Chinese Journal of Cancer Research最新文献

The clinical efficacy of immunotherapy in acute myeloid leukemia (AML) remains significantly limited by early relapse and treatment-associated toxicities. This review examines recent advances in antibody- and cell-based immunotherapies for AML, focusing on established targets (CD33, CD123, and CLL1) as well as emerging targets (including CD7, CD70, CD38, and FLT3). Therapeutic modalities discussed include immunoconjugates, bispecific T-cell engagers and chimeric antigen receptor T (CAR-T) cells. Furthermore, we summarize the current challenges impeding the success of immunotherapy in AML and propose strategies to enhance its efficacy. These include combination therapies, structural optimization of CAR constructs, functional enhancement of CAR-T cells, identification of novel targets, and the development of next-generation cellular therapies. Collectively, these approaches aim to offer new insights for improving immunotherapeutic outcomes in AML.

Objective: Acral melanoma (AM), a unique subtype prevalent in China, develops on the palms, soles, and nail beds. Despite its distinct clinical and pathological features compared to cutaneous melanoma (CM), the molecular basis underlying these differences remains poorly understood. This study aims to perform a comprehensive comparative transcriptomic analysis of AM and CM at the single-cell level to uncover key molecular distinctions.

Methods: We analyzed single-cell RNA sequencing (scRNA-seq) data from 39 AM patients and 18 CM cases. Single-cell transcriptomic profiling was used to compare tumor cell subpopulations and microenvironmental differences. Bioinformatics tools were employed for cell clustering, differential gene expression analysis, cell-cell communication network inferences, and survival analysis.

Results: AM exhibited a significantly higher proportion of MPZ ⁺ melanoma cells, a subpopulation with Schwann cell-like properties associated with poor prognosis. These MPZ ⁺ melanoma cells established extensive communication networks with AM-specific immune and stromal components, prompting an immunosuppressive microenvironment and enhancing angiogenic potential. Survival analysis further indicated that the presence of MPZ ⁺ melanoma cells is closely linked to worse clinical outcomes in AM patients.

Conclusions: This study provides novel insights into the molecular distinctions between AM and CM, highlighting the critical role of MPZ ⁺ melanoma cells in AM progression. These findings enhance our understanding of AM pathophysiology and may contribute to the development of more targeted therapeutic strategies.

Objective: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the only potentially curative method for treating myelodysplastic syndrome (MDS). Post-HSCT measurable residual disease (post-HSCT MRD) is associated with inferior transplant outcomes. In this prospective study, we aimed to investigate the prognostic value of post-HSCT MRD in relapse prediction in MDS.

Methods: A total of 166 patients diagnosed with MDS were prospectively enrolled in this study. The Kaplan-Meier method was used to calculate the survival probabilities. Potential risk factors for outcomes after transplantation were evaluated through univariate and multivariate Cox regression models.

Results: For patients with negative and positive post-HSCT MRD, the cumulative incidence of relapse (CIR) and disease-free survival (DFS) at 3 years were 5.9% and 69.6% (P<0.001) and 82.7% and 26.1% (P<0.001), respectively. In the multivariate analysis, post-HSCT MRD (HR=22.801, P<0.001) and Revised International Prognostic Scoring System (IPSS-R) risk stratification (HR=4.346, P=0.003) were independently correlated with relapse. A scoring system for relapse prediction was built based on post-HSCT MRD and IPSS-R stratification. The cumulative incidence of relapse at 3 years was 1.1%, 15.8%, and 91.7% for patients with scores of 0, 1, and 2, respectively (P<0.001).

Conclusions: Our results demonstrated both post-HSCT MRD and IPSS-R scores were independent prognostic factors for OS, DFS, and relapse for MDS patients after allo-HSCT. The risk score system could better predict transplant outcomes and refine the risk stratification than alone in patients with MDS.

Objective: Advanced gastric cancer remains highly refractory to therapy, with limited immunotherapy efficacy due to tumor microenvironment heterogeneity. Primary cilia, microtubule-based organelles involved in tumor progression, remain insufficiently explored in gastric cancer. This study aimed to define primary cilia subtypes and establish prognostic signatures for personalized treatment strategies.

Methods: Bulk transcriptomic data from over 1,500 gastric cancer samples were integrated to define distinct primary cilia subtypes. A primary ciliary phenotype-associated signature (PCS) was established using a multi-machine learning survival framework incorporating ten algorithms. The prognostic predictive value and immunotherapy response prediction capability of PCS were validated across multiple independent cohorts. Single-cell RNA sequencing analysis was performed to identify cellular populations associated with high-PCS phenotype. Causal weighted gene co-expression network analysis (WGCNA) was employed to identify driving factors, followed by functional validation through cell culture experiments and xenograft models.

Results: Two distinct primary cilia subtypes were identified and validated across all cohorts, with C2 patients exhibiting significantly worse overall survival compared to C1 patients. PCS demonstrated robust predictive value for both prognosis and immunotherapy response, with superior accuracy compared to existing models across multiple validation cohorts. High-PCS patients showed reduced tumor purity, increased stromal cell infiltration, and poor response to immunotherapy. Single-cell analysis revealed that fibroblasts had the highest PCS scores and identified a novel secreted modular calcium-binding protein 2 (SMOC2)^high myofibroblastic cancer-associated fibroblast (mCAF) population as the key driver of high-PCS phenotype. Functional experiments confirmed that SMOC2 knockdown significantly suppressed gastric cancer cell proliferation, migration, and invasion, while promoting mCAF-to-inflammatory cancer-associated fibroblasts (iCAF) transition.

