Dose-dense regimen versus conventional three-weekly paclitaxel combination with carboplatin chemotherapy in first-line ovarian cancer treatment: a systematic review and meta-analysis.

BACKGROUND: Paclitaxel dose-dense regimen has been controversial in clinical trials in recent years. This systematic review and meta-analysis tried to evaluate the efficacy and safety of paclitaxel dose-dense chemotherapy in primary epithelial ovarian cancer. METHODS: An electronic search following PRISMA guidelines was conducted (Prospero registration number: CRD42020187622), and then a systematic review and meta-analysis of included literature were initiated to determine which regimen was better. RESULTS: Four randomized controlled trials were included in the qualitative evaluation, and 3699 ovarian cancer patients were included in the meta-analysis. The meta-analysis revealed that the dose-dense regimen could prolong PFS (HR0.88, 95%CI 0.81-0.96; p = 0.002) and OS (HR0.90, 95%CI 0.81-1.02; p = 0.09), but it also increased the overall toxicity (OR = 1.102, 95%CI 0.864-1.405; p = 0.433), especially toxicity of anemia (OR = 1.924, 95%CI 1.548-2.391; p < 0.001), neutropenia (OR = 2.372, 95%CI 1.674-3.361; p < 0.001). Subgroup analysis indicated that the dose-dense regimen could significantly prolong not only PFS (HR0.76, 95%CI 0.63-0.92; p = 0.005 VS HR0.91, 95%CI 0.83-1.00; p = 0.046) but also OS (HR0.75, 95%CI 0.557-0.98; p = 0.037 VS HR0.94, 95%CI 0.83-1.07; p = 0.371) in Asian, and overall toxicity was significantly increased in Asians (OR = 1.28, 95%CI: 0.877-1.858, p = 0.202) compared to non-Asians (OR = 1.02, 95%CI 0.737-1.396, p = 0.929). CONCLUSION: Paclitaxel dose-dense regimen could prolong PFS and OS, but it also increased the overall toxicity. Therapeutic benefits and toxicity of dose-dense are more obvious in Asians compared to non-Asians, which need to be further confirmed in clinical trials.

Single-cell profiling of mouse and primate ovaries identifies high levels of EGFR for stromal cells in ovarian aging.

Increased ovarian fibrosis and an expanded stromal cell compartment are the main characteristics of aging ovaries. However, the molecular mechanisms and the key factor of stromal cells underlying ovarian aging remain unclear. Here, we explored single-cell transcriptomic data of ovaries from the adult mouse (4,363 cells), young (1,122 cells), and aged (1,479 cells) non-human primates (NHPs) to identify expression patterns of stromal cells between young and old ovaries. An increased number of stromal cells (p = 0.0386) was observed in aged ovaries of NHPs, with enrichment processes related to the collagen-containing extracellular matrix. In addition, differentially expressed genes of stromal cells between young and old ovaries were regulated by ESR1 (p = 7.94E-08) and AR (p = 1.99E-05). Among them, EGFR was identified as the common target and was highly expressed (p = 7.69E-39) in old ovaries. In human ovaries, the correlated genes of EGFR were associated with the process of the cell-substrate junction. Silencing of EGFR in human ovarian stromal cells led to the reduction of cell-substrate junction via regulating phosphorylation modification of the AKT-mTOR signaling pathway and stromal cell marker genes. Overall, we identified high levels of EGFR for stromal cells in ovarian aging, which provides insight into the cell type-specific molecular mechanisms underlying ovarian aging at single-cell resolution.

Integrative Analyses of m6A Regulators Identify that METTL3 is Associated with HPV Status and Immunosuppressive Microenvironment in HPV-related Cancers.

Although m6A modifications are associated with tumor progression, and anti-tumor immune responses, the role of m6A regulators in HPV-related carcinogenesis has not been well resolved. To provide evidence for the role of m6A regulators in HPV-related carcinogenesis and identify potential therapeutic targets for HPV-related cancers, integrative analyses of m6A regulators in 1,485 head and neck squamous cell carcinoma (HNSC) patients and 507 cervical squamous cell carcinoma (CESC) patients was performed and identified that an m6A regulator, METTL3, was highly expressed in tumors and was related to the poor prognosis in HNSC and CESC. In HPV-positive tumors, METTL3 was positively associated with tumor HPV status, such as HPV integration status, E6 and unspliced-E6 expression, and p16 expression. Further analysis demonstrated that METTL3 high status was negatively correlated with tumor immune cell infiltrations and facilitated the expression of immunosuppressive immune checkpoint molecules (i.e., PD-L1). Cell-derived xenograft models demonstrated that METTL3 inhibitor combined with anti-PD1 therapy promoted immunotherapy of CESC in vivo. Overall, this study identified that METTL3 high status, is associated with poor prognosis and HPV status, and serves as a mediator of the immunosuppressive tumor microenvironment in HPV-associated cancer, which provides a promising therapeutic target for anti-cancer immunotherapy.

Genomic mutation features identify distinct BRCA-associated mutation characteristics in endometrioid carcinoma and endometrioid ovarian carcinoma.

