A randomised, double-blind, placebo-controlled, first-in-human phase I study to characterise the safety, pharmacokinetics and immunogenicity of 9MW1411 in healthy Chinese subjects.

INTRODUCTION: 9MW1411 is a humanised monoclonal antibody against Staphylococcus aureus alpha-toxin. The safety, pharmacokinetics (PK) and immunogenicity of 9MW1411 should be characterised in humans before further clinical development. METHODS: A single-centre, randomised, double-blind, placebo-controlled phase I clinical study was conducted in humans for the first time. A total of 42 healthy Chinese subjects were randomised to receive a single ascending dose of 9MW1411 (200, 600, 1500, 3000 or 5000 mg) or placebo. Safety, PK parameters and anti-drug antibody (ADA) were analysed. Monte Carlo simulations (MCS) were performed to predict the probability of target attainment (PTA) after single dose IV administration of 1500, 3000 and 5000 mg of 9MW1411. RESULTS: Thirty-four subjects received 9MW1411, completed the study and were included in data analysis. Five cases of drug-related AEs occurred in four subjects. All the adverse events (AEs) were mild or moderate. The Cmax, AUC0-t and AUC0-∞ of 9MW1411 increased with dose after IV administration of 200 to 5000 mg 9MW1411. The mean Cmax increased from 85.40 ± 5.43 to 2082.11 ± 343.10 µg/mL and AUC0-∞ from 29,511.68 ± 5550.91 to 729,985.49 ± 124,932.18 h·µg/mL. The elimination half-life (T1/2) was 19-23 days. 9MW1411 ADA was positive in three subjects. MCS indicated that a single dose of 3000 or 5000 mg 9MW1411 could achieve PTA > 90% for S. aureus. CONCLUSIONS: 9MW1411 has shown a good safety profile in healthy Chinese subjects after a single dose up to 5000 mg. A single dose of 3000 mg 9MW1411 is appropriate for use in subsequent studies.

Timing of interval debulking surgery and postoperative chemotherapy after neoadjuvant chemotherapy in advanced epithelial ovarian cancer: a multicenter real-world study.

BACKGROUND: To investigate the prognostic relevance of the time to interval debulking surgery (TTS) and the time to postoperative adjuvant chemotherapy (TTC) after the completion of neoadjuvant chemotherapy (NACT). METHODS: A retrospective real-word study included 658 patients with histologically confirmed advanced epithelial ovarian cancer who received NACT at seven tertiary hospitals in China from June 2008 to June 2020. TTS was defined as the time interval from the completion of NACT to the time of interval debulking surgery (IDS). TTC was defined as the time interval from the completion of NACT to the initiation of postoperative adjuvant chemotherapy (PACT). RESULTS: The median TTS and TTC were 25 (IQR, 20-29) and 40 (IQR, 33-49) days, respectively. Patients with TTS > 25 days were older (55 vs. 53 years, P = 0.012) and received more NACT cycles (median, 3 vs. 2, P = 0.002). Similar results were observed in patients with TTC > 40 days. In the multivariate analyses, TTS and TTC were not associated with PFS when stratified by median, quartile, or integrated as continuous variables (all P > 0.05). However, TTS and TTC were significantly associated with worse OS when stratified by median (P = 0.018 and 0.018, respectively), quartile (P = 0.169, 0.014, 0.027 and 0.012, 0.001, 0.033, respectively), or integrated as continuous variables (P = 0.018 and 0.011, respectively). Similarly, increasing TTS and TTC intervals were associated with a higher risk of death (Ptrend = 0.016 and 0.031, respectively) but not with recurrence (Ptrend = 0.103 and 0.381, respectively). CONCLUSION: The delays of IDS and PACT after the completion of NACT have adverse impacts on OS but no impacts on PFS, which indicates that reducing delays of IDS and PACT might ameliorate the outcomes of ovarian cancer patients treated with NACT.

Development and validation of a nomogram to predict cancer-specific survival of mucinous epithelial ovarian cancer after cytoreductive surgery.

