Chinese Journal of Cancer Research最新文献

Thyroid cancer (TC) is one of the most common endocrine system tumors, and its incidence continues to increase worldwide. Although most TC patients have a good prognosis, especially with continuous advancements in surgery, radioactive iodine therapy, chemotherapy, endocrine therapy and targeted therapy, the effectiveness of disease treatment has significantly improved. However, there are still some cases with a higher risk of death and greater aggressiveness. In these more challenging advanced or highly aggressive cases, tyrosine kinase inhibitors appear to be an effective treatment option. Unfortunately, these drugs are less than ideal in terms of efficacy because of their toxicity and potential for intrinsic or acquired resistance. Therefore, exploring new strategies targeting the metabolic characteristics of TC cells and overcoming drug resistance barriers in existing treatments have become key topics in the current field of TC research. In recent years, lipid metabolic reprogramming has gained attention as an important aspect of cancer development. Lipid metabolic reprogramming not only participates in the formation of the cell membrane structure, but also plays an important role in signal transduction and promoting cell proliferation. In particular, fatty acid (FA) metabolic reprogramming has attracted widespread attention and plays an important role in multiple aspects such as tumor growth, metastasis, enhanced invasive ability, immune escape, and drug resistance. Although TC is considered a disease that is highly dependent on specific types of metabolic activities, a comprehensive understanding of the specific mechanism of action of FA metabolic reprogramming in this process is lacking. This article aims to review how FA metabolic reprogramming participates in the occurrence and development of TC, focusing on the impact of abnormal FA metabolic pathways and changes in the expression and regulation of related genes over the course of this disease. By examining the complex interactions between FA metabolic disorders and carcinogenic signaling pathways in depth, we aim to identify new therapeutic targets and develop more precise and effective treatments for TC.

Objective: Early predicting response before neoadjuvant chemotherapy (NAC) is crucial for personalized treatment plans for locally advanced breast cancer patients. We aim to develop a multi-task model using multiscale whole slide images (WSIs) features to predict the response to breast cancer NAC more finely.

Methods: This work collected 1,670 whole slide images for training and validation sets, internal testing sets, external testing sets, and prospective testing sets of the weakly-supervised deep learning-based multi-task model (DLMM) in predicting treatment response and pCR to NAC. Our approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations, and controls the expressiveness of each representation via a gating-based attention mechanism.

Results: In the retrospective analysis, DLMM exhibited excellent predictive performance for the prediction of treatment response, with area under the receiver operating characteristic curves (AUCs) of 0.869 [95% confidence interval (95% CI): 0.806-0.933] in the internal testing set and 0.841 (95% CI: 0.814-0.867) in the external testing sets. For the pCR prediction task, DLMM reached AUCs of 0.865 (95% CI: 0.763-0.964) in the internal testing and 0.821 (95% CI: 0.763-0.878) in the pooled external testing set. In the prospective testing study, DLMM also demonstrated favorable predictive performance, with AUCs of 0.829 (95% CI: 0.754-0.903) and 0.821 (95% CI: 0.692-0.949) in treatment response and pCR prediction, respectively. DLMM significantly outperformed the baseline models in all testing sets (P<0.05). Heatmaps were employed to interpret the decision-making basis of the model. Furthermore, it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis exploration.

Conclusions: The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

Objective: Prostate cancer (PCa) is a complex disease characterized by diverse cellular ecosystems within the tumor microenvironment (TME) and high tumor heterogeneity, which challenges clinically stratified management and reinforces the need for novel strategies to fight against castration-resistant PCa (CRPC).

Methods: We performed single-cell RNA sequencing (scRNA-seq) on 10 untreated primary PCa tissues and integrated public scRNA-seq resources from three normal prostate tissues, two untreated primary PCa tissues, and six CRPC tumors to portray a comprehensive cellular and molecular interaction atlas of PCa. We further integrated the single-cell and bulk transcriptomes of PCa to establish a molecular classification system.

Results: scRNA-seq profiles revealed substantial inter- and intra-tumoral heterogeneity across different cell subpopulations in untreated PCa and CRPC tumors. In the malignant epithelial reservoir, cells evolved along decoupled paths in treatment-naive PCa and CRPC tumors, and distinct transcriptional reprogramming processes were activated, highlighting anti-androgen therapy-induced lineage plasticity. Based on the specifically expressed markers of the epithelial subpopulations, we conducted unsupervised clustering analysis in The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) cohort and identified three molecularly and clinically distinct subtypes. The C1 subtype, characterized by high enrichment of CRPC-enriched epithelial cells, had a high risk of rapid development of anti-androgen resistance and might require active surveillance and additional promising intervention treatments, such as integrin A3 (ITGA3) + integrin B1 (ITGB1) inhibition. The C2 subtype resembled the immune-modulated subtype that was most likely to benefit from anti-LAG3 immunotherapy. The C3 subtype had a favorable prognosis.

