Peng Peng

Toripalimab combined with bevacizumab plus chemotherapy as first-line treatment for refractory recurrent or metastatic cervical cancer: a single-arm, open-label, phase II study (JS001-ISS-CO214)

To evaluate the efficacy and safety of adding toripalimab to bevacizumab and platinum-based chemotherapy as first-line treatment for refractory recurrent or metastatic (R/M) cervical cancer (CC). Patients were administered toripalimab (240 mg) + bevacizumab (7.5 mg/kg) combined with platinum-based chemotherapy once every three weeks for six cycles, followed by the maintenance therapy involving toripalimab + bevacizumab once every 3 weeks for 12 months or when disease progression or intolerable toxicity occurred. The primary endpoint was the objective response rate (ORR) per Response Evaluation Criteria in Solid Tumors version 1.1. The secondary endpoints were safety profiles, disease control rate (DCR), progression-free survival (PFS), and overall survival (OS). Twenty-four patients were enrolled in this study and in the final analysis. The median follow-up duration was 18.6 (range, 3.3-28.5) months. The ORR was 83.3% (95% confidence interval [CI]=62.6-95.3) and the DCR was 95.8% (95% CI=78.9-99.9); 9 (37.5%) patients achieved complete response, 11 (45.8%) achieved partial response, and 3 (12.5%) had stable disease. The median PFS was 22.6 (95% CI=10.4-34.7) months and the median OS was not reached. The most common grade 3 treatment-related adverse events (AEs) were neutropenia (41.7%) and leukopenia (16.7%). The most common immune-related AEs (irAEs) were thyroid dysfunction (37.5%) and increased adrenocorticotropic hormone (37.5%) and serum cortisol levels (33.3%). No grade ≥3 irAEs were observed. Toripalimab combined with bevacizumab and platinum-based chemotherapy show promising clinical efficacy and favorable safety profile, providing an alternative first-line treatment option for patients with R/M CC. ClinicalTrials.gov Identifier: NCT04973904.

Metastatic Cervical Cancer in the Asia-Pacific Region: Current Treatment Landscape and Barriers

Abstract Despite treatment advances for metastatic cervical cancer (mCC), the Asia-Pacific region faces significant barriers in treatment accessibility, availability, and healthcare infrastructure. This study explored the treatment landscape and barriers for mCC in the Asia-Pacific. A descriptive, cross-sectional, web-based study evaluating cervical cancer treatment patterns was conducted among medical, radiation, and gynecologic oncologists and gynecologists in the Chinese mainland (n = 80), Australia, the Philippines, South Korea, and Taiwan (n = 20 each). Eligible respondents were primarily involved in direct patient care (≥60%) and were key treatment deciders with ≥5 years of experience. Among patients with cervical cancer of 160 physicians, 10.9% had metastatic disease, of which 50.3% were aged 41 to 60 years and had Eastern Cooperative Oncology Group scores of 0 to 2 (78.7%). Top treatment modalities included systemic therapy (ST) alone (43.6%) and radiotherapy + ST (33.4%). Top first-line regimens were carboplatin/cisplatin + paclitaxel ± bevacizumab (42.3% and 33.1%, respectively), and the top second-line treatment regimens were carboplatin + paclitaxel + bevacizumab (12.0%) and cisplatin + paclitaxel + bevacizumab (11.5%). PD-L1 testing was more common in South Korea (80.8%) than in the Chinese mainland (48.8%) and Taiwan (26.4%). Treatment drivers included National Comprehensive Cancer Network guidelines (82.7%), disease stage (87.4%), Eastern Cooperative Oncology Group status (83.5%), comorbidities (59.1%), drug efficacy (88.2%), safety (84.3%), and accessibility (66.9%). Treatment challenges included poor prognosis (26.8%), patient affordability (21.3%), and limited treatment option availability (19.7%). In bevacizumab-reimbursed locations, patient tolerability and insufficient medical resources persisted. In conclusion, approximately 11% of cervical cancer cases were metastatic. Treatment preferences were radiotherapy and ST, with funding, cost, accessibility, and availability challenges. Policies supporting reimbursement and accessibility could encourage the adoption of effective alternative therapies. Significance: The findings offer valuable insights about current treatments and the related unmet needs in funding, cost, accessibility, and availability across the Asia-Pacific region. These further highlight areas of importance and the need for implementing reimbursement policies and enhancing accessibility to support the adoption of effective, advanced treatments.

Early prediction and risk stratification of ovarian cancer based on clinical data using machine learning approaches

Our study was aimed to construct a predictive model to advance ovarian cancer diagnosis by machine learning. A retrospective analysis of patients with pelvic/adnexal/ovarian mass was performed. Potential features related to ovarian cancer were obtained as many as possible. The optimal machine learning algorithm was selected among six candidates through 5-fold cross validation. Top 20 features having the most powerful predictive significance were ranked by Shapley Additive Interpretation (Shap) method. Clinical validation was further performed to confirm whether our model could advance diagnosis of ovarian cancer. A total of 9,799 patients were collected. The inclusion criteria included age >18 years old, the first diagnosis being pelvic/adnexal/ovarian mass of undetermined significance, and pathological report indispensable. Four hundred and thirty-eight dimensional features were obtained after filtration. LightGBM showed the best performance with accuracy 88%. Among the top 20 features, 55% belonged to laboratory test report, 35% came from imaging examination report, and 10% were attributed to basic demographics and main symptom. Age, CA125, and risk of ovarian malignancy algorithm were the top three. Our predictive model performed stably in testing and clinical validation datasets, and was found to advance the diagnosis of ovarian cancer about 17 days before clinical pathological examination. LightGBM was the optimal algorithm for our predictive model with accuracy of 88%. Laboratory test and imaging examination played essential roles in diagnosing ovarian cancer. Our model could advance the diagnosis of ovarian cancer before clinical pathological examination.

Toripalimab Combined With Chemotherapy and Bevacizumab as First-Line Treatment in Patients With Advanced Cervical Cancer

This is a single-arm, multicenter, phase II study to investigate efficacy and safety of Toripalimab combined with chemotherapy (paclitaxel and cisplatin) and Bevacizumab as first-line treatment in patients with recurrent, refractory and metastatic cervical cancer