Therapeutic strategies targeting cancer metabolism are advancing rapidly. However, perturbing distinct nodes within the same metabolic pathway often yields divergent outcomes. Ferroptosis, a metabolic cell death driven by lipid peroxidation, has garnered attention for potentiating antitumor immunity. Here, we demonstrate that interruption of fatty acid oxidation (FAO) at hydroxyacyl-CoA dehydrogenase (HADHA) node promotes tumoral ferroptosis, whereas targeting upstream enzymes does not. HADHA inhibition causes accumulation of hydroxylated C18 (C18-OH) acylcarnitine to exacerbate mitochondrial lipid peroxidation. In vivo, HADHA ablation or acylcarnitine C18-OH supplementation suppresses tumor growth, enhances antitumor T-cell immunity, and potentiates PD-1 blockade therapy. Clinically, elevated plasma acylcarnitine C18-OH correlates with improved prognosis and immunotherapy response in lung cancer patients. Trimetazidine, an approved anti-ischemic drug and HADHA inhibitor, similarly delays tumor progression and augments immunotherapy. Together, our findings identify HADHA as a ferroptosis regulator and offer a clinically actionable strategy to enhance ferroptosis and immunotherapy through metabolic intervention.

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6is) are standard therapy for HR+/HER2 − metastatic breast cancer (MBC), yet treatment outcomes vary substantially and individualized prognostic tools based on real-world data remain limited. This study evaluated real-world effectiveness and developed prognostic models integrating Cox regression and machine learning. This multicenter retrospective study included 1,008 HR+/HER2 − MBC patients treated with CDK4/6is across 20 cancer centers in central China. Treatment patterns were analyzed in the overall cohort. PFS and prognostic factors were evaluated in patients receiving first- or second-line CDK4/6is using Kaplan–Meier and Cox regression analyses. These patients were randomly divided into training and validation cohorts (7:3). A Cox model and seven machine learning algorithms (GBM, RSF, Lasso-Cox, CoxBoost, XGBoost, SuperPC, and plsRcox) were developed and compared using time-dependent AUC, calibration, and decision curve analysis. CDK4/6is were used as first- and second-line therapy in 65.68

Breast cancer (BC) is the most common malignancy among women globally. Despite significant advances, therapeutic resistance and tumor recurrence remain major clinical challenges. Personalized cancer vaccines have recently emerged as a promising strategy to induce durable, highly specific anti-tumor immune responses and improve long-term survival. However, challenges persist in neoantigen prediction, vaccine formulation, and practical implementation, which can hinder the overall effectiveness and widespread adoption of these personalized cancer vaccines. Ongoing technological advancements are expected to facilitate the development of more efficient and accessible personalized vaccines for BC. This review systematically summarizes recent progress in personalized BC vaccines, focusing on neoantigen identification, emerging vaccine platforms, nano-delivery systems, and subtype-specific vaccine strategies. It also discusses the pharmacological basis of these vaccines, including immune activation mechanisms, pharmacokinetic influences on efficacy, and mechanisms of vaccine resistance. Furthermore, we explore combination strategies integrating cancer vaccines with immune checkpoint inhibitors (ICIs), radiotherapy, and chemotherapy to overcome the immunosuppressive tumor microenvironment (TME) and enhance therapeutic efficacy. Ultimately, this review provides a comprehensive reference to guide future research and promote the clinical translation of personalized vaccine-based immunotherapies for breast cancer.

Accurate liver and tumor segmentation from CT images is essential for cancer diagnosis, treatment planning, and response assessment. However, manual segmentation is labor-intensive and variable, while standard automated models lack the flexibility to adapt to diverse clinical needs or inherent image uncertainties. To bridge this gap, we introduce User-Preference Alignment with Uncertainty-Aware Interactive Rectification (UAIR), a novel framework designed for efficient and adaptive segmentation. Instead of requiring laborious pixel-level corrections, UAIR presents the clinician with a small, curated set of diverse segmentation candidates generated by quantifying model uncertainty. The user simply selects the most suitable option, allowing the framework to iteratively refine its results and align with specific clinical preferences. This selection-based approach drastically reduces the human interaction cost. We validated UAIR on a large-scale, multi-center CT dataset, demonstrating superior accuracy (DSC 0.776) over existing manual positional prompting (DSC 0.685) and less prompting efforts. UAIR provides a clinically-viable solution that integrates seamless human guidance, enabling rapid and robust segmentation for downstream quantitative analysis.

