Shu Wang

Tryptophan metabolism enzymes are potential targets in ovarian clear cell carcinoma

AbstractAimAs the second most prevalent subtype of epithelial ovarian cancers, ovarian clear cell carcinoma (OCCC) is known for its chemoresistance to conventional platinum‐based therapy. In this work, we examined the tryptophan (Trp) metabolism enzymes' differential expression in patients with OCCC to assess the potential for personalised treatment.MethodsA total of 127 OCCC tissues were used to construct tissue microarrays, and immunohistochemistry (IHC) staining of the Trp enzymes IDO1, IDO2, TDO2 and IL4I1 was performed. The correlations between Trp enzyme expression and clinical characteristics were analysed.ResultsPositive IDO1, IDO2, TDO2 and IL4I1 staining was identified in 26.8%, 94.5%, 75.6% and 82.7% of OCCC respectively. IDO1‐positive samples were more common in the chemoresistant group than in the platinum‐sensitive group (46.7% vs. 19.8%). Moreover, positive expression of IDO1, TDO2 and IL4I1 was related to advanced stage, metastasis, bilateral tumours, endometriosis and tumour rupture (p < 0.05) respectively. Univariate analysis revealed a significant association between bilateral tumours, lymph node metastasis, advanced stage, distant metastasis and aberrant cytology with a poor prognosis for OCCC, while the absence of residual tumour was correlated with a favourable outcome (p < 0.05). However, only bilateral tumours and lymph node metastases were related to a poor prognosis after multivariate analysis.ConclusionThis is the first study to investigate the expression of the Trp enzymes IDO1, IDO2, TDO2 and IL4I1 in OCCC tissues. IDO2, TDO2 and IL4I1 were detected in the majority of OCCC. Clinical traits were correlated with IDO1, IDO2, TDO2 and IL4I1 expression. IDO1 may be used as a therapeutic target given the large percentage of chemoresistant cases with IDO1 expression. These results will aid the development of personalised therapies for OCCC.

Oncologic outcomes in older women with endometrial carcinoma (≥70 years)

Data are limited in the management of elderly women with endometrial cancer as they are under-represented in clinical trials. The aim of this study was to evaluate the outcomes of women ≥70 years who underwent hysterectomy. One hundred and twenty-one patients met the inclusion criteria. The median age among the cohort was 75 years (range: 70-91), and 52% underwent robotic surgery. The five-year overall survival (OS) rate was 67%. The five-year cumulative incidence of recurrence was 19%. Based on univariate analysis, white race, lower ASA score, higher pre-operative and post-operative haematocrit, lower estimated blood loss, stage I and robotic surgery were associated with improved OS. On multivariable analysis, ASA score, preoperative haematocrit, estimated blood loss and stage were associated with survival.Survival rates among older women were low and disease recurrence was high. Robotic surgery was safe and appeared to improve perioperative outcomes in older women with endometrial cancer.Impact Statement

Development and validation of Nomograms for predicting overall survival and Cancer-specific survival in patients with ovarian clear cell carcinoma

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort ( n  = 1079) and a validation cohort ( n  = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3- and 5-year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups. Conclusions We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

Survival and recurrence after intraperitoneal chemotherapy use: Retrospective review of ovarian cancer hospital registry data

AbstractBackgroundIntraperitoneal/intravenous chemotherapy (IP/IV) was associated with improved survival for ovarian cancer (OC) patients in several randomized clinical trials. However, the uptake of IP/IV in clinical practice is varied due to conflicting evidence about its impact on survival and recurrence. The aim of this study was to explore the uptake of IP/IV treatment and to evaluate its impact on survival and recurrence in OC patients.MethodsDemographic and clinical information on OC patients (N = 2916) who underwent treatment for OC between 2000 and 2017 was obtained from the large healthcare system cancer registry. Duplicate records, grade 1, rare (eg, gelatinous carcinoma), and non‐epithelial (eg, granulosa cell carcinoma) tumors were excluded. Kaplan‐Meier survival curves were constructed to compare 5‐ and 10‐year survival based on the chemotherapy type, surgery type, and stage. Multivariable Gray's piecewise constant time‐varying coefficient models were fitted to evaluate the effect of IP/IV on adjusted hazard ratio (AHR) of OC survival and recurrence adjusting for potential confounders.ResultsThe final sample consisted of 1846 OC patients, 14% (250/1846) of which received IP/IV chemotherapy. IP/IV was significantly associated with improved 10‐year survival (P < .001). Multivariable Gray's model demonstrated that IP/IV therapy significantly reduced the AHR of death (AHR = 0.39‐1.07, P < .001) with the beneficial effect gradually declining over time. Use of IP/IV chemotherapy had no impact on OC recurrence.ConclusionsThese findings demonstrated that only a small fraction of eligible patients underwent IP/IV chemotherapy. We report a significant 10‐year survival, but not necessarily recurrence benefit is associated with IP/IV chemotherapy compared to IV only, suggesting the need for novel ways of identifying patients who may benefit from IP/IV chemotherapy.

