Front‐line platinum‐base chemotherapy for advanced thymoma and thymic carcinoma (TC) improves resectability and prolongs patients' overall survival (OS). In this study, we evaluated patients' outcomes given different front‐line regimens: cisplatin, doxorubicin, and cyclophosphamide (CAP); cisplatin and etoposide (EP); or cisplatin and paclitaxel (TP).We retrospectively evaluated the medical records of patients with advanced thymoma and TC who were treated at our medical center between 2005 and 2015. We investigated objective response rates (ORRs), progression‐free survival (PFS), and OS after undergoing different front‐line regimens.p = 0.424), respectively, and the 5‐year OS rates were 84.9%, 70.6%, and 60.0% (p = 0.509). In patients with stage IVB disease, the median PFS were 9.4, 8.2, and 11.6 months after undergoing CAP, EP, and TP (p = 0.173), respectively, and the 5‐year OS rates were 41.1%, 39.1%, and 14.3% (p = 0.788). TC pathology subtype and liver metastasis were associated with poor OS. Three patients with stage IVB TC had an OS of more than 5 years.Among the 108 enrolled patients, 37 (34%) had thymoma and 71 (66%) had TC; 45 received CAP, 36 received EP, and 27 received TP regimens. The ORRs of patients receiving CAP, EP, and TP were 51%, 50%, and 41%, respectively. For patients with stage III and IVA disease, the median PFS after CAP, EP, and TP were 34.5, 26.4, and 18.0 months ; cisplatin and etoposide (EP); and cisplatin and paclitaxel (TP). Patietns with stage III/IVA or stage IVB thymoma and thymic carcinoma had similar long‐term overall survival after receiving three different front‐line chemotherapy regimens. This paper also provided comprehensive clinical information and studied clinical prognostic factors in patients with advanced thymoma and thymic carcinoma. We found that up‐front surgery led to the best OS, and patients with advanced thymic cancer usually received induction chemotherapy with or without further surgery.Therefore, this study was designed to compare objective response rates (ORRs), progression‐free survival (PFS), and OS in patients with thymoma and TC after undergoing treatment with different front‐line platinum‐based chemotherapy regimens. Moreover, we evaluated clinical prognostic factors for patients' long‐term OS.22.1We retrospectively reviewed the medical records of patients with thymoma or TC who received front‐line chemotherapy regimens at our medical center from 2005 to 2015. The patients underwent pretreatment assessments, including a history report, physical examinations, complete blood cell counts, blood chemistry analysis, and whole‐body computed tomography (CT) for the purpose of staging. Positron emission tomography/CT was arranged as indicated for the specific condition. No patients received endobronchial ultrasound‐guided transbronchial needle aspiration for lymph‐node biopsy at initial staging, but chest surgeons arranged lymph‐node dissection during the operation if lymph node metastasis was suspected.The enrolled patients were divided into three groups based on undergoing different front‐line chemotherapy regimens: group 1 comprised patients receiving a cisplatin, doxorubicin, and cyclophosphamide (CAP) regimen; group 2 comprised patients receiving a cisplatin and etoposide (EP) regimen; and group 3 comprised patients receiving a cisplatin and paclitaxel (TP) regimen. Attending physicians decided on one of these three front‐line chemotherapy regimens after discussing previous study results and treatment‐related side effects with their patients, and the final dosages were adjusted according to the patients' clinical conditions.The paclitaxel chemotherapy could be administered weekly or every 3 weeks at the attending physician's judgment. The attending physicians decided on the total number of chemotherapy cycles according to patients' tolerability and clinical benefit, and patients received the minimum of 2 cycles of chemotherapy. Since some patients presented with clinical stage III and IVA thymoma or TC, they needed to undergo surgery after receiving front‐line chemotherapy. These surgical procedures included total thymectomy and complete excision of gross tumors in the thoracic cavity. After surgery, patients received radiotherapy with 45–54 Gy for close (1–2 mm) or microscopically involved (<1 mm) resection margins, and with up to 60 Gy for gross residual tumors.Pathological diagnosis of thymoma or TC was based on the 2015 World Health Organization (WHO) classification system, and two pathologists (Y.L.H. and Y.L.C.) with a professional focus on thoracic malignancies confirmed the diagnoses.2.2Most patients underwent CT assessments every 3–6 months, according to the Taiwan National Health Insurance regulations. We administered a response evaluation to patients undergoing front‐line chemotherapy regimens according to Revised RECIST guidelines (version 1.1).Clinical stage III/IVA or stage IVB for thymic cancer strongly affects patients' prognoses and indications for treatment modalities. Most patients diagnosed with stage III/IVA thymic cancer receive curative‐intent treatment, while most stage IVB patients receive palliative‐intent treatments. Thus, our patients' long‐term PFS and OS in accordance with different front‐line chemotherapy regimens were analyzed according to stage III/IVA or stage IVB classifications.The National Taiwan University Hospital Research Ethics Committee (NTUHREC) approved this retrospective study (NTUHREC No. 202106012RINA) and waived the informed consent, as all patient identifiers were removed from the dataset prior to data analysis. This study was conducted in accordance with the Declaration of Helsinki and its later amendments.2.3We compared the distributions of categorical variables for patients receiving different front‐line chemotherapy regimens using Fisher's exact test. Continuous variables and medians were compared using the Kruskal–Wallis test. Long‐term PFS and OS were calculated using the Kaplan–Meier method.p‐values of <0.05 were used to denote statistical significance.Univariate and multivariate Cox proportional hazard models were used to explore associations between OS and the following potential prognostic factors: patient age, sex, pathological subtypes, initial stages, front‐line chemotherapy regimens, and visceral metastasis. The 33.1Table The median age of the 36 patients receiving front‐line EP chemotherapy was 51 years (range: 13–77 years), and 23 of these patients (64%) were male. Two patients (6%) had a WHO classification of type B3 thymoma, and 23 patients (62%) were diagnosed with TC. Nineteen patients (53%) had stage IVB disease. A total of 15 patients (42%) underwent surgery for thymic tumors, and 32 patients (89%) had lung metastases.The median age of the patients in the front‐line TP chemotherapy group was 58 years (range: 28–77 years), and 12 of these patients (44%) were male. Twenty‐three (81%) patients had TC, and 15 (56%) had stage IVB disease. In total, 16 patients (59%) underwent surgery for thymic tumors, 17 patients (63%) had pleural and pericardial tumors, and 17 patients (63%) had lung metastases.A total of 29 of 45 patients (64%) receiving a CAP regimen, 17 of 36 patients (47%) receiving an EP regimen, and 12 of 27 patients (44%) receiving TP chemotherapy were diagnosed with stage III/IVA cancer. These chemotherapy regimens were addressed in the neoadjuvant setting.p = 0.025). The vast majority of patients in the CAP regimen group underwent surgery . In addition, patients receiving front‐line CAP or EP regimens developed more pleural/pericardial or lung metastases, respectively . Among a total of 37 (out of 108) patients who developed liver metastases, six and 31 patients were diagnosed with thymoma and TC, respectively. All 37 patients had metastases in the liver parenchyma and two patients also had liver surface involvement due to thymic tumors seeding. Table When comparing three groups of patients receiving different front‐line chemotherapy regimens, the vast majority of patients diagnosed with TC received the TP regimen      received surgery and 58 patients (100%) received radiotherapy for thymic malignancies after undergoing chemotherapy. Thus, a total of 58 patients with stage III and IVA cancer received curative‐intent treatments. Patients' median PFS were 34.5, 26.4, and 18.0 months  Figure , and the) Figure . In pati) Figure , and the) Figure .The PFS or OS of patients with stage III/IVA or stage IVB thymoma or of TC patients according to different front‐line chemotherapy regimens are listed on Table Table 3.3The most common side effects in patients receiving a CAP regimen were leukopenia, which occurred in 34 (76%) patients, including 22 (49%) patients with grade I/II and 12 (27%) patients with grade III/IV leukopenia. In addition, grade I/II thrombocytopenia and anemia occurred in 11 (24%) and eight (18%) patients, respectively. In patients receiving an EP regimen, 15 (42%) patients had grade I/II leukopenia and nine (25%) patients had grade III/IV leukopenia, respectively. Grade I/II thrombocytopenia and anemia occurred in eight (22%) and seven (19%) patients, respectively. Moreover, 10 (37%) patients had grade I/II leukopenia and five (19%) patients had grade III/IV leukopenia after undergoing a TP regimen. Grade I/II thrombocytopenia, anemia, and neuropathy occurred in four (15%), five (19%), and 17 (63%) patients, respectively.3.4p = 0.005), Masaoka–Koga system stage IVB disease , and liver metastasis  were associated with poor survival. In contrast, pleural or pericardial metastasis  was associated with a favorable prognosis. In multivariate analysis, pathological subtype of TC  and liver metastasis  were associated with poor survival.Table Table 4Although platinum combined with anthracycline, etoposide, or taxane are among the most common front‐line chemotherapy regimens for treating thymoma and TC, studies comparing patients' long‐term outcomes are limited. One study conducted by Fornasiero et al. assessed 32 patients with stage III or IV thymoma who received the first‐line cisplatin, doxorubicin, vincristine, and cyclophosphamide (ADOC) regimen. They found an ORR to the ADOC regimen of 91.8%, and likewise found that the median OS for patients undergoing this regimen was 15 months.n = 21) or TC (n = 23) were treated with chemotherapy regimens containing carboplatin and paclitaxel. These researchers found ORRs of 42.9% and 21.7% in patients with thymoma or TC, respectively. The median PFS durations in the thymoma and TC cohorts was 16.7 and 5.0 months, respectively. Moreover, the median OS durations in the thymoma and TC cohorts were “not reached” and 20.0 months, respectively.In a study by Giaccone et al., 16 patients with recurrent or metastatic malignant thymoma received EP chemotherapy, yielding an ORR of 56% and median PFS and OS durations of 2.2 and 4.3 years, respectively.In this study, we divided patients into three groups according to different front‐line chemotherapy regimens. Notably, more than 80% of the patients in the TP group were diagnosed with TC. The percentages of patients with stage IVB disease in the CAP, EP, and TP groups were approximately 30%, 50%, and 50%, respectively. Moreover, the ORRs of patients with thymoma or TC receiving front‐line chemotherapy were nearly 60% or 40%, respectively. Importantly, the ORRs observed in each chemotherapy group in this study were not consistent with those reported in previous studies. This may be due to the evaluation of treatment response by different attending physicians in our study as well as differences in imaging follow‐up intervals. However, in the current study, patients with TC were less responsive to platinum‐based chemotherapy regimens than were those with thymoma, which conforms to the findings of previous investigations. Moreover, more patients in the front‐line TP chemotherapy group had stage IVB disease and TC. This explains why patients in this group had a worse ORR to chemotherapy as well as a worse OS as compared with those receiving front‐line CAP or EP chemotherapy regimens. Considering that the OS differences between the front‐line chemotherapy groups were not statistically significant within either the Kaplan–Meier curve or the univariate analysis, we speculated that front‐line CAP, EP, and TP regimens produce similar results in terms of long‐term OS , has been studied in thymic cancer.In Taiwan, attending physicians may choose different chemotherapy regimens to treat patients with thymic cancer according to the results of previous studies, medical guidelines, and clinical judgment. With regard to first‐line platinum‐based chemotherapy regimens, taxane is not covered by