BACKGROUND: Mucinous epithelial ovarian cancer (mEOC) is a relatively uncommon subtype of ovarian cancer with special prognostic features, but there is insufficient research in this area. This study aimed to develop a nomogram for the cancer-specific survival (CSS) of mEOC based on Surveillance, Epidemiology, and End Results (SEER) database and externally validate it in National Union of Real World Gynecological Oncology Research and Patient Management (NUWA) platform from China. METHODS: Patients screened from SEER database were allocated into training and internal validation cohort in a ratio of 7: 3, with those from NUWA platform as an external validation cohort. Significant factors selected by Cox proportional hazard regression were applied to establish a nomogram for 3-year and 5-year CSS. The performance of nomogram was assessed by concordance index, calibration curves and Kaplan-Meier (K-M) curves. RESULTS: The training cohort (n = 572) and internal validation cohort (n = 246) were filtered out from SEER database. The external validation cohort contained 186 patients. Baseline age, tumor stage, histopathological grade, lymph node metastasis and residual disease after primary surgery were significant risk factors (p < 0.05) and were included to develop the nomogram. The C-index of nomogram in training, internal validation and external validation cohort were 0.869 (95% confidence interval [CI], 0.838-0.900), 0.839 (95% CI, 0.787-0.891) and 0.800 (95% CI, 0.738-0.862), respectively. The calibration curves of 3-year and 5-year CSS in each cohort showed favorable agreement between prediction and observation. K-M curves of different risk groups displayed great discrimination. CONCLUSION: The discrimination and goodness of fit of the nomogram indicated its satisfactory predictive value for the CSS of mEOC in SEER database and external validation in China, which implies its potential application in different populations.

Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study.

Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main outcomes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889-0.948), 0.923 (0.855-0.990), 0.918 (0.872-0.963), and 0.887 (0.813-0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889-0.955) in the combined validation cohort; 0.955 (0.912-0.997) in VC1; 0.939 (0.901-0.977) in VC2; 0.917 (0.824-1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.

Does the primary treatment sequence affect post-relapse survival in recurrent epithelial ovarian cancer? A real-world multicentre retrospective study.

OBJECTIVE: To explore the impact of the primary treatment sequence (primary debulking surgery, PDS, versus neoadjuvant chemotherapy and interval debulking surgery, NACT-IDS) on post-relapse survival (PRS) and recurrence characteristics of recurrent epithelial ovarian cancer (REOC). DESIGN: Real-world retrospective study. SETTING: Tertiary hospitals in China. POPULATION: A total of 853 patients with REOC at International Federation of Gynaecology and Obstetrics stages IIIC-IV from September 2007 to June 2020. Overall, 377 and 476 patients received NACT-IDS and PDS, respectively. METHODS: Propensity score-based inverse probability of treatment weighting (IPTW) was performed to balance the between-group differences. MAIN OUTCOME MEASURES: Clinicopathological factors related to PRS. RESULTS: The overall median PRS was 29.3 months (95% CI 27.0-31.5 months). Multivariate analysis before and after IPTW adjustment showed that NACT-IDS and residual R1/R2 disease were independent risk factors for PRS (p < 0.05). Patients with diffuse carcinomatosis and platinum-free interval (PFI) ≤ 12 months had a significantly worse PRS (p < 0.001). Logistic regression analysis revealed that NACT-IDS was an independent risk factor for diffuse carcinomatosis (OR 1.36, 95% CI 1.01-1.82, p = 0.040) and PFI ≤ 12 months (OR 1.59, 95% CI 1.08-2.35, p = 0.019). In IPTW analysis, NACT-IDS was still significantly associated with diffuse carcinomatosis (OR 1.29, 95% CI 1.05-1.58, p = 0.015) and PFI ≤ 12 months (OR 1.90, 95% CI 1.52-2.38, p < 0.001). CONCLUSIONS: The primary treatment sequence may affect the PRS of patients with REOC by altering the recurrence pattern and PFI duration.

The first Chinese National Union of Real-world Gynaecological Oncology Research and Patient Management Platform: A retrospective study.

