Jie Lin

Efficacy and safety of cadonilimab combined with chemotherapy as the first-line treatment for primary advanced or recurrent endometrial cancer: a prospective single-arm open-label phase II clinical trial

IntroductionRecently, immunotherapy has significantly transformed the treatment landscape of endometrial cancer (EC). Results from KEYNOTE-158, RUBY and AtTEnd showed programmed cell death 1 (PD-1) or programmed cell death-ligand 1 inhibitors with promising efficacy in primary advanced or recurrent EC. However, few studies focused on the role of dual immune checkpoints in primary advanced or recurrent EC. Cadonilimab is an immune checkpoint inhibitor targeting the PD-1 and T-lymphocyte antigen-4, which is expected to show substantial clinical efficacy in EC. Combining cadonilimab with standard chemotherapy may have synergistic effects, making this combination a promising first-line treatment for primary advanced or recurrent EC. Furthermore, incorporating molecular classification for guidance on the use of cadonilimab may hold valuable clinical benefits.Methods and analysisIn this multicentre, open-label, phase II study, patients with histologically confirmed EC were eligible. Forty-five patients will be recruited. Seventeen patients will be enrolled in stage I, and at least seven cases of complete response (CR) and partial response (PR) should be observed before entering stage II. All patients will receive cadonilimab at a dosage of 10 mg/kg along with carboplatin (area under the curve (AUC)=4–5) plus paclitaxel (175 mg/m2) every 3 weeks (Q3W) for 6–8 cycles. Subsequently, patients with CR, PR or stable disease will receive maintenance of cadonilimab at 10 mg/kg Q3W for 24 months or until progressive disease or adverse events are reported. The objective response rate is the primary endpoint. The secondary endpoints include the disease control rate, duration of response, progression-free survival, overall survival and safety. Additionally, exploratory endpoints involve biomarkers that may predict the efficacy of cadonilimab and chemotherapy, as well as their relationship with molecular classifications. The interim analysis will be conducted after 17 patients have been enrolled.Ethics and disseminationThe study protocol meets the approval of the ethical committee of Fujian Cancer Hospital (K2023-173-04) and all other participating hospitals. Study findings will be disseminated in peer-reviewed publications.Trial registration numberNCT06066216.

A novel five-gene metabolism-related risk signature for predicting prognosis and immune infiltration in endometrial cancer: A TCGA data mining

Metabolism dysfunction can affect the biological behavior of tumor cells and result in carcinogenesis and the development of various cancers. However, few thoughtful studies focus on the predictive value and efficacy of immunotherapy of metabolism-related gene signatures in endometrial cancer (EC). This research aims to construct a predictive metabolism-related gene signature in EC with prognostic and therapeutic implications. We downloaded the RNA profile and clinical data of 503 EC patients and screened out different expressions of metabolism-related genes with prognosis influence of EC from The Cancer Genome Atlas (TCGA) database. We first established a metabolism-related genes model using univariate and multivariate Cox regression and Lasso regression analysis. To internally validate the predictive model, 503 samples (entire set) were randomly assigned into the test set and the train set. Then, we applied the receiver operating characteristic (ROC) curve to confirm our previous predictive model and depicted a nomogram integrating the risk score and the clinicopathological feature. We employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways of the model. Afterward, we used ESTIMATE to evaluate the TME. Also, we adopted CIBERSORT and ssGSEA to estimate the fraction of immune infiltrating cells and immune function. At last, we investigated the relationship between the predictive model and immune checkpoint genes. We first constructed a predictive model based on five metabolism-related genes (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA). This model showed the ability to predict EC patients' prognosis accurately and performed well in the train set, test set, and entire set. Then we confirmed the predictive signature was a novel independent prognostic factor in EC patients. In addition, we drew and validated a nomogram to precisely predict the survival rate of EC patients at 1-, 3-, and 5-years (ROC The metabolism-related genes signature (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA) is a valuable index for predicting the survival outcomes and efficacy of immunotherapy for EC in clinical settings.