national health insurance and patients must, therefore, pay out‐of‐pocket expenses. This may have caused fewer of our patients to select front‐line TP (27 patients) as compared with EP (36 patients) and CAP (45 patients) regimens. In addition, with regard to later‐line treatments, patients still need to pay out‐of‐pocket costs for gemcitabine, taxane, pembrolizumab, or oral fluorouracil‐based regimens. In contrast, infused fluorouracil is covered by national health insurance. Although capecitabine or S‐1 were the most studied fluorouracil‐based regimens in later‐line treatments, more than half of the patients received infused fluorouracil‐based treatment in our study. In addition, some patients received oral cyclophosphamide in later‐line treatments. Metronomic oral cyclophosphamide may exert anti‐tumor effect by immunomodulation, though this treatment strategy was not well studied in thymic malignancies.Prior studies have reported that advanced Masaoka–Koga stage and incomplete resection are the two leading prognostic factors for poor OS in patients with thymoma and TC.The limitations of this study include its retrospective design and the heterogeneous patient sample comprising different thymic neoplasm subtypes, diverse treatment protocols, and varying imaging follow‐up intervals. The study data were recorded from 2005 to 2015, and therefore, the use of the latest surgery and clinical care techniques might have resulted in improved outcomes. Moreover, the number of enrolled patients was low, especially after grouping patients according to different front‐line chemotherapy regimens. Nevertheless, this study presented the substantial strength of evaluating clinical characteristics, long‐term outcomes, and prognostic factors for patients receiving different front‐line chemotherapy regimens. Additional studies are warranted to investigate other potential molecular prognostic factors of thymic neoplasms.5In this retrospective study conducted at a single medical center, we identified 108 patients with thymoma or TC who had undergone front‐line chemotherapy within an 11‐year interval. We found that long‐term PFS and OS among patients with stage III/IVA and stage IVB diseases were similar after undergoing different front‐line chemotherapy regimens. However, in the multivariate analysis, patients with TC or liver metastasis showed poor OS. Rarely, patients with stage IVB TC achieved a long‐term OS. Additional studies investigating other molecular prognostic factors are warranted.The authors have no conflicts of interest to declare.Wei‐Li Ma and Chia‐Chi Lin were responsible for literature collection, data management and interpretation, and manuscript writing. Wei‐Li Ma, Chia‐Chi Lin, Feng‐Ming Hsu, Jang‐Ming Lee, Jin‐Shing Chen, Yen‐Lin Huang, and Yih‐Leong Chang contributed to patient care and clinical data. Wei‐Li Ma and Chin‐Hao Chang were responsible for data management and interpretation and statistical analysis. Wei‐Li Ma and James Chih‐Hsin Yang treated patients; planned, designed, and coordinated the study throughout the study period; and wrote the manuscript.The National Taiwan University Hospital Research Ethics Committee (NTUHREC) approved this retrospective study (NTUHREC No. 202106012RINA) and waived the requirement for informed consent. The medical charts were retrospectively reviewed and all processes was carried out in accordance with the principles of the Declaration of Helsinki.Table S1–S4Click here for additional data file.


The frequent incidence of postsurgical recurrence issues in papillary thyroid cancer (PTC) patients is a primary concern considering the low cancer‐related mortality. Previous studies have demonstrated that epithelial–mesenchymal transition (EMT) activation is closely related to PTC progression and invasion. In this study, we aimed to develop a novel EMT signature and ancillary nomogram to improve personalized prediction of progression‐free interval (PFI).First, we carried out a differential analysis of PTC samples and pairwise normal thyroid samples to explore the differentially expressed genes (DEGs). The intersection of the DEGs with EMT‐related genes (ERGs) were identified as differentially expressed EMT‐related genes (DE‐ERGs). We determined PFI‐related DE‐ERGs by Cox regression analysis and then established a novel gene classifier by LASSO regression analysis. We validated the signature in external datasets and in multiple cell lines. Further, we used uni‐ and multivariate analyses to identify independent prognostic characters.We identified 244 prognosis‐related DE‐ERGs. The 244 DE‐ERGs were associated with several pivotal oncogenic processes. We also constructed a novel 10‐gene signature and relevant prognostic model for recurrence prediction of PTC. The 10‐gene signature had a C‐index of 0.723 and the relevant nomogram had a C‐index of 0.776. The efficacy of the signature and nomogram was satisfying and closely correlated with relevant clinical parameters. Furthermore, the signature also had a unique potential in differentiating anaplastic thyroid cancer (ATC) samples.The novel EMT signature and nomogram are useful and convenient for personalized management for thyroid cancer. We e identified a 10‐EMTgene signature and constructed a prognostic nomogram with relevant clinical parameters involved. The novel 10‐gene classifier and the nomogram could provide a novel and reliable tool for prognosis prediction with PTC, therefore help decision making for the purpose of individualized surveillance. Further, crucial genes and relevant coefficients were selected from the PFI‐related DE‐ERGs by Least absolute shrinkage and selection operator (LASSO) regression analysis with the “glmnet” R package.After differential gene expression analysis, all the 488 PTC cases from TCGA thyroid cancer (THCA) dataset were allocated randomly into the training and the testing datasets on a ratio of 0.8.2.5We explored the potential turbulence in the ERG‐based signature at the transcriptional level by GSEA.2.62, 37°C, in RPMI Medium 1640 (Invitrogen) with 10% FBS (Gibco), non‐essential amino acids (Invitrogen), Glutamax (Invitrogen), and Sodium Pyruvate  added. TRIzol (Lablead) was used to lysate and isolate RNA from cells according to the manufacturer's protocol.Normal human thyroid follicular cell line Nthy‐ori 3.1,2.7−ΔΔCt method.RT‐qPCR was conducted after isolation of RNA and performed essentially as described previously with housekeeper (GAPDH) mRNA for normalization via the 22.8p > 0.1 for further multivariate analysis.Univariate and multivariate Cox analyses were implemented to extract the correlating prognostic parameters. EMT risk score and clinical information included age, gender, BRAF mutation status, RAS mutation status, Neoplasm_size, TNM stage, extrathyroidal extension, residual tumor, primary focality, and anatomic site. Categorical variables including gender, age (over 55 or not), BRAFV600E, RAS, TNM, extrathyroidal extension, residual tumor, primary focality, anatomic site, and histological type were transformed into rank variables. We firstly conducted univariate analysis and excluded factors with 2.9We constructed a stepwise Cox model after collinearity tests for predicting the PTC's PFI. Clinical factors in multivariate analysis and other previously reported significantly correlated ones were included. Thereafter, the model was visualized as a nomogram. The cutoff value to define high‐ or low‐risk groups was generated by X‐Tile based on the nomogram points for each case. We then assessed the predictive ability of the model based on the AUC of the ROC curve, the C‐index, and the calibration curve, which was generated with 1000 times bootstrap resampling.2.10t‐tests. Categorical variables were analyzed using chi‐squared tests. K–M survival curves were analyzed with the Gehan–Breslow–Wilcoxon test. Multiple testing correction were applied with Benjamini and Hochberg method for difference and enrichment analyses. p‐values <0.05 were considered to indicate statistical significance.Statistical analyses were conducted using R version 4.0.3 (Bunny‐Wunnies Freak Out) and GraphPad Prism 8.4.3 . Codes used for analyses are listed in supplementary document 33.1The workflow of our study is shown in Figure 3.2Annotation of the 244 DE‐ERGs by GO and KEGG pathway analyses is shown in Figure 3.3p‐value, and hazard ratio of each item are shown in Figure The baseline information of the training and testing sets allocated from the 488 PTC cases is shown in Table 3.4p < 0.0001, Figure p < 0.05). In GSE82208, the difference of risk score between follicular adenoma and follicular thyroid cancer (FTC) was statistically significant (p < 0.0001). Also, in GSE60542, GSE58545, GSE29265, and GSE33630, the difference of risk score between normal and PTC samples was statistically significant (p < 0.0001). And for discriminating adenoma from FTC, the optimal cutoff value would be −0.95. . The differences of FHL1, TGFBR3, CTGF, FBLN5, WNT11, and AQP9 were not statistically significant (p > 0.05), as shown in Figure p < 0.05), as shown in Figure p < 0.05), as shown in Figure The relative 10‐gene expression level of EMT signature in B‐CPAP and KTC‐1 cell were generated through RT‐qPCR quantification. In 10 genes of EMT signature, the expression level of FOXP2, WT1, IL11, and UHRF1 were higher in KTC‐1 than B‐CPAP, the differences were statistically significant (3.7p < 0.05). For the terms divided by gender, the difference was not statistically significant (p > 0.05), as shown in Figure Next, we analyzed the correlation between the EMT signature and clinical characters. In groups divided by age, patients who were over 55 had higher risk score than patients who were younger than 55 Figure . In grou3.8p < 0.05, Table p < 0.1 in univariate analysis, a total of 422 patients were enrolled in multivariate analysis. In multivariate analysis, neoplasm_size (≥2 cm) and risk score were significantly associated with prognosis , neoplasm_size ≥2 cm), histological_type , T stage (T3/T4), M stage (M1), extrathyroidal extension, and risk score were significantly associated with prognosis .We constructed a stepwise Cox regression model including the parameters of risk score, age, TNM stage, neoplasm size, residual tumor, histological type, and RAS status. The model was visualized in a predictive nomogram as shown in Figure 4As previously described, the progression of PTC, lymph node metastasis, stemness, and anaplastic transformation are closely related to EMTAmong our novel panel, half of them were previously identified in association with TC invasion and progression. For example, downregulated expression of FHL1 was associated with invasion of PTC.Compared with the relatively low risk of cancer‐related death, recurrence or persistent disease remains the main issue that influence the prognosis of PTC patients.Some limitations of our study should be noted. First, due to the lack of array‐based independent gene expression profiling or sequence datasets with complete follow‐up information, we had to apply internal verification of our results. Thus, our findings require further validation using an external independent dataset. In addition, as compared to the 26.57% to 26.95% of CV in TCGA‐THCA cohort, a 10–20% CV for regular qRT‐PCR is considered more acceptable. At last, the primary source of our study was from TCGA program, in which the samples were collected from North American people. Possible biases would occur when applying the classifier in different countries or regions, which should be addressed in further clinical study.5In this study, we successfully identified a novel 10‐gene classifier and established a classifier‐based regression model combining relevant clinical characteristics for prediction of prognosis in PTC patients. The novel gene classifier and nomogram may be useful and convenient for personalized management for thyroid cancer.ZC and RL designed the study, carried out experiments, and obtained the data; RL, ZC, MP, and WM analyzed the data and conducted statistical analyses; RL, ZC, MP, and XL wrote the manuscript; HY and ZL revised and approved the manuscript.This research was supported by the National Natural Science Foundation of China (grant number: 82172727), the Nature Science Foundation of Beijing [grant number: 7202164], CAMS Innovation Fund for Medical Sciences (CIFMS) [grant number: 2016‐12 M‐3‐005], and CAMS Innovation Fund for graduate students [grant number: 2019–1002‐44].The authors declare that they have no competing interests.Not applicableAppendix S1 Supplementary FiguresClick here for additional data file.Appendix S2 Supplementary TablesClick here for additional data file.