Conclusions: Our study provides a comprehensive and high-resolution landscape of the intricate architecture of the PCa TME, and our trichotomic molecular taxonomy could help facilitate precision oncology.

Objective: Neuroblastoma is the most common extracranial solid tumor in children and has complex genetic underpinnings. Previous genome-wide association studies (GWASs) have identified many loci associated with neuroblastoma susceptibility; however, their application in risk prediction for Chinese children has not been systematically explored. This study seeks to enhance neuroblastoma risk prediction by validating these loci and evaluating their performance in polygenic risk models.

Methods: We validated 35 GWAS-identified neuroblastoma susceptibility loci in a cohort of Chinese children, consisting of 402 neuroblastoma patients and 473 healthy controls. Genotyping these polymorphisms was conducted via the TaqMan method. Univariable and multivariable logistic regression analyses revealed the genetic loci significantly associated with neuroblastoma risk. We constructed polygenic risk models by combining these loci and assessed their predictive performance via area under the curve (AUC) analysis. We also established a polygenic risk scoring (PRS) model for risk prediction by adopting the PLINK method.

Results: Fourteen loci, including ten protective polymorphisms from CASC15, BARD1, LMO1, HSD17B12, and HACE1, and four risk variants from BARD1, RSRC1, CPZ and MMP20 were significantly associated with neuroblastoma risk. Compared with single-gene model, the 8-gene model (AUC=0.72) and 13-gene model (AUC=0.73) demonstrated superior predictive performance. Additionally, a PRS incorporating six significant loci achieved an AUC of 0.66, effectively stratifying individuals into distinct risk categories regarding neuroblastoma susceptibility. A higher PRS was significantly associated with advanced International Neuroblastoma Staging System (INSS) stages, suggesting its potential for clinical risk stratification.

Conclusions: Our findings validate multiple loci as neuroblastoma risk factors in Chinese children and demonstrate the utility of polygenic risk models, particularly the PRS, in improving risk prediction. These results suggest that integrating multiple genetic variants into a PRS can enhance neuroblastoma risk stratification and potentially improve early diagnosis by guiding targeted screening programs for high-risk children.

Objective: The trial was designed to evaluate the efficacy of prophylactic hyperthermic intraperitoneal chemotherapy (HIPEC) with cisplatin for patients with locally advanced gastric cancer (LAGC).

Methods: Between March 2015 and November 2016, a phase II clinical trial was performed. Fifty consecutive patients with LAGC were randomly assigned to two groups: the experimental group (radical gastrectomy + HIPEC with cisplatin + adjuvant chemotherapy) and the control group (radical gastrectomy + adjuvant chemotherapy). Survival rates were closely monitored.

Results: The 5-year overall survival (OS) rate of all patients was 80.0%. The 5-year OS rate in the experimental group was lower than that in the control group, at 75.8% and 88.2%, respectively, with no statistical significance. In addition, 5-year recurrence-free survival (RFS) rates of patients who underwent HIPEC or not were also 75.8% and 88.2%, respectively. In the multivariate analysis, only pT stage [risk ratio (RR)=7.079, P=0.018] was significantly associated with prognosis. The most common recurrence pattern was peritoneal recurrence in both groups. The experimental group had a lower incidence of peritoneal recurrence than the control group with no statistical significance.

Conclusions: This trial clearly revealed that prophylactic HIPEC with cisplatin neither decrease the risk of peritoneal recurrence nor improve the prognosis of patients with LAGC. Thus, HIPEC with cisplatin is not recommended as a prophylactic treatment for peritoneal recurrence of LAGC after radical gastrectomy.

Objective: The expression of programmed death 1 (PD-1) on CD8⁺ T cells is associated with their activation and exhaustion, while CD57 serves as a senescence marker. The impact of PD-1⁺ and CD57⁺CD8⁺ T cells on the prognosis of patients with advanced high-grade serous ovarian cancer (HGSOC) remain unclear.

Methods: We assessed the percentages of PD-1⁺ and CD57⁺CD8⁺ T cells in tumor-infiltrating lymphocytes (TILs, n=85) and tumor ascites lymphocytes (TALs, n=87) using flow cytometry. The optimal cutoffs for these markers in TILs and TALs were determined through the log-rank maximization method. Gene expression analysis elucidated the tumor immune microenvironment (TIME, n=36).

Results: Patients with higher PD-1⁺CD8⁺ TILs (>87.8%) exhibited longer platinum-free interval (PFI) and overall survival (OS). In contrast, those with elevated CD57⁺CD8⁺ TALs (>28.69%) were more likely to experience chemotherapy and had lower complete remission rates, shorter PFI and OS. PD-1⁺CD8⁺ TILs are primarily displayed an effector memory state with strong proliferative and secretory capabilities. Approximately 50% of CD57⁺CD8⁺ TALs were terminally differentiated, exhibiting significantly impaired proliferation. Based on the proportions of PD-1⁺CD8⁺ TILs and CD57⁺CD8⁺ TALs, patients were categorized into good, median and poor prognosis groups, with median PFI of 47.78, 27.29 and 11.96 months, respectively (P<0.0001). Median OS for these groups was not reach, 49.23 and 30.92 months, respectively (P<0.0001). Patients with poor prognosis exhibit significantly reduced CD8⁺ T cell proportion and increased M2 macrophage in the TIME, alongside downregulation of multiple T cell activation-related pathways.