Although endometrioid carcinoma (EC) and endometrioid ovarian carcinoma (EnOC) display similar pathological features, their molecular characteristics remain to be determined. Somatic mutation data from 2777 EC, 423 EnOC, and 57 endometriosis patients from the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset were analyzed and showed similar profiles with different mutation frequencies among them. By using 275 overlapping mutated genes, EC was clustered into two groups with different disease outcomes and different clinical characteristics. Although BRCA-associated mutation characteristics were identified in both EC and EnOC, the mutation frequencies of BRCA1 (P=0.0146), BRCA2 (P=0.0321), ATR (P=3.25E-11), RAD51 (P=3.95E-08), RAD1 (P=0.0003), TP53 (P=6.11E-33), and BRIP1 (P=2.90E-09) were higher in EnOC. Further analysis showed that EnOC cell lines with BRCA-associated mutation characteristics were more sensitive to poly ADP-ribose polymerase (PARP) inhibitors than EC cell lines, including olaparib, talazoparib, rucaparib, and veliparib. Moreover, based on BRCA-associated mutational and transcriptomic profiles, EC with BRCA-associated mutational burdens shows lower levels of immune cell infiltration, higher expression of immunosuppressive checkpoint molecules and worse prognosis than EC without BRCA mutation. Our study comprehensively analyzed the genome mutation features of EC and EnOC and provide insights into the molecular characteristics of EC and EnOC.

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).

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.

Efficiency and safety evaluation of prophylaxes for venous thrombosis after gynecological surgery.

BACKGROUND: In this study, we investigate the incidence of venous thrombosis (VT), and evaluate the effectiveness and safety of 3 major thromboprophylaxes and the potential risk factors for VT in women undergoing surgery for a gynecological malignancy. METHODS: We performed a randomized controlled trial of 307 patients undergoing laparoscopic surgery for gynecological malignancies at a single institution from January 2016 to October 2017. Patients were divided into 3 groups: one receiving a half dose of low-molecular-weight heparin sodium injection (FLUXUM, Alfa Wassermann, Italy) delivered by injection, one receiving a full dose of FLUXUM, and a third group receiving an Argatroban injection. RESULTS: None of the patients in our study developed a pulmonary embolism, bleeding, or infectious complications. There were no statistical differences in the rate of deep venous thrombosis (DVT) (0%, 0%, and 2.38%) and the superficial venous thromboembolism (SVT) (15.66%, 8.97%, and 18.6%) among the 3 groups. None of the patients developed symptomatic VT. The effect of treatment on alanine aminotransferase and aspartate aminotransferase differed between the groups, with a minimal effect in the Argatroban group, and all 3 methods resulted in minimal impairment of renal function. Decreased hemoglobin, elevated levels of D-dimer, and prothrombin time were closely related to thrombogenesis. CONCLUSION: In conclusion, the incidence of postoperative thrombosis in gynecological malignancy among these Chinese people is not as low as we had originally presumed. Argatroban is not more effective than Parnaparin as a direct thrombin inhibitor, but it has less influence on liver function, which is beneficial for patients undergoing chemotherapy. Hemoglobin, D-dimer, and prothrombin time may be used to predict or detect thrombogenesis.

Risk factors for developing into critical COVID-19 patients in Wuhan, China: A multicenter, retrospective, cohort study.

BACKGROUND: The ferocious global assault of COVID-19 continues. Critically ill patients witnessed significantly higher mortality than severe and moderate ones. Herein, we aim to comprehensively delineate clinical features of COVID-19 and explore risk factors of developing critical disease. METHODS: This is a Mini-national multicenter, retrospective, cohort study involving 2,387 consecutive COVID-19 inpatients that underwent discharge or death between January 27 and March 21, 2020. After quality control, 2,044 COVID-19 inpatients were enrolled. Electronic medical records were collected to identify the risk factors of developing critical COVID-19. FINDINGS: The severity of COVID-19 climbed up straightly with age. Critical group was characterized by higher proportion of dyspnea, systemic organ damage, and long-lasting inflammatory storm. All-cause mortality of critical group was 85•45%, by contrast with 0•58% for severe group and 0•18% for moderate group. Logistic regression revealed that sex was an effect modifier for hypertension and coronary heart disease (CHD), where hypertension and CHD were risk factors solely in males. Multivariable regression showed increasing odds of critical illness associated with hypertension, CHD, tumor, and age ≥ 60 years for male, and chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), tumor, and age ≥ 60 years for female. INTERPRETATION: We provide comprehensive front-line information about different severity of COVID-19 and insights into different risk factors associated with critical COVID-19 between sexes. These results highlight the significance of dividing risk factors between sexes in clinical and epidemiologic works of COVID-19, and perhaps other coronavirus appearing in future. FUNDING: 10.13039/100000001 National Science Foundation of China.

HPV-CCDC106 integration alters local chromosome architecture and hijacks an enhancer by three-dimensional genome structure remodeling in cervical cancer.

Integration of human papillomavirus (HPV) DNA into the human genome is a reputed key driver of cervical cancer. However, the effects of HPV integration on chromatin structural organization and gene expression are largely unknown. We studied a cohort of 61 samples and identified an integration hot spot in the CCDC106 gene on chromosome 19. We then selected fresh cancer tissue that contained the unique integration loci at CCDC106 with no HPV episomal DNA and performed whole-genome, RNA, chromatin immunoprecipitation and high-throughput chromosome conformation capture (Hi-C) sequencing to identify the mechanisms of HPV integration in cervical carcinogenesis. Molecular analyses indicated that chromosome 19 exhibited significant genomic variation and differential expression densities, with correlation found between three-dimensional (3D) structural change and gene expression. Importantly, HPV integration divided one topologically associated domain (TAD) into two smaller TADs and hijacked an enhancer from PEG3 to CCDC106, with a decrease in PEG3 expression and an increase in CCDC106 expression. This expression dysregulation was further confirmed using 10 samples from our cohort, which exhibited the same HPV-CCDC106 integration. In summary, we found that HPV-CCDC106 integration altered local chromosome architecture and hijacked an enhancer via 3D genome structure remodeling. Thus, this study provides insight into the 3D structural mechanism underlying HPV integration in cervical carcinogenesis.

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.