Anti‐drug antibodies are associated with treatment failure to anti‐TNF agents in patients with inflammatory bowel disease (IBD).To assess whether immunogenicity to a patient's first anti‐TNF agent would be associated with immunogenicity to the second, irrespective of drug sequenceWe conducted a UK‐wide, multicentre, retrospective cohort study to report rates of immunogenicity and treatment failure of second anti‐TNF therapies in 1058 patients with IBD who underwent therapeutic drug monitoring for both infliximab and adalimumab. The primary outcome was immunogenicity to the second anti‐TNF agent, defined at any timepoint as an anti‐TNF antibody concentration ≥9 AU/ml for infliximab and ≥6 AU/ml for adalimumab.p = 0.002). Similarly, in patients treated with adalimumab and then infliximab, immunogenicity to adalimumab was associated with subsequent immunogenicity to infliximab . For each 10‐fold increase in anti‐infliximab and anti‐adalimumab antibody concentration, the odds of subsequently developing antibodies to adalimumab and infliximab increased by 1.73  and 1.99 , respectively. Patients who developed immunogenicity with undetectable drug levels to infliximab were more likely to develop immunogenicity with undetectable drug levels to adalimumab . Commencing an immunomodulator at the time of switching to the second anti‐TNF was associated with improved drug persistence in patients with immunogenic, but not pharmacodynamic failure.In patients treated with infliximab and then adalimumab, those who developed antibodies to infliximab were more likely to develop antibodies to adalimumab, than patients who did not develop antibodies to infliximab (OR 1.99, 95%CI 1.27–3.20, Irrespective of drug sequence, immunogenicity to the first anti‐TNF agent was associated with immunogenicity to the second, which was mitigated by the introduction of an immunomodulator in patients with immunogenic, but not pharmacodynamic treatment failure. In 1058 patients with inflammatory bowel disease who underwent therapeutic drug monitoring for both infliximab and adalimumab, those who developed antibodies to a first anti‐TNF were more likely to develop antibodies to a second anti‐TNF, irrespective of drug sequence. Commencing an immunomodulator at the time of switching to the second anti‐TNF was associated with improved drug persistence in patients with immunogenic but not pharmacodynamic failure. Obesity, cigarette smoking, higher baseline markers of disease activity, anti‐TNF monotherapy and the development of anti‐drug antibodies are associated with low drug levels and anti‐TNF treatment failure.Whilst it is generally accepted that there is a diminishing return from second‐ and subsequent anti‐TNF therapies,The aim of the IMplications for Sequencing of biologic therapy and choice of second Anti‐TNF in patients with IBD (IMSAT) study was to evaluate the relationship between immunogenicity to the first anti‐TNF therapy and immunogenicity and drug persistence to second anti‐TNF therapy. We hypothesized that immunogenicity to the first anti‐TNF would be associated with immunogenicity to the second anti‐TNF, irrespective of drug sequence.22.1Risk of immunogenicity to a second anti‐TNF drug, stratified by immunogenicity to the first anti‐TNF drug.Rates of drug persistence to a second anti‐TNF, following treatment failure to the first anti‐TNF, stratified by immunogenicity to the first anti‐TNF drug.Strategies to mitigate the development of immunogenicity to a second anti‐TNF drug.We sought to define the:We conducted a UK‐wide, multicentre, retrospective cohort study to report rates of immunogenicity to second anti‐TNF therapies in patients with IBD.The Academic Department of Blood Sciences at the Royal Devon and Exeter NHS Foundation Trust provides a therapeutic drug monitoring (TDM) service to hospitals throughout the United Kingdom (UK).Patient eligibility was confirmed by local research sites. We included patients with a diagnosis of Crohn's disease, ulcerative colitis (UC), and IBD‐unclassified (IBD‐U) as determined by local sites. Using case note reviews of secondary care records, their disease courses were followed to the point of data entry or drug withdrawal. Patients who had historically been treated with an anti‐TNF drug prior to the index course with TDM measurement, those who had not been exposed to two anti‐TNF drugs, and where the clinical data were incomplete were excluded.2.2The primary outcome was immunogenicity to the second anti‐TNF drug, defined at any timepoint as an anti‐TNF antibody concentration ≥9 AU/ml for infliximab and ≥6 AU/ml for adalimumab, using the Immundiagnostik anti‐drug antibody ELISA.Treatment failure endpoints were primary non‐response at week 20, loss of response after week 20 and adverse events leading to drug withdrawal:Primary non‐response: was defined as exit before week 20 because of treatment failure , corticosteroid use at week 20 (new prescriptions or if the previous dose had not been stopped), or physician global assessment of no meaningful response at any time prior to drug withdrawal, even if the drug continues beyond standard induction period.Loss of response: in patients who did not have primary non‐response was defined as symptomatic inflammatory bowel disease activity that warranted an escalation of steroid, immunomodulator or anti‐TNF therapy, resectional surgery, or exit from the study due to treatment failure.Adverse events were coded centrally according to the Medical Dictionary for Regulatory Activities (MedDRA) version 23.1. Serious adverse events included those that required hospitalisation, were life‐threatening or resulted in persistent, permanent or substantial disability or incapacity. Causality was graded according to the Good Clinical Practice framework guidelines as not related, unlikely, possibly, probably or definitely related to treatment by local research sites.Immunogenic—pharmacokinetic failure was defined as treatment failure with low anti‐TNF drug levels , and the presence of anti‐TNF antibodies .Immunogenic—pharmacodynamic failure was defined as treatment failure despite adequate anti‐TNF drug levels , and the presence of anti‐TNF antibodies .Non‐immunogenic—pharmacokinetic failure was defined as treatment failure with low anti‐TNF drug levels , and without the presence of anti‐TNF antibodies .Non‐immunogenic—pharmacodynamic failure was defined as treatment failure despite adequate anti‐TNF drug levels , and without the presence of anti‐TNF antibodies .We subsequently incorporated the use of TDM‐based decision making in the setting of primary non‐response or loss of response, according to the results of their most recent drug level and anti‐drug antibodies to the first anti‐TNF.Time to loss of response was defined as the duration of time from initiation of anti‐TNF therapy to treatment failure. Non‐treatment failure endpoints were withdrawal of anti‐TNF therapy in patients with quiescent disease, by treating physician or patient choice.2.3We recorded demographic , IBD‐related data  according to Montreal Classification, and immunomodulator status , with no minimum duration required and anti‐TNF treatment data .2.4All laboratory analyses were performed at the Academic Department of Blood Sciences at the Royal Devon and Exeter NHS Foundation Trust. Anti‐TNF drug and anti‐drug antibodies were measured on the Dynex Technologies  DS2 automated ELISA platform.The Immundiagnostik (IDK) AG  IDKmonitor infliximab (K9654) and adalimumab (K9651) total anti‐drug antibody assays allow semi‐quantitative measur‐drug antibodies.The IDKmonitor free infliximab (K9655) and adalimumab (K9657) drug level assays permit quantitative measurement of a free therapeutic drug in serum.2.5At the time of study design, we identified approximately 1000 patients who had TDM results for both anti‐TNF drugs: 78% were treated with infliximab first, and 22% with adalimumab first. We assumed that the crude rates of immunogenicity according to biologic type were generalisable across the cohort and allowed for a 30% attrition rate. We calculated that our sample size provided 93% and 79% power at the 0.025 significance threshold level to detect a significant association between immunogenicity to the first and second anti‐TNF, in the infliximab‐ and adalimumab‐ treated first cohorts, respectively.p‐values <0.05 were considered significant. We included patients with missing clinical data in analyses for which they had data and specified the denominator for each variable.Data were pseudonymized and entered into a purpose‐designed electronic database in REDCap .We performed univariable analyses using Fisher's exact and Mann–Whitney U tests to identify categorical and continuous variables associated with immunogenicity and treatment failure outcomes. Logistic regression analyses were used to assess whether the magnitude of anti‐drug antibodies to the first anti‐TNF was independently associated with antibody formation to the second anti‐TNF. We performed sensitivity analyses according to drug clearance, which was defined as undetectable anti‐TNF drug levels , and the presence of anti‐TNF antibodies .Rates of drug persistence were estimated using the Kaplan–Meier method, and comparative analyses were performed using Cox proportional hazards regression. Patients were censored at the time of treatment failure to their second anti‐TNF.Youden's formula33.1n = 10 patients) so were not approached and eight sites (n = 233 patients) opted not to take part.Between 1 May 2013 and 31 November 2020, we identified 38,940 and 14,847 TDM results, from 13,708 and 8662 patients, treated with infliximab  and adalimumab , respectively. One thousand six hundred eighty‐three patients from 51 sites had both infliximab and adalimumab TDM results Figure . Six sitA total of 1440 patients were screened by research sites for eligibility, and data for 97.3% (1401/1440) patients were submitted. We excluded 11.1% (156/1401) patients who had received a previous course of anti‐TNF therapy; 6.3% (88/1401) patients where a requisition error had occurred and who had never received a second anti‐TNF; 5.4% (75/1401) patients with incomplete clinical data; and 1.7% (24/1401) patients who did not have IBD.3.2p = 0.179). The first anti‐TNF was discontinued in 80% (846/1058) patients because they did not respond or lost response; 6.6% (70/1058) patients developed an adverse event leading to drug cessation and the drug was withdrawn in 13.4% (142/1058) patients for non‐treatment failure reasons .Of the 1058  patients in the final analysis: 71.4% (755), 24.4% (258), and 4.3% (45) patients were diagnosed with Crohn's disease, UC, and IBD‐U, respectively. The median time of follow‐up from starting the first anti‐TNF to the point of data entry or drug withdrawal was 3.84 years (IQR 2.34–5.68). 