OBJECTIVE: To produce high-quality, real-world evidence for oncologists by collating scattered gynaecologic oncology (GO) medical records in China. DESIGN: Retrospective study. SETTING: The National Union of Real-world Gynaecological Oncology Research and Patient Management Platform (NUWA platform). SAMPLE: Patient-centred data pool. METHODS: The NUWA platform integrated inpatient/outpatient clinical, gene and follow-up data. Data of 11 456 patients with ovarian cancer (OC) were collected and processed using 91 345 electronic medical records. Structured and unstructured data were de-identified and re-collated into a patient-centred data pool using a predefined GO data model by technology-aided abstraction. MAIN OUTCOME MEASURES: Recent treatment pattern shifts towards precision medicine for OC in China. RESULTS: Thirteen first-tier hospitals across China participated in the NUWA platform up to 7 December 2021. In total, 3504 (30.59%) patients were followed up by a stand-alone patient management centre. The percentage of patients undergoing breast cancer gene (BRCA) mutation tests increased by approximately six-fold between 2017 and 2018. A similar trend was observed in the administration rate of poly(ADP-ribose) polymerase inhibitors as first-line treatment and second-line treatment after September 2018, when olaparib was approved for clinical use in China. CONCLUSION: The NUWA platform has great potential to facilitate clinical studies and support drug development, regulatory reviews and healthcare decision-making.

Oncological big data platforms for promoting digital competencies and professionalism in Chinese medical students: a cross-sectional study.

OBJECTIVES: Advancements in big data technology are reshaping the healthcare system in China. This study aims to explore the role of medical big data in promoting digital competencies and professionalism among Chinese medical students. DESIGN, SETTING AND PARTICIPANTS: This study was conducted among 274 medical students who attended a workshop on medical big data conducted on 8 July 2021 in Tongji Hospital. The workshop was based on the first nationwide multifunction gynecologic oncology medical big data platform in China, at the National Union of Real-World Gynecologic Oncology Research & Patient Management Platform (NUWA platform). OUTCOME MEASURES: Data on knowledge, attitudes towards big data technology and professionalism were collected before and after the workshop. We have measured the four skill categories: doctor‒patient relationship skills, reflective skills, time management and interprofessional relationship skills using the Professionalism Mini-Evaluation Exercise (P-MEX) as a reflection for professionalism. RESULTS: A total of 274 students participated in this workshop and completed all the surveys. Before the workshop, only 27% of them knew the detailed content of medical big data platforms, and 64% knew the potential application of medical big data. The majority of the students believed that big data technology is practical in their clinical practice (77%), medical education (85%) and scientific research (82%). Over 80% of the participants showed positive attitudes toward big data platforms. They also exhibited sufficient professionalism before the workshop. Meanwhile, the workshop significantly promoted students' knowledge of medical big data (p<0.05), and led to more positive attitudes towards big data platforms and higher levels of professionalism. CONCLUSIONS: Chinese medical students have primitive acquaintance and positive attitudes toward big data technology. The NUWA platform-based workshop may potentially promote their understanding of big data and enhance professionalism, according to the self-measured P-MEX scale.

Sintilimab combined with bevacizumab in relapsed/persistent ovarian clear cell carcinoma (INOVA): an investigator-initiated, multicentre clinical trial-a study protocol of clinical trial.

BACKGROUND: Ovarian clear cell carcinoma (OCCC) has an abysmal prognosis with a median overall survival (OS) of 25.3 months because of a low response to chemotherapy. The 5-year disease-specific survival rate after recurrence is 13.2%, with more than two-thirds of the patients dying within a year. Therefore, it is urgent to explore new therapeutic options for OCCC. Based on the characteristic immune-suppressive tumour microenvironment derived from the gene expression profile of OCCC, the combination of immunoantiangiogenesis therapy might have certain efficacy in recurrent/persistent OCCC. This trial aims to evaluate the efficacy and safety of sintilimab and bevacizumab in patients who have failed platinum-containing chemotherapy with recurrent or persistent OCCC. METHOD AND ANALYSIS: In this multicentre, single-arm, open-label, investigator-initiated clinical trial, 38 patients will be assigned to receive sintilimab 200 mg plus bevacizumab 15 mg/kg every 3 weeks. The eligibility criteria include histologically diagnosed patients with recurrent or persistent OCCC who have been previously treated with at least one-line platinum-containing chemotherapy; patients with Eastern Cooperative Oncology Group (ECOG) performance status 0-2 with an expected survival greater than 12 weeks. The exclusion criteria include patients previously treated with immune checkpoint inhibitor and patients with contraindications of bevacizumab and sintilimab. The primary endpoint is the objective response rate. The secondary endpoints are progression-free survival, time to response, duration of response, disease control rate, OS, safety and quality of life. Statistical significance was defined as p<0.05. ETHICS AND DISSEMINATION: This trial was approved by the Research Ethics Commission of Tongji Medical College of Huazhong University of Science and Technology (2020-S337). The protocol of this study is registered at www. CLINICALTRIALS: gov. The trial results will be published in peer-reviewed journals and at conferences. TRIAL REGISTRATION NUMBER: NCT04735861; Clinicaltrials. gov.

Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study.

BACKGROUND: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model that automates evaluation of ultrasound images and to facilitate a more accurate diagnosis of ovarian cancer than existing methods. METHODS: In this retrospective, multicentre, diagnostic study, we collected pelvic ultrasound images from ten hospitals across China between September 2003, and May 2019. We included consecutive adult patients (aged ≥18 years) with adnexal lesions in ultrasonography and healthy controls and excluded duplicated cases and patients without adnexa or pathological diagnosis. For DCNN model development, patients were assigned to the training dataset (34 488 images of 3755 patients with ovarian cancer, 541 442 images of 101 777 controls). For model validation, patients were assigned to the internal validation dataset (3031 images of 266 patients with ovarian cancer, 5385 images of 602 with benign adnexal lesions), external validation datasets 1 (486 images of 67 with ovarian cancer, 933 images of 268 with benign adnexal lesions), and 2 (1253 images of 166 with ovarian cancer, 5257 images of 723 benign adnexal lesions). Using these datasets, we assessed the diagnostic value of DCNN, compared DCNN with 35 radiologists, and explored whether DCNN could augment the diagnostic accuracy of six radiologists. Pathological diagnosis was the reference standard. FINDINGS: For DCNN to detect ovarian cancer, AUC was 0·911 (95% CI 0·886-0·936) in the internal dataset, 0·870 (95% CI 0·822-0·918) in external validation dataset 1, and 0·831 (95% CI 0·793-0·869) in external validation dataset 2. The DCNN model was more accurate than radiologists at detecting ovarian cancer in the internal dataset (88·8% vs 85·7%) and external validation dataset 1 (86·9% vs 81·1%). Accuracy and sensitivity of diagnosis increased more after DCNN-assisted diagnosis than assessment by radiologists alone (87·6% [85·0-90·2] vs 78·3% [72·1-84·5], p<0·0001; 82·7% [78·5-86·9] vs 70·4% [59·1-81·7], p<0·0001). The average accuracy of DCNN-assisted evaluations for six radiologists reached 0·876 and were significantly augmented when they were DCNN-assisted (p<0·05). INTERPRETATION: The performance of DCNN-enabled ultrasound exceeded the average diagnostic level of radiologists matched the level of expert ultrasound image readers, and augmented radiologists' accuracy. However, these observations warrant further investigations in prospective studies or randomised clinical trials. FUNDING: National Key Basic Research Program of China, National Sci-Tech Support Projects, and National Natural Science Foundation of China.

Safety, tolerability, pharmacokinetic characteristics, and immunogenicity of MW33: a Phase 1 clinical study of the SARS-CoV-2 RBD-targeting monoclonal antibody.

MW33 is a fully humanized IgG1κ monoclonal neutralizing antibody, and may be used for the prevention and treatment of coronavirus disease 2019 (COVID-19). We conducted a randomized, double-blind, placebo-controlled, single-dose, dose-escalation Phase 1 study to evaluate the safety, tolerability, pharmacokinetics (PK), and immunogenicity of MW33. Healthy adults aged 18-45 years were sequentially enrolled into the 4, 10, 20, 40, and 60 mg/kg dose groups and infused with MW33 over 60 ± 15 min and followed for 85 days. All 42 enrolled participants completed the MW33 infusion, and 40 participants completed the 85-day follow-up period. 34 participants received a single infusion of 4 (n = 2), 10 (n = 8), 20 (n = 8), 40 (n = 8), and 60 mg/kg (n = 8) of MW33. 27 subjects in the test groups experienced 78 adverse events (AEs) post-dose, with an incidence of 79.4% (27/34). The most common AEs included abnormal laboratory test results, vascular and lymphatic disorders, and infectious diseases. The severity of AEs was mainly Grade 1 (92 AEs), and three Grade 2 and one Grade 4. The main PK parameters, maximum concentration (Cmax), and area under the concentration-time curve (AUC0-t, and AUC0-∞) in 34 subjects showed a linear kinetic relationship in the range of 10-60 mg/kg. The plasma half-life was approximately 25 days. The positive rates of serum ADAs and antibody titres were low with no evidence of an impact on safety or PK. In conclusion, MW33 was well-tolerated, demonstrated linear PK, with a lower positive rate of serum ADAs and antibody titres in healthy subjects.Trial registration: ClinicalTrials.gov identifier: NCT04427501.Trial registration: ClinicalTrials.gov identifier: NCT04533048.Trial registration: ClinicalTrials.gov identifier: NCT04627584.