Breast cancer remains one of the most deadly cancers, highlighting the urgent need for better prognostic markers and treatment targets. Emerging evidence suggests that inflammation-driven immune dysregulation plays a pivotal role in breast cancer progression and therapy resistance. Single-cell RNA sequencing and comprehensive gene network analysis based on inflammation identified FLT3LG as a crucial protective factor in breast cancer. Tumors with lower FLT3LG expression showed poorer survival outcomes across multiple patient cohorts. Functional analysis revealed its role in immune regulation, particularly in the activation of B cells and T cells, as well as in chemokine signaling. Genetic profiling revealed distinct mutation patterns: PIK3CA mutations were enriched in tumors with high FLT3LG expression, whereas TP53 mutations predominated in tumors with low FLT3LG expression. Correlations with immune cell infiltration and checkpoint markers suggested that FLT3LG may predict enhanced responses to immune checkpoint inhibitors and certain targeted drugs. These findings support its potential as a multifunctional biomarker for prognosis and therapeutic decision making in breast cancer.

To assess whether the pCASL technique and its radiomics features can enhance the differentiation between tumor recurrence (TR) and pseudoprogression (PsP) in postoperative glioma patients. A retrospective study of 120 postoperative glioma patients (WHO Grade 2–4) from Tongji Hospital, Wuhan, was conducted. MRI data, including T1WI, T2WI, T2FLAIR, contrast-enhanced T1WI, and pCASL, were analyzed. Final diagnoses of TR or PsP were confirmed through pathology or follow-up. Among the patients, 65 had recurrence, and 55 had PsP. Process the pCASL images to generate the CBF parameter map, then perform N4 bias correction and Z- score standardization to obtain the standardized CBF parameter map for group analysis. The lesion areas were outlined, and mean values for ROI were calculated. Statistical analysis included the Mann-Whitney U test and ROC curve analysis. Radiomics features were extracted from the CBF maps. These features were then further selected and divided into training and testing sets. Machine learning models, including Support Vector Machine (SVM), logistic regression, random forest, and Gaussian Naive Bayes, were developed and subsequently validated. The Mann-Whitney U test showed a significant difference in mean CBF values between TR and PsP groups (p < 0.001). ROC analysis revealed an AUC of 0.879 (95

Hepatocellular Carcinoma (HCC) is related to dysregulated lipid metabolism and immunosuppressive microenvironment. This study developed a genetic risk model using lipid metabolism-related genes to predict survival and immune patterns in HCC patients. Differentially expressed genes (DEGs) related to lipid metabolism were identified in HCC via the TCGA-LIHC dataset. A risk model for survival prediction was constructed via DEGs related to survival. The immune signature associated with the risk model was also evaluated by the CIBERSORT algorithm, tumor immune dysfunction and exclusion algorithm, and single sample gene set enrichment analysis. This study identified six lipid metabolism-related genes, ADH4, LCAT, CYP2C9, CYP17A1, LPCAT1, and ACACA, to construct a lipid metabolism-related gene risk model that can divide HCC patients into low- and high-risk groups. Internal and external validation verified that the risk model could be a signature that could effectively predict HCC patient prognosis. High-risk patients showed disrupted immune cell profiles, reduced tumor-killing capacity, and increased expression of immune checkpoint genes. However, they responded more favorably to immune checkpoint inhibitor (ICB) therapy. The top ten hub genes related to the risk model were associated with tumor progression and deteriorating prognosis. In vitro experiments verified that the downregulation of the top 1 hub gene CDK1 was correlated to the HCC cell proliferation. The risk model constructed using lipid metabolism-related genes could effectively predict prognosis and was related to the immunosuppressive microenvironment and ICB immunotherapy. The hub genes related to the risk model were potential therapeutic targets.

