Multiomics sequencing and immune microenvironment characteristics define three subtypes of small cell neuroendocrine carcinoma of the cervix.

Small cell cervical carcinoma (SCCC) is the most common neuroendocrine tumor in the female genital tract, with an unfavorable prognosis and lacking an evidence-based therapeutic approach. Until now, the distinct subtypes and immune characteristics of SCCC combined with genome and transcriptome have not been described. We performed genomic (n = 18), HPV integration (n = 18), and transcriptomic sequencing (n = 19) of SCCC samples. We assessed differences in immune characteristics between SCCC and conventional cervical cancer, and other small cell neuroendocrine carcinomas, through bioinformatics analysis and immunohistochemical assays. We stratified SCCC patients through non-negative matrix factorization and described the characteristics of these distinct types. We further validated it using multiplex immunofluorescence (n = 77) and investigated its clinical prognostic effect. We confirmed a high frequency of PIK3CA and TP53 alterations and HPV18 integrations in SCCC. SCCC and other small cell carcinoma had similar expression signatures and immune cell infiltration patterns. Comparing patients with SCCC to those with conventional cervical cancer, the former presented immune excluded or 'desert' infiltration. The number of CD8+ cells in the invasion margin of SCCC patients predicted favorable clinical outcomes. We identified three transcriptome subtypes: an inflamed phenotype with high-level expression of genes related to the MHC-II complex (CD74) and IFN-α/ß (SCCC-I), and two neuroendocrine subtypes with high-level expression of ASCL1 or NEUROD1, respectively. Combined with multiple technologies, we found that the neuroendocrine groups had more TP53 mutations and SCCC-I had more PIK3CA mutations. Multiplex immunofluorescence validated these subtypes and SCCC-I was an independent prognostic factor of overall survival. These results provide insights into SCCC tumor heterogeneity and potential therapies. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Targeting the Cdc2-like kinase 2 for overcoming platinum resistance in ovarian cancer.

Platinum resistance represents a major barrier to the survival of patients with ovarian cancer (OC). Cdc2-like kinase 2 (CLK2) is a major protein kinase associated with oncogenic phenotype and development in some solid tumors. However, the exact role and underlying mechanism of CLK2 in the progression of OC is currently unknown. Using microarray gene expression profiling and immunostaining on OC tissues, we found that CLK2 was upregulated in OC tissues and was associated with a short platinum-free interval in patients. Functional assays showed that CLK2 protected OC cells from platinum-induced apoptosis and allowed tumor xenografts to be more resistant to platinum. Mechanistically, CLK2 phosphorylated breast cancer gene 1 (BRCA1) at serine 1423 (Ser1423) to enhance DNA damage repair, resulting in platinum resistance in OC cells. Meanwhile, in OC cells treated with platinum, p38 stabilized CLK2 protein through phosphorylating at threonine 343 of CLK2. Consequently, the combination of CLK2 and poly ADP-ribose polymerase inhibitors achieved synergistic lethal effect to overcome platinum resistance in patient-derived xenografts, especially those with wild-type BRCA1. These findings provide evidence for a potential strategy to overcome platinum resistance in OC patients by targeting CLK2.

Biomarker-driven targeted therapy in patients with recurrent platinum-resistant epithelial ovarian cancer (BRIGHT): protocol for an open-label, multicenter, umbrella study.