Clinical characteristics and prognostic factors of stage IC ovarian clear cell carcinoma: a Surveillance, Epidemiology, and End Results (SEER) analysis

The study aimed to investigate the clinical characteristics and prognostic factors of stage IC ovarian clear cell carcinoma (OCCC). The Surveillance, Epidemiology, and End Results (SEER) database was accessed for medical records of patients with stage IC OCCC from 1992 to 2016. The clinical and prognostic features of stage IC OCCC from several therapeutic perspectives were identified with Kaplan-Meier method and Cox proportional hazards model. Totally, 1079 patients were enrolled for the analysis. The median age was 55 (range 24-91) years. 850 (78.8%) patients were treated with chemotherapy, 877 (81.3%) received lymph node (LN) dissection, and 20 (1.9%) underwent radiotherapy. LN dissection (P = 0.501) and chemotherapy (P = 0.130) did not significantly impact cancer-specific survival (CSS). Among patients younger than 45 years, 23 received fertility-sparing surgery (FSS). No significant difference in CSS was observed between the FSS and non-FSS group (P = 0.523). Bilateral tumor (P < 0.001) and larger tumor size (P = 0.010) were significantly and independently associated with poor CSS. Older age (P = 0.001), bilateral tumor (P < 0.001), and larger tumor size (P = 0.005) were significantly and independently associated with poor overall survival (OS), while LN dissection (P = 0.005) was significantly and independently associated with better OS. Significant differences in CSS (P = 0.005) and OS (P < 0.001) were observed between the low- and high-risk groups, which were divided by median risk score. LN dissection and chemotherapy did not significantly impact CSS, while LN dissection was an independent prognostic factor for OS. Convincing evidence from clinical trials with a large number of patients are further required to develop treatment guidelines.

The application of risk models based on machine learning to predict endometriosis‐associated ovarian cancer in patients with endometriosis

AbstractIntroductionThere is currently no satisfactory model for predicting malignant transformation of endometriosis. The aim of this study was to construct and evaluate a risk model incorporating noninvasive clinical parameters to predict endometriosis‐associated ovarian cancer (EAOC) in patients with endometriosis.Material and MethodsWe enrolled 6809 patients with endometriosis confirmed by pathology, and randomly allocated them to training (n = 4766) and testing cohorts (n = 2043). The proportion of patients with EAOC in each cohort was similar. We extracted a total of 94 demographic and clinicopathologic features from the medical records using natural language processing. We used a machine learning method – gradient‐boosting decision tree – to construct a predictive model for EAOC and to evaluate the accuracy of the model. We also constructed a multivariate logistic regression model inclusive of the EAOC‐associated risk factors using a back stepwise procedure. Then we compared the performance of the two risk‐predicting models using DeLong's test.ResultsThe occurrence of EAOC was 1.84% in this study. The logistic regression model comprised 10 selected features and demonstrated good discrimination in the testing cohort, with an area under the curve (AUC) of 0.891 (95% confidence interval [CI] 0.821–0.960), sensitivity of 88.9%, and specificity of 76.7%. The risk model based on machine learning had an AUC of 0.942 (95% CI 0.914–0.969), sensitivity of 86.8%, and specificity of 86.7%. The machine learning‐based risk model performed better than the logistic regression model in DeLong's test (p = 0.036). Furthermore, in a prospective dataset, the machine learning‐based risk model had an AUC of 0.8758, a sensitivity of 94.4%, and a specificity of 73.8%.ConclusionsThe machine learning‐based risk model was constructed to predict EAOC and had high sensitivity and specificity. This model could be of considerable use in helping reduce medical costs and designing follow‐up schedules.