Misi He

Risk factors associated with overall survival in patients with cervical cancer: a prospective cohort study in Western China comparing random survival forest and Cox proportional hazards models

Cervical cancer (CCa) significantly affects female fertility and quality of life. This study aimed to construct and validate a random survival forest (RSF) model to identify the factors that affect the overall survival (OS) in patients with CCa in China and compare its performance with that of the Cox proportional hazards model (Cox model). Data on CCa patients were collected from Chongqing University Cancer Hospital. The performance and discrimination ability of the models were evaluated via the C-index, integrated Brier score (IBS), accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The Kaplan-Meier (K-M) survival curve was used to analyze the difference in OS between patients with high and low risk predicted by RSF model. A total of 3,982 patients were included in this study. Comparing to Cox model, the RSF model ranked important variables and identified radiotherapy (RT) as an important treatment measure. A comprehensive analysis of the evaluation indices confirmed that the RSF model outperformed the Cox model (IBS: 0.152 vs. 0.162, C-index: 0.863 vs. 0.764). The RSF model metrics for the validation cohort (VC) were as follows: 1-, 3-, and 5-year AUC (0.908, 0.884, and 0.869), sensitivity (0.746), specificity (0.825), and accuracy (0.808). The OS of low-risk patients predicted by RSF was greater than that of high-risk patients. The RSF model demonstrated excellent discrimination, calibrated predictions, and stratified risk for CCa patients. Furthermore, it outperformed the Cox model in predicting risks, thus enabling the delivery of personalised treatment and follow-up strategies.

Predictors of para-aortic lymph node metastasis based on pathological diagnosis via surgical staging in patients with locally advanced cervical cancer: A multicenter study

Para-aortic lymph node (PALN) metastasis of patients with locally advanced cervical cancer (LACC) is associated with multiple risk factors. This study aimed to identify risk factors and develop a predictive model for PALN metastasis based on the pathological diagnosis via surgical staging to determine the patient-population suitable for extended-field irradiation (EFRT) and clarify the prognosis of patients with LACC. Five parameters were identified as predictors by logistic regression analysis. The predictive model was displayed as a nomogram and then modified into a simple scoring system. The concordance indices for the prediction nomogram were 0.939 in the training cohort, and 0.954 in the validation cohort, respectively. The scoring system consisted of tumor size, histological type, number of pelvic lymph nodes (PLNs), common iliac lymph node, and shorter diameter of the largest PLN. With a cutoff value of 8 points, the sensitivity and specificity of the predictive model were 91.04 % and 85.37 %, respectively, in the training cohort, and 89.47 % and 84.68 %, respectively, in the validation cohort. Using this system, patients were divided into high- and low-risk groups. Patients in the high-risk group showed a greater likelihood of PALN metastasis and worse PFS and OS than those in the low-risk group. The predictive model displays promise for the pathological diagnosis of PALN via surgical staging, offering good accuracy. It provides a non-invasive, practical tool to guide precise radiation strategy and stratify prognosis of patients with LACC.

Genetic analysis of cervical cancer with lymph node metastasis

To find out the differences in gene characteristics between cervical cancer patients with and without lymph node metastasis, and to provide reference for therapy. From January 2018 to June 2022, recurrent cervical cancer patients 39 cases with lymph node metastasis and 73 cases without lymph node metastasis underwent testing of 1,021 cancer-related genes by next-generation sequencing. Maftools software was used to analyze somatic single nucleotide/insertion-deletion variation mutation, co-occurring mutation, cosmic mutation characteristics, oncogenic signaling pathways. EP300 and FBXW7 were significantly enriched in lymph node-positive patients. Lymph node-positive patients with EP300 or FBXW7 mutations had lower overall survival (OS) after recurrence. Both lymph node-positive and -negative patients had plenty of co-occurring mutations but few mutually exclusive mutations. Lymph node-positive co-occurring mutation number ≥6 had lower OS, while lymph node-negative co-occurring mutation number ≥3 had lower OS after recurrence. The etiology of SBS3 was defects in DNA double strand break repair by homologous recombination, which exclusively exist in lymph node-positive patients. There was no difference in median tumor mutation burden (TMB) between positive and negative lymph nodes, but TMB was significantly associated with PIK3CA mutation. The somatic SNV/Indels of EP300 and FBXW7, SBS3 homologous recombination-mediated DNA repair defect were enriched in lymph node-positive patients. For lymph node-positive patients, EP300 or FBXW7 mutations predicted poor prognosis. No matter lymph node-positive or negative, more co-occurring mutation number predicted poor prognosis. PIK3CA mutation may account for the higher TMB and help identify patients who benefit from immunotherapy.

Early detection, clinicopathological subtyping, and prognosis prediction for endometrial cancer patients using fragmentomics liquid biopsy assay

Endometrial cancer (EC) is among the most prevalent gynecological malignancies worldwide. This study explores the use of cell-free DNA (cfDNA) fragmentomics to develop a non-invasive liquid biopsy assay, aiming to improve early detection, subtyping, and prognostication of EC, thereby enhancing therapeutic outcomes and reducing associated mortality. A cohort of 120 patients with diagnosed EC and 120 healthy volunteers was used to develop a novel non-invasive liquid biopsy assay for EC. Five distinct fragmentomic features were analyzed from preoperative plasma samples using low-pass whole-genome sequencing. Ensemble models were created by integrating base models that utilized four different machine learning algorithms for early cancer detection, clinicopathological subtyping, and prediction of recurrence-free survival. An independent test cohort of 62 EC patients and 62 healthy controls was used to assess the final ensemble model's performance. The liquid biopsy assay demonstrated high efficacy in early EC detection, achieving an area under the curve (AUC) of 0.96, with 75.8% sensitivity and 96.8% specificity in the independent test cohort. Consistent sensitivities were observed across EC stages I-IV at 74.4%, 85.7%, 75%, and 75%, respectively. The assay moderately predicted clinicopathological features including stage (AUC = 0.72), histological subtypes (AUC = 0.73), and microsatellite instability status (AUC = 0.77). The model also effectively predicted recurrence-free survival, identifying high-risk patients [hazard ratio (HR) 8.6, P < 0.001]. Additionally, similarity network fusion stratified patients into high- and low-risk clusters, with high-risk individuals exhibiting a notably increased recurrence risk (HR 6.2, P = 0.049). Patients identified as high-risk by both methods exhibited an even greater risk (HR 10.1, P < 0.0001) for recurrence. This DECIPHER-UCEC-2 study (Detecting Early Cancer by Inspecting ctDNA Features) demonstrates that by integrating cfDNA fragmentomics with machine learning, our liquid biopsy assay shows significant promise for EC's early detection, subtyping, and prognosis, potentially paving the way for enhanced patient outcomes.