BACKGROUND: Platinum-resistant, recurrent ovarian cancer has an abysmal prognosis with limited treatment options. Poly-(ADP-ribose)-polymerase (PARP), angiogenesis, and immune checkpoint inhibitors might improve the outcomes of platinum-resistant, recurrent ovarian cancer, but accurate patient selections for those therapies remain a significant clinical challenge. PRIMARY OBJECTIVE: To evaluate the efficacy and safety of biomarker-driven combinatorial therapies of pamiparib, tislelizumab, bevacizumab, and nab-paclitaxel in platinum-resistant, recurrent ovarian cancer. STUDY HYPOTHESIS: A precision medicine combination of PARP inhibitors, anti-angiogenic therapy, immunotherapy, and chemotherapy will improve disease outcomes of platinum-resistant, recurrent ovarian cancer by accounting for genomic and immunologic features. TRIAL DESIGN: The BRIGHT Trial is a prospective, open-label, multicenter, phase II, umbrella study planning to enroll 160 patients with serous, endometrioid, or clear cell platinum-resistant, recurrent ovarian cancer from 11 clinical centers in China. Patients are assigned to one of three experimental arms based on biomarkers. Patients with BRCA1/2 mutations will receive pamiparib plus bevacizumab (arm 1, n=40) regardless of CD8+ tumor-infiltrating lymphocytes count. Patients with wild-type BRCA1/2 (BRCAwt) and ≥3 CD8+ tumor-infiltrating lymphocytes count will receive the combination of tislelizumab, bevacizumab, and nab-paclitaxel (arm 2, n=50), while BRCAwt patients with <3 CD8+ tumor-infiltrating lymphocytes count will receive bevacizumab plus dose-dense nab-paclitaxel (arm 3, n=50). After completing patient enrollment in arm 2, another 20 BRCAwt patients with ≥3 CD8+ tumor-infiltrating lymphocytes count will be included as an arm 2 expansion. Treatment will continue until disease progression or intolerable toxicity, and all adverse events will be recorded. MAJOR INCLUSION/EXCLUSION CRITERIA: Eligible patients include those aged ≥18 with serous, endometrioid, or clear cell ovarian cancer, platinum-resistant recurrence, and Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1. PRIMARY ENDPOINT: Objective response rate (ORR) assessed by the investigators by the RECIST 1.1 criteria. SAMPLE SIZE: 160 patients. ESTIMATED DATES FOR COMPLETING ACCRUAL AND PRESENTING RESULTS: Recruitment is estimated to be completed by 2024 and results may be published by 2027. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05044871.

Graph learning for particle accelerator operations.

Particle accelerators play a crucial role in scientific research, enabling the study of fundamental physics and materials science, as well as having important medical applications. This study proposes a novel graph learning approach to classify operational beamline configurations as good or bad. By considering the relationships among beamline elements, we transform data from components into a heterogeneous graph. We propose to learn from historical, unlabeled data via our self-supervised training strategy along with fine-tuning on a smaller, labeled dataset. Additionally, we extract a low-dimensional representation from each configuration that can be visualized in two dimensions. Leveraging our ability for classification, we map out regions of the low-dimensional latent space characterized by good and bad configurations, which in turn can provide valuable feedback to operators. This research demonstrates a paradigm shift in how complex, many-dimensional data from beamlines can be analyzed and leveraged for accelerator operations.

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis.

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

Value of novel thrombotic markers for predicting occurrence of the malignant cerebral artery infarction: a prospective clinical study.

Objective: This study investigated the diagnostic performance of thrombin-antithrombin complex (TAT), plasmin-α2 plasmin inhibitor complex (PIC), tissue plasminogen activator-plasminogen activator inhibitor complex (t-PAIC), and thrombomodulin (TM) in predicting the progression of massive cerebral infarction to the malignant cerebral artery infarction. Method: A total of 71 patients with massive cerebral infarction confirmed by imaging examination were divided into malignant cerebral artery infarction group (MCAI) and non-malignant cerebral artery infarction group (NMCAI) based on whether they progressed to MCAI after admission. TAT, PIC, t-PAIC, and TM were measured immediately after admission. The predictive performance was analyzed by the receiver characteristic operating curve (ROC). Result: The median plasma concentrations of TM, PIC, TAT, and t-PAIC in the MCAI patients at admission were 10.65 IU/mL, 1.17 µg/mL, 12.25 ng/mL, and 13.85 ng/mL, respectively, which were higher than those in the NMCAI patients (9.00 IU/mL, 1.07 µg/mL, 4.60 ng/mL, and 8.70 ng/mL), and the difference was statistically significant (p = 0.045, p = 0.035, p = 0.004, and p = 0.003). Elevated plasma t-PAIC concentration was shown to be an independent risk factor for progression of massive cerebral infarction to MCAI (OR = 1.131) by multivariate logistic regression analysis. ROC curve analysis showed that t-PAIC was the best predictor of MCAI (AUC = 74.7%), with a sensitivity of 75.0% and specificity of 75.9% when t-PAIC concentration was ≥12.4 ng/mL; TAT had the highest specificity in predicting MCAI, with a specificity of 90.7% when the TAT concentration was ≥13.5 ng/mL. Conclusion: The detection of PIC, TAT, t-PAIC, and TM is a comprehensive assessment of vascular endothelial damage and activation of the coagulation and fibrinolytic systems and has predictive value for poor prognosis in patients with MCAI. The widespread use of these tests will likely greatly improve the early diagnosis rate of MCAI.