Tyrosine kinase inhibitors (TKIs) are the standard treatment for advanced hepatocellular carcinoma (HCC). However, their therapeutic efficacy is often limited by drug resistance, primarily driven by tumoral intrinsic mechanisms. In this study, we demonstrate that IFNγ in the tumor microenvironment can potentiate TKI response, and that ablation of IFNγ receptor on HCC cells leads to TKI resistance in vivo. Mechanistically, IFNγ synergizes with TKI to induce GSDME-mediated pyroptosis of HCC cells. The PERK-mediated unfolded protein response (UPR) protects HCC cells from TKI-induced pyroptosis. IFNγ attenuates PERK activation by inducing the expression of PDIA1, which alleviates the stress of protein unfolding. In vivo, PERK inhibition augments TKI therapy, and elevated PERK expression correlates with poor overall survival of patients with HCC. Moreover, IFNγ-producing CD8+ T cells can potentiate TKI efficacy. Combining PD-1 blockade to activate T-cell response with TKI therapy synergistically suppresses the growth of GSDME-expressing HCC tumors, which is further enhanced by the PERK inhibitor. Our findings reveal how IFNγ signaling modulates TKI response and demonstrate the potential of a sequential combination of ICB-mediated immunotherapy and TKI therapy for patients with GSDME+ HCC.

Background:Utidelone (UTD1), a genetically engineered epothilone derivative, has been approved in China for use in combination with capecitabine in treating metastatic breast cancer (MBC) patients previously treated with anthracyclines or taxanes. Objectives:To evaluate the real-world efficacy and safety of UTD1 in Chinese patients with MBC and to explore potential predictors of therapeutic effectiveness. Design:A multicenter, retrospective, real-world study. Methods:MBC patients who received UTD1 between March 2021 and August 2023 were identified using an electronic database. Outcome variables included progression-free survival (PFS), overall survival (OS), time to treatment failure (TTF), objective response rate (ORR), clinical benefit rate (CBR), and adverse events (AEs). Results:A total of 270 MBC patients were included, with 81.1% presenting with visceral metastasis and 23.7% with brain metastasis. The median number of treatment lines for UTD1 was 3. UTD1 showed a median PFS of 3.97 months (95% confidence interval (CI) 3.33-4.61) and a median OS of 20.63 months (95% CI 16.72-24.54). Among the patients, 17.4% received UTD1 monotherapy, and 82.6% received UTD1-based combination therapy. The median TTF was 2.80 months (95% CI 2.31-3.29). The ORR was 8.4%, and the CBR was 33.5%. The most common AE was peripheral neuropathy (PN, 55.2%). Patients with unresolved PN from previous therapy or receiving UTD1 through intravenous infusion on days 1-5 were more likely to develop ⩾grade 3 PN. Conclusion:UTD1 is a new option for patients who have previously received taxanes and anthracyclines, with its clinical toxicity controllable.

Today, breast cancer (BC) is the most frequently diagnosed malignancy and a leading cause of cancer-related deaths among women worldwide. Brain metastases (BMs) are a common complication among individuals with advanced breast cancer, significantly impacting both survival rates and the overall condition of life of patients. This review systematically analyzes the innovative approaches to drug treatment for breast cancer brain metastases (BCBMs), with particular emphasis placed on treatments targeting molecular mechanisms and signaling pathways and drug delivery strategies targeting the blood brain barrier (BBB). The article discusses various drugs that have demonstrated effectiveness against BCBM, featuring a mix of monoclonal antibodies, nimble small-molecule tyrosine kinase inhibitors (TKIs), and innovative antibody-drug conjugates (ADCs). This study of various drugs and techniques designed to boost the permeability of the BBB sheds light on how these innovations can improve the treatment of brain metastases. This review highlights the need to develop new therapies for BCBM and to optimize existing treatment strategies. With a deeper comprehension of the intricate molecular mechanisms and advances in drug delivery technology, it is expected that more effective personalized treatment options will become available in the future for patients with BCBM.