80.0% (846) patients were treated with infliximab and then adalimumab, and 20.0% (212) patients were treated with adalimumab and then infliximab. There was no difference in the duration of treatment with the first anti‐TNF drug  .In patients treated with infliximab then adalimumab, patients who developed antibodies to infliximab were more likely to develop antibodies to adalimumab, compared to patients who did not develop antibodies to infliximab . A similar observation was seen for patients who developed antibodies to adalimumab who were subsequently treated with infliximab .For each 10‐fold increase in anti‐infliximab antibody concentration, the odds of subsequently developing antibodies to adalimumab increased by 1.73 . This was not seen for patients treated with adalimumab‐first and infliximab‐second   showed that patients who developed immunogenicity with undetectable drug levels to infliximab‐first were more than twice as likely to then develop immunogenicity with undetectable drug levels to adalimumab‐second  Tables  and S5.Youden's method demonstrated that the optimal anti‐drug antibody titre cut‐off to first anti‐TNF to determine immunogenicity with undetectable drug level to second anti‐TNF was 109 AU/mL for patients treated with infliximab first, with an area under the curve of 0.66 (95% CI 0.60–0.71). The sensitivity and specificity were 0.63 (95% CI 0.49–0.90) and 0.68 (95% CI 0.38–0.80), respectively. For patients treated with adalimumab first, the optimal anti‐drug antibody titre cut‐off was 11 AU/mL, with an area under the curve of 0.57 (95% CI 0.51–0.64). The sensitivity and specificity were 0.58 (95% CI 0.42–0.70) and 0.61 (95% CI 0.55–0.74), respectively.3.4Overall, 39.3% (416/1058) patients did not respond or lost response to the second anti‐TNF, 4.3% (45/1058) patients developed an adverse drug reaction leading to drug cessation, and the drug was withdrawn electively in 4.3% (45/1058) patients.Of the 846 patients who did not respond or who lost response to the first anti‐TNF, 57.6% (487/846) and 18.2% (154/846) patients were classified with immunogenic‐pharmacokinetic and immunogenic‐pharmacodynamic failure, respectively. A further 6.9% (58/846) and 17.4% (147/846) patients were classified with nonimmunogenic‐pharmacokinetic failure and nonimmunogenic‐pharmacodynamic failure, respectively (Table p = 0.564), however, more patients treated with infliximab as first anti‐TNF were treated with a concomitant immunomodulator . Similar proportions of infliximab‐ and adalimumab‐treated patients had their first anti‐TNF dose escalated before switching drugs.The median duration of first anti‐TNF treatment was similar between patients treated with infliximab as first anti‐TNF and patients treated with adalimumab as first anti‐TNF . No differences were seen in drug persistence in patients treated with adalimumab as a second anti‐TNF, according to infliximab treatment failure status  .Adverse events leading to withdrawal of the second anti‐TNF occurred in 5.7%  patients treated with infliximab and 3.9%  patients treated with adalimumab. The most common adverse events were infusion and injection‐site reactions , rash , arthritis , and viral infections  Table .p < 0.001]. Overall, infusion reactions to infliximab were associated with higher anti‐infliximab antibody levels; for every 10‐fold increase in antibodies, there was an 8 times risk of having an infusion reaction . No association was seen between injection site reactions and anti‐adalimumab antibody levels.Infusion reactions to infliximab, which occurred after a median of 20.4 weeks (IQR 14.4–58.5), were associated with subsequent injection site reactions to adalimumab, which occurred after a median of 30.5 weeks (IQR 6.8–49.2) [69.0% (40/58) vs. 18.2% (6/33) 4Irrespective of the anti‐TNF sequence, immunogenicity to the first anti‐TNF, was associated with immunogenicity to the second anti‐TNF. We report here that 34% (95% CI 30%–37%) and 64% (95% CI 54%–73%) of patients subsequently developed anti‐drug antibodies to adalimumab and infliximab, respectively. Patients who developed immunogenicity with undetectable drug levels to infliximab were more than twice as likely to develop immunogenicity with undetectable drug levels to adalimumab. Commencing an immunomodulator at the time of switching to the second anti‐TNF was associated with improved drug persistence in patients with immunogenic‐, but not pharmacodynamic‐treatment failure to the first anti‐TNF.It is widely accepted that infliximab is more immunogenic than adalimumab. This has been attributed to the chimeric formulation of infliximab and the more variable drug levels and associated discontinuity of immune responses seen across the standard 8‐week dosing interval.Why some individuals have a propensity to develop antibodies to unrelated epitopes of infliximab and then adalimumab is unknown.We, like others, have shown previously that carriage of one or more HLA‐DQA1*05 alleles confers an almost two‐fold increased risk of immunogenicity to both infliximab and adalimumab, irrespective of concomitant immunomodulator use.We have replicated findings of a recent open‐label randomised controlled trial (RCT) that demonstrated reduced clinical failure rates in 90 patients with immunogenic‐pharmacokinetic treatment failure to first anti‐TNF who commenced azathioprine at the time of the switch to a second anti‐TNF.Herein, at the time of the first anti‐TNF treatment failure about one in five patients had anti‐drug antibodies that were detectable in the presence of the drug.As commonly performed in clinical practice, we incorporated the use of TDM‐based decision making in the setting of primary non‐response or loss of response. Consistent with recently published systematic reviews,We acknowledge, however, the following limitations. First, inherent to our retrospective study design, we have no data on patients who failed an anti‐TNF drug but did not have TDM undertaken. Because of this we may have underestimated the rates of immunogenicity and overestimated drug persistence. This, and the lack of alternative biologic treatments during the timeframe of the study, probably accounts for why over half of patients, regardless of their immunogenicity status, were being treated with their second anti‐TNF after 4 years. Second, our results are potentially subject to interpretation bias, and bias because of missing data, including anti‐TNF and immunomodulator dose optimization data.Third, we accept that our data would have been strengthened by objective markers of disease activity and endoscopic outcomes. Fourth, this was an unselected TDM referral cohort and although we recommend blood sampling just before the next dose, inevitably, some non‐trough samples will have been processed. Even drug‐tolerant anti‐drug antibody assays are not completely drug‐tolerant and therefore we are likely to have underestimated the rates of immunogenicity.Finally, although we were able to show that patients who developed immunogenicity with low drug levels to infliximab also developed this outcome to subsequent adalimumab because only 20% of our cohort were treated with adalimumab first, we were probably underpowered to demonstrate this association for patients treated with second‐line infliximab.We collected data from multiple sites across the UK, who, based on the variability in the number of tests per patient, used a range of TDM practices. However, because we were able to confirm associations with immunogenicity that we reported in the prospective UK‐wide PANTS study,5Patients who developed antibodies to their first anti‐TNF were more likely to develop antibodies to their second anti‐TNF, irrespective of drug sequence. Our findings support international recommendations for the management of anti‐TNF treatment failure, to switch out of biologic class when drug levels are therapeutic, and within class with an immunomodulator when anti‐TNF drug levels are low and associated with antibody development.Neil Chanchlani: conceptualization ; data curation ; formal analysis ; investigation ; methodology ; writing –original draft ; writing –review and editing . Simeng Lin: data curation ; formal analysis ; investigation . Rachel Nice: Data curation ; investigation . Timothy J McDonald: Data curation . Nicholas Alexander Kennedy: Data curation ; formal analysis ; investigation ; methodology , supervision . James R Goodhand: data curation ; investigation ; methodology ; supervision ; writing – review and editing .Tariq Ahmad: Conceptualization ; data curation ; funding acquisition ; investigation ; methodology ; supervision ; writing – review and editing ; IMSAT study investigators: data curation ; investigation .The study was funded by unrestricted grants from Janssen Pharmaceuticals and Cure Crohn's Colitis (Scottish‐registered charity). The funders had no role in the study design, data collection, data analysis, data interpretation or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.Neil Chanchlani has nothing to declare. Simeng Lin reports non‐financial support from Pfizer and Ferring, outside the submitted work. Nicholas A Kennedy reports grants from F. Hoffmann‐La Roche AG, grants from Biogen Inc, grants from Celltrion Healthcare, grants from Galapagos NV and non‐financial support from Immundiagnostik, during the conduct of the study; grants and non‐financial support from AbbVie, grants and personal fees from Celltrion, personal fees and non‐financial support from Janssen, personal fees from Takeda, personal fees and non‐financial support from Dr Falk, outside the submitted work. James R Goodhand reports grants from F. Hoffmann‐La Roche AG, grants from Biogen Inc, grants from Celltrion Healthcare, grants from Galapagos NV and non‐financial support from Immundiagnostik, during the conduct of the study. Tariq Ahmad reports grants and non‐financial support from F. Hoffmann‐La Roche AG, grants from Biogen Inc, grants from Celltrion Healthcare, grants from Galapagos NV and non‐financial support from Immundiagnostik, during the conduct of the study; personal fees from Biogen inc, grants and personal fees from Celltrion Healthcare, personal fees and non‐financial support from Immundiagnostik, personal fees from Takeda, personal fees from ARENA, personal fees from Gilead, personal fees from Adcock Ingram Healthcare, personal fees from Pfizer, personal fees from Genentech, non‐financial support from Tillotts, outside the submitted work.The choice of treatment, care or services was that of the healthcare professional and patient. In line with Health Research Authority guidelines, formal ethics approval for our study and patient consent were not required. The sponsor of the study is the Royal Devon and Exeter NHS Foundation Trust.Appendix S1 Supporting InformationClick here for additional data file.


Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high‐grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)‐assisted cytological diagnosis for such lesions.Low‐grade squamous intraepithelial lesion or worse was set as the diagnostic threshold for AI‐assisted diagnosis. The performance of AI‐assisted diagnosis was evaluated and compared to that of manual diagnosis. Feasibility in large‐scale screening was also assessed.p = 1.52 × 10−76) and a better interobserver agreement . AI‐assisted detection showed a higher diagnostic accuracy , sensitivity , and negative predictive value (NPV) . Specificity and positive predictive value (PPV) were not significantly differed. AI‐assisted diagnosis demonstrated a smaller proportion of participants of interest , a higher consistence between cytology and endoscopy , specificity , and PPV  in community‐based screening. Sensitivity and NPV were not significantly differed. AI‐assisted diagnosis as primary screening significantly reduced average cost for detecting positive cases.AI‐assisted diagnosis for abnormal cells was superior to manual reading by presenting a higher efficiency for each slide (50.9 ± 0.8 s vs 236.8 ± 3.9 s, Our study provides a novel cytological method for detecting and screening early ESCC and HGIN. This study has provided an automated classfication for esophageal epithelial squmaous cells. This AI‐based system is competent in identifying atypical squamous cells and low‐grade squamous intraepithelial lesion.Hence, our research may contribute to promotion of cytological detection of early esophageal squmaous cell carcinoma. Besides the heavy workload, some crucial problems in assessing all available slides from population‐based screening remain to be unsolved, including follows. Firstly, atypical squamous cell (ASC)‐or‐worse is adopted as the diagnostic threshold for detecting abnormal cells of interest. Due to the low‐rank clinical significance of ASC,Application of artificial intelligence (AI)‐assisted cytological diagnosis has been developed to detect dysplasia cells,22.1chineseclinicaltrials.gov (ChiCTR1900028524). The protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki, and has been approved by review board of Zhongda Hospital Southeast University, with Ref No. 2019ZDSYLL092‐P01. Written consent was obtained from each participant before enrollment into this study.An AI‐based cytological diagnosis system from Froeasy Tech Co., which is designed for automated classification of esophageal squamous epithelial cells, has been applied for cytological detecting early ESCC and HGIN. By setting LSIL‐or‐worse as the diagnostic threshold, the performances of AI‐assisted diagnosis were evaluated in patients with pathologically confirmed esophageal lesions and asymptomatic participants with high risks of ESCC. This study has been registered on 2.2On the basis of our previous study,2.3Cytological samples were collected by patients swallowing a sponge device , which consists of a microscope with a ×20 objective, a condenser numerical aperture (NA) of 0.75 and a camera adapter of factor 0.87, a double charge‐coupled‐device camera, and a computer with a high‐resolution monitor, was used for manual classification of cells. Manual diagnosis was made based on DNA content and morphological features. AI‐assisted diagnosis was performed as AI‐reading for abnormal cells followed by manual confirmation.2.6From July 2019 to August 2020, 2090 asymptomatic participants (aged ≥40 years) with high risks of ESCC from 10 communities in Nanjing were recruited.2.7t test was used for the comparison of continuous variables. All statistical tests were performed using the SPSS statistical software for Windows, version 20.0 (SPSS Inc.) and R software (version 3.5.3).The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated, and 95% confidence intervals (CIs) were calculated using the Clopper–Pearson method. A receiver operating characteristic (ROC) curve was used to show the diagnostic performance of the deep learning algorithm in classifying esophageal squamous epithelial cells. A Student's 33.1After 300 iterations through the entire training set, the training procedure was concluded due to no improvement in diagnostic accuracy Figure . As show3.2p = 1.52 × 10−76). The interobserver agreement of identifying LSIL or worse by AI‐assisted reading was better than that of manual reading .Slides from 127, 28, 14 and 24 patients with early ESCC, HGIN, LGIN, and REE, respectively, were selected for manual diagnosis and AI‐assisted diagnosis. The time taken to read each slide by AI‐assisted diagnosis was significantly shorter than that of manual diagnosis , a higher sensitivity  and a higher NPV  than those of manual diagnosis. And, specificity  and PPV  were not significantly differed.As listed in Table Slides from misdiagnosed cases, including one from AI‐assisted diagnosis and 49 from manual diagnosis, respectively, were re‐evaluated by three expert cytopathologists. The missed case by AI‐assisted diagnosis, which was HGIN instead of NLIM, was caused by inadequate staining and scanty abnormal cells. Whereas, 48 misdiagnosed cases by manual reading only were due to interpretive errors. Among these, 21 cases with LSIL‐or‐worse were underdiagnosed as ASC or NLIM due to lack of positive control cells for diagnosis. Twenty‐seven cases with LSIL‐or‐worse were missed due to few abnormal cells were present in all slides. By contrast, AI‐assisted reading altered the cytologists by presenting LSIL‐or‐worse abnormality. Typical samples of these cases are shown in Figure 3.3p = 1.59 × 10−58). Based on endoscopic and histopathological outcomes, 1969, 88, 24, 23, and 13 participants were diagnosed as negative, REE, LGIN, HGIN and early ESCC, respectively. These results are shown in Table According to cytological results, NILM, ASC, LSIL, HSIL, and SCC were present in 1845, 193, 57, 20, and 2 participants, respectively. The proportion of participants with abnormal cells by using LSIL‐or‐worse as diagnostic threshold was significantly lower than that by using ASC‐or‐worse ; the specificity  and PPV  were also enhanced. Sensitivity  and NPV  were not significantly differed between these two settings .The cost of upper gastrointestinal endoscopy including Lugol's iodine staining, histopathological diagnosis, and sponge‐based cytology were 600, 130, and 400 Yuan (RMB) per case, respectively. In this screening cohort, the per capita cost for detecting early ESCC and HGIN by using AI‐based cytology as pre‐endoscopy detection was significantly lower than that of endoscopy screening only , therefore, cytology was negative in these three cases. Subsequently, timely follow‐up cytology is recommended in participants with high risks of ESCC and negative cytological outcomes. Also, good quality control should be executed in sample collection and slides preparation in such procedures.A key limitation of this study is that the performance was carried out by one tertiary center. Possible bias in participant recruitment may be involved. Another limitation is that the community‐based screening was conducted in a region with a relatively low incidence of disease, as reflected by total positive rate of all identified early ESCC and HGIN at 0.71% (15/2117). However, the feasibility of this AI‐assisted diagnosis with a rigid diagnostic threshold has been verified owing to the high efficiency of a well‐trained AI‐based automated recognition and classification.Taken together, we have developed an AI‐assisted diagnosis for automatically cytological detection of esophageal squamous lesions and validated its efficiency. A rigid diagnostic threshold has been firstly established in such detection. This automated classification also provides a novel pre‐endoscopy screening method for large‐scale screening of early ESCC and HGIN.Yadong Feng, Huazhong Shu, and Ruihua Shi conceptualized this study. Yadong Feng, Bin Yao, Kai Zhao, and Yan Liang performed cytology and endoscopy. Peilin Huang and Juncai Zang reviewed cytological and histopathological results and performed statistical analysis. Jie Song, Mengjie Li, and Xiaofen Wang were in charge of follow‐up and data collection. Yadong Feng drafted the original manuscript. Bin Yao, Kai Zhao, and Ruihua Shi edited the manuscript. All authors reviewed and approved the final edition of this manuscript.This work is funded by Jiangsu Provincial Special Program of Medical Science (BE 2019710 and BE 2018620), Nanjing Municipal Social Development Program (201803056), Medical Innovation Program of Jiangsu Commission of Health (2017XKJQW08), Major Project of Changzhou Municipal Commission of Health (ZD2019028) and Changzhou Municipal Social Development Program (CE20195001).The authors declare that there is no conflict of interest.Bin Yao andJuncai Zang are employees of Foreasy Tech Co. They are the main members of two projects of Jiangsu Provincial Special Program of Medical Science (BE 2019710 and BE 2018620). They do not have any financial arrangement for this research and the preparation of this manuscript.Bin Yao is pursuing a PhD degree at Southeast University.The protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki, and has been approved by review board of Zhongda Hospital Southeast University, with Ref No. 2019ZDSYLL092‐P01. Written consent was obtained from each participant before enrollment into this study.