Causes of death analysis and the prognostic model construction in neuroendocrine carcinoma of the cervix: A SEER‐based study

AbstractPurposeNeuroendocrine carcinoma of the cervix (NECC) is rare but results in poor prognosis. The causes of death (CODs) in NECC patients are rarely reported. Our study aimed to explore the distributions of death causes of NECC patients compared with squamous cell carcinoma (SCC) and adenocarcinoma (ADC) and to develop a validated survival prediction model.MethodsPatients diagnosed with NECC, SCC, or ADC were identified from the Surveillance, Epidemiology, and End Results Program database from 1975 to 2019. We analyzed the standardized mortality ratio (SMR) to determine each cause of death for each survival time category. The Kaplan–Meier method was used for survival analysis. Univariate and multivariate Cox regression analyses were used to establish a nomogram model.ResultsA total of 358 NECC patients were included in this study, and 270 (75.4%) died during the follow‐up period. Patients with NECC had 5.55 times (95% CI, 4.53–6.79, p < 0.0001) higher risk of death compared with patients with SCC and 10.38 times (95% CI, 8.28–13.01, p < 0.0001) higher compared with ADC. Cervical cancer is the main cause of death in NECC. As the diagnosis time increased, the risk of death from all causes and cervix cancer gradually decreased. While after at least 10 years of follow‐up time, the highest and most dramatical SMR values were observed for metastasis (SMR, 138.81; 95% CI, 37.82–355.40; p < 0.05) and other cancers as the reason for death has an over 7‐fold higher SMR (SMR: 7.07; 95% CI: 2.60–15.40, p < 0.05) more than 5 years after the cancer diagnosis. Race, FIGO stage, and surgery were independent risk factors for the overall survival (OS) of NECC patients. For the predictive nomogram, the C‐index was 0.711 (95% CI: 0.697–0.725) and was corrected to 0.709 (95% CI: 0.680, 0.737) by bootstrap 1000 resampling validation.ConclusionCompared with SCC and ADC, NECC patients have an elevated risk of mortality due to cervical cancer and metastasis. We successfully constructed a prognostic nomogram for patients with NECC. Based on refractoriness and high mortality of NECC, targeted treatment strategies and follow‐up plans should be further developed according to the risk of death and distribution characteristics of CODs.

A novel nomogram based on inflammation biomarkers for predicting radiation cystitis in patients with local advanced cervical cancer

AbstractBackgroundsPlatelet‐to‐albumin ratio (PAR) is a new systemic inflammatory prognostic indicator associated with many inflammatory diseases. However, its role in radiation cystitis (RC) is obscure. This study aimed to explore whether PAR could be used as an effective parameter for predicting the RC risk in local advanced cervical cancer (CC) treated with radiotherapy.MethodsA total of 319 local advanced CC patients who received radical radiotherapy at Fujian Cancer Hospital were enrolled between December 2018 and January 2021. Demographics and clinical parameters were retrospectively analyzed. Univariate and multivariate analyses were used to identify the risk factors for RC. Backward and stepwise regression was applied to construct two monograms‐one with primary significant factors and the other with extra inflammatory biomarkers. A DeLong test was applied to compare the prediction abilities of two nomograms. Calibration curves and decision curve analysis (DCA) evaluated its prediction consistency, discrimination ability, and clinical net benefit.ResultsUnivariate analysis showed that age, tumor size, stage, total radiation dose, pelvic radiation dose, Systemic Immune‐Inflammation Index (SII), platelet‐to‐lymphocyte ratio (PLR), and PAR were significantly associated with RC occurrence (all p < 0.05). Multivariate analyses indicated that age, tumor size, stage, total radiation dose, and PAR were independent factors (all p < 0.05). Then, the area under curve (AUC) value of the nomogramSII+PAR was higher (AUC = 0.774) compared to that of the baseline nomogram (AUC = 0.726) (pDelong = 0.02). Also, the five‐cross validation confirmed the stability of the nomogramSII+PAR. Moreover, the calibration curve and DCA exhibited the nomograms' good prediction consistency and clinical practicability.ConclusionsPAR and SII could be valued for CC patients who are treated with radiation therapy. The nomogram based on PAR and SII could stratify patients who need extra intervention and nursing care to prevent bladder radiation damage and improve patients' quality of life.