Radiation therapy is a crucial adjunct treatment for head and neck tumors, as well as primary or metastatic brain tumors. Radiation-induced brain injury is one of the most severe complications, postirradiation, in patients with head and neck tumors, and significantly impacts their quality of life. Currently, there are no effective treatments for radiation-induced brain injury, making the study of radiation-induced molecular mechanisms and the identification of early damage biomarkers critical for the early diagnosis and treatment of such injuries. In this study, twelve male C57 mice aged 6-8 weeks were randomly divided into a control group, a 15 Gy irradiation group, and a 30 Gy irradiation group. Mice were exposed to 6 MV X rays. The control group underwent the same anesthesia procedure as the irradiated groups but did not receive radiation. General health and weight changes were monitored and recorded. Four months postirradiation, mice were subjected to intracranial magnetic resonance imaging [T2-weighted imaging (T2WI)], open field test (OFT), novel object recognition (NOR), followed by a collection of brain tissues for immunofluorescence, SA-β-gal staining, and transcriptomic and metabolomic analyses. Compared to the control group, the 15 Gy and 30 Gy irradiated mice showed reduced activity and weight loss. The irradiated mice exhibited impaired recognition memory in the NOR test and decreased body weight, but radiation had no significant effect on weight or performance in the OFT. Electron microscopy reveals significant demyelination of mouse cortex after irradiation, and MRI T2-weighted imaging demonstrated varying degrees of brain atrophy and ventricular enlargement in irradiated mice compared to the control group. Immunofluorescence staining showed a significant increase in astrocytes and microglia activated after irradiation. SA-β-gal staining revealed significant increases in the numbers of β-gal+ cells in irradiated mice compared to those in untreated control mice. Bioinformatics analysis identified enriched pathways primarily related to lipid metabolism and neuroinflammatory responses; associated metabolites and genes were variously upregulated or downregulated. The findings suggest that radiation-induced brain injury involves complex biological processes, with lipid metabolism disorders and neuroinflammation being the predominant pathological changes observed. Further studies on these metabolic pathways and genes could enhance our understanding of the pathogenic mechanisms underlying radiation-induced brain injury and identify potential therapeutic targets.

Breast cancer metastasis remains the primary driver of patient mortality, involving dynamic interactions between tumor cells and distant organ microenvironments. In recent years, tumor cell-derived extracellular vesicles (EVs) have emerged as critical information carriers, playing central roles in breast cancer metastasis by mediating organ-specific pre-metastatic niche formation, immune modulation, and tumor cell adaptive evolution. Studies have demonstrated that EVs drive the metastatic cascade through the delivery of bioactive components, including nucleic acids (e.g., miRNAs, circRNAs), proteins (e.g., integrins, metabolic enzymes), and lipids, which collectively regulate osteoclast activation, immune cell polarization, vascular permeability alterations, and extracellular matrix (ECM) remodeling in target organs such as bone, the lungs, and the liver. Molecular heterogeneity in EVs derived from different breast cancer subtypes strongly correlates with organotropism, providing potential biomarkers for metastasis prediction. Leveraging the organotrophic mechanisms of EVs and their dual regulatory roles in metastasis (pro-metastatic and anti-metastatic), strategies targeting EV biogenesis, cargo loading, or delivery exhibits translational potential in diagnostics and therapeutics. In this review, we summarize recent advances in understanding the role of breast cancer-derived exosomes in mediating metastatic organotropism and discuss the potential clinical applications of targeting exosomes as novel diagnostic and therapeutic strategies for breast cancer.

RATIONALE AND OBJECTIVES:The purpose of this study is to investigate whether six diffusion models derived from multi-b-value diffusion-weighted imaging can enhance the differentiation between pseudoprogression (PsP) and postoperative tumor recurrence (TR) in glioma patients, with the aim of providing clinical insights. MATERIALS AND METHODS:A retrospective study was conducted on 82 patients with WHO grade 2-4 gliomas who underwent surgery at our hospital, with MRI sequences including T1WI, T2WI, T2FLAIR, contrast-enhanced T1WI, and multi-b-value DWI. Postoperative follow-up or secondary surgery pathology confirmed 46 cases of TR and 36 cases of PsP. Six diffusion models were fitted based on multi-b-value DWI sequences, including monoexponential DWI (Mono_DWI), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched-exponential model (SEM), fractional-order calculus (FROC), and continuous-time random-walk (CTRW) model. ROIs were manually outlined to calculate the average values of each parameter. Differences between the two groups were compared using T-tests or Mann-Whitney U tests. The diagnostic performance of individual parameters was analyzed using ROC curve analysis, and the diagnostic performance of each model was compared using multivariate logistic regression. RESULTS:Among the 14 parameter maps, significant differences were found in all models (P<0.0036) except for IVIM_D*, IVIM_f. ROC curve analysis showed that CTRW_D demonstrated the highest AUC of 0.8484 (0.7549-0.9240). Further analysis of the diffusion models showed that CTRW performed the best among all models, with an AUC of 0.8635 (0.7816-0.9454), slightly higher than the FROC model, which had an AUC of 0.8629 (0.7839-0.9420). CONCLUSION:The various diffusion models derived from multi-b-value DWI sequences can effectively distinguish between postoperative recurrence and pseudoprogression in gliomas. Among these models, the CTRW and FROC models are the two optimal models, demonstrating comparable diagnostic performance.