The MYBL2-CCL2 axis promotes tumor progression and resistance to anti-PD-1 therapy in ovarian cancer by inducing immunosuppressive macrophages.

BACKGROUND: An immunosuppressive tumor microenvironment in ovarian cancer facilitates tumor progression and resistance to immunotherapy. The function of MYB Proto-Oncogene Like 2 (MYBL2) in the tumor microenvironment remains largely unexplored. METHODS: A syngeneic intraovarian mouse model, flow cytometry analysis, and immunohistochemistry were used to explore the biological function of MYBL2 in tumor progression and immune escape. Molecular and biochemical strategies-namely RNA-sequencing, western blotting, quantitative reverse transcription-polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay, multiplex immunofluorescence, chromatic immunoprecipitation assay (CHIP) and luciferase assay-were used to reveal the mechanisms of MYBL2 in the OVC microenvironment. RESULTS: We found tumor derived MYBL2 indicated poor prognosis and selectively correlated with tumor associated macrophages (TAMs) in ovarian cancer. Mechanically, C-C motif chemokine ligand 2 (CCL2) transcriptionally activated by MYBL2 induced TAMs recruitment and M2-like polarization in vitro. Using a syngeneic intraovarian mouse model, we identified MYBL2 promoted tumor malignancyand increased tumor-infiltrating immunosuppressive macrophages. Cyclin-dependent kinase 2 (CDK2) was a known upstream kinase to phosphorylate MYBL2 and promote its transcriptional function. The upstream inhibitor of CDK2, CVT-313, reprogrammed the tumor microenvironment and reduced anti-PD-1 resistance. CONCLUSIONS: The MYBL2/CCL2 axis contributing to TAMs recruitment and M2-like polarization is crucial to immune evasion and anti-PD-1 resistance in ovarian cancer, which is a potential target to enhance the efficacy of immunotherapy.

A novel marker integrating multiple genetic alterations better predicts platinum sensitivity in ovarian cancer than HRD score.

Introduction: Platinum-based chemotherapy is the first-line treatment strategy for ovarian cancer patients. The dismal prognosis of ovarian cancer was shown to be stringently associated with the heterogeneity of tumor cells in response to this therapy, therefore understanding platinum sensitivity in ovarian cancer would be helpful for improving patients' quality of life and clinical outcomes. HRDetect, utilized to characterize patients' homologous recombination repair deficiency, was used to predict patients' response to platinum-based chemotherapy. However, whether each of the single features contributing to HRD score is associated with platinum sensitivity remains elusive. Methods: We analyzed the whole-exome sequencing data of 196 patients who received platinum-based chemotherapy from the TCGA database. Genetic features were determined individually to see if they could indicate patients' response to platinum-based chemotherapy and prognosis, then integrated into a Pt-score employing LASSO regression model to assess its predictive performance. Results and discussion: Multiple genetic features, including bi-allelic inactivation of BRCA1/2 genes and genes involved in HR pathway, multiple somatic mutations in genes involved in DNA damage repair (DDR), and previously reported HRD-related features, were found to be stringently associated with platinum sensitivity and improved prognosis. Higher contributions of mutational signature SBS39 or ID6 predicted improved overall survival. Besides, arm-level loss of heterozygosity (LOH) of either chr4p or chr5q predicted significantly better disease-free survival. Notably, some of these features were found independent of HRD. And SBS3, an HRD-related feature, was found irrelevant to platinum sensitivity. Integrated all candidate markers using the LASSO model to yield a Pt-score, which showed better predictive ability compared to HRDetect in determining platinum sensitivity and predicting patients' prognosis, and this performance was validated in an independent cohort. The outcomes of our study will be instrumental in devising effective strategies for treating ovarian cancer with platinum-based chemotherapy.

Suppressive stroma-immune prognostic signature impedes immunotherapy in ovarian cancer and can be reversed by PDGFRB inhibitors.