Abnormal uterine bleeding (AUB), alopecia, low quality of life, and acne are considered as complications of polycystic ovary syndrome (PCOS). We hypothesized that magnesium supplementation would yield beneficial effects on PCOS related complications.To examine the effects of magnesium supplementation on AUB, alopecia, quality of life, and acne.n = 32) or placebo group (n = 32) for 10 weeks. AUB, alopecia, quality of life, and acne were assessed by the International Federation of Gynecology and Obstetrics criterion, the Sinclair Scale, the Health Survey Quality of Life Questionnaire, and the Global Acne Grading System, respectively. This randomized clinical trial was registered at IRCT.ir (IRCT20130903014551N9).In this parallel randomized clinical trial, we randomly assigned 64 women with PCOS to the magnesium group (p = 0.011), role limitations due to physical health (p = 0.012), role limitations due to emotional problems (p < 0.001), energy/fatigue (p = 0.005), emotional wellbeing (p < 0.001), social functioning (p = 0.002), general health (p = 0.013), and total quality of life (p < 0.001), compared with placebo. No significant effect was observed on acne, alopecia, and AUB.Magnesium supplementation significantly improved the components of quality of life including physical functioning .This randomized clinical trial was registered at IRCT.ir on 2020–10-18 (Registration Code: The online version contains supplementary material available at 10.1186/s12958-022-00982-7. Polycystic ovary syndrome (PCOS) is characterized by several cysts and follicles in enlarged ovaries, and production of infertile eggs . GeneticAUB is prevalent among women with PCOS; indeed, evidence suggests that 50% of patients had oligomenorrhea and 20% had amenorrhea . AndrogeEvidence suggests that magnesium deficiency may play an important role in women's health in several clinical conditions, including premenstrual syndrome, dysmenorrhea, and PCOS . Women wAccording to previous studies, we hypothesized that magnesium supplementation might elicit beneficial effects on complications of PCOS. Therefore, this study sought to evaluate the effect of magnesium supplementation on AUB, alopecia, quality of life, and acne in women with PCOS.This study was carried out in the period of November 2020 to November 2021 in Isfahan, Iran. Subjects were included if they: 1) were aged 18 to 45 years old; 2) were diagnosed with PCOS according to the Rotterdam criteria  / Δ2 = 2 [(1.96 + 0.85) 2 × (0.66) 2] / (0.47) 2 = 30.n = 32 in each group) were included in the study because of possible withdrawal. Participants were randomly allocated in a ratio of 1:1 to either magnesium supplement or placebo using a computer-generated randomization sequence. We did not use blocks in randomization. We assigned a code to each subject and entered the codes into SPSS. Then participants were randomly divided in to 2 groups by SPSS.Finally, 64 subjects . This randomized clinical trial was registered at IRCT.ir (IRCT20130903014551N9).Comprehensive information regarding the study were explained to participants. In the magnesium group, a 250 mg magnesium oxide tablet  per day was administered for 10 weeks. In placebo group, we used a tablet that contained 5 mg starch and its color, appearance, smell, and taste were similar to the 250 mg magnesium oxide tablet. Participants were asked to consume tablets after breakfast. We used telephone calls and virtual networks to monitor use of supplements.Women in both groups received a list of dietary recommendations, including: 1) limit consumption of refined or simple carbohydrates, such as white bread, white rice, sugar and sweets; 2) increase consumption of fresh vegetables; 3) use more mini-meals instead of big meals; 4) drink at least 8 glasses of fluid, especially water; 5) be cautious about your weight and avoid overeating; 6) consume leafy vegetables such as lettuce and cabbage instead of starchy vegetables such as potatoes; 7) increase consumption of fresh fruits and avoid using industrial and sugar sweetened fruit juices; 8) limit consuming salty foods, fast foods and high-fat dairy products; and 9) use healthy oils such as olive oil and canola oil and limit consuming saturated, partially saturated vegetable oil or animal fat.Frequency of menses: The duration of the menstrual cycle is normally 24 to 38 days. Therefore, regular episodes of bleeding at intervals of ≤ 24 days or > 38 days were considered as abnormal.Regularity of menses: Irregular menses was defined as shortest to longer cycle variation was ≥ 10 days.Duration of menses: If the duration of menstrual bleeding was more than 8 days or less than 3 days in each period, it was considered as abnormalVolume of monthly blood loss: If a woman's bleeding volume was between 5 to 80 ml in a period, it was considered normal and less than 5 ml or more than 80 ml was abnormal.Based on the definitions provided by the International Federation of Gynecology and Obstetrics (FIGO), following criteria were considered as components of AUB , 25:FreqThe number of AUB criteria in each subject was assessed at baseline and after 10 weeks of intervention.Clinical manifestations of alopecia was assessed at the beginning and end of the study using the Sinclair Scale . The valTo evaluate the severity of acne, we examined existence of acne according to Global Acne Grading System . ValiditTo assess the effect of magnesium on quality of life, we asked patients to complete the Health Survey Quality of Life Questionnaire SF-36) before and after the intervention , 33. The before aPhysical activity of the participants was presented as metabolic equivalent per hour per day (MET.h.d). Each participant completed 5 one-day physical activity diaries during the study. Individuals were asked to report their activities such as walking, exercise, sleep, watching TV, housework, studying, bathing, and so on. The total metabolic equivalent was calculated by multiplying the frequency, duration, and intensity of each physical activity in 24 h.To assess dietary intakes during the study, each participant was asked to complete 5 one-day food records, including 3 weekdays and 2 weekends. Nutrient content of the foods was calculated by Nutritionist IV based on the United States Department of Agriculture food composition database.The serum level of magnesium was measured at baseline. A 5 ml blood sample was collected and serum was isolated. We measured magnesium by Atomic Absorption Spectrophotometry method.To classify patients in terms of economic status, they were asked about the amount of family income and based on the amount of income, they were classified into three groups: 1) Poor economic status (for incomes less than three million ،Tomans per month), 2) Medium economic status (monthly income Between four to ten million Tomans) and 3) Good economic situation (for people whose average family income was above ten million Tomans per month). This division was based on living conditions in Iran and the income range of clients. The level of education of each person was asked, and they were divided into three groups: 1) under diploma, 2) Diploma, and 3) University education.We ran an intention to treat (ITT) analysis by using the linear regression method in the current study . The Koln = 844). Then we called them and 780 patients were excluded because: 1) they did not meet the inclusion criteria (n = 376); 2) PCOS was treated (n = 24); 3) patients were on insemination in vitro fertilization treatments (n = 50); 4) they refused to participate in the study (n = 248); 5) they were pregnant (n = 20); or 6) other reasons (n = 52). Then women who reported signs and symptoms of PCOS were invited to run an assessment based on the Rotterdam criteria to ensure that they had PCOS [n = 32) or placebo (n = 32). During the follow-up process, five patients in the magnesium group were lost to follow-up because they: 1) refused to continue the study (n = 2); 2) were pregnant (n = 1); or 3) did not want to participate in blood sampling (n = 2). Similarly, five subjects were lost to follow-up in the placebo group because they: 1) refused to continue the study (n = 2); 2) did not want to participate in blood sampling (n = 2); or 3) personal reasons (n = 1). Therefore, 54 patients completed the study. Nevertheless, data of 64 people (32 subjects in each group) were analyzed based on the ITT method.The process of patient recruitment is shown in Fig. had PCOS . Therefop = 0.615), height (p = 0.439), weight (p = 0.918), weight status (p = 0.987), BMI (p = 0.808), educational status (p = 0.382), economic status (p = 0.186), marital status (p = 0.306), and the level of physical activity (p = 0.733) were not different between two groups. Baseline serum magnesium was higher in the intervention group compared with placebo (p = 0.047). More data regarding physical activity are presented in the Supplementary File Table p = 0.325), protein (p = 0.583), fat (p = 0.760), cholesterol (p = 0.102), linoleic acid (p = 0.480), saturated fatty acids (p = 0.591), monounsaturated fatty acids (p = 0.332), polyunsaturated fatty acids (p = 0.959), vitamin A (p = 0.434), vitamin E (p = 0.704), vitamin K (p = 0.403), vitamin C (p = 0.086), vitamin B1 (p = 0.250), vitamin B2 (p = 0.386), vitamin B3 (p = 0.532), vitamin B5 (p = 0.312), vitamin B6 (p = 0.179), folate (p = 0.859), magnesium (p = 0.481), potassium (p = 0.341), calcium (p = 0.606), zinc (p = 0.560), iron (p = 0.609), sodium (p = 0.561), and dietary fiber (p = 0.412) had no significant differences between the two groups.Table p = 0.011), role limitations due to physical health (p = 0.012), role limitations due to emotional problems (p < 0.001), energy/fatigue (p = 0.005), emotional wellbeing (p < 0.001), social functioning (p = 0.002), general health (p = 0.013), and total quality of life (p < 0.001) were significantly improved after intervention compared with baseline. In contrast, number of AUB items (p < 0.001) and score of alopecia (p = 0.009) decreased after the trial in magnesium group. In placebo group, scores of physical functioning (p = 0.028), number of items of AUB (p = 0.001) and score of alopecia (p = 0.009) were significantly decreased at the end of the trial compared with baseline. More data regarding alopecia, acne, physical activity, AUB scores are presented in the Supplementary File Table p = 0.001), energy/fatigue (p = 0.010), emotional wellbeing (p < 0.001), general health (p = 0.042), and total quality of life (p < 0.001) compared with placebo.After adjusting for baseline serum magnesium and initial measurements, magnesium supplementation improved scores of role limitations due to emotional problems (The results of this study showed that supplementation with 250 mg of magnesium for 10 weeks improved the quality of life components in women with PCOS. Quality of life in PCOS is lower than healthy subjects and those with other gynecological diseases, which can lead to several negative consequences , 40, 39.Previous studies confirmed that magnesium supplementation had a favorable effect on quality of life. Indeed, a clinical trial showed that oral magnesium sulfate significantly improved the quality of life in women with dysmenorrhea , whilst We found that magnesium supplementation had no significant effect on acne in patients with PCOS. Acne vulgaris is a cosmetic problem that affects 80% of the population, especially women with PCOS . It is aWe found that magnesium supplementation had no significant effect on alopecia. We hypothesized that magnesium supplementation may improve alopecia because previous studies showed that magnesium deficiency contributed to alopecia and disrupted cholesterol-enhanced hair loss . Also, tWe did not observe any significant change in AUB after magnesium supplementation. Although some previous studies evaluated the efficacy of magnesium supplementation in PCOS, its effect on AUB was not assessed, or due to publication bias and adverse outcomes, results remain unpublished. Therefore, we could not compare our findings with previous results.In this study, magnesium supplementation resulted in improvement of physical function and physical health in women with PCOS. According to previous studies, physical activity is associated with increased magnesium requirement and intake . Also, dMagnesium supplementation in women with PCOS improved emotional and mental aspects of quality of life. Previous studies showed that low magnesium intake was significantly associated with externalizing behaviors , whilst In the present study, an improvement in the general health of patients with PCOS was observed after magnesium supplementation. Since magnesium is a coenzyme of more than 300 enzymes in the body, and many chemical reactions require sufficient magnesium level, it is unsurprising that the general health of the body depends on adequate intake of magnesium . In clinSeveral strengths and limitations should be stated with regard to the present study. the Covid-19 pandemic and subsequent lockdown was a reason for several participants ceasing study enrolment. In addition, some variables in the present study were collected by subjective methods, which is often accompanied by recall bias; however, we utilized validated methods in an effort to ameliorate this issue. A strength of the current study was using ITT method in statistical analysis, which was a priori defined. Also, all components of quality of life were reported in the results, allowing detailed insight into numerous aspects of quality of life.Magnesium supplementation in women with PCOS had a significant positive effect on improving total quality of life and its components. However, data regarding the effect of magnesium supplementation on alopecia, AUB and acne was not sufficient to draw a consensual conclusion. Future studies should assess the effect of magnesium supplementation on AUB, acne and alopecia.Additional file 1.


