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

Sintilimab combined with bevacizumab in relapsed or persistent ovarian clear cell carcinoma (INOVA): a multicentre, single-arm, phase 2 trial

BACKGROUND

Ovarian clear cell carcinoma rarely responds to second-line chemotherapy, the recommended treatment for relapsed epithelial ovarian cancer. Here, we report the activity and safety of sintilimab in combination with bevacizumab in patients with relapsed or persistent ovarian clear cell carcinoma.

METHODS

In the prospective, multicentre, single-arm, phase 2 INOVA trial, patients aged 18-75 years with histologically confirmed relapsed or persistent ovarian clear cell carcinoma were enrolled from eight tertiary hospitals in China. Eligible patients had an Eastern Cooperative Oncology Group performance status score of 0-2 and previous exposure to at least one cycle of platinum-containing chemotherapy. Enrolled patients received sintilimab (200 mg) and bevacizumab (15 mg/kg) intravenously every 3 weeks until disease progression. The primary endpoint was objective response rate assessed by independent central review based on Response Evaluation Criteria in Solid Tumours version 1.1. Eligible enrolled patients who received at least one cycle of treatment and had at least one tumour response assessment following the baseline assessment per protocol were included in the activity analysis. Patients who received at least one dose of study drug were included in the safety analysis. The study is registered with ClinicalTrials.gov (NCT04735861) and is ongoing.

FINDINGS

Between April 8, 2021, and July 3, 2023, 51 patients were screened and 41 patients received at least one dose of sintilimab in combination with bevacizumab. Response evaluation was completed in 37 patients. Objective responses were observed in 15 patients (objective response rate 40·5%; 95% CI 24·8-57·9), of which five (14%) were complete responses and ten (27%) were partial responses. At data cutoff (Jan 29, 2024), the median follow-up was 16·9 months (IQR 7·5-23·4). Three (7%) patients developed grade 3 treatment-related adverse events including one patient with proteinuria, one patient with myocarditis, and one patient with rash. No treatment-related adverse events of worse than grade 3 severity were recorded. Treatment-related serious adverse events occurred in two (5%) patients including one patient with immune-related myocarditis and another with hypertension and renal dysfunction. No treatment-related deaths occurred.

INTERPRETATION

Sintilimab in combination with bevacizumab showed promising anti-tumour activity and manageable safety in patients with relapsed or persistent ovarian clear cell carcinoma. Larger, randomised trials are warranted to compare this low-toxicity, chemotherapy-free combinatorial regimen with standard chemotherapy.

FUNDING

National Key Technology Research and Development Program of China, National Natural Science Foundation of China, Beijing Xisike Clinical Oncology Research Foundation, and Innovent Biologics.

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

The global burden, trends and cross-country inequalities of female breast and gynaecologic cancers: A population based study

OBJECTIVE

To analyse the global burden, trends and cross-country inequalities of female breast and gynaecologic cancers (FeBGCs).

DESIGN

Population-Based Study.

SETTING

Data sourced from the Global Burden of Disease Study 2019.

POPULATION

Individuals diagnosed with FeBGCs.

METHODS

Age-standardised mortality rates (ASMRs), age-standardised Disability-Adjusted Life Years (DALYs) rates (ASDRs) and their 95% uncertainty interval (UI) described the burden. Estimated annual percentage changes (EAPCs) and their confidence interval (CI) of age-standardised rates (ASRs) illustrated trends. Social inequalities were quantified using the Slope Index of Inequality (SII) and Concentration Index.

MAIN OUTCOME MEASURES

The main outcome measures were the burden of FeBGCs and the trends in its inequalities over time.

RESULTS

In 2019, the ASDRs per 100 000 females were as follows: breast cancer: 473.83 (95% UI: 437.30-510.51), cervical cancer: 210.64 (95% UI: 177.67-234.85), ovarian cancer: 124.68 (95% UI: 109.13-138.67) and uterine cancer: 210.64 (95% UI: 177.67-234.85). The trends per year from 1990 to 2019 were expressed as EAPCs of ASDRs and these: for Breast cancer: -0.51 (95% CI: -0.57 to -0.45); Cervical cancer: -0.95 (95% CI: -0.99 to -0.89); Ovarian cancer: -0.08 (95% CI: -0.12 to -0.04); and Uterine cancer: -0.84 (95% CI: -0.93 to -0.75). In the Social Inequalities Analysis (1990-2019) the SII changed from 689.26 to 607.08 for Breast, from -226.66 to -239.92 for cervical, from 222.45 to 228.83 for ovarian and from 74.61 to 103.58 for uterine cancer. The concentration index values ranged from 0.2 to 0.4.

CONCLUSIONS

The burden of FeBGCs worldwide showed a downward trend from 1990 to 2019. Countries or regions with higher Socio-demographic Index (SDI) bear a higher DALYs burden of breast, ovarian and uterine cancers, while those with lower SDI bear a heavier burden of cervical cancer. These inequalities increased over time.

Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study

BACKGROUND

Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer.

METHODS

In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer.

FINDINGS

Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values.

INTERPRETATION

The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer.

FUNDING

Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China.

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.