BACKGROUND: As the most lethal gynecologic cancer, ovarian cancer (OV) holds the potential of being immunotherapy-responsive. However, only modest therapeutic effects have been achieved by immunotherapies such as immune checkpoint blockade. This study aims to propose a generalized stroma-immune prognostic signature (SIPS) to identify OV patients who may benefit from immunotherapy. METHODS: The 2097 OV patients included in the study were significant with high-grade serous ovarian cancer in the III/IV stage. The 470 immune-related signatures were collected and analyzed by the Cox regression and Lasso algorithm to generalize a credible SIPS. Correlations between the SIPS signature and tumor microenvironment were further analyzed. The critical immunosuppressive role of stroma indicated by the SIPS was further validated by targeting the major suppressive stroma component (CAFs, Cancer-associated fibroblasts) in vitro and in vivo. With four machine-learning methods predicting tumor immune subtypes, the stroma-immune signature was upgraded to a 23-gene signature. RESULTS: The SIPS effectively discriminated the high-risk individuals in the training and validating cohorts, where the high SIPS succeeded in predicting worse survival in several immunotherapy cohorts. The SIPS signature was positively correlated with stroma components, especially CAFs and immunosuppressive cells in the tumor microenvironment, indicating the critical suppressive stroma-immune network. The combination of CAFs' marker PDGFRB inhibitors and frontline PARP inhibitors substantially inhibited tumor growth and promoted the survival of OV-bearing mice. The stroma-immune signature was upgraded to a 23-gene signature to improve clinical utility. Several drug types that suppress stroma-immune signatures, such as EGFR inhibitors, could be candidates for potential immunotherapeutic combinations in ovarian cancer. CONCLUSIONS: The stroma-immune signature could efficiently predict the immunotherapeutic sensitivity of OV patients. Immunotherapy and auxiliary drugs targeting stroma could enhance immunotherapeutic efficacy in ovarian cancer.

Overlapping filter bank convolutional neural network for multisubject multicategory motor imagery brain-computer interface.

BACKGROUND: Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition. METHODS: This paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label. RESULTS: Experiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods. CONCLUSION: The proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.

The prognosis of patients with small cell carcinoma of the cervix: a retrospective study of the SEER database and a Chinese multicentre registry.

BACKGROUND: Small cell carcinoma of the cervix is a rare but poor prognosis pathological type of cervical cancer, for which advice in clinical guidelines is unspecific. We therefore aimed to investigate the factors and treatment methods that affect the prognosis of patients with small cell carcinoma of the cervix. METHODS: In this retrospective study, we collected data from the Surveillance, Epidemiology, and End Results (SEER) 18 registries cohort and a Chinese multi-institutional registry. The SEER cohort included females diagnosed with small cell carcinoma of the cervix between Jan 1, 2000, and Dec 31, 2018, whereas the Chinese cohort included women diagnosed between Jun 1, 2006, and April 30, 2022. In both cohorts, eligibility was limited to female patients older than 20 years with a confirmed diagnosis of small cell carcinoma of the cervix. Participants who were lost to follow-up or those for whom small cell carcinoma of the cervix was not the primary malignant tumour were excluded from the multi-institutional registry, and those with an unknown surgery status (in addition to those for whom small cell carcinoma of the cervix was not the primary malignant tumour) were excluded from the SEER data. The primary outcome of this study was overall survival (length of time from the date of first diagnosis until the date of death from any cause, or the last follow-up). Kaplan-Meier analysis, propensity score matching, and Cox-regression analyses were used to assess treatment outcomes and risk factors. FINDINGS: 1288 participants were included in the study; 610 in the SEER cohort and 678 in the Chinese cohort. Both univariable and multivariable Cox regression analysis (SEER hazard ratio [HR] 0·65 [95% CI 0·48-0·88], p=0·0058; China HR 0·53 [0·37-0·76], p=0·0005) showed that surgery was associated with a better prognosis. In subgroup analyses, surgery remained a protective factor for patients with locally advanced disease in both cohorts (SEER HR 0·61 [95% CI 0·39-0·94], p=0·024; China HR 0·59 [0·37-0·95]; p=0·029). Furthermore, the protective effect of surgery was observed among patients with locally advanced disease after propensity score matching in the SEER cohort (HR 0·52 [95% CI 0·32-0·84]; p=0·0077). In the China registry, surgery was associated with better outcomes in patients with stage IB3-IIA2 cancer (HR 0·17 [95% CI 0·05-0·50]; p=0·0015). INTERPRETATION: This study provides evidence that surgery improves outcomes of patients with small cell carcinoma of the cervix. Although guidelines recommend non-surgical methods as first-line treatment, patients with locally advanced disease or stage IB3-IIA2 cancer might benefit from surgery. FUNDING: The National Key R&D Program of China and the National Natural Science Foundation of China.