The incidence of colorectal cancer is increasing among young adults and more rectal cancers are reported. This study aimed to identify the clinical features specific for early‐onset rectal cancer and provide insights on cancer management.Early‐onset (<50 years) and late‐onset (≥50 years) rectal cancer patients from a referral tertiary care center (SYSU cohort) and Surveillance Epidemiology and End Results database (SEER cohort) were included to perform a comprehensive comparison on clinical information.p < 0.05). In addition, early‐onset patients presented more frequently with advanced node stage  and high‐risk features, including mucinous or signet cell carcinomas , poorly differentiated tumors , and perineural invasion  compared with late‐onset patients. However, early‐onset patients received more neoadjuvant  and adjuvant treatments , and they had better overall survival in both SYSU  and SEER  cohorts.A total of 552 and 80,341 patients with stages I–III rectal cancer were included in the SYSU and SEER cohorts, respectively. In the SYSU cohort, early‐onset diseases had significantly higher prevalence of family history of cancer and history of HBV infection and lower incidence of comorbidities (Early‐onset rectal cancers are distinct from late‐onset cases in clinicopathological features, treatment modalities, and outcomes. The clinical trials and studies that are specific for young populations are needed to develop optimal strategies for cancer screening, treatment, and surveillance. This study including two cohorts with a total of 552 and 80,341 stages I–III rectal cancer patients demonstrated that there existed a wide set of disparities in disease history, clinicopathological features, treatment modalities, and outcomes between early‐onset and late‐onset rectal cancer patients. The patient data, including demographic and clinicopathological characteristics and follow‐up data, were collected from the prospectively maintained institutional database program of colorectal disease (IDPCD) as introduced previously.2.2According to the NCCN Guideline for Rectal Cancer, patients in SYSU cohort were treated and followed up with the details described in previous publications.2.3U test because of their abnormal distribution. Categorical variables were reported as numbers with percentages, and they were assessed using the chi‐square test or the Fisher exact test where appropriate. DFS and OS were calculated using the Kaplan–Meier method and performed through log‐rank test between group comparisons. Univariate survival analysis for OS and DFS was evaluated using a Cox proportional hazards model. The statistical analyses were performed through SPSS 22.0 and R‐project 3.5.1. A two‐sided p value <0.05 was regarded with statistical significance.Continuous variables were described as median and interquartile range and were compared using the Mann–Whitney 33.1In the SYSU cohort, a total of 552 rectal cancer patients with stages I–III were included. There were 322 male (58.3%) and 230 female 41.7%) patients, with a median age of 59 years (21–89 years). The TNM staging among those patients was distributed as 25.5%, 32.8%, and 41.7% for stages I, II, and III, respectively. Among them, there were 124 (22.5%) early‐onset and 428 (77.5%) late‐onset rectal cancer cases, with a median age at the time of diagnosis at 39 years (21–49 years) and 64 years (50–89 years), respectively. With a median follow‐up time of 46.0 (35.0–61.8) months, the DFS rates at 1, 3, and 5 years were 87.1%, 71.2%, and 66.0% and the OS rates at 1, 3, and 5 years were 96.7%, 84.1.0%, and 76.1%. In the SEER cohort, the early‐onset cases were substantially less than late‐onset cases, though the proportion of early‐onset cases was increasing per year in the past decades  Interestingly, more patients with history of hepatitis B virus (HBV) infection were found in the early‐onset group . As expected, late‐onset cases were more susceptible to age‐related comorbidities, including hypertension (1.6 vs. 20.3%), diabetes mellitus (0.8 vs. 12.1%), and heart diseases (0 vs. 7.0%) .The disease history of early‐ and late‐onset patients in the SYSU cohort is summarized in Figure 3.3p = 0.051). There was no significant difference in circumferential tumor location between early‐onset and late‐onset rectal cancer patients in the SYSU cohort. The early‐onset patients had higher incidence of low tumor differentiation , mucinous and signet ring rectal carcinoma , and presented more with advanced N stage  in SYSU cohort  and adjuvant  chemo(radio)therapy  in the SYSU cohort.The overall incidence of postoperative anastomotic complications, including any disease of anastomotic leakage, bleeding, and stenosis, did not differ significantly between two age groups (p = 0.350) and were detected in 9.4% and 19.5% of the late‐onset patients (p = 0.970) in the SYSU cohort  metastasis compared with late‐onset cases  and SEER  cohorts therapy according to the treatment protocols from NCCN guideline.It is widely acknowledged that standard neoadjuvant and adjuvant therapies administered to colorectal cancer patients could significantly improve their oncological outcomes.Although the two cohorts with sufficient cases could provide robust evidence to support our findings, this study has some limitations. First, this study was limited by its retrospective nature, and the specific results derived from the single‐center analysis may not be generalized to other populations. Second, the sample size of early‐onset cases in the SYSU cohort was relatively small, which makes it difficult to further split the early‐onset cases into younger groups, such as patients aged <40 and 30 years, to conduct a robust statistical analysis. Then, the details about neoadjuvant regimens and response to neoadjuvant treatment were not well documented in SYSU cohort, which limits the in‐depth analysis in this study. Finally, we focused on rectal cancer in this study, while future studies are of interest to specifically compare the early‐onset and late‐onset colon cancers to provide valuable sources for next‐generation management of colon cancer. As a result, the optimal multimodal regimen and surveillance strategies specific for early‐onset colorectal cancers should be confirmed in the future clinical trials.5This study characterized the demographic and clinicopathological features of early‐onset rectal cancer, an increasingly diagnosed subgroup of colorectal cancer patients, by comparing them with late‐onset rectal cancer patients. We found that early‐onset patients were more likely to have advanced node staged tumors and high‐risk pathological features, including low differentiation, mucinous or signet cell carcinoma, and perineural invasion. However, early‐onset patients got more access to neoadjuvant and adjuvant treatment with better survival outcomes compared with late‐onset patients. Our findings indicated that this survival advantage might be attributed to the more sufficient treatment early‐onset patients received. Considering that early‐onset rectal cancer had distinct clinical and pathological features, the specific clinical trials and studies for young populations are needed to develop optimal strategies for cancer screening, treatment, and surveillance.Study concept and design: DS, YL, and HY. Acquisition of data: MZ, PW, XW, and MH. Analysis and interpretation of data: DS, YX, ZZ, and PW. Drafting of the manuscript: DS and HY. Critical revision of the manuscript for important intellectual content: all authors. All authors had access to the study data and reviewed and approved the final manuscript.The authors declare that they have no conflicts of interest.The study protocol was reviewed and approved by the Institutional Review Board of the Sixth Affiliated Hospital of Sun Yat‐sen University (SYSU) (no. 2017ZSLYEC‐006), and written informed consent was obtained from all subjects or their representatives for the study participation. The study was performed in accordance with the Declaration of Helsinki.Figure S1Click here for additional data file.Table S1Click here for additional data file.


Recent advances in single cell RNA sequencing (scRNA‐seq) empower insights into cell–cell crosstalk within specific tissues. However, customizable data analysis tools that decipher intercellular communication from gene expression in association with biological functions are lacking. The authors have developed InterCellDB, a platform that allows a user‐defined analysis of intercellular communication using scRNA‐seq datasets in combination with protein annotation information, including cellular localization and functional classification, and protein interaction properties. The application of InterCellDB in tumor microenvironment research is exemplified using two independent scRNA‐seq datasets from human and mouse and it is demonstrated that InterCellDB‐inferred cell–cell interactions and ligand–receptor pairs are experimentally valid. User‐defined analysis: A platform is developed for user‐defined analysis on intercellular crosstalk using single cell RNA sequencing (scRNA‐seq) datasets. Protein annotation including expression change, cellular localization and functional classification are considered. Protein interaction properties including evidence source, credibility score, action mode, and action effect are provided as well. Therefore, biological meaningful processes can be explored further by InterCellDB. Under disease conditions, intercellular networks change dramatically due to the alterations in the extracellular matrix. For instance, tumor cells modify the surrounding micro‐environment by secreting factors to recruit immune cells, which in turn impact on tumor growth and its responses to therapies. Elucidating intercellular communications will enrich our knowledge of disease development and promote new therapeutic approaches.Cell crosstalk with adjacent or remote partners are commonly observed in multicellular organisms throughout the life span.1 Cont Several analytical tools have been developed to unravel intercellular interactions, such as CellPhoneDB, SingleCellSignalR, CellChat, iTALK, and NicheNet. However, there are still unmet needs in this field. First, all current analytical tools provide limited types of interaction. In reality, intercellular networks are much more complicated, encompassing ligand–receptor, receptor–receptor, extracellular matrix–receptor, and vesicle–cell interactions. Besides, all these published tools have limited capability in providing insight in intercellular interactions for specific functions. For instance, in recent study, we found no available analysis approaches able to predict how ligand–receptor interaction between regulatory T cells (Treg cells) and microglia promotes oligodendrogenesis and white matter repair after ischemic stroke. Finally, most current approaches have fixed build‐in searching criteria, which limited the dimensions of analysis. A new platform is in demand to analyze intercellular networks in a user‐defined manner. We, therefore, develop a new analytical platform called InterCellDB to address these gaps and provide a user‐defined comprehensive analysis of intercellular communication using single cell RNA sequencing (scRNA‐seq) datasets in combination with specific biological functions of interest.Single‐cell‐omics technologies provide sufficient resolution to map intercellular networks.5 Seve22.1Figure  Subsequently, based on the references provided by the NCBI genomes database, we converted proteins into corresponding coding genes and generated two gene lists  in each cluster compared with other clusters  and myeloid cells . According to the function and cellular localization of proteins, we classified two core collections of cells: 1) cells releasing signals ; 2) cells receiving signals . Intercellular network analysis between these two collections of cells showed that myeloid cells were one of the main signal recipients in response to the ligands from CAF1, CAF2, and myeloid cells  with high levels of IL6 as the most prominent cluster among six distinct fibroblast subsets. Their biological studies proved that vCAFs secrete IL6 to induce significant epigenetic alterations in ICC cells expressing IL6 receptors and thereby enhance malignancy .18 ZhaWe applied InterCellDB to a group of ligand–receptor pairs specified in Zhang's analysis and evaluated whether IL6–IL6R was the potential messenger between vCAFs and ICC cells Figure . The res2.3 Distinct from other analytical tools, InterCellDB aggregates all gene‐coded proteins into 132 types according to the annotated information from the Uniprot database . Three subsets were constructed according to the credibility of protein interactions. The first subset contained those protein interactions that were validated by experiments. These protein interactions were labelled with “experiments > 0” in the STRING database. The