Potential Diagnostic and Prognostic Values of CBX8 Expression in Liver Hepatocellular Carcinoma, Kidney Renal Clear Cell Carcinoma, and Ovarian Cancer: A Study Based on TCGA Data Mining

Background. Chromobox protein homolog 8 (CBX8), a transcriptional repressor, participates in many biological processes in various carcinomas. Cell differentiation, aging, and cell cycle progression are examples of such processes. It is critical to investigate CBX8 expression and its relationship with clinicopathological characteristics in liver hepatocellular carcinoma (LIHC), kidney renal clear cell carcinoma (KIRC), and ovarian cancer (OV) to investigate CBX8’s potential diagnostic and prognostic values. Methods. TCGA and CPTAC databases were used to compare the data between cancer and matched normal tissues on RNA and protein expression profiles and their relevant clinical information to determine the relationship between CBX8 and clinicopathological features. Kaplan–Meier analyses were used to assess CBX8 relationship’s with disease-free survival (DFS), relapse-free survival (RFS), and overall survival (OS). The multivariate Cox regression analysis was used to identify independent risk factors which affect prognosis. DNA methylation and genetic changes and their impact on prognoses were evaluated by cBioPortal and MethSurv websites. Spearman’s correlation was used to determine the relationship of CBX8 expression with somatic mutation. Tumor immune estimation resource (TIMER) was adopted to investigate the relationship between CBX8 and immune cell infiltration (ICI). CBX8-relevant genes and proteins are analyzed by EnhancedVolcano and STRING databases. The gene set enrichment analysis (GSEA) was performed to investigate the potential functions of CBX8. Results. CBX8 RNA and protein overexpression were confirmed in LIHC, KIRC, and OV ( p < 0.05 ). High CBX8 was significantly related to the clinical features and poor prognoses. The CBX8 genetic alteration rate was 3%. DNA methylation was also associated with prognoses. CBX8 closely interacted with ICI, TMB, MSI, purity, and ploidy. GO analyses revealed that CBX8-associated genes were enriched in biological processes, cell cycle regulation, and molecular functions. KEGG analyses exhibited that CBX8 was gathered in signaling pathway regulation. GSEA revealed that cell cycle, DNA replication, and Wnt signaling pathways were differentially enriched in the high CBX8 expression group. Conclusions. CBX8 could be a potential diagnostic and prognostic biomarker for LIHC, KIRC, and OV cancers.

A novel nomogram and risk stratification for early metastasis in cervical cancer after radical radiotherapy

AbstractObjectThis study aimed to establish an effective risk nomogram to predict the early distant metastasis (EDM) probability of cervical cancer (CC) patients treated with radical radiotherapy to aid individualized clinical decision‐making.MethodsA total of 489 patients with biopsy‐confirmed CC between December 2018 and January 2021 were enrolled. Logistic regression with the stepwise backward method was used to identify independent risk factors. The nomogram efficacy was evaluated by using the area under the receiver operating characteristic curve (AUC), C‐index by 1000 bootstrap replications, etc. Finally, patients were divided into high‐ and low‐risk groups of EDM based on the cut‐off value of nomogram points.Results36 (7.36%) CC patients had EDM, and 20 (55.6%) EDM had more than one metastatic site involved. Age below 51 (OR = 2.298, p < 0.001), tumor size larger than 4.5 cm (OR = 3.817, p < 0.001) and radiotherapy (OR = 3.319, p < 0.001) were independent risk factors of EDM. For the nomogram model, C‐index was 0.701 (95% CI = 0.604–0.798), and 0.675 (95% CI = 0.578–0.760) after 1000 bootstrap resampling validations. The Hosmer–Lemeshow test demonstrated no overfitting (p = 0.924). According to the Kaplan–Meier curve of risk score, patients with high risk were more prone to get EDM (p < 0.001).ConclusionThis is the first research to focus on EDM in CC patients. We have developed a robust scoring system to predict the risk of EDM in CC patients to screen out appropriate cases for consolidation therapy and more intensive follow‐up.

Cadonilimab (AK104) Plus Chemotherapy in Patients With Recurrent or Advanced Endometrial Cancer

This is an open-label, multi-center Phase II study of cadonilimab (AK104) combined with chemotherapy in patients with recurrent or advanced endometrial cancer. The primary objective is to evaluate objective response rate of cadonilimab plus chemotherapy.