Spatial-Temporal Networks for Antibiogram Pattern Prediction.

An antibiogram is a periodic summary of antibiotic resistance results of organisms from infected patients to selected antimicrobial drugs. Antibiograms help clinicians to understand regional resistance rates and select appropriate antibiotics in prescriptions. In practice, significant combinations of antibiotic resistance may appear in different antibiograms, forming antibiogram patterns. Such patterns may imply the prevalence of some infectious diseases in certain regions. Thus it is of crucial importance to monitor antibiotic resistance trends and track the spread of multi-drug resistant organisms. In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future. Despite its importance, tackling this problem encounters a series of challenges and has not yet been explored in the literature. First of all, antibiogram patterns are not i.i.d as they may have strong relations with each other due to genomic similarities of the underlying organisms. Second, antibiogram patterns are often temporally dependent on the ones that are previously detected. Furthermore, the spread of antibiotic resistance can be significantly influenced by nearby or similar regions. To address the above challenges, we propose a novel Spatial-Temporal Antibiogram Pattern Prediction framework, STAPP, that can effectively leverage the pattern correlations and exploit the temporal and spatial information. We conduct extensive experiments on a real-world dataset with antibiogram reports of patients from 1999 to 2012 for 203 cities in the United States. The experimental results show the superiority of STAPP against several competitive baselines.

Loss of HRD functional phenotype impedes immunotherapy and can be reversed by HDAC inhibitor in ovarian cancer.

In recent years, homologous recombination deficiency (HRD) has not achieved the expected substantial promotion of immunotherapeutic efficacy in ovarian cancer. This study aims to explore the role of HRD functional phenotype as a powerful biomarker in identifying HRD patients who may benefit from immunotherapy. HRD functional phenotype, namely HRD-EXCUTE, was defined as the average level of the 15 hub genes upregulated in HRD ovarian cancer. A decision tree was plotted to evaluate the critical role of HRD-EXCUTE in HRD patients. Agents inducing HRD-EXCUTE were identified by CMAP web (Connectivity Map). The mechanisms and immunotherapeutic effect of PARPi and HDACi in promoting HRD-EXCUTE was examined in vitro and in vivo. The decision tree plotted on the basis of HRD and HRD-EXCUTE indicated the HRD patients without the HRD functional phenotype were largely unresponsive to immunotherapy, which was validated by the immunotherapeutic cohorts. Furthermore, loss of HRD-EXCUTE in the HRD patients attenuated immunogenicity and inhibited immune cells in tumor microenvironment. Moreover, Niraparib combined with Entinostat induced HRD-EXCUTE by activating the cGAS-STING pathway and increasing the histone acetylation. The combination therapy could enhance the cytotoxicity of immune cells, and promote pro-immune cells infiltrating into ascites, resulting in inhibited ovarian cancer growth. The HRD functional phenotype HRD-EXCUTE was set up as a potent biomarker to identify whether HRD patients can benefit from immunotherapy. Loss of HRD-EXCUTE in HRD patients were largely insensitive to immunotherapy. The combination of PARPi with HDACi could improve the efficacy of the PARPi-based immunotherapy in ovarian cancer by augmenting the HRD functional phenotype.

Learning Representations by Graphical Mutual Information Estimation and Maximization.

The rich content in various real-world networks such as social networks, biological networks, and communication networks provides unprecedented opportunities for unsupervised machine learning on graphs. This paper investigates the fundamental problem of preserving and extracting abundant information from graph-structured data into embedding space without external supervision. To this end, we generalize conventional mutual information computation from vector space to graph domain and present a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graph and hidden representation. Except for standard GMI which considers graph structures from a local perspective, our further proposed GMI++ additionally captures global topological properties by analyzing the co-occurrence relationship of nodes. GMI and its extension exhibit several benefits: First, they are invariant to the isomorphic transformation of input graphs-an inevitable constraint in many existing methods; Second, they can be efficiently estimated and maximized by current mutual information estimation methods; Lastly, our theoretical analysis confirms their correctness and rationality. With the aid of GMI, we develop an unsupervised embedding model and adapt it to the specific anomaly detection task. Extensive experiments indicate that our GMI methods achieve promising performance in various downstream tasks, such as node classification, link prediction, and anomaly detection.

