of the 4 replicates was calculated. Knockdowns with <3 images were removed, as well as knockdowns with <150 accepted tracks’18. Each predictor was trained on in vitro cell migration or proliferation as the dependent variable and gene expression of cell lines as the independent variables in the regression. CellToPhenotype adopts two level feature selection to reduce testing error. First genes that are significantly associated with patient survival (in an independent dataset – METABRIC) were selected to be included in the subsequent regression model. Secondly, CellToPhenotype uses LASSO shrinkage to regularize the predictor that enables a data-driven feature selection using a cross-validation. Both above feature selection was conducted in dataset independent of the testing set on which performance of CellToPhenotye was evaluated. This ensures an unbiased evaluation of the predictive power of CellToPhenotype.CellToPhenotype predictors consists of two expressions based supervised predictors – one for predicting cell proliferation and other for cell migration. The gene expression data was obtained from the Cancer Cell Line Encyclopedia projectTo achieve the robust final estimate of migration and proliferation, CellToPhenotype uses bootstrapping. CellToPhenotype predictors were conducted on the training data and then phenotypes were predicted for test samples. The process is repeated for 50 bootstraps. Median of each bootstrap are taken as the final estimates of migration and proliferation levels (details are provided in the Supplementary Note).in vitro data leaving one sample. Migration and proliferation was estimated for the left-out sample. Spearman ρ between the predicted and actual phenotypes was computed.Leave-one-out cross validation was conducted to assess the CellToPhenotype predictive power as follows. The migration and proliferation models were trained on all Using CellToPhenotype predictors, migration and proliferation levels were predicted for 937 breast cancer patients in TCGA Table . Out of Using CellToPhenotype predictors, migration and proliferation levels were predicted for 1706 breast cancer patients whose cancer grade information was available in the METABRIC dataset . Accordingly, the above model systematically controls for these confounders. Strata (race) in the above model implies Cox regression was conducted in each patient stratification based on race separately and likelihood were combined. We repeated the procedure for 10 iterations, and median coefficients (risk factor) of migration were computed. The association of survival with proliferation was estimated similarly. Each Kaplan Myer (KM) analysis was done by comparing the migration/proliferation levels of on the top 25 percentile of patients with bottom 25 percentile patients.To estimate the relative contribution of migration and proliferation to predict patient survival we fit following Cox regression, which also controls for age, race, and genomic instability:42. Following the siRNA, we experimental measured change cell migration by measuring factors including Major Axis score (Z-score). Migration-suppressive genes (n = 26) were identified by selecting genes whose knockdown led to high migration (above 90 percentile) in MDA-MB-231 cell line and whose gene expression was significantly negatively correlated with experimentally determined migration values in 40 cell lines. Migration-enhancer genes (n = 24) are those whose knockdown significantly decreased cellular migration (below 10 percentile) and whose gene expression was significantly positively correlated with experimentally determined migration values in 40 cell lines. We count the number of lowly expressed  migration-suppressive genes and highly expressed (above 50 percentile) upregulated migration-enhancer genes; and assign the count as KD-migration-score of each sample (cell line or patient). For this analysis, we use the median expression of all genes as the threshold for upregulation or downregulation.Out of around ~4600 druggable genes that we considered, we selected 475 genes whose expression was significantly correlated with migration across 40 cell lines . Out of this, 248 genes are negatively correlated and 227 genes are positively correlated. We conducted siRNA knockdown of 475 genes described above in MDA-MB-231 breast cancer cell line. MDA-MB-231 was chosen because it is highly migratory breast cancer cell lineet al.30. Among these genes, we select 1248 genes whose expressions are significantly correlated with experimentally determined proliferation values in 40 BC cell lines (606 genes positively correlated and 642 genes negatively correlated). Proliferation-suppressive genes were identified by selecting genes whose knockdown led to high proliferation (above 90 percentile) in MDA-MB-231 cell line and whose gene expression was significantly negatively correlated with experimentally determined proliferation values in 40 cell lines. Proliferation-enhancer genes are those whose knockdown significantly decreased cellular proliferation (below 10 percentile) and whose gene expression was significantly positively correlated with experimentally determined proliferation values in 40 cell lines. The count of lowly expressed proliferation suppressive and highly expressed proliferation-enhancer genes in a sample was assigned as KD-proliferation-score of the sample.siRNA/shRNA-based KD-proliferation-score of a sample is determined in an analogous manner described for migration. Briefly, we used proliferation measurement post ~15400 genes shRNA knockout in MDA-MB-231 cell lines from Marcotte Drug response information is available for 720 TCGA breast cancer patients in TCGA: 389 patients administering at least one of the 7 cytoskeletal drugs, and 331 patients administering at least one of the 31 drugs targeting only proliferation . The siRNA-based analysis data based on MDA-MB-231 cell line is from Fokkelman et al.40. The R code for developing CellToPhenotype predictors is available as free software with the GNU General Public License in the GitHub repository (https://github.com/nishanth83/CellToPhenotype).The experimentally measured proliferation values are added in Table Supplementary note and figuresSupplementary Table S1Supplementary Table S2Supplementary Table S3