second subset included those protein interactions that were extracted from pathways and labelled with “database > 0” in the STRING database. The last subset contained those protein interactions from various sources including “neighborhood,” “neighborhood_transferred,” “fusion,” “cooccurence,” “homology,” “coexpression,” “coexpression transferred,” “experiments transferred,” “database transferred,” “textmining,” and “textmining transferred,” which showed low evidence and were labelled with score > 0 in at least one of the above sources in the STRING database. We named these three subsets as experiment validated, pathway curated, and predicted databases. In addition, annotation information including credibility score, action mode, and action effect was added for each protein–protein pair. Credibility score ranging from 1 to 1000 are divided into four levels of confidence: highest (score ≥ 900), high (score ≥ 700), medium (score ≥ 400), and low (score < 400). Action mode refers to functional interplay between protein x and y, including “activation,” “binding,” “catalysis,” “expression,” “inhibition,” “post‐translational modification (ptmod),” “reaction,” and “other” .12 ThrA and B, the power was calculated by:Exprs(A) and Exprs(B) mean the normalized expression levels of the proteins.The power of interaction was calculated by the product of expressions of involved proteins. For one interacting protein pair p‐value of one given interaction between protein A and B was calculated as:(n) is the power of the interaction in n‐th permutation. N is the total number of permutations (N = 100 by default). Those interactions with p‐value < 0.05 are considered significant.To calculate the confidence of interaction, cell label permutation test was used to randomly assign clusters labels to all cells and recalculate the power of all interactions. The W between any two cells were calculated to evaluate the intensity of cell–cell interaction. The aggregated power W is the sum of interaction power for all protein pairs. Given two cell clusters i and j, the aggregated power was calculated by:M represents the overall count of protein pairs between cell cluster i and j. For k‐th interaction, the power of protein Ak and Bk are calculated.The number of protein–protein pairs and aggregated power The major advantage of the InterCellDB was that it provided custom settings for specific analysis accommodating to complicated biological process. There were two main modules of custom settings, including the selection of proteins in the analysis and the restriction of functional scope. The users could set specified thresholds for expression levels, cellular localizations, and functional features to select appropriate proteins for analysis. The users could also restrict functional scope by setting the evidence sources, credibility score, action mode, and action effect of the protein interactions.https://www.ebi.ac.uk/gxa/sc/experiments/E‐EHCA‐2/downloads. scRNA‐seq data preprocessing was completed by Seurat (v3.2) before applying InterCellDB for intercellular analysis. First, the count matrix was correctly formatted by setting the row names as gene names and the column names as cell barcodes. Then, the data were normalized and scaled. Cell clusters were annotated by the references of the experiment metadata, which included cancer‐associated fibroblasts (CAF 1–3), fibroblast from lymph node (fibroblast LN), tumor endothelial cells (Endo tumor), lymphatic endothelial cell (Endo lymphatic), endothelial cells from lymph node (Endo LN), conventional dendritic cells (cDC 1–2), plasmacytoid dendritic cell (pDC), migratory dendritic cell (migDC), T cell from lymph node (LN T cell), mucosal‐associated invariant T cell (MAIT), tumor T cells, natural killer cell (NK), myeloid, and B cells. Further, t‐SNE dimensionality reduction was performed to visualize the result of clustering and the differentially expressed genes were calculated for all clusters.Raw count files and experiment metadata were downloaded for scRNA‐seq data of mouse melanoma generated by Davidson et al. from analysis.21 FirGO: 0 006955). All further analysis was restricted in this cluster of genes. Cells with proteins releasing to the extracellular region were set as y‐axis and cells with receptors expressing in the plasma membrane were set in x‐axis. Action mode of protein pairs was set as “binding” to ensure that factors released from cells of the y‐axis could bind to their receptors in cells of the x‐axis. Heatmap was used to present the interaction intensity. Dot size showed the count number of protein pairs, and the color of dots indicated the strength of interaction.The study performed by Davidson et al. aims to explore immune–stromal interactions. Hence, the functional scope was restricted within immune response. Briefly, 1671 unique genes were extracted from the GO term of immune response  and “positive” (action effect) between CAF1 and myeloid cells were extracted. Heatmap was used to present the intensity and confidence of protein pairs in communications between CAF1 and myeloid cells. Dot size represented the confidence of protein pairs. The degree of color saturation of a dot represented the power of protein pairs by summing up the logT cells, NK cells, malignant cells, macrophages, hepatocytes, fibroblasts, endothelial cells, dendritic cells, cholangiocytes, and B cells. Further, t‐SNE dimensionality reduction was performed to visualize the result of clustering and the differentially expressed genes for all clusters were calculated.Raw gene expression count matrix of human ICC scRNA‐seq data were downloaded from GEO with accession code GSE138709. To determine gene expression, the same version of Seurat (v2.3.4) as described in the original study was applied. After normalization, 1000 variable genes were selected for cell clustering (resolution: 0.3). Cell clusters were annotated by the references of the experiment metadata as y‐axis and IL6‐received cells were set in x‐axis. Action mode of protein pairs was set as “binding” to ensure that factors released from cells of the y‐axis could bind to their receptors in cells of the x‐axis. Dot size and color indicated the count and strength of interaction, respectively.To evaluate potential off‐target effects, all protein pairs involving either IL6 or IL6R were assessed. Briefly, a set of ligand–receptor pairs predicted by Zhang et al. was extracted and it was reanalyzed by using InterCellDB. Heatmap showed the intensity and confidence of these ligand–receptor pairs for all possible interactions among any two cell types. All interleukin receptors that were expressed in malignant cells and had at least one putative ligand expressed in the other cell types were calculated to screen candidate ligand–receptor pairs. Finally, IL6‐released cells were set in The spatial pattern of target protein pairs was shown in a schematic diagram. Two separate cell‐mimic areas represented two independent cells. Each cell area contained four subcellular locations, including extracellular space, cytomembrane, cytoplasm, and cell nucleus. Interaction type and effect were denoted by line color and arrow. Proteins were drawn as dots, and gene expressions were indicted by size. The up‐regulated genes were denoted as red and downregulated genes were denoted as green.i is given as:Pairs(InterCellDB) is protein pairs from InterCellDB, and Pairs(i) is protein pairs in previous database i.The coverage of protein interactions was assessed among InterCellDB, iTALK, CellPhoneDB, SingleCellSignalR, NicheNet, and CellChatDB. Three major subsets of InterCellDB including experiment validated (InterCelldDB.exp), pathway curated (InterCelldDB.know), and predicted databases (InterCelldDB.pred) were also incorporated into the comparison. The formula for coverage ratio for previous database 2fold change > 0.25 and belonging to genes associated with immune response (GO: 0 006955) were preserved. To compare the precision between methods, literatures about these gene pairs were manually collected , astrocytes (ASC), endothelial cells (EC), ependymocytes (EPC), immature neurons (ImmN), macrophages (MAC), microglia (MG), mature neurons (mNEUR), neuroendocrine cells (NendC), neural stem cells (NSC), olfactory ensheathing glia (OEG), oligodendrocytes (OLG), oligodendrocyte precursor cells (OPC), and pericytes (PC). The potentially interacting protein pairs between any two cell clusters were calculated by InterCellDB or any previous methods. The average count of fetched protein pairs for one method k was given as:N is the total number of cell clusters, and interactions generated by method k between cell cluster i and cell cluster j is given as i,j(k)Pair.To compare the performance across all methods, a testing protein dataset was generated that only included those proteins existing in all databases Pair and i,j(l)Pair, respectively.One fetched protein pair for one method could be defined as overlapped to the other if it could be validated by at least one of other methods. The average count of overlapped protein pairs for one method Details about runtime parameters and the version of each method are given in Note S1 (Supporting Information). The analysis of computational efficiency was performed in Linux on a computer with 2.70 GHz Intel(R) Core (TM) i7‐10850H CPU, and 64.0 GB RAM. All programs were run in one core for three repeats. In order to be comparable, these methods were tested using the same number of protein pairs which set as 500, 1000, 1500, and 2000. Runtime were measured with “/user/bin/time ‐v” command in Linux.The scRNA‐seq data for mouse brain (GSE129788) was used a testing dataset.19 TheMale C57BL/6 CX3CR1‐GFP mice (8–10 weeks old) were housed in plastic cages with controlled temperature and humidity and a 12/12 h light/dark cycle. All animal experiment protocols were approved by the Institutional Ethics Committee of the Second Affiliated Hospital, Zhejiang University School of Medicine and were in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health.Mice were deeply anesthetized and perfused transcardially with 25 mL of ice‐cold phosphate‐buffered saline (PBS), followed by 20 mL of 4% paraformaldehyde (PFA). Brains were post‐fixed in 4% PFA for 24 h and dehydrated in serial 15% and 30% sucrose solutions at 4 °C. Next, the brain samples were sectioned into 25 µm thick coronal slices. The sections were stored in cryoprotectant  and kept at −20 °C until immunostaining. Brain sections were washed twice with PBS, followed by permeabilization in 0.5% Triton X‐100 at room temperature. Next, brain sections were blocked with 5% normal donkey serum in PBS for 1 h at room temperature and incubated overnight at 4 °C with the following primary antibodies: anti‐CD31 , anti‐IBA1 , anti‐CXCL12 . The sections were then incubated in the dark with donkey secondary antibody conjugated with Alexa Fluor 555 or 647  at room temperature for 1 h. After washing with PBS for three times, the sections were mounted on glass slides with mount‐G containing DAPI (Yeasen Biotech). Sections were observed and analyzed with a Leica TCS SP8 confocal microscope (Leica Microsystems). Images were adjusted for brightness and contrast using Fiji 2.1.0/1.53c. All confocal images were represented as maximum intensity projections.Tool.ShowGraph. The tables were output by using integrated function named Tool.WriteTables.The results of InterCellDB analysis were displayed in both graphs and tables. The graphs were drawn by using https://www.ebi.ac.uk/gxa/sc/experiments/E‐EHCA‐2/downloads. The scRNA‐seq data for human intrahepatic cholangiocarcinoma was downloaded from the Gene Expression Omnibus (GEO) repository with accession ID: GSE138709. The scRNA‐seq data for mouse brain was downloaded from GEO repository with accession ID: GSE129788.The scRNA‐seq data for mouse melanoma was downloaded from https://github.com/ZJUDBlab/InterCellDB). All the code and data used for processing of data sources and following analysis are available in figshare, whose links are given as follows:1)Database generationa)https://doi.org/10.6084/m9.figshare.17057342Mouse: b)https://doi.org/10.6084/m9.figshare.17057339Human: 2)Comparison of InterCellDB and previous methods https://doi.org/10.6084/m9.figshare.17057357Database comparison: b)https://doi.org/10.6084/m9.figshare.17057525Performance comparison: 3)Case studies on human and mouse dataa)https://doi.org/10.6084/m9.figshare.17029997Human example data processing https://doi.org/10.6084/m9.figshare.17029985Mouse example data processing (Figure InterCellDB is publicly accessible as an R package in GitHub (The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supplemental Table 2Click here for additional data file.Supplemental Table 3Click here for additional data file.Supplemental Table 4Click here for additional data file.Supplemental Table 8Click here for additional data file.