A novel combination of niraparib and anlotinib in platinum-resistant ovarian cancer: Efficacy and safety results from the phase II, multi-center ANNIE study.

Background: Patients with platinum-resistant recurrent ovarian cancer (PROC) face poor prognosis and limited treatment options. Single-agent antiangiogenics and poly (ADP-ribose) polymerase (PARP) inhibitors both show some activities in platinum-resistant diseases. The ANNIE study aimed to evaluate the efficacy and safety of the novel combination of the PARP inhibitor niraparib and the antiangiogenic anlotinib in patients with PROC. Methods: ANNIE is a multicentre, single-arm, phase 2 study (ClinicalTrials.gov identifier NCT04376073) conducted at three hospitals in China. Eligible patients had histologically confirmed epithelial ovarian, fallopian tube, or primary peritoneal cancer that recurred within 6 months of last platinum-based chemotherapy. Patients with prior PARP inhibitor exposure were excluded. The enrolled patients received oral niraparib 200 mg or 300 mg (baseline body weight-directed) once daily continuously and anlotinib 10 mg (12 mg before protocol amendment) once daily on days 1-14 of each 21-day cycle until disease progression or intolerable toxicity. The primary endpoint was objective response rate (ORR). Findings: Between May 22, 2020, and April 22, 2021, 40 patients were enrolled and treated. Thirty-six patients underwent post-baseline tumour assessments. By data cut-off (January 31, 2022), median follow-up was 15.4 months (95% CI 12.6-17.7). Intention-to-treat ORR was 50.0% (95% CI 33.8-66.2), including one complete response and 19 partial responses. Median (95% CI) progression-free survival and overall survival were 9.2 months (7.4-11.9) and 15.3 months (13.9-not evaluable), respectively. Drug-related, grade ≥3 TEAEs were reported in 26 (68%) patients. There were no treatment-related deaths. Interpretation: Niraparib plus anlotinib showed promising antitumour activity in patients with PROC. This oral combination warrants further investigation as a potential novel, convenient treatment option for patients with PROC. Funding: Zai Lab (Shanghai) Co., Ltd; Jiangsu Chia Tai-Tianqing Pharmaceutical Co., Ltd; the National Natural Science Foundation of China (No. 82102783).

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.

RAD21 amplification epigenetically suppresses interferon signaling to promote immune evasion in ovarian cancer.

Prevalent copy number alteration is the most prominent genetic characteristic associated with ovarian cancer (OV) development, but its role in immune evasion has not been fully elucidated. In this study, we identified RAD21, a key component of the cohesin complex, as a frequently amplified oncogene that could modulate immune response in OV. Through interrogating the RAD21-regulated transcriptional program, we found that RAD21 directly interacts with YAP/TEAD4 transcriptional corepressors and recruits the NuRD complex to suppress interferon (IFN) signaling. In multiple clinical cohorts, RAD21 overexpression is inversely correlated with IFN signature gene expression in OV. We further demonstrated in murine syngeneic tumor models that RAD21 ablation potentiated anti-PD-1 efficacy with increased intratumoral CD8+ T cell effector activity. Our study identifies a RAD21-YAP/TEAD4-NuRD corepressor complex in immune modulation, and thus provides a potential target and biomarker for precision immunotherapy in OV.

Phase I study of adjuvant immunotherapy with autologous tumor-infiltrating lymphocytes in locally advanced cervical cancer.

BACKGROUNDAdoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TILs) has achieved remarkable clinical efficacy in metastatic cancers such as melanoma and cervical cancer (CC). Here, we explored the safety, feasibility, and preliminary tumor response and performed translational investigations of adjuvant immunotherapy using infusion of autogenous TILs (auto-TILs) following concurrent chemoradiotherapy (CCRT) in patients with CC who had locally advanced disease.METHODSTwenty-seven patients with CC with stage III-IV disease were recruited in this single-center, phase I study. TILs were isolated from lesions in the uterine cervix and generated under good manufacturing practice (GMP) conditions and then infused after CCRT plus i.m. IL-2 injections.RESULTSTILs from 20 of the 27 patients were successfully expanded, with a feasibility of 74.1%. Twelve patients received TILs following CCRT. Adverse events (AEs) were primarily attributable to CCRT. Only 1 (8.3%) patient experienced severe toxicity with a grade 3 hypersensitivity reaction after TIL infusion. No autoimmune AEs, such as pneumonitis, hepatitis, or myocarditis, occurred, and there were no treatment-related mortalities. Nine of 12 patients (75.0%) attained a complete response, with a disease control duration of 9-22 months. Translational investigation showed that the transcriptomic characteristics of the infused TIL products and some immune biomarkers in the tumor microenvironment and serum of patients with CC at baseline were correlated with the clinical response.CONCLUSIONTIL-based ACT following CCRT was safe in an academic center setting, with potentially effective responses in patients with locally advanced CC. "Hot" inflammatory immune environments were beneficial to the clinical efficacy of TIL-based ACT as adjuvant therapy.TRIAL REGISTRATIONClinicalTrials.gov NCT04443296.FUNDINGNational Key R&D Program; Sci-Tech Key Program of the Guangzhou City Science Foundation; the Guangdong Province Sci-Tech International Key Program; the National Natural Science Foundation of China.