Conclusions: PCS serves as a robust prognostic biomarker for gastric cancer patients. Additionally, targeting SMOC2 ^high mCAFs represents a potential therapeutic strategy for patients with high-PCS gastric cancer.

Objective: This study aimed to develop and validate a predictive model for postoperative complications in gastrointestinal cancer patients using a large multicenter database, based on machine learning algorithms.

Methods: We analyzed the clinicopathological data of 3,926 gastrointestinal cancer patients from the Prevalence of Abdominal Complications After GastroEnterological surgery (PACAGE) database, covering 20 medical centers from December 2018 to December 2020. The predictive performance was evaluated using receiver operating characteristic (ROC) curves and Brier Score.

Results: The patients were divided into gastric (2,271 cases) and colorectal cancer (1,655 cases) groups and further divided into training and external validation sets. The overall postoperative complication rates for gastric and colorectal cancer groups were 18.1% and 14.8%, respectively. The most common complication was the intra-abdominal infection in both gastric and colorectal cancer groups. In the training set, the Random Forest (RF) model predicted the highest mean area under the curve (AUC) values for overall complications and different types of complications, in both the gastric cancer group and the colorectal cancer group, with similar results obtained in the external validation set. ROC curve analysis showed good predictive performance of the RF model for overall and infectious complications. An application-based clinical tool was developed for easy application in clinical practice.

Conclusions: This model demonstrated good predictive performance for overall and infectious complications based on the multi-center database, supporting clinical decision-making and personalized treatment strategies.

Objective: Regular cancer screening must be monitored to improve gastric cancer (GC) survival rates and maximize participation. This study examined adherence to regular GC screening over a 10-year period and identified the factors influencing adherence.

Methods: This retrospective cohort study was conducted using data from the Korean National Cancer Screening Program (KNCSP) between 2011 and 2020. The total cohort comprised 400,113 adults aged 40 years who were newly eligible for and participated in GC screening in 2011. The participants were followed up for 10 years to assess their adherence to biennial screening recommendations. They were categorized into two groups: the non-regular screening (non-RS) group, which included individuals who did not participate in subsequent screenings, and the regular screening (RS) group, which included those who participated in at least one follow-up screening. Multiple logistic regression analyses were performed to identify the factors associated with adherence to regular GC screening.

Results: Over 10 years, 59% of the participants completed at least four of the five recommended screenings, while 10% did not participate after their initial screening. Male participants had higher odds of non-adherence than females [adjusted odds ratio (aOR)=1.429, 95% confidence interval (95% CI): 1.394-1.464; P<0.001]. Non-adherence was more prevalent among self-employed individuals (aOR=1.208, P<0.001). Among males, those in the lowest income group were 1.267 times more likely to not undergo regular screening than those in the highest income group.

Conclusions: Long-term adherence to regular GC screening in South Korea remains suboptimal. Socioeconomic disparities persist, highlighting the need for tailored interventions to improve adherence and enhance public health.

Objective: The previously integrated tumor-inflammation-nutrition (HI-GC) score has demonstrated dynamic monitoring value for recurrence and clinical decision-making in patients with postsurgical gastric cancer (GC). However, its failure to incorporate clinical-pathological factors limits its capacity for baseline risk assessment. This study aimed to develop a model that accurately identifies patients for adjuvant chemotherapy and dynamically evaluates recurrence risk.

Methods: This retrospective, multicenter, longitudinal cohort study, spanning nine hospitals, included 7,085 patients with GC post-radical gastrectomy. A baseline prognostic model was constructed using 117 machine-learning algorithms. The dynamic survival decision tree model (dySDT) was employed to combine the baseline model with the HI-GC score.

Results: A Cox regression model incorporating six factors was used to create a nomogram [Harrell's C-index: training cohort: 0.765; 95% confidence interval (95% CI): 0.747, 0.783; validation set: 0.810; 95% CI: 0.747, 0.783], including pT stage, positive lymph node ratio, pN stage, tumor size, age, and adjuvant chemotherapy. The best-performing machine learning model exhibited similar predictive accuracy to the nomogram (C-index: 0.770). For the short-term dySDT at 1 month, the mortality hazard ratios (HRs) for groups IIa, IIb, and III were 2.61 (95% CI: 2.24, 3.04), 5.02 (95% CI: 4.15, 6.06), and 8.88 (95% CI: 7.57, 10.42), respectively, compared to group I. Stratified analysis revealed a significant interaction between adjuvant chemotherapy and overall survival in each subgroup (P<0.001). The long-term dySDT at 1 year showed HRs of 3.25 (95% CI: 2.12, 4.97) for group II, 6.73 (95% CI: 4.29, 10.56) for group IIIa, and 17.88 (95% CI: 10.71, 29.84) for group IIIb.

Conclusions: The dySDT effectively stratifies mortality risk and provides valuable assistance in clinical decision-making after gastrectomy.