Conclusions: Lower levels of PD-1⁺CD8⁺ TILs and higher CD57⁺CD8⁺ TALs, assessed prior to treatment, correlated with poor prognosis and suppressive TIME in advanced HGSOC.

[This corrects the article DOI: 10.21147/j.issn.1000-9604.2024.06.11.].

The latest data from the NATALEE trial showed the absolute 3-year invasive disease-free survival benefit was 4.9% between the experimental and control groups. That is to say, in the intermediate-risk hormone receptor positive/human epidermal growth factor receptor-2 negative subgroup, there are also some patients with primary resistance to ribociclib. These patients benefit less from ribociclib, and they are unable to gain significant benefit even with the intensive adjuvant therapy of ribociclib. Considering the drug toxicity and health economic benefits, a 3-year course of ribociclib may not be appropriate for all intermediate-risk populations. Therefore, how to screen out the prime population for intensive adjuvant therapy of ribociclib needs to worth explored. In this paper, we discussed that the adaptive neoadjuvant endocrine therapy can screen out the prime population for intensive adjuvant therapy of ribociclib.

Objective: Recurrence continues to be a pivotal challenge among hormone receptor-positive (HR⁺)/human epidermal growth factor receptor 2-negative (HER2^-) breast cancers. In the international consensus guidelines, HR⁺/HER2^- breast cancer relapse patterns are divided into three distinct types: primary resistant, secondary resistant, and endocrine sensitive. However, owing to the lack of cohorts with treatment and follow-up data, the heterogeneity among different recurrence patterns remains uncharted. Current treatments still lack precision.

Methods: This analysis included data from a large-scale multiomics study of a HR⁺/HER2^- breast cancer cohort (n=314). Through the analysis of transcriptomics (n=312), proteomics (n=124), whole-exome sequencing (n=290), metabolomics (n=217), and digital pathology (n=228) data, we explored distinctive molecular features and identified putative therapeutic targets for patients experiencing recurrence.

Results: We explored distinct clinicopathological characteristics, biological heterogeneity, and potential therapeutic strategies for recurrence. Based on a shared relapse signature, we stratified patients into high- and low-recurrence-risk groups. Patients with different relapse patterns presented unique molecular features in primary tumors. Specifically, receptor tyrosine kinase (RTK) pathway activation in the primary resistant group suggested the utility of RTK inhibitors, whereas mammalian target of rapamycin (mTOR) and cell cycle pathway activation in the secondary resistant group highlighted the potential of mTOR and CDK4/6 inhibitors. Interestingly, the endocrine-sensitive group displayed a quiescent state and high genomic instability, suggesting that targeting quiescent cells and using poly-ADP-ribose polymerase (PARP) inhibitors could be effective strategies.

Conclusions: These findings illuminate the clinicopathological and molecular landscape of HR⁺/HER2^- breast cancer patients with distinct recurrence patterns, highlighting potential targeted therapies.

Objective: The neglect of occult lymph nodes metastasis (OLNM) is one of the pivotal causes of early non-small cell lung cancer (NSCLC) recurrence after local treatments such as stereotactic body radiotherapy (SBRT) or surgery. This study aimed to develop and validate a computed tomography (CT)-based radiomics and deep learning (DL) fusion model for predicting non-invasive OLNM.

Methods: Patients with radiologically node-negative lung adenocarcinoma from two centers were retrospectively analyzed. We developed clinical, radiomics, and radiomics-clinical models using logistic regression. A DL model was established using a three-dimensional squeeze-and-excitation residual network-34 (3D SE-ResNet34) and a fusion model was created by integrating seleted clinical, radiomics features and DL features. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). Five predictive models were compared; SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM) were employed for visualization and interpretation.

Results: Overall, 358 patients were included: 186 in the training cohort, 48 in the internal validation cohort, and 124 in the external testing cohort. The DL fusion model incorporating 3D SE-Resnet34 achieved the highest AUC of 0.947 in the training dataset, with strong performance in internal and external cohorts (AUCs of 0.903 and 0.907, respectively), outperforming single-modal DL models, clinical models, radiomics models, and radiomics-clinical combined models (DeLong test: P<0.05). DCA confirmed its clinical utility, and calibration curves demonstrated excellent agreement between predicted and observed OLNM probabilities. Features interpretation highlighted the importance of textural characteristics and the surrounding tumor regions in stratifying OLNM risk.

Conclusions: The DL fusion model reliably and accurately predicts OLNM in early-stage lung adenocarcinoma, offering a non-invasive tool to refine staging and guide personalized treatment decisions. These results may aid clinicians in optimizing surgical and radiotherapy strategies.