Phase II trial of efficacy, safety and biomarker analysis of sintilimab plus anlotinib for patients with recurrent or advanced endometrial cancer.

BACKGROUND: Although co-inhibition of the angiogenesis and programmed death 1 (PD-1) pathways is proposed as an effective anticancer strategy, studies in Chinese patients with endometrial cancer are sufficient. Anlotinib is an oral multi-targeted tyrosine kinase inhibitor affecting tumor angiogenesis and proliferation; sintilimab is an anti-PD-1 monoclonal antibody. METHODS: This was a phase II trial using Simon's two-stage design. This study enrolled patients with endometrial cancer who had progressed after platinum-based chemotherapy. Sintilimab 200 mg was administered intravenously on day 1 every 3 weeks, and anlotinib 12 mg was administered on days 1-14 in a 21-day cycle. The primary endpoint was the objective response rate (ORR) using the immune-related Response Evaluation Criteria in Solid Tumors criteria. Immunohistochemistry and whole-exome sequencing were used as correlative investigations. RESULTS: Between November 2019 and September 2020, 23 eligible patients were enrolled. The ORR and disease control rates were 73.9% (95% CI, 51.6 to 89.8) and 91.3% (95% CI, 72.0 to 98.9), respectively, with 4 complete and 12 partial responses. With a median follow-up of 15.4 months (95% CI, 12.6 to 18.3), the median progression-free survival was not reached, and the probability of PFS >12 months was 57.1% (95% CI, 33.6 to 75.0). Exploratory analysis revealed that mutations in the homologous repair pathway showed a trend for higher ORR (100% vs 0%, p=0.07). Treatment-related grade 3/4 adverse events were observed in 50.0% of the patients. CONCLUSIONS: Sintilimab plus anlotinib demonstrated robust therapeutic benefits with tolerable toxicity in endometrial cancer. TRIAL REGISTRATION NUMBER: NCT04157491.

Development and External Validation of a Novel Model for Predicting Postsurgical Recurrence and Overall Survival After Cytoreductive R0 Resection of Epithelial Ovarian Cancer.

Purpose: Treatment of epithelial ovarian cancer is evolving towards personalization and precision, which require patient-specific estimates of overall survival (OS) and progression-free survival (PFS). Patients and Methods: Medical records of 1173 patients who underwent debulking surgery in our center were comprehensively reviewed and randomly allocated into a derivation cohort of 879 patients and an internal validation cohort of 294 patients. Five hundred and seventy-seven patients from the other three cancer centers served as the external validation cohort. A novel nomogram model for PFS and OS was constructed based on independent predictors identified by multivariable Cox regression analysis. The predictive accuracy and discriminative ability of the model were measured using Harrell's concordance index (C-index) and calibration curve. Results: The C-index values were 0.82 (95% CI: 0.76-0.88) and 0.84 (95% CI: 0.78-0.90) for the PFS and OS models, respectively, substantially higher than those obtained with the FIGO staging system and most nomograms reported for use in epithelial ovarian cancer. The nomogram score could clearly classify the patients into subgroups with different risks of recurrence or postoperative mortality. The online versions of our nomograms are available at https://eocnomogram.shinyapps.io/eocpfs/ and https://eocnomogram.shinyapps.io/eocos/. Conclusion: A externally validated nomogram predicting OS and PFS in patients after R0 reduction surgery was established using a propensity score matching model. This nomogram may be useful in estimating individual recurrence risk and guiding personalized surveillance programs for patients after surgery, and it could potentially aid clinical decision-making or stratification for clinical trials.