Intercellular communication can be mediated by direct cell-to-cell contact and indirect interactions through secretion of soluble chemokines, cytokines, and growth factors. Extracellular vesicles (EVs) have emerged as important mediators of cell-to-cell and cell-to-environment communications. EVs from tumor cells, immune cells, and stromal cells can remodel the tumor microenvironment and promote cancer cell survival, proliferation, metastasis, immune evasion, and therapeutic resistance. Most importantly, EVs as natural nanoparticles can be manipulated to serve as a potent delivery system for targeted cancer therapy. EVs can be engineered or modified to improve their ability to target tumors and deliver therapeutic substances, such as chemotherapeutic drugs, nucleic acids, and proteins, for the treatment of cancer. This review provides an overview of the biogenesis and recycling of EVs, discusses their roles in cancer development, and highlights their potential as a delivery system for targeted cancer therapy.

Precise targeting has become the main direction of anti-cancer drug development. Trophoblast cell surface antigen 2 (Trop-2) is highly expressed in different solid tumors but rarely in normal tissues, rendering it an attractive target. Trop-2-targeted antibody-drug conjugates (ADCs) have displayed promising efficacy in treating diverse solid tumors, especially breast cancer and urothelial carcinoma. However, their clinical application is still limited by insufficient efficacy, excessive toxicity, and the lack of biological markers related to effectiveness. This review summarizes the clinical trials and combination therapy strategies for Trop-2-targeted ADCs, discusses the current challenges, and provides new insights for future advancements.

Immunomodulatory effects of long-chain fatty acids (LCFAs) and their activating enzyme, acyl-coenzyme A (CoA) synthetase long-chain family (ACSL), in the tumor microenvironment remain largely unknown. Here, we find that ACSL5 functions as an immune-dependent tumor suppressor. ACSL5 expression sensitizes tumors to PD-1 blockade therapy in vivo and the cytotoxicity mediated by CD8+ T cells in vitro via regulation of major histocompatibility complex class I (MHC-I)-mediated antigen presentation. Through screening potential substrates for ACSL5, we further identify that elaidic acid (EA), a trans LCFA that has long been considered harmful to human health, phenocopies to enhance MHC-I expression. EA supplementation can suppress tumor growth and sensitize PD-1 blockade therapy. Clinically, ACSL5 expression is positively associated with improved survival in patients with lung cancer, and plasma EA level is also predictive for immunotherapy efficiency. Our findings provide a foundation for enhancing immunotherapy through either targeting ACSL5 or metabolic reprogramming of antigen presentation via dietary EA supplementation.

BACKGROUNDS:Ovarian cancer (OC) is the second most common gynecological tumor with the highest mortality rate worldwide. High FAM111B expression has been reported as a predictor of poor prognosis in other cancers, but its correlation with OC has not been reported.METHODS:Immunohistochemistry of tissue microarrays was performed to detect FAM111B expression levels in 141 OC patient tissues. The prognostic value of FAM111B was determined by Kaplan-Meier survival analysis, and correlations between FAM111B expression and clinicopathologic features were investigated by the Clu-square test. The significance of FAM111B expression was verified bioinformatically using the Gene Expression Omnibus database. Protein-protein interaction were performed to explore downstream mechanisms of FAM111B in OC.RESULTS:Among 141 OC patients, FAM111B was positively expressed in 87.23%, 58.16%, and 87.94%; and highly expressed in 8.51%, 17.02%, and 19.86%, as evaluated by cytoplasmic, nuclear, and combined cytoplasmic/nuclear staining. FAM111B expression was positively correlated with the expression of tumor protein markers KI67, EGFR, and PDL-1. Patients with high FAM111B expression had aggressive clinicopathologic features and shorter overall survival (P value 0.0428, 0.0050, 0.0029) and progression-free survival (P value 0.0251, 0.012, 0.0596) compared to the low FAM111B expression group for cytoplasmic, nuclear, and combined cytoplasmic/nuclear groups, respectively. These results were verified using patient data from the Gene Expression Omnibus. Seventeen genes co-expressed with FAM111B were primarily involved in "negative regulation of histone modification", "hippo signaling" and "inner ear receptor cell differentiation".CONCLUSIONS:High FAM111B expression may serve as a novel prognostic predictor and molecular therapeutic target for OC.

