About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.

Management of tumors has become more complex owing to tumor heterogeneity. Fewer studies have been performed on intra-tumor heterogeneity of endometrial cancer (EC) until now. Therefore, it is of great clinical value to explore the intra-tumor heterogeneity of EC based on clinical features and gene expression profiles. A total of 1688 patients with EC were screened and 114 patients were finally selected, including specimens from 84 patients with primary EC without relapse (PE) and the paired metastases (P-M) specimens, as well as specimens from 30 patients with primary EC with relapse (RPE) and the paired relapsed EC (P-RE) specimens. Microarray and RNA-seq were used to detect gene expression of EC samples. Clinicopathological characteristics and molecular data were compared between PE and P-M groups and between RPE and P-RE groups to explore the intra-tumor heterogeneity of EC. The clinical intra-tumor spatial heterogeneity of pathological type, grade, ER status, and PR status between PE and P-M were 17.9%, 13.1%, 28.6%, and 28.6%, respectively. The clinical intra-tumor spatiotemporal heterogeneity of pathological type, grade, ER status, and PR status between RPE and P-RE were 16.7%, 33.3%, 25.0%, and 37.5%, respectively. Cluster analysis sorts EC samples based on progression type of lesion and their pathological type. There were differentially expressed genes between PE and P-M and between RPE and P-RE, of which gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were mainly enriched in cell proliferation, the p53 signaling pathway, etc. Clinical and molecular data showed that there was spatiotemporal heterogeneity in intra-tumor of EC, which may add to the complexity of diagnosis and therapeutics for EC. Considering the intra-tumor heterogeneity, sequential chemotherapy and precision medicine may be a more suitable treatment plan for EC. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0

Background: This retrospective multi-center study aimed to describe the epidemiological characteristics, clinical features, and management of patients with cervical cancer in pregnancy (CCIP) and evaluate maternal and infant outcomes.Methods: The data of patients with CCIP were retrospectively collected from those diagnosed and treated in 17 hospitals in 12 provinces in China between January 2009 and November 2017. The information retrieved included patients' age, clinical features of the tumor, medical management (during pregnancy or postpartum), obstetrical indicators (i.e., gestational age at diagnosis, delivery mode, and birth weight), and maternal and neonatal outcomes. Survival analyses were performed using Kaplan–Meier survival curves and log-rank tests that estimated the overall survival of patients.Results: One-hundred and five women diagnosed with CCIP (median age = 35 years) were identified from ~45,600 cervical cancer patients (0.23%) and 525,000 pregnant women (0.020%). The median gestational age at cancer diagnosis was 20.0 weeks. The clinical-stage of 93.3% of the patients with CCIP was IB1, 81.9% visited the clinic because of vaginal bleeding during pregnancy, and 72.4% had not been screened for cervical cancer in more than 5 years. To analyze cancer treatments during pregnancy, patients were grouped into two groups, termination of pregnancy (TOP, n = 67) and continuation of pregnancy (COP, n = 38). Analyses suggested that the TOP group was more likely to be diagnosed at an earlier gestational stage than the COP group (14.8 vs. 30.8 weeks, p < 0.001). The unadjusted hazard ratio for the COP group's overall survival was 1.063 times that of the TOP group (95% confidence interval = 0.24, 4.71). There were no significant differences between the TOP and COP groups in maternal survival (p = 0.964). Thirty-three of the infants of patients with CCIP were healthy at the end of the follow-up period, with a median age of 18 ± 2.8 months.Conclusions: Most patients with CCIP had not been screened for cervical cancer in over 5 years. The oncologic outcomes of the TOP and COP groups were similar. A platinum-based neoadjuvant chemotherapy regimen could be a favorable choice for the management of CCIP during the second and third trimesters of pregnancy.

Platinum chemosensitivity detection plays a vital role during endometrial cancer treatment because chemotherapy responses have profound influences on patient's prognosis. Although several methods can be used to detect drug resistance characteristics, studies on detecting drug sensitivity based on dynamic and quantitative phase imaging of cancer cells are rare. In this study, digital holographic microscopy was applied to distinguish drug‐resistant and nondrug‐resistant endometrial cancer cells. Based on the reconstructed phase images, temporal evolutions of cell height (CH), cell projected area (CPA) and cell volume were quantitatively measured. The results show that change rates of CH and CPA were significantly different between drug‐resistant and nondrug‐resistant endometrial cancer cells. Furthermore, the results demonstrate that morphological characteristics have the potential to be utilized to distinguish the drug sensitivity of endometrial cancer cells, and it may provide new perspectives to establish optical methods to detect drug sensitivity and guide chemotherapy in endometrial cancer.

This study aimed at developing an available recurrence-free survival (RFS) model of endometrial cancer (EC) for accurate and individualized prognosis assessment. A training cohort of 520 women with EC who underwent initial surgical treatment and an external validation cohort of 445 eligible EC patients from 2006 to 2016 were analyzed retrospectively. Multivariable Cox proportional hazards regression models were used to develop nomograms for predicting recurrence. The concordance index (C-index) and the area under the receiver operating characteristic curve (AUC) were calculated to determine the discrimination of RFS prognostic scoring systems. Calibration plots were generated to examine the performance characteristics of the predictive nomograms. Regression analysis revealed that an advanced International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade 3, primary tumor diameter ≥2 cm, and positive peritoneal cytology were independent prognostic factors for RFS in EC in the training set. The nomograms estimated RFS according to these four variables, with a C-index of 0.860, which was superior to that of FIGO stage (2009 criteria), at 0.809 (P=0.034), in the training cohort. Encouragingly, consistent results were observed in the validation set, with a C-index of 0.875 for the nomogram and a C-index of 0.833 for the FIGO staging (P=0.0137). Furthermore, the calibrations of the nomograms predicting 3- and 5-year RFS strongly corresponded to the actual survival outcome. In conclusion, this study developed an available nomogram with effective external validation and relatively appreciable discrimination and conformity for the accurate assessment of 3- and 5-year RFS in Chinese women with EC.

Fusion genes may be play an important role in tumorigenesis, prognosis, and drug-resistance, while the study of fusion genes in endometrial cancer(EC) is rare. This study aimed to identify the new fusion genes and explore the clinical significance in EC. A total of 28 patients diagnosed EC were enrolled in this study. RNA sequencing was used to obtain entire genomes and transcriptomes. STAR comparison and STAR-fusion predict were applied to predict the fusion genes. Chi-square test and Student-t test were used to verify the clinical significance by SPSS 13.0 software. The new fusion genes were found and the number of fusion genes varies from 3 to 110 in all endometrial cancer patients. The type of fusion genes varies including mRNA-mRNA, LncRNA-LncRNA, LncRNA-mRNA. There were 6 fusion genes that have high fusion rate including RP11–123O10.4--GRIP1, RP11–444D3.1--SOX5, RP11–680G10.1--GSE1, NRIP1--AF127936.7, RP11–96H19.1--RP11–446N19.1, and DPH7--PTP4A3. Further studies have shown that these fusion genes were related to stage, grade and recurrence, in which NRIP1--AF127936.7 and DPH7--PTP4A3 only be found in stage III endometrial cancer patients. DPH7--PTP4A3 be found in grade 2, 3 and recurrence endometrial cancer. Fusion genes act as an essential role in endometrial cancer. There were six over-expression and high fusion rate genes. NRIP1--AF127936.7 and DPH7--PTP4A3 might related to stage and DPH7--PTP4A3 are related to grade and recurrence. Thisis an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0