C/EBPß promotes poly(ADP-ribose) polymerase inhibitor resistance by enhancing homologous recombination repair in high-grade serous ovarian cancer.

PARP inhibitors (PARPi) are efficacious in treating high-grade serous ovarian cancer (HG-SOC) with homologous recombination (HR) deficiency. However, they exhibit suboptimal efficiency in HR-proficient cancers. Here, we found that the expression of CCAAT/enhancer-binding protein ß (C/EBPß), a transcription factor, was inversely correlated with PARPi sensitivity in vitro and in vivo, both in HR-proficient condition. High C/EBPß expression enhanced PARPi tolerance; PARPi treatment in turn induced C/EBPß expression. C/EBPß directly targeted and upregulated multiple HR genes (BRCA1, BRIP1, BRIT1, and RAD51), thereby inducing restoration of HR capacity and mediating acquired PARPi resistance. C/EBPß is a key regulator of the HR pathway and an indicator of PARPi responsiveness. Targeting C/EBPß could induce HR deficiency and rescue PARPi sensitivity accordingly. Our findings indicate that HR-proficient patients may benefit from PARPi via targeting C/EBPß, and C/EBPß expression levels enable predicting and tracking PARPi responsiveness during treatment.

Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters.

BACKGROUND: Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. METHODS: We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. RESULTS: Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979-1.000) in internal validation cohort and 0.999 (95% CI 0.998-1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. CONCLUSIONS: The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. TRIAL REGISTRATION: This study was retrospectively registered in the Chinese Clinical Trial Registry ( ChiCTR2000032161 ). vv.

Immunological alternation in COVID-19 patients with cancer and its implications on mortality.

Patients with malignancy were reportedly more susceptible and vulnerable to Coronavirus Disease 2019 (COVID-19), and witnessed a greater mortality risk in COVID-19 infection than noncancerous patients. But the role of immune dysregulation of malignant patients on poor prognosis of COVID-19 has remained insufficiently investigated. Here we conducted a retrospective cohort study that included 2,052 patients hospitalized with COVID-19 (Cancer, n = 93; Non-cancer, n = 1,959), and compared the immunological characteristics of both cohorts. We used stratification analysis, multivariate regressions, and propensity-score matching to evaluate the effect of immunological indices. In result, COVID-19 patients with cancer had ongoing and significantly elevated inflammatory factors and cytokines (high-sensitivity C-reactive protein, procalcitonin, interleukin (IL)-2 receptor, IL-6, IL-8), as well as decreased immune cells (CD8 + T cells, CD4 + T cells, B cells, NK cells, Th and Ts cells) than those without cancer. The mortality rate was significantly higher in cancer cohort (24.7%) than non-cancer cohort (10.8%). By stratification analysis, COVID-19 patients with immune dysregulation had poorer prognosis than those with the relatively normal immune system both in cancer and non-cancer cohort. By logistic regression, Cox regression, and propensity-score matching, we found that prior to adjustment for immunological indices, cancer history was associated with an increased mortality risk of COVID-19 (p < .05); after adjustment for immunological indices, cancer history was no longer an independent risk factor for poor prognosis of COVID-19 (p > .30). In conclusion, COVID-19 patients with cancer had more severely dysregulated immune responses than noncancerous patients, which might account for their poorer prognosis. Clinical Trial: This study has been registered on the Chinese Clinical Trial Registry (No. ChiCTR2000032161).

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.