Cancer remains a significant global health challenge with limited treatment options beyond systemic therapies, such as chemotherapy, radiotherapy, and molecular targeted therapy. Immunotherapy has emerged as a promising therapeutic modality but the efficacy has plateaued, which therefore provides limited benefits to patients with cancer. Identification of more effective approaches to improve patient outcomes and extend survival are urgently needed. Drug repurposing has emerged as an attractive strategy for drug development and has recently garnered considerable interest. This review comprehensively analyses the efficacy of various repurposed drugs, such as transforming growth factor-beta (TGF-β) inhibitors, metformin, receptor activator of nuclear factor-κB ligand (RANKL) inhibitors, granulocyte macrophage colony-stimulating factor (GM-CSF), thymosin α1 (Tα1), aspirin, and bisphosphonate, in tumorigenesis with a specific focus on their impact on tumor immunology and immunotherapy. Additionally, we present a concise overview of the current preclinical and clinical studies investigating the potential therapeutic synergies achieved by combining these agents with immune checkpoint inhibitors.

Objective:Radiotherapy is a cornerstone of breast cancer therapy, but radiotherapy resistance is a major clinical challenge. Herein, we show a molecular classification approach for estimating individual responses to radiotherapy.Methods:Consensus clustering was adopted to classify radiotherapy-sensitive and -resistant clusters in the TCGA-BRCA cohort based upon prognostic differentially expressed radiotherapy response-related genes (DERRGs). The stability of the classification was proven in the GSE58812 cohort via NTP method and the reliability was further verified by quantitative RT-PCR analyses of DERRGs. A Riskscore system was generated through Least absolute shrinkage and selection operator (LASSO) analysis, and verified in the GSE58812 and GSE17705. Treatment response and anticancer immunity were evaluated via multiple well-established computational approaches.Results:We classified breast cancer patients as radiotherapy-sensitive and -resistant clusters, namely C1 and C2, also verified by quantitative RT-PCR analyses of DERRGs. Two clusters presented heterogeneous clinical traits, with poorer prognosis, older age, more advanced T, and more dead status in the C2. The C1 tumors had higher activity of reactive oxygen species and response to X-ray, proving better radiotherapeutic response. Stronger anticancer immunity was found in the C1 tumors that had rich immune cell infiltration, similar expression profiling to patients who responded to anti-PD-1, and activated immunogenic cell death and ferroptosis. The Riskscore was proposed for improving patient prognosis. High Riskscore samples had lower radiotherapeutic response and stronger DNA damage repair as well as poor anticancer immunity, while low Riskscore samples were more sensitive to docetaxel, doxorubicin, and paclitaxel.Conclusion:Our findings propose a novel radiotherapy response classification system based upon molecular profiles for estimating radiosensitivity for individual breast cancer patients, and elucidate a methodological advancement for synergy of radiotherapy with ICB.

目的 探讨卡培他滨在标准化辅助治疗后的早期三阴性乳腺癌患者中的临床疗效及安全性.方法 回顾性收集 2015 年 9 月至 2022 年 6 月期间于华中科技大学同济医学院附属同济医院就诊的早期三阴性乳腺癌患者资料,根据标准化辅助治疗后是否服用卡培他滨(节拍治疗 1 年),分为卡培他滨组(78 例)及对照组(82 例),主要研究终点为无进展生存期(progression-free survival,PFS),随访记录服药期间的安全性数据,包括不良反应及生活质量评分.结果 共入组 160 例患者,中位随访 46 个月,观察到 28 例复发事件,其中卡培他滨组 4 例,对照组 24 例.卡培他滨组PFS生存获益明显高于对照组(HR=0.32,95％CI:0.15～0.70,P=0.029).卡培他滨相关的常见不良事件是骨髓抑制、手足综合征、肝功能异常.生活质量评分量表结果 显示,两组无明显差异(P>0.05).结论 在接受标准化辅助治疗的早期三阴性乳腺癌患者中,卡培他滨节拍化疗,与对照组相比,具有较显著的生存获益;不良反应较轻,生活质量未见明显下降.